esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
## 5 Conclusions  
In this paper, the multi-temporal Sentinel-1A data and/or Landsat-8 were classified by RF and SVM classifiers in an urban agriculture region of Southern China. The main conclusions of this paper are: (1) In the urban agriculture region with a complex planting structure, using early- and late-season winter wheat growth multi-temporal Sentinel-1A data for classification, satisfactory classification of winter wheat can be obtained. This indicates that optical data can be replaced by Sentinel-1A images when the effective optical image cannot be obtained due to weather; (2) The classification accuracy of backscatter intensity, alone, was higher than that of the coherence and texture images; (3) The classification accuracy can be effectively improved by coherence and texture information of SAR data, but the overall accuracy of the combination of both features was less than 86%; (4) The result of the combination of SAR and optical data was the best, with the highest overall accuracy of 99.81%, which was better than using optical data alone. For winter wheat, the addition of SAR images to the optical images increased the F1 measure by up to 3.23%; (5) The SVM classifier outperformed the RF slightly in terms of overall accuracy and kappa coefficient, and SVM was much faster than RF; however, RF performed better than SVM in terms of the F1 measure.  
As an important component of the Chinese High-Resolution Earth Observation System, the GaoFen-3 (GF-3) was successfully launched in August 2016. GF-3 is China's first 1-m resolution C-band synthetic aperture radar (SAR) satellite, and has 12 imaging modes. The reported results of this study are also important for the application of GF-3 images in agricultural applications.  
This research was supported by a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions. [PERSON] envisioned and designed this research and wrote the paper. [PERSON] provided suggestions and modified the paper. [PERSON], [PERSON] and [PERSON] conducted the analysis. [PERSON], [PERSON] revised the manuscript draft.  
The authors declare no conflict of interest.
# Analysis of Drone Flight Stability for Building a Korean Urban Air Traffic (K-UAM) Delivery System
## 2 K-Drone Delivery Demonstration City Construction Project  
The K-drone delivery commercialization project is a sub-task of the Korean Urban Air Transport Core Technology R&D Project. This is a drone delivery service implemented in islands, ports, and parks by applying the K-drone delivery commercialization standard model to discover drone utilization models suited to regional characteristics and apply them to public services, etc. The K-drone delivery project plans to begin building drone delivery infrastructure, including 41 drone delivery bases and 183 drone delivery points, by the first half of 2024, and to launch full-scale drone delivery of daily necessities and groceries in 38 island regions from the second half of the year. Figure 1a shows the delivery of parcels, convenience store items, chicken, etc., between inhabited islands and the mainland and reverse delivery of agricultural and marine products (3 or more bases, 10 to 20 delivery sections). Figure 1b refers to the delivery of convenience store items, chicken, kimbap, etc., to urban parks, campgrounds, beaches, tourist destinations, etc., in the form of park delivery (3 or more bases, 10 to 20 delivery sections). Figure 1c is a concept of port delivery, which includes delivery of food, ship supplies, fishing and daily necessities to anchored ships and marine leisure areas around ports (one to three bases, delivery area near the port). In Figure 2, 14 local governments are selected for the drone demonstration city construction project. In this study, flight data of drones for delivery were acquired from 3 out of 14 local governments and their stability was analyzed.  
Figure 1: Establishing K-drone delivery infrastructure [20]. **(a)** Island shipping, **(b)** Park delivery, **(c)** Port shipping.  
Figure 2: Five local governments in yellow box selected for the drone demonstration city construction project (Uluju, Tongyeong, Yeosu, Gongju, Pocheon).
