esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
According to KEGG pathway analysis, histones H2A, H2B, H3, and H4, which are involved in the function of autoantigens, were associated with SLE due to active MHCII, B-cell receptor signal transduction, and autoantibodies. In general, H2A and H2B form octamers with H3 and H4 and are involved in the packaging of DNA with nucleosomes [66]. As cells struggle to repair chemotherapy-induced DNA damage, decreased histone transcript levels may indicate cell cycle arrest [67]. Our data support that low expression of histone variants in DSS-induced IBD suggests a correlation with DNA repair mechanisms associated with DNA damage. DNA silencing and repair pathways are activated upon mineral water consumption because miRNA-processing proteins that regulate the genes involved are Mg\({}^{2+}\)-dependent [68]. However, it remains unknown how minerals affect the expression of these histone variants involved in DNA repair. DSW contains various minerals. In particular, DSW was found to be richer in magnesium than in calcium. The importance of calcium-to-magnesium intake ratios in minimizing the risk of rectal cancer has been reported; a calcium-to-magnesium ratio of 1.7 or 2.5 significantly reduced cancer, cardiovascular risk, and mortality in a dose-dependent manner [69; 70; 71]. Calcium and magnesium can antagonize each other's absorption because they are both divalent ions [72]. This suggests that a balanced mineral intake based on in-vitro and in-vivo experiments should be validated in human studies. Numerous studies have illustrated that DSW-derived mineral water can serve as a functional food with anti-diabetic, anti-obesity, and anti-inflammatory properties. Further, various natural products extracted using DSW had better functionality than those extracted using distilled water [73; 74; 75; 76; 77]. This suggests that DSW has application potential in the development of many functional foods, which requires further study. Certain limitations were noted in this study. First, calcium and magnesium ratios are important, but this study did not identify the differences in each ratio. Second, in the in vitro model, inflammation is caused by macrophages, causing TJ collapse, whereas DSS induces collapse of the mucosa, causing colitis and ulcers. Therefore, the mechanism of inflammation differs. Moreover, the 5% DSS concentration was too high to clearly observe the anti-inflammatory effects following intake of DSW-derived mineral water. Third, effects of the intestinal microbiota and mucus layer involved in immunity should not be overlooked, but were not directly addressed in this study. Finally, we did not verify protein expression of the inflammatory and TJ genes, which must be confirmed in further studies.
# Damage Identification of Stay Cables Based on a Small Amount of Deflection Monitoring Data
## 5 Experimental Verification
### Influence of Noise on Damage Identification  
Following the location identification method described in Section 3, after adding noise, the location identification of damage cable results are shown in Figure 25.  
Figure 23: Damage location identification results. (**a**) Single-cable damage; (**b**) Double-cable damage.  
Figure 22: Deflection distribution curves of main beam under concentrated load. (**a**) Single-cable damage; (**b**) double-cable damage.  
Figure 24: Damage degree identification results. (**a**) Single-cable damage; (**b**) Double-cable damage.  
As shown in Figure 25, the identified damage location is the same as the preset damage rope location.  
After adding noise, the results show that the difference between the recognized damage index and the actual damage index (the preset damage index) is very small (the maximum error is 2.3%). It can be seen that the addition of noise will not affect the accuracy of identifying cable damage degree based on the Kriging proxy model.
## 4 Discussion  
In the conducted research, various GNSS solutions were compared under simulated real-time conditions using 1 s interval observations for static positioning. The analysis revealed significant differences in performance, particularly regarding solution availability, convergence time, accuracy, and ZTD estimation. Notable differences were observed between solutions based on broadcast ephemerides (BRDM), real-time precise corrections (CNES RTS), post-processed precise products (CODE MGEX), and the HAS service.  
Convergence time was one of the key factors differentiating GNSS solutions based on PPP positioning. For the BRDM and HAS services, more lenient accuracy thresholds were applied (20 cm horizontally (2D) and 40 cm vertically (U) and spatially (3D)), whereas stricter thresholds (10 cm both horizontally and spatially) were used for CNES and CODE. BRDM and HAS exhibited longer and more variable convergence times compared to CNES and CODE, especially when using GPS-only data. However, integrating signals from Galileo (or combined GPS+Galileo) improved performance, although convergence times remained longer. These differences stem from the limitations associated with broadcast ephemerides in BRDM solutions and the focus of the HAS service on the Galileo system.  
