esa-sceva/satcom-chunk-collection huggingface dataset:
Geochemical Assessment and Mobility of Undesired Elements in the Sludge of the Phosphate Industry of Gafsa-Metlaoui Basin, (Southern Tunisia)  
[PERSON]  
1 University of Tunis El Manar, Faculty of Sciences of Tunis, Department of Geology, Tunis 2092, Tunisia; [EMAIL_ADDRESS] (M.S.); sousis  
to eliminate fine wastes (FW) in the form of mud with a particle size <71 um. The final product matches the marketable phosphates (MP) with a particle size ranging from 2 mm to 71 um[4].  
The water that is required to wash the phosphate ore is provided by deep boreholes often located nearby the laundries. Due to the excessive consumption of drilling water (DW), the CPG has opted, for many years now, for another strategy that consists of recycling water from the washing circuit. This process is conducted by recovering water from the settling pond and mixing it with new drilling water. In this work, the discharge filtrates are sludge water (SW), which is recovered from the natural decantation of the sludge collected from the laundries.  
At the laundries, the process water is recycled after recovery from the sludge by flocculation in settling ponds. Once settled, the resulting thick mud is discharged either to tailings disposal impoundments or into the hydrographic network. However, these fine wastes (FW) may contain mineral fractions carrying undesired elements such as Cd, U, Zn, F\({}^{-}\), NO\({}_{3}\)\({}^{-}\), PO\({}_{4}\)\({}^{3-}\), etc. Several scholars have focused on both mineralogical and geochemical studies of solid discharges from laundries[5; 6; 7; 8], but without establishing a geochemical assessment between the input and output products of the phosphate laundries.  
Other scholars have focused on the impact generated by heavy metals from these discharges at the soil-plant interface[7; 8; 9; 10], but they did not highlight the influence of mineralogy and geochemistry of the solid effluents on the mobility of metallic trace elements (MTE).  
In this study, we have tried, firstly, to determine the mineralogical and geochemical characterization of the input and output products of the two laundries of Zone L (ZL) of M'Dhilla and Redeyef, which are installed east and west of the Gafsa-Metlaoui Basin, respectively. Second, this work is innovative in terms of mobility assessment of MTE (Cd and U) contained in the sludge being discharged by the aforementioned laundries, with reference to batch tests.
# Influence of Posture Change on Train Running Safety under Crosswind
## 4 Results
### Aerodynamic Responses
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Coefficient**} & \multirow{2}{*}{**Car**} & \multicolumn{2}{c}{**Entering**} & \multicolumn{2}{c}{**Exiting**} \\ \cline{3-6}  & & **Without Posture** & **With Posture** & **Without Posture** & **With Posture** \\ \hline \multirow{3}{*}{\(C_{y}\)} & Leading & 0.82 & 1.09 & 0.86 & 1.16 \\  & Middle & 0.49 & 0.57 & 0.55 & 0.59 \\  & Trailing & 0.10 & 0.12 & 0.11 & 0.15 \\ \hline \multirow{3}{*}{\(C_{z}\)} & Leading & 0.11 & 0.16 & 0.10 & 0.23 \\  & Middle & 1.48 & 2.13 & 1.53 & 2.25 \\  & Trailing & 0.35 & 0.45 & 0.49 & 0.57 \\ \hline \multirow{3}{*}{\(C_{M_{z}}\)} & Leading & 0.69 & 0.55 & 0.53 & 0.41 \\  & Middle & 0.04 & 0.05 & 0.03 & 0.03 \\  & Trailing & 0.32 & 0.63 & 0.20 & 0.28 \\ \hline \multirow{3}{*}{\(C_{M_{y}}\)} & Leading & 0.14 & 0.28 & 0.18 & 0.40 \\  & Middle & 0.12 & 0.18 & 0.19 & 0.15 \\  & Trailing & 0.37 & 0.37 & 0.56 & 0.59 \\ \hline \multirow{3}{*}{\(C_{M_{z}}\)} & Leading & 0.02 & 0.03 & 0.02 & 0.02 \\  & Middle & 0.29 & 0.18 & 0.30 & 0.15 \\ \cline{1-1}  & Trailing & 0.92 & 0.61 & 1.11 & 0.58 \\ \hline \end{tabular}
\end{table} 
Table 2: Peak-to-peak values of the aerodynamic coefficients when each car entered and left the crosswind region with and without the posture.
