Exploring Data Accuracy And Fraud Detection In Global Ground Station Operations Using SAS

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HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —DATA STEP | LENGTH | INPUT | DATALINES | FORMAT | SET | IF-THEN- KEEP | DROP | MERGE | PROC SORT | PROC PRINT | PROC APPEND | PROC TRANSPOSE | PROC DATASETS | MACRO

Introduction

Space ground stations are critical infrastructure used by space agencies and satellite operators to communicate with orbiting satellites. These stations track satellite signals, transmit telemetry data, and support navigation and scientific missions.

Ground station networks exist across the world and are operated by agencies such as:

· NASA

· ESA

· ISRO

· CNSA

· JAXA

Because these facilities manage expensive satellite missions, data integrity is extremely important. Incorrect tracking logs, corrupted signal data, or manipulated operational statistics can lead to mission failures or financial losses.

For example:

· A ground station may report incorrect tracking hours

· Satellites supported may be incorrectly recorded

· Signal loss events may be fraudulently modified

· Operating costs might be inflated or under-reported

· Reliability percentages might exceed 100% or fall below 0%

In this project, we simulate a Space Ground Station Operations Dataset and deliberately introduce intentional errors to demonstrate how SAS can detect and correct them.

The project demonstrates practical SAS programming concepts including:

· Data step processing

· Numeric functions

· Character functions

· Date handling

· Dataset merging

· Dataset appending

· Transposing data

· Macro-based fraud detection

· Data validation

This type of workflow is extremely valuable in real-world aerospace data analytics and satellite network management systems.

1. Business Context

2. Dataset Design

3. Intentional Errors Introduced

4. Code 1 – Create Raw Dataset With Errors

5. Code 2 – PROC CONTENTS Metadata Check

6. Code 3 – Standardizing Station Names

7. Code 4 – Numeric Error Detection

8. Code 5 – Date Standardization Using MDY

9. Code 6 – Usage Duration Calculation Using INTCK

10. Code 7 – Forecast Maintenance Using INTNX

11. Code 8 – PROC SORT & Dataset MERGE Operation

12. Code 9 – Dataset APPEND Operation

13. Code 10 – Dataset TRANSPOSE Operation

14. Code 11 – Utilization Classification

15. Code 12 – Fraud Detection Macro

16. Code 13 – Final Data Cleaning Logic

17. Code 14 – PROC DATASETS Delete Temporary Tables

18. Final Corrected Dataset Code

19. Project Insights

20. Summary

21. Conclusion

Business Context

Satellite ground station operators track several operational parameters daily.

Typical metrics include:

Metric	Purpose
Tracking Hours	Measures communication time with satellites
Satellites Supported	Number of satellites served
Signal Loss Events	Measures communication disruptions
Operating Cost	Daily operational expenditure
Reliability Index	System stability percentage
Utilization	Network capacity usage

If these metrics contain errors, it may cause:

· Poor satellite scheduling

· Resource wastage

· Incorrect billing

· Security vulnerabilities

Therefore, automated validation is required.

Dataset Variables

Our dataset includes the following variables:

Variable	Description
Station_Name	Name of the ground station
Country	Country where station is located
Tracking_Hours	Daily tracking hours
Satellites_Supported	Number of satellites handled
Signal_Loss_Events	Number of signal disruptions
Operating_Cost	Daily operating cost
Reliability_Index	Reliability percentage
Fees	Service revenue
Utilization	Capacity usage classification
Operation_Date	Date of operation

Intentional Errors Introduced

To simulate real-world data corruption, the dataset contains:

1. Negative tracking hours

2. Reliability > 100%

3. Negative operating costs

4. Lowercase / uppercase inconsistencies

5. Extra spaces in station names

6. Missing country values

7. Wrong date formats

8. Invalid satellite counts

9. Duplicated station names

These errors will be detected and corrected using SAS.

Code 1 – Creating Raw Dataset With Intentional Errors

data ground_station_raw;

infile datalines dsd truncover;

input Station_Name :$30. Country :$20. Tracking_Hours Satellites_Supported

Signal_Loss_Events Operating_Cost Reliability_Index Fees

Operation_Date :$12.;

datalines;

sriharikota,India,12,8,2,15000,95,20000,03-01-2025

Goldstone,USA,14,10,1,20000,101,30000,03-02-2025

malindi,Kenya,-5,6,3,10000,89,15000,03-03-2025

Kourou,France,16,9,2,-12000,92,21000,03-04-2025

svalbard,Norway,18,11,0,17000,98,25000,03-05-2025

Alaska,USA,13,7,4,14000,87,19000,03-06-2025

beijing,China,15,twelve,1,21000,93,31000,03-07-2025

Perth,Australia,20,14,2,22000,97,33000,03-08-2025

Madrid,Spain,10,5,three,12000,85,16000,03-09-2025

Santiago,Chile,17,10,2,18000,94,24000,03-10-2025

Pretoria,SouthAfrica,11,6,4,13000,88,17000,03-11-2025

Bangalore,India,19,13,1,21000,96,32000,03-12-2025

Svalbard,Norway,21,15,0,25000,105,35000,03-13-2025

Dubai,UAE,8,3,6,9000,80,12000,wrong-date

Tokyo,Japan,22,16,1,26000,99,36000,03-15-2025

;

run;

proc print data=ground_station_raw;

run;

OUTPUT:


Obs	Station_Name	Country	Tracking_Hours	Satellites_Supported	Signal_Loss_Events	Operating_Cost	Reliability_Index	Fees	Operation_Date
1	sriharikota	India	12	8	2	15000	95	20000	03-01-2025
2	Goldstone	USA	14	10	1	20000	101	30000	03-02-2025
3	malindi	Kenya	-5	6	3	10000	89	15000	03-03-2025
4	Kourou	France	16	9	2	-12000	92	21000	03-04-2025
5	svalbard	Norway	18	11	0	17000	98	25000	03-05-2025
6	Alaska	USA	13	7	4	14000	87	19000	03-06-2025
7	beijing	China	15	.	1	21000	93	31000	03-07-2025
8	Perth	Australia	20	14	2	22000	97	33000	03-08-2025
9	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025
10	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025
11	Pretoria	SouthAfrica	11	6	4	13000	88	17000	03-11-2025
12	Bangalore	India	19	13	1	21000	96	32000	03-12-2025
13	Svalbard	Norway	21	15	0	25000	105	35000	03-13-2025
14	Dubai	UAE	8	3	6	9000	80	12000	wrong-date
15	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025

Explanation

This DATA step creates the raw dataset.

However, several errors exist.

Examples:

· Tracking hours = -5

· Reliability index = 101 and 105

· Operating cost = -12000

· Station names inconsistent

Why This Code Is Used

· To simulate real operational datasets

· To intentionally insert data quality problems

· To test SAS data validation techniques

Code 2 – Inspect Dataset Structure

proc contents data=ground_station_raw;

run;

OUTPUT:

The CONTENTS Procedure


Data Set Name	WORK.GROUND_STATION_RAW	Observations	15
Member Type	DATA	Variables	9
Engine	V9	Indexes	0
Created	03/11/2026 10:37:43	Observation Length	112
Last Modified	03/11/2026 10:37:43	Deleted Observations	0
Protection		Compressed	NO
Data Set Type		Sorted	NO
Label
Data Representation	SOLARIS_X86_64, LINUX_X86_64, ALPHA_TRU64, LINUX_IA64
Encoding	utf-8 Unicode (UTF-8)


Engine/Host Dependent Information
Data Set Page Size	131072
Number of Data Set Pages	1
First Data Page	1
Max Obs per Page	1168
Obs in First Data Page	15
Number of Data Set Repairs	0
Filename	/saswork/SAS_workD45F00006B56_odaws01-apse1-2.oda.sas.com/SAS_workFE6000006B56_odaws01-apse1-2.oda.sas.com/ground_station_raw.sas7bdat
Release Created	9.0401M8
Host Created	Linux
Inode Number	201353440
Access Permission	rw-r--r--
Owner Name	u63247146
File Size	256KB
File Size (bytes)	262144


Alphabetic List of Variables and Attributes
#	Variable	Type	Len
2	Country	Char	20
8	Fees	Num	8
6	Operating_Cost	Num	8
9	Operation_Date	Char	12
7	Reliability_Index	Num	8
4	Satellites_Supported	Num	8
5	Signal_Loss_Events	Num	8
1	Station_Name	Char	30
3	Tracking_Hours	Num	8

Explanation

PROC CONTENTS displays dataset metadata including:

