esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Engineering Applications
### Analysis of Calculation Results
#### 4.4.2 Factor of Safety Solution for Storm Conditions  
In the numerical simulation under heavy rainfall conditions, the boundary conditions used are the side boundaries, fixed in the corresponding directions, and the bottom, fixed in the x, y and z directions. The damage criterion of the slope is the Mohr-Coulomb criterion, and its mechanical properties are listed in Table 2. The values of the deformation rate for the calculation refer to Table 3 for the adoption of a deformation rate threshold of 10 mm/d, i.e., 1.158 \(\times\) 10\({}^{-8}\) m/s.  
From Figures 10 and 11, it can be seen that under heavy rainfall conditions, the range of the discount factor solution is between 0.930 and 1.250, and the range of the deformation rate is between 7.500 \(\times\) 10\({}^{-8}\) m/s and 2.050 \(\times\) 10\({}^{-2}\) m/s. The greater the discount factor is, the more the safety factor is calculated, and the larger the discount factor, the larger the calculated safety factor. When the discount factor is 1.250, the maximum value of the deformation rate is 2.050 \(\times\) 10\({}^{-2}\) m/s; when the safety factor is between 1.023 and 1.250, the results of the deformation rate calculation do not converge, while between 0.930 and 0.987, the results of the calculation of the discount factor converge and are less than the threshold value. The rate of deformation changes abruptly at the discount factor of 0.987, so the safety factor obtained is 0.987. The stability analysis of the rocky slope using the safety factor obtained by the method of this study shows that the slope is unstable under the heavy rainfall condition, and there is a slippage phenomenon, which is consistent with the results of the on-site monitoring.  
Figure 9: Deformation rate versus discount factors under natural working conditions.  
Figure 10: Deformation rates corresponding to different discount factors under heavy rainfall conditions. (**a**) A discount factor of 1.250. (**b**) A discount factor of 1.125. (**c**) A discount factor of 1.035. (**d**) A discount factor of 1.029. (**e**) A discount factor of 1.023. (**f**) A discount factor of 0.987. (**g**) A discount factor of 0.960. (**h**) A discount factor of 0.930.
# Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications
## 4 Role of XAI in Alzheimer's Disease Detection  
Because clinical adoption of XAI is dependent on the transparency and understandability of AI-driven judgments, XAI is essential to the diagnosis of AD [6; 32]. Because AD is characterized by a steady deterioration in cognitive function, early identification is essential to successful treatments. Through medical imaging, AI models have shown potential in correctly diagnosing AD [12; 31]. However, general physicians are hesitant to rely only on these instruments due to the \"black box\" aspect of many AI models. To overcome this difficulty, XAI offers precise explanations of how AI models make their predictions, such as pinpointing the precise parts of the brain that the illness affects [5; 55]. This openness boosts clinician confidence in addition to facilitating early diagnosis by making small changes in brain structure more intelligible [13; 57]. Furthermore, XAI encourages more in-depth conversations about diagnosis and available treatments between patients and healthcare professionals [45; 68]. XAI facilitates clinical decision-making by allowing the validation of AI-driven insights against additional clinical data, resulting in more dependable and knowledgeable outcomes [7; 43]. Moreover, XAI plays a crucial role in creating and enhancing AI models by offering perceptions that boost the model's resilience and functionality [20; 50]. Ultimately, XAI improves the use of AI in AD detection, helping patients and promoting medical research [38; 41].
## 4 The COASYM-MAC Protocol
### Data Collection
#### 4.4.2. In Case of Beacon Loss Due to Link Asymmetry  
If a child (\(u\)) is ready to send data, it will hold until the parent (\(P_{u}\)) sends it the beacon. The sender will wait for \(t\) time, where \(t\) is chosen to be double a node's wakeup time. If the child is unable to receive the beacon from its parent because of link asymmetry in under \(t\) time, the child will use a cooperative node for data forwarding. The cooperative node may be the sibling of the child node or any other neighbor node with equal or lower hop count than the child node. In dense networks, keeping all neighbor node information is very difficult because of memory constraints. Thus, in this paper, we do not use the neighbor table. We only include parent ids and hop counts in the beacon message. By using the beacon information, the child node can make the decision of data forwarding. Now, after exceeding \(t\) time, the child \(u\) waits for the beacon of its sibling. The sibling that has a comparatively high CSI, high RE, and low distance from parent first sends the beacon as per Equation (4). After getting the beacon child \(u\) immediately sends the data. Other children of the parent \(P_{u}\) take the Short Random Backoff (SRB) and when it checks the channel it finds the ongoing transmission, as described in Algorithm 5. In the tree topology, any parent can receive messages other than its child. When children send data packets to siblings, they add parent IDs to the data packets.  
