esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Spectral Scattering Imaging
### Analysis of Scattering Images
#### 3.2.3 Feature Extraction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Function** & **Equation Form 11 ** & **No. of Parameters** \\ \hline Exponential & \(R(x)=\alpha+\beta\times\exp(-|x|/\delta)\) & 3 \\ \hline Gaussian & \(R(x)=\alpha+\beta\times\exp[-0.5(x/\delta)^{2}]\) & 3 \\ \hline  & \(R(x)=\alpha+\dfrac{\beta}{1+(x/\delta)^{\eta}}\) & 4 \\ [PERSON] & \(R(x)=\beta+\dfrac{1-\alpha}{1+(x/\delta)^{\eta}}\) & 4 \\  & \(R(x)=\dfrac{1}{1+(x/\delta)^{\eta}}\) & 2 \\ \hline  & \(R(x)=\alpha+\beta(1-e^{-\exp(\delta-\eta x)})\) & 4 \\ [PERSON] & \(R(x)=1-(1-\alpha)e^{-\exp(\delta-\eta x)}\) & 3 \\  & \(R(x)=1-e^{-\exp(\delta-\eta x)}\) & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mathematical functions used for fitting scattering profiles 11.  
dataset into a few informative or effective vectors. Wavelength selection is another way to identify several key wavelengths relevant to prediction of quality attributes. Step-wise multiple linear regression (MLR), while time consuming in searching for the optimal wavelengths, has been utilized to select the best single wavelength and combinations of wavelengths for assessing firmness and SSC of apples [43; 54]. [PERSON] and [PERSON] [60] proposed the use of a hierarchical evolutionary algorithm (HEA), a variant of genetic algorithm (GA), coupled with subspace decomposition and partial least squares (PLS) modelling, for fast selection of effective wavelengths. The HEA encoded multiple scattering parameters in a hierarchical manner. Each chromosome in the HEA was composed of an upper control layer and a lower parameter layer; the parameter layer was used to store the scattering profiling parameters and the control layer was used for wavelength selection. By calculating the mean value of each scattering profile, a 2-D hyperspectral image can be reduced to a 1-D mean spectrum, which then enables the implementation of commonly used methods in NIR spectral analysis for wavelength selection. For mean spectra extracted from hyperspectral scattering images, uninformative variable elimination (UVA), affinity propagation and interval partial least squares have been used for wavelength selection for quality prediction [61; 62; 63; 64]. The aforementioned feature extraction techniques are mostly used to extract features from a 1-D scattering profile, without considering the pixel intensity distribution in a 2-D scattering image, which may provide additional information to improve quality prediction. [PERSON] et al. [65; 66] explored various image processing methods to extract features from hyperspectral scattering images, which were used, coupled with the features from 1-D scattering profiles, for prediction of apple firmness and SSC. The authors extracted image texture features based on grey-level co-occurrence matrix, first-order statistics and Fourier analysis, as well as multi-resolution image features obtained by wavelet transform.
# RJA-Star Algorithm for UAV Path Planning Based on Improved RSDOS Model
###### Abstract  
To improve the obstacle avoidance ability of agricultural unmanned aerial vehicles (UAV) in farmland settings, a three-dimensional space path planning model based on the R5 DOS model is proposed in this paper. The direction layer of the R5 DOS intersection model is improved, and the RJA-star algorithm is constructed with the improved jump point search A-star algorithm in our paper. The R5 DOS model is simulated in MATLAB. The simulation results show that this model can reduce the computational complexity, computation time, the number of corners and the maximum angles of the A-star algorithm. Compared with the traditional algorithm, the model can avoid obstacles effectively and reduce the reaction times of the UAV. The final fitting results show that compared with A-star algorithm, the RJA-star algorithm reduced the total distance by 2.53%, the computation time by 97.65%, the number of nodes by 99.96% and the number of corners by 96.08% with the maximum corners reduced by approximately 63.30%. Compared with the geometric A-star algorithm, the running time of the RJA-star algorithm is reduced by 95.84%, the number of nodes is reduced by 99.95%, and the number of turns is reduced by 67.28%. In general, the experimental results confirm the effectiveness and feasibility of RJA star algorithm in three-dimensional space obstacle avoidance.  
