esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Methodology for Reliability and Availability Evaluation
### Introduction  
As aforementioned in the Section 2, the reliability evaluation of a general network is a NP-hard problem. Nevertheless, as it will be discussed in the next section, this problem can be tractable for a low-medium number of field devices, as is the case of networks typically found in industrial applications.  
Figure 4 overviews the proposed methodology. The process starts by providing information about the network topology, device types and redundancy, device's failure and repair process and network failure condition. The latter one is defined by a logical expression that combines the failure status of field devices. For attaining flexible failure conditions and to support self-healing routing protocols, it  
is necessary to find all paths between the gateway (sink) and the devices that encompass the failure condition. Next, a fault tree is generated using all the previous data. From that, the respective minimal cut sets are obtained using an inversion technique. This cut set is re-expressed as a (minimal) fault tree, which is used to produce input data for the tool that computes the results. For this task we use the sharpe tool [9], which is able to compute the metrics of interest, either symbolically or numerically. It is possible to evaluate the reliability, availability and mean time to failure (MTTF) of the WSN, and also the Birnbaum's and the criticality measures for all field devices. Finally, we also have developed a software tool that automates the previous steps.
## 3 Results
### Topic Analysis
#### 3.2.3 Applied Machine Learning Methods  
Over 2/3 of the analyzed papers that reported the application of machine learning of a specific type indicated supervised learning, whereas over 1/5 of them indicatedunsupervised learning, with merely 6% indicating the use of reinforcement learning, and half of that share of papers referring to various types of machine learning (see Figure 8).  
The machine learning technique most frequently applied in the analyzed works was artificial neural networks of various kinds, with the second place taken by random forests and extremely randomized trees. However, the use of many other techniques has been reported as well (including ensemble algorithms), yet with much lesser frequency. The ten ML techniques that were most frequently reported as used are depicted in Figure 9.  
Only less than 1/3 of the analyzed papers specified the used ML software tool or library. The retrieved information is visualized in Table 1. As can be observed, there is a clear leader: scikit-learn library [29], reported to be used by 10 papers. The second-place TensorFlow [30] was mentioned merely by three papers. Overall, 18 distinct ML software tools or libraries have been reported as used in the analyzed papers. In terms of programming languages used, Python is the clear leader: its use has been reported in seventeen papers vs. six reporting the use of R and four reporting the use of Java.  
Figure 8: Applied machine learning type.  
Figure 9: Applied machine learning technique (Top 10).
# Performing Arithmetic Operations with Locally Homogeneous Spiking Neural P Systems
## 3 An Locally Homogeneous Spiking Neural P System for Addition
### Comparison  
This section implements two different types of addition LHSNP systems \(\Pi_{Add2}\) and \(\Pi_{Add3}\). The two additive LHSNP systems designed in this article and the additive P systems designed in [25; 34] require the number of neurons, the time required to complete the operation, and the number of rules in the system, as shown in Table 3.  
There is no SN P system with one input for two \(k\)-bit binary number addition designed in [25; 34]. Therefore, \(\Pi_{Add2}\) is analyzed in comparison with \(\Pi_{Add3}\). \(\Pi_{Add3}\) adds 2 neurons compared to \(\Pi_{Add2}\). \(\Pi_{Add3}\) completes calculations with (\(n-2\))\(k\) more time slice than \(\Pi_{Add2}\). \(\Pi_{Add3}\) has 5 more rules than \(\Pi_{Add2}\). Although \(\Pi_{Add2}\) is superior to \(\Pi_{Add3}\). But \(\Pi_{Add3}\) can implement the addition operation of \(n\)\(k\)-bit binary numbers. \(\Pi_{Add3}\) has a wider range of applications.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **SN P System** & **SN P System with 1 Input for 2 Natural** & \multicolumn{3}{c}{**SN P System with 1 Input for \(n\) Natural**} \\  & \multicolumn{3}{c}{**Number Addition**} & \multicolumn{3}{c}{**Number Addition**} \\ \hline  & **Number of** & **Complete** & **Number of** & **Number of** & **Complete** & **Number of** \\  & **Neurons** & **Time** & **Rules** & **Neurons** & **Time** & **Rules** \\ \hline This work & \(2k+9\) & \(2k+3\) & \(8\) & \(2k+11\) & \(nk+3\) & \(13\) \\ [PERSON] [25] & - & - & - & \(3k+5\) & \((n+1)k+4\) & \(9\) \\ [PERSON] [34] & - & - & - & \(2k+4\) & \((n+1)k+1\) & \(5k-1\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Two types of LHSNP systems for addition.  
