esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis  
Data analysis was performed using the IBM-SPSS version 25 software (IBM, Armonk, NY, USA). The Kolmogorov-Smirnoff test was used to test for normal distribution. Variables were expressed as the mean and standard deviation or as medians and interquartile range. The Mann-Whitney test was used for comparing two level factors. Proportions of participants with associated chronic diseases were compared using the chi-square test. Proportions of weekend/weekdays days in each 24-h recall set were compared using chi-square, and statistical significance was adjusted using the Bonferroni method. For each participant, three 24-h recalls performed on weekdays were also averaged and further used in analysis. Average nutrient intakes from four ordered 24-h recalls and averaged nutrient intakes from three 24-h recalls (resulting from the exclusion of either the first, second, third, or fourth ordered 24-h recall) were also used in the analyses. Three different types of models for repeated design were used separately each nutrient category: energy, macro, and micronutrient intakes in the following circumstances: (1) the four ordered 24-h recalls; (2) the weekend and mean weekday intakes; (3) the mean of four 24-h recalls compared to mean of three 24-h (resulting from the exclusion of the first ordered 24-h recall); (4) the mean of four 24-h recalls compared to the mean of three 24-h (resulting from the exclusion of the second ordered 24-h recall); and (5) the mean of four 24-h recalls compared to the mean of three 24-h (resulting from the exclusion of the third ordered 24-h recall); and (5) the mean of four 24-h recalls compared to the mean of three 24-h (resulting from the exclusion of the fourth ordered 24-h recall). The general linear model and Wilcoxon-signed rank test were used for comparisons.  
For the general linear model, results of pairwise comparisons between the four ordered 24-h recalls were adjusted using the Sidak method. All results were also adjusted for false discovery rate (FDR) using an online tool ([[https://tools.carbocation.com/FDR](https://tools.carbocation.com/FDR)]([https://tools.carbocation.com/FDR](https://tools.carbocation.com/FDR)), accessed 15 March 2020) [19], which adjusted the p-values to the number of comparisons per research hypothesis. After the adjustments, the new \(p\)-values < 0.05 were considered statistically significant. For size effect interpretation, r was calculated and interpreted according to [PERSON]'s criteria: for r = 0.10-0.30, there was a small effect size; for r = 0.30-0.50, there was a medium effect size; and for r > 0.50, there was a large effect size [20].
## 3 Effects of Storage and Maintenance Modes on Battery Performance
### Effects of Charge-Discharge Modes on Battery Performance
#### 3.1.3 Effects of Charge-Discharge Modes on Battery Efficiency  
Figure 9 shows the effects of different charge-discharge modes on the efficiency of Ni-MH battery modules. It can be seen that the _FDC_ maintenance mode was the best approach to maintain the efficiency of Ni-MH battery modules, as it produced the lowest (absolute value) efficiency change rate.
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 6 Applications: XAI's Impact across Diverse Domains
### Autonomous Systems  
XAI has risen as an invaluable asset in enhancing the safety and dependability of autonomous systems such as self-driving cars and drones. By unveiling the intricate decision-making processes of these systems, XAI plays a pivotal role in the identification and rectification of errors or biases, thereby elevating their trustworthiness. Noteworthy instances abound in this realm.  
For instance, [PERSON]'s framework [85] stands as an exemplar of XAI's impact. By harnessing attention mechanisms and layer-wise relevance propagation, this framework delves into the depths of deep learning models, illuminating their inner workings and rendering insights accessible.  
Equally compelling is the framework proposed by [PERSON] et al. [86]. This innovation ingeniously amalgamates adversarial training, model introspection, and user feedback to cultivate an adaptable and explicable model for self-driving cars. The outcome is a harmonious blend of adaptability and transparency that resonates with real-world applications.  
Moreover, XAI finds resonance in the realm of drones. [PERSON]'s evolutionary approach [87] encapsulates its utility by developing an interpretable decision support system for unmanned aerial vehicles in precision agriculture. The experimental outcomes stand as a testament to the promises that XAI holds in this evolving landscape.  
