esa-sceva/satcom-chunk-collection huggingface dataset:
# FD-YOLO: A YOLO Network Optimized for Fall Detection
## 3 Materials and Methods
### Attention Module
to the CBAM, the GAB accounts for both channel and spatial information. However, the GAB goes a step further by considering interactions across the entire feature map when calculating the relationships between different positions, thereby applying both channel and spatial attention on a global scale. This global perspective allows the model to capture more comprehensive contextual information, improving its ability to detect falls, even in cases where critical parts of the body are partially occluded or when fall-related features are dispersed across the image. CBAM methods often use max or average pooling to extract features, which can result in capturing only specific features of the human body while losing other important details. To solve this, the GAM (Global attention mechanism) uses two convolutional layers to preserve and combine spatial information, reducing the loss of structural details and improving fall detection accuracy. This needs to be validated with experimental results. Pooling methods summarize data by averaging or taking the maximum value, which limits the ability to capture global context but is efficient in terms of parameters. The GAM removes pooling and uses convolutional layers to maintain information and enhance global interactions. While removing pooling may lower parameter efficiency, this was addressed with additional computations. The calculation of the channel attention mechanism is outlined in Equations (1) and (2):  
\[M_{c}(F_{1})=\sigma(W_{2}*ReLU(W_{1}*Pooling(F_{1}))) \tag{1}\]  
\[F_{2}=M_{c}(F_{1})\otimes F_{1} \tag{2}\]  
\(\sigma\) is a sigmoid activation function. \(W_{1}\) and \(W_{2}\) are the weights matrix. For the input feature map (\(F_{1}\)), the intermediate channel map (\(F_{2}\)) is obtained through element-wise multiplication between the channel attention map (\(M_{c}\)) and the input feature map (\(F_{1}\)). Similarly, the calculation of the spatial attention mechanism is depicted in Equations (3) and (4):  
\[M_{s}(F_{2})=\sigma(ConvV_{1}(ConvV_{2}(F_{2})))) \tag{3}\]  
\[F_{3}=M_{s}(F_{2})\otimes F_{2} \tag{4}\]  
For the intermediate channel map (\(F_{2}\)), \(ConvV_{1}\), \(ConvV_{2}\) is convolution operation. The final feature map (\(F_{3}\)) is produced via element-wise multiplication between the spatial attention map (\(M_{s}\)), and the input feature map (\(F_{2}\)). The distinguishing characteristic of this approach is its ability to compute interactions across all positions on the feature map globally, allowing the model to capture more comprehensive feature relationships. However, this increased consideration of positional interactions also leads to heightened computational complexity. To address this, we conducted experiments using both the CBAM and GAB to compare their effectiveness in improving detection accuracy under these conditions [38]. The overall architecture of the system is illustrated in Figure 3.  
Figure 3: Global attention mechanism.
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## 3 The Importance of Quantum Key Distribution (QKD)
### The Challenges of QKD in 5G Implementation
#### 3.1.7 Big Data Handling  
The seventh challenge is regarding the capability of handling a big amount of data. Several smart grid tools generate a flow of data stream based on the sampling period and it requires the accuracy of data. One of these tools is the synchronous phase measurement unit's tool. Mostly, the data stream records a few Mb/s. Consider the capacity of optical fiber that can transmit terabits per second or the capacity of 5G network that able to handle gigabit per second. QKD protocol encounters a challenge in supporting and coping with the requirements of data rate in each application where the secret key must be shorter than the data rate if it used with the OTP symmetric cryptographic algorithm. However, QKD can only produce a secret key at a rate of 1 Mb/s on 50 km. and it is rare for QKD implementation to generate a secret key at 10 kb/s over 100 km. In cases such as in UAV domain, due to the continuous data collection procedure, the data collected from UAV sensors is huge in volume. As a result, if there are insufficient resources tomanaging the data, it will create a problem when the data processing and data handling occur simultaneously.
## 3 Materials, Methods, and Findings
### Decision Tree (DT)  
The decision tree was structured with the gini index criterion and used without a depth limit. This algorithm branched and predicted class labels based on the features in the dataset. The decision paths clearly showed the importance of each feature and its contribution to class distinction. During training, attention was paid to the dataset class balance to avoid overlearning. Figure 6 shows the complexity matrix of the K-NN algorithm.  
