esa-sceva/satcom-chunk-collection huggingface dataset:
# Small-Size Square Ring 1-Bit Reconfigurable Transmitarray Unit Cell for C-Band Applications
## 3 Switching States Approach
The radiation characteristics of the proposed unit-cell are the point of interest. The unit-cell realized gain for different frequencies is shown in Figure 5. The gain is 5.5 dB at the central frequency, while the gain loss due to mismatching is about 2.5 dB in 200 MHz (3.4%) frequency bandwidth. It should be noted that the structure can be modified by placing an additional coupling element to the vertical arm of the square ring antennas to provide dual-polarization operation [12]. The unit cell has been fabricated using a standard PCB process and characterized inside the waveguide. The measurement setup includes two coax SMA to rectangular waveguide transitions with cross-section dimensions of \(40\times 20\) mm\({}^{2}\) (5.6 \(\div\) 6.2 GHz). The measured frequency response obtained employing VNA R&S ZNB20 is presented in Figure 6. The forward bias current is equal to 15 mA. The measured S-parameters are identical for both states; the insertion loss is equal to 2.3 dB at 5.9 GHz in both states. The 3-dB transmission bandwidth is 180 MHz (3.4%). The phase error does not exceed \(\pm\) 1\({}^{\circ}\) in the passband. A good agreement of the measured results with the simulated ones is obtained. The higher insertion loss is associated with higher metallic and dielectric losses and more narrowband response. Due to the soldering process of the diodes a change in the coupling between the antennas occurs, the reflection coefficient resonances are stuck together. The comparative study of unit cell realizations with p-i-n-diodes is summarized in Table 1. The study shows that the proposed unit-cell has insertion loss and dimens  
Figure 4: The simulated results of the unit cells scattering parameters modules for both p-i-n diodes states and phase difference. Figure 5: The unit-cell radiation pattern for 5.8 GHz, 5.9 GHz, and 6 GHz. other structures yet excellent phase error of \(\pm 1^{\circ}\) within the given bandwidth. Using p-i-n-diodes inside the radiating element ensures the distortion of the radiation parameters of the unit-cell [9; 13; 14]. The other unit cells not inserting p-i-n-diodes into radiating elements have to combine two separate PCBs [11; 15]. Whereas the structure presented in this paper can be implemented within a single PCB process and then populated with p-i-n-diodes, which makes this approach more robust and advantageous for mass-manufacturing.
###### Abstract  
Co-doped and Ni-doped hematite (\(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)) nanorod arrays were prepared on fluorine-doped tin oxide (FTO) conductive glass via aqueous chemical growth, in which the doping and the formation of nanorods occurred simultaneously (i.e., in situ doping). These samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), ultraviolet (UV)-visible spectrophotometry, linear sweep voltammetry and Mott-Schottky (M-S) measurement. Results showed that the introduction of 5% Co or Ni into \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) (the molar ratio of dopant to Fe is 1:20) did not change its crystal phase, morphology, energy gap and flat band potential. Both the undoped and the doped \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) showed a direct band gap of 2.24 eV, an indirect band gap of 1.85 eV, and a flat band potential of \(-\)0.22 V vs. saturated calomel electrode (SCE). At an applied potential of 0.2 V vs. SCE, the Co-doped and the Ni-doped \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) exhibited a photocurrent of 1.28 mA/cm\({}^{2}\) and 0.79 mA/cm\({}^{2}\), respectively, which were 2.1 times and 1.3 times that of the undoped \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\). After the Co or Ni doping, the charge carrier concentration increased from \(1.65\times 10^{25}\) m\({}^{-3}\) to \(3.74\times 10^{25}\) m\({}^{-3}\) and \(2.50\times 10^{25}\) m\({}^{-3}\), respectively. Therefore, the increase in the photocurrent of the doped \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) was likely attributed to their enhanced conductivity.  
photoelectrochemical water splitting; hydrogen production; aqueous chemical growth; hematite nanorod; in-situ doping +
Footnote †: journal: Applied Sci. 2020, 10, 3567; doi:10.3390/app10103567
## 3 Metrics  
The Dice coefficient is widely used to assess the agreement at the pixel level between a predicted segmentation and its corresponding ground truth. It quantifies the similarity by calculating twice the area of overlap divided by the sum of the total number of pixels in both images. Equation (6) expresses the Dice coefficient as [21]:  
\[\text{Dice-coef}=\frac{2\times|X\cap Y|}{|X|+|Y|} \tag{6}\]  
where \(X\) and \(Y\) represent the predicted set of pixels and the ground truth, respectively.  
