esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Shield Tunneling Parameters
### Cutter Torque  
The correlation between the cutter torque and the shield tunnel ring number is illustrated in Figure 11. Table 7 presents the statistical characteristics of the cutter torque across three distinct stratum conditions.  
From the analysis of Figure 11 and Table 7, it is observed that in stratum I, shield tunneling exhibits relative stability, with the cutter torque controlled at approximately 5287.8 kN\(\cdot\)m. In stratum II, cutter resistance increases, leading to significant fluctuations and an overall increase in the cutter torque. At the strata II and III junction, the cutter resistance diminishes, resulting in a reduction in the cutter torque to 2960.6 kN\(\cdot\)m. In stratum III, the cutter resistance progressively increases, and the corresponding torque of the cutter rises. However, the tunneling process is smoother than in stratum II. When the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stratum Serial** & **Mean** & **Standard** & **Median** & **Minimum** & **Maximum** & **Skewness** \\
**Number** & **Value** & **Deviation** & **Value** & **Value** & **Value** & **Skewness** \\ \hline I & 5287.8 & 584.3 & 5418.0 & 3897.9 & 6318.0 & \(-\)0.544 \\ II & 5831.5 & 1555.0 & 5876.2 & 2769.7 & 9378.4 & 0.089 \\ III & 4736.7 & 1218.5 & 4482.5 & 2602.3 & 8786.1 & 0.974 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistical characteristics of different strata with the cutter torque.  
Figure 10: The curve of the total thrust changing with the ring number.  
shield is propelled with high torque in the stratum, the cutter wear is minimal, ensuring safe and rapid tunneling.  
In contrast to other strata [34; 35], the torque of the shield cutter head in volcanic ash formations is notably higher. This is attributed to several factors. (1) Volcanic ash strata typically possess high compressibility and viscosity. The shield cutter must overcome substantial formation resistance during excavation, necessitating an increase in the cutter torque to overcome this resistance. (2) High hardness of rock debris: volcanic ash strata contain rock debris and particles with high hardness, leading to significant wear on the shield cutter. To ensure both tunneling efficiency and tool durability, the torque of the shield cutter must be sufficiently large to provide ample cutting force during excavation. (3) Fluidity of volcanic ash strata: volcanic ash strata exhibit a degree of fluidity, causing particles to easily accumulate on the shield cutter head surface. This accumulation increases the resistance of shield tunneling, requiring an increase in cutter torque to overcome this resistance.
## 2 Influence of MAI on Acquisition Module
### The Influence of Mai  
In this subsection, we will analyze the interference experienced by weak signals arising from other users within the system. In MEO communication systems, the free space loss determined by the transmission distance from the ground terminals to the satellite is the main loss suffered by signals, which is defined as  
\[L_{f}=20\lg\frac{4\pi df_{r}}{c}(\mathrm{dB}), \tag{3}\]  
where  
\(f_{r}\) is the carrier RF point, and 1.6 Ghz is taken in this paper.  
\(c\) is the speed of light, and \(3\times 10^{8}\) m/s is taken in this paper.  
\(d\) is the actual transmission distance of the user's signal.  
In our analysis, we assume the altitude of MEO satellites is 21,500 km, which means that the transmitter closest to the satellite experiences a loss of \(L_{f1}\) equal to 183.1729 dB, while the furthest transmitter suffers a loss of \(L_{f2}\) equal to 185.1959 dB.  
Figure 2: Transmitter.  
Although the interference caused by a strong user towards a weak user is merely 2 dB, the increasing number of transmitters results in an amplified presence of interference within the received signals. This leads to a serious performance degradation of the receiver system, impeding its ability to accurately capture the time and frequency bias information from each user. The subsequent functioning of the receiver is also influenced as depicted in Figure 3.  
Figure 4 illustrates the MAI experienced by weak signals with the number of strong signals increasing, quantified through the interference-to-signal ratio (ISR).
