esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conclusions  
The paper has highlighted the capacities of the PFGI-POF able to support both the transmission of the high data rate baseband standard (up to 10 Gbps) and the radiofrequency signal with RoF technology. It is clear that the fiber length is restricted to 100 m owing to the multimode behavior of the PFGI-POF which limits the fiber modal bandwidth both in the radio and digital domains at 850 nm and 1,300 nm. The penalties inherent to the fiber have been exhibited in the two domains and high transmission quality has been obtained for the transmitted signal. The multi-services concept has been demonstrated with such fibers. Even if the 120 \(\upmu\)m based PFGI-POF seems to be penalized due to the core diameter, the simultaneous transmission of a 10 GbE signal and a RF wireless signal respectively at 850 nm and 1,300 nm, with commercial off the shelf devices, has been achieved through 100 m of PFGI-POF (Lucina X and GigaPO62-SR). The results obtained with such topology clearly  
Figure 22: **(Left)** Evolution of the EVM as a function of the PFGI-POF length (WCDMA, IEEE802.11g and Wimedia BG1). The measurements have been performed with the system including the WDM devices. **(Right)** Illustration of the EVM penalties owing to the WDM devices for the IEEE802.11g WLAN transmission.  
demonstrated the bandwidth capacities of the PFGI-POF for the deployment of small office/home office network based on the multi-services concept.
## 4 Case Study
### Electricity Consumption Sensors  
The number of sensors, i.e., smart plugs, for analyzing electricity consumption is due to the need to individually monitor the consumption of the users present in the rooms and to correlate their data with the presence data from the cameras to simulate consumption profiles. The sensors can monitor the instantaneous power required by the user (W), the total electrical consumption (kWh) by integrating the power over time (min) and, if necessary, switch the connected devices on or off to reduce consumption due to the presence of hardware on standby or to service utilities (coffee machines, floor lamps, etc.).  
The presence of a panel power meter allows the monitoring of total consumption (kWh) and power demand (W), considering and separating data from lighting, air conditioning, sockets not covered by smart plugs and energy losses due to transport.
## 6 Computational Results using AO Algorithms in ISVN  
The development of AO algorithms was motivated by the need to address real-life optimization problems characterized by high dynamism. AO algorithms combine heuristic, biased-randomization techniques, and parallel computing to deal with the dynamic part of the problem. These algorithms belong to the field of parallelized heuristics, and can be used to generate efficient solutions to large-scale combinatorial optimization problems in real time. In the T&M field, these dynamic elements can be, for example, changing demands or travel times. In real life, we face a huge variety of optimization problems, including vehicle routing problems (VRP) [119], facility location problems (FLP) [7], arc routing problems (ARP) [120], team orienteering problems (TOP) [121], etc. Typically, the goal of each problem is to maximize the total reward or to minimize the total cost associated with the activity. Figure 5 presents an illustrative example of the structure of each of these main optimization problems. For example, the objective function in an VRP is usually to minimize the total cost, which is the sum of fixed plus variable cost. In the TOP, the objective is to maximize the rewards collected by vehicles when visiting customers. Many of the problems that arise in real-life T&M are dynamic in nature. These challenges make the problem troublesome.  
Figure 6 shows a cross-problem analysis of the performance of AO algorithms when compared with the best-known solution (BKS), which might require minutes or even hours of computation, and the solution provided by a greedy heuristic--which, as the AO, usually requires less than a second of computation. The problems analyzed are: the uncapacitated facility location problem in an Internet of Vehicles context (UFLP-IoV) [7], the team orienteering problem (TOP) [122], the permutation flow-shop problem with deadlines and payoffs (PFSP-DP) [123], the vehicle routing problem (VRP) [90], the basic permutation flow-shop problem (PFSP) [90], the basic uncapacitated facility location problem (UFLP) [124], and the arc routing problem (ARP) [125].  
