esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Ceramic Wool Diameter by Motor Frequency Using Improved BP Neural Network
## 4 The Calibration of Measurement Model
### LSM Model  
LSM is a mathematical optimization technique. It has been broadly used in the engineering application to minimize the squared error and find the best matched functions for the data. The anticipated data could be easily calculated by the LSM, and the sum of squared errors between the actual data and the obtained data could be obtained. Based on its simple characteristics, no statistical parameters were required [26].  
For error calculation, the motor frequency was set to be \(x\) as the input layer and the corresponding mean ceramic diameter was set to be \(y_{i}\) as the output layer, a series of paired data \((x_{1},y_{1})\), \((x_{2},y_{2})\)... \((x_{m},y_{m})\) was obtained. These points were assumed to near a line, the equation of this line could be set as  
\[y_{i}=a_{0}+a_{1}x, \tag{1}\]  
where \(a_{0}\) and \(a_{1}\) were the required determined parameters, respectively.  
To estimate the constants, the optimization criterion was defined as the minimum of the sum of squares between the measured value \(Y\) and the calculated value \(Y_{j}\) where  
\[Y_{j}=a_{0}+a_{1}X_{i}, \tag{2}\]  
Suppose the optimization criterion was  
\[Z=\sum\bigl{(}Y_{i}-Y_{j}\bigr{)}^{2}=\sum(Y_{i}-a_{0}-a_{1}X_{i})^{2}, \tag{3}\]  
In order to get the minimum value of \(Z\), the partial derivatives for \(a_{0}\) and \(a_{1}\) of \(Z\) were calculated and set to zero. The equations were  
\[\sum 2(Y_{i}-a_{0}-a_{1}X_{i})=0, \tag{4}\]  
\[\sum 2(Y_{i}-a_{0}-a_{1}X_{i})X_{i}=0, \tag{5}\]  
The parameters \(a_{0}\) and \(a_{1}\) were:  
\[a_{0}=\frac{\sum Y_{i}-a_{1}\sum X_{i}}{n}, \tag{6}\]  
\[a_{1}=\frac{n\sum(X_{i}Y_{i})-\sum X_{i}\sum Y_{i}}{n\sum X_{i}^{2}-\sum X_{i} \sum X_{i}}, \tag{7}\]  
Based on the data in Table 1, we could figure out the parameters \(a_{0}\) and \(a_{1}\). The line equation could be expressed as  
\[y_{i}=5.50961-0.00662x, \tag{8}\]  
The measuring model obtained based on LSM is shown in Figure 5. The black spots are the original measured data, and the red line is the fitted curve. The residual sum of squares was 0.09154 and the adj. R-Square was 0.91421.
# Off-Axis Diffractive Optics for Compact Terahertz Detection Setup
## 2 Design and Modeling
However, in the third step the obtained focal spot is circular and has  
Figure 3: The comparison of the phase distributions (**top** panels) and focal spots (**bottom** panels) of the off-axis lens in the first three steps of the iterative algorithm for: (**a**). a single propagation, (**b**). the second iteration and (**c**). the third iteration). dimensions in order of diffraction limit. Full widths at half maximum (FWHM) of calculated focal spots are equal to 3.33 mm, 2.22 mm and 1.99 mm, respectively. These values have been obtained by averaging FWHMs for horizontal and vertical axes. The diffraction limit of light for this wavelength and focusing parameters (Airy dot diameter, described as \(A_{d}=2\times 1.22\times\frac{d\Gamma}{d}\)) is equal to 1.88 mm. Thus, FWHM of the focal spot obtained from the optimized off-axis lens is only 6% higher than the theoretical limit. Further iterations do not provide any significant improvements which was verified in numerical simulations.
# Robotic System for Post Office Package Handling
## 1 Introduction  
Internet shopping is becoming more and more popular, leading to a rapid increase in postal parcels that need to be processed and shipped [1]. High-throughput parcel sorting lines have been developed and utilized in logistic centers that automate most of the work to redistribute the incoming parcels to their destinations [2]. However, manual work is still required to separate the incoming parcels from the pile and place them on the conveyor belt, forming a bottleneck in otherwise highly elaborate sorting centers. These bottlenecks are prone to get worse when demand increases, especially during holidays. In combination with an overall increase in unit labor cost, higher hourly compensation levels, and a decrease in the cost of robots, the upcoming installations of robotic systems will have decreased the required time for the return on investment [3]. From an economic point of view, these aspects highly motivate the research and development of new approaches to tackle processes that traditionally require human resources.  
