esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Divide Grouping Non-Orthogonal Multiple Access in Terrestrial-Satellite Integrated Network
## 2 Related Work
In [30], the author proposed a NOMA method based on millimeter wave, in which the maximum minimum resource allocation problem is modeled as a mixed integer nonlinear programming problem. Further, combining NOMA technology with multiple input multiple output (MIMO) technology, [PERSON] et al. proposed a power allocation approach aiming at maximizing the sum capacity under the interference constraint of the satellite user [31]. In [32; 33], the analytical results of outage probability and achievable sum-rate are derived for downlink NOMA in a satellite cell scenario. To improve the system throughput, [PERSON] et al. [34] proposed a satellite user selection schemebased on channel quality, where the user pairing problem is modeled as a maximum and minimum channel correlation problem and an optimization algorithm is designed to maximize the user communication capacity. In [35], a terrestrial-satellite network model is constructed, where a satellite and numerous BSs served all users, collaboratively. In this model, Lagrange dual method was used to study the power allocation algorithms of terrestrial users and satellite users. To reduce the outage probability of satellite users, [PERSON] et al. proposed a terrestrial-satellite cooperative relay transmission method, which ground node is used to amplify and forward signals [36]. Similarly, in [37], a downlink NOMA mechanism based on user cooperation is proposed, where the user with larger channel gain is used to decode the signals with poor channel conditions. The results show that user cooperation-based NOMA method can improve the reliability and fairness of the system. Based on CR technology, a NOMA algorithm for terrestrial-satellite cognitive network is proposed to improve the spectrum efficiency and the total system capacity [38]. However, CR technology will introduce signal interference between networks, which can leads to the reduced of system throughput. Table 1 summarizes the core ideas and limitations of the existing methods. It can be observed that the integration of satellite network and terrestrial network will increase the interference between users, resulting in the rate reduction or even communication interruption of cell edge users. Therefore, to reduce the interference between cells, it is necessary to realize the reasonable user pairing selection and power allocation of BS users and satellite users. Similarly, to improve the system throughput, the channel characteristics of satellite-terrestrial link need to be analyzed.
# Fixed Wireless Access in Flexible Environment: Problem Definition and Feasibility Check
## 6 Radio Throughput Simulations and an Experimental Link
A high fading margin is unnecessary during sunny days. Automatic transmit power control reduces the output power dynamically to avoid unwanted interference in other links and to save energy. When intensive rainfall reduces the Received Signal Level (RSL), ATPC increases the transmitter output power. For instance, during intensive rainfall (Figure 8a), ATPC increased transmitter power from 0 to +5 dBm on April 24 th and from 0 to +9 dBm on May 15 th. Even with increased output power, the RSL decreased with approximately 1 dB on both rainy days (Figure 8b). Despite the power increase, throughput briefly dropped to  
Figure 7: Antenna and _E_-band transceiver with Gbps optical ports. 6.14 Gbps and 6.2 Gbps on these days (Figure 8c) [52]. Figure 8d shows systematic rainfall intensity data recorded by a meteorological station 1.8 km away from the transceiver [53; 54].
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 5 Simulations, Results, and Discussions  
### Simulation System Setup  
This paper constructs a virtual hybrid network using MATLAB, utilizing graphs to describe the network's topology and communication properties. As illustrated in Figure 11, the system comprises a backbone network and two access networks, forming a two-level hierarchical network structure. Each node in the network generates random values for various parameters, including priority, destination nodes, data volume, and traffic characteristics. The priority values range from 1 to 4, and the traffic characteristic values sum to 1. The data volume is always an integer multiple of 64 bits. The minimum time interval for network scheduling is 0.2 milliseconds, and the minimum time slot for traffic allocation is 5 microseconds. The main simulation metrics are set in Table 3.  
Figure 10: HSN scheme transmission flowchart.
