esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Destructive Testing Applications for Steel Bridges
## 5 Complementary to NDT Methods
### Global Structural Response Testing (GSR)
\begin{table}
\begin{tabular}{|c|c|} \hline
**NDT Technology** & **Applications and Defects/Anomalies Detected by Relevant Method** \\ \hline Visual Inspection & \(\bullet\) & Corrosion, cracks, and geometrical defects \\ \hline Dye-Penetrant Testing & \(\bullet\) & Defects opened to the surface of homogenous material \\  & \(\bullet\) & Surface cracks, breaks, and weld defects \\ \hline Vision-based method/Digital & \(\bullet\) & Surface rust, cracks, and defective welds \\ Image Processing & \(\bullet\) & Subsurface and surface flaws \\ \hline Eddy Current Testing & \(\bullet\) & Cracks in welds with and without coating \\ \hline Radiographic Testing & \(\bullet\) & Welding cracks, separations, and inclusions \\  & \(\bullet\) & Corrosion \\  & \(\bullet\) & Defects in cables and tendons \\ \hline Ultra-Sonic Testing & \(\bullet\) & Presence and size of welds defects, cracks, and loss of cross-section \\  & \(\bullet\) & Thickness measurements with access from one side \\  & \(\bullet\) & Wire break and cross-section loss in cables and tendons \\ \hline Infrared Thermography & \(\bullet\) & Fatigue cracks \\  & \(\bullet\) & Internal damage and determining the material strength \\  & \(\bullet\) & Checking integrity of corrosion protection in cables and tendons \\ \hline Laser Technology & \(\bullet\) & Defects including bent members, ruptures, and points of discontinuity in steel members \\  & \(\bullet\) & Detection of corrosion and other types of defects in cables and tension elements \\ \hline Magnetic flux leakage & \(\bullet\) & Surface or shallow cracks \\  & \(\bullet\) & Corrosion, section loss, and break of wires in cables and tendons \\ \hline Chemical and Electrical Testing (CET) & \(\bullet\) & Corrosion, breaks, and cross-section loss \\  & \(\bullet\) & It can be used for inspection of external tendons \\ \hline Vibration Method & \(\bullet\) & Section loss in cables and tendons \\  & \(\bullet\) & Detection of damages through modal test \\ \hline Magnetostriction & \(\bullet\) & Flaws and damages in ropes, cables, and other tension elements \\ \hline Acoustic Emission & \(\bullet\) & Active and incipient cracks \\  & \(\bullet\) & Wire break detection in cables, tendons and prestressing strands \\ \hline \end{tabular}
\end{table} 
Table 2: The applications of NDT methods discussed in this study for steel bridges.
## 4 Data Analysis
### Steps, Results, and Validation
Consider the SVM algorithm's output matrix (Figure 9), where I\({}_{00}=719\) represents the correctly detected legitimate nodes, I\({}_{01}=0\) means no false positives for Sybil nodes, which is desirable, I\({}_{10}=2\) indicates that two Sybil nodes went undetected that were labeled as legitimate. And, I\({}_{11}=336\) specifies the correct and accurate detection of Sybil nodes. The algorithm's accuracy in this case was 99.81%, calculated by adding I\({}_{00}\) and I\({}_{11}\) and dividing it by the sum of all the elements in the matrix. The accuracy was computed for every algorithm after processing each matrix. This study encountered issues obtaining SVM outputs for some of the matrices; this is not uncommon [2; 11; 31; 57]. In our study, we employed a linear kernel for the training phase; other kernel types might yield less or no issues with SVM computing.
# Bingo: A Semi-Centralized Password Storage System
## 5 System Architecture
### Data Objects  
1. Domains: When an online service wishes to use Bingo for authentication of its users, the service is required to register itself with Bingo through a series of configuration details. We refer to a service registered with Bingo as a \"Domain\". Domains are assigned a unique ID which is the Domain's URL domain hashed with M. A unique hash ID prevents an attacker from being able to discern the site origin of a secret share on a stolen device. Figure 2a displays the attributes of a Domain object.