Figure 5: Illustrative examples of the main addressed problems with AO in L&T.  
Notice that, while the greedy heuristic typically offers solutions with a gap between 2% and 8% with respect to the BKS, the AO algorithm is capable of generating solutions with a gap lower than 2% in most cases. This is a noticeable result if we take into account that these gaps are obtained in wall-clock times below one second.
# Lightweight Semantic Segmentation Network for Real-Time Weed Mapping Using Unmanned Aerial Vehicles
## 3 Model Design and Optimization  
### Model Design  
Related studies have demonstrated that a FCN is appropriate for weed mapping tasks. In contrast to a traditional convolutional neural network, an FCN is an end-to-end network structure that can perform dense prediction tasks, allowing it to determine the weed densities at a target site, which can be directly used as references for accurate spraying tasks. For an FCN, input data can be images of arbitrary size and a corresponding output size can be obtained through effective inference. An FCN transforms all fully-connected layers into convolutional layers. In this form, the spatial information of an input image is preserved. By using deconvolutional operations, the feature maps screened from the final convolutional layer are up-sampled and the output image is restored to the same size as the input image. The general architecture of an FCN is presented in Figure 5.  
Figure 4: Task assignment on the Jetson TX2 module.  
Figure 3: Overall workflow for image collection and processing.  
Similar to other studies, we adopted a pre-trained network and fine-tuned it using a relevant dataset. Commonly used pre-trained networks include the ResNet [18] and VGGNet [19] models. These models tend to outperform their counterparts in terms of accuracy. However, according to the research presented in [14], these models are relatively time consuming, that is, they are inappropriate for real-time tasks. Therefore, we selected AlexNet as a pre-trained model because it is a relatively lightweight framework. As shown in Figure 6, AlexNet contains five convolutional layers and three fully connected layers. To reduce the number of model parameters, we removed the FC6 and FC7 layers. Additionally, the number of neurons in the final fully connected layer was reduced to three to match the number of classes (each pixel was classified as \"rice\", \"weed\", or \"other\"). Next, the last fully connected layer was transformed into a convolutional layer and a deconvolutional layer was appended to restore the feature map to the original image resolution.
## 1 Introduction
[15] presented a broad review of the state of the art in the industry involving onboard instrumentation techniques for detecting damage to railway vehicles. In 2022, [PERSON] et al. [16] presented a recent review of drive-by methodologies for highway bridges and railway tracks updating the previous work conducted by [PERSON] et al. [13]. In the same year, [PERSON] et al. [17] presented an extensive review of drive-by methodologies for the condition assessment of bridges. The review covered theoretical, numerical, and experimental approaches, with most of the works reviewed relating to road applications. Nonetheless, a few works related to the railway transport system were also presented. Table 1 summarizes the available literature review articles highlighting the subjects discussed in comparison with those that are covered by this work. A clear gap exists in the literature when it comes to works dedicated to the application of drive-by methodologies in railway subsystems. This is especially true when it comes to bridges, where most of the reviews are focused on highway applications, and only a few studies related to the railway are cited. To the best of the authors' knowledge, no review has been conducted to cover the application of drive-by techniques to all the three railway subsystems, namely vehicle, track, and bridge. Therefore, this work aims at filling this gap by presenting a critical state-of-the-art review of drive-by methodologies applied to the three railway subsystems. Due to the extension of the subject addressed in this work, the authors present the discussion on the theme split into two articles: Part I is dedicated to bridge condition assessment while all the content regarding track and vehicle condition assessment is presented in Part II [18]. Combined, the two papers comprise applications of drive-by methodologies applied to all the main railway infrastructure subsystems which will contribute to this field of knowledge. This paper analyzes the literature surrounding drive-by methodologies as applied to railway bridges, examining each technique in detail and evaluating their associated benefits and limitations. Additionally, it explores potential difficulties that could arise during practical implementation of these techniques within rail transportation systems, with a particular focus on environmental and operational factors. To provide readers with a comprehensive overview of this topic, recent advancements made by authors in this field are highlighted, along with promising areas for future research aimed at addressing the challenges discussed throughout the article review. Finally, through careful analysis of current knowledge on this subject matter, this work aims to make a valuable contribution towards facilitating more effective utilization of drive-by methodologies across railway infrastructure networks.
