esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Design of the Underwater Monitoring System  
The underwater monitoring system is divided into two components. The Buoy System and the Underwater ROV System. This system functions as a communication relay node, establishing connections with the cloud server via 4G mobile communication networks. Its primary functions include the following:  
1. Receiving and processing real-time MQTT control commands from the cloud server and forwarding them to the underwater ROV system.
2. Integrating RTSP video streams and MQTT sensor data from the underwater ROV system and forwarding them to the cloud server.
3. Collecting local water quality sensor data and transmitting it to the cloud server using the MQTT protocol.  
Figure 5 illustrates the communication system components, showing both the deployed buoy system and the ROV communication module configuration. The underwater ROV system communicates with the buoy system via wired connections, with primary functions including the following:  
1. Receiving and executing real-time control commands transmitted from the buoy system.
2. Real-time transmission of underwater camera images to the buoy system using the RTSP protocol.
3. Collecting local water quality sensor data and transmitting it to the cloud server using the MQTT protocol. Table 1 provides detailed specifications of the water quality sensors used in the system.  
Figure 4: System control interface displaying a real-time monitoring dashboard that includes video streaming, sensor data visualization, and controls for remotely operated vehicle functions.
## 5 Discussion  
From the survey, the results conclude that 76% of participants believe that with the onset of autonomous shipping, Licensed Deck Officer skillsets will significantly change; 62% of participants believe the MET will also significantly change in the next decade; and almost half of the participants believe that those currently sailing are not adequately prepared for changes in the industry. Just over 90% believe that shipping companies, unions, and MET facilities are not adequately prepared for changes that will be brought on by autonomous shipping, and 76% do not think that cadets are well prepared for these changes. Of the participants, 79% agree that RCCs will require operators who possess experience and credentials similar to that of current License Deck Officers; 57% believe that an increase in vessel automation will increase Licensed Deck Officers' over-reliance on sensors and could cause information overflow; while almost 60% disagree that an increase in shipboard automation will reduce the number of navigational accidents or decrease their daily duties. Additionally, 71% disagree that reducing onboard crew while increasing shipboard automation is safe, and 76% disagree that SA can be accurately replicated shoreside. Two-thirds of participants believe that it will not be safe to operate vessels in the future as fully autonomous (level 4).  
Using a Theoretical Thematic Analysis and qualitative analysis software program, ATLAS.ti, the semi-structured interviews were analyzed. The overarching themes of the effects of the implementation of autonomous shipping on the Licensed Deck Officer that emerged are as follows: Barriers, Purpose, the Unknown, External Factors, and Adaptability. These are detailed in Figure 5, followed by their associated codes and subcodes, which overlap into more than one theme.  
Figure 4: Likert Bar Graph representing scaled Feasibility Category question responses.
# 5G-MEC Testbeds for V2X Applications
## 5 Summary of Available Tools
### 5G and 4G Systems  
Available open source tools can help to implement and deploy the 5G and 4G core functionalities. However, most analyzed testbeds used commercial infrastructure, where the product included the SDKs. Commercial infrastructure SDKs are proprietary and licensed. Some open source tools and SDKs are still in development. Here, we present the most commonly used open source solutions and tools in the analyzed testbeds.  
**OAI**[35] provides the RAN and CN implementation that can be used to deploy the 4G and, partially, 5G functionalities. In the future, OAI will be able to deploy full standalone 3 GPP-compliant 5G CN. The OAI CN implementation has been tested with commercial RAN solutions such as Amarisoft gNB. Furthermore, in order to avoid the transmission of radio signals over the licensed spectrum, OAI can be connected to the open source state-of-the-art 5G UE and RAN (gNodeB) simulator, UERANSIM [72]. OAI CN supports primary functionalities such as UE registration, service requests, and PDU session management. OAI can support multiple UE deployments.  
**Open5 GCore**[73] was developed by the Fraunhofer FOKUS to provide 3 GPP Release 15 and 16 core network functionalities. Open5 GCore runs on typical hardware components such as a Raspberry Pi and computing servers using containers, pods, or virtualized environments. Open5 GCore is licensed.  
**MagmaEPC**[74] was developed within the Linux foundation projects. MagmaEPC is an open source tool that supports extendable mobile core network functionalities. It can be deployed both by using the Docker and on bare metal. MagmaEPC has three significant elements: an access gateway, an orchestrator, and a federation gateway.  
**NextEPC**[75] is an open source tool for implementing the 4G/5G CN and is not fully compliant with 3 GPP. NextEPC includes CN functionalities such as a Mobility Management Entity (MME), Home Subscriber Server (HSS), Serving Gateway (SGW), Packet Data Network Gateway (PGW), and Policy and Charging Rules Functions (PCRF).  
**Open5 GS**[76] is an open source implementation for 4G, 5G NSA CN, and 5G SA CN. Open5 GS can be deployed using Docker and virtual machines. This CN implementation can be integrated with the open source RAN simulator UERANSIM. Using UERANSIM, multiple UEs can be deployed and supported by the Open5 GS CN.  
