esa-sceva/satcom-chunk-collection huggingface dataset:
# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 2 Materials and Methods
### Classification Accuracy Evaluation  
In this study, we used the confusion matrix (Table 1) and F1 score to assess the performance of our method. The confusion matrix [66] is a very popular measure used to solve classification problems. Confusion matrices represent counts from predicted and actual values. The output \"TN\" stands for True Negative, which shows the number of negative examples that were classified accurately. Similarly, \"TP\" stands for True Positive, which indicates the number of positive examples that were classified accurately. The term \"FP\" shows False Positive value--i.e., the number of actual negative examples classified as positive--while \"FN\" means a False Negative value, which is the number of actual positive examples classified as negative [66].  
Figure 8: Examples of training data and validation data. Blue: water; green: other land types.  
Figure 7: The WaterUNet structure for water extraction (total params: 7,771,585; trainable params: 7,765,697; non-trainable params: 5888) The numbers in the green box are for the input dimension of the convolution block and the numbers in the blue box are for the output dimension.  
The metrics most frequently used while performing classification are accuracy, precision, recall, Cohen's Kappa coefficient, and F1 score. The accuracy of an algorithm is represented as the ratio of correctly classified samples (_TP + TN_) to the total number of samples (_TP + TN + FP + FN_) [67].  
\[Accuracy=\frac{TN+TP}{TN+FP+FN+TP} \tag{2}\]  
The precision of an algorithm is represented as the ratio of samples correctly classified as positive (_TP_) to the total samples predicted to be positive (_TP + FP_) [67].  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
The recall metric is defined as the ratio of samples correctly classified as positive (_TP_) divided by the total samples actual positive (_TP + FN_). Recall is also called as sensitivity [67]:  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
[PERSON]'s Kappa coefficient (_k_) [68] is a statistic that assesses the level of agreement between two classifiers on a classification problem. [PERSON]'s Kappa coefficient can range from \(-1\) to \(+1\), where a value of \(1\) means that the agreement between the classifiers is perfect and a value of \(0\) indicates that any agreement is expected to be the result of random chance [68]. It is calculated by:  
\[k=\frac{Accuracy-P_{e}}{1-P_{e}} \tag{5}\]  
where \(P_{e}\) is the probability of random agreement and calculated by:  
\[P_{e}=\frac{(TP+FN)(TP+FP)+(TN+FN)(TN+FP)}{(TP+TN+FP+FN)^{2}} \tag{6}\]  
The \(F1\)_score_ is the harmonic mean of precision and recall, where an \(F1\)_score_ reaches its best value at \(1\) and worst at \(0\). It is more objective than overall accuracy in our binary classification case because a water body mostly covers a small portion of the image under evaluation [43; 52]:  
\[F1\;score=\frac{2\times(Precision\times Recall)}{Precision+Recall} \tag{7}\]
## 2 Materials and Methods
### DNN-Bernoulli Model for FSI Simulation  
In the previous subsection, the DNN-Bernoulli model has been implemented for various synthetic shapes generated by Equation (6). In this subsection, we aim to apply the DNN-Bernoulli model in FSI simulation. To this end, FSI simulation is first performed by coupling the Bernoulli model with a finite-element method (FEM) based solid dynamics solver [36] to obtain the self-sustained vibrations. Then various glottal shapes at different time instants of one vibration cycle are extracted and simulated  
Figure 6: Architecture of the deep neural network (DNN).  
with the N-S solver. Similar to the procedures in Section 2.2, the input features \(x\) are extracted from these shapes, the corresponding output target \(y\) is obtained from the N-S solution, the data set \((x,y)\) is fed into the neural network to train and evaluate the DNN model. The final FSI simulation is conducted by coupling the DNN-Bernoulli model with the FEM solver and the results are compared with those obtained by the Bernoulli and N-S solver to demonstrate the improvement of the present model.  
The abstract workflow is illustrated in Figure 7.  
