esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Research Goal and Contributions  
To predict the demand for railway track tamping maintenance, we developed a prediction model of the longitudinal-level deterioration process within the tamping maintenance cycle for a track segment. Based on the model, the tamping maintenance cycle period of each track segment is predicted combined with the maintenance limit value of the longitudinal level indicator. This study makes the following key contributions.  
(1) To our knowledge, this study is the first attempt to use the power-time-transformed Wiener process (PTT-WP) to predict the tamping maintenance cycle. Simultaneously, this study estimates the deterioration model parameters individually for each track segment, achieving simultaneous consideration of deterioration characteristics, including heterogeneity, uncertainty, and historical dependency, avoiding the limitations that the Markov property assumption may impose on the accuracy of the prediction model.  
(2) Considering the characteristics of the problem, two parameter estimation methods were designed: the maximum likelihood estimation (MLE) and the adaptive Markov chain Monte Carlo (MCMC). Furthermore, under the framework of the MLE method, the effectiveness of three different parameter-solving algorithms, namely the marine predators algorithm (MPA), the genetic algorithm (GA), and the gradient descent algorithm (GD), are compared. To our knowledge, this is the first attempt to use MPA for this type of problem. Finally, by conducting a prediction error analysis based on actual cases, the effectiveness and performance of the different parameter estimation methods were assessed, providing useful guidance for similar studies.  
The Methodology section (Section 2) presents the prediction model developed for the tamping cycle of a track segment. The Estimation of Model Parameters section (Section 3) describes the methods used to estimate the proposed prediction model parameters. The Case Study section (Section 4) presents a case study using actual data from the Chinese Lanxin Railway line to verify the effectiveness of the proposed method. The main conclusions and directions for future research are presented in the final section (Section 5).
## 2 Methods  
### Acquisition of Terahertz Image  
In this work, the terahertz image dataset is acquired by a linear terahertz imaging system; the structure of the system is shown in Figure 1. The system consists of two main components: a 0.3 THz terahertz generator and a high-speed linear terahertz camera. The terahertz generator produces terahertz waves at a frequency of 0.3 THz, which are transmitted through a flared antenna and dispersed uniformly to ensure that the power of the camera at the receiving end is the same everywhere; the THz beam is transmitted through a concave mirror, so that the THz beam emitted by the radiation source covers every pixel of the camera uniformly and efficiently. The high-speed linear terahertz camera has a scanning frequency of up to 5000 Hz and a scanning speed of 2.5 m/s. The camera operates at 300 GHz, which compensates for the lack of spatial resolution due to high-frequency scanning, and the detector's individual pixel size is 0.5 mm \(\times\) 0.5 mm, with several pixels of a 256 \(\times\) 1 resolution. The camera can be fixed underneath the conveyor belt, and the samples that will be tested will pass through the conveyor belt to the location of the camera. The sample that will be detected passes through the conveyor belt and reaches the camera, and the phase information of the sample can be observed in real time via imaging in the visualisation interface of the computer's software. Moreover, we can manually adjust the spectral intensity of the system to change the brightness and hue of the image in the software's interface according to our own needs.  
In order to adapt to the arbitrary placement of objects to be tested in real contraband detection scenarios, we have prepared eight kinds of samples of concealed objects with multiple types (five dangerous objects, namely scissors, pistol, blades, lighter, and nails, and three kinds of non-dangerous objects, namely keys, nail scissors, and pens; some of the physical diagrams are shown in Figure 2). The blade category includes different types of blades, such as metal blades, utility knives, ceramic blades, and plastic knives. We have also prepared different sizes of scissors and nails. We did them randomly with different poses in two kinds of packages, as shown in Figure 3, and we used a terahertz imaging system to acquire the terahertz image data. Each image acquired by the device is a pseudo-colour image of size 512 \(\times\) 256, where pixel size is 0.5 mm \(\times\) 0.5 mm, and the colour of each image provides information about the spectral intensity.  
Figure 1: Terahertz scanning imaging system. (**a**) The structure of the system; (**b**) the physical diagram of the system; (**c**) the software interface of the system.
