esa-sceva/satcom-chunk-collection huggingface dataset:
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
## 2 Methods
### Rapid-DEM
#### 2.1.2 Land Cover Classification and Change Detection

Table 2: OpenStreetMap keys and tags used to train land class classification model. of their high correlation with the red and green bands. The spectral indices, NDVI [36; 37], normalized difference water index (NDWI) [40], BSI [35] are also used in addition to the textural features contrast, angular second moment, and correlation generated from the gray-level co-occurrence matrix (GLCM) [41; 42]. A random forest model is used to create a pixel-based thematic classification model using training samples from a weighted stratified random sample to ensure all land cover classes were represented. The sample sets are segmented into 70% training and 30% testing datasets [43] and the features are selected based on the evaluation of feature importance. Multiple random forest models are created at multiple tree levels (10, 50, 100, 250, 500, 1000) before the final model with the greatest overall accuracy is chosen to classify the image. During post-classification, water and street features from OSM are superimposed onto the classification results [11; 29]. The two thematically classified images are then used to develop the priority queue using the priority queue algorithm during the planning phase.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 3 ITS Sensing
### Aerial Sensing for ITS
#### 3.4.1 Road User Detection and Tracking  
Road user detection and tracking are the initialization processes for traffic interaction detection, pattern recognition, and traffic parameter estimation. Conventional UAV-based road user detection often uses background subtraction and handcrafted features, assuming UAV is not moving or stitching frames in the first step [186; 187; 188; 189; 190; 191]. Recent studies tended to develop deep learning detectors for UAV surveillance [192; 193; 194; 195; 196]. Road user detection itself can acquire traffic flow parameters, such as density and counts, without any need for motion estimation or vehicle tracking. [PERSON] et al. [196] proposed an enhanced Single Shot Multibox Detector (SSD) for vehicle detection with manually annotated data, resulting in high detection accuracy and a new dataset. [PERSON] et al. [197] identified the challenge in UAV-based pedestrian detection, particularly at night time, and proposed an image enhancement method and a CNN for pedestrian detection at nighttime. In order to conduct more advanced tasks in UAV sensing on the road user side, road user tracking is a must because it connects individual detection results. Efforts have been made on UAV-based vehicle tracking and motion analysis [186; 187; 188; 189; 199; 200]. In many previous works, existing tracking methods, such as particle filter and SORT, were directly applied and had fairly good tracking performance. Recently, [PERSON] et al. [201] developed a new tracking algorithm that incorporated lane detection information and improved tracking accuracy.
## 4 Monitoring Results and Analysis
The relation curve of level gauge deformation with the construction time of each stage at Site 2 is shown in Figure 11. The analysis shows that the deformation of level gauges W-1 and W-2 on the upper side of the culvert fluctuates with the construction of the composite foundation and shows an overall upward trend until the completion of the composite foundation. The deformation values are 0.676 and 0.877 mm, respectively. The deformation of level gauges W-3 and W-4 increases first and then decreases in the transition section position with the construction of the composite foundation. At the late stage of composite foundation construction, due to the material accumulation in the transition section near the downlink side, the deformation values of the level gauge on the downlink side are lower than those on the uplink side for the upper side of the culvert and the transition section. When the culvert extension structure is constructed, the deformation values of W-1 to W-4 decrease abruptly under the pouring stage of the bottom slab, side slab, top slab, and splayed wall. Until the culvert extension structure is completed, the deformation values of W-1 to W-4 are \(-\)2.636, \(-\)3.009, \(-\)1.109, and \(-\)1.310 mm, respectively, and the deformation values of W-1 and W-2 are significantly higher than those of W-3 and W-4. The maximum difference values of the upper side of the  
Figure 11: Symmetrical construction drawing. culvert and the transition section position are 0.373 and 0.201 mm, respectively. Combined with Figure 9, the analysis shows that lateral symmetrical construction can significantly reduce the influence of the difference in deformation values between the upper side of the culvert and the transition section position on the horizontal values of the existing track for culvert extension section construction.
