esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study investigates the feature extraction of axis motion trajectories, constructing a method based on distance sequence representation and validating it with measured data from a multi-disk rotor test rig. The conclusions are as follows:  
1. SVD is well suited for purifying and denoising axis trajectories. With a small parameter count, simple parameter determination, and brief processing steps, it effectively achieves the purification and denoising of rotor system axis trajectories by truncating smaller singular values and reconstructing the signals using larger singular values that contain more feature information.
2. The distance sequence representation method accurately captures the significant geometric features of axis motion trajectories under various operating conditions, including both macroscopic shape features and microscopic texture features, facilitating further research on the classification and identification of axis trajectories.
3. The axis motion trajectory feature extraction method is applicable to multiple unbalanced states. It can effectively extract significant features of various operating states of the rotor system from the original signals, with an approximately 80% reduction in data volume compared to the initial axis trajectories, making it significant for judging the operating states of the rotor system.  
Conceptualization, F.Y., J.P. and E.D.; Methodology, E.D., F.Y. and Y.S.; Validation, F.Y., J.P. and Z.Z.; Supervision, J.P, F.Y. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key R&D projects in Henan Province (231111222900) and the joint fund (industrial sector) of R&D projects in Henan Province (225101610003).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Cover Time in Edge-Uniform Stochastically-Evolving Graphs +
## 1 Introduction  
In the modern era of the Internet, modifications in a network topology can occur extremely frequently and in a disorderly way. Communication links may fail from time to time, while connections amongst terminals may appear or disappear intermittently. Thus, classical (static) network theory fails to capture such ever-changing processes. In an attempt to fill this void, different research communities have given rise to a variety of theories on dynamic networks. In the context of algorithms and distributed computing, such networks are usually referred to as temporal graphs [1]. A temporal graph is represented by a (possibly infinite) sequence of subgraphs of the same static graph. That is, the graph is evolving over a series of (discrete) time steps under a set of deterministic or stochastic rules of evolution. Such a rule can be edge- or graph-specific and may take as input graph instances observed in previous time steps.  
In this paper, we focus on stochastically-evolving temporal graphs. We define a model of evolution, where there exists a single stochastic rule, which is applied independently to each edge. Furthermore, our model is general in the sense that the underlying static graph is allowed to be a general connected graph, i.e., with no further constraints on its topology, and the stochastic rule can include any finite number of past observations.  
Assume now that a single mobile agent is placed on an arbitrary node of a temporal graph evolving under the aforementioned model. Next, the agent performs a simple random walk; at each time step, after the graph instance is fixed according to the model, the agent chooses uniformly at random a node amongst the neighbours of its current node and visits it. The cover time of such a walk is defined as the expected number of time steps until the agent has visited each node at least once. Herein, we prove some first bounds on the cover time for a simple random walk as defined above, mostly via the use of Markovian theory.  
Random walks constitute a very important primitive in terms of distributed computing. Examples include their use in information dissemination [2] and random network structure [3]; also, see the short survey in [4]. In this work, we consider a single random walk as a fundamental building block for other more distributed scenarios to follow.
# A Single Wearable Sensor for Gait Analysis in Parkinson's Disease: A Preliminary Study
## 2 Design of the Proposed System  
The system proposed in this work consists of an inertial wearable sensor placed on a subject's lower back and an algorithm specifically developed for the estimation of gait analysis parameters. The inertial wearable sensor used in this study is an NGIMU (Next Generation Inertial Measurement Unit) from X-IoT Technologies Limited (Bristol, UK). It is a wireless, small and non-invasive MARG (Magnetic Angular Rate and Gravity) sensor embedding a tri-axial accelerometer, a tri-axial gyroscope, and a tri-axial magnetometer. A velcro band was positioned on the lower back of the subject and the sensor was placed on it,as shown in Figure 1. The NGIMU was positioned following specific orientations: the \(x\)-axis of the NGIMU was used as the medial lateral (ML)-axis and pointed laterally, the \(y\)-axis as the vertical-axis and pointed upward, and the \(z\)-axis as the anterior posterior (AP)-axis and pointed forward. Starting from the data acquired by the inertial wearable sensor, an algorithm for the home assessment of gait parameter was developed for monitoring the PD subjects' responses to medication administration over time. The block diagram of the proposed algorithm is shown in Figure 2 and each step of it is described in the following.
