esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Definition of the Problem
### Tunnel Face Effect during Tunneling  
As shown in Figure 1, a support pressure \(p\) is applied on the tunnel periphery by the tunnel face effect, and its value gradually decreases to 0 with the advance of the tunnel face. Therefore, due to the tunnel face effect, the behavior of a deep tunnel during tunneling is essentially a three-dimensional problem. The bolts are installed behind the tunnel face, where the support pressure is \(\beta p_{0}\) (\(\beta\) is the coefficient of the tunnel face effect) and the corresponding rock displacement is \(u_{r}^{ini}\) (the initial radial displacement of the rock mass), while the bolts are yet to work at this moment. Then, the support pressure decreases during the advance of the tunnel, which leads to an increase of the radial displacement of rock mass \(u_{r}\) and the interaction between the rock mass and bolts. The tunnel face effect can be approximated using the \(\beta\)-method.  
The \(\beta\)-method consists of two steps: (1) the tunnel is excavated and a support pressure \(\beta p_{0}\) is applied to the tunnel periphery, and (2) the bolts are installed and the stress \(\beta p_{0}\) is shared by the ground and the bolts during the advance of the tunnel. The magnitude of \(\beta\) is related to the distance between the installation position and the tunnel face, where a large distance is associated with a small \(\beta\) and vice versa. The magnitude of \(\beta\) must be known a priori, and its value strongly depends on the ground condition, excavation method, etc. The value of \(\beta\) can range from 0.2 to 0.8 [2; 30].
## 3 Classification
### Pre-Crash Scenarios  
Pre-crash scenarios may help understand the requirements of an effective V2P crash prevention system. The pre-crash scenarios for different types of VRUs may differ from each other. Pedestrian fatality analysis shows that 88% of the pedestrian fatalities are tied to a scenario where a pedestrian is crossing the road in front of a vehicle moving on a straight road and 12% are tied to a scenario where the pedestrian is moving on a straight road parallel to a vehicle's direction [43]. Also, the majority of the pedestrian crashes occur at non-junctions [44]. In case of cyclists, the majority of crashes happen when a vehicle is turning right or left into the cyclist's path [45]. Also, the majority of cyclist crashes occur at intersections or junctions [45]. Figure 3 depicts the most common pre-crash scenarios for pedestrians and cyclists. In these scenarios, various factors may cause the crash. A few examples of such factors are obstructed view, speed of the vehicle and VRU, visibility (day/night) etc.  
V2P system developers may design crash prevention systems that are adapted to these pre-crash scenarios in order to maximize effectiveness.
# Retrospective Spectrum-Conversion Method Based on Time-Modulated Van Atta Array
## 6 Conclusions  
This paper proposes a retrospective method based on time-modulated Van Atta array. The Van Atta array includes eight antenna elements and four transmission lines connecting the antenna elements. The structure possesses the larger RCS at a wide angle due to its directional retrospective property. The SPST switches are added to the transmission line to control the on-off state of the structure. The switch-type active Van Atta array can switch between the on and off state by changing the bias voltage of the switches to obtain the RCS fluctuations. The measurement results show that the array in different states has 5 dB RCS difference. On this basis, the time-varying amplitude modulation model is established. Compared with the common time-varying modulation models implemented by artificial electromagnetic meta-materials, the method implemented by Van Atta array has greatly improved the modulation effect for oblique incidence and has a wider range of application scenarios. The symmetrical multiple harmonic components near the center frequency are generated in the modulated reflected signal. This phenomenon is expected to be applied in wide-angle wireless communication, electronic countermeasures, and other fields.  
Conceptualization, F.Z. and J.W. ([PERSON]); methodology, K.S.; formal analysis, F.Z.; investigation, W.H.; resources, D.F.; data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]), W.H. and D.F. 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 number 62201589, and grant number 62371455.  
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflicts of interest.
## 4 Propagation Loss Calculation Using the Improved TWPE Model
### Analysis of Remote Interference between Different Intercity Links  
Comparing Figures 22-25, the Shanghai-Wuxi link is relatively close to the Jiaxing-Wuxi link, and base stations on one of the links from 0 to 45 km might receive an uplink signal from another city, resulting in remote signal interference. Therefore, this section compares the PL of the four links with the receiving antenna heights of 60 m and 40 m, respectively (the heights are both within the trapping layer of the surface-based duct assumed in this paper), as shown in Figure 27. The probability of possible remote interference of inter-city signals was analyzed.  