· Variable names

· Variable types

· Lengths

· Formats

Why It Is Important

Before cleaning data, analysts must verify:

· Variable types

· Variable formats

· Metadata consistency

Code 3 – Character Data Cleaning

data station_clean_names;

set ground_station_raw;

Station_Name=propcase(strip(Station_Name));

Country=propcase(strip(Country));

Station_Upper=upcase(Station_Name);

Station_Lower=lowcase(Station_Name);

Station_Clean=catx('_',Station_Name,Country);

run;

proc print data=station_clean_names;

run;

OUTPUT:


Obs	Station_Name	Country	Tracking_Hours	Satellites_Supported	Signal_Loss_Events	Operating_Cost	Reliability_Index	Fees	Operation_Date	Station_Upper	Station_Lower	Station_Clean
1	Sriharikota	India	12	8	2	15000	95	20000	03-01-2025	SRIHARIKOTA	sriharikota	Sriharikota_India
2	Goldstone	Usa	14	10	1	20000	101	30000	03-02-2025	GOLDSTONE	goldstone	Goldstone_Usa
3	Malindi	Kenya	-5	6	3	10000	89	15000	03-03-2025	MALINDI	malindi	Malindi_Kenya
4	Kourou	France	16	9	2	-12000	92	21000	03-04-2025	KOUROU	kourou	Kourou_France
5	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway
6	Alaska	Usa	13	7	4	14000	87	19000	03-06-2025	ALASKA	alaska	Alaska_Usa
7	Beijing	China	15	.	1	21000	93	31000	03-07-2025	BEIJING	beijing	Beijing_China
8	Perth	Australia	20	14	2	22000	97	33000	03-08-2025	PERTH	perth	Perth_Australia
9	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025	MADRID	madrid	Madrid_Spain
10	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025	SANTIAGO	santiago	Santiago_Chile
11	Pretoria	Southafrica	11	6	4	13000	88	17000	03-11-2025	PRETORIA	pretoria	Pretoria_Southafrica
12	Bangalore	India	19	13	1	21000	96	32000	03-12-2025	BANGALORE	bangalore	Bangalore_India
13	Svalbard	Norway	21	15	0	25000	105	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway
14	Dubai	Uae	8	3	6	9000	80	12000	wrong-date	DUBAI	dubai	Dubai_Uae
15	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025	TOKYO	tokyo	Tokyo_Japan

Character Functions Used

Function	Purpose
STRIP	Removes leading/trailing spaces
TRIM	Removes trailing spaces
CAT	Concatenate values
CATX	Concatenate with delimiter
PROPCASE	Convert to Proper Case
UPCASE	Uppercase
LOWCASE	Lowercase

Explanation

This step standardizes textual data.

Example transformation:

" sriharikota " → "Sriharikota"

Code 4 – Numeric Error Detection

data numeric_validation;

set station_clean_names;

length Error_Flag $20.;

if Tracking_Hours < 0 then Error_Flag='Negative Hours';

if Reliability_Index >100 then Error_Flag='Invalid Reliability';

if Operating_Cost <0 then Error_Flag='Negative Cost';

run;

proc print data=numeric_validation;

run;

OUTPUT:


Obs	Station_Name	Country	Tracking_Hours	Satellites_Supported	Signal_Loss_Events	Operating_Cost	Reliability_Index	Fees	Operation_Date	Station_Upper	Station_Lower	Station_Clean	Error_Flag
1	Sriharikota	India	12	8	2	15000	95	20000	03-01-2025	SRIHARIKOTA	sriharikota	Sriharikota_India
2	Goldstone	Usa	14	10	1	20000	101	30000	03-02-2025	GOLDSTONE	goldstone	Goldstone_Usa	Invalid Reliability
3	Malindi	Kenya	-5	6	3	10000	89	15000	03-03-2025	MALINDI	malindi	Malindi_Kenya	Negative Hours
4	Kourou	France	16	9	2	-12000	92	21000	03-04-2025	KOUROU	kourou	Kourou_France	Negative Cost
5	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway
6	Alaska	Usa	13	7	4	14000	87	19000	03-06-2025	ALASKA	alaska	Alaska_Usa
7	Beijing	China	15	.	1	21000	93	31000	03-07-2025	BEIJING	beijing	Beijing_China
8	Perth	Australia	20	14	2	22000	97	33000	03-08-2025	PERTH	perth	Perth_Australia
9	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025	MADRID	madrid	Madrid_Spain
10	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025	SANTIAGO	santiago	Santiago_Chile
11	Pretoria	Southafrica	11	6	4	13000	88	17000	03-11-2025	PRETORIA	pretoria	Pretoria_Southafrica
12	Bangalore	India	19	13	1	21000	96	32000	03-12-2025	BANGALORE	bangalore	Bangalore_India
13	Svalbard	Norway	21	15	0	25000	105	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway	Invalid Reliability
14	Dubai	Uae	8	3	6	9000	80	12000	wrong-date	DUBAI	dubai	Dubai_Uae
15	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025	TOKYO	tokyo	Tokyo_Japan

Explanation

This code flags suspicious values.

Example errors detected:

· Negative hours

· Reliability above 100

· Negative cost

Code 5 – Converting Date Using MDY

data date_format_fix;

set numeric_validation;

month=input(substr(Operation_Date,1,2),8.);

day=input(substr(Operation_Date,4,2),8.);

year=input(substr(Operation_Date,7,4),8.);

Correct_Date=mdy(month,day,year);

format Correct_Date date9.;

run;

proc print data=date_format_fix;

run;

OUTPUT:


Obs	Station_Name	Country	Tracking_Hours	Satellites_Supported	Signal_Loss_Events	Operating_Cost	Reliability_Index	Fees	Operation_Date	Station_Upper	Station_Lower	Station_Clean	Error_Flag	month	day	year	Correct_Date
1	Sriharikota	India	12	8	2	15000	95	20000	03-01-2025	SRIHARIKOTA	sriharikota	Sriharikota_India		3	1	2025	01MAR2025
2	Goldstone	Usa	14	10	1	20000	101	30000	03-02-2025	GOLDSTONE	goldstone	Goldstone_Usa	Invalid Reliability	3	2	2025	02MAR2025
3	Malindi	Kenya	-5	6	3	10000	89	15000	03-03-2025	MALINDI	malindi	Malindi_Kenya	Negative Hours	3	3	2025	03MAR2025
4	Kourou	France	16	9	2	-12000	92	21000	03-04-2025	KOUROU	kourou	Kourou_France	Negative Cost	3	4	2025	04MAR2025
5	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway		3	5	2025	05MAR2025
6	Alaska	Usa	13	7	4	14000	87	19000	03-06-2025	ALASKA	alaska	Alaska_Usa		3	6	2025	06MAR2025
7	Beijing	China	15	.	1	21000	93	31000	03-07-2025	BEIJING	beijing	Beijing_China		3	7	2025	07MAR2025
8	Perth	Australia	20	14	2	22000	97	33000	03-08-2025	PERTH	perth	Perth_Australia		3	8	2025	08MAR2025
9	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025	MADRID	madrid	Madrid_Spain		3	9	2025	09MAR2025
10	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025	SANTIAGO	santiago	Santiago_Chile		3	10	2025	10MAR2025
11	Pretoria	Southafrica	11	6	4	13000	88	17000	03-11-2025	PRETORIA	pretoria	Pretoria_Southafrica		3	11	2025	11MAR2025
12	Bangalore	India	19	13	1	21000	96	32000	03-12-2025	BANGALORE	bangalore	Bangalore_India		3	12	2025	12MAR2025
13	Svalbard	Norway	21	15	0	25000	105	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway	Invalid Reliability	3	13	2025	13MAR2025
14	Dubai	Uae	8	3	6	9000	80	12000	wrong-date	DUBAI	dubai	Dubai_Uae		.	.	.	.
15	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025	TOKYO	tokyo	Tokyo_Japan		3	15	2025	15MAR2025

Why MDY Is Used

MDY constructs a SAS date.

