esa-sceva/satcom-chunk-collection huggingface dataset:
# Agent-Based Model for Automaticity Management of Traffic Flows across the Network
## 4 Architecture of the Agent-Based Model
### SEHA Social Election with Hidden Authorities Algorithm  
According to the social choice theory, the group decision-making process in a multi-agent system should consider that each agent will have their preferences and will vote to choose a winner [15]. The proposed model considers a set of agents A = \(\{Ag_{1},...,Ag_{n}\}\) in a network environment composed by \(n\) number of nodes and \(k\) links with different costs \(c\).  
The tuple that represents an agent is \(Ag_{i}=\)\(<\Omega,\beta,h>\), where \(\Omega\) is a set of preferences' relations \(\Omega\) = { \(\omega_{1}\succ\omega_{2}\succ\omega_{3}\)}, they are arranged according to the flow type to transmit and its priority \(\beta\). Both parameters \(\Omega\) and \(\beta\) are configured by the network administrator during the initialization process. The hierarchy of a node \(h\) is calculated by the centrality of each node among the network using the Equation (1).  
The proposed algorithm is activated when a congestion emerges in a node, as is described in Algorithm 1. The SEHA algorithm returns a winner \(\omega\) flow type (data, video or voice) after a voting process between the neighbor's nodes. The winner flow will have the higher priority in the queue of the congested node. The next step is transmitting the flow through one of its neighboring nodes using the shortest path with minimum cost \(c\) to its destination, the agent of the selected node obtains an utility \(u\) of +1. The rest of the non-preferred flows in the queue of the congested node picks randomly  
Figure 2: Multi-Agent Architecture for Management of Network Flows.  
a neighboring node to continue their paths, but the utility \(u\) of the agent is set to \(-1\) because they are not transmitting the preferred flows.  
```
functionFindwinner( \(\Omega\), \(\beta\), \(h\)) set choices [\(\omega\) with max \(\beta\)] of my_neighbors set choices
```  
if choices length \(>1\) set prefered_choice [\(\omega\) with max \(\beta\)] of my_neighbors with max \(h\) else set\(prefered\_choice\) choices return\(prefered\_choice\) endfunction ```  
**Algorithm 1** SEHA Social Election with Hidden Authorities Algorithm
## 5 Conclusions
### Limitations and Future Work Plan  
This study focused on refining the parameter setting of the improved BPGA-NSGA-II model and used the improved model to conduct a dynamic analysis of the stability of the case slope. The analysis results were verified using actual monitoring data, confirming the reliability of the model. However, there are notable shortcomings:  
(1) This study included a limited number of research cases, warranting further verification to assess the applicability of the method to different slope types;  
(2) Research on improving the key parameters of the NSGA-II algorithm is limited by calculation speed. This study was confined to population sizes in the order of 102 and the number of iterations in the order of 101. To establish parameter rules with different numbers of objective functions and different magnitudes, further investigation is required.  
Figure 17: Displacement monitoring data for JCK07.  
**Author Contributions:** Conceptualization, B.H. and X.D.; methodology, B.H.; software, B.H.; validation, J.Y. and D.M.; formal analysis, X.D.; investigation, M.B.; resources, M.B.; data curation, B.H.; writing--original draft preparation, B.H.; writing--review and editing, B.H.; visualization, B.H.; supervision, X.D.; project administration, J.Y. and D.M.; funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by The National Key Research and Development Project of China, the Ministry of Science and Technology of China (Grant 2018 YFC1505504).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** Authors [PERSON] and [PERSON] was employed by the company China Railway 16 th Bureau Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
## 5 Experiment and Analysis  
In this section, we evaluate the performance of the proposed SAL method by conducting experiments in an indoor circular hall environment and an indoor lab classroom environment based on our UWB system. Specifically, we first introduce the experiment settings. Then, we evaluate the performance of the system. The experiment results show that our system can achieve decimeter-level localization accuracy.