## 2 Materials and Methods  
### WRF-LES and Radiance Simulation  
The Weather Research and Forecasting Large Eddy Simulation (WRF-LES) passive tracer was applied to generate plumes upon turbulent field dynamics [29]. Following the definition of the methane plume point source [3], the horizontal resolution of the simulation area was set to 10 m, with plume emission originating from a grid. This specific grid has the highest concentration, and is typically used as the reference for determining the location  
Figure 1: A schematic of the study.  
of emission sources. The simulation domain was 4 km \(\times\) 4 km \(\times\) 2 km along the x, y, and z axes, respectively. The model parameters, such as wind speed, mixed layer height, and temperature profile entered used in simulations were similar to those described by [PERSON] et al. [30]. For the daytime troposphere, a mixed layer height of 1100 m and a uniform temperature of 300 K are assumed. A three-dimensional simulation of methane plumes was performed, with \(\Delta\)XCH\({}_{4}\) obtained vertically, integrating the simulated concentration profiles. The simulation lasted for three hours, with the first two dedicated to establishing the turbulent field. The final hour was selected the for instantaneous plume analysis. Figure 2 illustrates the instantaneous plume morphology for a 10 m wind of 3 m/s and an emission rate of 1000 kg/h. Emission rates for other levels can be derived by applying a scale factor, enabling simulations for 100 kg/h and 10,000 kg/h emission rates. These specific rates allow for an evaluation of how various satellite parameters influence the detection and quantification of methane plumes of differing levels, thus assessing the sensitivity and performance of satellite-based methane monitoring systems.  
Based on the simulated emission plume, the SCIATRAN radiative transfer model was employed to calculate the radiance of varying methane concentrations [31]. Absorption cross-sections were obtained from the HITRAN 2020 database, with a line-by-line absorption calculation employed to simulate radiative transfer accurately. The forward radiance simulation included all gases with absorption features within the selected spectrum to depict a realistic state of the atmosphere. Additionally, an aerosol module was incorporated to enhance the realism of the simulations. Methane profiles were derived from simulated plume data, while the profiles of other gases were based on the U.S. Standard Atmosphere. Meanwhile, a reference 30\({}^{\circ}\) solar zenith angle (SZA) and 0.3 surface reflectivity with clear sky were set, respectively. Pseudo-observations were subsequently generated by convolving with the spectral response function (SRF), which varies across different parameter settings.
###### Abstract  
The current work is devoted to the numerical determination of the wind speed value, which can cause the overturning of the mobile elevating work platform of the scissor lift type. In the first step of the analysis, the scaled model of the real vehicle is prepared. In the second step, the model is used in the aerodynamic tunnel to determine the aerodynamic force values and moment, which act on the vehicle. The three different configurations of the work platform are considered, namely: (a) The work platform raised to the maximum height with an additional bridge extended, (b) the work platform raised to the maximum height, and (c) the work platform half raised. In each position, the direction of the wind is changed from the range from 0\({}^{\circ}\) to 180\({}^{\circ}\) with an increment equal to 15\({}^{\circ}\). In the next step of the analysis, the CFD simulations are carried out. The ANSYS Fluent R22 software is used. As a model of turbulent airflow, the standard k-\(\epsilon\) with standard wall function is adopted. The obtained experimental results are used to verify the numerical model. A very good agreement between the results of the experiment and the results of numerical simulations is obtained. As the main result of the numerical study, the values of the tipping moment and corresponding wind speed that cause the overturning of the analyzed real scissor lift are determined. It occurred that the lowest value of the wind speed is obtained for the first variant of the vehicle configuration V\({}^{1}\)\({}_{\text{ct}}\) = 22.315 m/s for the angle of the wind speed direction \(\beta\) = 30\({}^{\circ}\) and the highest one for the third variant V\({}^{3}\)\({}_{\text{ct}}\) = 34.534 m/s and \(\beta\) = 15\({}^{\circ}\), without any persons on the work platform. The presence of human beings on the work platform is also considered.  