In contrast, for HAS solutions, the use of Galileo signals significantly improved availability and accuracy, whereas relying solely on GPS limited these parameters. The reduced availability of HAS orbit and clock corrections for GPS satellites indicates the need for the further development of this service. Similarly, [PERSON] et al. (2024) [44] demonstrated that HAS products for GPS are of a lower accuracy and that the positioning results for GPS were worse compared to those for Galileo. In the case of CNES, slightly worse results were obtained for Galileo signals compared to GPS. However, the combined use of both signals (GPS+Galileo) provided better results, both in terms of positioning accuracy and ZTD estimation. This clearly demonstrates that employing both systems together substantially enhances the overall solution performance.  
The ZTD analysis showed that BRDM solutions had a poorer ZTD estimation quality, especially for GPS, where large standard deviations were observed. In comparison, integrating GPS with Galileo improved the results by reducing ZTD variability. A similar dependency was reported by [PERSON] et al. (2024) [49], confirming that the addition of Galileo signals enhances the stability and precision of ZTD retrieval. Moreover, this study also demonstrated that the use of broadcast ephemeris products allows for a ZTD estimation with sufficient accuracy, especially when using the Galileo signal. HAS further showed improvements when using Galileo signals, with standard deviations of ZTD being significantly lower than for GPS. However, the CNES and CODE solutions, which utilize precise corrections and products, proved to be the most accurate and stable in ZTD estimation, making them suitable for high-reliability applications such as weather forecasting.
## 4 Applications, Results, and Discussion  
The objective of this study is the development of a novel detection model for the effective detection of cyber-attacks on WDSs by applying various machine learning approaches and determining the  
\begin{table}
\begin{tabular}{l} \hline \hline
**Input**: A training set \(\{X,T\}=\{x_{i},\,t_{i}\}^{\text{N}}{}_{i=1}\subset R^{n}\times R\) \\ The hidden node output function \(H(a,\,b,\,x)\), the continuous target function \(f\) and the hidden node number \(L\), and the expected learning accuracy \(\eta\). \\
**(Initialization)** \\  Let the number of hidden nodes \(L=0\), residual error \(E=T\), where \(t=[t_{1},...,\,t_{N}]\). \\
**(Learning step)** \\ _while_\(L<L_{max}\), \(||\text{E}||>\eta\) \\  Increase by one the number of hidden nodes \(L\):\(L=L+1\) \\ _if_\(L\)\(\{2n+1,\,n\in\)\(2\}\)_then_ \\ -Assign random input weight \(a_{L}\) and bias \(b_{L}\) for new hidden node \(L\) \\ -Calculate the output weight \(\beta_{L}\) for the new hidden node \(\beta_{L}\): \(\beta_{2n+1}=(e_{2n},\,H^{e}{}_{2n+1})||H^{e}{}_{2n+1}||^{2}\) \\ -Calculate \(E\) after adding the new hidden node \(L\):\(E=E\):\(H_{L}\)\(\cdot\)\(\cdot\)\(\cdot\)\(\cdot\)\(\cdot\)\(\cdot\)\(\cdot\)\(\cdot\)_end if_\(L\)\(\{2n,\,n\in\)\(2\}\)_then_ \\ -Calculate the error feedback function sequence \(H_{L}\): \(H_{2n}\)\(\epsilon=e_{2n-1}\)\((\beta_{2n-1})^{-1}\), where, \(e_{n}=f-f_{n}\). \\ -Calculate the input weight \(a_{L}\), bias \(b_{L}\) and update \(H_{L}\) for the new hidden node \(L\) \\ -Calculate the output weight \(\beta_{L}\) for the new hidden node \(\beta_{L}\): \(\beta_{2n}=(e_{2n-1},\,H^{e}{}_{2n})||H^{e}{}_{2n}||^{2}\) \\ -Calculate \(E\) after adding the new hidden node \(L\):\(E=E\):\(H_{L}\)\(\beta_{L}\) \\ _end if_ \\ _end while_ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Pseudo code of B-ELM [49].  
machine learning approach with the best detection performance. To evaluate the performance of each approach in the detection of cyber-attacks on WDSs, the commonly used approaches (i.e., KNN, SVM, ANNs, and ELM) are applied to the BATADAL dataset, simulating various cyber-attack scenarios.  
