esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Vegetation Indices  
A total of 21 common vegetation indices were used to analyze their correlation with ground measured _f_PAR. These vegetation indices have been used to estimate _f_PAR  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & **28** & **30** & \multirow{2}{*}{**1 April**} & **8 April** & **11** & **13** & **15** & **18** & **27** & **29** & \multirow{2}{*}{**2 May**} \\  & **March** & **March** & & & & & & & & & & \\ \hline _f_PAR & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Reflec. & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Weather 1  & C & C/S & S & S/R & S & C & C & C & C/S & S & C/R \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dates of reflectance and _f_PAR measurements and the weather conditions.  
for different vegetation types and have shown varying degrees of estimation capability. Therefore, the aim of this study is to evaluate their ability to estimate _f_PAR during the main growth stages of winter wheat. Their definitions and calculation methods are shown in Table 3.  
The correlation between vegetation indices and _f_PAR is typically represented by linear, power, and exponential functions. We simulated the ground-based ASD reflectance data as Sentinel-2 vegetation indices and established three different estimation models: linear, exponential, and logarithmic. We then selected the most stable vegetation index in terms of correlation during key growth stages and validated its accuracy at the satellite scale using Sentinel-2 data.
# Seepage Characteristics Study of Single Rough Fracture Based on Numerical Simulation
## 4 Conclusions  
Based on the Fluent numerical simulation software and a 2D numerical model, this paper studies the rough fracture seepage characteristics under the conditions of different shapes, densities, and fracture apertures of roughness elements. The main conclusions are as follows:  
1. In wider rough fractures, the flow rate mainly depends on fracture aperture, while in narrow and close rough fracture medium, the surface roughness of fracture wall becomes the main factor of head loss of seepage.  
Figure 11: \(K\)-\(L\) relation curves under conditions of different roughness element shapes and densities in fractures with (**a**) triangular, (**b**) rectangular, and (**c**) sinusoidal roughness elements.  
2. In rough fracture, there is a negative power exponential relation between the hydraulic gradient index \(m\) and the average fracture aperture \(\overline{e}\), i.e., with the increase of fracture aperture \(\overline{e}\), the relative roughness of fracture and the influence of hydraulic gradient both decrease.
3. In symmetrical-uncoupled rough fractures, there is a super-cubic relation between the discharge per unit width \(q\) and average aperture \(\overline{e}\).
4. The value of rough fracture permeability coefficient \(K\) is not a constant, and it is affected by the scale effect (the horizontal distance from the measuring point to the fracture inlet) and the density of the roughness elements.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12147328/s1](https://www.mdpi.com/article/10.3390/app12147328/s1)]([https://www.mdpi.com/article/10.3390/app12147328/s1](https://www.mdpi.com/article/10.3390/app12147328/s1)), Table S1: Relation between discharge per unit width \(q\) and hydraulic gradient \(J\).  
Conceptualization, S.W.; methodology, Q.Z.; software, Q.Z.; validation, Y.J.; formal analysis, S.W.; investigation, L.Z.; resources, J.Q.; data curation, Q.Z.; writing--original draft preparation, S.W.; writing--review and editing, Y.J. and J.Q.; visualization, Q.Z.; supervision, L.Z.; project administration, J.Q.; funding acquisition, Y.J. and J.Q. 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 Nos. 41831289 and 41972175; the Open Fund Project of Shaanxi Key Laboratory of Prevention and Control Technology for Coal Mine Water Hazard, China, Grant No. 2021 SKMS04; the Science and Technology Research Key Project for Development and Promotion of Henan Province in 2021, Grant No. 212102310502; and the Research Fund of Henan Key Laboratory of Coal Green Conversion (Henan Polytechnic University), Grant No. CGCF202005.  
The authors would thank the editors and reviewers for their help and constructive comments.  
The authors declare no conflict of interest.
## 4 Conclusions  
In this paper, to boost the RS image search performance in the Hamming space, we propose a novel deep hashing method called triplet ordinal cross entropy hashing (TOCEH) to learn an end-to-end binary feature of an RS image. Generally, most of the existing hashing methods place emphasis on preserving point-wise or pair-wise similarity.  
