esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
### The Complex Causes of Fatigue and the Role of Psychological Factors  
Fatigue driving is commonly attributed to physiological causes, such as prolonged driving, insufficient rest, or sleep deprivation [37; 38; 39]. However, with the advancement of research, psychological factors have increasingly been recognized as critical contributors to driving fatigue [40]. While existing studies have predominantly focused on physiological causes, findings from the present study suggest that psychological elements--such as stress, loneliness, and emotional fluctuations--also have a significant impact on the level of fatigue. These factors can exacerbate drivers' fatigue states, particularly during long-haul or monotonous driving conditions, underscoring the importance of a more comprehensive approach to fatigue prevention.  
Existing research has primarily focused on the physiological causes of driver fatigue. Prolonged driving durations and sleep deprivation have been consistently identified as the dominant physiological contributors. Additionally, monotonous driving environments--such as long stretches of straight roads--are known to accelerate the onset of fatigue by reducing cognitive stimulation and attentional engagement [41]. However, these studies often overlook the role of psychological factors. In contrast, the present study highlights the significance of psychological factors in fatigue driving, especially during extended driving periods or night-time driving. Psychological stress and emotional fluctuations can exacerbate fatigue. The research finds that when drivers engage in long periods of solitary driving or face high-pressure transport tasks, emotional instability and stress significantly impact their cognition and attention, thereby intensifying the fatigue state.  
Fatigue detection has predominantly focused on assessing driver fatigue through physiological signals, such as heart rate and eye movements [42], often overlooking the influence of psychological factors. This study reveals that psychological factors, such as loneliness and emotional fluctuations, especially during night-time driving or long-duration transportation, significantly increase drivers' fatigue and may exacerbate cognitive fatigue and attention decline. This contrasts with traditional research, which has been dominated by physiological factors, offering a more comprehensive perspective.  
Furthermore, research indicates that psychological stress not only affects drivers' emotional states but also alters their coping strategies and response capabilities to driving tasks [43]. These effects are particularly pronounced during long periods of driving, making it difficult for drivers to effectively manage the cognitive burden caused by fatigue. Therefore, in contrast to traditional studies that focus solely on physiological fatigue, this research emphasizes the fatigue states of drivers under high pressure and emotional fluctuations, thereby expanding the understanding of fatigue's underlying causes.
## Case Study: Data Analysis and Results
### Posterior Predictive Checks: Model Verification  
In Section 4.3.2, a normal distribution was chosen in order to model the observed integrated pixel intensities. Here it shall be demonstrated that this model is indeed plausible and adequate.  
Focusing on the transponder data \(y_{t,d}\), test statistics \(T(y_{t,d})\) of the observed data can be compared to the test statistics of replicated data samples \(T(y_{t,d}^{\mathrm{rep}})\), _i.e._, samples which were generated numerically through the model [12]. If this analysis is conducted for the four test statistics mean, standard deviation, minimum, and maximum value across all eight overpasses, Figure 14 results. A good model fit is found if the test statistic of the observed data (vertical line) lies close to the center of mass of the histogram. As a criterion, the \(p\) value (relative number of samples above observed test statistic) can be used. At a confidence level of 95%, the \(p\) value should therefore be within the range of 2.5% to 97.5%. This is observed for all four test statistics, and especially the most important aspect of the model, its mean, is well reproduced by the model with a \(p\) value close to \(\
u_{2}\).  
Figure 13: Visualization of the Kalibri transponder drift compensation with data from Table 4. For visual guidance, the gray line marks the sample mean of the data before transponder drift compensation.
## 2 Results  
### Optimization of the HS-SPME Technique  
Figure 1 shows the optimization process for the HS-SPME technique, analyzing the variables of extraction time, conditioning time, sample volume, and fiber type based on the response factors (total area and number of peaks). The goal was to identify the optimal conditions for extracting volatile compounds from CB coffee.  
