esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, most of the existing research has concentrated on either saturated rocks or rocks with constant saturation levels. In practical engineering scenarios, the saturation of rocks during long-term freeze-thaw cycles is not constant. For example, in some cold regions north of the Tropic of Cancer, the autumn and winter climates are characterized by cold, dry conditions, significant diurnal temperature variations, and low annual precipitation [25; 26; 27]. During freeze-thaw cycles, rocks cannot adequately replenish or absorb external moisture, resulting in the natural transition of rock masses from a saturated state to an unsaturated state as freeze-thaw deformation progresses. Furthermore, the crack planes in cracked engineering rock masses serve as weak failure surfaces, making the rock highly susceptible to failure along these planes under external loads. Additionally, the freeze-thaw deformation of rocks containing cracks has a more severe impact on the stress equilibrium of rock masses, significantly affecting their stability [28; 29; 30]. However, the current research predominantly focuses on the freeze-thaw deformation of rocks with through-going cracks, with crack length and width being the primary research variables. Studies on rocks containing initial non-penetrating cracks of varying depths remain scarce. The strain characteristic values of rocks during freeze-thaw cycles may exhibit significant differences with increasing crack depth. Therefore, the research on the deformation characteristics of saturated rocks with initial non-penetrating cracks of varying depths under long-term freeze-thaw cycles, where external water cannot be replenished and saturation changes naturally, remains limited but highly significant. Based on these considerations, this study simulates realistic freeze-thaw cycle conditions to investigate the deformation evolution and damage mechanisms of red sandstone specimens containing initial non-penetrating cracks with depths of 20 mm, 30 mm, and 40 mm under freeze-thaw cycles without external water supplementation. Freeze-thaw cycle experiments and strain monitoring were conducted on red sandstone specimens with different crack depths under isolated freeze-thaw conditions. The deformation behavior of red sandstone with varying crack depths was examined over time and temperature during freeze-thaw cycles. The evolution of freeze-thaw strain characteristic values were also analyzed, alongside the freeze-thaw deformation evolution mechanisms of cracked red sandstone. The experimental results provide valuable data references for understanding the deformation and failure mechanisms of cracked rocks subjected to freeze-thaw damage effects in cold regions. Additionally, the naming conventions for the symbols used in this paper are shown in Appendix A Table A1.
# A Survey of Algorithms and Systems for Evacuating People in Confined Spaces
## 3 Evacuation Wayfinding Algorithms
### On-Line Evacuation Wayfinding Algorithms
#### 3.2.2 Geometric Algorithms  
Geometric algorithms normally use a graph model to represent a hazardous environment and take advantage of the unique properties of geometric graphs to calculate safe egress paths for evacuees. For instance, the research in [84] adopted the localized Delaunay triangulation method [113; 114] to partition a wireless sensor network into triangular areas and construct area-to-area egress paths with the aid of a distributed navigation protocol; each sensor, which was the shared vertex of all the adjacent triangles, maintained the node ID, hops to the exits and the sensed hazard level (temperature) of the neighbour sensors; the direction of an egress path was generated from vertices with a larger hop count to the exit to vertices with a smaller hop count; the safety level of a triangle area was classified into three colour-coded levels (red \"high\", yellow \"moderate\" and green \"low\") by comparing the average detected temperature of the associated sensors with a pre-set temperature threshold; in built environments with multiple exits, additional wireless access points (AP) that can cover the whole environment were deployed in the vicinity of each exit to count the number of evacuees nearby, and a load-dispersion algorithm was employed to distribute evacuees by limiting the number of users per exit. The research in [85] proposed a WSN-based evacuation wayfinding system to guide evacuees without the aid of any pre-knowledge of sensor or user locations; the process of navigating evacuees to the exit contained three stage: firstly, a road map was generated as the backbone route; secondly, the exit was connected to the backbone route via a virtual power field algorithm; thirdly, evacuees were directed to the backbone route via the virtual power field algorithm and then followed the backbone route all the way to the exit; the road map was constructed via concatenating the medial axis of the boundary of any two safe areas; as was proven in [115], the medial axes of the safe regions, which can form continuous curve graphs, retained the topological and geometric features of the safe areas; in the virtual power field algorithm, the virtual power of a point was inversely proportional to its distance from the hazard; the route from any point to the backbone route would follow the most descending direction of the virtual power field; owing to the expanding or shrinking of the hazard, the dangerous areas varied during the evacuation process; hence, a local road map updating algorithm was proposed to rebuild the backbone route of the affected areas instead of reconstructing the entire backbone route when a variation of the dangerous areas was detected. However, the effectiveness of these approaches highly depended on the topology of the deployed wireless sensor network. The change of the topology would induce redeployment and re-calibration of these algorithms.
