esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, a compact photoacoustic spectroscopy system-based multi-pass cell was developed for methane gas detection. To concentrate the radiation within a limited space, a multi-pass cell composed of two non-coaxial mirrors with a diameter of 25.4 mm and a radius of curvature of 50.8 mm was designed. A total of 21 reflections occurred in the developed multi-pass cell, which cooperated with an acoustic resonant cavity with a radius of 4 mm and a length of 36 mm. The developed PA cell with the developed MPC presented a compact dimension with a total length of 102 mm. An 11-fold enhancement in photoacoustic signal was obtained through the multi-pass cell. PA signals with different methane concentrations shows a good linear relationship. The detection sensitivity of 0.114 ppm and 5.7 ppb was achieved with the average time of 10 s and 300 s, respectively, which shows the excellent detection performance of the system. The developed MPC-PA sensor was validated by atmospheric methane measurement and presented a long period of stability and a high sensitivity in the detection of methane. In addition, although this multi-pass cell may not offer significant advantages over the traditional Herriott cell in TDLAS, due to the optical path being confined within a plane, it can still be employed in QEPAS systems.  
Conceptualization, S.H., G.W. and L.W.; Methodology, L.W., T.Z., Y.H. and Y.Z.; software, L.W. and Y.Z.; Validation, L.W. and Y.H.; data curation, L.W.; formal analysis, L.W. and T.Z.; funding acquisition, S.H.; Investigation, L.W., Y.H. and Y.Z.; resources, S.H.; supervision, S.H. and G.W.; Writing--original draft, L.W.; Writing--review and editing, S.H. and G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"Pioneer\" and \"Leading Goose\" R&D Program of Zhejiang Province (2022C03051, 2023C03135), Ningbo Natural Science Foundation (2022J159), the Key Research and Development Program of Zhejiang Province (Grant Nos. 2021C03178), Zhejiang Provincial Natural Science Foundation of China (LQ22F050014), National Natural Science Foundation of China (Grant No. 62305290). National Key Research and Development Program of China (Grant Nos. 2022 YFC3601002 and 2022 YFC2010003), and Ningbo Science and Technology Project (2023Z179).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors are grateful to [PERSON], [PERSON], [PERSON] and [PERSON] for valuable discussion and help.  
The authors declare no conflicts of interest.
# An Intelligent Time-Series Model for Forecasting Bus Passengers Based on Smartcard Data
## 4 Experimental Comparison  
From the procedure proposed in Section 3, the initial data analysis and acquisition of necessary attributes were selected. This section describes the experiments implemented to  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Algorithm**} & \multicolumn{1}{c}{**No lag**} & \multicolumn{1}{c}{**Lag 1**} & \multicolumn{1}{c}{**Lag 2**} & \multicolumn{1}{c}{**Lag 3**} & \multicolumn{1}{c}{**Lag 4**} & \multicolumn{1}{c}{**Lag 5**} & \multicolumn{1}{c}{**Lag 6**} & \multicolumn{1}{c}{**Lag 7**} \\ \hline \multirow{3}{*}{LSTM} & RMSE & 346.983 & 323.136 & 314.640 & 313.599 & 311.582 & **270.137** & **246.327** & **235.838** \\  & MAPE & 55.297 & 51.271 & 54.108 & 53.805 & 46.663 & 42.201 & **30.713** & **29.091** \\  & RMSE & 419.087 & 457.096 & 440.914 & 464.523 & 487.629 & 465.230 & 444.713 & 441.920 \\  & MAPE & 50.414 & 40.028 & 42.689 & 39.118 & **38.048** & 39.447 & 42.019 & 42.427 \\  & RMSE & 413.770 & 422.451 & 437.535 & 442.538 & 454.443 & 413.121 & 425.137 & 473.599 \\  & MAPE & 53.621 & 48.826 & 43.573 & 42.355 & 40.548 & 54.963 & 46.756 & 38.588 \\  & RMSE & 417.011 & 436.597 & 606.395 & 496.521 & 520.526 & 451.678 & 455.457 & 481.922 \\  & MAPE & 51.544 & 43.844 & 58.925 & 38.235 & 39.951 & 41.111 & 40.371 & 38.435 \\  & RMSE & 423.232 & 456.767 & 463.386 & 427.124 & 433.571 & 448.466 & 435.913 & 466.868 \\  & MAPE & 48.477 & 40.148 & 39.307 & 46.883 & 44.817 & 41.475 & 43.736 & 38.926 \\  & RMSE & 407.477 & 406.968 & 436.204 & 416.700 & 413.988 & 406.394 & 405.928 & 400.344 \\  & MAPE & 67.738 & 