# Fault Detection in Active Magnetic Bearings Using Digital Twin Technology
## 4 Digital Twin Fundamentals and Application in AMB  
A digital twin represents the physical object, process, or system in virtual representation [32]. It includes both the physical and digital domains through duplicated real-time data from the physical system with a simulation model to enhance its physical counterpart's behavior, characteristics, and performance [33]. It works as a link between the real and simulated model to allow real-time monitoring, analysis, and optimization of the system [34]. The digital twin updated by capturing the data from the sensors of the physical system continuously. By implementing machine learning algorithms and simulation models, digital twins can achieve comprehensive insights, decision-making, and optimized performance by simulating and predicting the physical system's behavior [35]. The digital twin can play an essential role in monitoring and optimizing, diagnosing failure, and enabling predictive maintenance of the AMB. The system behavior, including rotor position, magnetic force, and control signals, can be covered by the digital twin through integrating real-time data from the position sensor, amplifier, and other related sources for the AMB. This makes it possible to identify faults earlier, analyze performance, and simulate \"what-if\" scenarios to determine the effects of faults and strategies for optimization. The digital twin offers a simulation model for applying a control algorithm, optimizing control parameters, and testing system performance under various operating conditions. Also, it provides system monitoring and fault detection, permitting preventative measures to reduce failures, improve the system's reliability, and reduce downtime.
## 2 Engineering Background  
### Engineering Overview  
Tunnel No. 1 of the Jakarta-Bandung High-Speed Railway spans from DK2 + 540.0 to DK4 + 410.0, covering a total length of 1885 m, with a maximum depth of approximately 36 m (see Figure 1 for details). This single-hole, two-lane tunnel has a shield range extending from DK2 + 771.5 to DK4 + 238.5, and the inner track is situated at a depth ranging from 5.0 to 24.0 m. The tunnel's geological composition includes clay, volcanic ash-cemented gravel, sandy soil, and fine soil. The construction presents challenges due to the complex conditions, high difficulty, and associated risks. The geological features involve a high water table, abundant surface water, and passage through volcanic accumulation layers with fragmented rock, low slurry stone strength, and significant weathering and cementation effects. Moreover, the tunnel intersects densely populated areas with buildings, highways, and interchange ramps, and it passes adjacent to two mosques. To tackle the intricate geological challenges and risks in Tunnel No. 1, the first oversized diameter slurry-water balanced shield machine developed by China High-Speed Railway Overseas is employed--a pioneering technology in Southeast Asia. This shield machine boasts a cutter diameter of 13.19 m, a length of approximately 101 m, and a weight exceeding 2600 tons. The cutter tool arrangement is depicted in Figure 2. The chosen lining method involves assembled reinforced concrete pipe sheets combined with box culverts. The pipe sheets use universal pipe sheet rings, with a lining ring width of 2000 mm, inner diameter of 11,700 m, outer diameter of 12,800 mm, and a thickness of 550 mm.
# Microbiological Safety and Health Properties of Markleted Fermented Root Vegetables
## 4 Statistical Analysis Results
### Kruskal-Wallis Test  
It was found that botanical families were characterized by interdependencies of antioxidant potential, which were as follows: _Apiaceae_ (\(H\) = 46.234; \(p\) = 0.000), _Brassicaceae_ (\(H\) = 46.230; \(p\) = 0.000), and _Amarantthaceae_ (\(H\) = 46.287; \(p\) = 0.000). Moreover, the Kruskal-Wallis test revealed relationships among the antioxidant methods used, i.e., FC (\(H\) = 25.677; \(p\) = 0.000), CUPRAC (\(H\) = 21.533; \(p\) = 0.000), DPPH (\(H\) = 21.842; \(p\) = 0.000). The pasteurization process appeared to be relevant to antioxidant potential. The Kruskal-Wallis test showed significant differences between pasteurized and unpasteurized pickled roots using the results from the FC assay (\(H\) = 9.967; \(p\) = 0.002) and the CUPRAC assay (\(H\) = 6.327; \(p\) = 0.012). Interestingly, pasteurized pickles and a lack of LAB were correlated with higher antioxidant potential. The strongest dependence was for FC and LAB (Figure 5). High temperatures induce a decrease in the content of some ingredients, e.g., vitamin C. Still, there are compounds that show greater antioxidant properties when subjected to processing, including short-term heating. At the same time, compounds such as phenols [91; 92; 93] and carotenoids [86] are chemically stable under temperatures ranging from 70 to 90 \({}^{\circ}\)C [85]. It was shown that the mentioned compounds are released into the analyzed extract in greater amounts at high temperatures than at low ones [94; 95].  
