esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Compound Strata and Surface Settlement
### Regression Analysis of the Peck Formula  
We take the logarithm on both sides of the Peck formula, obtaining  
\[\ln S(x)=\ln S_{\max}+\frac{1}{i^{2}}\times\left(-\frac{x^{2}}{2}\right) \tag{4}\]  
\[Y=\ln S(x)\]  
\[X=-x^{2}/2\]  
We perform regression analysis using \(Y\) and \(X\) as the regression variables. Let \(\ln S(x)\) be the constant term after regression and \(1/i^{2}\) be the linear coefficient after regression. To simplify notation, we define  
\[S_{\rm xx}=\sum\left(\frac{-x_{i}^{2}}{2}\right)^{2}-\frac{1}{n}\left(\sum \frac{x_{i}^{2}}{2}\right)^{2} \tag{5}\]  
\[S_{\rm xy}=\sum\left[\left(\frac{-x_{i}^{2}}{2}\right)\ln S(x_{i})\right]- \frac{1}{n}\sum\left(\frac{-x_{i}^{2}}{2}\right)\sum\ln S(x_{i}) \tag{6}\]  
\[S_{\rm yy}=\sum\ln^{2}S(x_{i})-\frac{1}{n}\left[\sum\ln S(x_{i})\right]^{2} \tag{7}\]  
\[b=\frac{S_{\rm xy}}{S_{\rm xx}} \tag{8}\]  
\[a=\frac{1}{n}\left[\sum\ln S(x_{i})-b\sum\left(\frac{-x_{i}^{2}}{2}\right)\right] \tag{9}\]  
where \(a\) is the constant term in the regression equation, \(b\) is the linear coefficient in the regression equation, \(x_{i}\) is the distance from the \(i\)-th settlement monitoring point to the tunnel centerline, and \(n\) is the number of sample points.  
We use the \(r\)-test method in mathematical statistics to determine the goodness of fit, which indicates the closeness of the monitoring values of the variable \(\ln S(x)\) to the regression curve. The linear correlation coefficient \(r\) is commonly used for this purpose.  
\[r=\frac{S_{\rm xy}}{\sqrt{S_{\rm xx}\times S_{\rm yy}}} \tag{10}\]  
When \(r>0.8\), \(X\) and \(Y\) are highly correlated. When \(0.5<r<0.8\), \(X\) and \(Y\) are significantly correlated. When \(0.3<r<0.5\), \(X\) and \(Y\) have a low correlation. When \(r<0.3\), \(X\) and \(Y\) are not correlated. Based on this, we obtain the optimal fitting parameters for \(S_{\max}\) and \(i\) after linear regression:  
\[S_{\max}=\exp(a) \tag{11}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(k\) & \(V_{1}\)/\% & \(i\)/m & \(S_{\max}\)/m \\ \hline Stratum 6 & 0.52 & 2.0 & 11.44 & 14.02 \\ Stratum 7 & 0.88 & 0.53 & 19.36 & 3.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter values for [PERSON]’s empirical formula.  
The widely adopted Abaqus2020 finite element software has found extensive application in engineering research, aiding engineers and scientists in gaining a deeper understanding and facilitating the prediction of the behavior of engineering systems. In order to investigate the uneven settlement range and the variation in \(S_{\text{max}}\) and \(i\) values in the direction of shield tunnel excavations in composite geological formations, the finite element software Abaqus2020 was used to simulate the sixth and seventh geological strata (soil-rock composite formations) in the studied project area.
# Low-Complexity Online Calibration for Large-Scale Multi-Beam Antennas
## 4 Simulation Results  
In this section, system simulations in various scenarios are provided. We use MATLAB as the simulation software to investigate the system performance of the proposed online calibration method and verify the closed-form expressions for \(A_{k,1}^{\text{RMSE}}\) and \(\phi_{k,1}^{\text{RMSE}}\).  
