esa-sceva/satcom-chunk-collection huggingface dataset:
# Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images
## 4 Experiments
### Experiments and Analysis
#### 4.2.5 Discussion  
The reasons that our method can achieve good performance in the experiments are mainly due to the following aspects: (1) through multimodal fusion, the size of image pixels is calibrated by using the spatial position information of the point cloud, which effectively solves the problem that a single mode cannot accurately calculate the ore rock  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Mean Error of \(D_{E}\) (cm)** & **Mean Error of \(D_{L}\) (cm)** & **Mean Error of \(D_{S}\) (cm)** \\ \hline Our method & **0.66** & **1.42** & **1.06** \\
[3] & 0.85 & 1.94 & 1.87 \\
[6] & 0.68 & 1.52 & 1.08 \\
[21] & 0.93 & 1.68 & 1.92 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the calculation errors in the comparative experiments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Mean Error of \(D_{E}\) (cm)** & **Mean Error of \(D_{L}\) (cm)** & **Mean Error of \(D_{S}\) (cm)** \\ \hline Our method & 0.66 & 1.42 & 1.06 \\ Gradient map & 1.09 & 2.87 & 2.31 \\ Canny & 0.97 & 2.53 & 1.94 \\ Edge drawing & 0.84 & 2.24 & 1.76 \\ SBED & 0.76 & 1.96 & 1.61 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of the calculation errors in the ablation experiments.  
fragmentation; and (2) through the construction of the standard deviation map and multi-scale adaptive edge detection, the marked watershed image segmentation algorithm was improved, effectively solving the problem of image under-segmentation.
# Image Dehazing Algorithm Based on Deep Learning Coupled Local and Global Features
### Defogging Method Based on Convolutional Neural Network  
In recent years, with the rapid development of machine learning, particularly deep learning, and its superior performance in the field of image processing, more and more scholars are using convolutional neural networks to deal with image defogging problems. The earliest deep learning fog removal network is the DehazeNet network proposed by [PERSON] et al. [8], which chunked the complete image as the input to the network, used multi-scale convolutional neural networks and MaxPooling to learn the haze features and estimate the transmittance map of the fogged image, and then inverse performed the fog-free image. The method achieved a good defogging effect, but because the dataset was a local image after cut, the features learned by the network lacked global representation, and the method did not fuse deep and shallow information, the defogged image showed incomplete defogging, inaccurate color reproduction, etc. [PERSON] et al. [9] proposed the MSCNN network, which takes the whole image as the input and first estimates the scene transmittance map using a coarse-scale network, and then refines it using a fine-scale network to obtain the fine transmittance map, which improved the accuracy of transmittance, but the method used the pooling layer to lose the detailed information, and only estimated the transmittance map, which is not accurate for atmospheric light estimation and affects the defogging effect. The above method only uses neural networks to learn the haze features to estimate the transmittance, but for the estimation of the atmospheric light value, another important parameter in image defogging, the a priori-based method, is still used, resulting in image defects such as color distortion in the recovered image due to the estimation error of the atmospheric light value.
###### Abstract  
With the increasing exploitation and use of marine resources, the limitations of acoustic, optical, and radio frequency technologies for underwater communications have become increasingly apparent. Magnetic induction (MI) is a new communication technology that enables wireless data transmission via magnetic field coupling between transmitting and receiving coils. MI offers advantages such as channel stability, small antenna size, and no multi-path loss. Multi-input-multi-output (MIMO) is a multi-antenna technology that significantly increases system capacity and spectrum utilization without increasing bandwidth. The whale optimization algorithm (WOA) is a well-known bio-inspired algorithm that mimics the hunting behavior of whales to optimize swarm intelligence. This paper proposes a model for an underwater MIMO communication system based on magnetic induction. We then construct a signal detection algorithm for MI-MIMO systems using the advanced whale optimization algorithm (AWOA) and conduct simulation experiments to compare the performance and complexity of three standard signal detection algorithms: zero-forcing (ZF), minimum mean square error (MMSE), and maximum likelihood (ML). The experimental results show that AWOA achieves suboptimal results, as its bit error rate (BER) is close to that of the ML algorithm. Furthermore, the complexity of AWOA is comparable to that of the MMSE strategy. This work supports the development of a high-performance MI-based underwater communication system.  
