esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### End-to-End Communication System Emulation in Interfering Model  
To emulate the previous scenario, Figure 11 shows the bench-emulated test setup of the TX and RX communication system with interference, with details of the RF devices/equipment used in the lab (Figure 12 is the setup picture). The specifications and description of the transmission and reception communication system are detailed below.  
In Figure 11, we have the CORTEX equipment, indicated as (1)--high-data rate receiver Cortex Series from the French company Zodiac Safran, which has a dual function in this setup. Firstly, this equipment is a TEST MODULATOR that generates the modulated carrier signal with the WFI camera information data provided by the WFI CAMERA SIMULATION-DEMO 1 (2). In this sense, we emulated the satellite data transmitter in the communication system. For practicality, the bit rate \(R_{b}\) was programmed at 50 Mbps (close to the actual WFI value of 51.28 Mbps).  
The TEST MODULATOR output is combined with a signal generated by the Rohde Schwarz SMJ 100A synthesizer (3), which emulates the interfering signal. The actual FS signal specification was used to set the synthesized signal, with \(BW\) = 37 MHz and QPSK  
Figure 11: Setup of the communication system.  
Figure 12: Picture of the communication system setup.  
modulation. The interference output power is varied according to the required SINR to measure the interference effects on the received images. The various microwave devices (attenuators, isolators, and couplers) are used for impedance matching and adjusting the signal level properly.  
At the output of the combiner (4), we have the compound signal: desired and interference signal plus additive white Gaussian noise (AWGN), which is looped back to CORTEX equipment (1). The demapping of the I/Q signal from the MAPSK modulation and the decoding are performed in the DEMODULATOR on CORTEX.  
Finally, the I/Q bit stream at the DEMODULATOR output, corresponding to the camera image data received by ERG, is sent to the acquisition board in the computer DEMO 2 (5) for image processing, according to Figure 13.  
The total losses between 'IF TEST' (MODULATOR OUT) and 'IF NOMINAL' (DEMODULATOR IN) is \(L_{T}\) = 19.0 dB.
# Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System
## 4 Simulation
### Lighting Environment Simulation
The overall illuminance uniformity is measured at 0.76, successfully meeting the previously established target. At night, the model calculates the dimming coefficients as K = [0.34, 1, 1, 1, 1, 0.22], leading to the following luminous flux outputs for each luminaire: [1360, 4000, 4000, 4000, 4000, 4000, 880] lm. Figure 9 shows that the illuminance for work planes F1 and F6 reaches 300 lx, with the other occupied work planes exceeding 500 lx as well. The uniformity of illuminance during nighttime is 0.78, indicating that all lighting indices have successfully reached the target values. Figure 8: 15 June 2023 at 12 noon, sunny day, with blinds, algorithm optimization, indoor 0.75 m space illumination. Figure 7: 15 June 2023 at 12:00, sunny day, with blinds, indoor 0.75 m space natural illumination. To summarize the above simulation results, the daytime and nighttime illuminance uniformity is 0.76 and 0.78, respectively, with illuminance values reaching the target value. Without any control system in place, the daytime illuminance uniformity was only 0.60. Therefore, the intelligent lighting control system effectively addresses the issue of low daytime illuminance uniformity.
## 3 Dynamic Influence Map
### Dynamic Influence Map
#### 3.3.1 Dynamic Influence Map Calculation  
Because the object is in motion, its influence decays at different rates in different directions. When the target point is in front of the moving direction of the object, the influence of the object decays slowly. When the target point is behind the moving direction of the object, the influence of the object declines faster. One way to adjust the rate at which an object's influence decays is to change the distance in the formula between the object and the target point.  
As shown in Figure 2, the spread source point is a, and the target point is b. \(\overrightarrow{v}\) is the motion direction of node a, and \(\overrightarrow{I_{a,b}}\) is the direction from a to b. The specific method for adjusting the distance is given by Equation (6) [41].  
\[d^{\prime}{}_{a,b}=\frac{d_{a,b}}{1+\cos\left(\overrightarrow{I_{a,b}},\overrightarrow{v}\right)\cdot\alpha}, \tag{6}\]  
where \(d_{a,b}\) indicates the actual distance from point \(a\) to point \(b\), \(d^{\prime}{}_{a,b}\) represents the adjusted distance, \(\overrightarrow{I_{a,b}}\) represents the direction vector from point \(a\) to point \(b\), and \(\overrightarrow{v}\) represents the current movement direction of spread source point \(a\). Moreover, \(\alphaof distance adjustment, which is between 0 and 1. The larger the value of \(\alpha\), the greater the value of the spreading influence of source point \(a\) in the direction of its movement. In this study, \({d^{\prime}}_{a,b}\) is used to calculate the influence of \(b\) according to Equation (5).  