Figure 14: The ablation experiments on SAT-4. The data are separately encoded as (**a**) 64- and (**b**) 128-bit binary code.  
Figure 15: The ablation experiments on SAT-6. The data are separately encoded as (**a**) 64- and (**b**) 128-bit binary code.  
In contrast, TOCEH establishes a tensor graph to capture the triplet ordinal relation among RS images and defines the triplet ordinal relation preserving problem as the formulation of minimizing the cross entropy value. Then, TOCEH achieves the aim of preserving triplet ordinal relation by minimizing the inconsistency between the probability distributions of the triplet ordinal relations in different spaces. During the training process, to avoid the NP hard problem, we apply continuous relaxation to the binary encoding process. Furthermore, we define a quantization function based on the triplet ordinal relation preserving restriction, which can reduce the loss caused by the continuous procedure. Finally, the extensive comparative experiments conducted on three large-scale RS image datasets, including UCMD, SAT-4 and SAT-6, show that the proposed TOCEH outperforms many state-of-the-art hashing methods in RS image search tasks.  
Conceptualization, Z.W. and P.L.; methodology, Z.W. and N.W.; software, P.L. and X.Y.; validation, N.W., X.Y. and B.Y.; formal analysis, Z.W. and N.W.; investigation, P.L. and X.Y.; resources, B.Y.; data curation, B.Y.; writing--original draft preparation, Z.W.; writing--review and editing, P.L.; visualization, N.W. and X.Y.; supervision, Z.W. and P.L.; project administration, Z.W. and P.L.; funding acquisition, Z.W. 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 61841602, the Natural Science Foundation of Shandong Province of China, grant number ZR2018 PF005, and the Fundamental Research Funds for the Central Universities, JLU, grant number 93K172021K12.  
The authors express their gratitude to the institutions that supported this research: Shandong University of Technology (SDUT) and Jilin University (JLU).  
The authors declare no conflict of interest.
## 5 Analysis and Discussion  
Both Elasticsearch and MySQL Document Store are easy to install and configure. Regarding scalability, ElasticSearch is very easy to scale horizontally, being built to always be available. Nodes can be added to a cluster to increase capacity, and Elasticsearch automatically distributes data and load to all available nodes. On the other side, MySQL Document Store uses the advantages of MySQL Group Replication and InnoDB Cluster to achieve high availability. The documents are reproduced by all members of the group and the transactions are synchronized.  
From the functionality and complexity point of view, ElasticSearch offers many APIs that can be used and eliminates the need to write extra code to process large amounts of data, being very easy to integrate and use. On the other side, MySQL Document Store provides APIs for create, read, update, and delete operations but requires extra code to process large amounts of data.  
Regarding the performance of CRUD operations, Table 10 presents a synthesis of the results obtained.  
Speaking about data replication issues, as shown from the data replication solution implemented in the case study, ElasticSearch allows the use of _BulkProcessor_ to process  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Elasticsearch** & **MySQL Document Store** \\ \hline \multicolumn{3}{c}{**Data insertion (INSERT)**} \\ \hline Make full use of the Bulk API features. The code for indexing documents is simple and easy to understand. Processes large amounts of data much faster offering many possibilities of data processing configuration. The use of three threads brings a significant improvement average insertion times based on the number of documents is 0.069 ms; average insertion time using three threads is 0.032 ms. & Document insertion is completed through multiple batches, each batch being inserted through a transaction. Data processing and insertion is slower compared  
large amounts of data, with no additional implementations required, just _BulkProcessor_ integration and configuration.  
Consequently, it offers many possibilities to configure the way of data processing and simplify the application logic because the indexing process is performed by _BulkProcessor_, which will receive the indexing requests. Therefore, ElasticSearch exhibits very good performance, the average replication time being 1270 s. On the other side, MySQL Document Store requires the implementation of a data processing strategy and implies writing extra code to replicate data. The resulting implemented solution generally does not offer as many configuration possibilities as _BulkProcessor_. However, it also exhibits very good performance, close to Elasticsearch, the average time obtained to replicate data being 1274.8 s.  
Although the processing times are very good in both cases, comparing the replication methods, it is possible, due to the fact that MySQL Document Store does not offer a Bulk Api, that MySQL Document Store may require more memory.
# Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism
## 6 Discussion and Future Works  
By incorporating the DSC and CBAM modules, the representation capability of the network model for fine road cracks is effectively enhanced, and the loss of feature information is reduced. When compared to the SegNet, DeepLab, and DeepCrack networks, the proposed method in this paper demonstrates superior performance in detecting discontinuous and incomplete cracks. Both quantitative and qualitative evaluation metrics indicate the effectiveness of the proposed method.  
However, the incorporation of DSC and the CBAM results in a relatively large network, which requires more memory and training time. Future improvements will focus on reducing the number of parameters. The current output of the network is limited to the morphological characteristics of cracks and does not include geometric parameter information. This limitation poses a certain degree of inconvenience for further analysis and processing by inspection personnel. To enhance the comprehensiveness and practicality of the detection results, future research should focus on improving the algorithm to enable the output to simultaneously encompass both the morphological and geometric information of cracks. This improvement would more effectively support the in-depth work of detection.  
Figure 9: Comparative experimental results of each network.  
**Author Contributions:** Conceptualization, X.C.; Data curation, S.F. and X.C.; Formal analysis, X.Y.; Funding acquisition, S.F. and X.C.; Investigation, S.F. and X.Y.; Methodology, Y.N.; Resources, X.C., X.Y. and J.Z.; Software, Y.N. and Z.W.; Writing--original draft, Y.N.; Writing--review and editing, Y.N., X.C. and J.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded in part by the Open Project of Shandong Key Laboratory of Smart Transportation (Preparation) under Grant 2021 SDKLT5004; China Postdoctoral Science Foundation under Grant 2023T160129; Shaanxi Transportation Scientific Research and Development Project under Grant 21-05X; Key Science and Technology Project of Ministry of Transport under Grant 2022-ZD6-079; and the Key Research and Development Program of Shaanxi under Grants 2023-YBGY-120 and 2022 QCY-LL-29.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Study Area and Data Processing
### Data Representation Method for Three-Dimensional Deep Learning Models of Landslides  
The primary challenge of integrating deep learning models into landslide risk prediction is constructing a suitable representation of landslide data for the model. This study adopted a three-dimensional representation of landslide data, which is more intuitive and less likely to lose data accuracy.  
First, factors closely related to landslides, such as slope, aspect, and elevation, were selected. All raster grid rows and columns were aligned to 6737 \(\times\) 10,053 pixels, with a uniform resolution of 30 m. The various factor layers were stacked and placed to form an array with a shape of (6737, 10,053, 15), as illustrated in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factors** & **Tolerance** & **VIF** \\ \hline Profile curvature & 0.088 & 11.304 \\ TRI & 0.089 & 11.261 \\ Elevation & 0.263 & 3.808 \\ Land cover type & 0.414 & 2.417 \\ Rainfall & 0.469 & 2.134 \\ Slope & 0.469 & 2.133 \\ NDVI & 0.491 & 2.038 \\ NDVI & 0.607 & 1.648 \\ Road density & 0.746 & 1.341 \\ Plane curvature & 0.799 & 1.251 \\ Fault density & 0.815 & 1.227 \\ TWI & 0.865 & 1.156 \\ Stream density & 0.960 & 1.041 \\ Aspect & 0.978 & 1.022 \\ Distance to fault & 0.996 & 1.004 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Collinearity diagnosis results of landslide evaluation factors.  
Figure 4: Landslide factor layers.
## 5 Experiment
### Evaluation on Concept Drift
#### 5.6.2 Results  
The closed-world results are shown in Table 5, all attacks suffer the detrimental effect of concept drift. In a period of 56 days after training, the accuracy of DF dropped by nearly  
Figure 6: The performance of WF attacks in the open-world scenario.  
25%, and the accuracy of Var-CNN dropped by nearly 22%. Our bionic trace can strongly weaken the influence of concept drift with the accuracy dropped by 18%. At every point in time, our method shows an advantage over HDA. The performance of TF is impressive to stay at high accuracy. However, the attack that applies our bionic traces and the SRP-based cumulative feature performs the best.  