Moreover, the loss of the Dice coefficient is employed as a measure of dissimilarity between the predicted and ground-truth segmentation. It is computed by subtracting the Dice coefficient from 1. Equation (7) presents the formulation of the loss of the Dice coefficient [21].  
\[\text{Dice-coef}\text{ loss}=1-\frac{2\times|X\cap Y|}{|X|+|Y|} \tag{7}\]  
The Intersection over Union (IoU) is commonly utilized as an evaluation metric for object detection accuracy in the dataset by calculating the ratio of the overlap and the union areas between the predicted and ground-truth regions. Equation (8) [22] represents the IoU formula as:  
\[\text{IoU}=\frac{\text{Area of overlap}}{\text{Area of union}} \tag{8}\]  
When developing a depth estimation model, accuracy is used as a classification metric to measure the proportion of correctly predicted instances where the predicted depth exceeds the actual depth. It provides insight into the model's performance on the dataset. The accuracy is computed by dividing the sum of the True Negatives (\(TN\)) and True Positives (\(TP\)) by the total number of samples. Equation (9), illustrates the accuracy formula [23]:  
\[\text{Accuracy}=\frac{TP+TN}{\text{total sample}} \tag{9}\]  
Figure 6: Estimating depth of absorbing structure with deep learning model: (**a**) training process and (**b**) depth estimating process.
## 3 Materials and Methods  
### Baseline Feature Extraction of Spatiotemporal Characteristics  
Given a dataset composed by \(\{X_{n}\in\mathbb{R}^{C\times T}\}_{n=1}^{N}\), consisting of \(N\in\mathbb{N}\) EEG trials--where \(C\) denotes the number of channels and \(T\) the number of temporal samples per trial--the classification task aims to learn a function \(f:\mathbb{R}^{C\times T}{\rightarrow}\mathbb{R}^{K}\) that predicts the binary label \(y_{n}{\in}\{0,1\}\) (\(K=2\)), corresponding to the brain's neural responses to left- and right-hand MI, respectively.  
As a classical baseline, we include the Filter Bank Common Spatial Pattern (FBCSP) method, widely used in MI-BCI systems for its effectiveness in extracting subject-specific spatio-spectral features. The baseline MI binary classification approach involves spatial filtering via linear transformations of the EEG data to enhance class separability--specifically, by maximizing the variance of signals associated with one class while minimizing it for the other [63; 64]. The EEG signals are first passed through a filter bank comprising band-pass filters, typically centered on canonical sub-bands \(b_{i}\) (e.g., mu, alpha, beta) or custom frequency ranges. This process yields filtered EEG segments \(X_{b_{i}}\in\mathbb{R}^{C\times T}\). Following the FBCSP procedure [65], features are extracted by computing the log-variance of the spatially filtered signals \(\bar{X}_{b_{i}}\), as given by the following:  
\[v_{b_{i}}=\log\left(\frac{\text{diag}(\bar{X}_{b_{i}}\bar{X}_{b_{i}}^{\top})}{ \text{tr}(\bar{X}_{b_{i}}\bar{X}_{b_{i}}^{\top})}\right),\]  
where \(\text{diag}(\cdot)\) extracts the diagonal elements of the covariance matrix, \(\text{tr}(\cdot)\) denotes the trace operator, and \(\bar{X}_{b_{i}}=\bar{W}_{b_{i}}^{\top}X_{b_{i}}\) represents the spatially filtered signal using projection matrix \(\bar{W}_{b_{i}}\), which contains the most discriminative spatial filters for sub-band \(b_{i}\).
## 2 Materials and Methods
### Geology and Soils  
From north to south (Figure 2a), the Himalaya in Uttarakhand includes four tectono-stratigraphic units [64; 65]. (1) The Tethys, or Tibetan, Himalayan Zone comprising Palaeozoic to Eocene units deposited on the northern margin of the Indian plate [66; 67]; (2) the Higher, or Greater, Himalaya Zone comprising Proterozoic crystalline rocks intruded by leucogranites [68; 69]; (3) the Lesser Himalaya Zone with non-metamorphosed or weakly metamorphosed Indian continental crust and its sedimentary cover [69], intruded by Proterozoic plutons [70], along with Paleogene foreland deposits [71]; (4) the Outer Himalaya Zone comprising a thick Palaeocene to Quaternary sedimentary succession composed mainly of continental units derived from the Himalayan orogen [72; 73; 74; 75].  