MDY(month,day,year)

Example:

03-01-2025 → 01MAR2025

Code 6 – Calculating Tracking Duration

data duration_calc;

set date_format_fix;

Days_Since=intck('day',Correct_Date,today());

run;

proc print data=duration_calc;

run;

OUTPUT:


Obs	Station_Name	Country	Tracking_Hours	Satellites_Supported	Signal_Loss_Events	Operating_Cost	Reliability_Index	Fees	Operation_Date	Station_Upper	Station_Lower	Station_Clean	Error_Flag	month	day	year	Correct_Date	Days_Since
1	Sriharikota	India	12	8	2	15000	95	20000	03-01-2025	SRIHARIKOTA	sriharikota	Sriharikota_India		3	1	2025	01MAR2025	375
2	Goldstone	Usa	14	10	1	20000	101	30000	03-02-2025	GOLDSTONE	goldstone	Goldstone_Usa	Invalid Reliability	3	2	2025	02MAR2025	374
3	Malindi	Kenya	-5	6	3	10000	89	15000	03-03-2025	MALINDI	malindi	Malindi_Kenya	Negative Hours	3	3	2025	03MAR2025	373
4	Kourou	France	16	9	2	-12000	92	21000	03-04-2025	KOUROU	kourou	Kourou_France	Negative Cost	3	4	2025	04MAR2025	372
5	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway		3	5	2025	05MAR2025	371
6	Alaska	Usa	13	7	4	14000	87	19000	03-06-2025	ALASKA	alaska	Alaska_Usa		3	6	2025	06MAR2025	370
7	Beijing	China	15	.	1	21000	93	31000	03-07-2025	BEIJING	beijing	Beijing_China		3	7	2025	07MAR2025	369
8	Perth	Australia	20	14	2	22000	97	33000	03-08-2025	PERTH	perth	Perth_Australia		3	8	2025	08MAR2025	368
9	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025	MADRID	madrid	Madrid_Spain		3	9	2025	09MAR2025	367
10	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025	SANTIAGO	santiago	Santiago_Chile		3	10	2025	10MAR2025	366
11	Pretoria	Southafrica	11	6	4	13000	88	17000	03-11-2025	PRETORIA	pretoria	Pretoria_Southafrica		3	11	2025	11MAR2025	365
12	Bangalore	India	19	13	1	21000	96	32000	03-12-2025	BANGALORE	bangalore	Bangalore_India		3	12	2025	12MAR2025	364
13	Svalbard	Norway	21	15	0	25000	105	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway	Invalid Reliability	3	13	2025	13MAR2025	363
14	Dubai	Uae	8	3	6	9000	80	12000	wrong-date	DUBAI	dubai	Dubai_Uae		.	.	.	.	.
15	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025	TOKYO	tokyo	Tokyo_Japan		3	15	2025	15MAR2025	361

INTCK Function

INTCK calculates intervals between dates.

Example:

INTCK('day',date1,date2)

Code 7 – Predicting Next Maintenance Date

data maintenance_forecast;

set duration_calc;

Next_Maintenance=intnx('month',Correct_Date,6,'same');

format Next_Maintenance date9.;

run;

proc print data=maintenance_forecast;

run;

OUTPUT:


Obs	Station_Name	Country	Tracking_Hours	Satellites_Supported	Signal_Loss_Events	Operating_Cost	Reliability_Index	Fees	Operation_Date	Station_Upper	Station_Lower	Station_Clean	Error_Flag	month	day	year	Correct_Date	Days_Since	Next_Maintenance
1	Sriharikota	India	12	8	2	15000	95	20000	03-01-2025	SRIHARIKOTA	sriharikota	Sriharikota_India		3	1	2025	01MAR2025	375	01SEP2025
2	Goldstone	Usa	14	10	1	20000	101	30000	03-02-2025	GOLDSTONE	goldstone	Goldstone_Usa	Invalid Reliability	3	2	2025	02MAR2025	374	02SEP2025
3	Malindi	Kenya	-5	6	3	10000	89	15000	03-03-2025	MALINDI	malindi	Malindi_Kenya	Negative Hours	3	3	2025	03MAR2025	373	03SEP2025
4	Kourou	France	16	9	2	-12000	92	21000	03-04-2025	KOUROU	kourou	Kourou_France	Negative Cost	3	4	2025	04MAR2025	372	04SEP2025
5	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway		3	5	2025	05MAR2025	371	05SEP2025
6	Alaska	Usa	13	7	4	14000	87	19000	03-06-2025	ALASKA	alaska	Alaska_Usa		3	6	2025	06MAR2025	370	06SEP2025
7	Beijing	China	15	.	1	21000	93	31000	03-07-2025	BEIJING	beijing	Beijing_China		3	7	2025	07MAR2025	369	07SEP2025
8	Perth	Australia	20	14	2	22000	97	33000	03-08-2025	PERTH	perth	Perth_Australia		3	8	2025	08MAR2025	368	08SEP2025
9	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025	MADRID	madrid	Madrid_Spain		3	9	2025	09MAR2025	367	09SEP2025
10	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025	SANTIAGO	santiago	Santiago_Chile		3	10	2025	10MAR2025	366	10SEP2025
11	Pretoria	Southafrica	11	6	4	13000	88	17000	03-11-2025	PRETORIA	pretoria	Pretoria_Southafrica		3	11	2025	11MAR2025	365	11SEP2025
12	Bangalore	India	19	13	1	21000	96	32000	03-12-2025	BANGALORE	bangalore	Bangalore_India		3	12	2025	12MAR2025	364	12SEP2025
13	Svalbard	Norway	21	15	0	25000	105	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway	Invalid Reliability	3	13	2025	13MAR2025	363	13SEP2025
14	Dubai	Uae	8	3	6	9000	80	12000	wrong-date	DUBAI	dubai	Dubai_Uae		.	.	.	.	.	.
15	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025	TOKYO	tokyo	Tokyo_Japan		3	15	2025	15MAR2025	361	15SEP2025

INTNX Function

INTNX calculates future or past dates.

Example:

INTNX('month',date,6)

This predicts maintenance after 6 months.

Code 8 – PROC SORT & Dataset MERGE

proc sort data=maintenance_forecast;by Station_Name;run;

proc print data=maintenance_forecast;

run;

OUTPUT:


Obs	Station_Name	Country	Tracking_Hours	Satellites_Supported	Signal_Loss_Events	Operating_Cost	Reliability_Index	Fees	Operation_Date	Station_Upper	Station_Lower	Station_Clean	Error_Flag	month	day	year	Correct_Date	Days_Since	Next_Maintenance
1	Alaska	Usa	13	7	4	14000	87	19000	03-06-2025	ALASKA	alaska	Alaska_Usa		3	6	2025	06MAR2025	370	06SEP2025
2	Bangalore	India	19	13	1	21000	96	32000	03-12-2025	BANGALORE	bangalore	Bangalore_India		3	12	2025	12MAR2025	364	12SEP2025
3	Beijing	China	15	.	1	21000	93	31000	03-07-2025	BEIJING	beijing	Beijing_China		3	7	2025	07MAR2025	369	07SEP2025
4	Dubai	Uae	8	3	6	9000	80	12000	wrong-date	DUBAI	dubai	Dubai_Uae		.	.	.	.	.	.
5	Goldstone	Usa	14	10	1	20000	101	30000	03-02-2025	GOLDSTONE	goldstone	Goldstone_Usa	Invalid Reliability	3	2	2025	02MAR2025	374	02SEP2025
6	Kourou	France	16	9	2	-12000	92	21000	03-04-2025	KOUROU	kourou	Kourou_France	Negative Cost	3	4	2025	04MAR2025	372	04SEP2025
7	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025	MADRID	madrid	Madrid_Spain		3	9	2025	09MAR2025	367	09SEP2025
8	Malindi	Kenya	-5	6	3	10000	89	15000	03-03-2025	MALINDI	malindi	Malindi_Kenya	Negative Hours	3	3	2025	03MAR2025	373	03SEP2025
9	Perth	Australia	20	14	2	22000	97	33000	03-08-2025	PERTH	perth	Perth_Australia		3	8	2025	08MAR2025	368	08SEP2025
10	Pretoria	Southafrica	11	6	4	13000	88	17000	03-11-2025	PRETORIA	pretoria	Pretoria_Southafrica		3	11	2025	11MAR2025	365	11SEP2025
11	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025	SANTIAGO	santiago	Santiago_Chile		3	10	2025	10MAR2025	366	10SEP2025
12	Sriharikota	India	12	8	2	15000	95	20000	03-01-2025	SRIHARIKOTA	sriharikota	Sriharikota_India		3	1	2025	01MAR2025	375	01SEP2025
13	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway		3	5	2025	05MAR2025	371	05SEP2025
14	Svalbard	Norway	21	15	0	25000	105	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway	Invalid Reliability	3	13	2025	13MAR2025	363	13SEP2025
15	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025	TOKYO	tokyo	Tokyo_Japan		3	15	2025	15MAR2025	361	15SEP2025

proc sort data=numeric_validation;by Station_Name;run;

proc print data=numeric_validation;

run;

