esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### GC/GC-MS Method  
Headspace SPME and GCxGC-ToF-MS analyses were performed on the LECO Pegasus 4D apparatus, equipped with the Agilent 6890N GC, a high-speed ToF mass spectrometer (LECO, St. Joseph, MI, USA), and a MultiPurpose Sampler (MPS 2) autosampler (Gerstel GmbH, Mulheim an der Ruhr, Germany). The column set was 5% phenyl and 95% polysilphenylene-siloxane BPX5 (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) capillary column (SGE Analytical Science, Melbourne, Australia) in the first dimension, coupled to medium-polar 50% phenyl polysilphenylene-siloxane BPX50 (2 m \(\times\) 0.1 mm \(\times\) 0.1 \(\upmu\)m) (SGE Analytical Science, Melbourne, Australia) in the second dimension. Helium was the carrier gas at a flow rate of 1.5 mL/min for the entire run. The injector performed in the splitless mode. Modulation parameters consisted of a 6 s modulation period (1.6 s hot pulse time and 1.2 s cool time between stages) and a modulator temperature offset of 15 \({}^{\circ}\)C relative to the secondary oven temperature. The GC oven temperature was programmed at 35 \({}^{\circ}\)C for 5 min, then increased at 3 \({}^{\circ}\)C/min up to 245 \({}^{\circ}\)C, and held for 3 min (total time 78 min), while the secondary oven programming offset was 5 \({}^{\circ}\)C above the primary oven. The mass spectrometer operated in the Electron Impact mode at -70 eV and in a scan range (\(m/z\)) from 33 to 550 amu, with an ion source at 220 \({}^{\circ}\)C and a transfer line at 250 \({}^{\circ}\)C.
## 2 Testing Ethernet Based QoS
### QoS of Ethernet over RR Transport  
In contrast to traditional TDM-based services, where QoS is mostly expressed in terms of (un)availability and bit and block errors, Ethernet-based services' quality is determined by throughput, latency, frame loss rate, and maximal lossless frame rate (back-to-back frames) [10, 11, 12, 13, 14].  
Accordingly, in Figure 1, the Carrier Ethernet system consists of several Ethernet sections, each covering the physical (L1) and data link (L2) OSI layers. The IP traffic is controlled by the network layer (L3) responsible for end-to-end transmission and routing of packets, which is conditioned by error-free transmissions of Ethernet frames carrying encapsulated IP packets over each Ethernet section.  
QoS testing is mostly done end-to-end, and followed up with single-section tests only in the case of pure end-to-end performance that needs to be localized by detecting the section(s) with, for example, reduced throughput or increased delay.
# A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit
## 4 DTU Fault Analysis Method  
DTU fault databases contain significant amounts of data; moreover, they are updated in real time. Therefore, the Eclat algorithm was utilized due to its best performance among the three algorithms.  
The proposed DTU fault analysis method is shown in Figure 5.  
Firstly, DTU fault data was sampled and recorded. By scanning the database, item sets _FA_ were constructed. Then, Based on the Eclat algorithm, two types of strong rules were obtained:  
\[R_{a}:F_{f}(B_{b})\Rightarrow T_{t} \tag{9}\]  
\[R_{b}:T_{t}\Rightarrow M_{m} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{c} Medium \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Strengths and weaknesses of three algorithms.  
Figure 5: DTU fault analysis method.  
\(R_{a}\) represents the relationship between factor \(F\) and fault type \(T\). It indicates that certain factor \(F_{f}\) may result in certain type of fault \(T_{t}\), which reveals the rule of the fault. Brand \(B_{b}\) is one kind of factor \(F\) (family defect), but it is separated as an individual item in order to show if products of certain manufacturers \(B_{b}\) have a certain type of fault \(T_{t}\). \(R_{b}\) includes fault type \(T\) and fault module \(M\). It indicates when a certain type of fault \(T_{t}\) happens; there is a high probability that fault exists in corresponding module \(M_{m}\). When fault \(T_{t}\) happens, diagnosing module \(M_{m}\), before other modules, can shorten fault elimination time. Based on strong rules \(R_{a}\) and \(R_{b}\), fault rule bases and diagnosis rule bases can be constructed. These two rule bases can be utilized to arrange planned maintenance and facilitate fault repair.
