esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Thin-Film Materials and Their Applications
### Complex Metal Oxides
#### 3.1.2 Halide Perovskite Thin-Films  
Apart from oxide perovskite (ABO\({}_{3}\)), halide perovskite (ABH\({}_{3}\)) materials are of interest in both scientific and technological fields. The most common way to realize the perovskite structure is by using highly efficient and stable semi-transparent ABH\({}_{3}\) materials. This structure has a good reaction to light, which produces solar energy (e.g., solar cells) [112]. In addition, it can be used as light-emitting diodes [113], sensors [114; 115], and memristors [77].  
Under the prevalence of transient/degradable electronics, halide perovskites are exploited to fabricate the aforementioned devices applicable for environmental-friendly degradable sensors, dissolvable consumer devices, and hardware-secured digital systems. In 2017, [PERSON] et al. prepared a thiocyanate ion (SCN)-doped organometal halide perovskite (CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3}\)) thin-film, denoted as CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3-x}\)(SCN)\({}_{x}\) thin film [77]. Perovskite-based thin films were used as a sensing material to develop high-performance gas sensors for the detection of acetone or nitrogen dioxide (NO\({}_{2}\)) at room temperature. To date, some efforts have been carried out using perovskite materials as a promising candidate for transient and disposable devices [80; 81; 116; 117]. In 2018, [PERSON] et al. for the first time proposed that the transient resistive-switching electronic device with the perovskite CsPbBr\({}_{3}\) thin-films acting as resistive-switching functional layer [80]. The investigations on transient characteristics of perovskite-based resistive-switching devices reveal that the CsPbBr\({}_{3}\) thin-films and resistive-switching memory device can be dissolved in deionized water within 60 s. Another work used Cs\({}_{4}\)PbBr\({}_{6}\) thin-films as the switching insulators for fabricating transient perovskite-based resistive-switching memory devices [81]. In that research, dissolution behaviors suggested that Cs\({}_{4}\)PbBr\({}_{6}\) layers can be rapidly dissolved in a way whereby it fully vanished in DI water after 2 s. It is the reason that the higher sensitivity of Cs4 PbBr\({}_{6}\) thin-films with smaller thickness toward DI water than the aforementioned CsPbBr\({}_{3}\) thin-films. These researches demonstrate that the halide perovskite-based transient resistive-switching memory devices have great potential for disposable electronics, secure data storage, and artificial synapses. If dissolvable metals were to be selected as electrodes, the fully self-destructive transient electronic device can be fabricated with the entire electronic unit being degradable in DI water.
## 5 Post-Processing EMG Signals: Proposed Supervised Learning Model
### Data Pre-Processing  
The pre-processing phase of sEMG data plays a crucial role to ensure that the raw signals are effective for input into the advanced 1D-CNN. Pre-processing techniques provide help to enhance the quality of the data, reduce noise, and improve the performance of the neural network [55, 56]. At the beginning, sEMG.txt files was converted into matrix.xlc files of [20,000, 6] dimension. sEMG data represent three different voltage values over time, measured by the six electrodes placed on the biceps of the subjects. The data loading procedure included reading these files in an organized manner, ensuring they were  
Figure 6: sEMG data on healthy subjects (V-s).  
Figure 7: sEMG data on subjects with potential neuropathy (V-s).  
consistently structured for the subsequent pre-processing and analysis. Thereafter, normalization was performed to scale the sEMG signals to a consistent range. Normalization was performed by dividing each value in the sEMG signal by the maximum value in the dataset, resulting in normalized values between 0 and 1, in order to stabilize the training process and improve the convergence of the network. Right after normalization, the data from multiple subjects were concatenated to form a comprehensive dataset. This step involved combining the normalized sEMG signals of healthy subjects and those with neuropathic conditions into a single array. Each subject's data were assigned a label (0 for healthy and 1 for neuropathic) to facilitate supervised learning. The dataset was divided in an 80-20 working split; 80% of the data was used for training, and the remaining 20% was reserved for testing. This split ensured that the network was trained on a substantial portion of the data while being evaluated on unseen examples, allowing for an accurate assessment of its generalization performance. The last pre-processing step was data augmentation. Data augmentation is important to enhance the robustness of the neural network. For sEMG signals, augmentation included adding noise, shifting the signal in time, and varying the amplitude slightly. These augmentations help to simulate different scenarios and improve the network's ability to generalize to new unseen data [57, 58].