## 2 Structural Design  
### Compression Elements  
The design consists of three main rigid elements representing the human pelvic bone, the femur and the tibia (Figure 1). The compression elements are carbon fiber rods assembled with 3D-printed acrylonitrile butadiene styrene (ABS) pieces. The length of the compression elements were chosen to match the dimensions of the bones in the OpenSim 3.0 model _Gait2345_Simbody_ described in the Simulation Modeling Section. In Figure 1, the upper compression element is equivalent to a human pelvic bone, the middle element mimics a femur bone, and the lowest segment acts as the tibia and fibula, simplified into one rigid body. The hip joint performs similar to a ball and socket joint, which is not confined to one axis of rotation like most conventional robotic joint designs (Figure 2a,b). The knee joint has a dominant degree of freedom during flexing using a single actuating muscle, which resembles the biological biceps femoris (Figure 3a,b).  
The flexural joints are secured by a complex network of tensile components that perform similarly to muscles and other tendon-based bone-element connections [26]. To achieve multi-axis compliance, the _Y-shape_ design for the femur and tibia compression elements (Figure 2) is capable of generating flexion, extension, adduction, and abduction motion. The positional displacement of the tensegrity flexural hip joint produces a lifting motion advancing the structure to begin gait (see Figure 2). The tibia compression element is attached to a three-rod base, designed similarly to the patella and femur. It creates a flexural knee joint that flexes along one axis of rotation (as shown in Figure 3). When the cable-driven actuator shortens the hamstring, it pulls the tibia segment to flex the knee joint. This also pulls the femur, allowing the structure to maneuver and provide one fluid gait motion.
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 3 Methodology
### Machine Learning Technique Overview
#### 3.1.3 Step 3: Data Management  
Based on the research mentioned above, we will proceed to our analysis where each employee is denoted as Node \(N_{i}\), and for each one, there is a correlation between each task weight (TW) the employee undertakes, described as \(TW_{i}\) and measured in time units (minutes). Therefore, the interconnectedness among these tasks will be established, with \(TW_{1}\) as the fundamental reference point, exerting minimal influence on employee productivity. Hence, the relationship, for instance, between \(TW_{5}\) and \(TW_{4}\), \(TW_{4}\) and \(TW_{3}\), \(TW_{3}\) and \(TW_{2}\), \(TW_{2}\) and \(TW_{1}\), can be expressed as a function, where the minimum task weight is represented by \(TW_{1}\). This approach, outlining task interdependencies, gives rise to a generalized principle that applies to every task pair \(TW_{1}\) and \(TW_{i}\), as referred to in [PERSON] et al. (2022) [1], such as  
\[TW_{i}=iTW_{1},\hskip 28.452756 pt\forall i>0 \tag{1}\]  
In their work, [PERSON] et al. (2022) [1] utilized a four-tier factor profile, with the skill set grounded in adherence to the Greek Council of State's Decision No. 540/2021 in alignment with Council Directive 90/270/EEC, as shown in Figure 1.  
**K1 (Academic Proficiency)**  
* Count of seminars related to the current work (maximum of three)--(S1, S2, S3).
* Number of Bachelor's degrees (maximum of two)--(B1, B2).
* Possession of a Master's degree.
* Certification from the National School of Public Administration.
* PhD diploma.  
**K2 (Public Sector Work Experience)**  
Figure 1: Employee profile.  
* Total years of experience (maximum 35 years).
* Nature of responsibilities:
* Supervision of a small department.
* Leadership of a department.
* General management.
* **K3 (Private Sector Work Experience)**
* Total years of experience (maximum 35 years).
* Nature of responsibilities:
* Supervision of a small department.
* Leadership of a department.
* General management.
* **K4 (Age)**
* Age in years within the range of 20 to 70 years.  
Based on the algorithm applied on Node B, a unique Time Factor is calculated for every dataset profile.  
In this study, employee capability is measured on a continuous scale through the Time Factor (TF), which represents the average time an employee takes to complete a task. The Time Factor is derived from the dataset, which includes information on employees' work experience (both in the public and private sectors), educational history, and age. The Capacity Factor (CF), introduced by [PERSON] et al. [1], is calculated as the mean value of the number of \(TW_{1}\) tasks (tasks with the minimum task weight) completed per time unit. This approach allows for a continuous and granular assessment of employee capability, enabling more precise predictions and comparisons.  