scissor lift; wind load; CFD simulation; aerodynamic forces; tip-over +
Footnote †: journal: Applied Sciences
# Image Interpolation with Regional Gradient Estimation
## 2 Fundamental Issues and Ideas
The specific method of using the gradient to determine the properties of pixels is as follows. As shown in Figure 1, for diagonal pixels, such as the points at the \((2i-1,\,2j-1)\) and \((2i,\,2j)\) pixel positions, if a pixel in the \(45^{\circ}\) or \(135^{\circ}\) direction satisfies \(\,\mid u_{45^{\circ}}-u_{135^{\circ}}\mid\,\geq T\) (\(u_{\theta}\) denotes the \(\theta\)-directional gradient for pixels), the pixel can be regarded as an edge point, and the direction of the edge on which the pixel is located can then be determined by Equation (6), where \(u_{45}^{(h)}\) denotes the pixel's \(45^{\circ}\)-directional gradient in the HR image. \[\left\{ \begin{array}{l}\theta=135^{\circ},u_{45}^{(h)}\ \geq\ u_{135}^{(h)}\\ \theta=45^{\circ},\ \ u_{45}^{(h)}<u_{135}^{(h)}\end{array} \right. \tag{6}\]  
Similarly, for a pixel in the horizontal or vertical direction, e.g., the pixels at the \((2i-1,\,2j)\) and \((2i,\,2j-1)\) positions, if the pixel gradient \(u\) in the \(0^{\circ}\) or \(90^{\circ}\) direction satisfies \(\,\mid u_{0^{\circ}}-u_{90^{\circ}}\mid\,\geq T\), the pixel can be determined as an edge point, and the edge where the pixel is located can be determined by Equation (7). According to the experiment in [19], the value of \(T\) is about \(0.01\). \[\left\{ \begin{array}{l}\theta=90^{\circ},u_{0}^{(h)}\ \geq\ u_{90}^{(h)}\\ \theta=0^{\circ},\ \ u_{0}^{(h)}<u_{90}^{(h)}\end{array} \right. \tag{7}\]  
To obtain the HR image, we first copy the pixel values and properties at the \((i,\,j)\) position in the LR image to the \((2i-1,\,2j-1)\) position in the HR image, as shown in Equation (8). \[I_{\text{HR}}(2i-1,2j-1)=I_{\text{LR}}(i,j) \tag{8}\]  
To interpolate the remaining unknown pixels in the HR image, we need to judge the properties of the unknown pixels according to the known pixel properties. As shown in Equation (8) [12], the CGI method uses the nearest-neighbor strategy to determine the properties of unknown pixels. \[\left\{ \begin{array}{l}u_{\theta}^{(h)}(2i-1,2j)=u_{\theta}^{(h)}(2i-1,2j- 1)\\ u_{\theta}^{(h)}(2i,2j-1)=u_{\theta}^{(h)}(2i-1,2j-1)\\ u_{\theta}^{(h)}(2i,2j)=u_{\theta}^{(h)}(2i-1,2j-1)\end{array} \right. \tag{9}\]The CGI method is not very precise in judging the properties. When the image's edge information is rich, this strategy may incorrectly judge the properties of unknown pixels, affecting the final interpolation effect. The arrows in Figure 5a represent the actual gradient directions of the a and b pixels in the test image. Because the CGI method uses the nearest-neighbor strategy, the gradient and direction of pixel b in Figure 5b are consistent with pixel a, and the direction is \(135^{\circ}\). At this point, pixel b is interpolated as an edge pixel in the \(135^{\circ}\) direction. However, Figure 5a shows that the gradient direction of pixel b is \(0^{\circ}\). So, we propose a region-based gradient estimation strategy to effectively judge the properties of pixels. The details are discussed in the following section.