Figure 5: K-NN confusion matrix.  
The model made 87 correct predictions in the \"no verbal violence\" class but incorrectly labelled 15 instances as \"verbal violence present\" (false positive). In the \"verbal violence present\" class, the model made 85 correct predictions and incorrectly labelled 23 instances as \"no verbal violence\" (false negative). These results show that the model performed reliably in the \"no verbal violence\" class (81.90% accuracy) but suffered from a slight lack of sensitivity in the \"verbal violence present\" class (21.30% false negative). The limited number of false positives (14.71%) shows that the model kept its errors low, except for the positive class.
## 2 Background
### Blockchain-Based Federated Learning (BFL) for Data Sharing in IoMT  
A key limitation of federated learning (FL) lies in its reliance on a central server, which significantly impacts the privacy and performance of all devices within the system. In order to address this issue by reducing the network's dependence on a singular node and improving communication efficiency, blockchain-based federated learning (BFL) leverages blockchain technology to eliminate the necessity of a central server. Figure 3 illustrates how BFL handles model updates by treating them as data within a block, which is shared through a consensus mechanism.  
Figure 2: Federated learning (FL) for data sharing in IoMT.  
The blockchain is initiated with a single block that includes the initial global model. On the device side, the local model is initialized with the global model \(\mathbf{g}^{(t)}\) and trained on the data \(D_{i}\) from the \(i^{\text{th}}\) device. Subsequently, the device uploads data to the blockchain by constructing a block comprising a header, the trained model, and the uploader's identifier (ID). Devices engage directly with a group of miners to acquire the Merkle root of the data before transmitting the block. A smart contract (SC) on the blockchain enforces specific rules to decide when to update the global model. The aggregation mechanisms of BCFL employ an averaging function akin to federated averaging (FedAVG).
# Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking
## 2 Related Work
### Tracking Method Based on Transformer  
Because Transformer [43] was successfully used in natural language processing (NLP), the subsequent versions of Transformer [44; 45] have been continuously shining in this field. Later, when DETR [46] was introduced as a vision variant of Transformer, researchers started to realize that Transformer also has great potential value in the MOT field. As more researchers follow up, Transformer is also fruitful in more computer vision tasks [47; 48; 49], like classification, segmentation, image caption, lane line detection, etc.  
TransTrack [50] was the first work to use Transformer for MOT. TransTrack consisted of one encoder and two parallel decoders. The encoder input the feature map processed by CNN. The two decoders input the object query and the existing track query, respectively. The outputs of the two decoders were matched by intersection of union (IoU) to obtain the tracklets of objects' motion.  
The main difference between the main structure of TrackFormer [51] and TransTrack is that the object query and track query were input into a shared decoder in TrackFormer, and the decoder output two heads, which were the heads for classes and positions of boxes for the tracklets.  
TransCenter [52] is also a piece of work that used Transformer for MOT. When TransCenter processes a frame from a video, it needs the feature maps of the current frame and the previous frame to input two parallel deformable encoders (DE) and then output the memory feature maps for the current frame (\(M_{t}\)) and previous frame (\(M_{t-1}\)), respectively. Then they are sent to the query learn network (QLN) to generate dense multiscale query (\(DQ_{t}\)) and (\(TQ_{t}\)) for tracking and detection, respectively. Followed by two parallel deformable track decoders (DTD) and deformable detect decoders (DDD), respectively, for tracking and detection, DDD retrieves the current frame information from \(M_{t}\) through \(DQ_{t}\) to output multiscale detection feature (\(DF_{t}\)), and DTD retrieves the previous frame information from \(M_{t-1}\) through \(TQ_{t}\) to output the multiscale tracking feature (\(TF_{t}\)). Finally, the center heatmap (\(C_{t}\)) and bounding box sizes are estimated by \(DF_{t}\), and the tracking displacement (\(T_{t}\)) is estimated by the previous frame heatmap C\({}_{t-1}\), \(DF_{t}\) and \(TF_{t}\).
