esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Decoding the Intricacies of Blind Source Separation (BSS): A Deep Dive into Core Principles
### Canonical Correlation Analysis (CCA)  
This approach considers two sets of variables resulting from different electrode configurations or from EEG measurements under different experimental conditions. The goal is to discover the maximum correlations between these two sets of signals, which can represent, for example, different mental states or responses to different stimuli [98; 99].  
Let \(\mathbf{X}\) be the \(m\times n\) matrix representing the EEG signals during an experimental condition, and \(\mathbf{Y}\) the \(p\times n\) matrix representing the EEG signals, possibly from the same set of electrodes or from a different set, during another experimental condition. CCA finds linear combinations \(\mathbf{X}a\) and \(\mathbf{Yb}\), with \(a\) and \(b\) weight vectors, such that the correlation between \(\mathbf{X}a\) and \(\mathbf{Yb}\) is maximized. The canonical correlation, \(r\), between these linear combinations is expressed as:  
\[r=\frac{\mathbf{a}^{T}\mathbf{C}_{XY}\mathbf{b}}{\sqrt{(\mathbf{a}^{T} \mathbf{C}_{XX}\mathbf{a})(\mathbf{b}^{T}\mathbf{C}_{YY}\mathbf{b})}}, \tag{4}\]  
where \(\mathbf{C}_{XY}\) is the covariance matrix between \(\mathbf{X}\) and \(\mathbf{Y}\), \(\mathbf{C}_{XX}\) is the covariance matrix of \(\mathbf{X}\), and \(\mathbf{C}_{YY}\) is the covariance matrix of \(\mathbf{Y}\). The objective is to maximize \(r\) regarding \(\mathbf{a}\) and \(\mathbf{b}\), subject to the constraints that the variances of the linear combinations \(\mathbf{X}a\) and \(\mathbf{Yb}\) are normalized (i.e., \(\mathbf{a}^{T}\mathbf{C}_{XX}\mathbf{a}=1\) and \(\mathbf{b}^{T}\mathbf{C}_{YY}\mathbf{b}=1\)).  
The solution to this problem is obtained through the analysis of the eigenvalues of the matrices \(\mathbf{C}_{XX}^{-1}\mathbf{C}_{XY}\mathbf{C}_{YY}^{-1}\mathbf{C}_{XX}\) per \(\mathbf{a}\) and \(\mathbf{C}_{YY}^{-1}\mathbf{C}_{XX}\mathbf{C}_{XX}^{-1}\mathbf{C}_{XY}\)\(\mathbf{b}\), where the eigenvalues represent the squared canonical correlations \(r^{2}\), and the corresponding eigenvectors \(\mathbf{a}\) and \(\mathbf{b}\) indicate the weight vectors that maximize these correlations.
## 3 Materials and Methods
### Hybrid ARIMA-SVR Model  
In this study, a hybrid ARIMA-SVR model was developed by combining the traditional Autoregressive Integrated Moving Average (ARIMA) and Support Vector Regression (SVR) methods. ARIMA is utilized to model the intrinsic dynamics of time series data and conduct  
Figure 4: LSTM model architecture.  
regression analysis on corrected data, while SVR is integrated to address the model's complexity and nonlinear features. This hybrid model aims to enhance the performance of time series forecasting by amalgamating the regulatory capabilities of ARIMA with the flexible learning capabilities of SVR. The flowchart of the ARIMA-SVR model is presented in Figure 5.  
To determine the hyperparameters of the developed hybrid ARIMA-SVR model, a tuning process for support vector regression (SVR) was conducted using the Grid Search method. A dictionary named 'param_grid' was created to define the parameter range, encompassing options for determining the kernel type of the SVR model ('linear', 'poly', 'tbf', and'sigmoid'), values for the cost parameter ('C') (0.1, 1, 10, 100, 1000), and values for the epsilon parameter ('epsilon') (0.1, 0.2, 0.5, 0.3) to establish various parameter combinations. The Grid Search method was employed to execute trial and evaluation processes among these parameter combinations, identifying the ones with the best performance to obtain the optimal configuration for the model. Subsequently, the best parameters determined by the Grid Search method were utilized in creating the ARIMA-SVR model. The optimized parameters for the SVR model are as follows: 'kernel' is 'linear', 'C' is 1000, and 'epsilon' is 0.5. These parameters were selected to enhance the predictive ability of the ARIMA-SVR model for Ecological Footprint time series data.
