esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Modeling of MET during Six Training Sessions (Group Vise during Exercise Period)  
During the exercise period of six training sessions, the GEE model significantly predicted MET. For the untrained group, the respiratory exchange ratio (RER) (\(p\) = 0.004* and 95% CI = 0.152\(\sim\)0.789) and heart rate (HR) (\(p\) = 0.000* and 95% CI = 0.009-0.022) were significantly predicted MET while all other parameters including systolic blood pressure (SBP), diastolic blood pressure (DBP), blood lactate (BL), and Borg rate of perceived exertion (RPE) did not show a significant relationship with MET. For the training group, RER (\(p\) = 0.021* and 95% CI = 0.073-0.915), HR (\(p\) = 0.001* and 95% CI = 0.005-0.018), SBP  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Groups** & **Untrained (n = 5)** & **Trained (n = 6)** \\ \hline Age (years) & 22.00 \(\pm\) 2.00 & 25.83 \(\pm\) 3.66 \\ \hline Male/female & 0/5 & 4/2 \\ \hline Weight (kg) & 53.32 \(\pm\) 3.38 & 81.68 \(\pm\) 19.48 \\ \hline Height (cm) & 158.60 \(\pm\) 5.03 & 173.33 \(\pm\) 10.38 \\ \hline Body mass index (kg/m\({}^{2}\)) & 21.26 \(\pm\) 1.66 & 26.87 \(\pm\) 4.54 \\ \hline \% Body fat & 23.60 \(\pm\) 2.43 & 26.22 \(\pm\) 1.86 \\ \hline Habitual activity/week (h) & 3.14 \(\pm\) 0.92 & 4.40 \(\pm\) 1.13 \\ \hline RM 60\% of shoulder press (kg) & 9.10 \(\pm\) 1.60 & 16.08 \(\pm\) 3.68 \\ \hline RM 60\% of deadlift (kg) & 19.50 \(\pm\) 4.11 & 34.42 \(\pm\) 3.32 \\ \hline RM 60 \% of squat (kg) & 16.00 \(\pm\) 2.74 & 32.33 \(\pm\) 3.76 \\ \hline \hline \end{tabular}
\begin{tabular}{c} mean \(\pm\) SD: RM: repetition maximum: Training loads in kilogram (kg) for both side dumbbells. \\ \end{tabular}
\end{table}
Table 2: Study baseline characteristics (n = 11).  
(\(p\) = 0.033* and 95% CI = \(-\)0.011-0.000), and BL (\(p\) = 0.000* and 95% CI = \(-\)0.012\(\sim\)\(-\)0.004) reached a significant level in modeling MET. The QIC for exercise models were 74 and 187 while for resting models were 48 and 95 for untrained and trained groups, respectively (Tables 3 and 4).
# A Survey on the Use of Graph Convolutional Networks for Combating Fake News
## 3 Using GCNs for the Detection of Fake News
### Detecting Fake Content Using GCNs
The heterogeneous graph contains four types of nodes (i.e., news, domains, reviews and sources) and its edges link the news nodes with other news nodes with similar content or with their domain, source or review nodes. The neighbor sampling strategy allows controlling the number of nodes that are considered (indirectly, through a common domain or a common source, for example) relevant to a news' node and the respective explosion in the number of neighbors that may occur at each graph convolution layer. This approach attempts to include side information about the news, such as the reviews, which in a way goes beyond the news content itself and examines the actual response of the social network. However, it does not examine the diffusion of a fake news item in the network. The problem formulation begins with a set of news posts \(\mathcal{P}=\{p_{1},...,p_{n}\}\), which contain textual or visual content and side information that may correspond to the profile of the user that made the post. The content of each \(p\in\mathcal{P}\) is pre-processed to extract key concepts, terms or features, which are mapped to the respective textual \(R_{t}\) or visual \(R_{v}\) embeddings either using pre-trained word or image embedding models, such as GloVE or VGG-19. The next step, as shown in Figure 3, is the creation of an undirected graph \(\mathcal{G}=V,E\), with concepts/features as nodes \(u\in V\) and edges \(e\in E\) that denote relations between concepts (e.g., based on co-occurrence in posts). Using the embeddings \(R\) (concatenations of \(R_{t}\) and \(R_{v}\)) of all nodes, the input matrix \(X\in\mathbb{R}^{n\times t}\) is created, where \(n\) is the number of nodes and \(d\) is the dimensionality of the node embeddings. The adjacency matrix \(A\in\mathbb{R}^{n\times n}\) is used to represent the set of edges \(E\). The Graph Convolution Network (GCN) in the next step aims to capture the effect of neighboring nodes (i.e., concepts) to the embedding of each node. Each GC layer is formally represented as a function that takes as input the feature matrix from the previous layer \(Z^{(j)}\) and outputs a higher level matrix \(Z^{(j+1)}\), as shown in Equation (2):  
