esa-sceva/satcom-chunk-collection huggingface dataset:
# Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa
## 1 Introduction
Therefore, its feasibility needs to be further verified. (2) In the process of walking, the body shape and movement mode of the target will affect the propagation of wireless signals. How to extract appropriate features representing individual uniqueness to effectively recognize the target identity is also a challenge for the research of this paper. This paper introduces a gait recognition system based on LoRa signals, designed to enable identity recognition in single target scenarios by analyzing the impact of different individuals on wireless signals. The proposed system offers simplicity in deployment, ease of operation, and practicality. To address the aforementioned challenges, the research focuses on comparing and analyzing the signal variation patterns caused by different individuals. The findings demonstrate that even individuals with similar height and weight exhibit distinct signal variation patterns due to their unique walking gaits. These results serve as an experimental foundation for gait recognition based on LoRa signals. In the feature extraction stage, an autoencoder is employed to automatically learn the features of data samples. The autoencoder is then combined with a Softmax layer to construct a deep learning network. The network is trained using the gradient descent method, enabling single-target identity recognition. By leveraging these approaches, the proposed gait recognition system based on LoRa signals offers promising potential for the accurate and efficient identification of individuals in real-world applications. The main contributions of this paper are as follows. * This paper is the first work to apply the LoRa signal to gait recognition. We verify the feasibility of gait recognition based on LoRa signals and construct a gait recognition dataset. The gait recognition algorithm is studied by analyzing and processing the wireless signals received by the LoRa gateway. * We analyze the signal change pattern when the target walks. Based on the amplitude data of the received signal, the features of the target when walking are extracted using an autoencoder. We transform it into a multi-classification problem to further identify the target identity. * Many gait recognition experiments are carried out to compare the various impacts of the recognition accuracy. The results show that the recognition accuracy of 2-6 people in an indoor environment is 99.7-95.6%. In the coal mine underground environment, the recognition accuracy of six people can still reach 90.2% at a long distance of 20 m.
## 3 Results
### Accuracy Performance Evaluations  
This part compares the accuracy performance of the proposed Gabor tracking approach to that of PCA-only tracking. The data base is the BTR picture shown in Figure 10, in which the genuine trajectories are indicated with blue dots and the tracking results with red dots. The performance metric is the mean absolute bearing deviations of three separate runs.  
The tracking data are compared in Figure 11. The PCA-only curve carrying error may be approximated to a horizontal line, illustrating that PCA-only migrates the target tracing. The mean absolute deviations of the proposed implementation diminish linearly as SNRs increase. It should be emphasized, however, that these tracking results are predicated on the assumption that the target is effectively and stably tracked.  
Figure 8: Tracking results of the LTM (yellow line) and Proposed Method (purple line) over different SNRs.  
Figure 9: Tracking results at SIR = \(-23\) dB. (**a**) Jamming target at SIR = \(-23\) dB. (**b**) Tracking result with proposed method.
## 3 Research Framework
### Feature Extraction Based on Deep-Learning Model
For ResNet-50, we extracted the output from the last global average pooling layer; for EfficientNet-B7, we extracted the feature map after the final convolutional layer; for ViT-L/16, we extracted the output features from the last Transformer encoder layer; for DenseNet-121, we extracted the feature map before the final fully connected layer; for Inception-v4, we extracted the output features from the final mixed pooling layer; for Xception, we extracted the features from the last depthwise separable convolution layer; and, the for Swin Transformer, we extracted the output features from the last Transformer layer. For feature downscaling, we applied principal component analysis (PCA) [43] and t-distributed stochastic neighbor embedding (t-SNE) [44] for dimensionality reduction after completing the feature extraction. For PCA, we extracted the top five principal components, while, for t-SNE, we extracted three features. The naming of features follows these rules: \"front\", \"side\", and \"top\" are used to distinguish the types of images; \"PCA\" and \"t-SNE\" are used to indicate the feature dimensionality reduction methods; and numerical indices are used to represent the rank of the feature within the principal components. For example, \"top_PCA4\" represents the fourth principal component extracted using PCA from the top-view image, while \"side_tSNE3\" represents the third dimension extracted using t-SNE from the side-view image. Feature extraction is a critical step in data preprocessing and model training, making it essential to evaluate the effectiveness of the extracted features. Based on the feature extraction data obtained from the three models, we established simple multiple linear regression models (with the regression target being the prediction of roof stress). By comparing the models (as shown in Table 2), we selected the Swin Transformer, which demonstrated the best performance, as the feature extractor. Figure 4: Mined-out area laser scanning image display: (**a**) front view; (**b**) side view; and (**c**) top view. The Swin Transformer introduces the \"shifted windows\" mechanism, where the input image is divided into multiple fixed-size windows, within which self-attention calculations are performed. This design significantly reduces the computational complexity, lowering it from the quadratic complexity in traditional Transformers to linear complexity, while preserving the integrity of local information. The core principle of the model lies in its hierarchical structure, which progressively expands the receptive field layer by layer, enabling the model to perform feature extraction at different resolutions [45]. This not only improves the computational efficiency but also enhances its ability to capture multi-scale features [46].