## 5 Characterization Using the Time Domain Measurement Data
### Modeling of Time-Varying Characteristics
#### 5.2.1 CHMM Definition  
CHMM has three main elements: state transition probability matrix \(P\), state observation vector probability model \(B\), and initial state probability vector \(I\)[22]. If the event has two states and they all obey the Gaussian distribution, then:  
\[P=\left[p_{ij}\right]_{2\times 2}=\left[\begin{array}{cc}p_{11}&p_{12}\\ p_{21}&p_{22}\end{array}\right] \tag{6}\]  
where \(p_{ij}\) means the probability of making a transition to state \(j\), given currently in state \(i\).  
\[B=\left\{b_{1}(y),b_{2}(y)\right\} \tag{7}\]  
Figure 9: Pulse separation distribution (PSD) results. (**a**) PSD of vertical polarization at 315 MHz; (**b**) PSD of horizontal polarization at 315 MHz; (**c**) PSD of vertical polarization at 779 MHz; (**d**) PSD of horizontal polarization at 779 MHz; (**e**) PSD of vertical polarization at 916 MHz; (**f**) PSD of horizontal polarization at 916 MHz.  
where  
\[b_{1}(y)=\frac{1}{\sqrt{2\pi}\sigma_{1}}e^{-\frac{(y-\mu_{1})^{2}}{2\sigma_{1}^{2 }}},b_{2}(y)=\frac{1}{\sqrt{2\pi}\sigma_{2}}e^{-\frac{(y-\mu_{2})^{2}}{2\sigma_{2 }^{2}}} \tag{8}\]  
\(b_{1}(y)\), and \(b_{2}(y)\) are the probability density function of two states.  
\[I=(I_{i})=(I_{1},I_{2}) \tag{9}\]  
where \(I_{i}\) is the probability of the initial moment of state \(i\).  
To model the signal with CHMM (\(\lambda=(P,B,I)\)), we give an approximate estimate value of the three elements according to the real measurement data using the Baum-Welch algorithm [PERSON] (1953). Then the CHMM is used to simulate the non-stationary conditions of the electromagnetic noise.
# Progress in Improving Photovoltaics Longevity
## 4 Results
### PV Degradation Mechanisms
#### 4.1.1 Intrinsic Degradation (Internal Stress Factors)
The proper design and subsequent matching of all adjacent PV components is vital for the reliability, flawless performance, and extended lifespan of the module. Among all relevant components of the PV module, the backsheet and the encapsulant are more prone to degradation effects. It is, therefore, crucial to find a way to properly exploit the thermo-mechanical properties of these components [81]. In the primary stages of PV module operation, material incompatibilities included delamination, metal corrosion, and discoloration, i.e., yellowing [82]. However, over the past few years, the incorporation of new materials in PV field technologies has led to the expansion of material incompatibilities. For instance, specific combinations of polypropylene-based backsheets and polyethylene-based encapsulants comprise adhesion failures [83], while acetic acid accumulation was connected to cracking formation and propagation [84]. In order to limit--or even eliminate, if possible--the compatibility problems inside a module, it is imperative to modify the already existing encapsulant-backsheet combinations or turn to the quest for new, well-promising ones with reinforced properties.
# An Improved Target Network Model for Rail Surface Defect Detection
## 3 Experimental Design
### Evaluation Metrics  
The paper uses a variety of metrics to evaluate the performance of the rail surface defect detection model, specifically including precision (P), recall (R), [EMAIL_ADDRESS], F1 score, AP (average precision), and mAP (mean AP).  
The precision represents the proportion of all true positive results in a positive sample that is tested, while the recall represents the model's ability to correctly detect positive samples. Precision (P) and recall (R) are defined as follows.  
\[P=\frac{TP}{TP+FP} \tag{13}\]  
\[R=\frac{TP}{TP+FN} \tag{14}\]  
where TP is the count of accurately identified model checks, FP is the sum of errors in model detection and target classification, and FN is the quantity of model misses. A threshold value of 0.5 was employed, whereby a prediction box was classified as a positive sample only if its intersection over union (IOU) with the ground truth box surpassed 0.5; otherwise, it was deemed a negative sample.  
Figure 6: Randomly chosen flawed samples from the dataset.  
To assess the model's comprehensive classification and recall capabilities, the harmonic mean of the above two metrics was used as the F1 score. A higher F1 score indicates a more effective testing methodology. F1 scores are defined as follows:  
\[F1=\frac{2\times P\times R}{P+R} \tag{15}\]  
The equations for calculating AP (average precision) and mAP (mean average precision) are as follows:  
\[AP=\int\limits_{0}^{1}\!P\big{(}R\big{)}\,\mathrm{d}R \tag{16}\]  
\[mAP=\frac{\int_{0}^{1}P(R)\,\mathrm{d}R}{N} \tag{17}\]  
where PR denotes the precision on the precision-recall curve, while N indicates the object type of the model detection set.
