esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Impact of Particulate Matter Distribution on Pressure Drop of Wall-Flow Particulate Filters
## 1 Introduction
Previous works [13] have hypothesized the water drag of the particulate matter towards the inlet channels end as the main cause of the pressure drop reduction without soot mass removal. This kind of restructuring is in agreement with findings about the influence of the ash deposition pattern along the inlet channels. While ash deposition mixed with soot on the particulate layer produces a great pressure drop [14], ash deposition in the rear end region leads to lower pressure drop. Rear end deposition of ash is explained by exhaust gas drag during long-term engine operation [15]. This kind of mechanism, i.e., soot restructuring during engine operation, would also contribute to explain why different pressure drops are usually found for the same engine operating conditions and soot loading under real driving operation. In the case of pre-DPF water injection, the drag process is forced by controlled periodic injection events affecting both soot and ash. In this work, the impact of the particulate layer characteristics along the inlet channel is discussed. The analysis is guided by a computational study carried out with a one-dimensional gas dynamic code for wall-flow DPFs [16]. Parametric studies focus on the effect of the particulate layer thickness profile (soot mass distribution) combined with porosity to identify the solution domain that would provide the pressure drop obtained experimentally and its increased rate after pre-DPF water injection. The conclusions of the theoretical analysis are also supported by visualization of monolith substrates analyzed after pre-DPF water injection and in baseline conditions. As a final step, the influence of the particulate layer restructuring on the regeneration process is also explored by means of the modeling of experimental data obtained during active regeneration.
## 5 Discussion  
In Section 4 we shared our findings that demonstrated the effectiveness of our proposed neural network approach for solving the inverse source problem of dynamic load identification by incorporating physics information. The neural network structure and best working hyper-parameter choices were obtained by performing systematic hyper-parameter tuning. The network was later trained on different data instances generated by simulating spring mass damper systems subjected to different types of forcing functions, including smooth, abrupt changes in gradient, and jump discontinuities.  
The neural network predictions in all cases were excellent, with slight overshoot and undershoot at the cusp of both piece-wise functions and some small oscillations at the start of the triangular function. However, some numerical oscillations were observed in step function predictions that resemble the Gibbs phenomenon. The findings suggest that the proposed neural network approach was effective in predicting different types of forcing functions.  
The study also tested the network to recover the forcing functions of non-linear oscillators from the data. The data were generated by solving duffing equations subjected to various smooth forcing functions. The network was able to predict sinusoidal functions and sinusoidal functions with increased non-linearity and frequency with small amounts of instability and minor overshoot and undershoot at the peak and trough of the periodic function.  
Finally, the network was used to predict functions given by the sum of two sinusoidal functions and an impulse, and the network prediction was in close agreement with the actual function. However, some under and over-predictions with small oscillations were observed at the peaks and valleys of the functions in the case of the sum of two sinusoidal functions. In the case of a forcing function involving an impulse, numerical oscillations were observed at the start that dampened out in the later stages of predictions.  
Overall, the findings of this study demonstrate that the proposed technique works well in predicting a variety of forcing functions from response data, although, only smooth functions were considered in the case of non-linear oscillators. Additionally, the analysis was based on simulated data without any noise used to train the neural network. Future studies should investigate the effectiveness of this technique using real-world data with noise and compare its performance with other established techniques for dynamic load identification.
# A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices
## 3 Vulnerability Analysis, Discovery, Detection, and Mitigation
### Research on Vulnerability Mitigation
#### 3.4.1 Automated Patch Generation  
The technology of automated patch generation in this section is not specifically targeted at the IoT field but an extension of the traditional security field. The vulnerability repair work is usually done at the source level by the vendor development team. After obtaining the external vulnerability report, they eliminate the vulnerability by reproducing the vulnerability trigger condition and analyzing the vulnerability mechanism. Automatic patch generation holds out the promise of automatically correcting software defects without the need for developers to diagnose, understand, and correct these defects manually [97].  