# Design and Application of Logical Range Framework Based on Digital Twin
## 3 Key Technologies
### Proxy-Based Equipment Dynamic Extension Method
In the proxy management platform, a specified proxy can be selected and a proxy object can be created for it, where each proxy object matches a specific piece of equipment. Proxy objects and equipment are bound to each other through _equipmentID_. After binding, the equipment is taken over by the binding proxy object. The equipment only needs to interact with the proxy object, while the upper layer application software only needs to interact with the proxy object. Figure 2: Location of the equipment proxy in the proposed system. When the system needs to extend to new equipment, it needs to check whether the corresponding proxy exists for the new equipment. If the new equipment has a corresponding proxy, the proxy object is created for the equipment in the proxy management platform and the proxy object is bound to the equipment to complete the equipment expansion. If the corresponding proxy does not exist for the new equipment, the corresponding proxy needs to be developed (according to the proxy development process described above), following which the proxy is uploaded to the proxy management platform. A proxy object is then created for the new equipment, binding between the proxy object and the equipment is completed and, finally, the equipment extension is implemented. A flowchart of the process for adding new equipment is shown in Figure 4.  
From the above description of the whole proxy process, it can be seen that, after adopting the proxy mechanism in this system, the intrusion to the system in the process of dynamically adjusting the physical layer equipment is minimal, and the whole process is in accordance with the \"Open Closed Principle.\" The equipment that is added to the system will not only be limited to physical equipment, as simulation equipment can also be connected to the system through proxies to achieve system integration. Figure 3: Schematic diagram of proxy creation and operation.
## 3 Methods
### SRI Calculation and Run Theory  
The SRI, which is used to characterize the hydrological droughts, is based on the SPI proposed by [PERSON] [15; 17]. In this study, due to the seasonal drought that exists in many of the selected river basins, SRI on a 3-month scale (SRI-3) was obtained as the drought indicator. The reason why 3-month timescale was adopted is to consider seasonally wet/dry conditions as well as to avoid redundant information if a longer timescale were used (e.g., 6-24 months). Assuming that the runoff volume for a certain time period is \(x\), the probability density function \(f(x)\) of its gamma distribution can be expressed as:  
\[f(x)=\frac{1}{\gamma^{g}\Gamma(\beta)}x^{\beta-1}e^{\frac{-x}{\gamma}}\ \ \ \ \ \ \ \ \ x>0 \tag{8}\]  
where \(\gamma>0\) and \(\beta>0\) are the shape and scale parameters, respectively, which can be calculated by the maximum likelihood method. \(\Gamma(\beta)\) is the gamma function. For a particular time scale, the cumulative probability of runoff \(x_{0}\) is as the following:  
\[F(x)=\int_{0}^{x}f(x)dx_{0} \tag{9}\]  
After standardizing the probability of the \(\Gamma\) distribution, the SRI can be calculated by the following equation:  
\[SRI=S\frac{t-(c_{2}t+c_{1})t+c_{0}}{((d_{3}t+d_{2})t+d_{1})t+1}\ \ \ \ \ t=\sqrt{-2\ln(F)} \tag{10}\]  
\(F\) is the probability derived from Equation (8) or Equation (9); when \(F>0.5\), S = 1; when \(F\leq 0.5\), S = \(-1\); the other values are empirical parameters, and their specific values are: \(c_{0}=2.515517\), \(c_{1}=0.802853\), \(c_{2}=0.010328\), \(d_{1}=1.432788\), \(d_{2}=0.189269\), \(d_{3}=0.001308\)[19].  
Based on the run theory [52], the drought characteristics include drought duration (Dd) and drought severity (Ds). Previous studies usually set \(-0.5\)[19; 53] or \(-1.0\)[54] as a threshold to identify drought events. In this study, the threshold was defined as \(-1.0\) in order to better separate the more severe drought events from the moderate drought events. The count of continuous months at which the value of the SRI is below the threshold \(x_{0}\) is Dd and the absolute sum of all SRI during the drought period is Ds. The run theory schemes are shown in Figure 3.
## 4 Discussion
### Analysis of the Influence of Window Position (Upper/Centered)  
The position of the window--either centered or upper--significantly influences \(DA\) and \(CSA\) values, impacting the spatial distribution of daylight within the room. Centered window positions generally offer superior performance for both the \(DA\) and \(CSA\) metrics, providing a more uniform distribution of light throughout the room. This configuration minimizes areas with low daylight exposure, thus enhancing overall visual comfort and circadian stimulus. For \(DA_{500}\), centered windows with WWRs of 25% to 45% consistently achieve higher values, maintaining levels at over 80% throughout the room's depth in bright-coated scenarios.  