# Low-Complexity Pupil Tracking for Sunglasses-Wearing Faces for Glasses-Free 3D HUDs
## 4 Discussion
Here, we assumed that only eyes are occluded by sunglasses, leaving the nose, mouth, and face boundaries unobstructed by other accessories such as masks or caps. To handle a greater variety of user face-shape occlusions, the proposed system structures require multiple different eye trackers, each trained using a separate image database, which requires enhanced multiclass classification capabilities. Additionally, the number of publicly available images of drivers wearing sunglasses for use in face occlusion training image datasets is limited, presenting an obstacle for state-of-the-art deep-learning-based algorithm training. Therefore, data augmentation is required to construct large face occlusion datasets for the designated eye trackers. Finally, to handle various user face occlusion cases, further study is necessary to develop and optimize our algorithm. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Bare-Face Eye Tracker** & **Sunglasses-Wearing Eye Tracker** \\ \hline
3D crosstalk & AR 3D HUD prototype (3D margin 12 mm) \\ experimental system & & \\
3D content & & 3D arrows with glow effects \\ Number of participants reporting & & \\
3D crosstalk (static) & & 0/20 \\ Number of participants reporting & & \\
3D crosstalk (dynamic) & & 11/20 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results for 3D crosstalk with the proposed eye-tracking algorithms for bare and sunglasses-wearing faces. All experiments were performed using a commercial embedded system with an AR 3D HUD prototype.
# Routing Protocol for Heterogeneous Wireless Sensor Networks Based on a Modified Grey Wolf Optimizer
## 4 Proposed Algorithm
### Proposed Algorithm
#### 4.3.3 Selection of the Optimal Cluster Set  
According to the clustering algorithm, a network can be divided into multiple clusters, and multiple clusters in the network are called a cluster set. In this study, the selected initial clusters are referred to as the initial cluster set, which is taken as the current optimal cluster set, and the objective function value of the current optimal cluster set is calculated. Each cluster in the current optimal cluster set is randomly changed by the MGWO to generate a new cluster, and a plurality of the new clusters forms a new cluster set; the objective function value of the new cluster set is then calculated. When the value of the objective function of the current optimal cluster set is greater than that of the new one, the new cluster set is accepted as the current optimal cluster set; otherwise, the new cluster set is accepted as the current optimal cluster set in a probabilistic manner. At the end of the iteration, the optimal cluster set is formed. The objective function is defined as  
\[F_{3}=a_{3}\times d_{TCH}+\left(1-a_{3}\right)\times d_{TBS}. \tag{18}\]  
where \(a_{3}\) is the weight, \(d_{TCH}\) is the total intra-cluster communication distance, and \(d_{TBS}\) is the total communication distance from the CH to the BS. The objective function is designed based on the total inter-cluster communication distance and the total communication distance from the CH to the BS. A smaller objective function value indicates that the CH selection is more reasonable, the CH is optimal in the cluster, and the CH set is also optimal relative to the whole network. The HMGW pseudo-code is described in Algorithm 2.  
