esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Low-Cost Control System for Fish Farm Facilities
## 4 Proposal for Smart Low-Cost Control Systems for Aquaculture
### Adaptations for Smart Low-Cost Control Systems for Aquaculture Facilities with RAS
#### 4.2.3 Communication Protocol and Communication Technologies  
As for cages in the sea, in RAS facilities, a group-based collaborative protocol for fish farming monitoring is proposed. The differences are in the employed communication technologies. In the RAS facilities, it is possible to connect all the devices using a sole wireless technology, WiFi. A local WiFi network connects all the fish farm facilities' sensor nodes and actuator nodes, and a gateway sends the information through an Ethernet connection, as seen in Figure 13.  
Figure 13: Deployment of aquaculture control system in RAS. The main difference is to be found in the location and typology of sensors and actuators. A general perspective of the water reconditioning system and the detail of one tank is shown.
# A Novel Deep Reinforcement Learning Approach for Task Offloading in MEC Systems
## 2 Related Work
In [21], the authors presented the framework for offloading computation tasks from an MD to an edge server with high CPU availability so that processing time may be reduced in both the MD and the server. However, these solutions, in nature, are concentrated on the decision-making problem of a single user and do not take more complex cases into account. In [22], the authors considered the task scheduling in an edge computing system and proposed an online learning iterative algorithm to jointly minimize the total cost of the execution delay and energy consumption. In [23], the authors studied a novel vehicular edge computing (VEC) task-offloading system where the roadside units (RSUs) can switch their states between sleep and work and then proposed a greedy algorithm based on DQN approach for minimizing the total delay of tasks. In [24], the authors explored the scenario of centralized user clustering with different priority tasks and proposed a DQN-based algorithm to minimize the system cost. In [25], the authors proposed a temporal attentional deterministic policy gradient (TADPG) which is based on the deep deterministic policy gradient (DDPG), and was formulated to solve the computation offloading and resource allocation problem in a single-server multi-MD MEC system. Furthermore, it is worth noting that we extend this model to a multiple-server multi-MD scenario. In [26], the authors discussed a novel task dependence model with server clusters and proposed a novel offloading approach Com-DDPG to optimize the energy consumption and latency of the system. In [27], the authors considered the multichannel access and task-offloading problem in a multiagent system and proposed a novel multiagent deep reinforcement learning (MADRL) to reduce the computation delay and increase the channel access success rate. In [28], the authors studied a multiuser offloading model and presented a decentralized epoch based offloading (DEBO) to optimize the total rewards. Furthermore, they proved that DEBO had good performances on various common scenarios. In [29], the authors paid attention to VEC considering offloading decisions in congestion situation. They modeled a system where tasks can be divided into subtasks and proposed an offloading algorithm based on a double deep Q-network.
# Recent Advances in Wearable Devices for Non-Invasive Sensing
## 2 Materials and Structural Design
### Materials
#### 2.1.2 Carbon-Based Materials
By mixing single-walled CNT (SWCNT) with a thermally reversible, self-healing polymer, a flexible thermal sensor with mechanical adaptability was developed to monitor the temperature of human skin in real-time [52].
## 5 Discussion  
### Highlighted Difference by Using UALR-Adjusted NDVI and MODIS NDVI  
The UALR-adjusted NDVI, which incorporates uncropped arable land information, should be used for crop condition assessment rather than the original MODIS NDVI. It generates more accurate crop condition estimates, since it eliminates the influence of inter-annual variability in arable land utilization. The article proposes a method for obtaining NDVI that is adjusted for the fact that parts of a pixel may not be cropped in one year, but may be cropped in another year; adjusting for this difference and then using this UALR-adjusted NDVI for crop monitoring will, by definition, yield more accurate monitoring results, simply by removing the variance in NDVI that is caused by the change in crop area, not crop condition. The UALR-adjusted NDVI then is a more accurate measure of the real crop condition.  
As is shown in Figure 11, uncropped arable land area (b) was much less than that in 2010 (a). Using MODIS NDVI, normal crop condition dominated the area in Figure 11. However, the more cropping areas in 2011 contributed to the normal crop condition. Actually, the greenness of the cropped area in 2011 was lower than that in 2010. As a result, nearly half of the area in Figure 11d was recognized as in worse condition using UALR-adjusted NDVI.  
