esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Road Traffic Accidents Reduction Strategy Based on Intelligent Transportation Systems (TARS)
## 1 Introduction
Many car manufacturing companies like Ford, Daimler, BMW, General Motors, and Audi have been inspired by the ITS and are motivated to manufacture smart vehicles for passenger safety. In VANETs, the topology of the network keeps changing due to the high mobility of vehicles, so the network is considered as an ad-hoc network. It enables vehicles to sense their environment and exchange their sensed data with surrounding vehicles. An infrastructure named Road Side Units (RSU) is installed along the roads to assist the vehicles moving in its vicinity. Standard IEEE 802.11p [14] has been introduced, particularly for vehicular communication, which allows ad-hoc communication (the \(p\) denotes the specific version for communication between vehicles). There are three modes of communication in VANETs: Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V), and Vehicle 2 Hybrid (V2X) communication. Vehicles communicate with each other in order to get a better understanding of the surrounding environment to prevent any hazardous situations. Drivers must be given timely warnings about any expected hazardous situations in order to avoid accidents. Another concern is that drivers become doubtful as to which route to follow next even if warned about emergency situations, which can cause inefficient traffic flow and congestion on alternate routes [15]. Traffic safety and smooth flow can be achieved by assisting the driver with appropriate suggestions. The aim of this project was to design and develop a possible accident detection strategy that provides timely warnings to the driver about possible accident situations and gives appropriate suggestions accordingly. The objectives of this research were focused on the following aspects:  
1. The design of a new protocol for accident prevention and, hence, reduction. 2. To forecast/guess the probability of the occurrence of an accident in advance before it occurs. 3. To re-route vehicle traffic to prevent traffic jams on the road that may cause accidents. * To maintain traffic flow efficiently. * To assist drivers to reach a destination on time. * To reduce safety messages broadcasting in order to avoid a broadcasting storm and network congestion. * To minimize the delay in re-routing of traffic to other available paths. The roadmap of this research is arranged as follows. An overview of the existing protocols proposed to avoid traffic accidents is presented in Section 2. In Section 3, a smart road traffic accident reduction strategy is presented in detail. The simulation parameters of the proposed strategy are presented in Section 4. The simulation results of the proposed scheme are discussed and evaluated in Section 5. Finally, the research work conclusions are presented in Section 6.
## 2 Related Works
### Routing Based on History Information  
Pioneering studies on routing in DTNs have mainly focused on the actions required for the next hop transmission with consideration of historic information. Typical protocols include Epidemic [1], Prioritized Epidemic (PREP) [2], Spray and Wait [3], Seek and Focus [4] and Probabilistic Routing Protocol using History of Encounters and Transitivity (PROPHET) [5] and some other improvements and variations have been proposed. By the Epidemic scheme, a node with messages will forward a copy of each message to any node it meets and the messages are distributed transitively through the networks. Through such transitive transmission, the messages have a higher probability to reach their destinations. Based on the Epidemic scheme, the PREP scheme prioritizes the messages based on the costs to the destination, the source and the expiring time. Different from the Epidemic and the PREP schemes, the Spray and Wait routing approach limits the number of message copies and works in two phases, namely the Spray phase and the Wait phase. For each message originating at a source node, \(L\) message copies are initially spread to \(L\) distinct \"relays\" and then the \(L\) relays will forward each message only to its destination. By the Seek and Focus scheme, local forwarding decisions are made based on the current connectivity and the information of the predictions of future connectivity for a single copy of each message. The PROPHET scheme is a routing mechanism working based on the history information, which is the history of the encounters and transitivity to select next hop for the message forwarding. The transmit predictability \(P_{(a,b)}\in(0,1]\) has been adopted as the probability metrics. The main characteristic of the PROPHET scheme is that node \(i\) will forward a message to node \(j\) if node \(j\) has a higher predicted probability to the destination of the message than that of node \(i\).
