esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing
###### Abstract  
Recently, with the development of mobile devices and the crowdsourcing platform, spatial crowdsourcing (SC) has become more widespread. In SC, workers need to physically travel to complete spatial-temporal tasks during a certain period of time. The main problem in SC platforms is scheduling a set of proper workers to achieve a set of spatial tasks based on different objectives. In actuality, real-world applications of SC need to optimize multiple objectives together, and these objectives may sometimes conflict with one another. Furthermore, there is a lack of research dealing with the multi-objective optimization (MOO) problem within an SC environment. Thus, in this work we focused on task scheduling based on multi-objective optimization (TS-MOO) in SC, which is based on maximizing the number of completed tasks, minimizing the total travel costs, and ensuring the balance of the workload between workers. To solve the previous problem, we developed a new method, i.e., the multi-objective task scheduling optimization (MOPSO) model that consists of two algorithms, namely, the multi-objective particle swarm optimization (MOPSO) algorithm with our fitness function Alabbadi, et al. and the ranking strategy algorithm based on the task entropy concept and task execution duration. The main purpose of our ranking strategy is to improve and enhance the performance of our MOPSO. The primary goal of the proposed MOPSO model is to find an optimal solution based on the multiple objectives that conflict with one another. We conducted our experiment with both synthetic and real datasets; the experimental results and statistical analysis showed that our proposed model is effective in terms of maximizing the number of completed tasks, minimizing the total travel costs, and balancing the workload between workers.  
task scheduling; spatial crowdsourcing; ranking strategy; MOO; MOPSO
## 2 Methodology
### Objects Generated from the High-Resolution Multispectral Data  
Objects are generated by image segmentation, which is the subdivision of an image into separate regions [57; 58; 59]. We will use the multi-resolution segmentation (MRS) method [60; 61], implemented in the eCognition software 10.1, to obtain the image objects composed of grouped pixels. MRS is a clustering procedure, and smaller image objects are merged into bigger ones. The underlying optimization procedure minimizes the weighted heterogeneity. Heterogeneity, in eCognition, is considered as a primary object that features color and shape. The increase in heterogeneity, f, should be less than a certain threshold. The specific calculations are shown in Equations (10) and (11):  
\[f=w_{\text{color}}\cdot\Delta h_{\text{color}}+w_{\text{shape}}\cdot\Delta h_{ \text{shape}} \tag{10}\]  
Subject to:  
\[w_{\text{color}}+w_{\text{shape}}=1,w_{\text{color}}\in[0,1],w_{\text{shape}} \in[0,1] \tag{11}\]  
The weight parameters (\(w_{\text{color}}\), \(w_{\text{shape}}\)) allow us to adapt the heterogeneity definition to the application. The spectral heterogeneity, \(\Delta h_{\text{color}}\), allows for multi-variant segmentation to different image channels. The shape heterogeneity, \(\Delta h_{\text{shape}}\), is a value that describes the improvement of the shape with regard to the smoothness and compactness of an object's shape.  
The scale parameter is the stop criterion for the optimization process. Prior to the fusion of two adjacent objects, the resulting increase in heterogeneity is calculated. If this resulting increase exceeds a threshold value determined by the scale parameter, then no further fusion takes place and the segmentation stops. The larger the scale parameter, the more objects can be fused and the larger the objects grow. Since one MODIS LST pixel (1000 m) covers more than 120 ASTER LST pixels (90 m), the level 1 scale was chosen as 200 to make the area of the segmentation object larger than one MODIS LST at this level. While the object segmented at level 1 was composed of different landcovers, we chose the scale of 20 to obtain the child object for level 1. Each object, at level 2, can be classified as one landcover category (e.g., water, vegetation, soil, building) with relatively similar spectral information. Level 3 generates the smallest object, and its purpose is to identify the small difference in the spectral data of the object at level 2.
# Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavior
## 7 Conclusions  
This paper presented a fast navigation approach for a dynamic environment where the behavior of passive agents is partially predictable. The concept of safe control space is presented by generalizing the nonlinear velocity obstacle, which makes it possible to consider the robot model for computing the  
Figure 11: It shows the performance of AVO algorithm and the proposed \(U_{safe}\) approach. The effect on average computation time, success rate, and elapsed time, are shown against varying number of passive agents. AVO: Acceleration-Velocity Obstacle.  
Figure 12: Global navigation plan. Robot employs a heuristic graph search strategy. The color gradient shows the Dijkstra heuristic field for the maze. The interaction horizon is \(\tau=4\) s. The safe control inputs are computed, which are neither in control obstacle nor did they lead to a collision with the static obstacle. At the same time, Dijkstra heuristic function computes the cost for reaching the goal. A safe control input is executed which minimizes the cost and has margin greater than or equal to \(\beta=1.2\).  
system compliant collision-free trajectories. The safety margin defined for safe control inputs allows the robot to make a safe decision in the environment where the predicted trajectories of passive agents change frequently. The ability of the proposed algorithm to compute a safe decision in real-time is demonstrated. The comparative study validates that under the proposed approach the robot reaches the goal with high success rate. The proposed approach can be incorporated into a waypoint plan for global navigation. One possible implementation of a global navigation plan is presented in the paper.  
In future, authors will attempt the problem of dynamically adapting the minimum safety margin parameter based on the circumstances. Also, an interesting direction for future work will be the extension of the proposed approach to the environment with multiple active and passive agents. In addition to this, the authors are interested in planning for situations with dynamic obstacles have deterministic behavior.  
This research was partially supported by the Higher Education Commission of Pakistan by the award letter No. HRDI-UESTPs/Batch-II/South Korea/2012.  
[PERSON] proposed the idea, implemented the simulated experiments and wrote the manuscript; [PERSON] supervised the study and the manuscript writing process; [PERSON] co-supervised the study. All the authors discussed the results and contributed to the final manuscript.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} VO & Velocity Obstacle \\ NLVO & Nonlinear Velocity Obstacle \\ AVO & Acceleration Velocity Obstacle \\ GVO & Generalized Velocity Obstacle \\ \end{tabular}
## 4 The Influence of Power Consumption on Communication Reliability in Islanded Microgrids
### Microgrid Communications Reliability  
Reliability in a communication system is the concept used to describe if a system is behaving properly without failures for a given period [43]. Controls' rules are made to decrease BS power consumption by deactivating some devices. The deactivation impacts the BS awake time causing damages to microgrid operation. To evaluate the grid reliability behavior, the BS turned off time is considered a failure. Thus, the microgrid communication network operation can be modeled as a continuous-time Markov chain (CTMC) [44].  
A CTMC is a stochastic process, which says that, for any given system, given the present state \(n\) and its future state \(n+1\), the probability of being in state \(k\) at the time \((t+\Delta t)\), with transition rates \(\lambda_{k-1,k}\) between states \(k-1\) and \(k\), is written as in [43]:  
\[P_{k}(t+\Delta t)=\lambda_{k-1,k}\Delta tP_{k-1}(t)+P_{k}(t)-\lambda_{k,k+1} \Delta tP_{k}(t). \tag{3}\]  
\[P_{2}(t)=\frac{\triangledown_{1,2}\triangledown_{0,1}[(\triangledown_{0,1}- \triangledown_{1,2})e^{-\triangledown_{2,3}t}+(\triangledown_{2,3}-\triangledown_{0,1 })e^{-\triangledown_{1,2}t}+(\triangledown_{1,2}-\triangledown_{2,3})e^{- \triangledown_{0,1}t}]}{(\triangledown_{1,2}-\triangledown_{2,3})(\triangledown_ {0,1}-\triangledown_{1,2})(\triangledown_{2,3}-\triangledown_{0,1})}, \tag{15}\]  
\[P_{3}(t)=1-P_{0}(t)-P_{1}(t)-P_{2}(t). \tag{16}\]  
The communications system will remain in operation as long as it stays in states \(\triangledown_{0}\), \(\triangledown_{1}\) and \(\triangledown_{2}\). The system becomes unavailable when it reaches state \(\triangledown_{3}\). Therefore, the reliability of the network over time is dictated by the reliability functions, which describe the role of each operational state:  
\[R(t)=P_{0}(t)+P_{1}(t)+P_{2}(t). \tag{17}\]  
Simulations will be presented in the next section to investigate the feasibility of the proposed method and its application to an islanded microgrid scenario.