Prephonatory Geometry of the Vocal Folds  
The prephenatory geometry of a vocal fold (left half) is shown in Figure 8. The length \(L\) along the anterior-posterior direction (\(z\)) and thickness \(T\) along the inferior-superior direction (\(y\)) are 1.5 cm and 0.3 cm, respectively. To simplify the model, only the lateral vibration is allowed, the vertical motion is fixed. An initial gap \(\Delta x=0.002\) cm along the lateral direction (\(x\)) exists between the left and right counterpart.  
Figure 8: Prephonatory geometry of the vocal fold (left half).  
Figure 7: Workflow of the DNN-Bernoulli model for fluid structure interaction (FSI) simulation.  
Each vocal fold is divided into three layers including the cover, ligament and body. Each layer is assumed to be invariant in the anterior-posterior direction. The vocal fold tissue is modeled as viscoelastic, transversely isotropic material. The material properties of each layer of the vocal fold [15; 37] are listed in Table 3.
## 4 Experiments
### Semantic Segmentation on the ADE20K Dataset  
**Experiment Settings** For the semantic segmentation task, ADE20K [54] is the most commonly used dataset, covering a broad range of 150 semantic categories with 20K images for training and 2K images for validation. Similar to models used on the COCO [53] dataset, we initialize the backbone with weights pre-trained on ImageNet1K [52] and take UperNet [72] in MMSegmentation [73] as the segmentation head. For UperNet [72], an AdamW [55] optimizer with an initial learning rate of \(1\times 10^{-4}\) and a weight decay of 0.01 is used, the models are trained for 160K iterations with a batch size of 16 on 8 RTX 3090 GPUs. In this task, we use mean Intersection over Union (mIoU) as the metric.  
**Results** The results are illustrated in Table 5. Like object detection, LGViT shows better performance than others. We achieved similar FLOPs with Swin Transformer [23] even though we expand the key set and value set during the LSA and GSA module. This is because we set the window size in our model as 8 while in Swin [23] the window size is 7, thus the number of windows in our model is less than Swin [23].  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **\#Params** & **FLOPs** & \(AP^{b}\) & \(AP^{b}_{50}\) & \(AP^{b}_{75}\) \\ \hline ResNet-50 [3] & 37.7 M & 234.0 G & 36.3 & 55.3 & 38.6 \\ CAT-B [68] & 62.0 M & 337.0 G & 41.4 & 62.9 & 43.8 \\ ViL-M [61] & 50.8 M & 338.9 G & 42.9 & 64.0 & 45.4 \\ RegionViT-B [71] & 83.4 M & 308.9 G & 43.3 & 65.2 & 46.4 \\ Swin-T [23] & 38.5 M & 245.0 G & 41.5 & 62.1 & 44.2 \\ \hline LGViT (ours) & 40 M & 261 G & **43.4** & **65.3** & **46.9** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of object detection task on the COCO2017 dataset for different models with RetinaNet. The FLOPs are computed on 1280 \(\times\) 800 resolution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **\#Params** & **FLOPs** & **mIoU** \\ \hline ResNet-101 [3] & 86 M & 1029 G & 44.9 \\ Shuffle-T [50] & 60 M & 949 G & 46.6 \\ TwinsP-S [51] & 54.6 M & 919 G & 46.2 \\ Twins-S [51] & 54.4 M & 901 G & 46.2 \\ Swin-T [23] & 60 M & 945 G & 44.5 \\ \hline LGViT (ours) & 62 M & 946 G & **47.1** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of semantic segmentation task on the ADE20K dataset for different models. The FLOPs are computed on 2048 \(\times\) 512 resolution.
## 3 Anticipated Results
### MFCS Environmental Impact calculation  
The LI and the WF system uses 38% desalination makeup water and 62% condensate water as recorded by the EMCS. The proposed MFCS calculation method will provide the environmental impact this mixed use of water will have on the environment in terms of GHG emission or decarbonization in kgCO2e/m3.  
