esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Coaxial Cable PTFE Thermal Expansion Characterisation Experiment  
During this experiment, the antennas were completely submerged in peanut oil in pairs and fixed such that the antenna tips were centred approximately in the middle of a small viewing window. In order to ensure consistent imaging conditions, antennas were positioned vertically in alignment with the inner wall of the beaker, thereby minimising distortions caused by glass refraction, oil opacity, and convective turbulence. The peanut oil was replenished around every three cycles to mitigate oxidation and prevent particulate contamination during the exchange of antennas.  
The experiment was initiated at an oil temperature of 30 \({}^{\circ}\)C, which was raised in stages (70 \({}^{\circ}\)C, 80 \({}^{\circ}\)C, 100 \({}^{\circ}\)C, 120 \({}^{\circ}\)C, and 140 \({}^{\circ}\)C). The temperature was controlled at each stage until it was stabilised within \(+/-1\)\({}^{\circ}\)C. Four pictures were taken at each temperature stage. The oil temperature was then cooled back to 30 \({}^{\circ}\)C, again taking four pictures at each temperature stage. Before and after the experiment, each antenna's length was measured using a toolmaker's microscope. In total, 16 annealed and 16 non-annealed antennas were investigated with this method.  
The change in length was determined by measuring the change in pixels with open-source image processing software (US National Institutes of Health NIH-Image) 1.54f [36]. The image was digitally focused on the antenna length (D) (refer to Figure 1), using the centre conductor as a guide to measure along the centre. The percentage change in pixels over the initial pixel value was then converted to a change in length in mm. This was achieved by exploiting the known initial length measured using a toolmaker's microscope. A pixel-based mean PTFE length measurement was determined by calculating the mean from the four pictures at each temperature stage. The correlation of the percentage change in pixels to the percentage change in mm was validated through another experiment using a vernier calliper's depth rod as a reference. The length of the depth rod varied between  
Figure 3: The complete system used to investigate the expansion of PTFE: (1) hotplate/stirrer and insulated beaker filled with peanut oil on an adjustable lifting platform, (2) camera and macro lens on an adjustable tripod, (3) software control and data logging, (4) immersion heaters and control hardware, and (5) thermometry system.  
12 mm and 14 mm, with a step size of 0.05 mm. The pixel-based measurement method was compared with the vernier calliper measurement, obtaining a mean error of just 0.92% over the whole population.
# FPGA Implementation of IEC-61131-3-Based Hardware Aided Counters for PLC
## 3 FPGA Counters Implementations
### Hardware Counters  
In the hardware counter function block design the main problem is not to design the counter, but the whole environment. The counter itself is not a problem, because this task is well controlled in the HDL synthesis. The description of the counter presented in the standard is presented in Listing 4a, while the Verilog description is presented in the Listing 4b.  
However, the main problem presented in detail in Section 2 is the execution moment of the counter. It is not the problem in the hardware counter as described in Listing 4b. The active clock edge executes the counter. However, such a counter is executed based on the state of inputs like 'CU', 'CD', etc. Those inputs must be stored somehow, because the counter can be driven only by means of CR_b. Moreover, one of the most important parts of the counter is PV that must also be remembered. It can be constructed as presented in Figure 7.  
The counter presented in Figure 7 receives an active clock edge together with other parts of the environment. It is unambiguous with the counter execution, so it is compliant with conclusions drawn in Section 2 for Listing 3 and short operators. When the design should be compliant with the action presented in Listing 2, every counter's input must be remembered, but the counter may be executed after CAL instruction. The proper structure with counter's Verilog description is presented in Figure 8 and Listing 5, respectively. The call (CAL) is implemented by means of clock enable (CE) input that is built-in in the FPGA D flip-flop. The RTL (Register-Transfer Level) schematic of the counters function block is presented in Figure 9.  
One more thing needs some comment. The structure presented in Figures 7 and 8 returns 'cv', 'qu' and 'qd'. However, the CPU expects only CR_b and CR_W inputs, so 'qu' and 'qd' must be multiplexed and every single counter must be connected to the CR_b and CR_W by MUxes too. The structure presented in Figure 10 is a complete hardware counters function block for four counters.  
Figure 6: The interface between function unit and hardware-supported blocks.  
```
**Listing 4**. (**a**) The description of the counter presented in the standard, (**b**) the Verilog description of the counter.