Figure 7 illustrates the performance of attacks in the open-world scenario. Our method leads the deep learning model to learn the deep abstract features of traffic traces, even though concept drift affects the data distribution of target datasets. Furthermore, as illustrated in Figure 8a, the proposed SRP-based cumulative features show a greater advantage at a time gap of 56 days. Figure 8b shows the enhancement of our method to the Var-CNN model. As the time gap grows, the enhancement of the \(F_{1}\)-score increases from 12% to 15%, which proves that our method can enhance the model's ability to combat concept drift.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **0-Day** & **3-Days** & **10-Days** & **28-Days** & **42-Days** & **56-Days** \\ \hline DF [24] & 91.5 & 91.5 & 87.1 & 78.6 & 72.2 & 66.4 \\ TF [11] & 95.5 & 95.5 & 92.8 & 87.3 & 82.1 & 78.7 \\ Var-CNN [10] & 87.2 & 87.2 & 84.2 & 76.4 & 70.3 & 65.4 \\ Var-CNN + HDA [14] & 89.6 & 89.9 & 88.2 & 79.8 & 72.9 & 70.2 \\ \hline Var-CNN + BionicT & 95.7 & 95.7 & 94.5 & 88.7 & 81.8 & 77.2 \\ Var-CNN + BionicT * & 96.3 & 96.5 & 94.7 & 88.2 & 83.1 & 78.9 \\ \hline \hline \end{tabular}  
* Using the proposed SRP-based cumulative feature.  
\end{table}
Table 5: Results of closed-world concept drift WF attack on awf\({}_{100}\). Metrics: accuracy.  
Figure 7: The performance of WF attacks at each point in time in the open-world scenario.
## 5 Experimental Results and Analysis
### Spatial Correlation Analysis of Traffic Black Spots
#### 5.3.3 Summary of Spatial Correlation of Traffic Black Spots  
The global Moran index analysis of traffic black spots at road intersections indicates that the distribution of traffic black spots is visibly spatially aggregated and positively  
Figure 26: Localized local Moran’s \(I\) for traffic black spots.  
correlated in the whole Kowloon Peninsula area. The local Moran index analysis of traffic black spots at road intersections indicates that there are visible traffic black spot aggregation areas and non-traffic black spot aggregation areas, which also indicates that adjacent road intersections will influence each other, increasing or decreasing the likelihood of each other becoming traffic black spots.  
The above experiments show that there exists a spatial correlation of traffic black spots at road intersections in the Kowloon Peninsula region of Hong Kong, i.e., adjacent road intersections will affect each other in terms of their probability of becoming traffic black spots. Based on this situation, this paper improves the recognition method of road intersection traffic black spots, constructs a traffic black spot network topology, takes road intersections as nodes, uses the connecting lines between nodes as edges of the network, and the built environment of road intersections as node features, and uses the GCNN graphical convolutional neural network for traffic black spot recognition, which is a better way to perform contextual learning and feature aggregation of neighboring node information, and achieve better traffic black spot recognition.
# A Momentum-Based Local Face Adversarial Example Generation Algorithm
## 6 Conclusions  
This paper proposed a face adversarial example generation algorithm based on local regions. The algorithm uses the principle of a face recognition system to build a local area containing key features and generates momentum-based adversarial examples. This algorithm is a typical white-box attack method but still achieves good results in the black-box attack scenario.  