In sum, the integration of XAI into the realm of autonomous systems not only upholds the principles of accountability and reliability but also shapes the trajectory toward a safer and more dependable future for these technologies.
# Usability and Acceptance Analysis of Wearable BCI Devices
## 1 Introduction
Other options include electro-corticography (ECoG), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS) [18]. The sensitivity of each type of measurement with neuroimaging technologies may be variable depending on the level of workload experienced by the user [2] and the configuration of the wearable device used. Three criteria can be used to characterize the technology under consideration: (1) ease of use in terms of human factor/ergonomics; (2) temporal resolution in terms of determining the timing of neural processing; and (3) spatial resolution in terms of localizing neural activity throughout the brain [1]. In addition to being widely employed in psychological, neurological, and cognitive research, as well as in the development of brain-computer interfaces, functional neuroimaging is used to identify metabolic disorders and injuries. Recent years have seen notable advancements in the field of research known as neuro-engineering, or brain-computer interface (BCI). Numerous studies have focused on the possibility of using EEG for applications in everyday contexts [7; 18] and BCI for controlling exoskeletons, robotic arms, cursors, and computer spelling [15; 19]. Several prototypes of EEG-based BCI have already been validated in real-world contexts; however, there are still problems that limit the development and use of such technologies, such as their ease of use and user acceptability. Although brain-computer interface (BCI) technology has advanced significantly, there is still a lot to learn about how end users see and use these devices in practical settings. To maximize user experience, enhance device accessibility, and guarantee the smooth integration of BCI technology into several contexts, these gaps must be filled. This study proposes to investigate, through the performance and usability analysis of case studies, wearable devices that integrate neuroimaging technologies currently on the market, evaluating their strengths and possible transversality in various intended uses.
# Retrieval of Similar Evolution Patterns from Satellite Image Time Series +
###### Abstract  
Technological evolution in the remote sensing domain has allowed the acquisition of large archives of satellite image time series (SITS) for Earth Observation. In this context, the need to interpret Earth Observation image time series is continuously increasing and the extraction of information from these archives has become difficult without adequate tools. In this paper, we propose a fast and effective two-step technique for the retrieval of spatio-temporal patterns that are similar to a given query. The method is based on a query-by-example procedure whose inputs are evolution patterns provided by the end-user and outputs are other similar spatio-temporal patterns. The comparison between the temporal sequences and the queries is performed using the Dynamic Time Warping alignment method, whereas the separation between similar and non-similar patterns is determined via Expectation-Maximization. The experiments, which are assessed on both short and long SITS, prove the effectiveness of the proposed SITS retrieval method for different application scenarios. For the short SITS, we considered two application scenarios, namely the construction of two accumulation lakes and flooding caused by heavy rain. For the long SITS, we used a database formed of 88 Landsat images, and we showed that the proposed method is able to retrieve similar patterns of land cover and land use.  
pattern recognition; dynamic time warping; maximum likelihood criterion; similarity measure; multitemporal data; multispectral information +
Footnote †: journal: Accepted: 22 November 2018; Published: 1 December 2018
# Broadband Generalized Sidelobe Canceler Beamforming Applied to Ultrasonic Imaging
## 4 Results  
In this section, several examples are provided to compare performance of the proposed beamforming with DAS, SA, and GSC in terms of lateral resolution, sidelobe level and contrast. A simulation tool named Field II is used to calculate the simulated ultrasonic echo data, which is highly accepted in the medical ultrasonic imaging field [22; 23]. Simulated echo data can be used to verify the beamforming performance of the algorithm. Field II is used to obtain the simulated echo signal of scattering points and the circular cyst of a linear phased array ultrasonic system. Key parameters of the simulated linear phased array ultrasonic system are listed here: width of array \(\mathrm{d}=0.2413\) mm; center frequency of the ultrasonic \(\mathrm{f_{0}}=3.33\) MHz; array number \(\mathrm{M}=64\); ultrasonic velocity \(\mathrm{c}=1500\) m/s; sampling rate of system \(\mathrm{f_{s}}=71.04\) MHz.