``` (a) IFR  THENCV:=0; ELSEIFLD  THENCV:=PV; ELSE  IFNOT(CUANDCD)  THEN  IFCUAND(CV<PV_MAX)  THENCV:=CV+1;  IFCDAND(CV>PV_MIN)  THENCV:=CV-1; END_IF END_IF END_IF  
QU:=(CV>=PV); QD:=(CV<=0); //------- (b) always@(posedge clk)  if(reset)  cv<=32'b0; else  if(ld)  cv<=pv;  else  if(!(cu&&cd))  if(cu&&(cv<PV_MAX))  cv<=cv+1'b1; else  if(cd&&(cv>PV_MIN))  cv<=cv-1'b1;  assignqu=cv>=pv;  assignqd=cv<=0; ```
## 3 Sound Quality Analysis of Pattern Noise
### SQI of the Tire Pattern Noise  
To evaluate the sound quality of pattern noise objectively, a prediction model was developed using psychoacoustic metrics [21]. There are several metrics in this field [22]. The psychoacoustic metrics for 10 pattern noises and their correlation with subjective evaluation was calculated. It was found that loudness and sharpness were the major metrics affecting the subjective evaluation of tire pattern noise. Figure 8 shows the correlation between psychoacoustic metrics and subjective rating. Finally, to develop the predictive model, the multiple regression method [23] was employed. The results of the multiple regression are listed in Table 1.  
Figure 7: Results of subjective evaluation.  
In Table 1, the non-standardized coefficient, \(B\), is the coefficient of the SQI. The standard error denotes the value of standard deviation divided by the square root of the number of samples. The standardized coefficient, \(\beta\), is the influence of the SQI. If the absolute value of \(\beta\) is high, it has a high impact on the results. In contrast, if the absolute value of \(\beta\) is low, then the influence of each model is low. The \(t\)-value is given by \(B\)/standard-error. It gives the location of the \(t\)-value on the distribution curve, so probability can be estimated by the \(t\)-value. From the \(B\) value in Table 1, the SQI for the tire pattern noise from multiple linear regression analysis can be given as follows:  
\[SQI=13.743-0.088\cdot L-0.104\cdot S \tag{6}\]  
where \(L\) is loudness and \(S\) is sharpness. In Table 1, the \(p\)-value for sharpness is 0.779 and is larger than 0.05. The reason of this high \(p\)-value is the high mutual correlation of loudness and sharpness (correlation coefficient: 0.94). This high \(p\)-value indicates that sharpness does not add information to the model and sharpness is excluded. Therefore, Equation (6) is replaced by  
\[SQI=162.89-10.97\cdot L \tag{7}\]
# Joint Communication-Motion Planning in Networked Robotic Systems
## 3 Cognitive-Relay-Assisted Robot System with Interference Constraints
### Resource Sensing: Observe  
We consider the narrow-band Rayleigh block-fading channel model as in the previous section, and the instantaneous received signal-to-noise power ratio (SNR) is expressed as \(\gamma_{ij}=\frac{P_{i,j}-\theta_{ij}^{-\beta}|h_{ij}|^{2}}{\mathrm{N}\mathrm{B }}\), where the channel bandwidth is \(B\), and \(N_{0}\) is the additive white Gaussian noise (AWGN) power spectral density, the distance \(d_{ij}=\|x_{i}-x_{j}\|\) between the transmitter \(i\) and the receiver \(j\) and the pathloss exponent \(\beta\). In this paper, we assume that each robot is capable of measuring the instantaneous received SNR.