65.773 & 44.045 & 51.765 & 54.012 & 65.769 & 64.782 & 65.439 \\  & RMSE & 415.328 & 438.604 & 444.973 & 460.935 & 440.719 & 434.932 & 435.827 & 435.218 \\  & MAPE & 52.564 & 43.338 & 41.875 & 39.496 & 43.074 & 45.093 & 43.985 & 43.888 \\  & RMSE & 428.524 & 413.265 & 424.142 & 434.607 & 419.408 & 417.863 & 422.436 & 423.037 \\  & MAPE & 46.397 & 54.061 & 41.223 & 44.322 & 50.989 & 52.713 & 49.107 & 48.805 \\  & RMSE & 437.040 & 414.256 & 422.609 & 415.376 & 419.644 & 421.224 & 420.811 & 423.770 \\  & MAPE & 43.773 & 53.320 & 48.703 & 52.607 & 50.366 & 50.886 & 50.222 & 48.335 \\  & RMSE & 416.775 & 445.232 & 448.546 & 446.987 & 500.864 & 441.484 & 406.373 & 410.386 \\  & MAPE & 51.681 & 41.895 & 41.223 & 41.478 & 38.510 & 43.311 & 62.067 & 54.371 \\ \hline \hline \end{tabular} Model abbreviations are shown in Table 4, and the bold print denotes the top three of the 80 combined models in terms of the RMSE and MAPE.  
\end{table}
Table 5: Results of 80 combined models for Route 1 on week data.  
compare the proposed model with the listed models based on the minimum RMSE and MAPE criteria and then gives some findings.
## 4 Test Results and Discussion
### Particle Breakage Characteristics
#### 4.2.3 Blows per Layer (Compaction Degree)  
In order to analyze the effect of blows per layer of samples on the particle breakage properties of weathered granite, four red-brown weathered granite samples with the same initial particle size distribution were prepared, and four different heavy compaction tests were conducted, with blows per layer (BPL) of 30, 50, 75, and 98. The sieving results before and after the tests are shown in Figure 19.  
As shown in Figure 20, the relative breakage increased with an increase in blows per layer, but the increasing level of relative breakage decreased. Furthermore, with the further increase of blows, \(B_{r}\) tended toward a certain limit value. Considering the engineering practice, the compaction degree of  
Figure 19: Sieving curves of samples under different blows per layer. BPL: blows per layer.  
Figure 18: Relationship curve between feldspar content and \(B_{r}\).  
samples was analyzed, and the compaction degrees of samples and corresponding relative breakage are depicted in Figure 21. It can be concluded from this figure that there is an approximate linear growth relationship between compaction degree and \(B_{r}\). It can be found that, for the same weathered granite fillings, relative breakage can be used to reflect the compaction performance indirectly on the basis of the relationship between compaction effect and compaction performance. Excessive compaction may lead to excessive particle breakage of soils and is not conducive to the long-term stability of subgrade.
# A Study on the Heat Transfer of Surrounding Rock-Supporting Structures in High-Geothermal Tunnels
## 4 Results and Discussion
### Thickness of the Surrounding Rock Heat-Adjusting Layer  
Before excavation of the tunnel, the temperature of the rock mass is in an equilibrium state (initial rock temperature). After tunnel excavation and ventilation, heat transfer occurs from deep to wall at each point in the surrounding rock due to the temperature difference between air and rock mass. The distance from the center of the tunnel to the limit boundary of the heat regulating circle is the disturbance range of the temperature field, that is, the thickness of the heat-adjusting layer. With the extension of ventilation time, the thickness of the heat-adjusting layer keeps increasing, and the increasing rate keeps decreasing, until the disturbance range of the temperature field becomes stable. Through the iterative calculation and comparison of the model, the disturbance range of the temperature field under different initial surrounding rock temperature conditions was obtained. In general, the temperature field of the surrounding rock is stable when the secondary support starts concreting. Moreover, the lining structure may provide some insulation effect because of the difference in thermal conductivity. Therefore, 50d was chosen as the final time to calculate the range of thermoregulation effects.  