## 2 Finite Element Analysis of the Original Structure  
### Introduction of the Reflector Antenna  
MICAP is a package of active and passive microwave instruments including an L-band scatterometer and L/C/K band radiometers. The scatterometer and radiometers share the parabolic cylinder reflector antenna with linear array feeds.  
The dimensions of the parabolic cylindrical reflector antenna's generatrix were designed as illustrated in Figure 1.  
As shown in Figure 1, the equation of the parabola is \(x^{2}=4\times F\times Z\), where the focal distance F equals 1500 mm. Then, selecting two lines that are 5.72 degrees and 92.79 degrees from the \(z\)-axis, respectively, and that intersect with the parabola, the parabola between the two intersection points is the generatrix of the parabolic cylindrical reflector surfaces.  
A structural concept diagram of the parabolic cylindrical antenna is shown in Figure 2. The overall structure consists of three reflector surfaces, two sets of side hinge mechanisms, one set of root hinge mechanisms, and one set of feed source compartment structures. The overall connection relationship is as follows: the left and right reflector surfaces are connected to the central reflector surface through the side hinge mechanisms, and the central reflector surface is connected to the feed source compartment through the root hinge mechanism.  
\begin{table}
\begin{tabular}{l l} \hline
**Literature** & **Research Gate** \\ \hline
[11] & Reflector antenna simulations through varying the number and height of the reinforced ribs, no parametric analysis for the thickness of reinforced ribs \\
[13] & Reflector antenna, no optimization and parametric analysis for reinforced ribs \\
[17] & Primary mirror topology optimization with the objective of the minimization of the overall structural flexibility, size optimization for height, no analysis for the thickness of reinforced ribs \\
[19] & Used topology and size optimization to obtain the rib layout and thickness, no analysis for the height of the ribs \\
[20] & Used topology optimization to obtain the rib layout, no analysis for rib height and thickness \\ This study & Reflector antenna, uses topology optimization to obtain the layout of reinforced ribs, parametric analysis on thickness and height of reinforced ribs \\ \hline \end{tabular}
\end{table}
Table 1: Literature research.  
The size of a single reflector surface is 1.91 \(\times\) 3.5 m, and the final overall size after deployment in orbit is 5.6 \(\times\) 3.5 m. Through preliminary analysis, it was determined that due to the larger size of the antenna reflector surface, its influence on RMS would be significantly higher than the feed source compartment. Therefore, the subsequent finite element analysis, topology optimization, parametric analysis, and RMS calculations only focus on the antenna reflector surface.
## 7 Experimental Verification
However, in this paper for simplicity and since the parasitic resistance of the boost converter different components (Lb, Qb, Db, Cb) does not have more effects in the converter THD, the parasitic resistances were neglected in the modeling of the PFC boost converter in the simulation process. The effect of the parasitic resistance of the boost converter different components can be observes in the experimental results for the efficiency which is less than the simulation results for the same operating and loading condition. At 1000 W loading condition, the designed converter total power losses calculated using the simulation were about 16.5 W, but with the same loading condition the total power losses calculated with the experiment were about 24.5 W.
