esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.4 Correlation Analysis  
The Pearson correlation coefficient method quantifies the linear correlation between drought indicators and vegetation indices, thereby revealing the relationship between drought and vegetation growth. The correlation coefficients indicate the degree to which the SPI reflects the relationship between water changes and vegetation; a positive correlation indicates that the drought index is strongly correlated with the vegetation condition. The larger the correlation coefficient, the closer the relationship between precipitation and vegetation. The Pearson correlation coefficient can not only analyze the correlation between drought and vegetation but also reveal the lagged phenomenon between drought and vegetation [39; 48]. Currently, the Pearson correlation coefficient is widely used in drought research. It helps to comprehensively assess the relationship between drought and vegetation growth and provides a scientific basis for ecosystem protection and management. The formula for the correlation coefficient is as follows:  
\[\text{R}_{\text{xy}}=\frac{\sum_{i=1}^{n}\left[\left(\text{x}_{i}\ -\ \overline{\text{x}}\right)\left(\text{y}_{i}\ -\ \overline{\text{y}}\right)\right]}{\sqrt{\sum_{i=1}^{n}\left(\text{x}_{i}\ -\ \overline{\text{x}}\right)^{2}\sum_{i=1}^{n}\left(\text{y}_{i}\ -\ \overline{\text{y}}\right)^{2}}} \tag{5}\]  
where R\({}_{\text{xy}}\) is the correlation coefficient of x and y, x\({}_{i}\) and y\({}_{i}\) are the SPI and NDVI values in year i, respectively, while \(\overline{\text{x}}\) and \(\overline{\text{y}}\) are the mean values of SPI and NDVI, respectively, and n is the length of the time series.  
To ensure spatial and temporal consistency between the daily SPI and the 16-day synthetic NDVI dataset, the data were spatially and temporally processed. Spatially, the SPI data were interpolated to match the raster resolution of the NDVI dataset. Temporally, the daily SPI data were aggregated into 16-day averages to correspond with the NDVI period. This processing unified the spatial and temporal scales of the two datasets, enabling Pearson correlation analysis. Following the calculation of the Pearson Correlation Coefficients, a significance test was conducted to assess the significance of these coefficients (\(p<0.05\)).
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
## 3 Interactive Model and Architecture of IoV
### IoV Architecture
#### 3.2.2 Control Layer  
In the control layer, cognitive engines are employed to reinforce the permanency and dependability in the network and to meet Quality of Service (QoS) necessities of intra-vehicle applications. The resource of intra-vehicle is reliable for the actual processing of driving data. In this, speediest decision-making can be secured. Resource organized on cloud perform network optimization in a centralized system all through efficient deployment of the global intelligence of IoV. Implementation on cloud is at the price of large integrated data storage, processing and bandwidth resources. In particular, the major work of cloud is to observe resource deployment on edge network and to perform dynamic scheduling for resources in actual time. In addition, cloud receives developing messages sent by edge and performs a sequence of emergency treatment through high-performance computing.
## 3 Multi-Source Information Fusion Method
### Leakage Detection Based on Kalman Filter and SPRT  
The traditional SPRT method requires a training set to perform anomaly detection. However, the brake cylinder pressure is different due to the influence of the vehicle load and the braking level. The training set has difficulty achieving full coverage, which hinders the use of SPRT in the on-line detection of latent leakage faults in the relay valve.  
The Kalman filter optimally estimates the state of the system through the input and output signals. This filter is especially suitable for the prediction of motion behavior with frequent changes in motion state and can realize real-time prediction [20]. In this paper, the innovation sequence of the Kalman filter is input into the SPRT model to realize the online detection function.
## 5 Conclusions  
We investigated suitable body points for estimating the anterior and mediolateral MoSs from triaxial translational velocities, assuming the use of an IMU, such as those installed in smartphones. To the best of our knowledge, no previous studies have reported similar findings. Among the 10 points evaluated, the sacral crest and knee were identified as the most accurate for MoS estimation. When using the sacral crest, the mean correlation coefficients between the estimated and observed MoSs were 0.563 for the mediolateral direction and 0.542 for the anterior direction, respectively. For the knee, these coefficients were 0.560 and 0.518, respectively. These two features are considered as promising points to attach IMUs. Future research will explore the feasibility of IMU-based MoS estimation as a method for accessible MoS measurement in everyday life. Our findings pave the way for developing an accessible system to estimate fall risks.  