Figure 5: Symmetric link situation.  
Conversely, if a child Node \(u\) does not get a beacon from any of its siblings within another \(t\) time, then it waits for the beacon from any of its neighbor that have an equal or lower hop count to the Base Station. If the child gets the beacon from any of its neighbors who satisfy the condition, then it immediately sends the data packet with identifying relay mark so that the neighbor node does not discard the data packet. Figure 6 shows the relay selection among siblings and Figure 7 displays the timing diagram of the proposed protocol in the case of beacon loss.
## 3 Our Advanced Proposed Methodology
### Interleaving Physical and Virtual Sensors  
Once the virtual sensor data are generated, we combine them with the physical sensor data to create the final supervector, HSPV. The _interleaving_ procedure alternates between appending physical sensor values and virtual sensor predictions for each reading. This results in the new HSPV configuration with a combination of 1, 2, or 3 virtual sensors placed between each pair of physical sensors, as shown in Figure 1.  
\[\text{HSPV}=\text{interleave}(\text{physical base\_data},\text{virtual\_ sensors\_data})\]  
Each row in the final HSPV represents a hybrid reading, with the initial columns corresponding to the physical sensor readings and the subsequent columns representing the predicted virtually sensed data values, based on the configuration presented in Figure 1.  
This process enables the use of both the original physical sensor data and the newly generated virtual sensor data in subsequent predictive ML models, such as classification or regression tasks.
# SACT: A New Model of Covert Communication Based on SDN
###### Abstract  
Anonymous tracking technology of network watermarking is limited by the deployment of tracking devices in traditional network structure, resulting in poor scalability and reusability. Software Defined Network (SDN) boasts more freedom thanks to its separation of the control plane from the data plane. In this paper, a new anonymous communication tracking model SDN-based Anonymous Communication Tracking (SACT) is proposed, which introduces network watermarking into SDN and combines IP time hidden channel and symbol expansion technology. In addition, we introduce a hopping protection mechanism to improve the anti detection ability of the watermark as well. The experimental results show that in a variety of simulated network environments, SACT achieves excellent detection rate and bit error rate, thus it is sufficient to determine the communication relationship between the two parties. Meanwhile, SACT solves the deployment problem of anonymous tracking and improves the availability and scalability of covert communication.  
Keywords: watermarking; SDN; covert communication; dynamic protection; security analysis +
Footnote †: journal: Sensors
# GNSS Applications to Assess Performance in Olympic Sailors: Laser Class
## 2 Materials and Methods  
### Participants  
The study sample consisted of 159 Olympic sailors (67 females) in the Laser class, with an age range of 17 to 45 years. The data were collected form World-Sailing(r) [24], which is the commercial entity of the International Sailing Federation (ISAF). However, this organization no longer exists.
## 5 Simulation Framework and Experimental Scheme
### Performance Metrics
#### 5.4.1 Call Drop Rate  
The CDR quantifies the percentage of communication sessions that are prematurely terminated due to insufficient network resources. It is defined as follows:  
\[\text{CDR}=\left(\frac{\text{Number of Dropped Calls}}{\text{Total Number of Initiated Calls}}\right)\times 100\%. \tag{3}\]  
As drivers deviate from their anticipated routes, the network must dynamically adapt its resource allocation to maintain service quality. For VFH applications, maintaining a CDR below 1% is critical to ensure high-quality service. An elevated CDR indicates that the system is struggling to manage the additional uncertainty introduced by human behavior, particularly during periods of peak congestion.
