esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Forward kinematic solution is meaningful and vital for the building and analysis of the Stewart-Gough platform. In this paper, the link between the length of six outriggers and posture in six dimensions is built by the proposed optimum algorithm. Through the calculation of the inverse kinematic solution and optimum of the input posture, the result gradually converges to the target solution. To overcome the disadvantage of the standard PSO method, a simulated annealing method is applied to the PSO method to help the algorithm remove the local optimum. The feasibility simulation shows that the SA-PSO is a good method for solving the forward kinematics. Based on this, a group of simulations with different accuracy levels is carried out. And the results indicate the method works well for high accuracy with more iterations. Also, the improved algorithm performs better than the standard PSO method in repetitive simulation experiments with higher success rate and faster convergence speed. The continuous trajectory simulation and the workspace random simulation further verify the better reliability and the superior robustness of the proposed algorithm. As for the convergence dilemma of NR method in some special points, the SA-PSO method performs better than the traditional numerical algorithm in success rate and real-time application, that means it is a powerful complement to traditional algorithms. Finally, a physical platform is built to test the proposed SA-PSO method. It is concluded that for the Stewart-Gough platform, the accurate prediction of posture is based on the precise model of the structure, efficient forward kinematic solving, and high precision measurement of six outriggers. Furthermore, it is significative to study the application of this algorithm in platform design, assembly, parameter calibration and motion control in the future.  
Conceptualization, Z.Y.; methodology, Z.Y.; software, Z.Y.; validation, Z.Y. and R.Q.; formal analysis, Z.Y. and R.Q.; investigation, Z.Y. and R.Q.; resources, Y.L.; data curation, Z.Y. and R.Q.; writing--original draft preparation, Z.Y.; writing--review and editing, Y.L.; visualization, Z.Y.; supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Major Program of National Natural Science Foundation of China, grant number 42192582, and National Key R&D Program of China, grand number 2016 YFB0500400.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 11: The SA–PSO results and the error for the experiments. (**a**) X rotation results and errors. (**b**) X translation results and errors. (**c**) Y rotation results and errors. (**d**) Y translation results and errors. (**e**) Z rotation results and errors. (**f**) Z translation results and errors.
## 3 Methodology
### Cross-Layer Attention Upsampling (CLAU)
#### 3.3.2 Scaled Dot Product Attention  
The scaled dot product attention mechanism proposed in [31] has shown impressive performance in sequential-to-sequential modeling tasks, and is summarized here. The attention mechanism takes tensor queries and keys of dimension \(d_{k}\) and values of dimension dv as inputs, which can be expressed as **Q**, **K**, and **V**, respectively. Then, the dot products of the query are computed with all keys, each is divided by \(\sqrt{d_{k}}\), and a softmax function is applied to obtain the weights of the values. The formula is expressed as follows (6):  
\[Attention(Q,K,V)=Softmax(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{6}\]  
In recent years, attention mechanisms have been widely used in the visual field to replace or improve upon convolutional neural networks (CNNs). However, because the input tensor of the model is the output of a certain layer of convolution in the neural network, the global information of the current layer can be fully learned after the self-attention model to make up for the deficiency that convolution can only obtain local information. However, the details lost in the current layer cannot be retrieved, meaning that the resulting models have only a limited effect on improving the performance of network detection of small targets.
# Online Forecasting and Anomaly Detection Based on the ARIMA Model
## 2 Materials and Methods  
### Classical ARIMA Model  
The ARMA model is described by an equation from [6]:  
\[\hat{X}_{t}=\sum_{i=1}^{q}\beta_{i}\epsilon_{t-i}+\sum_{i=1}^{p}\alpha_{i}X_{t- i}+\epsilon_{t}+c \tag{1}\]  
where \(X_{t}\) is the value of the \(X\) time series at moment \(t\); \(\alpha\in\mathbb{R}^{p}\) and \(\beta\in\mathbb{R}^{q}\) are the vectors of the weight coefficients; \(\epsilon\) is a noise term; \(c\) is a constant; \(\sum_{i=1}^{p}\alpha_{i}X_{t-i}+\epsilon_{t}+c\) is an autoregressive model with order \(p\), which is a linear combination of the previous \(p\) values of series, constants and noise terms; \(\sum_{i=1}^{q}\beta_{i}\epsilon_{t-i}+\epsilon_{t}\) is the moving average model with order \(q\), which is a linear combination of the previous \(q\) noise terms and current noise term.  