The studied soils cover geological units of the Lesser Himalaya and Greater Himalaya zones (Figure 2A,B). In the investigated region, the Greater Himalaya is mainly composed of micaceous schists, engisses, calc-silicate gneiss, and locally metabasic rocks [69]. The Lesser Himalaya units are from its inner metasedimentary belt (diverse siliciclastic and carbonate metasedimentary and volcanic units) and outer sedimentary belt (diamictite, sandstone, slate, carbonates, chert/quartzite) [69; 74].  
Figure 2B presents the adapted soil cartography of Uttarakhand, according to the FAO/UNESCO [75]. The soil map reveals the strong link between geology, relief, and soil-type (Figure 2B). The higher-elevation areas with crystalline rocks of the Greater Himalaya are occupied  
Figure 1: Study area in the high hills of Uttarakhand Himalaya, India.  
by lithosols, the lower Lesser Himalaya dominated by sedimentary units displays cambisols, and the transitional zone between these two geological units can be covered by either cambisols or lithosols.
# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 4 Results
### Experimental Setup and Results Analysis
#### 4.1.4 Model Comparison  
The PR curves of the YOLOv5s model and the improved YOLOv5s model after training are shown in Figure 7.  
The PR curves for each class in different models are presented in Figure 7, and the specific information is summarized as shown in Table 2. In the improved YOLOv5s model, only the average accuracy of giraffe detection was not improved, and the average accuracy of detection of the other five grassland animals was improved. It can be seen that the overall performance of the improved YOLOv5s model was better than that of the original model.  
During the training process, tensorboard was used to draw the relevant curve. The data curves of Precision, Recall, mAP_0.5 and mAP_0.5:0.9 are shown in Figures 8 and 9. The blue color corresponds to the improved YOLOv5s data curve, and the pink color represents the YOLOv5s data curve. In terms of speed and accuracy, the improved YOLOv5s model is better.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **YOLOv3(AP)** & **YOLOv5s(AP)** & **Improved YOLOv5s(AP)** \\ \hline Elephant & 0.923 & 0.995 & **0.996** \\ Zebra & 0.706 & 0.970 & **0.982** \\ Bison & 0.942 & 0.988 & **0.993** \\ Horse & 0.805 & 0.992 & **0.994** \\ Giraffe & 0.783 & **0.996** & **0.996** \\ Hippo & 0.812 & 0.964 & **0.985** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average Precision (IOU = 0.5) obtained for each evaluated object detection algorithm.  
Figure 7: (a) YOLOv5s PR curve; (b) improved YOLOv5s PR curve.
# Compensation of Frequency Drift in Frequency-Sweep Polarization-Modulation Ranging System
## 3 Experimental Results and Discussion
The temperature dependence of crystal property included the refractive index and the length change on the optical path, and air-path distance variation was mainly caused by the variation of the air refractive index. Figure 5: Mean square of \(f_{\rm b}\) and N with different frequency interval numbers. Figure 6: Measurement results under different sweep methods.
## 3 Experiment and Validation of the Proposed Method
### Visualization of Fault Forecast Task Results  
The results of sensor fault predictions shown in Figure 7(a)-c represented the results of predicting 1000, 1100, and 1200 points, respectively. From Figure 8, the prediction results of traditional statistical models are relatively poor, followed by the statistical models DeepAR and Deep State which combine with neural networks. Because AT-LSTM has the attention mechanism and was combined with the LSTM model, which was suitable for the prediction task, the prediction effect is the best among the comparison models.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Prediction Period \(I\)** & & **[2900, 4700]** & **[3000, 4800]** & **[3100, 4900]** \\ \hline
**Prediction Length \(O\)** & **Loss** & **1000** & **1100** & **1200** \\ \hline ARIMA & MSE & 3.75 & 4.96 & 8.8 \\  & MAE & 1.56 & 1.89 & 2.36 \\ Garch & MSE & 0.85 & 1.48 & 2.66 \\  & MAE & 1.03 & 1.21 & 1.31 \\ DeepAR & MSE & 0.09 & 0.14 & 0.18 \\  & MAE & 0.21 & 0.28 & 0.3 \\ DeepState & MSE & 0.08 & 0.1 & 0.13 \\  & MAE & 0.21 & 0.25 & 0.27 \\ AT-LSTM & MSE & 0.07 & 0.09 & 0.11 \\  & MAE & 0.2 & 0.23 & 0.26 \\ GLTrans. & MSE & 0.04 & 0.08 & 0.10 \\  & MAE & 0.15 & 0.2 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fault forecast task of methane sensor results.
