esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Mobility Environment
### Experimental Results  
Figure 5 denotes the comparison of the network lifespan for the three routing protocols, namely OCS-AODV, CS-DSDV, and ACO-AODV, under various environmental conditions, namely 0 to 60, 60-80, and 80-100 internals. When nodes are in an idle state, they switch to a sleep mode to save battery. We observed that, compared to other routing protocols, the proposed scheme was the best performing scheme. The advantage of the proposed scheme is that it utilizes the cluster-heads, optimization strategies, and improved data-transmission techniques to conserve energy of the nodes. The graph shows the performance of the three routing protocols during the simulation. The time ranges are divided into three categories reflecting observation of the mobile nodes on how long they could operate before they could ran out of power. It shows that the proposed scheme consumes the least power compared to the other routing protocols.  
For time interval, from 0 to 60 s shows that the energy is still sufficient; continuing to 60 to 80 s shows that the CS-DSDV and the ACO-AODV routing protocols' residual energy is decreasing, while the proposed scheme still has sufficient energy. Finally, between 80 to 100, it shows how long the average energy can last between the schemes. Hence, we observed that the CS-DSDV and ACO-AODV energy was almost depleted, whereas the proposed scheme still had sufficient energy. Figure 6 present the packet loses and packet delivery rates of the three schemes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Source Nodes** & **Destination Nodes** & **File-Transfer Protocol** \\ \hline Node 39 & Node 37 & ftp 0 \\ Node 14 & Node 26 & ftp 1 \\ Node 29 & Node 36 & ftp 2 \\ Node 30 & Node 15 & ftp 3 \\ Node 23 & Node 28 & ftp 8 \\ Node 24 & Node 31 & ftp 7 \\ Node 38 & Node 32 & ftp 6 \\ Node 34 & Node 13 & ftp 4 \\ Node 35 & Node 12 & ftp 5 \\ Node 0 & Node 33 & ftp 9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Demonstrates configuration connections between the source and destination nodes.  
Figure 5: Energy consumption of the three routing protocols during the three time intervals.
# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 6 Experimental Results and Discussion  
### Experimental Setup  
All experiments were implemented by using Python programming language and Python libraries such as Scikit-learn, TensorFlow, Keras, etc. They were run on the Google Colab Pro+ platform. The performances of the investigated models were measured by the accuracy score, which is obtained from  
\[Accuracy=\left[\frac{1}{K}\sum_{k=1}^{K}\frac{TP_{k}+TN_{k}}{TP_{k}+FP_{k}+TN_{k} +FN_{k}}\right]\times 100, \tag{22}\]  
where \(K\) is the number of classes, \(TP_{k}\) is the number of true positives of the \(k\)-th class, \(FP_{k}\) is the number of false positives of the \(k\)-th class, \(TN_{k}\) is the number of true negatives of the \(k\)-th class, and \(FN_{k}\) is the number of false negatives of the \(k\)-th class. There are two basic approaches to evaluate the model performances: training-validation-testing split and \(k\)-fold cross validation. The training-validation-testing split will divide a dataset into three separated parts: training set, validation set, and testing set. Therefore, the performance results of the investigated model will highly depend on the data in the testing set. In order to avoid this problem, similar to [18; 22; 25; 27], we applied the \(k\)-fold cross validation, where \(k\) is set to 10, to the experiments. The 10-fold cross validation will divide a dataset into 10 parts. One part will be selected as the testing set while the remaining nine parts will be the training set. We evaluate an investigated model 10 times. For each time, we select a different part to be the testing set. Thereafter, the performance results will be the average of the testing scores. The investigated models were trained by minimizing the categorical cross-entropy using the Adam optimizer with the settings \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), and \(\epsilon=10^{-7}\). The training rate was set to 0.001. The batch size was 32. The number of epochs was 40. We did not experience an overfitting issue. Our training scores are slightly higher than the testing scores.