## 3 Communication Characteristics of a QADA Receiver
### Field of View  
To understand both the communications and positioning characteristics of a VLP system using a QADA, it is important to define two different fields of view. This is because, for positioning, light from a given transmitter must reach all four quadrants of the PD, whereas for communications it is sufficient for light to reach a single quadrant. To make clear the difference, we introduce new terminology. FOV-C is used to designate the FOV for communications and FOV-P is used to designate the FOV for positioning. Figure 3 shows the difference between FOV-C and FOV-P. It shows that the FOV-P is smaller than FOV-C. The FOV-C is defined by  
\[\psi_{\rm C}=\tan^{-1}\left(2L_{\rm PD}/d\right) \tag{5}\]  
and the FOV-P is given by  
\[\psi_{\rm P}=\tan^{-1}\left(L_{\rm PD}/d\right) \tag{6}\]  
Figure 2: Normalized 3-D channel gain of the 4 th quadrant of the QADA receiver. The gain of the other three quadrants will look similar, but ‘point’ in different directions  
The FOV of a QADA depends mainly on ratio \(L_{\text{PD}}/d\). Figure 4 shows how increasing the aperture height reduces FOV-C which makes the receiver more directional. This also applies to FOV-P.
## 1 Introduction
### Contributions  
This paper proposes a dynamic area optimization approach employing a simple neural network (NN) model, which is pretrained on the area information and the solutions generated by GA. This model is easy to implement and allows for instant adaptation to unexpected user distribution changes. We train separate NN models for each beam direction of the cells and then utilize the solutions predicted by these models. Nonetheless,the models face the difficulty of capturing the dependencies of neighboring cells. To address this problem, classifier chain (CC), which chains multiple classifiers to consider the relationships among outputs, is integrated into the NN. However, the capability of the CC fluctuates depending on the output sequence. Therefore, we further incorporate an ensemble approach that combines the CCs with different sequences and chooses the best solution. In our previous work [25], we proposed the dynamic area optimization algorithm based on an NN for HAPS mobile communications and evaluated the communication performance primarily. However, the cost caused by the algorithm was not sufficiently evaluated in that study. Therefore, this study extends the evaluation to costs, such as the amount of training data and optimization time. The main contributions of this study are summarized as follows:  
* The integration of the CC and ensemble approach into the simple NN facilitates the learning of various sequences, aiding in capturing intercell relationships. Our result demonstrates the total throughput performance is comparable to that of GA for randomly selected distributions in Japan.
* Compared to GA, the application of NN facilitates immediate adaptation to unknown distributions, thereby reducing the optimization time. Our evaluation indicates an improvement in time efficiency.
* The ability to learn from past optimization results enables the effective utilization of prior solutions obtained by the search method, even in other areas, avoiding unnecessary search calculations.
# Research on Shear Behavior of Sand-Structure Interface Based on Monotonic and Cyclic Tests
## 4 Cyclic Shear Tests  
### Test Results  
#### 4.1.1 The Relationship between Shear Displacement and the Shear Stress Ratio  
The loose sand and dense sand are subjected to a half-period cyclic shear test at two different roughness interfaces (\(R_{n1}\), \(R_{n3}\)). This work was carried out in 12 groups. Each group was divided into three tests, which were the 30%, 60% and 90% of the peak shear stress loading and unloading tests. Figure 7a-c show three sets of tests, all of which show the relationship between shear displacement and shear stress ratio. They are the test results of loose sand with different normal stress on the relatively smooth interface. The expression of the shear stress ratio (\(P_{\tau}\)) is as follows  
\[P_{\tau}=\frac{\tau}{\tau_{p}} \tag{3}\]  
where \(\tau\) is the interface shear stress, and \(\tau_{p}\) is the interface peak shear stress.  