# Comparison of Current Five-Point Cylindicity Error Separation Techniques
## 3 Applicative and Comparative Analysis for In Situ Measurement of Cylindricity
### Applicative Analysis of Spiral Scan EST
Now, how to estimate the output of Probes 2-4 in this angular range would affect the precision of the measured spiral line profile on the cylinder. Here, the outputs of Probes 2-4 in the angular range from \((2\pi M-\varphi)\) to \(2\pi M\) are assigned with the data collected by Probe 1 in the angular range from 0 to \(\varphi\) so as to best agree with the requirement for closed profile. Figure 12 demonstrates the verified results of this EST in the case of \"actual measurement\". From Figure 12, it may be found that the given spiral line profile on the cylinder with the median line deviation, radius deviations and roundness errors, and the measured spiral line profile is  
Figure 12: Comparison of the measured spiral line profile with given one in the case of actual measurement. Here, angular position of probes \(\varphi_{n}\)\((n=1,2,\cdots,5)\) is \(0^{\circ}\), \(395^{\circ}\), \(838^{\circ}\), \(1236^{\circ}\), \(1659^{\circ}\), respectively, and pitch of spiral line is \(T=10\) mm. Number of total rotating circles of spiral line is \(M=29\). almost the same as the given one except for its slight translation. Their differences also present the periodicity of the 29 th harmonic. Because the data substitutions of Probes 2-4 introduce some errors, the difference between the measured spiral line profile and given one has the high-frequency components. In addition, the measured spiral line profiles at both ends of the cylinder are deteriorated significantly. However, in engineering practice, we pay attention to low frequency parts of the measured profile, high frequency error in the measured profile can be eliminated by filtering. Therefore, the shortened effective measurement length of the spiral line profile is a weakness of this EST. It is worth noting that the above numerical verification results were obtained under the condition that the radial error motions of two imaginary rolling bearings and carriage error motions were only 20% of the given spiral line profile on the cylinder. If this ratio goes up, the confidence level of the spiral line profile measured by this EST will be greatly decreased. In Reference [10], a prototype instrument was developed based on the spiral scan EST. Comparing the results of the instrument with that of a commercial roundness measuring machine indicated the feasibility of measuring out-of-roundness, but the instrument seemed to be not able to identify the curved median line profile of the cylinder. In the opinion of the authors of Reference [10], the \(M\)th harmonic suppression issue in this EST was worth in-depth examined. Whereas, they did not touch the issue of the multi-point straightness EST based on DFT.
# Inf-OSRGAN: Optimized Blind Super-Resolution GAN for Infrared Images
## 4 Experiments
### Discussion  
Our model has a few limitations that are worth mentioning. Firstly, due to the diverse range of degradation modes present in infrared images, the convergence of our model requires a longer training time. This is because the model needs to learn and adapt to various degradation patterns, which leads to a more complex optimization process. Additionally, our model does not specifically simulate certain degradation models unique to infrared images, such as blind pixels or elemental stripes. These degradation modes tend to result in overcompensation, leading to worse results. This limitation arises from the fact that the training data and the designed loss function may not fully capture the complexities associated with these specific degradation modes.  
It is important to acknowledge that these limitations suggest potential areas for future research and improvement. Future work could focus on developing more robust models that can handle a wider variety of degradation modes and specifically address the challenges posed by unique infrared degradation patterns. By addressing these limitations, we can further enhance the performance and applicability of our proposed model. Additionally, we plan to explore the applicability of the optimization techniques proposed in this paper to other super-resolution models. By applying these optimization methods to different models, we aim to assess their effectiveness and potential for improving the performance of various super-resolution approaches. Furthermore, we intend to investigate the transferability of these optimization strategies across different domains, such as medical imaging, satellite imagery, or low-light photography. This will involve conducting a series of experiments to evaluate how well these techniques perform when applied to datasets specific to these domains, which often present unique challenges, such as the varying anatomical structures in medical images. By evaluating the performance of the proposed techniques in these diverse domains, we will gain insights into their adaptability and potential for wider adoption. In addition, if there is sufficient demand, we may develop a software interface for the Inf-OSRGAN model, to enhance the user experience and accessibility.
