esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Practical Implementation  
Our proposed work provides a practical solution to enhance the efficiency and scalability of existing ETC system. The key areas of applicability is as follows:  
* Uninterrupted Toll system: This model is applicable for any highway or expressway toll system. The suggestion of eliminating lane system and implementing minimum number of reader antenna for vehicle detection makes the system fast and less congested. No vehicle need to wait in a queue to pass the gate.
* Multi-reader environment: Proposed Modified Pulse protocol reduce the reader collision while multiple reader cover the toll gate area in no-lane system. Our suggested algorithm enhance the throughput by optimizing reader transmission timing.  
Figure 15: Throughput with varying no. of tags of deterministic algorithms.  
* High dense vehicle management: Number of vehicles need to manage at the highway toll gate causes tag collision. Trimming method of our proposed M-ary trimming query tree algorithm minimize the number of queries and ensure faster tolling system.
* Cost effective: Our proposed work offers a cost effective toll system. It established a fast, efficient and economical system for managing large amount of vehicles applying minimum arrangements.
* Future Intelligent Transport system (ITS): The proposed model can integrate to vehicle to vehicle (V2V) or vehicle to Infrastructure (V2I) communication for smart city.
## 5 The Proposed Solution
### Signal Features
#### 5.2.1 Number and Length of Signal Bursts  
The first time domain feature includes information about the burstiness of the observed signal, employing a threshold-based burst detection. The feature is an \(M\)-element vector in which each element represents the number of bursts of a certain sample length. Hence, we define:  
\[\mathbf{F}_{B}=\{F_{1},\ F_{2},\ F_{3},\ F_{4},\..,\ F_{M}\} \tag{5}\]  
where \(F_{n}\in\mathbf{N}\) represents the the number of bursts of length \(n\) found in the RSSI trace in the analysis, while with \(F_{M}\), we mark all of the bursts with sample length \(L\geq M\). In particular, we require a certain number of samples under the selected threshold to identify the end of a burst. This is to avoid the case where a single or a few incorrect readings of the RSSI register will lead to a misclassification of long signal bursts into shorter ones. As will be discussed in Section 7, the detection of longer (i.e., \(>5\) ms) bursts is extremely important because it is a specific feature of the RF emissions of microwave ovens. The choice of a proper value of the threshold with respect to the calibration of the radio nodes will be discussed in Section 6.
## 2 Mathematical Description of the Proposed Hybrid System Components
### PV System  
The PV system is considered as a number of cells connected in series. The output power of the PV system is presented based on many parameters as introduced in Equation (1) [23]:  
\[P_{pv}=I(t)\times\eta_{pv}(t)\times A_{pv} \tag{1}\]  
where \(I\) represents the solar irradiation, \(A_{pv}\) represents the area covered with PV modules and \(\eta_{pv}\) is the efficiency of the PV system that can be calculated as follows:  
\[\eta_{pv}(t)=\eta_{r}\times\eta_{t}\times\left[1-\beta\times(T_{a}(t)-T_{r})- \beta\times I(t)\times\left(\frac{NOCT-20}{800}\right)\times(1-\eta_{r}\times \eta_{t})\right] \tag{2}\]  
Figure 1: Configuration of the proposed microgrid hybrid energy system.  
where \(NOCT\) is the nominal operating cell temperature (\({}^{\circ}\)C), \(\eta_{r}\) is the reference efficiency, \(\eta_{t}\) is the efficiency of the maximum power point tracking (MPPT) equipment, \(\beta\) is the temperature coefficient, \(T_{a}\) is the ambient temperature (\({}^{\circ}\)C), \(T_{r}\) is the solar cell reference temperature (\({}^{\circ}\)C).  
Figure 2: Power management of the PV/Biomass hybrid renewable energy sources (RES).
# The State of the Art of the Control Strategies for Single-Phase Electric Springs
## 2 Control Strategies of ES-1  
For ES-1 without an energy storage unit, the amount of reactive power injected from the power converter is controlled by a reference voltage whose vector is always leading or lagging the current vector by 90\({}^{\circ}\)[6, 10]. A typical control scheme is depicted in Figure 1[10].  
