esa-sceva/satcom-chunk-collection huggingface dataset:
# A Short Note on Physics-Guided GAN to Learn Physical Models without Gradients
## 2 GAN and Physics-Informed GAN  
A conditional GAN model consists of a generator network \(G\) and a discriminator network \(D\), as illustrated in Figure 1. The input of the generator network is a noise vector \(z\) and label \(\theta\), and the output represents fake data \(\mathbf{x}^{\prime}\), expressed as \(\mathbf{x}^{\prime}=G(z\mid\theta)\). The input of the discriminator network is given by real \(\mathbf{x}\) and fake \(\mathbf{x}^{\prime}\) data, and the network distinguishes the real data from the fake data. The loss function is defined as  
\[V(G,D)=E_{\mathbf{x}\sim p_{x}}[\log D(x)]+E_{\mathbf{z}\sim p_{z}}[\log(1-D(G(z)))], \tag{1}\]  
and the generator minimizes \(V\), whereas the discriminator maximizes \(V\), i.e., \(\min_{G}\max_{D}V(G,D)\). The discriminator only considers data \(\mathbf{x}\) and \(\mathbf{x}^{\prime}\); physical reasonableness is not considered.  
A PINN can be coupled with a GAN model. The resulting architecture is called PI-GAN in the present article. Suppose that a physical model is expressed as \(P(\mathbf{x})=0\). For a variable \(\mathbf{x}^{\prime}\), the residual is given by \(r=P(\mathbf{x}^{\prime})\). The PINN adds the residual into the loss function, which is minimized. In the GAN model, the loss function of the generator is modified as \(V(G,D)+\lambda r\), where \(\lambda\) is a constant. In the numerical example described later on, \(\lambda\) was set to 0.1. The loss function of the discriminator remains the same as the original GAN.  
Figure 1: Architecture of GAN.
## 2 Results and Discussion
### Ligand Efficiency (LE) and Lipophilic Efficiency (LipE)  
In the current study, for optimization assessment, LE was calculated [18]. The parameter LE has a crucial role in \"lead optimization for drug-like candidate\" properties [19]. Compounds with the highest activity were selected for evaluation against sensitive cancer cell lines (breast and colon cancer cells).  
LE was calculated using the following equation [20]:  
\[\text{LE}=(\text{pIC}_{50}\times 1.37)/\text{NHA}\]  
\(\text{IC}_{50}=\text{half-maximal inhibitory concentration (in terms of molar concentration); NHA = non-hydrogen atom.  
The compounds had an LE value in the range of 0.19-0.26 except for compound **5n** (Table 3). All compounds exhibited higher LE values in breast cancer cells than in colon cancer cells, especially compounds **5c**, **5e**, and **5l** (LE = 0.26), all of which were structural isomers.  
The recommended LE value should be in the range of 0.3. The acceptable LE value should be higher than 0.3.
###### Abstract  
In the field of slope landslide prevention and monitoring in open-pit mines, addressing the lag issues associated with the traditional GNSS inverse-velocity method, this study introduces a novel strategy that integrates high-spatiotemporal-resolution monitoring data from ArcSAR with a time log model for prediction. The key findings include the following: (1) This strategy utilizes the normal distribution characteristics of deformation velocities to set confidence intervals, accurately identifying the starting point of accelerated deformation. (2) Coupled with coordinate transformation, the time logarithm prediction method was constructed, unifying the units of measurement and resolving convergence issues in data fitting. (3) Empirical research conducted at the Kambove open-pit mine in the Democratic Republic of the Congo demonstrates that this method successfully predicts landslide times four hours in advance, with an error margin of only 0.18 h. This innovation offers robust technical support for slope landslide prevention and control in open-pit mines, enhancing safety standards and mitigating disaster losses.  
Keywords:lands lide prediction; inverse-velocity method; circular synthetic aperture radar; logarithmic model; normal distribution
# VPNFilter Malware Analysis on Cyber Threat in Smart Home Network
## 2 Taxonomy of Malware in the Smart Home Network
### Attacks Based on the Smart Home Architecture
#### 2.1.3 Middleware Layer  
This provides enterprise activation and integration required to connect engagement systems. The middleware layer obtains from the network layer data that links together the system to the database and the cloud and also performs processing and data storage [19]. The security of the database and cloud is considered the main problem in the middleware layer, which greatly affects the quality of service at the application layer.  