## 3 Results
### Experiment with High-Intensity Picosecond Laser Pulses  
In the experiments with high-intensity picosecond laser pulses, we studied the threshold of the optical breakdown of water \(I_{thr}\) induced by a 20 ps laser pulse (\(\lambda\) = 1064 nm). In this experiment, the onset of optical breakdown was fixed by the appearance of a bright light flash (the basic endpoint of optical breakdown); that is, the stage of optical coalescence should be completed by the time the flash appears. Typical photographs of breakdown flashes at the intensities \(I\approx\) 0.9 TW/cm\({}^{2}\) and 1.2 TW/cm\({}^{2}\) are shown in Figure 9a,b. The plasma flash was recorded at an angle of 90\({}^{\circ}\) to an optical axis (this photodetector is not shown in Figure 4).  
As shown in Appendix A, at the laser pulse intensity of about \(10^{7}\) W/cm\({}^{2}\), stimulated optical coalescence occurs at times of the order of \(10^{-10}\) s. However, with increasing intensity, all dynamic processes leading to the stimulated optical coalescence of the bubston cluster are significantly accelerated. Therefore, at intensities of the order of \(10^{12}\) W/cm\({}^{2}\), the stimulated optical coalescence should be completed within times less than 20 picoseconds, and in this case a plasma flash of optical breakdown occurs. In experiments with high-intensity picosecond pulses, the threshold intensities \(I_{thr}\) for the occurrence of optical breakdown flash in degassed water and water, saturated with dissolved gas, were studied. Degassing was carried out by the prolonged boiling of the liquid under vacuum; the degassing technique is described in detail in a recent paper [46]. A highly sensitive receiver (photoelectron multiplier FEU-79, LLC Zapadpribor, Moscow, Russia, not shown in Figure 4) made it possible to fix the plasma emission of optical breakdown \(Lum_{BR}\) at \(I\approx I_{thr}\). In Figure 10, we exhibit the dependence of \(Lum_{BR}\) vs. the laser pump intensity in the focal area of a short-focus lens (\(F\) = 20 mm) for degassed Milli-Q water. The threshold value of the breakdown intensity for such water was \(I_{thr}\) = 3.0 TW/cm\({}^{2}\), and this value remained unchanged in time providing that the liquid sample was kept in a hermetically sealed cell (i.e., without access of atmospheric air).  
Figure 9: Flash patterns at the focus of a long-focus lens (\(F\) = 100 mm) in water saturated with bubston clusters, induced by laser pulse (\(\tau\) = 20 ps, \(\lambda\) = 1064 nm). Panel (**a**) is related to intensity at the breakdown threshold, \(I\approx\) 0.9 TW/cm\({}^{2}\); panel (**b**) is related to intensity \(I\approx\) 1.2 TW/cm\({}^{2}\).  
It turned out that the contact of degassed water with atmospheric air leads to a decrease in the optical breakdown threshold from \(I_{thr}\) = 3.0 TW/cm\({}^{2}\) to \(I_{thr}\) = 0.85 TW/cm\({}^{2}\) within the time T = 12 h of settling the degassed sample in open atmosphere. Note that this value significantly exceeds the threshold intensity for stimulated optical coalescence, which is about \(10^{7}\) W/cm\({}^{2}\).