The simulation parameters are set as follows. The length-of-spread spectrum code for calibration is \(L=1023\). The power of the communication signal is 30 dB larger than that of the calibration signal. The phase \(\varphi_{k}\) is randomly distributed [23] in the range \([-\pi,+\pi]\). Due to the large scale of the antenna to be calibrated, we define average RMSE (ARMSE) of the estimated relative amplitude (in dB) and phase (in degrees) as follows:  
\[A_{K}^{\text{ARMSE}} =\frac{1}{K-1}\sum_{k=2}^{K}A_{k,1}^{\text{RMSE}}, \tag{28}\] \[\phi_{K}^{\text{ARMSE}} =\frac{1}{K-1}\sum_{k=2}^{K}\phi_{k,1}^{\text{RMSE}}. \tag{29}\]  
Figure 6 depicts the ARMSE of the relative amplitude and phase with various \(K\). It can be seen that our theoretical analysis results, which are approximated by (26) and (27), match all the simulation curves well in various K scenarios. Meanwhile, the proposed online calibration method can effectively eliminate the strong interference from high-power communication signals.  
In practical application system, the received power of multiple calibration signals is affected by different link losses. In order to evaluate the calibration accuracy in this case, we set \(\gamma_{1}-\gamma_{k}=20\) dB (the power of calibration signal corresponding to the \(k\)-th antenna is 20 dB lower than that of the reference antenna) and \(\gamma_{1}-\gamma_{k}=10\) dB, compared with \(\gamma_{1}-\gamma_{k}=0\) dB (equal power condition). Figure 7 shows the RMSE of the relative amplitude and phase with unequal power conditions. Theoretical results are approximately by (24) and (25). Simulation results demonstrate that when the power of calibration signal is 20 dB lower than the reference antenna, the relative amplitude and phase performance deteriorate by 6.12 dB and 40.96 degrees, respectively.  
Figure 6: Theoretical and simulation calibration accuracy. (**a**) ARMSE of the relative amplitude. (**b**) ARMSE of the relative phase.  
Figure 8 indicates the ARMSE of the relative amplitude and phase versus \(\delta_{k}\), validating the \(20\text{log}_{10}(L)\) relationship in (19). On the one hand, the longer the code length, the greater the spread spectrum gain and the higher the calibration accuracy. If the code length is doubled, RMSE curves shifts by 6 dB to the left. On the other hand, the correlation operation is performed in a period of code length and one measurement result is obtained. The longer the code length, the longer the measurement time. Therefore, it is necessary to compromise between high calibration accuracy and short measurement time in application.
## 1 Introduction
#### 2.2.2 Model CO\({}_{2}\) Flux Components
The provided CarbonTracker Europe files come with 1\({}^{\circ}\) latitude and longitude spatial resolution and three-hourly temporal resolution. They are 3D molefraction files, which were converted from the forward run's molefraction files with the optimized fluxes from CTE2017-FT. As previously suggested by [PERSON] et al. [73], strong vertical gradients affecting near-surface mixing ratios in the first two vertical levels of CarbonTracker were smoothed by averaging over the first three levels weighted by thickness of each vertical level. This approach conserves the mass and removes excessive accumulation or depletion caused by incorrect representation of vertical mixing in the first levels above the surface. The CTE2017-FT three-hourly simulated mixing ratios were then interpolated onto our WRF boundaries and continuously advected into the domain. The interpolation of pressure levels and mass balance conservation was achieved using the algorithm described in [PERSON] et al. [74], which has been adapted from a script originally developed by [PERSON] (University of Chile, Santiago, Chile) and [PERSON] (NOAA/ESRL/GSD, Boulder, CO, USA), and has adaptations specifically suited to a WRF-Chem setup using multiple tracers. The mean CO\({}_{2}\) fluxes emitted from each of the respective components are shown in Figure 3 for the innermost 1 km WRF domain, showing both the spatial and temporal means. Spatially, the means are taken across every land pixel for the land-based fluxes and across every ocean pixel for the ocean flux. Temporally, the mean is taken across all hours in the three periods of interest: Summertime 2016, Wintertime 2017, and Springtime 2017. We chose to include all three biogenic components here, namely, GPP, NEE, and Respiration, for illustrative/comparative purposes. Fire emissions were left out because they are point sources, meaning that the spatial average over the whole domain is effectively zero even during emission events. The summertime fluxes are the most dynamic, as was expected. The magnitudes of both the GPP and Respiration are significantly higher in summer than in winter. The dryness of the Marseille cityscape explains the respiration; however, the region around Marseille is well forested (in fact, forests represent almost half of the PACA territory [75; 76]), which accounts for the increased GPP. Unlike many regions, NEE is positive in the summertime and negative in the winter. This is a known feature of this climate (e.g., [77]) and is primarily driven the hot and dry the summers are, which lead to increased respiration, while the winters are relatively mild and wet. Because of how unusual this feature is, we include the Springtime 2017 period for comparison. During the spring, there is significantly more GPP with a moderate amount of respiration, leading to a significantly more negative NEE than during the winter, as would be expected.