underwater communication; magnetic induction; multi-input-multi-output; the advanced whale optimization algorithm; signal detection
# Blockchain-Based E-Voting Mechanisms: A Survey and a Proposal +
## 3 Comparative Analysis
### Security Requirements  
We utilize the security requirement model from [2; 42; 43] and we analyze the following security requirements.  
* Voter Anonymity: It is essential that there is no feasible way to trace how the voters cast their votes. Anonymity can be maintained by using cryptographic techniques, zero-knowledge proofs, secure multi-party computation methods, and other techniques.
* Election Auditability: The voting process must be fully auditable once the results are declared.
* Vote Verifiability: Every voter in the election should have the ability to confirm that their vote has been recorded and counted correctly in the election.
* Vote Uniqueness: The system must ensure that each voter can cast only one vote, preventing duplicate voting.
* Election Fairness: The system should avoid revealing real-time results which could potentially influence subsequent voting decisions.
# Optimal Crew Scheduling in an Intensive Care Unit: A Case Study in a University Hospital
## 5 Conclusions  
This study proposed an optimized workforce scheduling model for a Pediatric Intensive Care Unit (PICU) by integrating nurses, junior doctors, senior doctors, and caregivers into a unified framework. The developed Mixed-Integer Programming (MIP) model successfully minimizes operational costs while ensuring fair workload distribution across different healthcare personnel. The results demonstrated a reduction in shift conflicts and an improvement in workload balance, confirming the efficiency of the proposed approach.  
The findings highlight the advantages of an automated scheduling system over traditional manual methods by significantly reducing scheduling errors and improving staff satisfaction. The proposed model offers a scalable and adaptable solution that can be extended to other hospital departments and healthcare facilities.  
To further enhance hospital scheduling efficiency, future research should focus on several key areas. One important direction is real-time scheduling adjustments, which involve incorporating dynamic scheduling techniques to accommodate emergency cases, unexpected absences, and fluctuating patient volumes. Another promising avenue is the integration of artificial intelligence (AI) and machine learning, where AI-driven predictive models can be utilized to anticipate staffing needs and automate scheduling decisions, reducing administrative workload and improving responsiveness. Additionally, multi-objective optimization should be explored by expanding the model to account for factors such as employee fatigue, job satisfaction, and overall hospital-wide cost efficiency, ensuring a more balanced and sustainable workforce. Finally, the application of this scheduling model to different hospital units--such as emergency rooms and operating heaters--would help validate its adaptability and effectiveness in diverse healthcare settings. Addressing these aspects in future studies will further enhance the flexibility, scalability, and practicality of healthcare workforce-scheduling models, ultimately leading to improved staff well-being, operational efficiency, and patient care outcomes.  
Conception and design of the research: O.D. and M.N. Acquisition of data: O.D. and M.N. Analysis and interpretation of the data: O.D. Writing of the manuscript: O.D. and M.N. Critical revision of the manuscript for important intellectual content: O.D. and M.N. All authors have read and agreed to the published version of the manuscript.  
The study did not receive any funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors thank the Pediatric Intensive Care Unit (PICU) supervisor and the responsible PICU nurse in charge of scheduling, as well as the responsible junior assistant doctor at the PICU service of Faculty of Medicine, Cukurova Universityfor the information provided in this study.  
The authors declare that they have no competing interests.