Compared with the traditional adversarial attack method, the adversarial perturbance generated by our method only needs to cover a small part of the original image. Because the region contains the key features of the face, it can successfully mislead the face recognition system. In addition, the generated adversarial example is patch-like, which is highly similar to the original image and therefore more inconspicuous. Our algorithm can selectively attack any target in the image, so it can be extended to attack other types of images. the experiments show that the proposed algorithm can effectively balance the modified pixel area and attack successfully, achieving good transferability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Attack Method** & **ResNet50** & **MobileFaceNet** & **SphereFace** & **ArcFace** \\ \hline \multirow{5}{*}{ResNet50} & FGSM & 77.00\% & 34.81\% & 31.88\% & 30.26\% \\  & I-FGSM & 100.00\% & 24.41\% & 21.76\% & 18.82\% \\  & AdvGlasses & 100.00\% & 51.05\% & 48.02\% & 40.58\% \\  & AdvHat & 97.80\% & 52.92\% & 44.87\% & 44.24\% \\  & AdvLocFace & 99.10\% & 57.15\% & 51.35\% & 59.00\% \\ \hline \multirow{5}{*}{MobileFaceNet} & FGSM & 39.99\% & 67.67\% & 27.83\% & 29.04\% \\  & I-FGSM & 38.86\% & 100.00\% & 24.59\% & 20.75\% \\  & AdvGlasses & 69.39\% & 100.00\% & 46.06\% & 45.64\% \\  & AdvHat & 77.61\% & 97.90\% & 46.29\% & 37.38\% \\  & AdvLocFace & 61.62\% & 99.20\% & 52.76\% & 40.92\% \\ \hline \multirow{5}{*}{SphereFace} & FGSM & 38.55\% & 32.34\% & 59.20\% & 28.74\% \\  & I-FGSM & 41.94\% & 33.37\% & 99.88\% & 25.91\% \\ \cline{1-1}  & AdvGlasses & 76.59\% & 65.61\% & 99.58\% & 53.53\% \\ \cline{1-1}  & AdvHat & 68.03\% & 54.06\% & 93.91\% & 64.16\% \\ \cline{1-1}  & AdvLocFace & 62.96\% & 58.90\% & 96.65\% & 67.91\% \\ \hline \multirow{5}{*}{ArcFace} & FGSM & 37.01\% & 33.58\% & 30.76\% & 75.19\% \\ \cline{1-1}  & I-FGSM & 31.56\% & 25.65\% & 21.96\% & 98.67\% \\ \cline{1-1}  & AdvGlasses & 57.88\% & 52.43\% & 50.20\% & 97.35\% \\ \cline{1-1}  & AdvHat & 68.61\% & 65.90\% & 63.19\% & 100.00\% \\ \cline{1-1}  & AdvLocFace & 73.24\% & 70.64\% & 70.57\% & 100.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy and success rates of different algorithms.  
Investigation, J.H.; Methodology, D.L.; Software, S.L.; Writing--Original draft, D.C.; Writing--Review & editing, J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the State Administration of Science, Technology and Industry for National Defense, PRC, Grant No. JCKY2021206B102.  
Not applicable.
## 4 Methodology
### Fuzzy Logic Groups of Predictor Variables  
The study in [1] identified 28 parameters, of which only 14 were found to significantly impact the outcomes. With these influential predictors as a foundation, we separated them into four groups:  
* **Time-decision group**--focuses on factors related to decision and timing;
* **Criticality group**--assesses criticality factors;
* **Eye group**--considers eye-related metrics;
* **Experience group**--evaluates factors related to driver experience and age.  
These 14 predictors, organized into their respective groups, are shown in Table 3 including their corresponding units.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group of Predictors** & **Predictor** & **Unit** \\ \hline \multirow{3}{*}{Time-decision} & _videoLength_ & s \\  & _decisionTime_ & s \\  & _correctDecision_ & - \\  & _decisionMade_ & - \\ \hline \multirow{3}{*}{Criticality} & _difficulty_ & Likert scale \\  & _danger_ & Likert scale \\ \hline \multirow{6}{*}{Eye} & _backMirror_ & - \\  & _road_ & - \\ \cline{1-1}  & _pupilMean_ & mm \\ \cline{1-1}  & _fMean_ & m \\ \cline{1-1}  & _pupilChange_ & mm \\ \cline{1-1}  & _fMax_ & ms \\ \hline \multirow{2}{*}{Experience} & _drivingFrequency_ & - \\ \cline{1-1}  & _age_ & year \\ \hline \hline \end{tabular}
\end{table}
Table 3: Top 14 predictors categorized into four groups with corresponding units.
## 5 Classification of Harmonic Emissions Profiles
### Feature Selection
#### 5.2.2 Features for Classifier SVM-2  
The classifier SVM-2 uses two different features to distinguish between the classes (D1, D4) and (D2, D3). The mean of the emission profiles between 9 p.m. and 12 p.m. in the evening addresses the emission \"peaks\" in the evening hours for residential areas. The range between midnight and 3 a.m. in the morning highlights the higher variation of the emission in the morning hours for the classes D1 and D4.