Figure 3: An overview of the fake news detection process using GCN. \[Z^{(j+1)}=\sigma(\hat{D}^{-1/2}\hat{A}\hat{D}^{-1/2}Z^{(j)}W) \tag{2}\]  
where \(\hat{A}=A+I\), \(I\) is the identity matrix, \(\hat{D}_{il}=\sum_{j}\hat{A}_{j}\), W is the transformation matrix for the j-th layer, and \(Z^{(0)}=X\). \(\sigma\) is a non-linear activation function, e.g., a ReLU \(\sigma(x)=max(0,x)\). The next step is to apply a global mean pooling to aggregate the vertices of each graph and obtain the representation vector of posts. The vector of each post is fed to a binary classifier that employs a cross-entropy classification loss to distinguish between fake and non-fake news.
# Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning
## 3 Problem Definition  
In this work, we aim to propose a fair and efficient coexistence scheme for uncoordinated LTE-U and WiFi networks. In our previous work [8], we have developed a CNN-based solution that classifies saturated and unsaturated WiFi networks. The developed solution can be used by advanced coexistence schemes that aim to achieve fair and spectrum efficient coexistence between technologies co-located with WiFi. In this work, we aim to develop a coexistence scheme that is used for autonomous selection of optimal LTE-U ON and OFF duration ratio which maximizes aggregated throughput of the two technologies and fairness between the technologies.  
The main goal of the algorithm is to select an optimal LTE-U ON and OFF duration ratio based on LTE-U and WiFi network traffic loads. The LTE-U estimates the traffic demand of the WiFi network based on the saturation sensing model and classifies the WiFi networks as either saturated or unsaturated based on their offered traffic load. A WiFi network is considered as unsaturated if its aggregated throughput has not reached the maximum system throughput limit and saturated otherwise [33]. Similarly, for the LTE network, LTE offered traffic load (\(LTE_{\text{OH}}\)), the target throughput (\(LTE_{\text{Tar}}\)), and obtained throughput (\(LTE_{\text{Ob}}\)) are considered to select the optimal LTE-U ON and OFF duration ratio. \(LTE_{\text{Ob}}\) refers to the throughput obtained by the LTE network when a certain TXOP is selected and \(LTE_{\text{Tar}}\) is set as the offered load throughput if the offered data rate is less than the saturation throughput of the LTE network. On the other hand, \(LTE_{\text{Tar}}\) is set as the LTE saturation throughput if the offered data rate is greater than the saturation throughput of the LTE network. Note that the LTE saturation throughput is the maximum capacity of the LTE network in a standalone LTE network. In other words, the saturation throughput is the maximum aggregated capacity of all the eNBs of the LTE network for all the User Equipments (UEs) when the LTE uses the whole spectrum.  
The goal of the coexistence scheme is to select an optimal LTE-U ON and OFF duration ratio so that the WiFi network remains unsaturated while the \(LTE_{\text{Ob}}\) is close to \(LTE_{\text{Tar}}\), if possible. Otherwise, the LTE-U ON and OFF duration ratio has to be selected in such a way that the aggregated throughput and fairness of the two technologies are maximized. In this article, we aim to achieve these objectives considering the following constraints: (a) no collaboration protocol is used to exchange the traffic status between the technologies(b) the coexistence is implemented on the LTE-U eNB side and there is no modification required in the WiFi network's commercial devices.