As shown in Figure 27, due to large terrain fluctuations, the propagation distance on Hangzhou-Wuxi and Nanjing-Wuxi links were about 15 and 40 km, respectively. From the city orientation shown in Figure 8, it can be seen that Hangzhou, Jiaxing, and Shanghai are all located within a radius of about 100 km southeast of Wuxi, and signal serialization may occur between the links from each city to Wuxi. As shown in Figure 9d, the Jiaxing-Wuxi link had a sudden increase in PL at different receiving antenna heights due to relatively  
Figure 26: The propagation loss using the improved TWPE at a height of 40 m using different land covers, on Jiaxing–Wuxi. (**a**) PL on J–W at different LCs at an antenna height of 40 m. (**b**) PL on J–W at a range of 0–40 km at different LCs. (**c**) PL on J–W at a range of 40–70 km at different LCs. (**d**) PL on J–W at a range of 70–93 km at different LCs.  
large terrain fluctuations and switching of LCs. In the 0-40 km stage, the signals transmitted from Hangzhou, Jiaxing, and Shanghai were relatively stable under the effect of surface-based duct and tropospheric turbulence. Therefore, within a certain radius close to Wuxi, remote interference may occur among the signals of the three urban links, where a useful signal from one city may become an interfering signal received by another user. Based on the analysis of this phenomenon, it can be considered that when the base stations are deployed among relatively close cities, combined with a variety of possible ducting conditions, the tropospheric turbulence effect, and the annual update of the distribution of LCs, the simulation prediction of the propagation of over-the-horizon signals between cities can be carried out. According to the results, within the relevant radius, methods such as high obstacle blocking, alternating high and low antennas, and flexible site selections of base stations could be adopted to carry out preventive 5G base station deployment between cities with similar terrains to what we simulated in this paper, where remote interference may occur.
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
## 4 Security Evaluation of the Proposed Methodology
### Informal Analysis
#### 4.1.8 Man-in-the-Middle Attack (MITM)  
According to Section 3.4, the three participants authenticate each other. As a result, everyone involved can verify one another. As a result, the suggested system can withstand a MITM attack.
# Laplacian Eigenmaps Dimensionality Reduction Based on Clustering-Adjusted Similarity
## 4 Results
### Results on Different Datasets
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **FMI** & **F-measure** & **PU** \\ \hline \multicolumn{4}{c}{Musk} \\ \hline LE-CAS & **0.6832 \(\pm\) 0.0216** & **0.4724 \(\pm\) 0.0222** & **0.8804 \(\pm\) 0.1846** \\ LE & 0.4956 \(\pm\) 0.0375 & 0.2121 \(\pm\) 0.0247 & 0.5376 \(\pm\) 0.1643 \\ S-LE & 0.5982 \(\pm\) 0.0299 & 0.3055 \(\pm\) 0.0200 & 0.5895 \(\pm\) 0.1302 \\ GGLE & 0.6458 \(\pm\) 0.0321 & 0.3623 \(\pm\) 0.0132 & 0.6989 \(\pm\) 0.1033 \\ SCPLE & **0.6823 \(\pm\) 0.0370** & 0.4078 \(\pm\) 0.0219 & 0.7277 \(\pm\) 0.1023 \\ \hline \multicolumn{4}{c}{CNAE-9} \\ \hline LE-CAS & **0.9285 \(\pm\) 0.0514** & **0.7051 \(\pm\) 0.0193** & **0.9409 \(\pm\) 0.1860** \\ LE & 0.6541 \(\pm\) 0.0427 & 0.4621 \(\pm\) 0.0237 & 0.6746 \(\pm\) 0.1283 \\ S-LE & 0.7028 \(\pm\) 0.0365 & 