# Europium, Yttrium, and Indium Recovery from Electronic Wastes
## 2 Materials and Methods
### Methodology for Indium Recovery  
The raw material that was used in the project was collected from discarded liquid crystal displays (LCD) of different sizes, brands, and models. The LCDs were dismantled manually; the outer frame, the plastic layer of the LCD monitor, image diffusers, and anti-glare layers were removed until the LCD panel was obtained. After that, a manual size reduction was made to reach a particle size between 0.5-2.0 cm; then, the material was pulverized with a Siebtechnik model T 100 equipment (Siebtechnik, Mulheim an der Ruhr, Germany). The resulted powder was submitted to a granulometric analysis, and three representative sizes of the sample were collected: +298 \(\upmu\)m; \(-149\) + 105 \(\upmu\)m; and \(-105\) + 74 \(\upmu\)m. The powder was characterized by X-ray fluorescence analysis with a S8 Tiger, Bruker equipment (Bruker, Karlsruhe, Germany), and a scanning electron microscopy (SEM)-electric (TESCAN, Brno, Czech Republic) dispersive scanning (EDS) Vega TSCAN. The leaching process was investigated through the use of two acids: HCl and H\({}_{2}\)SO\({}_{4}\) with a concentration of 90 g/L. Leaching essays were carried out with magnetic stirring for 24 h. Indium, iron, and aluminum dissolution was monitored by atomic absorption spectroscopy with a Perkin Elmer Analyst 300 spectrometer (Perkin Elmer,Shelton (CT), USA). The LCD powder was submitted to acid disintegration with HNO\({}_{3}\), HF, and HCl in order to quantify the undissolved elements by atomic absorption. Indium recoveries were obtained by making a mass balance through using Equation (1). For indium recovery form acidic solutions, solvent extraction was carried out. Di-(2-ethylhexyl) phosphoric acid (DEHPA), tributyl phosphate (TBP), and methyl trioctyl ammonium chloride (Aliquat 336) were employed as extractants, whereas \(n\)-hexane and diesel were used as diluents. Two ratios of 1:1 and 1:6 between the organic and the aqueous phase respectively were assessed, as well as the concentration of the extractant (0.025-1.000 M). In addition, two organic solvents were tested to determine the optimal conditions to separate indium from iron and aluminum. Once the best extraction results were obtained, the organic phase that had the highest indium load was submitted to re-extraction. The aqueous phase tested was H\({}_{2}\)SO\({}_{4}\) at different concentrations (6 M, 8 M, and 10 M), and the amount of indium that was recovered was measured by atomic absorption. Finally, the acid solution was evaporated up to indium precipitated as indium sulfate.
## 1 Introduction
In the fine alignment stage, the most widely used are those based on the Normal Distribution Transformation (NDT) [19] algorithm and those based on the Iterative Closest Point (ICP) [20] algorithm. The ICP algorithm needs to be iterated, and when the quantity of point cloud data is large enough, the algorithm will take longer. In order to solve this time-consuming problem, researchers have conducted a significant amount of research. [PERSON] et al. [21] proposed the HT-ICP algorithm, which reduces the computation time by eliminating the incorrectly corresponding point pairs. [PERSON] et al. [22] proposed the GICP algorithm, which improves the accuracy of the algorithm through the introduction of oriented quantities and curvature geometric features. [PERSON] et al. [23] used the kd-tree accelerated ICP algorithm on robot disassembly targets to realize point cloud fine alignment. Due to the large amount of data obtained from rail collection, the time complexity of point cloud alignment tends to be high, which makes the algorithm suffer in terms of both operation efficiency and alignment accuracy [24]. To address the above problems, a point cloud alignment method combining ISS-4 PCS and KD-ICP is proposed in this work. That is, ISS feature points are integrated on the basis of the 4 PCS algorithm, a 4 PCS coarse alignment algorithm based on ISS feature points is proposed, and the KD-ICP algorithm proposed by [PERSON] et al. is applied to rail abrasion detection for fine alignment. The method consists of two stages: ISS-4 PCS coarse alignment and KD-ICP fine alignment; the specific realization steps are as follows. In the coarse alignment stage, the voxel filtering is used to downsample the source and target point clouds to be aligned, then the ISS feature point extraction algorithm is used to extract the source and target point cloud feature point sets, and finally the 4 PCS algorithm is used to complete the coarse alignment. In the fine alignment stage, the kd-tree near-neighborhood search is used to accelerate the ICP algorithm to complete the fine alignment. This method uses the ISS algorithm to extract the feature points to replace the original data points, which can improve the alignment accuracy of 4 PCS algorithm; the use of kd-tree to accelerate the ICP algorithm can improve the alignment efficiency of the ICP algorithm, and the flow of the method used is shown in Figure 1. Experiments show that the combined algorithm proposed in this paper has better robustness in rail point cloud alignment.
# Maintaining Secure Level on Symmetric Encryption under Quantum Attack
## 2 Literature Review  
### Quantum Computer  
Quantum computing is a method that uses a quantum as the basic computing [8] unit and operates with quantum algorithms. As long as a quantum reaches the quantum superposition state and the quantum entanglement state at the same time, it becomes a qubit, providing a calculation. The reading and writing of qubits can be done using laser or microwave techniques. The current mainstream quantum computing methods are divided into five types: silicon-based spin qubits, ion traps, diamond nitrogen vacancy centers, topological qubits, and superconducting rings. In 2021, IBM released Eagle with 127 bits, and it is expected for this to increase to 433 bits in 2022.  