# Learning Motion Constraint-Based Spatio-Temporal Networks for Infrared Dim Target Detections
## 2 Materials and Methods
### Inference Phase  
Infrared targets in long-range distances always occupy a few pixels of the entire image and represent only little semantic information [31]. The methods used early to improve the detection rate generally include pixel-by-pixel methods [32; 33]. This causes many redundant calculations. A substantial amount of computation results are insignificant. This tends to cause online stability detection failures [34]. In this paper, we proposed an innovation called a state-aware module (STAM) to tackle the dilemma. It is a variable interval search strategy. The STAM records the historical diagonal of the prediction box, which is denoted as (Dmax, Dmin). Dmax = Dmin = D is initialized with the diagonal of the image to be detected. (Dmax, Dmin) is updated after the completion of k consecutive frames detection. k is set up as 5 due to the speed used in this paper. Provided that (Dmax, Dmin) is updated, the next search region will be replaced from the global image to the local neighborhood. The region is defined as the rectangular box enclosing (Dmax, Dmin), as shown in Figure 10. Such a pattern of local searches can greatly reduce the parameters of network computation and speed up the real-time applicability of the algorithm.  
Next, consider the interruption case. If the target is temporarily lost from the local area due to sudden fast movements or frame loss, (Dmax, Dmin) is reinitialized to the global search. However, if the obstruction occurs, global research would also be futile. Inspired by the Simple Online and Realtime Tracking with a Deep Association Metric (Deep SORT) [35], we introduce the Kalman Filter (KF) to solve this situation, as shown in Figure 11. KF utilizes the motion correlation of the object to estimate the position [36; 37]. When the obstruction is over, the model can quickly switch to the local search. As the parameters passing into the KF are only the positions, the computation does not consume many resources.  
The stability of the algorithm matters for real applications, but uncontrollable factors (such as occlusion and frame loss) are unavoidable. STAM can improve the resilience and robustness of the model to solve unexpected events.
## 2 Materials and Methods
### Data Analysis
#### 2.4.2 Semiempirical Multiplicative Model Construction at the Seasonal Scale  
According to previous studies [34; 38; 60; 61; 62], the soil temperature (T\({}_{\text{s}}\)), U\({}^{*}\), SWC, vapour pressure deficit (VPD) and GPP are essential variables controlling daily CH\({}_{4}\) fluxes.  
The semiempirical multiplicative model has been proven to be an effective tool to model daily CH\({}_{4}\) fluxes with biophysical controls in paddy fields [49]. Therefore, a semiempirical multiplicative model was employed to rank the factors influencing daily CH\({}_{4}\) emissions:  
\[\text{F}_{\text{CH4}}=\text{a}*\text{b}^{\text{Tsolin}}*\text{c}^{\text{Un}}* \text{d}^{\text{VFDn}}*\text{e}^{\text{SWCn}}*\text{f}^{\text{GPPn}} \tag{4}\]  
where Tsoiln, Un, VPDn, SWCn and GPPn are the normalized T\({}_{\text{s}}\), U*, VPD, SWC and GPP, respectively, following Equation (5):  
\[\text{X}_{\text{n}}=\frac{\text{X}-\text{X}_{\text{min}}}{\text{X}_{\text{max} }-\text{X}_{\text{min}}} \tag{5}\]  
where X\({}_{\text{n}}\) is the normalized variable, X is the original variable, X\({}_{\text{max}}\) and X\({}_{\text{min}}\) are the maximum and minimum variables during the period.  
First, all 5 variables were incorporated into the model, and then the p-values of the 5 factors were obtained and ranked. Then, stepwise multivariate regression models were constructed based on the rank according to Equation (5). The coefficient of determination (R\({}^{2}\)) was adopted as the indicator to evaluate the performance of each model.  
The semiempirical models were constructed in 2 different ways. 'Model-1' was constructed using the data from the whole growing season. Considering that the main controlling factors might vary in different periods of rice growth, 'Model-2' was constructed according to the pattern of the seasonal CH\({}_{4}\) flux. The whole growing season was split into 2 or 3 periods when different models were developed. Owing to the single-peak pattern of the seasonal CH\({}_{4}\) fluxes, the whole growing seasons of late rice in 2018 and of early rice in 2020 were split into 2 periods by mid-season drainage. However, the whole growing seasons of late rice in 2019 and in 2020 were split into 3 periods by mid-season drainage, as well as the double-peak pattern of the seasonal CH\({}_{4}\) fluxes. The periods of mid-season drainage were included in 'Model-2'.