## 4 Discussion
### Limitation and Future Work  
In general, this study provides a solid framework for evaluating vehicle interfaces with SAT information to test trust behaviors in regards to ADS. However, as mentioned above, it is possible that this study failed to test one of the key hypotheses because some of the data were not significant due to the design of the variable of scenario urgency. From the experiment results, the advantage of the SAT level \(1+3\) interface was numerically more evident in high-urgency situations. Therefore, more reasonable experimental variables should be designed to test this hypothesis. Due to the inability of some participants to understand the differences between the scenarios, in future studies, scenario urgency could be used as a between-subject variable to delineate additional levels. Another limitation involving the experiment design was the inadequate amount of time during which the SAT information interface was presented to the participants. This may also account for some of the hypothesized effects not being statistically significant. As one participant stated, he would pay more attention to the road when the event occurred, and by the time he tried to evaluate the interface again, it was too late. Therefore, future research should design more hazardous events during one drive, which would allow participants to evaluate the SAT interface more accurately.  
Future research should also attempt to present SAT information in more ways than in just a bird's eye view, with the inclusion of some text on an additional screen. As previous studies have mentioned, \"an invisible interface is also not usable\" and \"placing the screen closer to the instrument cluster\" was suggested [47]. Also, several participants suggested various alternatives for the presentation of transparency, for example, striking light effects, voice prompts, and a heads-up display (HUD). The effectiveness of different methods of displaying SAT information in increasing user trust and reducing cognitive load is also worth investigating. After all, the ultimate goal of researching SAT is to enhance user trust in ADS and to promote human-vehicle collaboration. Researchers and designers should focus not only on what SAT information should be communicated in different scenarios, but also on how the information is communicated.  
Finally, driving simulations in the lab do not provide participants with experience on real roads, especially in high-risk scenarios. We also recognized that a low number of participants in this research had previous autonomous driving experience, which may have resulted in users retaining supervisory habits from manual driving. Future studies could recruit more autonomous vehicle owners or make use of longitudinal methods to evaluate the evolution of user experience and trust in order to obtain more realistic and valid data regarding automated driving performance and user evaluation. And, it is widely accepted that users form and modify their judgments about automation over time [48], so research concerning trust in automation should inlcude sufficient consideration regarding longitudinal study.
## 5 Challenges and Limitations
### Practical Challenges  
* Integration Complexity: Integrating the proposed framework with existing systems can be challenging. It requires significant customization to accommodate the specific needs of different IoT applications, which may vary widely in their complexities and operational contexts.
* Scalability Concerns: While the framework aims to handle a broad range of requirements efficiently, scaling it for extremely large datasets or highly complex projects may require additional optimization to prevent performance bottlenecks, especially in the Analytical Hierarchical Process (AHP), when dealing with many pairwise comparisons.
* Stakeholder Alignment: Gaining consensus among stakeholders can be a significant hurdle, particularly in environments with diverse interests and priorities. The framework's reliance on stakeholder input for prioritizing requirements can lead to delays if not managed efficiently.
## 2 Materials and Methods
### Statistical Analysis
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Characteristic** & **N** & **n (\%)** \\ \hline Cobb angle & 262 & 42.00 (25.00, 66.75) a \\ Scoliosis type: & 262 & \\ Single & & 149 (56.87\%) \\ Double & & 113 (43.13\%) \\ Scoliosis severity: & 262 & \\ Mild & & 43 (16.41\%) \\ Moderate & & 85 (32.44\%) \\ Severe & & 134 (51.15\%) \\ \hline \hline \end{tabular} Note: a: Mdn (Q1, Q3); N—sample size; n—group size; Mdn—median; Q1—first quartile (25%); Q3—third quartile (75%).  