Let \(IM(x)\) indicate the influence value at point \(x\) in the influence map. The calculation formula of the influence map can then be expressed as Equation (7).  
\[IM(x)=\sum_{i\in U}I_{i,x}, \tag{7}\]  
where \(I_{i,x}\) represents the magnitude of influence exerted by game unit \(i\) on point \(x\).
# Estimation of River Velocity and Discharge Based on Video Images and Deep Learning
## 2 Theory and Methods
### Structure of ShuffleNetV2  
ShuffleNetV2 [19], introduced by Megvii Technology, is an efficient and lightweight convolutional neural network. ShuffleNetV2 retains the core operations from its predecessor ShuffleNetV1 [20], such as channel shuffle, group convolution, and depthwise separable convolution. Beyond that, ShuffleNetV2 takes into account the impact of Memory Access Cost (MAC) on the model performance on the basis of ShuffleNetV1, ensuring that the network architecture is optimized for speed. The overall design of the network adheres to four guidelines: keep the width of the input and output channels equal, employ group convolution appropriately, minimize the degree of network fragmentation, and reduce the element operations.  
The basic structure of ShuffleNetV2 is depicted in Figure 4; it is mainly composed of a downsampling unit and a basic unit. The downsampling unit is designed with a stride of 2, which effectively halves the spatial dimensions of the input features. The feature maps are dually processed through two distinct branches. The main branch undergoes two \(1\times 1\) conventional convolutions, and employs a 3 \(\times\) 3 depthwise separable convolution (DWConv) in the middle for feature extraction, while the other branch applies a conventional \(1\times 1\) convolution and a 3 \(\times\) 3 depthwise separable convolution with a stride of 2. The final step involves concatenating the feature maps and performing a channel shuffling operation. In the basic unit with a stride of 1, the input feature map is divided into two branches by channel split. The main branch operates the same as the downsampling unit, while no operation is performed in the other branch. Ultimately, the channels from both branches are concatenated and a channel shuffling operation is carried out.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{5}{c}{**Image Class**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-6}  & **Normal** & **Exposure** & **Turbulence** & **Blur** & **Synthetic** \\ \hline Train set/sheet & 810 & 810 & 810 & 1620 & 4050 & 8100 \\ Test set/sheet & 162 & 162 & 162 & 324 & 810 & 1620 \\ Total/sheet & 972 & 972 & 972 & 1944 & 4860 & 9720 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset distributions.  
Figure 4: Basic structure of ShuffleNetV2.  
Figure 3: Dataset images of different types.
# Practical Comparison Between the CI/CD Platforms Azure DevOps and GitHub
## 5 Security Aspects  
Security has become a critical component of modern software development, particularly as cyber threats targeting CI/CD pipelines, open-source dependencies, and software supply chains have become increasingly sophisticated. Organizations adopting cloud-based DevOps solutions must ensure that their repositories, automation workflows, and deployment environments remain protected against unauthorized access, data breaches, and malicious code injections. Azure DevOps and GitHub provide robust security frameworks, but their architecture, security policies, and approach to mitigating vulnerabilities differ in ways that significantly impact an organization's security posture. Azure DevOps is designed for enterprise-scale security governance, whereas GitHub integrates security seamlessly into the developer workflow, prioritizing automation, proactive vulnerability detection, and open-source security intelligence. A foundational aspect of security in any CI/CD platform is authentication and access control, as unauthorized access to repositories can lead to code leaks, intellectual property theft, and compromised software releases. Azure DevOps and GitHub implement multi-factor authentication (MFA), single sign-on (SSO), and role-based access control (RBAC) to ensure that users have appropriate permissions based on their roles and responsibilities [28]. Azure DevOps benefits from deep integration with Microsoft Azure Active Directory (Azure AD), enabling enterprises to enforce centralized identity management, conditional access policies, and Just-In-Time (JIT) privileged access across all DevOps activities. Organizations can configure identity governance policies that restrict repository access based on geographical locations, IP address whitelisting, and device compliance checks. This level of integration makes Azure DevOps the preferred choice for enterprises that require strict identity verification and federated authentication across multiple business units. While historically developer-centric, GitHub has significantly improved identity management and authentication security, particularly with the introduction of GitHub Enterprise Cloud and GitHub Enterprise Server. Enterprise customers can now enforce single sign-on (SSO) through SAML-based authentication providers, implement OAuth-based authorization policies, and utilize hardware security keys for added layers of authentication. GitHub and Azure DevOps also introduced fine-grained personal access tokens (PATs), which limit the scope and duration of authentication tokens, reducing the risk of token leakage or misuse.