OUTPUT:


Obs	Station_Name	Country	Tracking_Hours	Satellites_Supported	Signal_Loss_Events	Operating_Cost	Reliability_Index	Fees	Operation_Date	Station_Upper	Station_Lower	Station_Clean	Error_Flag
1	Alaska	Usa	13	7	4	14000	87	19000	03-06-2025	ALASKA	alaska	Alaska_Usa
2	Bangalore	India	19	13	1	21000	96	32000	03-12-2025	BANGALORE	bangalore	Bangalore_India
3	Beijing	China	15	.	1	21000	93	31000	03-07-2025	BEIJING	beijing	Beijing_China
4	Dubai	Uae	8	3	6	9000	80	12000	wrong-date	DUBAI	dubai	Dubai_Uae
5	Goldstone	Usa	14	10	1	20000	101	30000	03-02-2025	GOLDSTONE	goldstone	Goldstone_Usa	Invalid Reliability
6	Kourou	France	16	9	2	-12000	92	21000	03-04-2025	KOUROU	kourou	Kourou_France	Negative Cost
7	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025	MADRID	madrid	Madrid_Spain
8	Malindi	Kenya	-5	6	3	10000	89	15000	03-03-2025	MALINDI	malindi	Malindi_Kenya	Negative Hours
9	Perth	Australia	20	14	2	22000	97	33000	03-08-2025	PERTH	perth	Perth_Australia
10	Pretoria	Southafrica	11	6	4	13000	88	17000	03-11-2025	PRETORIA	pretoria	Pretoria_Southafrica
11	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025	SANTIAGO	santiago	Santiago_Chile
12	Sriharikota	India	12	8	2	15000	95	20000	03-01-2025	SRIHARIKOTA	sriharikota	Sriharikota_India
13	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway
14	Svalbard	Norway	21	15	0	25000	105	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway	Invalid Reliability
15	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025	TOKYO	tokyo	Tokyo_Japan

data station_merge;

merge maintenance_forecast

numeric_validation;

by Station_Name;

run;

proc print data=station_merge;

run;

OUTPUT:


Obs	Station_Name	Country	Tracking_Hours	Satellites_Supported	Signal_Loss_Events	Operating_Cost	Reliability_Index	Fees	Operation_Date	Station_Upper	Station_Lower	Station_Clean	Error_Flag	month	day	year	Correct_Date	Days_Since	Next_Maintenance
1	Alaska	Usa	13	7	4	14000	87	19000	03-06-2025	ALASKA	alaska	Alaska_Usa		3	6	2025	06MAR2025	370	06SEP2025
2	Bangalore	India	19	13	1	21000	96	32000	03-12-2025	BANGALORE	bangalore	Bangalore_India		3	12	2025	12MAR2025	364	12SEP2025
3	Beijing	China	15	.	1	21000	93	31000	03-07-2025	BEIJING	beijing	Beijing_China		3	7	2025	07MAR2025	369	07SEP2025
4	Dubai	Uae	8	3	6	9000	80	12000	wrong-date	DUBAI	dubai	Dubai_Uae		.	.	.	.	.	.
5	Goldstone	Usa	14	10	1	20000	101	30000	03-02-2025	GOLDSTONE	goldstone	Goldstone_Usa	Invalid Reliability	3	2	2025	02MAR2025	374	02SEP2025
6	Kourou	France	16	9	2	-12000	92	21000	03-04-2025	KOUROU	kourou	Kourou_France	Negative Cost	3	4	2025	04MAR2025	372	04SEP2025
7	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025	MADRID	madrid	Madrid_Spain		3	9	2025	09MAR2025	367	09SEP2025
8	Malindi	Kenya	-5	6	3	10000	89	15000	03-03-2025	MALINDI	malindi	Malindi_Kenya	Negative Hours	3	3	2025	03MAR2025	373	03SEP2025
9	Perth	Australia	20	14	2	22000	97	33000	03-08-2025	PERTH	perth	Perth_Australia		3	8	2025	08MAR2025	368	08SEP2025
10	Pretoria	Southafrica	11	6	4	13000	88	17000	03-11-2025	PRETORIA	pretoria	Pretoria_Southafrica		3	11	2025	11MAR2025	365	11SEP2025
11	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025	SANTIAGO	santiago	Santiago_Chile		3	10	2025	10MAR2025	366	10SEP2025
12	Sriharikota	India	12	8	2	15000	95	20000	03-01-2025	SRIHARIKOTA	sriharikota	Sriharikota_India		3	1	2025	01MAR2025	375	01SEP2025
13	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway		3	5	2025	05MAR2025	371	05SEP2025
14	Svalbard	Norway	21	15	0	25000	105	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway	Invalid Reliability	3	13	2025	13MAR2025	363	13SEP2025
15	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025	TOKYO	tokyo	Tokyo_Japan		3	15	2025	15MAR2025	361	15SEP2025

Why SORT Is Used

Sort is used to order the observations in ascending or descending.

Why MERGE Is Used

MERGE combines datasets using a common key.

Code 9 – Dataset APPEND

proc append base=station_merge

data=maintenance_forecast;

run;

proc print data=station_merge;

run;

OUTPUT:


Obs	Station_Name	Country	Tracking_Hours	Satellites_Supported	Signal_Loss_Events	Operating_Cost	Reliability_Index	Fees	Operation_Date	Station_Upper	Station_Lower	Station_Clean	Error_Flag	month	day	year	Correct_Date	Days_Since	Next_Maintenance
1	Alaska	Usa	13	7	4	14000	87	19000	03-06-2025	ALASKA	alaska	Alaska_Usa		3	6	2025	06MAR2025	370	06SEP2025
2	Bangalore	India	19	13	1	21000	96	32000	03-12-2025	BANGALORE	bangalore	Bangalore_India		3	12	2025	12MAR2025	364	12SEP2025
3	Beijing	China	15	.	1	21000	93	31000	03-07-2025	BEIJING	beijing	Beijing_China		3	7	2025	07MAR2025	369	07SEP2025
4	Dubai	Uae	8	3	6	9000	80	12000	wrong-date	DUBAI	dubai	Dubai_Uae		.	.	.	.	.	.
5	Goldstone	Usa	14	10	1	20000	101	30000	03-02-2025	GOLDSTONE	goldstone	Goldstone_Usa	Invalid Reliability	3	2	2025	02MAR2025	374	02SEP2025
6	Kourou	France	16	9	2	-12000	92	21000	03-04-2025	KOUROU	kourou	Kourou_France	Negative Cost	3	4	2025	04MAR2025	372	04SEP2025
7	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025	MADRID	madrid	Madrid_Spain		3	9	2025	09MAR2025	367	09SEP2025
8	Malindi	Kenya	-5	6	3	10000	89	15000	03-03-2025	MALINDI	malindi	Malindi_Kenya	Negative Hours	3	3	2025	03MAR2025	373	03SEP2025
9	Perth	Australia	20	14	2	22000	97	33000	03-08-2025	PERTH	perth	Perth_Australia		3	8	2025	08MAR2025	368	08SEP2025
10	Pretoria	Southafrica	11	6	4	13000	88	17000	03-11-2025	PRETORIA	pretoria	Pretoria_Southafrica		3	11	2025	11MAR2025	365	11SEP2025
11	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025	SANTIAGO	santiago	Santiago_Chile		3	10	2025	10MAR2025	366	10SEP2025
12	Sriharikota	India	12	8	2	15000	95	20000	03-01-2025	SRIHARIKOTA	sriharikota	Sriharikota_India		3	1	2025	01MAR2025	375	01SEP2025
13	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway		3	5	2025	05MAR2025	371	05SEP2025
14	Svalbard	Norway	21	15	0	25000	105	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway	Invalid Reliability	3	13	2025	13MAR2025	363	13SEP2025
15	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025	TOKYO	tokyo	Tokyo_Japan		3	15	2025	15MAR2025	361	15SEP2025
16	Alaska	Usa	13	7	4	14000	87	19000	03-06-2025	ALASKA	alaska	Alaska_Usa		3	6	2025	06MAR2025	370	06SEP2025
17	Bangalore	India	19	13	1	21000	96	32000	03-12-2025	BANGALORE	bangalore	Bangalore_India		3	12	2025	12MAR2025	364	12SEP2025
18	Beijing	China	15	.	1	21000	93	31000	03-07-2025	BEIJING	beijing	Beijing_China		3	7	2025	07MAR2025	369	07SEP2025
19	Dubai	Uae	8	3	6	9000	80	12000	wrong-date	DUBAI	dubai	Dubai_Uae		.	.	.	.	.	.
20	Goldstone	Usa	14	10	1	20000	101	30000	03-02-2025	GOLDSTONE	goldstone	Goldstone_Usa	Invalid Reliability	3	2	2025	02MAR2025	374	02SEP2025
21	Kourou	France	16	9	2	-12000	92	21000	03-04-2025	KOUROU	kourou	Kourou_France	Negative Cost	3	4	2025	04MAR2025	372	04SEP2025
22	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025	MADRID	madrid	Madrid_Spain		3	9	2025	09MAR2025	367	09SEP2025
23	Malindi	Kenya	-5	6	3	10000	89	15000	03-03-2025	MALINDI	malindi	Malindi_Kenya	Negative Hours	3	3	2025	03MAR2025	373	03SEP2025
24	Perth	Australia	20	14	2	22000	97	33000	03-08-2025	PERTH	perth	Perth_Australia		3	8	2025	08MAR2025	368	08SEP2025
25	Pretoria	Southafrica	11	6	4	13000	88	17000	03-11-2025	PRETORIA	pretoria	Pretoria_Southafrica		3	11	2025	11MAR2025	365	11SEP2025
26	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025	SANTIAGO	santiago	Santiago_Chile		3	10	2025	10MAR2025	366	10SEP2025
27	Sriharikota	India	12	8	2	15000	95	20000	03-01-2025	SRIHARIKOTA	sriharikota	Sriharikota_India		3	1	2025	01MAR2025	375	01SEP2025
28	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway		3	5	2025	05MAR2025	371	05SEP2025
29	Svalbard	Norway	21	15	0	25000	105	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway	Invalid Reliability	3	13	2025	13MAR2025	363	13SEP2025
30	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025	TOKYO	tokyo	Tokyo_Japan		3	15	2025	15MAR2025	361	15SEP2025

