esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Functional Model of the Framework
### Technologies Used at Each Level
#### 4.5.1 First Level--Intersections  
The proposed algorithm (see Listing 1) calculates the number of vehicles based on the data received from different sources. The algorithm decides the time of opening the signal for each street at the intersection based on the number of vehicles and the level of congestion in them. The technologies used include:  
* A control camera (model number FoMaKo NDI PTZ Cameras): It captures an image of each side of the intersection periodically (every 10 s) and sends the image to the fog node to calculate the number of vehicles, shown as object 1 in Figure 4.
* WSNs are distributed on both sides of the road to calculate the number of cars passing. These sensors sense the presence of new cars once they pass through them and immediately send this information to the fog node shown as object 2 in Figure 4.
* Smart vehicles with embedded RFID tags are identified by RFID readers placed in the smart traffic lights, shown as object 3 in Figure 4.  
We used a wireless network to link these devices. At the traffic light, the cameras send the images to fog nodes via a Wi-Fi network. The sensors and radio identifiers use two types of protocols (Bluetooth and Zigbee) to send their data to the side-unit or default gateway, which forwards that to a fog node via Wi-Fi. The fog node communicates with the cloud to send its data periodically using an internet connection.
## 5 Experimental Validation
### Results and Discussion  
#### 5.2.1 Sealing Performance Analysis  
Experimental validation of sealing performance revealed significant differences between rectangular and concave contact geometries. Figure 15 illustrates the comparative sealing behaviors of both designs across the operating pressure range.  
The measurement methodology involved gradually increasing the inlet pressure while monitoring the outlet pressure. The sealing pressure threshold was determined as the specific inlet pressure at which the outlet pressure dropped to zero, indicating complete sealing had been achieved. The data presented in Figure 16 specifically represents the Result #1 specimen of each design configuration, selected as a representative example for visual clarity. The complete testing results for all specimens are summarized in Table 12.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Design Model**} & \multicolumn{6}{c}{**Sealing Pressure (MPa)**} \\ \cline{2-7}  & **Result \#1** & **Result \#2** & **Result \#3** & **Result \#4** & **Result \#5** & **Mean \(\pm\) SD** \\ \hline Rectangular & 5.6 & 6.5 & 5.0 & 4.9 & 6.2 & 5.6 \(\pm\) 0.7 \\ Concave & 1.7 & 1.5 & 1.8 & 1.6 & 1.9 & 1.7 \(\pm\) 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Sealing pressure results for different contact geometry models.  
Figure 15: Sealing performance comparison between rectangular and concave contact geometries.  
Quantitative measurements presented in Figure 16 show the sealing pressure thresholds for five specimens of each design configuration.  
The horizontal reference line at 2.0 MPa represents the AIS-195 industry standard [24] for hydrogen control components in fuel cell vehicles. This standard specifies that effective sealing must be achieved at or below 2.0 MPa to ensure operational reliability and safety in hydrogen applications. The rectangular contact geometry exhibited sealing pressures ranging from 4.9 to 6.5 MPa, failing to meet the AIS-195 requirement, while the concave contact geometry achieved sealing between 1.5 and 1.9 MPa, successfully satisfying the standard's specifications.  
As summarized in Table 12, the mean sealing pressure for the rectangular contact geometry was 5.6 \(\pm\) 0.7 MPa, whereas the concave contact geometry demonstrated a mean sealing pressure of 1.7 \(\pm\) 0.2 MPa. This represents a 69.6% reduction in the pressure required to achieve effective sealing, confirming the significant improvement in low-pressure sealing performance predicted by theoretical analysis and FEA simulations.
# Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review
## 4 Crowd Related Research Approaches
### Crowd Monitoring
#### 4.2.3 Crowd Behavior  
Crowd behavior analysis and detection have become a primary part everywhere for peaceful event organization [65]. The difficulties of behavior identification and abnormal behavior detecting are very important issues in video processing [66]. The researchers have proposed different methods and techniques for the crowd behavior detection. Some of the close related works are elaborated here. In [66; 67], image processing with optical flow and motion history image techniques were used to detect the behavior of crowd. Similarly in [54], an optical flow method with Support Vector Machine (SVM) was used for abnormal behavior detection. In [68], Cascade Deep AutoEncoder (CDA) and with the combination of multi-frame optical flow information have been proposed for the detection of crowd behavior. Isometric Mapping (ISOMAP) [45], spatio- temporal [46] and spatio-temporal texture [49] models were used to detect the anomalous crowd detection. In [51] Hybrid Random Matrix (HRM) and deep neural network were used for the detection of violent behavior detection. One uses SIFT feature extraction technique [1] and Fixed-width clustering algorithm with YOLO were used to detect crowd behavior [53].
# Optimal Routing in Urban Road Networks: A Graph-Based Approach Using [PERSON]'s Algorithm
## 4 Shortest Path Improvement by Applying [PERSON]'s Algorithm on a Sparse Counter Network  
### Organizational Structure of the Algorithm  
The application of [PERSON]'s algorithm to real-world problems requires additional effort in determining the parameters that will influence the edge weight values. Within a transportation network, it is possible to use travel time, driving speed, edge length, or any other parameter, depending on the desired output result. For the shortest path, time is the key parameter, and since it depends on other factors, it will be necessary to link them through preparatory steps.  
In the Figure 2, the required sequence of steps is shown for determining the shortest vehicle route in a network with sparse traffic counters, measured by travel time.  
As the first step, it is necessary to create or obtain a relevant map of the study area from available sources. Within the defined coverage area, traffic counters must be positioned based on their geolocation data.  
Positioned counters serve as the foundation for executing the third operational step. This step defines traffic conditions in the network by creating one-way streets, roundabouts, and traffic signal controls. This process not only integrates physically existing elements into the map but also adapts their representation within the core computational part of the code to ensure unambiguous identification. Speed is defined at this stage by assigning an appropriate value to each street based on its hierarchical level and sorting them into a separate file. Additionally, street lengths are graphically added to the drawing for those segments where the impact of counters is considered unchanged.  
The previously created attributes are then synthesized into a single file containing all necessary data for route calculation. Since these files are generated using specialized  
Figure 2: Algorithm flowchart.  
AutoLISP algorithms, they remain independent of the main code and do not affect the overall computation time, as they only need to be created once.  
The final step involves loading the latest generated file into the computational Dijkstra code, where the route is calculated based on the selection of the start and end points and the time of day for which the route is being analyzed. If multiple routes need to be calculated, there is no need to reload the file, as the data it provides remain active within the drawing. The user is also given the flexibility to modify the endpoints and the calculation time, as needed.
## 6 Discussion
### The Limitations of UAV Technology in Complex Terrain and Future Research  
We know that the application of UAV technology can help us obtain the discontinuity data of high and steep rock slopes, but UAVs are greatly affected by weather and wind during collection. Another issue is that the application of UAV technology is also affected by season. For example, vegetation in summer and snow cover in winter will block parts of the slope surface and cause data acquisition failure. Therefore, to reduce the potential impact of these factors on the final results, it is recommended that the rock mass discontinuity data of the slope's surface is collected in clear weather with no wind or low wind and in the absence of snow cover and vegetation.  
Regarding limitations in data and analysis methods in our study, the calculated orientation dominant groups are mostly depended on the preset threshold (i.e., angle threshold \(\theta_{\text{max}}\) of the normal vector and deviation threshold, \(\eta_{\text{max}}\)). Therefore, more reasonable threshold selection needs to be discussed in detail for subsequent studies. Moreover, the slope data model established by UAV scanning technology only identifies  
Figure 14: Schematic diagram of slope stability analysis effected by the different rock discontinuity angles. (**a**) Slope diagram with large angle and small rock discontinuity angles. (**b**) Anti-dip layered slope with interaction of large- and small-rock discontinuity angles.  
the rock mass discontinuities on the slope's surface, and its internal rock mass discontinuity distribution cannot be identified directly. How to obtain the distributions of large areas of internal discontinuities in slopes is still a difficult ongoing problem.  