## 8 Expected altimeter performances
### Range bias and noise over ocean-like surfaces  
Estimating performances of the instrument over sea surfaces is performed using simulation tools based on the [PERSON]'s model [35]. The following results are based on 200 second simulations of tracking loops, and a ground retracking process based on a four parameter MLE (Maximum Likelihood Estimator) algorithm estimating range, SWH, \(\sigma\)0 and pointing. Performance is evaluated by comparing the estimated parameters with simulation entries, and issuing biases and noise measurements. A 12 dB SNR is assumed, and the carrier satellite is assumed to fly at an 800 km altitude.  
Figure 13 shows the expected 1 second range noise. For a 2 meter SWH, the range noise is less than 1 cm. Compared with the Ku-band range noise (in the same conditions of altitude and SNR as for the Ka band), there is an improvement of about 40%, mainly related to the increase of the PRF, leading to 40 elementary measurements in one second instead of 20.  
Figure 12: Land to sea transition conditions associated with simulation results plotted on Figure 11.  
In terms of significant waveheight, noise at 2 m is less than 5 cm for a 2 m SWH, and remains less than 8 cm for a 8 m SWH.  
Biases on the measurements are very low: they are about 5 mm on the range and 1.5 cm on the SWH, and they are stable with SWH.  
At the beginning of the section, we pointed out the sensitivity of Ka-band echoes to satellite mispointing. This topic has been addressed in terms of ground retracking performances. In that respect, it has been proved [36] that a four parameter MLE algorithm is very well suited to simultaneously estimate the mispointing angle together with the classical geophysical parameters from the waveform: noise and bias figures are fully stable as a function of mispointing, as far as mispointing is lower than 0.25 degree.
First, we discuss in short the global frequency spectrum used by different regions for 5G application [32], as shown in Figure 1. The global snapshot elaborates the frequency ranges for each region where work for 5G communication is in progress. Under the umbrella of (ITU-R), the master international frequency register (MIFR) is the key element of international frequency management, which is a permanent database that contains the spectrum characteristics for radio stations' operations throughout the world. The international recognition and protection against interference is conferred by these stations. This database is managed by BR and currently contains 2.6 million frequency assignments for terrestrial services, and over 200,000 are added every year. More than 1.1 million assigned frequencies are contained in this database. In addition, about 350,000 assigned frequencies for the broadcasting-satellite service and 25,000 allotted frequencies for the fixed-satellite service are planned for future uses. In [32], the authors discussed the global frequency spectrum as shown in Figure 1. A detailed summary of frequency bands both licensed and unlicensed are shown. Furthermore, the paper is split out into six sections. The first section of \"Introduction\" covers 5G technology's goals as well as a summarized package of the literature review consisting of the latest state-of-the-art work focused on the latest performance improvement strategies and techniques in performance parameters. There are three main categories of frequencies in the assigned global frequency spectrum. The first one is assigned as high bands above 24 GHz, the midband frequencies range is from 1 GHz to 7 GHz, and the low band is below 1 GHz. Furthermore, these bands are divided into licensed bands, over 40 bands globally for LTE, which remains the industry's top priority. the unlicensed spectrum is 2.4 GHz/5.9-7.1 GHz globwise. After introduction, the second section of the paper is focused on 5G antenna trends for base stations or access points and for mobile terminals. The third section of the paper summarizes the latest performance enhancement techniques discussed in detail in the literature review. The fourth section is about the beam steer ability and beam forming techniques. In section five, the reconfigurability for different environmental scenarios are discussed. The sixth section presents a deep focus on the MIMO antenna performance-enhancement techniques such as mutual coupling reduction/ decoupling techniques, which might effectively play a vital role in mutual coupling reduction. The seventh section is the future challenges and opportunities. Section eight is the conclusion, which concludes the paper in a summarized package. There are a variety of strategies that must be used in conjunction with traditional antenna design techniques, data rate, signal-to-noise ratio and a lot more.