The double PI closed-loop control, shown in Figure 1, are adopted, to control the DC link voltage and the grid voltage. In order to ensure that there is no active power transferred between the DC link and the AC port of the ES, two coordinated closed-loops are used to regulate the DC link voltage \(V_{dc}\) and also the magnitude of the grid voltage \(v_{S}\). Therefore, the magnitude and phase of the ES voltage are interrelated and inherently controlled, and the degree of control freedom in the method is one. Under this control strategy, the power angle of the SL is not controlled and varies, during the grid voltage regulation process. The main advantage of this control strategy is that its structure is simple, which is easy to implement. Additionally, it does not need detection of the grid voltage and a priori knowledge of the circuit parameters. The shortcomings are also obvious. For example, the decoupling feature is weak, and it can only operate at the pure reactive power compensation mode, which causes an inability to adjust the power angle of the SL and can only stabilize the grid voltage.
# Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model
## 3 Methodology
### Temporal Convolutional Network (TCN) Model  
Temporal convolutional network (TCN) is an algorithm that can be used to solve time series predictions. At the same time, TCN also provides a unified method to capture spatiotemporal information hierarchically. These layers have time attributes and are used to learn global and local patterns in data. Its main features are: (1) Since each prediction can only rely on its previous prediction TCN, using causal convolution, will not have data leakage; (2) TCN combines the deep neural network and extended convolution to form a model that can save a long effective history. When the model is used for multi-dimensional data parallel input, it can maintain efficient computing efficiency [35]; (3) TCN also introduces residual network and hole convolution to construct long-term dependence, so as to effectively improve the performance of the model. The TCN model structure is shown in Figure 1. The hole coefficient at the lowest layer of the hidden layer is \(d=1\), which means that samples are taken at each time point during input. The next layer \(d=2\) means that every two time points are taken as an input, and so on. In Figure 1, the third layer is taken as an example. The convolution kernel size \(k\) = 3, and the expansion convolution calculation formula is:  
\[F(s)=(x\times df)(s)=\sum_{i=0}^{k-1}f(i)X_{s-d\cdot i} \tag{1}\]  
Figure 2 shows the residual network structure. Dropout means that during the neuron propagation process, the activation value of a neuron stops working with a certain probability to enhance the generalization of the model. ReLU represents the linear rectification function, which is used as the activation function of the neural network. Weight Norm means to normalize the weight value, and Dilated Causal Conv represents a dilated convolutional layer.
# Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm
## 6 Electricity Load-Forecasting Model
### Federated Learning Model Based on LSTM  
The LSTM (Long Short-Term Memory) network excels at handling time-series problems, and the electricity load of each user is inherently a type of time series. Figure 3 shows the internal unit structure of LSTM, which consists of input gates, forget gates, and output gates.  
In Figure 3, \(X_{t}\) is the input, \(h_{t-1}\) and \(h_{t}\) are the outputs, and \(C_{t-1}\) and \(C_{t}\) are the storage cell states. The calculation formulas for these variables are as follows:  
\[f_{t}=\sigma\Big{(}W_{f}X_{t}+U_{f}h_{t-1}+b_{f}\Big{)} \tag{20}\]  
\[i_{t}=\sigma(W_{i}X_{t}+U_{i}h_{t-1}+b_{i}) \tag{21}\]  
\[o_{t}=\sigma(W_{o}X_{t}+U_{o}h_{t-1}+b_{o}) \tag{22}\]  
\[\tilde{C}_{t}=\tanh(W_{c}X_{t}+U_{c}h_{t-1}+b_{c}) \tag{23}\]  
\[C_{t}=f_{t}\odot C_{t-1}+i_{t}\odot\tilde{C}_{t} \tag{24}\]  
\[h_{t}=o_{t}\odot\tanh(C_{t}) \tag{25}\]  
where \(\sigma\) is the sigmoid function and \(f_{t}\), \(i_{t}\), and \(o_{t}\) are the outputs of the forget gate, input gate, and output gate, respectively, \(W_{c}\), \(U_{c}\), \(W_{o}\), \(U_{o}\), \(W_{i}\), \(U_{i}\), \(W_{f}\), and \(U_{f}\) are the corresponding weights, and \(b_{c}\), \(b_{o}\), \(b_{i}\), and \(b_{f}\) are the bias terms. This paper adopts the framework of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature Type** & **Feature Name** & **Data Type** & **Unit/Range** & \begin{tabular}{c} **Transformation** \\ **Method** \\ \end{tabular} & \begin{tabular}{c} **Transformed** \\ **Feature** \\ \end{tabular} \\ \hline \multirow{3}{*}{Weather} & Temperature & Continuous & °F & Normalization & temperature \\  & Humidity & Continuous & \% & Normalization & humidity \\  & Pressure & Continuous & kPa & Normalization & pressure \\ \hline Load & Load & Continuous & kW & Normalization & load \\ \hline \multirow{6}{*}{Time} & Year & Discrete & 2016–2018 & \begin{tabular}{c} One-hot \\ encoding \\ \end{tabular} & \begin{tabular}{c} oh-2016, oh-2017, \\ oh-2018 \\ \end{tabular} \\  & Month & Discrete & 1–12 & \begin{tabular}{c} sin/\(\cos\) Cyclic \\ encoding \\ \end{tabular} & \begin{tabular}{c} month-sin, \\ month-cos \\ \end{tabular} \\  & Day & Discrete & 1–30 & \
\begin{tabular}{c} sin/\(\cos\) Cyclic \\ encoding \\ \end{tabular} & day-sin, day-cos \\  & Hour & Discrete & 0–23 &
\begin{tabular}{c} sin/\(\cos\) Cyclic \\ encoding \\ \end{tabular} & hour-sin, \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feature selection and feature transformation of datasets.  