###### Abstract  
The aim of this work is to assess the crystalline structure modification of an SLM Co-Cr-W dental alloy, veneered with two different ceramics, by means of X-ray diffraction (XRD), atomic force microscopy (AFM), and scanning electron microscopy, coupled with energy-dispersive spectroscopy (SEM-EDS). Ten identical plates were fabricated using SLM and were subsequently subjected to ceramic veneering. Following the repeated firing of the ceramic layers, carried out at temperatures exceeding 900 \({}^{\circ}\)C, new crystalline phases and variations in the crystallite sizes in the SLM Co-Cr-W dental alloy used for the substructure were detected via XRD. The two veneering ceramics showed significant differences in their behavior, beginning with the first firing, accompanied by structural changes. AFM micrographs and histograms of the surface heights over the whole scanned area of the samples showed that the surface of the Co-Cr-W dental alloy is strongly affected by the repeated firings necessitated by the ceramic firing process, a finding in accordance with the XRD results. The SEM investigation revealed that the different firing parameters had an impact on the alloy, the ceramic microstructure, and the surface quality. The differences in the chemical composition of the ceramics, highlighted by EDS, are reflected in their behavior. The crystalline alloy structure is influenced by the repeated firings of the ceramic layers.  
Co-Cr-W dental alloy; 3D printing selective laser melting; ceramic veneering: crystalline structure; X-ray diffractometry; atomic force microscopy; scanning electron microscopy; energy-dispersive spectroscopy
## 4 Sensitivity Analyses: Results and Discussion
### Influence of the Far Field Stresses and Pore Fluid Pressure on Mud Weight Prediction
The distribution of the angles \(\beta_{w}\) was calculated at different far field stresses (\(R=1\)-\(1.5\)), pore fluid pressures (8 MPa, 11 MPa and 15 MPa) and inclination of the weakness planes (\(\delta=0^{\circ}\)-\(15^{\circ}\)-\(30^{\circ}\)-\(45^{\circ}\)-\(50^{\circ}\)-\(60^{\circ}\)-\(90^{\circ}\)). The peaks calculated with HBm occur in a wide range of \(\beta_{w}=15^{\circ}\)-75\({}^{\circ}\). The range of \(\beta_{w}\) is affected by the far field stress anisotropy \(R\). Furthermore, the pore fluid pressure  
Figure 11: (**a**) Comparison between the mud weight trends at different \(R=1.1\)–1.25 and constant pore fluid pressure \(p_{f}=11\) MPa. (**b**) Comparison between the mud weight trends at different R = 1.5–2.0 and constant pore fluid pressure \(p_{f}=11\) MPa. (**c**) Percentage change in SG related to percentage change in far field stresses \(\Delta R=+36\%\) and \(\Delta R=+14\%\) at constant pore fluid pressure \(p_{f}=11\) MPa. (**d**) Comparison between SG of the weakness planes and rock matrix (plateau of constant strength) when \(R=1.5\) and \(p_{f}=11\)–15 MPa. Figure 12: Frequency distribution of the angles \(\beta_{w}\) at which the mud weight peaks occur by varying the weakness plane inclination \(\delta\), far field stresses \(R=\sigma_{MAX}/\sigma_{min}\) and pore fluid pressures \(p_{f}\). magnitude does not seem to influence the location of the peaks, although the high variation \(\Delta p_{f}=+37\%\). The peaks calculated with WPM occur in the range \(\beta_{w}=50^{\circ}\)-\(70^{\circ}\). Most peaks occur at \(\beta_{w}=60^{\circ}\), which corresponds to the minimum strength \(\beta_{w}=45^{\circ}+\phi_{w}^{\prime}2\). This result suggests that the mud weight is lightly influenced by the anisotropy of the far field stresses \(R\) and heavily depends on the minimum strength. Furthermore, the pore fluid pressure magnitude negligibly affects the location of peaks. These outcomes can explain why SG calculated with WPM are higher/lower than the peaks calculated with HBm. Finally, HBm is more sensitive to the far field stress anisotropy than WPM, in terms of \(\beta_{w}\).