An employee profile with precise skills recorded on the dataset will produce a Capacity Factor that determines its ability to manage task workload as shown in Figure 2 above.
## 2 Methodology
### Multi-Criteria Clustering  
Multi-criteria clustering is considered to be an important advancement in the field of complex decision-making problems with many objectives. The six nominal things are taken for optimal CH election: node energy, node degree, cluster formation, neighborhood node distance, cluster distance, and residual energy. These parameters are utilized as an energy efficient clustering approach. The multi-criteria clustering is aimed to minimize cell impact in the outer boundaries with the aid of grid structure.  
\begin{table}
\begin{tabular}{c c c c c} \hline (5,1) & (5,2) & (5,3) & (5,4) & (5,5) \\ (4,1) & (4,2) & (4,3) & (4,4) & (4,5) \\ (3,1) & (3,2) & (3,3) & (3,4) & (3,5) \\ (2,1) & (2,2) & (2,3) & (2,4) & (2,5) \\ (1,1) & (1,2) & (1,3) & (1,4) & (1,5) \\ \hline \end{tabular}
\end{table}
Table 1: Grid ID representation in a sensor network area.  
Figure 2: Node deployment based on the grid method.  
_(i) Node energy_  
It is important to conserve energy at the time of information transmission. This model was proposed by [PERSON] et al. [23] for minimizing the energy consumption.  
\[NE=\frac{Optimal\ No.\ of\ clusters}{Size\ of\ CH\ set} \tag{3}\]  
_(ii) Node degree_  
The node degree provides the summation of approachable cluster members and their balance with loads of CH.  
\[ND=\sum_{i=1}^{m}CM_{i} \tag{4}\]  
where \(CM_{i}\) indicates the \(i\)th member of CH.  
_(iii) Cluster formation_  
Cluster formation is used to increase the quality of links among clusters. It provides information about the cost required for transmitting the information between the sink nodes (SN) to the CH.  
\[CI=\sum_{i=1}^{R}\frac{dis(CH_{i},N_{m})}{Min[dis(CH_{i},N_{m})]} \tag{5}\]  
where \(CH_{i}\) represents CH at \(i\)th member and \(N_{m}\) is number of nodes, \(R\) is a number of clusters, and \(dis(CH_{j},N_{m})\) provides the Euclidean distance.  
_(iv) Neighborhood node distance_  
Neighborhood node distance provides the ratio between the Euclidean distance and half of the total area. This parameter reduces the distance between the CH and BS.  
\[NN=\frac{1}{R}\sum_{i=1}^{R}\frac{dis(CH_{i},BS)}{\frac{A^{2}}{2}} \tag{6}\]  
where \(A^{2}\) is the total area covered by BS.  
_(v) Intra-Cluster distance_  
It provides the distance among CH and SN of its members. It aids the improvisation of cluster quality. The value '1' is assigned for a specific node when it is connected to CH; otherwise, the value '0' is assigned.  
\[I_{n}C=\sum_{i=1}^{R}\left[\sum_{i=1}^{CM_{i}}\frac{dis(CH_{i},CM_{i})}{|CM_{i} |}\right] \tag{7}\]  
where the term \(dis(CH_{i},CM_{i})\) represents the Euclidian distance among the \(i\)th node of and CH and \(i\)th cluster member (CM).  
_(vi) Residual Energy (ReE)_  
It provides the details about the available energy for each node after nominating the CH.  
\[R_{e}E=\sum_{i=1}^{n}\frac{1}{E_{CH_{i}}} \tag{8}\]  
where \(n\) is number of CH, \(E_{CH_{i}}\) is remaining \(i\)th node energy of CH.