Figure 3: Internal structure of LSTM.  
federated learning, using LSTM as the load-forecasting model, leveraging the three gates mentioned above to utilize long-term user historical data for load forecasting. Each participating user acts as an independent client, and the network topology is illustrated in Figure 1.
## 3 Numerical Solution
### Beam Space Discretization  
The equations of motion of the beam are fourth-order partial differential equations in which the unknown functions, displacement \(u\) and \(v\), are functions of space and time. The solution of those equations is obtained by the finite element method (FEM). The finite element form of (7) and (6) involves transforming the equations into their weak form, splitting the domain into smaller segments (finite elements) and introducing approximation functions for the displacement fields. This procedure leads to a set of ordinary differential equations with respect to time, which is written in a matrix form as  
\[\mathbf{M}_{e}\vec{q}_{e}(t)+\mathbf{C}\hat{q}_{e}(t)+\mathbf{K}q_{e}(t)= \boldsymbol{p}(t)+\boldsymbol{r}_{e}(t) \tag{15}\]  
For more details, the reader can refer to [38; 39]. In the equation above, \(\mathbf{M}_{e}\) is the mass matrix, \(\mathbf{C}\) is the damping matrix, \(\mathbf{K}\) is the stiffness matrix, \(\boldsymbol{p}(t)\) is the vector load, and \(\boldsymbol{r}_{e}(t)\) is the vector of contact forces, and they all appertain to the beam. Within the finite element method, the displacements of the nodal points are obtained. The vectors \(\boldsymbol{q}_{e}(t)\), \(\hat{\boldsymbol{q}}_{e}(t)\) and \(\tilde{\boldsymbol{q}}_{e}(t)\) collect, respectively, the displacements, velocities, and accelerations of the nodal points, which are obviously functions of time. For the analysed beam, each node of the beam has three displacement components (or degrees of freedom) [42], namely, the displacements in the \(x\) and \(y\) directions and a rotation.
## 3 Proposed Methodology
### Model Training
#### 3.3.5 Tuned Hybrid LSTM-Volume  
Hyperparameters, including the number of neurons, batch size, number of epochs, learning rate, and dropout rate, were fine-tuned for the Hybrid LSTM-Volume from Section 3.3.4. These adjustments aimed to reduce model complexity, shorten training time, and enhance overall performance and accuracy.  
Fine-tuning hyperparameters ensured the model struck a balance between training efficiency and predictive accuracy. By iteratively adjusting parameters, the model's capacity to generalise was enhanced, mitigating issues like overfitting or underfitting. Using three LSTM layers ensured the model maintained sufficient depth to capture complex temporal dependencies.  
After fine-tuning the Hybrid LSTM-Volume model, the training loss (MSE) decreased consistently from an initial value of 0.0045 in the first epoch to 0.0003 by the final epoch, indicating the model effectively minimised errors. Validation loss (MSE) closely tracked training loss throughout the process, starting at a slightly lower value and stabilising by the final epoch. The absence of significant fluctuations in the validation loss indicated strong generalisation without overfitting.  
The final validation loss achieved was 0.0001, reflecting minimal error between predicted and actual price values on the validation set. These results confirmed that the Hybrid LSTM-Volume Model successfully leveraged multivariate input features to capture complex temporal relationships influencing Bitcoin price trends. Figure 6 displays the training and validation error trends for MSE, MAE, and MAPE.  