Conceptualization, S.O. and Y.A.; methodology, S.O. and T.K.; software, Z.L. and T.K.; validation, Z.L. and T.K.; formal analysis, Z.L., T.K. and S.O.; investigation, Z.L. and T.K.; resources, T.K.; writing--original draft preparation, Z.L., S.O. and T.K.; writing--review and editing, S.O. and Y.A.; visualization, Z.L. and S.O.; supervision, S.O.; project administration, S.O. All authors have read and agreed to the published version of the manuscript.  
This study was in part supported by MEXT Kakenhi (21H01572).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** This study is based on a semi-open data [30] that are accessible under certain conditions. We are not allowed to redistribute the data.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids
## 2 Identifying the Distinctions between S-MPC and AR-RNN-LSTM
### Problem-Solving Method  
By analyzing the control processes illustrated in Figure 1a,b, it is possible to identify a number of expressions with total or partial equivalence between the two methods.  
S-MPC can be solved implicitly by performing switching logic, forecasting, and resolving a dynamic optimization problem at each time step or explicitly by learning a control policy from data generated by a S-MPC with any function approximation. Consequently, S-MPC has a higher online computational cost, because every control step requires estimation of the states and dynamic optimization. Typically, the optimization problem in S-MPC is solved using numerical optimization techniques, such as nonlinear programming or quadratic programming (QP) (in this paper, QP has been used), to solve the optimization problem. The solution to the optimization problem over the prediction horizon provides the optimal control signal. At each time step, the first component of the optimal control signal is applied to the system, and the process is repeated with the updated state and prediction horizon values. S-MPC requires the solution of an optimization problem at each time step, which can be computationally expensive for large systems.  
The training process for AR-LSTM involves back-propagation through time (BPTT), a variation in the back-propagation algorithm that considers temporal dependencies in the data. The RNN is unrolled throughout the training for a predetermined number of time steps, and gradients are calculated at each step. The RNN's weights are then updated based on the gradients accumulated across all time steps. The most prevalent optimization algorithm for training RNNs is gradient descent, which involves updating the weights iteratively in the direction of the loss function's negative gradient [69]. However, the standard gradient descent algorithm is susceptible to issues such as vanishing gradients, in which the gradients become extremely small and the weights do not update. Several variants of gradient descent, such as the adaptive gradient descent algorithms AdaGrad, RMSProp, and Adam, have been developed to address this issue [71].
# A Size-Controlled AFGAN Model for Ship Acoustic Fault Expansion
## 3 The Size-Controlled AFGAN
### The Generator Objective Function  
The objective function of the traditional GAN is \[L_{GAN}(D,G)=E_{x\sim p_{data}(x)}[logD(x)]+E_{z\sim p_{z}(z)}[log(1-D(G(z)))], \tag{5}\]  
where \(E[\ ]\) is the mathematical expectation, and \(P_{data}()\) and \(P_{z}()\) are the probability density distributions of the real samples and random noise, respectively. In the GAN training process, the generator adjusts parameters to minimize \(L_{GAN}(D,G)\), while the discriminator adjusts parameters to maximize \(L_{GAN}(D,G)\). With a number of iterations, the final objective is  
\[G*=argmin_{G}max_{D}L_{GAN}(D,G). \tag{6}\]  
If the size constraint is taken into consideration, the task of the discriminator remains the same, which is to calculate the probabilities that the generated samples belong to the real samples. In addition to generating samples, the generator also needs to control the sample size. The objective function can be expressed as  
\[G*=argmin_{G}max_{D}(1-\lambda)L_{GAN}(D,G)+min_{G}\lambda L_{SIZE}(G), \tag{7}\]  
where \(\lambda\) is a constant (0 or 1). During the training period, \(\lambda=0\) and the size-controlled AFGAN function is used to train the generator and discriminator to generate realistic expanded samples. After the network has been trained stably, \(\lambda=1\), and its function is used to control the expanded sample size. The generator will continue generating samples until the size has reached the optimal value.