**Technical requirements:** The ability to fetch and update IoT device firmware.  
The researchers in the field of software engineering proposed to automatically generate the patch by learning the correct code in the C language [97; 98], Java language [99], and other source code levels, which achieved the initial feasible effect. Another idea is to change the form of the program without changing its function. _GenProg_[100] uses an extended form of genetic programming to evolve a program variant that retains required functionality but is not susceptible to a given defect. However, it can generate nonsensical patches due to the randomness of mutation operations. Thus, [PERSON] et al. [101] proposed the Pattern-based Automatic program Repair (PAR) to solve the above problem. In terms of the android platform, [PERSON] et al. [102] proposed _AdaptKpatch_, which is an adaptive kernel hotfix framework and LuaKpatch which inserts a type-safe dynamic language engine into the kernel to execute patches. These solutions solve the problem that the patch chain of the Android platform is too long, the fragmentation and the ecological layout are not matched, and the subdivision repair is not timely. However, they do not consider solving the problem of the automatic generation of hotfixes in the cross-CPU architecture. They still need to be manually written based on the field of knowledge and experience. The Cyber Grand Challenge (CGC) [103] of DARPA drives researchers to work on automated defense methods at the binary code level. However, these methods mainly adopted generalized defense mechanisms such as binary code hardening [104; 105], boundary checking, and pointer patching [106].
# An Improved Energy-Efficient Routing Protocol for Wireless Sensor Networks
## 3 Proposed Scheme
### Improved Energy-Efficient LEACH Protocol  
To decrease the energy loss and increase the energy efficiency of the WSNs, we propose a novel improved energy-efficient LEACH protocol called IEE-LEACH in this section.
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 3 Sensing Connectivity and K-Node (Edge) Sensing Connectivity  
In this section, we first elaborate on the disaster-resilient survivability requirements of optical sensing and optical communication in OSNs. Then, the concepts of content connectivity and \(k\)-node (edge) content connectivity are introduced. The \(k\)-node (edge) content connectivity was used to quantitatively measure content connectivity and achieve disaster-resilience. Based on \(k\)-node (edge) content connectivity, a new concept of \(k\)-node (edge) sensing connectivity, which ensures the connectivity between sensing data and users, is proposed. Moreover, the key technologies of implementing \(k\)-node (edge) sensing connectivity are also elaborated.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Survivability Technology** & **Routing** & **Spectrum Allocation** \\ \hline The Fiber Bus Protection Scheme & Line [40; 41; 42; 43; 44; 45] \\ The Self-Healing Architecture & Ring [46; 47; 48; 49; 50], Ring-Mesh [49] Two-Level Ring [51], Star-Ring-Bus [52], Star-Ring [53] \\ The \(1+1\) Protection Scheme and Extensions & Mesh [56; 58; 59; 61; 62], Ring [57; 66; 67; 68; 69; 70; 71; 72], Star [60; 63], \\ The Photonic Millimeter-Wave Bridge Scheme & Line [75; 78], Star [73; 74; 76; 77] \\ The P-Cycle Scheme & Mesh [79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 83; 88; 89; 84; 85; 87; 89; 90; 91; 92; 93; 94; 95; 96; 97] \\ The Pre-Configured \(K\&K\) Structure Scheme & Mesh [98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120] \\ The Multi-Path Protection Scheme & Mesh [121; 122; 123; 124; 125] \\ \hline \hline \end{tabular}
\end{table}
Table 4: The applicable topologies of each kind of survivability technology.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Survivability Technology** & **Routing** & **Spectrum Allocation** \\ \hline The \(1+1\) Protection Scheme and Extensions & Dijkstra’s Algorithm [56; 67; 70; 71; 72], The ILP Formulation [58; 59; 61; 67; 70; 72], Spectrum Window Plane [61; 67; 70; 72], Available Spectrum Block [68], First-Fit [69] \\ \multicolumn{2}{c}{The ILP Formulation} \\  & Straddling Link Algorithm [79; 93; 94; 95; 96; 97], Genetic Algorithm [81; 91], Distinct K-Shortest Path Algorithm [82], Protection Cardinality of Demand [83], The ILP-Genetic Algorithm [90], Breadth-First Search [92] \\ \multicolumn{2}{c}{The ILP Formulation} \\  & [98; 101; 104; 105; 106; 107; 109; 110], Cycle Extension [98; 99; 103], Depth-First Search Algorithm [102], Greedy Algorithm [105], Disjoint Paths [108] \\ \multicolumn{2}{c}{Bhandari’s link-disjoint Paths} \\ The Multi-Path Protection Scheme & Algorithm [111; 112; 113; 117; 113; 115; 117], The ILP Formulation [112; 113; 114; 115; 117; 118; 119; 120], KSP Algorithm [115; 119], KSP Algorithm [121], The ILP Formulation [124], KSP [124] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Technologies used to conduct routing and spectrum allocation in backup paths.
