esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Local Spatial and Temporal Attention Network for Video-Based Person Re-Identification
## 5 Conclusions  
Person Re-ID based on video is an important task that has received much attention in recent years. In this paper, we proposed a non-local attention model (NLAM) that can be added between CNN blocks for frame-level feature extraction and a non-local temporal pooling (NLTP) method for temporal feature aggregation. The experiments showed that the two methods we proposed have shown excellent results on the video-based person Re-ID datasets. Compared with most existing methods, the advantage of our proposed network architecture (SE-ResNet50 +NLAM (4,5) + NLTP) is that it better describes the relationship between frames in the video. It focuses on the spatial and temporal relationships of all frames in a non-local way and gives different weights, thus forming a more accurate representation of the video. The results performed better compared to state-of-the-art methods. Our proposed NLAM and NLTP methods can also be applied to other video-based tasks, such as target tracking and pose estimation.  
Conceptualization, Z.L. and Q.Z.; methodology, Z.L. and W.L.; software, Z.L. and W.L.; validation, Z.L., W.L. and F.D.; formal analysis, Z.L.; investigation, Z.L. and X.L.; resources, Z.L., W.L. and Q.Z.; data curation, Z.L.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L., Q.Z. and F.D.; visualization, Z.L.; supervision, Q.Z.; project administration, Q.Z.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Project of Supporting Plan of Tianjin (China) (16-YFZCSY00850). The authors declare no conflict of interest.
# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 7 Conclusions  
In this paper, we provided a comprehensive review of digital image watermarking regarding conventional techniques and learning-based techniques. First, we discussed the background of the image watermarking domain, including the watermarking phases and different kinds of attacks. Second, the image watermarking requirements and their corresponding evaluation metrics were described. We then reviewed and summarized some of the new conventional image watermarking schemes in detail, including the approaches used, their objectives, advantages, and weaknesses. Next, we presented another new comprehensive review of learning-based watermarking techniques. These methods were discussed in depth and then summarized as well in detail, including the architectures employed, their objectives, embedding locations, benefits, and drawbacks. Finally, we examined recent challenges and issues in the image watermarking field based on the reviewed methods, as well as some potential solutions. In this regard, we believe that this paper illustrates the transition of image watermarking from relying solely on conventional methods to adopting learning-based techniques, and we hope that this research can help readers understand the conventional and modern techniques in topic. Hence, future work can be expanded by combining traditional and novel learning methodologies to meet the critical needs of watermarking techniques. Furthermore, in order to improve robustness and security, researchers should focus on developing new, advanced methodologies and exploiting the benefits of both modern and conventional techniques.  
Writing--original draft, S.B.; Writing--review & editing, S.B., R.R. and F.R.; Validation, R.R., H.D., F.R. and R.H.; Supervision, R.R., H.D., F.R. and R.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Dynamic Stability Assessment of High-Speed Railway Bridges Using Numerical Model Updating
## 2 Experimental Setup for Ambient Vibration Test
### Target Bridges  
Prestressed concrete (PSC) box girder bridges represent more than 70% of high-speed railway bridges in South Korea. In this study, three PSC box girder bridges (Imgi 2 nd Bridge [one span], Maeaji Bridge [two spans], and Hwalchun Bridge [three spans]) were selected as target bridges based on the number of spans. These three bridges were designed to connect Busan, Gyeongju, and Daegu on the Gyeongpu high-speed railway. The Imgi 2 nd Bridge consists of 11 girders of 40 m and a single span, and the second span was selected as the target position. The Maeaji Bridge has two continuous girders of 50 m and four spans with a total length of 100 m, and the third and fourth spans were selected as the target positions. The Hwalchun Bridge has one continuous girder of 75 m and three spans. Figure 1 shows the target bridges, while Table 1 outlines their specifications.
## 2 Materials and Methods
### Satellite Precipitation Data
#### 2.4.1 TRMM 3B42 V7  
The TRMM 3B42 V7 precipitation products were generated by using the TRMM 3B42 Version 7 algorithm [18]. It was designed to combine various microwaves MW, and infrared IR satellite-based precipitation estimates with gauge adjustments observations to provide 3-hourly quasi-global quantitative precipitation estimates. The 3B42 V7 product was derived by bias adjusting the near-real-time product with the GPCC monthly gauge-analysis precipitation data set, and it has a two-month latency [1]. The product can produce rational precipitation estimates in a 0.25\({}^{\circ}\) spatial resolution with a quasi-global coverage (50\({}^{\circ}\) S-50\({}^{\circ}\) N). In this study, the TRMM 3B42 V7 daily precipitation product was acquired from the Precipitation Measurement Mission (PMM) website ([[https://giovanni.gsfc.nasa.gov/giovanni/](https://giovanni.gsfc.nasa.gov/giovanni/)]([https://giovanni.gsfc.nasa.gov/giovanni/](https://giovanni.gsfc.nasa.gov/giovanni/)) (accessed on 16 May 2022)).