# Feature Mining and Sensitivity Analysis with Adaptive Sparse Attention for Bearing Fault Diagnosis
## 3 The Proposed Adaptive Sparse Attention Network (ASAN)
### The Network Structure  
The structure of the proposed deep network for fault diagnosis is depicted in Figure 3. To further illustrate, the bearing vibration envelope spectrum signal is used as the model input.  
Figure 2: (a) Attention structure. (b) Adaptive sparse attention structure.  
First, the input data sample is divided into \(N_{seg}\) subsamples, i.e., \(N_{seg}\) sequences, and each subsample contains \(N_{sub}=\frac{N_{input}}{N_{seg}}\) data points (all adjusted to integers for the convenience of subsequent calculation). Next, the convolution layers Conv1 and Conv2 are used to extract features for each signal segment, and then the max-pooling operation is carried out. The two convolution layers use the same structure, but the parameters are slightly different. Multiple local convolutional kernels with a window length of \(F_{L}\) are used for each layer respectively, and the zeroing operation is carried out to keep the dimension of the feature extraction layer unchanged. Each sequence signal's spatial characteristics can be deduced in this manner.  
Then, a full-connection layer was added to each segment of the signal for feature extraction, and softmax was used to assign attention weight to each segment of the signal. Subsequently, the attention weight is filtered by an adaptive threshold, and a sparse attention weight is obtained. Finally, the enhanced representation vector of the signal is created by multiplying the sparse attention weight by each segment signal.  
After the signal is enhanced by sparse attention, all the sequence features are integrated into a bidirectional LSTM layer. The bidirectional LSTM can connect forward and backward hidden states to capture sequential information in the input data.  
Dropout technology is a useful regularization technique that can prevent training data from being over-fitted. The authors of [36] recommended using dropout technology with a ratio of 0.4 at multiple levels of the network. In addition, the rectified linear unit (ReLU) activation function is generally used in the network. Because they do not have the problem of gradient disappearance or gradient diffusion during training, they can generally achieve better performance, especially in deep architectures [37]. Among them, the leaky ReLU first proposed is a variant of ReLU, which can be used as the attention module's activation function to determine the difference in how attention weight is distributed. In this study, the cross-entropy function was used as the loss function [38]. The parameters of the proposed network are shown in Table 1.  
Figure 3: A deep learning architecture for fault diagnosis based on the adaptive sparse attention mechanism.
# A Survey of the Applications of Text Mining for the Food Domain
## 3 Text Mining Overview
### Named Entity Recognition
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Algorithms** & **Comments** & **Papers** \\ \hline \multirow{3}{*}{Rules-Based} & \multirow{3}{*}{Regular Expressions, Lexicon Lookup, and so on.} & Easy to understand and implement, but requires writing many rules manually and is difficult to generalize to new domains. & [PERSON] et al. (2017) [48], [PERSON] et al. (2019) [49], [PERSON] and [PERSON] and [PERSON] et al. (2019) [50] \\ \cline{3-4}  & & & \\ \hline \multirow{3}{*}{Deep Learning} & \multirow{3}{*}{CNN, RNN, LSTM, and so on.} & Capable of learning more complex semantic and contextual features, but requires large computational & [PERSON] et al. (2018) [51], [PERSON] et al. (2020) [47], [PERSON] et al. (2022) [52], [PERSON] et al. (2022) [53], [PERSON] et al. (2023) [54] \\ \cline{1-1} \cline{3-4}  & & & \\ \cline{1-1} \cline{3-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: General methods of named entity recognition. the integration of knowledge graphs and transfer learning has improved the adaptability of NER systems [54]. NER stands as a cornerstone technology within NLP, with significant application potential in the food domain. Its evolution from rules-based to deep learning methodologies has not only enhanced performance, but also broadened its application scope. As language technology and artificial intelligence continue to advance, NER is expected to play an increasingly pivotal role in supporting intelligent decision-making and services.
