esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Collection and Processing
### Data Set Creation  
After processing the collected data using the above method, the outliers in the 25 parameter variables that were initially selected were manually cleaned and removed. The collected data were consolidated based on time series correlation to create an initial data set for constructing the blast furnace permeability model. The blast furnace permeability index dataset was then randomly divided into two groups: 80% of the data set is used as a training set to train the blast furnace permeability index prediction model. The remaining 20% of the data set is used as a test set to comprehensively evaluate the effect of the blast furnace permeability index prediction model.
###### Abstract  
This study investigates the energy dynamics of sandstone subjected to failure in conditions typical of deep underground construction. Research was conducted using both standard triaxial compression and cyclic loading-unloading techniques at six distinct confining pressures, with the objective of elucidating the deformation and failure processes of rock materials. The tests demonstrated that, regardless of the stress path, sandstone primarily fails through shear under different confining pressures, which also reduces the formation of secondary cracks. The energy transformation observed during cyclic loading and unloading processes exhibits a distinctive peak-like distribution, marked by an inflection point that indicates changes in energy distribution. In the initial stages of the loading cycle, the energy profile of the rock increases, characterized by a condition in which the energy stored elastically exceeds the energy dissipated. Nevertheless, subsequent to reaching peak stress, there is a rapid transmutation of elastic strain energy into other forms, culminating in a pronounced elevation in the ratio of dissipated energy, which ultimately achieves a state of equilibrium influenced by the confining pressures. The study introduces the energy consumption ratio (Ke) as a metric for assessing rock damage accumulation and stability, noting a critical pattern where Ke decreases and then spikes at the rock's failure point, with K = 1 identified as the critical threshold for failure. This comprehensive analysis illuminates the intricate relationship between energy distribution patterns and the stability of rock structures, thereby enhancing our understanding of failure mechanisms from an energetic perspective.  
triaxial tests; cyclic loading and unloading; energy evolution; damage +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
In addition, it can be seen that 5% to 10% exergy loss occurs due to heat loss to the surrounding. Exergy destruction due to optical losses is found constant and the destruction due to pressure drop is found insignificant. The temperature difference between absorber plate and the heat transfer fluid contribute to very low portion of exergy destruction. The graph pattern is in good agreement for the curve for the parabolic  
Figure 8: Variation of exergetic efficiency and thermal efficiency with mass flow rate. Figure 7: Variation of exergetic efficiency and thermal efficiency with irradiance. through receiver [26]. Figure 11 shows that the exergy loss due to optical loss reduces with the increasing optical efficiency. Figure 11: Variation of dimensionless exergy with optical efficiency. Figure 10: Variation of dimensionless exergy with ambient temperature. Figure 9: Variation of dimensionless exergy with fluid inlet temperature. Figures 14-18 graphically show the relationships between EDR and various parameters. With increasing value of fluid inlet temperature and irradiance, the EDR value decreases. In contrast, the response of EDR to ambient temperature, optical efficiency and mass flow rate is the opposite, i.e., EDR increases with these parameters. Figure 14: Variation of exergetic destruction ratio with fluid inlet temperature. Figure 12: Variation of dimensionless exergy with irradiance. Figure 5:0 Variation of dimensionless exergy with mass flow rate. Figure 5:1 Variation of exergetic destruction ratio with optical efficiency. Figure 5:2 Variation of exergetic destruction ratio with irradiance. Figure 5:3 Variation of exergetic destruction ratio with ambient temperature. For this type of passive solar collectors, it is not possible to measure the mass flow rate of water, so it was calculated numerically, as mentioned in Section 3.1. However, the response of various parameters with respect to mass flow rate was consistent with the results mentioned in other research. To simply the calculation of overall heat loss coefficient, collector heat removal factor and plate efficiency factor were considered constant, which is not the case practically.