## 2 Related Work
\begin{table}
\begin{tabular}{c p{142.3 pt} p{142.3 pt}} \hline \hline
**Reference** & **Main Contribution** & **Evaluation/Results** \\ \hline
[17] & Resource orchestration for micro-service-based 5G applications & Savings in network and computing resources \\ \hline
[18] & Hierarchical availability model for Edge-Fog-Cloud continuum & Demonstrated improved availability \\ \hline
[19] & Model-based approach for software deployment in Edge gateways & Support for software assignment and deployment in IoT-Edge-Cloud continuum through a fleet concept \\ \hline
[20] & Quantum computing-inspired solution for task allocation & Improvement in prediction efficiency and error reduction \\ \hline
[22] & IFS-CMR: Integrated Fuzzy logic System for Cloud–Fog-Edge computing & Efficient coordination and management in Software-Defined Vehicular Networks \\ \hline
[23] & STEP-ONE: Simulation tools for IoT systems & Evaluation of a monitoring application in a smart city scenario \\ \hline
[24] & FRATO: Framework for Adaptive Task Offloading & Comparative analysis of offloading strategies \\ \hline
[25] & ETA-GA: Genetic Algorithm approach for Task Allocation & Efficient allocation in a simulated Cloud environment \\ \hline
[26] & Metaheuristics for multi-objective task-scheduling problem & Metaheuristics outperformed more-traditional methods \\ \hline
[27] & PSO for resource allocation and offloading strategy for multi-user multi-MEC servers in heterogeneous networks & Minimized computing overhead \\ \hline
[28] & Custom PSO for computing offloading strategy in MEC scenarios & Reduced delay and efficient load balancing of the MEC server \\ \hline
[29] & GA for task caching optimization and mixed-integer nonlinear programming for the maximization of the total utility of the system in Fog computing & Effectiveness of the proposed scheme \\ \hline
[30] & DeepCord: DRL for service Coordination based on real-world conditions & Superior throughput and network utility on realistic network topologies \\ \hline
[31] & RL for task scheduling in a Cloud–Fog ecosystem & Enhanced CBTSA with intelligent resource allocation \\ \hline
[34] & RL for real-time task assignment in Fog computing & Overcomes greedy and conventional methods \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related efforts.
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 4 Traffic Measurement with SDN  
Although many different types of network traffic occur in a transportation system, traffic measurement for monitoring all transport data should be improved when the service provider employs SDN. To manage the specific traffic measurement, considering each link of the system individually will be improving the accuracy of measurement when using SDN. For instance, the data traffic of the transport system has many links, such as the data between the user and service provider, the data of the speed check, the data of signals, etc. For measurements, each system can use some network parameters. Within the transportation system, data management depends on reliable network communication channels. With the SDN, FSTS allows the users and service providers to handle the measurement of the traffic load used during the transportation service.  
Traffic measurements depend on the latency, jitter, and packet loss, which need to be considered during the data transmission. Packet loss may happen in many ways when users exchange voice or text messages.
## 2 Leakage Model of Shield Joints Based on the Persson Model
### Influencing Factors of Shield Joints  
#### 2.1.1 Configuration of Shield Joints  
The lining of a shield tunnel is composed of prefabricated reinforced concrete segments linked by high-strength bolts. Its joints seal is achieved by ethylene-propylene-diene monomer (EPDM) gaskets around the segments. An EPDM gasket is the primary component of the waterproof structure of shield joints. Under assembly pressure, a rubber gasket deforms greatly and provides a great elastic restoring force, so as to be squeezed to fill the uneven contact surface and block the seepage channel of groundwater. The lining segments and the joint configurations are shown in Figure 1.
