esa-sceva/satcom-chunk-collection huggingface dataset:
# High Speed Decoding for High-Rate and Short-Length Reed-Muller Code Using Auto-Decoder
## 4 Conclusions  
In this paper, we proposed \(\mathfrak{R}\)(1,4) and \(\mathfrak{R}\)(2,4) decoders using an AD, with MLD performance and shorter decoding process time than the FHT decoder. The decoding time of the RM decoder using the AD is 1.8 times faster than that using FHT decoding for \(\mathfrak{R}\)(1,4), and 125 times faster for \(\mathfrak{R}\)(2,4). This is because the FHT decoding method relies heavily on masking bits. We presented PAD with multiple CADs to improve the BER performance of the RM decoder using a single AD, and found that PAD-2 and PAD-3 showed the best performance for \(\mathfrak{R}\)(1,4) and \(\mathfrak{R}\)(2,4), respectively; however, the performance difference is not significant. The proposed fast decoding method with MLD performance can be useful in mobile communication systems, such as 5G and 6G, which require low latency and BER. Since the AD shows better performance then the AE when input size is small, it can be useful for noise removal in signal processing with a relatively small data size. The AD can be used not only in communication fields, but also in fields using various sensors. As we have confirmed the performance of the proposed model based on the AD and PAD for high-rate and short-length RM codes in terms of decoding speed and BER, we will extend the result to other error-correction coding schemes.  
Software, H.W.C.; Writing--review & editing, Y.J.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1F1A1061907).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multicolumn{5}{c}{**Number of Nodes**} \\ \cline{2-6}  & **1 st** & **2 nd** & **3 rd** & **4 th** & **5 th** \\  & **CAD** & **CAD** & **CAD** & **CAD** & **CAD** \\ \hline
1 st hidden & \(n\) & \(2n\) & \(3n\) & \(4n\) & \(5n\) \\
2 nd hidden & \(n\) & \(4n\) & \(9n\) & \(16n\) & \(25n\) \\
3 rd hidden & \(n\) & \(2n\) & \(3n\) & \(4n\) & \(5n\) \\ output & \(n\) & \(n\) & \(n\) & \(n\) & \(n\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: The number of nodes in PAD with 5 CADs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Time (ms)**} & \multicolumn{2}{c}{**Parameters**} \\ \cline{2-5}  & \(\mathfrak{R}\)(1,4) & \(\mathfrak{R}\)(2,4) & \(\mathfrak{R}\)(1,4) & \(\mathfrak{R}\)(2,4) \\ \hline FHT & 0.6012 & 46.625 & - & - \\ PAD-1 & 0.3327 & 0.3704 & 5808 & 40,080 \\ PAD-2 & 0.3472 & 0.3785 & 6896 & 41,168 \\ PAD-3
## 7 Conclusions  
For the MRS with the limited CSI feedback, the path selection using the end-to-end SNR correlation was newly introduced in this paper, and the exact and closed-form expression for the end-to-end SNR correlation coefficient was presented with only the Nakagami parameters and average SNRs for all the hops. Our proposed PPS with local CSI based on the highest end-to-end SNR correlation can achieve the most similar performance to the BPS with global CSI for a given limited feedback overhead. In particular, when the Nakagami parameters and the gap between average channel powers for hops are large, the PPS using CSI for only one of multiple hops can attain the analogous outage performance to the BPS using global CSI. The SNR correlation approach may be also applied to local CSI-based multi-hop cooperative systems such as multi-input multi-output relaying system, cognitive relaying system, NOMA-based relaying system, and physical-layer secure relaying system so as to improve their performances.  
The contributions of [PERSON]-[PERSON] were to propose the path selection scheme using the SNR correlation and analyze the SNR correlation coefficient and the end-to-end performance, while [PERSON] advised the ideas about the system model and the proposed scheme and suggested the mathematical analysis methodology and the simulation scenarios.  