\(\Pi_{Add3}\) has 13 rules, requiring \(2k+11\) neurons and \(nk+3\) time slices to complete the calculation. Compared to the SN P system for addition proposed by [PERSON] in [25]. Our designed \(\Pi_{Add3}\) has increased the number of rules by 4. But the number of neurons has been reduced by \(k-6\) compared to [25], and the time required to complete the calculation has been reduced by \(k+1\) time slice. It greatly optimizes the additive SN P system. In the SN P system with weights and delays for addition proposed by [PERSON] et al. in [34], we removed weights and delays by increasing the number of neurons. Compared with [34], our designed \(\Pi_{Add3}\) adds 7 neurons, but the time required to complete the calculation has been reduced by \(k-2\) time slice, and the number of rules has been reduced by \(5k-14\). This not only simplifies the system but also greatly improves its performance.
# An Integrated Cellular Automata Model Improves the Accuracy of Secondary Fragmentation Prediction
## 5 Conclusions  
The new model combining the fragmentation model with a stress model developed in a flow simulator showed satisfactory results when replicating experiments under high confinement pressure conditions at laboratory scale, reaching a margin of error close to 10%. However, when confinement pressures were lower, the discrepancy between simulated and experimental secondary fragmentation results increased considerably, which was hypothesized to be due to the absence of shear deformation. This discrepancy was observed particularly in the fine fragmentation simulation, since in the case of coarser fragments, such as d\({}_{50}\) or d\({}_{80}\), the margin of error was smaller. The main conclusions are summarized below:  
* With the inclusion of the shear strain effect, the results obtained with the new integrated model, which better represented the dynamics of the gravity flow and fragmentation of caved material, showed a notable improvement in fine granulocyte, while for coarser sizes under conditions of high stresses, a slight improvement was also observed;
* Shear deformation influences smaller fragments due to secondary fragmentation more significantly, validating its integration into the proposed model and improving the model's prediction accuracy.  
Given that fines are commonly identified in block caving mines, applying a model such as the one described here should be considered to support planning measures in the presence of fine material that will likely appear at drawpoints.  
Conceptualization, R.G. and R.C.; methodology, C.S.M.; software, C.S.M.; calibration, C.S.M.; formal analysis, R.G. and C.S.M.; investigation, R.G., C.S.M. and R.C.; resources, R.C.; data curation, C.S.M.; writing--original draft preparation, R.G.; writing--review and editing, R.C.; visualization, R.G. and C.S.M.; supervision, R.G. and R.C. project administration, R.C.; funding acquisition, R.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Agencia Nacional de Investigacion y Desarrollo, grant numbers 1230749 and AFB230001.  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
The authors also thank [PERSON] for her support in the editing.  
The authors declare no conflicts of interest.
## 3 Cryptography and the Erosion of the Security Perimeter
### Public Key Cryptography  
The invention of public-key cryptography had two effects on data protection. First, the distribution and re-distribution of cryptographic keys was manageable in much better ways based on a public-key infrastructure (PKI). Second, based on a PKI, so-called certificates could be issued to users or organizations. Certificates can identify a person (and thereby constitute an electronic identity), or they can provide a role privilege. Here, a clear division in strategies for data protection turned up: the efforts divided into the re-establishment of perimeter security (by using PKI to extend the security perimeter through authenticated data links, authenticated identities and authenticated hardware and software) and into the efforts of establishing new paradigms (such as multi-party encryption, blind digital signatures, trusted platforms and data minimization). Many of the strategies to keep security guarantees up to defined levels included the re-erection of the security perimeter, wherein remote terminals or sessions had to perform additional security measures to be admitted to the still centralized main system resources. Encrypted data link lines, password security, code cards and one-time number generators are examples for additional security measures.
# Positioning Errors Measurement of CNC Machine Tools Based on J-DBB Method
## 2 Principle of the J-DBB  
### Structure of the J-DBB  
Generally, the length of the bar in traditional DBB is constant. Several bars with different lengths are required for different errors measurement radius of the machine tool.  
