esa-sceva/satcom-chunk-collection huggingface dataset:
# Uses of Nanoclays and Adsorbents for Dye Recovery: A Textile Industry Review
## 2 Sewage Treatment
### With Nanotechnologies
#### 2.2.4 Halloysite  
Today, scientists are working on employing adsorption by nanotechnologies because they consider it a very efficient method to remove different types of very harmful pollutants from water. Considerable importance has been attached to clayey minerals given  
Figure 4: Zeolite adsorption of dye.  
Figure 3: Zeolite SEM (**left**) and TEM (**right**).  
their natural origin, the possibility of chemically synthesizing them, and their unique characteristics [147]. Halloysite is an example of a 1:1-type clayed that abounds in nature and is classified as a biocompatible material. The diameter of halloysite nanotubes (Figure 5) is approximately 40-70 nm, and their length can be 200-2000 nm [148]. Their specific Surface Area (SSA), 47 m\({}^{2}\cdot\)g\({}^{-1}\), and CEC, 9.45 cmol\(\cdot\)kg\({}^{-1}\), have been determined by the authors [149] The interstratified phases are high-charge halloysite-smectite mixed-layered clays (CEC, 30-72 cmol\(\cdot\)kg\({}^{-1}\) clay) [150]. Their outer surface is negatively loaded and is composed of SiO\({}_{2}\), while their inner area is positively loaded and composed of Al\({}_{2}\)O\({}_{3}\)[151]. The use of halloysite nanotubes (HNT) has considerably increased thanks to them being cylindrical in nature and them having certain properties; e.g., their large area in relation to their size, drug release, low-cost, and thermal stability [148; 152; 153; 154; 155].  
In order to explain their aforementioned characteristics, HNT belong to the aluminosilicates family, where one octahedric aluminum oxide layer alternates with another tetrahedric silica layer. From this mismatch in the two described layer types, a characteristic hollow tubular form of this nanoclay is generated, which confers it specific characteristics that come about because of its nanoarchitecture [155]. It is important to consider that HNT properties derive mostly from their specific geological deposit, which is explained in the consulted literature [155; 156; 157; 158; 159; 160]. Their biocompatibility makes them very useful for adsorbing dyes [161; 162], such as methylene blue (MB) [163; 164; 165; 166; 167; 168; 169; 170; 171], azo dyes [172; 173; 174; 175; 176; 177; 178], triaryl and diaryl methane dyes [179; 180; 181; 182; 183; 184; 185; 186; 187], and xanthine dyes [161; 188; 189; 190], and also for the biosorption of heavy metals [191]. One study has eliminated Orange G from simulated and real waters by combining halloysite with ZnO nanoparticles and by photocatalytic degradation. Up to 94% dye removal was achieved [192].  
One of the most frequent problems in natural halloysite is its high concentration of impurities, which must be eliminated to improve adsorption capacity. Some authors describe how adsorption performance can be enhanced by using acid treatment for 12 h and by laminar surface injection [193].
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 4 Resource Allocation
### Allocation Based on DRL
[78] used a constrained discrete-continuous soft actor-critic algorithm to maximize the throughput in an environment with a discrete channel and continuous energy-harvesting time division. This actor-critic variation modeled the space of states considering the channel, battery and queue state. Furthermore, it represented its actions regarding the subchannel allocation and the harvesting time duration. Ref. [79] proposed a resource allocation mechanism that uses multiagents DQN and an SDN controller to allocate radio resources to uRLLC and eMBB to maximize the data rate. The DQN agents represented the space of states regarding the set of end-users, preallocated radio resource blocks, the channel gain, the minimum data rate, and the maximum delay. The agents operated with actions intended to assign the preallocated resource blocks to the end-users and request additional blocks from other agents.
## 3 Materials and Methods
### Mapping in Dense Vegetation by the Fusion of FPCS Components from Different Data Sources Using GEE
#### 3.3.1 Pre-Processing and Selection of Suitable Multispectral Bands
Since satellites have different revisit times, due to varying weather conditions, the images of Sentinel-2 reported higher vegetation than the Landsat-8 ones.