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant number: NRF-2018R1D1A1B07042499 and NRF-2016R1D1A1B03930060).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
IoTInternet of things  
WSNWireless sensor network  
BPSBest path selection  
PPSPartial path selection  
CSIChannel state information  
SNRSignal-to-noise ratio  
AFAmplify-and-forward  
DFDecode-and-forward  
NOMANon-orthogonal multiple access  
MRSMulti-hop parallel relay system  
CDFCumulative distribution function  
PDFProbability density function
## 2 Materials and Methods
### HFFN (Hierarchical Feature Fusion Network) Module  
In the process of chili pepper detection, it is inevitable that chili pepper targets with different levels of size will appear in the same image, which will seriously interfere with the recognition accuracy of chili peppers. However, the original YOLOv5 feature pyramid is only applicable to the detection of chili pepper targets with a small degree of hierarchical change in chili pepper size, and performs poorly in the detection process where there are chili peppers with large hierarchical changes in an image. In this paper, we introduce adaptive spatial feature fusion (ASFF) in the model to address the above drawbacks and set the convolution kernel in ASF to a size of 3 \(\times\) 3 to adapt it to the chili pepper targets in this paper's dataset. Therefore, YOLO-chili contains a total of three ASF prediction layers, which are responsible for processing different levels of chili pepper feature information, among which the first layer is the smallest layer of the feature map, with a channel number of 512, which is responsible for processing the feature information of small-scale chili peppers. The second layer is the layer with moderate feature map size and channel number 256, which deals with the feature information of medium-scale chili peppers. The third layer is the layer with the largest feature map, channel number 128, dedicated to processing feature information of large-scale peppers. The YOLO-chili containing three ASF prediction layers is able to handle chili data with large variations in chili levels in the same map, and thus is fully adapted to the task of chili detection in orchards under complex conditions. The HFFN structure is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configure** & **Para** \\ \hline CPU & core i5-11400H \\ GPU & Nvidia GeForce RTX 3050 TI \\ Accelerated environment & CUDA10.1 CDNN7.5.0 \\ development environment (computer) & Pycharm2020.1.3 \\ operating system & Windows 10 64-bit system \\ software environment & Anaconda 4.8.4 \\ storage environment & Memory 16.0 GB \\  & Mechanical Hard Disk 2 T \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental Environment.  
Figure 2: Distribution of chili peppers at different scales in the chili pepper dataset.
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 3 Outdoor Measurements and Wireless Link Characterization
### Ambient Noise Density Measurements  
As the sub-1 GHz ISM band is used, interference can considerably affect the wireless link quality and needs to be investigated. Figure 8 shows the measurement setup of the ambient noise density \(N_{A}\) by using the MS2721A spectrum analyzer. It is given by  
\[N_{A}=k_{b}T+\Delta N, \tag{10}\]  
where \(k_{b}\) is the Boltzmann constant, \(T\) is the ambient temperature, and \(\Delta N\) is the noise density arising from other interfering emissions in the same frequency band. The spectrum analyzer's noise marker functionality was used to get a 1 Hz resolution bandwidth measurement. Although the measurement setup is different, the reader can refer to [56] to better understand the measurement methodology. Noise densities of -151.11 dBm/Hz and -154.27 dBm/Hz were measured in the urban and suburban areas respectively. The 3.16 dB difference can be explained by the fact that the urban area is a more industrialized and densely populated area [57].  
Figure 7: WMCU communication range measurement setup.
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 6 System Design and Implementation
### Measurement Procedure  
As described in Section 5.4, one measurement consists of two scans from locations that are separated by a distance of \(d_{\text{ss}}\). The measurement results in a heading direction, which provides the DOA of the BLE beacon. This estimate can be used by the user to move closer to the beacon and take another measurement if required. To start the estimation of DOA using the calibrated scanner application, the user must select _'Start Scanning'_, which will start concurrently recording the RSS, heading, and timestamp information. The user rotates 360\({}^{\circ}\) and selects _'Stop Scanning'_, which will populate the result for _'Scan1'_ on the  
Figure 8: iOS application for tracking the Eddystone beacon.  
scanner application. Another scan from an adjacent location will result in '_Scan 2_' followed by the DOA estimate labeled as '_Beacon angle_'. After navigating in the direction provided by '_Beacon angle_', another measurement can be made if necessary to get closer to the beacon. The scanner application notifies the user if the beacon is within 1-2 m, allowing them to look for the beacon physically.  
Algorithm 2 describes in detail the measurement procedure for DOA estimation using the scanner device.  
