esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Scheme
### Methodology of Data Analysis  
As explained in Section 3.1, we adopted a 2D inverse SAR (ISAR) data collection geometry with a fixed incidence angle. Thus, the frequency domain backscatter data of rice samples were acquired for the complete azimuth angles from 0\({}^{\circ}\) to 359\({}^{\circ}\). Figure 5 shows the reconstructed images of one sample of data observed on 30 August 2016, for LL, RL, and RR polarizations. A spherical back-projection algorithm is used to process this wide-angle data [28]. From the reflectivity images in Figure 5, it can be clarified that all rice stocks are clearly identified for each polarization thanks to the high-resolution capability of our system. In practical rice monitoring applications, however, the resulted SAR images cannot usually maintain such a high-resolution feature, and a single resolution cell consists of the superposition of different scattering contributions from a few rice plants. Thus, for our situation, all eight rice plants should be confined inside a single resolution cell for a reasonable analysis of rice scattering mechanisms, as [PERSON] et al. mentioned [29]. For this reason, our analyses are not performed on image data but the scatterometric data obtained by ensemble averaging of the frequency domain data along with all the azimuth angles (0-359\({}^{\circ}\)) and frequencies (4.5-7.5 GHz). As a result, the whole scattering behavior is combined into a single coherency matrix as also adopted in [29; 30; 31], and the decomposition theories explained in Section 2 are applied to this matrix.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Date** & **DoY (Day of Year)** & **Mean Height (cm)** & **BBCH Code** & **Phenological Stage** \\ \hline Soil and water & NA & NA & 0 & 1: Germination \\
7 June 2016 & 159 & 19 & 21-29 & 2: Tillering \\
22 June 2016 & 174 & 27 & 21-29 & 2: Tillering \\
6 July 2016 & 188 & 34 & 30-39 & 3: Stem elongation \\
21 July 2016 & 203 & 42 & 30-39 & 3: Stem elongation \\
3 August 2016 & 216 & 49 & 41-49 & 4: Booting \\
22 August 2016 & 235 & 52 & 83-85 & 5: Ripening \\
30 August 2016 & 243 & 52 & 87-89 & 5: Ripening \\
14 September 2016 & 258 & 45 & 93 & 5: Ripening \\ \hline \hline \end{tabular}
\end{table}
Table 1: BBCH code and phenology stage of each observed data set.  
Figure 5: Reconstructed circularly polarized SAR (CP-SAR) images for the rice observed on 30 August 2016. The images are normalized to the maximum value of three images. (**a**) LL polarization; (**b**) RL polarization; (**c**) RR polarization.
## 3 Methodology
### Time-Frequency Fusion Block  
Figure 2 depicts the structure of the proposed Time-Frequency Fusion (TFF) block, including two branches designed to process input signals from different domains. The left branch primarily operates in the time domain, utilizing a single fully connected layer (colored in orange) to learn input patterns and project them into vectors within the latent space. Specifically, the size of the input to the current block is denoted as \(c_{in}\), and the desired output size is represented as \(c_{t}\), where the subscript \(t\) represents the time domain processing. It is worth noting that, similar to the single fully connected layer, the input size \(c_{in}\) and output size \(c_{t}\) can share the same value or have different values. In the right branch, which is dedicated to frequency domain processing, the input is initially converted from the time domain to the frequency domain using the Fast Fourier Transform (FFT). Subsequently, two separate fully connected layers are employed to learn the real (highlighted in purple) and imaginary (highlighted in green) parts of the complex output obtained from the FFT operation. As the FFT operation maintains the data size, both the real and imaginary parts of the FFT results remain the size of \(c_{in}\). Consequently, two layers in the frequency branch change the size from \(c_{in}\) to \(c_{f}^{\prime}\) and \(c_{f}^{\prime}\), respectively. The subscript \(f\) denotes the frequency domain branch, and superscripts \(r\) and \(i\) represent the real and imaginary parts of frequency features, respectively. In this work, the values of \(c_{t}\), \(c_{f}^{\prime}\), and \(c_{f}^{\prime}\) are all the  
Figure 1: The pipeline of the proposed anomaly detection framework and system.  
same; this value is denoted as \(F\) in this paper, i.e., \(c_{t}=c^{\prime}_{f}=c^{i}_{f}=F\). The inverse FFT operation is then applied to the complex values within the latent space, where features for reconstructing the real and imaginary parts have already been learned by the fully connected layers. Although the inverse FFT yields results in the time domain, the process inherently distills the knowledge and feature representation acquired from the frequency domain. Features from both branches are combined by summation to integrate information from both domains. Finally, another fully connected layer processes the combined features with size \(F\) to generate the final output of the TFF block, the desired dimension of which is denoted by \(c_{out}\). Additionally, in this work the value of \(c_{out}\) is determined to be the same as \(c_{in}\) of each block. In short, this new block enables learning features in the frequency domain to improve the reconstruction of normal time series.