In future research on the identification of the orientations of discontinuity, we will pay more attention to the combined application of artificial intelligence technology and UAV technology to realize intelligent detection and early warnings and improve monitoring efficiency and accuracy.
## 3 Materials and Methods
### Evaluation  
The metrics implemented to evaluate the performance of the described classification task (Figure 3 ) include Accuracy, Precision, Recall, and F1 Score, all depending on the confusion matrix shown in Table 2([PERSON], 2018).  
* **Accuracy** is the proportion of correctly labeled instances (True Positives and True Negatives) among the total number of instances. When considering Table 1, accuracy is calculated as follows \[Accuracy=\frac{TP+TN}{TP+FP+FN+TN}\] (1)  
\begin{table}
\begin{tabular}{c|c|c|c|} \multicolumn{3}{c}{**PREDICTED**} \\ \cline{3-4} \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{**Negative**} & \multicolumn{1}{c|}{**Positive**} \\ \cline{2-4} \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{**Negative**} & \multicolumn{1}{c|}{TN} & \multicolumn{1}{c|}{FP} \\ \cline{2-4} \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{**Positive**} & \multicolumn{1}{c|}{FN} & \multicolumn{1}{c|}{TP} \\ \cline{2-4} \end{tabular}
\end{table}
Table 2: Confusion matrix showing the True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) labeled instances.  
* **Precision** refers to the degree of dispersion of the results; the less dispersed, the greater the precision. From Table 1, the precision is \[Precision=\frac{TP}{TP+FP}\] (2)
* **Recall** calculates the proportion of actual positives correctly labeled. Recall is also known as the true positive rate (TPR) or sensitivity. From Table 1, this measure is calculated by \[Recall=\frac{TP}{TP+FN}\] (3)
* **F1-score** is defined as the harmonic mean of precision and recall, and is commonly used in the information retrieval field. Based on Table 1, the F1-score is \[F_{1}=2\times\frac{Precision\times Recall}{Precision+Recall}\] (4)  
Finally, the models' performances were compared or averaged, depending on the followed validation strategy (Figure 3 ).
## 3 Results
### Validation with Real Gravity Data
Due to the incorporation of physical prior constraints during training, the network not only removes noise but also processes the data more reasonably according to the physical characteristics of the gravity field. The left subplots in Figure 17 display the original gravity field data for each region, with clear geological features. The middle subplots show the data after noise addition, where image clarity is significantly reduced, severely interfering with the identification of geological structures. The right subplots, after U-Net denoising, not only effectively suppress noise but also restore many detailed features, reconstructing more realistic geological structures compared to the original data. Notably, the U-Net network with physical prior constraints performs better in marine regions than in land areas. This indicates that the U-Net network incorporating physical prior information can provide more robust and accurate denoising capabilities for gravity data across different geographic regions. Figure 15: No physical constraint training history and physically constrained training history. Figure 16: Comparison of PSNR values between physically constrained and no physically constraint models. Figure 17: Denoising results of real gravity data.
# Gelatin-Siloxane Hybrid Monoliths as Novel Heavy Metal Adsorbents
###### Abstract  
Novel gelatin-siloxane hybrid monoliths for heavy metal removal were prepared in the chemical reaction of gelatin with organomodified silicone containing epoxy group. Obtained porous hybrid materials were applied for adsorption of Cu(II), Cd(II) and Pb(II) from aqueous solutions. In this paper, the influence of siloxane amount used for the modification of gelatin on adsorbent stability and heavy metal removal was examined. The effect of pH values of the immersion liquid, as well as the contact time, was studied. Morphology, compressive strength and water absorption of hybrid monoliths were investigated. Desorption tests were also performed. The results showed that the higher the amount of the siloxane, the better stability of the hybrid monoliths in aqueous solutions. The highest values of adsorption capacity were observed for Pb(II) ions. The experimental maximum adsorption capacity determined for hybrid monoliths was 3.75 mg/g for Pb(II), 1.76 mg/g for Cu(II) and 1.5 mg/g for Cd(II). The desorption of metal ions for hybrid monoliths stable in aqueous solutions reached 70%.  