Figure 5: Training and validation error trends for the Hybrid LSTM-Volume model across metrics: MSE, MAE, and MAPE. The decreasing trends indicate effective learning and generalisation.
## 5 Discussions
### Technical Speculations for the New Generation of Nightlight Satellite Sensors  
#### 5.2.1 Spectral Resolution  
Although, it is highly convincing that moonlight can be a very useful illuminating source for Earth observations via optical remote sensing. Our knowledge of utilizing moonlight for remote sensing purposes is still very limited. We have proposed some new views and ideas for future designs of nightlight satellite sensors. First, for detecting nightlight non-self-emitting objects, the current mono-spectral nightlight remote sensing data are very limited and more bands are required. We believe that the bands of future sensors must contain at least three bands in the visible range, as well as a near-infrared band (IR). With three bands, the ISS and UAV data can overcome the limitations of traditional mono-spectral VIIRS/DNB data [46; 47; 48]. Whether for urban lighting or light pollution, a single wide waveband is no longer able to meet our requirements. It has been discussed that the scotopic band from 0.454 to 0.549 mm, the photopic band from 0.51 to 0.61 mm, and a broad red and near-infrared band spanning from 0.61 to 0.9 mm are needed for future sensors. Additionally, IR bands are also needed to detect sodium vapor emission lines [57]. Combining the current experience of lightings in urban areas [58], the perspective of light pollution [59] and this study, the settings of a new generation of nightlight satellite mission should contain at least four-color channels in the visible light band (at 383-503, 493-619, 568-584, and 797-833 nm), and an additional \(\sim\)10 \(\upmu\)m thermal infrared band for cloud and fire detection.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**ID** & **Camera** & **Len** & **ISO** & **Exposure** & **AV** & **Distance** & **WB** \\ \hline ISS026-E-12438 & Nikon D3S & 180 mm & 12,800 & 1/15S & F/2.8 & 409 KM & Auto-adjustment \\ ISS045-E-155029 & Nikon D4 & 400 mm & 10,000 & 1/25S & F/2.8 & 394 KM & Auto-adjustment \\ UAV-Mosaic & Hasselblad & 28 mm & 3200 & 1S & F/2.8 & 120 M & Auto-adjustment \\ \hline \end{tabular}
\end{table}
Table 4: Parameters of different moonlight sensors.
# A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction
## 4 A Case Study on Interpretability
### Results  
Since it is difficult to investigate all significant GO terms determined in 200 targeted side effects, we narrowed the investigation range down by a specific criterion. First, among the GO terms, those with more than 100 proteins belonging to the term were excluded. That is, terms corresponding to a wide range of biological phenomena are excluded. Then, among side effects, side effects having two or more significant GO term were excluded. Consequently, 24 side effects, each has one significant GO term only, were selected as our investigation targets.  
Table 6 summarizes the results. In 16 out of 24 side effects (66.6%), the relationships with the corresponding GO terms were found through the existing literatures. Despite the GO terms of a narrow range of biological phenomena and the side effects of a narrow range of symptoms, there were existing literatures supporting the relationships. In particular, a very strong relationship was shown in some cases. For example, folliculitis has an intuitive relationship with cilium. And there is a study that mentions a direct relationship between myocarditis and cyclin/CDK. Moreover, there are many existing studies on the relationship between nail and amyloid.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Side Effect ID** & **Significant GO Term** & **GO ID** & **Overlap \({}^{*}\)** & \begin{tabular}{c} **Adjusted** \\ _p-Value_ \\ \end{tabular} & **Related Literatures and Notes** \\ \hline \multirow{2}{*}{Calculus ureteric} & Regulation of amyloid precursor protein biosynthetic process & GO:0042984 & \multirow{2}{*}{5/11} & \multirow{2}{*}{0.008806} & \multirow{2}{*}{\begin{tabular}{c} Ureteric calculus is related to amyloid [34; 35]. \\ \end{tabular} } \\  & Regulation of amyloid precursor protein biosynthetic process & GO:0042984 & \multirow{2}{*}{5/11} & \multirow{2}{*}{0.007394} & \multirow{2}{*}{\begin{tabular}{c} Weaken of eye function is related to amyloid [36; 37]. \\ \end{tabular} } \\ \cline{1-1}  & \begin{tabular}{c} Intricalinary transport involved \\ in cilium assembly \\ \end{tabular} & GO:0035735 & 10/40 & 0.00145 & \
\begin{tabular}{c} Folliculitis is directly related to cilia (cilium) [38; 39]. \\ \end{tabular} \\ \hline \multirow{2}{*}{Hernia inguinal} & Histone lysine methylation & GO:0034968 & 10/34 & 0.003045 &
\begin{tabular}{c} Hernia is related to histone lysine methylation [40; 41]. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Significant term for each 24 side effects, determined by unified embedding.
