esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved NSGA-II-Based Method for Cutting Trajectory Planning of Boom-Type Roadheader
## 3 Simulations and Experiments
### Simulation Verification
#### 3.1.2 Ablation Comparison  
To validate the effectiveness of algorithm improvements, ablation comparison experiments were conducted, comparing initial population and crossover processes. \(\sim(F+P_{C}+P_{V})\): no improved initialization or crossover with adjacency constraints; \(F\): only NSGA-II with adjacency-constrained initialization; \(P_{C}+P_{V}\): only crossover with adjacency constraints; \(F+P_{C}+P_{V}\): both improved initialization and crossover with adjacency constraints. Experimental results are shown in Figure 14, including convergence generations, optimal objective function value, and running speed. The improved method yields the best objective function value.  
Through ablation experiments and statistical significance analysis, the proposed method demonstrates significant improvements over other modifications in terms of convergence speed, total path length, steering angle, and runtime (\(p<0.001\)). Specifically, the proposed method reduces the number of iterations by 60.9% (\(p<0.001\)). In terms of runtime, the proposed method reduces the time by 91.6% compared to other methods (\(p<0.001\)).  
Figure 12: Path sets and their corresponding joint space coordinates.  
Figure 13: Improved NSGA-II trajectory point optimization results. (**a**) Pareto frontier solution; (**b**) Convergence of the total distance; (**c**) Convergence of total Angle.  
The total path length and runtime of the proposed method are significantly better than those of the other improvements.  
This study validates the effectiveness and superiority of the improved NSGA-II path optimization method in cross-sectional forming cutting trajectory optimization from both convergence and performance improvement perspectives. However, further verification is required for its specific application in boom-type roadheader path optimization. Given the complexity and difficulty of actual cutting path testing, a virtual debugging system is constructed in a virtual environment to verify the correctness of the cutting trajectory by comparing planning results with virtual motion results.
# Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications
## 1 Introduction
Section 3 provides CS reconstruction algorithms like the convex-optimization, greedy, Bayesian, and noniterative-reconstruction algorithms. Section 4 briefly presents compressed-sensing applications. Lastly, conclusions are presented in Section 5.
# Design and Implementation of Virtual Security Function Based on Multiple Enclaves
## 7 Conclusions  
In this paper, we propose a virtual security function architecture based on multiple enclaves to ensure the security and scalability of VNFs. In our system, the modules of VNF can be put into different enclaves and the communication can be done through local attestation to improve the scalability of the system while providing runtime security protection for VNFs. Furthermore, a new hot swapping mechanism is also presented to enable the system to be reconfigured at runtime without stopping the whole system. In order to reduce the overhead caused by SGX protection, we use DPDK to speed up data packet processing and present the methods of reduced system calls, batch and shared memory to improve system performance. Finally, an IDS based on our architecture is implemented and evaluated. The results show that the performance overhead is within an acceptable range while improving the scalability of VNFs protected by SGX.  
To our knowledge, our work is the first attempt to put the elements of virtual network functions in different enclaves, to protect the security of key modules while supporting the scalability and composability of virtual network functions. In future work, we will design more virtual security functions based on our architecture and evaluate their performance. Moreover, we will optimize the method of hot-swapping and improve its security.  
**Author Contributions:** Conceptualization, J.W.; methodology, J.W. and Y.Y.; software, Y.L. and C.F.; validation, Y.Y.; formal analysis, Y.Y.; investigation, Y.L.; resources, S.H.; data curation, Y.Y. and C.F.; writing--original draft preparation, Y.Y. and Y.L.; writing--review and editing, J.W., Y.Y., S.H.; visualization, Y.L.; supervision, J.W.; project administration, J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is sponsored by the National Natural Science Foundation of China granted No.61872430, 61402342, 61772384 and the National Basic Research Program of China 973 Program granted No.2014 CB340601, and Foundation of Science and Technology on Information Assurance Laboratory (No. KJ-17-103).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Methodology
### Identification of SOTIF-Related Hazardous Factors with the Extended STPA
#### 2.1.2 Identification of Causal Scenarios by SOTIF-Related Classification  
This analysis aims to find the SOTIF-related causal scenarios from the performance limitation of situational awareness and the related reasonably foreseeable human misuse.  
In the control structure, the performance limitation of situational awareness should be analyzed in the feedback part, and the reasonably foreseeable human misuse should be analyzed around the driver. However, the existing STPA is a domain-independent method that does not provide a targeted classification of SOTIF-related causal scenarios for the IRDAS. Therefore, based on the structural characteristics of IRDAS, the classification of the SOTIF-related causal scenarios regarding the process model and the feedback is proposed, which is shown in three phases, as follows.  