# Modeling Actual Evapotranspirationiration with MSI-Sentinel Images and Machine Learning Algorithms
## 4 Discussions
Approach 2 had a total of 12 predictors: the same as approach 1 and some others, mainly the short infrared wavelength and red edge. The main results and discussion found in the literature when applying METRIC to predict the evapotranspiration fraction is to consider ET\({}_{\mathrm{r}}F\) to be equal to K\({}_{\mathrm{c}}\) when using the reference evapotranspiration of alfalfa [7; 48; 49; 50]. However, this interpretation is not correct because sensors on board satellites or other platforms capture the information that is occurring in the field under natural conditions, and one cannot be sure that the plant is in maximum water comfort and with nutritional, pest, disease, and weed management adequate for maximum water uptake. Thus both METRIC and the models trained in this study estimate the product between K\({}_{\mathrm{c}}\) and K\({}_{\mathrm{s}}\) (stress coefficient), the latter being responsible for reducing ET\({}_{\mathrm{r}}F\) to values lower than K\({}_{\mathrm{c}}\) when the crop is under stress or making it equal to K\({}_{\mathrm{c}}\) when the plant is in favorable conditions for maximum water uptake.
# An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks
## 4 Motivation and System Model
### System Model
#### 4.2.1 Service Category Server (SCS)  
It has four missions in this paper. Firstly, SCS should compute and prepare pseudo-IDs \(PID_{i}\) = \(\{PID_{1},PID_{2},...\}\) for users registration, the details can be referred to the paper [8]. Secondly, SCS generates the parameters (\(n\), \(q\), \(\delta\), \(R\), \(R_{q}\)) of BVHE, which enable users to generate a public-private key pair (\(pk\), \(sk\)). Thirdly, according to the different \(th_{l}\) in different regions, SCS generates the corresponding service category table. Fourthly, SCS regularly updates the corresponding relationship between the kind of service category and binary number in the table, and users' preference privacy can  
Figure 8: Scenario 2.  
Figure 7: Scenario 1.  
be well protected in LBS query by this way. Note that how to make reasonable value \(th_{l}\) in different regions is out of our discussion, the reasonable value \(th_{l}\) in a region may be obtained from the analysis of big data or other approaches, but in this paper, we should make sure that there are only one pair thresholds (i.e., \(th_{k}\), \(th_{l}\)) in one region.
# Fatigue Life Prediction of High Modulus Asphalt Concrete Based on the Local Stress-Strain Method
## 5 Determination of Local Stress and Strain
### Neuber Equation Correction
#### 5.2.2. Neuber Equation Correction  
To show the plastic penetration level and equivalent stress and strain levels, the right end of the Neuber equation was multiplied by two coefficients, and the following corrections were made to the Neuber equation:  
\[\Delta\sigma\cdot\Delta\varepsilon=m\cdot g\cdot\Big{(}K_{f}\cdot\Delta S \Big{)}^{2}/E \tag{15}\]  
\[m=\Big{(}1-\mu^{\prime}+{\mu^{\prime}}^{2}\Big{)}^{\frac{1}{2}} \tag{16}\]  
\[g=m/\Big{(}1-{\mu^{\prime}}^{2}\Big{)} \tag{17}\]  
\[\mu^{\prime}=0.5-(0.5-\mu)E_{s}/E \tag{18}\]  
where: \(m\)--plastic penetrable degree, \(g\)--coefficient of equivalent stress and strain, \(\mu^{\prime}\)--elastic-plastic Poisson's ratio, considering the compressibility of material, \(\mu\)--Poisson's ratio, \(E\)--elasticity modulus, \(E_{s}\)--secant modulus, determined by the monotone tensile curve.  