# An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach
## 2 Related Research
### New Customer Churn Model Variables
In addition, reference [47] predicted customer churning in the telecommunication industry based on rough set theory. They used historical data on a publicly available dataset and found some essential attributes in the customer churn prediction, such as evening minutes, customer service calls and day minutes. Reference [45] proposed a prediction model for a customer churn by using different data mining techniques. They used customer information, such as contract length, customer complaints and call details. Reference [48] used three hybrid models over two stages: data clustering and churning prediction. They collected the three-month call data of customers of a Jordanian telecommunication company. Reference [49] predicted customer churn according to the call details and contract information they gathered from interviews with telecom experts. Reference [7] proposed a model for predicting high value customers and churner customers. They used customer information, such as age, sex and call details. Numerous studies recognised the importance of including customer complaints as an attribute in their churn prediction model [6; 7; 25; 31; 32; 34; 35; 39; 41; 43; 45; 50]. After reviewing the literature, we listed the most common techniques in Table 3. As shown in the literature, decision trees and logistic regression are the most common techniques used in churning prediction models. A decision tree offers a graphical representation of the relations between churning variables [51]. CART or CHAID are examples of the algorithms used to develop a decision tree [52]. Both logistic regression and decision tree are effective and easy techniques to predict churning and analyse the characteristics that cause a churn [36; 42; 53; 54]. However, there are some disadvantages in using a decision tree, such as it being affected by the complex relations between the variables [71]. The next technique commonly used in the literature is a neural network, which has some limitations, including its need for an extensive dataset and extensive time consumption in training [42]. Support vector machine and naive Bayes were likewise used.
# A Review in Fault Diagnosis and Health Assessment for Railway Traction Drives
## 3 Fault Diagnosis in Railway Systems
Among the model-based approaches, a consistency-based approach, given analytical redundancy relations (ARR), is proposed for traction power supply fault detection [61]. An Observer-based approach is presented in [62] for sensor fault detection in the suspension system. A hypothesis test is utilized for residual evaluation.
## 3 Gene Discovery and Gene Expression Analysis in the Pre-NGS Era
### Other Plant Metabolites Produced by Endophytic Fungi
#### 4.6.5 1,8-Cineole  
In 2015, [PERSON] et al. described a new fungal 1,8-cineole synthase gene, _hyp3_, in the genome of isolate E7406B of _Hypoxolon_ sp. [82]. It was the first monoterpene synthase discovered in fungi to be structurally and biochemically characterized, exhibiting the same specificity and mechanism for water capture as observed in unrelated plants species. The whole genome sequencing of isolate E7406B enabled identification of thirteen terpene synthases, out of which two were excluded and four were proven to conduct terpene biosynthesis. Only hyp3 demonstrated a strong affinity for GPP substrate and production specificity of 1,8-cineole (Figure 1) as a major monoterpene product of this enzyme.  
Phylogenetic analysis of 1,8-cineole synthases in bacteria, plants, and fungi revealed interesting relationships between these proteins, suggesting convergent evolution. However, terpene synthases from strain E7406B do not belong in a monophyletic clade and are related with ascomycete terpene synthases, while hyp3 is specifically related with fungal aristolochene synthases. These results suggest independent evolution of this pathway in fungi and argue against hypothetical horizontal gene transfer.
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 6 Conclusions  
At present, although valuable research results have been achieved in multi-mode process monitoring technology, there remain many difficult problems worthy of further research: (1) Data acquisition is difficult. In particular, the amount of historical data available in the transition mode is far less than that in the steady-state mode. The process monitoring of transition modes is still a challenge; (2) The mechanism of the batch process is complex, and the model is difficult to obtain. The monitoring technology for the batch process is far behind that for the continuous process; (3) The monitoring of complex industrial production processes in the multi-mode is divided into offline modeling and online monitoring stages. The dilemma for pattern recognition of offline data lies in the means of distinguishing modeling data for steady-state modes and transition modes, while that of online data is how to identify the mode category corresponding to online data in time.  
In conclusion, there have been many research studies concerning multi-mode process monitoring technology. However, few studies relate to fault prediction and residual effective life prediction techniques which have significant development potential.  
This research was funded by the National Key Research and Development Program of China (No. 2019 YFB1705403) and the National Natural Science Foundation of China (No. 61973041).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# BDS-3 Integrity Risk Modeling and Probability Evaluation
## 3 BDS-3 Integrity Failure Model and Effect Analysis Algorithm
### BDS-3 Integrity Risk Factors Analysis
#### 3.3.1 System Anomaly  
System anomaly factors can be divided into space segment anomalies and ground control segment anomalies from the system composition perspective.  