0.5738 \(\pm\) 0.0224 & 0.5478 \(\pm\) 0.2302 \\ GGLE & 0.8731 \(\pm\) 0.0417 & **0.7023 \(\pm\) 0.0122** & 0.8032 \(\pm\) 0.1053 \\ SCPLE & 0.8392 \(\pm\) 0.0347 & 0.6708 \(\pm\) 0.0238 & 0.7978 \(\pm\) 0.1926 \\ \hline \multicolumn{4}{c}{SECOM} \\ \hline LE-CAS & **0.6967 \(\pm\) 0.0416** & **0.5930 \(\pm\) 0.0192** & **0.7931 \(\pm\) 0.1427** \\ LE & 0.5852 \(\pm\) 0.0437 & 0.3247 \(\pm\) 0.0233 & 0.4388 \(\pm\) 0.1904 \\ S-LE & 0.6082 \(\pm\) 0.0367 & 0.5552 \(\pm\) 0.0296 & 0.5478 \(\pm\) 0.1309 \\ GGLE & 0.6552 \(\pm\) 0.0345 & 0.4925 \(\pm\) 0.0334 & 0.6546 \(\pm\) 0.1071 \\ SCPLE & **0.6923 \(\pm\) 0.0570** & **0.5892 \(\pm\) 0.0270** & 0.6339 \(\pm\) 0.1286 \\ \hline \multicolumn{4}{c}{DrivFace} \\ \hline LE-CAS & **0.6738 \(\pm\) 0.0227** & **0.5771 \(\pm\) 0.0280** & **0.7563 \(\pm\) 0.0470** \\ LE & 0.3691 \(\pm\) 0.0322 & 0.1917 \(\pm\) 0.0267 & 0.4614 \(\pm\) 0.0234 \\ S-LE & 0.4452 \(\pm\) 0.0237 & 0.3156 \(\pm\) 0.0193 & 0.5324 \(\pm\) 0.0193 \\ GGLE & 0.5892 \(\pm\) 0.0334 & 0.4622 \(\pm\) 0.0138 & 0.6474 \(\pm\) 0.0291 \\ SCPLE & 0.6037 \(\pm\) 0.0190 & 0.5112 \(\pm\) 0.0142 & 0.6890 \(\pm\) 0.0391 \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 2: Results of FMI on ten datasets (Msplice, W1a, Soccer-sub1, Medelon, FG-NET, ORL, Musk, CNAE-9, SECOM, DrivFace) under different target dimensionalities (_c_).
## 3 Results and Discussion
This suggests that the ability to oxidize alcohol into acetic acid was reduced at 8% alcohol. Therefore, the optimal alcohol concentration for the six isolates was determined to be 5%, where the GV-5, GV-12, and GV-17 strains showed high acid production. At 8% alcohol concentration, the GV-12 strain showed a high acid production ability, exhibiting alcohol tolerance. Although it is reasonable not to consider the ethanol concentration as the limit for AAB growth, other parameters are important considering the efficiency and successful oxidation of ethanol. To identify the optimal pH of the isolates, AAB were inoculated into a liquid medium adjusted to pH 2.0-4.0 and fermented at 30 \({}^{\circ}\)C for 15 days. Then, the acid production ability and growth were investigated. The initial acidity was increased by decreasing the pH (acidity as pH decreases: pH 2.0, 5.51%; pH 3.0, 3.24%; pH 4.0, 0.37%) (Table 4). The six isolates produced acetic acid while growing at pH 3.0 and 4.0 but failed to grow at pH 2.0. In particular, isolates showed a high average tittatable acidity of 6.92 \(\pm\) 1.02% at pH 4.0 on day 9, which decreased as the fermentation period elapsed. The growth increased 15.8 times on day 15 compared with that on day 0. Isolates showed an average tittatable acidity of 3.58 \(\pm\) 1.13% at pH 3.0 on day 9, and the growth increased 8 times on day 15. The average fermentation efficiency on day 9 of fermentation was 53.70 \(\pm\) 17.50% and 105.80 \(\pm\) 15.74% at pH 3.0 and 4.0, respectively (Table S1). This suggests that the ability of the isolates to oxidize ethanol to acetic acid increases in an environment of pH 4.0; therefore, pH 4.0 was considered optimal for the strains. In particular, the GV-8, GV-12, GV-22, and GV-5 strains showed high acid-producing ability at pH 4.0. Acetic acid alters the flavor of vinegar and increases the survival advantage of AAB; however, acetic acid accumulation may have induced acid stress that inhibited AAB growth [40].