R5 DOS intersection matrix; RJA-star algorithm; jump point search algorithm; path planning Article  
R5D 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Classical P&O and dp-P&O
### Issues under Rapidly Changing Irradiance  
As previously mentioned, it has been reported that the P&O method can become confused under rapidly changing irradiance scenarios [5; 16; 19; 31]. This issue occurs because the algoritm assumes that the change observed in power output is triggered exclusively by the perturbation of MPPT. Nevertheless, if a significant enough shift in irradiance occurs, it can have an even more substantial effect on power variation than MPPT-induced perturbation. Therefore, the algorithm drifts away from the MPPs reported in [8; 18; 19]. The classical drift behavior is illustrated in Figure 2. A steady irradiance increment over time is shown, the MPP line (red dot) ramps up, and the system power (solid) increases along with it. At the starting operation point \(p_{k-2}\), increment \(\delta\) in the wrong direction is introduced by the MPPT driving the power away from the MPP line. During the next perturbation stage, it can be observed that power \(p_{k-1}\) is greater than the previous power state \(p_{k-2}\) due to the rapidly increasing irradiance. Since \(\Delta p_{-1}>0\), the classical P&O algorithm decides to apply an increment in the wrong direction for the second time. Notice that since \(\Delta p>0\), the P&O method would keep driving the operating point away from MPP for future iterations. This would be repeated until the effect caused by the controller action is greater than the impact of external factors. An approach to determine whether the perturbation was made in the right direction is presented in this work.  
Figure 1: Flowchart of the classical P&O MPPT.
# Two Universality Properties Associated with the Monkey Model of Zipf's Law
## 2 Proof that \(-\beta\) Tends Towards \(-1\) from the Asymptotics of Log-Spacings
If the term \(-\sum_{i=1}^{K}q_{i}\ln q_{i}\) is sufficiently large, then \(\beta\approx 1\). For the class of random variables that we considered in partitioning \([0,1-s]\) to populate the letter probabilities, this will be the case for large \(K\). Figure 2 presents graphical results using simulations of sample spacings from several distributions to illustrate our result for \(\beta\). In Figure 2a we plot \(\log_{10}P_{r}\) by \(\log_{10}r\) for the [PERSON] model with exactly the parameter values he used, _i.e._, \(K=26\) letters, a space probability \(s=0.18\) and equal letter probabilities \(q_{1}=\cdots=q_{26}=(1-0.18)/26\). The graph is based on the ranks \(1\leq r\leq 475,255=\sum_{i=0}^{4}26^{j}\), corresponding to all words of length \(\leq 4\) non-space letters. Figure 2b through 2d are based on generating \(K=26\) letter probabilities from three different continuous distributions with bounded densities \(h(x)\) on \([0,1]\) using a generalized broken stick method to obtain the sample spacings. Again \(s=0.18\) was used for the probability of the space character and the letter probabilities were populated with values from the spacings with \(q_{i}=(1-s)D_{i}\), \(1\leq i\leq 26\), so that in each case, \(\sum_{i=1}^{26}q_{i}=0.82\), as in [PERSON]'s example. For each graph in Figure 2b-d, we generated the  
largest 475,255 word probability values in order to match the [PERSON] example of Figure 2a. The three continuous distributions, all defined on \([0,1]\), are:  
* a uniform distribution with density \(h(x)=1\);
* a beta \(B(3,2)\) distribution with density \[q_{1}^{1/\beta}+q_{2}^{1/\beta}+\cdots+q_{K}^{1/\beta}=1, \tag{2}\]4 (13)
* a triangular distribution with density \[h(x)= \begin{cases}4x,&\text{if }0\leq 1/2\\ 4(1-x)&\text{if }1/2\leq x\leq 1.\end{cases} \] (14)  
The graphs in Figure 2b-d illustrate our theoretical derivation, but we also note that the very linear plots indicate what appears to be an almost \"immediate\" convergence to approximate power law behavior exceeding what might be expected from the asymptotics of Conrad-Mitzenmacher and Bochkarov-Lerner.