According to the BDS-3 navigation satellite composition, the space segment anomalies can be divided into satellite payload anomalies and satellite platform anomalies. According to the integrity monitoring process in Section 2.2, the payload anomalies directly related to integrity monitoring can be further decomposed into satellite clock anomalies, signal measurement anomalies and signal power anomalies.  
According to the ground integrity processing flow described in Section 2.2.1, the anomalies directly related to integrity monitoring in the ground control segment include ground monitoring network data anomalies, the uplink injection station injection anomalies, and the master station information processing anomalies (including satellite orbit calculation anomalies, satellite clock calculation anomalies, ephemeris fitting parameter calculation anomalies, and orbit and time synchronization processing equipment anomalies).
## 3 Results
### Maturity Progress  
VSP showed a faster velocity of ripening compared to all SMPH treatments (Figure 1) resulting in a higher brick value at a given GDD. Through all thinning treatments, maturity progress was accelerated and the delay in ripening reduced.  
On average VSP required 2818 GDD (DOY 236) reaching the legal threshold of TSS (Riesling: 14.1 \({}^{\circ}\)Brix) for the category of quality wine of origin at Rheingau valley (Table S2). For the SMPH, a delay of 129 GDD (7 days) was determined. Whereas, thinning accelerated maturity progress. Across three vintages the delay to VSP was reduced to 71 GDD (3 days) by shoot thinning, 101 GDD (5 days) by bunch thinning and 102 (5 days) by biotechnological thinning. Reducing yield caused an earlier ripening (14.1 \({}^{\circ}\)Brix) in SMPH ST (58 GDD; 3 days), SMPH BT (28 GDD; 2 days) and SMPH GA (27 GDD; 2 days) compared to non-thinned SMPH.  
The threshold of TSS for the category of cabinet wine quality (Riesling: 18.2 \({}^{\circ}\)Brix) was reached on average 269 GDD (16 days) later in SMPH (3358 GDD; DOY 268) compared  
Figure 1: Maturity progress and thresholds of two German wine categories (14.1 \({}^{\circ}\)Brix and 18.2 \({}^{\circ}\)Brix): Total soluble solids (TSS) plotted against cumulative growing degree days in Vertical Shoot Positioning (VSP *) and Semi-Minimal-Pruned Hedge (SMPH) training system with non-thinned control (SMPH \(\blacksquare\)), shoot thinning (SMPH ST \(\blacktriangledown\)), biotechnological thinning (SMPH GA \(\blacklozenge\)) and bunch thinning using a harvester (SMPH BT \(\blacktriangle\)) of cv. Riesling between 2017 and 2019. Shapes indicate empirical observed data, lines represent calculated progress according to sigmoidal equation type \(y=a/(1+e^{-((x-w)/\beta)})\). Error bars are representing \(\pm\)SD.  
to VSP (3089 GDD; DOY 251) (Table S2). Across three vintages, the delay to VSP was reduced to 164 GDD (10 days) by bunch thinning, 191 GDD (12 days) by biotechnological thinning and 210 GDD (13 days) using shoot thinning. Compared to the non-thinned SMPH 18.2 \({}^{\circ}\)Brix was reached earlier in SMPH BT (105 GDD; 6 days), SMPH GA (78 GDD; 4 days) and SMPH ST (59 GDD; 3 days).  
GDD and DOY for both wine categories (14.1 \({}^{\circ}\)Brix and 18.2 \({}^{\circ}\)Brix) and all treatments as well as years respectively are presented in Table S2.