# A Survey on Blockchain-Based Federated Learning
## 3 Blockchain-Based Federated Learning
### Functions of BFL
#### 3.2.4 Privacy Protection  
To forestall privacy breaches, certain BFL schemes incorporate additional privacy protocols [79; 113; 114; 115]. For instance, [PERSON] et al. [116] implemented homomorphic encryption to safeguard the privacy of the training model. [PERSON] et al. [111] developed Bisocotti, a decentralized P2P scheme based on blockchain, employing a verifiable secret sharing scheme for secure model aggregation to fortify individual privacy. Blockchain and FL technologies were combined by [PERSON] et al. [117] to devise an intrusion detection algorithm suitable for lightweight network devices, with the aim of safeguarding the data privacy of network users during data sharing. [PERSON] et al. [78] harnessed the decentralization and tamper-proof attributes of blockchains, storing data records and critical information on the blockchain while the complete data were encrypted and stored in a distributed database, ensuring secure storage to prevent the leakage of users' private data. [PERSON] et al. [103] employed the Paillier algorithm to encrypt users' model parameters, subsequently uploading them to the blockchain. After the completion of the model updates, collaborative decryption was executed by a consortium of users. In the context of data security and sharing requisites in the IIoT and Smart Transportation, [PERSON] et al. [89] and [PERSON] et al. [118] deployed local differential privacy techniques. They introduced noise to the raw data prior to feature extraction and sharing to thwart privacy attacks. The Adaptive Differential Privacy FedAvg (ADPFe-dAvg) algorithm was presented by [PERSON] et al. [119] to protect the client's historical data during the entire training phase and prevent member inference attacks in visual object identification modeling. ADPFedAvg introduced user-level differential privacy technology, complemented by adaptive clipping technology. To establish a data-privacy-preserving mechanism, [PERSON] et al. [120] encrypted all data via a public key infrastructure (PKI) comprising a public key and a private key, achieving a BFL mechanism that preserved data privacy with multi-layered security.
## 3 Results and Discussion
### Third Experiment  
To assess the conditions that gave the best results, we recorded the data from the sensors sealed inside the plastic container for half an hour, without changing the soil and the sensor's conditions, to acquire the values for dry soil. This record was shorter than in the other experiments because we know the previous state of the soil. After the waiting period, we added 5% water in mass to the soil four times per hour, directly below the RADAR.  
We arranged the readings to make a graphic of the RADAR's signal, control signal, temperature, and humidity of the air by the time to see the evolution of the RADAR's signal over time and compare it to the rest of the data. Figure 7 presents two results from this experiment.  
Figure 7 shows that the effect of SWC in the RADAR's readings is not constant. When the SWC is high enough, the RADAR's signal, instead of decreasing, increases with SWC.  
Figure 5: RADAR’s readings vs. control signal at 2 cm depth. The vertical red solid line represents the moment when the water was added.  
Figure 6: RADAR’s readings vs. control signal at 21 cm depth. The vertical red solid line represents the moment when the water was added.  