In contrast, upper window positions, while still effective for larger WWRs, tend to exhibit a more pronounced decline in light levels towards the back of the room. This configuration often results in greater variability and less consistent light distribution, which can compromise visual comfort and circadian effectiveness. For instance, \(DA_{500}\) values in upper window positions decrease more steeply, resulting in lower average values at the room's rear compared to centered configurations.  
Similarly, \(CSA\) values are affected by the window position, with centered configurations yielding more favorable outcomes, particularly in rooms with bright finishes and larger WWRs. \(CSA2\) values are notably higher in centered positions, with maximum levels occurring closer to the window and gradually declining with a more controlled gradient compared to upper positions.  
Therefore, centered window positions are recommended for achieving the best balance of daylight distribution and circadian stimulus, especially in hospital settings where uniform lighting is critical for patient well-being. Upper window configurations can still be selected in specific cases but may require additional design considerations, such as increasing the WWR or optimizing the reflectance, to mitigate their less favorable light distribution characteristics.
## 3 The PSOTSC Algorithm  
The PSOTSC algorithm is developed in this section where the high-quality segmentation solution is found through particle swarm optimization to achieve better segmentation accuracy and compression rate. First, the overall framework of the algorithm is introduced. Then some key processing links in the algorithm are illustrated in detail, such as particle swarm segmentation optimization method, particle update strategy based on NARJ and the satisfaction judgment method.
# A Topical Category-Aware Neural Text Summarizer
## 1 Introduction
After a text-CNN identifies the topic category of a given text, the category is used to generate the CAM of the text. Since the map identifies relevant words to the topic category, it is regarded as a sort of attention mechanism for an encoder-decoder model. The LSTM decoder of the proposed summarizer produces a summary using the context vector from the LSTM encoder like other encoder-decoder models. The main difference from other models is that the proposed decoder uses the combination of class-activation map and legacy attention as attention weights in writing a summary. The class-activation map provides relevancy weights to the words in the original text for the topic category and legacy attention [13] does relevancy weights for generating a summary. As a result, the summary does not simply summarize a text but also reflects the topic category into a summary. The contribution of this paper is three-folds. Note that the proposed model learns the relation between a text and its topic category. Thus, the first contribution is that it can produce a summary for a desired topic category without learning from topic-specific summaries, which leads to saving on the costs of preparing topic-specific summaries. The second is that CAM is applied to text summarization for the first time. The proposed model combines CAM and the attention mechanism \begin{table}
\begin{tabular}{l} \hline \hline \(\mathbf{S}\): A review on Tuesday about two concerts at the Metropolitan Museum of Art misstated the surname of a pianist at two points. As noted elsewhere in the review, he is [PERSON], not [PERSON]. \\ \(\mathbf{R}\): Review of two concerts at Metropolitan Museum of Art: Pianist’s name is [PERSON]. \\ \hline \hline \(\mathbf{S}\): Most campaigns slow down in the heat of the summer, but voters itching for the governor’s race to pick up speed can now turn to the Internet. Governor [PERSON] unveiled a campaign web site yesterday that offers video and audio clips as well as the text of speeches, position papers and a biography. The web page, at www.christie97.org, even shows supporters how to make their own campaign buttons. \\ \(\mathbf{R}\): Governor [PERSON] unveils campaign site on Internet. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Two examples on text summarization that show the importance of topic category. within an encoder-decoder model. The last is that it is proven empirically that the use of topic category increases the summarization performance. According to the experiments on the New York Times corpus, the proposed topic category-aware summarizer outperforms the legacy attention-based sequence-to-sequence model. The rest of this paper is organized as follows. Section 2 introduces the previous studies on automatic text summarization. Section 3 presents the proposed approach to extracting topical information from a text, and Section 4 explains the proposed automatic text summarization model which uses the topical information. Section 5 gives the experimental results, and finally Section 6 draws conclusions and future work.