gelatin; siloxane; heavy metal; adsorption; copper; cadmium; lead Article
## 5 Experimental Results
### Encoder Training and Results Using Autoencoder  
The encoder model was trained using the autoencoder described in Section 4.4. For the hyperparameters, Optuna was used to search for the optimal parameters. However, the number of LSTM units was set to 512, the same number as in contrastive learning, because the larger the number of LSTM units, the better the accuracy of the model. Table 7 shows the details of the parameters and their values.  
The learning transition of the autoencoder is shown in Figure 16.  
The resulting error was MAE = 0.027, with an average of approximately 3 cm. Figure 17 shows an example of the results of motion restoration using the autoencoder. Blue is the correct joint position and orange is the result of visualizing the restored position in a skeleton.  
Next, the trained encoders were used to train the reference-guided dance evaluation model described in Section 4.5. The hyperparameters used the same architecture as the evaluation model with contrastive learning, also described in Section 4.5. Table 8 shows the results of training as a class classification problem for each evaluation item. LMA, a rule-based motion feature extraction method, and random forest (RF), a statistical machine learning method, were used as baseline models, and autoencoder (AE) was used for the feature extraction model and their accuracy was compared.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Search Range** & **Value** \\ \hline Number of GCN layers & 1–3 & 2 \\ \hline Number of GCN filters & 16–512 & 128 \\ \hline Bidirectional (encoder) & True, False & True \\ \hline Bidirectional (decoder) & True, False & False \\ \hline Number of LSTM units & 32–1024 & 512 \\ \hline Number of units in dense layers & 32–1024 & 256 \\ \hline Dropout rate & 0.0–0.5 & 0.1 \\ \hline Learning rate & 0.00001–1 & 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Autoencoder model parameters.
# Experimental Validation of Peer-to-Peer Distributed Voltage Control System
## 5 Device-to-Device Communication
### D2D Communication Modules
#### 5.2.2 Synchronized Protocol  
The radio thread handles control over the radio transceiver and implements a synchronized radio protocol. The synchronized protocol is a simple slotted protocol, where each of the D2D modules is assigned a periodic time slot for transmission of its data and receiving the transmissions from the other modules in their respective slots, as shown in Figure 8.  
The parameters of the protocol, namely the number of slots (M) in the superframe and the duration of each slot (T-slot) are hardcoded in the firmware and cannot be changed without reprogramming the module. Each module uses for its transmission the slot with the number equaling to its programmed identifier (i.e., a module with ID 1 will send in slot 1, etc.). Empirically it was found out that the need of using low-speed UART interface between the main module and the radio transceiver chipset and  
Figure 7: Structure of the embedded software.  
the slow operation of the chipset itself introduces substantial overheads (e.g., packet transmission, switching between transmit and receive, etc.). Due to this reason, the duration of one slot cannot be set below 150 ms.  
When enabled the first time, the D2D module based on this protocol first scans the radio channel for several superframe periods. If it does no-t find any transmissions and it has data to send, it will start the transmission right away. If during scanning a module finds some transmissions ongoing, it will use this transmission as a reference for defining its designated slot. After each superframe, a module adjusts its synchronization. As a reference point for adjusting the synchronization, each module uses the timestamp of the packet with minimum identifier not exceeding the identifier of the module. If such a reference is not available, no compensation is applied. As a practical example, module 1 transmitting in slot 1 never adjusts its synchronization. If modules 2, 3 and 4 hear transmission of module 1, they will adjust their synchronization based on it. If module 5 does not hear module 1 but hears modules 2 and 3, it will adjust its synchronization based on the transmission of module 2.