With elevated SWC, the soil gets to the point that it cannot absorb water anymore, making the water reflect the RADAR's signal.
## 2 Wind Speed Modeling  
### Probability Distribution Model  
The probability distribution modeling is a data-based method that extracts characteristics in a statistical way to depict the probability distribution of wind speed. It reflects the expectations of wind speed over a period of time, and provides guidance for wind resource assessment. It can be classified into parameter distribution models and non-parameter distribution models.
# Multi-Sensor GA-BP Algorithm Based Gearbox Fault Diagnosis
## 3 Simulation Analysis
### Data Processing
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & \multicolumn{3}{c}{**Test Data**} & \multicolumn{1}{c}{**Fault**} \\ \hline
1 & 0.99770 & 0.00045 & 0.00069 & 0.00041 & 1 \\
2 & 0.99773 & 0.00044 & 0.00075 & 0.00036 & 1 \\... &... &... &... &... &... \\
9 & 0.99746 & 0.00245 & 0.00061 & \(-\)0.0015 & 1 \\
1 & 0.00008 & 0.99924 & \(-\)0.00009 & 0.00185 & 2 \\
2 & \(-\)0.00011 & 0.99989 & \(-\)0.00009 & 0.00231 & 2 \\... &... &... &... &... &... \\
9 & \(-\)0.00004 & 0.99828 & 0.00022 & 0.00306 & 2 \\
1 & 0.00088 & \(-\)0.0006 & 0.99893 & 0.00153 & 3 \\
2 & 0.00082 & \(-\)0.0010 & 0.99942 & 0.00151 & 3 \\... &... &... &... &... &... \\
9 & 0.00056 & \(-\)0.0003 & 0.99911 & 0.00126 & 3 \\
1 & 0.00262 & \(-\)0.0022 & 0.00028 & 1.00032 & 4 \\
2 & 0.00150 & 0.00057 & 0.00068 & 0.99861 & 4 \\... &... &... &... &... &... \\
9 & 0.00300 & \(-\)0.00165 & \(-\)0.00007 & 0.99899 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: BP algorithm test data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & \multicolumn{3}{c}{**Test Data**} & \multicolumn{1}{c}{**Fault**} \\ \hline
1 & 1.00060 & 0.00851 & 0.00863 & 0.00106 & 1 \\
2 & 1.00114 & \(-\)0.00561 & 0.00484 & 0.00142 & 1 \\... &... &... &... &... &... \\
9 & 1.00023 & 0.00005 & \(-\)0.00362 & 0.00738 & 1 \\
1 & 0.00865 & 0.99963 & 0.00050 & 0.00063 & 2 \\
2 & 0.00613 & 1.00053 & 0.00490 & 0.00860 & 2 \\... &... &... &... &... &... \\
9 & 0.00901 & 1.00047 & 0.00635 & 0.00399 & 2 \\
1 & 0.00575 & 0.00282 & 1.00095 & 0.00134 & 3 \\
2 & 0.00845 & 0.00539 & 1.00051 & 0.00031 & 3 \\... &... &... &... &... &... \\
9 & 0.00661 & \(-\)0.00853 & 1.00067 & 0.00025 & 3 \\
1 & 0.00730 & 0.00874 & 0.00842 & 1.00216 & 4 \\
2 & 0.00891 & 0.00270 & 0.00559 & 1.00057 & 4 \\... &... &... &... &... &... \\
9 & 0.00121 & \(-\)0.00082 & 0.00331 & 1.00024 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: GA-BP algorithm test data. Figure 7: The number of iterations of the GA-BP algorithm. The GA-BP algorithm is compared with the BP algorithm by means of the performance metrics of mean square error, number of iterations, time required, speed of convergence, and accuracy. In addition, one-dimensional convolutional neural network (1D-CNN) in machine learning was used to compare the accuracy of the BP algorithm and the GA-BP algorithm in simulation and experiment. The accuracy comparison of these algorithms are shown in below Figure 8.  
