esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Rock fragmentation by blasting influences ore recovery and the cost of downstream operations. The development of electronic detonators makes it possible to improve fragmentation by controlling the initiation timing in blasting projects, and the effect of the mechanism of delay timing on rock fragmentation should be studied. Fragmentation of granite bench specimens with different initiation timing was investigated in blast experiments. Conclusions are obtained by studying the surface strain field and post-blast specimens. A total of six blasting tests were carried out on granite bench specimens with four boreholes each having a diameter of 10 mm and a length of 450 mm. Each borehole used penetraythful tetannitte (PETN) as the explosive charge, which was approximately 4.84 g with a charge diameter of 5.5 mm. Delay times between adjacent boreholes in the same row were set as 0, 50, 100, 150, 200, and 250 us. The surface strain field of the bench specimen under blast loading was analyzed using three-dimensional digital image correlation (3D-DIC) techniques based on two cameras that captured high-speed images. Additionally, the post-blast specimen was also observed and recorded. Fragments of each bench specimen were carefully collected, weighed, and sieved with a set of sieves, including very fine particles. According to the 3D-DIC analysis for bench specimens, the propagation pattern of the main strain concentration zone transformed from horizontal to vertical with the increase in inter-hole delay. The maximum blast excavation weight was obtained by the bench specimen with an inter-hole delay of 100 us, while the bench specimen with the longest inter-hole delay (250 us) obtained the minimum blast excavation weight. By combining the results for blast excavation weight with the results from fragment size distribution analysis of all specimens, the optimal inter-hole delay was 200 us. Compared to simultaneous detonation, the median size was decreased by about 14.5% for the inter-hole delay of 200 us. The results of experiments show that delay time significantly influences rock fragmentation, but the stress wave superposition in short delays cannot improve rock fragmentation. For long delays, the blast-induced crack propagation time should be regarded as an influential factor when choosing the proper delay time. The experimental findings of this study could provide a better understanding of the effect of the mechanism of delay time on rock fragmentation.  
blast loading; delay time; 3D-DIC analysis; surface strain field; fragment size distribution Article
## 4 Simulations and Discussion  
The effectiveness of the designed algorithm is demonstrated in three daily traffic scenarios. The main contribution of this paper is that the designed controller only requires the speed and position information of the vehicle, without the need for accurate models or parameters. The vehicle parameters are listed in Table 1 and are only used for model updates. The vehicle interference is \(\delta(\cdot)=0\); the vehicle spacing strategy parameters are \(h=0.5\) s and \(d_{st}=2\) m; the initial state of the vehicle is \(z^{0}=0\) m and \(v^{0}=15\) m/s; the initial state of the leader vehicle is \(z^{L}(0)=20\) m and \(v^{L}(0)=30\) m/s; the reference speed for the following vehicle is \(v_{ref}=36\) m/s; the velocity error constraint function is \(\phi_{v}(k)=\left(22\epsilon^{-0.2k}+0.2\right)^{-1}\); and the spacing error constraint function is \(\phi_{d}(k)=10\).
# Secure Image Signal Transmission Scheme Using Poly-Polarization Filtering and Orthogonal Matrix
## 6 Numerical Result
In addition, if there were errors in both parameters \(\gamma\) and \(\eta\), the BER performances worsened, as shown in Figure 9, where the BER curves with \((\Delta\gamma,\Delta\eta)\)=\((3^{\circ},3^{\circ}),(5^{\circ},5^{\circ}),(10^{\circ},10^{\circ}),(15^{\circ},15^{ \circ}),(20^{\circ},20^{\circ})\) are plotted. If there were errors in both parameters, the BER performance would be worse than that shown in Figure 8. Moreover, larger errors in both \(\gamma\) and \(\eta\) would lead to worsening BER performances. As analyzed in Section 5, for Eve, the polarization parameters were difficult to obtain, and the modulation techniques were unknown; therefore, the errors in polarization parameters were inevitable. Therefore, in this case, the BER performance of Eve would be worse. Figure 6: BER performance of PPF-OM (PSK). In this simulation, the security performance was evaluated in terms of the average secrecy rate (ASR). Both 4 PSK and 8 PSK were utilized to modulate the two information sequences, which yielded the \(10^{6}\) 4 PSK and \(10^{6}\) 8 PSK symbols, respectively. We assumed Eve could correctly reshape the received signal vectors into the \(M\times\frac{2\mu}{l}\) matrices \(\mathbf{T}_{\text{E\_RH}}\) and \(\mathbf{T}_{\text{E\_RV}}\) as well as obtain the matrices \(\mathbf{F}_{\text{ER}}^{\text{o}}=\mathbf{F}^{\text{o}},\mathbf{F}_{\text{ER} }^{\text{e}}=\mathbf{F}^{\text{e}}\) correctly. However, the PSs that had been used to process the signals were unknown. As analyzed in Section 5, the PSs were difficult to obtain, and when there were errors in PSs, based on Equations (29) and (30), there would be interferences in the recovered signals. Therefore, the ASR was improved, as shown in Figure 10, where the ASR curves are plotted. Here, the errors were added only to the polarization parameter \(\gamma\) while the other parameters were distortionless. When the error was zero, the ASR equaled zero and did not change with the SNR. In addition, when the error was non-zero, the ASR increased alongside the SNR, and the larger error  
Figure 7: BER performance of the PPF-OM scheme with errors in OM. in \(\gamma\) led to a larger ASR. These simulation results were consistent with the theoretical derivation process. Therefore, with the PPF-OM scheme, a positive ASR was ensured, and the transmission security was enhanced. Figure 8: BER performance with PS errors (\(\Delta\gamma\)). Figure 5.0 Average secrecy rate versus SNR with errors in \(\gamma\). Figure 5.1 BER performance with PS errors \((\Delta\gamma,\Delta\eta)\).