Time series representing real-world processes are often non-stationary. Deterministic components, such as trends, seasonality, slow and fast variation, and cyclical irregularity are usually represented in this manner. An effective way to remove most of these components is to compute their differences. For the time series \(X\), at time moment \(t\), the first order differences (lag = 1) are equal to \(\
abla X_{t}=X_{t}-X_{t-1}\), while the second order differences are equal to \(\
abla^{2}X_{t}=\
abla X_{t}-\
abla X_{t-1}\). To remove the seasonality, seasonal differences can be calculated, which are pairwise differences in values in neighboring seasons.  
If \(\
abla^{d}X_{t}\) is described by the ARMA model (\(p\), \(q\)), it can be said that \(X_{t}\) is described by the ARIMA model (\(p\), \(d\), \(q\)):  
\[\
abla^{d}\hat{X}_{t}=\sum_{i=1}^{q}\beta_{i}\epsilon_{t-i}+\sum_{i=1}^{p} \alpha_{i}\
abla^{d}X_{t-i}+\epsilon_{t}+c \tag{2}\]  
where \(p\), \(d\), \(q\) are the hyperparameters or orders of a model, \(\alpha\in\mathbb{R}^{p}\) and \(\beta\in\mathbb{R}^{q}\) are the vectors of the weight coefficients.  
The predicted value of \(\widehat{X}_{t}\) can be calculated as:  
\[\widehat{X}_{t}=\
abla^{d}\widehat{X}_{t}+\sum_{i=0}^{d-1}\
abla^{i}X_{t-1} \tag{3}\]  
As stated earlier, hyperparameter selection is a separate, non-trivial task. The Box-Jenkins method for solving this problem is described in [6].
## 6 Applications of Actinobacteria in Biotechnology
### Biofuels  
Bioconversion of plant materials to sugars and their transformation into other compounds enhances the growth of a sustainable biofuel industry [89]. Literature search has validated that the genera _Streptomyces_, _Cellomonas_, _Mycobacterium_, _Propionibacterium_, _Nocardia_, _Corynebacterium_,and _Micromonospora_ are very rich in carbohydrate degrading enzymes (glycoside hydrolase) [90]. Interestingly, the enzymes are important in the production of simple sugars which are subsequently converted into biofuels and other compounds of economic value. Actinobacteria also play an important role in detoxification of fuel associated toxic compounds.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Use** & **Enzyme** & **Applications** \\ \hline \multirow{5}{*}{Detergent (laundry and dish wash)} & Protease & Protein stain removal \\ \cline{2-3}  & Amylase & Starch stain removal \\ \cline{2-3}  & Lipase & Lipid stain removal \\ \cline{2-3}  & Cellulose & Cleaning, color clarification, anti-redeposition (cotton) \\ \cline{2-3}  & Mannanase & Mammana stain removal \\  & (reperpearing stains) \\ \hline \multirow{5}{*}{Starch and fuel} & Amylase & Starch liquefaction and saccharification \\ \cline{2-3}  & Amyloglucidase & Saccharification \\ \cline{2-3}  & Pullulanase & Saccharification \\ \cline{2-3}  & Glucose isomerase & Glucose to fructose conversion \\ \cline{2-3}  & Cyclodextrin-glyocyosyltransferase & Cyclodextrin production \\ \hline Xylanase & Viscosity reduction (fuel and starch) & \\ \hline \multirow{5}{*}{Food (including dairy)} & Protease & Milk clotting, infant formulas \\ \cline{2-3}  & Protease & (low allergenic), flavor \\ \cline{2-3}  & Lipase & Cheese flavor \\ \cline{2-3}  & Lactase & Lactose removal (milk) \\ \cline{2-3}  & pectin methyl esterase & Firming fruit-based products \\ \cline{2-3}  & Pecinase & Fruit-based products \\ \cline{2-3}  & Transglutaminase & Modify visco-elastic properties \\ \hline \multirow{5}{*}{Baking} & Amylase & Bread softness and volume, flour \\  & Adjustment & adjustment dough conditioning \\ \cline{2-3}  & Xylanase & Dough stability and conditioning (in situ emulsifier) \\ \cline{2-3}  & Lipase & Dough stability and conditioning (in situ emulsifier) \\ \cline{2-3}  & Phospholipase & Dough strengthening \\ \cline{2-3}  & Glucose oxidase & Dough strengthening \\ \cline{2-3}  & Lipoxygenase & Bread whitening \\ \cline{2-3}  & Protease & Discuits, cookies \\ \cline{2-3}  & Transglutaminase & Laminated dough strengths \\ \hline \multirow{5}{*}{Animal feed} & Phytase & Phytate digestibility- \\ \cline{2-3}  & Alphase & phosphorus release \\ \cline{2-3}  & Alphase & Digestibility
# From Patient to Musician: A Multi-Sensory Virtual Reality Rehabilitation Tool for Spatial Neglect
## 5 Implementation Details
### Interfaces  
In order to allow easy modifiability with little constraint on the _data storage_ and _dashboard_ components, the technologies for the interfaces between the components are based on the TCP/IP protocol stack. Figure 10 shows the interfaces between the identified components. Both interfaces between the _VR system_ and the _dashboard_ and the _VR system_ and the _data storage_ use a network-based message queue. The communication between the _dashboard_ and the _data storage_ happens over the database's TCP/IP communication protocol.