# Follow-Up Control and Image Recognition of Neck Level for Standard Metal Gauge
## 2 Experimental Design
### Design of Automatic Acquisition System for Gauge Neck Level  
The NIST standards [2] stipulate that the reading of a glass tube should be performed with the line of sight and liquid level at the same height. For a given camera, a larger field of vision and a smaller focal length result in greater distortion, increased image deformation, and lower precision. As shown in Figure 2, a larger angular view and increased reading errors occur when the concave meniscus level is not at the same horizontal height as the camera. Concave meniscus readings exhibit large errors when the deviation angle exceeds a certain limit and precision requirements for pipe prover verification cannot be met.  
A camera-based liquid level follow-up control system was designed to solve this problem, by achieving automated tracking of liquid levels and improving the accuracy of image acquisition other than the inclined viewing angle. In this process, the camera is mounted on a linear guide with a ball screw, allowing the lens to be controlled manually. A camera with a wide-angle lens in initial position A first acquires an image of the entire glass tube. This image is only used to approximate the liquid level and calculate the vertical distance between the camera and the height of the liquid column. The camera is then driven by a stepper motor to position C, where the angle of inclination provides a view of the concave meniscus within the allowed range. After the camera is moved to the  
Figure 2: Readings of a concave surface from different view angles, including (**a**) outside of readings of a concave surface, (**b**) looking down, (**c**) same horizontal height as the eye, (**d**) looking up, (**e**) outside of readings of a concave surface.  
required position, the focal length is adjusted and another image is acquired for accurate liquid level measurements, thereby achieving automated tracking.  
The hardware for the proposed liquid level acquisition system, shown in Figure 3, primarily consists of a linear guide rail, a camera, and an infrared photoelectric detection switch, used to determine the recognition range. A programmable logic controller (PLC) drives the step motor. Liquid level photos taken by the camera are transmitted to the upper computer, which processes the images in real time. An orange background plate was placed behind the tube to reduce background interference, and highlight the scale numbers/lines and concave meniscus.
## 2 Materials and Methods  
### XGBoost  
The XGBoost method provides an advanced boosted tree model [42] and is a common machine learning method with high accuracy. This method implements a new regularized learning objective, which is simpler than the regularized greedy forest model. The predicted function of XGBoost is defined as follows:  
\[\hat{y}=\sum\limits_{k=1}^{K}f_{k}(x_{i}),\qquad f_{k}\in\Gamma \tag{1}\]  
where \(\hat{y}_{i}\) is the predicted output value of the \(i\)th instance; \(K\) is the number of regression trees; \(f_{k}\) is the tree structure; \(x_{i}\) is the feature vector of the \(i\)th sample; and \(\Gamma\) is the space of regression trees.  
To overcome overfitting problems, a penalty function is used to smooth the learning weights as follows:  
\[Obj=\sum\limits_{i=1}^{n}l(y_{i},\hat{y}_{i})+\sum\limits_{k=1}^{K}\Omega(f_{ k}) \tag{2}\]  
\[\Omega(f_{k})=\gamma T+\frac{1}{2}\lambda\sum\limits_{j=1}^{T}w_{j}^{2} \tag{3}\]  
where \(Obj\) is the regularized objective function; \(\sum\limits_{i=1}^{n}l(y_{i},\hat{y}_{i})\) is the loss function, which measures the model accuracy; \(\Omega(f_{k})\) is a penalty function handling overfitting; \(y_{i}\) is the real value of the \(i\)th sample; \(\gamma\) is the complexity cost of introducing additional leaves; \(T\) is the number of leaves; \(\lambda\) is a regular item parameter; and \(w_{j}^{2}\) is the weight of the \(j\)th leaf node.  