The difference between a quantum computer and a conventional computer is that quantum computers exploit the properties of quantum entanglement and superposition. Because of this unique property, computers made of qubits can process large amounts of data in parallel.  
However, the computing power of current quantum computers is not sufficiently strong [9; 10], therefore there are still has many problems to overcome.
# Tree Internal Defected Imaging Using Model-Driven Deep Learning Network
## 3 Experimental Results and Analysis
### Heterogeneous Multi-Defect Inversion Imaging  
To further expand the application for real tree internal defect detection, three internal defects are set up for an inverse imaging test, and the electrical properties of each defect are different to consider the existence of three different defects in the same two-dimensional section of the wood. The relative dielectric constants of the three defects are 20, 40, and 60, respectively, and the live wood defect model is set up as shown in Figure 6a, where the relative dielectric constant of the defect on the right side of the xylem is 20, the relative dielectric constant of the defect above the xylem is 40, and the relative dielectric constant of the defect below the xylem is 60. The effect of each algorithm for defect inversion is shown in Figure 6.  
As shown in Figure 6, for the detection of heterogeneous multi-defects inside the trees, the CSI cannot locate the defect location. The BP neural network better inverts the defect size and location, while the boundary between wood and air in the result is not clear enough, and the _IOU_ values for BP are 0.928 and 0.941, indicating that this algorithm is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Contrast Source** & **BP Neural Network** &
\begin{tabular}{c} **Model-Driven Deep** \\ **Learning Networks** \\ \end{tabular} \\ \hline Mean Square Error & 0.3526 & 0.1932 & 0.0937 \\ Single Detection Time & 119 s & 0.078 s & 0.066 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean square error and average single detection time for each algorithm.  
Figure 6: Heterogeneous multi-defect model inversion imaging. (**a**) Heterogeneous multi-defect model with 2 cm radius. (**b**) CSI inversion results. (**c**) BP neural network inversion results. (**d**) Model-driven deep learning network inversion results.  
not accurate enough for feature extraction of the training data. The model-driven deep learning inversion has less noise, accurately reflecting the defect size and location, and also clearly reflecting the media boundary between wood, defect and air, and the _IOU_ value reaches 0.961.  
As shown in Table 5, under the standard of mean square error, the result of the model-driven depth neural network is significantly better than that of the BP neural network. The consumption of the two methods is roughly the same.
## 1 Introduction
### Aim, Objectives, Object, and Subject of the Study  
Based on the above reviewed scientific works, it has been established that for the application of MPC algorithms, these algorithms must be parametrised by the user and that this parametrisation determines the quality of the results. Therefore, the main aim of this article is to improve the hardware and software provisions for controlling asynchronous motors by developing and validating a computerised method for the multiparameter optimisation of predictive control algorithms based on the criteria of reducing computational costs and increasing the accuracy of task development, which is achieved by applying methods and approaches of multiple-criteria decision making (MCDM) in determining the optimal combinations of control parameters and influences. This is particularly important because the selected parameters of the MPC algorithms, such as the number of sampling points over the prediction horizon, the weighting coefficients of the cost function, the maximum number of gradient search iterations, and others, directly affect the control system's ability to generate energy-optimal trajectories. Improperly tuned parameters may lead to overcorrection, oscillations, or inefficient voltage commands, all of which increase energy consumption during dynamic operation. A key innovative aspect of this research lies in the integration of the GRAMPC predictive control framework with a taxonomic multiple-criteria decision-making method for systematic multiparameter optimisation. The objectives of this study are as follows: * Analysis of the influence of the main parameters of the prediction algorithm (length of the prediction horizon, sampling step, number of iterations, indicators of the loss function) on the efficiency of electric drive control in the dynamic mode; * Development of a computer model that allows for conducting a series of simulation experiments to identify and configure the parameters of predictive control tools, followed by a comparative analysis of the results in terms of accuracy, computation time, and energy efficiency; * Studying the feasibility of using the taxonomic method as a basic mechanism for the multiparameter selection of optimal combinations of control parameters; * Analysis of computer experiment results at qualitative and quantitative levels for different sets of parameters in order to identify those configurations that provide the best balance between speed, accuracy, and energy consumption; * Validation of the obtained research results through a series of simulation experiments with a modelled load on the electric drive and variable control settings. The object of the study is the process of multiparameter optimisation of gradient algorithms for the predictive control of electric drives with a rotating magnetic field in real time. The subject of the study comprises algorithms and methods for reducing the computation time, improving the control accuracy, and reducing the energy consumption of electromechanical systems based on electric drives with a rotating magnetic field.