\end{table} 
Table 2: Distribution of Cobb angle measurements, types, and stages of scoliosis in a study sample of radiographic images. severity of curvature across the studied sample. In terms of scoliosis type, the majority of cases were classified as single (56.87%), with double scoliosis observed in 43.13% of the samples. Regarding the staging of scoliosis, the data showed that severe scoliosis was the most common stage, accounting for 51.15% of cases, followed by moderate and mild stages at 32.44% and 16.41%, respectively.
# Calibration Method of PFC3D Micro-Parameters under Impact Load
## 1 Introduction
Therefore, based on the SHPB rock test and the FLAC3D/PFC3D coupling method, a new calibration method for the SJM model of particle discrete element PFC3D software is proposed. Firstly, according to the in situ rock samples, the parallel bond model (PBM) and smooth joint model (SJM) in PFC3D were used to construct numerical specimens with bedded surfaces. The linear elastic model of FLAC3D was used to construct the incident bar and transmission bar of the SHPB experimental device. The bridging domain method was used to conduct the impact load between the PFC particles and the FLAC element. Then, several micro-parameters of SJM affecting the macro-mechanical parameters were determined using single-factor and double-factor orthogonal SHPB numerical tests. Furthermore, through multiple linear regression analysis, the quantitative relationship between the micro-parameters of SJM and the macro-parameters of the rock sample was obtained. A set of the calibration processes for the micro-parameters of SJM under impact load was determined. Finally, the reliability of the proposed calibration method was verified by comparing it with the macro-parameters obtained from laboratory tests. A more accurate and reliable calibration method is proposed to determine the micro-parameters of the SJM model under impact load.
# Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method
## 5 Simulation
### Analysis of the Measured Signal
Similarly, the method proposed in this paper was used to address the ambient noise signal shown in Figure 7; the main mode signals extracted from the ambient signal by the first decomposition are shown in Figure 5.0 The signal energy method was used to screen the modes, and all the oscillation modes were retained finally. The decomposed signals were sorted into four dominant modes with decreasing amplitudes. The modal parameters of each dominant mode were identified using the three methods; the results are shown in Figure 5.1 As shown in Table 3, the frequencies of the dominant oscillation modes are 1.47, 1.78, 3.59, and 0.32 Hz, with decreasing amplitude. Compared with the results in Table 2, the frequencies of the potential dominant modes extracted from the ambient noise signal are close to those of the dominant modes in the obvious oscillation signals, which are both near 1.48 Hz. As shown in Table 3, the dominant frequency of the EMD-Prony method is 1.55 Hz, and the dominant frequency of the MF-Prony method is 3.74 Hz. There are some deviations between the identification results of the EMD-Prony method and the IA-VMD-Prony method. In comparison, the deviation between the identification results of the MF-Prony method and the IA-VMD-Prony method is very large. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method and IMF**} & \multicolumn{3}{c}{**IA-VMD-Prony**} & \multicolumn{3}{c}{**EMD-Prony**} & \multicolumn{3}{c}{**MF-Prony**} \\ \cline{2-10}  & **1** & **2** & **3** & **1** & **2** & **3** & **1** & **2** & **3** \\ \hline Frequency/Hz & **1.48** & 2.02 & 0.49 & **1.48** & 1.51 & 1.08 & **1.44** & 1.61 & 2.32 \\ Amplitude/MW & **32.7** & 17.9 & 3.2 & **30.6** & 16.3 & 8.7 & **28.2** & 13.7 & 8.5 \\ Damping Ratio & \(\mathbf{-0.94}\) & \(\mathbf{-1.40}\) & 0.18 & \(\mathbf{-0.52}\) & \(\mathbf{-1.01}\) & \(\mathbf{-1.03}\) & \(\mathbf{-1.18}\) & \(\mathbf{-1.59}\) & \(\mathbf{-2.31}\) \\ \hline \hline \end{tabular} The dominant oscillation mode is indicated in the bold part.  