# A Clustering WSN Routing Protocol Based on k-d Tree Algorithm
## 5 Simulation and Results Analysis
### Other Tests Performed
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Nodes** & **LEACH (r)** & **LEACH-C (r)** & **H-kdtree (r)** \\ \hline
100 & Average & 29.34 & 13.703 & 28.16 \\  & Variance & 42.4871 & 34.0309 & 4.7477 \\
200 & Average & 28.01 & 34.15 & 24.03 \\  & Variance & 37.2625 & 34.8312 & 2.9384 \\
300 & Average & 24.80 & 43.4356 & 32.43 \\  & Variance & 39.3804 & 48.0083 & 6.4618 \\
400 & Average & 34.11 & 39.5644 & 32.67 \\  & Variance & 46.3957 & 39.7683 & 5.6382 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Number of cluster head. (r) random. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Nodes** & **LEACH (r)** & **LEACH-C (r)** & **H-kdtree (r)** \\ \hline
100 & 0.06682 & 0.0901 & 0.07915 \\
200 & 0.07959 & 0.11681 & 0.09122 \\
300 & 0.08839 & 0.1217 & 0.09623 \\
400 & 0.09272 & 0.1433 & 0.1062 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Average energy of each node. (r) random. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Nodes** & **LEACH (r)** & **LEACH-C (r)** & **H-kdtree (r)** \\ \hline
100 & 0.283324844 & 0.8021 & 0.10480334 \\
200 & 0.928 & 0.8758 & 0.114 \\
300 & 0.841188642 & 0.6348 & 0.177976 \\
400 & 0.6150795862 & 0.9217 & 0.104979 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Delay (seg). (r) random. H-kdtree shows a 50% throughput increase as compared to LEACH and LEACH-C, as shown in Table 15.  
In the observed metrics in Table 16, show that the stability periods in the half-life of network nodes for the proposed H-kdtree protocol is longer than in LEACH and more short that in LEACH-C. After 50% of the rounds, in H-kdtree, we have found node death to be stepped and maintaining low variability in delay and jitter. This was not only due to its reactive mechanism but also because of its stability derived from using TDMA for medium access. The WSN we studied did not present node mobility: all nodes maintained their positions. This characteristic was used by H-kdtree and its cluster formation mechanism, which is based on the k-d tree algorithm and adds more stability by keeping the majority of nodes in the same clusters after each configuration phase. The results on PDR showed that TDMA-based packet transmission planning did not show packet loss in LEACH, LEACH-C and H-kdtree, in all scenarios with 100, 200, 300, and 400 nodes.
## 2 Data and Methods
### Extraction of Impervious Surface Distribution
#### 2.2.3 Method for Accuracy Assessment  
Accuracy is determined in terms of precision, recall, and the F1 score.  
According to their actual and predicted values, pixels are divided into true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN), and then the precision (P), recall (R), and F1 score are calculated using Equations (13)-(15) [34]:  
\[P=\frac{TP\_N}{TP\_N+FP\_N}\times 100\% \tag{13}\]  
where TP_N and FP_N are the total numbers of pixels in TP and FP, respectively.  
\[R=\frac{TP\_N}{TP\_N+FN\_N}\times 100\% \tag{14}\]  
where FN_N is the total number of pixels in FN.  
\[F1=\frac{2\times P\times R}{P+R}\times 100\% \tag{15}\]  
The closer the F1 score is to 1, the higher the accuracy of the extraction of impervious surface distribution is.