Phase 1 is to identify the set of situational information that, if incomplete, incorrect, or uncertain, can lead to the UCAs. In this phase, the mental model of the driver and the IRDAS decision-making part that uses situational information are examined. For drivers and the IRDAS, the critical situational information includes, for example,  
* Operational area.
- Track and other infrastructures.
- Trains and other objects on the track.
- The situation of the controlled train (obtained by the perception of the environment).
- Weather and emergency.
* A priori knowledge (e.g., the operational design domain (ODD)).
* The operational mode of the IRDAS. Phase 2 is to analyze the understanding part of the IRDAS and the driver in order to identify the potential reasons for the improper information in phase 1. This phase includes causes of:  
* Aleatoric uncertainties.
- Epistemic uncertainties.
* Improper information input. For the understanding of IRDAS, the information comes from sensors and for drivers, the information comes from IRDAS.  
Figure 2: The customized control structure for the SOTIF of the IRDAS.  
* For drivers, the improper use or setting on IRDAS. Phase 3 is to identify the potential performance limitation of the sensing part, including,
* The performance parameter of sensors compared with particular operational environments.
* The deficient setting and algorithm of sensors compared with particular operational environments.
* The improper feedback or guidance from understanding or deciding parts in some particular operational environment.
* For drivers, the improper use or setting of the IRDAS.  
It should be noticed that the causal scenarios consist of many interrelated causal factors. For the purpose of practical hazard control, the causal factors, instead of causal scenarios, are used for the evaluation in the following content.
# Analysis of Security Issues and Countermeasures for the Industrial Internet of Things
## 3 Security Issues in IIoT Systems
### Security Issue 8--Data Confidentiality  
Confidentiality ensures that the data are protected from unauthorized entities. In a centralized IIoT system, a user's confidential data are stored inside a traditional server,; however, in the case of a large-scale IIoT system, sensitive information can also be stored in distributed locations. Therefore, IIoT systems experience issues, e.g., protecting business's confidential information and individual confidential information [105, 106]  
One of the common ways to get a user's confidential and private information (e.g., username, password, bank details, etc.) unlawfully is a phishing attack. This can be done by sending emails, seemingly legitimate emails, that contain a malicious link (e.g., malware or spyware), and when a user clicks on the link, their sensitive information is transferred online to the attacker's device [107]. Another possible attack on data confidentially could be the MITM attack, which may be caused due to insufficient authentication and authorization mechanisms, lack of transport encryption schemes, and weak access control enforcement. It could be a potential threat of _disclosure_ of users' sensitive data unlawfully by the attackers. Therefore, protecting the databases as well as taking security measures for local data (i.e., data located inside an IIoT device) is important in the context of data confidentiality in an IIoT system [69]. It is also imperative to design security protocols for resource limited IIoT devices that require a minimum of identifiable information of users and devices.
## 2 Reactive Routing Protocols and Application of Jitter
### Basic Operations of Reactive Routing Protocols
#### 2.1.2 Route Maintenance  
Route maintenance is performed when an actively used route fails, _i.e._, when a data packet cannot be delivered to the next hop towards the intended destination. On detecting that a route has failed, a route error (RERR) message is generated. On receiving such an RERR message, the source of the failed data packet can initiate a new route discovery procedure to re-establish connectivity.
# Thermogravimetry Applicability in Compost and Composing Research: A Review
## 3 Results and Discussion
### Thermogravimetry in Compost Pyrolysis  
The pyrolysis of the compost samples was studied by TGA. Pyrolysis of diverse compost from different raw materials has been studied with different objectives in the last years. The main weight loss was observed between 209 and 373 \({}^{\circ}\)C by several authors. This range is narrower than that found by other authors in the pyrolysis of lignocellulosic biomass for which the highest weight loss is normally found between 200 and 450 \({}^{\circ}\)C [75]. Differences in lignin content may explain this discrepancy because lignin is the thermally most important material among the components of biomass and compost. In addition, pyrolytic carbon is mainly derived from lignin [85]. On the other hand, other authors point to the high ash content of the compost as being responsible for the difference in temperature ranges mentioned above [86]. [PERSON] et al. [87] investigated the technology of manufacturing pellet blends for energy production from two discarded materials in industry: coal tailings from coal cleaning processes and spent mushroom compost (SMC) a mixture of fibrous compost substrate and a wet casing layer used during mushroom production. Differential thermogram (DTG) of air-dried SMC and coal tailings demonstrated the differential behavior of the components. SMC materials under two temperature ranges (230-580 \({}^{\circ}\)C and 680-790 \({}^{\circ}\)C) have been degraded. Pyrolysis of the lignocellulosic components has been attributed to the first range. In addition, a peak corresponding to 310\({}^{\circ}\)C (DTG) to cellulose decomposition is usually attributed. The second interval to CaCO\({}_{3}\) decomposition is normally attributed although also carbonaceous residues are responsible for the degradation suffered between 350-670 \({}^{\circ}\)C with a large mass loss peak. Thermal degradation of these components at 670 \({}^{\circ}\)C is assumed to be complete.