Different extraction times were evaluated to determine the time required to reach an optimal equilibrium between the volatile compounds and the HS-SPME fiber. As seen in the first plot of Figure 1, the response factor (total area) increased with time and plateaued at approximately 50 min. Therefore, an extraction time of 50 min was chosen to ensure maximum extraction efficiency. The second plot in Figure 1 illustrates the effect of conditioning time on the response factors. A conditioning time of 5 min was sufficient to stabilize the sample matrix without a significant loss of volatiles, as indicated by the steady response factor after this time. The third plot in Figure 1 depicts the relationship between the sample volume and response factor. A volume of 6 mL was determined to be optimal, providing enough sample for effective volatile extraction while maintaining practical handling and analysis conditions. The type of fiber used in HS-SPME can significantly affect the efficiency of volatile extraction. The fourth plot in Figure 1 compares the performance of the fibers with different polarities. The DVB/PDMS/CAR fiber was the most effective, owing to its mixed polarity characteristics, capturing a broad range of volatile compounds.  
In summary, the optimal parameters for the HS-SPME technique in this study were a 50-min extraction time, a 5-min conditioning time, a sample volume of 6 mL, and Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CAR/PDMS) fibers. These conditions provided a comprehensive and representative profile of volatile compounds in CB (Figure 1).
# Geometric Similarity Measurement Method for Micro Scene Generalization
## 2 Methodology
### Shape Similarity
#### 2.1.5 Micro Scenes' Shape Similarity  
Humans' expression and cognition of space are hierarchical, and the multilevel expression of space aligns with the human cognitive perception of space [36; 37]. The morphology similarity calculation is based on multilevel grids, where the larger the grid size, the fewer the number of grids; the details of spatial objects are generalized, which can only reflect the global distribution of features. The smaller the grid, the more detailed the description of objects, and the more detailed the extraction of local structural features [38].  
Initial grids are constructed and the regions are divided into several levels according to hierarchical control index \(N\). The calculation of \(N\) is shown in Equation (8), using the maximum density, minimum density, and the visual discrimination coefficient to estimate the number of levels [39; 40].  
\[N=\frac{logD_{max}-logD_{min}}{logq}+1 \tag{8}\]  
Here, \(D_{max}\) is the maximum density, \(D_{min}\) is the minimum density, and \(q\) is the visual discrimination coefficient of the map load.  
The geometric similarity of each grid is calculated according to:  
\[\text{Sim}_{Grid}=1-\left|\frac{H_{M,Grid_{A}}(G_{A},G_{B})-H_{M,Grid_{B}}(G_{ A},G_{B})}{\text{max}\big{(}H_{M,Grid_{A}}(G_{A},G_{B}),H_{M,Grid_{B}}(G_{A},G_{B}) \big{)}}\right| \tag{9}\]  
where \(H_{M,Grid_{A}}(G_{A},G_{B})\) and \(H_{M,Grid_{B}}(G_{A},G_{B})\) are the median Hausdorff distance, and \(\text{Sim}_{Grid}\) is the shape similarity between \(Grid_{A}\) and \(Grid_{B}\).  
Finally, the shape similarity calculation formula is obtained according to the percentage of the length (Equation (10)):  
\[\text{Sim}_{\text{shape}}=w_{1}\text{Sim}_{Grid_{1}}+w_{2}\text{Sim}_{Grid_{2 }}+\ldots+w_{n}\text{Sim}_{Grid_{n}} \tag{10}\]  
where \(w_{1}\), \(w_{2}\) and \(w_{n}\) are the percentages of each type of grid in the total length, and \(\text{Sim}_{\text{shape}}\) is the shape similarity.
## 5 Conclusions  
Six blasting experiments were conducted using bench specimens with four boreholes. The following conclusions can be made:Delay time markedly influenced full-field strain on the monitored front surface of the bench specimen. The strain field results analyzed using the 3D-DIC technique show that the surface strain field transformed from horizontal strain concentration zone dominated to vertical strain concentration zone dominated as the delays increased.
* It was observed that short delays with a stress wave superposition produced a larger range of toe rock above the bench floor compared to long delays. The maximum blast excavation weight of 85.2 kg was obtained for BS3 with an inter-hole delay of 100 \(\upmu\)s, while the smallest blast excavation weight of 47.1 kg was obtained for BS6 with the longest inter-hole delay.
* The fragment size distributions were well constructed by the extended Swebrec function. The fragmentation results indicate that stress wave superposition had a limited effect on improving rock fragmentation. For long delay times without stress wave interaction, rock fragmentation improved as delay time increased. BS6, with the longest delay time, obtained the best fragment size distribution.