Figure 11: Crop monitoring using UALR-adjusted NDVI _vs._ MODIS NDVI. (**a**) Cropped and uncropped map in 2010; (**b**) cropped and uncropped map in 2011; (**c**) crop condition using MODIS NDVI; (**d**) crop condition using UALR-adjusted NDVI.
## 4 Results
### Local Explainability Results  
Table 2 presents the comparison of our local explainability frameworks with LIME on the HELOC in terms of point fidelity, neighborhood fidelity, and stability. Both our pre hoc and co hoc local explainability frameworks significantly outperform LIME across all metrics.  
On the HELOC dataset, our frameworks achieve neighborhood fidelity scores of 0.9587 (pre hoc) and 0.9647 (co hoc), significantly higher than LIME's score of 0.6600. Similarly, on the ML-100k dataset, our frameworks achieve neighborhood fidelity scores of 0.9597 (pre hoc) and 0.9647 (co hoc), outperforming LIME's score of 0.7410. The stability of our explanations, measured by total variation, is also significantly better than LIME, indicating more consistent explanations across different instances.  
Figure 1: Effect of explainability regularization parameter \(\lambda_{1}\) on accuracy and fidelity for Pre Hoc Explainability Framework on the ml-100k (**a**), ml-1M (**b**), and HELOC (**c**) datasets. The pre hoc predictor is our proposed model, BB is the original black-box predictor model, and WB is the explainer model.  
Figure 2: Effect of explainability regularization parameter \(\lambda_{1}\) on accuracy and fidelity for Co Hoc Explainability Framework on the ml-100k (**a**), ml-1M (**b**), and HELOC (**c**) datasets. The co hoc predictor is our proposed model, BB is the original black-box predictor model, and WB is the explainer model.
# One-Shot Distributed Generalized Eigenvalue Problem (DGEP): Concept, Algorithm and Experiments +
## 2 Distributed GEP Algorithm and Its Extensional Applications
### Distributed GEP in One-Shot Communication  
Then, the general one-shot distributed GEP algorithm (DGEP in Algorithm 1) is shown as below.  
```
1: In the local servers, calculate local covariance matrix \(M_{i}\) in \(i\)-th local server and broadcast it to the central server.
2: In the central server, calculate \(\hat{M}=\sum_{i=1}^{N}M_{i}\) as the approximation of \(M_{0}\).
3: Computing the leading \(k\) eigenvectors \(\hat{W}\) of the approximate matrix \(\hat{M}\).
4: Return \(\hat{W}\).
```  
**Algorithm 1** One-shot Distributed GEP algorithm (DGEP).  
We focus on the distributed optimization of GEP in (5) with symmetric \(M_{i}\in\mathbf{R}^{d\times d}\) for \(i\in\{1,2,\ldots,N\}\). \(N\) local servers broadcast the data covariance \(M_{i}\) to the central server in one-shot communication for efficiency. Learning the generalized eigenvector \(\hat{w}\in\mathbf{R}^{d}\) occurs in the central server. To be more specific, the whole process is depicted in Figure 1, among which 'Local calculation' denotes calculating the local covariance matrix, which is different from the traditional one-shot method in Figure 2. When solving (5) in the central server, SVD and the power method [30] can be carried out, among which the power method is to find a dominant eigenvalue and a corresponding eigenvector and is summarized in Algorithm 2.  
```
1: Given the covariance matrix \(M\in\mathbf{R}^{d\times d}\) and the value of \(max\_iter\).
2: Initialize \(w_{0}\in\mathbf{R}^{d}\).
3:for\(t=1\) to \(max\_iter\)do
4: Calculate \(w_{t}=Mw_{t-1}\).
5: Calculate \(w_{t}=w_{t}/\|w_{t}\|\).
6:endfor
7:\(\hat{w}=w_{max\_iter}\).
```  
**Algorithm 2** The power method (PM) in the central server.  
Figure 1: The one-shot communication process of Algorithm 1.
