esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of the Thermal Performance and Temperature Field  
### Analysis of the Temperature Distribution of the Rolling Bearing  
In order to study the heat transfer law of each component of the rolling bearing, the corrected mathematical model is used to calculate heat generation and convective heat transfer coefficient. Take the temperature rise experiment condition shown in Table 5 as an example; the transient temperature field of the rolling bearing is analyzed. The final steady-state temperature distribution of the overall ball bearing and cross-section temperature distribution are shown in Figure 9, which reveals that the temperature gradient boundary of the rolling bearing after reaching the thermal stability state is obvious: the temperature is highest in the inner ring and lowest in the outer ring, with the rolling temperature gradually decreasing from the inner ring contact point to the outer ring contact point, and there is no pronounced temperature difference between the outer ring, inner ring and cage. The 6311 ball bearing has eight rolling bodies, with its overall temperature distribution showing 1/8 periodic symmetry. Five points of the longitudinal section centerline are taken as research objects, namely the outer surface temperature node of the outer ring, the temperature node of the contact point between the outer ring and the rolling body, the center temperature node of the rolling body, the temperature node of the contact point between the rolling body and the inner ring, and the inner surface temperature node of the inner ring. The temperature distribution on the five points is shown in Figure 10.  
Figure 8: Durability experiment verification results.  
Figure 7: Comparison curve of the corrected temperature rise simulation value and the experimental value.  
As can be seen from Figure 9, of the five temperature nodes, the temperature is the highest at the contact point between the inner ring and rolling body and the inner surface of the inner ring. The contact points of the inner ring and the rolling body and the outer ring and the rolling body are where friction heat and bearing heat are generated. The generated heat then causes the temperature to rise in the rolling body, inner ring and outer ring via heat conduction. The temperature in the outer surface of the outer ring is lower than that of the inner ring since the outer surface is in contact with the bearing chamber, which allows the heat to dissipate through conduction.
## 6 Results and Discussion
### Group Specific Discussion
#### 6.1.2 Group #2  
This trial set was characterised by the data listed in Table 2. Throughout this group of experiments, 268 packets out of the 300 transmitted were received (i.e., the 89.33%). Table 6 outlines the mean values, the standard deviations of the RSSIs and SNRs and the number of received packets out of 50 for each SF. The best RSSI mean value was experienced for \(SF=7\) (i.e., \(\mu_{RSSI}=-100.717\) dBm) while the best SNR mean value was observed for \(SF=11\) (i.e., \(\mu_{SNR}=9.396\) dB). However, because of the limited difference amid the mean RSSIs of \(SF=7\) and \(SF=8\) and due to the fact that the latterhas the smallest standard deviation, it is plausible to assert that \(SF=8\) is the one having the best performances throughout this trial setup. Figures 14-17 respectively show the RSSI PMF, the RSSI temporal trend, the SNR PMF and the SNR temporal trend dividing them by SF. As it was averred for the analysis of group #1, Figures 15 and 17 do not present any value in the horizontal axis since their purpose is just to visualize how the RSSIs and the SNRs varied during the tests. It can be seen that the number of the negative values for SNR was increased with respect to the ones belonging to group #1.  
Figure 15: Group #2 RSSIs temporal trend: (**a**) \(SF=7\); (**b**) \(SF=8\); (**c**) \(SF=9\); (**d**) \(SF=10\); (**e**) \(SF=11\); (**f**) \(SF=12\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SF** & \(\mu_{RSSI}\) **[dBm]** & \(\sigma_{RSSI}\) **[dBm]** & \(\mu_{SNR}\) **[dB]** & \(\sigma_{SNR}\) **[dB]** & **Received Packets** \\ \hline \(7\) & \(-100.717\) & \(1.893\) & \(6.744\) & \(4.093\) & \(46\) \\ \(8\) & \(-101.978\) & \(1.406\) & \(7.716\) & \(4.446\)  
Figure 16: Group #2 SNRs PMFs: (**a**) \(SF=7\); (**b**) \(SF=8\); (**c**) \(SF=9\); (**d**) \(SF=10\); (**e**) \(SF=11\); (**f**) \(SF=12\).