The environmental impact calculation includes decarbonization (Scope 3) from the condensate water used by the WF and the LI system less the GHG emissions emitted using makeup desalinated water; and less the GHG emissions emitted by the MFCS electrical system (Scope 2) of the treated non-potable water.
## 3 Tactical Calculation Methodology and Situational Awareness Evaluation Algorithms
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Quantity** & **Potential** & **Type** \\ \hline \multicolumn{4}{c}{**Unit Type: Motorized Infantry Battalion**} \\ \hline APC KTO Rosomala & 53 & 0.92 & armor \\ UKM-2000 & 36 & 0.4 & infantry \\ PKT Machine gun & 7 & 0.3 & infantry \\ sniper rifle & 13 & 0.5 & infantry \\
0.50 rifle & 4 & 0.55 & infantry \\ Recon Vehicle & 3 & 0.45 & armor \\ RPG-7 & 43 & 0.12 & infantry \\
60-mm Mortar & 12 & 0.56 & artillery \\
98-mm Mortar & 6 & 0.58 & artillery \\ PPK SPIKE & 6 & 0.75 & artillery \\ Mk-19 & 12 & 4.0 & infantry \\ \hline \multicolumn{4}{c}{**Unit Type: Mechanized Infantry Battalion**} \\ \hline Machine gun & 41 & 0.3 & infantry \\ sniper rifle & 13 & 0.5 & infantry \\
0.50 rifle & 4 & 0.55 & infantry \\ Mk-19 & 12 & 4.0 & infantry \\ RPG-7 & 53 & 0.12 & infantry \\ Recon Vehicle & 3 & 0.45 & armor \\ BWP-1 & 53 & 0.8 & armor \\
120-mm Mortar & 6 & 0.97 & artillery \\
9P133 malyutka & 6 & 1.0 & artillery \\ \hline \multicolumn{4}{c}{**Unit Type: (Armored) Tank Battalion**} \\ \hline machine gun & 2 & 0.3 & infantry \\ RPG-7 & 5 & 0.12 & infantry \\ PT-91 tank & 58 & 2.35 & armor \\ BWR-1D & 3 & 0.4 & armor \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hostile unit composition equipment with potential values and type. Figure 5: Heterogeneous combat potential influence graph [14] (a configurable mechanism for tactical calculations) (**a**); mCOP view of potential allocation (**b**); TARV showing battlespace entity details within a tactical scenario (**c**). potential: armor, air, anti-air, infantry, and artillery (Figure 5). A more detailed description of the implemented Lanchester model can be found in References [4; 10; 18].
## 3 Experimental Results and Analysis
### Analysis of Experimental Results
#### 3.3.3 Display of Actual Test Results  
The faster R-CNN target detection model, using the Resnet50 network with the ECA-net module as the backbone feature extraction network, is tested on the testing set, and the insulator detection results of aerial transmission lines are shown in Figure 11. It can be seen that good results can be achieved for the multi-target detection of insulators of different sizes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Backbone** & **Parameters** & **Train Loss** & **Val Loss** & **AP** \\ \hline Original faster R-CNN & VGG16 & 136,689,024 & 0.535 & 0.716 & 87.74\% \\ The first improved & RESNET50 & 28,275,328 & 0.438 & 0.642 & 87.91\% \\ faster R-CNN & \multirow{2}{*}{ECA-net+RESNET50} & \multirow{2}{*}{28,275,376} & \multirow{2}{*}{0.412} & \multirow{2}{*}{0.612} & \multirow{2}{*}{89.37\%} \\ The second improved & & & & & \\ faster R-CNN & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the original faster R-CNN and the improved faster R-CNN.  
Figure 10: Precision–recall curve graph under the ECA-net+Resnet50 backbone network.  
Figure 9: Precision–recall curve graph under the Resnet50 backbone network.