It can be seen in Figure 13 that the maximum radius of the heat regulating circle of the surrounding rock of the tested tunnel is 15.8 m. The thickness of the heat-adjusting layer is the quadric function of initial rock temperature and it increases with the rise of initial rock temperature at an early time, but the curve becomes smooth and slow when the rock temperature exceeds 70 \({}^{\circ}\)C. Moreover, the thickness will increase by an average of 2.3 cm for every degree the temperature increases.  
As we all know, the heat released by radial surrounding rock mostly depends on the high-temperature rock that is in the range of the heat-adjusting layer. Based on the analysis of the above results and heat transfer theory, the tunnel construction under different initial rock temperature conditions gives off different amounts of heat. As shown in Figure 14, the heat released by the rock is part of the heat source in a high geothermal tunnel. When the temperature is from 40 to 80 \({}^{\circ}\)C, the accumulated amount of heat released ranged from 2.29 to 9.93 MJ, and it increased linearly with initial rock temperature in 50 days. What was more, the released heat from rock increased, appearing slowly first and fast afterwards, with the thickness of the adjusting layer. The final calculation results may provide basic data for the design of ventilation and a comprehensive cooling system.  
Figure 13: The change rule of heat-adjusting layer thickness under different initial rock temperatures.
# A Novel Hybrid Model for Cantonese Rumor Detection on Twitter
## 1 Introduction  
With the rapid development of the Internet, social media have provided a convenient online platform for users to obtain information, express opinions, and communicate with each other. As one of the most popular social networks and microblogging platforms, Twitter has attracted more and more people to publish and share their opinions. As more and more people participate in discussions about hot topics and exchange their opinions on social networks, many rumors appear on Twitter. A rumor refers to a story or statement in general circulation without confirmation or certainty as to facts. Due to the large number of users and easy access to social networks, rumors can spread widely and quickly on Twitter, causing public panic and disrupting social order, even endangering national security [1]. Therefore, it is necessary to detect rumors and stop them from spreading widely on Twitter. There are some fact-checking websites, such as Snopes [2] and PolitiFact [3], that provide information and reports for rumor analysis and checking. However, these websites rely heavily on manual labor to track and debunk rumors, and the verified rumors are often limited to specific topics.  
In this case, automatic rumor detection methods should be proposed for saving human effort and debunking rumors more efficiently.  
There has been a lot of work related to rumor detection on Twitter concerning English and Chinese [4; 5; 6; 7], but Cantonese rumors are seldomly studied. Cantonese is a branch of Chinese and it is mostly used in Guangdong Province and Hong Kong in China. Although Cantonese is widely used in speaking, it is in increasing use for informal communication like in forums or blogs. However, due to the complexity of Cantonese semantics and the lack of benchmark datasets, Cantonese rumor detection is a challenging task.  
To address the problem of Cantonese rumor detection, we have proposed a novel hybrid model XGA and conducted experiments to evaluate its performance. The main contributions of this paper are summarized as follows:  
* To cope with the complexity of Cantonese semantics and extract the deep features of Cantonese, we take both semantic and sentiment features into account for rumor detection. An XLNet model is used to produce text-based and sentiment-based embeddings. We perform joint learning of character and word embeddings to better fit in with the structre of Cantonese.
* A novel hybrid model XGA is proposed to improve the performance of Cantonese rumor detection, which takes advantage of XLNet, BiGRU and the attention mechanism. The Cantonese rumor dataset we constructed before is used to train our proposed model. The evaluation results show that XGA significantly outperforms other widely used rumor detection approaches in the Cantonese rumor detection.  
The rest of this paper is organized as follows. Section 2 introduces related work on rumor detection. Section 3 elaborates the proposed model. Section 4 describes the experimental setup and gives out evaluation results. Section 5 concludes the research in this paper and discusses future work.
# Spam Email Detection Using Long Short-Term Memory and Gated Recurrent Unit
## 4 Proposed Method  
The method presents a hybrid architecture based on LSTM and GRU for spam email detection. The LSTM and GRU both are the advanced types of RNNs that have been developed to solve specific challenges that are inherent in conventional RNNs, including the vanishing gradient problem that may affect the capturing of long-term dependencies on sequential data. Our proposed hybrid model seeks to improve the precision of antispam resources. The LSTM component is far superior at capturing long-term dependencies and contexts, while the GRU gives a much simpler architecture and low time complexity while training. Therefore, by using such an approach, the model would be able to provide a complex solution to address the issue of detecting spam emails since it deals with the sequence of the emails.