## 3 Classical Routing Protocols
### Modification of LEACH Based on CH Selection
#### 3.2.10 Ec-Leach  
[PERSON] et al. (2018) proposed a variant of LEACH based on a centralized approach with a multi-hop clustering methodology. The CH selection process is conducted by \(BS\) by calculating threshold \(T(n)\) using Equation (12).  
\[T(n)=\frac{R_{E}(n)}{\sum_{i=1}^{m}\frac{d(i,m)}{R_{E}(i)}} \tag{10}\]  
The residual energy of each node is \(R_{E}(n)\) and \(d(i,n)\) is the distance of node \(i\) to node \(n\) in a network of \(m\) nodes. The node with the highest value of \(T(n)\) is selected as the CH and then its distance from the node with second-highest \(T(n)\) is computed. This leads to the following two possibilities:  
* If this distance is greater than or equal to the minimum distance between every CH and the next (MDCH), then the second-highest \(T(n)\) node is declared as CH.
* If this distance is lesser than the minimum distance between every CH and the next (MDCH), the BS does not select any of these nodes as CH.  
As a result, a node with less energy will never be chosen as CH, saving significant amounts of energy. However, the protocol suffers a problem with scalability and extra overhead.
# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 3 Methods
### Correlation Filter Tracking Model Based on the Deep-Pruned Feature Network
#### 3.4.3 Target Location Prediction  
During the tracking process, a search box \(S\) of the same size as the initial target box T is constructed with the target location as the center. The deep-pruned feature network is used to extract the features of the area in which search box S is located and to record them as \(F_{CL,l}(S)\ (l=1,2,\ldots,KN_{CL})\). Then the response \(\left(r_{i,j}\right)_{M\times N}\) can be obtained from Equations (16) and (17) [30]:  
\[g^{l}=\left(\hat{W}^{l}\right)^{\ast}\odot F_{CL,l}(S)\ (l=1,2,\ldots KN_{CL}) \tag{16}\]  
\[\left(r_{i,j}\right)_{M\times N}=\mathcal{F}^{-1}\left(\sum_{l=1}^{KN_{CL}}g^{ l}\right) \tag{17}\]  
where, \(g^{l}\) represents the frequency output response of the \(lth\) tracking filter, \(\mathcal{F}^{-1}\) represents the inverse Fourier transform, \(r_{i,j}\in R^{M\times N}\) represents the total output response of all channels. Based on Equations (16) and (17), the coordinates \((x,y)\) of the predicted position of the target can be obtained as:  
\[(x,y)=\underset{RM\times N}{max}\left(r_{i,j}\right)_{M\times N} \tag{18}\]
###### Abstract  
In order to improve the robustness and imperceptibility in practical application, a novel audio watermarking algorithm with strong robustness is proposed by exploring the multi-resolution characteristic of discrete wavelet transform (DWT) and the energy compaction capability of discrete cosine transform (DCT). The human auditory system is insensitive to the minor changes in the frequency components of the audio signal, so the watermarks can be embedded by slightly modifying the frequency components of the audio signal. The audio fragments segmented from the cover audio signal are decomposed by DWT to obtain several groups of wavelet coefficients with different frequency bands, and then the fourth level detail coefficient is selected to be divided into the former packet and the latter packet, which are executed for DCT to get two sets of transform domain coefficients (TDC) respectively. Finally, the average amplitudes of the two sets of TDC are modified to embed the binary image watermark according to the special embedding rule. The watermark extraction is blind without the carrier audio signal. Experimental results confirm that the proposed algorithm has good imperceptibility, large payload capacity and strong robustness when resisting against various attacks such as MP3 compression, low-pass filtering, re-sampling, re-quantization, amplitude scaling, echo addition and noise corruption.  
audio watermarking; robustness; blind extraction; discrete wavelet transform; Discrete cosine transform 2018 8 723 309/app8050723