## 3 Results
### Bacterial Pre-Concentration with Aptamer Decorated Magnetic Beads  
The principle of the direct PCR method for _E. coli_ detection in salted media is described in Figure 3. The first step was pre-concentration of bacterial cells with magnetic beads decorated with the specific E1 aptamer that recognizes an epitope on the surface of _E. coli_ vegetative cells. The second step was a direct PCR detection of _E. coli_ attached to beads.  
The feasibility of _E. coli_ detection by an integrated aptamer-magnetic direct PCR method was studied on bacterial cells cultured in 0.4 M NaCl, BP medium. This supplemented medium was considered as artificial seawater because most natural oligotrophic seawaters contain about 1 mg of dissolved organic carbon in addition to the high salt concentration [11].  
Streptavidin-biotin conjugated chemistry was used to decorate magnetic beads with the E1 aptamers. Bright monodisperse metallic beads of a spherical shape were observed before bacterial immobilization (Figure 4a). The technique did not enable to visualize aptamer molecules on the beads surface because of nanometric sizes of biotinylated aptamer molecules. When admixed to a PB + 0.4 M NaCl culture containing _E. coli_, functionalized beads captured efficiently bacterial cells. Figure 4b shows that functionalized beads after 30 min incubation with bacteria were covered with bacterial cells, even after multiple washings.  
The efficiency of capturing was calculated by enumerating bacteria in supernatant after magnetic concentration using a drop plate method remained. It was estimated to 90% for \(10^{4}\) CFU/mL of _E. coli_ in BP + NaCl.  
Figure 4: **(a) Phase contrast transmission micrographs of magnetic beads functionalized with the E1 aptamer. Scale bar, 10\(\upmu\)m. (b) Phase contrast study of _E. coli_-bead complex after capturing. Scale bar, 10\(\upmu\)m stains for both images.**  
Figure 3: Schematic representation of the developed integrated apta-magnetic direct PCR method.
## 3 Pre-Processing
### RGB to Grayscale  
The camera acquires images of the road characteristics, providing the functionality of video acquisition in real-time, consisting of \(\times\) frames/sec, in which a frame is a collection of pixels available in RGB format [21]. Therefore, the acquired frame represents the image of the road according to the field of vision of a camera. Therefore, the calibration and fixed focus configuration are necessary assignments. In addition, the image acquired may have noise, which can be removed based on applying the binary buffering technique over the grayscale image.  
The conversion from RGB to grayscale or intensity is one of the first steps of image processing algorithms, reducing the amount of image information. It allows us to reduce the processing demand, while keeping the POI (Point of Interest) features in the image, and process the object recognition strategy faster. In addition, the ROI (Region of Interest) adoption provides effective process demands for just the specific region of interest, which in our case is the lane recognition. This conversion defines specific weights that must be applied to R, G, and B channels. Although a grayscale image contains less information than an RGB one, most essential features are maintained, such as edges.  
The RGB to intensity conversion consists of the concatenation of three channels of a pixel, based on the multiplication of each channel by a specific factor and summed, to result in a single channel pixel. The conversion process keeps the dimensions of rows and columns of the frame and decreases the dimensions of each pixel from three to one. The multiplication factors based on the NTSC CCIR 601 standard are demonstrated in  
Figure 4: (**a**) For LKA function, the input is the image captured by the camera, and calculation of lane and vehicle positions; (**b**) if the vehicle gets out of the lane boundary, an Electronic Power Steering (EPS) system is activated to keep the vehicle within the lane.  