```
0:\(N\) = number of alive nodes \(K\) = number of desired clusters
```
0: A set of \(K\) clusters Steps:
19: Initialization;
20: Initial CHs selection (described above);
21: Each of the remaining nodes decides to join its nearest CH according to the Euclidean distance;
22: Form the initial clusters;
23: Current optimal cluster set = initial cluster set;
24: Calculate the objective function value of the current optimal cluster set (\(F_{opt}\));
25: While\(t\leq t_{max}\)do
26: For all current optimal clusters
27: If (the number of cluster members \(\geq\) 3)
28: Implement the MGWO algorithm (presented above) to select the CHs in each current optimal cluster;
29: Else
30: All the nodes are regarded as normal nodes;
31: End if
32: End for
33: Each of the remaining nodes decides to join its nearest CH according to the Euclidean distance;
34: Form the new clusters;
35: Calculate the objective function value of the new cluster set (\(F_{new}\));
36: If (\(F_{new}<F_{opt}\))
37: Current optimal cluster set = new cluster set;
38: \(F_{opt}=F_{new}\);
39: Else If (\(rand()>probability\))
40: Current optimal cluster set = new cluster set;
41: \(F_{opt}=F_{new}\);
42: End if
43: End while ```  
**Algorithm 2** HMGWO pseudo-code
## 4 Three-Dimensional Magnetic Field Modeling
### Easternmost Mediterranean
The considered block of the Earth's crust is essential for assessing the Early Permian stage of tectonic-thermal activation, corresponding to the initial spreading of the Neotethy  
Figure 16: Results of 3D combined magnetic–gravity modeling along profile IV (see the location of this profile in Figure 12). DST, Dead Sea Transform. Ocean. Traces of this activation are widely seen in the radiometric dates (253-283 Ma) of zircons in xenoliths from Late Cretaceous volcanic apparatuses [56; 57]. According to mineralogical and petrological data [58], this igneous complex of the Carmel-Galilee region corresponds to granulite xenoliths. According to the above authors, these rocks were uplifted from the upper mantle to the depths of 18-34 km, which agrees with the proposed physical-geological model (Figure 16). Figure 17 displays the contoured oceanic crust block with the Kaima paleomagnetic hyperzone (EMM) and the narrow Earth's crust block (Lower Galilee) with inverse polarities. A reconstruction of the position of a reverse magnetized block of the Earth's crust enabled us to contour an inverse magnetization zone with an S-N orientation with a width reaching 70 km and a length of about 200 km [23]. Such a large, thick (about 10 km) block of inverse magnetization (the calculated entire volume of this zone exceeds 120,000 km\({}^{3}\)) must correspond to a significant and prolonged effect of inverse polarity in the Earth's magnetic field history. Analysis of the paleomagnetic scale indicates that the Early Cretaceous or Early Jurassic periods, as was assumed in [28], do not contain sufficiently prolonged periods of the Earth's magnetic field inverse polarity. Therefore, it was suggested that this is the Kaima paleomagnetic hyperzone of inverse polarity [23], first detected in Australia's Late Carboniferous and Permian rocks [59]. Tectionically, it is necessary to note the discordant position of the recognized Kaima oceanic crust block concerning the sea shores. Considering this fact and many other geological-geophysical indicators [23], it was determined that this block and some surrounding blocks of oceanic crusts are allochthonous; it was proposed that this block was initially formed in the region of the modern Persian Gulf [60]. Figure 17: Final generalization of the 3D combined magnetic–gravity modeling results. (1) Intraplate faults, (2) intraplate faults, (3) deep fault separating the Alpine belt and oceanic depression, (4) contour of the block of oceanic crust with the Kaima paleomagnetic hyperzone, (5) block of the reversely magnetized Earth’s crust. DST, Dead Sea Transform; SF, Sinai Fault; An, Antilebanon; JS, Judea–Samaria.
# Comparison of Key Nutrient Content of Commercial Puppy Foods with Canine Dietary Requirements
## 2 Materials and Methods
### Proximate Analyses  
In the product samples, the proximate composition was analyzed following AOAC [61] guidelines. Dry matter (DM), crude protein (CP), crude fiber (CF), crude fat (EE-ether extract) and crude ash (CA) were analyzed, while metabolizable energy (ME) and nitrogen-free extract (NFE-an approximation of carbohydrates) were calculated. To determine dry matter, samples were dried at 105 \({}^{\circ}\)C to constant weight (method 945.15). The Kjeldahl method was used to assess the sample crude protein content (method 954.01) using a Buchi B-324 distillation unit (Buchi Labortechnik AG, Flawil, Switzerland). Once the total nitrogen content in the samples was determined, the crude protein content was calculated using a conversion factor of 6.25, since most meta proteins contain about 16% nitrogen [62]. Crude fat was determined by means of the Soxhlet method with diethyl ether (method 920.39) and crude ash (method 920.153) by incineration in a muffle furnace at 580 \({}^{\circ}\)C. Crude fiber was determined using an ANKOM220 Fiber Analyser (ANKOM Technology, New York, NY, USA). Nitrogen-free extract was calculated via the subtraction of crude protein, crude fat, crude fiber and ash from the total dry matter.