# Analysis of Potent Odour-Active Volatile Thioids in Foods and Beverages with a Focus on Wine
## 4 Analytical Instrumentation
### LC vs. GC  
Overall, the LC methods have been demonstrated to be more sensitive, selective, and faster than GC approaches for quantitative volatile thiol analysis in foods and beverages. Perhaps more importantly, another significant advantage of LC-based methods is that the sample preparation steps are less complicated, markedly so in a number of cases. Furthermore, LC with MS/MS in DPIS mode has potential for untargeted thiol profiling and screening (Table 2, Entry 16). However, as discussed in the next section, the LC-MS approaches have to be treated carefully (particularly for quantitation) to solve analytical challenges from matrix effects [73].  
GC approaches generally involve more complicated sample preparation, but are still attractive when compared to LC-MS methods, particularly with respect to the aroma qualities that are obtained by GC-O, which can provide valuable information for thiol identification (or verification) [23] and related sensory studies [74]. In addition, multidimensional GC offers great separation power [75], and with fast GC in the second dimension improving the detection limits and reducing the matrix background, GC\(\times\)GC coupled with different types of detectors could serve as a powerful tool for discovering new thiols. For instance, GC\(\times\)GC-TOF-MS has been recently used for thiol screening in wine and fruit, where a total of 11 volatile thiols were identified (Table 2, Entry 31) [23].  
GC and LC based methods for thiol analysis are both very versatile and they have great potential in identification and quantitation. The choice of method is often based on instrument availability in the given laboratory, along with capital, operating, and maintenance costs, and most importantly, the analytical methods that are required to address the aims of the research.
# CQFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation
## 5 Experiments
### Results and Discussion  
#### 5.4.1 Performance Evaluation  
The performance of CQDFormer and comparison models is presented in Table 2, and the proposed model achieves the best in all four metrics. The RSME and MAE intuitively reflect the difference between the estimated and true OD matrices, and the RMSE and MAE of the proposed model are 4.1796 and 3.0373, which are 46.98% and 45.40% improved compared to the MLP, and 53.89% and 56.40% improved compared to the c-SPSA. MAPE, on the other hand, portrays the relative accuracy of the models, and since the magnitude of OD flows has large variations from location to location and time to time, and commonly the higher the magnitude of the OD flow reaches, the larger the error is, MAPE provides a relatively fairer metric. The MAPE of the proposed model is 10.10%, showing a performance improvement of 29.76% and 46.81% with respect to MLP and CycleGAN. The \(R^{2}\) measures the coefficient of determination of models, and it varies from negative infinity to 1, with better performance the closer it is to 1. When \(R^{2}\) takes a positive value, it indicates the model is better than the simple and direct method (i.e., averaging all data directly), so in many cases, only \(R^{2}\) between 0-1 is considered meaningful. Nevertheless, the presence of negative \(R^{2}\) in this experiment is reasonable because the average of realistic OD matrices actually remains unknown. The \(R^{2}\) of the proposed model is equal to 0.8053, being the highest among all models, and the MLP is the next highest, equal to 0.6536.
## 4 Discussion  
### Insights from the Dynamic Tendency of GPP and Climate Change  
The findings of this study demonstrate a significant overall increasing trend in GPP, exhibiting a clear southeast-to-northwest decreasing gradient, with non-significant decreasing trends detected in the western part of the study area. Research indicates that the implementation of ecological projects such as the \"Three Norths\" shelterbelt plan may be the fundamental reason why GPP still maintains an increasing trend in the NFYS region under the trend of dry heating [42; 43; 44]. Due to more favorable hydrothermal conditions and vegetation topography in the eastern regions compared to the west, GPP exhibits a clear decreasing gradient from east to west [45; 46]. Notably, significantly low GPP values were observed in 2005 and 2014, primarily driven by severe drought events [47]. Furthermore, the western part of the study area, located at the transition zone between desert steppes and desert, is characterized by greater ecological fragility and heightened sensitivity to climate change, leading to less pronounced GPP trends compared to the eastern regions [48].  