## 3 Secure and Precise DM Scheme Based on A Single-Carrier Multiple Antenna Arrays Model
### The Architecture of the LU Based on a Single-Carrier Multiple Antenna Arrays Model
Therefore, even if the Eve is aligned with or very close to one transmit direction, the transmission security can also be ensured owing to \(K\) symbols emitting simultaneously from different directions, which outperforms traditional AN-aided DM schemes using a single antenna array.
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 6 Performance Study of the UWOC Link
### Relaxing the Transmit Power Constraint  
So far, our study was mainly based on the assumption of constrained transmit power, considering the Tx energy efficiency as the main criterion. Consider now as objective the maximization of the LBI in order to obtain the most flexible link operation, while relaxing the requirement of energy efficiency. Accordingly, considering a data rate of 20 Mbps and based on the results of Figure 7, we set \(P_{\text{Tx,e}}\) to 50, 80, and 185 mW for ACO, LACO, and DCO, respectively, which allow us to maximize the LBI for each scheme. The BER performances are contrasted in Figure 9, where it is seen that DCO provides the best performance, as it could be expected.  
The corresponding LBIs and \(Z_{\text{max}}\) are summarized in Table 4 for a target BER of \(10^{-3}\).  
In the sequel, we again fix \(P_{\text{Tx,e}}\), considering as the main criterion the link energy efficiency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(R_{b}\) (Mbps) & LBI (m) & \(Z_{\text{max}}\) (m) \\ \hline \multirow{3}{*}{DCO} & 20 & 81 & 95 \\  & 50 & 64.9 & 78.9 \\  & 100 & 48 & 61.8 \\ \hline \multirow{3}{*}{ACO} & 20 & 73.8 & 88.6 \\  & 50 & 58.6 & 73 \\  & 100 & 42.8 & 56.3 \\ \hline \multirow{3}{*}{LACO} & 20 & 74.3 & 88.8 \\  & 50 & 58.9 & 73.2 \\ \cline{1-1}  & 100 & 43.1 & 56.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: LBI and maximum attainable range for DCO, ACO, an LACO-OFDM transmission schemes according to Figure 9; \(\text{BER}=10^{-3}\), \(\eta\approx 1\,\text{bps}/\text{Hz}\).  
Figure 9: BER as a function of \(Z\) for ACO-, LACO-, and DCO-OFDM, with \(P_{\text{Tx,e}}=50\), 80, and 185 mW, respectively. \(R_{b}=20\), 50, and 100 Mbps; \(N\) and \(N_{\text{CP}}\) as in Figure 8, \(\eta\approx 1\,\text{bps}/\text{Hz}\).
## 2 Modeling and Numerical Methods
### Reference Model and Computational Method
Combined with the sensitivity analysis of the subgrid-scale models described in the next section, the LES model with the Smagorinsky-Lilly subgrid-scale model is used for the transient simulation. The detailed description and equations of the LES model can be found in the study [23]. The body force and plasma active region are encoded into the conservation equations by the macro function DEFINE_SOURCE. The conservation equations are discretized by the finite volume method and the discretized equations are calculated using the SIMPLE-Consistent (SIMPLEC) algorithm, where SIMPLE is the semi-implicit method for pressure-linked equations. The spatial discretization has a second-order accuracy, and the bounded second order implicit Euler scheme is adopted for the time integration. According to the discretization-scheme sensitivity analysis described in the next section, the discretization schemes for the diffusion and convection terms are the bounded central differencing scheme. According to the numerical analysis [23], the most economical non-dimensional time step of \(\Delta t\times U_{\infty}/\hbar=0.175\) (\(\Delta t=0.005\) s) is adopted by the present study with 25 sub-iterations, where \(U_{\infty}\) is the wind speed of 7 m/s. The convergence criterion of all dependent variables is set as \(10^{-5}\) for the normalized residuals. Figure 4: Profiles of mean velocity and turbulence intensity under different wind speeds \(U_{\infty}\): (a) \(U_{\infty}=4\) m/s; (b) \(U_{\infty}=7\) m/s; (c) \(U_{\infty}=10\) m/s.