Parameters \(\mu^{\prime}=0.4221\), \(m=0.87\), \(g=1.059\) can be obtained by putting \(\mu=0.25\), \(E_{s}=374\) MPa, and \(E=1200\) MPa into Equation (15), which becomes the following final form of the Neuber equation that predicts the fatigue life of HMAC in the strain-controlled mode:  
\[\Delta\varepsilon\cdot\Delta\sigma=\frac{0.92\Big{(}K_{f}\cdot\Delta S\Big{)} ^{2}}{E} \tag{19}\]  
As nominal stress is in the elastic range, \(\Delta\varepsilon=\frac{\Delta S}{E}\) and \(K_{t}=1.976\) were substituted into Equations (9) and (19) to develop Equation (20):  
\[\varepsilon\sigma=0.92\times 1.976^{2}se \tag{20}\]
# Context-Aware Gossip-Based Protocol for Internet of Things Applications
## 4 System Design
```
1:\(E\gets 0.5\)\(\triangleright\) Initial energy
2:\(N\gets TotalNumberOfNodes\)
3:\(X\gets SourceNodeXcoordinate\)
4:\(Y\gets SourceNodeYcoordinate\)
5:\(msg\gets MessagePriority\)\(\triangleright\) zero if low and one if high
6:SelectionList\(\leftarrow\) empty
7:neighbours[]\(\leftarrow\) ListOfNeighbours
8:\(sX\gets sink^{s}sXcoordinate\)
9:\(sY\gets sink^{s}sYcoordinate\)
10:\(sToSinkDistance=max(|X-sX|,|Y-sY|)\)
11:while\(Energy>0\)do
12:if data sensed \(\parallel\) packet received then
13:for all node \(n\) : neighbours do
14:\(nEnergy\gets n^{\prime}sEnergy\)
15:\(nX\gets n^{\prime}sXcoordinate\)
16:\(nY\gets n^{\prime}sYcoordinate\)
17:\(localDensity\gets n^{\prime}sNumberOfNeighbours\)
18:\(neighbourDistance=max(|X-nX|,|Y-nY|)\)
19:\(nToSinkDistance=max(|nX-sX|,|nY-sY|)\)
20:\(Energy=nEnergy/E\)
21:\(NeighbourDist=1/neighbourDistance\)
22:\(sinkDist=1-(nToSinkDistance/sToSinkDistance)\)
23:\(nDensity=localDensity/N\)
24:\(w=(f1\cdot Energy)+(f2\cdot NeighbourDist)+(f3\cdot sinkDist)+(f4\cdot nDensity)+(f5 \cdot msg)\)
25:add\(w\) to SelectionList
26:endfor
27:SortDescending (SelectionList)
28: Send data to the first node in SelectionList
29:endif
30:endwhile
```  
**Algorithm 1** The algorithm for the proposed protocol
## 3 Methodology
### CNN-Based Airport Delay Level Prediction Model
#### 3.2.1 Classification Method  
Airport delay level provides an overall description of various types of delays occurring at an airport within a specific period. It serves as an important indicator for evaluating airport operational efficiency. Using a single feature to describe delay can only reflect one aspect of the situation and fails to capture the full picture of flight delays and their evolving patterns. Therefore, this paper selects two indicators--the average departure delay time and the on-time departure rate--to assess the airport departure delay level from the perspectives of both delay severity and flight reliability.  
Airport delay classification can be regarded as an unsupervised learning problem, so in this paper, we will use the fuzzy C-mean (FCM) clustering [27]. The principle of the FCM algorithm is to minimize the criterion function through continuous iteration. After FCM cluster analysis, the flight delay dataset can be divided into different clusters, each representing a level of delay. Therefore, each cluster can be regarded as a delay level, which can be used to classify the degree of delay at the airport. Figure 6 demonstrates the trend of the contour coefficient with the number of clusters, where the number of clusters of 2 corresponds to the highest contour coefficient, indicating that the samples in the clustering results are more cohesive and separated at this time. Figure 7 demonstrates the distribution of different delay features within each cluster when the number of clusters is 2, in which the distribution of average departure delays between two clusters is relatively more separated, while there is a staggered distribution of flight punctuality, but the overall flight punctuality of the samples in Cluster 0 is higher, and the overall flight punctuality of the samples in Cluster 1 is lower.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Combination Name** & **Channels** \\ \hline \
\begin{tabular}{c} \hline
\begin{tab  
Figure 5: Framework structure of the ADLCNN method.  
Figure 6: Trend in the number of clusters and profile coefficient.  
Thus, we divide the airport delay level into two categories. Cluster 0 represents the general delay level, whose average departure delay is less than 50 min, and flight punctuality is higher. Cluster 1 represents the severe delay level, whose average departure delay is higher than 50 min, and flight punctuality is lower. The later experiments on airport delay prediction will be viewed as dichotomous predictions.  