## 5 Results
### Performance of the Models Based on Execution Time  
The execution time was computed to further evaluate the performance of the proposed method; the time was calculated at different stages, which included the time required for preprocessing, EEG signal decomposition, feature extraction, k-fold training, and testing of the model. Figure 12 shows the execution time of our various proposed methods. From the Figure, the combined features with DNN (Hybrid-DNN) require longer to classify the focal and non-focal EEG signals with an execution time of 13.2 s, while the WSST  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Author** & **Year** & **Feature Extraction** & **Type of Classifier** & **Accuracy(\%)** \\ \hline [PERSON] et al. [53] & 2017 & Fuzzy Entropy, FAWT & LS-SVM & 84.67 \\ [PERSON] et al. [54] & 2017 & Entropies, WFB & LS-SVM & 94.25 \\ [PERSON] et al. [46] & 2017 & Entropy, FAWT & LS-SVM & 94.40 \\ [PERSON] et al. [55] & 2018 & Time-domain, IMFs & LS-SVM & 84.01 \\ [PERSON] et al. [15] & 2018 & RPS, EWT & LS-SVM & 90.0 \\ [PERSON] et al. [56] & 2018 & Nonlinear Features & LS-SVM & 87.93 \\ [PERSON] et al. [57] & 2018 & Entropies & CNN & 83.0 \\ [PERSON], et al. [18] & 2018 & Entropies & Exemplars classifier & 99.0 \\ [PERSON] and [PERSON] [58] & 2019 & Entropies, IMFs & LS-SVM & 83.18 \\ [PERSON] et al. [59] & 2019 & WPT & Random Forest & 99.2 \\ [PERSON] and [PERSON] [60] & 2020 & DCAE & MLP & 93.2 \\ [PERSON] and [PERSON] [24] & 2020 & B-LSTM & B-LSTM & 99.60 \\ [PERSON] et al. [61] & 2021 & STFT & CNN & 94.3 \\ This study & 2022 & DWT, hybrid & 1D-CNN & 99.4 \\ This study & 2022 & Hybrid Features & LS\_SVM & 99.3 \\ This Study & 2022 & WT-SST & 2D-DCNN & 99.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of recent works that used the same database with our work and our proposed method.  
Figure 11: Performance of Different SVM types for Hybrid features.  
DCNN only took around 11.3 s. These values indicate that the proposed methods could be used in real-time and online applications. Moreover, by using higher computationally optimized software, the time required to execute the algorithm could be further decreased. A comparison of our proposed model's execution time with other work provided in Table 7 is not shown, as none of the work provided the time required to implement their proposed method.
# On Site Investigation and Health Monitoring of a Historic Tower in Mantua, Italy
## 5 Monitoring Results and Data Analysis
### Damage Detection under Changing Environment
Eventually, an ARX240 model has been selected, characterized by \(n_{a}=2\) previous estimates of the experimental frequency, \(n_{b}=4\) past hourly values of the outdoor temperature, and no delay between input and output (\(n_{k}=0\)). To calibrate the parameters of the ARX240 model, the same training period used for the DR relationship has been considered, _i.e._, between 17/12/2012 and 16/06/2013, in order to leave out the structural variation due to the earthquake of 21/06/2013. The evolution in time of the cleaned natural frequencies of modes B\({}_{1}\), B\({}_{2}\), and T\({}_{1}\) is shown in Figure 16: the non-negligible dispersion of the values and the evident fluctuations characterizing the results of the DR are no longer observed, suggesting that the influence of environmental factors has been completely removed. Unfortunately, none of the depurated frequencies exhibits any sudden variation after the Garfagnana earthquake. A possible explanation for this unexpected behavior could lie in the fact that--as shown in Figure 11a, Figure 12a, and Figure 13a--the frequency drop caused by the seismic event has the same order of magnitude as the fluctuations induced by the temperature effects. Therefore, since the ARX model is able to follow the temperature-induced frequency variations with high accuracy, the sudden drop caused by the seismic event is reproduced as well. This, in turn, jeopardizes the possibility of detecting anomalous occurrences from environment-independent features obtained through ARX models.
