esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Regression Analysis  
Each eye-tracking and subjective evaluation metric was treated as the dependent variable, with \(\Delta\)E, \(\Delta\)L, \(\Delta\)C, and \(\Delta\)h as the independent variables. Based on the trends observed in Figure 6, linear regression models were established for pairs of variables that exhibited linear changes. First, single-factor regression models were created. When the regression coefficients of these models showed statistical significance, multi-factor regression models were subsequently developed (Table 3).  
In the single-factor linear regression models established between the color element differences and the eye-tracking metrics and subjective comfort, all regression coefficients were statistically significant (Table 3). To develop a mathematical model for the relationship between \(\Delta\)E, \(\Delta\)L, \(\Delta\)C, \(\Delta\)h, and user visual comfort, multi-factor linear regression analysis models were created, resulting in fitted equations. These equations revealed the trends in variations between the color metrics and the eye-tracking metrics, clarifying the impact of different colors on visual comfort.  
This study established multi-factor linear regression models to predict APD, VD, FC, TFD, VC, and ACP based on differences in color elements. The R\({}^{2}\) values and regression coefficients of these models were tested and found to be statistically significant (\(p<0.05\); Table 4).  
Figure 6: The correlation analysis between \(\Delta\)E, \(\Delta\)L, \(\Delta\)C, and \(\Delta\)h and eye-tracking metrics and subjective comfort metrics APD, ASA, FFD, FC, TIFF, TFD, ACP, and VC is depicted in the figures: (**a**) scatter plot matrix and (**b**) correlation analysis chart, with Pearson correlation coefficients. ***: \(p<0.001\).  
Conversely, the influence of \(\Delta\)h on VC was positive; when \(\Delta\)h increased by 1 unit, the VC value increased by 0.008 units.
## 2 G-R for HSRs
### Handover Measurement of SCell and NCell
#### 2.3.3 Dynamic Hysteresis Margin of SS-RSRP/SS-RSRQ  
In order to suppress ping-pong handover, a hysteresis margin is required between the SCell and the NCell. The default hysteresis margin of 5G is difficult to adapt to 5G-R because the HST is moving at a high speed. The relationships between the dynamic hysteresis margin of SS-RSRP/SS-RSRQ and the speed of HST are shown in Figure 8. The traditional SS-RSRP/SS-RSRQ based on the trigger scheme of the handover generally takes the approximate 5 dB/6 dB as the default hysteresis margin. The hysteresis margin of SS-RSRP/SS-RSRQ is 1.84 dB/2.23 dB when the speed of HST is 350 km/h. With the increase of HST's speed, the hysteresis margin of SS-RSRP/SS-RSRQ is reduced accordingly to improve the trigger probability of the handover and enhance the success rate of the handover.  
Figure 8: Relationship between the dynamic hysteresis margin of SS-RSRP/SS-RSRQ and the speed of the high-speed train (HST).  
Figure 7: Statistics of the synchronization signal reference signal receiving quality (SS-RSRQ) measurement. The box plots denote the statistic of SS-RSRQ in each 80 m. For a certain box, the bottom is the lower quartile Q1, the top is the upper quartile Q3, the horizontal line in the middle of the box is the median Q2; the top of the whisker is O3 + 1.5 IQR, and the bottom is Q1 \(-\)1.5 IQR, where IQR is the interquartile range IQR = Q3 \(-\) Q1.  
Based on the above measurement analysis, the trigger point of the handover is shown in Figure 9. In addition to the measurement results of the Scell and the Ncell, the hysteresis-1, the hysteresis-2, and the time to trigger (\(TTT\)) are also considered during the process of the handover. In the optimization of wireless communications, these parameters can be adjusted on the station to improve the handover performance, and different values of parameters can adapt to different scenarios.
## 4 Results
### Velocity Structure Changes in the Linfen Basin  
The 1303 Hongtong earthquake occurred in a high P-wave velocity region along the Huoshan Fault (F1) (Figure 8). The bottom interface of the velocity layer shows a significant mutation in the lateral direction (high in the east and low in the west), and the Hongtong earthquake occurred at the location of an abrupt change in P-wave velocity. Small earthquakes in the middle-lower crust are relatively densely distributed and are concentrated at locations where P-wave velocity changes abruptly and where the velocity change zone favours strain accumulation [20,21,51,65,66]. When the stress reaches a certain level, the fault activates and causes an earthquake (Figure 8).  