## 1 Introduction
Based on the literature found that analyzed UTM conflict detection by conducting a flight test, there were three categories of communication architectures in conflict detection, which were vehicle-to-ground (V2G), vehicle-to-vehicle (V2V), and mixed V2G and V2V architecture.
# Synthesizing Individual Consumers' Credit Historical Data Using Generative Adversarial Networks
## 4 Results
### Statistical Confidentiality  
[PERSON] et al. (2017) introduces general statistical utility measures, including propensity scores and interval-overlap [23]. However, we concluded that in this consumer credit case, such statistical measures would not be sufficient considering exposure risks altogether. To be specific, previously known methods are applied to cross-sectional data, an observation on a sample at one given time, but consumer credit observes a group sample over a successive period. Even if the propensity scores and interval-overlap agree at 5% confidence interval, it does not necessarily mean that they are distributed accordingly to the corresponding period. Therefore, for the statistical confidentiality, we only compare the distributions of the attributes from the original and synthetic data to see if the model can generate the mixture of Gaussian distribution.  
As shown in Figure 5, the distributions of attributes in the synthetic dataset fit very close to the distributions of the original dataset. It is also notable that they show sharperpeaks than those in the original dataset. It seems to be that applying a GMM to data generation has highlighted its features of the multimodal distribution.
# Two-Level Sharing and Extraction of Sensing Information with Hybrid V2X Communications
## 5 Performance Evaluation  
### Simulation Setup  
We carried out a computer simulation to assess the performance of the proposed scheme by a C++ program on a Linux system (Ubuntu 18.04). The objective of the simulation is to examine the efficiency of sensing-information extraction regarding the traffic load at the edge server and the effectiveness of local cooperation among vehicles.  
In the evaluation, the main evaluation metrics are as follows.  
(1) The number of global reports  
We measured the main traffic load in sensing information extraction at the edge server by the number of global reports of sensing data from the vehicles to the edge servers. We measured the necessary number of global reports after each round of local exchanges of sensing information among vehicles.  
(2) The consumption of wireless resources in terms of accumulated interference area  
Interference area is a key factor that is associated with the consumption of spectrum resources, the power of transmission, and the potential contention status in using the wireless resources.  
Let the local transmission range for the data exchange between vehicles be \(r\). Let the global transmission range between a vehicles and the edge server be \(R\). Suppose that there are in total \(n\) local broadcasts at vehicles, and \(N\) global reports from vehicles to the edge server. We measure the accumulated interference area by  
\[AIR=n\times\pi\times r^{2}+N\times\pi\times R^{2}. \tag{1}\]  
(3) The MinFactor extracted at each round of exchange  
This metric shows the results of local extraction of object information that needs to be extracted.  
Two schemes are evaluated: (1) a direct report scheme, i.e., the fundamental scheme of sensing information extraction in which each vehicle reports its sensing results to the edge server, and (2) the proposed scheme of sensing information extraction.  
The parameter setup in the simulation is shown in Table 1. One edge server and 30 vehicles were employed in the evaluation. The communication range between a vehicle and the edge server was set to 1000 m. Two local communication ranges, 300 m and 100 m, were employed. Such local communication ranges were regarded as two typical examples, referring to the communication ranges of DSRC and Wi-Fi. The local communication range affects the connectivity of vehicles and scope of the local exchanges of sensing information. The vehicles were randomly distributed in two lanes. The object sensing results at the vehicles were random integer values in between \(-50\) and \(-100\). The object information in the extraction of sensing information is the MinFactor, which is the minimum value of sensing results among the vehicles. The maximum number of exchange rounds was set to 3. In the evaluation, to concentrate on the mechanism of the sharing and extraction of sensing information, we supposed that there was reliable communication between vehicles and between the edge server and the vehicles.  