# Texture-Guided Graph Transform Optimization for Point Cloud Attribute Compression
## 2 Related Work on Point Cloud Attribute Compression
### Point Cloud Graph Transform Optimization
Commencing with the initial graph \(\mathcal{G}_{d}=(\mathcal{V}_{d},\mathcal{E}_{d},\mathbf{W}_{d})\) and its corresponding desired graph sparsity \(k_{d}\) as determined from the preceding texture-guided intra-cluster analysis outlined in Section 3.4, we employ the primal-dual algorithm outlined in [38] to address our proposed graph optimization problem using a splitting optimization strategy. Within this framework, the weight optimization step automatically discerns the edges to be nullified and sets their optimal values. Subsequently, with the optimized graph structure in hand, the graph Laplacian is computed to facilitate the compression of attribute residuals via transform. Consequently, point cloud attribute residuals are efficiently compressed by leveraging the optimized graph transform. Finally, the transformed point cloud attribute coefficients undergo quantization and entropy coding to produce the total bitstream. In our scheme, we do not compress the entire graph structure but rather encode a graph sparsity parameter into the bitstream. The construction of the graph in the point cloud local clusters utilizes both the decoded geometry information and the graph sparsity parameter extracted from the intra-cluster attribute analysis. Specifically, the decoded geometry information is available in both the attribute encoder and decoder. The extracted graph sparsity parameter is then encoded into the bitstream to facilitate consistent construction of the graph in the decoder. In the decoder, the geometry information of the point cloud is first decoded and then used as side information to guide the partitioning of the point cloud attributes into local clusters. Later, graph sparsity parameters of point cloud clusters are decoded. By leveraging these two types of decoded information, the graph structure in point cloud local clusters can be consistently reconstructed in the decoder, ensuring accurate representation and decoding of the attribute information.
## 3 Results  
### Ionospheric Scintillations Observed with LOFAR PL612 Station  
To show the importance of the results, it is worth mentioning that LOFAR stations are a very good tool for observing scintillation of radio waves from distant bright, point sources [7; 8; 9]. Of course, when observing astrophysical objects, we strive to reduce the influence of the ionosphere as much as possible, however, this is not always a routine activity. On the other hand, monitoring the phenomena connected to ionosphere is one of the observation points in the single station mode for a few LOFAR stations. Although the current observation technology does not allow simultaneous observations of pulsars and ionospheric phenomena, below we present examples of such observations. In studies of the ionosphere, we observe two discrete astronomical radio sources for LOFAR system: Cyg A and Cas A.  
Cygnus A (Cyg A) is a radio galaxy, one of the strongest radio sources in the sky. It contains an active galactic nucleus (AGN). The super-massive black hole at the core has a mass of \(3\times 10^{9}\) solar masses. Cassiopeia A (Cas A) is a supernova remnant (SNR) and it is the brightest extrasolar radio source in the sky at frequencies above 1 GHz.  
Figure 7 shows examples of dynamic spectra obtained in the direction of Cassiopeia A (left part) and Cygnus A (right part). Data were taken from LBA part of LOFAR PL612 station and represent frequency span from 18 MHz up to 88 MHz. Temporal resolution is 1 s. The pipeline for this kind of observations is described in detail in [30]. The colors represent relative flux. The slightly stretched structures (left) and vertical structures (right) show how the electron density changes towards the observed sources, which affects the transfer of radio waves through the ionosphere with also taking into account the frequency of the radio wave. Such observations, as they are carried out at lower frequencies, are much more sensitive than the commonly used GNSS methods [3; 8].