This seemingly easy and dull job of taking parcels from the pile and placing them on the belt poses several challenges that make its automation difficult. First, packages come in various sizes, shapes, and colors, so any computer vision algorithm, if used, must be highly flexible and not tied to any particular object type. Second, the mix of soft and hard objects, some being very thin, poses challenges to the mechanical design of their transfer mechanisms. Third, objects are piled and partially occluded, so the order in which they can be picked must be taken into account. Fourth, the resulting system has to provide high throughput and work reliably without the need for manual intervention.  
The main scientific contribution of this article lies in the evaluation of an AI-based robot solution for automating the parcel-handling process. The proposed architecture tackles the challenges posed by diverse parcel characteristics, ensuring adaptability, efficiency, and reliability in parcel-sorting centers. The developed system and both the design choices and implementation in the physical setup are shown and described in detail. Various crucial aspects for the implementation of similar systems are discussed and possible shortcomings are identified. Universal Robot UR5 is employed, which is a hardware-agnostic robot control and a computer-vision system for detecting and moving parcels from an unstructuredpile to the required location as illustrated in Figure 1. The solution can handle arbitrarily shaped objects, such as postal parcels, without additional training, and the achieved results show the reliability of the developed system in different scenarios.
# Scalability Analyses of Premium Connections Characterized by a Surface Fractal Function
## 3 Contact Analysis of Premium Connections
### Analysis of Influencing Factors
#### 3.3.2 The Influence of Axial Tension  
The Von Mises stress nephograms when the axial tension was \(4\times 10^{5}\), \(8\times 10^{5}\) and \(1.2\times 10^{6}\) N are shown in Figure 11, and the distribution of the contact pressure on the sealing surface is shown in Figure 12. As shown in Figure 11, with an increase in the axial tension, the area of concentrated stress concentration on the sealing surface decreases and there is little change in the maximum Von Mises stress. When the axial tension was less than \(8\times 10^{5}\) N, there was a significant concentration of stress on the torque shoulder and sealing surface. When the axial tension was \(1.2\times 10^{6}\) N, the concentration of stress on the sealing surface is higher than \(1.2\times 10^{6}\) N, and the concentration of stress on the sealing surface is higher than \(1.2\times 10^{6}\) N.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fractal Dimension \(D\)** & **2.1** & **2.5** & **2.9** \\ \hline Maximum Von Mises stress (Pa) & \(9.00\times 10^{8}\) & \(8.81\times 10^{8}\) & \(8.53\times 10^{8}\) \\ Maximum contact pressure (Pa) & \(1.39\times 10^{9}\) & \(1.20\times 10^{9}\) & \(7.45\times 10^{8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The maximum Von Mises stress and contact pressure with different fractal dimensions.  
Figure 10: Distribution of contact pressure with different fractal dimensions \(D\).  
torque shoulder disappeared, but stress concentration appeared on the thread, indicating that the thread bore all the axial tension.  
As shown in Figure 12, both the contact pressure and effective contact length at the fractal surface decrease with an increase in the axial tension. This is because the box tip is fixed and fully constrained in the FEM. Under the action of axial tension, the sealing surface of the pin tip is displaced along the direction of the load, resulting in a decrease in the effective contact length (from \(2.72\times 10^{-3}\) to \(2.24\times 10^{-3}\) m). When the sealing surface of the  
Figure 11: Nephograms of premium connection under axial tension. (a) \(4\times 10^{5}\) N; (b) \(8\times 10^{5}\) N; (c) \(1.2\times 10^{6}\) N.  
Figure 12: Distribution of contact pressure under axial tension.  
pin tip is displaced in the direction of the load, the interference in the assembly decreases due to the influence of the cone angle. This causes the deformation of the asperities in the fractal surface to decrease, so the contact pressure also decreases. It can be concluded from the above analysis that when the axial tension is 1.2 \(\times\) 10\({}^{6}\) N, there is large displacement of the sealing surface at the pin tip along the direction of the load, and the contact pressure and the effective contact length of the fractal surface decrease sharply, finally resulting in a decrease in the sealability.