# Efficient Inorganic Stabilization Materials for Chromium and Arsenic Pollution in Water and Soil
## 2 Inorganic Stabilization Materials
### Polymetallic Sulfate  
Metal sulfides have the characteristics of high surface reactivity, strong reducibility, good selectivity, wide sources, and easy synthesis. Currently, their application in the treatment of chromium/arsenic pollution has received widespread attention. Common metal sulfides include pyrite (FeS\({}_{2}\)), molybdenum disulfide (MoS\({}_{2}\)), etc. Fe(II) and S\({}^{2-}\) were alternately injected by [PERSON] et al. [49] to prepare Fe sulfide coatings. Firstly, As(III) reacted directly with Fe sulfide coatings to form As sulfide precipitate, effectively removing As. Secondly, the surface composite reaction of Fe sulfide coatings also promoted the immobilization of As, forming As-S bonds and As-O-H bonds, which enhanced the removal ability of As and achieved a removal rate of As(III) at 109.7 mg/g. Potassium sulfide (K\({}_{2}\)S) was added by [PERSON] et al. [50] to promote a combination of As\({}^{3+}\) with S\({}^{2-}\) to form an insoluble arsenic sulfide (As\({}_{2}\)S\({}_{3}\)) precipitate. Finally, As was removed through a co-precipitation mechanism, with the reduced As content below 0.1 ppm. However, these metal sulfide stabilization materials also have some drawbacks, such as difficulty in  
Figure 6: (**a**) The distribution of chromium species between the solution and the material surface after reaction with nFe\({}^{0}\) and CMC-nFe\({}^{0}\)[44]; (**b**) SEM images of magnetite before and after ball milling [48]; (**c**) The effect of magnetite on the occurrence form of As in soil before and after ball milling [48]; (**d**) The stabilization mechanism of As by ball-milling magnetite [48].  
solid-liquid separation and the need to improve their adsorption capacity for chromium and arsenic pollutants. Therefore, it is necessary to further study the modification of these metal sulfides to solve the problems faced in the promotion of practical application. Compounding of \(\beta\)-cyclodextrin(\(\beta\)-CD) with Fe\({}_{3}\)S\({}_{4}\), CD-Fe\({}_{3}\)S\({}_{4}\) was prepared by [PERSON] et al. [51]. Compared with Fe\({}_{3}\)S\({}_{4}\), CD-Fe\({}_{3}\)S\({}_{4}\) has an enhanced removal rate of Cr(VI), reaching over 99%. Applied to actual wastewater containing Cr(VI) 5.61 mg/L, 99.91% of chromium can be removed.