###### Abstract  
The ability of sixteen wood- and soil-inhibiting basidiomycete strains and four ascomycete strains to degrade the most hazardous, widespread, and persistent pollutants (polycyclic aromatic hydrocarbons, oxyethylated nonylphenol, alkylphenol, anthraquinone-type synthetic dyes, and oil) was found. The disappearance of the pollutants, their main metabolites, and some adaptive properties (activities of ligninolytic enzymes, the production of emulsifying compounds and exopolysaccharide) were evaluated. The toxicity of polycyclic aromatic hydrocarbons decreased during degradation. New data were obtained regarding (1) the dependence of the completeness of polycyclic aromatic hydrocarbon degradation on the composition of the ligninolytic enzyme complex; (2) the degradation of neonol AP-12 by higher fungi (different accessibilities of the oxyethyl chain and the aromatic ring of the molecules to different fungal genera); and (3) the production of an emulsifying agent in response to the presence in the cultivation medium of hydrophobic pollutants as the common property of wood- and soil-inhibiting basidiomycetes and ascomycetes. Promise for use in mycoremediation was shown in the wood-inhibiting basidiomycetes _Plentrotus estratus_ f. Florida, _Schizophyllum commune, Trametes versicolar_ MUT 3403, and _Trametes versicolar_ DSM11372; the litter-decomposing basidiomycete _Stropharia rugosannulata_; and the ascomycete _Cladosporium herbarum_. These fungi degrade a wide range of pollutants without accumulation of toxic metabolites and produce ligninolytic enzymes and emulsifying compounds.  
fungi; biodegradation; hazardous pollutants; ligninolytic enzymes; emulsifying activity; mycoremediation +
Footnote †: journal: Applied Sciences
# Qom--A New Hydroologic Prediction Model Enhanced with Multi-Objective Optimization
###### Abstract  
The efficient calibration of hydrologic models allows experts to evaluate past events in river basins, as well as to describe new scenarios and predict possible future floodings. A difficulty in this context is the need to adjust a large number of parameters in the model to reduce prediction errors. In this work, we address this issue with two complementary contributions. First, we propose a new lumped rainfall-runoff hydrologic model--called Qom--which is featured by a limited set of continuous decision variables associated with soil moisture and direct runoff. Qom allows to separate and quantify the volume of losses and excesses of the rainwater falling in a hydrographic basin, while a Clark's model is used to determine output hydrograms. Second, we apply a multi-objective optimization approach to find accurate calibrations of the model in a systematic and automatic way. The idea is to formulate the process as a bi-objective optimization problem where the Nash-Sutcliffe Efficiency coefficient and percent bias have to be minimized, and to combine the results found by a set of metaheuristics used to solve it. For validation purposes, we apply our proposal in six hydrographic scenarios, comprising river basins located in Spain, USA, Brazil and Argentina. The proposed approach is shown to minimize prediction errors of simulated streamflows with regards to those observed in these real-world basins.  
hydrologic model; prediction; multi-objective optimization; metaheuristics +
Footnote †: journal: Applied Sciences
# Software Reliability Growth Model with Dependent Failures and Uncertain Operating Environments
## 2 Software Reliability Growth Model  
### Poisson Processes  
The counting process is represented by \(\mathrm{N(t)}\), i.e., the total number of events up to time \(\mathrm{t}\). The counting process satisfies the following conditions [30]:  
1. \(\mathrm{N(t)}\geq 0\).
2. \(\mathrm{N(t)}\) is an integer.
3. If \(\mathrm{s}\leq\mathrm{t}\), \(\mathrm{N(s)}\leq\mathrm{N(t)}\).
4. When \(\mathrm{s}\leq\mathrm{t}\), \(\mathrm{N(t)}-\mathrm{N(s)}\) represents the number of events within time interval \(\mathrm{(s,\ t]}\).  