* Flooding attack: This type of attack is considered to be a form of denial of service attack wherein a network or a service becomes so weighed down with packets initiating incomplete connection requests that it can no longer process genuine connection requests. Attackers can attack the service to affect the quality of service [29].
* Cloud malware attack: An attacker launching this type of attack tries to inject a malicious service in the cloud and creates its own malicious service implementation module and tries to add it in the cloud system. Therefore, if the attacker succeeds, the cloud automatically redirects the request of the valid user for the attacker code to start to be executed.
* SQL injection attack: In this type of attack, many attackers use SQL statements for writing, deleting operations, and reading when the web application is being hacked by SQL injection. Pages show different outcomes compared with the actual information on the network.
* Signature wrapping attack: Based on the cloud system, this type of attack uses the XML signature to ensure service integrity. Attackers can easily modify the communication between nodes on this layer by eavesdropping without invalidating the signature [30].
## 3 Results and Discussion
### UPLC-Q-TOF/MS Analysis
As for the lactic acid treatment groups, 10 main differentiation compounds were observed according OPLS-DA analysis, which included 8 organic acids, 1 amino acid and 1 other (Figure 3A). It can be seen from Figure 3B that the concentrations of most organic acids were reduced in LC-3.50, but there were no significant effects on their metabolism after increased lactic acid addition. Most interestingly, the addition of lactic acid before fermentation significantly reduced the consumption of malic acid in cherry must, and the lactic acid content of cherry wine did not increase but decreased in the LC-3.50, LC-4.15 and LC-4.80 groups; a significant accumulation of lactic acid was found in LC-5.45. This indicated that lactic acid could be utilized during cherry wine fermentation, and its addition was excessive in LC-5.45. In the MLC group, more than 15 compounds had high VIP scores, which meant it was considered the most metabolically active group. Similar to the malic acid and lactic acid treatment groups, their combined treatment had significant effects on organic acid, amino acid and nitrogen glycoside metabolism (Figure 4A). According to Figure 4B, the metabolism of malic acid in the MLC group was the same as that in the MC group, and  
Figure 4: OPLS-DA results (**A**) and heatmap (**B**) of wine samples obtained by adjusting the initial acid contents (3.50, 4.15, 4.80 and 5.45 g/kg, respectively) of cherry must with the combination of malic acid and lactic acid. its accumulation could also be observed when the initial acid content was 4.80 g/kg. The difference is that the lactic acid content in the MLC group showed a downward trend until the initial acid concentration was adjusted to 5.45 g/kg, and then returned to the same level as the control. Conversely, the concentrations of amino acids (L-tryptophan, D-glutamine and aspartic acid) and nitrogen glycoside (cytidine) decreased significantly in MLC-5.45. Apart from this, lower concentrations of quercetin, kaempfer, isorhamentin-3-O-neohespeidoside and cyanidin-3-O-glucose were found in MLC-4.80 and MLC-5.45. This demonstrated that there was no positive effect on the metabolism of flavones and anthocyanins when the initial acidity was adjusted to more than 4.15 g/kg, and this result was consistent with the analysis results for total flavonoids.
# Design of a Compact Multiband Monopole Antenna with MIMO Mutual Coupling Reduction
## 3 Antenna Implementation and Measurement Results
### MIMO Mutual Coupling Reduction  
The measured and simulated reflection coefficients of the MIMO antenna with slits, with both antennas excited, are shown in Figure 17. The measurements indicate good agreement with the simulations at the lower two bands, but there is a narrower bandwidth, as shown in Figure 17a, at the higher band. However, the reflection coefficient is almost below \(-10\) dB at that band and covers the required bandwidth. The measured and simulated isolations of the MIMO antenna with and without slits, with both antennas excited, are  
Figure 16: The measured and simulated radiation patterns of the compact multiband monopole antenna at 5.45 GHz, 6.32 GHz, and 7.02 GHz.  