```
Open Application; ifScanner deviceCalibratedthen [start]: Start measurement; Hold scanner device steady and rotate 360\({}^{\circ}\); Stop measurement; Move to the left or right by 1 to 2 feet; Start measurement; Hold smartphone steady and rotate 360\({}^{\circ}\); Stop measurement; ifIs the BLE beacon in close proximitythen BLE beacon found; else goto: start; else  Calibrate the scanner device; goto: start.
```  
**Algorithm 2**Measurement procedure
# Local Dependency-Enhanced Graph Convolutional Network for Aspect-Based Sentiment Analysis
## 5 Discussion
### The Impact of Sentiment Words with Different Scores  
To investigate the impact of sentiment words with different intensity levels on model performance, we sorted all sentiment words in descending order based on the absolute values of their sentiment scores. We constructed the dependency-enhanced graphs using an equal number of sentiment words from different score ranges and conducted experiments on the Rest14 dataset. The result is shown in Figure 7, revealing that using sentiment words with higher scores to construct the LDE graph yielded better performance. This indicates that sentiment words with stronger polarity contribute to higher word correlations within sentences, resulting in more accurate derived word correlation graphs. In other words, the stronger the sentiment tendency of the sentiment words, the stronger the word correlations in the sentence, leading to more precise word correlation graphs.
## 4 Analysis of the Existing Datasets
### Subway Dataset  
The Subway dataset [81], consisting of two sub-datasets, Subway Entrance and Subway Exit, was published in 2008. This dataset contains anomalous events such as going in the incorrect direction, avoiding payment, and similar actions. Some examples of abnormal frames are shown in Figure 2. The videos were produced at 25 FPS and a 512 \(\times\) 384 resolution. The entrance subset contains 1 h 36 min of video (144,250 frames), and the exit subset contains 43 min (64,901 frames). Subway Entrance has 66 unusual events, and Subway Exit has 19 unusual events. Two restrictions apply to this dataset: the count of anomalies and predictable spatial localizations [48]. The ground truth is available only at the frame level.  
Figure 1: Examples of abnormal frames from the CUHK Avenue dataset are presented.  
Figure 2: Examples of abnormal frames from the Subway dataset are presented. Abnormal frames are shown in the first row from the Subway Entrance, and abnormal frames are shown in the second row from the Subway Exit sub-datasets.
# Detecting Deception from Gaze and Speech Using a Multimodal Attention LSTM-Based Framework
## 2 Materials and Methods  
### Dataset  
In this work, we used and adhered to the terms of this license the Bag-of-Lies dataset [12], which is a multimodal deception database composed of video, audio, gaze and EEG recordings from 35 subjects (25 male and 10 female), although EEG signals are only available for 22 users. As requested by the authors of the database, data from one of the female participants (user 12) were not used. So, in total there are 315 recordings, of which 157 corresponded to lies and 158 to truths.  
The database was collected in realistic conditions. Participants were shown 6-10 images and requested to describe them in such a way that they were free to lie or tell the truth. Video and audio were recorded with a conventional smartphone, and therefore, the audio recordings present a significant amount of noise. Gaze data was captured by using a Gazepoint GP3 Eye Tracker (Gazepoint, Vancouver, BC, Canada), whereas a 14-Channel Emotic EPOC+EEG headset (Emotiv, San Francisco, CA, USA) was used for the recording of EEG signals.  
In our experiments, we have only used the gaze and audio (speech) modalities. Gaze data is sampled at 60 Hz and follows the GazePoint Open Gaze API specification [35], providing 26 channels that encode different information related to eye tracking. Audio was extracted from the video recordings by using the FFmpeg software [36] and downsampled to 16 KHz. As some of these audio files contain speech from both, the person in charge of the recordings and the participant, the first voice was eliminated by cropping the initial seconds of the audio recordings.
# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 1 Introduction
### Purpose and Structure of the Paper  
This review provides a systematic analysis and methodological comparison of two-dimensional human pose estimation (2D HPE). In response to rapid technical evolution and the growing diversity of approaches, we categorize and analyze the principal task paradigms. These paradigms include single-person versus multi-person estimation, regression-based versus heatmap-based methods, and top-down versus bottom-up architectures. This classification helps readers understand the field's developmental trajectory and current design trends. To facilitate model selection and performance evaluation, we compile and compare six representative models that consistently rank among the top performerson authoritative benchmark datasets. The comparison covers network architecture, input resolution, evaluation metrics, and key technical innovations, enabling readers to assess each model's strengths and limitations. In addition, we summarize widely used evaluation metrics and highlight the design characteristics of public datasets commonly adopted for 2D HPE. By examining current technical challenges, we outline several potential directions for future research. The remainder of the paper is organized as follows: Section 2 reviews core methodologies; Section 3 introduces evaluation metrics and datasets; Section 4 presents a comparative analysis of top-performing models; and Section 5 discusses future development trends and concludes the paper.
# Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing
## 2 In Search of the Effectiveness of a Diagnosis Support System--Results

Table 4: Classification result for Central Class 184 (herpangina) vs. all other classes. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Classifier** & **Sensitivity** & **Specificity** & **Balanced Accuracy** & **Sensitivity SD** & **Specificity SD** & **Balanced Accuracy SD** \\ \hline LogisticRegression & 1.0 & 0.94 & 0.97 & 0.0 & 0.07 & 0.04 \\ RandomForest & 1.0 & 0.92 & 0.96 & 0.0 & 0.08 & 0.04 \\ AdaBoost & 1.0 & 0.93 & 0.96 & 0.0 & 0.08 & 0.04 \\ ExtraTreesClassifier & 1.0 & 0.92 & 0.96 & 0.0 & 0.07 & 0.03 \\ Perceptron & 0.99 & 0.9 & 0.95 & 0.01 & 0.07 & 0.04 \\ SGDClassifier & 1.0 & 0.9 & 0.95 & 0.0 & 0.09 & 0.04 \\ kNN & 1.0 & 0.88 & 0.94 & 0.0 & 0.09 & 0.04 \\ DecisionTree & 1.0 & 0.88 & 0.94 & 0.0 & 0.1 & 0.05 \\ GradientBoosting & 1.0 & 0.88 & 0.94 & 0.0 & 0.09 & 0.04 \\ BaggingClassifier & 1.0 & 0.89 & 0.94 & 0.0 & 0.09 & 0.04 \\ GausianNB & 0.95 & 0.88 & 0.92 & 0.01 & 0.1 & 0.05 \\ SVM & 1.0 & 0.82 & 0.91 & 0.0 & 0.11 & 0.06 \\ SVC & 1.0 & 0.82 & 0.91 & 0.0 & 0.11 & 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification result for Central Class 51 (hemiplegia) vs. all other classes. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Classifier** & **Sensitivity** & **Specificity** & **Balanced Accuracy** & **Sensitivity SD** & **Specificity SD** & **Balanced Accuracy SD** \\ \hline ExtraTreesClassifier & 1.0 & 0.92 & 0.96 & 0.0 & 0.07 & 0.04 \\ LogisticRegression & 1.0 & 0.88 & 0.94 & 0.0 & 0.1 & 0.05 \\ RandomForest & 1.0 & 0.89 & 0.94 & 0.0 & 0.1 & 0.05 \\ GausianNB & 0.99 & 0.89 & 0.94 & 0.01 & 0.08 & 0.04 \\ AdaBoost & 0.99 & 0.89 & 0.94 & 0.0 & 0.11 & 0.06 \\ Perceptron & 0.99 & 0.89 & 0.94 & 0.0 & 0.11 & 0.05 \\ DecisionTree & 1.0 & 0.87 & 0.93 & 0.0 & 0.09 & 0.05 \\ GradientBoosting & 1.0 & 0.86 & 0.93 & 0.0 & 0.1 & 0.05 \\ BaggingClassifier & 1.0 & 0.87 & 0.93 & 0.0 & 0.1 & 0.05 \\ kNN & 0.99 & 0.82 & 0.91 & 0.01 & 0.12 & 0.06 \\ SGDClassifier & 1.0 & 0.82 & 0.91 & 0.0 & 0.14 & 0.07 \\ SVM & 1.0 & 0.66 & 0.83 & 0.0 & 0.15 & 0.08 \\ SVC & 1.0 & 0.66 & 0.83 & 0.0 & 0.15 & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification result for Central Class 112 (kidney stones) vs. all other classes.