## 3 Experimental Study
### Experimental Results and Discussion
#### 3.2.3 Ablation Study of the Proposed Feature-Engineering-Based Approach  
In order to evaluate the effectiveness of the proposed feature-engineering-based approach, an ablation study was performed on the aforementioned neural networks in this study. First, original networks, only with the encoded data of fused sensors, were used for the RUL prediction. After that, the difference feature was added to the fused sensor data, and the RUL prediction was performed. The results of the ablation study can also be found in Table 7.  
The results demonstrate that all networks, when augmented with the difference feature in the fused sensor data, outperformed the original networks lacking this feature, highlighting the effectiveness of the feature construction approach. Furthermore, it can be concluded that the 1D-CNN-LSTM exhibited superior performance compared with the  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Methods** & **RMSE** & **Score** \\ \hline PELT & 35.8 \(\pm\) 1.6 & 3576.5 \(\pm\) 726.0 \\ pwlf with 2 lines & 34.1 \(\pm\) 1.1 & 3092.5 \(\pm\) 395.7 \\ pwlf with 3 lines & 17.0 \(\pm\) 1.0 & 503.7 \(\pm\) 118.6 \\ pwlf with 4 lines & 16.4 \(\pm\) 0.6 & 522.2 \(\pm\) 128.8 \\ pwlf with 5 lines (proposed) & **16.1 \(\pm\) 0.4** & **437.2 \(\pm\) 119.9** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Evaluations of change point detection methods.  
Figure 17: Difference between actual and predicted RUL values on the test set.  
base 1D-CNN, base LSTM network, and other related networks, whether with or without additional features, in terms of the RMSE and score function. The predicted results of the proposed 1D-CNN-LSTM neural network compared with the actual RUL values are shown in Figure 18 with and without the difference feature for all engines in the data set. As is noticeable in Figure 18b, the variation of prediction results with the added difference feature was much smaller than using only the fused sensor signals for the task of the RUL prediction.
# Group Authentication Scheme for Neighbourhood Area Networks (NANs) in Smart Grids
## 4 Security Analysis
### Sybil Attack  
In a Sybil attack, a malicious node assumes multiple fake identities and attempts to inject traffic into the network. Our scheme prevents vulnerability to such attacks by falling back on the need to validate the authentication value and time stamp value in a packet. The authentication value is derived from the random number shared by the gateway. This value is encrypted with the key shared with the gateway and verified at the gateway. So, in order to fake multiple identities, the attacker node must have access to all the shared keys of the nodes it intends to fake [28]. If the attacker is able to get these keys and the random value from the gateway, the ID of the node can be faked. However, in order to successfully transmit data the attacker will require having valid time stamps that the NOC can validate. Like in the earlier cases, the scheme will be broken if the attacker successfully synchronizes the clock tick values of the nodes that are being faked. In such a scenario, assumption 12 in Section 3.1 is broken.  
The attacker can simply re-initiate an authentication process, to overcome the time stamp problem. Re-authentication is a directed activity controlled by the NOC and therefore, any attempt to re-authenticate will immediately be detected by the NOC, thereby mitigating the attack. If this authentication attempt is successful, then the scheme breaks. This can occur if assumption 13 in Section 3.1 is broken.
# Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning
## 3 Methodology
### Regime Identification Based on Probabilistic Modeling
#### 3.2.2 Feature Construction  
A key step in forecasting modeling is to determine the representative features. For the multi-regime model, the identified regimes are treated as the most important features, and will be used in the modeling. In addition, the temporal correlations of the forecasted traffic flow measure and the interactive correlations of the multiple traffic flow measures are also two significant factors that can be used to improve the forecasting accuracy [14; 24; 45]. With this in mind, the time-lagged and interactive features of the traffic flow are also added to the representative feature pool. Table 1 lists the constructed features for the forecasting modeling. Note that in the study, the time interval is set as 5-min and the forecasted time is denoted as \(t\).