## 6 Experimental Results and Analysis
### Analysis of Dynamic Optimization Based on Prediction  
Here, two methods are used to compare the average time for processing each video frame under different conditions. One is random allocation, which distributes videos to the agents by the RPM; the other is dynamic optimization allocation, which assigns videos to the agents using the MGA with prediction.  
The experiments are simulated by assigning 4, 6, 8, 10, 12, and 14 videos to two agents, and the results are shown in Figure 9a. In random allocation, the processing time increases as the number of videos increases. In the dynamic optimization allocation, the amount of task at the agent side is always balanced. As the total number of videos increases, the processing time gradually approaches a certain value. When the total number of videos reaches the highest level, the processing time in dynamic optimization is 27.18% lower than in random allocation.  
Also, the experiments are simulated by assigning 8, 12, 16, 18, 20, and 28 videos to four agents, and the results are shown in Figure 9b. In random allocation, the processing time increases rapidly as the number of videos increases. In the dynamic optimization allocation, as the number of videos increases, the processing time grows gently. The processing time in dynamic optimization is 20.44% to 26.03% lower than in random allocation.  
Figure 8: (**a**) Comparison of the actual value and the predicted value from Number 991 to Number 1000; (**b**) comparison of actual and predicted numbers of pedestrians in the video.  
Figure 9: (**a**) Comparison of the average time for processing each frame when performing random allocation and dynamic optimization allocation; (**b**) comparison of the average time of processing each frame when performing random allocation and dynamic optimization allocation in the case of four agents.
# Deep Contrast Learning Approach for Address Semantic Matching
###### Abstract
How to construct examples is an important issue in contrastive learning. For the translation task, [PERSON] changed the number of omitted words, word frequency, and part of speech according to the actual translation, designed different types of negative examples to realize data augmentation [29]. [PERSON] and [PERSON] proposed to use word deletion, reordering, and substitution to achieve it [30; 31]. However, due to the inherent discrete characteristics of Chinese addresses, it is extremely difficult to implement data augmentation simply through text processing. In summary, the approaches are difficult to cope with addresses that contain complex structures or contain redundant information. The reason is that these methods are lacking in terms of understanding the semantics of the address, and they also cannot extract the semantic features of the address well. At the same time, these models often focus on dataset built with specific conditions that do not provide effectively help on enhancing generalization ability, which cannot fundamentally improve the model performance. To address these problems, this paper proposes a contrast learning address matching algorithm. First, ABLC use the Trie syntax tree structure to construct a standard address tree to extract address elements, then uses Bi-LSTM and CNN models to embed the address into vectors with semantic information. Following that, we introduce the attention mechanism to get position-aware information from the context, so as to further improve the accuracy of semantic representation. In the end, the corresponding Manhattan distance is calculated between two semantic vectors of address pair, which can be considered as the similarity of two addresses. Furthermore, we introduced a data augmentation method to extend the existing address dataset, and then the model has been significantly improved in the stability and the ability to perceive similar addresses is competitive. The contributions of this paper are as follows: (1) Propose a contrast learning address matching algorithm that captures similarities and differences between the input address pairs so as to achieve the judgment of similarity and dissimilarity of address pairs. (2) Propose a semantic-based address representation model with a hybrid neural network that incorporates an attention mechanism. The model extracts local and global features of the input data in addition to giving higher weights to important information in the address, so as to more effectively capture key information from addresses. (3) Propose an address data augmentation method to improve the performance of the model. By constructing an address enhancement dataset based on the uniqueness of addresses and combining the dropout strategy to achieve data enhancement, the overall performance of the model is improved with better generalization capability.
# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 3 Experiments and Results
### Study Datasets  
In this section, we provide a brief introduction to the public S3 DIS dataset and the data we collected. Both datasets were preprocessed as described in Section 2.1. These two datasets cover a diverse range of indoor spaces, from single-floor layouts to multi-floor environments, enabling us to evaluate our method under different conditions. The following is a brief overview of each dataset.
# SpaFLEX: Field Campaign for Calibration and Validation of FLEX-S3 Mission Products +
## 3 Results  
### Seasonal Campaign  
Figure 2 shows an example of the values obtained by the FLOX system throughout 2024 in the Manzanera area. These data sets, along with those from the eddy covariance system, will be used to optimize the time scaling process from half-hourly data to the FLEX temporal resolution (27 days). On the other hand, these data will be used to gain knowledge about the relationships between fluorescence, photosynthesis, and gross primary production (GPP) in order to fine-tune Level 2 algorithms [9].  
We observed in preliminary analysis that seasonal trends in SIF retrievals correlate well with changes in PAR and a significant relationship with GPP, which supports the reliability of the data for future temporal scaling efforts. Changes in the behavior of both sites after rainfall events were also observed and will be further investigated in the next step of the project.
## 3 Methodology
### Generative Adversarial Networks
Considering the trade-off between data quality and efficiency, we found that the best performance of the GAN model was achieved with a learning rate of 0.02, batch size of 128, noise dimension of 100, and 1000 iterations. The loss function of the generator adopts the mean absolute error (MAE), and the loss function of the discriminator utilizes the binary cross-entropy (BCE), defined as follows:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{4}\]  
\[BCE=-\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}\cdot\text{log}\,p(y_{i})+(1-y_{i}) \cdot\text{log}(1-p(y_{i}))\rvert \tag{5}\]  
where \(\hat{y}_{i}\) is the predicted value, \(y_{i}\) is the true value, \(n\) is the sample size, and \(p(y_{i})\) denotes the probability that
# Assessment of Daytime HONO Emission Source from Asphal Surface to Urban Air
## 2 Experimental
### Conversion Rate of NO\({}_{2}\) to HONO  
In our chamber reaction setup with a continuous flow rate (\(f\)), the conversion rate of NO\({}_{2}\) to HONO, R\({}_{\text{HONO}}\), on the asphalt surface can be expressed by a first-order reaction rate constant (\(k_{\text{HONO}}\)) and input NO\({}_{2}\) concentration. This rate can be estimated with a simple molar balance of the entering (C\({}_{i}\)) and exiting HONO concentrations (C\({}_{o}\)) within the chamber volume (\(V\)) as in (1).  
\[R_{\text{HONO}}=k_{\text{hono}}\bullet NO_{2}=\ \frac{(C_{o}-C_{i})\bullet f}{V} \tag{1}\]  
Figure 1: Schematic diagram of continuous flow reaction chamber setup.  
As the initial HONO concentration (\(C_{i}\)) is zero, \(k_{HONO}\) can be calculated using Equation (2) from the ratio between produced HONO and initial NO\({}_{2}\), flow rate, and chamber volume.  
\[k_{hono}\ =\ \frac{(C_{o}-C_{i})\bullet f}{NO_{2}\bullet\ V}\ =\ \frac{C_{o}\bullet f}{NO_{2}\bullet\ V}\ =\ \alpha\frac{f}{V} \tag{2}\]
## 3 Example Analysis
### Model Establishment  
By observing the satellite carrier-phase data and the sea-surface height change for a period of time, a primary model between the triple-frequency combined-phase observation value for tide level monitoring and the effective inversion height is established. Figure 4 shows the periodic diagram (Figure 4a) of the observed value of the triple-frequency combined phase in the low-altitude angle area of a typical GPS satellite varying with the satellite altitude angle and the corresponding Lomb-Scargle spectrum (Figure 4b). It can be seen from Figure 4a that the observed value of the triple-frequency combined phase changes periodically with the change of satellite height, and the oscillation amplitude changes between \(-0.5\) and \(0.5\). The abscissa corresponding to the peak in Figure 4b is the estimated frequency value of the combined phase, and the frequency value of the combined phase is 37.07 Hz.  
Figure 3: The geographical location of the sc02 station.  