2. Users: Bingo associates users with the Domains for which they register and login. When a new user wishes to register with a given Domain, Bingo generates a \"User\" object containing a unique key, which is a tuple of the Domain's unique ID and a hashed identifier for the user (e.g., an email) within the Domain. Figure 2b displays the attributes of a user object.
3. Secret Shares: A Secret Share is a fractional piece of a larger piece of secret information, such as a hashed password. Bingo distributes these shares among peers in the network alongside a tuple identifier consisting of a Domain ID and a user ID. Peers are tasked with storing these shares in a manner that allows them to later retrieve them given the same tuple identifier. Figure 2c displays the attributes of the Secret Share object.  
Figure 2: Bingo’s data model utilizes a set of three basic objects.
## 2 Related Work
### Single-Objective Optimization Approaches  
Single-objective optimization focuses on optimizing a single criterion, such as resource allocation, the response time, or crime coverage. For example, [PERSON] et al. introduced Patrolling Security Games (PSGs), modeling strategic patrolling as extensive-form infinite-horizon games [18]. [PERSON] et al. proposed a patrol strategy combining the ant colony algorithm with Bayesian decision making, focusing on optimizing local decisions [19]. [PERSON] et al. developed an algorithm that maximizes the coverage of high-crime areas [20]. [PERSON] et al. applied game theory to PPOP, focusing on minimizing deployment costs based on risk values derived from the Nash equilibrium [21]. [PERSON] et al. developed the GRAND-VISION patrol scheduling system, which uses mixed-integer programming to optimize resource allocation across regions and time periods [22]. [PERSON] et al. designed patrol paths based on static historical crime data, focusing solely on maximizing the coverage of crime hotspots [23]. [PERSON] et al. proposed a smart patrol system that integrates machine learning with real-time crime predictions, focusing solely on maximizing crime coverage [24].  
Despite their promising efforts, the aforementioned single-objective optimization approaches--incorporating game theory, mathematical programming, machine learning, and heuristic algorithms--exhibit notable limitations. For instance, [PERSON] et al.'s [18] PSGs lack scalability for multiple patrollers and empirical validation. [PERSON] et al.'s [19] greedy strategy does not guarantee global optimality. [PERSON] et al.'s [20] algorithm fails to guarantee optimal solutions in complex scenarios. [PERSON] et al.'s [21] method does not address practical patrol path generation. [PERSON] et al.'s [22] approach neglects patrol route optimization. [PERSON] et al.'s [23] reliance on static historical crime data limits their system's ability to handle dynamic environments. [PERSON] et al.'s [24] system relies on simplistic linear regression for crime predictions, limiting its accuracy. More broadly, these methods often depend on static data or overly simplistic models, constraining their effectiveness in dynamic and complex environments. Their narrow focus on single objectives frequently results in suboptimal solutions when multiple objectives need to be balanced simultaneously. Given that real-world patrol tasks inherently involve trade-offs among competing objectives, the inherent constraints of single-objective approaches underscore the necessity for more comprehensive frameworks capable of addressing multiple objectives effectively.
# Optimizing the Scheduling of Teaching Activities in a Faculty
## 2 Related Studies
[PERSON] [20] examined the utilization of OptaPlanner [21], an open-source constraint solver and predecessor to Timefold Solver [22], to create an automated course scheduling system for the Computer Science Department at California State University. The study focuses on the complexity of manual scheduling by utilizing advanced algorithms to optimize course assignments, ensure balanced faculty workloads, and meet student requirements. The results indicate significant improvements in the effectiveness and efficacy of scheduling, highlighting the potential of OptaPlanner in handling difficult scheduling problems in academic settings with a smaller dataset (70 courses, 50 instructors, 20 rooms, and 60 timeslots). [PERSON] [23] tackles the complex problem of generating university timetables that comply with various constraints, such as room boundaries, teacher availability, and course prerequisites. By applying the algorithms of the Optaplaner API, [PERSON] successfully generated timetables that not only satisfied all hard restrictions but also optimized multiple soft constraints, resulting in increased satisfaction among both teachers and students. The issue instance examined by [PERSON] consisted of a small dataset covering three semesters. It involved scheduling 14 courses and 70 lectures within a school week that consisted of 40 periods (with 8 time slots each day) and had 8 accessible rooms. The resulting schedules were generated within approximately 7 s and were found to be comparable to the existing timetables currently being utilized for the semester. This exemplifies the efficacy and pragmatic utility of [PERSON]'s technique in real-life contexts. However, none of the aforementioned studies have explored or addressed the complexities involved in scenarios with a significantly larger scale, such as those involving thousands of courses, teachers, student groups, rooms, and time slots. The existing research primarily focuses on smaller datasets with relatively limited numbers of variables, which might not capture the full spectrum of challenges faced by large educational institutions. In contrast, the present study undertakes the challenge of optimizing timetables for extensive datasets, addressing a broader range of constraints, and ensuring the scalability of the solution. This effort marks a significant advancement in the application of algorithmic approaches to timetable generation, catering to the needs of institutions with substantial student and faculty populations.