## 4 Empirical Modeling
The analysis of Figure 9a,b revealed that the heat flux has no effect on the estimated convective boiling contribution, whereas it highly affects the nucleate boiling contribution: considering a mean vapor quality of 0.4, the nucleate boiling heat transfer coefficient at \(HF\) = 50 kW m\({}^{-2}\) was 3.1 times the value at \(HF\) = 10 kW m\({}^{-2}\) in the case of R450A, whereas it was 2.8 times in the case of R515B. Moreover, it appears that R450A had a stronger contribution of nucleate boiling compared to R515B. Figure 9c,d show the effect of mass velocity on the two contributions at \(HF\) = 10 kW m\({}^{-2}\) (Figure 9c) and at \(HF\) = 50 kW m\({}^{-2}\) (Figure 9d) for R450A and R515B. The heat transfer coefficient due to convective boiling at \(G\) = 400 kg m\({}^{-2}\) s\({}^{-1}\) was, on average, 19% higher than that at \(G\) = 100 kg m\({}^{-2}\) s\({}^{-1}\), and R515B showed a convective boiling contribution that was 20% higher compared to R450A. In addition to models for the estimation of the heat transfer coefficients, models for the evaluation of frictional pressure drops are needed for a complete design of evaporators. The following models were selected for the comparison against the experimental data: [PERSON] and [PERSON] [43], [PERSON] et al. [39], and [PERSON] [40]. Only data characterized by an experimental pressure gradient higher than 0.5 kPa m\({}^{-1}\) were considered in the following comparisons in order to omit data with high experimental uncertainty. The model of [PERSON] and [PERSON] [43] considers the homogeneous model for the momentum term and a friction factor that considers the presence of fins for the frictional contribution; considering the three fluids, the model was able to predict the experimental pressure drop with a relative and absolute deviation of \(-12.1\%\) and \(20.5\%\), respectively. The models of [PERSON] et al. [39] and of [PERSON] and [PERSON] [40] estimate the \(\alpha\)-dependent \(\alpha\)-dependent  
the frictional pressure drop; considering the three fluids, the model of [PERSON] et al. [39] presented a relative and absolute deviation of \(-4.6\%\) and \(11.1\%\), respectively, whereas the model of [PERSON] and [PERSON] [40] presented a relative and absolute deviation of \(-8.2\%\) and \(20.4\%\), respectively. Table 5 summarizes the relative and absolute deviations of the implemented models split for each tested refrigerant. Figure 10 reports the calculated frictional pressure gradient plotted against the experimental counterpart for the model developed by [PERSON] et al. [39], which presents the lowest deviations.
## Instrument Description
### Control and Recording System  
At the beginning of the project, no commercial high-speed recording solution that would fit our needs (320 MB/s sustained recording with time-tagging and flexible front-end control) was available. Thus, we implemented our own high-speed control and recording system, which turned out to be the major technological bottleneck of the project.  
The control and recording system consists of an industrial computer running Linux with a DE4-Terasic development board inserted into one of the PCIe slots. The DE4 board is populated with a Stratix IV FPGA and its function is to interface with the SPIR front-end to control and program  
Figure 3: Block diagram of SPIR’s clock synthesizer.  
the different subsystems, in addition to gathering the 16 digital data-streams to be recorded on the computer. Although these two functions are clearly different (control and data recording), they have been implemented in one single FPGA and computer system. The reason for this implementation is that both actions need to be well synchronized, and this can be better achieved by implementing them in the same subsystem.  
Figure 4 shows a block diagram for the control and recording system. The FPGA contains three subsystems: the instrument controller, the data recorder, and the PCIe interface of the computer. The implementation has been achieved using VHDL.
## 4 Proposed Filter Architecture and Determination of Stopband Frequencies  
This section describes the specification of the proposed filter architecture and the logic behind the formation of stopband frequencies using the mathematical model analyzed in Section 3. The filter is made up of a UTL loaded with three folded \(\lambda/4\) short-circuited rectangular pads. Out of these, one stub placed in the middle section of one side of the UTL, and the other two are placed at the symmetrical position on the opposite side of the UTL. The stubs have been placed at a distance of a half wavelength from each other to achieve five TPs in the passband. The layout of the proposed filter is shown in Figure 12 with optimized dimensions in millimeters (mm) displayed in Table 1. The stub arrangement made the proposed filter compact with a covered area of \(0.52\,\lambda_{\rm g}\times 0.32\,\lambda_{\rm g}\). It has been fabricated on Roger 4350 substrate material, with specifications listed in Table 1, and analyzed using a ZNB20 vector network analyzer. For the rejection of radio frequency bands at 4.9 GHz, 8.3 GHz, and 11.5 GHz, the stopband characteristics have been attained utilizing a quad-mode stub-loaded resonator coupled to the basic filter structure in Figure 2. All the stopbands can be independently controlled with the help of the lengths mentioned in the respective equations using the corresponding mathematical models listed in Table 2. Therefore, the resonance frequencies in the proposed resonator are controlled independently and mathematically verified, simulated, and then fabricated using a low-loss substrate material.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline L & 22.5 & W & 3.2 & \(L_{1}\) & 4 & \(L_{2}\) & 1 \\ \(L_{3}\) & 1.5 & \(L_{4}\) & 9 & \(L_{5}\) & 2 & \(L_{6}\) & 6 \\ W\({}_{1}\) & 1 & \(L_{8}\) & 2.9 & \(L_{9}\) & 1.2 & \(L_{7}\) & 0.8 \\ W\({}_{2}\) & 0.3 & W\({}_{3}\) & 1.5 & W\({}_{4}\) & 0.5 & S & 0.1 \\ R\({}_{1}\) & 0.55 & R\({}_{2}\) & 0.3 & PCB height & 1.5 & PCB & RO-4350 \\ \(\epsilon_{r}\) & 3.6 & \(\tan\delta\) & 0.0003 & W\({}_{t}\) & 3.1 & & \\ \hline \end{tabular}
\end{table}
Table 1: Dimensions in millimeter (mm) of the proposed SUWB-BPF with a stub-loaded resonator.  
