esa-sceva/satcom-chunk-collection huggingface dataset:
# Model for the Prediction of Rain Attenuation Affecting Free Space Optical Links
## 3 Comparison with Analytical Methods
### Performance Assessment  
In this section, the prediction performance of the analytical models briefly described above is evaluated. Specifically, the same \(P(R)\) used to produce the set of synthetic maps through MultiEXCELL provides the input for the prediction models. As for the DSD, we have used the different values of \(\mu\) listed in Table 1. Furthermore, though \(k\) and \(\alpha\) do not depend on the wavelength \(\lambda\) (according to the asymptotic theory of optical scattering, which is valid for particles that are much larger than \(\lambda\)[29]), we have selected \(\lambda=1.55\)\(\upmu\)m, as the ITU-R model also requires the frequency \(f\) as the input (see e.g., (3)). Finally, we have considered path lengths ranging from 1 to 5 km.  
Examples of the results are shown in Figure 6, which compares the \(P(A)\) as obtained numerically from MultiEXCELL maps (blue line), with the curves estimated by the models: in this case, \(\mu=1\) and \(L=1\) km.  
Both the Lin and Moupfouma models provide accurate predictions of the \(P(A)\), while the worst performance is achieved by the current ITU-R method. This finding is likely due to the high number of coefficients defining the path reduction factor \(r\) in (3), which were regressed using a large set of rain attenuation statistics derived from millimeter-/micro-wave links as the reference. Furthermore, the Brazilian model has a poor performance, likely for the same reason. Similar conclusions can be drawn from Figure 7, which reports the same curves, but for a link with \(L=3\) km: while the Moupfouma model deviates more from the reference curve, the prediction obtained from the Lin model is still very accurate.  
The models' prediction error is quantified in terms of root mean square (RMS) and mean (M) values of the following error figure \(\varepsilon\), defined in recommendation ITU-R P.311-15 [30]:  
\[\varepsilon(P)=\begin{cases}100\Big{[}\frac{A_{m}(P)}{10}\Big{]}^{0.2}\ln \Big{[}\frac{A_{e}(P)}{A_{m}(P)}\Big{]}&A_{m}(P)\leq 10\,\text{dB}\\ 100\,\ln\Big{[}\frac{A_{e}(P)}{A_{m}(P)}\Big{]}&A_{m}(P)>10\,\text{dB}^{\prime }\end{cases} \tag{15}\]  
where \(A_{m}(P)\) and \(A_{e}(P)\) represent the path attenuations, both corresponding to exceedance percentage \(P\), extracted from the reference and the estimated \(P(A)\), respectively.  
Figure 6: Assessment of the models’ prediction performance: \(L=1\) km and \(\mu=1\).  
Figure 8 shows the mean value M (left side) and RMS (right side) of \(\varepsilon\) obtained by testing all the models considered above for various path lengths. As is clear from the figure, overall, the Lin model gives the best result (the RMS of error figure is approximately 2%), which is almost independent of \(L\). Though not shown here for brevity's sake, very similar results were obtained for different values of \(\mu\), which allows the conclusion that the Lin model can be accurately and efficiently used to predict rain attenuation statistics on FSO links.
## 3 Method
### Algorithm Training  
Two parameters in the proposed algorithm are user-defined: the significance level \(\alpha\) for the KS test in Step 1 and the scale factor \(\beta\) for determination of the change date in Step 2. The significance level is usually valued at 0.1, 0.05, or 0.01 [27], while the scale factor is a positive number reasonably smaller than 4. A grid search technique was used to select the values of the two parameters with training samples. The values in the grid search ranged from 0.01 to 0.1 with an incretion of 0.005 for significance level and from 1 to 3 with an incretion of 0.05 for the scale factor. The parameters were determined when the both lowest root mean squared error (RMSE, refer to Section 3.4) for the time of detected change and lowest RMSE for the number of changes were obtained.