# Triaxial Loading and Unloading Tests on Dry and Saturated Sandstone Specimens
## 3 Results
### Energy Evolution of Rock Failure
In contrast, the energy dissipation curve shows a nearly vertical increase for the triaxial unloading test, indicating a sudden and violent brittle failure of rock specimens. The relationship between the \(U_{d}\)/\(U\) and initial confining pressure at the corresponding critical failure point is plotted in Figure 11. The \(U_{d}\)/\(U\) values increase as \(o_{3}^{0}\) increases. When the rock specimen is under high initial confining pressures, it has more time for specimen deformation and damage. Thus the dissipative strain energy takes a large proportion of the total absorption energy in the rock. The deformation and failure process of rock in loading tests includes the micro-cracks compaction stage (OA), the elastic deformation stage (AB), the crack initiation and propagation stage (BC), the unstable crack development up to the failure stage (CD), the post-failure stage (DE) and so on. However, the rock in unloading tests enters the failure stage almost directly from the elastic deformation stage. Figure 10: Evolution of \(U_{d}\)/\(U\) with loading time under triaxial loading and unloading tests for dry and saturated sandstone specimens (**a**) TC-D; (**b**) TC-S; (**c**) TU-D; (**d**) TU-S. Thus, relatively fewer cracks develop inside the rock, resulting in less dissipation of rock energy and the energy is mainly consumed for the circumferential deformation and converted into kinetic energy for the failure of rock, such as rockbursts. The \(U_{d}\)/\(U\) of rock in unloading test is significantly lower than that of conventional triaxial compression test, as shown in Figure 11. Furthermore, the \(U_{d}\)/\(U\) of saturated rock is greater than dry rocks in the deformation and failure process of triaxial tests. Saturated by water, the internal friction strength of the rock is weakened as the water immersion, which results the rocks more prone to be deformed and damaged. With the accumulated damage within the rock, the energy account for the dissipation increases and the energy release decreases, leading to a relatively moderate failure of saturated rock, and there are less possibilities for rockbursts. It is also one of the basic theoretical foundations for the rockburst prevention through water injection in engineering. In addition, comparing the influence of stress paths and water conditions of the rock in Figure 11, it can be found that the effect of stress paths on energy evolution is more pronounced than the water conditions of the rock.
# Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping
## 4 Results
### Comparison with Other Works  
Table 8 compares the proposed ensemble system with other works. The proposed customer-churn prediction ensemble system shows the best accuracy.
# Pan-Cancer Analyses Reveal Genomic Features of FOXM1 Overexpression in Cancer
## 2 Results
### FOXM1 Overexpression in Pan-Cancer Correlates with Genomic Instability  
_FOXM1_ mRNA expression and the FOXM1 transcriptional program have been reported to be enriched in tumors with genomic instability [36; 37; 38]. To test the relationship between FOXM1 expression and genomic instability using TCGA pan-cancer data, we correlated FOXM1 expression with genomic instability using a recently defined tumor aneuploidy score [39]. In this context, aneuploidy is defined as somatic copy number alterations of whole chromosomes and chromosome arms. Thus, the aneuploidy score reflects the total number of chromosome arms with arm-level copy-number alterations in a sample. Consistent with previous work, we observed a strong association between FOXM1 mRNA and protein expression with genomic instability (Figure 3A,B). To control for _FOXM1_ copy number increases in aneuploid tumors as a confounding variable, we performed the same analysis only using _FOXM1_ diploid samples. Importantly, we again observed a strong association between FOXM1 expression and genomic instability (Figure 3C,D). Next, we compared FOXM1 expression using previously reported TCGA pan-cancer aneuploidy clusters [38]. Surprisingly, group 10, which is characterized by moderate aneuploidy, showed the highest _FOXM1_ expression (Figure S3A). This aneuploidy cluster is enriched for TGCT, which shows _FOXM1_ amplification or copy number gains in \(\sim\)100% of tumors (Figure S1). _FOXM1_ is located at 12p13.33; 12p gains are pathognononic for TGCT [40], and isochromosome 12p gains occur in 87% of TGCT [41], likely accounting for increased FOXM1 expression in this cancer type. As expected, aneuploidy groups 1, 2, 3 and 5, which show high aneuploidy, were enriched for cancer types with the highest _FOXM1_ mRNA expression, while group 7, which has low aneuploidy, was enriched for cancer types that show low _FOXM1_ mRNA expression (Figure S3A,B; Figure 1A). The assignment of _FOXM1_-linked aneuploidy clusters to _FOXM1_ diploid tumors resulted in an almost identical pattern of cluster enrichment [42]. Finally, FOXM1 protein expression correlated with a highly similar pattern of aneuploidy clusters and tumor types as seen for _FOXM1_ mRNA (Figure S3C,D).