# A Novel OFDM Format and a Machine Learning Based Dimming Control for LiFi
## 6 Dimming Control for DPO and HDAP Systems
Applying this concept, a wide dimming range with better spectral efficiency is achieved. Figure 11 shows a block diagram of the transmitter of this combining technique. By applying this technique, greater spectral efficiency with a wide dimming range can be achieved using different elements of HDAP and HDAP itself. A light sensor at the start of the transmitter section observes the ambient light and its changes. This light sensor will calculate the dimming level. After obtaining the required dimming level, the scheme selection block will select the suitable scheme. In short, when the brightness is very low or very high, then the ACO-PAM scheme will carry the data. Again, in the lower mid brightness, a stand-alone HDAP scheme will be activated and carry the data. Lastly, when the brightness is in the mid-level, then DCO will carry the data. The term \(\eta_{r}\) is assumed as the required dimming level. The target bit error rate, BER, is \(10^{-3}\). Furthermore, \(M_{ACO}\)  
Figure 11: Block Diagram of the transmitter part for switching between different components of hybrid DC biased asymmetrically clipped pulse amplitude modulated discrete multitone modulation optical OFDM (HDAP-OFDM), where OFDM stands for orthogonal frequency division multiplexing. Figure 10: Bit error rate (BER) versus electrical energy per bit to noise power spectral density (\(E_{b(dec)}/N_{O}\)) for dc-biased pulse amplitude modulated orthogonal frequency division multiplexing (DPO-OFDM) for different dimming levels, \(\eta\). \(M_{DCO}\), \(M_{PAM}\) are the sizes of the constellation points for ACO, DCO, and PAM-DMT, respectively. By which spectral efficiency can be achieved, and it is measured in bit/s/Hz. Here, \(I_{L}=0\) and \(I_{H}=1\), and the average amplitude of the signal \(I_{D}\). With these assumptions, Algorithm 1. presents the switching method between ACO-PAM, HDAP, and DCO for obtaining wider dimming range and better spectral efficiency. ```
1: Input \(I_{L}=0\), \(I_{H}=1\), BER = \(10^{-3}\), \(N\geq 128\);
2: Compute the required dimming level \(\eta_{r}\);
3: If (\(\eta_{r}=2\%\) to 10% or \(\eta_{r}=90\%\) to 98%);
4: THEN select the ACO-PAM scheme;
5: ELSE IF (\(\eta_{r}=10\%\) to 30% or \(\eta_{r}=70\%\) to 90%);
6: THEN select the stand-alone HDAP (50% DCO and 50% ACO-PAM) scheme;
7: ELSE;
8:-select the DCO scheme. ```  
**Algorithm 1** Pseudocode for switching method between ACO-PAM, HDAP, and DCO
# Testing of Indoor Obstacle-Detection Prototypes Designed for Visually Impaired Persons
## 3 Results
### Tests with Various Materials Placed in Front of the Prototype  
The upcoming series of tests aimed to assess whether materials within a building can absorb or influence the 40 kHz ultrasonic wave emitted by the HC-SR04 ultrasonic sensor.  
These tests were performed utilizing only the WAVE prototype, as they pertain to the sonic wave and not variations between prototypes. The materials positioned at a distance of 50 cm from the prototype will encompass: metal, glass, mirror, concrete, plastic, wood, particleboard, MDF, canvas, denim, cotton, linen, aluminum, cardboard, leather, and paper. The prototype will conduct distance measurements for each material type over a period of 10 min.  
The prototypes were moved to all the already marked testing distances and a log file was saved. Analyzing all the log files for those prototypes, the accuracy results are shown in Figures 20 and 21.  
In conclusion, no material was identified as being able to absorb the 40 kHz wave, while there might be specifically engineered materials designed to deflect or absorb this wave, the probability of encountering them in a civilian building is quite low. Therefore, it can be inferred that, regardless of the material composition of the obstacle, detection will occur, and the user will receive a precise message.  
Figure 20: Materials first figure.  
Based on the tests conducted thus far, it is evident that the shape and surface characteristics of the obstacle exert a more significant impact on the distance readings by the prototype than the material composition itself.