Figure 15: The measured and simulated radiation patterns of the compact multiband monopole antenna at 2.46 GHz, 3.47 GHz, and 4.41 GHz.  
shown in Figure 17b,c. Those isolations follow a similar trend and are below \(-13\) dB at the higher band. Furthermore, Figure 17c shows the difference between the MIMO antennas with and without slits. The simulated and measured curves are quite close at the 2.4 GHz band, highlighting an impressive reduction in mutual coupling. However, there is only a 4 dB reduction in mutual coupling at 3.47 GHz, but another suppression response occurs at 3.2 GHz, showing the same level of isolation as the simulation. This may be the designed suppression frequency, but the lower meander slit is too long for some unknown reason. Additionally, there are two response frequencies at 2.8 GHz and 3.9 GHz, respectively. These frequencies result from the physical size and first-order harmonics, and all four curves exhibit the same response frequencies.  
The measured and simulated radiation patterns of the MIMO antenna with both antennas excited are shown in Figures 18 and 19, respectively. The patterns demonstrate good directional coverage in the azimuth angle (XY plane). The gain decreases noticeably as the frequency increases. The elevation angle (YZ plane) also exhibits a similar trend and is not smooth. Due to the measured frequency points not resonating at the same frequencies as the simulated points, there are notable differences, especially at 4.41 GHz, where it is not even below \(-6\) dB.  
Figure 17: The measured and simulated S-parameters of the MIMO antenna. (**a**) The reflection coefficient. (**b**) The isolation of the full band. (**c**) The isolation of the partial band.  
Figure 18: The measured and simulated radiation patterns of the MIMO antenna with slits at 2.46 GHz, 3.47 GHz, and 4.41 GHz.
# Tension Force Detection of Short Suspender by Using Laser Doppler Vibrometer
###### Abstract  
In this study, the vibration frequency of steel rod-type short suspenders simulated by three types of prestressed steel rebars with aspect ratios of 14, 20.8, and 37.8 was detected accurately and remotely by using Laser Doppler Vibrometer (LDV); then the tension force state of short suspenders was evaluated through the quadratic regression relationships between vibration frequency and tension force. The results showed that the vibration frequency of steel rod-type short suspenders increases with the increase in tension force, and there is a good correlation between vibration frequency and prestress. Furthermore, when the prestress applied is the same, the vibration frequency of short suspenders decreases with the increase of their aspect ratios, indicating aspect ratio plays a decisive role on the change of vibration frequency. The errors between the tension force obtained by experiment and tension string theoretical calculation for short suspenders are large, due to the nonrigid boundary condition. The establishment of quadratic regression relationships between vibration frequency and tension force effectively avoids the influence of various factors on the existing calculation model such as mass, stiffness, and constraint type, and makes the detection accuracy of tension force achieve 98%.  
Laser Doppler Vibrometer (LDV); short suspender; force state; vibration frequency; remote detection +
Footnote †: journal: Applied sciences
## 1 Introduction
Research demonstrates that NAPL presence in clay alters porewater ionic concentrations and double-layer structures, inducing resistivity elevation, while contaminant-soil particle interactions may further modify dielectric properties [25; 26]. Nevertheless, a quantitative interpretation of electrical anomalies in low-permeability media remains challenging: (1) soil resistivity exhibits coupled dependence on moisture content, porosity, and temperature, allowing contamination signals to be easily obscured by background noise [27], and (2) conventional inversion methods relying on empirical parameters struggle to adapt to heterogeneous field conditions [28]. These limitations necessitate the development of intelligent interpretation methodologies integrating physical mechanisms with data-driven approaches [29; 30] to improve contamination identification reliability. Current applications of ERT in contamination