## 2 System and Channel Model
Considering the relay with fixed gain, the signal will be amplified with a fixed relay gain, G, and forwarded to the destination. Hence, the end-to-end instantaneous SNR of the mixed RF-FSO system could be expressed as \(\gamma=\frac{\gamma_{1}\gamma_{2}}{\gamma_{2}+C}\), where \(C\) is a constant associated with G [26]. The CDF of the hybrid RF-FSO system is defined as [9]:  
\[ \begin{array}{l}F_{\gamma}(\gamma)=\Pr\bigl{(}\frac{\gamma_{1}\gamma_{2}}{ \gamma_{2}+C}<\gamma_{th}\bigr{)}\\ =\int\limits_{0}^{\infty}\Pr\bigl{(}\frac{\gamma_{1}\gamma_{2}}{\gamma_{2}+C}< \gamma_{th}\bigr{|}\gamma_{2}\bigr{)}f_{\gamma_{2}}(\gamma_{2})d\gamma_{2}\\ =\int\limits_{0}^{\infty}F_{1}\biggl{(}\frac{\gamma_{th}(\gamma_{2}+C)}{\gamma _{2}}\biggr{)}f_{\gamma_{2}}(\gamma_{2})d\gamma_{2}.\end{array}  \tag{3}\]  
Figure 1: End-to-end mixed radio frequency (RF) and free space optical (FSO) communication system. Using \(\Gamma(\mathsf{g}_{r},x)=\int\limits_{0}^{x}t^{s-1}e^{-t}dt\), the CDF of Rayleigh distribution, and Equation (3), the CDF of the hybrid RF-FSO system is  
\[F_{\gamma}(\gamma)=1-\frac{2^{(\alpha+\beta)}A\sqrt{C}}{16\pi\sqrt{\overline{ \gamma_{1}}}}\sqrt{\gamma}\exp(-\frac{\gamma}{\overline{\gamma_{1}}})\times G \begin{array}{c}6,0\\ 1,6\end{array} \left(\frac{(\alpha\beta)^{2}C\gamma}{16\overline{\gamma_{1}} \overline{\gamma_{2}}}\right] \begin{array}{c}\frac{\xi^{2}+1}{2}\\ \kappa_{1}\end{array} \right) \tag{4}\]  
where \(A=\alpha\beta\xi^{2}/\big{(}2\sqrt{\overline{\gamma_{2}}}\Gamma(\alpha)\Gamma (\beta)\big{)}\) and \(\kappa_{1}=\frac{\xi^{2}-1}{2}\), \(\frac{\alpha-1}{2}\), \(\frac{\alpha}{2}\), \(\frac{\beta-1}{2}\), \(\frac{\beta}{2}\), \(-\frac{1}{2}\).
## 2 Methods
### Threshold Setting  
#### 2.3.1.  
The thresholds in the Conventional Early Warning Method  
The thresholds for \(S_{ref}\), \(S_{ia}\), and \(S_{ku}\) are calculated as follows:  
After the monitoring equipment is deployed, the data acquired in the first week are collected for statistical analysis, and the upper whisker is taken as \(S_{ref}\). \(S_{ia}\) and \(S_{ku}\) are about 1.5-2 times and 3-4 times that of \(S_{ref}\), respectively. The data from one week after the monitoring begins was chosen for two main reasons: First, special thresholds and parameters for the current landslide must be established as soon as possible. If it is too late, the early warning's timeliness and accuracy will be compromised. Second, a certain amount of data is required to analyze the characteristics of data fluctuations caused by monitoring sensor measurement errors, mainly extreme values and periodic characteristics, in order to avoid data fluctuations influencing thresholds and parameters.  
The thresholds for \(v_{21}\), \(v_{22}\), \(v_{31}\), \(v_{32}\), \(v_{33}\), and \(v_{34}\) are calculated as follows:  
Use a time window to scan the data acquired in the first week to obtain the velocity series, and then take the upper whisker of the speed series as \(v_{ref}\). Let \(v_{21}=4v_{ref}\), \(v_{22}=8v_{ref}\), \(v_{31}=2v_{ref}\), \(v_{32}=4v_{ref}\), \(v_{33}=6v_{ref}\), and \(v_{34}=8v_{ref}\).  
As the amount of landslide displacement monitoring data increases, the thresholds should be dynamically adjusted. When the landslide has obvious CDS, the velocity of constant deformation can be used as \(v_{ref}\), and then reset \(v_{31}=2v_{ref}\), \(v_{32}=4v_{ref}\), \(v_{33}=6v_{ref}\), and \(v_{34}=8v_{ref}\). It should be clarified here that the reason for using 2, 4, 6, and 8 as the boundaries separating the different warning levels is based on the study of [PERSON] et al. [10]. This study proposes to use the displacement speed ratio (DSR), which is the ratio of current landslide deformation speed to the deformation speed of the CDS, as the evaluation index of landslide deformation state, after analyzing and summarizing several landslide deformation processes. This research also offers a DSR-based warning criterion: CDS for DSRs between 0 and 2, initial acceleration stage for DSRs between 2 and 6, medium-acceleration stage for DSRs between 6 and 8, and critical sliding stage for DSRs larger than 8. To meet the warning levels of five levels, the initial acceleration stage with a DSR of 2-6 was separated into two parts, 2-4 and 4-6, in this study.