The predicted delay levels in this study not only serve as a classification target but also provide practical value for air traffic control by enabling controllers to receive early warnings. For example, if a high delay level is predicted in advance, controllers can prepare diversion resources or adjust flight release priorities accordingly, thereby mitigating congestion and improving decision efficiency.
## 2 Materials and Methods
### Data Sets of Gross Primary Productivity
#### 2.2.1 Gosif-Gpp  
The monthly 0.05\({}^{\circ}\) GOSIF-GPP product (2001-2018) used in this study is generated based on a global SIF data set from the Orbiting Carbon Observatory-2 (OCO-2) (i.e., GOSIF) and its biome-specific linear relationship with the measured GPP [30]. The GOSIF-GPP data set was estimated using a data-driven model, in which variables reflecting vegetation conditions, meteorological conditions, and land cover information were used as model input [31]. It is based on SIF observations made by NASA's sun-synchronous and polar Orbiting Carbon Observatory-2 (OCO-2) (launched 2014). SIF retrieved by OCO-2 has a higher resolution (1.3 \(\times\) 2.25 km\({}^{2}\)) and data acquisition rate compared to, e.g., GOSAT or Global Ozone Monitoring Experiment (GOME). These measurements are still too spatially sparse to provide a data set with 0.05\({}^{\circ}\) (spatial) resolution over a long-term period. The authors of [30] used eight biome-specific linear relationships between SIF and GPP, in combination with machine-learning techniques to create a global coverage (0.05\({}^{\circ}\)) of SIF-based GPP from the year 2001 to 2018. Climate data, such as air temperature, photosynthetic active radiation (PAR), and VPD, was used in combination with the Enhanced Vegetation Index (EVI) retrieved from MODIS MCD43C4v006 to help with calibration of SIF. Compared to GPP retrieved from Eddy Covariance (EC) measurements of 91 sites ([[https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)]([https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)), accessed on 6 March 2022, FLUXNET tier1) an overall correlation coefficient (R\({}^{2}\)) between EC measurements and GOSIF-GPP is 0.71 (\(p<0.001\)).
## 6 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**UPWARD**} & \multicolumn{2}{c}{**DOWNARD**} \\ \cline{2-5}  & \begin{tabular}{c} **Case A** \\ **(DG Only)** \\ \end{tabular} & \begin{tabular}{c} **Case B** \\ **(DG + End-Users)** \\ \end{tabular} & \begin{tabular}{c} **Case A** \\ **(DG Only)** \\ \end{tabular} & \begin{tabular}{c} **Case B** \\ **(DG + End-Users)** \\ \end{tabular} \\ \hline Market Potential (GWh/year) & 9.026 & 37.271 & 42.293 & 70.519 \\ \hline Feasible Flexibility (GWh/year) & \begin{tabular}{c} 8.126 \\ (\(-10\%\)) \\ \end{tabular} & \begin{tabular}{c} 32.503 \\ (\(-13\%\)) \\ \end{tabular} & \begin{tabular}{c} 37.254 \\ (\(-12\%\)) \\ \end{tabular} & \begin{tabular}{c} 60.659 \\ (\(-14\%\)) \\ \end{tabular} \\ \hline Cost of feasible flexibility (MEUR/year) & 0.502 & 1.849 & 0.253 & 1.499 \\ \hline Needed Reactive support (GVarh/year) & 29.344 & 27.900 & 0.298 & 3.026 \\ \hline Total extra cost for Reactive support (kEUR/year) & 73.3 & 70.6 & 0.9 & 9.2 \\ \hline Unitary cost (without extra cost—feasible) & 61.78 & \
\begin{tabular}{c} 56.89 \\ (\(-8\%\)) \\ \end{tabular} & 6.79 & 24.71 \\ \hline Unitary cost (with extra cost—feasible) & 70.80 &
\begin{tabular}{c} 59.06 \\ (\(-17\%\)) \\ \end{tabular} & 6.82 & 24.86 \\ \hline \hline \end{tabular}  
* (1): the percentages refer to the potential quantity for the same case. (2): the percentages refer to the same quantity in case A, considered as a reference case.  