## 1 Introduction
The application of an FRM-based simulation is limited because it lacks the support of test data and a theoretical basis. The BPM for rock applies cohesive bonds between elementary balls to simulate the behaviors of solid rocks. A standard BPM (e.g., parallel bond model) is capable of carrying both the contact force and moment [35]. However, an intrinsic problem is encountered when simulating brittle rock materials with a traditional BPM because the model may produce unrealistically low ratios of compressive to tensile strength [35; 36; 37], as the spherical particles cannot provide sufficient grain interlocking and rotational resistance. This means that if one matches the unconfined compressive strength of a typical compact rock, then the direct-tension strength of the model will be too great. The cluster [35] and clump [36] logic, as well as the particle rotation-prohibited model [38], were proposed to address this problem, but they all have obvious limitations. Recently, the concept of a flat-joint model was proposed [39; 40; 25; 41]. A flat-joint contact defines the bond by simulating the behavior of an interface between two notional surfaces. The notional surface is a disk (3D) and is connected rigidly to a piece of a body. The interface is discretized into elements that can be either bonded or unbonded with friction. A flat-joint contact can provide rational resistance even after the interface breakage, since the notional surfaces will not be immediately deleted. Thus, the traditional BPM problem has been overcome [40; 25; 41]. In this study, a spherical particle contact test was carried out to analyze the behavior of gypsum particle breakage under ball-plane contact conditions. The experiments revealed that a cone core exists under the contact point, which is a significant feature. A corresponding numerical simulation using particle flow code was then performed to further explore the process and mechanism of particle breakage from a microscopic perspective. This paper also provides a brief introduction to the flat-joint model, by which the physical characteristics of the numerical model fit well with those in the experiment. The results obtained by experiment and simulation demonstrate the formation of the cone core and reveal its effect on particle breakage.
## 4 Magnetic Suspension and Positioning Test
### Comparison with an FSM Guided by a Gimbal Mechanism  
For comparison, an FSM using a gimbal mechanism and flexure pivots (5006-400, Riverhawk Corp., New Hartford, NY, USA) was designed and prototyped, as shown in Figure 13. The mirror size (\(\phi\)80 mm), the design and allocation of the VCMs, the controller algorithm, and the feedback displacement sensors are the same as those used for the magnetic suspension type. The parameters of the PID controller for each axis were adjusted independently to increase the bandwidth by as much as possible. The reference characteristics in the \(\theta_{x}\) and \(\theta_{y}\) directions are shown in Figure 14. The moment of inertia in the \(\theta_{x}\) direction, including the mirror and the gimbal ring, is larger than that in the \(\theta_{y}\) direction. The servo bandwidth in the \(\theta_{x}\) direction could not be made as wide as that in the \(\theta_{y}\) direction. Furthermore, vibration modes other than the tip-tilt motion of the mirror were excited as calculated by FEM and are shown in Figure 14b. These have an effect on the servo bandwidth. By adjusting the control parameters only, the bandwidth for the FSM with the gimbal mechanism could not reach that achieved with the magnetic suspension.  
Figure 12: Reference characteristics of the FSM with the magnetic suspension; (**a**) Bode plot for the \(\theta_{x}\) and \(\theta_{y}\) directions; (**b**) Bode plot for the \(Z\) direction.  
Figure 13: The test rig using a gimbal ring and flexural pivots: (**a**) Configuration of the gimbal mechanism; (**b**) configuration of the test rig; (**c**) photograph of the test rig.
## 3 Methodology
### Architecture  
The proposed system architecture comprised two models, which used a bidirectional LSTM layer and the other a GRU layer. Every other part of the architecture was the same for both models. Figure 2 illustrates a sample training cycle of a text containing fourteen words and the punctuation after each word, if any, present. The transformer model was used to create embeddings for each text, which was represented with T1, T2,..., T14. The different transformer models were used in different experiments. The result was fed into the single bidirectional layer of either LSTM or GRU. The result from this layer was passed into a fully connected linear layer, y1, y2,..., y14, and the output layer produced one class for each word, which was one of the four classes (PERIOD, COMMA, QUESTION, O).  
Step 1: The dataset was passed to the pre-trained transfer model as input and labels. Output was a vector representation of the input text.  