* By combining blast excavation weights and the fragment size distribution of all experiments, BS5 produced the best blast excavation quality, and the optimum delay time was 200 \(\upmu\)s.
* This study investigated the effect of delay time on rock fragmentation through model blasting. In the future, full-scale benches should be used to conduct similar experiments. Furthermore, the influence of crack propagation on fragmentation can be further investigated by combining numerical simulations, which is instrumental for understanding the effect of the mechanism of delay time on rock fragmentation.  
Conceptualization, J.Y. and Q.Y.; methodology, H.-L.T. and Q.Y.; software, Q.Y.; validation, J.Y.; formal analysis, H.-L.T.; investigation, H.-L.T.; resources, J.Y.; data curation, H.-L.T. and Q.Y.; writing--original draft preparation, H.-L.T.; writing--review and editing, H.-L.T. and J.Y.; visualization, H.-L.T. and Q.Y.; supervision, J.Y. and Q.Y.; project administration, J.Y.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 51774043.  
Not applicable.  
The authors declare no conflict of interest.
# Reconfigurable Intelligent Surface in Wireless-Powered Interference-Limited Communication Networks
## 4 System Performance Analysis  
To analyse the performance of the concerned system, the closed-form expressions of the outage probability, average throughput under the delay-tolerant transmission mode and the average BER for binary and coherent modulation are derived in this section.
# The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting
## 3 Transformation of Time Series
### Logarithmic Transformation  
In this paper, the following logarithmic transformation is applied on time series \(\{x_{t}\}\):  
\[y_{t}=\log(C+x_{t}), \tag{9}\]  
where \(C\) is a constant value, large enough to guarantee that the term inside the logarithm is positive. As mentioned before, log-transform is a common preprocessing to handle variance instability or right skewness. Furthermore, one may use log-transform as a form of preprocessing to convert a time series with a multiplicative structure to an additive one. Given that SSA can be applied to time series with both additive and multiplicative structures, it does not necessarily need log-transform pre-processing [26]. However, the authors in [PERSON] and [PERSON] [26] showed that using log-transform could affect SSA's forecasting accuracy. In fact, SSA's forecasting accuracy will increase if the rank of a transformed series is smaller than the original one.
## 3 Results and Discussion
### Optimal Allocation Considering Return Flow (Model II)
#### 3.3.3 Results of Optimal Allocation of Water Resources (Model II)
This paper incorporates return flow into the optimal allocation model, describing its uncertainty using interval numbers. Subsequently, the optimal allocation of irrigation water resources is achieved through an interval linear programming model. The results demonstrate that the uncertainty in the optimal allocation of return flow can be more effectively addressed using the interval linear programming model.
# Evaluating the Quality of Light Emitted by Smartphone Displays
## 3 Results
### Static Contrast  
An example of static contrast measurement is presented in Figure 8, and a summary of the results is provided in Table 7. The contrast values for the selected smartphones are varied. The ratio of the average white field luminance to the black field luminance displayed on the screen at the same time ranged from 312:1 to 527:1. The static contrast for devices with LCDs was significantly lower and was closer to a value of 300:1. Moreover, the static contrast for devices with AMOLED displays was typically around 500:1.  