# Noise Modeling to Build Training Sets for Robust Speech Enhancement
## 1 Introduction  
Speech enhancement [1] (SE) is the extraction of speech signals while suppressing sources of interference and eliminating noise. SE plays an important role in improving the intelligibility and quality of noisy speech recordings. In recent years, Deep Neural Network (DNN)-based SE methods have received significant attention as part of a broader interest in learning-related Artificial Intelligence (AI). Important contributors are the Recurrent Neural Networks (RNNs) [2, 3] and Generative Adversarial Nets (GANs) [4, 5], along with other DNN-based architectures [6, 7, 8] that have already been explored in SE tasks.
# Preliminary Seismic Hazard Analyses for the Ugandan Region
## 3 Materials and Methods  
Seismic hazard analysis can be conducted using deterministic or probabilistic methods. In this study, probabilistic seismic hazard analysis (PSHA) is employed as the deterministic approach relies on the developer and provides maximum values in the results. The procedure for carrying out PSHA can be summarized in the following three steps: (1) identifying and characterizing all potential earthquake sources which can produce strong ground motions, (2) selecting appropriate GMPEs, and (3) calculating the hazard integral given the relevant source characteristics and GMPEs selected. The seismic hazard integral is expressed as:  
\[\lambda(IM>x)=\int_{i=1}^{number\ of\ sources}\lambda_{i}(M>m_{min})\int_{m_{ min}}^{m_{max}}\int_{r_{min}}^{r_{max}}P[IM>x|m,r]P[M=m]P[R=r] \tag{1}\]  
where \(\lambda=\) annual rate of exceedance; \(M\) and \(m=\) magnitude; \(m_{max}\), \(m_{min}\) = maximum magnitude and minimum magnitude, respectively; and \(r_{max}\), \(r_{min}\) = maximum and minimum site-to-source distances, respectively. One of the ways the probabilistic approach integrates uncertainty is through the GMPE in the \(P[IM>x|m,r]\) component of the hazard integral.
# Commonly Used External TAM Variables in e-Learning, Agriculture and Virtual Reality Applications
## 3 Method
### Phase 1--Definition of the \"What's\"
#### 4.1.3 Technology Acceptance When the Technology Is a Device  
The TAM was initially conceived as a framework to understand the grounds for computer technology acceptance [7]. In keeping with its initial purpose, [PERSON] et al., [PERSON] [55; 56] validated the TAM in an attempt to identify the factors predicting pre-service teachers' intention to use computers at the National Institute of Education in Singapore, and [PERSON] et al. [57] studied teachers' intention to use computers in their pedagogical practices in a university in Malaysia. Analogously, [PERSON] et al. [58] examined the behavioral intention of primary education teacher students of the University of Salamanca, towards to use mobile devices for teaching practices to be developed in the future. Cellular technologies are attractive tools for educational purposes and also have a fundamental role in fostering rural development. With that in mind, [PERSON] and [PERSON] [59] surveyed the adoption reasons for mobile phones among farmers in Bangladesh. The proposed Rural Technology Acceptance Model (RUTAM) pointed out the significant influence of social ties at the early stages of adoption. Carrying on with the agricultural sector, [PERSON] and [PERSON] [60] delved into the attributes responsible for a favorable reception of solar water pump (SWP) technology among rural farmers of Pakistan. The outcomes confirm the influence of perceived usefulness and perceived ease of use on SWP usage. On VR technologies, the TAM was tested to determine the acceptance of google's [61] and virtual reality devices in general [62]. In both cases, perceived enjoyment was a significant and positive predictor of attitude towards using and purchasing.
# Analyzing the Corporate Business Intelligence Impact: A Case Study in the Financial Sector
## 2 Proposed Research Framework, Methodology, and Case Study
### ETL Processes
#### 2.4.1 Extract Phase  
* Cheque Inquiry Trigger: When a customer presents a cheque to XY Financial Institution or when a cheque needs to be processed, the institution automatically sends an inquiry about the cheque to the CRB or the relevant source. This inquiry includes information such as the cheque's validity, the unclosed cheque's status, and the cheque's payment status.