## 5 Computational Experiments
### Experimental Experiments and Comparative Analysis
#### 5.3.3 Heuristic Optimization Under Limited Capacity of CI  
This section presents computational results for CI capacity-limited instances solved using the HACO, BR-GRASP, SA, GA and GA-GSK algorithms, with optimal solutions highlighted in bold in Table 6. The comparative analysis confirms HACO's superior performance and stability while highlighting the impact of CI capacity constraints on the problem and algorithms. The experimental results demonstrate that the HACO algorithm consistently achieves the optimal or near-optimal costs across all instances, indicating its superior performance in objective function optimization. Compared to BR-GRASP, GA, and GA-GSK, HACO exhibits a significant advantage, with performance gaps of 0.16-12.36% (GAP7), 0.45-23.31% (GAP9), and 0.14-15.97% (GAP10), confirming its comprehensive outperformance. While HACO maintains lower total costs than SA in 85% of cases, SA occasionally surpasses HACO in specific instances (e.g., C25_1, C50_1, and C100_10), as indicated by negative GAP8 values, though with less overall stability. Regarding operational efficiency, HACO demonstrates optimal vehicle utilization with the lowest average fleet size and moderate battery swapping frequency, effectively exploiting the collaborative strengths inherent in HERI. BR-GRASP, GA, and GA-GSK show slightly higher vehicle usage than HACO but compensate through the frequent utilization of BSI. SA, while employing the largest fleet size and minimal times of battery swapping, achieves better total costs than BR-GRASP through optimized routing and energy replenishment strategies. The study demonstrates that HACO's performance advantage over BR-GRASP grows significantly with the problem scale, with GAP7 increasing from 4-5% to 8-10%. A parallel trend is observed in the comparative analysis between the HACO and GA/GSK algorithms, with both GAP9 and GAP10 metrics demonstrating progressive escalation as the problem scale increases. In contrast, HACO's improvement over SA remains stable at 3-4% across most scales, except in large-scale instances with 100 customer points where SA's performance substantially improves, reducing GAP8 to 0.72%. This enhancement likely stems from SA's stochastic nature and ability to accept inferior solutions, which proved particularly effective in navigating the complex solution spaces of large-scale problems through improved global search and local optimization capabilities. Given HACO's consistent superior performance in both Sections 5.3.2 and 5.3.3, it is adopted as the reference algorithm for analyzing the impact of CI capacity constraints on EVRP. The accompanying pie charts in Figure 12a,b depict the cost composition of instance set C100 with and without CI capacity constraints, respectively. Figure 12: The cost structure of instance set C100. (**a**) Cost structure of CI with capacity constraints. (**b**) Cost structure of CI without capacity constraints.
###### Abstract  
The vibration isolator equipped with a negative stiffness corrector (NSC) excels at vibration isolation, but its stiffness often presents complex nonlinearity which needs to be approximated in calculation. To avoid the harmful effects of approximate stiffness, the NSC formed by the cam-roller mechanism with a quadratic polynomial trajectory (QCRM) is proposed to construct the vibration isolation system. From the inherent geometrical relationship in the structure, the generation mechanism of high-static-low-dynamic stiffness is analyzed, and the quasi-zero stiffness (QZS) condition of the system is derived. Based on the dynamic model of the QZS vibration isolator, the functions of response characteristics are solved by the harmonic balance method. Then, the absolute displacement transmissibility with different parameter values, and the vibration isolation performance under sinusoidal, multi-frequency wave, and random excitations are discussed. The simulated results show that the stiffness expression of the proposed QZS vibration isolator is directly a quadratic function, which removes the calculation error caused by approximate stiffness at large displacement and broadens the available isolation displacement range. Introducing the QCRM-NSC can significantly suppress the low-frequency vibration and resonance response without changing the load-bearing capacity of the vibration isolator. Under various excitations, the vibration isolation performance of the QZS vibration isolator all outperforms the linear counterpart.  