Four scenarios were employed in the evaluation. In Scenario 1,
# Concerto: Dynamic Processor Scaling for Distributed Data Systems with Replication
## 5 Performance Evaluation
### Benchmark through a Replica  
The master-replica model typically allows the replica to accept the read queries, thereby enhancing the availability and performance capabilities of data service platforms. This characteristic is at odds with the concept of Concerto because scaling down the replica can deliver a decrease in the user experience.Concerto is augmented to offload the independent part of the read path to the fast-running thread. To evaluate the efficacy of the optimization process, we measured performance by reading data from the replica. Figure 9 shows the IOPS of Memtier and the YCSB benchmarks when sending the read requests to the replica. For YCSB, we only report the result of workload C because the others include write requests, and, they, therefore, fail when directed to the replica. We study two versions of Concerto: CNCT processes all requests (e.g., read requests from users and write requests from master) with a single thread, and CNCT+ performs the offloading optimization for read requests. CNCT generates 20% of the performance loss compared to ALL-H, which is identical to the outcome of ALL-L. However, CNCT+ significantly reduces the performance gap with the partial offloading technique, which is only 3% on average. Considering that this measurement is performed under the high-bandwidth network configuration, this performance penalty will be negligible in practice.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**WK** & **Description** & **Ratio** \\ \hline Load & Initial database establishment & 100\% Insert \\ A & Session store recording actions & 50\% Read, 50\% Update \\ B & Photo tagging and tag reads & 95\% Read, 5\% Update \\ C & User profile cache & 100\% Read \\ D & User status update & 95\% Read, 5\% Insert \\ E & Recent post scan in conversation & 95\% Scan, 5\% Insert \\ F & Database & 50\% Read, 25\% Read-Modify-Write, 25\% Update \\ \hline \hline \end{tabular}
\end{table}
Table 3: YCSB workload characteristics.  
Figure 8: Energy consumption with YCSB.  
Figure 7: IOPS with YCSB.
# Towards Strengthening the Resilience of IoV Networks--A Trust Management Perspective
## 3 Trust Management in the Internet of Vehicles
### Trust Models
#### 3.2.3 Evaluation of Trust Models  
There are two important steps in trust management: establishing a trust model and evaluating the trust model. The above discussion introduced the existing trust model in detail, and trust evaluation is also a crucial step. Up to now, most papers have used evaluation parameters to evaluate trust models; specific evaluation parameters are shown in Table 4. In general, trust evaluation parameters include: precision (P), recall (R), F-score (F), accuracy (A), TPR, TNR, FPR, FNR, etc., which are mainly used to evaluate whether the established trust model is feasible. For example, using the combination of multiple parameters to evaluate the model [52], this paper proposed a novel trust model: NOTRINO. In order to evaluate the trust model, the authors verified three parameters under several different attacks: precision, recall, and F-score. By comparison, the three parameters of this model reached the highest values, which shows that this trust model was feasible and can be used to detect malicious attacks and ensure the safety of the IoV. In [47], the authors proposed a hierarchical hybrid trust management model (HHTM). In this model, three trust values need to be calculated and then mixed according to different weights. Three parameters are also used to evaluate the accuracy of the HHTM scheme: P, R, and F. The experimental results show that with the increase in the number of nodes, these three parameters all increased, and their values were higher than those of the basic model.  
Every established trust model is evaluated in various ways. To date, there have been few evaluations of the network, with most of the existing papers failing to achieve this. Nevertheless, the network is very important to the trust management mechanism, so network evaluation is the next topic that we should focus on.
# Fault Diagnosis of HV Cable Metal Sheath Grounding System Based on LSTM
###### Abstract
Secondly, the sheath currents of four types of faults, namely, open circuit in sheath loop, the breakdown of cable joint, the flooding in link box, and the sheath grounding fault, are analyzed, and 14 feature vectors are constructed based on the amplitude ratio and phase difference of the sheath currents of the same circuit and the same grounding box to comprehensively reflect the operation status of the metal sheath grounding system. Then, 18 types of metal sheath grounding systems were simulated and operated to establish a fault database, and finally, the faults of HV cable grounding systems were intelligently and accurately identified by the LSTM algorithm.
## 5 Semi-Active Control Analysis of the Multi-DOF Seat Damping Suspension  
For a passive vibration isolation system, the vibration transmissibility can be shown in the following equation:  
\[T_{d}=\sqrt{\frac{1+\left(2\zeta\lambda\right)^{2}}{\left(1-\lambda^{2} \right)^{2}+\left(2\zeta\lambda\right)^{2}}} \tag{12}\]  
In this formula, \(T_{d}\) is the vibration transmissibility of the passive vibration isolation system, \(\lambda\) is the ratio of the excitation frequency and natural frequency, and \(\zeta\) is the damping ratio.  
Figure 19 shows that when the excitation frequency was 1.414 times higher than the natural frequency (\(\lambda>1.414\)), the vibration transmissibility \(T_{d}\) was less than 1, meaning that the vibration isolation system could effectively reduce vibration. However, when \(\lambda<1.414\), \(T_{d}>1\), indicating that the vibration was amplified.  
Figure 19: Vibration transmission characteristics of the passive vibration isolation system.  
Figure 18: Test results of the multi-dimensional damping seat platform. (**a**) Vertical direction test results; (**b**) horizontal direction test results; (**c**) roll direction test results.  
Therefore, to achieve better damping performance, it was necessary to reduce the natural frequency of the seat as much as possible. However, if the natural frequency is too low, the deflection of the vibration isolation system will be too large, so it is not easy to reach a low natural frequency. Therefore, it is necessary to use active or semi-active control methods, in order to attenuate low-frequency vibrational excitation.