## 7 Conclusions  
In this paper, we have designed a novel delay-aware routing (QDAR) mechanism based on the Q-learning technique to extend the network lifetime of underwater sensor networks. The QDAR mechanism can reduce average latency as well as extend the network lifetime. The data_ready phase and the packet structures are designed for data collection. Then, the sink node applies the QDAR algorithm to determine the routing path. In QDAR, we extend the action-utility function with both residual energy-related cost and delay-related cost. In order to have a better tradeoff between residual energy of nodes and delay, an adaptive detouring path strategy is designed. When the residual energy is sufficient, a path with shorter delay is chosen. When the residual energy of a node is lower than a threshold, the weight of the delay-related cost is decreased so as to construct an adequate path avoiding nodes with relatively less energy, even though these nodes may be nearer to the sink node. Thus, QDAR can distribute the residual energy more evenly, which is crucial to extend the network lifetime. Moreover, as QDAR takes both direct rewards and future rewards into account, it can choose a global optimal next hop, whereas greedy algorithms only pay attention to the direct reward. After a  
Figure 7: Total energy consumption between QDAR and QELAR with different MAC protocols.  
routing decision, the path is constructed during the interest phase. Then, packets are forwarded and the communication ends with an acknowledgement. The QDAR mechanism can work adaptively and distributively in the dynamic underwater environment. We evaluate the performance of QDAR with different parameters and compare it with QELAR and VBF. The simulation results show that QDAR reduces the total energy consumption effectively and decreases the average latency significantly by 20-25% at the cost of only a little reduction in network lifetime. Therefore, QDAR is more adequate for time-critical applications.  
This work was supported by the National Natural Science Foundation of China (61571318), (61571323), and the Guangxi Science and Technology Project AC16380094.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 5 Future Work
### Data Assimilation  
One advantage of the use of CMLs for rainfall estimation is the availability of a sufficient network of virtual sensors; however, it is common that there may be more than one mobile network operator in a region. This means that their data need to be assimilated with each other because the sampling strategy and data format are quite likely to be different for different operators. [PERSON] et al. [181] recently reported the case of using CMLs from Germany and the Czech Republic to generate transboundary rainfall maps and found different anomalous behavior for heterogeneous datasets, emphasizing the importance of quality control algorithms. In addition, the situation is potentially improved in the future if access to the operator's NMS is available as in Section 5.1.  
In addition, in areas where dedicated precipitation observations exist, excessive focus should not be placed on discussing CML-only-based rainfall estimates. It may be more meaningful to assimilate CMLs with traditional rain sensors or even other opportunistic sensors [91] to complement the spatiotemporal resolution and accuracy of precipitation products.
## 4 Discussion  
RS data contain the growing condition of the crop at the observation time only, making it difficult to estimate crop yield directly [72]. In addition, although RS-based empirical forecasting models are relatively simple to build, these models cannot take into account temporal changes in crop yields without long-term field experiments [7]. Recently, the integration of RS data into crop growth models has been investigated as a way to estimate crop yield, becoming increasingly recognized as a potential tool for yield forecasting [7; 72]. Moreover, various types of satellite imagery need to be considered to fit the intended utilization and resolution (e.g., spatial, spectral and temporal). However, the limited synergy between RS-based methods and crop models is complicated and so many input parameters are required, including several biophysical parameters (e.g., soil and meteorological variables) and plant parameters (e.g., biomass, LAI and height, age, etc.) which are usually expensive, labor-intensive and time-consuming to acquire [6; 7]. In this study, we combined the advantages of the AquaCrop model [20; 40] and moderate-resolution HJ1A/B data to eliminate such limitations, achieving successful results with satisfactory validation.