The homogeneous Poisson process (HPP) is given by  
\[\Pr\{\mathrm{N(t)=n}\}=\frac{\lambda\mathrm{t}^{n}}{n!}\mathrm{e}^{-\lambda \mathrm{t}},\ n=0,\ 1,\ 2,\ \ldots \tag{1}\]  
where \(\mathrm{N(t)}\) is the number of failures up to time t, and \(\lambda\) is a constant. Thus, the failure rate \(\lambda\) does not change over time t. The number of failures \(\mathrm{N(t)}\) has a Poisson distribution with the mean \(\lambda\)t.  
Most software reliability models assume that \(\mathrm{N(t)}\) follows a non-homogeneous Poisson process (NHPP). When \(\mathrm{N(t)}\) follows a/n NHPP, it is given by  
\[\Pr\{\mathrm{N(t)=n}\}=\frac{[\mathrm{m(t)}]^{n}}{n!}\mathrm{e}^{-\mathrm{m( t)}},\ n=0,\ 1,\ 2,\ \ldots \tag{2}\]  
where \(\mathrm{m(t)}\) is used instead of the constant \(\lambda\), unlike with a HPP. This means that the failure rate \(\mathrm{m(t)}\) depends on time t. Here, \(\mathrm{m(t)}\) can be written as follows:  
\[\mathrm{m(t)=E[N(t)]=\int_{0}^{t}\lambda(s)ds.} \tag{3}\]
# Comparing Approaches for Explaining DNN-Based Facial Expression Classifications
## 2 Background and Related Work
### Background on Explainable AI
#### 2.1.1 Shap  
SHAP (SHapley Additive exPlanation) [22] is a widely used method for generating explanations. Its main goal is to calculate the contribution of each feature to the prediction, thus explaining what features are the most important for a prediction. This is done using Shapley values, which have their foundation in game theory [23]. In short, importance of a feature \(f_{i}\) is calculated using a weighted average of the difference in prediction \(f(S\cup f_{i})-f(S)\), where \(S\) is a subset of the original feature set and the values of the complement set are assumed missing. SHAP also comes with some desirable properties: local accuracy (fidelity), missingness, and consistency [22].  
We chose to use the SHAP method-more specifically the model-agnostic version of SHAP: KernelSHAP-over e.g., LIME (Locally Interpretable Model-Agnostic Explanations) [24], since the KernelSHAP implementation extends the heuristically driven LIME, but with the desirable properties of SHAP included. KernelSHAP is a model-agnostic, post-hoc method for generating explanations. It can thus work on any pre-made model.  
SHAP can also be used for explaining CNNs, where the input does not consist of distinct features, but an image. In that case, SHAP groups pixels together as so-called'super-pixels' and calculates the values with these super-pixels as features. However, this approach is computationally much more expensive than gradient based methods, e.g., Grad-CAM, which is described below.
# Wireless Energy Harvesting Two-Way Relay Networks with Hardware Impairments
## 2 System and Signal Model
### Instantaneous Achievable Sum Rate: AF Relaying  
Since with the AF protocol directly amplify the received original data, its achievable sum rate is dependent on the end-to-end SNDR. By substituting Equations (5) and (8) into Equation (6), the received signal at \(S_{i}\) is rewritten as  
\[\begin{split} y_{S_{i}}&=h_{i}\sqrt{P_{R}}G\cdot y_{i ^{D}}+h_{i}\sqrt{P_{R}}\tau+n_{i}\\ &=\underbrace{h_{i}h_{i}\sqrt{(1-\rho)P_{i}P_{R}}Gx_{i}}_{\text {self-interference signal}}+\underbrace{h_{i}h_{i}\sqrt{(1-\rho)P_{i}P_{R}}Gx_{i}}_{ \text{required signal}}\\ &+\underbrace{h_{i}\sqrt{P_{R}}G[(h_{1}\sqrt{P_{1}}+h_{2}\sqrt{ P_{2}})\tau_{tx}+\tau_{rx}]+h_{i}\sqrt{P_{R}}\tau}_{\text{impairments distortion noise}}\\ &+\underbrace{h_{i}\sqrt{P_{R}}Gn_{r}+n_{i}}_{\text{AWGN}},\end{split} \tag{15}\]  
where \(i,\tilde{i}\in\{1,2\}\) and \(i\
eq\tilde{i}\), \(\tau_{tx}\sim\mathcal{CN}(0,\kappa_{1}^{2})\), \(\tau\sim\mathcal{CN}(0,\kappa^{2})\), and \(\kappa^{2}=\sqrt{\kappa_{1}^{2}+\kappa_{2}^{2}}\).  