# Fault-Diagnosis Method for Rotating Machinery Based on SVMD Entropy and Machine Learning
## 5 Discussion and Open Issues  
### Classification-Model Design  
In this paper, we used four classification models: SVM, KNN, RF, and GBDT. Table 8 shows the comparison of the classification models for reference. However, in practical industrial applications, the design of a classification method requires the consideration of several factors, including the type of data, the type of classification problem, the size of the data set and the number of features, the accuracy requirements, the interpretability, and the complexity of the model. When designing a model, these factors need to be considered comprehensively, and the most appropriate model should be selected based on practical applications.
# Performance Improvement of an NMP Mini Segway Using Sample and Hold Inputs
## 6 Experimental Implementation  
### Inner-Loop LQR Controller Tuning  
After the MS system platform was developed, the inner-loop LQR\({}_{c}\) was implemented to stabilize the MS system. A baseline system performance was established based on \(Q_{ci}\) and \(R_{ci}\), which are the initial LQR\({}_{c}\) weighting matrices. Starting from this baseline controller with the \(Q_{ci}\) matrix fixed, the weighting matrix \(R_{c}\) was tuned by introducing a gain \(G_{Rc}\) such that \(R_{c}=G_{Rc}R_{ci}\). Experiments were conducted with \(G_{Rc}\) from 0.6 to 1.2 with an increment of 0.1. The cart displacement and pitch angle are shown in Figure 8a,b. The corresponding control voltage is shown in Figure 8c.  
Figure 8: The experimental result for inner-loop LQR\({}_{c}\) optimization.  
Figure 7: MS robot.  
The mean absolute values of the cart oscillation magnitude (\(|x_{cart}|_{avg}\)), pitch angle (\(|\theta_{p}|_{avg}\)), and the corresponding control voltage (\(|v|_{avg}\)) for each \(G_{Rc}\) are shown in Table 2, and the data are plotted in Figure 9.  
From Figure 8, when \(G_{Rc}\) is 0.6, the resulting LQR control gain is high, and the MS system outputs (pitch angle and displacement) oscillate with very high frequencies. When \(G_{Rc}=0.7\) with a reduced control gain, the cart oscillation magnitude is reduced but is still unstable. As a result, these two \(G_{Rc}\) values are not feasible since they do not result in feasible LQR\({}_{c}\) controllers for stabilization purposes. For \(G_{Rc}=0.8\), the closed-loop system takes about 5 s to stabilize; as a result, the closed-loop MS system transits from unstable with \(G_{Rc}=0.7\) to stable with \(G_{Rc}=0.8\) (as shown in Figure 9). The closed-loop system performance is further improved with \(G_{Rc}=0.9\), and the cart is also stabilized with \(G_{Rc}=1.0\) but with an increased oscillation magnitude. When \(G_{Rc}\) reaches 1.1, the closed-loop system has an unbounded oscillation magnitude for cart displacement, indicating an unstable system. The data in Table 2 are plotted in Figure 9. In spite of the undesired range and unstable range, the feasible (stabilizing) \(G_{Rc}\) is between 0.8 and 1.0.
# Studying the Impact of Different TCP DoS Attacks on the Parameters of VoIP Streams
## 4 Results and Discussion
### Results for Video Streams Only
#### 4.2.2 Results during the TCP ACK Attack  
Figure 29 represents the number of different TCP packets. As can be seen from the graphs, there were no TCP SYN or TCPSYNACK packets because the VoIP server was not being accessed at this moment. Only TCP RST packets were generated because the web server of Asterisk had detected a problematic TCP session and, through these packets, it tried to terminate this session. Asterisk was accessible through a browser.  
Figure 28: Instantaneous values of the jitter for the video stream between VM_4 and Asterisk (**a**) and VM_3 and Asterisk (**b**) during the TCP SYN attack.  
Figure 29: Number of different TCP packets sent during the TCP ACK attack for the video conversation.  