\end{table} 
Table 2: Modal identification of the obvious oscillation signal. Figure 9: IMFs extracted from the ambient signal. On the one hand, the experimental results show that the ambient noise signal contains some of the oscillation characteristics of potential LFOs. On the other hand, the results prove that the method proposed in this paper is suitable not only for analyzing obvious oscillation signals but also for analyzing ambient signals in normal operation; it has high accuracy. Relatively speaking, all the identification results of the other two classical identification methods have large errors. The experimental results show that the method proposed in this paper has a superior result when analyzing ambient signals.
## 3 Background
### Process  
Operating systems like Windows and Linux utilize an abstraction known as _process_ to execute programs [3]. This abstraction creates the illusion that each process has its own dedicated CPU, even though it shares a single or multiple CPUs with other processes. Typically, a process can exist in one of three different states:  
* **Running:** this state indicates that the process is actively running on a CPU.  
* **Ready:** here, the process is prepared to run but has not been selected by the scheduler yet.
* **Blocked/waiting:** in this state, a process has usually undergone some form of 1/O operation and is currently blocked or waiting.  
Processes can comprise one or multiple threads. For simplicity, this paper only considers processes with a single thread.  
Typically, the operating system schedules processes and threads with unknown running time; hence, some assumptions are made for simulations. We will assume that the runtime for each incoming process is known, enabling us to simulate the same load on different scheduling algorithms. The word _job_ is used in the book but will be used interchangeably with process and thread for the rest of this paper. Each job will retain essential information for simulation purposes, enabling the generation of metrics upon completion of the simulation. The produced jobs will encompass the following:  
* **PID:** a unique identifier for the job.
* **Arrival time**: the moment the job was generated.
* **Started time**: the initial scheduling time of the job.
* **Finish time**: the completion time of the job.
* **Remaining time**: the duration remaining for the job.
* **Total time**: the overall time required for job execution.
* **Scheduled**: the number of times the job has been scheduled (due to context switching).
* **Migrated**: the count of times the job has been migrated.  
For some specific scheduling algorithms, extra fields are necessary.
## 2 Vision based Target Tracking for Autonomous Vehicles Navigation
### Visual Target Tracking for Autonomous Underwater Vehicles  
In the underwater environment visual target tracking can be broadly used for Autonomous Underwater Vehicles (AUV) navigation. For example in order to study the behavioral patterns of unknown underwater life forms, it is necessary to observe them carefully for a longer period. It is therefore, important to stay close to the object being observed by moving with it such as fish following. Also, for a reliable functioning of underwater man-made systems, it is necessary to maintain them by routine observations. Therefore, the underwater exploration and maintenance require proper observations and, as a result the close navigation along or with these objects is necessary [21]. The visual target tracking system can be used for these autonomous underwater vehicles navigation applications.  
Underwater pipe inspection is one example of a class of problems that bear many similarities with visual target tracking for autonomous land vehicles. Extracting the contours of a pipe is equivalent to extract and track the landmarks. In [7] [PERSON] _et al._ take a visual servoing approach and devise a controller that take the inputs as the lines extracted from the image of a pope and uses this information to generate steering commands for the ROV Vortex vehicle. In [20] [PERSON] and [PERSON] propose a sensor fusion technique for the Autonomous Underwater Vehicle to track underwater cables. They propose a sensor fusion scheme using the dead reckoning position uncertainty with a \(2D\) position model of the cable to predict the region of interest in the image. They solve two practical problems encountered in the optical vision based systems in underwater environments: first, the navigation of AUV when the cable is invisible in the image; second, the selection of the correct cable (interest feature) when there are many similar features appearing in the image.  
[PERSON] _et al._[22] also propose the vision based tracking system for underwater docking using correlation based underwater images template matching.  
One of the most recent underwater visual target tracking methods is proposed in [21] for AUV navigation (Figure 8). In [PERSON]'s work the objects of interest are extracted from the images by using the dynamic properties (optical flow techniques) and their optical features (color, texture, shape and so on). The consecutive dynamic behavior of the objects of interest is then estimated based on the current dynamics. By using this predicted dynamics, the amount of data for the region of interest identification can be reduced. This also increases the speed of processing for the hardware available in the small and limited hulls of AUV. After the image dynamics and feature position information are fused with the AUV's other onboard sensor data, the navigation commands are derived for AUV to track the object.