## 2 Materials and Methods
### Determination of Microbial  
Total genomic DNA samples were extracted using the OMEGA Soil DNA Kit (M5635-02) (Omega Bio-Tek, Norcross, GA, USA), following the manufacturer's instructions. PCR amplification of the bacterial 16S rRNA genes V3-V4 region was performed using the forward primer 338F (5\({}^{\prime}\)-ACTCCTACGGGAGGCACA-3\({}^{\prime}\)) and the reverse primer 806R (5\({}^{\prime}\)-GGACTACHVGGGTWTCTAAT-3\({}^{\prime}\)). Seven base pair sample-specific barcodes were incorporated into the primers to facilitate multiplex sequencing. The PCR components contained 5 \(\upmu\)L of buffer (5\(\times\)), 0.25 \(\upmu\)L of Fast Pfu DNA Polymerase (5 U/\(\upmu\)L), 2 \(\upmu\)L (2.5 mM) of dNTPs, 1 \(\upmu\)L (10 \(\upmu\)M) of each Forward and Reverse primer, 1 \(\upmu\)L of DNA Template, and 14.75 \(\upmu\)L of ddH\({}_{2}\)O. The thermal cycling protocol involved an initial denaturation step at 98 \({}^{\circ}\)C for 5 min, followed by 25 cycles of denaturation at 98 \({}^{\circ}\)C for 30 s, annealing at 53 \({}^{\circ}\)C for 30 s, and extension at 72 \({}^{\circ}\)C for 45 s, with a final extension step at 72 \({}^{\circ}\)C for 5 min. The PCR amplicons were purified with Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China), and the resulting products underwent individual quantification and quality assessment using the Illumina MiSeq sequencing platform, provided by Illumina Inc. (San Diego, CA, USA). The high-quality sequences, exhibiting a homology threshold of 97%, were subsequently classified into operational taxonomic units (OTUs) for further analysis.
## 4 System Model
### Successful Transmission
#### 3.2.3 Void Hole Recovery  
On the occurrence of void holes, we have to perform some recovery methods. In this work, GEDPAR is proposed for void hole recovery purpose. GEDAR and LMPC are also used for the same purpose. However, both of these methods have some limitations, e.g., GEDAR always takes depth adjustment on the occurrence of a void hole. On the other hand, LMPC uses the concept of the binary tree and forward multiple copies of each packet.  
In the proposed scheme, we can use the option of an increase in transmission range or depth adjustment. Depth adjustment is used in the case when a neighbor node could not found even in maximum transmission range. Figure 3 presents the transmission range adjustment. The solid circle shows the original transmission range while dotted circle shows the enhanced transmission range. Depth adjustment procedure is shown in Figure 4. In this figure, R1 shows the minimum transmission range and R2 indicates the maximum transmission range. In case, if a node cannot find next forwarder in R2 then the current node must adjust its depth vertically. The node in blue color shows the new depth.  
Figure 3: Transmission range adjustment.  
Figure 2: Void node.
# Reconfigurable Intelligent Surface Assisted Target Three-Dimensional Localization with 2-D Radar
## 3 Three-Dimensional Target Localization
### Localization Method
As shown in Figure 2, the constraint (16) depicts that the \(k\)-th target is located in a fan-shaped area where the measured azimuth angle \(\theta_{k}\) is the central angle and the incremental angle around \(\theta_{k}\) is \(\Delta\theta_{k}\). In addition, the constraint in (17) and (18) represents a rectangular area, which is also a convex set. Therefore, the overlapping area of these two convex sets is still a convex set, as shown by the shaded area in Figure 2.  