detection have achieved notable progress. Internationally, [PERSON] et al. [11] successfully monitoredpetroleum plume migration using time-lapse resistivity imaging, while domestic scholar [PERSON] [31] elucidated chlorinated hydrocarbon distribution in chemical sites through 3D ERT surveys. However, existing research predominantly focuses on high-permeability sandy soils [32], with limited systematic analyses of electrical response characteristics in low-permeability clays. Furthermore, resistivity data interpretation remains heavily reliant on expert experience, exhibiting low efficiency and strong subjectivity when processing massive datasets [33]. Recent breakthroughs in deep learning techniques for image segmentation provide new perspectives for automated interpretation [34]. By training neural networks to recognize mapping relationships between resistivity anomalies and contamination zones, rapid localization of polluted areas can be achieved with minimized human intervention, enhancing monitoring system responsiveness [21]. Therefore, this study presents a novel integration of high-density ERT and an enhanced UNet model to form an intelligent and automated interpretation system, significantly improving the accuracy and objectivity of contamination detection in complex subsurface environments. Through field resistivity testing, we acquire electrical datasets from contaminated sites, achieving precise contamination localization via data preprocessing, inversion imaging, and UNet network segmentation. This paper is structured as follows: Section 2 details the study area's geological setting and ERT operational principles, including comprehensive descriptions of data acquisition, preprocessing, and inversion workflows. Section 3 analyzes field test data, revealing contamination extent through time-lapse resistivity imaging. Section 4 develops a UNet-based contamination segmentation model and evaluates its localization accuracy. Section 5 concludes with research findings and future directions. By integrating multidisciplinary methodologies, this research aims to establish an efficient and reliable technical framework for NAPL contamination identification in low-permeability sites.
# Drought and Carbon Cycling of Grassland Ecosystems under Global Change: A Review
## 2 Drought and Grassland \(C\) Cycling under Global Change
### Land-Use Change  
Land-use change enhanced the vulnerability to drought and posed a threat to grasslands ecosystem. Land-use and management change can strongly influence variations in grassland SOC [125]. The impacts of land-use change on drought mostly focus on land-use change as a driver of climate change [126]. Grasslands could be influenced by potential variations in droughts and by alterations in land-use changes [127]. Land-use change and droughts may induce multidirectional impacts on an ecosystem [128]. In addition, land-use change can cause a decline in grassland biodiversity and plant cover [129]. Land use can also strongly affect the resistance and resilience of soil food webs to drought [130]. Drought has significant effects on vegetation and may enhance the risk of wildfires [131]. Also, large-scale and destructive shifts in grasslands can change climate conditions and affect vegetation regimes and the surface energy budget, deteriorating water depletion [132; 133]. However, drought and land-use change can deteriorate soil erosion, causing soil \(C\) to be lost at a faster rate and in larger amounts [134]. It is difficult to distinguish which one is the driver. Land-use change can influence the frequency and duration of drought [135]. Changes in land-use and plant species components can affect the decomposition of litter via a number of mechanisms, such as alterations of litter quality, but also reshape the pattern of temperature and moisture at the soil surface [136; 137]. Furthermore, grasslands serve as a significant \(C\) sink via improved management, and are highly regulated by biome type and climate conditions [115]. To mitigate drought effects on SOC, land-use change is important to avoid in management practice within a dryland ecosystem [2; 138]. Drought and land-use change have a direct impact on the \(C\) source/sink function of a grassland ecosystem, and may be major drivers affecting the \(C\) budget of grasslands.
