esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Validation
### Analysis Result  
#### 3.3.1 Response of Each Transducer  
This section presents the electrical current and the vibration velocity obtained from the impedance matrix equation in the form of a transfer function for the applied voltage. The results are compared to the FE analysis results. The 32 operated transducers and their numbers are shown in Figure 7. Each operating transducer has a unique transfer function depending on its position and the direction of the projected beam.  
Figures 8-11 show the resulting electrical and mechanical transfer functions for randomly selected transducers #7 and #9. In each graph, the \(x\)-axis is the normalized frequency, \(ka\), for the size of the transducer, where \(a\) is the length of one side of the radiating surface of the head (0.2 m in this case). The amplitudes of the electrical admittances of transducers #7 and #9 are shown in Figures 8 and 9, respectively. The amplitudes of the transfer function between the applied voltage and the head velocities of transducers #7 and #9 are shown in Figures 10 and 11, respectively. Graphs are shown for elevation angles of the projected beam of \(0^{\circ}\) and \(30^{\circ}\). Here, the analyzed frequency range is approximately \(ka=1.5\sim 4.2\)  
Figure 7: Transducer number (front view).  
Figure 8: Electrical admittance of transducer #7 calculated by the proposed method (solid line) and the conventional FEM (thick dashed line) when (**a**) \(\theta=0^{\circ}\) and (**b**) \(\theta=30^{\circ}\).  
Figure 10: Voltage-to-velocity transfer function of transducer #7 calculated by the proposed method (solid line) and the conventional FEM (thick dashed line) when (**a**) \(\theta=0^{\circ}\) and (**b**) \(\theta=30^{\circ}\).  
Figure 9: Electrical admittance of transducer #9 calculated by the proposed method (solid line) and the conventional FEM (thick dashed line) when (**a**) \(\theta=0^{\circ}\) and (**b**) \(\theta=30^{\circ}\).  
The conventional FEM results and the results of the proposed method match well. The validity of the proposed method can be confirmed by the fact that the transfer functions of each transducer obtained by the two methods are very similar. In Figures 8-11, the analysis results are rather complex. This occurs because the sound diffraction, transmission, and vibration mode of the acoustic window have complex effects on the acoustic field produced by the sonar (the crosstalk phenomenon).
# The Electric Vehicle Routing Problem with Time Windows, Partial Readers, and Parcel Lockers
## 7 Conclusions
**Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request. **Conflicts of Interest:** The authors declare no conflict of interest. +++ ==ERROR: No output for this page==
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multicolumn{8}{c}{**EVRPTW-PR-PL**} & \multicolumn{8}{c}{**EVRPTW-PR**} \\ \cline{2-11}  & \multicolumn{4}{c}{**GUROBI**} & \multicolumn{4}{c}{**ALNS**} & \multirow{2}{*}{**\(\Delta\)\(\mathbf{ \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multicolumn{6}{c}{**EVRPTW-PR-PL**} & \multicolumn{4}{c}{**EVRPTW-PR**} & \multirow{2}{*}{\(\Delta|K|\)} & \multirow{2}{*}{CR} \\ \cline{2-2} \cline{5-8}  & 1K1 & \(f_{b}\) & \(f_{a}\) & \(\Delta f_{(a-b)}\) & \(\bar{t}\) & 1K1 & \(f_{b}\) & \\ \hline R101 & 12 & 806.21 & 827.956 & 2.7 & 45.20 & 18 & 1642.86 & 6 & 2.04 \\ R102 & 10 & 723.69 & 730.41 & 0.93 & 66.20 & 15 & 1471.88 & 5 & 2.03 \\ R103 & 11 & 776.12 & 818.944 & 5.52 & 47.00 & 13 & 1261.76 & 2 & 1.63 \\ R104 & 9 & 757.79 & 795.34 & 4.98 & 53.80 & 11 & 1081.43 & 2 & 1.43 \\ R105 & 12 & 807.09 & 863.804 & 7.03 & 28.60 & 14 & 1375.04 & 2 & 1.70 \\ R106 & 14 & 742.53 & 787.044 & 5.99 & 29.00 & 13 & 1294.95 & \(-1\) & 1.74 \\ R107 & 10 & 753.41 & 803.188 & 6.61 & 43.80 & 12 & 1127.02 & 2 & 1.50 \\ R108 & 8 & 557.93 & 600.61 & 7.65 & 38.00 & 11 & 1039.99 & 3 & 1.86 \\ R109 & 10 & 681.17 & 730.158 & 7.19 & 49.60 & 12 & 1184.68 & 2 & 1.74 \\ R110 & 9 & 715.32 & 756.246 & 5.72 & 41.80 & 11 & 1092.22 & 2 & 1.53 \\ R111 & 9 & 781.97 & 815.734 & 4.32 & 39.80 & 11 & 1102.33 & 2 & 1.41 \\ R112 & 11 & 799.56 & 849.464 & 6.24 & 35.60 & 11 & 1001.79 & 0 & 1.25 \\ \hline Average & 10.42 & 741.90 & 781.59 & 5.41 & 43.20 & 12.67 & 1222.96 & 2.25 & 1.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Detailed results on EVRPTW-PR-PL large-size instances of subset R-2.
# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 5 Threat Analysis and Risk Assessment
### Overview of System Components and Threats  
A DMS consists of several critical components, each susceptible to specific threats. These components include the following:  
* Processes: These are the core functional units responsible for processing sensor data and making decisions based on that information. Processes are highly vulnerable to all six STRIDE threat categories because they are central to the system's operation;
* Sensors: These collect data on driver behavior and environmental conditions, such as steering input and facial recognition. Sensors can be targeted by Spoofing attacks or tampered with to deliver incorrect data to the system;
* Actuators: These devices, such as steering systems and brakes, respond to the system's decisions and influence vehicle operation. Actuators are vulnerable to Tampering, where unauthorized changes could lead to dangerous vehicle behavior;
* Dataflows: Data transmission between components, from sensors to the processing unit, is crucial for system operation. These flows must maintain confidentiality, integrity, and availability to prevent data interception and Tampering;
* Ports: These are the physical or logical entry points through which external devices and systems communicate with the DMS. Ports are at risk of DoS attacks and unauthorized access.  
The total number of threats in the system is computed by categorizing each component and determining the potential attacks it might face. For example, processes are susceptible to all six STRIDE categories, ports are vulnerable to three, and data stores need to follow the three Confidentiality, Integrity, and Availability (CIA) principles. Using this method, the total number of threats in the system was calculated to be 115, consistent with the findings from running the analysis using Ansys Medini Analyse.
# Erasure Codes for Cold Data in Distributed Storage Systems
## 4 Implementation  
### Architecture  
We combined the erasure codes with replication for our system in this paper. Erasure codes are used to decrease the storage consumption, and replication can improve the read performance. The system framework is shown in Figure 7.  
After the read or write request is initiated by the application, it is sent to the gateway, which can judge whether the data block is located in the local server node or not. If it is located in the local server node, it will be classified into replication or erasure code according to the request type. Otherwise, it will be forward to other nodes.  
The cluster management module is the most important module in the system. It can locate the object into the replication or erasure code. At the same time, it can maintain the consistency as soon as the node information has been changed in the system.
# Improved Branch-and-Bound Antenna Selection Algorithm for Massive MIMO
## 2 Representative Methods in Antenna Selection
### Branch and Bound Algorithm  
The Branch and Bound algorithm is a systematic search methodology designed for solving combinatorial optimization problems [17; 18; 19; 20]. Its core principle lies in constructing a solution space tree for the problem, iteratively decomposing the problem into sub-problems (branching), and employing upper or lower bound computations to prune non-optimal branches (bounding). This dual mechanism effectively narrows the search space, thereby circumventing exhaustive enumeration of all possible solutions. The algorithm ultimately identifies the optimal antenna subset through this process. The search procedure is implemented as follows (with channel capacity maximization as the selection criterion):  
1. Initialization: Build the root node of the solution space tree, set the lower capacity bound of the current optimal subset \(B=-\infty\), and the initial antenna subset \(S=\emptyset\).
2. Branching: Generate child nodes from the current node, each corresponding to adding a new receiving antenna \(k_{i}\) to the current antenna combination, \(S_{i}=S_{i}\cup\{k_{i}\}\).  