# Research on Key Technologies of Elastic Satellite Optical Network Based on Optical Service Unit
## 4 Conclusions  
The proposed OISL-OSU method provides an effective solution to the bottleneck of carrying small-granularity, high-quality dedicated line services in current satellite optical networks. The comparison test results demonstrates that OISL-OSU outperforms OISL-MPLS in terms of bandwidth utilization, delay, jitter, and burst processing capabilities. The hard-channel, deterministic delay, and high reliability characteristics of OISL-OSU align with the stringent requirements of 6G networks for space-terrene-sea integration and industrial Internet. OISL-OSU can be applied in specialized scenarios such as high-frequency financial trading (microsecond-level delay-sensitive), industrial automation (with deterministic delay requirements), and ultra-high-definition video transmission (ensuring smoothness with low jitter), and is expected to become one of the core transmission technologies in 6G networks.  
Conceptualization, W.Z. and Q.L.; methodology, W.Z. and B.C.; software, W.Z. and B.C.; hardware, W.Z. and B.P.; validation, W.Z. and B.G.; writing--original draft preparation, W.Z.; writing--review and editing, B.G.; supervision, Q.L.; project administration, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was fully funded by the 2024 Guangxi Key R&D Program Project (Grant No. GuiKe AB24010272, Approval No. 2023 AB07026) and Development Fund Project of the 34 th Institute of China Electronics Science and Technology Group Corporation in 2022 (Grant No. K1340020225702).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. [PERSON], [PERSON], and [PERSON] are employees of The 34 th Research Institute of China Electronics Technology Group Corporation. The authors declare that this study received funding from The 34 th Research Institute of China Electronics Technology Group Corporation. The funder had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AOSs & Advanced orbiting systems \\ CCSA & China Communications Standards Association \\ CCSDS & Consultative Committee for Space Data Systems \\ CETC & China Electronics Technology Group Corporation \\ DCN & Data communication network \\ DDPG & Depth deterministic policy gradient \\ DRL & Deep reinforcement learning \\ FEC & Forward error correction \\ FIB & Forwarding information base \\ \end{tabular}
# Latency Analysis of Blockchain-Based SSI Applications +
###### Abstract  
Several revolutionary applications have been built on the distributed ledgers of blockchain (BC) technology. Besides cryptocurrencies, many other application fields can be found in smart systems exploiting smart contracts and Self Sovereign Identity (SSI) management. The Hyperledger Indy platform is a suitable open-source solution for realizing permissioned BC systems for SSI projects. SSI applications usually require short response times from the underlying BC network, which may vary highly depending on the application type, the used BC software, and the actual BC deployment parameters. To support the developers and users of SSI applications, we present a detailed latency analysis of a permissioned BC system built with Indy and Aries. To streamline our experiments, we developed a Python application using containerized Indy and Aries components from official Hyperledger repositories. We deployed our experimental application on multiple virtual machines in the public Google Cloud Platform and on our local, private cloud using a Docker platform with Kubernetes. We evaluated and compared their performance benchmarked by Read and Write latencies. We found that the local Indy ledger reads and writes 30-50%, and 65-85% faster than the Indy ledger running on the Google Cloud Platform, respectively.  
blockchain; Self Sovereign Identity; Hyperledger Indy; latency analysis Article
# Diagnosing Schizophrenia Using Effective Connectivity of Resting-State EEG Data
## 2 Materials and Methods
### Data Pre-Processing  
To minimize the artifacts, we first band-pass-filtered the EEG signal with cut-off frequencies of 0.5 Hz and 50 Hz. We then used the wavelet-enhanced independent component analysis (wICA) method to detect and remove the components that were contaminated with the artifacts [42]. wICA uses the wavelet threshold to enhance artifact removal with independent components analysis and can therefore better recover the neural activities that are hidden in the artifacts.