The worst performance among the phones with AMOLED displays was achieved for sample 2. It achieved the highest white field luminance values of all the smartphones tested. However, the black field luminance values it achieved were also high. It is the only phone not using LCD technology for which the black field luminance exceeded 1.0 cd/m\({}^{2}\). The older model obtained a slightly higher static contrast value (sample 8). The best static contrast values were achieved by samples 3 and 4. These achieved both high white luminance and low black luminance. It is beneficial for proper content display. Over the years, it has not improved significantly, and for AMOLED displays, it will remain at 500:1 regardless of the price of a given smartphone. It is surprising because high static contrast is desirable and determines the display's observation comfort.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Left Side Test**} & \multicolumn{2}{c}{**Right Side Test**} & \multirow{2}{*}{\(\mathbf{L_{wm}}\) (\(\frac{\text{cd}}{\text{m}^{2}}\))} & \multirow{2}{*}{\(\mathbf{L_{b}}\) (\(\frac{\text{cd}}{\text{m}^{2}}\))} & \multirow{2}{*}{\(\mathbf{L_{w}}\) (\(\frac{\text{cd}}{\text{m}^{2}}\))} & \multirow{2}{*}{\(\mathbf{L_{b}}\) (\(\frac{\text{cd}}{\text{m}^{2}}\))} & \multirow{2}{*}{\(\mathbf{L_{b}}\) (\(\frac{\text{cd}}{\text{m}^{2}}\))} & \multirow{2}{*}{\(\mathbf{S_{static}}\) **Contrast**} \\ \cline{2-2} \cline{6-6}  & \(\mathbf{L_{w}}\) (\(\frac{\text{cd}}{\text{m}^{2}}\)) & \(\mathbf{L_{b}}\) (\(\frac{\text{cd}}{\text{m}^{2}}\)) & & & & \\ \hline
1 & 413.6 & 0.813 & 431.0 & 0.874 & 422.3 & 0.844 & 500:1 \\ \hline
2 & 517.5 & 1.065 & 538.1 & 1.206 & 527.8 & 1.136 & 465:1 \\ \hline
3 & 460.9 & 0.890 & 486.1 & 0.920 & 473.5 & 0.905 & 523:1 \\ \hline
4 & 448.9 & 0.820 & 488.0 & 0.955 & 468.5 & 0.887 & 528:1 \\ \hline
5 & 431.3 & 0.851 & 480.0 & 0.961 & 455.7 & 0.906 & 503:1 \\ \hline
6 & 367.7 & 1.081 & 377.1 & 1.041 & 372.4 & 1.061 & 351:1 \\ \hline
7 & 425.3 & 0.888 & 462.7 & 0.888 & 444.0 & 0.888 & 500:1 \\ \hline
8 & 381.0 & 0.815 & 361.6 & 0.755 & 371.3 & 0.785 & 473:1 \\ \hline
9 & 393.4 & 1.272 & 406.9 & 1.286 & 400.2 & 1.279 & 313:1 \\ \hline \end{tabular}
\end{table}
Table 7: The results of contrast measurements.  
Figure 8: The example of static contrast measurement outcome: (**a**) left-side test and (**b**) right-side test (values in cd/m\({}^{2}\)).
# An Overview of Infrared Remote Sensing of Volcanic Activity
## 4 Historical Perspective on Sensors Used for Volcanic Observation
The spatial resolution of the ATSR bands was 1000 m \(\times\) 1000 m, making them less useful for detailed studies than their American counterparts at the time (those of Landsat TM), but providing an enhanced temporal resolution in the period before MODIS [31]. Given the temporal superiority of ATSR compared to Landsat TM, use of the sensor became common place for several years, and it was used to examine temporal variations in volcanic activity at various locations, including Lascar, Chile [31], Fernandina, Ecuador [76] and Etna, Italy [77], with its surveillance capabilities reviewed in [78]. Findings from the use of ATSR were also used to assess the potential volcanic-utility of the then, not-launched MODIS sensor [79]. Japan also entered the field in 1991 with the launch of the OPtical Sensor (OPS) on-board the Japanese Earth Resources Satellite (JERS-1), possessing, for the first time, four high spatial resolution (18 m \(\times\) 24 m) SWIR bands; it is arguably data from this sensor that highlighted the potential of the shorter-infrared wavelength part of the spectrum for high-temperature surface monitoring [31; 80].
# Shapley's Value as a Resource Optimization Strategy for Digital Radio Transmission over IBOC FM
## 2 Materials and Methods
### Spectrum for the Extended Hybrid Mode  
In the extended hybrid mode, additional sidebands are introduced alongside the primary sidebands of the hybrid mode, resulting in an expansion of the hybrid sidebands toward the central area where the analog FM component is situated. This modification enhances the digital capacity of the system. However, it impacts analog coverage as the  
Figure 1: IBOC FM hybrid waveform spectrum.  
analog bandwidth is reduced. Depending on the service mode, one, two, or four frequency divisions are added at the inner edge of the primary main sideband, thereby reducing the bandwidth allocated to the analog FM signal [20]. Moreover, in addition to the subcarrier structure described in the hybrid mode, the extended sidebands encompass subcarriers ranging from \(-356\) to \(-280\) and from \(280\) to \(356\). The amplitude of the subcarriers within the extended sidebands matches that of the main primary subcarriers. Figure 2 depicts the spectrum corresponding to the IBOC FM extended hybrid model [5].