## 5 Case Studies and Discussions
### Comparison between DO, ADMM and CO
The reason why the time difference is so large is that the optimization times of each mode are different. The discrete operation mode solves the electric network subproblem and the gas network subproblem once. The step-by-step cooperative operation model solves the electric network subproblem once and the gas network subproblem twice. When the synchronous ADMM runs for 129 iterations, the electric network subproblem and the gas network subproblem are solved for 129 iterations, the asynchronous ADMM only runs for 35 iterations. The solution of the gas network subproblem is the multi-layer iterative sequential cone optimization method, as proposed in this paper. Under the research background of the collaborative operation of the electricity-gas interconnection system, this case shows the specific modes and advantages and disadvantages of the discrete operation of the electricity-gas interconnection system, the coordinated operation, based on the alternating direction multiplier method, and the proposed step-by-step collaborative operation method. The following observations are obtained:  
1. The discrete operation mode of the electricity-gas interconnection system will produce unbalanced coupling dispatch when the gas network is blocked, and the gas generators cannot get enough natural gas supply, which does not meet the optimal economic dispatch decision-making results. 2. Based on the alternating direction multiplier method, the coordinated scheduling of the electricity-gas interconnection system can be realized. It can be divided into asynchronous and synchronous cooperative operation modes with or without the participation of third-party organizations. The above two modes can achieve coordinated scheduling when gas network pipelines are blocked, that is, gas generating units can get enough natural gas supply, and there will be no unbalanced coupling scheduling due to asymmetric information. 3. This case proposes a new idea of realizing step-by-step coordinated operation, based on credit indicators, and establishes a corresponding mathematical model and specific operation framework. This operating mode belongs to the concept of distributed scheduling. Through case analysis, the feasibility and superiority of the proposed method are discussed, and the sensitivity analysis of credit changes is carried out. Compared with other methods, the proposed method has the advantages of simplicity, reliability, and strong applicability. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **DO** & **ADMM** &
\begin{tabular}{c} **Proposed** \\ **Framework (CO)** \\ \end{tabular} \\ \hline CPU time (s) & 1.51 & 241.39 & 2.29 \\ Gas shortage (kcf) & 280.38 & 0 & 0 \\ Load shedding (MW) & 17.76 & 0 & 0 \\ Power generation cost (\$) & 22,358.3 & 22,969.2 & 22,969.5 \\ Load shedding cost (\$) & 1176 & 0 & 0 \\ Total cost of electricity (\$) & 23,534.3 & 22,969.5 & 22,969.5 \\ Gas network income (\$) & 15,781.9 & 17,794.9 & 17,794.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison.
## 1 Introduction
By investigating the effect of different smoothing techniques, valuable insights can be gained regarding the selection of the most suitable technique that minimises noise, maintains the true signal components, and ensures the validity of the acceleration data.
## 3 Uhs
### The Application of UHS in SOFCs
[131] applied UHS to a sinter cermet system composed using the Ni-based superalloy and YSZ, realizing high-density and homogeneous microstructures while minimizing the evaporation of Ni metal due to the reduced sintering time. The results demonstrated that the cermet composed of a Ni-based superalloy and YSZ, which was sintered using UHS, exhibited exceptional mechanical properties and high-temperature oxidation resistance. Furthermore, [PERSON] et al. [132] investigated the application of UHS in the synthesis of LaCrO\({}_{3}\) (LCO). As a promising interconnect material for SOFCs, due to its high melting point and exceptional stability at high temperatures, conventional sintering techniques typically require several tens of hours at high temperatures to achieve the desired density of LCO. Even with hot-press sintering, which reduces the time to two hours, prolonged holding times can result in Cr evaporation and increased energy consumption. The research findings indicate that the ultra-fast heating rate and extremely short sintering time of UHS promote the densification of LCO, which can reach 96%, while reducing the Cr evaporation. The UHS equipment is characterized by its simplicity, user-friendly interface, and ability to achieve high heating rates and elevated maximum temperature thresholds, facilitating the densification of ceramics that are conventionally difficult to sinter. The UHS process is effectively managed through the precise regulation of current in order to control the heating rate and temperature. Importantly, unlike FS and SPS, UHS can conduct sintering on numerous samples at the same time and fabricate samples with complex forms. Although the present application of UHS in SOFCs is limited, it signifies a novel method for the fabrication and improvement of critical materials in SOFCs. Figure 15: Electrode and electrolyte multilayered materials obtained by UHS multistep process. (Reproduced from [102] with permission from Elsevier).
## 3 LB-MPC in Path Tracking of Mobile Platforms
### Learning and Optimizing for Controller  
It is important to control the mobile platforms to follow a complex curvature path. The prediction model is the core element of the model predictive controller, and other elements, such as cost functions, constraints, and real-time response, also have a significant impact on the final closed-loop control performance.
