esa-sceva/satcom-chunk-collection huggingface dataset:
# Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System
## 3 Solution Method
### BPNN Prediction  
A standard BPNN is a multilayer feed-forward neural network with error backward propagation, including an input layer, one and more hidden layers, and an output layer. Each layer is composed of a number of neurons that are connected by weights and thresholds. To solve a complicated problem, a complex neural network structure must be established by increasing the number of neurons and layers, and the structure of the network should match the problem [28]. Figure 2 illustrates a three-layer BPNN with a sigmoid function for the hidden layer and a linear function for the output layer.  
During the training progress, an input pattern is given to the input layer of the network. Based on the given input pattern, the network will compute the output in the output layer. This network output is then compared with the desired output pattern. The aim of the back-propagation learning rule is to define a method of adjusting the weights of the networks. Eventually, the network will give the output that matches the desired output pattern given any input pattern in the training set.  
In this paper, a dynamic artificial neural network integrated with the non-linear auto-regressive model with exogenous inputs (NARX) [29,30] is employed to forecast the output power of the generation, which contains an input layer, a hidden layer with delays, and an output layer with 2, 10, and 1 neurons, respectively.  
Figure 1: Three-point discrete distribution of wind power.  
Figure 2: Basic structure of a back-propagation network.  
As can be seen from Figure 3, a non-linear autoregressive with exogenous inputs dynamic neural network model establishes a relationship between the optimal scheduling of diesel generation and the actual data of wind power and the load, and is detailed in Equation (19).  
\[P_{diest}(t)=f[P_{diest}(t-1),\ldots,P_{diest}(t-d);P_{wind}(t-1),\ldots,P_{wind }(t-d)]+\varepsilon(t),\ d\geq 1 \tag{19}\]  
where \(d\) is the delay-order of independent variable; \(P_{wind}(t)\) is \(wind\) power at \(t\) hour; \(P_{diest}(t)\) is the output power of the diesel generation power in one-hour ahead scheduling. It should be noticed that, in the NARX model, load demand is not the independent variable because it has been contained in \(P_{diest}(t)\).
# The Importance of Incorporating Denitrification in the Assessment of Groundwater Vulnerability
## 3 Results and Discussion
### Specific Vulnerability Assessment to NO3-  
Usually, denitrification is not involved in the standard vulnerability assessment methodologies, the only parameters considered in the most used methods, namely DRASTIC and SINTACS, that can be directly correlated to denitrification are the soil and recharge factors. Both DRASTIC and SINTACS refer to the soil characteristics in terms of texture and thickness, neglecting other common parameters linked to denitrification, like the soil OM and Ks. For three of the previously discussed study areas, two new SINTACS-based approaches have been proposed and applied: SINTACS-SVN [12] and SVAP [5]. These methodologies integrate LOSN-P index in the assessment of the specific groundwater vulnerability to NO3-, replacing the common soil parameters. In particular, SINTACS-SVN was applied to the Campania plain while SVAP was applied to the Garigliano plain and the Florina basin. In those new applications, some of the pre-existing SINTACS parameters were replaced with new ones (LOSN, LOSW, aquifer thickness, groundwater velocity and hydraulic resistance) while weights and classes were calibrated against NO3- concentrations for the SINTACS-SVN method and with a general pollution factor for the SVAP method. In both the applications a strong correlation between the final new indices and NO3- concentrations was found, despite the weak and sometimes negative correlation previously obtained using the standard methodologies, like SINTACS or DRASTIC.  
The LOSN-P factor registered three different weights of impact in the three study areas, but generally the parameter influence on the final vulnerability assessment was equal to approximately 10%. Moreover, occasionally a negative correlation between NO3- concentration and LOSN-P was found. This finding confirmed how LOSN-P could be a useful parameter to be included in the vulnerability assessment, but at the same time it cannot stand alone for the correct assessment of groundwater vulnerability to NO3- concentration. This is because other factors intervene in determining NO3- concentrations in groundwater and must be involved in the final assessment, like the dilution factors, the groundwater velocity and the characteristics of the whole vadose zone, among others. Furthermore, it is crucial to consider the amount of fertilizers used for each kind of land use, that in some cases could overcome the attenuation role of the denitrification process.