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Stochastic Models
#### 3.2.6 LaSAR-PHREEQC Approach  
LaSAR (Lagrangian Stochastic Advection Reaction) is an analytical stochastic approach for modelling coupled transport and reaction processes in heterogeneous flow media [474; 475]. According to the LaSAR approach, transport takes place between the injection layer, in which the solute (pollutant) enters the system, and the control layer. Several assumptions must be made along the flow path. The solute is injected uniformly, transport between the two layers occurs along single and independent flow paths (streamlines) that do not cross each other, water flow is assumed to be steady and unidirectional, advection is the dominant transport mechanism, and diffusion and dispersion in the pore space are generally neglected [431]. The heterogeneity of the soil means that moving particles have different probabilities of reaching the control plane. The variability of the flow is therefore reflected in the differences in travelling time along the individual streamlines, while the statistics of the residence time can be represented by a PDF (probability density function), which can follow one of the usual statistical distributions (e.g., lognormal or bimodal). Geochemical and biochemical processes take place along the streamlines, e.g., the dissolution or precipitation of minerals, the sorption or biodegradation of organic pollutants, or even radioactive decay. These reactions are simulated using the PHREEQC code [476] and the one-dimensional transport option. This approach has been tested in environmental studies, including the modelling of acid mine drainage [477] and the transport and biodegradation of hydrocarbons [478].
# Joint Embedding of Semantic and Statistical Features for Effective Code Search
## 3 Approach
### Joint Embedding of Semantic and Statistical Features
#### 3.2.3 Joint Embedding  
Figure 5 shows an example of a joint embedding between code snippet and natural language query. There are two natural language description texts (_Text_1: \"read a text file line by line\"; _Text_2: \"sort an array\") and a code snippet (_Code_), which is a function that implements the function of reading text in a file line by line. After embedding _Text_1, _Text_2, and Code into the same vector space with the natural language description embedding network and code embedding network, respectively, _Text_1 and _Code_ with similar functions are closer in space. A supervised code search model [12] is selected as the baseline model of the search, and it is extended on this basis.  
A joint embedding model consists of three parts: two different embedding functions \(\varphi:X\rightarrow\mathbb{R}^{d}\) and \(\psi:Y\rightarrow\mathbb{R}^{d}\). These are combined to form the similarity measure function \(\tilde{f}(.)\). They are connected through deep neural networks. The entire code search model consists of three modules, which correspond to the three components of the joint embedding  
Figure 5: Schematic diagram of joint embedding of code and natural language query.  
technology: code embedding, query embedding network, and similarity calculation. This is a general framework of the designed code search model based on supervised learning.
## 3 Results and Discussion
### Results for Time-Frequency-Domain
#### 3.1.5 Influence of Pore Fluid  
In this subsection, the pore fluid is changed from water to oil. The engine oil studied by [PERSON] et al. [8] is analyzed here, such that longitudinal velocity \(c_{O}=1740\) m/s and  
Figure 5: Dispersion curves of guided waves in water-filled pipe embedded in a finite porous medium of layer thickness \(d=\) 0.025, 0.05, 0.10 m. Porosity is \(\beta=\) 0.1. Black, red, and blue lines represent layer thicknesses \(d=\) 0.025, 0.05, and 0.10 m, respectively.  
density \(\rho_{O}=870\) kg/m\({}^{3}\). The pipe is filled with water (see properties in the previous section) and the internal and external of pipe radii are \(a=14\) mm and \(b=16\) mm. Note that the values of \(\beta=0.1\) and \(d=0.05\) m are used for the infinite and finite media in this subsection. The influence of pore fluid on the dispersion curves is shown in Figure 6.  
It can be seen in Figure 6 that when engine oil is present, the phase velocity of each mode does not change significantly. The dispersion curves of the liquid-filled pipe when the pore fluid is engine oil are almost the same as when the pore fluid is water. Therefore, the propagation of the guided wave in the liquid-filled pipe embedded in porous medium is mainly determined by the external medium and the pipe. The fluid viscosity is not considered, given its small influence.