# A Study of Applying Fuzzy Theory in Simulation-Based Education
## 4 Empirical Analysis
### First Stage of Questionnaire Analysis
In the scenario \"Recognized 'Air Traffic Control' (ATC) unsafe terrain clearance,\" SA means \"Perceives and comprehends all of the relevant information available and anticipates what could happen that may affect the operation.\" Most experts thought that nonstandard landing zones or hindrances around mountain areas were unpredictable. Normally, the flight crew should conduct pre-landing checks in advance and remind and communicate with each other to confirm hindrances around mountain areas and execute an emergency departure procedure when necessary. Thus, SA was not the most important consideration and should be deleted. 4. In the scenario \"Increasing tailwind on final approach (not reported),\" communication means \"Demonstrates effective oral, non-verbal, and written communications in normal and non-normal situations.\" Both sides should use standard terminology with correct, clear, and precise communication in mind to communicate to reduce communication errors. Therefore, most of the experts thought that harsh wind might have a greater influence on helicopters. In addition, unlike fixed-wing aircraft, helicopters possess low airspeed and low-altitude hovering characteristics. It is unnecessary for a long and straight runway for the final approach for landing; but when the altitude and speed decreased, ground hindrances were closer, and sudden crosswind or gust wind was encountered, which might have resulted in operations due to rotary-wing aircraft limitations and increased the risk of coming closer to ground hindrances. Thus, the experts thought that most control pilots would respond to handle the situation first instinctively but not engage in crew communication. Thus, communication was inapplicable in this situation and should be deleted. After screening via FDM, the results were as follows: In the scenario \"Increasing tailwind on final approach (not reported),\" workload management was deleted; in the scenario \"High-altitude area or high payload made horsepower insufficient,\" workload management was deleted; in the scenario \"Multiple external NAV failure. NAV capability deteriorated,\" automation and workload management were deleted; in the scenario \"Recognized Air Traffic Control (ATC) unsafe terrain clearance,\" SA was deleted. Six competencies within six scenarios were deleted, so of the 24 competencies, 18 were left as the basic reference for \"Evidence-based training on the crew's helicopter resources management\" second-stage questionnaire survey (see Table 5).
## 3 Proposed Method  
### Method Overview  
An important feature of the proposed method is to reduce the load on the server side by extracting and transmitting only Regions of Interest (ROI) through preprocessing of images captured in the edge systems (i.e., surveillance cameras). Figure 2 illustrates the overall process from capturing camera images to object-detection results in the proposed method.  
As shown in Figure 2, our proposed method is implemented at both the edge and the server. The traditional method is server-side, receiving images directly from the camera, which places a heavy load on the server. The proposed method removes the background region from the edge side where the camera is located and sends the image in a low-bandwidth format. For this purpose, the Reference Image generator, DSSIM-MAP generator, and RO filter are additionally located on the edge side. On the server side, an additional Background Color Switcher module is deployed to receive images and exchange background colors.
# Value of Information Based Data Retrieval in UWSNs
## 2 Related Work
### Some Applications of IQ and VoI in Sensor Networks  
IQ (information quality) aware routing schemes make data routing decisions based on the threshold of information required by application or mission objectives [29; 30; 31]. The data is fused en-route to the sink node and routed on paths which can potentially satisfy the aggregated IQ threshold or constraints. Data is kept fusing incrementally on each next forwarding node until it reaches the IQ threshold. When the threshold is met, the information is sent to the main fusion center or sink node.  
We use Vol in an event driven context. An event-based information quality aware routing (IQAR) has been proposed in [32]. The unique aspect about IQAR is its use of event-based data fusion, whereas, in the previous IQ aware routing algorithms, the fusion process would be initiated from the fusion centers. In the initial phase of the IQAR method, an aggregation tree is constructed that spans the whole network. In the case of an event, the sensors in vicinity forward the data using the preexisting links as established during the tree initialization. After data reaches the fusion center, the network uses a greedy approach to prune the initial tree in order to maximize the information retrieved and to minimize the energy consumption of the nodes on the data forwarding links.  
One of the applications where IQ and Vol have been used in sensor networks is for tracking [30; 33]. In [33] a Vol-based approach has been proposed for intruder tracking objectives. The sensor network, based on predictive measures centered around Vol, makes data routing decisions in the network for more efficient target tracking. The network is able to prioritize among high and low-value targets. Tracking a higher value target should result in a higher amount of Vol accumulated. In [34], the authors further proposed IVE which is an improved version of the Vol-based intruder tracking system. The network is able to control the demand of data packets while balancing trade-offs between network energy consumption and required Vol.  