# 5G V2X Performance Comparison for Different Channel Coding Schemes and Propagation Models
## 4 Simulation Results
### Simulation Results for 128-Bit Frame Length
As the SNR rises, noise has less impact on the communication channel and the signal is dominant. If we consider that the NLOSv state comes from three equiprobable transmission cases, we can assume that the NLOSv state will have slightly better performance compared to the NLOS state. This fact can be seen from Table 8, where BER is slightly better in the fourth scenario than in the third one. Scenarios 2 and 5 are based on NLOSv channel state. Although they have different traffic environments, they have common small-scale features, except for the Doppler effect. BER for these two scenarios is identical when the SNR is low, and as the SNR increases, the second scenario has a little better performance. Bearing in mind that the NLOSv state is a partially bad LOS and partially NLOS state, we can conclude that the combination of low SNR and poor channel state makes the effect of the Doppler effect small, since the bad large-scale characteristics dominate system performance. As the SNR increases, the channel performance improves and the small-scale differences become prominent. These high SNR variations are due to the appearance of the Doppler effect [70]. Vehicles in the fifth scenario (urban-NLOSv) move slower than the second scenario (highway-NLOSv), causing less fading and improving the performance by a tiny factor. SOVA has the worst overall performance, even though it behaves slightly better than polar and LDPC decoding for low SNR. Although LDPC decoding starts from an error level similar to BCJR, initially, it has slow BER convergence, which gradually accelerates as the SNR increases. The main characteristic of LDPC is the presence of error floor, which is sometimes smoother and sometimes steeper, depending on the scenario. BER in polar decoding starts from a significantly larger initial BER, which is bigger than the uncoded case [71], but it rapidly decreases. The diminishing rate gradually increases until BER becomes zero. Polar is the only algorithm that has no error floor and the only one that its BER nulls for all the aforementioned scenarios. From scenarios 1 to 5, we can deduce that BCJR, logBCJR and max logBCJR decoding algorithms have superior performance for low and middle SNR. Above this SNR, polar coding has better performance and is more suitable for coding implementations. Obviously, this is due to the fact that the parity bits of turbo codes contain more information for the data bits than the parity bits of LDPC, since the former are created by a convolutional process while the latter by a block process. Further, polar coding does not perform as efficiently as turbo because the frame binary data are too small to effectively apply the polarization effect, especially in poor SNR transmissions. Eventually, polar coding becomes superior at the high SNR region, because the BER declines rapidly in polar coding, while BER in turbo-based algorithms is limited by the presence of the error floor.
## 2 Materials and Methods
### LANGMIIR Isotherm, Activation and Adsorption Parameters  
The same equation (Equation (1)) is used for studying the temperature effect on the corrosion rate, i.e., the inhibition efficiency. During this test, the concentration of AO and HERA was 2 g/L and the immersion time is fixed at 1 h; whereas the temperature was fixed at 308, 318, 328, and 338 K.  
Activation energy \(E_{a}\) is a main key to understanding the metal-inhibitor interactions. Comparing the inhibitor and the blank activation energies, [PERSON] distinguished between two types of metal-inhibitor interactions: an electrostatic (\(E_{ai}>E_{a}\)) and a chemical (\(E_{ai}<E_{a}\)) interaction [19]. The Arrhenius logarithmic equation (Equation (2)) gives access to the activation energy \(E_{a}\), while the Arrhenius equation alternative formula (Equation (3)) gives access to other thermodynamic parameters such as adsorption entropy \(\Delta S_{ads}^{0}\) and \(\Delta H_{a}{}^{0}\) adsorption enthalpy.  
\[{In}C_{R}={Ln}(A)-\frac{E_{a}}{RT} \tag{3}\]  
\[{ln}\left(\frac{W}{T}\right)=\left({ln}\left(\frac{R}{Nh}\right)+\frac{\Delta S _{a}^{0}}{R}\right)-\frac{\Delta H_{a}^{0}}{RT} \tag{4}\]  
where: \(A\) is a constant, \(R\) is the gas constant, \(N\) is the Avogadro's number, and \(h\) is [PERSON]'s constant.  
Based on the results of the temperature effect, and in order to define the adsorption thermodynamic parameters, LANGMIR isotherm was plotted using Equation (4):  
\[\frac{C_{inh}}{\theta}=\frac{1}{K_{ads}}+C_{inh} \tag{5}\]where \(C_{inh}\) is the concentration of the tested inhibitor, \(\circ\) is the area coverage, and \(K_{ads}\) is the the adsorption-desorption equilibrium constant [20]. The calculated \(K_{ads}\) from Equation (4) allows for the calculation of the free adsorption energy \(\Delta G^{\circ}\)_ats_ using Equation (6):  
\[\Delta G^{\circ}_{ads}=-RT\times ln(K_{ads}\times C_{M}(solvent)) \tag{6}\]  
The \(C_{M}\)(_solvent_) is the solvent's molar concentration which is equal to 55.5 mol\(\cdot\)L\({}^{-1}\) for H\({}_{2}\)O. \(R\) is the gas constant (R = 8.314 J\(\cdot\)K\({}^{-1}\cdot\)mol\({}^{-1}\)).