esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on a Comprehensive Evaluation Method of Train Anti-Slip System Performance
## 7 Conclusions  
This article addresses the problem of single and simplistic evaluation indexes for train anti-slip control, and proposes a comprehensive evaluation method based on the analytic hierarchy process by adding a load layer and a braking level layer. This evaluation method for train anti-slip systems uses an adhesion coefficient utilization index, an air consumption increase ratio index, a wheelset slipping energy index, and an anti-slip valve action frequency index, which enhances the comprehensiveness and scientific basis of evaluation.  
To accurately simulate the operating environment of trains, this article establishes a semi-physical simulation platform for anti-slip control by developing vehicle models, adhesion models, and physical design of braking air circuits. The platform was validated, providing a simulation platform for implementing anti-slip strategies.  
Based on the constructed semi-physical simulation platform for anti-slip control, this article conducted a series of performance tests on anti-slip control strategies. A multi-dimensional comprehensive evaluation was conducted on the anti-slip control performance of two anti-slip strategies under different loads, brake levels, and adhesion coefficient levels. The results showed that the R1 and R2 values of the two anti-slip control strategies under complex working conditions were 0.8389 and 0.7912, respectively. Therefore, it can be clearly shown that the control effect of anti-slip control strategy 1 was better than that of anti-slip control strategy 2.  
The model used in the semi-physical simulation platform established in this article is based on the POLACH theory [12]. In subsequent research, it was found that the theory ignored the adhesive improvement effect. Therefore, in further research, more accurate adhesive models can be used to update the semi-physical simulation platform and comprehensive evaluation methods established in this article, which is an important research direction in the future.  
Conceptualization, G.Z. and H.Q.; software, J.Z.; validation, H.Y. and J.Z.; writing--original draft preparation, H.Y.; writing--review and editing, J.Z.; project administration, G.Z and F.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Adhesion Control and Experimental Verification Of Rail Transit Mobile Equipment (No. 2022 CY7005), the National Natural Science Foundation of China (No. 52072266) and the Open Cooperative Innovation Foundation of the High Speed Wheel-Rail System Laboratory (No. 2021Y264).  
Not applicable.  
The data presented in this study are available in this article.  
Thanks to the China Automotive Engineering Research Institute for their support of this research work.  
Author [PERSON] and [PERSON] were employed by the company CRRC Industrial Academy Corporation Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial rela-tionships that could be construed as a potential conflict of interest.
## 3 Results and Discussion
### The Comparison of Lp_HOGS with Lp_LOGS  
Lp_HOGS and Lp_LOGS were compared to verify the effect of adding the second-order overlapping group sparse TV regularization term. We added 30%, 40%, and 50% salt-and-pepper noise to the Gaussian, box, and motion blurs to obtain nine degradation combinations and compared the quality of the deblurred images. Nine distinct IR images were selected as the test images. Images of a passerby, a station, a truck, a car, and some buildings were 384 \(\times\) 288 pixels each, and images of a garden, some stairs, a corridor, and a zebra crossing were 506 \(\times\) 408 pixels each.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 6 Results
### Results Obtained in the Signature Generation Phase
#### 6.3.1 Comparative Analysis of SPHINCS-QRNG and SPHINCS-Chacha12 Based on Execution Time  
Figure 16 shows that the execution time of SPHINCS QRNG is more than that of the existing SPHINCS algorithm with PRNG. The mean execution time of SPHINCS-QRNG public key is 160 \(\upmu\)s, and of the private key is 238.89 \(\upmu\)s. Whereas, in SPHINCS-Chacha12 algorithm, the average time to generate the public key 112.15 \(\upmu\)s and the private key is 110.12 \(\upmu\)s.  
Figure 15: Percentage of passed tests, by RSA, ECDSA and SPHINCS-QRNG key pairs, based on NIST statistical test suite on randomness.  
Moreover, we also performed a comparative analysis based on theoretical performance. Since SPHINCS is a _h/d-ary_ certification tree, [PERSON] et al. [13] provided a rough theoretical run time values based on the count of pseudo random number functions (PRFs), PRNG and, hashes. The total height of hyper tree is \(h\) with \(d\) multiple layers of trees. It takes \(d2^{h/d}\) OTS key generations, \(d2^{h/d}+2\) PRF calls, \(d2^{h/d}+1\) PRNG calls and \(2t+d((\) (\(w+1)\)) \(2^{h/d}-1)\) hashes. HORST uses parameter \(t\), such that \(t=2^{\tau}\). The height of the HORST tree is \(\tau=\log t\). WOTS uses a signature size and runtime tradeoff parameter \(w\) such that \(w\in\mathbb{N}\). However, the time complexity of RSA is O(\(n^{2}\)) and that of ECC is O(\(n^{3}\)) [58]. Further, all pseudo-random number generator requires O(n) bit operations. In contrast, QRNG based on Hadamard transformation can be computed in O(nlogn) operations in classical hardware and, in O(1) in quantum hardware [59; 60]. Therefore, we conclude that theoretically, the computational complexity of our proposed algorithm is higher than that of existing SPHINCS based on PRNG and AGA-12.