## 4 Results  
### Preliminary Test at the University  
Prior to the application at the mining site, the developed digital device was attached to a RoboMaster S1 unit (Figure 6a), and a preliminary test was performed at Samcheok Campus, Kangwon National University. RoboMaster S1, developed by DJI, is a radio-controlled vehicle with video shooting and autonomous driving capabilities. It is equipped with a two-axis mechanical gimbal, a Mecanum wheel, and a high-performance motor. Additionally, it is equipped with advanced AI technology that supports the scratch and python programming languages. Because the roads at Samcheok Campus, Kangwon National University, are all paved, simulating an environment with rough road surface conditions is challenging, such as mine sites with ordinary vehicles. However, RoboMaster S1, a small radio-controlled vehicle, can perform experiments on rough terrain or unpaved paths. Furthermore, because RoboMaster S1 has a built-in battery, the power of the digital device is supplied by RoboMaster without requiring a separate battery to operate the device. The route of the RoboMaster for the preliminary test at Kangwon National University is shown in Figure 6b. Section 1 was a relatively flat paved road, and Section 2 was a rougher sandy road. After Section 2, the road was paved again, and Section 3 was adjacent to the road on which cars passed. The location and sensor measurements could be checked in real time through the developed application and recorded on a desktop computer using python codes.  
The data logging results obtained using the developed application are shown in Figure 7. RoboMaster S1 has a small body and wheels; therefore, a significant vibration was measured even on a relatively flat surface. Vibration values of up to 9 m/s\({}^{2}\) were observed during the test. The distance to the ground using LiDAR also changed significantly. A distance of approximately 10 cm was measured on average. Distances ranging from 1 to 16 cm were recorded when the body of the RoboMaster vibrated during the test. Because RoboMaster S1 has a low height, only short-range results were obtained when using LiDAR; however, it can be used for longer-distance measurements in high-height dump trucks and heavy equipment. In Section 2, where the road surface was rough, the vibration and distance from the ground surface changed significantly. In addition, unlike other paved routes, Section 2 was an environment where dust could accumulate; thus, the dust density was seen to significantly increase up to 39 \(\upmu\)g/m\({}^{3}\). The gas sensor did not fluctuate significantly and maintained a generally constant value; however, the gas concentration increased in Section 3. This was because, during the test, vehicles passed along the adjacent road and emitted exhaust gases. Through a preliminary test, we confirmed that the developed application could detect, share, and record changes in the surrounding environment.  
Figure 6: Images of **(a)** RoboMaster S1 and **(b)** preliminary test site (Samcheok Campus, Kangwon National University).
# Efficient On-Off Keying Underwater Acoustic Communication for Seafloor Observation Networks
### Relation to Existing Works  
In our previous work [17], the IrCC [18] was introduced to UACs with some preliminary results presented. In this paper, a more extensive investigation is made. First, the complexity of the ITE algorithm is significantly reduced; second, the effects of iteration number and interleaver depth on the performance are thoroughly investigated; third, the placement of the communication nodes near the seafloor is analyzed; finally, the IrCC design is continuously improved by accounting for actual channel information.  
The rest of this paper is structured as follows. In Section 2, we introduce the system model and the high spectral efficiency of the OOK modulation is demonstrated. In Section 3, the IrCC is designed and compared with the RSC, and the ITE algorithm for demapping is proposed. The Undersea application and experimental results are shown in Sections 4, and Section 5 concludes the paper.
## 4 Methodology
### Problems Formulation  
The land cover classification problem in this study can be formulated as a supervised classification problem where bands (or typical indices) are selected as features into a supervised classifier for training. In this study, the Sentinel-2A/B image pixels are represented by \(\mathcal{D}=\{1,\cdots,n\}\) where \(n\) means the total number of individual pixels in the original satellite map. Here, the pixel matrix of this image with \(f\) being the number of features (bands or indices) is defined as \(x=(x_{1};\cdots;x_{n})\in R^{n\times f}\). Let \(\mathcal{L}=\{1,\cdots,k\}\) and \(C=(c_{1},\cdots,c_{n})\) be a set of class labels and classification map corresponding to the label, respectively, where \(k\) denotes the number of class. Therefore, the training dataset \(\mathcal{T}\) can be generated by the number of features \(f\) and the corresponding labels \(C\) in the form of \(\mathcal{T}=\{(x_{1},c_{1}),\cdots,(x_{\tau},c_{\tau})\}\) with the number of training samples \(\tau\). As a result, after the classification model is built, the classification evaluation matrix and also the corresponding classification map can be generated by sending the training dataset \(\mathcal{T}\) into the classifier. The aim of this study is to evaluate the classification performance of three different supervised classifiers including the random forest classifier with Bayesian parameter optimization.