Figure 30 presents the summarized results for the video stream that was exchanged in both directions between VM_4 and VM_3 during the attack. Figure 30a shows the summarized results for the video stream that was exchanged between VM_4 and Asterisk, and Figure 30b shows the summarized results for the video stream that was exchanged between VM_3 and Asterisk. During this attack, the jitter values decreased because this was a TCP ACK attack, which is not highly burdensome, unlike a TCP SYN attack. As a result, there were no packet losses.  
Figure 31a,b shows the variation in jitter values for the video stream between VM_4 and VM_3 and Asterisk in both directions. There was an improvement in the values because a TCP ACK attack is not highly burdensome, unlike a TCP SYN attack.
# Towards Automatic Depression Detection:
## 3 Proposed Approach and Models
### Multi-Modal Fusion  
To integrate different types of information extracted from audio and text content, embeddings generated from the last layer of both the BiLSTM model and the 1D CNN model are concatenated  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer Name** & **Parameter Settings** \\ \hline \multirow{2}{*}{BiLSTM} & Hidden: 128 \\  & Layers: 2 \\  & Dropout: 0.5 \\ \hline Attention & \\ \hline Dropout & 0.5 \\ \hline \multirow{2}{*}{FC1} & Out features: 128 \\  & Activation: ReLU \\ \hline Dropout & 0.5 \\ \hline \multirow{2}{*}{FC2} & Out features: 128 \\  & activation: ReLU \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings of the BiLSTM model with an attention layer.  
horizontally. The concatenated embeddings are then passed to a one-layer FC network, which serves as a merge node. The concatenation of two types of embeddings is defined in Equation (3).  
\[\begin{split}\mathbf{a}&=\textbf{BiLSTM}(\mathbf{X}_{embedds}) \\ \mathbf{b}&=\textbf{CNN}_{\textbf{1D}}(\mathbf{X}_{spc})\\ \mathbf{x}_{fuse}&=[a_{1},a_{2},\dots,b_{1},b_{2},\dots ]\end{split} \tag{3}\]  
In the depression detection problem, there are two modalities, i.e., audio and text embeddings, that actually contribute to the final prediction. The loss function is able to measure the contributions of the two modalities separately. Similar to [32], we opted to derive a loss function that updates the parameters based on the contributions of different modalities during the back propagation process. The derived loss function is defined in Equation (4), where \(M\) is the number of modalities (\(M=2\) in this paper), \(\ell\) is the criterion measuring the difference between the prediction and the ground-truth, \(\mathbf{x}_{m}\) is the output features of the \(m\)-th modality, \(\mathbf{\omega}_{m}\) is the weight of the FC network with respect to the \(m\)-th modality, and \(y\) is the ground-truth.  
\[\mathcal{L}=\sum_{m=1}^{M}\ell(\mathbf{x}_{m},\mathbf{\omega}_{m},y) \tag{4}\]  
In particular, \(\ell\) varies depending on the evaluation tasks. In the task of classifying depressed patients, Huber loss is utilized. As defined in Equation (5), Huber loss is a smooth L1 loss with great robustness to the outliers. It is a trade-off between the mean squared error and mean absolute error.  
In the task of depression severity assessment, cross entropy loss, as defined in Equation (6), is chosen to be the criterion function.  
\[\ell_{huber}=\begin{cases}0.5(x-y)^{2}&|x-y|<1\\ |x-y|-0.5&\text{otherwise}\end{cases} \tag{5}\]  
\[\ell_{ce}=-\frac{1}{n}\sum_{x}[y\cdot logx+(1-y)\cdot(1-x)] \tag{6}\]
## 2 Materials and Methods
### Code Implementation and System Logic  
The full drone control system was implemented in Python, integrating hand gesture recognition, autonomous obstacle avoidance, 3D mapping, and AI-Deck video streaming. Upon startup, all necessary software libraries are imported, and the system establishes a wireless radio connection to the Crazyflie 2.1 drone. A socket connection is simultaneously opened to the AI-Deck for FPV streaming. Google's MediaPipe Hands framework is initialized to process webcam input and track 21 hand landmarks, which are later used for gesture classification. In parallel, the drone's position is logged using the Kalman filter output from the Flow Deck V2, and a blank 3D map is initialized using Plotly to visualize flight data and obstacle encounters.  