## 2 Literature Review
### Adaptive Neuro Fuzzy System (ANFIS) Method  
The structure of the ANFIS model is shown in Figure 5. There are five layers to this system [29]:  
1. The first layer calculates the membership degree. Each node produces a membership degree. The fuzzy sets apply membership functions [29]. \[\begin{array}{l}O_{1,i}=\mu_{Ai}(x)i=1,2,\ldots\\ O_{1,i}=\mu_{Bi-2}(y)i=3,4,\ldots\end{array}\] (1) where \(x\) and \(y\) are outputs, \(A_{i}\) and \(B_{i}\) are linguistic labels, and \(\mu_{Ai}\) and \(\mu_{Bi-2}\) are the degree of membership function for \(A_{i}\) and \(B_{i}\), respectively.
2. The output of the second layer (fire strengths) is computed based on the computed membership degrees. In fact, membership functions of the previous layer are compounded together to generate the firing strengths. \[O_{2i}=w_{i}=\mu A_{i}(x)*\mu B_{i}(y),\] (2) where \(O_{2i}\) is the output of this layer called fire strength.
3. The firing strengths are normalized in this level. The contribution of the firing strengths is computed by the constant nodes: \[O_{3,i}=w_{i}=\frac{W_{i}}{w_{1}+w_{2}}.\] (3)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Simulation** & **Parameter** & **Time Lag** & **Output** \\ \hline Group 1 & SSL & t-3 & Suspended Sediment Load (SSL) (t) \\ Group 2 & Q, SSL & t-3 & SSL (t) \\ Group 3 & SSL, R & t-3 & SSL (t) \\ Group 4 & Q, R & t-3 & SSL (t) \\ Group 5 & SSL, Q, R & t-3 & SSL (t) \\ Group 6 & SSL, Q, R & t-6 & SSL (t) \\ Group 7 & SSL, Q, R & t-9 & SSL (t) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The suggested combination.  
In this layer, consequent parameters are used to determine the proportion of the \(i\)th rule to the overall outcomes:  
\[O_{4,i}=\overline{w}_{i}f_{i}=\overline{w}_{i}(p_{i}x+q_{i}y+r_{i}), \tag{4}\]  
where \(p_{i}\), \(q_{i}\), and \(r_{i}\) are consequent parameters.
5. This layer uses summation of input signals to obtain the overall output. \[O_{5,i}=\sum_{i}\overline{w}f_{i}=\frac{\sum\limits_{i}w_{i}f_{i}}{\sum\limits _{i}w_{i}}.\] (5a) A previous study showed that a Gaussian membership function led to accurate outputs [30]. Thus, the current study uses this membership function [29]: \[\mu_{A_{i}}(x)=\left(-\frac{(x-c_{i})^{2}}{2\sigma_{i}2}\right),\] (5b) where \(c_{i}\) and \(\sigma_{i}\) are the parameters for the membership function.
## 4 Results and Discussion
### Results
#### 4.1.1 Comparison of Energy Performance: Low and High Usage of the Kindergarten
Under low-usage conditions, the GWP decreases by 25.7% compared to the baseline, primarily due to lower energy consumption during periods of limited occupancy. In the high-usage scenario, lifetime GWP is 8.2% lower than the baseline but 23.5% higher than the low-usage case, reflecting the increased energy consumption associated with higher operational intensity under normal usage conditions. The observed values also account for the impact of gradual component degradation on energy performance and environmental outcomes over time. The KPIs highlight the interplay between operational energy demands, the efficiency of the renewable energy system, and lifecycle events such as maintenance and component replacements. While the results confirm the kindergarten's ability to achieve emission reductions through PV system upgrades, they also emphasize the importance of ongoing system optimization to sustain these benefits over the long term. The trends observed in the KPIs are further contextualized and analyzed in the subsequent discussion of the PED and GHG emissions plots, providing deeper insights into the system's performance under varying operational conditions. Figures 6 and 7 illustrate the annual trends in PED and GWP over the 25-year analysis period, highlighting key lifecycle events and differences between low-usage and high-usage scenarios:  
Figure 6: Annual PE Demand for Low and High Usage over 25 years of analysis. Figure 7: Annual GHG Emissions for Low and High Usage over 25 years of analysis. * Initial Peak (2021): The sharp peak observed in 2021 reflects the embodied energy associated with the installation of the PV system. This includes energy requirements for the manufacturing, transportation, and installation processes, which significantly increase PED and GWP in the first year;
* Steady Trend with Gradual Increase: Following the initial installation, PED and GWP stabilize but exhibit a gradual increase over time. This trend is driven by the 0.5% annual degradation assumed in the PV system's performance. As the system's efficiency declines, its renewable energy output decreases, causing a greater reliance on grid electricity to meet the building's energy demand;
* Spikes (2031 and 2041): Distinct spikes are expected to occur in 2031 and 2041, corresponding to the replacement of the air conditioning (assumed to occur in 2031) and the heat pump (in 2041) systems. These lifecycle events temporarily increase PED due to the embodied energy required for the manufacturing and installation of the replacement components.