Moreover, to compare the quantitative performance of each algorithm, various performance indices are used. Such performance indices are introduced in the next subsection. The learning approaches are first compared in terms of performance, and the algorithm with the best detection performance is determined. Then, the improved versions of the said learning algorithm are tested and analyzed. All computations were performed using MS Excel 2019, and such machine learning techniques as KNN, ANNs, SVM, ELM and Data Manager were developed using MATLAB 2019 (MathWorks, Inc., Natwick, MA, USA).
## 2 Materials and Methods
### NMR Spectroscopy and Data Processing  
All HR-MAS NMR spectra were obtained on a Bruker Avance 500 MHz spectrometer (Fallanden, Switzerland), operating at a \({}^{13}\)C resonance frequency of 125.758 MHz, equipped with a BVT-3000 device for temperature control and an HR-MAS 5 mm probe. All measurements were made at a spin rate of 4 KHz and at room temperature, acquiring 32 k points with 5000 scans and using the inverse-gated pulse sequence (zgig) in order to avoid the nuclear Overhauser effect (NOE) and obtain quantitative \({}^{13}\)C information from the \({}^{13}\)C-[1H] NMR spectrum. Data were processed using Bruker TopSpin 3.6 software (Bruker BioSpin, Rheinstetten, Germany) [38]: the time-domain spectra were apodised with exponential function, and then Fourier-transformed, phased, and baseline-corrected automatically. In order to remove the background signal due to the Kel-F rotor cap, to each spectrum has been subtracted the \({}^{13}\)C spectrum obtained from a blank sample consisting in D\({}_{2}\)O and TMS. The chemical shifts were referred to the TMS signal (\(\delta\) = 0.00 ppm). In the semi-quantitative procedure, the global chemical shift range of carbon in the spectra was subdivided into sub-regions, as shown in Table 1.  
Each signal was integrated using the TopSpin 3.6 software and was expressed as a percentage respect to the total integration area. The integration errors were calculated using the root-mean-square (RMS) method, by which the noise RMS was calculated as:  
\begin{table}
\begin{tabular}{c c} \hline
**Chemical Shift Range (ppm)** & **Assignment** \\ \hline
220 to 190 & C=O of ketones and aldehydes \\
190 to 160 & C=O in carboxylic acids, esters and amides \\
160 to 140 & C=O–R and C–N–R aromatics; C–1 in polystyrene \\
140 to 110 & Aromatic C–H; C–2, C–3, C–4, C–5 and C–6 in polystyrene \\
110 to 90 & C–1 in carbohydrates; C–2 and C–6 in the lignin syringes unit \\
90 to 60 & C–2 and C–6 in carbohydrates; side chain in lignin \\
60 to 50 & Methylene groups, Ca in amino acids \\
50 to 40 & Methylene groups (lipids, proteins, polyethylene, polyisoprene, polyamide). Long chain \\  & aliphatic C, also biopolymers \\
40 to 25 & Ca and C\(\beta\) in polymers such as polyvinylchloride, polypropylene, polystyrene \\
25 to 0 & Methyl groups (lipids, peptides, polypropylene). Short chain aliphatic C \\ \hline \end{tabular}
\end{table}
Table 1: Chemical shift regions of \({}^{13}\)C–[1H] NMR spectra of OFMSW [29; 30].  
Figure 1: Experimental steps, oxidative reaction conditions, and NMR samples collected for each process the initial sample of the organic fraction of municipal solid waste (OFMSW) was subjected to.  
\[RMS=\sqrt{\frac{\sum_{i}^{n}\lambda_{i}}{n}} \tag{1}\]  
where \(\lambda\) is the intensity of the _i_-point (in ppm) out of the total _n_-points. The _RMS_ value, obtained in this way, represents the height of the noise. Subsequently, this value was used to estimate the error expressed as a percentage of the intensity of the signal and this percentage has been used to estimate the error bar of the integral of each NMR peak [39].
# Electromagnetic Assessment of UHF-RFID Devices in Healthcare Environment
## 3 Results
### Anechoic Chamber
#### 3.2.1. EMF Exposure near MO-RFID Reader  
The dimensions of an anechoic chamber, where measurements on the EMF emitted by the MO-RFID reader were made, were as follows: 9.76 m \(\times\) 6.71 m \(\times\) 6.10 m. The reader was set on a manual positioning device, and, thus, the former could be rotated to facilitate the measurement of the radiation pattern. The radiation pattern, (maximum values from measurements for horizontal and vertical positions of measuring antenna and adjusted RFID antenna position) was sampled out around antenna, starting in front of an UHF RFID antenna with a 45\({}^{\circ}\) angle step, to cover major, lateral and, back radiation.  