Equation (1) according to [20]. For \(Pixel_{i,\ j}\), it means the specific pixel value in the image frame with index \(i=row\) and \(j=column\)[22], is obtained by using the following formula  
\[Pixel_{i,\ j}=0.2989R+0.5870G+0.1140B, \tag{1}\]  
where \(R\), \(G\), and \(B\) are color channel values corresponding respectively to red, green, and blue (R: Red, G: Green; and B: Blue). For the grayscale image, we still have a sharp image of the ROI. Nevertheless, the image output in grayscale enables an edge detection filter to be applied later. The edge detection allows characterizing and highlighting the objects contained in the image. There are several edge detection filters, such as Canny, Deriche, Sobel, and Prewitt.  
Overall, the image in grayscale is an artifact to be delivered to perform the buffering method at the pixel level to remove undesirable noises [22].
## 2 Methodology
### NBTI Effect and Its Impact on the PVTC Method  
At present, the NBTI effect is the dominant aging mechanism for VLSI circuits; this effect occurs when pMOS transistors are negatively biased, which causes a shift in the threshold voltages (\(V_{th}\)) and in turn causes changes in circuit characteristics during its lifetime. [PERSON] _et al._ (2016) noted that in small-sized devices in CMOS logic circuits, the threshold voltage change induced by the NBTI effect is dominated by stochastic charging/discharging events of individual oxide defects; therefore, each device shows a different degradation curve. Clearly, this type of NBTI model will greatly complicate the analysis process. Because our aim was to estimate the transient fault rate that considers both environmental noise and the NBTI effect and provide a design basis for transient fault mitigation techniques, in this paper, pMOS transistors are assumed to suffer the same degree of degradation during the lifetime of the circuits, and the R-D model is applied to analyze the NBTI effect. According to the R-D model, the \(V_{th}\) degradation of pMOS transistors caused by NBTI has two phases: the stress phase and the recovery phase. The magnitude of \(V_{th}\) increases during the stress phase (_i.e._, \(V_{gs}=-V_{dd}\)), whereas the magnitude of \(V_{th}\) partially decreases towards its initial value during the recovery phase (_i.e._, \(V_{gs}=0\)); however, recovery can only partially alleviate the effect of NBTI. The threshold voltage change during stress and recovery cycles is illustrated in Figure 8.  
Therefore, the magnitude of \(V_{th}\) degradation is strongly dependent on the duty cycle (\(\alpha\)) of the input signal, which reflects the fraction of time that the pMOS transistor spent in the stress state in one clock period. In this paper, we apply the long-term prediction model proposed in (Kip  
The change in the SVTC curve for an inverter gate caused by the \(V_{th}\) increase is shown in Figure 10; this change, in turn, affects the occurrence probability of transient faults. Therefore, to acquire accurate transient fault rate estimations during the lifetime of circuits, changes in circuit characteristics caused by NBTI effect should be considered in the PVTC analysis framework.
## 2 Equipment and Methodology  
In this section, the measurement equipment used to determine the meteorological and radiation variables is detailed, as well as the methodology followed to develop the models that predict I\({}_{\text{UV}}\) levels. In the present work, these models are developed for all-sky conditions.
# Blind Image Separation Method Based on Cascade Generative Adversarial Networks
## 3 Experiments
### Datasets  
The natural images are from datasets of shoe and bag images. We selected 1000 images from both the shoe dataset [28] and bag dataset [29] as known samples and performed UGAN processing on the selected images. After processing, 600 images were used as training samples, and 400 images were used as test samples. To test the separation performance of the PAGAN, we also selected 1200 images from both the shoe and bag datasets as known samples and randomly mixed the selected images at a ratio of 7 to 3. In this way, we could test the ability of PAGAN to separate strong sources from synthetic mixed images, and prove the separation performance of the network. Of these images, 1000 were used as training samples and 200 images were used as test samples. For different datasets, we trained models separately for testing.  
Because of the potential demonstrated by our method, we further extended our proposed method to the practical application of remote sensing images and conducted experiments on different remote sensing image datasets.  