The limited work scope and discontinuous error measurement radius have afflicted scholars and mechanical engineers for several decades. A new device named J-DBB with a freely measuring radius was proposed to measure the errors of the CNC machine tool [23]. As shown in Figure 1, this new device consists of a spherical joint, two connecting bars, and two precision balls. Compared with DBB, the J-DBB realizes the full range error measurement of the machine tool by adding a spherical joint, which helps to better understand the spatial distribution of machine tool errors and provides guidance for the reasonable selection of working areas to improve the machining accuracy of parts. Unfortunately, there is currently no sensor that can accurately measure the spatial angle of the spherical joint. Therefore, J-DBB adopts the mechanical decoupling frames (MDF) structure to measure the angle of the spherical joint.  
The angle measuring device of the spherical joint is shown in Figure 2, which consists of two MDFs, which measure the rotation angle through a rotary encoder. The two MDFs, which are fixed on the supporting structure, vertically intersect and form a square hole that can grip the second bar. When moving with the spindle of the machine tool, the angle of the spherical joint is converted to the angle between the two connecting bars, and the resolution of the spherical joint is transformed into the resolution of the rotary encoder through two MDFs.  
When the second ball moves with the CNC machine tool spindle, the motion is divided into the rotary movement of the two MDF. Because the two MDF are vertical, the motion of the spherical joint is turned into two vertical rotations. The angle of rotation can be measured by a rotary encoder. The friction of the spherical joint can affect the measurement accuracy. To measure the error with high accuracy, the spherical joint needs a high rotation accuracy and low frictional force. In this paper, the spherical joint consists of a sun sphere and several planetary spheres, as shown in Figure 2b. Because of the unitized structure, low frictional force and zero clearance is achieved by the spherical joint with high rotation accuracy.
## 2 Materials and Methods
### Statistical Analysis  
The data were analyzed using IBM SPSS software for Windows (version 29.0, IBM Corp, Armonk, NY, USA). Counts (and proportions), means, standard deviations (SDs), and 95% confidence intervals (CIs) were calculated to describe categorical and continuous variables. The data were presented (total sample) and then divided into the following groups: BMI \(\geq\) 25 kg/m\({}^{2}\) group, BMI \(\leq\) 25 kg/m\({}^{2}\) group, the group that does not exercise, and the group that does exercise. The Kolmogorov-Smirnov test (n \(>\) 50) and [PERSON] test were used to verify the normality and homogeneity of the data, respectively. Pearson's correlation coefficients were also used to determine the association between all variables under analysis. The magnitudes of the associations were also determined (\(r\) = 0.10 to 0.29--small; \(r\) = 0.30 to 0.49--moderate; \(r\) = 0.50 to 1--strong) [40]. An MANOVA was performed to investigate the effect of each group (BMI group \(\geq\) 25 kg/m\({}^{2}\) vs. BMI group \(<\) 25 kg/m\({}^{2}\) and non-exercise group vs. exercise group\({}^{\dagger}\) on the set of dependent variables related to mental health. If MANOVA was significant, univariate ANOVAs were calculated for each dependent variable, followed by [PERSON]'s post hoc analysis. For paired comparisons, the effect size was assessed using partial eta squared (partial \(\eta^{2}\)), while for overall differences between groups, eta squared (\(\eta^{2}\)) was applied. The effect sizes were interpreted based on [PERSON]'s guidelines, with the following thresholds: \(\leq\)0.10 (small), \(\geq\)0.25 (medium), and \(\geq\)0.40 (large). For all tests, the significance level was set at \(p<\) 0.05.