Since the optimization problem (20) for localization is convex, the convex optimization method is available to find its optimal solution, and the three-dimensional localization of the targets can be obtained in the RIS-assisted 2-D radar. Figure 2: The constraint area in the optimization problem (20). The methods to solve convex functions include the gradient descent method, conjugate gradient method, Newton method, quasi-Newton method, interior point method and simplex method. There are also some special libraries and toolkits, such as CVXPY, MOSEK, Gurobi, SCIP and MATLAB optimization toolkits, which can be used to solve convex optimization problems. For the analysis of computational complexity, the interior point method is taken as an example, which is a classical algorithm for solving convex optimization problems. The computational complexity of the interior point method is usually O(\(N^{3}\)), where \(N\) is the number of unknowns. The target localization problem in this paper is a small-scale problem, \(N=3\), and therefore, this complexity is completely acceptable.
###### Abstract  
The objective of this study was to prepare bio adsorbents from agro-industrial wastes from yarn starch (YSR) and plantain (PSR) production for its use in the removal of Cr (VI) and Ni (II) in aqueous solution in batch and continuous packed-bed column systems. Bromatological analysis showed that the biomaterials are rich in cellulose, lignin, hemicellulose, and SEM micrographs that evidence a mesoporous structure characteristic of materials of lignocellulosic origin. FTIR evidenced functional groups such as hydroxyl, carbonyl, and methyl, possibly involved in the uptake of metal ions. EDS and FTIR analysis after adsorption confirmed that the retention of the metals on the surface of the adsorbent materials was successful. Cr (VI) and Ni (II) removal efficiencies above 80% were achieved using YSR and PSR in batch systems at the different conditions evaluated. The optimum conditions for removing Ni (II) on PSR were a bed height of 11.4 cm and a temperature of 33 \({}^{\circ}\)C, while for YSR, they were: 43 \({}^{\circ}\)C and 9 cm for temperature and bed height respectively. The variable with the most significant influence on the removal of Cr (VI) in a batch system on the two bio adsorbents was temperature. In contrast, the adsorbent dose and temperature are relevant factors for PSR Ni (II) removal. Therefore, the residues from the preparation of yarn and plantain starch have high potential for removing heavy metals from wastewater and are presented as an alternative for their final disposal.  
adsorption; biomaterials; packed bed column; adsorption kinetics +
Footnote †: journal: Applied Sciences
# Modeling GPR Wave Propagation in Complex Underground Structures Using Conformal ADI-FDTD Algorithm
## 4 Field Experiment  
The correctness and effectiveness of the conformal ADI-FDTD algorithm were verified by a GPR detection experiment of rectangular voids. As shown in Figure 18, a carton with a length of 0.5 m, a width of 0.3 m, and a height of 0.16 m was buried in the soil, and the top surface of the carton was 0.16m from the ground. In this experiment, GSSI SIR-4000 road radar was used for detection, and the center frequency was 400 MHz.  
The experimental model was simplified to a two-dimensional case, as shown in Figure 19. It was assumed that the relative permittivity of the soil layer was 6.8 and the  
Figure 17: GPR B\(-\)scan images of underground cavity mode.  
Figure 18: Road radar rectangular cavity experimental diagram.  
conductivity was 3 mS/m. The time step was set to 0.001 ns; the space step was set to 0.005 m, and the simulation iterations were taken as 2000. Figure 20 was a single-channel wave comparison diagram of the numerical simulation results of the conformal ADI-FDTD algorithm and the measured results.  
It can be seen from Figure 20 that the simulated waveform obtained by the algorithm in this paper is in good agreement with the measured waveform in terms of amplitude and delay, with differences only in details. This is mainly due to the fact that we considered the soil layer as a homogeneous and isotropic medium, which led to the difference between the assumed dielectric constant of the material and the real dielectric constant of the material during the simulation. This example verifies the applicability and effectiveness of the proposed algorithm for practical engineering inspection.
# STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction
## 4 Experiments
### Quantitative and Qualitative Comparison of Different Methods  
In this section, we provide a comprehensive comparison of the proposed method with the current state-of-the-art single-image super-resolution (SISR) and reference-based super-resolution (RefSR) methods on multiple test datasets. The SISR methods involved in the comparison include Bicubic, ESRGAN [13], SPSR [14], etc., whereas among the RefSR methods, we choseRSGAN for the comparison to represent the state-of-the-art in the field. We aim to demonstrate the performance and advantages of the methods under different remote sensing scenarios and image quality evaluation metrics. Six standard metrics, namely PSNR, SSIM, LPIPS, PI, RMSE, and SAM, were used to quantitatively evaluate the super-resolution (SR) results to ensure accuracy and comprehensiveness. In the results presentation, the best performing values in each row are highlighted in dark blue font. As shown in Table 2, STGAN performs best in several of the evaluated metrics, and in particular, it obtains the lowest values in all test sets of LIPIS and PI, indicating the best perceived quality. Secondly, it also outperforms the other models in terms of RMSE and SAM, especially with the lowest RMSE value and the smallest error.
# Filter Pruning via Attention Consistency on Feature Maps
## 3 Methods  
### Preliminaries  
For a given pre-trained CNN model with \(K\) convolution layers. The model weights are denoted by \(\left\{\mathbf{W}_{i}\in\mathbb{R}^{N_{i+1}\times N_{i}\times k_{i}\times k_{i}},1\leq i\leq K\right\}\). Here, \(N_{i}\), \(N_{i+1}\) and \(k_{i}\) represent the number of input channels, the output channels, and the kernel size for \(i\)-th convolution layer, respectively. The \(j\)-th filter of the \(i\)-th convolution layer is also represented as \(\mathbf{W}_{i}^{j}\in\mathbb{R}^{N_{i}\times k_{i}\times k_{i}}\), \(\mathbf{F}_{i}^{j}\in\mathbb{R}^{h_{i}\times m_{i}}\) represents the feature maps generated by \(\mathbf{W}_{i}^{j}\). Furthermore, \(h_{i}\) and \(w_{i}\) represent the height and width of the feature maps, respectively. In filter pruning, a binary indicator \(m_{i}^{j}\in\{0,1\}\) is introduced to determine whether the \(j\)-th filter of \(i\)-th convolution layer ought to be pruned, where \(m_{i}^{j}=1\) means the \(j\)-th filter and the corresponding feature maps \(\mathbf{F}_{i}^{j}\) are pruned.
# Deep Convolutional Neural Network with KNN Regression for Automatic Image Annotation
## 3 Our Proposal
### Annotating New Images  
Let us suppose that we have a new input image \(I_{new}\) without labels and we want to assign annotations to it. Similarly, T-JSEG algorithm will be employed to segment the image \(I_{new}\) and produce a set of regions \(\widetilde{r}=\{\widetilde{r}_{1},\ldots,\widetilde{r}_{s}\}\). Since we have assumed that each region \(\widetilde{r}_{i}\) corresponds to one annotation \(c_{i}\) from the annotation space, then we must calculate the conditional probabilities \(P(c_{i}\,|\,\widetilde{r}_{i})\) to find out the best annotation that fits the region.  
To assign a set of annotations, we perform a KNN regression while maximizing a Bayesian probability as follows:  
1. Embed \(\widetilde{r}_{i}\) descriptor into the appropriate manifold using the trained autoencoder model from N2D.
2. Retrieve k-nearest clusters using a simple Euclidean distance \(C_{ri}=\{c_{1},c_{2},\,\ldots,c_{k}\}\) and calculate, for each annotation \(a_{i}\) in the dataset, a regression probability: \(P(a_{i})=\sum_{i=1}^{k}M(a_{i},c_{i})\). This regressed value will be considered as a representative of the region \(\widetilde{r}_{i}\).