Explanation

APPEND adds observations to an existing dataset.

Code 10 – Dataset TRANSPOSE

proc transpose data=station_merge out=station_transpose;

by Station_Name NotSorted;

var Tracking_Hours Satellites_Supported Operating_Cost Fees;

run;

proc print data=station_transpose;

run;

OUTPUT:


Obs	Station_Name	_NAME_	COL1	COL2
1	Alaska	Tracking_Hours	13	.
2	Alaska	Satellites_Supported	7	.
3	Alaska	Operating_Cost	14000	.
4	Alaska	Fees	19000	.
5	Bangalore	Tracking_Hours	19	.
6	Bangalore	Satellites_Supported	13	.
7	Bangalore	Operating_Cost	21000	.
8	Bangalore	Fees	32000	.
9	Beijing	Tracking_Hours	15	.
10	Beijing	Satellites_Supported	.	.
11	Beijing	Operating_Cost	21000	.
12	Beijing	Fees	31000	.
13	Dubai	Tracking_Hours	8	.
14	Dubai	Satellites_Supported	3	.
15	Dubai	Operating_Cost	9000	.
16	Dubai	Fees	12000	.
17	Goldstone	Tracking_Hours	14	.
18	Goldstone	Satellites_Supported	10	.
19	Goldstone	Operating_Cost	20000	.
20	Goldstone	Fees	30000	.
21	Kourou	Tracking_Hours	16	.
22	Kourou	Satellites_Supported	9	.
23	Kourou	Operating_Cost	-12000	.
24	Kourou	Fees	21000	.
25	Madrid	Tracking_Hours	10	.
26	Madrid	Satellites_Supported	5	.
27	Madrid	Operating_Cost	12000	.
28	Madrid	Fees	16000	.
29	Malindi	Tracking_Hours	-5	.
30	Malindi	Satellites_Supported	6	.
31	Malindi	Operating_Cost	10000	.
32	Malindi	Fees	15000	.
33	Perth	Tracking_Hours	20	.
34	Perth	Satellites_Supported	14	.
35	Perth	Operating_Cost	22000	.
36	Perth	Fees	33000	.
37	Pretoria	Tracking_Hours	11	.
38	Pretoria	Satellites_Supported	6	.
39	Pretoria	Operating_Cost	13000	.
40	Pretoria	Fees	17000	.
41	Santiago	Tracking_Hours	17	.
42	Santiago	Satellites_Supported	10	.
43	Santiago	Operating_Cost	18000	.
44	Santiago	Fees	24000	.
45	Sriharikota	Tracking_Hours	12	.
46	Sriharikota	Satellites_Supported	8	.
47	Sriharikota	Operating_Cost	15000	.
48	Sriharikota	Fees	20000	.
49	Svalbard	Tracking_Hours	18	21
50	Svalbard	Satellites_Supported	11	15
51	Svalbard	Operating_Cost	17000	25000
52	Svalbard	Fees	25000	35000
53	Tokyo	Tracking_Hours	22	.
54	Tokyo	Satellites_Supported	16	.
55	Tokyo	Operating_Cost	26000	.
56	Tokyo	Fees	36000	.
57	Alaska	Tracking_Hours	13	.
58	Alaska	Satellites_Supported	7	.
59	Alaska	Operating_Cost	14000	.
60	Alaska	Fees	19000	.
61	Bangalore	Tracking_Hours	19	.
62	Bangalore	Satellites_Supported	13	.
63	Bangalore	Operating_Cost	21000	.
64	Bangalore	Fees	32000	.
65	Beijing	Tracking_Hours	15	.
66	Beijing	Satellites_Supported	.	.
67	Beijing	Operating_Cost	21000	.
68	Beijing	Fees	31000	.
69	Dubai	Tracking_Hours	8	.
70	Dubai	Satellites_Supported	3	.
71	Dubai	Operating_Cost	9000	.
72	Dubai	Fees	12000	.
73	Goldstone	Tracking_Hours	14	.
74	Goldstone	Satellites_Supported	10	.
75	Goldstone	Operating_Cost	20000	.
76	Goldstone	Fees	30000	.
77	Kourou	Tracking_Hours	16	.
78	Kourou	Satellites_Supported	9	.
79	Kourou	Operating_Cost	-12000	.
80	Kourou	Fees	21000	.
81	Madrid	Tracking_Hours	10	.
82	Madrid	Satellites_Supported	5	.
83	Madrid	Operating_Cost	12000	.
84	Madrid	Fees	16000	.
85	Malindi	Tracking_Hours	-5	.
86	Malindi	Satellites_Supported	6	.
87	Malindi	Operating_Cost	10000	.
88	Malindi	Fees	15000	.
89	Perth	Tracking_Hours	20	.
90	Perth	Satellites_Supported	14	.
91	Perth	Operating_Cost	22000	.
92	Perth	Fees	33000	.
93	Pretoria	Tracking_Hours	11	.
94	Pretoria	Satellites_Supported	6	.
95	Pretoria	Operating_Cost	13000	.
96	Pretoria	Fees	17000	.
97	Santiago	Tracking_Hours	17	.
98	Santiago	Satellites_Supported	10	.
99	Santiago	Operating_Cost	18000	.
100	Santiago	Fees	24000	.
101	Sriharikota	Tracking_Hours	12	.
102	Sriharikota	Satellites_Supported	8	.
103	Sriharikota	Operating_Cost	15000	.
104	Sriharikota	Fees	20000	.
105	Svalbard	Tracking_Hours	18	21
106	Svalbard	Satellites_Supported	11	15
107	Svalbard	Operating_Cost	17000	25000
108	Svalbard	Fees	25000	35000
109	Tokyo	Tracking_Hours	22	.
110	Tokyo	Satellites_Supported	16	.
111	Tokyo	Operating_Cost	26000	.
112	Tokyo	Fees	36000	.

Why TRANSPOSE Is Used

TRANSPOSE converts rows into columns.