# Edge-Nodes Representation Neural Machine for Link Prediction
## 4 Experiments and Results
### Baselines and Experimental Setting
[21], Adamic-Adar (AA) [4], resource allocation (RA) [11], preferential attachment (PA) [10], Katz [12], resistance distance (RD) [14], PageRank (PR) [1], SimRank (SR) [13], Stochastic block model (SBM) [9], matrix factorization using a classification loss function (MF-c) [23], matrix factorization using a regression loss function (MF-r) [23], and two popular methods including WLNM [16] which employs edge' representation as link's formation mechanism and Node2 vec [18] which combines two nodes' embeddings on the two sides of the edge as link's formation mechanism. For some methods, different parameter settings lead to unequal results and we often use the best \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Logistic Regression** & **Decision Tree** & **Random Forest** & **Adaboost** & **Neural Network** \\ \hline
**Cele** & 0.8429 & 0.7186 & 0.757 & 0.7581 & **0.870** \\
**USAir** & 0.958 & 0.8697 & 0.8803 & 0.8756 & **0.968** \\
**PB** & 0.937 & 0.7992 & 0.8663 & 0.8488 & **0.9453** \\
**NS** & 0.937 & 0.9273 & 0.9345 & 0.9218 & **0.9832** \\
**Ecoli** & 0.9563 & 0.8762 & 0.9212 & 0.889 & **0.9787** \\
**Yeast** & 0.9378 & 0.8861 & 0.9066 & 0.8878 & **0.9618** \\
**Power** & 0.7968 & 0.6534 & 0.6924 & 0.6693 & **0.866** \\
**Router** & 0.9134 & 0.6158 & 0.623 & 0.7768 & **0.921** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: AUC results of different classifiers for ENRNM. results. For Katz, we set the damping factor \(\beta\) to \(0.001\) and for PageRank, we set the damping factor to \(0.85\), which is suggested in the [16]. For SBM, we adopt the implementation straightly in [38], where the number of latent groups is chosen in \(\{4,6,8,10,12\}\). For MF, we utilize the libFM [39] software, where the number of latent factors of MF is searched in \(\{5,10,15,20,50\}\). For WLNM, we set the nodes' number K of subgraph to \(10\), which is presented to have good performance on different networks. For Node2 vec, we generate 45-dimension vector as node embedding. To show our results more intuitively, we draw a line chart according to the corresponding result table. We conduct two groups experiments. The first group experiments compare the heuristic methods with the proposed method ENRNM as shown in Table 6 and Figure 8, the second group experiments compare the Node2 vec, WLNM and ENRNM as shown in Table 7 and Figure 9. Moreover, we analyze the characteristics of heuristics, Node2 vec, WLNM and ENRNM in Table 4. In Figure 5, we compare the link's representation of Node2 vec, WLNM and ENRNM.
# A Nonlinear Optimization Design Algorithm for Nearly Linear-Phase 2D IIR Digital Filters
## 6 Experimental Results  
This section is dedicated to implementing Equations (12) and (13) to optimize the design of 2D IIR filters with nearly linear phase. Our proposed approach will be evaluated against other methods described in the literature. Specifically, we will compare the designs obtained using the proposed method to the ones presented in [8]. Some of these examples  
Figure 1: Flowchart of the optimization process.  
are discussed in [52; 53] as well. The desired frequency response will be defined in each example, and the group delays \(\tau_{1}\) and \(\tau_{2}\) along the \(\omega_{1}\) and \(\omega_{2}\) directions are the same, i.e., \(\tau_{1}=\tau_{2}=\tau\). In each of the designed examples, the starting values for \(\tau_{10}\) and \(\tau_{20}\) are set as the mean values of the passband group delays of the initial filter, and the stability margin \(\varepsilon_{s}\) is set to 0.02, unless otherwise mentioned in the design example. The MATLAB environment is used to solve the constrained optimization problems. The MATLAB Symbolic Math Toolbox and the interior-point method (IP) were used to implement the constrained optimization problem.