## 4 Simulation Results
### Experimental Results
#### 4.2.2 Network Lifetime  
The network lifetime is an important metric for evaluating the performance of WSNs. Here, we discuss the impact of our proposed protocol on network lifetime by comparing the result of our proposed protocol with existing protocols.  
Figure 8 shows the percentage of nodes alive in the network in EDACP-CS, EECDA, and LEACH-Hetero. It is obvious that both the dead time of the first and the last nodes of the proposed  
Figure 6: Average energy consumption during error occurrence in ECADP-CS, energy efficient clustering and data aggregation (EECDA), and Low-Energy Adaptive Clustering Hierarchy (LEACH)-Hetero protocols.  
protocol came after those of LEACH-Hetero and EECDA. Also, it shows that EECDA achieves better performance compared with LEACH-Hetero, whereas in LEACH-Hetero the CH elected based on the residual energy of the individual node calculation and the re-clustering scheme is adopted in each cluster of the WSNs. However, a path with a maximum sum of energy residual would be selected for data transmission in spite of the path with minimum energy would be selected in case of EECDA. The prolongation of the network lifetime in EDACP-CS because it efficiently compresses data using CS in addition to considering the distance from the BS and the residual energy for the CH selection while other protocols select the CH by considering only the residual energy.  
Figure 9 shows the lifetime for heterogeneous sensor nodes in terms of energy and transmission range. It has been observed that when the transmission range increases there is a significant improvement in lifetime of the network, this is because extending the transmission range will increase the number of CHs within the BS's transmission range. Therefore, it reduces the amount of aggregated data packets which are forwarded to the BS since CHs near the BS have higher burden to receive and forward data packets. It has been shown that EDACP-CS greatly prolongs the sensor network's lifetime compared with LEACH-Hetero and EECDA, because in EDACP-CS, each sensor node independently elects itself as a CH depends on both the energy level of each sensor and distance between sensor to neighbor's sensor and sensors to BS. Besides, EDACP-CS efficiently compresses data using CS.  
Figure 8: Number of alive nodes over rounds.  
Figure 7: Energy consumption for the wireless sensor networks (WSN).
# Interaction in eXtended Reality Applications for Cultural Heritage
## 3 Related Work
### Web-Based Cultural Heritage Applications  
In the widespread adoption of web-based 3D applications, the emergence of WebGL was crucial. Before WebGL, applications were built for proprietary 3D web plugins, which often had a short lifetime and high maintenance cost. Today, most modern game engines support WebGL builds. Having this technology at their disposal allowed developers to offer rich 3D applications to users without the need for installation, configuration, or some additional hardware. This property is essential for cultural heritage applications because it allows more people to have access to the heritage, not just the museums visitors, but also internet users. In the COVID-19 pandemic, this was particularly useful during the lockdown, enabling people to visit cultural heritage sites from their homes.  
Even though WebGL solved some problems, there are still issues with the limited power of web-based 3D rendering. Authors in [41] described a method of reducing geometry complexion to accelerate loading and rendering speed.  
Web-based solutions can be practical because they inherently support multimedia through HTML5. Including and combining different types of content simultaneously is easy. One solution that combines videos, web pages, and 3D scenes on the web-based application is presented in [42].  
Web technologies in combination with AR or XR applications to create more accessible solutions are described in many papers. Some of these papers are listed below.  
Unity3D was used in [43] to create a web application in mixed reality. The authors concluded that using web languages (HTML, CSS, and JavaScript) lowered the bar for content creators. The problems that persist are for hardware makers to solve by loosening some restrictions on hardware configurability and adaptability to different software technologies.  
The development of some VR features on a web-based application for an online virtual reality control laboratory is presented in [44]. In the laboratory, students and teachers had immersive experiences using Head-Mounted Displays. This increased the students' engagement, motivation, and sense of presence. The application is based on the Tree.js library, which uses WebGL.  
Virtual Reality in web technologies is explored in clinical imaging and 3D data visualization in [45]. Here, the authors described the transformation of the QuickTime VR format, which is a proprietary format in 3D models that can be displayed in HTML5 but is not supported by all devices. The authors used the Object2 VR software to successfully convert objects into a format more suitable for HTML5.