Bounding: For each child node \(S_{i}\), its corresponding channel capacity \(C_{i}\) is calculated.
4. Pruning: If the \(C_{i}\) of a child node is less than the \(B\) of the current optimal subset, pruning the child node avoids further searching.
5. Update optimal result: If the number of antenna subsets corresponding to a child node \(\mid S_{i}\mid=M_{r}\) and its channel capacity \(C_{i}>B\), update \(S_{opt}=S_{i}\), \(B=C_{i}\).
6. Iteration: Repeat steps 2 to 4 until the search space is fully explored or the termination condition is met.  
The Branch and Bound algorithm guarantees the identification of a globally optimal receiver antenna combination, thereby optimizing system performance. By employing pruning operations, this algorithm significantly reduces the number of antenna combinations requiring evaluation, demonstrating superior computational efficiency compared to exhaustive search approaches. However, the implementation of the Branch and Bound algorithm involves relative complexity, necessitating precise design of branching and bounding strategies along with effective methods for calculating upper and lower bounds. Although the pruning mechanism reduces computational complexity, the algorithm may still incur substantial computational costs when handling large-scale antenna configurations.
## 4 Security Threat Landscape in 5G Networks
### Threat Landscape in 5G Networks  
There are a number of key areas of concern within the 5G security landscape. First and foremost is the threat of cyberattacks, with the aim of either disrupting network operations or gaining unauthorized access to sensitive data. Cyberattacks can take many forms, from distributed denial-of-service (DDoS) attacks, which aim to overwhelm network resources and disrupt services, to advanced persistent threats (APTs), which are long-term targeted attacks that seek to remain undetected while si network operations [83]. Another area of concern in 5G networks is the threat to user privacy. With an increased amount of data being transferred and processed, there is a corresponding increase in the risk of unauthorized data access or leakage [64]. Given the range of services and applications running on 5G networks, from IoT devices to mission-critical communications, ensuring the privacy of user data is of paramount importance. Further, the enhanced capabilities of 5G networks, such as network slicing and the use of edge computing, while offering significant benefits, can also introduce new security vulnerabilities [56; 57]. For instance, the virtualization of network functions may open up new vectors for cyberattacks. Meanwhile, the use of edge computing, while reducing latency, may increase the attack surface by distributing data processing and storage across numerous devices and locations [68; 69].  
Table 4 encapsulates this evolutionary path, identifying the key security threats and features pertinent to each mobile network generation. Initial 1G networks were focused mainly on analog voice services, offering a somewhat insular threat landscape that chiefly included risks like eavesdropping. The digital revolution heralded by 2G prompted novel security concerns related to data interception but also marked the debut of encryption algorithms such as A5/1 as a countermeasure. The introduction of broadband and IP-based services in 3G extended the threat landscape to include malware, phishing, and data leakage, counterbalanced by enhanced security features like robust encryption and mutual authentication. With 4G's all-IP network structure, the threats evolved to include DoS, MitM, and data sniffing attacks, necessitating advanced security measures such as stronger encryption algorithms and firewalls. Lastly, 5G presents a decentralized, software-driven architecture, introducing a panoply of advanced cyber threats ranging from edge computing vulnerabilities to large-scale botnet attacks. This complexity is met with equally sophisticated countermeasures, including zero-trust architecture and AI-based anomaly detection.
## 4 Experimental Results and Discussion
### Results
#### 4.4.4 Host Image Adaptability  
Because the proposed method does not use any layers that are dependent on the host image's resolution, such as the FC layer, it is adaptable to the resolution of the host image. The WM invisibility and the robustness against attack were evaluated by changing the host image's resolution from 64 \(\times\) 64 to 512 \(\times\) 512, as shown in Table 5. Here, we fixed the WM capacity to about 0.0039. Note that the network has been trained with 128 \(\times\) 128 host images and 8 \(\times\) 8 WM data. As shown in Table 5, the WM invisibility increased as the host image resolution increased.  
Figure 6 shows the results from robustness experiments as graphs, in which each graph includes the results for one kind of attack with the different attack strengths and the different resolution of the host images. Note that the legends in other graphs for the resolution are the same as (a). According to Figure 6, the robustness decreases as the resolution increases for the most pixel-value change attacks, except the Gaussian noise addition and the high-compression JPEG attacks. Those two attacks showed not much difference in robustness for different resolutions and did not follow the tendency. For most of the geometric attacks, the robustness tends to decrease as the resolution increases, but the dropout attack showed increasing robustness as the resolution increases.  