# 3D Behavior Recognition Based on Multi-Modal Deep Space-Time Learning
## 2 Related Work
### Behavior Recognition based on Skeleton Space-Time Expression  
Given a corresponding 3D joint position, a behavior can be represented by a time-domain sequence of a human skeleton that is close to it. Existing joint-based behavioral recognition methods use different features to model the motion of a joint or a group of joints. Various features are extracted from the skeleton data in order to obtain the relevance of body joints in 3D space. [PERSON] et al. [31] proposed a new descriptor called EigenJoints which contained the pose, motion and offset features. The differences between the current frame and neighboring frames were used to encode the temporal and spatial information. [PERSON] et al. [32] estimated human joint positions from the video and then divided the estimated joint positions into five different sections. Each action can be expressed by calculating the changes in the spatial and temporal parameters of these body parts. The author applied the bag-of-words method for classification. In reference [33], the behavior was modeled as a space-time structure that used a set of 13 joint locations in a 4D space (3D space plus time) to describe a human behavior. In reference [10], [PERSON] et al. designed seven feature vectors, such as joint position, bone position, and joint angle etc. Then a Hidden Markov Model was used to learn the movement of each action for each feature. [PERSON] et al. [34] proposed a space-time hierarchical skeleton configuration in which each pose represented the motion of a set of joints in a special time domain. In [35], the joint-space covariance matrix in a fixed-length time window was used to obtain the space-time relationships between joints. [PERSON] et al. [36] proposed a descriptor called motion gesture which was formed by the position, velocity, and acceleration of a skeleton joint in a short time window around the current frame. They considered both the spatial attitude information and the time difference of the human joint. The work in reference [37] proposed a more complex skeletal representation that described the relative geometry between two rigid limbs as the combination of a rigid body rotation and translation. Thus, the skeletal sequence could be expressed as a curve in the Lie group, which in turn used a Fourier temporal pyramid [38] to represent skeleton space-time features. In reference [39], each behavior was represented by the space-time motion trajectory of the joint, and the trajectory was a curve in the Riemannian manifold of an open-curve shape space. In general, skeleton space-time expression-based methods usually need intricate feature designs to classify actions without joint selection, and all the joints are all treated the same way during behavior identification.
## Materials and Methods
### Risk Decision Based on RUL Prediction
#### 2.2.1 Feature extraction based on variation coefficient  
When multiple sensors are used to monitor the health of a system, a key step for predicting health states is selecting features from the multidimensional time series data, because only parameters without commonality can be treated as attributes features. Therefore, parameters screening is the prerequisite to ensure the non-commonality of parameters. Intuitively, practitioners should firstly deal with correlations of parameters to make the transformed parameters independent.  
In this paper, the statistic of variation coefficients is used to select the parameters features. Similar to the concept of standard deviation and variance in statistics, the variation coefficient is described as the dispersion degree of observations. The variation coefficient is calculated by the ratio of the standard deviation to the mean, as Equation (6) shows. Therefore, it is dimensionless.  
\[VC=(\textit{StandardDeviation}/\textit{Mean})\times 100\%. \tag{6}\]  
Variables with larger variation coefficients show more obvious features and therefore are more suitable for describing characteristics of data.