The differences in E-field values measured (Em) and calculated (Ec) are shown in Table 6 and Figure 8. The calculated measurements are obtained based on the ERP (Effective Radiated Power) values, and an equivalent calculation of the E-field intensity is performed at the measurement distance. The test equipment, with a transmission button pressed, delivers emissions for an approximate time of 1 min and then emission stops.  
Figure 7: Spatial distribution of E-field measured near AS-RFID reader (V/m).
# Blind Source Separation of Intermittent Frequency Hopping Sources over LOS and NLOS Channels +
## 1 Introduction  
Frequency hopping (FH) spread spectrum signals have been widely studied and adopted for wireless communications due to a multitude of advantages, such as their low probability of detection and their inherent robustness to jamming [1, 2, 3]. Estimating and tracking parameters of multiple FH signals have important applications in both civilian and military fields, such as collision avoidance [4, 5], cognitive radio [6, 7], and interception of non-cooperative communications [8, 9]. The estimation of FH signal parameters for the purpose of intercepting non-cooperative sources is the focus of this work.  
The FH sources assumed in this paper transmit at frequencies that change pseudorandomly within a block of spectrum. Parameters such as hop time, hopping pattern, and frequencies are random and unknown at the receiver. The localization and separation of multiple FH sources without knowledge of these parameters is posed as a blind source separation (BSS) problem [10].  
To perform BSS of multiple FH sources, it is not sufficient to produce a frequency versus time map of power of the observed signals; it is also necessary to associate the signals to physical sources. We refer to the task of associating frequency hops to a source as the problem of labeling of FH signals. Since frequency hop estimates cannot determine which of several sources transmitted the signal, it is necessary to estimate information that is source specific and extraneous to the FH pattern. To achieve this, one can leverage theknowledge that all signals transmitted by a source via line-of-sight (LOS) propagation have the same direction of arrival (DOA) information.  
Estimating the DOAs of the sources is made more challenging if signals are received via multipath propagation. Measurement data analysis in [11] demonstrates that physical structures in the channel act as secondary sources, forming separable clusters with narrow angular spread around the clusters [12; 13; 14]. Several cluster-based channel models can be found in the literature, such as the 3 GPP Spatial Channel Model (SCM) [15; 16; 17], WINNER II [18], and the 3 GPP Clustered Delay Line (CDL) model [19]. In [15; 16; 17], the SCM is defined for different scenarios, namely suburban macro, urban macro and urban micro. The urban micro channel propagation environment deals with LOS sources, and the other two propagation environments take the effects of multipath propagation into account and deal with non-line-of-sight (NLOS) sources. In this work, signals are observed over channels that follow the SCM model for three propagation environments: (i) LOS, (ii) single-cluster, and (iii) multiple-cluster settings.
## 3 Installation Methods and Circuit Protection
### Installation Methods of Fiber Optic Displacement Meter  
The installation method of the fiber optic displacement meter included the following steps (Figure 9):  
1. Drilling was affected by the size of the equipment. The diameter of the drilled recess was more than 75 mm, and the depth of the hole was more than 10 m. Combined with the site construction conditions and construction environment, the C-6 drilling rig was used for drilling. The boreholes are shown in Figure 9a. In order to prevent the borehole from collapsing and ensure the quality of the borehole, after the borehole was punched, the debris in the borehole were rinsed during the lowering of the instrument to prevent sticking.
2. The fiber Bragg grating displacement sensor group was then lowered. First, the displacement meter and grouting pipe were lowered. Subsequently, the 4 m grouting pipe was put into the hole, and extreme care was taken not to stick it to the cable and to position it on the opposite side, as shown in Figure 9b.
3. After the lowering was completed, the orifice sealing work was started. Cotton wool was inserted into the borehole and mixed with mineral polyurethane expansion quick-drying glue in a ratio of 1:1 for good stirring and inserted into the borehole. The sealing length of the orifice was about 0.3 m, and the sealing effect could be achieved through a reaction cycle of about 30 min. The mineral polyurethane expansion quick-drying adhesive is shown in Figure 9c.
4. The grouting principle is shown in Figure 9d, and the technical requirements of grouting are shown in Table 2. The cement was stirred in a tank at the site, and the entire hole was grouted to prevent cement solidification. After a certain amount of grouting, the PVC pipe began to produce gas, and the slurry flowed back. At the beginning, the slurry was concentrated, and a ball valve was used to control the slurry to flow outward until a slurry with the same concentration was obtained. The ball valve switch was then closed, and grouting was continued. When the grouting amount was approximately equal to the calculated grouting amount, the grouting work was stopped. At the same time, the grouting pipe was closed, and the hole grouting work was completed.