As shown in Figure 8, three algorithms are relatively volatile. However, the GA-BP algorithm is 2.3% more accurate than the traditional BP algorithm and GA-BP algorithm is 3.99% more accurate than the traditional 1D-CNN algorithm.
## 5 Conclusions  
In conclusion, this paper presents a novel controlled mixing strategy, the Dual-Channel Mixing Scheduling (DCMS) Strategy, for the external circulation devices of cylindrical rollers in the Double-Disk Straight Groove Lapping (DDSGL) process. This strategy successfully achieves roller mixing while minimizing collision damage, enabling full size comparison among the entire batch of rollers, and achieving rapid convergence of the batch diameter variation.  
Figure 13: Evolution process of batch diameter variation of rollers, (**a**) Leap interval \(S_{n}\) = 30, (**b**) Leap probability \(k\) = 0.3.  
The model simulation results indicate that a higher leap probability and leap space lead to a more thorough and uniform size comparison among rollers. However, an excessive increase in these parameters can cause a rapid rise in the range of cycle times, which may negatively impact the batch diameter variation. Therefore, based on the simulation results, it is recommended to maintain moderate values for both jump probability and jump space.  
Experimental results corroborate the simulation findings, confirming that the DDSGL method combined with the controlled mixing strategy can significantly enhance the efficiency and precision of roller production. A larger leap probability and leap space accelerate the convergence speed of roller size consistency. However, the parameter values suggested by the model simulation allow convergence to the best accuracy at a relatively fast speed. The industrial relevance of this research is significant, as it can reduce processing time by up to 50%, and the batch diameter change of the rollers can converge from 1.15 \(\upmu\)m to as low as 0.76 \(\upmu\)m. These improvements can lead to higher efficiency in the manufacturing process and improved quality of bearing rollers.  
Future research could further fine-tune the parameters of the DCMS to optimize the DDSGL process for different production scenarios. The proposed approach provides a fresh perspective and solution for improving the size consistency of cylindrical rollers in batch roller production. This has potential implications for the bearing industry and other related fields.  
Conceptualization, W.L. and C.R.; methodology, W.L.; validation, W.L. and C.R.; formal analysis, W.L.; writing--original draft preparation, W.L.; writing--review and editing, W.L.; funding acquisition, C.R. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China under Grant No. 51935008. The authors would like to acknowledge the support from the foundation of Xinchang Research Institute of Zhejiang University of Technology (Grant number: 2021 GKF-0316).  
Not applicable.  
The authors declare no conflict of interest.
# Stability Analysis in Multi-VSC (Voltage Source Converter) Systems of Wind Turbines
## Appendix A State-Space Submodels  
The state variables of the single-VSC system in Figure 1 are shown below, where the variables with a superscript \(c\) are in the control dq frame:  
\[x_{1}=\begin{bmatrix}\theta_{\text{PLL}}&\Phi_{\text{PLL}}&q_{\text{errd}}&q_{ \text{errq}}&V_{\text{PCCd,LFF}}&V_{\text{PCCq,LFF}}&q_{\text{errac}}&V_{\text {M,LFF}}&I^{c}_{Ld}&I^{c}_{Lq}\\ V_{\text{PCCd}}&V_{\text{PCCq}}&I_{\text{od}}&I_{\text{eq}}&x_{\text{del,1d}}&x_{ \text{del,2d}}&x_{\text{del,3d}}&x_{\text{del,1q}}&x_{\text{del,2q}}&x_{\text {del,3d}}\end{bmatrix} \tag{1}\]  
where \(V_{\text{M}}\) corresponds to the absolute value of the PCC voltage \(|V_{PCC}|\).  