# A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification
## 3 The Proposed Method
### Extracting Interference Noise from the Reference PRNU
In the covariance matrix, \(\Omega_{X}(i,j),\ i\
eq j\) means the correlation between \(\vec{x}_{i}\) and \(\vec{x}_{j}\). Then we can calculate the orthonormal PCA transformation matrix for \(X\). In order to preserve more information about the interference noise, we chose to save all the feature vectors. \(\Omega_{X}\) is used to transform the sample training dataset \(X\) into the PCA domain. \[Z=\Omega_{X}X, \tag{9}\]  
where \(Z\) is the decorrelated dataset for \(X\). We have  
\[\Omega_{z}=(\frac{1}{n})XX^{T}=\Lambda_{X}, \tag{10}\]  
where \(\Omega_{Z}\) is the covariance matrix of \(Z\). In the PCA transform domain, the scene detail information is concentrated on several important components, and the scene detail irrelevant information is evenly distributed throughout all components [20; 21].We chose sample vectors similar enough to the central vector, meaning the information of the central vector and its local structure accounts for the largest amount of information retained by the PCA. Therefore, we can extract the scene detail content better. We chose to use linear minimum mean square error (LMMSE) estimation to suppress the uninteresting component in \(Z\)[19], as follows,  
\[\hat{Z}_{k}=w_{k}Z_{k}, \tag{11}\]  
where \(Z_{k}\) is the \(k\)-th row of \(Z\), \(\hat{Z}_{k}\) is processed using LMMSE for \(Z_{k}\), and \(w_{k}\) is the shrinkage coefficient which can be calculated as follows,  
\[w_{k}=(\Lambda_{X}(k,k)-\sigma_{PRNU}^{2})\bigg{/}\Lambda_{X}(k,k), \tag{12}\]  
where \(\sigma_{PRNU}^{2}\) is the variance of the reference PRNU noise, and \(\Lambda_{X}(k,k)\) is the \(k\)-th eigenvalue of \(\Omega_{X}\). In the PCA domain, variance could measure the volume of information, which in a sense also tells us what kind of information should be removed. Although constructing training samples using LPM and selecting samples, reference PRNU noise is inevitably mixed in the samples. In our view, extracting scene details in the PCA domain, the main interference factor is the camera's reference PRNU noise, so in Equation (12) we use the difference of the variances as the numerator. After extracting the image details in the PCA transform domain, the \(\hat{Z}\) inverse transforms to the spatial domain. We obtain the interference noise in the local pixel block as follows,  
\[\hat{X}=\Omega_{x}^{T}\hat{Z}, \tag{13}\]  
where \(\hat{X}\) is the \(\hat{Z}\) inverse transforms to the spatial domain. During the above operation, we can extract the central block \(\hat{x}_{0}\), and extract its central pixel \(x_{target}\). Applying the above procedure to each pixel in the reference PRNU noise, we obtain the complete interference noise. Finally, we obtain a better quality of the reference PRNU noise, calculated as follows,  
\[\hat{R}=R-V, \tag{14}\]  
where \(\hat{R}\) is the noise residual between the reference PRNU noise \(R\) and interference noise \(V\). We evaluated the effectiveness of the program in the experimental section in detail.
# An Adaptive INS-Aided PLL Tracking Method for GNSS Receivers in Harsh Environments
## 2 The Principle of Delta Doppler Aided PLL  
Usually, two kinds of INS information are used to aid the carrier loop: Doppler derived from the integration filter [4] and delta Doppler over integration time [18]. For a second-order carrier loop, there is no steady-state error when tracking the velocity under ideal conditions, while there will be a steady-state error under acceleration. Velocity and acceleration correspond to Doppler frequency and Doppler rate, respectively. As a matter of fact, the performance of the carrier loop is mainly determined by the Doppler rate and less so by the Doppler frequency. Therefore, we use the delta Doppler derived from INS as the aiding information. The delta Doppler aided PLL architecture can be described as in Figure 1.  