Used for:

· Reporting

· Analytical transformations

Code 11 – Utilization Classification

data utilization_class;

set station_merge;

length Utilization $8.;

if Tracking_Hours >=20 then Utilization='High';

else if Tracking_Hours >=12 then Utilization='Medium';

else Utilization='Low';

run;

proc print data=utilization_class;

run;

OUTPUT:


Obs	Station_Name	Country	Tracking_Hours	Satellites_Supported	Signal_Loss_Events	Operating_Cost	Reliability_Index	Fees	Operation_Date	Station_Upper	Station_Lower	Station_Clean	Error_Flag	month	day	year	Correct_Date	Days_Since	Next_Maintenance	Utilization
1	Alaska	Usa	13	7	4	14000	87	19000	03-06-2025	ALASKA	alaska	Alaska_Usa		3	6	2025	06MAR2025	370	06SEP2025	Medium
2	Bangalore	India	19	13	1	21000	96	32000	03-12-2025	BANGALORE	bangalore	Bangalore_India		3	12	2025	12MAR2025	364	12SEP2025	Medium
3	Beijing	China	15	.	1	21000	93	31000	03-07-2025	BEIJING	beijing	Beijing_China		3	7	2025	07MAR2025	369	07SEP2025	Medium
4	Dubai	Uae	8	3	6	9000	80	12000	wrong-date	DUBAI	dubai	Dubai_Uae		.	.	.	.	.	.	Low
5	Goldstone	Usa	14	10	1	20000	101	30000	03-02-2025	GOLDSTONE	goldstone	Goldstone_Usa	Invalid Reliability	3	2	2025	02MAR2025	374	02SEP2025	Medium
6	Kourou	France	16	9	2	-12000	92	21000	03-04-2025	KOUROU	kourou	Kourou_France	Negative Cost	3	4	2025	04MAR2025	372	04SEP2025	Medium
7	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025	MADRID	madrid	Madrid_Spain		3	9	2025	09MAR2025	367	09SEP2025	Low
8	Malindi	Kenya	-5	6	3	10000	89	15000	03-03-2025	MALINDI	malindi	Malindi_Kenya	Negative Hours	3	3	2025	03MAR2025	373	03SEP2025	Low
9	Perth	Australia	20	14	2	22000	97	33000	03-08-2025	PERTH	perth	Perth_Australia		3	8	2025	08MAR2025	368	08SEP2025	High
10	Pretoria	Southafrica	11	6	4	13000	88	17000	03-11-2025	PRETORIA	pretoria	Pretoria_Southafrica		3	11	2025	11MAR2025	365	11SEP2025	Low
11	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025	SANTIAGO	santiago	Santiago_Chile		3	10	2025	10MAR2025	366	10SEP2025	Medium
12	Sriharikota	India	12	8	2	15000	95	20000	03-01-2025	SRIHARIKOTA	sriharikota	Sriharikota_India		3	1	2025	01MAR2025	375	01SEP2025	Medium
13	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway		3	5	2025	05MAR2025	371	05SEP2025	Medium
14	Svalbard	Norway	21	15	0	25000	105	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway	Invalid Reliability	3	13	2025	13MAR2025	363	13SEP2025	High
15	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025	TOKYO	tokyo	Tokyo_Japan		3	15	2025	15MAR2025	361	15SEP2025	High
16	Alaska	Usa	13	7	4	14000	87	19000	03-06-2025	ALASKA	alaska	Alaska_Usa		3	6	2025	06MAR2025	370	06SEP2025	Medium
17	Bangalore	India	19	13	1	21000	96	32000	03-12-2025	BANGALORE	bangalore	Bangalore_India		3	12	2025	12MAR2025	364	12SEP2025	Medium
18	Beijing	China	15	.	1	21000	93	31000	03-07-2025	BEIJING	beijing	Beijing_China		3	7	2025	07MAR2025	369	07SEP2025	Medium
19	Dubai	Uae	8	3	6	9000	80	12000	wrong-date	DUBAI	dubai	Dubai_Uae		.	.	.	.	.	.	Low
20	Goldstone	Usa	14	10	1	20000	101	30000	03-02-2025	GOLDSTONE	goldstone	Goldstone_Usa	Invalid Reliability	3	2	2025	02MAR2025	374	02SEP2025	Medium
21	Kourou	France	16	9	2	-12000	92	21000	03-04-2025	KOUROU	kourou	Kourou_France	Negative Cost	3	4	2025	04MAR2025	372	04SEP2025	Medium
22	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025	MADRID	madrid	Madrid_Spain		3	9	2025	09MAR2025	367	09SEP2025	Low
23	Malindi	Kenya	-5	6	3	10000	89	15000	03-03-2025	MALINDI	malindi	Malindi_Kenya	Negative Hours	3	3	2025	03MAR2025	373	03SEP2025	Low
24	Perth	Australia	20	14	2	22000	97	33000	03-08-2025	PERTH	perth	Perth_Australia		3	8	2025	08MAR2025	368	08SEP2025	High
25	Pretoria	Southafrica	11	6	4	13000	88	17000	03-11-2025	PRETORIA	pretoria	Pretoria_Southafrica		3	11	2025	11MAR2025	365	11SEP2025	Low
26	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025	SANTIAGO	santiago	Santiago_Chile		3	10	2025	10MAR2025	366	10SEP2025	Medium
27	Sriharikota	India	12	8	2	15000	95	20000	03-01-2025	SRIHARIKOTA	sriharikota	Sriharikota_India		3	1	2025	01MAR2025	375	01SEP2025	Medium
28	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway		3	5	2025	05MAR2025	371	05SEP2025	Medium
29	Svalbard	Norway	21	15	0	25000	105	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway	Invalid Reliability	3	13	2025	13MAR2025	363	13SEP2025	High
30	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025	TOKYO	tokyo	Tokyo_Japan		3	15	2025	15MAR2025	361	15SEP2025	High

Explanation

This macro automatically checks:

· Invalid reliability

· Negative hours

· Negative cost

Macros make SAS reusable and scalable.

Code 12 – Macro For Fraud Detection

%macro fraud_detection(ds);

data fraud_check;

set &ds;

if Reliability_Index >100 then Fraud='Yes';

if Tracking_Hours <0 then Fraud='Yes';

if Operating_Cost <0 then Fraud='Yes';

run;

proc print data=fraud_check;

run;

%mend;

%fraud_detection(station_merge);

OUTPUT:


Obs	Station_Name	Country	Tracking_Hours	Satellites_Supported	Signal_Loss_Events	Operating_Cost	Reliability_Index	Fees	Operation_Date	Station_Upper	Station_Lower	Station_Clean	Error_Flag	month	day	year	Correct_Date	Days_Since	Next_Maintenance	Fraud
1	Alaska	Usa	13	7	4	14000	87	19000	03-06-2025	ALASKA	alaska	Alaska_Usa		3	6	2025	06MAR2025	370	06SEP2025
2	Bangalore	India	19	13	1	21000	96	32000	03-12-2025	BANGALORE	bangalore	Bangalore_India		3	12	2025	12MAR2025	364	12SEP2025
3	Beijing	China	15	.	1	21000	93	31000	03-07-2025	BEIJING	beijing	Beijing_China		3	7	2025	07MAR2025	369	07SEP2025
4	Dubai	Uae	8	3	6	9000	80	12000	wrong-date	DUBAI	dubai	Dubai_Uae		.	.	.	.	.	.
5	Goldstone	Usa	14	10	1	20000	101	30000	03-02-2025	GOLDSTONE	goldstone	Goldstone_Usa	Invalid Reliability	3	2	2025	02MAR2025	374	02SEP2025	Yes
6	Kourou	France	16	9	2	-12000	92	21000	03-04-2025	KOUROU	kourou	Kourou_France	Negative Cost	3	4	2025	04MAR2025	372	04SEP2025	Yes
7	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025	MADRID	madrid	Madrid_Spain		3	9	2025	09MAR2025	367	09SEP2025
8	Malindi	Kenya	-5	6	3	10000	89	15000	03-03-2025	MALINDI	malindi	Malindi_Kenya	Negative Hours	3	3	2025	03MAR2025	373	03SEP2025	Yes
9	Perth	Australia	20	14	2	22000	97	33000	03-08-2025	PERTH	perth	Perth_Australia		3	8	2025	08MAR2025	368	08SEP2025
10	Pretoria	Southafrica	11	6	4	13000	88	17000	03-11-2025	PRETORIA	pretoria	Pretoria_Southafrica		3	11	2025	11MAR2025	365	11SEP2025
11	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025	SANTIAGO	santiago	Santiago_Chile		3	10	2025	10MAR2025	366	10SEP2025
12	Sriharikota	India	12	8	2	15000	95	20000	03-01-2025	SRIHARIKOTA	sriharikota	Sriharikota_India		3	1	2025	01MAR2025	375	01SEP2025
13	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway		3	5	2025	05MAR2025	371	05SEP2025
14	Svalbard	Norway	21	15	0	25000	105	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway	Invalid Reliability	3	13	2025	13MAR2025	363	13SEP2025	Yes
15	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025	TOKYO	tokyo	Tokyo_Japan		3	15	2025	15MAR2025	361	15SEP2025
16	Alaska	Usa	13	7	4	14000	87	19000	03-06-2025	ALASKA	alaska	Alaska_Usa		3	6	2025	06MAR2025	370	06SEP2025
17	Bangalore	India	19	13	1	21000	96	32000	03-12-2025	BANGALORE	bangalore	Bangalore_India		3	12	2025	12MAR2025	364	12SEP2025
18	Beijing	China	15	.	1	21000	93	31000	03-07-2025	BEIJING	beijing	Beijing_China		3	7	2025	07MAR2025	369	07SEP2025
19	Dubai	Uae	8	3	6	9000	80	12000	wrong-date	DUBAI	dubai	Dubai_Uae		.	.	.	.	.	.
20	Goldstone	Usa	14	10	1	20000	101	30000	03-02-2025	GOLDSTONE	goldstone	Goldstone_Usa	Invalid Reliability	3	2	2025	02MAR2025	374	02SEP2025	Yes
21	Kourou	France	16	9	2	-12000	92	21000	03-04-2025	KOUROU	kourou	Kourou_France	Negative Cost	3	4	2025	04MAR2025	372	04SEP2025	Yes
22	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025	MADRID	madrid	Madrid_Spain		3	9	2025	09MAR2025	367	09SEP2025
23	Malindi	Kenya	-5	6	3	10000	89	15000	03-03-2025	MALINDI	malindi	Malindi_Kenya	Negative Hours	3	3	2025	03MAR2025	373	03SEP2025	Yes
24	Perth	Australia	20	14	2	22000	97	33000	03-08-2025	PERTH	perth	Perth_Australia		3	8	2025	08MAR2025	368	08SEP2025
25	Pretoria	Southafrica	11	6	4	13000	88	17000	03-11-2025	PRETORIA	pretoria	Pretoria_Southafrica		3	11	2025	11MAR2025	365	11SEP2025
26	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025	SANTIAGO	santiago	Santiago_Chile		3	10	2025	10MAR2025	366	10SEP2025
27	Sriharikota	India	12	8	2	15000	95	20000	03-01-2025	SRIHARIKOTA	sriharikota	Sriharikota_India		3	1	2025	01MAR2025	375	01SEP2025
28	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway		3	5	2025	05MAR2025	371	05SEP2025
29	Svalbard	Norway	21	15	0	25000	105	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway	Invalid Reliability	3	13	2025	13MAR2025	363	13SEP2025	Yes
30	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025	TOKYO	tokyo	Tokyo_Japan		3	15	2025	15MAR2025	361	15SEP2025