# Numerical Simulation of Rockfill Materials Based on Fractal Theory
## 4 Analysis of the Results of the Numerical Test  
### Compactness Test  
RD is an important indicator for controlling the shear strength of granular material [3]. Thus, the numerical test on RD is performed to analyze the influences of the fractal dimension \(D\) on the compactness of rockfill materials. The maximum void ratio \(e_{max}\) and minimum void ratio \(e_{min}\) of the numerically simulated specimen must be determined first. In this paper, the maximum void ratio is determined by using a method presented by [PERSON] et al. [13], that is, frictional particle aggregate under the action of dead load reaches the loosest state defined by the natural accumulation state. However, the minimum void ratio is obtained by compressing the rigid boundary of frictionless particle aggregate in all directions. The specimen size is \(\varphi\) 300 mm \(\times\) 600 mm, and the maximum particle size \(d_{max}\) is 60 mm. The relationship between dense degree index and the fractal dimension \(D\) is shown in Figure 3.  
Figure 3 shows that the fractal dimension \(D\) increases from 1.27 to 2.03, and the maximum void ratio \(e_{max}\) and the minimum void ratio \(e_{min}\) of the numerically simulated specimen, as well as the void ratio \(e\) that corresponds to the RD of 0.89, decrease. With the increasing fractal dimension \(D\), the scale method changes, and the number of fine particles contained in the specimen increases gradually with the number of contacts between the inner particles. However, the number of contacts is closely related to the compactness. A high content of fine particles corresponds to great compactness and low void ratio. For smooth round particles without breakage, a small particle size corresponds to great compactness and a large fractal dimension \(D\).
## 2 Materials and Methods
### Comparison of Ultrasound-Induced and Rotation-Induced Cupula Displacement  
The SCC, responsible for detecting rotational movement, consists of endolymph and the cupula [3]. When the body rotates, the endolymph tends to remain stationary due to inertia, while the SCC moves with the body [1; 3; 27]. This interaction exerts directional forces on the cupula, leading to its deformation. The displacement of the cupula during rotational movement was extracted from previous studies (Table 2), representing displacement values under various rotational movements.  
Due to the experimental limitations of directly measuring human cupula displacement, most existing experiments have been conducted on animals such as bullfrogs and oyster toadfish [48; 49]. These studies offer reference values for cupula displacement in animals with similar SCC structures. In the case of humans, the direct measurement of the displacement is challenging. Indirect methods, such as vestibulo-ocular reflex [46; 50] or vestibular evoked myogenic potential [51], assess the vestibular response through eye movements or muscle activity. Alternatively, the displacement values have primarily been obtained through finite element simulations [25; 28; 46; 52]. Therefore, the displacements from these simulations, along with animal model data, were used as references to evaluate the significance of the displacement induced in this study.  
By comparing the displacement data obtained from the other studies and our results, we can evaluate whether the displacement induced by LIFU matches the range of displacement experienced during human rotational movement. This comparison allows us to understand how ultrasound-induced displacement aligns with or differs from the natural SCC dynamics.