# A Review on LiFi Network Research: Open Issues, Applications and Future Directions
## 5 Component and Architecture of a LiFi System
### Downlink Receiver DLR  
LiFi technology typically utilizes LEDs and Photodetectors (PDs) as transmitters and receivers, respectively. However, camera PDs or Image sensors (ISs) are used as physical receivers. IR, VL, or Ultraviolet (UV) spectra are used as a communication media [21]. Compared with the conventional usage of electromagnetic waves, such as Wi-Fi, LiFi utilizes light as a medium. LiFi signals cannot penetrate walls, and it is usually carried out using white LED bulbs on the DLT [167]. Different components, such as I PIN PDs, may be used as receivers [168; 169]. An intrinsic zone is inserted between two vast regions.  
When the volume increases, an incident photon can be generated by: (i) electron-hole pairings and (ii) silicon APDs [170]. A high reverse bias is added to raise the electric field and the velocity of the carriers responsible for the avalanche effect. Owing to the phenomenon of avalanches, APD shows great photo sensibility relative to a PIN PD, (iii) SPADs, (iv) CMOS sensors [171; 159; 172; 173] and (v) solar photovoltaic PV cells [161; 174]. Several benefits and drawbacks of using a PV-based receiver are shown in Table 3. Figure 6 shows the DL transmitter and receiver in a room environment. For example, from LiFi AP to cellphone: (d) refers to the distance between the AP and UE, (z) refers to half the angle of the radiation angle, and (a) refers to the angle of the receiver.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Benefits** & **Drawbacks** \\ \hline \multirow{4}{*}{**Purchase**} & 1. & Elevated bandwidth. & 1. & Poor surface detection. \\  & 2. & The output voltage of the illumination is linearly & 1. & Poor surface detection. \\  & & related to the transmitted optical LED power. PD can & 2. & Requires the use of a very high-performance, \\  & & detect low-level light variations. & & high-cost circuit amplifier. \\  & & 3. & The avalanche effect increases the identification of & 3. & The power received at the PD is affected by \\  & & slight changes in light intensity. & & the channel gain and noise. \\  & & 4. & Low noise and high bandwidth are produced. & \\ \hline \multirow{4}{*}{**Purchase**} & 1. & LiFi receiver with auto power (neutral energy). & 1. & Low bandwidth standard. \\  & 2. & Allows energy harvesting detection of LiFi signs. & 2. & A small spectrum of light variations is \\  & & 3. & Broad surface detection. & earned (i.e., the received light interpretations \\  & & 4. & Allows partial shading to be avoided and angular & for preserving a close linear relationship with the difference in output voltage are \\  & & acceptance to be larger than that of PD. & significantly low). \\ \hline \hline \end{tabular}
\end{table}
Table 3: Benefits and drawbacks of PD- and PV-based receivers.  
Figure 6: DLT and DLR in a room scenario.
# NTPP-MVSNet: Multi-View Stereo Network Based on Neighboring Tangent Plane Propagation
## 1 Introduction
We also incorporated a neighborhood bias learning module in our depth sampling module, obtained using deformable convolution, that enables adaptively depth-smoothing neighborhood pixels for each pixel. This mechanism circumvents the presence of erroneous depth assumptions offered by neighborhood points at discontinuous surfaces. Notably, our network achieves higher accuracy with equal depth hypotheses and acquires the current image's normals outcome that can offer significant benefits for tasks like model editing and texture mapping. In summary, the main contributions of our algorithm are as follows:  
Firstly, we proposed a novel multi-view stereo network that leverages neighborhood tangent plane cues, which incorporates a carefully designed depth sampling module and a neighborhood bias learning module, providing surface-continuous neighbor pixel positions for each pixel. Secondly, we introduced a normal estimation module to the multi-view stereo network. The predicted depth map can provide input for the normal map calculation and the generated normal map can constrain the detail distribution of the depth map to make it closer to the true value. The addition of this module does not significantly reduce the operating efficiency of the algorithm or GPU memory consumption. Thirdly, we proposed a new depth sampling method based on the normal vector estimation results of the scene. Compared with the uniform sampling and the sampling method based on neighborhood depth propagation, the depth sampling accuracy obtained by our method is higher, and the premise assumption is more in line with the actual situation. Experiments conducted on diverse datasets like DTU, Tanks, and Temples, displayed the potential of our method for various real-world applications.