# Pinion Failure Analysis of a Helical Reduction Gearbox in a Kraft Process
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Steps** & **Case-I Gearbox Operated at 3 MW** & **Case-II Gearbox Operated at 3.75 MW** \\ \hline
1 & \(N_{g}=111,N_{p}=20,m_{G}=N_{g}/N_{p}\) & \(N_{g}=111,N_{p}=20,m_{G}=N_{g}/N_{p}\) \\  & \(=111/20=5.55\) & \(=111/20=5.55\) \\ \hline
2 & Torque on the pinion shaft: & Torque on the pinion shaft \\  & \(T_{p}=P/\omega_{p}=3.0\times 10^{6}/(2\times\pi\times 8350/60)\) & \(T_{p}=P/\omega_{p}=3.75\times 10^{6}/(8350\times 6.28/60)\) \\  & \(=3430N\cdot\) m & \(=4290.78N\cdot\) m \\ \hline
3 & Output torque: & Output torque: \\  & \(T_{q}=m_{G}\times T_{p}=5.55\times 3340=19,036.5N\cdot\) m & \(T_{q}=m_{G}\times T_{p}=5.55\times 4291=23,815.05N\cdot\) m \\ \hline
4 & Transmited load: & Transmited load: \\  & \(W_{t}=T_{p}/(d_{p}/2)=3430/(0.1319/2)\) & \(W_{t}=T_{p}/(d_{p}/2)=3430/(0.1319/2)\) \\  & \(=52\times 10^{5}N\) & \(=52\times 10^{5}N\) \\ \hline
5 & Velocity factor (\(K_{y}\)): & Velocity factor (\(K_{y}\)): \\  & Pitch line velocity \(V_{t}=(d_{p}/2)\omega_{p}\) & Pitch line velocity \(V_{t}=(d_{p}/2)\omega_{p}\) \\  & \(=(0.1319/2)\times(2\pi\times 8350/60)=57.6\) m/s & \(=(0.1319/2)\times(6.28\times 8.350/60)=57.6\) m/s \\  & \(K_{y}=(78/(78+(200\times V_{t})^{0.5}))^{0.5}=0.65\) & \(K_{y}=(78/(78+(200\times V_{t})^{0.5}))^{0.5}=0.65\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Design checks of pinion and gear as per AGMA for assessing the safety factor. Figure 9: Isometric view of gear pair illustrating the normal stress in y-direction. \begin{table}
\begin{tabular}{c l l} \hline \hline
**Steps** & **Case-I Gearbox Operated at 3 MW** & **Case-II Gearbox Operated at 3.75 MW** \\ \hline
6 & Various factors: & Various factors: \\  & Size factor \(K_{\rm s}=1.0\) & Size factor \(K_{\rm s}=1.0\) \\  & Rim thickness factor \(K_{B}=1.0\) & Rim thickness factor \(K_{B}=1.0\) \\  & Load distribution factor \(K_{m}=1.8\) & Load distribution factor \(K_{m}=1.8\) \\  & Application factor \(K_{a}=1.25\) & Application factor \(K_{a}=1.25\) \\  & Idler factor \(K_{I}=1.0\) & Idler factor \(K_{I}=1.0\) \\  & Geometry factor \(l_{pinion}=0.428\) & Geometry factor \(l_{pinion}=0.428\) \\ \hline
7 & Pinion-tooth bending stress: & Pinion-tooth bending stress: \\  & \(\sigma_{hp}=\left(W_{t}\times p_{d}/\left(F\times I\right)\right)\) & \(\sigma_{hp}=\left(W_{t}\times p_{
## 3 Preliminaries
### Formal Security Models
#### 3.6.2 BPR Model (Bellare-Pointcheval-Rogaway)  
The Bellare-Pointcheval-Rogaway (BPR) model [15], and its variants like the Real-or-Random (RoR) model, employed in several contemporary IoD security papers [1; 2; 5; 6; 13; 19], provides a standard and rigorous framework for proving the semantic security of Authenticated Key Exchange (AKE) protocols against active adversaries, often in the random oracle model. The model typically defines the following:  
* **Participants** (oracles): Legitimate protocol participants (\(U_{O}\) via OD, GCS in our case) are modeled as oracles. An oracle \(\Pi^{s}_{U}\) represents instance \(s\) of principal \(U\) engaging in a protocol session.  