Figure 11: (**a**) First even-mode excitation circuit. (**b**) Second even-mode excitation circuit.
## 4 CRISPR/Cas Mechanism
### Expression, crRNA Generation and Interference
#### 4.2.2 Type II Systems  
A very different mechanism of CRISPR expression and crRNA maturation was discovered in the type II-A system of _Streptococcus pyogenes_[84]. An abundant short _trans_-encoded transcript is essential for processing of pre-crRNA into mature crRNA. Therefore, these RNAs are termed _trans_-**a**ctivating **cr**RNAs (tracrRNA). These tracrRNAs contain a 25 bp stretch with almost perfect complementarity to the type II CRISPR repeats within the pre-crRNA (Figure 3B). The type II repeats do not form stem-loops and the authors suggest this deficiency is overcome by pairing with the tracrRNA [84].  
In addition to the tracrRNA, Cas9 (Csn1) and host RNase III are needed for the processing of pre-crRNA into mature crRNA. The requirement of RNase III is the first discovery of a non-Cas protein being an essential part of CRISPR/Cas defense machinery (Figure 3B) [84]. Interestingly, the _S. thermophilus_ type II-A system provides phage resistance in _E. coli_ when recombinantly expressed [59], which suggests diverse RNase III enzymes are compatible for crRNA generation. Cas9 is not only involved in crRNA processing but, along with the tracrRNA:crRNA hybrid, recognizes and degrades the target (Figure 3C) [85]. In complex with the tracrRNA:crRNA structure, Cas9 binds the target dsDNA and creates a double strand break by cleaving the complementary and non-complementary strand with its HNH- and RuvC-like domains, respectively [85]. _In vivo_, blunt cleavage of phage and plasmid dsDNA was observed at a position 3 bp from the PAM within the protospacer [53]. The PAM is important for the affinity towards the target and may have implications on the unwinding of the duplex, as cleavage of targets with mutated PAMs is only impaired in dsDNA but not ssDNA. Indeed, phages with PAM mutations also have the ability to avoid targeting by type II systems [37]. Furthermore, similar to the seed sequence in type I systems, complementarity between crRNA and target over a 13 bp stretch proximal to the PAM is required for interference [85] and hence, phages with mutations in this region of the protospacer region can evade interference [37].
# Research on Roughness and Microhardness of C45 Material Using High-Speed Machining
## 4 Conclusions  
The experiment was carried out in three alternative cutting speeds--the first alternative 700 m/min, the second alternative 1400 m/min, and the third alternative 2000 m/min. In each alternative, the depths of cuts--0.5 mm, 1 mm, and 1.5 mm--were used for each cutting speed. The tested material was machined in practice with a standard tool, a monolithic face-cylindrical cutter with a diameter of 16 mm. The conducted experiments formed the basis for creating a mathematical prediction for determining the quality of the machined surface of the C45 material by HSM technology, which reflects changes in roughness depending on the cutting speed. The created mathematical prediction can be rewritten in the form: \(Ra=-0.004176v_{c}{}^{2}+0.1873\ v_{c}-0.4834\), through which it is possible to realize the prediction of the resulting roughness for the given material depending on the cutting speed, for example, for a cutting speed of 1400 m/min it is based on the created relation predicted surface roughness 1.61 \(\upmu\)m. A significant finding is the fluctuating course of surface roughness when changing the cutting speed:  
* At 1400 m/min, the surface roughness was expected to have a decreasing tendency; however, it increased.
* A decreasing tendency was not recorded until at speeds of 1800 m/min, but during this process, the material already crept.  