## 4 Methodology
### Evaluation Metrics  
The accuracy of an object detector can be evaluated according to a variety of parameters. The most typical assessment metrics include precision, recall, and Average Precision (AP) or mAP. The AP metric is intended to offer a reliable and consistent assessment of the categorization and detection processes. In addition, the frame rate is a key indicator of the speed of the object detector; therefore, it is important to pay attention to both metrics. To describe the mAP metric, it is important to define the following terms first:  
* Precision is the ratio of successfully recognized occurrences, denoted as True Positives (TPs), to all positively detected instances (TP + False Positivess (FP)), as shown in Figure 4. Recall is defined as the ratio of correctly recognized instances to all tested instances (TPs + False negatives (FNs)).
* The IoU algorithm is used to calculate the amount of overlap that exists between the anticipated and ground truth bounding boxes. Then, the detection of TP is referred to be a match between bounding boxes that is greater than a particular threshold. The occurrence of FP takes place when the detection level falls below a predetermined threshold. The fact that the proper detection was not made is indicated by the FN instance.  
The accuracy of the prediction can be determined by using the precision/recall (PR) curve and the Area Under Curve (AUC). The AP measure that was decided upon for The PASCAL Visual Object Classes Challenge 2010 [48] is calculated from the PR curve through the process of interpolating the precision at eleven different recall levels [0, 0.1,..., 1] (Equation (1)). \(\rho_{\text{interp}}\)(r) specifies the precision at each recall level, and it is interpolated by taking the greatest precision measured for which the corresponding recall is greater than r.  
\[\text{AP}=\frac{1}{11}\sum_{\text{r}\in\{0,0.1,\...,1\}}\rho_{\text{interp}}(\text{r}) \tag{1}\]  
Figure 4: Visual comparison of evaluation metrics.  
The notation of mAP is an abbreviation for \"mean absolute precision,\" which is determined by taking the average of all n categories (Equation (2)). The mAP is typically evaluated with different values of IoU threshold, e.g., mAP@.5 means the mean of AP calculated for each class with an IoU threshold \(>\) 0.5; if not given, mAP is calculated across all IoU threshold values. The mAP@ [0.5:0.95] is a popular measure that provides the most information about the quality of object identification and it was used as part of the COCO Detection Challenge [49]. It refers to the mAP calculated across an IoU threshold between 0.5 and 0.95. A high score for this metric indicates that the model can identify and correctly classify visual objects.  
\[\text{mAP}= \frac{1}{\text{n}}\ \sum_{\text{i}=1}^{\text{n}}\text{AP}_{\text{i}} \tag{2}\]
## 2 Materials and Methods
### Previous Work  
#### 2.2.1 YOLO and YOLOv10  
The YOLO series [7; 8; 9; 10; 11; 12; 13] has become a benchmark in real-time object detection due to its single-stage architecture and high efficiency. Starting with YOLOv1 [7], it introduced a regression-based approach for detection, improving processing speed. Later versions successively enhanced accuracy and feature fusion capabilities.  
YOLOv10 introduces three significant architectural advancements, Figure 4 displays the model's structure: the SCDown module for more efficient downsampling while preserving critical information, the C2 fCIB module for enhanced multi-scale feature integration, and the PSA module to focus attention on important regions in dense scenes. These innovations help YOLOv10 outperform previous versions with better accuracy and lower latency, making it well-suited for real-time tasks such as plant disease detection.  
However, YOLOv10 retains fixed-scale convolutional operations that limit its adaptability to the diverse scale variations of plant disease symptoms. Additionally, its feature concatenation mechanism lacks adaptive weighting, treating all input features equally regardless of their diagnostic significance. Our WD-YOLO addresses these limitations through dynamic-scale convolutions (DSConv) that autonomously adjust to lesion morphology and learnable weighted concatenation (WTConcat) that prioritizes diagnostically significant features, specifically optimized for agricultural constraints like lightweight design and edge deployment.  
Figure 3: Augmentation techniques: (**a**) original image, (**b**) horizontal flip (orientation variance), (**c**) vertical flip (perspective variance), (**d**) affine transformation (scale/rotation variance), (**e**) Gaussian noise (noise robustness).