# Occlusion-Aware Worker Detection in Masonry Work: Performance Evaluation of YOLOv8 and SAMURAI
## 2 Methodology
### YOLOv8 and SAMURAI Architecture
#### 2.3.1 YOLOv8 Architecture  
YOLOv8 is a deep learning-based object detection model that builds upon the efficiency and accuracy of its predecessors in the YOLO algorithm [70; 71]. It maintains the fundamental one-stage detection pipeline, allowing for rapid object localization and classification within a single forward pass [72; 73]. YOLOv8 consists of three primary components: the backbone, neck, and head, as illustrated in Figure 3[74; 75].  
Compared to YOLOv5, YOLOv8 introduces several architectural improvements. It replaces the C3 modules of YOLOv5 with Cross-Stage Partial connections with two convolutional layers and fusion (C2f), which improves feature fusion while reducing the number of parameters. Additionally, YOLOv8 removes the anchor box mechanism (anchor-free), simplifying the detection head and enabling more flexible object localization [76]. The backbone is responsible for extracting low-level to high-level spatial features from input images using convolutional layers. The enhanced modules such as C2f and the use of the Sigmoid Linear Unit (SiLU) activation function in place of Leaky Rectified Linear Unit (Leaky ReLU) further contribute to better convergence and detection precision [76]. It employs Cross-Stage Partial (CSP) connections to enhance gradient flow and reduce computational redundancy while maintaining detection performance. The extracted features are then forwarded to the neck, which utilizes Spatial Pyramid Pooling-Fast (SPPF) layers to aggregate multi-scale features. This enhances the model's ability to detect objects of varying sizes, making it particularly useful in dynamic environments. Finally, the YOLO head performs object classification and bounding box regression, producing the final detection results.  
For this study, YOLOv8 was applied to detect construction workers performing brick transportation and bricklaying tasks. The model was trained on an annotated dataset consisting of worker images captured in an outdoor environment. The detection performance was evaluated across different occlusion conditions, ensuring a comprehensive analysis of the model's effectiveness in real-world construction scenarios. Figure 3 illustrates the improved architecture of YOLOv8, including updated backbone modules and anchor-free detection head, which distinguish it from previous YOLO versions and contribute to its enhanced performance.
# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
## 2 Related Work
### Edge-Assisted Visual SLAM  
To enable visual SLAM on resource-constrained devices such as mobile devices and robots, [PERSON] et al. [19] proposed edgeSLAM, an edge-assisted mobile semantic visual SLAM system. This method integrates an efficient computational offloading strategy into ORB-SLAM2 and improves localization and mapping accuracy through semantic segmentation.  
Similarly, [PERSON] et al. [20] introduced Edge-SLAM, which ensures long-term, accurate operation under limited computational resources by offloading local mapping and loop-closing computations to edge servers.  
These edge-assisted visual SLAM approaches require transmitting image frames to edge servers, which then return point clouds and poses after processing. However, this transmission process poses significant privacy risks [6; 7; 10].
## 3 Materials and Methods
### Traffic Density  
The intuitive estimation of traffic density in maritime areas helps identify congested areas, enabling vessel operators and air traffic controllers to rapidly respond to emergencies in high-risk, high-density areas. It can also inform the optimization of port operations and logistics management by minimizing bottlenecks and improving traffic flow. Several studies have used AIS data to assess maritime traffic density maps. [PERSON] et al. [27] applied a spatiotemporal density analysis to evaluate traffic patterns and identify both open sea and coastal routes. Their approach calculates ship occupancy time within specific cells, producing heat maps that provide valuable data for national maritime planning and safety. [PERSON] et al. [28] predicted maritime traffic in the Strait of Malacca using time-series models. They found that exponential smoothing techniques offered the most accurate predictions, a key factor for managing congestion in heavily trafficked waterways. Furthermore, [PERSON] et al. [29] developed an open-source toolbox to create maritime density maps. This toolbox addressed common issues such as data noise and spoofing and provided a scalable method for visualizing large datasets, making it a valuable tool for maritime spatial planning, particularly in high-density areas. [PERSON] et al. [30] proposed a framework that uses clustering algorithms to detect vessel routes from AIS data. Their approach identified key waypoints and established routes, which are crucial for further evaluating safety and developing autonomous maritime systems. Finally, [PERSON] [31] introduced a fine-grained grid-based algorithm to calculate maritime traffic density using ship trajectory data, providing a scalable and effective tool for identifying global maritime traffic hotspots and analyzing trade trends.  
Figure 1: Process flow of the proposed maritime traffic behavior visualization method.