* Retrieval of Data from Data Source: XY Financial Institution typically sends a query to the CRB platform to collect cheque information. This platform sends the cheque number, the date the cheque was issued, and other necessary information. The response from the CRB includes the current status of the cheque and the payment history.
* Data Format: Data are retrieved from the platform via the Rest API in JSON format. These formats allow for the data to be retrieved in a structured format. Cheque-related data typically include the following types of information:
* Cheque number
* Issuer/institution information
* Cheque amount
* Cheque status (paid, unpaid, bounced, etc.)
* Cheque date
* Cheque payment status (e.g., bank approved, voided, etc.)
## 2 Results and Discussion  
### Prediction of Bioactive Potential of Identified Peptides from Protein Hydrolysates Samples  
Identified peptides from LC-MS/MS analysis were analyzed using \"Peptide Ranker (PepRank)\" web-based application. PepRank is an in silico tool that predicts the probability of a peptide to be bioactive by ranking them within the range between 0 and 1 [16]. Computational predictions of bioactivity in PepRank generally cover the different classes of bioactive peptides. In addition, it considers the amino acid composition and impact of extracellular status on predictions [17]. Upon submitting the list of identified peptide sequences from each sample, PepRank predicts the probability of the bioactivity of each submitted peptide. The general threshold value is referred as 0.5 and peptides that possess scores above 0.5 are considered bioactive [18]. It is also noteworthy that false positive rates decrease with increasing the threshold values. For example, a false-positive rate of 16% of short peptides at 0.5 threshold will decrease to 6% by increasing the threshold to 0.8. For long chain peptides this will change from 11% to 6% [17]. Furthermore, it may also affect the true-positive rate. Therefore, it is always prudent to select the threshold value carefully based on the purpose of the analysis, i.e., identification of all the true-positives or screening out or reduce the number of false-positives [17; 18]. For our study, we selected 0.9 as the threshold value based on suggestion that the probability of a peptide to be bioactive is significantly high when the predicted score is close to 1 [16]. As shown in Table 1, 8 peptides were over 0.9 threshold score in flower whereas 6 peptides were identified from internal organs samples. Among all the predicted values, flower and internal organs shared the same peptide sequence (GPPQPQWPLDF with 0.96) for the highest predicted bioactivity. The predicted bioactive peptides were then analyzed for their specific bioactive potentials with emphasis on antioxidant and ACE inhibitory activities using BIOPEP data base.
## 2 Model Experiments
### Results and Discussion
#### 2.5.4 Deflection and Stress  
As shown in Figure 8, the deflection decreases exponentially with the increase of distance from the tamping point. Their relationship can be expressed as an exponential equation (Equation (3)). Among them, the deflection increases gradually with the increase of A (Figure 8a) or C (Figure 8c).  
\[S_{g}=a\cdot e^{b}+c, \tag{3}\]  
where \(S_{g}\) is the deflection of first reinforced geogrid, \(b\) is the distance from the tamping point, and \(a\) and \(c\) are empirical fit constant.  
Figure 9a shows that the closer the geogrid is to the tamping point, the greater the strain. The stress increases gradually with the increase of A. The main reason is that the thicker the soil above the geogrid, the greater the impact energy, which leads to a large strain distribution as well as lateral deformation of geogrid. As shown in Figure 9b, B has little effect on the stress distribution of first reinforced layer, the variation trends of strain distribution are almost the same case with each other. On the horizontal direction, between 0 and 330 cm away from the tamping point, the stress is exponentially decreased. Figure 9c shows the strain increases with interval increasing and decrease greatly with increasing horizontal distance away from tamping point. Since it is harmful to have a bigger stress on the geogrid, a smaller interval should be selected to prevent the yield failure by excessive displacement of reinforced geogrid.  