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 4 Theoretical Basis for ANNs to Improve Lighting System Reliability
### Parametric Characteristics of Tunnel Lighting System  
Based on the comparison and analysis of the above mainstream lighting modes, one VCT lighting control system is chosen as the research object. This system comprehensively considers the influence of multiple input factors, and the output contains two parameters, color temperature and luminance, that is, the input and output can be conceptually expressed as the following function,  
\[ICT=Fun1(Direction,\,Weather,\,Volume,\,OCT,\,OLuminance,\,Speed) \tag{11}\]  
\[ILuminance=Fun2(Direction,\,Weather,\,Volume,\,OCT,\,OLuminance,\,Speed) \tag{12}\]  
This lighting system is, therefore, a multiple-input and multiple-output (MIMO) control system. Input and output parameters are described in Table 14.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Classification Criteria** & **Categories** \\ \hline \multirow{2}{*}{Model structure} & Feedforward network (multi-layer perceptron network). \\  & Feedback network (Hopfield network). \\ \hline \multirow{2}{*}{Learning mode} & Supervised learning. \\  & Unsupervised learning. \\  & Semi-supervised learning. \\ \hline \multirow{2}{*}{Working mode} & Deterministic neural network. \\  & Stochastic neural network. \\ \hline \multirow{2}{*}{Time characteristics} & Continuous neural network. \\  & Discrete neural network. \\ \hline \hline \end{tabular}
\end{table}
Table 13: Categorization of ANNs.
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 3 Materials and Methods
### Pose Estimation and Fall Detection Algorithm
#### 3.4.4 Posture Classification Criteria  
Based on these metrics, the system categorizes posture into predefined classes. Table 4 summarizes the heuristic conditions used to determine each posture category. If the number of detected keypoints is insufficient or the metrics are inconclusive, the posture is labeled as Unknown.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Posture** & **Conditions** \\ \hline Fall Detected & body\_angle < 0.5 and y\({}_{\text{hips}}\) < y\({}_{\text{Shoulders}}-20\) and \(\mid y_{\text{hips}}-y_{\text{feet}}\mid\) < 30 \\ \hline Sitting & body\_angle > 0.6 and y\({}_{\text{hips}}\) < y\({}_{\text{Shoulders}}\) \\ \hline Bent Over & 0.3 < body\_angle < 0.6 and y\({}_{\text{hips}}\) > y\({}_{\text{feet}}\) + 30 \\ \hline Standing & body\_angle > 0.7 and y\({}_{\text{hips}}\) > y\({}_{\text{Shoulders}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Rule-based criteria for posture classification.  
To evaluate the effectiveness of the proposed posture detection system, multiple experiments were conducted using a dual-camera configuration. This setup allows for simultaneous observation of the user from different angles, which enhances robustness in cases of occlusion or limited visibility. The following figures present the results obtained from processing the image streams captured by both cameras.  
Figure 1 illustrates pose detection results obtained from both Camera 1 and Camera 2, enabling simultaneous monitoring from different angles, and correct identification of the user's posture as \"Standing\" and \"Fall detected!\" based on skeletal keypoints extracted in real time. The red dots correspond to detected body keypoints, while green lines connect adjacent keypoints to form a skeletal representation of the posture.  
Figure 2 shows examples of real-time detected postures such as \"Sitting\" and \"Bent over,\" demonstrating the system's capability to operate correctly even when one of the cameras is not functional.
# Force Analysis of Masonry Cave-Dwelling Structure Based on Elastic Center Method
## 2 Research Object and Methodology
### Basic Assumptions Adopted in the Calculation Diagram  
Considering the construction characteristics of masonry cave-dwelling structure, the following assumptions are adopted when determining the calculation diagram:  
* Because the curve form of the arch axis has little effect on the internal force results, the upper arch ring of the cave dwelling is simplified as a circular arc axis with equal sections;
* The support constraint between the arch foot and the lower cave-dwelling leg is assumed to be fixed; that is, the arch foot is considered to resist bending;
* Generally, the ratio of the arch height f to the arch span length l is greater than 0.2, so when calculating the coefficients and free terms of the force method equation, only the influence of bending deformation is considered, and the effect of shear force and axial force on deformation is ignored.