For the remote sensing image datasets, two benchmark datasets from RICE [30] were adopted: RICE-I and RICE-II. RICE-I was collected from Google Earth, and it con tained a total of 500 pairs of cloud and corresponding cloudless images with a size of the \(512\times 512\) pixels [30]. The coverage areas of these images did not overlap each other. The image size in the RICE-II dataset was the same as that in RICE-I, which was \(512\times 512\) pixels. The RICE-II dataset contained 700 pairs of images without overlapping, and this dataset was part of the Landsat 8 OLI/TIRS dataset [30].
# DDPG-Based UAV-RIS Framework for Optimizing Mobility in Future Wireless Communication Networks
###### Abstract  
The development of beyond 5G (B5G) future wireless communication networks (FWCN) needs novel solutions to support high-speed, reliable, and low-latency communication. Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are promising techniques that can enhance wireless connectivity in urban environments where tall buildings block line-of-sight (LoS) links. However, existing UAV-assisted communication strategies do not fully address key challenges like mobility management, handover failures (HOFs), and path disorders in dense urban environments. This paper introduces a deep deterministic policy gradient (DDPG)-based UAV-RIS framework to overcome these limitations. The proposed framework jointly optimizes UAV trajectories and RIS phase shifts to improve throughput, energy efficiency (EE), and LoS probability while reducing outage probability (OP) and HOF. A modified K-means clustering algorithm is used to efficiently partition the ground users (GUs) considering the newly added GUs as well. The DDPG algorithm, based on reinforcement learning (RL), adapts UAV positioning and RIS configurations in a continuous action space. Simulation results show that the proposed approach significantly reduces HOF and OP, increases EE, enhances network throughput, and improves LoS probability compared to UAV-only, RIS-only, and without UAV-RIS deployments. Additionally, by dynamically adjusting UAV locations and RIS phase shifts based on GU mobility patterns, the framework further enhances connectivity and reliability. The findings highlight its potential to transform urban wireless communication by mitigating LoS blockages and ensuring uninterrupted connectivity in dense environments.  
DDPG; GUs connectivity; RIS configuration; LoS probability; UAV trajectory; UAV-RIS; user grouping +
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Article
# Using Mixed Reality (MR) to Improve On-Site Design Experience in Community Planning
## 1 Introduction
Additionally, portable devices can be a tangible input interface to provide real-time manipulation behaviors (e.g., moving, rotating and zooming) for designers to adjust their virtual design objects in AR environments [4]. At the same time, real-time environmental analysis (such as wind, light and heat) for the virtual design objects, combined with remote computing, can be rendered in AR environments [8; 20]. Since this 2D display on the screen of the portable device barely describes the real 3D spatial relationship, it was difficult for designers to make intuitive design perceptions, even if they returned to the design site [21]. Similarly, the input interface of the AR design support system (AR-DSS) on the screen of the portable device was relatively stiff. These interactions did not support flexible design actions, and it was difficult for designers to make accurate judgments [21]. In short, depending on portable devices, AR technology can bring virtual design objects into real design environments, thus avoiding the repeated trips between the design studios and design site and providing possibilities relating to the improvement of the on-site design convenience. Nevertheless, the visualization and interaction of virtual design objects are limited by the 2D screen display of portable devices, which hardly improve on-site design intuitiveness and accuracy. Mixed reality (MR) technology combines the advantages of both VR and AR [22] since it can merge real and virtual worlds to produce new visual environments where physical and digital objects coexist and interact in real time [23]. The rapid development of hardware, especially the advent of the Microsoft HoloLens (Microsoft, Redmond, WA), a head-mounted display, makes MR available for some professional fields. Until now, MR has been used in the medical field for 3D visualization in surgery planning [24; 25], the aerospace field for the simulation of the space exploration environments [26], the tourism field for interactive acoustic and visual navigation [27], the engineering field to promote communication and safety in on-site construction [28; 29], and in other fields. Overall, these successful applications demonstrate some important technical characteristics of MR, such as 3D on-site visualization, natural control mode (based on gaze and gesture), accurate spatial mapping, and real depth perception, which offer the potential to improve the current on-site design intuitiveness, accuracy, and convenience. This study intends to apply mixed reality (MR) to community planning to improve on-site design with regard to intuitive perception, accurate judgment, and convenient decisions. Thus, an on-site design experiment, using an MR design support system (MR-DSS) for a typical community planning and design scene, was performed to examine the effectiveness of MR.