## 4 Statistical Analysis
### Multiple Regression Analysis  
Multiple linear regression analysis for predicting the compression index using geotechnical properties was performed using OLS analysis to investigate the effects of the natural water content, liquid limit, and plastic limit on the compression index and present regression equations. Multiple linear regression analysis expresses the relationship between multiple explanatory variables and the dependent variable (whether ground subsidence occurs or not) as a regression equation, as shown in Equation (6). The coefficients of the regression equation are estimated through the Ordinary Least Squares method (OLS), which minimizes the Sum of Squared Residuals [29; 30].  
\[\mathrm{y}=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\cdots+\beta_{i}x_{i}+\varepsilon \tag{6}\]  
\(\mathrm{y}\): dependent variable; \(x_{i}\): independent variable; \(\beta_{i}\): estimated regression coefficient; \(\varepsilon\): error.  
Table 7 shows the results of the OLS analysis performed in this study. The weights of the natural water content and liquid limit were found to be statistically significant (\(p<0.001\)). The weight of the natural water content was higher than that of the liquid limit. These results were consistent with the Pearson correlation and simple linear regression analysis above. However, in the case of the plastic limit, the weight was relatively small and not statistically significant. Therefore, the compression index regression equation using the geotechnical properties of the southern coastal area of Korea is composed of the natural water content and liquid limit, excluding the plastic limit, as shown in Equation (7), below.  
\[C_{c}=0.0152W_{n}+0.0018 LL-0.3165 \tag{7}\]
## 5 Methodology
### Network Performance Metrics  
The primary tools used in this methodology are iperf and netperf, which are capable of measuring:  
1. Bandwidth: This is evaluated for both TCP and UDP protocols. The system performs multiple test runs (five for TCP and two for UDP by default), with results averaged to provide robust data for performance evaluation;
2. Latency: Measured under different network conditions using the same set of tuned profiles, giving insights into network latency, as network latency can be a key performance indicator for specific workloads;
3. Packet Size and Frame Configurations: Tests are conducted with varying packet and frame sizes to simulate real-world network loads and conditions.  
Using these tools in these different settings means that our methodology can simulate different workload network traffic profiles in various configurations and environments.
# DDPG-Based UAV-RIS Framework for Optimizing Mobility in Future Wireless Communication Networks
## 4 Numerical Results and Analysis  
In this section, we evaluate the effectiveness of the proposed UAV-RIS framework and the DDPG scheme through extensive simulation in terms of EE, HOF, throughput, OP, and LoS probability in an mmWave network. The proposed scheme classifies users according to distances and SINR values while jointly optimizing the UAV trajectory and RIS phase shifts to ensure uninterrupted connectivity for GUs in urban environments. To validate the proposed scheme, we compared it with UAV-only, RIS-only, and without UAV-RIS deployments.
# A Machine Learning Approach for Breast Cancer Risk Prediction in Digital Mammography +
## 2 Materials and Methods
### Pre-Processing Dataset and Features Selection  
Different data visualization tools were used to study the datasets. In particular, it was found that the features are located in different ranges. Therefore, to be treated equally, it was necessary to report them all in the same range. Then, the normalization of the dataset was performed. Before selecting the features, the correlation of the features was evaluated using a correlation matrix, and through the visualization of the boxplots in relation to the feature density categories, the most discriminating features were also highlighted. There was, therefore, a first reduction in the dataset. In particular, it was noted that the characteristics of wavelets are not very discriminating, while Haralick characteristics turn out to be strongly correlated with each other. There was, therefore, an initial reduction to 187 total features. Feature selection is an indispensable step for selecting only non-redundant and discriminating features.  
To eliminate redundant features, this study employed a filter feature selection. Wrapper feature selection treats the task of choosing an appropriate set of features as a search problem, exploring different combinations of features. On the other hand, filter feature selection utilizes statistical measures to assign a specific score to each feature. In the first scenario, the features are categorized based on their scores, and in accordance with the classification, they are either selected or excluded from the dataset. In the second scenario, a prediction model evaluates each combination using various techniques. In this study, a correlation-based filter method for feature selection is chosen because, being not dependent on any machine learning algorithm implemented and given the unbalance of the dataset in multiclass classification, it appears as the best choice for eliminating the non-discriminative features.