\end{table} 
Table 4: Flexibility quantities for the whole substation adopted as Case Study (Figure 2). Figure 8: Potential profile during all representative days, feeder R2—case B (“DG + end-users”). in Table 3, the end-users' participation allows a cost reduction in the upward part, until 17%, taking into account the additional cost for the Q control. On the contrary, in the downward part, the unitary cost increases, but these results depend on the downward bids adopted by the RES owners, who might bid at zero or even at a negative price, if allowable by the regulatory framework. A zero price bid means that the producer will receive the money corresponding to the energy curtailed at the market clearing price. In these conditions, starting from a zero price (Table 1), the increase is unavoidable. Further details about this topic can be found in [46]. Finally, it is important to remark that the flexibility analysis cannot be stopped at the \"potential quantity\" (first row of Table 4) but on the effective energy that can be used for the flexibility. Even though the fit and forget approach caused an extremely cautious development of distribution systems to host distributed generation, bottlenecks in distribution networks still impede the provision of flexibility or cause adjunctive costs.
# Enhanced Pet Behavior Prediction via S2 GAN-Based Heterogeneous Data Synthesis
## 3 Pet Behavior Prediction via S2 GAN-based Data Synthesis
### Pet Behavior Prediction  
Real and fake data are used to predict pet behavior. The network model structure for behavior prediction includes a CNN-LSTM hybrid model (Figure 6), which is systematically segmented into three principal components: (1) a section dedicated to feature extraction through convolutional processes, which effectively captures spatial features; (2) a section that leverages LSTM to analyze temporal sequences, thereby preserving the continuity and dynamics of behavioral patterns over time; and (3) a final integration section, which combines the spatial and temporal features for accurate behavior prediction. This integration ensures that video and sensor data are processed in their respective streams before being fused, enhancing the ability of the model to make informed predictions based on a multimodal dataset.  
Video data consists of preprocessed sequences for the head, body, front legs, and rear legs, which are fed into the model as separate streams. Within each stream, time-distributed 2D convolutional layers are applied twice to extract features of the joints. Subsequently, a 3D convolutional layer is applied, which performs convolutions across the time series, joint locations, and x/y coordinates, designed to reduce all dimensions except for the time series to a size of one. This dimensionality reduction is intended for input into the LSTM layer. Following each convolutional operation, the ReLU activation function and  
Figure 6: Behavior prediction network architecture.  
batch normalization are applied, normalizing the input data and stabilizing the learning process. The final stage of the convolutional operations includes a dropout at a rate of 0.5 to prevent overfitting. The data processed for the head, body, front legs, and rear legs are then concatenated and fed into the bidirectional LSTM layer. This bidirectional LSTM processes sequences in forward and reverse directions, considering the context before and after each point in time, effectively extracting behavior patterns.  
In the case of sensor data, the accelerometer, gyroscope, and magnetometer data were combined and concurrently input into the model. The overall process parallels that of video data handling; however, due to sensor data having one less dimension compared to video data, time-distributed 1D convolutional and 2D convolutional operations are conducted instead. All the steps, including dropout and LSTM, were performed similarly to those of the video sequence data.  
The extracted features from the video sequence data and sensor sequence data were first processed through a dense layer with 32 hidden units and then concatenated to combine their features. Following this, the combined features were passed through two additional dense layers and a softmax activation function to output the probabilities of the behaviors represented by the sequences.
# Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning
## 1 Introduction
### Contributions of the Manuscript  
This study responds to our responsibility as scholars to develop a solid theoretical foundation for this emerging discipline. In contrast to the previous reviews, this review is a systematic literature review (SLR) of high-quality literature published between 2018 and 2022 from pre-defined resources and based on pre-defined inclusion/exclusion criteria. It focuses on dynamic obstacle avoidance and path planning through reinforcement learning. Studies were classified into different categories based on how they addressed dynamic obstacle avoidance and path design. Out of the 546 papers that were found utilizing the search approach, 34 met the requirements and were finally included. Different RL-based path planning and dynamic obstacle avoidance algorithms were evaluated based on their performance, the environment's complexity, and the type of RL algorithm they employed. Based on how different studies addressed dynamic obstacle avoidance and path planning, several studies were developed.  
The process of the research in this area is well discussed. The obstacles in this field are then discussed, and the effectiveness of the method in terms of safety, effectiveness, and scalability is compared with the type of RL algorithm employed, the propagation model used, the complexity of the environment, and those factors.  
Finally, the research gaps are thoroughly examined, and recommendations for potential future study topics are provided. By giving a complete picture and highlighting the areas that require development in dynamic obstacle avoidance and reinforcement learning-based path planning, these contributions can progress this field of study. The information on dynamic obstacle avoidance and path planning needed to start this field of study's evolution is all included in this paper.