## 7 Conclusions and Future Research Needs
### Future Research Needs  
To promote the sustainable and responsible application of plastic-modified asphalt pavements, the following research priorities should be addressed:  
* **Long-term field validation** Execute ongoing investigations for several years across varied climates and traffic situations to evaluate pavement durability, ageing characteristics, and environmental impacts (e.g., MP release, VOC/PAH emissions).
* **Standardization of materials and processes** Develop globally accepted standards for plastic feedstock quality, incorporation methods, and performance evaluation to guarantee consistency and promote regulatory approval.
* **Advanced processing techniques** Investigate the use of functionalized plastics, hybrid modifiers (e.g., plastics combined with crumb rubber or nanomaterials), and optimized blending methods to enhance binder compatibility, dispersion, and long-term stability.
* **Comprehensive environmental assessment** Enhance testing methodologies to more accurately measure MP emissions, leaching characteristics, and cumulative ecosystem impacts under realistic service and ageing conditions.
* **Enhanced life cycle modelling** Refine LCA frameworks to include dynamic factors such as maintenance practices,evolving vehicle impacts, recyclability, and integration with complementary sustainable technologies (e.g., RAP, WMA).  
By addressing these priorities through interdisciplinary collaboration, the asphalt and waste management sectors can realize the full potential of plastic-modified pavements as a high-performance, sustainable alternative for future infrastructure.  
Conceptualization, A.G. and N.S.M.; methodology, A.G.; resources, A.G.; writing--original draft preparation, A.G.; writing--review and editing, A.G. and N.S.M.; visualization, A.G., N.S.M. and T.P.; supervision, N.S.M. and T.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.
# Simulation Study of Microwave Ablation of Porous Lung Tissue
## 3 Results
### Effect of Microwave Ablation Power  
In microwave ablation, ablation power is one of the most important factors on the ablation effect [28]. Figure 5a-c show the temperature distribution of the lung tissue for \(\phi_{l}=0.7\), \(\phi_{t}=0.6\), d = 2.0 cm, at t = 360 s, and ablation powers of 10 W, 20 W, and 30 W, respectively. It can be seen from the figure that the higher the power of microwave ablation is, the higher the highest temperature of the tissue is, and the transverse and longitudinal diameter of the ablation area is also increasing. As seen in Figure 5c, at P = 30 W, t = 360 s, the tumor was not completely ablated, so we believe that further adjustment of ablation parameters is required if the tumor with a diameter of 2.0 cm is to be completely ablated. Table 7 shows the values of the transverse and longitudinal diameter of the ablation zone, and the ablation volume under different ablation powers when the ablation power was increased from 10 W to 30 W; the maximum temperature increased from 87.3 \({}^{\circ}\)C to 142 \({}^{\circ}\)C, the transverse diameter of the ablation zone increased from 0.92 cm to 1.40 cm, the longitudinal diameter increased from 1.88 cm to 2.11 cm, and the ablation volume increased from 2.28 cm\({}^{3}\) to 4.06 cm\({}^{3}\). The higher the ablation power, the higher the maximum temperature of the tissue, and the larger the ablation area and volume.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Tumor Porosity (cm)**} & \multirow{2}{*}{**Maximum Temperature (\({}^{\circ}\)C)**} & \multicolumn{2}{c}{**Ablation Zone (cm)**} & \multirow{2}{*}{**Ablation Volume (cm\({}^{3}\))**} \\ \cline{3-3} \cline{5-5}  & & **Transverse Diameter (cm)** & & \\ \hline
0.5 & 95.3 & 1.23 & 2.02 & 2.94 \\
0.6 & 87.3 & 0.92 & 1.88 & 2.45 \\
0.7 & 83.9 & 0.62 & 1.82 & 1.80 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation temperature, ablation area and ablation volume of tumors with different porosities.  
Figure 5: Temperature distribution of porous lung tissue at different ablation powers. (t = 300 s, d = 2.0 cm, \(\phi_{t}=0.6\), \(\phi_{l}=0.7\)): (**a**) P = 10 W (**b**) P = 20 W (**c**) P = 30 W.