## 5 Conclusions  
In this study, we successfully developed an ANN model for predicting the settling velocity of both spherical and non-spherical particles in various fluid environments. Utilizing an extensive dataset of 931 data points, we demonstrated the robustness and accuracy of the model. Key achievements of our research include:  
(1) The development of an ANN model structured as 7-42-1, which achieved an average absolute relative error of 8.51%, highlighting its precision in predicting settling velocities. (2) A detailed assessment of the impact of each input variable on the settling velocity revealed the consistency index to be the most negatively impactful factor, while particle diameter showed the greatest positive influence. (3) The proposed model's predictions for particle settling velocities were reasonably accurate for both spherical and non-spherical particles in Newtonian and non-Newtonian fluids, showcasing its effectiveness when compared to empirical models. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14020826/s1](https://www.mdpi.com/article/10.3390/app14020826/s1)]([https://www.mdpi.com/article/10.3390/app14020826/s1](https://www.mdpi.com/article/10.3390/app14020826/s1)). File S1: Experimental data of particle settling velocity-931 data.xlsx. **Author Contributions:** Conceptualization, W.L. and X.S. ; methodology, W.L. and Z.X. ; validation, X.J. ; formal analysis, X.J. ; investigation, J.L. ; resources, Z.X. ; data curation, J.L. ; writing--original draft  
Figure 6: Relative importance of input variables in the ANN model for predicting particle settling velocity. preparation, Z.X. ; writing--review and editing, S.D. ; visualization, Z.X. ; supervision, S.D. ; project administration, W.L. ; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Natural Science Foundation of China (No. 52104009), China National Petroleum Corporation Scientific Research and Technology Development Project \"200C/105 MPa Continuous tube drilling and grinding/Jet operation Tool Development\" (No. 2020B-4018), and China National Petroleum Corporation Limited major technology promotion project \"Full series Continuous pipe operation Technology Promotion\" (No. 2022 ZT01). **Data Availability Statement:** The original contributions presented in the study are included in the Supplementary Material, further inquiries can be directed to the corresponding author. **Conflicts of Interest:** Authors [PERSON], [PERSON], [PERSON] were employed by the Jianghan Machinery Research Institute Limited Company of CNPC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare no conflicts of interest. **Nomenclature**
# Acoustic Emission for Evaluating the Reinforcement Effectiveness in Steel Fiber Reinforced Concrete
## 3 Results  
### Three-Point Bending AE Results  
Figure 5 shows the cumulative curves of individual specimens with a different vol.% content of steel fibers (SF). It is obvious that SFRC produces much more AE activity than reference plain concrete. This is a reasonable trend, as fibers add another failure mechanism (pull-out from the matrix) that does not physically exist in the reference concrete. Indeed, the total activity of reference concrete is almost negligible compared to the SFRC, as shown in Figure 5.  
Figure 4: Concrete specimen reinforced with hooked fibers after a bending test with the main crack visible.  
Apart from the basic behavior as shown through the cumulative activity, it is interesting to check specific waveform parameters. Figure 6 shows the RT of the same specimens. The difference in the level of RT is indicative of this extra shear character imposed by the fibers. The plain specimens break with a large crack from the tensile side, without exhibiting a serious shear character. Hence the RT of AE signals stays at relatively low levels. However, when fibers are present, this shear character is enhanced and results in higher RT values for 0.5% and even higher values for 1% of steel fibers.  
In Table 1, one can see the average values of some of the main AE parameters for the different fiber contents. The values were taken from the activity before the main cracking occurs, since we focused on the early behavior and the influence of the reinforcement on the initial stress field. It is obvious that the RT increases with the increase of fibers from 0% to 1%, showing the reinforcing effect of fibers as their content increases. Inversely, frequency indicators decrease even though slightly, something that will also be seen in the 4-point bending part of the study. The mechanical test results also depicted in Table 1 show that secant stiffness and toughness values increase with the increase of the fiber content. Hooked fibers work as a bridge to prevent and delay the crack progress which provides a more ductile behavior than the plain concrete. A correlation between AE parameters and toughness is noted with more details in the discussion section.
# XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones
## 6 Online Identification Method
### Intrinsic Identification via a Dual-DNN Approach
The input of the first DNN model is the data matrix mapping for the protocol data; each matrix corresponds to one protocol message, with dimensions equal to the number of lines in the protocol message and the ASCII-coded maximal line length of the sequences included by this message. The number of neurons in the hidden layer must be no less than the total number of elements contained in the input data matrix. The output of the first DNN model is the predicted normal matrix, which has the same dimensions as the input. The second DNN model is designed to replace the convolution calculation with the XOR of the base DNN model. The model focuses on all the image sections instead of the image margins. The input for this second DNN model consists of the data matrix that requires detection and the corresponding predicted data matrix. Furthermore, the number of neurons in the hidden layer must be at least equal to the total number of elements in the input data matrix. For any layer \(l\), \(d^{l}\) is the output vector, which is also the input to the  
Figure 9: Static and dynamic fields of one 5G protocol sequence. \(l+1\)th layer. The XOR operation is performed between the weights of the neurons in the \(l+1\)th layer. Further, the activation function denoted by \(f\) follows this operation to obtain \(a^{l+1}\), which is given by  
\[d^{l+1}=f\left(\omega^{l}\oplus a^{l}\right), \tag{8}\]  
where \(\omega^{l}\) denotes the weight. After performing the XOR calculation between the predicted data matrix and the data matrix to be detected, we obtain the detection result matrix. On the basis of the output of the 2 nd DNN, a label is assigned to each grayscale value, with 0 and 1 indicating normal and anomalous points, respectively. Anomalies are highlighted in red. Because the grayscale-to-signal transformation is reversible, anomalous signal points can be precisely located. With the former design, the intrinsic identify is achieved. The process is summarized below. The identification model begins with inputting an ASIC-encoded grayscale image, which is a matrix consisting of elements that are either zero or one. Then, the identification model continues to process, detect, and identify. One layer of the DNN predicts the expected value when the system is not under attack. In the second layer of the DNN, the predicted matrix and the input matrix undergo an XOR operation to output the detection matrix. If there is no attack, the detection matrix will be zero. Otherwise, there will be elements in the detection matrix that are one, corresponding to the anomalous signal fields. Lastly, the anomalous signal fields can be obtained by reversing the matrix to derive the protocol information.
## 2 Materials and Methods
### Data Processing
#### 2.4.2 Data Analysis  
Primary and secondary variables of interest were analyzed using a mixed-model ANOVA. The fixed effects of the condition (PD and CON) along with the random effects of the subject and limb within the subject were included in the model. Left and right limb data were analyzed, as asymmetry in turning strategies in IPD has been previously reported [16]. Normality and homogeneity of variance were verified with residual analysis. Comparisons for the primary dependent variables described above were analyzed between foot strike and foot off in the gait cycle. The significance level was set at \(p\) < 0.05. A Bonferroni adjustment of the significance level was used for ST parameter variables (\(p\) < 0.0056) and dynamic balance variables (\(p\) < 0.0028) to decrease the risk of a type 1 error with the use of multiple statistical tests. Since this was an exploratory study, the impact of a type I error would be low. In fact, type II errors, i.e., missing significance, are probably more of a problem. Thus, values of \(p\) less than the Bonferroni cutoffs were declared significant. Values of \(p\) < 0.05 but greater than the Bonferroni cutoff are reported as marginally significant. One participant presented as an outlier in ST parameter analysis but was not an outlier in dynamic balance variables analysis and therefore was excluded from ST parameter analysis only. The effect size was calculated as the difference between the means of the two groups defined by the factor levels after correcting for the other factors in the model and is in the same units as the dependent variable. Since there is limited research regarding COP-COM A/P and M/L, COP-XCOM A/P and M/L, and U\({}_{MAX}\)-XCOM A/P and M/L, and clinically meaningful effect sizes between these populations, mild, moderate, and large effect sizes were defined as mild effect sizes = 10-20 mm, moderate effect sizes = 20-30 mm, and large effect sizes \(\geq\) 30 mm.
## 7 Evaluation and Discussion
### Discussion
#### 7.4.1 Protection of Virtual Instruction  
The primary goal of Tamper-Proofing Instruction is to protect the virtual instruction from tampering. To our knowledge, _VMGuards_ is the first PVM-based scheme attempt at tamper-proofing software code. Previously, the adversary could, for instance, locate the critical component-Dispatcher, collect information of all Handlers, and make modifications to the virtual instructions. TPI-Ring can guard all the virtual instructions from tampering, as any changes made by adversaries will be detected and responded to appropriately. However, too much stress of the intensity of TPI adds serious performance to both runtime and space. In addition, the structure TPI-Ring itself has already added an additional layer of protection from tampering. With a better balance between TPI protection range and TPI-Ring, _VMGuards_ still can be used to protect the program from tampering effectively.