The Doppler rate \(df_{dopp}\) can be obtained as:  
\[df_{dopp}=-\frac{1}{\lambda}\cdot(\mathbf{A}_{u}-\mathbf{A}_{s})\cdot\frac{( \mathbf{R}_{u}-\mathbf{R}_{s})}{|\mathbf{R}_{u}-\mathbf{R}_{s}|} \tag{1}\]  
where \(\lambda\) is the wavelength of the carrier, \(\mathbf{A}_{u}\), \(\mathbf{A}_{s}\) are three-dimensional acceleration vectors of receiver and satellite, respectively, and \(\mathbf{R}_{u}\), \(\mathbf{R}_{s}\) are three-dimensional position vectors of receiver and satellite, respectively. With the integration time \(T\) of the PLL, the aiding information, _i.e.,_ delta Doppler, can be expressed as the product of Doppler rate and integration time as follows:  
\[A_{dopp}=df_{dopp}\cdot T \tag{2}\]
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 4 Model Architecture
### Deep Learning Experiments
#### 4.3.1 Convolutional Neural Network (CNN)  
In this paper, the CNN model was developed through the integration of five convolutional layers, each followed by a max-pooling layer and a dropout layer. The input of the neural network was a vector of 96 MFCC features. Each of these five CNN layers had 64 filters, with a kernel size of 3 \(\times\) 3, that were applied at a stride setting of three pixels. We used the RELU activation function instead of the typical sigmoid functions, which improved the efficiency of the training process. The max-pooling layer generates a lower resolution version of the convolution layer; we applied a pool size of 2 \(\times\) 2. Later, we added a dropout layer, which helped in avoiding overfitting; we set the dropout ratio at 20%. Finally, we added a fully connected layer and a dense layer that had 30 neurons; each represented one speaker in the dataset.
## 3 Result and Discussion
### Model Optimization and Generalization Performance
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Hyper-Parameter** & **Description** & **Tuning Range** & **Optimized Value** \\ \hline \multirow{4}{*}{RF} & Max\_depth & The maximum depth of tree & 1–20 & 11 \\  & Max\_n\_tree & The maximum number of tree in RF model & 1–1500 & 959 \\  & Max\_features & The maximum number of input parameter to consider & 1–6 & 3 \\  & C & Regularization parameter & 100–100,000 & 90,575 \\ \multirow{4}{*}{SVR\({}_{\text{BBF}}\)} & \(\varepsilon\) & It specifies the epsilon-tube in \(\text{SVR}_{\text{BBF}}\) model & 0.1–0.5 & 0.128 \\  & \(\gamma\) & The coefficient for radial basis function & 0.01–1 & 0.735 \\ \multirow{4}{*}{SVR\({}_{\text{Linear}}\)} & \(C\) & Regularization parameter & 100–100,000 & 47,107 \\  & \(\varepsilon\) & It specifies the epsilon-tube in \(\text{SVR}_{\text{Linear}}\) model & 0.1–1 & 0.123 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hyper-parameter tuning spaces and optimized values for each ML method. Figure 10: Cross-validated RMSE in descending order versus the number of iterations. The results show that the performance of \(\text{SVR}_{\text{BBF}}\) is about 1.8 times higher than that of the \(\text{SVR}_{\text{Linear}}\). This result implies that the relationship between the AE parameters and the degree of damage is a complex and nonlinear problem. As for comparison between two different nonlinear models, the RF (RMSE = 0.048) showed 2.2 times higher optimizing performance than the \(\text{SVR}_{\text{BBF}}\) (RMSE = 0.104). To verify a generalization performance, the optimized nonlinear models were examined for a testing set that was not used to construct the models (Figure 11). As evaluation indices for the models, R-squared (\(R^{2}\)), RMSE, and mean absolute percentage error (MAPE) were selected in this study and the formulation of \(R^{2}\) and MAPE can be expressed as Equations (9) and (10), respectively;  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}- \overline{y})^{2}} \tag{9}\]  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}}\right| \tag{10}\]  
In Figure 11, the solid line indicates the relationship \(y_{\text{predicted}}=y_{\text{Actual}}\). Data points for a good prediction would lie close to the solid line. The result showed that \(R^{2}\), RMSE, MAPE for the SVR\({}_{\text{RBF}}\) were 0.899, 0.101, and 0.058, whereas as for the RF, the values were 0.989, 0.032, and 0.014, respectively. In Figure 11, some data points far from the solid line seem to have increased the prediction error. On the other hand, the data points in Figure 11 were overall close to a solid line, which indicate that RF is higher generalization performance compared with SVR\({}_{\text{RBF}}\). Based on the results, we selected the RF model as the best fitting model and their information is summarized in Table 4.