# Prediction of Vacant Parking Spaces in Multiple Parking Lots: A DWT-ConvGRU-BRC Model
## 5 Conclusions  
This paper proposes a deep learning model for occupancy prediction of multiple parking lots. The model incorporates DWT, ConvGRU, and BRC modules and has the flexibility to take multiple spatial-temporal structured data sources as inputs. The performance of the model is evaluated using a case study from 11 public parking lots in Santa Monica, California, USA, in which VPS data, weather data, and weekday and weekend data are used. The experimental results show that our model can achieve considerably high accuracy with MAPEs of less than 2% for short-term predictions and less than 4% for long-term predictions. The DWT-ConvGRU-BRC model significantly outperforms the baseline LSTM and GRU methods. In general, we found that noise reduction of VPS data using a DWT can improve prediction accuracy and that combining weather information and weekday and weekend information can improve the performance of long-term predictions of parking occupancy.  
Prediction of available parking spaces is an integral part of parking guidance information systems. Available parking space predictions can improve the effectiveness of parking guidance system information, which can help drivers plan driving routes and find vacant parking spaces. Furthermore, if we have a reliable parking prediction algorithm, we can apply dynamic parking pricing to control the parking demand of each parking lot, thereby assisting traffic planning and management and reducing energy consumption and traffic congestion. In future work, we will concentrate on further improving the adaptability by considering other external influences, such as POI information, traffic incident data, traffic flow data, etc. At the same time, future research will also consider how the running time of the model can be optimized while ensuring prediction accuracy.  
Methodology, [PERSON]; Formal analysis, [PERSON]; Resources, [PERSON]; Writing--original draft, [PERSON]; Writing--review & editing, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by Fujian Natural Science Foundation (Project No.: 2020J05194, 2021)05226, and 2022J01938).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Development Stages of the Proposed Approach
### Development Stages of the Proposed Approach  
The multiplicative Holt-Winters model for seasonal demand forecasting consists of an iterative process by incorporating the estimated level (\(L_{T}\)) defined as:  
\[L_{T}=\alpha(D_{T}/SN_{T-l})+(1-\alpha)(L_{T-1}+b_{T-1}) \tag{1}\]  
Here, \(D_{T}\) is the actual demand for period \(T\), \(SN_{T-l}\) represents the seasonal index for period \(T-l\), where \(l\) denotes the seasonal length, and \(b_{T}\) is the growth rate expressed as:  
\[b_{T}=\beta(L_{T}-L_{T-1})+(1-\beta)b_{T-1} \tag{2}\]  
In the Holt-Winters method, the seasonal factors are updated at each iteration. Thus, \(SN_{T}\) and \(SN_{T-l}\), the seasonal indices respectively for the periods \(T\) and \(T-l\), are updated by using the following equation:  
\[SN_{T}=\gamma(D_{T}/L_{T})+(1-\gamma)SN_{T-l} \tag{3}\]  
Generally, starting with the data of a specific period, the Holt-Winters method is able to forecast for the next one or more periods denoted as \(\tau\) steps. Thus, for example, \(\tau\) = 3 means that it is possible to forecast for three periods, from period \(T\) to \(T+1\), \(T+2\) and \(T+3\). The \(\tau\) step ahead of forecasted demand (\(Y_{T+\tau}\)) can be exposed as:  
\[Y_{T+\tau}=(L_{T}+\tau b_{T})SN_{T+\tau-l} \tag{4}\]  
So, one step ahead forecasted demand reflecting \(\tau\) = 1 can be derived from Equation (4) and presented as Equation (5). The error, \(E_{T(L)}\) can be determined by Equation (6). This equation represents a computational process to measure the deficit in estimating the level parameter for each incremental time step, T. In other words, the error, \(E_{T(L)}\) expresses the de-seasonalized and de-trended one-step-ahead likelihood deficit which is used for training the rule base of the fuzzy inference system.  
\[Y_{T+1}=(L_{T}+b_{T})SN_{T+1-l} \tag{5}\]  
\[E_{T(L)}=((D_{T}/SN_{T-l})-b_{T})-L_{T} \tag{6}\]  
As mentioned earlier, the three smoothing constants (\(\alpha\), \(\beta\), and \(\gamma\)), also known as the trios and used in the conventional multiplicative Holt-Winters method, are often ascertained subjectively or by using some iterative techniques. In this work, the optimal values of the trios are determined by simulation through a coded program in MATLAB.