# Design of External Rotor Ferrite-Assisted Synchronous
## 4 Finite Element Simulations
### Anti-Demagnetization Ability Observation  
FEA simulations have evaluated the ability of the motor to withstand a higher demagnetizing field. The results are provided in Figure 15, where the flux density levels are shown for different current loading conditions. Figure 15a shows magnet flux densities under no-load, and Figure 15b shows magnet flux densities under rated-load. In both cases, PM-flux density levels are well above the proposed ferrite magnet's knee point flux density. The q-axis current component is responsible for creating the demagnetizing field across the magnets, and hence, the flux densities in the magnets for different values of q-axis current excitations are shown in Figure 15c,d. The magnetic flux density for q-axis current at two and three times the rated current is shown in Figure 15c,d, respectively. Figure 15d shows, under a q-axis current at three times the rated current, the flux density level in a PM is below the threshold level, which will cause permanent partial demagnetization of the magnet. This overloading condition will partially demagnetize all the ferrite magnets in the motor. The load and demagnetization curves are presented in Figure 15e. It shows the magnetization loss under demagnetizing \(\mathrm{I_{q}}\), (q-axis current) up to three times the rated current.
# A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
## 3 Tokenization  
### WordPiece  
The WordPiece (WP) tokenizer is a subword tokenization technique originally designed for solving Japanese-Korean segmentation problems in the Google speech recognition system [20]. [PERSON] et al. [9] introduced this tokenization algorithm to maximize the likelihood of the training data when given an evolving word definition. WordPiece selects a symbol pair that will lead to the largest increase in likelihood upon merging. This is similar to the method used by [PERSON] and [PERSON] [20] to handle uncommon/rare words to generate a segmentation for any word. One of the benefits of using the WordPiece algorithm is that it reduces the vocabulary size, thus resulting in more efficient encoding of text data. Additionally, the algorithm is considered a language-agnostic tokenizer, which means that it can be applied to different languages, thus making it adaptable for multilingual applications. First, the algorithm breaks down the input words into individual units called wordpieces. It then counts the most frequently repeated two-character, three-character, and longer sequences in the training corpus. The resulting vocabulary list will contain full words, individual characters, and subwords. To distinguish between a whole word and a subword token, a special symbol, usually '#', is added as a prefix to each subword token. This indicates that the token is part of a larger word rather than a complete word on its own. For example, the sentence \"I was uncomfortable\" might be split into \"I\", \"was\", \"un\", \"#comfortable\". In contrast to BPE, WordPiece selects the one that maximizes the likelihood of the training dataset given the current vocabulary, not the most common symbol pair. The wordpiece tokenizer has been used in many prominent language models such as BERT [5], DistillBERT [6], and in almost all Arabic langauge models such as Arabic-BERT [35], ARBERT [36], AraPoemBERT [37], and CAMELBERT [38].
# Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals
## 3 System Design  
### System Overview  
Wi-Senser aims to achieve non-device-based human head motion detection utilizing ubiquitous WiFi signals, without the need for users to wear any professional sensing devices. Figure 1 depicts the system architecture of Wi-Senser. Wi-Senser consists of three functional modules: a data collection module, a data processing module, and a head motion-detection module. In the data collection module, Wi-Senser directly uses a commercial WiFi router as a transmitter while using a laptop with an Intel 5300 network card to receive WiFi signals and extract CSI amplitude data. Since the original CSI amplitude data contain a large number of outliers and strong noise caused by the external environment and hardware equipment, Wi-Senser utilizes a filtering channel consisting of a Hampel filter, wavelet filter, and mean filter in the data processing module to filter out outliers and strong noise, and then uses a sensitivity measure to calculate the sensitivity of CSI amplitude fluctuations of each subcarrier to human head movement. As the CSI amplitude fluctuations of different subcarriers have different sensitivities to human head movement, Wi-Senser first selects the optimal subcarrier with the highest sensitivity from 30 subcarriers according to the calculated sensitivity in the head motion-detection module, and then uses the peak-finding algorithm to capture the real peak set caused by human head movement from the optimal subcarrier to achieve human head motion estimation. The following sections provide a detailed overview of each functional module of Wi-Senser for human head motion detection.  