It is observed that the loading and unloading curves of loose sand on the relatively smooth interface are not closed under the three stress level amplitudes. Among them, the stress level amplitude of 90% is the most significant. This means that during the cyclic shearing process, residual deformation occurs at the loose sand interface. The other nine groups of trials also have a similar curve shape trend, which is not shown here. The difference is that the loading and unloading hysteresis curve of dense sand is tighter than that of loose sand. In particular, the stress level amplitudes of 30% and 60% are close to the closed hysteresis curve. In the case of a rough interface, the shape of the loading and unloading hysteresis curve of loose sand or dense sand is denser than that when the interface is relatively smooth. It shows that only when the stress level amplitude is 90% is the dense sand interface similar to that of loose sand, and the residual deformation occurs in the cycle. The reason why the loading and unloading hysteresis curve is not closed is mainly due to the difference in shear stiffness between loading and unloading. Therefore, it is necessary to further excavate and analyze the residual deformation and load-unload shear stiffness generated during the cycle.  
Figure 6: Fitting results of initial shear stiffness under different interface roughness.
# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
## 3 Proposed Method  
### Overview  
As illustrated in Figure 2, the AuxDepthNet architecture employs a multi-faceted feature representation to enhance monocular 3D object detection. Specifically, our framework integrates three key feature types: depth-sensitive, context-sensitive, and depth-guided features--each capturing critical information at different processing stages.  
We adopt DLA-102x as the Backbone network, which takes an input image of size \(3\times H\times W\) and produces multi-scale feature maps. The final output of the Backbone, denoted \(F\), is a feature map with dimensions \(C\times H^{\prime}\times W^{\prime}\), where \(C\) is the number of channels and \(H^{\prime},W^{\prime}\) may be smaller than the original input dimensions due to downsampling operations. These feature maps feed into the Auxiliary Depth Feature (ADF) module, which encodes depth-related cues through an additional \"auxiliary\" learning task.  
This task eliminates the need for pre-computed depth maps by implicitly capturing depth prototypes, thereby strengthening depth-sensitive feature representation. In this context, \"auxiliary\" denotes an extra but complementary objective that supports the model's primary 3D detection goal by providing more accurate depth information for improved spatial reasoning.  
Meanwhile, context-sensitive features extracted by the Backbone are refined via a feature pyramid and the DepthFusion Transformer (DFT) to supply essential semantic and spatial context. In parallel, depth-guided features receive further enhancement from the Depth Position Mapping (DPM) module and positional encoding, embedding depth-related positional cues into the latent space. This combination of local and global spatial relationships, delivered at minimal computational cost, results in robust and efficient 2D and 3D object detection.
# Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna
## 3 Proposed Method
### Multi-Scale Convolutional Separation Network  
From the Formula (5), it can be observed that \(\mathbf{M}\) greatly affects the separation accuracy. It is significant to generate effective masks by using discriminative temporal information in the separation model. In order to capture long temporal information, the temporal convolutional network (TCN) including 1-D convolutional blocks with increasing dilation is used in Conv-TasNet. However, the signals is so long and the dilation factors can not adequately exploit the long temporal information.  
In order to solve this issue, MCS network is designed as shown in Figure 3. The MCS network consists of four convolutional layers, three TCN model and three deconvolution layers. The output of encoder model is the input of the MCS network. First, the convolutional layers with \(1\times 1\) kernel size is used to further extract features. Then, the TCN is utilized to capture long temporal information. The \(3\times 1\) convolution with stride 2 is used to compress the length of ADS-B signal. The more long temporal information is captured by this temporal compression. After the compression, the temporal information of the next  
Figure 2: The framework of proposed MConv-TasNet.  
scale is exploited using the TCN. In order to keep the same size of multi-scale features, the 1-D transposed convolution is utilized in the second and third TCN. Moreover, the multi-scale temporal information is fused to generate the separation masks using the \(1\times 1\) convolution. Compared with Conv-TasNet, our proposed MCS network can capture more temporal information and reduce the computational complexity.  
In TCN, eight 1-D convolutional blocks are used as shown in Figure 4a. Moreover, each block uses the different dilation factors to capture the relationship between different time-stamps. Each block consists of three convolution layer and uses residual structure as shown in Figure 4b. First, the \(1\times 1\) convolution followed by nonlinear activation function and normalization is used to improve feature dimensions. Then depth-wise convolution is used to decrease the number of parameters. Finally, the feature dimensions is changed to the same dimension of input using the \(1\times 1\) convolution. The dilation factors are used in the depth-wise convolution.