## 3 Results and Discussion
### Mechanical Characterization of Specimens
#### 3.1.2 UV Radiation  
Figure 11a presents the tensile strength of UN-FREI and UR-FREI specimens exposed to UV radiation at 500% and 7% strains, respectively. UV radiation caused an increase in the tensile strength of UN-FREI by 20.70% at 5 days and 39.65% at 9 days of exposure. The formation of new bonds could increase stiffness and initial deformation resistance; however, it could also make the rubber less elastic and reduce its ability to withstand high stresses before rupture [27; 57]-. Conversely, after 9 days of exposure, the UR-FREI samples exhibited a tensile strength 18.27% lower than the reference value.  
Figure 10: RH exposure results for the cyclic shear test: (**a**) shear modulus (G), and (**b**) damping ratio (\(\beta\)).  
The compressive stress at 70% deformation of the UN-FREI and UR-FREI specimens is shown in Figure 11b. Contrary to the results observed under RH exposure, the UR-FREI specimens exhibited higher compressive strength values than the UN-FREI specimens when exposed to UV radiation. The UN-FREI specimens experienced a strength decrease of 4.28% and 17.26% at 5 and 9 days of exposure, respectively. Conversely, the UR-FREI samples showed an increase in compressive strength of 13.93% at 5 days and 1.65% at 9 days. In general, after 9 days of exposure, both materials experienced a decline in strength compared to the initial days of UV radiation exposure.  
The residual compression of the UN-FREI and UR-FREI specimens is shown in Figure 11c. UV radiation caused a reduction in deformation for both materials. For UN-FREI, the reduction was 18.58% at 5 days and 19.33% at 9 days of exposure, while for UR-FREI, it was 22.07% at 5 days and 12.04% at 9 days.  
The cyclic shear test was conducted with a period of 5 s. In the UN-FREI samples, the shear modulus remained constant after 5 days of UV exposure but decreased by 17.88% after 9 days. In contrast, the UR-FREI samples exhibited a 5.62% increase in the shear modulus at 5 days, followed by a slight decrease of 1.03% at 9 days (Figure 12a). Regarding the damping ratio (Figure 12b), after 9 days of exposure, the UN-FREI samples showed an 8.83% increase in damping, whereas the UR-FREI samples exhibited a 1.80% decrease.  
Figure 11: UV radiation results for: (**a**) tension, (**b**) monotonic compression, and (**c**) residual compression (CB) tests.
# Superimposed Perfect Binary Array-Aided Channel Estimation for OTFS Systems
## 1 Introduction
Furthermore, in contrast to similar work in [17], the SPBA method can estimate delay-Doppler taps and complex path gains in the same superimposed frame, which means the additional non-superimposed EP frame is no longer required. As a result, the SPBA method has better adaptability to rapidly delay-Doppler varying channels. 2. A channel estimator for the SPBA framework is proposed. At the receiver side, local PBAs with different circular shifts in the DD domain are correlated with the received signal. By utilizing the perfect autocorrelation of PBA [19; 20; 21], we can find the propagation paths and determine the delay-Doppler taps together with the complex path gains by comparing the correlation values to a threshold. Hence, channel estimation is performed through a linear search for the correlation peak. The proposed channel estimator can achieve high estimation accuracy with a significant SE improvement. 3. The SPBA's optimal power ratio is calculated by deriving the signal-to-interference-plus-noise ratio (SINR) and maximizing it. We validate that the adoption of the optimal power ratio will further improve SE and bit error rate (BER) performance of the SPBA method. We validate the effectiveness of the proposed method and show that this method can approach the channel estimation performance of the EP method while significantly improving the SE. The remainder of this paper is organized as follows: Section 2 formalizes the superimposed PBA-aided OTFS (SPBA-OTFS) system model and derives the corresponding channel input-output relation. Section 3 proposes the PBA-based channel estimation algorithm and analyzes its modifications in the case of OTFS modulation using rectangular waveforms. Section 4 analyzes the SE of the proposed system and derives the optimal power ratio by means of maximizing the SINR. Simulation results are presented in Section 5, followed by conclusions in Section 6.