negative stiffness corrector; quasi-zero stiffness; quadratic polynomial trajectory; cam-roller mechanism Article
## 6 Discussion
Likewise, having too few duplicate classes can lead to significant inter-service communication while reducing the overhead of maintaining duplicate classes. Choosing the right balance depends on the needs of the system. Additionally, both the AE-C and VAE-C can be configured to vary the level of duplication by changing the maintainability threshold. As stated in the previous sections, the maintainability threshold is a predefined value that determines the minimum membership score a class must have to belong to a microservice. A higher maintainability threshold would reduce the number of duplicate classes, while a lower maintainability threshold would increase the number of duplicate classes. In our previous research [1], we demonstrated that our model, VAE-C, was highly effective at decomposing small-scale monolithic applications into microservices. In this current study, we take it a step further by applying VAE-C to a mid-sized monolithic system--and the results are just as promising. Specifically, VAE-C is able to match 70.53% of the classes with those in the actual microservice benchmark. This level of alignment suggests that the model not only scales well but also maintains its ability to identify meaningful service boundaries even as the system grows in complexity. Of course, a 70.53% match rate is not perfect, but that is expected. There is no universally correct way to decompose a monolith--different architects may arrive at different microservice designs even when working from the same set of requirements. Microservice boundaries often reflect architectural preferences, domain understanding, and performance goals, all of which can vary from one team to another. So, while VAE-C may not replicate every human-made decision, the high overlap demonstrates that it captures many of the key patterns and functional separations that define good microservice design. Overall, these findings reinforce our belief that VAE-C offers a practical and scalable approach for guiding the decomposition of monolithic systems. It can serve not only as a tool for automation but also as a decision-support framework for architects exploring different decomposition strategies.
# Maritime Supply Chain Optimization by Using Fuzzy Goal Programming
## 5 Conclusions  
International transshipment is the today's reality and should be considered as an important milestone for the conventional means of transport. Transshipment might involve in several processes such as consolidation, de-consolidation, customs controls, etc. Therefore, optimization of transshipment process is significant in terms of finance, time and safety.  
This paper aims to solve maritime supply chain problem operating under uncertain demand conditions. Two goals are targeted as minimization of total cost and minimization of loss or damage of containers returned from destination port, and problem is analyzed by using fuzzy goal programming. First, we define and formulate the problem, then assumptions and constraints are identified. The algorithm for fuzzy goal programming is introduced along with fuzzy decision variables and fuzzy parameters.  
The model is established to minimize the returns of customers with the total cost of maritime supply chain and container losses and losses in the network. Linear membership functions are used to represent the objective goals in addition Weighted average method for the defuzzification of fuzzy demand. A fuzzy constraint can be shown as \(\bar{C}\) fuzzy set or \(\mu_{\bar{C}}(x)\in[0,1]\) which is a subset of universe set where \(\mu_{\bar{C}}(x)=1\), \(0<\mu_{\bar{C}}(x)<1\) and \(\mu_{C}(x)=0\) represent fully satisfied, partially satisfied and not satisfied respectively.  
In the future, scenario-based stochastic models will be considered for maritime supply chain optimization models. Since this is one of our early efforts for maritime supply chain optimization, more complex problems, cases, models and scenarios will be developed for the future research.  
Finally, the proposed model can enable decision makers to decide on both situation possible pessimistic, probable and optimistic values of demand and the decisions priorities, decision maker can also decide on the minimum level of losses or damages.  
Conceptualization, B.S.; Methodology, B.S.; Writing--original draft, B.S.; Writing--review & editing, D.Y., A.A.H. and N.S.F.A.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 5: Summary of research in MSC [43].