## 2 Background on Physical-Layer Security in LoRaWAN and WSNs
### PLA for Spoofing Mitigation  
PLA schemes are particularly effective in combating spoofing attacks, which exploit the broadcast nature of wireless networks. By analyzing device-specific physical attributes, PLA offers a lightweight mechanism for detecting spoofing attempts without the overhead of traditional cryptographic solutions [13]. Early PLA methods relied heavily on single attributes, such as RSSI, due to their simplicity and low energy requirements. However, RSSI-based schemes are highly susceptible to environmental interference, including multipath fading and shadowing, which lead to increased false positives and reduced reliability [8]. To overcome these limitations, researchers have developed multi-attribute PLA schemes that integrate complementary features for improved detection accuracy. For instance, [PERSON]. [14] demonstrated the effectiveness of combining RSSI with Radio Frequency Fingerprints (RFF), using hardware-specific imperfections that are difficult to replicate. Similarly, advanced techniques using deep learning, as proposed by [PERSON]. [15], enable dynamic analysis of network traffic, adapting to varying environmental conditions to enhance spoofing detection. While PLA is primarily used for spoofing mitigation, its ability to detect signal anomalies also makes it applicable to jamming detection. For example, sudden drops in RSSI or consistent interference patterns may indicate jamming activity. Our study builds on these researches by integrating dynamic and static attributes like RSSI, Battery Level (BL), and altitude, enabling a multi-attribute approach that addresses spoofing threats in resource-constrained IoT environments.
## 6 Result and Discussion
### Estimation of Food Weight and Caloric Intake
This method of calorie estimation can either minimize or maximize the error, depending on whether the default calorie weightage is higher or lower than the actual weightage. Figure 10 compares the relative performance of the two methods of estimating caloric intake. The whiskers of each box represent the calorie distribution and intra-person variability in calorie consumption for the related food. The differences in whisker height for boxes of different colors represent the errors that occurred in estimating the calories of the related food classes. From this weight estimation, it is evident that calorie estimation error results from differences between the actual and estimated food weights. The estimated weight of food was observed to be less than the actual weight due to missed swallows and larger swallow sizes than the assumed 1 g. Clearly, error in the weight estimation is propagated in both methods of calorie estimation. Although the manual calorie estimation method requires the user to find out the calorie weightage of each food in advance, this method is only  
Figure 10: Calorie estimation methods and comparison with actual caloric intake. subject to error if there are discrepancies in the weight estimation. In contrast, the calorie measurement based on the application default values is affected by both the propagated weight error and the generalized calorie weightage. As shown in Figure 10, the manual method of calorie insertion was more accurate than the application default-based method for cookies and pizza. Because the application default-based method selected a smaller number of calories, the error difference increased as the gap between the actual and estimated calories increased. In contrast, the application-based method performed better in estimating the calories of chips and nuts because the default value was close to the actual value. The default calorie weightage clearly reduced the error gap caused by the weight estimation, so the calorie number was similar to the actual calorie number, as shown above in Figure 10. Thus, default values can regulate the error generated by weight differences, as they minimized the error for chips and nuts and maximized the error for cookies and pizza. The default calorie weightages of 5.25 for chips and 6.07 for nuts were higher than the actual values of 5.2 for chips and 5.67 for nuts. Similarly, smaller default values of 4.6 for cookies and 2.56 for pizza were chosen; the actual values are 5.125 for cookies and 3.01 for pizza. Thus, larger and smaller default values, reduced and increased the error, respectively, caused by weight discrepancy in the calorie estimation. It has been graphically established that the application default weightage method has a higher error than the manual insertion method for some foods and a smaller error for other foods. Overall calories are calculated as the sum of the calories of all foods. Knowledge of the overall caloric intake can help the user to consume the required number of calories and to balance excessive calorie intake through physical exercise.