# Evaluation and Validation of Viscous Oil Cavitation Model Used in Torque Converter
###### Abstract  
Hydraulic torque converter is widely used in transmission units as it is able to provide variable speed and torque ratio, isolate vibration, and absorb shock. The pursuit of a highly packed power unit requires a high capacity/speed torque converter, consequently resulting in a higher risk for cavitation and severe performance degradation, noise, vibration, and even failure. Existing cavitation models generally focus on water, and the empirical parameters are not suitable for the cavitation prediction of torque converter which utilizes high viscosity oil as its working medium. This paper focused on the influence of parameters on the performance and cavitation characteristics of torque converter. A full flow passage geometry and different computational fluid dynamics (CFD) models with cavitation were developed to predict torque converter fluid behavior by resolving Reynolds-averaged Navier-Stokes equations using finite volume method (FVM). The numerical results indicated that nuclei volume fraction, vaporization coefficient, mean nucleation site radius, and maximum density ratio have great influences on the cavitation behavior. These parameters altered the degree of cavitation and the pressure distribution on the surface of stator blades, and affected the stall performance such as stall capacity factor and torque ratio. The cavitation model was then modified to improve calculation accuracy. The test results showed that the prediction error under stall operating condition was decreased from 6.7% to 2%. This study provides insight on the influences of the empirical parameters on both internal cavitation behavior as well as overall hydrodynamic performance.  
torque converter; cavitation parameter; nuclei volume fraction; cavitation vaporization coefficient; CFD +
Footnote †: journal: Applied Sciences
# Fault-Tolerant Control for Carrier-Based Aircraft Based on Adaptive Fuzzy Sliding-Mode Method
###### Abstract
By contrast, active FTC can reconfigure the control actions to actively counter faults in different components of the system [32; 33]. However, the literature contains few relevant studies of landing control for CBA. In recent years, adaptive control theory has been applied widely to active FTC technology, and it has become a common theoretical method in FTC strategy after being combined with fuzzy theory [34], sliding-mode control theory [35; 36], backstepping method [37], etc. Regarding establishing a fault-tolerant flight control system, modelling the controlled object and measuring uncertain parameters must be done first, and then the adaptiveness and robustness of the system should be ensured. Also, the problem of the large range of parameter fluctuations of the controlled object can be solved by using adaptive control theory, which is quite effective for ensuring the robustness of the controlled system. Based on the characteristics of CBA landing, various unknown disturbances and possible actuator faults, presented herein is an FTC strategy based on adaptive fuzzy sliding-mode fault-tolerant control (AFSMTC). The strategy involves using an adaptive fuzzy network to conduct fuzzy approximation for the sliding-mode switching terms, and it effectively weakens the impacts of unknown disturbances on the control system without estimating the upper limit of perturbation. Adaptive laws are used to counter the actuator efficiency loss fault, automatically adjust the controller parameters in the light of actuator faults and complete FTC under fault conditions, and a sliding-mode controller is used to ensure CBA tracking along the expected flight path. Finally, the applicability of the proposed method is verified by comparing it with the typical proportional-integral-derivative (PID) control method.
# Stalling in Queuing Systems with Heterogeneous Channels
## 3 Queuing Characteristics and Optimization
### Optimization of Stalling Buffer  
An average number of queries in the system \(\overline{N}\) is the most important characteristic because it describes the whole system's capacity and the efficiency of the chosen queueing strategy. As obtained in the previous section, expression \(\overline{N}\) can be explored analytically, e.g., one can differentiate it in relation to the parameters. Further, following the findings presented in [9], the technique of Chebyshev polynomials for the study of the asymptotic behaviour of a heterogeneous multi-channel OS with stalling is developed. Denote by  
\[z_{j}=\frac{q+1+\frac{j}{m\cdot r}+\sqrt{\left(q+1+\frac{j}{m\cdot r}\right)^{ 2}-4\cdot q}}{2},t_{j}=\frac{q+1+\frac{j}{m\cdot r}-\sqrt{\left(q+1+\frac{j}{m \cdot r}\right)^{2}-4\cdot q}}{2},\quad 0\leq j\leq n.\]We can easily see that \(z_{j}+t_{j}=q+1+\frac{j}{t},z_{j}\cdot t_{j}=q\).  