# Integrating Blockchains with the IoT: A Review of Architectures and Marine Use Cases
## 3 BioT Architectures and Communication
### BIoT Interaction Models and Traffic Considerations
Furthermore, Table 4 provides a concise breakdown of these interaction models, highlighting their descriptions and key characteristics for a clearer understanding. In addition, the \"IoT-Blockchain traffic\" model, proposed by [PERSON] et al. [2], categorizes traffic based on the deployment of IoT devices, blockchain nodes, and their interconnections. In the structure where IoT devices function as nodes within the blockchain network, devices operate as lightweight nodes that do not store a complete copy of the blockchain or engage in mining (in the case of PoW networks). This setup maximizes decentralization and ensures network \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Description** & **Advantages** \\ \hline \multirow{3}{*}{Direct communication} & IoT devices communicate & \multirow{3}{*}{\begin{tabular}{c} Fastest transmission, no \\ blockchain or \\ centralized nodes \\ \end{tabular} } \\  & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Blockchain-based \\ communication \\ \end{tabular} } & IoT devices interact via & \multirow{3}{*}{\begin{tabular}{c} Enhanced security, reliability, \\ and data privacy through \\ decentralized validation \\ \end{tabular} } \\  & blockchain, ensuring & \\ \cline{1-1}  & consensus and distributed & \\ \cline{1-1}  & data integrity & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Fog- or cloud-based \\ communication \\ \end{tabular} } & IoT devices offload tasks to & \multirow{3}{*}{
\begin{tabular}{c} Efficient management of \\ blockchain’s computational \\ and storage needs, \\ improved scalability \\ \end{tabular} } \\  & IoT devices offload tasks to & \\ \cline{1-1}  & fog or cloud nodes, reducing & \\ \cline{1-1}  & local computational strain & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: IoT device communication models, [PERSON] et al. [8]. performance even if specific nodes are compromised. Alternatively, in the structure where IoT devices are outside the blockchain network, a central base station interacts directly with IoT devices, processing their requests and forwarding them to the blockchain. This model simplifies operations and maintenance by isolating IoT devices from direct blockchain interactions and using a proxy server to manage and regulate traffic efficiently.
## 4 Proposed Approach
### Design Methodology
#### 4.2.2 Confidentiality  
User data should be hardened against cracking attempts. To ensure robust privacy protection, our solution employs probabilistic metrics and entropy-based measures to assess and maintain user anonymity. By leveraging these techniques, we achieve an anonymity set size of over 500 for protected health information (PHI), making it significantly more difficult for malicious actors to re-identify individuals. This approach enhances privacy by ensuring that data points remain indistinguishable within a large pool of similar records.  
Furthermore, to ensure both trustworthiness and privacy, our solution integrates advanced cryptographic techniques, including zero-knowledge proofs, homomorphicencryption, and attribute-based credentials (ABCs). These mechanisms provide strong security guarantees by enabling verifiable trust assessments without exposing sensitive data. Moreover, zero-knowledge proofs allow one party to prove the validity of information without revealing the actual data, while homomorphic encryption ensures computations can be performed on encrypted data without decryption, preserving privacy throughout the process. Additionally, ABCs enhance access control by granting permission based on verified attributes rather than personal identifiers. These security measures work together to maintain data integrity, confidentiality, and user anonymity, strengthening the system's ability to resist unauthorized access and privacy breaches.
## 4 Performance Results  
### Performance Indicator  
#### 4.1.1 Confusion Matrix  
A confusion matrix is a matrix used in classification problems as an index to indicate the result of classification. It consists of a total of four cases: two cases as a result predicted by the model, and two cases of the actual correct answer label. Table 15 is an example of a confusion matrix.  
A True Positive is when the result predicted by the model is Positive and the actual correct answer is also Positive; a False Negative is when the result predicted by the model is Negative and the actual correct answer is Positive; a False Positive is when the result predicted by the model is Positive and the actual correct answer is Negative; a True Negative is when the result predicted by the model is Negative and the actual correct answer is also Negative.