* (\(\mathcal{A}\)): A probabilistic polynomial-time (PPT) adversary interacts with these oracles. \(\mathcal{A}\) has full control over the communication network (as per [PERSON] [26]). Its capabilities are modeled through a set of allowed oracle queries. Based on similar models in [5] and the original BPR model, common queries include the following:
* '\(\text{Send}(\Pi^{s}_{U},M)\)': \(\mathcal{A}\) sends message \(M\) to instance \(\Pi^{s}_{U}\) and receives the protocol-defined response.
* '\(\text{Execute}(\Pi^{s}_{U},\Pi^{s2}_{V})\)': \(\mathcal{A}\) eavesdrops on an honest execution of the protocol.
* '\(\text{Reveal}(\Pi^{s}_{U})\)': \(\mathcal{A}\) obtains the session key computed by instance \(\Pi^{s}_{U}\)'.
* '\(\text{Corrupt}(\text{U})\)': \(\mathcal{A}\) obtains all long-term secret keys of principal \(U\).
* '\(\text{Hash}(\text{M})\)': \(\mathcal{A}\) queries the random oracle for the hash function \(H(M)\) or KDF.
* '\(\text{FuzzyExtractor}(\text{Gen}/\text{Rec},\text{data})\)': \(\mathcal{A}\) can query fuzzy extractor oracles.
* '\(\text{Test}(\Pi^{s}_{U})\)': Queried once on a \"fresh\" instance. The oracle flips a secret random bit \(b\). If \(b=1\), it returns the actual session key; if \(b=0\), it returns a random string.
* **Freshness**: An instance \(\Pi^{s}_{U}\) is \"fresh\" if its session key has not been trivially revealed.
* **AKE** security definition: An AKE protocol is secure if \(Adv^{AKE}_{\text{Prot}}(\mathcal{A})=|2\cdot\Pr[\mathcal{A}\text{ correctly guesses that }b]-1|\) is negligible. Proofs usually proceed by game hopping.
# Enhanced Local Fisher Discriminant Analysis for Indoor Positioning in Wireless Local Area Network
## 2 Proposed E-LFDA Method for WLAN Indoor Positioning
### Signal Perturbation for LFDA  
The proposed signal perturbation method aims to generate more number of useful training samples for LFDA. We discover that, due to the high-dimensionality and uncertainty of RSS signals, the number of RSS training samples is not sufficient to include all useful samples, even for the most representative samples consisting of the most likely received RSS value from all APs. Denote all the \(N_{S}\) RSS samples collected at the reference point as a set \(C=\left\{\mathbf{x}_{1},\mathbf{x}_{2},\cdots,\mathbf{x}_{N_{S}}\right\}\), where \(\mathbf{x}_{i}=\left[x_{1}^{1},x_{2}^{2},\cdots,x_{i}^{m}\right]^{\mathrm{T}}\) is the sample vector with RSS values collected from \(m\) APs. RSS values from the \(j\)th AP comprise the set \(C^{j}=\left\{x_{1}^{j},x_{2}^{j},\cdots,x_{N_{S}}^{j}\right\}\). Assuming that the probability of the most likely received RSS value from \(j\)th AP is \(P_{\max}^{j}\), the probability of the most representative RSS sample occurs in set \(C\) can be given as:  
\[P_{\max}=1-\left(1-\prod_{j=1}^{m}P_{\max}^{j}\right)^{N_{S}} \tag{12}\]  
Assuming that there are 10 APs (\(m=~{}10\)) available in target environment, the probability of the most likely received RSS values for all APs equals to 0.4 (\(P_{\max}^{j}=0.4,j=1,\ldots,10\)), and the number of RSS samples is 100 (\(N_{S}=100\)), the probability for the most representative training sample is only about 0.01 (\(P_{\max}=0.01\)). Thus, we propose a signal perturbation method to generate more number of representative RSS samples, which may be absent in original RSS sample set.  