# Resolving Persistent Packet Collisions through Broadcast Feedback in Cellular V2X Communication +
## 4 Consecutive Error Prevention Performance: Evaluation
### Packet Reception Ratio
Furthermore, the proposed scheme works as an opportunistic enhancement but not a replacement to the standard SPS algorithm, so it would also be gracefully introduced in the evolution of the standard framework.
## 2 Connected Health  
The 5G network will support sensors with very low energy consumption, e.g., one battery charge every 15 years. This new paradigm will be very beneficial for medical connected devices such as blood pressure or insulin body sensors. 5G will provide healthcare solutions through connected health (CH), which could be for prevention, providing medical information and monitoring.  
In References [18; 19], a measurement of different medical sensors has been collected as shown in Table 1. These sensors are used to be connected to patient's body for continuous measurements and feedback. Some of them are used for rehabilitation, which need continuous transmission and monitoring. These sensors include Electroencephalography (EEG), electrocardiography (EKG), body temperature, heartbeat, insulin level and blood pressure. A new technology studying a prototype chip that is used to predict a heart attack before it happens by measuring some biological parameters and to warn its user.  
These sensors are classified into three types: (i) \"wearable\" sensor: where a patient can wear these sensors to start collecting their data. This type of sensor includes temperature, pressure and accelerometer sensors; (ii) The second type is \"implantable\" in the patient's body, such as camera pill and insulin level sensors, which need to be injected into the body; (iii) The third category is \"stationary\" sensors, which are placed in a medical centre for example, so that these sensors will surround patients  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor Type** & **Sampling Frequency (Hz)** & **Bits Per Sample** & **No. of Channels** & **Date Rate (bps)** \\ \hline EEG & 256 & 16 & 24 & 98,304 \\ EKG & 200 & 16 & 1 to 3 & 3200–9600 \\ blood pressure & 120 & 16 & 1 & 1920 \\ Pulse oximeter & 60 & 16 & 2 & 1920 \\ Cardiac output & 40 & 16 & 1 & 640 \\ Body temperature & 0.2 & 12 & 1 & 2.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data traffic for some medical sensors [18; 19].  
and be used to collect information about patient behaviours, such as visual sensors--for more details on body sensors please refer to Reference [20]. The connected health diagram is shown in Figure 2, where a group of patients are uploading their information through the wireless local area network (WLAN) of the medical centre. In this scenario, a user can use their smartphone as a gateway for M2M links; the smartphone will periodically collect information from these sensors through e.g., (Bluetooth, Wi-Fi) with a special M2M application made for this purpose. The M2M Gateway sends the data using the 5G cellular network to a special server, which is M2M data management to manage and store the data. After that, these data are sent to a medical centre for monitoring and further processing.
## 3 Results  
Spatial-Temporal Variations of Habitat Maintenance (NCP1), Climate Regulation (NCP4), and Water Quantity Regulation (NCP6)  
Based on the model assessment, Figure 4 showed the spatial and temporal distribution of supply and demand for three key NCPs across the QTP. From the supply perspective, NCP1 showed an obvious spatial discrepancy, with higher values in the southeast and lower values in the northwest of the QTP. The inter-annual variation of the main contributing factor of NCP1 showed a slight downward trend from 2000 to 2015 and an increasing trend from 2015 to 2020. NCP4 was higher in the southeast and lower in the northwest of the QTP. Over time, despite some fluctuations, NCP4 showed an overall increasing trend from 2005 to 2020. NCP6, based on the WTU scale assessment, showed higher values in the southern basins, especially in the southwest. The water supply index (WSI), which integrates precipitation, snow cover, glaciers, and lakes, had the multiyear average value increase from 2000 to 2020, causing the enhancement of NCP6 supply. It can be seen from the pie chart that NCP1 had a low proportion of high values, while NCP4 and NCP6 had high proportions of high values.  