Figure 2: The ssc02 station’s surrounding environment.  
The tide level inversion model is established based on the satellite observation data and the tide level data for 15 consecutive days from 1 August 2020 to 15 August 2020. The effective inversion height h is calculated by subtracting the tide level height solution from the height \(h\) of the antenna phase center. Through the spectrum analysis of the triple-frequency combined-phase observation values, the frequency \(f\) is obtained, and then the primary model of the effective reflection height and frequency \(f\) is established.  
Figure 5 shows a model between the height from the antenna phase center to the reflecting surface and the frequency of the combined phase observation which is obtained by Lomb-scargle spectrum analysis method. The relationship between them is obtained by the least square method in the sc02 station. It is a linear relationship. According to the least square fitting, the antenna height \(h\) from the sea surface (the reflecting surface) and the\(f\) constitute the following linear relationship:  
\[h=0.12674f-0.024363 \tag{15}\]  
Figure 4: Periodic diagram (**a**) The triple-frequency combined-phase observation and (**b**) the Lomb-Scargle spectrum; sin \(e\) is the sinusoidal value of the satellite altitude angle \(e\).  
Figure 5: The linear relationship diagram between the antenna phase center and the peak frequency of the Lomb–Scargle spectrum analysis. The longitudinal ordinate \(h\) represents the height from the antenna phase center to the reflective surface; the horizontal ordinate \(f\) represents the peak frequency corresponding to the Lomb–Scargle spectrum.
## 3 Results
### Alamar Blue Assay
The four highest dilutions of EEP resulted in a significant decrease in the percentage of reduction compared with the negative control (\(p<0.001\)). Conversely, the lower dilutions showed biocompatibility. A noteworthy observation was that when the EEP concentration was 6.25% or lower, regardless of the fermentation periods, there was a remarkable increase in the mean percentage of reduction, surpassing 90%. For EEP at concentrations of 3.13%, 1.57% and 0.78% at both fermentation periods, the percentage of reduction was comparable to the negative control, with no significant difference (\(p>0.05\)), as indicated by the symbol \"#\" in Figure 2. Similar results were observed when visually analysing the Alamar Blue assay, where the colour shift to pink indicated the presence of viable cells at the four lower concentrations of EEP during both fermentation periods, as depicted in Figure 3. The group treated with 2.5% NaOCl showed a significantly lower percentage of cell viability reduction compared with the negative control (\(p<0.001\)). Furthermore, when comparing different concentrations of EEP with 2.5% NaOCl, all concentrations of EEP exhibited significantly higher percentages of reduction at both fermentation periods (\(p<0.05\)), as illustrated by \"*\" in Figure 2 and Table 4. Notably, the percentage of reduction observed in the 2.5% NaOCl group was relatively low (less than 7%). \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Interaction between Different Treatment Groups and fermentation Periods**} \\ \hline
**Two-Way Mixed ANOVA** & F = 0.724, \(p=0.713\) \\
**Main Effect** & **Different Treatment Groups** & **Fermentation Periods** \\  & F = 38,354.39, \(p<0.001\) * & F = 3.408, \(p=0.068\) \\ \hline \hline \end{tabular}  
* \(p\) is statistically significant at \(\alpha\) = 0.05.  
\end{table} 
Table 2: Results of two-way mixed ANOVA and the subsequent main effect of fermentation periods and different treatment groups.