The additive method is used to train the model as follows [42]:  
\[Obj^{(t)}=\sum\limits_{i=1}^{n}l\left(y_{i},\hat{y}^{(t-1)}+f_{t}(x_{i}) \right)+\Omega(f_{t}) \tag{4}\]  
where \(\hat{y}_{i}^{(t)}\) is the prediction of the \(i\)th sample at the \(i\)th iteration, and \(f_{t}\) is applied to minimize the objective. To rapidly optimize the loss function of the first term in Equation (4), a second-order Taylor expansion can be written as  
\[Obj^{(t)}=\left[l\left(y_{i}-\hat{y}_{i}^{(t-1)}\right)+g_{i}f_{t}(x_{i})+ \frac{1}{2}h_{i}f_{t}^{2}(x_{i})\right]+\Omega(f_{t}) \tag{5}\]  
where \(g_{i}=\partial_{\hat{y}}(t-1)l\left(y_{i},\hat{y}^{(t-1)}\right)\) and \(h_{i}=\partial_{\hat{y}^{(t-1)}}^{2}l\left(y_{i}-\hat{y}^{(k-1)}\right)\) are the first and second order gradient statistics of the loss function, respectively. The constant term is removed to obtain the objective function of the \(i\)th step as follows:\[Obj^{(t)}=\sum_{i=1}^{n}\biggl{[}g_{i}f_{t}(x_{i})+\frac{1}{2}h_{i}f_{t}^{2}(x_{i}) \biggr{]}+\Omega(f_{t}) \tag{6}\]  
The parameters in Equation (6) can be continuously updated until the conditions are satisfied. More details on XGBoost can be found in ref. [42]. XGBoost has been used to predict shear strength of clay, and the results demonstrate that XGBoost is a promising tool for predicting geotechnical parameters [34]. The potential of XGBoost for predicting multicategory of clay sensitivity will be investigated.
## 2 Materials and Methods
### Statistical Analysis  
All data were analyzed using IBM SPSS Statistics for Macintosh, Version 25.0 (IBM Corp., Armonk, NY, USA) and shown as means with standard deviations (\(\pm\)SD) with their 95% confidence intervals (CIs). Statistical significance was set at \(p<0.05\). The normality of the data distribution, the assumption of variance homogeneity, and the assumption of variance sphericity were verified using Shapiro-Wilk, [PERSON]'s, and [PERSON]'s tests, respectively. Repeated measures ANOVAs (2 groups [males; females] \(\times\) 2 conditions [SHAM; IPC]) were used to investigate the influence of IPC on the following dependent variables: rowing performance, BLa concentrations, and HR. When a significant interaction or main effect was found, post hoc tests with the Bonferroni correction were used to analyze the pairwise comparisons. Furthermore, because the data distribution in TPP was not normal, the Wilcoxon signed-rank test was employed to assess the impact of sex. The magnitude of mean differences was expressed as a standardized effect size. The thresholds for the qualitative descriptors of [PERSON]'s \(d\) were interpreted as \(<\)0.20--\"trivial\", 0.2-0.49--\"small\", 0.5-0.79--\"moderate\", and \(>\)0.8--\"large\".
# Pavement Distress Detection Using Three-Dimension Ground Penetrating Radar and Deep Learning
## 1 Introduction
[31] proposed deep reinforcement learning (DRL)-based autonomous cognitive GPR (AC-GPR) to achieve the automatic detection of subsurface targets with superior accuracy and speed. The waveform characteristics of pavement distress vary from one channel to another in a 3D GPR. The majority of the reported studies focus on the longitudinal radar images since most distresses are obvious in these images. However, these methods ignore a fact that 3D GPR can obtain pavement internal information in three dimensions and the horizontal radar images also can provide a unique view of pavement distress detection. However, there are very few research studies that study this problem. Motivated by the above reason, this study aims to detect pavement distress based on the waveform characteristics of different distress in horizontal radar images. In horizontal radar images, anomalous waveforms are located by a CP-YOLOX model, which is a modified version of You Only Look Once X (YOLOX) [32]. On this basis, to investigate the capacity of horizontal GPR images to represent different pavement distresses, the localization results from the CP-YOLOX model were intercepted to build a classification data set according to three class-membership strategies. Five SViT models, as simplified versions of Vision Transformer [16], classify anomalous waveforms into one of the possible distress categories. The objective of this study is to identify distress types from horizontal radar images using deep learning methods and to provide assistance for distress detection in the future by combining longitudinal radar images. The rest of this paper is organized as follows. The methods of our study are presented in Section 2, which include the collection of 3D GPR images, the processing method of anomalous waveform localization using the CP-YOLOX model, and the method for distress classification using SViT models. Section 3 presents the numerical experiment results. The conclusions are summarized in Section 4.