## 3 Results and Discussion
### Thermo-Mechanical Properties
#### 3.2.2 Young's Modulus  
Figure 6 provides a comparison of Young's modulus behavior of recycled PS with virgin polystyrenes (HIPS 7240 and GPPS 1540) at different temperatures. Also, Young's modulus of recycled PP and virgin PP were compared. It was observed that Young's modulus for all the materials tested decreased significantly with the increase in temperature because the polymers become more ductile at higher temperatures due to Brownian mobility of the molecules [50]. The low Young's modulus at a higher temperature means that when the polymer is loaded, it increases deflections within the structure, which can limit the product in the requirements of serviceability [49].  
Recycled PS recorded a lower Young's modulus than HIPS 7240 and GPPS 1540 by 5.2-9.8% and 46.0-53.2%, respectively. [PERSON] et al. [39] did a similar experiment to determine how the amount of reprocessing affects the Young's modulus of HIPS and found out that the Young's modulus decreased with the number of reprocessing cycles. The reduced Young's modulus for recycled PS compared to HIPS 7240 after recycling was due to the reduced polymer cohesion resulting from a reduction in molecular weight [12]. GPPS 1540 is more rigid than HIPS 7240 and recycled PS since the crystallization behavior of amorphous GPPS 1540 increases the Young's modulus [18; 19; 20; 38]. Also, it was also observed that recycled PP had a lower Young's modulus than virgin PP by 39.2-52.0%. The result was similar to a study conducted by [PERSON] et al. [26] and [PERSON] et al. [47], which showed that Young's modulus reduced slightly after recycling. In this study, the lower Young's modulus of recycled PS and recycled PP than corresponding virgin polymers could be due to thermal degradation, which occurred due to physical ageing and thermo-oxidative degradation. The thermo-mechanical degradation is a temperature-dependent process which occurs due to a decrease of molecular weight [12]. [PERSON] et al. [39] demonstrated that successful mechanical recycling is  
Figure 5: Tensile strength for recycled PS, HIPS 7240, and GPPS 1540 at 24 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C.  
primarily dependent on sufficient melt processing stability. Processing causes oxidation, thermal, and mechanical stress to both virgin and recycled polymers. These three stress types are a permanent processing feature and can induce changes in mechanical properties.
# Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering
## 4 Machine Learning Models for Soil Layer Reconstruction
### Prediction Model
Through small-scale preliminary experiments, the model structure adopted by the algorithm was found to be sufficient to capture the key features in the soil layer data, while ensuring computational efficiency and avoiding overfitting. The model with two convolutional layers exhibited lower accuracy and did not meet practical requirements. In contrast, compared to the three-layer model, the framework models with four and five layers achieved an accuracy improvement of approximately 3%, but their computation times increased to about 1.4 and 1.8 times that of the original model, respectively, with a significantly increased risk of overfitting. We also conducted a hyperparameter tuning process to achieve better model performance. The final parameter settings are as follows. Activation function: ReLU; all layers use the ReLU activation function. Optimizer: Adam; we employ the Adam optimizer with a learning rate of 0.001. Training iterations: 20,000; the model is trained for 20,000 iterations. Figure 11: SCNN-10 network model structure diagram. Batch size: 128; a batch size of 128 is used during training. Dropout rate: 0.5; a dropout rate of 0.5 is applied after each pooling layer to help prevent overfitting. Weight decay: 1 \(\times\) 10\({}^{-4}\); a weight decay of 1 \(\times\) 10\({}^{-4}\) is used for regularization.
## 2 Data and Methods
### Methods
#### 2.3.2 Sulfur Oxidation Ratio (SOR) and Nitrogen Oxidation Ratio (NOR)  
The Sulfur Oxidation Ratio (SOR) and Nitrogen Oxidation Ratio (NOR) are the oxidation ratios that describe the conversion process of sulfur and nitrogen in the modified forms of the gas-particle distribution factors [28; 29]. In this study, these ratios are defined as follows [30]:  
\[\text{SOR}=\text{nSO}_{4}{}^{2-}/(\text{nSO}_{4}{}^{2-}+\text{nSO}_{2})\]  
\[\text{NOR}=\text{nNO}_{3}{}^{-}/(\text{nNO}_{3}{}^{-}+\text{nNO}_{2})\]where n is the concentrations of the chemical species in umol m\({}^{-3}\).  