3. Maximize the following Bayesian probability: \(arg\,\,max\,\,P(\widetilde{r}_{i})=\frac{P(\widetilde{r}_{i}\,|\,a_{i})P(a_{ i})}{P(\widetilde{r}_{i})}\), where \(P(a_{i})=\frac{1}{Number\,\,of\,annotations}\), and \(P(\widetilde{r}_{i})=\frac{1}{k}\sum_{l=1}^{k}g(c_{i}),g(c_{i})\) calculates the center of the cluster \(c_{i}\).
4. Assign the top fit concepts \(C^{*}=\{a_{j}\}\) to the input image.  
The rationale behind involving a neighborhood of clusters, rather than one cluster, to annotate one region is to ensure that we are taking into account information about blob-to-blob relationships, which grants higher error tolerance.
## 3 Results and Discussion
### Response Surface Methodology  
The RSM model is developed using a quadratic model [30] with two axes for the variables of the process, where \(x_{1}\) represents the culture time and variable \(x_{2}\) indicates the culture humidity percentage. Equation (2) is obtained with a statistical confidence level of 95%.  
\[\begin{array}{c}C(x_{1},x_{2})=-4183.487-7.400x_{1}+110.482x_{2}+0.219x_{1}x_ {2}-0.033{x_{1}}^{2}\\ -0.786{x_{2}}^{2}\end{array} \tag{2}\]  
where \(C(x_{1},x_{2})\) indicates the amount of condiisopores produced in millions of units.  
The influence of the two independent variables \((x_{1},x_{2})\) over the response variable \(C\) is shown in Table 2, which represents the calculation of the ANOVA [31] from the quadratic model of the surface response. A Fisher value of 29.041 is obtained, and a probability value  
Figure 1: Behavior of (**a**) \(R^{2}\), (**b**) \(RMSE\), and (**c**) \(AAD\) during the training of 10,000 ANNs.  
Figure 2: The ANN that presents a better performance, with R\({}^{2}=0.9952\).  
of \(p<0.05\) is obtained. The determination coefficient (R\({}^{2}\)) from the experimental data and the forecasted values with the RSM model is 0.8284 (Figure 3), which indicates that this model describes the data with a precision of 82.84%  
The regression analysis [32] for the quadratic equation (Table 3) reveals that the interaction between the independent variables presents a bigger effect on the response variable. Nevertheless, none of these terms can be considered meaningful because the \(p\)-value is bigger than 0.05.  
The contour graph [33] in Figure 4 shows the interaction between the independent variables and their effects on the conidiospores production. These graphs are three-dimensional representations of the surface response as a function of the two independent variables, where the maximum amount of conidiospores is obtained when the culture time increases as well as the humidity percentage current in the sample. Both input variables are directly proportional to the amount of produced conidiospores.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Model Term**} & \multicolumn{1}{c}{**Coefficient Estimate**} & \multicolumn{1}{c}{**Standard Error**} & \multicolumn{1}{c}{**F-Value**} & \multicolumn{1}{c}{\(p\)-Value**} \\ \hline Constant & \(-4183.5\) & 4071.1 & \(-1.0276\) & 0.31235 \\ A & \(-7.3998\) & 6.1014 & \(-1.2128\) & 0.23467 \\ B & 110.48 & 108.13 & 1.0218 & 0.31505 \\ AB & 0.21915 & 0.075181 & 2.915 & 0.0066662 \\ A\({}^{2}\) & \(-0.033194\) & 0.012558 & \(-2.6433\) & 0.012927 \\ B\({}^{2}\) & \(-0.78625\) & 0.71922 & \(-1.0932\) & 0.28301 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Regression analysis for the quadratic model.  
Figure 3: Regression fitting for the RSM model.