Even in the cases that the robustness decreases as the resolution increases, the proportion was not large, or the increased BER values are not so high. That is, the reduced robustness by resolution change can still be regarded as high robustness. Therefore, we can conclude that the proposed method applies to various resolutions of the host image. Especially most pixel-value change attacks, the proposed scheme is more suitable to the high-resolution trend because mostly it increases the robustness as the resolution increases.  
Figure 5: Examples of the extracted watermarks (WMs) according to BER: (**a**) 0, (**b**) 0.78, (**c**) 3.51, (**d**) 10.93, (**e**) 21.09.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Host Image Resolution** & **Watermark Resolution** & **Invisibility [dB]** \\ \hline
64 \(\times\) 64 & 4 \(\times\) 4 & 39.97 \\
128 \(\times\) 128 & 8 \(\times\) 8 & 40.58 \\
256 \(\times\) 256 & 16 \(\times\) 16 & 41.23 \\
512 \(\times\) 512 & 32 \(\times\) 32 & 42.35 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Watermark resolution, host image resolution, measured invisibility.
## 2 Results and Discussion
### Organic Compound Degradation and Biosurfactant Production
#### 2.8.1. Degradation of Aliphatic Hydrocarbons  
The genome of the ZCR6 strain carried _ladA_--a functional gene determining a novel type of alkane monooxygenase (LadA) (Table 8) that was originally detected in the genome of thermophilic _Geobacillus thermodentirificans_ NG80-2 [97]. LadA is a flavin-dependent long-chain alkane monooxygenase of the family of bacterial luciferasees that utilizes a terminal oxidation pathway for the conversion of long-chain alkanes (C15 and longer, up to C36) to corresponding alcohols [98; 99; 100]. To the best of our knowledge, this is the first work that shows the presence of the _ladA_ gene in the genome of the _Pseudomonas_ genus. The ability of ZCR6 to degrade crude oil hydrocarbons was confirmed by measuring the amount of residual hydrocarbons in the culture medium during 4 weeks of cultivation. The fastest degradation rate was observed in the first week of cultivation--45.6% of the initial hydrocarbon content was degraded. In the following weeks, the rate of degradation was slower, and 76.52% of the initial hydrocarbon content was finally removed after four weeks of bacterial culture (Figure 5).
## 2 Data Availability and Collection  
Owing to the promotion of International GNSS (Global Navigation Satellite System) Service (IGS) [20], the accuracy and reliability of GNSS products are improved significantly during the last two decades. The IGS network consists of more than 450 stations distributed around the world, and about 250 stations now track multi-GNSS satellites data, including GPS, GLONASS, Galileo, BDS, QZSS, IRNSS and SBAS. It should be noted that there are four frequencies of open service signals transmittedby BD3 satellites, namely, B1I, B1C, B2a and B3I, and, at present, more than 100 stations can record BD3 signals. However, affected by the firmware version, most of the receivers can only track B1I and B3I signals and even parts of those satellites. Until now, only three types of receivers that come from the IGS backbone network can track BD3 dual-frequency signals, which are made by JAVAD, Trimble and Septentrio.  