Figure 8: Velocity structure tomographic imaging profile of the Hongtong earthquake (C–C’). The black solid lines are velocity contour lines, the red solid line represents a normal fault within the cover layer, and the red dashed line illustrates the extension of the Huoshan Fault in the middle-lower crust. Brown spheres represent small earthquakes in the region from 1981 to 2018, while yellow spheres represent the 1303 Hongtong \(M\) 8 earthquake (F1: Huoshan Fault).  
Figure 7: Depth–frequency distribution map of small earthquake relocations from 1981 to 2018 in the (**a**) northern and (**b**) southern parts of the basin.  
The Luoyunshan Fault trends SE and is an upward-steepening, downward-gentle, 'listric' normal fault [23]. The surface exposure extends downward with a relatively steep dip angle, and the Quting and Fenxi faults converge with the Luoyunshan Fault at a depth of 10 km (Figure 4). A distinct intracrustal low-velocity body was observed within the upper crust (at a depth of 12-21 km). Some researchers have suggested that the Linfen \(M\) 7.8 earthquake occurred above this low-velocity body. This body represents a velocity transition zone that is conducive to strain accumulation, and faults passing through this region of the upper crust are considered to be structurally favourable for the occurrence of strong earthquakes (Figures 6 and 9) [20; 21; 37; 51; 66].
## 4 Results and Discussion
### High-Frequency Word Analysis  
Figure 6 presents word clouds depicting the top 50 high-frequency words for each year, offering a visual representation of the key terms associated with discussions on common prosperity. These visualizations highlight that the predominant strategy for promoting common prosperity in China is focused on development, frequently described as \"enlarging the economic cake\". In pursuit of these developmental goals, China has enacted a variety of measures. These include strengthening the leadership role of the CPC, revitalizing the rural economy, enhancing the well-being of the populace, and deepening market reforms.  
In response to concerns about wealth redistribution in China through \"robbing the rich to help the poor\", we expanded our analysis to include the top 500 high-frequency words. This more extensive search for sensitive terms revealed several insights.  
Firstly, the term \"tax\" only made it into the top 500 in 2021, ranking 450 th, and did not appear in subsequent years. This indicates that China is not actively reforming its current tax system specifically to facilitate common prosperity. Furthermore, there is no indication in China's legislative agenda for the next five years of plans to introduce direct taxes that would have clear wealth redistribution effects, such as property tax, capital gains tax, or inheritance tax. Thus, it is premature to suggest that significant changes to the tax framework are imminent.  
Secondly, the term \"communism\" did not appear in the top 500 words in any year, while \"Marxism\" was ranked 423 rd in the 2021-2023 comprehensive ranking, and it appeared sporadically in the annual rankings. This suggests that, despite China's socialist system, there is no indication of impending radical communist policies such as wealth confiscation from capitalists.  
Thirdly, an examination of terms related to \"poverty\" showed only neutral or positive associations like \"poverty alleviation\" and \"poverty eradication\", without negative connotations or expressions of class antagonism. Similarly, terms associated with \"rich\" such as \"enrich\", \"to be rich\", and \"wealthy\" also appeared without any extreme expressions such  
Figure 6: High-frequency word clouds.  
as \"robbing the rich\" or \"resenting the rich\". These findings suggest a lack of significant public antagonism between the rich and poor or a populist drive for wealth redistribution.  
To sum up, the mainstream public opinion, as reflected by high-frequency words, does not support radical populist or communist ideologies. Instead, it indicates that the promotion of common prosperity in China is primarily focused on enhancing wealth through development--an approach resembling Pareto improvement rather than redistributing existing wealth, akin to Kaldor-Hicks improvement. This analysis confirms that, while China has prioritized the goal of common prosperity, its policy direction remains consistent with broad economic development rather than fundamental redistributive reforms.