## 2 Materials and Methods
### Statistical Analysis  
The examiner ([PERSON]) measured the points on the lateral cephalometric radiographs once and again two weeks later. The measurements taken twice were analyzed with a paired \(t\)-test, with a \(p\)-value greater than 0.05 indicating no significant difference between the measurements. Averaged values were analyzed. All 13 measured values assessed using Shapiro-Wilk tests followed by a normal distribution. Homogeneity was verified for the total duration of treatment (T1-T3), including the chronological ages at T1 and T3 in both groups, cervical vertebral maturation stage (CVMS), maxillary protraction period (T1-T2), and follow-up duration (Table 2). Changes in measured values between the T-A and M-A groups were compared using Student's \(t\)-test during forward traction (T1-T2), follow-up (T2-T3), and throughout the entire study (T1-T3). Skeletal and dental changes at T1, T2, and T3 within each group were confirmed using repeated-measures analysis of variance (ANOVA) with the general linear model, followed by post hoc Bonferroni tests. Statistical significance was defined as \(p<0.05\). All data were statistically analyzed using SPSS 29.0 (IBM Corp., Armonk, NY, USA).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measured Item** & **Description** \\ \hline Y axis (\({}^{\circ}\)) & Sella Gnathion to Frankfurt Horizontal Plane \\ \hline IMPA & Mandibular Incisor to Mandibular Plane Angle \\ \hline Overjet & The Overlap of The Teeth in The Horizontal Dimension \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **T-A Group (n = 25)** & **M-A Group (n = 25)** & **Significance** \\ \hline \multirow{4}{*}{Age (T1) a  } & Mean & 7.92 & 9.84 & \multirow{4}{*}{\(<\)0.01 \({}^{*}\)} \\  & SD & 1.12 & 1.09 & & \\  & Min & 6 & 7.8 & & \\  & Max & 10.7 & 11.9 & & \\ \hline \multirow{4}{*}{Age (T3) a  } & Mean & 12.12 & 13.34 & 0.16 \\  & SD & 2.12 & 1.15 & & \\  & Min & 8.1 & 10.9 & & \\  & Max & 16.1 & 16.5 & & \\ \hline \multirow{4}{*}{CVMS (T1) b  } & Median & I & II & 0.15 \\  & SD & 0.76 & 0.80 & & \\  & 25\% & I & II & & \\  & 75\% & II & III & & \\ \hline \multirow{4}{*}{CVMS (T3) b  } & Median & IV & V & 0.2 \\  & SD & 0.86 & 0.71 & & \\  & 25\% & III & IV & \\  & 75\% & V & V & \\ \hline \multirow{4}{*}{Treatment period (T1–T2) a  } & Mean & 9.43 & 10.59 & 0.61 \\  & SD & 9.51 & 5.96 & & \\ (T1–T2) a  & Min & 2.93 & 4.40 & & \\  & Max & 35.9 & 31.10 & & \\ \hline \multirow{4}{*}{Treatment period (T1–T3) a  } & Mean & 52.05 & 42.19 & 0.33 \\  & SD & 20.19 & 9.09 & & \\ (T1–T3) a  & Min & 30.4 & 31.83 & & \\ \cline{1-1}  & Max & 96.1 & 74.77 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of age, CVMS, treatment duration between patients treated using tooth- and miniscrew-anchored devices.