## 3 Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**Accuracy**} & \multicolumn{3}{c}{**IoU**} \\ \cline{3-8}  & & **Case 1** & **Case 2** & **Case 3** & **Case 1** & **Case 2** & **Case 3** \\ \hline \multirow{8}{*}{Test 1} & Ground & 0.9740 & 0.9607 & 0.9659 & 0.9140 & 0.8462 & 0.8955 \\  & Vegetation & 0.8663 & 0.8528 & 0.8610 & 0.8035 & 0.7909 & 0.7986 \\  & Car & 0.4005 & 0.3804 & 0.3980 & 0.2615 & 0.2837 & 0.2822 \\  & Truck & 0.3731 & 0.1300 & 0.2562 & 0.0236 & 0.0198 & 0.0207 \\  & Powerline & 0.8661 & 0.8703 & 0.8731 & 0.3932 & 0.5568 & 0.4650 \\  & Fence & 0.0498 & 0.2979 & 0.1993 & 0.0391 & 0.1872 & 0.1136 \\  & Pole & 0.5767 & 0.7089 & 0.6230 & 0.1413 & 0.2527 & 0.1825 \\  & Building & 0.8966 & 0.8420 & 0.8820 & 0.8537 & 0.7747 & 0.8208 \\  & Ground & 0.9924 & 0.9898 & 0.9874 & 0.9302 & 0.8964 & 0.9166 \\  & Vegetation & 0.8826 & 0.8651 & 0.8773 & 0.8445 & 0.8147 & 0.8355 \\  & Car & 0.6838 & 0.3482 & 0.5332 & 0.2060 & 0.1711 & 0.2196 \\  & Truck & 0.4217 & 0.4077 & 0.3738 & 0.2750 & 0.3033 & 0.2431 \\ Test 2 & Powerline & 0.6757 & 0.7863 & 0.7677 & 0.5517 & 0.6407 & 0.5376 \\  & Fence & 0.1207 & 0.5763 & 0.3530 & 0.0907 & 0.2999 & 0.1816 \\  & Pole & 0.4210 & 0.4619 & 0.4332 & 0.1788 & 0.2546 & 0.2108 \\  & Building & 0.9208 & 0.8764 & 0.9044 & 0.9067 & 0.8464 & 0.8797 \\  & Ground & 0.8814 & 0.9046 & 0.8534 & 0.5666 & 0.4129 & 0.5556 \\  & Vegetation & 0.3350 & 0.2296 & 0.2831 & 0.2838 & 0.2098 & 0.2515 \\ New 1 & Car & 0.1852 & 0.0756 & 0.1270 & 0.0920 & 0.0517 & 0.0953 \\  & Building & 0.7523 & 0.3712 & 0.7208 & 0.6573 & 0.3342 & 0.5858 \\  & Ground & 0.9445 & 0.9570 & 0.9429 & 0.3292 & 0.2742 & 0.3299 \\  & Vegetation & 0.1712 & 0.0583 & 0.1332 & 0.1680 & 0.0564 & 0.1298 \\ New 2 & Car & 0.0237 & 0.0139 & 0 & 0.0588 & 0.0085 & 0 \\  & Building & 0.8512 & 0.3086 & 0.7760 & 0.7699 & 0.3797 & 0.6644 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification accuracy of test and new data. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Dataset**} & **Case 1** & **Case 2** & **Case 3** \\ \hline Test 1 & 0.7417 & 0.7948 & 0.7598 \\ Test 2 & 0.7691 & 0.7826 & 0.7851 \\ New 1 & 0.5158 & 0.2611 & 0.5063 \\ New 2 & 0.5938 & 0.1930 & 0.5344 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Recall of building boundaries.
# Secure Mimo Communication System with Frequency Hopping Aided OFDM-DCSK Modulation
###### Abstract  
In this paper, a multiple-input multiple-output (MIMO) communication system with frequency hopping (FH) aided orthogonal frequency division multiplexing differential chaotic shift keying (OFDM-DCSK) modulation is proposed. Our objective is to improve the security of MIMO communication system which is encoded by space time block coding (STBC). In order to combat the eavesdropping or malicious attacks due to the broadcast characteristics of wireless communication system, we propose to use DCSK and FH modules to encrypt the information, and hide the user data in the chaotic sequences, where the initial value of chaotic sequences and the method of generating FH module are only shared among legitimate users. Moreover, we derive the bit error rate (BER) and the secrecy capacity of the scheme in additive white Gaussian noise (AWGN) channel and Rayleigh fading channel. Simulation results show that the proposed scheme can effectively improve the security of MIMO communication system, which can be seen from the BER of eavesdroppers and legitimate users, and the secrecy capacity of the proposed scheme and the benchmark schemes.  
bit error rate; differential chaotic shift keying; frequency hopping; multiple-input multiple-output; security Article