## 4 Results and Discussion
### Practical Implications and Industrial Applications  
The proposed conveyor alignment and thermal detection systems are highly applicable in industrial recycling environments, particularly in eddy current separators. By reducing the belt misalignment and responding rapidly to overheating scenarios, the system minimizes machine downtime, prevents damage to key components, and enhances operational efficiency. Managers can rely on real-time alerts to prevent fire hazards, while analysts can use the system's data for process optimization and predictive maintenance planning.
## 3 Theories and Methodology  
### Basic Theory  
The ionosphere is a part of the Earth's upper atmosphere, extending from 65 to 1000 km above the surface, and is predominantly made up of plasma that has been ionized by solar radiation. This plasma exhibits a property of dispersion, which results in a frequency-dependent group delay for radio waves as they travel through the ionosphere. By disregarding the effects of the geomagnetic field, the higher-order terms in the 'quasi-longitudinal' expansion of the Appleton-Hartree equation, and the variations in displacement during the satellite detecting a pulse source, the group delay of time of arrival for radio waves of various frequencies traveling from the ground to the detector adheres to the following relationship:  
\[\tau_{toa}(f)=\frac{\alpha\times STEC}{f} \tag{1}\]  
In this formula, all variables are expressed in metric units, where \(\alpha=1.34\times 10^{-7}\), \(f\) signifies the different frequency components of radio waves, and \(STEC\) refers to the slant total electron content (STEC) along the path of the radiation source over the satellite's trajectory. The formula provided allows for two principal methodologies to ascertain the STEC. The first approach involves fitting the frequency-time-of-arrival coefficient for wideband signals, a method that has been explored in several scholarly works [2; 10; 24; 25; 26]. The second technique is the 'dechirping' of signals, as detailed by [6]. For this paper, the latter method has been selected to calculate the STEC from various signals, and this calculated value is designated as \(STEC_{dec}\).  
Figure 1 illustrates a simplified representation of a signal captured by the WEMPD. The ionosphere is conceptualized as a spherical shell, and the trajectory from the signal's origin to the satellite intersects the ionosphere at a point of presumed maximum electron  
density. At the ionospheric pierce point (IPP), the relationship between the STEC and the vertical total electron content (VTEC) is given by the following equation:  
\[VTEC=STEC\times\cos\delta=STEC\times\sqrt{1-\left(\frac{R_{e}}{R_{e}+H_{i}}\sin \theta\right)^{2}} \tag{2}\]  
Here, \(\delta\) denotes the zenith angle of the satellite with respect to the IPP; \(R_{e}\) is Earth's radius; and \(H_{i}\) is the altitude of the peak electron density layer, which is assumed to be 300 km in this study. Furthermore, \(\theta\) represents the angle of incidence of the signal as it travels from the radiation source to the satellite. For a radiation source located at a certain altitude \(H_{e}\) above the ground in the lower atmosphere, \(\theta\) can be approximated using the following formula once its two-dimensional position is established:  
\[\theta=\frac{\pi}{2}-\arctan\left(\frac{H_{s}+R_{e}(1-\cos\beta)}{R_{e}\sin \beta}\right)+\beta \tag{3}\]  
In this formula, \(\beta\) is the angle between the radiation source and the satellite footprint, and \(H_{s}\) is the altitude of the satellite, as depicted in Figure 1.