# Peculiarities of Applying Partial Convolutions to the Computation of Reduced Numerical Convolutions
## 3 Background Section  
Consider the usual expression for the convolution operation applied to functions taking discrete values  
\[U_{out}(i)=\sum_{j}K(j)\ U_{in}(i-j) \tag{1}\]  
where \(K(j)\)--convolution operator core, \(U_{out}(i)\) and \(U_{in}(i)\)--functions, which we will treat as \"output\" and \"input\", respectively.  
Let us assume that the functions \(U_{in}(i)\) and \(K(j)\) are digital, i.e., each of them at each of the index values takes one of \(N\) discrete values corresponding to the sampling scale arising from the nature of the problem to be solved.  
Besides, we will also assume that the function \(K(j)\) is different from zero only at \(M\) clock cycles  
Then the number of values \(N_{1}\), which can be taken by the function \(U_{out}(i)\), generally speaking, lies within the limits defined by the inequality  
\[0\leq N_{1}\leq MN^{2} \tag{2}\]  
One can see that this numerical range differs significantly from the range of variation of the original function \(U_{in}(i)\).  
The reduction of these two ranges to each other can be performed by the operation of division by a suitable number \(M_{1}\) followed by rounding to an integer.  
Operations of this kind, however, cannot be performed using operations only on integers, for example, they cannot be reduced to modulo operations.  
Consequently, the problem is to find algebraic operations in terms of integers, allowing for the reduction of the scale of the function \(U_{out}(i)\) to the same scale as the function \(U_{in}(i)\).  
This problem is solved in this paper using the method of partial convolutions.
# Satellite Network Task Deployment Method Based on SDN and ICN
###### Abstract  
With the rapid development of 5G and the Internet of Things, satellite networks are emerging as an indispensable part of realizing wide-area coverage. The growth of the constellation of low-orbit satellites makes it possible to deploy edge computing services in satellite networks. This is, however, challenging due to the topological dynamics and limited resources of satellite networks. To improve the performance of edge computing in a satellite network, we propose a satellite network task deployment method based on SDN (software-defined network) and ICN (information-centric network). In this method, based on the full analysis of satellite network resources, a mission deployment model of a low-orbit satellite network is established. The genetic algorithm is then used to solve the proposed method. Experiments confirm that this method can effectively reduce the response delay of the tasks and the network traffic caused by task processing.  
software-defined network (SDN); information-centric network (ICN); satellite networks; edge computing; task deployment +
Footnote †: journal: Sensors
# A Parameter-Driven Methodology of Wheel Flat Modeling for Wheel-Rail Impact Dynamics
## 3 Complex 3D Wheel Flat Surface Reconstruction
### Example of Wheel Flat Surface Reconstruction  
The reconstruction process of the wheel flat surface is shown in Figure 7.  
Based on this process, the algorithm for reconstructing the wheel flat surface was tested in this study. According to the speed limits specified in Table 4, the depth of the wheel flat was categorized into three levels as follows: 0.2 mm, 0.4 mm, and 0.6 mm, each machined onto the wheel tread. The shape of the machined flat was controlled using Equations (2) and (3).  
Figure 7: The reconstruction process of the wheel flat surface.  
The findings in Table 5 indicate a close match between the size and depth distribution of the reconstructed wheel flat surface and the actual machining results. Specifically, the maximum depths of the reconstruction model were 0.18 mm, 0.038 mm, and 0.014 mm for depths originally machined at 0.2 mm, 0.4 mm, and 0.6 mm, respectively, differing from the machining depths by only 2.3% in the case of the 0.6 mm depth. However, because of the limited number of sections and measurement accuracy, especially near the edge sections where the measurement instrument's accuracy is challenged, the reconstruction results exhibit some roughness at the endpoints. Furthermore, the interpolation results for depths at individual points within the wheel flat region tend to skew higher.