## 3 Description of the Approach  
### OCR Tools  
The OCR output files which form the starting point of this study were generated by the use of Google Cloud Vision tool, running in the document text mode. Google Cloud Vision document text detection was chosen as it provided an overall accuracy on the total data set of 89.03% compared to Tesseract (PSM3) which had and accuracy of 71.76% (Figure 2).  
In order to ensure that we had the best possible starting point for the system that could easily be reproduced for the reader of this study, a quick look at widely available, constantly maintained, and low cost/free OCR tools was done. The main deliverable from investigating the OCR tools was to find the most accurate OCR tool and use its representation of the documents as the baseline for the proposed system. Only some of the best maintained and most widely used OCR tools were considered namely Google Cloud Vision (GCV) [19] and Tesseract [1; 20]. It also allowed an unbiased comparison between two OCR tools where one was an \"in cloud\" and \"paid for\" service while the other is an \"on-premise\" and \"open source\" tool. The tools were each tested on the raw documents with various configurations of both tools [15; 19] and the configuration with the best character-to-character accuracy against the truth set was picked for each tool. To measure the accuracy between the OCR tools, the same data set of nineteen images differing in size, dimensions, and type was used and run through each of the OCR  
Figure 2: Average accuracy calculation—raw image digitalization across each platform (Google Cloud Vision versus Tesseract).  
tools. The ground truth document was created manually and formed the benchmark against which each of the OCR output files were compared. Figure 2 below illustrates the average accuracy achieved by each of the OCR tools used. The tools used were not optimized or given additional training and may thus not reflect the full capability of each tool.
# SBNNR: Small-Size Bat-Optimized KNN Regression
## 4 Proposed Method  
We aim to optimize the KNN algorithm for small-data regression problems, where datasets are limited to around 500 samples. Challenges include overfitting, instance and feature selection, and balancing model complexity with accuracy, which are hard to address due to data sparsity. By using the bat algorithm (BA) to optimize hyperparameters, feature selection, and instance selection, we seek to enhance model reliability and generalization. Our goal is to provide a solution that improves predictive accuracy and performance robustness across scientific and industrial domains with limited data.  
For instance, in scenarios characterized by sparse and irregularly distributed data points, our method offers valuable benefits. By utilizing the bat algorithm to optimize KNN regression, we improve the precision and dependability of predictive models in spite of the difficulties presented by limited data availability. Practitioners can use this to obtain valuable insights and make well-informed decisions in areas like environmental monitoring, anomaly detection, and financial forecasting, even when there is a lack of data or when the data are not evenly distributed.  
As introduced above, the bat algorithm (BA) is a metaphor-based metaheuristic (global optimization) algorithm that is derived from bat echolocation behavior. In this research, we used the bat algorithm to make optimal decisions with respect to the hyperparameters, features, and instances used in KNN regression for small datasets.  
Instance selection and feature selection are crucial because they directly influence the performance and efficiency of the KNN algorithm. By carefully selecting the most relevant instances, we minimize the risk of overfitting and ensure that the model is trained on the most representative data points. Similarly, feature selection helps identify the most informative instances to improve the model's accuracy and interpretability. In the context of small-data regression problems, these selections become even more critical, as they enhance the algorithm's ability to generalize from limited data, avoiding the pitfalls of noise and irrelevant information that can skew predictions. Alongside instance and feature selection, a GAN is used to generate synthetic data to address data sparsity, while a DNN is employed to extract features from both the original and synthetic datasets. The parameters of the GAN and DNN are also optimized using the BA. Through optimized instance and feature selection, feature extraction, and the generation of synthetic data, KNN can achieve better performance, ensuring robust and reliable predictions despite the constraints of small datasets.