## 5 Conclusions  
In this study, to monitor the performance of the utility tunnel structure in a soft foundation affected by ground traffic loads during the operation period, the field tests have been conducted in the typical engineering site (one utility tunnel engineering in Suqian City of Jiangsu Province, China). From the field test results, the five main disturbance factors (four vehicle operating load parameters and one operating time parameter) on the utility tunnel structure and the two main structural safety responses (displacement and stress) have been determined to construct the big datasets whose number is 15,376 for the prediction model. To treat the datasets, one new deep learning model called WO-DBN has been proposed, in which the WOA is used to optimize the hyperparameters of the DBN. Finally, using the WO-DBN model, and based on the datasets, the main safety responses of the utility tunnel structure have been predicted. From the studies, the following conclusions can be drawn: (1) For the utility tunnel structure in a soft foundation during the operation period, the two main safety responses affected by the five main disturbance factors such as the load and time are the displacement and stress at the mid span of the structure roof. (2) For its special structure and good computing performance, the deep learning method (DBN) can analyze the big data from the field tests well; however, for the numerous hyperparameters of DBN, which cannot be determined easily, the WOA is used to optimize those hyperparameters, and a new deep learning model (WO-DBN) is proposed to treat the big data. (3) Based on the new WO-DBN model, the dynamic safety status of the utility tunnel structure in a soft foundation during the operation period can be predicted well, whose results show that the computing errors are not significant (the average absolute error for the displacement is 0.1604 and that for the stress is 12.3726) and can meet the requirement of real engineering.  
Conceptualization, W.G.; methodology, W.G. and S.G.; software, S.G. and Y.G.; validation, S.G. and S.Y.; formal analysis, S.G.; investigation, S.Y.; resources, W.G.; data curation, S.G. and S.Y.; writing--original draft preparation, W.G.; writing--review and editing, W.G. and Y.G.;visualization, S.G.; supervision, W.G.; project administration, W.G.; funding acquisition, W.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Restrictions apply to the availability of these data. The data were obtained from Suqian High-speed Railway Construction and Development Co., Ltd. and are available from the authors with the permission of Suqian High-speed Railway Construction and Development Co., Ltd.  
The authors declare no conflicts of interest.
## 4 FTN Detection Scheme in Multipath Channels Based on Neural Network
### Proposed Deep-Bi-LSTM Architecture for FTN Signal Detection
#### 4.2.1 Use RNN-Based LSTM Unit for FTN Detection  
Existing research has proven that the RNN-based sequence detection architecture has proven effective in many fields. This is because the RNN network can map the effects of all previous inputs to the current output through feedback connections, which makes the RNN network'memorable' and causal. Aiming at the problem of FTN signal detection, these characteristics of the RNN network are suitable for solving this problem of strong correlation in the time dimension. The proposed network architecture is shown in Figure 3.  
Figure 2: Block diagram of using CNN network structure to detect FTN signaling.  
The input of the model is the preprocessed N-length received symbol sequence. After extracting features through two identical fully connected hiddenayers using Bi-LSTM, the fully connectedlayer with tanh as the activation function obtains an output in the range (\(-1\), \(+1\)). In addition, batch normalization is added after each hiddenayer operation to correct the data distribution to prevent gradient vanishing problems that are difficult toearn effectively. Unlike traditional two-classification problems that mostly use cross-entropy as theoss function, we found in the actual test that using the \(l2\) norm as theoss function can achieve relatively better performance in this problem. The possible reason is that the activation function in the fully connected network chooses to use tanh instead of sigmoid.  
The sequence detection model directly outputs the estimated symbol at each moment as \(\hat{a}_{k}\), and the correspondings function is  
\[\mathcal{L}(\mathbf{a},\mathbf{\hat{a}})=\frac{1}{N}\sum_{k=1}^{N}|a_{k}-\hat {a}_{k}|^{2} \tag{9}\]  
In the process of using traditional RNN, theoretically, it can handle arbitrarilyong sequences. However, in actual application, it often encounters aong-term dependencies problem. Generally, aong short-term memory (LSTM) unit [29] is used to alleviate this problem.