# Assessing Voltage Stability in Distribution Networks:
## 4 Assessment Process  
The probabilistic assessment method of the SVS of a distribution network proposed by this paper utilizes the Copula function and Nataf transformation to address correlations between variables. The 3 PEM was used to select samples for CPF calculations, transforming complex uncertainty issues into determinate issues. Finally, combined with the load margin index \(I_{\text{LM}}\), the probabilistic voltage-stability assessment of the distribution network with distributed generators was completed. The computational process is illustrated in Figure 3, with detailed steps as outlined below:  
Step 1: Establish typical probability models for PV generators and load, and the Copula correlation coefficient matrix \(p_{ij}\) between the stochastic variables.  
Step 2: Sample the input variables in the independent standard normal distribution space. Calculate the position and weight coefficients of each input variable's estimated point according to 3 PEM.  
Step 3: Cause the matrix Z to be evaluated within the independent standard normal space, and use the Nataf inverse transformation to convert it back into the original correlated variable space, obtaining the assessment matrix X.  
Step 4: Carry out CPF calculations with every column of the evaluation matrix \(X\) as input samples to determine the corresponding voltage stability critical values \(V_{\text{cr}}\) and load margin index \(I_{\text{LM}}\). From Equations (23)-(25), their expected values, standard deviations,  
Figure 2: Curve of the severity function S(\(I_{\text{LM}}\)).  
and raw moments are obtained, and the distribution model of critical value \(V_{\text{cr}}\) and the PDF of \(I_{\text{LM}}\) index are obtained by the Cornish-Fisher series expansion.  
Step 5: Apply a risk preference function as specified in Equation (28) to obtain the severity function, and combine it with the PDF from Step 1 to calculate the index-exceedance risk of the distribution network, thus completing the SVS assessment of the distribution network with distributed generators.  
Figure 3: Process for SVS probability assessment.
## 3 Results and Discussion  
### Characterization of the Commercially Available nZVZ  
In the present study, the XRD pattern obtained for the nZVZ shows the characteristic peak at the 20 angles of 36.0, 39.5 and 44.5 corresponding to (002), (100) and (101) facets, respectively. These peaks are compatible with the standard XRD data for the hexagonal Zn lattice (JCPDS file 04-0831) (Figure 6) [43]. The characteristic peaks of ZnO at 32\({}^{\circ}\) and 34.7\({}^{\circ}\) are assigned to (100) and (002) facets [44; 45]. The XPS spectrum of wide scan demonstrates that the binding energy of 284.5, 532.7 and 1023.6 are attributed to C1s, O1s and Zn 2p, respectively. In the spectrum of Zn 2p, the peaks for Zn 2p\({}_{3/2}\) and Zn 2p\({}_{1/2}\) are located at 1022.5 eV and 1047.6 eV, respectively (Figure 7a). The satellite peaks at around 1021.4 eV and 1023.6 eV are the feature peaks of Zn\({}^{2+}\) and Zn\({}^{0}\), respectively, which suggest that the surface of nZVZ was partially oxidized (Figure 7b) [45].  
Figure 5: Flowchart of Genetic Algorithm (GA) optimization.  
Figure 6: The XRD pattern of the commercially available nanoscale zerovalent zinc (nZVZ).  
Figure 7: The X-ray photoelectron spectroscopy (XPS) spectra of nZVZ (**a**); The XPS spectra of the Zn-2p core level. Regions (**b**).
## 1 Introduction  
Development of the Internet leads to a growing number of devices. The devices are more likely to connect with each other due to the rise of networking and communication technologies (i.e., wifi, ZigBee, etc.). The connection of devices has fastened the growth of the Internet of Things (IoT) networks. IoT is a promising technology that integrates physical world with the Internet. The IoT network is defined as; \"the connection of Internet enabled devices that share data, information, and resources with each other\". The connection among IoT devices is established without any human intervention. One of the main aspects of IoT is to share data, information, and resources among devices.  
The connection of devices extends the applications of IoT networks in various fields. Some of the applications of IoT networks include: vehicular networks (where cars are integrated with an existingentertainment, traffic, and navigation systems), home automation (i.e., smart homes), healthcare (i.e., health data sharing), supply chain management (asset tracking, forecasting, vendor relations, connected fleets, etc.), security systems (i.e., sensors, buzzer connected), and many others [1]. Because of the aforementioned applications, IoT devices are connected globally and the ratio of connectivity is increasing day-by-day.  