## 5 Demonstration Experiment and Position Identification in Actual Headrace Tunnel
### Plot of Chalking Positions in Headrace Tunnel  
To efficiently detect deformation positions on the inner wall of the headrace tunnel, in the proposed system, we made it possible to visualize the damage positions in the entire tunnel, as shown in Figure 14a, according to the positional information of the tunnel provided by the captured continuous images of the walls and the chalking-position detection results. This is based on the fact that YOLO can derive the bounding box of the detected damage, and here, the central point of the bounding box was used as the representative point. As shown in Figure 14a, there were many detections near the entrance (positions of 0-20 m in the direction of travel), but there were very few chalking positions after approximately 40-80 m. Figure 14b,c present continuous images of the wall 5-10 m and 60-65 m from the entrance, respectively. In Figure 14b, there are many chalking positions, whereas in Figure 14c, there are hardly any; thus, the detection result is considered to be  
\begin{table}
\begin{tabular}{l l} \hline \hline Total number of chalking locations & 614 \\ \hline Total number of detections & 754 \\ \hline Correctly detected (TP) & 608 (99.02\% of all chalking locations) \\ \hline False detections (FP) & 146 \\ \hline A part or multiple parts of white chalk area (e.g., number and symbols) & 126 (86.30\% of false detections) \\ \hline Background & 14 (9.59\% of false detections) \\ \hline Other colors (Bright yellow, white and yellow, etc.) & 6 (4.11\% of false detections) \\ \hline Repair sheet & 0 (0\% of false detections) \\ \hline No detection (FN) & 6 (0.98\% of all chalking locations) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Breakdown of the detection results.  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **Examples of Detectable/Not Detectable** \\ \hline True Positive (TP) & **Examples of Detectable/Not Detectable** \\ \hline False Negative (FN) & **Examples of Detectable/Not Detectable** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Example detection results for each class.  
valid. Additionally, for example, there was considerable damage at approximately 10 m, but this was a joint part, and the damage is attributed to the scattering of spatter during fabrication, which made leakage more likely, promoting corrosion. Thus, detecting the chalking positions throughout the tunnel made it possible to determine the distribution of damage locations and their tendencies, which will be useful for future maintenance and management.
# Machine-Learning-Based Side-Channel Evaluation of Elliptic-Curve Cryptographic FPGA Processor
## 1 Introduction
The complete methodology is formulated for formation of the input datasets for further machine-learning analysis. In the existing literature, machine-learning-based side-channel attacks are launched on the raw samples, but no clear information is provided about the attack methodology and input feature datasets. Moreover, the ECC double-and-add-always (public-key) algorithm is selected for attack, as not much analysis is done on key recovery in the public-key domain. Secondly, we propose to use signal properties as features for efficient analysis instead of using raw samples. This ensures the elimination of redundant data during processing, hence increasing the computational power and reducing the time to train and to test the network. The same proposed hypothesis has been tested for the AES (symmetric cryptography) algorithm in our previous work [26]. Based on the findings of our previous work for the symmetric key algorithm, we have tested the public-key algorithm for five particular signal properties only. Please note that this study is conducted for the ECC (public-key cryptography) algorithm. Thirdly, our contribution is the application of filters to datasets, before processing them using the classification process. We have used Principal Components and Chi-square filters for pre-processing. The purpose of applying filters is to avoid the problem of wrong classification and to check if the accuracy can be improved by selecting/extracting important features. Again, we have selected one feature-selection and one feature-extraction algorithm, based on our previous findings from the research related to the symmetric algorithm AES. This double layer of pre-processing is applied just to verify if this can improve the accuracy further. The rest of the paper is organized as follows. Section 2 describes related work, the ECC algorithm under test and the classification algorithms used for this analysis, Section 3 describes the implementation design of ECC (algorithm under analysis), Section 4 explains our attack methodology for key recovery using machine-learning-based classification techniques and describes the feature formation procedure, Section 5 outlines the hardware and software experimental setup, Section 6 gives an analysis of the results while Section 7 concludes the paper.