## 4 Discussion
### Environmental Risk Assessment  
Out of eighteen studies, seven assessed the environmental risk using the risk quotient (RQ), reported here as the quotient between MEC (measured environmental concentration) and PNEC (predicted no-effect concentration). The environmental risk assessments considered here evaluated the potential impact of the occurrence of chemical compounds discharged into the environment on the organisms of aquatic ecosystems, such as algae, daphnids and fish. Usually, the RQ results were classified as low, medium and high risk, where RQ with a value \(\leq\) 0.1 implies a minimum risk to the organisms, the range between 0.1 and 1 indicates medium risk and an RQ \(\geq\) 1 implies high risk. However, in one study, the high-risk interpretation started from RQ values \(\geq\) 10 [11]. RQs represented here (Table 6) are the resulting analyses on 20 countries, of which only one is not in Europe;  
Figure 7: Differences in sulfadiazine (**a,b**) and tetracycline (**c**) concentrations in influent and effluent hospital and municipal WWTPs (values in ng/L are reported on a log10 scale). Results of the independent \(t\)-tests are illustrated in the Supplementary Material (Tables S12–S18). therefore, they can provide a faithful representation of the environmental risk posed by PRs released solely in this area. Among the 27 antimicrobial substances assessed, azithromycin, ciprofloxacin, clarithromycin, sulfamethoxazole and ofloxacin were reported as the PRs with the highest RQ values. Once again, the need to spotlight the frequency these harmful PRs were detected at high concentrations must be stressed. In certain countries, such as Cyprus, Portugal, Ireland, Spain, Germany, Kenya and China, the presence of more than three PRs exceeding the moderate-risk value is a cause for concern [10; 12; 15; 17]. In contrast, in Finland, Switzerland, Norway and Italy, few or no PRs exceeding the highest RQ value were reported [10; 14]. Moreover, although the high concentrations of ofloxacin, ciprofloxacin and sulfamethoxazole described for the hospitals of Istanbul were worrying, the RQ values relative to the effluent of the biological WWTPs were moderately low, 0.1, 1.7 and 0.2, respectively [11]. Many PRs are discharged into the environment as a mixture; here, the harmful impact of these compounds may be exacerbated by the synergistic effects that naturally occur. Therefore, given that these effects could lead to mutagenic and genotoxic consequences [11; 37], the assessment of the risk to the environment is an essential parameter that should always be considered during the monitoring of PRs pollution in water bodies.
## 2 Related Work
However, industrial ground surface landmarks require frequent maintenance due to scratches or damage caused by worker movement and the contamination of production materials. Installing landmarks on a target surface rather than on the ground in front of the target can significantly reduce maintenance costs and increase flexibility since they do not require frequent readjustment when the target position changes. Side-mounted landmarks provide 6-DoF pose information that indicates the relative pose between the landmarks and robots. A general approach finds the landmark's corner point in both pixel and world coordinates, converting the problem into a classical perspective-n-point (PnP) problem. The PnP problem output is the landmark's pose relative to the camera coordinates, and calibration can obtain the extrinsic parameters between the camera and AMR, considering that they are rigidly connected. [PERSON] [25] utilized ArUco recognition technology for navigation and positioning in visually guided electric assistive devices, with the results showing that vision guidance is stable, accurate, and low-cost. [PERSON] [11] proposed an EKF-based relative pose estimation algorithm to handle polluted and intermittently available visual measurements of fiducial markers. In a similar substation application, [PERSON] [26] used ArUco on both the ground and wall, with each ArUco marker on the ground fused into a map as a specific landmark and each wall ArUco indicating the charging station location. Vision-based docking methods are also utilized in underwater environments [27; 28] and drone landings [29]. Despite the high accuracy and easy deployment of vision-based docking methods, they are sensitive to ambient light conditions. To address the problem of drastic illumination changes caused by sunlight, [PERSON] [30] proposed using narrow-bandwidth near-infrared active illumination at 940 nm coupled with a matching bandpass filter on the camera, which significantly reduced variations in ambient light, making the image quality stable and consistent regardless of lighting conditions. However, there has been little research on the use of near-infrared cameras in industrial AMR docking processes. Most studies on near-infrared cameras applications in robotics have focused on using infrared LEDs as landmarks to guide robots in navigation, landing, or gripping tasks [31; 32].