The connection of devices brings several challenges in the networks. Some of the challenges are: inefficient management of data, unauthorized access, malicious attacks, single point of failure through centralization, and many others [2]. The IoT devices consist of sensitive data; therefore, an efficient management of networks is highly required. As the IoT devices' data is of huge amount; so, centralized storage systems, i.e., cloud and fog are used to store that data. The cloud has the ability to process a huge amount of data in rapid manners. Moreover, it achieves accuracy, efficiency, and speed for data processing. Besides the storage and processing advantages of networks, cloud and fog also bring latency, security, and privacy issues. However, unauthorized access of data is the major issue [3; 4; 5].  
Many strategies are presented in the literature to overcome issues in the IoT networks. The challenging tasks of IoT networks are efficient data sharing and authorized access control. There are several strategies to manage data sharing and authorized access control of data. Such strategies are based on traditional models, which include: Attribute-Based Access Control (ABAC), Role-Based Access Control (RBAC), and many others. However, there are many limitations in the traditional systems, such as: single point of failure through centralization, untrustworthiness, unauthorized access to data, etc., [6; 7].  
Therefore, blockchain technology is integrated with access control and data sharing mechanisms, to eliminate the issues in traditional schemes. Blockchain provides solutions to many problems that are more effective to provide data integrity, fairness, authenticity, security, and distribution [8]. Furthermore, Section 1.1 provides an in-depth understanding of blockchain technology.
# Application and Assessment of Cooperative Localization in Three-Dimensional Vehicle Networks
## 8 Simulation Results  
Given a propagation time step size of \(\Delta t\), the seven state equations of vehicle \(i\) in (4) and (5) can be written in discrete form as:  
\[\begin{cases}r_{0}^{i}(k+1)=r_{0}^{i}(k)+R_{0}^{i}(k)\big{(}\eta^{i}(k)+\eta_ {\gamma}^{i}\big{)}\Delta t\\ \bar{q}^{i}(k+1)=\bar{q}^{i}(k)+\frac{1}{2}G^{i}(k)\big{(}\omega^{i}(k)+\eta_{ \omega}^{i}\big{)}\Delta t\end{cases} \tag{30}\]  
where \(\eta_{\upsilon}^{i}\) and \(\eta_{\omega}^{i}\) are the corresponding components of \(\eta^{i}\) in (24) representing the input velocity process noise. Similarly, \(R_{0}^{i}(\bar{q}^{i}(k))\) and \(\frac{1}{2}G^{i}(\bar{q}^{i}(k))\) are the components of \(\gamma^{i}\) in (24).  
The relative measurements obtained by all vehicles are polluted by additive Gaussian measurement noise (AWGN) with a standard deviation of 0.05 m for the position states (based on distances between vehicles) and 0.02 for the quaternion states of each vehicle. Vehicles may also be capable of obtaining absolute pose measurements with a standard deviation of 0.1 m for position and 0.02 for the quaternion states. As far as process noise is concerned, the standard deviations are 0.1\(v^{i}\) for \(\eta_{\upsilon}^{i}\) and 1\({}^{\circ}\)/s for \(\eta_{\omega}^{i}\) across all vehicles. Finally, since there is additive noise in both measurement and process steps, after several  
Figure 3: Centralized EKF-based CL diagram.  
trial runs, we concluded that about 100 runs in each simulation scenario are required to remove any significant effect stemming from outliers.  
All simulations are run for 200 s and use the same input velocities and process and measurement noise properties in order to remove any input bias from the assessments. Input velocities are applied to each vehicle with varying magnitudes, as listed in Table 2. Since vehicles normally have a \"forward\" velocity input, 3D movement is attained through orientation changes, as a result of input angular velocities. Therefore, linear velocity is only applied in the x-direction. We selected \(\Delta t=0.02\) s, which is sufficiently small for accurate propagation without any noise, given the magnitude of input velocities.  
To provide a comprehensive assessment, we initially present a test case with a changing measurement scenario. Next, we evaluate the effect of sequentially adding measurements that do not directly involve a given vehicle to show how CL can improve its localization and the localization of the entire network. We then assess the effect of redundant measurements when two or more vehicles simultaneously measure each other's poses. Next, we assess the effectiveness of CL when only relative positions are measured, followed by an investigation of how a single vehicle's absolute measurement can affect localization in this scenario.