Numerous studies have consistently shown a global decline in frost days (FD0) and ice days (ID0), accompanied by a significant increase in extreme high-temperature frequency and intensity indices [8]. These findings provide robust evidence supporting the prevailing trend of warming and drying in the study area. Specifically, the region has experienced a marked increase in warm days (T\({}_{\rm{X90p}}\)) and warm nights (T\({}_{\rm{N90p}}\)), while cold days and nights have shown a decreasing trend, consistent with observations across most regions of China [49]. Notably, the decline rate of light rain days (R\({}_{\rm{10}}\)) (\(-0.07\) d/yr) substantially surpasses that of moderate rain days (R\({}_{\rm{20}}\)) (\(-0.02\) d/yr) and heavy rain days (R\({}_{\rm{25}}\)) (\(-0.01\) d/yr), indicating a shift toward more frequent moderate and heavy precipitation events. This observed pattern may be attributed to enhanced atmospheric instability and significant alterations in aerosol concentrations, which warrant further investigation to better understand the underlying mechanisms.  
Figure 6: Spatiotemporal variation characteristics of GPP from 2025 to 2035.
## 4 Experiments
### Experiment Settings  
The Beijing transportation smart card data are very large, containing about 177 million records of passengers' bus transaction information. A single computer is too slow to process this, and sometimes even fails to produce results. We used the Apache Hadoop distributed computing platform, which consists of a cluster of eight Intel Xeon servers, to process the bus big data. We scaled the bus traffic flow value into the range \([-1,1]\) by the Min-Max normalization method. We re-scaled the predicted value back into the normal value in order to compare it with ground truth in the evaluation. The convolution kernel sizes were set to \(3\times 3\) with 64 filters in the convolution layer of both ConvIn and improved residual block. The convolution kernel sizes were set to \(3\times 3\) with two filters in ConvOut. We used the Tanh activation function in output weight layer, and the activation function in the other weight layers were ReLU [19]. The length of the three dependence sequences were set to \(l_{c}=3,l_{p}=1\), and \(l_{q}=1\).  
We trained our model in two stages. In the first stage, we split our training data into a 90% training set and 10% validation set, then we warmed up the training with 500 epochs, using early-stop methodsto stop training when the validation metric stopped decreasing. Figure 7 presents the warm-up training, which shows the change of loss value and normalized RMSE value in each epoch, respectively. From the figure, we find that our proposed methods are very easy to train, and the training curves are close to the validation curves. After about 400 epochs, the training process was stopped, which showed that the validation metrics had stopped decreasing. In this way, we got the selected best model on the validation set. In the second stage, we continued training on all training data with another fixed number of epochs, e.g., 1000 epochs, using the selected best model. The learning rate was set to 0.0001. We used the Mean Squared Error as our loss function.  
The models were built using Keras [20] and Tensorflow [21], and we trained our model on a cluster of eight NVIDIA Tesla P100 GPUs.
## 5 Simulation Analysis
### Simulation Results
#### 5.2.1 Without Altimeter Scenario
However, the LEO 1 satellite + INS algorithm cannot overcome the divergence of INS. In challenging environments that require a high accuracy, such as indoor and urban environments, this positioning result is difficult for people to accept.
## 3 Results and Discussion
### Analysis of CIM's Potential for the Calculation of the Indicators
#### 3.2.35 The Indicator to Measure Local Food Production  
This indicator encourages recommendations to ensure city dwellers have access to fresh products, promote community food production and improve residents' nutrition, and support the economic development of the city/urban area. This is developed by SBTool\({}^{\text{PT}}\) Urban, and LEED ND, and addressed by ISO 37120, and SDGs making it a prioritized social dimension of sustainability concern. According to the SBTool\({}^{\text{PT}}\) Urban Guide, to calculate this indicator there are two parameters: Local Food Production Index and Community Garden Promotion Index. The first one is determined by the ratio of the total area destined for food production and the total number of inhabitants. To calculate this ratio using the CIM model, at first, the areas destined for food production must be identified, and then a Dynamo routine must be applied. The second parameter is evaluated through a verification checklist, but only two of the eight criteria can be assessed by the model. Thus, this indicator is classified as \"NO\", in terms of its applicability in CIM modeling.