# Ten Years of TerraSAR-X Operations
## 4 Dedicated Mission Campaigns
### Modifications Induced by TanDEM-X Science Phase
#### 4.2.2 Large Horizontal Baseline Formation  
Following the pursuit monostatic phase, TSX and TDX returned to a close formation for bistatic data taking under large cross-track baseline conditions. A maximum horizontal separation of about 3.6 km was reached in the extreme at the equator. Thus, TerraSAR-X acquisitions that were performed by TDX were possibly taken outside the reference orbit tube leading to slightly different product characteristics. Therefore, the so-called preferred satellite concept was introduced. In case that the nominal perpendicular baseline between TSX and TDX exceeds a given (configurable) threshold and if the available resources like memory and downlink capacity allow for it, a scene is taken by TSX (presuming that the user order allows for both, TSX or TDX for the acquisition). In summary, four different satellite selection options are offered for ordering. Orders can specify the TSX satellite for data taking when the observation of the reference orbit tube is needed. TDX is chosen if one wants to explicitly exploit large baseline situations. No satellite is pre-selected, but preference should be given to TSX. Both TSX and TDX are accepted, if no TSX preference is needed. Hence, the user needs were observed as best as possible, while still allowing for mission planning a good load balancing between TSX and TDX.
# Developing a Virtual World for an Open-House Event: A Metaverse Approach
## 2 The Design and Development
### Architecture and Implementation  
The platform's architecture design, shown in Figure 1, was based on the gathered requirements and features a front-end named CAMT MetaEd, which is accessible by both end-users and administrators. The front-end application was developed using Unity for deployment on a WebGL platform for easy accessibility by any web browser [2]. The Unity game engine was adopted because it has a good reputation in cross-platform development. In other words, the developed application can be easily imported to other platform devices, such as VR, AR, mobile, and standalone applications. The front-end was connected to the Playfab as the back-end service [51], where the user credentials, user personalisation data such as avatar configurations, and user statistics are stored. The Playfab back-end service was chosen for several reasons. It has a well-documented use as a standard form of data storage, i.e., Java Script Object Notation (JSON) [52]. It is rich in features for this particular type of back-end service. For example, it has a secure server-side cloud function for virtual money transactions. It is scalable. Figure 2 shows an example screenshot of the use of the Playfab back-end to store user data. At the lower right of Figure 1, the state-of-the-art Colyseus real-time online multiplayer distributed engine can be seen [53]. This is used for real-time data synchronisation by concurrent online users. The engine is scalable to support thousands of concurrent users, which is essential for the metaverse. The application database serves as the metaverse content server on which the virtual world information is stored and can be retrieved via the provided application programming interface (API). It can be managed by an administrator through a web-based application, shown in the lower left of Figure 1. From a list of key metaverse features in Table 1, Table 2 shows the corresponding features implemented in this study.  
As a result of this implementation, a screenshot of the registration page is shown in Figure 3. The registration follows a common form of procedure: a user fills in the form and agrees with the term of service. Then, a confirmation email is sent to the provided email address for the user confirmation. Figure 4 shows a screenshot of an avatar's hair style and clothing, obtained using the customisation feature. Ready Player Me [54] was utilised as the system for avatar personalisation.
## 3 Results and Discussion
### Analysis of the Impact of the Rasterization Value
The full results of the pairwise post hoc comparisons for the factor _Algorithms_ can be found in Table 10. \begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{2}{c}{**Mean Difference**} & \multicolumn{1}{c}{**SE**} & \multicolumn{1}{c}{**t**} & \multicolumn{1}{c}{\(\mathbf{p_{bonf}}\)} \\ \hline ABC & FWA & 0.002 & \(7.760\times 10^{-5}\) & 24.560 & \(<\)0.001 \\  & PSO & \(1.452\times 10^{-7}\) & \(7.760\times 10^{-5}\) & 0.002 & 1.000 \\ FWA & PSO & \(-0.002\) & \(7.760\times 10^{-5}\) & \(-24.558\) & \(<\)0.001 \\ \hline \end{tabular}
\end{table} 