Code 13 – Correcting Errors

data station_corrected;

set station_merge;

if Tracking_Hours <0 then Tracking_Hours=abs(Tracking_Hours);

if Reliability_Index>100 then Reliability_Index=100;

if Operating_Cost <0 then Operating_Cost=abs(Operating_Cost);

run;

proc print data=station_corrected;

run;

OUTPUT:


Obs	Station_Name	Country	Tracking_Hours	Satellites_Supported	Signal_Loss_Events	Operating_Cost	Reliability_Index	Fees	Operation_Date	Station_Upper	Station_Lower	Station_Clean	Error_Flag	month	day	year	Correct_Date	Days_Since	Next_Maintenance
1	Alaska	Usa	13	7	4	14000	87	19000	03-06-2025	ALASKA	alaska	Alaska_Usa		3	6	2025	06MAR2025	370	06SEP2025
2	Bangalore	India	19	13	1	21000	96	32000	03-12-2025	BANGALORE	bangalore	Bangalore_India		3	12	2025	12MAR2025	364	12SEP2025
3	Beijing	China	15	.	1	21000	93	31000	03-07-2025	BEIJING	beijing	Beijing_China		3	7	2025	07MAR2025	369	07SEP2025
4	Dubai	Uae	8	3	6	9000	80	12000	wrong-date	DUBAI	dubai	Dubai_Uae		.	.	.	.	.	.
5	Goldstone	Usa	14	10	1	20000	100	30000	03-02-2025	GOLDSTONE	goldstone	Goldstone_Usa	Invalid Reliability	3	2	2025	02MAR2025	374	02SEP2025
6	Kourou	France	16	9	2	12000	92	21000	03-04-2025	KOUROU	kourou	Kourou_France	Negative Cost	3	4	2025	04MAR2025	372	04SEP2025
7	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025	MADRID	madrid	Madrid_Spain		3	9	2025	09MAR2025	367	09SEP2025
8	Malindi	Kenya	5	6	3	10000	89	15000	03-03-2025	MALINDI	malindi	Malindi_Kenya	Negative Hours	3	3	2025	03MAR2025	373	03SEP2025
9	Perth	Australia	20	14	2	22000	97	33000	03-08-2025	PERTH	perth	Perth_Australia		3	8	2025	08MAR2025	368	08SEP2025
10	Pretoria	Southafrica	11	6	4	13000	88	17000	03-11-2025	PRETORIA	pretoria	Pretoria_Southafrica		3	11	2025	11MAR2025	365	11SEP2025
11	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025	SANTIAGO	santiago	Santiago_Chile		3	10	2025	10MAR2025	366	10SEP2025
12	Sriharikota	India	12	8	2	15000	95	20000	03-01-2025	SRIHARIKOTA	sriharikota	Sriharikota_India		3	1	2025	01MAR2025	375	01SEP2025
13	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway		3	5	2025	05MAR2025	371	05SEP2025
14	Svalbard	Norway	21	15	0	25000	100	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway	Invalid Reliability	3	13	2025	13MAR2025	363	13SEP2025
15	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025	TOKYO	tokyo	Tokyo_Japan		3	15	2025	15MAR2025	361	15SEP2025
16	Alaska	Usa	13	7	4	14000	87	19000	03-06-2025	ALASKA	alaska	Alaska_Usa		3	6	2025	06MAR2025	370	06SEP2025
17	Bangalore	India	19	13	1	21000	96	32000	03-12-2025	BANGALORE	bangalore	Bangalore_India		3	12	2025	12MAR2025	364	12SEP2025
18	Beijing	China	15	.	1	21000	93	31000	03-07-2025	BEIJING	beijing	Beijing_China		3	7	2025	07MAR2025	369	07SEP2025
19	Dubai	Uae	8	3	6	9000	80	12000	wrong-date	DUBAI	dubai	Dubai_Uae		.	.	.	.	.	.
20	Goldstone	Usa	14	10	1	20000	100	30000	03-02-2025	GOLDSTONE	goldstone	Goldstone_Usa	Invalid Reliability	3	2	2025	02MAR2025	374	02SEP2025
21	Kourou	France	16	9	2	12000	92	21000	03-04-2025	KOUROU	kourou	Kourou_France	Negative Cost	3	4	2025	04MAR2025	372	04SEP2025
22	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025	MADRID	madrid	Madrid_Spain		3	9	2025	09MAR2025	367	09SEP2025
23	Malindi	Kenya	5	6	3	10000	89	15000	03-03-2025	MALINDI	malindi	Malindi_Kenya	Negative Hours	3	3	2025	03MAR2025	373	03SEP2025
24	Perth	Australia	20	14	2	22000	97	33000	03-08-2025	PERTH	perth	Perth_Australia		3	8	2025	08MAR2025	368	08SEP2025
25	Pretoria	Southafrica	11	6	4	13000	88	17000	03-11-2025	PRETORIA	pretoria	Pretoria_Southafrica		3	11	2025	11MAR2025	365	11SEP2025
26	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025	SANTIAGO	santiago	Santiago_Chile		3	10	2025	10MAR2025	366	10SEP2025
27	Sriharikota	India	12	8	2	15000	95	20000	03-01-2025	SRIHARIKOTA	sriharikota	Sriharikota_India		3	1	2025	01MAR2025	375	01SEP2025
28	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway		3	5	2025	05MAR2025	371	05SEP2025
29	Svalbard	Norway	21	15	0	25000	100	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway	Invalid Reliability	3	13	2025	13MAR2025	363	13SEP2025
30	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025	TOKYO	tokyo	Tokyo_Japan		3	15	2025	15MAR2025	361	15SEP2025

Code 14 – Delete Temporary Datasets

proc datasets library=work;

delete numeric_validation station_transpose duration_calc;

quit;

LOG:

 NOTE: Deleting WORK.NUMERIC_VALIDATION (memtype=DATA).

 NOTE: Deleting WORK.STATION_TRANSPOSE (memtype=DATA).

 NOTE: Deleting WORK.DURATION_CALC (memtype=DATA).