## 3 Results
### Simulation Results
#### 3.2.4 Simulation 4  
We consider a sectioning span of 0.5 to 5 m in both cantons with an increase of 0.1 m in each calculation. In all cases, a slope elevation of 0 m is considered, i.e., we use a horizontal slope in the sectioning to obtain the optimum length of the beam and, thus, to ensure a suitable configuration. In this case, the normal spans of both cantons are considered to have a length of 5 m to check the differences with the previous simulation.  
Figure 12: Graphs of simulation 3. Variation of the length of the overlapping span in the range of 0.5 to 5 m and height 0 of the slope. Normal spans are 10 m long. (**a**) Average value of pantograph effort, (**b**) standard deviation of pantograph effort, (**c**) maximum value of pantograph effort, and (**d**) total distances of take-offs.  
As in the previous cases, the analysis of the data obtained by the calculations is carried out in the common area and, as a result, we obtain the graphs shown in Figure 14, which represent the average, maximum, and minimum values of the pantograph effort on the  
Figure 13: Graphs of simulation 3 per cantons. Variation of the length of the overlapping span in the range of 0.5 to 5 m and height 0 of the slope. Normal spans are 10 m long. (**a**) Standard deviation of pantograph effort (canton 1), (**b**) total distances of take-offs for canton 1, (**c**) standard deviation of pantograph effort (canton 2), and (**d**) total distances of take-offs for canton 2.  
contact wire in Newtons, the standard deviation, and the average distance of take-offs. In this case, and as expected, take-offs may occur in the common area of the catenary; thus, we show the graph indicating the total length of take-offs produced in this area.  
Figure 14: Graphs of simulation 4. Variation of the length of the overlapping span in the interval from 0.5 to 5 m and slope height 0. Normal spans length of 5 m. (**a**) Average value of pantograph effort, (**b**) standard deviation of pantograph effort, (**c**) maximum value of pantograph effort, and (**d**) total distances of take-offs.  
The abscissa axis of the graphs represents the length of the overlapping span in meters and the ordinate axis represents the effort (mean, maximum, minimum, and deviation) in Newtons obtained in the integration calculated with the given elevation.  
An analysis of the graphs (Figure 14) shows that the deviation of the stresses is optimal for the span length of approximately 1.4 m or less, with slightly worse behavior at values below 1 m. From approximately 2 m, the behavior begins to worsen significantly and take-offs appear over greater distances.  
The behavior is similar to that of simulation 3, but because the normal spans are shorter, the effect of lifting the beam in the section is dissipated before the critical point at 1.4 m, from which the lifting of the beam is compensated by starting to fall below the nominal height, which causes problems with the passage of the pantograph.
# Magnetic Levitation Control Based on Flux Density and Current Measurement
## 2 Modeling
### Displacement Estimation  
As previously stated, a position sensor is not used and a combination of flux density and coil current measurements is proposed instead to calculate the mover displacement. To this end, the relation  
Figure 5: Numerical behavior of the measured flux density \(B_{s}\) as a function of current \(i\) and displacement \(q\).  