## 3 System Model and Problem Formulation
### Sensing Architecture of WSN for CPSS  
Generally, the CPSS is composed of three parts, i.e., social space, physical space, and cyber space, as shown in Figure 1. The WSN, also called the sensing network, consists of social sensors and physical sensors. The higher abstraction level represents different applications, such as smart home, smart city, intelligent transport and so on. The social and physical sensors in WSN connect to the cyber world through the communication network or Internet. The cyber space interplays with social space and physical space. The social sensor can interact with physical sensors and among each other. The progressive interaction between the two types of sensor nodes can provide the reliable operation, low cost and efficient control for cyber system. They are used to capture the monitoring data and offer the collection of large-scale data. In CPSS, the physical sensors are generally fixed and static. The social sensors are affected by individual affective and physical state [25; 26], which is a dynamic process. That is, these social sensors are carried by people and each social sensor moves among different locations based on the social dynamics. It should be noted that the concept of \"location\" has different definitions in the literature [25; 26]. The authors of [25] utilized Voronoi diagram to divide the plane into cells and each Voronoi cell is referred to as a location. In [26], an square cell (block) is a location. In this paper, we consider the definition of location in [25]. In this work, we use the energy efficient CB technique to improve the transmission performance of the social and physical sensing network for CPSS. The corresponding transmission model based on CB is presented in this section.
## 4 Analysis and Discussion
### Advantages of the HSCNN Model
Two types of kernels are used in the first convolutional layer. The 1 \(\times\) 1 \(\times\) 4 kernels are used to extract the feature of the pixels, and the 3 \(\times\) 3 \(\times\) 4 kernels are used to extract the spatial relation between pixels. This design takes advantage of the ability of convolution for extracting higher-level spatial semantics and for obtaining the rich pixel information contained in the remote sensing images. 3. Our model does not utilize pooling, instead the convolution result is taken as the eigenvalue of the central pixel of the pixel block. In the application of the convolutional network to image classification, the basic target (sample) for the classification is the entire image. Thus, pooling can produce the main features of the feature map and reduce the amount of subsequent computation. Although the information on the accurate position of the features is lost during this process, their relative positions are nevertheless retained, which ensures the normal operation of the subsequent computational steps. However, in image segmentation, the basic target (sample) for the classification is an individual pixel, whose exact location must be mapped by the eigenvalue. Therefore, the major advantage of our model is its ability to preserve the spatial location of the eigenvalue, which makes it possible to remove the deconvolution adopted by the traditional FCN. Accordingly, the amount of computation is reduced. Further, the loss in precision due to positioning error is reduced, as the accurate position of the eigenvalue is kept.
# An Anti-Interference Method Based on Energy Residual Searching in GNSS Positioning Applications
## 2 Related Work
For example, in [25] a GNSS interference detection method based on the smoothed pseudo-multi-resolution Hough transform was proposed, to improve detection performance by addressing the cross-term issue, transforming GNSS interference detection into a localized search for Hough transform energy peaks within a small region of the polar Hough parameter space. In comparison to traditional time-frequency analysis methods, the fractional Fourier transform (FrFT) [27; 28] has garnered significant attention, due to its effectiveness in handling the characteristics of signals such as LFM. The FrFT avoids the introduction of cross-interference terms, thereby better preserving the original time-frequency characteristics of the signal. Additionally, linear frequency-modulation interference can exhibit good energy concentration in the optimal fractional Fourier domain, while useful GNSS signals and additive white Gaussian noise (AWGN) are typically dispersed throughout the fractional Fourier domain without any energy concentration [29; 30]. This characteristic highlights the theoretical and practical significance of FrFT in GNSS receiver applications. Once the optimal fractional order of the FrFT is determined, effective detection of GNSS frequency-sweep interference signals can be achieved by searching for energy peaks within the fractional Fourier domain.