## 4 Field Validation Tests and Global Vibration Intensity Prediction
### Field Validation Tests
Figure 13: Photos of field validation test setup. (**a**) The construction site; (**b**) the vibratory sheet pile driving work in progress; (**c**) the first set of data acquisition systems integrated with (**d**) the single-axis accelerometers; (**e**) the second set of data acquisition system integrated with (**f**) the triaxial accelerometer. Vibration measurement results at the six points (P1-P6) by single-axis accelerometers were used to localize the vibratory driving location. Figure 16 shows the adopted segments of vibration signals recorded at the end of vibratory sheet pile driving (pile toe depth: 6 m). The cross-correlation analysis (Equation (5)) was performed to identify the \(\Delta\)ToA among them. The localization of the vibration source was determined according to the time-based localization method (Equation (6)). The localization results are shown in Figure 17, in which monitoring points in the linear layout (P1-P4) and right-angle layout (P1, P4-P6) were tested separately to verify the different localization performances by using different layouts of monitoring points. The estimated piling location reasonably approaches the actual piling location. As expected, the localization by using the monitoring points in the right-angle layout is better than that in the linear layout. The relative errors were calculated as the estimation errors in the coordinates \((x,y)\) divided by the site dimension (i.e., 50 m in  
Figure 16: Vertical vibration signals recorded at the six monitoring points (P1–P6) by single-axis accelerometers. Figure 15: Particle vibration paths at one plan distance (\(d=15\) m) by one triaxial accelerometer. the field tests). The calculated relative error decreases from 7.42% in the linear layout to 2.60% in the right-angle layout in the Y direction, but almost equal values (0.65% in the linear layout and 0.72% in the right-angle layout) are obtained in the X-direction.
## 2 Materials and Methods
### Statistical Analysis
The agreement was visually assessed via Bland Altman plots. To assess the differences and agreement between the methods, various statistical measures were applied. The Mean Absolute Deviation (MAD) was used as an indicator of the differences between measurements obtained by the two methods. The slope and intercept derived from the least squares regression analysis were utilized to evaluate the consistency of measurement trends between the methods, where an ideal agreement would be indicated by a slope of 1 and an intercept of 0. To assess the distribution of errors, the \(\beta\) (beta) value was calculated, representing systematic deviations between the methods. The Lin's Concordance Correlation Coefficient (CCC) was used to confirm the results of the agreement as follows: <0.8 is unacceptable, 0.81-0.89 is poor, 0.90-0.94 is moderate, 0.95-0.99 is substantial, and >0.99 is almost perfect agreement [32]. Moreover, ordinary least product regressions (OLP; Model II linear regression) were used to determine whether systematic and/or proportional bias existed between BIA and ADP. Systematic bias (consistent under/overestimations) was present when the 95% confidence interval of the intercept did not include 0. Proportional bias was present when the 95% confidence interval of the slope did not include 1.0. \(p\)-values below 0.05 were considered statistically significant in all analyses. The statistical analyses were performed with Statistica 13 (TIBCO Software Inc., Tulsa, OK, USA; StatSoft Polska, Cracow, Poland).
## 1 Introduction
When the dynamics of a PA are linearized at a fixed operating point to determine the linear state equation, [PERSON] and [PERSON] [16] used a linear optimal controller to regulate the pneumatic servo system for testrobotic applications. [PERSON] et al. [5] and [PERSON] et al. [17] proposed a sliding mode control (SMC) to control the motion for Pas, because the SMC compensates for nonlinearities. A SMC uses a model-based approach, which can produce an unstable system which is unstable due to modeling error. To attenuate the modeling error effect in the tracking control performance, [PERSON] et al. [9, 10, 18] proposed an adaptive SMC that regulates the position of the moving platform of LDR. [PERSON] et al. [19, 20] employed a function approximation technique to determine an unknown model for PAs and increase tracking performance over time for the overall system. Despite the pervasive development of control algorithms in literature and the widespread using of PAs, the practical solution to achieve positioning accuracy and repeatability still remains an open problem in pneumatic-driven systems. This study presents the development and implementation of a pneumatic-driven and horizontal-structure linear delta robot (PH-LDR) for industrial manufacturing that is cost-effective and highly reliable. The PH-LDR is a pneumatic-driven and horizontal-structure (PH) linear delta robot (LDR), that uses pneumatic actuators to achieve high trajectory tracking performance and reduce the cost, and which is designed as a horizontal structure to enlarge the workspace. The PH-LDR uses three rod-less PAs as the linear actuator to produce the 3-DOF motion, as well as adopting a hybrid control technique to compensate for nonlinearities and uncertainties. The rod-less PAs possess a long stroke, because unlike general pneumatic actuators, the stroke is contained within the envelope of a cylinder body. Without the need of parameter identification of the pneumatic actuating system for the controller implementation, the hybrid controller uses a simple and straightforward approach and contains two parts: a fuzzy-PID controller, as designed by using expert knowledge, to achieve the positional accuracy, and a neural network pre-compensator (NNPC), that predicts variations caused by the environmental changes such as temperature, and works with the fuzzy-PID controller. Experimentswith the implemented full-scale test rig are conducted to confirm the validness of the PH-LDR for complex 3-DOF trajectory tracking problems. The remainder of this paper is organized as follows. In Section 2, the layout of the test rig and its experimental setup are described. Furthermore, a kinematics analysis of the PH-LDR is analyzed in Section 3. The controller design for the PH-LDR is given in Section 4. Two experiments of the PH-LDR are presented in Section 5 to investigate the trajectory tracking performance. Finally, Section 6 concludes this paper.