# Adaptive Deployable Thin Spherical Shell Reflectors
## 1 Introduction
### Large Reflectors  
The goal of using larger reflectors in space is to improve the sensitivity and resolution of antennas, spectrographs and imaging devices while meeting the volume and weight constraints of the launcher. When the limited space in the fairing of the launcher does not allow a rigid reflector, folding is necessary during the launch and the reflector is unfolded once in orbit. This brings the challenge of maintaining the surface figure accuracy of the deployed reflector for the mission, which is becoming increasingly difficult as the wavelength \(\lambda\) becomes shorter (RMS wavefront accuracy of \(\lambda/14\) for optical systems). This often requires some kind of active control. The weight constraints associated with launching large reflectors to distant operating locations such as the Lagrange L2 point brings another requirement on the low areal density.  
The James Webb telescope with a primary mirror of 6.5 m relies on articulated rigid segmented mirrors. Several alternative concepts of deployable reflectors with low areal density (\(<\)3 kg/m\({}^{2}\)) and high stowability have been proposed: Lenticular pressure stiffened membranes [2]; and doubly curved form stiffened elastic shells [3, 4]. None of them offer accuracy acceptable in the optical range. Others rely on a deployable mesh antenna supporting a membrane reflector controlled by a set of electrostatic actuators [5].  
Our work [6; 7] investigates the control a thin spherical polymer shell covered with a thin film of electrostrictive copolymer (PVDF-TrFE) used as in-plane strain actuator (according to the \(d_{31}\) mode); the target shape is obtained by applying voltages to a set of independent electrodes. Earlier work has shown that strain actuators are very efficient to control a flat plate; they are widely used in Adaptive Optics (AO) (e.g., see [8]). However, the response of a thin spherical shell is far more complicated; the accurate shape control requires that the electrode size \(\varrho\) be such that  
\[\varrho<(R_{c}\,t)^{1/2} \tag{1}\]  
where \(R_{c}\) is the radius of curvature and \(t\) is the shell thickness [9; 10]. Any significant departure from this condition will lead to a wavy reflector shape at the transition between electrodes excited with different strains (i.e., different voltages for an electrostrictive material). According to the foregoing equation, assuming electrodes with an aspect ratio close to 1, the number of electrodes \(N\) follows \(N\propto(R_{c}\,t)^{-1}\); thus, more curvature and thinner shell will mean more electrodes, more sophisticated metrology and more complex control algorithms.
## 4 Conclusions  
A silico-aluminate material, perlite, was used as an alternative and cheap sorbent of lead ions, present in industrial wastewater. Solutions at different metal concentrations (2-10 mg\(\cdot\)L\({}^{-1}\)) were eluted at different flow-rates (0.15-0.4 L\(\cdot\)h\({}^{-1}\)) in packed bed columns, filled with different weights (2-4 g) and volumes (16-30 cm\({}^{3}\)) of perlite. The main results are summarized as follows:  
* From microscopic and BET measurements, metal retention was ascribed to surface area and to the large sorbent pores exposed to the liquid-phase, which allowed for free migration of hydrated lead ions to the functional groups of the silico-aluminate matrix.
* Metal removal efficiencies increased with increasing amounts of perlite, together with breakpoints being reached at increasing bed volumes.
* Increase in retention capacities was observed with an increase of initial metal concentration, together with anticipation of the breakthrough point.
* A more complete saturation of the column was detected at low flow-rates, associated with long liquid/solid contact times, and with corresponding higher values of the breakthrough points.
* The best lead ion retention was obtained at 0.3 L\(\cdot\)h\({}^{-1}\), with 4 g of perlite and 10 mg\(\cdot\)L\({}^{-1}\) of Pb\({}^{2+}\) (3.5 mg of Pb\({}^{2+}\)g\({}_{\text{Sorbent}}\)\({}^{-1}\)).
* Film diffusion control was the kinetic step of the process.