All presented features and structures provide a comprehensive overview of the research of problems and bring new knowledge to the academic community and for practice.  
Conceptualization, J.Z. and J.D.; methodology, J.Z.; software, J.D. and D.D.; validation, J.Z., J.D. and D.D.; formal analysis, J.Z., J.D. and D.D.; investigation, J.Z. and J.D.; writing--original draft preparation, D.D.; writing--review and editing, D.D. and J.D.; visualization, D.D.; supervision, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Scientific Grant Agency Ministry of Education, Science, Research and Sport of the Slovak Republic and Slovak Academy of Sciences, grant no. VEGA 1/0080/20 \"Research into the effect of high-speed and high-feed machining technologies on the surface integrity of hard-to-machine materials\" and by Cultural and Educational Grant Agency Ministry of Education, Science, Research and Sport of the Slovak Republic, grant no. KEGA 024 TUKE-4/2023 \"Update and innovation of methodology for practical education of Automotive Production Technologies study program in the field of heat treatment and surface finishing\". This work was supported by the Slovak Research and Development Agency under contract No. APVV-21-0293.  
Not applicable.  
Figure 14: Chip shape after machining.
# Research on the Path Planning Algorithm of a Manipulator Based on GMM/GMR-MPRM
## 4 MPRM Module
### Collision Detection and Path Segmentation  
Before resampling, it is necessary to divide the path into a collision path and a noncollision path, that is to separate the collision path from the path and record the start and end points of the collision path.  
In order to divide the path, it is necessary to calculate the Euclidean distance \(L_{d}\) from the points on the path to the obstacle surface and then select the smallest Euclidean distance \(L_{\min}\) to compare with the threshold \(L_{threshold}\), where \(L_{threshold}=r_{b}+r_{ manipulator}\), \(r_{b}\) stands for the radius of the obstacle, and \(r_{ manipulator}\) stands for the radius of the manipulator of the robot. If \(L_{\min}\leq L_{threshold}\), the point is considered to collide with the obstacle. The first collision point is recorded as the starting point of the collision path, then whether the next point is a collision point is determined, until the next point is a noncollision point, and the point is recorded as the end point of the collision path. Repeat the iteration until the last point on the path. The iterative process of the path segmentation algorithm is shown in Algorithm 1.  
```
0: Task trajectory
0: The Startpoints and Endpoints of the collision path
1: Startpoints=[]; Endpoints=[]; StartFlag=False; EndFlag=False;
2:\(i=1;j=1\);
3:while\(i\
eq size(\text{path})\)do
4:\(L_{\min}=\min(L_{d})\);
5:if\(L_{\min}<L_{threshold}\)then
6:if Startflag=False then
7: Startflag=True; Startpoints()=\(i\) ; \(j=j+1\);
8:endif
9:else
10:if Startflag==True then
11: Startflag=False; Endpoints()=\(i\) ;
12:endif
13:endif
14:endwhile
```  
**Algorithm 1** Path segmentation algorithm.
# Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy
## 5 Conclusions  
In this paper, we proposed an efficient RDHEI by using multi-MSB embedding strategy with a very high embedding rate, which is much higher than the related methods [31; 35; 36; 38; 39]. In the proposed method, the values of \(m\) may be different for different images when the maximum embedding rates are obtained. In general, the smoother the image is, the smaller the value of \(m\) selected to obtain the maximum embedding rate. Under a certain embedding rate, we can select optimal values of \((m,T)\) to obtain the highest PSNR of a decrypted image with only the encryption key. For the reconstructed image with only the encryption key, only some parts of edged pixels are damaged, and the other pixels of the reconstructed image are all restored to the original value. This means that within the maximum embeddable capacity, no matter how much data is embedded, the visual quality of the reconstructed image will not be significantly damaged, and most of the pixel values can be losslessly recovered. The experimental results show that the proposed method can achieve excellent embedding performance.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools; [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61762017, Grant 61962008, and Grant 81701780, in part by the Guangxi Natural Science Foundation under Grant 2017 GXNSFAA198222, and Grant 2017 GXNSFBA198221, in part by the Project of Guangxi Science and Technology under Grant GuiKeAD17195062, in part by the Project of Guangxi \"Bagui Scholar\" Team for Innovation and Research, in part by the Guangxi Talent Highland Project of Big Data Intelligence and Application.  
The authors would like to thank the anonymous referees for their valuable comments and suggestions.  
The authors declare no conflict of interest.