Larger values of SOR and NOR mean that higher fractions of sulfur or nitrogen exist in the particle phase rather than in the gas phase, and it can be considered that more of the oxidation process has progressed in the atmosphere. Thus, higher values of SOR and NOR in a site mean the observed particles in the atmosphere have a greater age.
## 4 Numerical Analysis and Discussion
### Measured vs. Theoretical Frequencies  
In this section, we discuss the properties of the measured vibration spectra and compare them to the theoretical analysis of the wind tower reported in Section 4.2. The 0.4 Hz frequency measured by both TLS and GB-RAR is in agreement with the value obtained using the analytical formulas for the first natural frequency. The frequency f = 3.1 Hz of the second oscillation mode is measured by GB-RAR but not by TLS, probably due to lower precision of TLS when compared to GB-RAR.  
The frequency peak at f = 40 Hz found in all TLS measurements is an artifact due to the rotating mirror. In fact, the laser beam used for measurements in the TLS VZ1000 is addressed and collected by a rotating three-sided mirror, i.e., by the lateral faces of a triangular-based prism. When a scan speed of 120 lines per second is selected, the three-sided mirror rotates with an angular speed of 40 revolutions per second. Even a slight vibration of the rotation axis can cause oscillations in the results at a frequency  
Figure 15: Ground distance from TLS of a point P on the tower located at H = 65 m during the activity of the turbine, from 13:37:50 to 13:38:12 (**a**) and from 14:00:00 to 14:01:06 (**b**). Below the graphics of the distances, the wind speed in m/sec. X axis is the time, both for upper and lower figures.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Start Time** & **End Time** & **Wind Speed [m/sec]** & **Measured D [m]** & **Theoretical D [m]** \\
**[CET]** & **[CET]** & **min** & **max** & **min** & **max** & **min** & **max** \\ \hline
13.37.50 & 13.38.12 & 2.72 & 3.25 & 47.247 & 47.225 & 47.247 & 47.227 \\
14.00.00 & 14.01.06 & 2.20 & 2.40 & 47.262 & 47.255 & 47.260 & 47.254 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Theoretical and measured ground distances from TLS of a point 65 m high.  
of 40 Hz (Figure 16a). To confirm this hypothesis, the results of the measurements performed with a slower scanning speed of 60 lines per second were analyzed.  
Figure 16b shows the displacements of a point 65 m high on the tower base during the acquisitions. Figure 16c shows the power spectrum for the ground distance from TLS of a point 65 m high on the tower base. Two peaks are present: the first at a low frequency, the second at 20 Hz. In Figure 16d the first peak is enlarged, corresponding to a frequency of 0.4 Hz. The results confirm the above hypothesis, so we can attribute the peak at the highest frequency to the vibration of the rotating mirror of the TLS.
## 3 Results
### Detailed Analysis of Developments in Selected Specialties
#### 3.7.5 Telemedicine and Digital Health Development in Laryngology (ENT)  
Telemedicine in ENT is gaining traction in Poland, focusing on remote screening, mobile applications, and adaptation to the pandemic. A telemedicine model for hearing screening in children developed in Poland was adopted in Tajikistan, demonstrating the feasibility of remote auditory testing [116]. The COVID-19 pandemic prompted clinical recommendations for otolaryngologists regarding high-risk procedures [117] and guidelines for managing patients while preventing infections [118]. Surveys assessed the impact of the pandemic on the Newborn Hearing Screening Program in Poland [119]. Research has evaluated a mobile application for tinnitus sound therapy, showing therapeutic potential through telehealth platforms [120]. A multicenter study investigated COVID-19's impact on head and neck cancer progression, addressing emergency airway management and telemedicine in acute care [121]. Trends in ENT telemedicine indicate that remote pediatric screening is among the earliest applications. This pandemic has influenced ENT practices and requires infection control guidelines. Service disruptions affect newborn hearing screening, highlighting the need for remote solutions. Digital therapeutics through mobile apps shows promise for patient support. Telemedicine remains vital in acute and oncological ENT care, with data showing delayed cancer diagnosis due to the pandemic. The integration of telemedicine reflects technological adoption and pandemic adaptation, while maintaining the quality of care.
# Remote Sensing in Urban Forestry:
## 2 Remote Sensing Applications in Urban Forests
### Theme 2: Multi-Temporal Input
#### 2.2.1 Short Series Data Input  
Some studies have utilised short series (usually only two) datasets to estimate changes in urban heat island effects and the urban forest structure during a given time span, especially before and after a certain event. [PERSON] et al. (2017) integrated satellite and aerial images to examine the change in urban heat island effect induced by eradicating the invasive Asian Longhorned Beetle, which caused the elimination of 30,000 trees since 2008 in central urban areas in Massachusetts, USA. Landsat-5 (TM) thermal images acquired in the summertime in 2008 and 2010 were used to obtain land surface temperature. NAIP images also acquired in both years during leaf-on conditions were used to extract tree canopy cover. The authors found that the large amounts of tree loss and increased impervious surface between 2008 and 2010 were significant in determining the increased land surface temperature.  