Step 2: Vector representation of the text was passed to the neural network layer (LSTM/GRU). The output was fed into a linear, fully connected neural network layer and finally to a SoftMax output layer with four neurons representing each class.  
In this paper, we also evaluate different BERT transformer models. The principle of transfer learning has been used in many NLP tasks, as discussed in the literature review. The model is trained for different NLP applications that can be fine-tuned to perform other NLP tasks. We briefly discuss some of the models used in this study.  
Figure 1: Simple Transformer encoder.
# Joint Deployment of Sensors and Chargers in Wireless
## 1 Introduction
Considering that the working state and the charging state of the sensor powered by radio-frequency radiation cannot be performed at the same time, any PoI has to be monitored by multi-sensors to ensure that the PoI is continuously monitored across all time domain, and a sensor can also monitor multi-Pols. This many-to-one working mode is achieved by scheduling monitoring and charging timeslots for sensors. Specifically, a sensor can monitor multi-Pols in a timeslot. Considering that multiple sensors sharing a charger can improve charging efficiency and reduce charger deployment costs, the aggregation effect of sensors in dense areas should be used to constrain sensor deployment and scheduling. In our deployment scheme for sensors, the life cycle of a sensor is first divided into several time slots, and these slots of all sensors are synchronized. The scheduling problem of a sensor is to select the working mode (working or charging) for the sensor in each time slot under the constraint of uninterrupted task monitoring. Finally, the chargers are deployed based on the deployment of sensors. The charging power provided to the sensor has a lower limit, such that the power received by the sensor during charging time slots should be no less than the energy it consumes during its working time slots. The main contributions of this paper are as follows:  
1. We present a new problem of joint deployment of sensors and chargers in a two-dimensional plane with the constraints of deployment cost. 2. We propose a strategy for deploying static sensors and chargers in a two-dimensional plane by scheduling the working state and the charging state of the sensors to realize the real-time monitoring of Pols. The target is to reduce the deployment cost of WSNs. We formulate the above problem and conclude two progressive problems, P1 and P2, to analyze the impact of sensor and charger deployment on deployment network costs and prove their NP-hardness. 3. The aggregation effect of sensors is revealed to effectively reduce the number of deployed chargers, and thus, the greedy heuristic approximate solution for deploying sensors by using the aggregation effect (GHDSAE) is proposed. Then, the greedy heuristic (GH) solution and the particle swarm optimization (PSO) solution are pro posed for the deployment of chargers. The accuracy and efficiency of the solutions are evaluated through a large number of simulations at different computational scales. The remainder of the paper is organized as follows. In Section 2, we present the related works. In Section 3, we introduce the relevant models, formulate our problem, and reformulate it for easier tractability. Next, we propose four solutions in Section 4. Numerical results are presented in Section 5. Finally, we conclude this work in Section 6.
# Satellite On-Board Change Detection via Auto-Associative Neural Networks
## 3 Results
### Quantitative Performance Analysis
OSCD ground truth image. Figure 17: ROC curve for test image 1 in Figure 11a1,a2 (**left**) and test image 2 in Figure 11d1,d2 (**right**). CVA ground truth image. recall scores, we selected from image number 1 a threshold value of 0.4 as the optimum one, and we used the same value to evaluate the change detection results on image 3. The final scores on test image 3 are reported in Table 3 for both the OSCD and the CVA datasets and always consider the performance obtained with the DWT as a benchmark. The main change elements in test image number 3 are seasonal land cover variations that are not labelled in the OSCD ground truth image: This factor affects the results that reported lower performance, as shown in Table 3. The metrics rise sharply considering the comparison with CVA image, as shown in the table. In fact, the ground truth image obtained via the CVA was specifically made to point out these alterations. In this case, F1 reaches 0.92 with an AANN reduction. Finally, it must be underlined that the developed algorithm is not heavy in terms of computational requirements. It was performed on NVIDIA(r) GeForce(r) GTX 1650 Max Q GPU with 4 GB RAM. During the test phase, the time taken to open a couple of satellite images with size of 772 \(\times\) 902 pixels, divide them in patches, normalize their values and return their encoded representation was 9.8 s and required 25.79 MB of RAM. The training weights of the AANN encoder part require less than 3 MB for storage.