From the demand perspective, the overall spatial distribution of NCP1 was similar to that of the supply perspective. However, the high-value areas were much smaller than those for supply, and the lower values were dispersed throughout the QTP. In addition, there existed an increased trend of NCP1 demand, opposite to the supply. A similar situation was found in NCP4, where the high-value areas were reduced, and the spatial distribution, though similar to the supply perspective, was more dispersed. For NCP6, the spatial distribution showed high values in the southeast of the QTP, differing from the WSI, reflecting the role of WDI and population factors.  
Figure 3: Identifying the different levels of ecological conservation area based on the importance and vulnerability of NCPs.
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 5 Experimental Setup
### Mobile Device Scene Parameter Settings  
In the experiments related to mobile device edge node placement, we employed a laboratory-developed inspection car as a mobile device to simulate the movement trajectory and task requests encountered by mobile devices within the mining environment. The structure of the trolley is depicted in Figure 8. The car consists of several key components, including a server, motor vision module, navigation module, power supply, and controller power supply module, among other parts.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** & **Value** \\ \hline SN & Number of fixed equipment & [10, 100] \\ SX & Number of potential candidate locations & [5, 15] \\ \(\hat{sd}_{in}\) & Amount of data input per task & [800, 1500] kb \\ \(\hat{sd}_{out}\) & Amount of data output per task & [1, 50] kb \\ \(sy\) & Load per task & [10, 50] MHz \\ \(\hat{sfre}\) & The main frequency of the edge node & [1,5, 4.5] GHz \\ C & Edge node cost & [800, 3000] \\ \(\beta_{1}\) & Delay weight coefficient & 0.6 \\ \(\beta_{2}\) & Placement cost weight coefficient & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations used in fixed device edge node placement.  
Figure 8: Mobile device edge node placement data collection trolley.  
Figure 7: Fixed equipment edge node deployment platform.  
A laboratory site measuring 60 by 60 m was designated for the experiments. Multiple inspection vehicles were deployed, with task data being recorded and subsequently used for experimental evaluations. The maximum coverage range of the edge node was set at 20 m, and the movement speed of the inspection vehicles was maintained at 5 m per second. Additional details and parameters are provided in Table 3.  
We conducted simulations on a computer equipped with a 3.60 GHz AMD CPU and 16 GB RAM (Advanced Micro Devices, Inc., Santa Clara, CA, USA), utilizing the parameters outlined in Table 4 for the algorithms.
# Towards Detection of Interest Using Physiological Sensors
## 1 Introduction  
Many theories, definitions, hypotheses, and experiments regarding interest were proposed in the late 19 th century. Each had its own perspective, addressed one or more aspects of interest, and was based partially on previous research and findings and/or new experimental work. This work led to the proposal of a variety of definitions, and thus concepts, regarding interest. It is therefore difficult to combine the conclusions of this research in one concept because of the uncontrolled settings of the experimental studies, or often, missing connections between the different fields of study. What could be so interesting about interest that grasped researchers' attention and led to efforts to study and explore this area of research? According to a large number of researchers, e.g., [PERSON] [1], [PERSON] and [PERSON] [2], and [PERSON] and [PERSON] [3], interest is highly related and linked to academic achievement. Many researchers have proposed the idea of interest as a motivational variable and an important and basic element of productive learning. These researchers include [PERSON] [4], [PERSON] [5], and [PERSON] [6]. It has been suggested that interesting information is significantly better remembered and more easily understood than non-interesting information. [PERSON] and [PERSON] [7] have studied the effects of interest on recognition and recall in young children. Interest is also an emotion that is often studied in the context of television commercials and consumers' responses. It is advised to utilize technology to trigger consumer interest toward specific products in order to increasesales and enhance brand image [8; 9]. There are other implicit rewards for \"interest\" that benefit individuals personally by raising their self-efficacy. In addition, interest not only has cognitive effects but also physiological effects.  