Figure 2: **(a) Crazyflie 2.1 Drone; (b) Crazyradio PA; (c) Flow Deck V2 Component; (d) Multi-Ranger Deck Component; (e) AI-Deck 1.1 Component.**  
Once the system is fully initialized, the main loop begins and handles multiple tasks concurrently. The AI-Deck continuously streams grayscale or color video, which is decoded and displayed in a live feed using OpenCV, enabling remote FPV-based navigation. Simultaneously, the Multi-Ranger Deck monitors the proximity of obstacles in the front, back, left, and right. If an object is detected within \(\rho_{th}\) in any direction, the drone overrides gesture commands and automatically moves \(\rho_{avoid}\) away from the object to avoid collision. The location of each obstacle encounter is recorded and visualized as a red rectangular prism on the 3D map.  
Meanwhile, MediaPipe Hands detects the user's hand in the webcam frame and calculates geometric angles between finger joints and distances between landmark pairs. These features are passed into custom classification functions that recognize specific gestures such as \"point\", \"thumb up\", or \"closed fist\". Each gesture is mapped to a unique drone command. For example, a \"point\" gesture triggers forward motion, \"hand open\" results in vertical ascent, and \"thumb up\" moves the drone laterally to the left. The drone executes each command in small increments (typically 0.05 m) to ensure precision and responsiveness. When the landing gesture is detected, the drone initiates a landing sequence and stops logging data. At that point, the full 3D map is saved as an interactive HTML file, and flight coordinates are exported as a CSV dataset.  
Throughout the flight, x, y, and z positions are recorded every 0.1 s using data from the Flow Deck. These positions are plotted and dynamically adjust the axis range of the 3D map. The AI-deck's FPV stream provides additional spatial awareness for navigating beyond the user's direct line of sight. These modules function in parallel, creating a responsive, beginner-friendly system that allows intuitive control while maintaining autonomous safety and environmental visualization. This system is visualized in Figure 3 below.  
Figure 3: A block flowchart illustrating the full system logic of the multifunctional drone control framework.
## 5 Experiments and Results
### Quality of the Rebalancing Operations
#### 5.5.2 Effect of the Contextualised Data Partitioning Strategy  
The results obtained varying the data partitioning strategy, and the default configuration for the other parameters, are shown in Figures 7 and 8.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Values** \\ \hline _data partitioning strategy_ & Per month partitioning, \\  & Per day of the week partitioning, \\  & Per timeslot partitioning, \\  & Per day of the week and timeslot partitioning \\ _minSupport_ & 10\%, 20\%, 30\% \\ _minConfidence_ & 50\% \\ \(N_{t}\) & 5, 10, 20 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Parameters ranges.  
Figure 7 shows that the per day of the week partitioning strategy is the best one in terms of absolute number of fixed stations and is the second best one when considering the stations fixed per rebalancing operation metric (see Figure 8). The per day of the week and timeslot partitioning strategy is slightly better than the per day of the week one in terms of stations fixed per rebalancing operation. However, it is significantly worse in terms of total number of fixed stations (173 vs. 225 fixed critical station status). The rules mined considering the per day of the week partition strategy are more general and robust with respect to those mined using the per day of the week and timeslot partitioning strategy. Hence, in the majority of the cases the operations planned at time \(t_{new}\) are still useful and can be applied at the reallocation time \(t_{new}+t_{reb}\). Differently, when other contextualised partition strategies are considered, the operations planned at time \(t_{new}\) are more frequently no longer applicable at time \(t_{new}+t_{reb}\) because they are too much specific (per timeslot, per day of the week and timeslot) or too much general (per month partitioning).
# The Optimal Deployment Strategy of Mega-Constellation Based on Markov Decision Process
## 4 Case Study  
In this section, the Markov decision process is used to find the optimal strategy for the LEO mega-constellation.