## 3 Testing Results
### Modelling of Damage Evolution
Initially, damage within a CLS is relatively minor during the early loading stages. Subsequently, damage escalates as the CLSs increase, and the incremental rate accelerates accordingly. Towards the end of the loading sequence, an inverted \"S\"-shaped pattern of damage propagation becomes evident in the last several CLSs. Based on the findings presented in Figure 9, we establish a self-defined damage evolution model that incorporates radial strain and deformation, as illustrated in Figure 10. The form of this proposed model is represented as follows:  
\[D=1-(1-(n/N)\eta)^{\text{a}})^{\text{b}} \tag{2}\]  
The damage variable D results from irreversible plastic deformation. Initially, rock damage is zero before loading (N = 0) and reaches one when failure occurs completely (N = Nf). Here, n denotes the number of loading cycles and Nf signifies the fatigue lifetime. Parameters a and b are associated with material characteristics. Figure 10 displays the fitting results using Origin 2021 software for the marble samples with RBLs of 10 mm,  
Figure 9: Damage variable versus cycle number at different loading stages. (**a–d**) Rock samples with RBLs of 10, 20, 30, and 40 mm, respectively. 20 mm, 30 mm, and 40 mm. These results, along with corresponding correlation coefficients, affirm the applicability of the proposed model in describing the evolution of rock damage.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 2 Particularities and Constraints
### Dynamic Network Topology  
While terrestrial sensor nodes are densely deployed, underwater the deployment is deemed to be sparser due to the cost involved and to the challenges associated to deployment itself. The majority of underwater sensor nodes are mobile due to water flow. From empirical observations, underwater objects may move at the speed of 2-3 knots or 36 km/h in a typical underwater condition, which results in a highly dynamic network topology.  
As discussed in Section 1, the cluster-based network architecture is widely used in UWSNs. Moreover, to expand the monitoring and communication region, AUVs are widely utilized in many applications. The AUVs may frequently join and exit the cluster or network, which will also result in a highly dynamic topology.  
As shown in Figure 2a, to communicate with a surface statin or on-shore sink, the AUV joined Cluster 3 as a member node to transmit packets via Cluster 3. As shown in Figure 2b, due to the movement, the AUV was out of the communication range of Cluster 3, the AUV exited and then joined Cluster 1 as its member node. The movement of the AUV led to variation of the network topology.  
As a consequence, these variations of the network topology mentioned above may change routing and influence the accuracy rate of data transmission which can affect the overall performance of the network. In particular, in some underwater applications with AUVs, due to the high mobility of AUVs, cooperation within the nodes and designing adaptive protocols can be a major challenge.
# VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain
## 1 Introduction
Most schemes directly fuse the trust of the neighbor node \(v_{kn}\) to \(v_{i}\) as an indirect trust, and include it in the measurement of \(v_{j}\)'s trust to \(v_{i}\). The problem in these schemes is that the influence of the reputation value is not considered at all. That is, if the historical reputation of \(v_{j}\) is greater than the historical reputation of \(v_{kn}\), for \(v_{j}\), the reference value of \(v_{kn}\)'s trust to \(v_{i}\) should be reduced or directly set to zero;
4. Most of the existing schemes give different measures of direct trust and indirect trust to generate the overall trust. In addition, some researchers add the social metrics of nodes. However, the process of connecting the two concepts of interactive trust and cumulative reputation and updating the reputation value of the target node through the trust value generated by a single interaction while resisting malicious attacks, is still an urgent problem that remains to be solved. In view of the above challenges, we propose a brand new VANET security model, called the double-layer blockchain-based reputation evaluation and management model (DBREMM), and based on this model, we designed a complete reputation update scheme, including:  
1. We propose a new two consortium blockchain-based reputation update model for VANET. It consists of the cooperation between two blockchains and helps to achieve the security for VANET. In detail, an event chain is used for safety verification, real-time emergency response, and the after-action accountability of road events. A reputation chain is used to establish a recessive vehicle reputation network, and to realize the storage and query of historical reputation value and social measures of vehicle nodes;
2. We design an improved Bayesian inference (BI) algorithm based on multi-factor measurements to calculate the local direct trust value of vehicle nodes. This algorithm can optimize the calculation method of trust values and objectively evaluate the observation content from multiple dimensions, so as to better realize the increase of trust values of a normal vehicle node and the rapid decline of a malicious vehicle node;
3. We design an adaptive improved algorithm based on historical reputation difference. When calculating the indirect trust value of vehicle nodes, the measurement weight of each node is modified by the adaptive difference attenuation factor according to the comparison between the neighbor node and its own historical reputation value stored in the chain. The importance of historical reputation is amplified;
4. In the process of reputation weighted fusion update, the attenuation factor is used to control the historical reputation weight and an adjustment factor is introduced into the social measurement. A security trust update scheme is proposed based on the number threshold and the fluctuation factor, which can resist an on-off attack and collusive attack.