# Gaussian-UDSR: Real-Time Unbounded Dynamic Scene Reconstruction with 3D Gaussian Splitting
## 4 Experimental Evaluation
### Experimental Setup  
We preprocessed LiDAR and camera data by aligning their timestamps, transforming the camera images into point clouds via SfM, and fusing them with LiDAR point clouds using iterative closest point (ICP) for spatial alignment. We implemented our method in Python3.20 using the Pytorch framework [41] and trained the proposed neural network using the Adam optimizer [42]. In our experiments, we set the position decay exponentially to 0.01, the opacity learning rate to 0.05, and the densification gradient threshold to \(2\times 10^{-4}\), reset the opacity every 3000 iterations to remove redundant points. We also set the feature learning rate to \(2.5\times 10^{-3}\), the number of feature map slices \(k=4\), the optimizable parameter \(\Delta T_{t}=0.005\) and \(\Delta R_{t}=0.001\) for object position change, and the hyperparameter settings including \(\lambda_{1}=1\), \(\lambda_{SSIM}=0.2\), \(\lambda_{LPLPS}=0.1\), \(\lambda_{2}=0.001\), \(\lambda_{3}=0.15\), \(\lambda_{4}=0.01\). We set the scene resolution to 1024 to capture high-frequency details in the sky, and the rest of the parameters were set based on 3 DGS [31]. All our experiments were performed on a system equipped with an Intel Xeon(R) Silver 4214R CPU and an Nvidia RTX 3090 GPU for 30,000 iterations.  
Dataset. We conducted experiments using the Waymo open dataset [43] and KITTI benchmarks [44]. Both datasets provide driving data in complex environments and cover driving scenarios under various road, weather, and lighting conditions. They contain many sensor data from self-driving vehicles, including high-definition cameras, LiDAR (laser radar), inertial measurement units (IMUs), GPS information, etc. The sensor data for each scenario are recorded at a high frame rate, meticulously capturing fast-moving and dynamic objects with high-resolution texture information, and the dataset is equipped with multiple LiDARs that are capable of capturing the precise 3D structure of the surrounding environment, which is suitable for 3D reconstruction and dynamic object separation. In our experiments, we selected four recorded sequences with many moving objects, significant self-car motion, and complex lighting conditions. All sequences were around 200 frames in length. We will select one from the sequences as a test frame every ten frames and use the rest for training.
# Hygro-Thermo-Mechanical Analysis of Brick Masonry Walls Subjected to Environmental Actions
## 2 Hygro-Thermal Model
### Initial and Boundary Conditions  
The hygro-thermal model is completed by introducing initial and boundary conditions. The Dirichlet boundary condition yields:  
\[T=\overset{-}{T}, \tag{15}\]\[\varphi=\bar{\varphi}, \tag{16}\]  
where \(\bar{T}\) [K] and \(\bar{\varphi}\) [-] are the prescribed temperature and relative humidity at the boundary, respectively.  
Conversely, the Neumann boundary condition is defined as a flux derived from a temperature or vapor pressure difference:  
\[q=h_{T}(T_{ext}-T), \tag{17}\]  
\[g=h_{m}(p_{v,ext}-p_{v}), \tag{18}\]  
where \(q\) [W/m\({}^{2}\)] is the convective heat flux, \(h_{T}\) [W/(m\({}^{2}\)-K)] is the heat transfer coefficient, \(T_{ext}\) [K] is the temperature of the environment, \(T\) [K] is the temperature at the boundary, \(g\) [kg/(m\({}^{2}\)-s)] is the convective moisture flux, \(h_{m}\) [s/m] is the convective mass transfer coefficient, and \(p_{v,ext}\) [Pa] and \(p_{v}\) [Pa] are the partial vapor pressures defined for the environment and at the boundary, respectively. In the present work, \(h_{T}\) accounts for the combined effects of convection and long-wave radiation exchanges with the environment. Thus, this lumped convective/radiative heat transfer coefficient is defined as  
\[h_{T}=h_{T,c}+h_{T,r}, \tag{19}\]  
where \(h_{T,c}\) [W/(m\({}^{2}\)-K)] is the convective heat transfer coefficient, and \(h_{T,r}\) [W/(m\({}^{2}\)-K)] is the radiative heat transfer coefficient. The heat transfer coefficient is dependent on the local air flow conditions, the temperature, the geometry, and the orientation of the studied element, which makes an accurate estimation very complex. For building applications, a simplified approach is usually adopted, and the heat transfer coefficient is assumed to be constant [38].