**Free5 GC**[77] was mainly developed by National Chiao Tung University (NCTU). This implementation was based on the NextEPC platform, and the current version only supports the basic 5G CN, although it is planned to develop 5G CN functionalities defined in 3 GPP Release 15 and beyond. Free5 GC is deployed using virtual machines.  
**Simu5G**[78] is an open source simulator for 5G new radio (NR) that is based on SimuLTE libraries. Simu5G was developed using the OMNET++ simulation framework. Simu5G's features can allow testing resource allocation and management in a 5G network in addition to the targeted UE, depending on the modulation schemes. Simu5G has also been tested with the ETSI MEC system model.
# Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
## 5 Results and Discussion
### Deep Learning Model Validation  
Performance metrics were obtained based on the four possibilities from the confusion matrix of TP, TN, FP, and FN, and were used to validate the proposed deep learning model (Table 3). These metrics included specificity, accuracy, precision, F1-score, and area under a receiver operating characteristic (ROC) curve (AUC) metrics, in addition to mean squared error (MSE).  
A report on the deep learning model for the 612 testing samples obtained through the confusion matrix (Table 3). It shows that the actual detected events (TP) numbered 222, the number of events that occurred but were not detected (FN) was 35, the number of events that did not occur and the system generated an absence event report (TN) was 304, and the number of events that were absent but the system reported as present (FP) was 51. Additionally, the outcome demonstrates an ability to ignore fake occurrences, with an average specificity of 85.6%. The accuracy of 86% reflects the percentage of times a model produced a prediction throughout the entirety of the dataset.  
The classification report (Table 4) shows the validation data average recall (sensitivity) for both classes (rock fall does not occur is 0 and rock fall occurs is 1) is 86%. That means, at the lowest sensitivity levels, only 14% of the rock-fall occurrences were improperly recognized. The average precision of 85% shows that the majority of the positive predictions made by the model were correct. The average F1-score is 86%.  
The deep learning model's loss curve for 200 iterations is shown in Figure 6. In contrast, during the neural network training session, the cost value drops with each iteration, reflecting the performance of the learning through time. Finally, the cost value eventually dropped to less than 0.14 points, which is regarded as an acceptable mean squared error (MSE) score.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Rock Fall (Not occur 0) & 91\% & 86\% & 88\% & 355 \\ \hline Rock Fall (Occurs 1) & 81\% & 86\% & 84\% & 275 \\ \hline Accuracy & & & 86\% & 612 \\ \hline Macro avg & 85\% & 86\% & 86\% & 612 \\ \hline \end{tabular}
\end{table}
Table 4: Classification report.  
Figure 6: The mean squared error (MSE) curve.  
The ROC curve shown in Figure 7 demonstrates the model's accuracy in predicting the occurrence of rock falls. The area under the ROC curve (AUC) value reached 0.946.
## 4 Results and Discussion
### Risk Metrics  
Risk evaluation plays a role in the optimization process to ensure that investment choices are profitable and resilient to adverse scenarios. Table 10 outlines the risk indicators derived from the model, which include VaRs, CVaRs, portfolio return rates, total investment, total capacity expansion, and Sharpe ratios. The Sharpe ratio is computed using Equation (10) in the Python 3.11.7 code. It gives insight into the excess return obtained relative to the additional volatility experienced by the investment.  
\[Sharpe\ ratio=\frac{R_{i,t,s}-R_{free}}{\sigma_{P}} \tag{10}\]  
Figure 5: Operational Decisions Heatmap.  
where \(R_{i,t,s}\) denotes the expected return on investment, \(R_{free}\) denotes a Risk-free rate, and \(\sigma_{P}\) denotes the standard deviation of the portfolio return.  
Table 11 summarizes the risk metrics related to the optimized renewable energy portfolio. The model attained an Expected Portfolio Return of USD 1,822,500.00 with a total investment of USD 16,500,000.00 and a significant capacity expansion of 925 MW. The Sharpe Ratio of 18.2250 signifies an exceptionally advantageous risk-adjusted return profile. The CVaR of USD 100,000.00, as shown in Figure 6, functions as a more thorough risk metric, indicating the anticipated loss exceeding the VaR threshold. Although the VaR is zero, a finite CVaR suggests that the model considers probable losses in unfavorable conditions, ensuring the portfolio's resilience against severe declines. This dual strategy highlights the model's dedication to harmonizing profitability with effective risk management. The expected portfolio return of USD 1,822,500.00 indicates the projected profitability of the investment strategy (see Figures 7 and 8). This value and the total investment of USD 16,500,000.00 demonstrate an optimized allocation that maximizes returns within the specified budgetary limitations. The total capacity expansion of 925 MW highlights the model's efficacy in augmenting renewable energy capacities, aligning with the company's strategic goals of advancing sustainable energy production. The Sharpe Ratio of 18.2250 is remarkably elevated, signifying that the portfolio provides a considerable return relative to its risk. This indicator underscores the efficacy of the investment strategy in producing profits while mitigating risk exposure, a vital factor for stakeholders seeking sustainable and lucrative growth in the renewable energy sector.