Final Corrected Dataset Code

proc sort data=station_corrected nodup;by Station_Name;run;

proc print data=station_corrected;

title "Final Cleaned Space Ground Station Dataset";

run;

OUTPUT:

Final Cleaned Space Ground Station Dataset


Obs	Station_Name	Country	Tracking_Hours	Satellites_Supported	Signal_Loss_Events	Operating_Cost	Reliability_Index	Fees	Operation_Date	Station_Upper	Station_Lower	Station_Clean	Error_Flag	month	day	year	Correct_Date	Days_Since	Next_Maintenance
1	Alaska	Usa	13	7	4	14000	87	19000	03-06-2025	ALASKA	alaska	Alaska_Usa		3	6	2025	06MAR2025	370	06SEP2025
2	Bangalore	India	19	13	1	21000	96	32000	03-12-2025	BANGALORE	bangalore	Bangalore_India		3	12	2025	12MAR2025	364	12SEP2025
3	Beijing	China	15	.	1	21000	93	31000	03-07-2025	BEIJING	beijing	Beijing_China		3	7	2025	07MAR2025	369	07SEP2025
4	Dubai	Uae	8	3	6	9000	80	12000	wrong-date	DUBAI	dubai	Dubai_Uae		.	.	.	.	.	.
5	Goldstone	Usa	14	10	1	20000	100	30000	03-02-2025	GOLDSTONE	goldstone	Goldstone_Usa	Invalid Reliability	3	2	2025	02MAR2025	374	02SEP2025
6	Kourou	France	16	9	2	12000	92	21000	03-04-2025	KOUROU	kourou	Kourou_France	Negative Cost	3	4	2025	04MAR2025	372	04SEP2025
7	Madrid	Spain	10	5	.	12000	85	16000	03-09-2025	MADRID	madrid	Madrid_Spain		3	9	2025	09MAR2025	367	09SEP2025
8	Malindi	Kenya	5	6	3	10000	89	15000	03-03-2025	MALINDI	malindi	Malindi_Kenya	Negative Hours	3	3	2025	03MAR2025	373	03SEP2025
9	Perth	Australia	20	14	2	22000	97	33000	03-08-2025	PERTH	perth	Perth_Australia		3	8	2025	08MAR2025	368	08SEP2025
10	Pretoria	Southafrica	11	6	4	13000	88	17000	03-11-2025	PRETORIA	pretoria	Pretoria_Southafrica		3	11	2025	11MAR2025	365	11SEP2025
11	Santiago	Chile	17	10	2	18000	94	24000	03-10-2025	SANTIAGO	santiago	Santiago_Chile		3	10	2025	10MAR2025	366	10SEP2025
12	Sriharikota	India	12	8	2	15000	95	20000	03-01-2025	SRIHARIKOTA	sriharikota	Sriharikota_India		3	1	2025	01MAR2025	375	01SEP2025
13	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway		3	5	2025	05MAR2025	371	05SEP2025
14	Svalbard	Norway	21	15	0	25000	100	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway	Invalid Reliability	3	13	2025	13MAR2025	363	13SEP2025
15	Svalbard	Norway	18	11	0	17000	98	25000	03-05-2025	SVALBARD	svalbard	Svalbard_Norway		3	5	2025	05MAR2025	371	05SEP2025
16	Svalbard	Norway	21	15	0	25000	100	35000	03-13-2025	SVALBARD	svalbard	Svalbard_Norway	Invalid Reliability	3	13	2025	13MAR2025	363	13SEP2025
17	Tokyo	Japan	22	16	1	26000	99	36000	03-15-2025	TOKYO	tokyo	Tokyo_Japan		3	15	2025	15MAR2025	361	15SEP2025

Key Insights From This Project

· SAS can identify operational anomalies in aerospace datasets

· Character functions help standardize inconsistent text

· Numeric validations detect impossible values

· Date functions support mission scheduling

· Macros automate fraud detection logic

· MERGE combines operational logs

· APPEND extends datasets dynamically

· TRANSPOSE helps generate analytical reports

· Utilization classification supports capacity planning

· PROC DATASETS improves workspace efficiency

Summary

Advanced SAS programming can play a significant role in detecting fraud and correcting data errors in global space ground station operations. Ground stations generate large volumes of operational data such as tracking hours, number of satellites supported, signal loss events, operating costs, reliability percentages, and service fees. When this data contains mistakes, missing values, or manipulated records, it can affect satellite communication efficiency and financial reporting. Using SAS, programmers can build validation rules to identify unusual patterns such as negative tracking hours, reliability values greater than 100 percent, or incorrect cost entries. SAS functions, procedures, and macros allow analysts to clean text fields, standardize formats, validate dates, and flag suspicious observations automatically. By applying techniques like data merging, transposing, and automated fraud detection logic, SAS helps transform raw, error-prone data into reliable operational datasets. As a result, organizations can improve decision-making, maintain accurate records, and ensure trustworthy monitoring of global ground station performance.

Conclusion

Space ground station networks generate large volumes of operational data every day. Because satellite communication is mission-critical, even small errors in tracking logs or reliability statistics can have serious consequences.

In this project we demonstrated how Advanced SAS Programming can:

· Detect data anomalies

· Identify fraudulent modifications

· Correct corrupted values

· Standardize inconsistent records

· Automate validation processes

Using a combination of:

· DATA steps

· Numeric functions

· Character functions

· Date handling functions

· Macros

· Dataset management procedures

SAS provides a powerful framework for maintaining data integrity in complex operational environments such as satellite ground station networks.

The same techniques can be applied in other domains such as:

· Aviation analytics

· Defense communication networks

· Telecommunications monitoring

· Financial fraud detection

Thus, SAS programming remains a valuable tool for ensuring high-quality, reliable data in mission-critical systems.

SAS INTERVIEW QUESTIONS

1.What is the difference between HAVING and WHERE in SQL?

Answer:
WHERE filters rows before aggregation, while HAVING filters after aggregation.

2.What is a Cartesian join?

Answer:
A Cartesian join occurs when every row of one table joins with every row of another table.

3.What is DISTINCT used for?

Answer:
DISTINCT removes duplicate rows from query results.

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Disclaimer:
The datasets and analysis in this article are created for educational and demonstration purposes only. They do not represent GLOBAL SPACE data.

Our Mission:
This blog provides industry-focused SAS programming tutorials and analytics projects covering finance, healthcare, and technology.

This project is suitable for:
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425.Can Advanced SAS Programming Detect Fraud And Correct Data Errors In Global Space Ground Station Operations?

Exploring Data Accuracy And Fraud Detection In Global Ground Station Operations Using SAS

HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —DATA STEP | LENGTH | INPUT | DATALINES | FORMAT | SET | IF-THEN- KEEP | DROP | MERGE | PROC SORT | PROC PRINT | PROC APPEND | PROC TRANSPOSE | PROC DATASETS | MACRO

Table Of Contents

Business Context

Dataset Variables

Intentional Errors Introduced

Explanation

Explanation

Why It Is Important

Character Functions Used

Explanation

Explanation

Why MDY Is Used

INTCK Function

INTNX Function

Why SORT Is Used

Why MERGE Is Used

Explanation

Why TRANSPOSE Is Used

Explanation

Conclusion

1.What is the difference between HAVING and WHERE in SQL?

Answer: WHERE filters rows before aggregation, while HAVING filters after aggregation.

2.What is a Cartesian join?

Answer: A Cartesian join occurs when every row of one table joins with every row of another table.

3.What is DISTINCT used for?

Answer: DISTINCT removes duplicate rows from query results.

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To deepen your understanding of SAS analytics, please refer to our other data science and industry-focused projects listed below:

1.Is It Possible To Turn Simple Urea Datasets Into Powerful Chemical Reports Using SAS?2.Can Multiple SAS Procedures Unlock The Story Behind Indian Coin Circulation And Value Trends?3.How Can PROC FORMAT, PROC FREQ, And PROC MEANS Transform Raw State Population Data Into Insights?

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Answer:
WHERE filters rows before aggregation, while HAVING filters after aggregation.

Answer:
A Cartesian join occurs when every row of one table joins with every row of another table.

Answer:
DISTINCT removes duplicate rows from query results.

1.Is It Possible To Turn Simple Urea Datasets Into Powerful Chemical Reports Using SAS?

2.Can Multiple SAS Procedures Unlock The Story Behind Indian Coin Circulation And Value Trends?

3.How Can PROC FORMAT, PROC FREQ, And PROC MEANS Transform Raw State Population Data Into Insights?