Figure 7: Crater depth and reinforced depth under different reinforced factors: (**a**) embedded ratio; (**b**) number of reinforced layers; (**c**) interval of reinforced layers.  
Figure 8: Deflection of reinforced geogrid under different reinforced factors: (**a**) embedded ratio; (**b**) number of reinforced layers; (**c**) interval of reinforced layers.  
Figure 9: Stress of reinforced geogrids under different reinforced factors: (**a**) embedded ratio; (**b**) number of reinforced layers; (**c**) interval of reinforced layers.  
Through the control variable method, the variation on these evaluating indicators of reinforced soil under dynamic compaction are obtained. The deformation characteristics of the reinforced foundation and the stress state of the geogrid under dynamic compaction are analyzed and optimized by model experiment. Unfortunately, the model experiment results only reflect the influence of a single factor on the evaluating indicators. However, for a specific field project, there are many influencing factors for the evaluating indicators, and the degree of influence of each factor on the evaluating indicators is different. Therefore, how to unify the influence of these factors on evaluating indicators is a problem that needs to be paid more attention. Range analysis is a common method for test results calculated by the orthogonal experimental method, which can find out the key influencing factors and determine the optimal value of influencing factors. In this study, range analysis and variance analysis are employed to analyze the experimental results. The analyses' results are discussed in the next chapter.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Device-to-Device (D2D)
#### 4.2.2 Communication Scenarios of D2D  
There are new features defined in 3 GPP Release 12 which enable the use of eNBs and core networks to facilitate D2D communication. Figure 8 shows the D2D communication scenarios in 3 GPP Release 12. Three coverage situations are specifically addressed, as follows:  
1. In coverage mode: In this communication mode, all Ues are within the eNB's coverage.
2. Out of coverage mode: In this communication mode, none of the Ues are under the eNB's coverage.
3. Partial coverage mode: In this communication mode, certain Ues are covered by the eNB while others are not. Ues under the eNB's coverage communicate with Ues that are not within the eNB's coverage [91].
# Analysis and Demonstration of First Cross-Support Interferometry Tracking in China Mars Mission
## 4 Discussion  
In order to support the joint cross-support DeltaDOR measurements conducted by CLTC and ESOC, this paper researches an algorithm and method which is different from the conventional processing within the CLTC, especially for the clock estimation and correlation for spacecraft scan. Results of the cross-support data sets are also analyzed in detail. Clock offset of stations between different agencies could be obtained effectively, and delay difference for spacecraft scan has RMS better than sub nanosecond, which could testify the effectiveness of the algorithm adopted in CLTC.  
In particular, processing DeltaDOR measurements of all the baselines shows that DeltaDOR measurements before Mars orbit insertion are accurate to better than 0.5 ns (the  
Figure 17: Orbit accuracy review with cross-support DeltaDOR measurement. (**a**) Baseline CEB-NNO; (**b**) baseline CEB-KSH; (**c**) baseline NNO-KSH.  
expected goal is 1 ns). Residuals of CEB-NNO/CEB-KSH/NNO-KSH baselines have one sigma accuracy of 0.6, 0.4 and 0.3 ns, respectively.  
Since the residuals provide a direct estimation of the predicted orbit accuracy, in order to make sure maneuver control parameters effective, such as Mars orbit insertion, the maximum orbit error must be limited to 40 km. Based on the basic DeltaDOR measurement geometry, DeltaDOR delay observables would be better than 1 ns. The results give confidence to the orbit determination and the maneuver planning. The DeltaDOR measurements jointly conducted by CLTC and ESOC successfully support the orbit accuracy review in China Mars mission.  
In addition, the station clock offset in the order of microseconds could be identified when correlated with raw data from different agencies; however, the reason is not presently understood and needs more research. It has been noticed that the station clock offsets between different agencies are relatively stable in all the DeltaDOR measurement data sets, in the same order of microseconds, but not exactly the same.