* After column breakthrough, the sorbent was considered a special waste and incorporated into cement conglomerates as a lightweight, eco-friendly aggregate.  
Figure 7: Cement/perlite composites, after mechanical tests (inset), SEM image of the structure of the conglomerate (**top**) and magnification of the ligand paste/perlite interface (**bottom**).  
With the increase of perlite volume, thermal insulating properties of the samples were improved, together with a decrease in mechanical strength.
* The cement conglomerates could be used in the construction industry in plaster or panels, without adverse environmental effects, as the metals would be incorporated into the matrix, with negligible leaching.  
A.P.: experimental tests and discussion of the results; D.S.: experimental tests and discussion of the results; V.R. and P.C.: characterization of the materials and discussion of the results; M.R.: elaboration data and discussion of the results; N.D.V.: porosity measurements.  
This research received no external funding.  
The authors thank [PERSON] for the SEM-EDX analysis. Regione Puglia is gratefully acknowledged for her financial support (X-ray Lab Project-Reti di Laboratori Pubblici di Ricerca, cod. n. 45 and 56).  
The authors declare no conflicts of interest.
###### Abstract
The primary contributions of this study are as follows: (a) we utilized the NGSIM vehicle trajectory dataset as the data source for our research and employed a more accurate method to extract the required trajectory data; (b) we comprehensively measured the driving behavior risk from three perspectives: driving, car-following, and lane-changing. The parameters used in the established index model are easily collectible; (c) we constructed a risk measurement model for driving behavior and clustered the driving behavior risk into four categories. This model can assess the risk status at each moment during the driving process of a vehicle in real-time. The research results can be used for monitoring and warning against hazardous driving behaviors, driver training, and the creation of driver behavior recommendation systems. This paper is organized as follows: Section 2 introduces the driving behavior risk indicators established in this study and the methods used; Section 3 conducts a case analysis through the NGSIM data set, establishes a driving behavior risk measurement model, divides the driving behavior risk categories through cluster analysis, and at the same time, tests the driving behavior risk measurement model; Section 4 provides a summary of this work.
## 1 Introduction  
Owing to the high heterogeneity and complexity in urban ecosystems, it is rather difficult to monitor or predict the hydrological dynamics of urban surfaces [1]. Some megacities, e.g., Beijing--the capital city of China--have experienced strong urbanization, large population inflow, island effect and climate change during the past few decades [2]. These changes induce urban hydrological processes to be highly uncertain and make policymakers face tough challenges in water use planning and management. Therefore, there is an urgent need to accurately estimate urban hydrological processes.  
Evapotranspiration (ET), as a key component of the urban hydrological processes and surface energy balance, plays an important role in regulating water resource supply and relieving the urban island effect (e.g., surface cooling) [3]. Different from natural ecosystems, the urban ecosystems include large proportions of artificial modifications in land cover, such as impervious surfaces including roofs, squares and cement or asphalt roads. These man-made reconstructions could contribute a large fraction of evaporation [4; 5; 6], but the quantification at city levels remains highly uncertain due to a lack of clearly distinguishing estimations of ET between impervious surfaces and vegetated or bare-soil lands. The good news is that recent developments of fine resolution remote sensing for land use and land cover classification, vegetation dynamics and environmental monitoring provide new opportunities to estimate urban ET more accurately [7]. For example, the Sentinel-2, as part of the European Commission's Copernicus program with the launch of satellite Sentinel-2A on 23 June 2015, are monitoring variability in global land surface conditions at a 10-60m resolution and a 5-10-day revisit [8].  
In this study, we take the sponge city project in Beijing city as a study case to estimate ET of the urban ecosystem at 10m resolution using the satellite-based land cover map and vegetation information derived from Sentinel-2 data. Beijing city has 5-year mean annual precipitation of 560 mm and mean annual temperature of 12 \({}^{\circ}\)C, with the potential evaporation of 550-600 mm year\({}^{-1}\). To reduce stress on water supply (e.g., ~30 m\({}^{3}\) per capita water use per year in Beijing) and urban environment, the Beijing Sponge City project was started on 4 December 2017, aiming at turning 20% of Beijing city into a sponge city covered area by 2020 [9]. Therefore, to evaluate the benefit of this project, it is essential to implement a city-level assessment of the project-induced ecohydrological changes at fine resolution.