The proposed signal perturbation method is founded on the following two assumptions:  
* For a realistic RSS vector sample, the probability of all the samples in the set \(C^{j}\) is identical;
* The existing probability of RSS values from any two APs is independent to each other.  
The process of the proposed signal perturbation method can be given as follows: Firstly, we randomly select a RSS value from the set \(C^{j}\), repeat this process over all the \(m\) sets of the related \(m\) APs, and then a new RSS training sample is generated by comprising the \(m\) randomly selected RSS values. Secondly, repeat the above process and generate more training samples for each reference point. Finally, by combining the new generated samples with the original ones, an enlarged radio map is produced and can be deployed to enhance the performance of LFDA. Note that all the computations of signal perturbation are achieved during offline phase, no additional computation cost is required during online phase on mobile devices.
## 2 Fault Analysis and Feature Extraction
### Single-Phase Ground Fault Simulation and Analysis  
Transmission line operating environment is complex and affected by many factors, including non-ideal conductors such as tree branches, sand, and turf, which can cause single-phase ground faults. These faults, caused by high-resistance media, are usually characterized by transition resistance higher than 200 \(\Omega\) and are collectively referred to as high-resistance ground faults. If not addressed in a timely manner, these faults can expand in scope and seriously affect the stable operation of transmission lines. When a fault occurs, the system's operation produces high-frequency electromagnetic oscillation, causing line-wide over-voltage for several hours and posing a serious threat to the weak insulation areas of the transmission line.  
Since arc ground faults are easy to recognize due to their distinct characteristics and high resistance ground faults and arc ground faults are common and difficult to identify in the actual working conditions of 66 kV transmission lines, this paper focuses on these two types of faults and proposes fault judgment criteria through simulation analysis of various fault types to achieve the goal of fault identification.
## 4 A Case Study
### Test Cases with a Single Dynamic Obstacle
#### 4.1.3 Crossing  
There are two crossing scenarios included in this case, called Crossing-1 and Crossing-2. The same configuration, an initial position at \((50,\ 300)\) m, a model of 2.0 m/s, and a course of \(0^{\circ}\), was chosen for the OS in the two scenarios. In Crossing-1, the TS's initial position and course were set to \((477,\ 477)\) m and \(225^{\circ}\), respectively, and the corresponding original position and course of TS in Crossing-2 were \((300,\ 550)\) m and \(270^{\circ}\). A velocity of 2.0 m/s was adopted for TS in both Crossing-1 and Crossing-2.  
According to the provisions of COLREGs Rule 15 [29], when two vessels are crossing, the vessel that has the other on its starboard should keep out of the way. Therefore, the OS is a give-way vessel in the two crossing scenarios. The same parameters for FM and corresponding safety distance thresholds were selected in this case as in the overtaking scenario.  
The simulation results are depicted in Figures 7, 9 and 10. Figures 9a and 10a display the planned path, and Figures 9b and 10b show the collision avoidance trajectories of the two crossing scenarios. Specific quantitative assessments are shown in Figure 7. The relative distances between the OS and the TS in Crossing-1 and Crossing-2 are illustrated by filled square blue and filled diamond cyan lines, respectively. The shortest distances in the two scenarios are 52.60 m and 62.04 m. The results illustrate that a good path planning capability can be achieved with the proposed method.  
Figure 8: The planning results and the collision avoidance trajectories in the overtaking scenario. (**a**) The path planning results, (**b**) The collision avoidance trajectories.
# Univariate Outlier Detection: Precision-Driven Algorithm for Single-Cluster Scenarios
## 4 Results And Discussion
### Discussion of Findings  
The ability to achieve near-perfect precision renders the algorithm valuable in critical infrastructure monitoring and safety-critical systems. By minimizing false positives while maintaining high confidence in identifying true anomalies, the algorithm enhances operational efficiency and enables timely intervention in potential fault scenarios. However, it is crucial to acknowledge the trade-offs associated with threshold selection. Careful consideration of application-specific requirements is essential to optimize performance based on the desired balance between precision and other metrics [24].