Figure 1: System architecture of Wi-Senser.
###### Abstract  
Generalized boundary conditions (GBCs) for electromagnetic surfaces are investigated in this paper, which can be used to analytically characterize field discontinuities across two-dimensional surfaces. First, five representative features are described to categorize various surface problems which require proper GBC-based characterization procedures. Next, the generalized boundary conditions are discussed in details, in association with impedance boundary conditions, generalized sheet transition conditions, and surface scattering coefficients. Then an extraction method for surface susceptibilities, the characteristic parameters of surfaces in GBCs, are proposed and validated. Finally, to illustrate the applications of GBCs, two representative surface problems are demonstrated, namely, isolated-aperture surface analysis and surface-wave mode characterization. For the isolated-aperture surface, a characterization procedure is derived based on GBCs and Babinet's principle, and the accuracy is validated through comparison with full-wave simulations. For surface wave analysis, a characterization procedure is also developed from GBCs, and the feasibility is verified by numerical examples as well.  
surface electromagnetics; generalized boundary conditions; electromagnetic surfaces +
Footnote †: journal: Applied Sciences
## 4 Proposed Framework
### Architecture Overview
#### 4.1.1 IoT Device Layer  
The layer of the IoT devices acts as the basic infrastructure for collecting information in the system, consisting of \(n\) IoT nodes. The nodes, being typically restricted in terms of resources, monitor their environment and transmit unprocessed information for analysis afterwards, creating the basis for the operation of the system.  
Composition  
* Environmental sensors (\(E_{s}\)) designed to measure various parameters, including temperature (\(T\)), humidity (\(H\)), and CO\({}_{2}\) levels (\(CO_{2}\)).
* Traffic monitoring devices (\(T_{m}\)) tracking vehicle counts (\(V_{c}\)), speeds (\(S\)), and congestion levels (\(C_{l}\)).  
Figure 1: Hierarchical architecture for IoT-edge federated learning with ontology management.  
* Energy meters (\(E_{m}\)) recording power consumption (\(P_{c}\)) and voltage (\(V\)).
* Security devices (\(S_{d}\)) such as cameras capturing images or motion data.  
Resposibilities
* **Data collection:** Each IoT device collects raw data over time as follows: \[D_{i}=\{x_{1},x_{2},\ldots,x_{t}\},\quad\text{where }x_{t}=\{T_{t},H_{t},CO_{2_{t}}\}\] (4) Data are either collected continuously or triggered based on specific events or thresholds.
* **Communication:** IoT devices use lightweight communication protocols (e.g., MQTT, CoAP) to transmit data securely to the nearest edge server \(E_{j}\).
# Spectral Domain-Based Data-Embedding Mechanisms for Display-to-Camera Communication
## 3 Performance Evaluation and Results
### Embedding in Different Areas of the MF Region  
In this sub-section, we evaluate the performance of the proposed D2C model by embedding data into different areas within the MF region of a DCT image. The MF region was split into three sub-regions, each of which holds 200 bits of \(\mathbf{b}\). Here, the \(\alpha\) value for M1 was set to 30, and those for M2 and M3 were set to 0.15. \(D\) and \(AOC\) were set to 15 cm and 90\({}^{\circ}\), respectively. To assign embedding positions for the sub-regions (A, B, and C), the coefficients of the MF region were sorted in descending order. The bit capacity for each sub-region was specified based on the sorted coefficients, where a total of 600 highest coefficients were selected (200 for each sub-region). Sub-region A contained the top 200 coefficients and so on for sub-regions B and C.  