Moreover, it is easily to verify yet that if \(0<q<1\), \(r\geq 1\), then \(z>1\), \(t<1\). Assume for simplicity that \(0<q<1\).  
Note that ratios \(A_{i,j}\) are polynomial functions of \(q\) and \(\frac{1}{r}\).  
**Lema A3.**_Ratios \(A_{i,j}\) follow to recurrent presentation:_  
\[A_{i,j}=\sum_{s=0}^{i}b_{i,s}\cdot\frac{z^{j+1-s}-t^{j+1-s}_{j}}{z_{j}-t_{j}}, \ 0\leq i\leq m+K+1,\ 0\leq j\leq n,\]  
where \(b_{i,\ j}=0\) if \(i\leq 0\) or \(s<0\), except \(b_{0,\ 0}=1\), and other coefficients follow to recursive relationship:  
\[b_{i,s}=\frac{m\cdot b_{i-1,s}}{min(i,m)}-\frac{(m-min(i-1,m))\cdot b_{i-1,s-1} }{min(i,m)}+\frac{m\cdot q\cdot(b_{i-1,s-2}-b_{i-2,s-2})}{min(i,m)},\ \text{if}\ i>1,s\geq 0.\]  
Proof of Lemma A3 is given in Appendix A.  
**Corollary.**_We have_  
\[\sum_{i=0}^{m+K}\frac{A_{i,1}}{A_{m+K,1}}= \frac{z_{1}}{z_{1}-1}+o\Big{(}q^{K}\Big{)},\ \text{and}\] \[\frac{A_{m+K+1,j}}{A_{m+K,j}}=z_{j}+O\Big{(}q^{K}\Big{)},\ 0\leq j\leq n.\]  
**Theorem A2.**_Assume that \(0<q<1\), \(r\geq 1\), \(K\geq 0\), \(M\geq 1\). Then, the optimal stalling buffer size, minimizing the average number of queries in QS, follows to approximate expressions:_  
_(i)_ \(K_{0}=\frac{\frac{h1}{R0}\cdot\frac{z_{1}}{z_{1}-1}+\frac{n\cdot S_{1}+S_{2}} {20}-\frac{q}{(1-q)^{2}}}{\frac{q}{(1-q)}-\frac{1}{10}}+\frac{q\cdot P_{2}}{1 -q}-m\cdot(1-q)-\frac{1}{ln(q)}+O\big{(}q^{K}\big{)},\) _and_ _(ii)_ \(K_{0}=r\cdot m\cdot(1-q)-\frac{1+q}{1-q}+\frac{p_{m}\cdot q}{(1-q)^{2}}-m\cdot( 1-q)-\frac{1}{ln(q)}+O\Big{(}\frac{1}{r}\Big{)}.\) _where_ \(h\) \(h\) _0 and_ \(h\)1 are the following:_  
\[h0=\sum_{s=1}^{n}\frac{n!}{s!\cdot(n-s)!}\prod_{j=1}^{s}\left( \frac{z_{j}}{q}-1\right)+1,\] \[h1=\sum_{s=2}^{n}\frac{n!}{s!\cdot(n-s)!}\prod_{j=2}^{s}\left( \frac{z_{j}}{q}-1\right)+n.\]
# Research on Soil Pesticide Residue Detection Using an Electronic Nose Based on Hybrid Models
## 2 Experimental Materials and Methods
### Soil Pesticide Odor Data Collection  
In this study, a self-developed electronic nose (Figure 2) is used to collect soil pesticide odor signals. The developed electronic nose consists of an air pump (D15S), a circuit board, a detection chamber, a gas sensor array, and a signal acquisition card (USB5631). The sensor array is the core component of the electronic nose system and is applied to sense the gas to be measured. When the gas comes into contact with the sensor, the resistance of the sensor decreases. Due to the variety of gases in the soil, we selected 26 semiconductor metal oxide sensors (Table 1) to form a sensor array. The change in the sensor will be reflected by the circuit diagram (Figure 2b). The decreases in the sensor resistance Rs will cause the output voltage across the load resistor RL to rise. The output voltage is read by a data acquisition card and converted to a digital signal to be stored in a computer-specified path. In addition, the air pump provides the power for gas transmission, and the detection chamber is the place where the sensor reacts with the gas. Its sealing has an important impact on the sensitivity of the detection system. The air pump and data acquisition card were purchased from Chengdu Hailin Technology (Chengdu, China) and ART Technology (Beijing, China).  