Similarly, Landsat-5 and -8 images from 2004 and 2014 were examined to detect changes in urban forest structure in Jinjiang city, southern China (2017). Combined together with ground observations and spatial statistical modelling, the mechanisms that influence the surface temperatures of urban forest canopies were successfully explored.  
[PERSON] et al. (2017, 2018) utilised HSR and multispectral satellite images to explore the changes in urban greenspace configuration induced by the 2008 Olympic Games in Beijing, China. Taking advantage of both the HSR and multispectral features of SPOT and ALOS images taken in 2005 and 2008, fine-scale greenspace coverage and landscape indices were derived for spatial analysis. The authors found that holding the Olympic Games in Beijing not only incentivised the local government to increase greenspace coverage but also changed the spatial pattern of greenspaces across the city.
## 4 Data Analysis and Results  
The data analysis included sunlight analysis, orientation optimization, psychometric chart analysis, wind analysis, thermal environment analysis, optical simulation analysis, and energy consumption simulation. The analysis started with exporting the model to gbXML format in Revit for saving, and transferring this file to Ecotect to set the geographical location and meteorological data of the region, as shown in Figure 10.
## 1 Introduction
In this paper, we propose to exploit an LSTM network for sequentially designing the CMM matrix. LSTMs are a class of recurrent neural networks (RNNs) that are well suited for handling time-series and other sequential data due to their inherent architecture [36; 37; 38; 39; 40; 41; 42]. The previous work used a fully connected deep neural network (FCDNN), where the received signal in each time snapshot was treated independently. However, in real-world scenarios, adjacent time samples of the signal have strong correlations with each other. Therefore, we use an LSTM network to sequentially process data by retaining temporal dependencies between the input data points. Preserving time-dependent information enables more effective optimization of the CMM in each time snapshot, leading to faster convergence and better DOA estimation performance. Notations: We use bold lower-case and upper-case letters to represent vectors and matrices, respectively. Particularly, we use \(I_{N}\) to denote the \(N\times N\) identity matrix. \((\cdot)^{\mathrm{T}}\) and \((\cdot)^{\mathrm{H}}\) respectively represent the transpose and Hermitian operations of a matrix or vector. The notations \(\mathbf{\upalpha}\) and \((\cdot)^{\mathbf{\upbeta}}\) are used to represent element-wise division and squaring, respectively. Additionally, \(|\cdot|\) denotes the determinant operator. The operator \(\mathbb{E}[\cdot]\) represents statistical expectation, whereas \(\mathcal{R}(\cdot)\) and \(\mathcal{I}(\cdot)\) respectively extract the real and imaginary parts of a complex entry. \(\mathcal{C}^{M\times N}\) denotes the \(M\times N\) complex space.
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 7 Challenges and Future Trends in EV Fast Charging  
EV fast charging systems are continuously evolving to meet the demands of the growing electric mobility landscape. Challenges and future trends in EV fast charging revolve around addressing infrastructure limitations, including expanding the charging network and managing grid capacity intelligently through technologies like load balancing and demand response. Universal standards and interoperability among charging systems are crucial for widespread EV adoption, requiring global collaboration to establish compatibility across networks and countries. Despite advancements, battery technology poses challenges, necessitating improvements in chemical compositions and thermal management for faster charging without sacrificing battery longevity. Future research areas recommend prioritizing faster charging, improving infrastructure components, standardizing charging processes, and optimizing energy utilization through smart grid solutions and bidirectional chargers. Exploration of battery advancements, wireless charging, battery swapping, and enhancing user experience is encouraged. Enriching the current trends involves detailing advancements in ultra-fast charging for quicker charging times, exploring battery technology advancements, emphasizing standardization efforts for compatibility across different EVs and charging stations, and highlighting the integration of renewable energy sources with charging stations for a more sustainable ecosystem.  
This research paper on EV fast charging technology highlights key areas for recommended future research, emphasising the need to prioritise faster charging, improve infrastructure components, standardize charging processes, and optimise energy utilization through smart grid solutions and bidirectional chargers. It encourages exploration of battery advancements, wireless charging, battery swapping, and enhancing user experience. Future research should comprehensively analyse the impact of EV fast charging on the power grid, delve into DC fast charging, ultra-fast charging, and V2G systems, advance infrastructure, and control techniques, integrate renewable energy sources, address standardisation challenges, implement intelligent charging algorithms, and strategies for efficiency enhancement. Achieving faster charging times and exploring various charging techniques, control strategies, and using EV batteries for grid services are crucial areas for ongoing exploration.