## 4 Mathematical Formalism of Consensus Through Path-Laplacian Matrices
### Consensus Dynamics on Undirected Networks
#### 4.2.3 Multi-Hop Consensus Dynamics  
The previous models can be extended to incorporate _long-range interactions_ using the \(k\)-path Laplacians \(L_{k}\). To model such phenomena, we introduce additional coupling terms that account for neighbors at distances \(2,3\), etc. Suppose we fix an integer \(k_{0}\geq 1\) and assign a weight coefficient \(\alpha_{k}\geq 0\) to interactions via \(k\)-hop neighbors (where \(k=1,2,\ldots,k_{0}\)). It is natural to assume \(\alpha_{1}\geq\alpha_{2}\geq\cdots\geq\alpha_{k_{0}}\), reflecting that closer neighbors exert a stronger influence than far neighbors. In some cases, the weights are considered as \(\alpha_{k}=e^{-\beta k}\), with \(\beta>0\), representing an exponential decay of the influence concerning distance. These transformations do not qualitatively transform the diffusion dynamics, i.e., for any parameter, the dynamics remain standard diffusion. However, the Mellin transformation (power law) transforms from normal diffusion to superdiffusion for certain values of the exponent [22; 44]. In a continuous-time setting, generalized consensus dynamics can be written as  
\[\frac{d\boldsymbol{\phi}}{dt}(t)=-\sum_{k=1}^{k_{0}}\alpha_{k}L_{k}\boldsymbol {\phi}(t). \tag{11}\]  
We recall that the standard nearest-neighbor model is obtained from (8) when \(k_{0}=1\) and \(\alpha_{1}=1\)). The matrix \(L_{\mathrm{tot}}:=\sum_{k=1}^{k_{0}}\alpha_{k}L_{k}\) from (11) acts as an effective Laplacian matrix for the multi-hop coupling. Like each \(L_{k}\), the combined matrix \(L_{\mathrm{tot}}\) is symmetric, positive, and semi-definite, and \(L_{\text{tot}}\mathbf{1}=0\). Analogously, a multi-hop consensus update can be formulated for the discrete case shown in (10):  
\[\boldsymbol{\phi}(t+1)=P_{c}\boldsymbol{\phi}(t), \tag{12}\]  
where \(P_{c}\) denotes the combined update matrix \(I-\varepsilon\sum_{k=1}^{k_{0}}\alpha_{k}L_{k}\), which generalizes the Perron matrix of the standard model. As before, for \(P_{c}\) to define a proper consensus update, it should have non-negative entries, and each row should sum to 1. Sufficient conditions can be derived from the step-size \(\varepsilon\) and the network's \(k\)-path degrees. For instance, if we take \(\alpha_{k}=1\) for all \(k\), a suitable choice of the step size would be  
\[0\ <\ \varepsilon\ <\ \frac{1}{\max_{i\in V}\ \sum_{k=1}^{k_{0}}\delta_{k}(i)}. \tag{13}\]
## 3 Types of Picosecond Lasers  
It was not until the late 1990s that picosecond lasers became available in clinical practice [4]. Currently, devices utilizing picosecond pulses with wavelengths of 532, 670, 730, 755, 785, and 1064 nm have been developed. They differ not only in the length of the emitted wave but also in pulse duration and peak power [12]. Additionally, these lasers can be equipped with specialized lenses which may fractionate the emitted energy. Depending on the type of the lens, PS lasers can be classified into those with diffractive lens arrays, microlenses, and holographic optical matrices [13]. The choice of the laser for the treatment depends on the characteristics of the lesions and patient skin type [14]. The Fitzpatrick skin phototype scale is presented in Table 1[15].  
[PERSON] and [PERSON] [16] compared the effects of a PS alexandrite laser (755 nm), with a diffractive lens system, and an Nd:YAG laser (532 nm/1064 nm), with a holographic optical system, in patients with Fitzpatrick skin types I-VI, using the energy ranges recommended by the manufacturers. Photos of the target area were taken (at 15 min. and 24 h post-procedure), and a histopathological examination of the skin samples biopsied 24 h after the procedure was performed. Their findings indicated that skin exposure to the PS Nd:YAG laser (532 nm/1064 nm) with a holographic optical system induced immediate erythema and small, scattered petechial hemorrhages, which in many cases persisted after 24 h post-procedure. In contrast, the alexandrite laser (755 nm) with a diffractive lens system also caused immediate erythema, which largely resolved within the next 24 h. Furthermore, the use of the fractional PS Nd:YAG laser (532 nm/1064 nm) with a holographic optical system was associated with focal areas of cutaneous and intraepidermal hemorrhage, together with vascular damage in some patients. Both lasers were shown to generate vacuoles within the skin, which were most likely associated with LIOB. Among the patients with Fitzpatrick skin types II-IV, these authors observed diffuse hemorrhagic areas in the skin due to vascular injury at 532 nm and 1064 nm wavelength, but not at 755 nm wavelength [16].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fitzpatrick Skin Phototypes** & **Reaction to Sun Exposure** & **Complexion** \\ \hline \hline
**I** & always burns, never tans & very fair \\ \hline
**II** & burns easily, burns develop into a light tan & fair to light \\ \hline
**III** & burns a moderate amount, burns develop into a light tan & light to medium \\ \hline
**IV** & burns a minimal amount, develops a moderate tan & moderately dark \\ \hline
**V** & does not burn, develops dark tan & dark \\ \hline
**V** & does not burn or change in appearance & very dark \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Fitzpatrick Skin Phototype Scale.