Embedding data in different sub-regions did not alter the BER performance of any mechanisms. However, the visual quality of the data-embedded image was affected instead. Table 2 shows the PSNR data of the three data-embedding mechanisms. In each case, three different sub-regions within the MF region were selected for embedding. Here, each sub-region acts as a separate coefficient vector of length 200. We can observe that the PSNR levels of the three sub-regions differed significantly, even though the data-embedding mechanism was identical. M1 exhibited the highest PSNR level when the data were embedded into sub-region C. With M1, the smaller coefficients of this region are directly added to the product of \(\alpha\) and \(\mathbf{S}\). Whenever such values are selected for embedding, the difference between the input image and data-embedded image is minimal, leading to a better PSNR. On the other hand, for M2, the PSNR increased significantly when data were embedded into sub-region C of the MF region. Here, the smaller coefficients of the sub-region are multiplied by \(\alpha\mathbf{S}\), yielding very small differences between the input and data-embedded images, and thus a higher PSNR. Similarly, for M3, the PSNR was highest when sub-region C was selected for embedding. Here, \(\alpha\mathbf{S}\) is exponentially multiplied by the selected coefficient vector, which presents also small values. Thus, a higher PSNR can be maintained, given only the slight difference between the input and data-embedded images. Note that these sub-regions tend to reside on the lower part of the MF coefficients, close to the HF region. The insertion of data into such regions prevails successful data embedding with less visual artifacts and the higher PSNR level.  
Figure 11: The BER against the PSNR [dB] for the proposed system under normal and ambient lighting conditions.
# HybridTabNet: Towards Better Table Detection in Scanned Document Images
## 3 Method
### Deformable Convolution  
Convolutional Networks [55] have been very successful over the past years on applications, including object detection and segmentation [56; 57; 58]. However, they cannot  
Figure 1: HybridTabNet for Table detection and segmentation. In the first step, the network performs feature extraction using ResNeXt-101 [54] with deformable convolution layers. The second step utilizes the Hybrid Task Cascade network to regress the bounding box and semantic mask coordinates of the table in the image.  
model complex geometric transformations due to their fixed kernel size. Deformable convolutional layers [21] were introduced to overcome this limitation. The intuition behind deformable convolutional layers is to add 2D offsets at regular grid sampling positions in the standard convolution operation, which deforms the constant receptive field of the preceding activation unit.  
The added offsets are learnable from the preceding feature maps. The receptive fields of the deformable layers are adaptive, which changes according to the scale of the object, and this allows the capture of objects at different scales [21]. The deformable layers use the same number of learnable parameters as convolutional layers, but exploit a much larger receptive field. This makes the performance of deformable layer s superior to that of convolutional layers [21].  
The deformable convolution operation can be defined as follows.  
\[\mathbf{y}(\mathbf{p}_{0})=\sum_{\mathbf{p}_{n}\in\mathcal{R}}\mathbf{w}( \mathbf{p}_{n})\cdot\mathbf{x}(\mathbf{p}_{0}+\mathbf{p}_{n}+\Delta\mathbf{p}_ {n}) \tag{1}\]  
where \(\mathcal{R}\) is any kernel of size \(n\times n\), \(\mathbf{w}\) is the weight of the kernel, \(\mathbf{x}\) is the input feature map, \(\mathbf{y}\) is the output of convolution operation, \(\mathbf{p}_{0}\) is the starting position of each kernel, \(\mathbf{p}_{n}\) is enumerating along with all the positions in \(\mathcal{R}\), and \(\Delta\mathbf{p}_{n}\) denotes the offsets added to the normal convolution operation.  
Figure 1(a) depicts that 2D offsets for the deformable layer are obtained by applying a convolutional layer over the input feature maps. The spatial resolution and dilation of the convolution kernel are the same as in the current convolutional layer. The output channels are of dimension 2N, where N corresponds to the number of 2D offsets.