This paper reviews researches of different objectives that employed different methods in order to provide interested researchers with direction for future studies. The motivation for writing this review was to help researchers recognize the astonishing benefits of triggering, detecting, and maintaining interest in individuals in different aspects of life, including well-being, education, society, and economy. This review also discusses the strengths and weaknesses of some current research on interest from an objective point of view. The paper is structured as follows. Section 2 describes our review method and research questions, along with the nature and level of interests. Section 3 highlights the physiological patterns and features of Interest. These patterns include EEG, ECG, Eye tracking and other patterns. The correlation between self-report and physiological sensors is also discussed. Section 4 illustrates the effect of experimental design on the output of the experiment. This includes task, stimulation, number of subjects, and their age. The paper concludes with some recommendations for future studies on the detection of interest using physiological methods.
# Doherty Power Amplifier Design via Differential Combining
## 2 Proposed Combiner
\begin{bmatrix}\begin{bmatrix}1&0\\ j\omega C_{OUT}&1\end{bmatrix}\begin{bmatrix}\frac{1}{\sqrt{2}}&j\frac{Z_{0}}{ \sqrt{2}}\\ j\frac{1}{\sqrt{2}Z_{0}}&\frac{1}{\sqrt{2}}\end{bmatrix}\begin{bmatrix}1&0\\ j\omega C_{OUT}&1\end{bmatrix}^{2}\begin{bmatrix}1&0\\ \frac{1}{\sqrt{2}}&1\end{bmatrix}\begin{bmatrix}V_{2}\\ -I_{2}\end{bmatrix}\end{bmatrix}  \tag{1}\]  
Thus,  
\[ \begin{bmatrix}V_{1}\\ I_{1}\end{bmatrix} = \begin{bmatrix}\frac{1}{\sqrt{2}}(1-\omega C_{OUT}Z_{0})&j \frac{1}{\sqrt{2}}Z_{0}\\ j\frac{1}{\sqrt{2}}\left(2\omega C_{OUT}-\omega^{2}C_{OUT}^{2}Z_{0}+\frac{1}{Z_ {0}}\right)&\frac{1}{\sqrt{2}}(1-\omega C_{OUT}Z_{0})\end{bmatrix} ^{2} \begin{bmatrix} 1&0\\ \frac{1}{\sqrt{2}}&1\end{bmatrix}\begin{bmatrix}V_{2}\\ -I_{2}\end{bmatrix} . \tag{2}\]  
Specifically, by choosing the characteristic impedance \(Z_{0}\) of the \(\lambda/8\) transmission lines to match the impedance of the output capacitance \(C_{OUT}\), such that \(Z_{0}=(\omega C_{OUT})^{-1}\), the resulting ABCD matrix simplifies significantly,  
\[ \begin{bmatrix}V_{1}\\ I_{1}\end{bmatrix} = \begin{bmatrix}-1&0\\ 0&-1\end{bmatrix}\begin{bmatrix}1&0\\ \frac{1}{\sqrt{2}}&1\end{bmatrix}\begin{bmatrix}V_{2}\\ -I_{2}\end{bmatrix}  \tag{3}\]  
and, therefore,  
\[ \begin{bmatrix}V_{1}\\ I_{1}\end{bmatrix} = \begin{bmatrix}-1&0\\ -\frac{1}{\sqrt{2}}&-1\end{bmatrix}\begin{bmatrix}V_{2}\\ -I_{2}\end{bmatrix} . \tag{4}\]  
By transforming the resulting ABCD matrix into its correspondent \(Z\) matrix, (4) can be written as  
\[ \begin{bmatrix}V_{1}\\ V_{2}\end{bmatrix} =R \begin{bmatrix}1&-1\\ -1&1\end{bmatrix}\begin{bmatrix}I_{1}\\ I_{2}\end{bmatrix} . \tag{5}\]  
Figure 1: The proposed combiner and its equivalent circuit. (**a**) Proposed combiner. (**b**) Equivalent circuit. Note that if \(I_{2}=-I_{1}\), the impedance seen by the current generators is exactly \(2R\) (i.e., \(Z_{1}=Z_{2}=2R\)). This demonstrates the combiner's ability to cancel out the effect of the parasitic \(C_{OUT}\) at any chosen frequency. However, it is important to note that the practical implementation of this combiner is subject to certain constraints. One such limitation arises when the characteristic impedance \(Z_{0}\) reaches values that are impractically high or low, which can occur depending on the relative permittivity (\(\epsilon_{r}\)) of the substrate. Depending on \(\epsilon_{r}\), the resulting transmission line dimensions may become physically unmanageable, making implementation difficult. Despite this, the proposed combiner remains a highly effective solution within the practical range of \(Z_{0}\), offering significant improvements in design flexibility and performance over existing combiners.