# Removal of Chromium from Synthetic Wastewater Using Modified Maghemite Nanoparticles
## 3 Results
### Effect of Adsorbent Dose on % Removal of Chromium  
The effect of adsorbent dose on chromium removal is shown in Figure 3a, c. It was observed that the chromium removal increased from 30% to 90% with an increase in the adsorbent dose. This increase in RE is due to the availability of greater number of active sites in the adsorbent. The chromium uptake increased up to an adsorbent dose of 8 g/L. The RE of Cr(VI) ions increased up to an optimum dose (8 g/L), beyond which the concentration did not significantly change. This result was expected because increasing the adsorbent dose provides greater adsorption sites for a fixed initial solute concentration. Thus, for an adsorbent dose less than 8 g/L, the removal of Cr(VI) ions increased with increasing adsorbent dose. However, after achieving the optimum dose (i.e., dose more than 8 g/L), increasing adsorbent dose led to decrease in the RE because of interfering adsorption sites together. Therefore, there was no further increase in the removal [34]. Similar observations were observed by [PERSON] et al. [35], where catecholamine-coated maghemite nanoparticles are used as the adsorbent for chromium removal. Lower amounts of the adsorbent may achieve acceptable results because of the higher surface area-to-volume ratio [26].
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 4 Comparison of Fake Packet Techniques  
This section provides a comparison of fake packet techniques regarding the use of: _Intervals_, _Timers_, _Random Numbers_, _Probability_, _Clusters_, _Proxies_, _Constant Distribution_, _Exponential Distribution_, and _Poisson Distribution_. The comparison is presented in Table 1.  
Intervals indicate that the lifetime of the network is divided into time intervals. The duration of the intervals can be equal or unequal based on the implemented distribution. _Timers_ are utilized as a trigger for a node to transmit either real or fake packets. _Random Numbers_ decide the probability of a node to transmit a packet. _Probability_ decides whether a node will transmit a packet. _Clusters_ indicate that the network is divided into groups of nodes (Figure 8). Each group has multiple member nodes and one head node. _Proxies_ are commonly used to filter fake packets towards the sink. _Constant Distribution_ breaks down the time into equal intervals, whereas _Exponential Distribution_ decides the next interval according to this equation: \(X=\log\frac{(1-u)}{-\lambda}\), where \(X\) is the next interval, \(u\) is the uniform random number, and \(\lambda\) is the transmission rate. _Poisson Distribution_ calculates its intervals distribution based on the following equation: \(P(x,\lambda)=\frac{\lambda^{x}e^{-\lambda}}{x! }\), where \(P(x,\lambda)\) is the probability, \(x\) takes a whole number, and \(\lambda\) is the average number of events per interval. All techniques are assumed to implement similar structure, format and size for both real and fake packets. Using different size or format for fake packets makes real packets easily detectable if an adversary applies a _Size/Structure Correlation_ analysis.
## 5 Improved NSGA-II Algorithm
### Crossover and Mutation  
The crossover method chosen is a simulated binary crossover, and the mutation method selected is a polynomial mutation.  
The simulated binary crossover operator (SBX) [41] is a crossover operator commonly used in real-coded genetic algorithms to address continuous variables. Its operation is based on the following fundamental principles:  
1. The mean of the values of the two parents and the two children before and after the crossover are equal; i.e., \[\frac{p_{1}+p_{2}}{2}=\frac{c_{1}+c_{2}}{2}\] (31) \[\int\ln\Big{(}x^{2}+1\Big{)}dx\] (32)  
Figure 6: Selection method density comparison.  