# Feasibility Study on Grouting Compactness Detection in Sleeves Using Piezoelectric Transducers
## 3 Experimental Setup  
### Specimen Fabrication  
In the test for the grouting compactness detection of sleeves, the grouting compactness was controlled by the cavity areas of the rubber plugs, as shown in Figure 2a. The rubber plugs were placed at the ends of the sleeve and then the grouting material was poured. Excess grouting material flowed out from the notch of the rubber plugs to control the compactness, as shown in Figure 2b. The PZT patch used in this experiment is type PZT-5, with a size of \(8\times 6\) mm and a thickness of 1 mm. The material properties of the PZT are shown in Table 1. To ensure that the PZT transducers operatednormally without being affected by the external environment, they were completely encapsulated with epoxy resin in the metal ring to protect them and the connecting wires, as shown in Figure 3. The schematic of the specimen is shown in Figure 4. Four transducers (i.e., actuator and sensor) were bonded to a predetermined position on the outer surface of the sleeve with epoxy resin to form two pairs of transmitting-receiving groups (i.e., A1S1 and A2S2). The distance between sensors and set screw were set as 80 mm. The length of the sleeves was 380 mm, and the diameter and thickness of the tendon duct were 51 and 4.5 mm, respectively. The length and diameter of the steel bars were 400 and 20 mm, respectively. During the experiment, the grout inlet and outlet of the sleeve were fixed with the ground; the ends of the steel bars were free. Three groups of 15 sleeve specimens were prepared in total. The cement used for grouting in this study was a high-strength micro-expansion grouting material specifically used in sleeve grouting connections. At the beginning of the study, the material properties of the grout were tested. The performance indices are presented in Table 2. The results satisfied the requirements of the code [31].
# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
## 3 Methods
### Ball Tracking  
We divide the ball-tracking process into two stages: multilevel collaborative matching and automatic trajectory correction. In the multilevel collaborative matching stage, the simplified multiobject tracking method and rule matching are mainly used to generate the ball motion trajectory online. In the automatic trajectory correction stage, the trajectory generated by multilevel collaborative matching is automatically corrected mainly through a simple linear motion model, and the tracking accuracy is increased in the form of batch processing.
# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 3 Sources and Detectors for Lidar Imaging Systems in Autonomous Vehicles
### Photodetectors
Whereas in an APD a single photon can produce in the order of tens to few hundredths of electrons, in a SPAD a single photon produces a large electron avalanche of thousands of electrons which results in detectable photocurrents. An interesting arrangement of SPADs has been proposed recently with MPPC, which are pixelated devices formed by an array of SPADs where all pixel outputs are added together in a single analog output, effectively enabling photon-counting through the measurement of the fired intensity [45; 120; 121; 122]. Finally, PMTs are based on the external photoelectric effect and the emission of electrons within a vacuum tube which brings them to collision with cascaded dynodes, resulting in a true avalanche of electrons. Although they are not solid-state, they still provide the largest gains available for single-photon detection and are sensitive to UV, which may make them useful in very specific applications. Their use in autonomous vehicles is rare, but they have been the historical detector of reference in atmospheric or remote sensing lidar, so we include them here for completeness. Several complete overviews of photodetection, photodetector types, and their key performance parameters may be found elsewhere and go beyond the scope of this paper [45; 123; 124; 125; 126]. Here we will focus only on main photodetectors used in lidar imaging. Due to the relevance of the concepts of gain and noise, where SNR ratios in detection are usually small, a brief discussion on these concepts will be introduced here before getting into the detailed description of the different detector families of interest.
## 4 Analysis of Results
### Example Optimization
As a result, the PNS attack is readily detectable with a high statistical confidence of (i.e., >99.9% or \(p<0.001\)) when considering 1000 rounds of quantum exchange with a total of 100,000 detections per round. While the decoy state occurrence percentage \(S_{v}\) can be further reduced, statistical significance begins to diminish because the number of decoy state detections per round of quantum exchange approaches zero during normal operations. Moreover, as the occurrence percentage is further reduced the protocol's integrity is jeopardized as the decoy state gain must be larger than the system's dark count rate (i.e., \(Q_{v}>Y_{0}\)).