## 5 Noise-Corrected Clustered-Sparse Channel Estimation
### Data Detection and Noise Measurement
The MSE bound is related to the hyperparameters of the cluster distribution, data symbols and noise distribution. Only when the perfect hyperparameters are obtained is the lower bound of MSE reached. Similarly to analyzing the PB-CSBL algorithm, we assume that all \(L\) paths are in one cluster (i.e., \(C=1\)) and consider the two special cases, namely the temporal correlation coefficient \(\beta=0\) and \(\beta=1\). For ease of analysis, the noise power keeps constant \(\overline{\lambda}\) over all subcarriers. Furthermore, each transmitted symbol is normalized to unit power. Then, the MSE bounds of \(\mathbf{h}^{(m)}\) can be evaluated by  
\[E\bigg{[}\|\mathbf{h}^{(m)}-\widehat{\widehat{\mathbf{h}}}^{(m)}\|_{2}^{2} \bigg{]}\geq\sum_{l=1}^{L}\bigg{(}\frac{K_{p}+K_{d}}{\overline{\lambda}}+ \frac{1}{\gamma_{l}}\bigg{)}^{-1}, \tag{40}\]  
\[E\bigg{[}\|\mathbf{h}^{(m)}-\widehat{\widehat{\mathbf{h}}}^{(m)}\|_{2}^{2} \bigg{]}\geq\sum_{l=1}^{L}\bigg{(}\frac{M(K_{p}+K_{d})}{\overline{\lambda}}+ \frac{1}{\gamma_{l}}\bigg{)}^{-1}, \tag{41}\]  
respectively. In addition to what has been discussed when analyzing the PB-CSBL algorithm, Equations (40) and (41) also prove that the NC-CSCE algorithm utilizing pilot symbols and unknown data symbols can achieve a smaller MSE compared with the PB-CSBL algorithm.
# 16-Channel Wavelength Division Multiplexers Based on Subwavelength Grating
## 2 Structure and Method
The parameters for the O-band and C-band are shown in Table 1.  
Table 1 shows the parameters of the central wavelength of the O-band and C-band. After multiple optimizations, we selected the grating with the above parameters for overall simulation to ensure the WDM performance of the O-band and C-band.
###### Abstract  
The classical bistable stochastic resonance algorithm has an inherent output saturation defect that restricts the amplitude of the output signal. This paper examines the causes of this phenomenon and its negative impact on the detection of weak signals. Proposing the Unsaturated Bistable Stochastic Resonance (UBSR) detection algorithm involves constructing a segmented potential function using a linear function to eliminate the effect of higher-order terms in the classical stochastic resonance algorithm. A new type of segmented potential function has been created by combining exponential and linear functions. This new function helps to eliminate the impact of higher-order terms in classical algorithms while also improving the noise immunity of the stochastic resonance system. This results in the development of the accelerated stochastic resonance (ASR) detection algorithm. In this paper, the Kramers escape rate and output signal-to-noise ratio of two improved stochastic resonance algorithms are theoretically derived and compared with the classical bistable stochastic resonance algorithms, and the proposed algorithms are able to effectively avoid the output saturation phenomenon and have more excellent detection performance under strong background noise.  
stochastic resonance; multistage potential well; correlation number; harmonic detection; voltage dips detection +
Footnote †: journal: Applied sciences
# Presenting a Human Pupil Orbit Model (HPOM) for Eye-Gaze Tracking
## 5 Conclusions  
In conclusion, in this paper we present a novel pre-processing method for eye-gaze tracking based on a camera recording the front of a subject's face that works regardless of the distance from the eye to the camera. The proposed method simplified the rotational movement of the eyeball observed in the camera as RMD analysis. The relationship between the pose of the pupil in 3D space and the PPC was analysed using RMD analysis. Based on this, the three-dimensional position and pose of the pupil could be approximated only with the information of the PPC. Finally, the HPOM could be estimated using the RANSAC method. The proposed method is performed in a short time of less than 1 s, and it is possible to obtain HPOM with an error of less than 1 pixel in ORCP and radius performance. The proposed method not only finds the centre of rotation of the eyeball, which was a problem in previous studies, but also improves eye tracking method by simplifying the 3D space into a 2D space. The validity and performance of HPOM estimation were confirmed by the proposed method through a series of experiments. In addition, the execution time to achieve this was less than 1 s, enabling adaptive HPOM estimation that responds to user movements in real time. Moreover, even when the pupil was photographed from a distance, the HPOM could be estimated. As such, we believe that our method will serve as the basis for development of single camera eye-tracking solutions as the need for this grows due to new technologies that depend on this ability.  
Conceptualization, S.L.; methodology, S.L.; software, S.L.; validation, S.L., J.J. and D.K.; formal analysis, S.L.; investigation, S.L. and D.K.; writing--original draft preparation, S.L.; writing--review and editing, J.J.; supervision, S.K.; project administration, J.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported and funded by the Dongguk University Research Fund of 2022(S-2022-C0001-00064).  
The authors declare no conflict of interest.