# A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis
## 4 Experiment
### Experimental Results
\begin{table}
\begin{tabular}{c|c c|c c} \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c|}{**SemEval-2013**} & \multicolumn{2}{c}{**SST-2**} \\ \cline{2-5}  & **Accuracy** & **Macro-F1** & **Accuracy** & **Macro-F1** \\ \hline TextCNN [22] & 80.07 (0.02) & 73.17 (0.02) & 79.88 (0.31) & 79.88 (0.31) \\ TextCNN\({}_{\text{S-EWE}_{t}}\) & 80.11 (0.02) & 73.27 (0.03) & 79.99 (0.01) & 79.99 (0.01) \\ TextCNN\({}_{\text{S-EWE}_{t}}\) & **80.62** (0.05) & **73.68** (0.19) & **80.14** (0.16) & **80.14** (0.16) \\ \hline TextRNN [23] & 73.07 (0) & 42.22 (0) & 49.92 (0) & 33.30 (0) \\ TextRNN\({}_{\text{S-EWE}_{t}}\) & 73.07 (0) & 42.22 (0) & 49.92 (0) & 33.30 (0) \\ TextRNN\({}_{\text{S-EWE}_{t}}\) & 73.07 (0) & 42.22 (0) & 49.92 (0) & 33.30 (0) \\ \hline TextRCNN [24] & 72.62 (1.00) & 64.63 (10.90) & 72.56 (3.51) & 72.45 (4.00) \\ TextRCNN\({}_{\text{S-EWE}_{t}}\) & **74.03** (0.78) & 66.40 (10.34) & 74.83 (0.30) & 74.78 (0.35) \\ TextRNN\({}_{\text{S-EWE}_{t}}\) & 73.25 (2.26) & **67.40** (1.38) & **75.65** (0.63) & **75.63** (0.63) \\ \hline TextBiRCNN [24] & 73.66 (2.57) & 66.37 (1.26) & 73.32 (0.83) & 73.29 (0.83) \\ TextBiRCNN\({}_{\text{S-EWE}_{t}}\) & 74.92 (1.13) & 66.81 (1.15) & 75.12 (0.85) & 75.10 (0.9) \\ TextBiRCNN\({}_{\text{S-EWE}_{t}}\) & **75.70** (0.26) & **67.50** (2.58) & **75.29** (0.19) & **75.25** (0.19) \\ \hline ABCDM [25] & 73.77 (5.42) & 64.29 (11.64) & 73.48 (0.71) & 73.43 (0.66) \\ ABCDM\({}_{\text{S-EWE}_{t}}\) & **74.76** (0.68) & **67.56** (1.50) & **74.97** (1.45) & **74.94** (1.46) \\ ABCDMs\({}_{\text{S-EWE}_{t}}\) & 73.94 (1.72) & 67.10 (0.27) & 74.31 (0.17) & 74.28 (0.15) \\ \hline CNN-LSTM [26] & 74.82 (3.77) & 67.84 (2.02) & 71.65 (3.74) & 71.56 (4.30) \\ CNN-LSTM\({}_{\text{S-EWE}_{t}}\) & 76.85 (0.39) & **68.91** (1.87) & 72.38 (0.27) & 72.35 (0.26) \\ CNN-LSTM\({}_{\text{S-EWE}_{t}}\) & **76.95** (0.49) & 68.72 (1.72) & **72.56** (0.06) & **72.51** (0.07) \\ \hline \end{tabular}
\end{table} 
Table 6: The accuracies and macro-F1 scores of different models using GloVe with/without S-EWE\({}_{t}\) and S-EWE\({}_{t}\) on SemEval-2013 and SST-2 datasets (%). The accuracies and macro-F1 values were obtained in the same way as those in Table 5. Moreover, the values in parentheses, bold text, and underlined text represent the common meanings in Table 5.
# Exploring the Effectiveness of the Phase Features on Double Compressed AMR Speech Detection
## 5 DC AMR Detection System
Given the input features \(X\) (spectrogram or phase spectrum features), the outputs of the classification layer yield the posterior probabilities \(p(C_{1}|X)\) and \(p(C_{2}|X)\), where \(C_{1}\) and \(C_{2}\) denote the SC and DC speech classes, respectively. Employing the posterior probabilities of SC and DC classes, the [PERSON] decision rule, ensuring the minimum classification error rate, is utilized to make decisions. This rule is defined as follows:  
\[Decision= \begin{cases}\texttt{Decide}\ C_{1},&p(C_{1}|X)>p(C_{2}|X)\\ \texttt{Decide}\ C_{2},&p(C_{2}|X)\geq P(C_{1}|X).\end{cases}  \tag{5}\]  
The second system proposed in this study is the parallel LSTM system, illustrated in Figure 5. The parallel LSTM system comprises two LSTM networks operating in parallel, where the deep features learned by each branch are fused through concatenation and subsequently applied to the fully connected (FC) and classification layers. The motivation behind proposing the parallel LSTM system is to explore whether the magnitude spectrum (spectrogram) and phase features convey complementary information when simultaneously utilized in each branch of the parallel system. Each branch of the parallel system is designed to learn different levels of information due to the diverse inputs, enabling the learned deep  
Figure 4: Proposed baseline LSTM system DC AMR speech detection. embeddings to be fused to enhance the performance of DC AMR detection. Each branch of the parallel LSTM system consists of two LSTM blocks, each containing 256 units with a \(\tanh\) activation function. The outputs of the second LSTM block of each branch are then flattened, resulting in 25,600 -dimensional deep features from each branch. These features are concatenated to form a single 51,200 -dimensional super vector, which is subsequently fed into two FC layers comprising 512 and 256 units with rectified linear unit (ReLU) activations, respectively. The classification layer consists of 2 units representing the SC and DC classes, utilizing softmax activation  
All parameters in both systems, such as the number of LSTM and FC blocks, the number of units in each layer, and the activation functions, are optimized based on preliminary experiments. The best-performing parameters are then employed in the proposed methods.