# Design and Implementation of a Digital Twin System for Log Rotary Cutting Optimization
###### Abstract  
To address the low level of intelligence and low utilization of logs in current rotary cutting equipment, this paper proposes a digital twin-based system for optimizing the rotary cutting of logs using a five-dimensional model of digital twins. The system features a log perception platform to capture three-dimensional point cloud data, outlining the logs' contours. Utilizing the Delaunay3D algorithm, this model performs a three-dimensional reconstruction of the log point cloud, constructing a precise digital twin. Feature information is extracted from the point cloud using the least squares method. Processing parameters, determined through the kinematic model, are verified in rotary cutting simulations via Bool operations. The system's efficacy has been substantiated through experimental validation, demonstrating its capability to output specific processing schemes for irregular logs and to verify these through simulation. This approach notably improves log recovery rates, decreasing volume error from 12.8% to 2.7% and recovery rate error from 23.5% to 5.7%. The results validate the efficacy of the proposed digital twin system in optimizing the rotary cutting process, demonstrating its capability not only to enhance the utilization rate of log resources but also to improve the economic efficiency of the factory, thereby facilitating industrial development.  
digital twin; log centering; control optimization; log rotary cutting simulation Article
## 4 Discussion
### Re-Training Effects on Model Performance and Result Interpretability  
In this study, multi-round training not only improved the model performance on test images with diminishing similarity, but also demonstrated the benefits of evaluating the learnability and performance robustness among different models. It is cautioned that retraining with extended data did not always improve model performance, and relevant images with high quality are needed, as shown in Tables 3-6. The progression in learning behaviors could be observed through the changes in extracted activation features, ROC curves, or heatmaps across different stages. The observation that the ROC curves of ResNet-50 were persistently more skewed to the upper-left corner was closely correlated with its superiority over the other three models at multiple stages (Figure 5 and Tables 2-6). Conversely, VGG-19 consistently presented a lower ROC curve in every round and concurrently exhibited a slower rate of improvement (Figure 5 and Tables 2-6). By tracking alterations in activation features and heatmaps (Figures 8-10), we can gain deeper insights into the network's perception of the disease (i.e., the predominant features for classification) and even enhance our understanding of the disease through features/heatmaps filtered by the CNN models. It should be emphasized that, at least within the context of this study, while a correlation between features/heatmaps and disease manifestation does exist, it remains tenuous. Explicit application of model-learned features/heatmaps to explain the classification is still impractical.  
The weak heatmap-disease correlation can be seen in Figures 8 and 9, while the irregularity in such correlations can be seen in the pneumonia case in Figure 9. A normal X-ray image of the lungs will typically show clear, dark spaces with distinct edges that represent healthy air-filled lung tissue [56,57]. There should be no signs of abnormal densities or infiltrations that are indicative of infection or inflammation, as shown in the upper panels of Figures 8 and 9. COVID-19 X-ray images may show patchy areas of opacity, which indicate fluid buildup in the lungs [58,59]. Additionally, COVID-19 may cause a condition called \"ground-glass opacity\", which appears as a hazy, cloud-like pattern. In Figures 8 and 9, patches of hotspots with intermediate to high intensity were also observed. X-ray images of pneumonia also show infiltrations and white patches in the lungs [60], but they may appear in a more patchy or lobar pattern, which is consistent with the large extent of hotpots of occlusion intensity in the lower panels of Figure 8. Yet, similar observations are not found in the pneumonia Grad-CAM heatmaps displayed in the lower panel of Figure 9. This suggests that directly using heatmaps to elucidate classification outcomes might not always be viable.
# Extraction, Characterization, and Applications of Pectins from Plant By-Products
## 3 Pectin Extraction Methods
### Green methods for Pectin Extraction
#### 3.2.4 Dielectric Barrier Discharge Plasma Extraction (DBD)  
Plasma, the fourth state of matter, involves partially ionized gases containing reactive species such as negative and positive ions, electrons, gas atoms, free radicals, and photons. Dielectric barrier discharge (DBD), radiofrequency, gliding arc discharge, and corona discharge are methods that generate atmospheric cold plasma [88]. DBD can modify the biomacromolecule's side chains through the action of the chemically active species constituting the plasma or break down specific bonds for the destruction of the secondary structure [89]. DBD can also be used to degrade biopolymers, including proteins and polysaccharides. The hydroxyl free radicals produced by high-energy electrons from the DBD process attack the pectin chains and degrade them into molecules of smaller sizes [36]. Pectin extraction by DBD plasma has not attracted much attention and there is limited research on this topic. DBD may also be used to modify pectin structure in the future.