There were no statistically significant differences between TPC and TAC in terms of the level of grinding. Unfortunately, the crop area was unknown. Therefore, it was not possible to determine any differences in the antioxidant potential of pickles depending on their cultivation characteristics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Botanical Family** & _Apiaceae_ & _Brassicaceae_ & _Amarantthaceae_ \\ \hline _Apiaceae_ & - & 0.904790 & 0.879573 \\ \hline _Brassicaceae_ & 0.904790 & - & 0.866554 \\ \hline _Amarantthaceae_ & 0.879573 & 0.866554 & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: The results of the [PERSON]’s rank correlation test between TAC and TPC results obtained by all methods for analyzed botanical families. Values that were statistically significant at \(p\) < 0.001 were marked in red.  
Figure 5: Comparison of FC results for pasteurized and unpasteurized products. Various letters indicate statistically significant differences between groups (\(p\) = 0.0346). GAE—gallic acid equivalent, SE—standard error, SD—standard deviation.  
The null hypothesis of the Kruskal-Wallis test was rejected, so a post-hoc Dunn's test was applied, which allowed for differences' identification. This test revealed statistically significant differences (\(p\) < 0.001) between the _Amaranthaceae_ family (beetroot) and the other families, i.e., _Apiaceae_ and _Brassicaceae_. There was no statistically significant difference between the _Brassicaceae_ and _Apiaceae_ families.
## 7 Simulation Assessment
### Performance of the Separated Phase-Current Controls Using IBDGs
#### 7.2.2 Temporary LL Fault  
The LL fault was estimated at 10% for all kinds of fault occurrences [24]. Phases \(a\) and \(b\) were short-circuited with 0.01 \(\Omega\) of \(R_{f}\). Figure 14a shows the total fault current compared to the grid current. After the fault occurred at 0.14 s, the total fault current amplitude was greater than the grid current amplitude. When the fault was detected at 0.155 s, the conventional current control decreased the total fault current amplitude to the grid current amplitude. Then, \(V_{dc}\) reached the withstanding voltage of \(C_{dc}\), as shown in Figure 14b. IBDG1 and IBDG2 were disconnected from the grid at 0.188 s and 0.173 s, respectively. The cause of excessive \(V_{dc}\) was the same as in the previous SLG fault. After the IBDGs were disconnected, the total fault current amplitude became greater than the grid current amplitude again. The operation of the recloser-fuse and SDG breaker is shown in Figure 14c. The recloser-fuse coordination was lost, as the fuse melted before the recloser operated. After the fuse melted at 0.315 s, the grid current and total fault current were greatly decreased.  
With the separated phase-current controls, after the fault was detected at 0.148 s, the total fault current amplitude was reduced approximately to the grid current amplitude, as shown in Figure 15a. Since there was no disconnection of IBDGs caused by excessive \(V_{dc}\), the total fault current amplitude remained close to the grid current amplitude until the recloser tripped. \(V_{dc}\) was regulated within the withstanding voltage, as shown in Figure 15b. The operations of the recloser-fuse and SDG breaker are shown in Figure 15c. The tripping operation of the recloser happened before the fuse melted; hence, the recloser-fuse coordination remained effective. When the recloser tripped, the SDG breaker operated. After the tripping event of the recloser in phase \(a\) and in the SDG breaker at 0.333 s and 0.34 s, respectively, the grid current in phase \(b\) was reduced. Similarly, the total fault current in phases \(a\) and \(b\) also decreased.  
Figure 14: Conventional current control during temporary LL fault: (**a**) total fault current and grid current; (**b**) DC-link voltage; (**c**) operating time of recloser–fuse and SDG breaker.