The international GNSS Monitoring and Assessment Service (iGMAS) was developed by China [21] and a backbone network has been set up in the mainland of China and around the world. The network would consist of 30 stations, in which 24 stations have been constructed. Note that all these stations can receive the BD3 open service signals. Three types of receivers, manufactured by Chinese companies, that is, the 20 th and 54 th Institutes of the China Electronics Technology group Corporation (CETC) and Unicore Communications, Inc., are employed by these stations. Table 1 lists the information about the capable receivers. The distribution of these stations that can track BD3 open service signals is shown in Figure 1. Given that only receivers from iGMAS can track all the BD3 open service signals, we evaluated the signal performance using the iGMAS receivers' data in Section 3. In addition, considering the influence of station distribution on POD, the stations from IGS and iGMAS were used in the experiments presented in Section 4.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Receiver Type** & **Firmware Version** & **Manufacturer** & **Tracking Frequency** & **Agency** \\ \hline JAVAD TRE\_3 & 3.7.2 & JAVAD & B1I/B3I & IGS \\ DELTA & 3.7.2 & Trimble & B1I/B2a/B3I & IGS \\ TRIMBLE NETR9 & 5.3.7 & Trimble & B1I/B2a/B3I & IGS \\ SEPT POLARRX5 & 5.2.0 & Septentrio & B1I/B3I & IGS \\ CETC-54-GMR-4016 & SW-Version1.0 & CETC-54 & B1I/B1C/B2a/B3I & iGMAS \\ gnss\_ggr & 1.0 & CETC-20 & B1I/B1C/B2a/B3I & iGMAS \\ UNICORE UB4B0I & 20100 & Unicore & B1I/B1C/B2a/B3I & iGMAS \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about the third generation of BeiDou (BD3) satellites capable receivers.  
Figure 1: The distribution of stations from the International GNSS Service (IGS) and international GNSS Monitoring and Assessment Service (iGMAS) backbone network which can track the BD3 open service signals. The block dots represent the IGS stations and the red stars represent the iGMAS stations.
## 2 Background and Related Work
### SDWSN-IoT
Task automation in the SDWSN includes a wide range of solutions such as classification, the prediction of future network conditions, and the installation of optimization rules based on measured network conditions. The integration between machine learning algorithms and SDWSN-IoT architecture is a promising approach to optimizing the energy consumption of sensor nodes, mainly for the following reasons:  
* Adaptive Optimization: Machine learning algorithms can analyze real-time network conditions and historical data to make intelligent decisions for optimizing energy consumption in IoT applications. By dynamically adapting to changing network conditions, the algorithms can optimize the operation of sensor nodes, reducing unnecessary energy consumption [21]. * Intelligent Routing: Machine learning algorithms can learn from past routing patterns and make predictions about future network conditions. This enables them to intelligently route data, considering factors such as energy constraints, network congestion, and node availability. By choosing efficient routing paths, energy consumption can be minimized while maintaining effective data transmission [22; 23; 24]. * Proactive Resource Allocation: Machine learning algorithms can analyze data patterns and predict resource demands in advance. This allows for proactive resource allocation, ensuring that resources, such as energy, computational power, and memory, are efficiently allocated to meet the demands of IoT applications. By optimizing resource allocation, energy efficiency can be significantly improved [25]. * Anomaly Detection: Machine learning algorithms can detect anomalies and abnormal behavior in the network. By identifying unusual patterns or events that may lead to energy wastage or inefficiency, proactive measures can be taken to mitigate such issues and optimize energy efficiency in IoT applications [26]. * Predictive Maintenance: Machine learning algorithms can analyze sensor data and predict potential failures or maintenance requirements. By proactively addressing maintenance needs, energy can be saved by preventing unexpected downtime and optimizing the overall performance of IoT devices [27]. The following subsection introduces the commonly applied ML algorithm applied to find optimal routes for the intelligent forwarding of collected data across the network in IoT applications. Figure 1: The traditional SDWSN-IoT architecture.
# Considerations about Parameters Estimation into a Minimum Variance Control System
## 1 Introduction
Many papers are focused on control performances and less on parameters estimation, which is a vital part of a minimum variance control system [2; 5; 8]. Precisely this punctual issue of parameters estimation is addressed in this paper. As the manuscript title states, the main goal is to perform a comparative analysis regarding the process parameters estimates (both in open and closed-loop) and not to analyze the control system performances (topic presented by many works, including those of the authors [8; 9; 14; 20]). As will be shown below, such an analysis of the estimation process, performed in open-loop or closed-loop operating conditions, leads to some interesting conclusions regarding the process parameters estimates, respectively the process gain estimate. All these findings are valid only for the identification and control of a nonlinear process (whose limited functionality around an operating point can obviously be described by a linearized model).
# A UAV-Based Visual Inspection Method for Rail Surface Defects
## 3 Experiment Results
### Performance Analysis  
Two groups of visual comparison experiments and a qualitative comparison experiment for defects inspection are presented in this section. Every defect in the images is marked by a red rectangle.