## 3 Preliminaries  
### Description of the Problem  
Assume that \(n\) underwater sensor nodes are deployed in the monitoring area \(A\) and \(s_{i}\) represents the \(i\)th node in the network, so that the corresponding sensor node set is \(S=\left\{s_{1},s_{2},\cdots,s_{n}\right\}\). The dynamic point \(e\), which users are interested in, is referred to as an event; thus, in monitoring area \(A\), the event set \(E=\left\{e_{i}\left|e_{i}\in A,i=1,2,\cdots,m\right.\right\}\). Assuming that any underwater node has the ability to sense, communicate, and move, \(B_{j}=\left(r_{j}^{s},r_{j}^{c},l_{j},P_{j}\right)\), where \(r_{j}^{s}\), \(r_{j}^{c}\), \(l_{j}\), \(P_{j}\) respectively represent the radius of perception, the radius of communication, the maximum moving step length of node \(s_{j}\), and the current position of the node \(s_{j}\), and \(r_{j}^{s}\geq 0\), \(r_{j}^{c}\geq 0\), and \(l_{j}\geq 0\) (\(0\leq j\leq n\)). In a homogeneous network, all nodes have the same attributes, which are \(r_{j}^{s}=r^{s}\), \(r_{j}^{c}=r^{c}\), and \(l_{j}=l\) (\(0\leq j\leq n\)). A sensor node can sense an event and communicate with its neighbor nodes to obtain status information (number of events covered) of neighbor nodes. The task of a node is to cover an event, collect information about the event, and maintain connectivity between nodes.
## 4 Quantify the Spire Retrieval Accuracy and Uncertainty
### Assessment of the Spire Bending Angle Accuracy by Comparing with ERA5
The results are summarized in Table 7. In particular, the height-dependent humidity differences over these three SZA groups are shown in Figure 18. The mean humidity biases over two height regions (below 4.2 km and 4.8-8.4 km) are calculated from the data shown in Figure 18. Over the height range above 4.8 km, the mean humidity differences between the UCAR Spire retrievals and RS41 are all less than 0.06 g/kg for both daytime and nighttime, which suggests that the day and nighttime UCAR Spire retrievals agree well with RS41. Over the height range below 4.2 km, Figure 18 shows that the Spire-RS41 humidity differences are all negative over the three SZA groups, and the nighttime difference is the largest (\(-0.23\) g/kg), which is about 0.07 g/kg more negative than the daytime difference.
# Unsupervised Scene Image Text Segmentation Based on Improved CycleGAN
## 4 Experiments
### Experimental Effects on Other Data Sets
#### 4.3.2 Qualitative Evaluation  
(1) The IIIT 5K-word data set  
The experimental results of the IIIT 5K-word data set are shown in Figure 7. Through observation, we find that the U-Net network as a generator is not good for segmenting the background and text in the scene image, and there is no significant improvement after the introduction of the ASPP module. The original generator of CycleGAN can segment the background and text. However, the segmentation effect is poor, and many noise points appear around the font, which affects the segmentation effect. After the introduction of the ASPP module into the original generator, the noise around the font is effectively handled, and the segmentation of background and text meets our expectation.  
(2) MACT data set  
The experimental results of the MACT data set are shown in Figure 8. By observation, we find that as a generator, the U-Net network does not achieve our expected results, and the segmentation effect is poor even if the ASPP module is added. The original generator of CycleGAN can segment the background and text better. However, blurred or redundant strokes will appear in the detail part, which is not allowed for Chinese recognition. After the introduction of the ASPP module, the blurred parts turn clear, and the redundant strokes are also removed.  
In terms of qualitative evaluation, we believe that the original generator of CycleGAN has good segmentation effects on both the IIIT 5K-word data set and the MACT data set with the addition of the ASPP module. Moreover, the generated images perform well in terms of details and overall effects.  
Figure 7: Qualitative results of the IIIT 5K-word data set.