# Novel Deep Learning-Based Facial Forgery Detection for Effective Biometric Recognition
## 2 Materials and Methods  
### Dataset and Experimental Environment  
Qualified datasets that have been thoroughly validated and widely used in various studies are employed for the experiment of the proposed algorithm.  
1. CASIA-FASD: CASIA-FASD [19] is a face database which can cover a diverse range of potential attack variations. The database contains 50 genuine subjects, and fake faces are made from those of the genuine faces. Each subject possesses 3 designated qualities (namely the low, normal, and high) and each quality has one genuine, and 3 forgeries (warped photo attack, cut photo attack, and video attack) derived from the genuine. Therefore, each subject contains 12 videos (3 genuine and 9 fake), and the final database contains 600 video clips. To properly apply the deep learning module and improve the level and convenience of learning, appropriate still images are extracted and organized based on the characteristics of the video frames [20].
2. CelebA-Spoof: CelebA-Spoof [21] is a large-scale face anti-spoofing dataset that has 625,537 images from 10,177 subjects, which includes 43 rich attributes on face, illumination, environment, and forgery types. Among 43 rich attributes, 40 attributesbelong to genuine images including all facial components and accessories such as skin, nose, eyes, eyebrows, lips, hair, hat, and eyeglasses. A total of 3 attributes belong to forged images including forgery types, environments, and illumination conditions. The forgery types include print (photo, poster, and A4), papercut (facemask, upper body mask, and region mask), replay (PC, pad, and phone), and 3D masks for various lighting conditions (4 brightness and 2 locations).
* NIA-ILD: NIA-ILD is the facial image dataset consisting of over 1.6 million images, obtained from the results of the Images for Liveness Detection [22] by the National Information Society Agency [23]. Facial image data are acquired by capturing videos and then extracting still frames through the capture process. Various capture devices, including smartphones and tablet PCs, are evenly utilized to avoid bias towards a specific device. The lighting conditions are categorized into three levels, and efforts are made to ensure an equal gender distribution among subjects, covering age groups from 10s to 60s, including individuals wearing glasses or masks to simulate real-world facial recognition scenarios. The forgery types include print attacks (categorized as directly attaching a printed photo with flattening and bending, and attaching a printed photo with holes cut in the  
As for the experimental environment, the hardware specifications and operating system information used in this study are listed below:  
* GPU: NVIDIA GeForce RTX 4080 (16 GB VRAM)
* CPU: Intel Core i9-13900K
* RAM: Samsung DDR5 5600 MHz 32 GB \(\times 2\) (total 64 GB)
* Operating System: Ubuntu 22.04
# Creating a Regional MODIS Satellite-Driven Net Primary Production Dataset for European Forests
## 3 Results
### Stand Density Effects  
We analyzed \(\Delta\)NPP\({}_{\text{EURO}}\) (differences in NPP between MODIS EURO versus NFI NPP) by SDI (Stand Density Index [36] calculated with Equation (S10) in the Supplementary Material) for all of Europe (Figure 6).  
\(\Delta\)NPP shows in Figure 6 a significant trend by stand density index SDI (using linear regression; R 0.31; \(\Delta\)NPP = \(103.1-0.247\times\) SDI; \(p<0.001\)), which confirms that differences in stand density have an effect in our data from the 12 European countries. MODIS EURO NPP estimates are higher than NFI NPP at low SDI classes, while at intermediate SDI classes no discrepancies are evident (Figure 6). At high SDI classes MODIS EURO are lower than NFI NPP.  
We analyzed the effect of SDI for each country, since SDI could be an explanation for the discrepancies visible in Table 2, Figures 4 and 5. Local effects of forest management intensity, disturbances or differences in the local inventory data design and methodology (Table S1) could  
Figure 5: NPP Difference (\(\Delta\)NPP) MODIS EURO minus NFI NPP by Elevation classes (**a**), by Latitude (**b**) and by Longitude (**c**), properties of illustration analogous to Figure 3, on the top the number of values represented by the boxplots are given.  