Figure 2: A self-developed electronic nose system. (**a**) The electronic nose system; (**b**) The sensor circuit’s schematic diagram.  
The electronic nose system is activated 30 min prior to collect data, which will allow the sensor surface to reach the operating temperature and clean the sensor surface with dry air. An amount of 10 g of the collected soil is taken and placed in a sealed beaker. The volatile soil gas is transported to the sensor surface via an air pump, causing a chemical reaction on the sensor's surface. This is converted into an electrical signal using the data acquisition card and uploaded to a computer via a data cable. After collecting the gas from a soil sample, it is necessary to pass the clean gas into the chamber for 1 min to completely exhaust the residual gas and bring the sensor signal to the baseline level before collecting the gas from the next sample. A total of 160 samples were collected for each pesticide, and 40 samples were collected for healthy soil. Finally, a total of 1000 odor samples were collected.
# GPSR: Gradient-Prior-Based Network for Image Super-Resolution
## 4 Experiments
### Results with Blur-Downscale (BD) Degradation  
Following [29], we also test the effectiveness of the proposed GPSR on the LR images with the blur-down (BD) degradation model. We selected the five state-of-the-art methods, including RCAN [29], DRN [40], HAN [12], PA-EDSR [14], and NLSN [11], to compare with our GPSR in objective quantitative metrics. Table 5 depicts the results of different SR models for the \(\times\)8 SR task. It can be seen from Table 5 that our GPSR also consistently outperforms other compared methods, even without self-ensemble in the SR task with large scaling factor. Specifically, the PSNR gain of our GPSR over NLSN is up to 1.05 dB and 1.25 dB on the Urban100 and Manga109 datasets, respectively.
## 6 Wireless Web Services  
Web services are also becoming important for use in mobile devices. The concept of dynamic access to web services allied with the flexibility of wireless accesses makes it possible to envisage a new type of applications, where the mobility of the user supplies the application with context elements. Possible applications include disaster management, location services, advertising, _etc._ Web services security standards are used for the secure design of the communications between a web service and a mobile client and for the storage of the web service and its data. However, because those standards are designed to be flexible, they are also complex and verbose. On their part, wireless devices have specific technological constraints as well as their own standards. To use web services standards in mobile devices, it is necessary to adapt these standards to consider the limitations of portable devices. We have shown the use of patterns as a way to adapt web services security standards to the wireless environment [26]. We have proposed two approaches for adapting standards to wireless systems. We showed the use of XACML for standard subsetting, where parts of the standard which are considered too wasteful of resources or unnecessary for this environment are left out. Another approach is to start from the functions themselves and see which ones are the ones needed for some targeted applications and adapt the standard to these functions. As an illustration for this latter approach the PAOS pattern, is a wireless-oriented version of the traditional HTTP binding for SOAP for web services.  
**Liberty Alliance PAOS Service [26]:** An adapted version of the traditional HTTP binding for SOAP, suitable for mobile devices.  
It is important that wireless devices use security models based on standards. This approach allows the incorporation of wireless devices into a unified enterprise policy. In this way, policies for wireless devices can be designed as part of the overall design as we do in our methodology [4], and not in an ad hoc way. Approaches that use separate policy models for wireless devices result in systems that will not be interoperable with other systems.