With the perfect knowledge of channel gains and assuming channel reciprocity, \(S_{i}\) can remove the self-interference signal from \(y_{S_{i}}\) through self-cancellation process. Therefore, we can compute some mathematic manipulations, the SNDR at each node is expressed as follows.  
\[\text{Y}_{1}=\frac{\Xi_{1}\gamma_{1}\gamma_{2}}{\Xi_{2}\gamma_{1}(\gamma_{1}+ \gamma_{2})+\eta\rho\gamma_{1}+\Xi_{3}V_{1}} \tag{16}\]  
for \(S_{1}\) (i.e., SNR received at \(S_{1}\) transmitted by \(S_{2}\)) and  
\[\text{Y}_{2}=\frac{\Xi_{1}\gamma_{1}\gamma_{2}}{\Xi_{2}\gamma_{2}(\gamma_{1}+ \gamma_{2})+\eta\rho\gamma_{2}+\Xi_{3}V_{2}} \tag{17}\]  
for \(S_{2}\) (i.e., SNR received at \(S_{2}\) transmitted by \(S_{1}\)), where \(\Xi_{1}=\eta\rho(1-\rho)\), \(\Xi_{2}=\eta\rho[(\kappa^{2}-\rho\kappa_{1}^{2})(1+\kappa^{2})+\kappa^{2}(1- \rho)]\), \(\Xi_{3}=\tilde{\rho}+\kappa^{2}-\rho\kappa_{1}^{2}\).  
Accordingly, the data rate at \(S_{i},i=1,2\), is given by \(R_{i}=C(\text{Y}_{i})\), where \(C(x)=\frac{1}{2}\log_{2}(1+x)\). Furthermore, the total achievable sum rate of the network with the AF protocol can be expressed as  
\[\begin{split}\text{R}_{sum}^{\text{AF}}&=R_{1}+R_{ 2}\\ &=C(\text{Y}_{1})+C(\text{Y}_{2}).\end{split} \tag{18}\]  
From Equations (16) and (17), note that under the impact of hardware impairments, a continuous increase of the transmit power does not lead to a continuous increase of SNDR and achievable rate performance for AF-TWR scheme as well.
# Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions
## 2 Materials and Methods  
### Study Area  
The Heihe River Basin (HRB) is located in the middle of the Hexi Corridor in arid and semi-arid areas of northwest China, and is located between 97.1\({}^{\circ}\) E to 102.0\({}^{\circ}\) E and 37.7\({}^{\circ}\) N to 42.7\({}^{\circ}\) N. It is about 390 km wide from east to west and 510 km long from north to south. The region covers an area of approximately 143,000 km\({}^{2}\), and is the second-largest inland river basin in China. The terrain in the Heihe River Basin is high in the south, low in the north, high in the west, and low in the east. The Qilian Mountains in the south have the largest change in altitude and are the main source of snowmelt water in the basin. The annual average temperature, rainfall, and evaporation potentials for the study area are 7.5 \({}^{\circ}\)C, 136.8 mm, and 1840.1 mm, respectively.  
There are abundant geomorphic types in the region, including glaciers, frozen soils, alpine meadows, forests, irrigated crops, riparian ecosystems, deserts, and gobi. Different regions have different surface landscapes and different water and soil conditions. As a result, the reconstruction performance can be thoroughly tested. Figure 1 shows the land use types of the Heihe river basin and distribution of sites.  
Figure 1: The land use types of the Heihe river basin and distribution of sites.