Table 8: Post hoc comparison for _Algorithm_ for two-dimensional Environment 1 with factor _Rasterization_. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Rasterization \(r_{v}\) [m]**} & \multicolumn{2}{c}{**ABC**} & \multicolumn{2}{c}{**FWA**} & \multicolumn{2}{c}{**PSO**} \\  & **Average** & **St. Dev. ** & **Average** & **St. Dev. ** & **Average** & **St. Dev. ** \\ \hline
0.1 & 0.0795 & 0.0001 & 0.0776 & 0.0011 & 0.0795 & \(<\)0.0001 \\
0.2 & 0.0760 & 0.0001 & 0.0743 & 0.0013 & 0.0761 & 0.0001 \\
0.3 & 0  
* The interaction between the factors _Algorithm \(*\) Rasterization_ is statistically significant: \[F(18,870)=10.454,\,p<0.001.\]  
The statistically significant interaction between the factors prompted us to conduct a post hoc analysis of all factors. This post hoc analysis showed that for the two-dimensional environment there is no statistically significant difference between the factors _Algorithms_ for \(r_{v}=0.1\,\mathrm{m}\) and \(r_{v}=0.2\,\mathrm{m}\), while for values of \(r_{v}\geq 0.3\,\mathrm{m}\) both ABC and PSO perform better than FWA, with no significant difference between them. For the three-dimensional environment, a post hoc analysis showed that ABC and PSO outperform FWA for all rasterization values. This can be seen in Figure 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Mean Difference**} & \multicolumn{2}{c}{**SE**} & \multicolumn{1}{c}{**t**} & \multicolumn{1}{c}{_Pbm\({}^{\prime}\)_} \\ \hline ABC & FWA & \(0.001\) & \(3.952\times 10^{-5}\) & \(31.621\) & \(<\)\(0.001\) \\  & PSO & \(-3.056\times 10^{-5}\) & \(3.952\times 10^{-5}\) & \(-0.773\) & \(1.000\) \\ FWA & PSO & \(-0.001\) & \(3.952\times 10^{-5}\) & \(-32.395\) & \(<\)\(0.001\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Post hoc comparison for _Algorithm_ for three-dimensional Environment \(1\) with factor _Rasterization_. Figure 4: Coverage value \(C\) for each of the algorithms based on the rasterization value \(r_{v}\) for Environment \(1\). As can be seen in Figure 5, the computation time increases exponentially with the smaller rasterization value \(r_{v}\) and it overlaps again for ABC and PSO, while it increases somewhat faster for FWA.
# Research on Laser Dual-Mode Fusion Detection Method of Ship Wake Bubbles
## 3 Simulation of Ship's Wake Bubble Laser Dual-Mode Detection Based on Monte Carlo
### Ship's Wake Laser Dual-Mode Fusion Detection Signal Processing Method  
#### 4.2.1 Multi-Timescale Fusion Hierarchical Signal Processing Method  
Aiming at the problems of the weak echo signal of ship's wake bubbles, the random discrete distribution of echo signal, a large environmental interference, and a large quantity of target echo signal data, this paper proposes a multi-timescale hierarchical signal processing method. Combined with integral processing, envelope detection, mean filtering, and other methods, the fusion and discrimination of single pulse cycle millisecond micro-time scale and multi-cycle cumulative second-level macro-timescale wake characteristics are realized, which effectively improves the SBR of wake signal detection [24; 25]. (1) Single-cycle millisecond-scale microscopic timescale  
For the single-cycle laser echo signal, the common target characteristic detection signal processing method is to detect the change in its energy peak and waveform pulse width. However, the highest SBR of these two methods is about 1.5, which is difficult to adapt to the laser echo detection of wake bubbles. In this paper, a signal processing method based on integral circuit for single-cycle millisecond micro-timescale is proposed. Figure 11: Laser detection test system in pool. \(SBR\) is the ratio of the intensity of the target signal to that of the backscattered signal from the waters in a given time or space. That is,  
\[SBR=\frac{S_{s}}{S_{b}} \tag{3}\]  
where \(S_{s}\) is the target echo signal amplitude and is the water background signal amplitude. For wake detection, the farther the detection SBR is from 1, the greater the discrepancy between the wake signal and the background signal of the water, and the better the detection performance. However, when detecting low-density, small-scale bubble groups, the laser echo signal is very weak, and its amplitude is often similar to the water background signal. In this case, the signal-to-background ratios of both amplitude and pulse width are close to 1, making it difficult to determine whether a wake is detected. As shown in Figure 12, in the coordinate system \(Oyt\), the blue curve \(y_{s}(t)\) is the simulated wake echo signal with amplitude \(V_{s}\) and pulse width \(P_{s}\). The red curve \(y_{b}(t)\) is the simulated water background signal with amplitude \(V_{b}\) and pulse width \(P_{b}\).