2. The quotient of the interpolated values of the two parents and the interpolated values of the two children before and after the crossover is approximately equal to 1; i.e., \[\beta=\left|\frac{c_{1}-c_{2}}{p_{1}-p_{2}}\right|\approx 1\] (33) where \(c_{1}\) and \(c_{2}\) represent the two offspring individuals, \(p_{1}\) and \(p_{2}\) represent the two parent individuals, and the SBX operator is used to invert the two offspring individuals: \[\begin{cases}c_{1}=0.5[(1+\beta)p_{1}+(1-\beta)p_{2}]\\ c_{2}=0.5[(1+\beta)p_{2}+(1-\beta)p_{1}]\end{cases}\] (34) where \(\beta\) is a random variable determined dynamically and randomly by the distribution factor \(\eta\) in the following way: \[\beta=\begin{cases}(2\mu)^{1/(\eta+1)},\mu\leq 0.5\\ \left(\frac{1}{2-2\mu}\right)^{1/(\eta+1)},\mu>0.5\end{cases}\] (35) where \(\mu\) is a uniformly distributed random number on \((0,1)\).  
Polynomial mutation (PM) [42] is a variation operation for sequential optimization. Its purpose is twofold: firstly, to introduce a small range of stochastic variations in order to ensure the population diversity of the solution space; secondly, to improve the global search capability of the algorithm. The following is a description of the method in question:  
1. The initial parameters must be defined, the probability of variation \(p_{m}\) must be determined, and the distribution index \(\eta_{m}\) must be set.
2. Select the variation points of variation and, for each variable, generate a random number \(r\). If \(r<p_{m}\), the variable is varied; calculate the variation \(\delta\): \[\delta=\begin{cases}(2u)^{\frac{1}{\eta_{m}+1}}-1,\ u<0.5\\ 1-(2-2u)^{\frac{1}{\eta_{m}+1}},\ u\geq 0.5\end{cases}\] (36) where \(0<u\leq 1\).
3. For the selected variation \(x_{i}\), the update is \(x_{i}^{\prime}=x_{i}+\delta\cdot(x_{max}-x_{min})\), where \(x_{max}\) and \(x_{min}\) are the upper and lower bounds of the variable.
## 3 Results
### Spatial Distribution of Soil pH  
Figure 8 presents the soil pH maps with significant differences predicted by the seven scenarios. Overall, the study region was dominated by acid soils, and the figure illustrates the increasing soil pH, from southwest to northeast. Averagely 20% of the agricultural soils were predicted to be very acid soils (pH \(<\) 5) and concentrated in the southwestern plain area. Scenarios 2, 3, and 7 assigned around 15% of the pixels to very acid soils, while this category occupied nearly 30% of the pixels for the scenarios 4, 5, and 6. Scenario 3 (Figure 8c), which only used Sentinel-1/2 composites, and scenario 6 (Figure 8e), which combined Sentinel data and crop rotation, had the highest (5.38) and lowest (5.25) mean soil pH value, respectively. Relatively acid soils (6 \(<\) pH \(<\) 6.5) were mainly distributed in the northern mountainous area. Notably, there were considerable differences in small-scale spatial characteristics, and introducing crop rotation maps could help reduce the salt-and-pepper effect.  
Figure 7: The relative variable importance in the sub-models built on different subsamples: rainfed (**a**), irrigated (**b**), paddy (**c**), vegetable (**d**), and orchard (**e**). The dots of green, blue, and yellow represent radar, optical, crop rotation system, cropland type, and soil variables, respectively.  
Figure 9 visualizes the soil pH maps predicted by the soil sample grouping strategies. The spatial patterns of soil pH are generally similar to those in Figure 8e. However, grouping by crop rotation generated a map with more pixels assigned to very acid soils (Figure 9b), and the predicted mean pH was the lowest (5.24).  
Figure 8: Predicted soil pH distribution in 2021 from scenarios 1 (**a**), 2 (**b**), 3 (**c**), 4 and 5 (**d**), 6 (**e**), and 7 (**f**). Figures (**a1–f1**) correspond to the zoomed-in maps for the black squares in (**a–f**).