## 6 Strengths, Limitations, and Future Research Directions
### Limitations
#### 6.2.6 Architecture  
CNNs typically have fixed, predefined model architectures, which may limit the flexibility to adapt to specific task requirements or incorporate domain-specific knowledge, potentially affecting performance optimization. For ViTs, the availability of established architecture variants is still limited, which may require more experimentation and exploration to find optimal architectures for specific tasks.
# Predictive Business Process Monitoring Approach Based on Hierarchical Transformer
## 4 Results and Discussion
### Experiment Setting  
Accuracy (Acc) and Mean Absolute Error (MAE) were used as the model evaluation metrics in this experiment for the next activity prediction task and the remaining time prediction task, respectively. The two metrics are calculated as follows:  
\[Acc=\frac{1}{N}\sum_{i=1}^{N}\left[y_{i}=y_{i}^{(pred)}\right]\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\left|y_{i}-y_{i}^{(pred)}\right|\]  
where \(N\) is the number of business process cases in the test set. \(y_{i}\) and \(y_{i}^{(pred)}\) are the true state (next activity, remaining time) and the corresponding prediction value of the \(i\)-th test case, respectively.  
In the evaluation of the prediction model, the experiment divides the datasets into an 80% training set, 10% validation set, and 10% testing set, where the training set is used for training the model parameters; the validation set is used to select the optimal model based on the above metrics; and the test set is used to calculate the final prediction effect of the model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Event Log**} & **Cases** & **Events** & **Activities** & **Average** & **Maximum** & **Average** & **Maximum** \\  & **Number** & **Number** & **Number** & **Length** & **Length** & **Duration** & **Duration** \\ \hline BPI2012 & 13,087 & 262,200 & 24 & 20 & 175 & 8.6 & 137.2 \\ BPI2012W & 9658 & 170,107 & 7 & 17.6 & 156 & 10.5 & 132 \\ BPI2013i & 568 & 6499 & 13 & 11.4 & 123 & 11.9 & 768 \\ BPI2015 & 1199 & 27,409 & 38 & 22.8 & 61 & 95.9 & 1486 \\ BPI2017 & 31,509 & 1,202,267 & 26 & 35 & 180 & 18 & 395 \\ Hospital & 3494 & 97,794 & 18 & 27.9 & 217 & 127.2 & 1034 \\ Sepsis & 1049 & 15,214 & 16 & 14.5 & 185 & 28.5 & 422.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of event logs. Duration related measures are shown in days.  
To obtain better prediction results, most parameters of the model were adjusted in the experiments based on the effect of the validation set, and better parameter combinations were obtained as: batch size is 64; the learning rate is 0.01; activity and attribute encoding dimension is 64; self-attention layer encoding dimension is 36; attention head number is 3; attention layer number is 2; dropping rate is 0.5; and step length is 2.
## 2 Materials and Methods
### Synthesis of the Nanocomites  
The OLNCs (10 mg, 20 mg, 30 mg, and 50 mg) were added to 100 mg of TiO\({}_{2}\) (99.8% trace metals basis) in 50 mL of 5 M NaOH (reagent grade, \(\geq\)98%). The mixture was refluxed for 6 h at 100 \({}^{\circ}\)C. The product was separated from the solution using centrifugation and then rinsed with ethanol (95% analytical grade) and HCl (37% ACS reagent) to remove unreacted materials. The material was finally rinsed with distilled water and vacuum dried at 60 \({}^{\circ}\)C for 24 h.  
The material was subjected to calcination at 450 \({}^{\circ}\)C for 4 h in an inert environment and called TC-10, TC-20, TC-30, and TC-50, according to the mass (mg) of OLNCs added to the TiO\({}_{2}\) (i.e., C:TiO\({}_{2}\) molar ratios of 1:2, 1:4, 1:6, and 1:10, respectively). The C content of the composites was determined from XPS data (see Tables S1 and S2). Adsorption studies were performed using MO (85% ACS reagent) as a model adsorbate. The adsorption of MO was studied using the OLNCs, and the nanocomposites were studied under constant conditions (pH = 7, solution volume 50 mL, the mass of adsorbent = 100 mg, for 120 min). The pH 7 was selected to mimic wastewater from the textile industry, which normally has a pH range from 7 to 12, and the pH\({}_{\text{PZC}}\) of the TiO\({}_{2}\)-OLNCs nanocomposites was around pH 8. Below pH 8, the composites would be protonated, resulting in an electrostatic attraction between the catalyst and the anionic dye (methyl orange).