## 1 Introduction
The RoF conceptallows transportation by optical means, a radio signal from a central office to multiple remote access points through a fiber network, in order to extend the wireless range and to provide a wireless coverage in shadowed area. Using Ultra Coarse Wavelength Multiplexing (_i.e._, 850 nm and 1,300 nm) which is affordable using WDM multimode couplers, the radio over fiber technology can be used with the digital baseband transmission on the same multimode PFGI-POF fiber converting, so this supports a multi-services transmission media (Figure 1). To the authors' knowledge, the combined transmission of high data rate (10 Gbps) digital baseband signal with radiofrequency signal, using low cost wavelength division multiplexing (WDM) devices and PFGI-POF fibers, is reported for the first time. To this end, PFGI-POF should be fully characterized as a multiple transmission media both at 850 nm and 1,300 nm. The methodology used to demonstrate the multi-services concept is described in this review manuscript. The first part will report the PFGI-POF properties of the commercially available fibers able to support the multi-services distribution (description of the core and cladding material, numerical aperture, mode field diameter, attenuation and bandwidth). Then, results obtained for the 10 GbE transmission over the PFGI-POF at 850 nm are reported in the second part, in which the advantages and drawbacks of each PFGI-POF will be highlighted. In a third part, Radio over Fiber system based on PFGI-POF will be presented. Finally, the multi-services distribution deployed on PFGI-POF is demonstrated for home/office network with a great robustness against the transmission of several radiofrequency standards which clearly demonstrated the bandwidth capacities of such optical fibers.
# Faster Post-Earthquake Damage Assessment Based on 1D Convolutional Neural Networks
## 3 Configuration of Three Neural Network Models  
In this study, three neural network models were trained and compared with the same training, validation, and test GMR datasets in order to evaluate their performances. GMRs in different earthquake events have highly variable durations, lasting from several seconds to hundreds of seconds. Therefore, a short pre-defined 30 s duration for each GMR, centered on the peak ground acceleration (PGA), as adopted in [23], was extracted if the GMRs were longer than the pre-defined duration. A time step of 0.02 s was adopted in this study as a common time step used in NLTHA under earthquake excitations [46]. Thus, each GMR inputted into the neural network models contained 1500 data points. Those raw GMRs that had less than 1500 data points were padded with the equivalent number of zeros to reach 1500. Figure 3 shows the architecture of the three neural network models: the 2D CNN model, the 1D CNN model, and the FNN model. For the 2D CNN model, the WT-encoded GMR image size was 128 \(\times\) 1500, indicating a scale ranging from 1 to 128. The 1D and 2D CNN models both had four convolutional layers and four max-pooling layers with the same feature map (vector) depth in each convolutional layer. The kernel sizes of the convolution and max-pooling layers are shown in Figure 3. In the fully connected layers, the dropout technique was applied with a probability of 0.3 to reduce overfitting in the deep networks and improve the generalization ability [47]. In addition to the two CNN models, the third FNN model was also trained with the same GMR inputs as were used in the CNN models. Similar to the fully connected layers in the two CNN models, the third FNN model had an extra hidden layer with 200 neurons because two hidden layers are usually recommended to form the feature space in FNNs [48] that do not have high-level extracted features as inputs. A dropout probability of 0.5 was used in the two hidden layers of the FNN model to further regulate the training process and avoid overfitting. These three models were trained and tested under the same computing configuration and using the same training, validation, and test datasets of GMRs in the following case study. Their performance was evaluated in terms of model computational efficiency and prediction accuracy. Details of these three models are presented at [[https://github.com/yuanxzMST/1d-cnn-GMRs](https://github.com/yuanxzMST/1d-cnn-GMRs)]([https://github.com/yuanxzMST/1d-cnn-GMRs](https://github.com/yuanxzMST/1d-cnn-GMRs)) (accessed on 8 October 2021).