# Cyclic Consistent Image Style Transformation: From Model to System
## 4 Method
### Overview
The training process of C3 GAN involves optimizing three key loss functions:  
Adversarial Loss: This loss ensures that the generators produce outputs that are indistinguishable from real images in the target domain. It drives the generators to create realistic and convincing images, fooling the discriminators into classifying them as real. Cycle Consistency Loss: The cycle consistency loss maintains the integrity of the content information during the transformation. It guarantees that an image translated from domain A to domain B and then back to domain A closely resembles its original form, preserving essential features and structures. Identity Loss: This loss encourages the generators to retain the style information of the input images when the output domain matches the input domain. For example, when \(G_{A}\) processes an image from domain B, the output should be identical to the input image, ensuring that the generator does not unnecessarily alter the image's style when no domain translation is required.
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## 4 Numerical Simulation and Results  
This section evaluates the results and the performance of the algorithm, including a description of the interference scheme based on a genetic algorithm and related parameters. Then the convergence of the genetic algorithm applied to the progressive pitch strategy is analyzed. The algorithm is evaluated based on the total communication capacity, angular isolation and overlap coverage metrics of the satellite system. Finally, the algorithm is compared with other interference avoidance methods, which can have an effective guarantee of communication capacity of satellites under the condition of seamless coverage and no harmful interference to GSO satellite systems.
## 4 Experimental Results
### Cross-Validation Experiments
** \\ \hline GB (1,150) & 96.8 (0.1) & 98.7 (0.3) & 96.3 (0.4) & 97.1 (0.3) & 12.0 (0.4) & 2.7 (0.1) \\ \hline GB (2,25) & 96.7 (0.2) & **99.0** (0.5) & 96.1 (0.5) & 96.9 (0.3) & **10.8** (0.5) & 2.6 (0.0) \\ GB (2,50) & 96.7 (0.1) & 99.1 (0.2) & 96.3 (0.3) & 97.0 (0.3) & 12.2 (0.4) & 2.7 (0.1) \\ GB (2,100) & 96.9 (0.2) & 98.9 (0.2) & 96.3 (0.4) & 97.1 (0.3) & 15.3 (0.4) & 3.0 (0.0) \\ GB (2,150) & 96.8 (0.3) & 98.8 (0.3) & 96.1 (0.4) & 97.1 (0.2) & 17.8 (0.5) & 3.1 (0.1) \\ \hline GB (3,25) & 96.4 (0.3) & **99.0** (0.3) & 96.0 (0.3) & 96.7 (0.3) & 11.4 (0.5) & **2.5** (0.1) \\ GB (3,50) & 96.7 (0.4) & 98.9 (0.2) & 96.2 (0.4) & 97.0 (0.3) & 14.8 (0.6) & 2.9 (0.1) \\ GB (3,100) & 96.9 (0.4) & 98.8 (0.3) & 96.2 (0.5) & 97.1 (0.5) & 22.6 (0.6) & 3.3 (0.0) \\ GB (3,150) & 96.9 (0.3) & 98.8 (0.3) & 96.2 (0.4) & 97.1 (0.4) & 23.5 (0.6) & 3.4 (0.0) \\ \hline SR (25) & — — & — & 94.4 (0.2) & — & 31.2 (1.7) & 2.7 (0.0) \\ SR (50) & — & — & 93.5 (0.4) & — & 49.5 (2.1) & 2.7 (0.0) \\ SR (100) & — & — & 93.0 (0.5)As shown in Table 5, on the \"Heart Disease\" classification problem, the DIMLP ensembles achieved the highest predictive accuracy (85.8%), the highest predictive accuracy of the rules (84.4%), and the highest predictive accuracy of the rules when ensembles and rules agreed (86.8%), on average. The decision stumps provided the highest average fidelity (99.0%) and the lowest average complexity of the rules (11.0 rules with 2.6 antecedents). It is worth noting that the average predictive accuracy obtained by the DIMLPs and GB (1, 50) was very close (84.4 versus 84.3), but GB produced fewer rules (15.2 versus 20.4). The average predictive accuracy attained by SR was the lowest; moreover, it decreased as the number of trees increased. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Acc.** & **Fid.** & **Acc. R. (a)** & **Acc. R. (b)** & **Nb. R.** & **Nb. Ant.** \\ \hline GB (3,150) & 97.5 (0.8) & 98.6 (0.8) & 96.9 (1.0) & 97.9 (0.7) & 3.8 (0.0) & **1.5** (0.0) \\ \hline SR (25) & — & — & 96.5 (0.7) & — & 13.9 (1.0) & 2.7 (0.0) \\ SR (50) & — & — & 96.1 (0.8) & — & 21.5 (1.4) & 2.6 (0.0) \\ SR (100) & — & — & 95.7 (0.8) & — & 32.6 (2.2) & 2.7 (0.0) \\ SR (150) & — & — & 94.4 (0.9) & — & 41.0 (1.8) & 2.7 (0.0) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
## 2 Omnidirectional Vision System
### Camera Model  
The omnidirectional camera realized through the catadioptric imaging system construction can be formulated using a unified sphere model, as shown in Figure 2. Given a point **X** in the 3D space, it can be projected onto a unified sphere at the point  
\[\mathbf{\chi}_{s}=\frac{\textbf{X}}{||\textbf{X}||}, \tag{1}\]  
Figure 1: The catadioptric projection model of an omnidirectional camera. It consists of a hyperbolic mirror for reflection and a pinhole model for perspective projection.  
as illustrated in the figure. The point \(\chi_{s}\) is then transferred to a new reference frame centered at \(\mathbf{C}_{p}=(0,0,\xi)^{\top}\), and projected onto an image plane at the point  
\[\mathbf{m}=(X_{s},Y_{s},Z_{s}+\xi)^{\top} \tag{2}\]  
where \(\xi\) is the distance between the center of the sphere \(\mathbf{C}_{m}\) and the image projection center \(\mathbf{C}_{p}\). The projected point \(\mathbf{p}\) is given by  
\[\mathbf{p}=\left[\begin{array}{c}x_{1}\\ x_{2}\\ x_{3}\end{array}\right]=\mathbb{K}\mathbf{m}=\left[\begin{array}{ccc}\gamma&s &u_{0}\\ 0&\gamma r&v_{0}\\ 0&0&1\end{array}\right]\mathbf{m} \tag{3}\]  
This projection model involves a generalized camera matrix \(\mathbb{K}\) with the focal length \(\gamma\), the principal point \((u_{0},v_{0})\), the skew factor \(s\) and the pixel aspect ratio \(r\). An image pixel \((x_{im},y_{im})\) in the omnidirectional image is then calculated by the ratios  
\[\begin{split} x_{im}&=x_{1}/x_{3}\\ y_{im}&=x_{2}/x_{3}\end{split} \tag{4}\]  
To calibrate the omnidirectional camera, a checkerboard pattern was adopted as the calibration plate. The parameters were initialized according to the type of reflection mirrors. After the focal length and principal point were estimated, the parameters were used to simplify the feature extraction process and further optimize the parameters. The procedure is given as follows.  
1. Provide the center and border of the reflection mirror as shown in Figure 3a, and then estimate the principal point \((u_{0},v_{0})\).
2. Select at least three points on the same line (distorted by catadioptric imaging) from the calibration pattern as shown in Figure 3b, and estimate the focal length \(\gamma\).
3. Select the four corners on the calibration pattern as illustrated in Figure 3c, and estimate the extrinsic parameters.
4. The rest feature points on the calibration can be detected at the sub-pixel resolution via the inverse projection, as illustrated in Figure 3d.
5. Finally, the overall optimization should be carried out based on Equation (3) to derive more accurate intrinsic and extrinsic camera parameters.  
Figure 2: The unified sphere model for omnidirectional cameras. It consists of two linear projection steps.