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 8 Discussion and Future Work  
The objective of this paper was to establish a roadside scenario where vulnerable road users are classified according to their distances and allocated resources accordingly. Simulation results show that the performance of our method is efficient compared to a one-to-one matching scheme. We have aimed to show results under varying vehicular speed and number of users. Under varying vehicular speed, our method has performed better than the benchmark [9] scheme. A higher threshold SINR for cellular users can result in better overall system throughput but can result in increased interference to the V2P links and hence decreases the capacity of these links. Therefore, an SINR threshold of 5 dB was chosen. Finally, a higher number of VUEs contribute better to the SINR probability of the V2P, which ensures a better capacity of the links. In future, we want to extend this approach for other crucial scenarios like an out-of-coverage or relay assisted scenario. The integration of this scenario with mobile edge computing (MEC) technology will bring newer challenges while overcoming the latency issues. These issues include interoperability between different standardization organizations, deployment, handover, and offloading decision [32].  
The resource allocation in D2D-based vehicular networks has seen a tremendous research interest in the recent past. Methods ranging from heuristic optimization [12] to machine learning [33], have been used under varying sets of users and their requirements. Machine learning, being the current hot topic in this domain; centralized vs. distributed reinforcement learning, multi-agent deep reinforcement learning [34], and other methods that could provide a faster convergence with better performance than traditional methods. Relay-assisted [35], out-of-coverage scenarios [36] and energy efficient resource allocation [37] is currently being researched upon. Resource allocation in UAV- enabled vehicular communication is another new area of research being investigated for emergency coverage [38]. Resource allocation for virtual reality (VR) content sharing in D2D multicast communication [39] is another interesting area of research where the authors have created D2D-based multicast clusters for video content sharing. This approach could be integrated in a vehicular network scenario in the future. Hybrid network architectures [40] including the already in-service DSRC along with emerging LTE V2X could call for a more complex resource management. Channel modeling for V2X need more research due to the delay in the availability of channel state information (CSI) at the base station (due to high vehicular speed). Moreover, interference mitigation techniques to overcome interference from adjacent frequencies and co-occurring technologies, need further research and consideration. Nevertheless, there is a vast room for research in the area of cellular V2X, which will facilitate in the implementation of advanced applications and become a major enabler for intelligent transportation system (ITS) and autonomous driving.
## 2 Background and Methods  
### Brief History In-Car Navigation Devices  
Specialized devices for navigation assistance have been available for over a century. An advertisement dated 30 December 1909 describes a scrolling map associated with a steering wheel. By 1930, the Italian \"Touring Club Italiano\" worked on a simple principle: before starting, the driver selected and threaded the appropriate map sheets, and then the device rolled these sheets from one roll to another. The problem was that when the vehicle deviated from the route or came to a fork, the driver had to change the map sheet and find the current position [3; 4].  
In 1966, General Motors introduced DAIR (Driver Aid, Information and Routing), which alerted the driver to road signs, speed limits and hazards along the route [5]. The first true automotive navigation system, the Electro Gyro-Cator, appeared in 1981 from Honda, Alpine and Stanley Electric [4; 6]. By 1985, the Etak Navigator, which used 'dead reckoning' to determine the vehicle's position [3; 7], was the world's first publicly available, in-car navigation system.  
GPS-based navigation based on the US satellite configuration was introduced in 1990 in the Mazda Eunos Cosmo as part of the touch-screen car control system [8]. Two years later, the world's first GPS with voice navigation was introduced in the Toyota Celsior (Lexus LS--luxury sedan). In Europe, the BMW E38 incorporated GPS navigation in 1994 [9]). In the US, it was introduced a year later by Oldsmobile, and called GuideStar. Selective availability was removed by the US government in 2000, making GPS signals more accurate, and GPS-based navigation of all forms became more prevalent. More and more car companies, businesses and tech giants have since entered the automotive GPS market.