# Vector Approximate Message Passing Based OFDM-IM Detection for Underwater Acoustic Communications
## 4 Numerical Simulations
### BER Performance with Channel Uncertainty  
In real applications, the UWA channel cannot be guaranteed in advance. CSI is the prerequisite for data detection. Figure 5 studies the effects of channel uncertainty \(\xi^{2}\) on the BER performance of detectors. The CSI uncertainty is incorporated according to \(\hat{\mathbf{h}}=\mathbf{h}+\mathbf{e}\), where \(\mathbf{h}\) and \(\hat{\mathbf{h}}\) are the true channel vector and estimated channel vectors, respectively. The channel error \(\mathbf{e}\sim\mathcal{CN}\left(0,\xi^{2}\mathbf{I}\right)\) with the error variance \(\xi^{2}\). Figure 5 considers two CSI conditions with \(\xi^{2}=[0.01,0.1]\). In Figure 5, the larger \(\xi^{2}\) causes severe performance losses for detectors. The IM-AMP is too fragile for channel deviation due to channel estimated errors, and it almost loses efficacy due to the non-Gaussianity of the UWA channel matrix. The IM-MMSE-AMP improves the performance and the gains are generated due to the consideration of the symbol prior. The proposed detector performs better than the IM-MMSE-AMP, which both consider the symbol prior but the VAMP framework achieves more gains. The main reasons lie in that the scalar message passing of the VAMP framework has more accurate posterior estimation and robustness to the measurement matrix. Furthermore, the proposed detector performs most approximately with the LLR and the ML. In the case \(\xi^{2}=0.01\) and BER\(=10^{-2}\), the proposed detector works slightly worse than the ML with a performance gap of about 1 dB and the gap with  
Figure 4: BER comparisons of detectors with different modulation methods. (a) BER of ML detector with different residual CFO effects when CFO = 0.2; (b) BER comparisons of detectors with CFO compensations when CFO = [0.05 0.2].  
Figure 3: BER performance of the proposed detector with different implementation forms.  
the LLR is within 0.3 dB. In the case \(\xi^{2}=0.1\), all detectors are challenged; the proposed detector performs better than the AMP-based benchmarks. The results show that the novel shrinkage function and scalar message passing are effective in recovering the symbol and robust to the measurement matrix with time-varying UWA channel components. It is worthy noting that we use the OMP-based channel estimation method. Actually, the OMP-based method easily achieves an error floor in real UWA channel conditions as in work [17]. To further improve the system performance, a more accurate channel estimation method deserves to be explored, which is not investigated in this paper.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 3 Grid Operations
### Outage Management and Reliability in Operation
#### 3.4.1 Strategies for Quick Detection and Restoration of Power Outages  
The shift in power distribution system operations increasingly focuses on rapid outage detection and restoration by leveraging data-driven technologies. Smart meters and remote fault indicators are pivotal for precise fault localization [95], utilizing digital tools for real-time data analysis. Distribution automation plays a key role in service restoration [29], which is crucial for quick network re-energization.  
Studies [186; 187] highlight the importance of fault diagnosis and prognosis through data analytics, offering methods for pinpointing faults in networks with DGs. Further research in [30; 188] discusses advanced data analytics for fault diagnosis and a digital approach for locating faults in DG-integrated networks, respectively. Together, these studies underline the transformative impact of data-driven practices and automation in improving outage detection and recovery in power distribution systems.