The corresponding state equations of the system are presented below. Further details of the single-VSC state-space model can be found in [22; 23]:  
\[\dot{\theta}_{\text{PLL}}=K_{\text{LPLL}}\Phi_{\text{PLL}}+K_{\text{PPLL}}V^{c}_{PCCq}+\omega_{\text{n}} \tag{2}\]  
\[\dot{\Phi}_{\text{PLL}}=V^{c}_{PCCq} \tag{3}\]  
\[q_{\text{errd}}=I_{\text{Ldref}}-I^{c}_{Ld} \tag{4}\]  
\[\dot{\theta}_{\text{errq}}=I_{\text{Lqref}}-I^{c}_{Lq} \tag{5}\]  
\[\dot{V}_{\text{PCCd, LFF}}=-\omega_{\text{FELPF}}V_{\text{PCCd, LFF}}+\omega_{\text{FELPF}}V^{c}_{PCCd} \tag{6}\]  
\[\dot{V}_{\text{PCCq, LFF}}=-\omega_{\text{FELPF}}V_{\text{PCCq, LFF}}+\omega_{\text{FELPF}}V^{c}_{PCCq} \tag{7}\]  
\[\dot{\theta}_{\text{errac}}=V_{\text{PCCref}}-V_{\text{M,LFF}} \tag{8}\]\[\dot{V}_{\text{M,LPF}}=-\omega_{\text{AVC,LPF}}V_{\text{M,LPF}}+\omega_{\text{ AVC,LPF}}V_{\text{M}} \tag{11}\]  
\[\dot{I}_{Ld}^{c}=-\frac{R_{F}}{L_{F}}\dot{I}_{Ld}^{c}+\left(-\frac{1}{L_{F}}V_{ PCCd}^{c}\right)+\omega_{\text{PLL}}\dot{I}_{Lq}^{c}+\frac{1}{L_{F}}V_{ \text{Id}} \tag{12}\]  
\[\dot{I}_{Lq}^{c}=-\frac{R_{F}}{L_{F}}\dot{I}_{Lq}^{c}+\left(-\frac{1}{L_{F}}V_{ PCCq}^{c}\right)-\omega_{\text{PLL}}\dot{I}_{Ld}^{c}+\frac{1}{L_{F}}V_{ \text{Id}} \tag{13}\]  
\[\dot{V}_{\text{PCCd}}=\frac{1}{C_{F}}I_{Ld}+\left(-\frac{1}{C_{F}}I_{Ld} \right)+\omega_{\text{n}}V_{PCCq} \tag{14}\]  
\[\dot{V}_{\text{PCCq}}=\frac{1}{C_{F}}I_{Lq}+\left(-\frac{1}{C_{F}}I_{Lq} \right)-\omega_{\text{n}}V_{PCCd} \tag{15}\]  
\[\dot{I}_{\text{od}}=\frac{1}{L_{S}}V_{PCCd}+\left(-\frac{R_{S}}{L_{S}}I_{Ld} \right)+\left(-\frac{1}{L_{S}}V_{Sd}\right)+\omega_{\text{n}}I_{Lq} \tag{16}\]  
\[\dot{I}_{\text{oa}}=\frac{1}{L_{S}}V_{PCCq}+\left(-\frac{R_{S}}{L_{S}}I_{q} \right)+\left(-\frac{1}{L_{S}}V_{Sq}\right)-\omega_{\text{n}}I_{od} \tag{17}\]  
\[\dot{x}_{\text{del,1 dq}}=0x_{\text{del,1 dq}}+1x_{\text{del,2 dq}}+0x_{\text{ del,3 dq}} \tag{18}\]  
\[\dot{x}_{\text{del,2 dq}}=0x_{\text{del,1 dq}}+0x_{\text{del,2 dq}}+1x_{\text{ del,3 dq}} \tag{19}\]  
\[\dot{x}_{\text{del,3 dq}}=-\frac{120}{T_{d}^{3}}x_{\text{del,1 dq}}-\frac{60}{T_ {d}^{2}}x_{\text{del,2 dq}}-\frac{12}{T_{d}^{1}}x_{\text{del,3 dq}}+V_{\text{ normdq}} \tag{20}\]