\(B_{\rm s}(i,q)\) (see Figure 5) can be linearized at the desired operating point \(x_{0}=[q_{0}\ v_{0}\ i_{0}]^{T}\) from the Taylor series first order approximation:  
\[\hat{B}_{\rm s} \approx B_{0}+\left.\frac{\partial B_{\rm s}}{\partial i}\right|_{x_{0}} (\hat{i}-i_{0})+\left.\frac{\partial B_{\rm s}}{\partial q}\right|_{x_{0}}( \hat{q}-q_{0})\] \[\approx B_{0}+k_{bi}(\hat{i}-i_{0})+k_{bi}(\hat{q}-q_{0}). \tag{9}\]  
where \(\hat{B}_{\rm s}\) and \(\hat{i}\) are the measurements of the flux density and the coil current, respectively. The term \(\hat{q}\) stands for the unmeasured position of the mover. When transforming (9) into the corresponding deviation variables at the operating point (i.e., \(B=\hat{B}_{\rm s}-B_{0}\), \(i=\hat{i}-i_{0}\) and \(q=\hat{q}-q_{0}\)), the displacement can be directly calculated as  
\[q=\frac{1}{k_{bq}}B-\frac{k_{bi}}{k_{bq}}i. \tag{10}\]  
The absolute values of the coefficients \(k_{bq}\) and \(k_{bi}\) are hereinafter named sensing coefficients. It is worth mentioning that any other more accurate displacement estimation based on, for instance, lookup tables or polynomial approximations derived from Figure 5 can be applied. However, for the sake of clarity, the linear relation (10) of the displacement estimates with the current and flux density sensors is proposed here. It makes sense as long as the mover does not deviate significantly from the nominal position.
## 5 TSN Scheduling Problem Formulation
### TSN Scheduling Problem Formulation Assumptions
#### 5.3.5 Assumptions on the Delays of the Network  
A simple delay model considers four types of delay: (i) the processing delay of a frame in a node, (ii) the queuing delay of a frame in a priority queue, (iii) the transmission delay of a frame from the queue to the medium, and (iv) the propagation delay of a frame on the link. Although this model is vastly adopted in the literature, there is no consensus on the actual delay values. Refs. [40, 50, 51, 65, 88, 98, 104, 112] considered the processing, transmission, and/or propagation delays constant, [37, 48, 73, 82, 98, 112] assumed them to be the same in all the switches, [47] considered the processing delay to be relative to the frame size, [49, 82, 104] assumed different values for the transmission delay ranging from 1 Mbps to 1 Gbps, [46, 70, 91, 120] ignored the propagation delay or considered it as part of the network sync error, and [49, 59, 60, 76, 80] considered a zero queuing delay. As for the delay in the end-hosts, [121] ignored the delay in the end-host, and [104] fixed it to 0.5 ms. This shows the need for a more detailed study that provides a better understanding of different delay components in TSN networks. For reference, according to IEEE802.1 Qcc [125], the bridge delay is defined as a tuple of (ingress port, egress port, traffic class). For every tuple, there are two types of delay: dependent on the traffic, and independent. Every type is represented by an interval that could be read from the bridge. However, the exact values depend on the configuration. Ref. [125] recommends reading these variables after configuring the bridge to obtain the most accurate values. Therefore, a better option is to consider the bridge delay as an interval and make realistic assumptions on interval boundaries.
## 4 Light Attenuation
### Atmospheric Attenuation  
According to several authors (e.g., [12; 22; 23]), the radiation attenuation due to atmospheric effects occurring between the heliostats and the target can be tentatively described using the following equations:  
\[D\leq 1000\ \text{m:}\qquad\eta_{atm}=0.99321-0.0001176D+1.97\times 10^{-8}D^{2} \tag{6}\]  
\[D\leq 1000\ \text{m:}\qquad\eta_{atm}=\exp(-0.0001106D) \tag{7}\]  
where \(D\) is the distance between the mirror center and the collecting target (\(D\) is actually implemented in the source code as the distance between the reflection point in the heliostat and the collecting target).