## 2 Materials and Methods
### Experimental Dataset  
In 2013, Mnih put forward the Massachusetts dataset [22] ([[https://www.cs.toronto.edu/~vmnih/data](https://www.cs.toronto.edu/~vmnih/data)]([https://www.cs.toronto.edu/~vmnih/data](https://www.cs.toronto.edu/~vmnih/data)), accessed on 15 October 2023) from the University of Toronto. This dataset consists of 151 remotely sensed images. Each image has a dimension of \(1500\times 1500\) pixels in terms of resolution, and the spatial resolution of these images is 1 m. The ground truth was obtained through the combination of high-resolution aerial imagery and professional geospatial analysis software. This remote sensing image dataset is divided into 137 training sets, 4 validation sets, and 10 test sets. In this study, the image size is cropped to 512 \(\times\) 512 pixels, and considering the small number of datasets, the cut images are rotated, flipped horizontally and vertically and thus expanded to four times the original dataset. An example image of the Massachusetts dataset is shown in Figure 6.  
The WHU buildings dataset ([[https://gpcv.whu.edu.cn/data/building_dataset.html](https://gpcv.whu.edu.cn/data/building_dataset.html)]([https://gpcv.whu.edu.cn/data/building_dataset.html](https://gpcv.whu.edu.cn/data/building_dataset.html)), 15 October 2023) from Wuhan University [33] includes an aerial dataset along with a satellite dataset, and this study uses an aerial image dataset, which covers an area of about 450 km\({}^{2}\) in New Zealand with a spatial resolution of 0.3 m. The ground truth data were created based on advanced photogrammetric techniques and multi-spectral remote sensing data processing. The image size in the dataset is 512 \(\times\) 512 pixels, including 4736 training set, 1036 validation set, and 2416 test set images. An example image of the WHU aerial imagery dataset is shown in Figure 7.  
Figure 5: F EPA-Net network model.
## 3 Applications of the CDS for the Fiber Optic Link
### Perceptor
#### 3.1.1 Three-Layered Bayesian Generative Model
In Table 2, first, second, and third tensor dimensions correspond to discretized cells for In-phase (I) axis, quadrature (Q) axis (after 2D discretization in Layer II, see Figures 9 and 10), and number of constellation points of QAM, respectively. For example, if we consider PF = 5 in the first row of Table 2, then \(33\times 29\times 16\) corresponds to 33 columns (I-axis), 29 rows (Q-axis), and QAM-16, respectively. In addition, PF = 5 means the discretization step is 0.5 (see Section 3.1.2). Figure 11: The simulation of BER improvement for various precision factor (data rate = 52 Gb/s and transmission distance, \(L\) = 1600 km). From Table 2, we see that the required memory for PF = 0.5 is 16 times more than that for PF = 2. As can be seen from Figure 11, the BER improves as PF decreases, but a lower PF implies a larger memory (see Table 2). Hence, there is a trade-off between the BER improvement and memory requirements. The extracted model in this layer can be called the statistical model for a fixed fiber network with specific signal and system parameters. This model is saved in the model library for future use. This model is not data dependent, although it has labels for various data rates, fiber lengths, launch power, or other system parameters such as nonlinear coefficient or span length. Unlike the DBP method, the proposed CDS does not need to perform extensive data dependent signal processing if the fiber optic channel does not change appreciably. The extracted statistical model will be used for prediction if there is a disturbance in a fiber optic network, such as a power fluctuation or a change in transmission length due to re-routing. For model extraction, we do not require training frames. Instead, we use the FEC output as an approximation to the transmitted symbol, and the executive controls the data rate to ensure that the input BER of FEC is always less than the FEC threshold and the data can be transmitted continuously without interruption. For example, when users watch live video on YouTube, the video quality automatically varies (which corresponds to variable data rate) for good quality-of-service (QoS) and minimal interruptions.