## 3 Results
### Evolution of Research Frontiers from Keyword Bursts
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Keywords** & **Year** & **Strength** & **Begin** & **End** & **1993-2024** \\ \hline Land cover classification & 2020 & 3.73 & 2020 & 2021 & \\ Vegetation mapping & 2020 & 4.35 & 2021 & 2024 & \\ Precision & 2021 & 4.51 & 2021 & 2024 & \\ Landsat 8 & 2021 & 3.91 & 2021 & 2024 & \\ Spatial variability & 2021 & 3.91 & 2021 & 2024 & \\ Evaporation & 2021 & 3.61 & 2021 & 2024 & \\ \hline \end{tabular}
\end{table} 
Table 7: _Cont._Since 2015, as UAV technology matured, \"unmanned aerial vehicle\" itself became a burst keyword, highlighting a shift toward intelligent data processing. Emphasis on data quality was reflected in emerging keywords such as \"accuracy\" and \"LiDAR.\" Machine learning-based preprocessing, radiometric calibration, and noise suppression significantly improved data quality at the acquisition stage. Deep learning frameworks achieved major advances in object classification, semantic segmentation, and feature extraction, enabling precise recognition in complex environments [147; 148; 149]. The integration of SfM 3D reconstruction with change detection algorithms expanded the capacity for large-scale spatiotemporal analysis [150; 151; 152]. Keywords like \"precision agriculture\" and \"classification\" emerged as frontier topics, underscoring the explosion in application-driven research. The most recent burst term, \"vegetation\" (from 2020 to present), reaffirms the central role of ecological and agricultural monitoring as primary drivers in the field. In recent years, real-time and edge processing capabilities have gained momentum. With the integration of edge computing and efficient wireless communication, UAVs can now perform preliminary image interpretation and data alerts on-site, supporting water quality monitoring [153], crop growth assessment [154], wetland ecosystem surveillance [14], and disaster response [155]. This evolution not only reduces data transmission latency significantly, but also lays the foundation for a collaborative \"UAV + cloud + user-end\" operational model.
# Intrusion Detection Model Based on Improved Transformer
## 4 Results
### Experimental Results and Discussion
#### 4.3.2 Experiments on Different Dimensionality Reduction Levels  
After numerical and standardization preprocessing, the dimension of the NSL-KDD dataset was reduced to 122. To prepare the NSL-KDD dataset for training the Transformer encoder classifier, a stacked auto-encoder was employed to further reduce the dataset's dimension after numerical and standardization preprocessing. The encoding layers of the SAE were used for dimensionality reduction, and various feature subsets consisting of different numbers of features were input into the overall model for binary and multi  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dimensionality Reduction**} & \multicolumn{2}{c}{**Binary Classification**} & \multicolumn{2}{c}{**Multi-Classification**} \\ \cline{2-5}  & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline PCA & 0.851 & 0.846 & 0.820 & 0.816 \\ AE & 0.878 & 0.871 & 0.835 & 0.829 \\ SAE & 0.887 & 0.882 & 0.841 & 0.838 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison experiments of different dimensionality reduction methods.  
class classification. The impact of varying degrees of dimensionality reduction on model accuracy was evaluated, and the results are shown in Figure 8.  
From Figure 8, it is observed that the accuracy increased rapidly in the beginning as the number of selected features increased, and it eventually stabilized. The highest accuracy was achieved when the number of features was approximately 35. Analysis of the reasons for this shows that when the feature dimension is reduced to a minimum, the amount of information contained in the features is not sufficient for the model to learn and train effectively, resulting in the lowest accuracy. As the feature dimension increases, the amount of information contained in the features also increases, leading to an improvement in model accuracy. When the feature dimension reaches 35, the accuracy tends to balance.