# The Effects of High-Performance Cloud System for Network Function Virtualization
## 2 Background and Related Works
### Background
The configuration of ovs-vswitchd is stored in ovsdb. Therefore, ovs-vswitchd connects to the ovsdb-server to retrieve the configuration. When a cloud system adopts the OVS network, the OVS kernel module looks up the flow table when a packet is received. If the flow matches a network rule, the packet is processed according to the actions associated with the flow. When the packet does not match any rule in the flow table, the OVS kernel module queues the packet to the user space. This procedure introduces context switches and results in slowing down the packet processing. Figure 1: NUMA architecture. * OVS-DPDK  
The data plane development kit (DPDK) aims to accelerate the packet processing. The network architecture of OVS-DPDK is shown in Figure 3. With the help of vhost-user, VMs share the memory space with user space processes in the host. Sharing memory between two processes is implemented by a file descriptor. The file descriptor can be accessed by the vhost process (i.e., virtual switch). The NIC driver, such as UIO or VFIO [32], is responsible for OVS-DPDK to handle the user space interrupt. Therefore, OVS-DPDK can process the packets in user space and pass them through the kernel. The kernel bypass eliminates the overhead of system calls and context switches. OVS-DPDK also provides a poll mode driver (PMD) [33] to poll the network queue. The dedicated CPU cores assigned to PMD are helpful to alleviate the interrupt overhead. Figure 3: Network architecture of OVS-DPDK. Figure 2: OVS architecture. SR-IOV  
With the help of the input-output memory management unit (IOMMU), the VM can access Peripheral Component Interconnect (PCI) devices directly, called PCI passthrough. Direct memory access (DMA) remapping [34] allows the VM access to physical memory via the virtual address. When a VM accesses the network device, the interrupt remapping is triggered to send an interrupt to the VM instead of the host. With DMA and interrupt remapping, the network interface is assigned directly to a VM so that the VMM will not affect the performance. In this regard, accessing the NIC directly to a VM can significantly improve the network performance. However, allocating a dedicated physical NIC for each VM is too expensive. The SR-IOV is proposed to tackle this problem. As shown in Figure 4, a NIC with SR-IOV support can provide physical function (PF) and virtual function (VF) [35]. The PF has all the functions of the NIC and is responsible for managing the VFs. The VF is a simplified PCIe function and allows the VM to access the physical NIC directly. Accordingly, the network performance of a VM can be improved in the cloud system. However, SR-IOV is a hardware-assisted technique, and it is not easy to live-migrate a VM with an SR-IOV VF. Procuring the specific NIC is also necessary while adopting the SR-IOV, so as to increase the cost of capital expenditure and management efforts for the cloud.
# Model Mediation to Overcome Light Limitations--Toward a Secure Tactile Internet System
## 4 Model Mediation
### Model Mediated Systems  
#### 4.2.1 Early Birds  
In 1975, [PERSON] developed the first predictor algorithm, which is referred to as the Smith predictor [66]. The Smith predictor was developed for the linear time-invariant (LTI) feedback control system, to enhance the system stability. No longer, the first predictor for a bilateral teleoperation system was developed [67]. It was a high-fidelity graphics model referred to as phantom robot that is used for controlling and monitoring a real robot with no delay. The graphics model aimed to make the phantom robot predict the real robot's motion and hence the real remote robot follows the phantom robot's motion on the computer with a counted time delay. The algorithm has been simulated for a PUMA arm and the experiments have indicated that the algorithm has boosted the task manipulation through the predictive display; however, the main disadvantage of the system was that the delay was large [68].  
Many other models and different algorithms have been developed and introduced after the past works to enhance the predictor performance and overcome the large delay problems. At first, most of the attempts were just modifications to the previous two systems where predefined models and simplified geometry environments were deployed, and then a leap occurred [69,70]. The main point to be mentioned is that early works have considered a one degree of freedom (DoF) only; however there are methods that recently developed for high DoF systems. In the following, we consider the main works and most common model mediated algorithms developed for haptic-based bilateral teleoperated systems. Furthermore, recent developed model mediated systems are considered and the ability of these systems to serve for tactile Internet system is discussed. The last part of this section considers the security issues of the model mediated systems and how model mediated systems will increase the security of the tactile Internet system and prevent attacks.