# An Energy-Efficient Coverage Algorithm for Macrocell--Small Cell Network Systems
###### Abstract  
The energy efficiency of a macrocell base station (MBS) can be substantially improved via the deployment of small cell base stations (SBSs) within the coverage area of the MBS. Moreover, this approach is expected to remain a key feature of communication network markets in the future. However, little research has been conducted to uncover effective solutions to the issue of coverage holes (i.e., specific locations or areas in which a user is not able to get an adequate signal from the wireless network) that may occur in the context of such a network architecture. To address this dearth of relevant research, the present study proposes an energy-efficient coverage algorithm utilizing novel system configurations, for use in such macrocell--small cell network systems. The goal of the proposed algorithm is providing the maximum possible reduction of the combined power consumed by the transceivers of all the BSs (that is, the SBSs and the MBS), while simultaneously guaranteeing the provision of comprehensive wireless signal coverage to users under various scenarios. In order to accomplish this aim in an efficient manner, the algorithm smartly adjusts the power levels of all the SBSs in a given system, including the full deactivation of a previously active SBS, or the activation of a previously inactive SBS, according to the dynamics of the given network traffic, thereby modifying their power consumption as necessary. The results for simulations of various test scenarios indicated that the algorithm exhibits better performance than two conventional methods in terms of its overall effects on coverage, power usage, and average transmission rate. The simulated power savings yielded by the proposed algorithm were particularly notable, as it garnered an improvement as high as 78% under the condition of light traffic volumes.  
efficiency; energy saving; coverage holes; small cells; next-generation cellular systems Article
## 4 Discussion
### Methodological Considerations  
The selection of horizontal reference planes plays a pivotal role in the accuracy of sagittal condylar inclination (SCI) measurements. This study employed the axis-orbital plane (AOP), a widely accepted standard in the literature due to its reproducibility and clinical applicability. Previous research [19; 23] has highlighted significant discrepancies in sagittal condylar inclination (SCI) values depending on the reference plane used. Notably, measurements taken with the axis-orbital plane (AOP) tend to produce higher SCI values compared to the Frankfort horizontal or Camper planes, with differences reported to reach up to 10 degrees. These findings emphasize the critical need for standardizing reference planes, especially when integrating digital workflows into articulator programming and prosthodontic rehabilitation.  
While the AOP offers reliable and consistent measurements, comparisons with other methodologies, such as intraoral and extraoral recordings, reveal inherent variability introduced by differing techniques. Digital axiography, particularly when paired with the AOP, has proven to provide superior consistency and accuracy. These insights reinforce the AOP's utility as a reference standard in both clinical and research contexts, paving the way for enhanced precision in condylar guidance assessments.
# Ontology-Driven Semantic Analysis of Tabular Data:
## 4 Software Implementation
### The Architecture of the Proposed System  
The first stage in the implementation of the table semantic analysis algorithm is the design of the system architecture and its components (modules). It is necessary to design the data processing pipeline so that it consists of a sequence of replaceable modules. The advantage of this architecture is the support for easily adding new file types for processing. To do this, a parser must be written that will translate data from the new file format into an internal structure. At the design stage of the module, the following architecture was proposed, as shown in Figure 3.  
The input contains a table file (XLSX, CSV or PDF). Depending on the file type, the table is processed by the appropriate parser and translated into an internal structure for further processing. The internal structure of the table consists of two matrices--a matrix of types and a matrix of values (Figure 4). A value matrix is a direct mapping of table  
Figure 3: Software module architecture.  
cell values from a file into a matrix, a format that the algorithm can work with. The type matrix has the same dimensions as the value matrix but stores information about the cell's content type. When reading the table, the type matrix is filled with two types--\"String\" and \"Number\". The type \"Number\" is assigned to a cell if it contains a number or its value can be easily converted to a number (for example, the value \"4254 km\" can be converted to the number \"4254\" if the units are removed).  