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 4 Results
### Magnitude Prediction  
Generally, if the predicted magnitude is within \(\pm\)0.5 of the actual magnitude, this is considered an accurate prediction. If the predicted magnitude is 0.5 or more above the actual magnitude, this is considered a false alarm; if it is 0.5 or more below the actual magnitude, it is considered a missed alarm [38]. As shown in Figure 6, the test set contains a total of 94 time windows for seismic events, and the LSTM model accurately predicts 64 seismic events, with an accuracy of 68%; 16 false alarms, with a false alarm rate of 17%, and 14 missed alarms with a missed alarm rate of 15%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metrics** & **RF** & **SVM** & **MLP** & **LightGBM** & **LSTM** & **BiLSTM** \\ \hline MAE & 0.61 & 0.50 & 1.22 & 0.52 & 0.48 & 0.53 \\ MSE & 0.46 & 0.35 & 2.01 & 0.35 & 0.30 & 0.38 \\ RMSE & 0.68 & 0.60 & 1.42 & 0.59 & 0.55 & 0.62 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results of model performance when the ratio of training set to test set is 8:2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **RF** & **SVM** & **MLP** & **LightGBM** & **LSTM** & **BiLSTM** \\ \hline MAE & 0.72 & 0.59 & 1.38 & 0.59 & 0.58 & 0.59 \\ MSE & 0.72 & 0.47 & 2.46 & 0.47 & 0.44 & 0.45 \\ RMSE & 0.85 & 0.69 & 1.57 & 0.69 & 0.67 & 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results of model performance when the ratio of training set to test set is 7:3.  
In earthquake prediction, earthquakes of magnitude five or below are generally not considered highly dangerous, so this study focuses on predicting earthquakes of magnitude more than 5. The LSTM neural network's prediction results for earthquakes greater than five within the time window are shown in Table 5. The model issues 67 predictions for earthquakes of 5 \(<M_{\text{L}}\leq 6\), accurately predicting 57, with an accuracy rate of 85%. There were ten false alarms, with a false alarm rate of 15%, and no missed predictions. For earthquakes with magnitudes greater than 6, the model issues 19 predictions, accurately predicting 7, with an accuracy rate of 37%. There were no false alarms, but 12 events were missed, resulting in a missed alarm rate of 63%.
## 3 Results
### Estimation of the Vibration Exposure Level in the Operator's Seat  
In this section, the vibration exposure of the operator is evaluated using the weighted r.m.s. acceleration in the vertical direction (\(a_{wz}\)), computed using the definitions and the weighting curves provided in [10]. Several transient simulations have been performed considering the 6 DoFs tractor, with and without a rear-mounted machine (RMM), wherein the tractor was driven over the rougher and the smoother track proposed in [42] with different velocities. The operating machine considered in the simulations was characterized by a mass \(m_{O}\) of 700 kg and a moment of inertia \(I_{O}\) of 235 kg m\({}^{2}\).  
Results in Figure 14 show that, as expected, the \(a_{wz}\) was higher for the rougher track than for the smoother track. Moreover, the presence of the rear-mounted machine had a more relevant effect on the rougher track than on the smoother track. In particular, the selected rear-mounted machine worsened the weighted r.m.s. acceleration when the operating speed was slower than 10 km/h, and gave rise to lower values of the \(a_{wz}\) for faster operating speed. Note that the standard [42] requires determining the weighted vibrations along the three axes, for operating speeds equal to 4, 5, and 7 km/h. Therefore, a higher velocity on the rougher track was considered for illustrative purposes only.
## 6 Evaluation
### Simulation Results
As we can see in the transition from scenarios A to G as the topology becomes dense, the exploration loss becomes more important. In most of the scenarios, ACTOR outperforms XRPL. The advantages of optimal exploration (UCB) are more evident in the scenario with unreliable links (E), as XRPL shows a lower PDR and higher delay without being able to reduce its transmission power. In the scenario with mobility (F), we see that ACTOR manages to keep the connectivity better than TPP and almost at the same level as RPL (which performs well due to its high transmission power). However, it is worth noting that sustaining connectivity for mobile nodes is even challenging for ACTOR, as the previously established rewards have become irrelevant.