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 4 Results
### Analysis of Estimated Drifts in TOC Products
#### 4.3.5 Statistical Analysis of Estimated Drifts for All Angular Conditions  
The statistical distribution of estimated drift for TOC and BRDF-normalized reflectances when observations from all angles are merged is presented in Figure 15. This condition corresponds to the typical user case scenario, when all clear observations are retained for multi-temporal analysis. Overall, the dispersion of the estimated drifts lies around the 0 value for most of the spectral bands, in particular for the Red and NIR, and land cover classes, with some remaining negative biases in the Blue and SWIR bands. This is a confirmation, at global scale, of the results observed over the four sample sites for the NIR band (see Figures 7-10). In particular, the opposite drifts, clearly identified in the two scattering directions, largely cancel out when observations from all angular conditions are merged and the differences between the drifts estimated for TOC and BRDF-normalized data are within their combined ranges of variability.
## 4 Experiments and Evaluation  
In order to evaluate the usefulness, usability, and learners' satisfaction of the system, several surveys and experiments have been conducted at the university and junior high school levels. The evaluation tools include questionnaires, pre-test, post-test and interview.
## 3 Results and Discussion
This research complements the possibilities presented in [7] due to the information technology formulated in Section 2.2, locked to control the \(<\)ssthresh\(>\) parameter of the SIS mechanism. Figure 11 demonstrates the effect of the introduction of the authors' information technology on an example of modification of the mentioned TCP Westwood+ parameter. The remaining parameters of the experiment are identical to those described in [7]. From the findings presented in Figure 11, our results show that the use of the authors' information technology for controlling the \(<\)ssthresh\(>\) parameter, according to the example of using the Westwood+ TCP to control the information interaction between a SNEIoTD environment and a hub, has a positive effect that increases with the increase in the value of the parameter \(T_{rt}\). In particular, it was possible to increase the stability of data transfer in situations where the signal \(s^{o}\) is 80% received (see the graphs shown in Figure 11 for the values \(T_{rt}>9\) of this argument).
## 1 Introduction
Unfortunately, most of the current studies either focus on presenting authentication mechanisms for traditional MQTT that are based on a single broker without taking into account multiple brokers' communication or propose distributed MQTT architectures with multiple brokers without taking into account the security solutions, such as authentication, while the studies that provided authentication schemes for distributed MQTT architectures that are based on multiple brokers lack lightweight mutual authentication and a scalable authentication architecture. The main contributions of this paper can be summarized as follows:  
1. The proposed architecture contributes to handling the massive amount of M2M communication in IIoT by clustering the clients into groups that connect to a single local broker. Further, the architecture will reduce latency, network congestion, and bottlenecks. 2. The proposed architecture is designed in a hierarchical structure to enable delivering the requested data to the MQTT client from other brokers that the client is not connected to. 3. Implementing a lightweight mutual authentication scheme using hash function and XOR operation is more feasible for resource-constrained IoT devices to mitigate computation costs and communication overhead. 4. Deploying an authentication manager in each broker will allow the management of security independently for the broker and its group of connected clients/brokers. This will reduce the possibility of security threats and decrease the complexity of security management. The rest of the paper is structured as follows: Section 2 provides a brief background of MQTT and presents the related works. Section 3 introduces the proposed architecture of the multi-tier MQTT broker based on fog computing (Mu-TiMB) as well as the proposed lightweight authentication scheme. Section 4 presents an informal security analysis and the performance results. The evaluation is discussed in Section 5. Finally, the conclusion and directions for future work are provided in Section 6.