After reading the table and the primary classification of cell types into the \"String\" and \"Number\" classes, the internal representation of the table enters the \"Classifier\" module, which also receives a file with an ontology in the OWL format. The ontology contains a description of the subject area--a set of objects, properties and relationships between them. This data is used in the process of table analysis to identify the dependence of relationships between cells and restore the semantic structure of the table.  
In this module, the table is processed in several stages, and as a result, triplets are issued that were found during the semantic analysis of the table. These triplets are then added to the current ontology.
## 2 Materials and Methods
### Methods
#### 2.3.2 Extraction of Vegetation Damage Areas  
Vegetation damage areas caused by the earthquake were the regions where _FVC_ descended rapidly. In this study, vegetation damage areas were characterized by two conditions: slowly and naturally descending _FVC_ and an abrupt decrease in _FVC_ resulting from a burst factor (described as an earthquake in this paper). Some authors [14, 15] have used the change detection threshold method to extract landslides, but they did not explain how to determine the threshold. Landslides can be detected by image classification [18], which requires complex computations and is dependent on field samples. This paper proposes a new method to quantitatively determine the threshold more efficiently with simple calculations.  
The level of vegetation coverage before the earthquake is represented by _FVC\({}_{0}\)_, and the vegetation coverage level after the earthquake is represented by _FVC\({}_{1}\)_. Vegetation Damage Rate (_VDR_) can be expressed by:  
\[VDR=\frac{FVC_{0}-FVC_{1}}{FVC_{0}}\times 100\% \tag{1}\]  
_VDR_\(>0\) indicates a decrease in _FVC_. Districts with decreasing _FVC_ are composed of natural degeneration and an abrupt decrease in _FVC_ caused by the earthquake; the observed limit between those components is the breakpoint. The pixel frequency distribution is described by the image histogram, where the curvature of each point indicates a pixel change. An increased curvature indicates more severe change, therefore, the breakpoint between natural degeneration and earthquake damage is the point where the curvature is maximal in the district of decreasing vegetation. In addition, because of disturbance from the image noise, there may be a pseudo breakpoint that is usually distributed on the two sides of the histogram. It can be excluded by standard deviation as follows: the average (\(\overline{FVC}\)), maximum (\(FVC_{\text{max}}\)) and standard deviation (\(\circ\)) will be computed within the area of _VDR_\(>0\) (the area of decreasing vegetation). Most of the data will fall within 3\(\circ\) (3\(\circ\) principle in statistics), and the maximum curvature in the histogram can be measured in the open interval of (\(\overline{FVC}\), \(\min\{\overline{FVC}+3\sigma,FVC_{\text{max}}\}\)). If the area with vegetation damage is more than 50% of the area being evaluated, the low end of the interval can be expressed as \(\max\{FVC_{\text{min}},\overline{FVC}-3\sigma\}\). The _VDR_ that corresponds to the maximum curvature is the required threshold \(T\).  
\[\left\{\begin{array}{c}T=CUR^{-1}(\max\{CUR(VDR)\}),VDR\in(\overline{FVC},\min\{ \overline{FV\bar{C}}+3\sigma,FVC_{\max}\})\\ CUR(VDR)=\left|\frac{f(VDR+2)+f(VDR)-2f(VDR+1)}{\{1+[f(VDR+1)-f(VDR)]^{2}\}^{ \frac{3}{2}}}\right|\end{array}\right. \tag{2}\]  
where _f(VDR)_ is the histogram function and \(CUR(VDR)\) is the curvature function of the _VDR_ histogram.  
If D-set is the vegetation damage area, the damage area can be expressed as:  
\[D=\{VDR|VDR>T\} \tag{3}\]
# Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics
## 4 Materials and Methods
### sEMG Feature Extraction
#### 4.4.5 Root Mean Square (RMS)  
The Root Mean Square (RMS) is another popular feature for EMG pattern recognition [30; 32], which is related to constant force and non-fatiguing contraction. RMS is similar to the standard deviation of the EMG signal, as the mean of the signal is close to zero.  
\[RMS=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}{X_{i}}^{2}} \tag{7}\]