# Identification of Damage in Beams by Modal Curvatures Using Acoustic Beamformers
## 4 Inverse Problem
### Pseudo-Experimental Data
#### 4.1.2 Damage Located in-between Sensors  
In this section, we consider damage located in between sensors #3 and #4. The exact damage location is \(s^{D}=0.442\), which is slightly closer to the 4 th sensor, and \(h^{D}=0.75\). In this case, the peak of the modal curvature variations cannot be captured by the sensors.  
Figure 9 shows the contour plots of the objective function together with its minimum, indicated by a circle. Compared to the case of the damage below sensor #2 (Figure 6), the minimum is located in a wider and flatter valley, which results in a higher sensitivity to measurement and modeling errors.  
The contour plot of the Bartlett beamformer is presented in Figure 10. The sidelobes are even more apparent and higher than in Figure 7, and there are several local maxima. The superposition of the contributions of individual modes reveals that the third modal curvature variation is dominant over the first and second since it has a distinct maximum with regard to the damage location. This is due to the shape of the third modal curvature, which has a very steep variation near the damage location considered. Additionally, note that the resolution of the damage depth is not satisfactory.  
Figure 11 shows the contour plots of the MVDR processor. Remarkably, both the damage location and severity are clearly pinpointed, as in the case with damage below sensor #2.  
Figure 8: Contour plots of the MVDR beamformer including one ((**a**) \(i=1\), (**b**) \(i=2\), (**c**) \(i=3\)) and three numerical modal curvatures ((**d**) \(i=1,2,3\)) (coordinates of the circle are \(s^{D}\) and \(h^{D}\) for D2.A).  
Figure 9: Contour plots of the objective function including one ((**a**) \(i=1\), (**b**) \(i=2\), (**c**) \(i=3\)) and three numerical modal curvatures ((**d**) \(i=1,2,3\)) (Circle: Correct damage parameters).  
Similar to the case of damage located below one of the sensors, all three processors provide a unique solution even when using a single mode. However, the sensitivity of the estimators to location is much higher than that to intensity. In all cases, these sensitivities are lower than when the curvature below the damage is included in the measurements. This can be understood from the width of valleys and peaks in the contourplots (Figures 9 and 10). The sensitivity of MVDR is highly improved with regard to both parameters. This is particularly relevant for both the early-stage identification of damage and for the limitation in the number of sensors to be installed.  
From comparing the results of the different processors used, it can be concluded that the MVDR processor shows its superiority in eliminating sidelobes and local maxima which may appear as candidate points for damage. This result applies both to cases with damage located in between sensors and below one sensor.
# SAViP: Semantic-Aware Vulnerability Prediction for Binary Programs with Neural Networks
## 2 Related Work  
### Vulnerability Prediction  
Vulnerability prediction is an important research direction in software security. Traditional solutions use pattern-based methods, whose vulnerability patterns are defined prior to detection. However, these methods are inefficient and have weak scalability. Recently, deep learning methods have proven to be useful in this area, but as with traditional methods, there are some shortcomings in current approaches. First, most of the works are based on source code [5; 6; 19], while in reality, application software is often closed source, and acquisition of the source code can be difficult. Second, binary-based methods always lack information and tend to be highly time-consuming.  
A typical deep learning-based method [8] uses LLVM IR [20] and word2 vec for word embeddings. This method merges the word vectors in the function into an array and uses a recurrent neural network (RNN) [21] for classification. However, the word2 vec model is a word embedding model; it cannot be used to discover the internal features at the instruction level. Moreover, the model ignores statistical and structural features, and only shallow semantic features are considered. The use of RNN also greatly increases the time costs of model training and requires stronger hardware support. BVDetector [11] improves upon this method by adding data flow and control flow analysis but still depends on the word2 vec model and RNN [22]. Furthermore, the method [8] and BVDetector [11] lack positional information when extracting semantic features, while the positions of instructions are important to instruction semantics. SAViP uses RoBERTa [14], a state-of-the-art NLP model, to extract semantic features, which combines instruction semantics and positional information and makes generated embeddings more comprehensive.  