Figure 6: NPP Difference (\(\Delta\)NPP) MODIS EURO minus NFI NPP by Stand Density Index classes (SDI), for details see Figure 5.  
lead to differences in SDI. We performed similar graphical analysis as shown in Figure 6 for each country and present here as examples two \"extreme\" countries: (i) France--positive \(\Delta\)NPP +10%, with MODIS EURO overestimating NFI NPP; and (ii) Germany--negative \(\Delta\)NPP \(-\)16%, where MODIS EURO underestimates NFI NPP.  
For France, MODIS EURO and NFI NPP results agree at high stand density and show discrepancies at low stand density (Figure 7a). Apparently, MODIS EURO does well in capturing the NPP of stands with high densities, but does not agree with NFI NPP from very open stands. The same patterns are also visible for other countries, where MODIS EURO overestimates NFI NPP such as Spain or Czech Republic (not shown).  
For Germany on the other hand, MODIS EURO and NFI NPP are similar at low stand density classes, but show increasing deviations with increasing stand densities (Figure 7b). We see the same result for other countries as well, where MODIS EURO underestimates NFI NPP such as Poland (not shown). This may be seen as an indication that besides stand density an additional driver might cause discrepancies between MODIS EURO versus terrestrial NFI NPP.  
Figure 7: NPP Difference (\(\Delta\)NPP) MODIS EURO minus NFI NPP by Stand Density Index classes (SDI) for selected countries: France (**a**)—MODIS EURO overestimates NFI NPP (on average positive \(\Delta\)NPP) and Germany (**b**)—MODIS EURO underestimates NFI NPP (on average negative \(\Delta\)NPP), for details see Figure 5.
## 2 Materials and Methods
### Collection Sites  
The ambient air samples for this study were collected in three communities with railyards in the Los Angeles Basin: Commerce (CM), Long Beach (LB) and San Bernardino (SB) (Figure 1). CM, with two railyards, is located in the midtown area of Los Angeles. LB is southwest of CM and neighbors the Pacific Ocean and the Port of Los Angeles, which is the largest container port in the United States as measured by container volume and cargo value. Its emission sources would include both local railyards and the Port. The SB site is located in the eastern end of the Basin, approximately 80 miles from the coast and represents a receptor site that receives air parcels that have been subjected to modifications by photochemical and chemical reactions as they move from east to west across the Basin [19], together with emissions from a SB railyard. We previously observed that as the air mass moves east, a trend toward higher levels of 9,10-phenanthroquinone was found in the air samples, consistent with its formation from phenanthrene by atmospheric processes [7].  
Figure 1: Map of the Los Angeles Basin with locations of the air collection sites: Long Beach (LB), Commerce (CM) and San Bernardino (SB). The Los Angeles Basin contains a coastal area bordered by the San Gabriel Mountains located in northern Los Angeles County and western San Bernardino County.  
The second set of collections focused on CM at different distances nearby its railyards to assess ambient air properties as a function of distance from emission sources (Figure 2).  
The sites, CM1 and CM2, were closest to the CM-Union Pacific railroad and downwind from the Burlington Northern and Santa Fe (BNSF) Hobart yard. Sites CM3 and CM4 were both upwind of the BNSF yard, but CM3 was closer to the yards and to a diesel truck processing center. CM4 was considered as a background site, minimally directly impacted by the railroad activities. It should be pointed out that as the samples were collected continuously 24-h/d for 5-d, changes in air movement during that period could have minimized differences due to changes from prevailing onshore to infrequent offshore winds. The distances from the railyards for the CM collections are summarized in Table 1.
## 3 Underwater Channel Characterization
### Electro-Magnetic Properties of Water  
The high frequency radio band facilitates the network with large bandwidth for high data rate applications. However, the disadvantage is that EM waves attenuate severely underwater and thus are not able to travel long distances. In order to realize the possible effects on EM waves that propagate wirelessly underwater, the following EM properties of water are to be considered [11; 18].  
\begin{table}
\begin{tabular}{l l} \hline
**Environment** & **Path Loss Exponent “n”** \\ \hline Free Space & 2 \\ Urban Area & 2.7 to 3.5 \\ Sub Urban Area & 3 to 5 \\ Indoor (Line of Sight) & 1.6 to 1.8 \\ Underwater (Line of Sight) & 2 to 4 \\ \hline \end{tabular}
\end{table}
Table 2: Path loss exponent for different environments.