5. After 2-3 days of natural curing, the cement slurry was able to meet the requirements of curing strength. Initial testing was performed, and the initial wavelength was recorded, as shown in Figure 9e.
6. The wires were then connected with welded steel bars, as shown in Figure 9f.  
\begin{table}
\begin{tabular}{c c} \hline
**Grouting Machine** & **Pumping Pressure: 0.5 MPa** \\ \hline Grouting pipe & L = 4000 mm, D = 40 mm \\ Cement & Mixed on site, water cement ratio: 2:1 \\ \end{tabular}
\end{table}
Table 2: Grouting technical requirements.  
Figure 8: Stationing position of steel arch strain gauge.
## 6 Conclusions  
We performed human subject experimentation to determine the QoE and QoS in augmented reality settings. Our prediction performance analysis employing the datasets with machine learning approaches for QoE and QoS predictions showcases that QoE/QoS predictions are feasible on an individual subject basis. Utilizing the mean absolute prediction error, we found that in the current configuration, all four available electrodes' data can be employed in fairly accurate predictions of QoE and QoS levels. Considering future wearable devices, a reduction of the number of electrodes is possible with only small penalties on prediction accuracies. Such a reduction would allow the generation of wearable devices with reduced complexity paired with good real-time QoE/QoS prediction performance overall, as long as at least two electrodes remain in the configuration.  
Throughout this paper, we describe the generation and utilization of the Visual Interface Evaluation for Wearables (VIEW) datasets. The VIEW datasets contain anonymous human subject ratings and EEG band information gathered during experimental sessions with commercially-available off-the-shelf equipment. We make them publicly available and describe their utilization to help the research community in furthering their inquiries in the domain of QoE in augmented reality scenarios.  
Several interesting avenues for additional evaluations exist, such as evaluations of more devices and types, with subsequent cross-performance analyses. Additional evaluations based on the VIEW dataset exist, as well, such as new approaches to data processing and prediction. In our ongoing research, we are investigating other wearables and actual system implementations in the context of the tactile Internet and edge computing.  
This work was supported in part by a Faculty Research and Creative Endeavors grant from Central Michigan University. [PERSON] developed the experimental framework, conducted the human subject experiments and generated the initial data. [PERSON] generated the publicly-available datasets, analyzed the data and prepared the manuscript. The authors declare no conflict of interest.
# Hybrid Early Warning System for Rock-Fall Risks Reduction
## 3 Data Used  
Past rock-fall evidence is a crucial element in the estimation of possible events. In this study, two of the sites most plagued by rock-fall injuries were identified and approved as a study location. The rock-fall positions such as inventory reports and field surveys were described by two approaches. Three sources were used to collect historical data on rock-fall and their related weather information databases: Geological Hazards Research Center, KSA Civil Defense, and General Authority of Meteorology and Environmental Protection. From January 2015 to December 2018, a total of 75 rock-fall accidents were registered, and date of occurrence, location, and weather data were stored for each. The period was broken into 192 equivalent sampling intervals to incorporate a non-rock-fall event into the data collection. Then, the value assigned to the survey period in which rocks existed was one; otherwise, it was assigned as zero. The rock-fall inventory data were separated into two subsets for research purposes--a training data set of 65% (134 samples of 54 rock-falls) and a test data set of 35% (58 samples with 21 rock-falls). For the generation of rock-fall models, the training and the testing data sets were used for model validation. One dependent variable (rock-fall event) and three independent variables (rainfall rate, temperature variation, and slope angle) were derived during the inventory data review. In the study of rock-fall incidence, three contributing factors were considered based on the available evidence and literature review [36; 37]. These variables include weather variables (rainfall intensity, temperature difference) and spatial variables (slope angle). The rainfall rate is a major rock-fall initiating factor [38]. The study area consists of rubble layers, boulders, and small rocks. The geological composition of mountains induces heavy rainfall to shift debris layers, contributing to rock movement and dropping. The fluctuations in temperature have an apparent effect on the rocks' stability. Cyclical variations in temperature allow rock surfaces to expand and compress, leading to deformation or rock cracks [39]. The angle of the slope has an apparent effect on susceptibility to rock-fall. The higher the angle of the slope is, the less stable the rocks are [40].