## 3 Results and Discussion
### Analysis of Experimental Results
#### 3.4.2 Ablation Experiment  
In order to perform a more comprehensive analysis of the contribution of each improved strategy to the performance of obstacle detection, ablation experiments were conducted on dataset 1, and the experimental results are presented in Table 4. Network A is the original YOLOv4+K-means clustering network; network B is the model after Mobilenetv2 was selected as the YOLOv4 backbone network; network C adds the SA module on the basis of network B; network D uses K-means++ clustering on the basis of network C; and network E introduces the focal loss function on the basis of network D.  
As can be observed from Table 4, using Mobilenetv2 as the backbone of YOLOv4 can effectively improve the detection speed of the network, which increases by 75.61% to 72 FPS; however, the detection accuracy also decreases. The addition of the SA attention mechanism strengthens the feature extraction ability of the network, which can effectively suppress background interferences, improve the detection accuracy of obstacles in a complex background environment, and improve mAP by 1.37%. The K-means++ algorithm was used to cluster the prior frames, detection accuracy was improved further without affecting the detection speed, and mAP increased by 0.86%. The focal loss function was introduced to replace the cross-entropy loss function to reduce the amount of simple-sample background, and the model focused on the detection of target objects. Without affecting the detection speed, detection accuracy was further improved and mAP increased by 0.53%.
## 3 Hardware Development
### Impedance Measurement  
In this paper, damage detection is based on the EMI method, which requires a system capable of measuring the electrical impedance of the PZT sensors. To design a lightweight and energy-efficient system, the impedance analyzer, AD5933 (Analog Devices Inc., Norwood, MA, USA), is employed. AD5933 can generate a sinusoidal wave to excite the external impedance and measure the response signal. Then, a DSP in this analyzer can calculate the impedance according to these two signals.  
The electrical impedance measurement circuit is based on AD5933, a programmable gain amplifier, and a calibration circuit. This configuration allows a wide measuring range from 100 \(\Omega\) to 10 M\(\Omega\) with a high degree of accuracy. The range is divided into six subranges, as shown in Table 1, and each subrange corresponds to a special feedback resistor of the gain amplifier.  
Two modes of the measuring system are illustrated in Figure 5. In the measurement mode, as illustrated in Figure 5a, the excitation signal is first generated by the AD5933, and then goes through a high-pass filter for suppressing the effects of direct current (DC) bias coming from the power supply. Then, the signal is processed by a voltage follower for impedance transformation. However, the voltage follower could introduce high-frequency noises due to a self-oscillation phenomenon. Thus, a low-pass filter (a resistor-capacitor network) is employed to address this issue. The mode selection unit (ADG752, a 2:1 analog switch) guides the signal to a PZT sensor determined by the scheduling module. Then, the response of the PZT, via the programmable gain amplifier (the Signal Amplifier block in Figure 5),  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Calibration Resistor (\(\Omega\))** & **Gain Resistor (\(\Omega\))** & **Impedance Measuring Range (\(\Omega\))** \\ \hline
1 & 100 & 100 & 0.1 K–1 K \\
2 & 1 K & 1 K & 1 K–10 K \\
3 & 10 K & 10 K & 10 K–100 K \\
4 & 100 K & 100 K & 100 K–1 M \\
5 & 100 & 100 K & 1 M–2 M \\
6 & 9 M & 9 M & \textgreater{}2 M \\ \hline \hline \end{tabular}
\end{table}
Table 1: The feedback resistors, the calibration resistors, and the corresponding measuring range.  
Figure 4: The scheme for the charge amplifier.  
is measured by AD5933. Finally, the DSP of AD5933 calculates the impedance. The gain resistor of the signal amplifier is selected according to Table 1.  