## 3 Results and Discussion
### Microstructure of the Hot Rolled and Direct-Quenched Steel Plates  
The microstructures of the hot rolled and direct-quenched steel plates have been examined in order to evaluate the effect of Cr content in the case of laboratory scale rolling. As a result of the accelerated cooling, the microstructure of the three steel plates consists mainly of one or more of the following microstructural components: BF, GB, PF, and coalesced bainite (CB) (see Figure 11). Figure 12 shows inverse pole figures (IPF) with superimposed image quality (IQ) maps, grain boundary misorientation distributions, and grain sizes. The effective grain was determined as the equivalent circle diameters (ECD) of grains surrounded by boundaries having misorientation angles \(>\)15\({}^{\circ}\). The effective high-angle grain sizes at the 90\({}^{\mathrm{th}}\) percentile in the cumulative grain size distribution (D\({}_{90\%}\)) were also determined. The grain boundary misorientation distribution of Steel I, with the lowest Cr content, supports the LSCM and SEM observations that Steel I consisted mainly of GB with a large fraction of PF. Steel I also had a large mean effective grain size and D\({}_{90\%}\). Increasing the Cr content to 2.5 wt.% in case of Steel II led to an increase in the hardenability, an increase in the fraction of BF, and a reduction in the fraction of GB and PF, which are associated with reductions in the mean effective grain size from 2.66 to 2.12 \(\upmu\)m and D\({}_{90\%}\) from 30.85 to 24.35 \(\upmu\)m. The frequency of misorientation at about 58\({}^{\circ}\) increased compared to that of Steel I. With the further increase in the Cr content in the case of Steel III, the hardenability increased significantly, which promoted the formation of BF and suppressed the formation of PF and GB. This was associated with further reductions in the mean effective grain size from 2.12 to 1.86 \(\upmu\)m and D\({}_{90\%}\) from 24.35 to 17.12 \(\upmu\)m, together with a further increase in the frequency of misorientation at about 58\({}^{\circ}\) compared to that of Steel II.  
In summary, increasing the Cr content was associated with a significant change in the microstructural components, an increasing grain boundary misorientation frequency at about 58\({}^{\circ}\), and a reduction in the mean effective grain size and D\({}_{90\%}\).  
Figure 11: Typical LSCM and inlens SEM micrographs of the investigated hot-rolled and direct-quenched plates. PF and GB in Steel I (**a**,**d**), BF and PF in Steel II (**b**,**e**), and BF and CB in Steel III (**c**,**f**). Abbreviations: BF = bainitic ferrite, GB = granular bainite, PF = polygonal ferrite, and CB = coalesced bainite.
# Partially versus Purely Data-Driven Approaches in SARS-CoV-2 Prediction
## 3 Material and Methods
### Predictive Analytics
#### Forecasting using Exponential Smoothing Model for Time Series Data  
The exponential smoothing model is a forecasting model that uses the weighted average of the past observations with the aim of forecasting new unseen values [22]. The main idea behind this model is to give more weight to the recent values in the series. Thus, the newer values will get more exponential weight than the older ones, and the weight of older data points decays exponentially to reflect their importance. In exponential smoothing methods, the error, trend, and seasonal values are combined either additively, multiplicatively, or will be left out of the model. The additive model will add the seasonal and trend result factor together, so if one of them is zero, the other will have value. In the multiplicatively model, two factors are multiplied, so if one of them is zero, then the other will be zero as a result of multiplication. This model was proposed by [23; 24] and is used to forecast COVID-19 confirmed, new, and death cases.  
Exponential smoothing is the most common model forecasting future realization of a time series. It requires a chosen smoothing factor with the aim of reducing the residual error to compute the average distances between the actual and predicted lines [25].  
In this study, we used the exponential smoothing model to predict the number of COVID-19 new, confirmed, and death cases for a new unseen time (future time). This model suggested the data may have a trend and seasonal pattern. The forecasting has used data from 22 nd of February 2020 to 20 th of June 2020 to create a forecast through to 24 th of August 2020, looking for a potential seasonal pattern every 7 days. The results of applying this model to the three selected countries are shown and discussed in Section 4.3.