Concepts of quality and utility have been frequently applied to a variety of scheduling activities in sensor networks. Some examples of scheduling algorithms and protocols, which employ concepts of quality based on priority and utility metrics, are [35; 36; 37; 38; 39; 40; 41].
# A Modified Compact Flexible Vivaldi Antenna Array Design for Microwave Breast Cancer Detection
## 5 Conclusions  
In this paper, a compact flexible Vivaldi antenna for breast imaging application is proposed. The resultant antenna has two resonant frequencies, at 4 and 9.4 GHz. To eliminate the second frequency and enhance the bandwidth of the proposed Vivaldi antenna, two slots have been etched on the ground side of the antenna. Moreover, two rectangular parasitic elements in the radiation side of the antenna have been added. Due to the added DGS and parasitic elements, the resonant frequency is shifted to 4.4 GHz and the simulated bandwidth increased by 20%. The antenna is fabricated on Polyimide flexible material with a thickness of 0.1 mm and dielectric constant of 3.5 using standard PCB manufacturing processes.  
The measured and the simulated reflection coefficient are compared, and it is found that they are in good agreement with each other, as discussed in Section 2.5. An array of 9 antennas is used as a microwave breast imaging model to detect and locate the tumor inside the proposed multilayer phantom model using Microwave Radar Based Imaging toolbox (MERIT). This simulation setup successfully identifies and locates the tumors of different sizes and positions inside the phantom model. A more complex breast phantom model can be used that includes more layers such as the fibroglandural tissue, with a dielectric constant close to that of tumor. Moreover, an imaging setup with a higher number of antennas may be used to enhance imaging quality. In the nine-antenna array, the resolution we obtained is good enough to detect a tumor of 5 mm in diameter, but if we consider more antennas, we might be able to detect tumors of smaller sizes. This will be considered in a future study to generate a more realistic model.  
Conceptualization, A.M.Q. and R.W.A.; methodology, A.M.Q., R.W.A., and K.H.A.; software, A.M.Q.; validation, A.M.Q., R.W.A., and K.H.A.; formal analysis, A.M.Q.; investigation, A.M.Q.; writing--original draft preparation, A.M.Q.; writing--review and editing, R.W.A.; visualization, A.M.Q.; supervision, R.W.A.; project administration, R.W.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia, under grant number FP-208-43.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Imaging Setup** & **Location (x,y)** & **Tumor Size (mm) (Diameter)** \\ \hline Figure 20 & \(-\)28, 28 & 5 \\ Figure 21 & \(-\)23, 28 & 10 \\ Figure 22 & 20, 13 & 5 \\ Figure 23 & \(-\)5, \(-\)17 & 10 \\ Figure 24 & 0, 0 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Tumor Locations and Sizes.
# Deep Learning for Anomaly Detection in CNC Machine Vibration Data: A RoughLSTM-Based Approach
###### Abstract  
Ensuring the reliability and efficiency of computer numerical control (CNC) machines is crucial for industrial production. Traditional anomaly detection methods often struggle with uncertainty in vibration data, leading to misclassifications and ineffective predictive maintenance. This study proposes rough long short-term memory (RoughLSTM), a novel hybrid model integrating rough set theory (RST) with LSTM to enhance anomaly detection in CNC machine vibration data. RoughLSTM classifies input data into lower, upper, and boundary regions using an adaptive threshold derived from RST, improving uncertainty handling. The proposed method is evaluated on real-world vibration data from CNC milling machines, achieving a classification accuracy of 94.3%, a false positive rate of 3.7%, and a false negative rate of 2.0%, outperforming conventional LSTM models. Moreover, the comparative performance analysis highlights RoughLSTM's competitive or superior accuracy compared to CNN-LSTM and WaveletLSTM across various operational scenarios. These findings highlight RoughLSTM's potential to improve fault diagnosis and predictive maintenance, ultimately reducing machine downtime and maintenance costs in industrial settings.  
LSTM; rough set theory; anomaly detection; CNC machines; predictive maintenance