## 2 Materials and Methods
### CMC Samples Characterization  
#### 2.3.1 Structural Characterization  
The structural analysis of CMC samples was attained by Fourier Transform Infrared Spectroscopy (FT-IR), Powder X-ray Diffraction (PXRD), and Scanning Electron Microscopy (SEM).  
The FT-IR spectra were recorded using the Frontier(tm) MIR/FIR spectrometer from PerkinElmer in a scanning range of 550-4000 cm\({}^{-1}\) for 16 scans at a spectral resolution of 4 cm\({}^{-1}\). All analyses were done in triplicate.  
Powder X-ray Diffraction Analysis (PXRD) was performed on Rigaku MiniFlex 600 diffractometer with Cu k\(\alpha\) radiation, with a voltage of 40 kV and a current of 15 mA (\(3^{\circ}\leq 20\geq 60^{\circ}\); a step of 0.01 and speed rate of 3.0\({}^{\circ}\)/min). The evaluation of the crystallinity index (%CI) was performed based on the profile and the baseline adjustment of the diffractogram based on reflection areas by amorphous subtraction using the program PDXL2 from Rigaku(r) (version 2.8.1.1). All measurements were done in duplicate.  
The morphology of CMC samples was evaluated by Scanning Electron Microscopy (SEM) on JSM-5600 LV Scanning Electron Microscope from JEOL, Tokyo, Japan. Before analysis, the CMC powdered samples were placed in observation stubs (covered with double-sided adhesive carbon tape (NEM tape, Nisshin, Japan) and coated with Aug/Pd using a Sputter Coater (Polaron, Bad Schwalbach, Germany). All observations were performed in high-vacuum with an acceleration voltage of 30 kV, at a working distance of 9-10 mm and a spot size of 4. The images presented here are representative images of the morphology of each sample.
# Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine
###### Abstract  
The improvements in collecting and processing data using machine learning algorithms have increased the interest in data mining. This trend has led to the development of real-life decision support systems (DSSs) in diverse areas such as biomedical informatics, fraud detection, natural language processing, face recognition, autonomous vehicles, image processing, and each part of the real production environment. The imbalanced datasets in some of these studies, which result in low performance measures, have highlighted the need for additional efforts to address this issue. The proposed method (HOUM) is used to address the issue of imbalanced datasets for classification problems in this study. The aim of the model is to prevent the overfitting problem caused by oversampling and valuable data loss caused by undersampling in imbalanced data and obtain successful classification results. The HOUM is a hybrid approach that tackles imbalanced class distribution challenges, refines datasets, and improves model robustness. In the first step, majority-class data points that are distant from the decision boundary obtained via SVM are reduced. If the data are not balanced, SLS is employed to augment the minority-class data. This loop continues until the dataset becomes balanced. The main contribution of the proposed method is reproducing informative minority data using SLS and diminishing non-informative majority data using the SVM before applying classification techniques. Firstly, the efficiency of the proposed method, the HOUM, is verified by comparison with the SMOTE, SMOTEENN, and SMOTETomek techniques using eight datasets. Then, the results of the W-SIMO and RusAda algorithms, which were developed for imbalanced datasets, are compared with those of the HOUM. The strength of the HOUM is revealed through this comparison. The proposed HOUM algorithm utilizes a real dataset obtained from a project endorsed by the Scientific and Technical Research Council of Turkey. The collected data include quality control and processing parameters of yarn data. The aim of this project is to prevent yarn breakage errors during the weaving process on looms. This study introduces a decision support system (DSS) designed to prevent yarn breakage during fabric weaving. The high performance of the algorithm may encourage producers to manage yarn flow and enhance the HOUM's efficiency as a DSS.  
data mining; imbalanced datasets; undersampling; oversampling; classification; decision support systems +
Footnote †: journal: Applied sciences  
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Footnote †: journal: Applied sciences  
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