# Analysis and Correction of Antenna Pattern Effects in AMAO Spaceborne SAR Images
###### Abstract  
Azimuthal multi-angle observation (AMAO) is a novel spaceborne synthetic aperture radar (SAR) technique proposed recently, which significantly improves the acquisition performance of target information. It has highly flexible antenna beams with powerful beam-steering capability while affecting the consistency of radiometric measurements, especially in the high-squint case. This paper mainly focuses on the antenna pattern effect analysis and correction method of AMAO spaceborne SAR. The antenna pattern characteristics of the squint steering beam are first analyzed. On this basis, an analytical model is developed to quantify the antenna pattern effects in AMAO SAR images. Different from the conventional approach that corrects the elevation antenna pattern, an improved antenna pattern correction method, which accounts for the spatial variation in the two-dimensional antenna pattern, is then proposed for AMAO spaceborne SAR. Finally, experiments are conducted to verify the proposed correction method. The results show that the newly proposed method considering the spatial-variant antenna pattern has higher performance than the conventional approach using a reference elevation pattern.  
azimuthal multi-angle observation (AMAO); synthetic aperture radar (SAR); antenna pattern; radiometric correction +
Footnote †: journal: remote sensing  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Analysis and Correction of Antenna Pattern Effects in AMAO  
Spaceborne SAR Images. _Remote Sens._**2022, 14, 2141. https:////doi.org/10.3390/rs14092141
## 3 Theoretical Analysis
### Model Derivation
During the rolling process, these blocks go through several consecutive bounces and swings, completing the axis change process at a position before reaching the slope bottom, as shown in Figure 21.0 With the change in the rolling axis and the axis itself, the rolling resistance arm, deviation, and rolling speed of the block vary to different extents. In addition, comparing the errors between the theoretical and experimental values for Block B and Blocks E and F under the same conditions, it is observed that as the block's aspect ratio increases, the number of axis changes during the rolling process also increases, showing a positive correlation. Therefore, for such shaped blocks, the actual rolling speed may lean towards the lower calculated value or even slightly below it  
Figure 21.1 Comparison of rolling speeds for Block F on a 60\({}^{\circ}\) slope. Figure 21.2 Variation of rolling resistance arm \(\delta\) as the rolling axis changes. Figure 21.3 Comparison of rolling speeds for Block G on a 60\({}^{\circ}\) slope. Figure 21.4 Comparison of rolling speeds for Block H on a 60\({}^{\circ}\) slope. For these types of blocks, it is possible to determine the two-dimensional cross-sectional shape perpendicular to the slope surface during the process of changing the rolling axis and to calculate the corresponding rolling friction coefficient. This coefficient can then be substituted into the calculation formula to obtain the corresponding rolling speed. The calculation results are shown in Figures 35 and 36, where the experimental values for Blocks E and F fall within the range of the theoretical high and low values. Among them, the average difference between the actual value of the test block and the high and low values is E (6.7%, 14%) and F (6.3%, 14.3%), respectively. Based on the above analysis, for blocks with 0.33 \(<\lambda<\) 1, the experimental speeds obtained from the model tests are close to the high and low theoretical speeds calculated by the formulas. Experimental data were compared with the results from the theoretical formulas, revealing a maximum error of 10% and an average error of 5.126%. Discrepancies between the experimental and theoretical values were analyzed, and corrections were made to address these errors. The experimental results fell within the theoretical formula range, confirming the validity of the theoretical calculation formulas for describing block motion characteristics.
## 4 Simulation Experiments: Security Analysis and Comparison  
We conducted simulation experiments using Matlab 2013a on a machine with an Intel Core i7-7500U processor running at 2.70 GHz and 8 GB of RAM to assess the effectiveness of the proposed encryption algorithm. To ensure a fair comparison, we performed experiments with the state-of-the-art schemes [10; 11; 12; 13; 14; 15; 16; 17; 18; 38; 39; 40; 41; 42; 43; 44] in our system and tested it on different texts of various sizes, ranging from 30 to 5995.  
We conducted both analytical and empirical evaluations of the proposed text encryption scheme. The empirical study focuses on the correlation analysis, running time, and encryption quality. On the other hand, the analytical study includes entropy analysis, NIST analysis, key sensitivity analysis, plaintext sensitivity analysis, key space analysis, ciphertext-only attack, known-plaintext attack, chosen-plaintext attack, and chosen ciphertext attack. Furthermore, we compared our text encryption scheme with the state-of-the-art text encryption schemes [10; 11; 12; 13; 14; 15; 16; 17; 18; 39; 40; 41; 42; 43; 44].