On the other hand, the calibration mode is illustrated in Figure 5b. In this mode, the signal from the voltage follower will be guided to a selected calibration resistor by the mode selection unit. Then, the response signal is measured and its impedance is calculated by AD5933. This signal helps the system take the environment changes into account, improving performance. More detailed information on impedance calculation is illustrated in Section 4.3.
## 5 Simulation Results  
In this section, we present simulations to test the proposed optimization scheme and test the performance of our algorithm in this paper. This scheme jointly optimizes the number of offloaded bits, energy harvesting time, and transmission bandwidth. To test the performance of the scheme from various aspects, we consider the following influencing factors in the experiment: the AP transmission power, the task size, and the time block length. All the following simulation programs are coded with Python programming language and use the PyOpt [46] tool package for simulation.
# Finding Patterns in Signals Using Lossy Text Compression
### Run Length Encoding (RLE)  
Given a string \(S\), which represents a signal, our goal is to compress \(S\) such that the optimal compression will allow us to resolve the protocol behind that signal. The compression scheme we present in this paper is called recursive run-length encoding (RRLE), and is a natural generalization of run-length-encoding (RLE), but is more suited for semi-periodic strings that are produced by sensors on robots.  
RLE is a very simple form of lossless string compression in which runs of letters (that is, sequences in which the same letter occurs in many consecutive elements of the string) are stored as pairs of one count and one single letter, rather than the original run. For example, the string \(S=(aaaaaabbbbaaaa)\) has three runs and can be represented as the vector \(S^{\prime}=(7,a,4,b,4,a)\), which means that string \(S\) is consists of seven a's, followed by four b's, followed by four a's. This way the string \(S\) can be represented using six letters/integers instead of 15. RLE is most useful on a string that contains many such runs.  
In this paper we also use the term \(run\) (or _period_) to denote a periodic string. That is, a string that can be divided into a number of identical adjacent non overlapping _substrings_. For example, if \(S=ababab\), the RLE will be \((3,ab)\) which means that the string \((ab)\) repeats itself three times in the string \(S\). Similarly, the RLE of \(S=ababcb\) is \((2,ab,1,cb)\). In RRLE we recursively define each run (period) so that it may be further compressed using RLE in order to get even better compression as in Figure 1. For a formal definition of RRLE, see Section 2.3.
# Mechanical Behaviour of Rock Samples with Burst Liability Under Different Pre-Cycling Thresholds
## 3 Results  
### Strength Characteristics of Rock Samples with Burst Liability  
Figure 4a shows the stress-strain curves of rock samples under pre-cycling thresholds. The hysteresis loop represented irreversible deformation caused by crack closure and development. It indicated that greater pre-cycling thresholds leaded to a faster accumulation rate of fatigue damage in rock samples.  
Figure 4b shows the relationship between average peak stress and pre-cycling thresholds. The average peak stress increased exponentially with the pre-cycling thresholds. However, the increasing trend became gentle. It showed that the pre-cycling load had a stress-strengthening effect. However, the stress-strengthening effect was gradually weakened with the increase in pre-cycling thresholds. This was similar to results obtained by [PERSON] et al. [36]. [PERSON] et al. [36] believed that when the pre-cycling thresholds were less than 75% of the peak stress, the peak stress gradually increased with the pre-cycling thresholds. There were three main reasons for the phenomenon occurring in this study:  
1. There was rock debris in rock samples in the disturbance stage during pre-cycling loading and unloading. After the rock debris filled primary pores in rock samples under pre-cycling load, the inter-pore density was enhanced. The compressive performance of rock samples was improved.
2. For rock samples, within the limiting closure stress of the original pore, pre-cycling thresholds could increase the bearing capacity of rock samples.
3. The maximum pre-cycling threshold was smaller than the damage threshold. Therefore, the accumulated fatigue was not enough to cause damage to rocks. Then, under the pre-cycling threshold, the uniaxial compressive strength (UCS) gradually increased with the pre-cycling threshold. As for the reason, it was explained that the compaction strengthening effect of the internal structure was prominent under the pre-cycling threshold. Moreover, the compaction degree was greater than the fatigue degree.