V-Fuzz [12] introduces the importance of prediction scores; by outputting the vulnerability probabilities of functions, it speeds up dynamic analysis [23; 24; 25]. The prediction model of V-Fuzz uses structural and statistical features and obtains prediction scores through a graph embedding network. The information from binary programs is retained to a large extent with this design; however, the semantic features are totally ignored, which makes V-Fuzz less robust against vulnerability variants. Moreover, V-Fuzz counts all of the instructions of the x86 architecture, which creates an over-redundant feature vector.
## 2 Study Area and Data
### Remote-Sensing Datasets  
#### 2.2.1 Sentinel-1 Data  
Since 2014, the European Space Agency (ESA) has freely provided its Sentinel-1 C-band SAR satellite data to the world. Sentinel satellites are the first projects for environmental monitoring and safety monitoring that are sponsored by ESA, and Sentinel-1 is one of six satellites now in service. The Sentinel-1 satellite is equipped with a C-band radar imaging system. A single satellite can map the whole world once every 12 days. In dual operation, satellites Sentinel-1A and -1B shorten the revisit period to six days (three days in the equatorial region) [58], realizing continuous monitoring of the surface water range. Sentinel-1 operates in three land modes with different observation strategies, strip widths, and spatial resolutions: IW mode (width 250 km, resolution 5 m \(\times\) 20 m), wave mode (WM) (width 20 km \(\times\) 20 km, resolution 5 m \(\times\) 5 m), and strip mode (SM) (width 80 km, resolution 5 m \(\times\) 5 m) [59].  
For this study, a total of 42 images of Sentinel-1 interference wide (IW) mode ground-range detected data (GRD) (29 scans of the WeiFang site and 13 scans of the Ji'An site) were downloaded from the Alaska Satellite Facility (ASF) website. In addition, Sentinel-2 data in the same period were collected for visual comparison.
# Analysis of the New Thomson Scattering Diagnostic System on WEST [PERSON]
## 2 Materials and Methods
### Hydraulic Study  
As the diagnostic is close to the plasma, the heat flux is not negligible and some components need to be actively cooled. The coolant available in the WEST tokamak is pressurized water at a temperature of 100 \({}^{\circ}\)C and a pressure of 36 bar.  
Pressure drop is calculated in all components with the CFD software Ansys Fluent. k-\(\varepsilon\) is chosen as turbulence model because of its numerical stability, convergence and feedback. Conversely, heat transfer is neglected to calculate pressure drop.  
For each component, the objects of the study are:  
1. Evaluating pressure drop and making design improvements if necessary;
2. Studying the natural distribution of mass flow rate (in a global CFD simulation);
3. Describing the sub-components by plotting curves of the pressure drop (output parameter) depending on the velocity (input parameter).  
Figure 7: (**a**) Heat balance on each face—Pe indicates the heat flux radiation emitted by the plasma. Pi, Pr and Pa represent respectively the incident, reflected and absorbed heat flux radiation on the component surface. (**b**) Symmetrical representation of the area of study—P represents the opening (hence the plasma), M indicates the mirror face and numbers 1 to 4 point to several surfaces of the bracket.  
This last point will allow for assessment of the relationship between pressure drop and velocity. At the end, diaphragms will be incorporated into the hydraulic system to properly distribute the water mass flow rate in all components.  
The curve of the pressure drop depending on the velocity allows for understanding of the relation between these two variables with the equation:  
\[\Delta P=a\times V^{2}+b\times V \tag{3}\]  
where \(\Delta P\) represents the pressure drop from inlet to outlet of the component, \(V\) indicates the inlet velocity, and \(a\) and \(b\) are respectively the inertial and viscous loss coefficient.
# Optimal Non-Integer Sliding Mode Control for Frequency Regulation in Stand-Alone Modern Power Grids
## 3 Description of an Isolated Fractional-Order Microgrid Model  
### An Isolated Microgrid  
In an isolated MG, the distributed loads are provided by various DG components such as PVs and WTGs, and backup system elements (e.g., battery energy storage system (BESS) and flywheel energy storage system (FESS)) [8; 31]. A general scheme of microgrids is illustrated in Figure 1. Usually, the MG dispatch system (MGDS) and the distribution management system (DMS) control the MG operation and the power grid, respectively. Moreover, communication links attain reciprocal information transition.