# Enhancing 3D-Printed Clay Models for Heritage Restoration Through 3D Scanning
## 2 Materials and Methods
### 3D-Printing Methodology
#### 2.3.1 Characterization of Clay  
For 3D printing, two high-plasticity clays of type Red (plasticity index 11.8%) and Yellow (plasticity index 23.3%) are used, which according to existing literature, are ideal for extrusion processes [63, 64]. These clays are selected for its ability to maintain fine details and its favourable response during the firing process. Two different types of clays have been selected to assess the detail accuracy of the case studies. The clay has a controlled granulometry to ensure smooth extrusion and high consistency in the printed pieces. Additionally, its rheological properties are adjusted to allow continuous flow through the printer nozzle without compromising the structural stability of the pieces during printing.  
The clay paste used for the 3D printing process is characterized through a cone penetration test according with the UNE-EN ISO 17892-12:2019 standard [65], aiming to determine the Atterberg limits (liquid limit and plastic limit) of the clay before printing, considering the optimal moisture content for this process. The results showed a liquid limit of 46% and a plastic limit of 25% and a plasticity index of 21%, indicating that the clay exhibits adequate plasticity for extrusion in 3D printing. Additionally, a calcimetry test is performed to determine the calcium carbonate (CaCO\({}_{3}\)) content in the clay, with a result of 26.8%, which is consistent with the desired characteristics to ensure proper dimensional stability and cohesion during the printing and firing processes.
# Nonlinear Wave-Induced Uplift Force onto Pipelines Buried in Sloping Seabeds
## 3 Model Validation  
For present research, the pressure applied by waves to the mudline was first simulated by the wave sub-model. Then, the time-varying pressure was imported to the same location of seabed sub-model built by ABAQUS. To ensure the convergence of the grid, models with different grid sizes were built for the present section. The wave-caused pore pressure below the pipeline under different grid densities is illustrated in Figure 3.  
To verify the accuracy of the present model to simulate wave propagation, the laboratory experiment from [PERSON] (2016) was selected and the parameters for this validation are listed on Table 1. Figure 4 shows the free water surface obtained from this numerical model and experiment. It can be concluded that the numerical result agrees well with the experiment ([PERSON] (2016)).  
To validate the accuracy of the present model for simulating the wave-seabed interaction, an experiment from [PERSON] et al. (2017) was selected, and Table 2 lists the parameters for this validation. The wave-caused oscillatory pore pressure obtained from this numerical model and experiment are shown in Figure 5. It can be concluded that the numerical result agrees well with the experiment ([PERSON] et al. (2017)).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Wave period (T), s & 1 \\ Water depth (d), m & 0.3 \\ Wave height (H), m & 0.0361 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for verifying the wave propagation.  
Figure 3: Variation of the pore pressure amplitude at the bottom of the pipeline with grid densities.  
To validate the accuracy of present model for simulating the pore pressure applied by waves to pipelines, an experiment from [PERSON] et al. [40] was selected, and Table 3 lists the parameters for this validation. Figure 6 shows the pore pressure next to the pipeline obtained from the present model and experiment. And it is clear that the numerical result overall agrees with the experiment ([PERSON] et al. [40]).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Wave period (T), s & 9 \\ Water depth (d), m & 5.2 \\ Wave height (H), m & 3.5 \\ Poisson’s ratio (\(\
u\)) & 0.3 \\ Seabed porosity (n) & 0.425 \\ Shear modulus (G), pa & \(1.27\times 10^{7}\) \\ Saturation (S\({}_{\rm r}\)) & 0.996 \\ Soil permeability (k\({}_{\rm s}\)), m/s & \(1.8\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for verifying the wave-seabed interaction.  
Figure 4: Comparison of the free water surface between numerical model and laboratory experiment [39].  
Figure 5: Comparison of the wave-induced oscillatory pore pressure between numerical model and laboratory experiment [17].  
According to the three validations above, the wave-seabed-pipeline interaction is able to be effectively simulated using present numerical model.