# A Numerical Investigation of Laser Beam Welding of Stainless Steel Sheets with a Gap
## 4 Keyhole Dynamics and Pore Formation
After about 0.25 ms, liquid filled the lower part of the gap sufficiently for the absorption at the front wall, and thus evaporation, to increase again. The expanding vapor pushed the now trapped ambient gas further down and towards the back of the keyhole (Figure 12d). Fast moving vapor and constantly changing absorption conditions led to mixing of the evaporated metal and the trapped gas (Figure 12e) and to the formation of a larger bulge. Approximately 0.25 ms later, a larger liquid melt wave in the upper region of the keyhole constricted the opening entirely and thus blocked laser energy from reaching the bulge in the lower plate. This caused fast condensation in the whole region of the gap and the lower plate where metal vapor was present (Figure 12f), and thus much more air szz pulled inwards. Again, increasing evaporation at the front of the keyhole trapped the ambient gas, creating a cavity at the bottom (Figure 12g). When closing was forced due to the surface tension becoming larger than the vapor pressure holding the keyhole open, it constricted, forming a pore (Figure 12h). If said pore is not formed far enough, or shed with enough momentum, behind the location of the optical axis, it is quickly rejoined with the keyhole and the processes described above repeat. When the pore is large enough and detaches far enough behind the current laser position, it remains stable inside the melt pool, being subject to oscillations due to changes in flow conditions and buoyancy forces (Figure 12i). After some time, the whole process starts again with a new potential pore, while the previous pore has settled in its final position (Figure 12j). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Unit** & **S5** \\ \hline material & – & AISI304L \\ sheet thickness & mm & 1 \\ welding speed & mm/s & 120 \\ laser power & kW & 4 \\ laser wavelength & nm & 1070 \\ laser M\({}^{2}\) & – & 12.4 \\ laser defocus & mm & 7 \\ laser radius on top sheet & mm & 0.31 \\ surface & mm & 0.45 \\ gap width & mm & 0.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Conditions of the LBW simulation S5 used for the investigation of the pore formation mechanism. In welding processes, the requirement to have full penetration through both plates to be welded helps the ambient gas trapped inside the pores to escape at the bottom of the lower plate. Therefore, pore formation through the previously described processes is only possible when full penetration is not achieved in all parts of the weld bead length. As seen in Figure 12)), pores are only present where full penetration is not achieved. Increasing the power further would change the dynamics inside the keyhole, leading to a mostly open keyhole at the bottom of the lower plate and disrupting the fine balance described above, so as to hinder pore formation entirely.
# Propagation Attenuation Maps Based on Parabolic Equation Method +
## 3 Propagation Attenuation Map
### Ide of PAM Algorithm  
The main idea of the proposed PAM is based on the determination of a sparse matrix for given terrain profiles on specific azimuths outgoing radially from the TX. In the next step, the full PAM matrix is determined based on the interpolation method. We suggest using PAM in the first stage and linear interpolation--in the second stage. However, it should be emphasized that both the PEM and linear interpolation can be replaced by other methods that will allow for determining the full PAM matrix. Our choice resulted from the low computational expenditure of these methods.  
The main idea of the PAM algorithm is presented in Figures 1 and 2. In this case, the PAM is identified with the path loss matrix, **PAM**, whose center cell corresponds to the TX location. In the first stage (see Figure 1), the so-called sparse PAM matrix is created based on the PEM for the terrain profile along a specific azimuth. In the second stage (see Figure 2), the sparse PAM matrix is completed based on interpolation of the non-zero values of the matrix cells. In this way, the final PAM is obtained, represented by the so-called full PAM matrix.  
Figure 1: The idea of creating sparse PAM matrix.  
Figure 2: Interpolation method for creating full PAM matrix.
## 5 Fingerprinting  
As an alternative to the range-based approach, fingerprint methods are based on the collected data to infer the position instead of relying on the model. This method can better handle the error caused by the modelling errors and noises. Fingerprinting is mostly used for RSS and CSI-based localisation, but it has also been exploited for other RF-based measurement [48; 49; 50; 51; 52] and for a hybrid scheme [53; 54; 55].  
This method usually results in more accurate estimation and comes at the cost of the laborious step of data collection. Generally speaking, fingerprinting consists of an offline mode and an online mode. In the offline step, a dataset consists of recorded measurements or RF features, and the ground truth position at references points (RP) (see Figure 5), which are called fingerprints, is generated. Based on this, in the online phase, estimation is made through matching (see Figure 6).  
Figure 5: Reference points in 3D.