# Depth Image Completion through Iterative Low-Pass Filtering
## 2 Approach
### Extraction of Boundaries and Surfaces  
The primary objective of the Extraction of Boundaries and Surfaces (EBS) is to identify appropriate surfaces with watertight boundaries. The EBS process takes the RGB image and the depth image as inputs. The RGB image contributes edge information, which is processed through a combination of Canny edge detection [31] and other edge detection methods [32; 33; 34; 35] to achieve a clear and watertight boundary. Among the edge detection methods, we opted for the deep network architecture introduced in holistic edge detection [32], known for its enhanced speed in edge detection and a reliable network structure. The network is a fully convolutional neural network (FCN) adopting VGGNet [36]. We utilized the BSD500 dataset [37] for training, as it demonstrated a high F-score and superior edge detection results [32] for our implementation.  
To enhance surface extraction, we dilated the results of the edge detection with Canny Edge detection as a guide to contract the rough boundary, facilitating larger operation blocks. The contracted result represents the extracted surface, referred to as the operation block. The binarized shrunken results were then segmented to obtain operation blocks. Through this process, we obtained a reliable result for each surface. Finally, we multiplied the depth image by the operation block to generate the raw depth matrix. At this stage, the raw depth matrix included a defective area, and the subsequent step was to address and recover this defective area.  
Figure 1: The flowchart of DICP.
# Semi-Supervised Drivable Road Segmentation with Expanded Feature Cross-Consistency
## 2 Related Work
### Semi-Supervised Semantic Segmentation  
As deep learning becomes mainstream, methods that can balance low data annotation and higher accuracy are deserving of our attention, such as deep transfer learning [5], domain adaptation [37], self-supervised learning [6; 7], and semi-supervised learning [8]. However, the most suitable scenarios for deep transfer learning tend to be large models with fine-tuning, which may conflict with arithmetic-poor on-board edge computing devices in drivable area segmentation tasks. Although self-supervised learning guarantees low-inference computation consumption and does not even require labeled data, it means that larger data volumes and training are required to ensure model accuracy. In the field of autonomous driving, safety, in this case accuracy, is prioritized. Thus semi-supervised methods are considered to be balanced.  
Further, semi-supervised methods in semantic segmentation models can be divided into five categories: adversarial methods, consistency regularization, pseudo-labeling, contrastive learning, and hybrid [8]. Among them, the idea behind consistency regularization methods is that the same input model should be given the same output. Based on this, CCT [38] and CPS [39] perform perturbations on intermediate feature maps and model weight parameters respectively, expecting the outputs of the models to be consistent with no perturbation. CutMix [40] ClassMix [41], and VAT [42] perturb the input data, and have been incorporated into data augmentation in a wide range of fields and achieved favorable results. Inspired by CCT, CPS, and input perturbation methods, we propose a semi-supervised drivable road segmentation method with expanded feature cross-consistency, which combines input perturbations and feature perturbations.