## 3 The Effects of the Second Phases on Properties
### TiC Phase  
The JMatPro simulation has indicated that the temperature range for TiC formation in AISI 321 stainless steel spans from approximately 850 \({}^{\circ}\)C to 950 \({}^{\circ}\)C (Figure 1). The TiC phase can significantly enhance the high-temperature strength and corrosion resistance in AISI 321 stainless steel. At high temperatures, the formation of TiC inhibits the precipitation of Cr\({}_{23}\)C\({}_{6}\) at the grain boundary [25]. [PERSON], [PERSON] discovered that Cr\({}_{23}\)C\({}_{6}\) was correlated with the adjacent grain plane indices, mostly (111) planes, whereas TiC was not correlated with the adjacent grain plane indices. The lower correlation between crystal planes demonstrated that the grain shape of TiC would exhibit irregular polygons, leading to a reduction in interfacial free energy between adjacent unit areas and enhancing the anti-cavitation and anti-creep properties of 321 stainless steel [26].  
[PERSON], [PERSON] and his team successfully synthesized AISI 321 stainless steel with equivalent-sized TiC and Cr\({}_{23}\)C\({}_{6}\) particles after sensitization. They then conducted high-temperature creep and impact experiments on the material. The resulting fracture morphology is depicted in Figure 5. The findings indicate that AISI 321 stainless steel containing TiC particles exhibits fewer pores and smaller toughness dimples compared to its counterpart containing Cr\({}_{23}\)C\({}_{6}\), which has a higher porosity. Furthermore, the fracture mode of the TiC-aged alloy is more ductile than that of the Cr\({}_{23}\)C\({}_{6}\)-aged alloy. This ductility can be  
Figure 4: AISI 321 carbonitride distribution of stainless steel [16].  
attributed to the strong interfacial affinity between TiC and the matrix, which acts as a barrier to void formation and growth [27]. Importantly, maintaining smaller carbide sizes amplifies their pinning effect on dislocations, thereby preventing a reduction in dislocation density during prolonged creep deformation, which is crucial for maintaining the material's structural integrity [28].
# Measurement and Modeling of 3D Solar Irradiance for Vehicle-Integrated Photovoltaic
## 4 Discussion
### Limitation of the Model  
First, the model discussed in this article relies on the random number and assumes that every parameter affecting the solar resource on the car roof and car side is distributed by a simple rule, for example, ranged uniform distribution. It may be meaningful to the averaged or integrated energy yield but cannot be applied to the power prediction in specific driving points and directions, specific climate as well as specific surrounding conditions. The given distributions may not be equal to the real situation that varies every position and every time. In this sense, it is to be applied to annual or other long-term integration like annual energy yield.  
Distribution of the shading objects (both spatial distribution and height distribution) is essential to the model. Our model is based on a linear trend (Figure 2) but is too simplified to the shading in the  
Figure 20: Distribution of the normalized efficiency of some design of the car roof PV affected by synthesized partial shading given by the Monte Carlo simulation. Note 1/6 or more cut is recommended for suppressing partial-shading loss to \(<\)10%.  
high-rise section in Figure 13, where the trend of Figure 2 may become trapezoid or other complicated shapes. Namely, the energy yield in the urban area may give an optimistically-biased value.  
For the moment, the impact by the partial shading has not been considered yet. We only estimated the impact of using a Monte Carlo simulation. Depending on the string configuration or the power-conditioner connected to the car-mounted module, the output power may drop by the mismatching loss that was not anticipated to the model.
## 4 Proposed Solution and Algorithm
### Proposed Algorithm  
In order to solve the problem formulated, we proposed a modified version of the Gale-Shapley algorithm (GSA) [33], which represents the state-of-the-art as regards the two-sided preferences matching problems [34; 35; 36]. More in depth, both the \(\mathcal{S}^{\prime}\) and \(\mathcal{S}^{\prime\prime}\) sets reside in \(\mathcal{S}\). More in depth, for the sake of simplicity, the \(\mathcal{S}\) set has been split into \(\mathcal{S}^{\prime}\) and \(\mathcal{S}^{\prime\prime}\) that are a virtual representation of the two communication modes in which an ED can receive the packet \(l\).  
The algorithm reaches the suboptimal solution throughout an iterative bargaining process between \(\mathcal{S}^{\prime}\) or \(\mathcal{S}^{\prime\prime}\) and \(\mathcal{D}\). By defining \(\mathcal{D}^{\prime}\) and \(\mathcal{D}^{\prime\prime}\) as the set of proposals received by \(\mathcal{S}^{\prime}\) and \(\mathcal{S}^{\prime\prime}\) respectively, the algorithm steps are  
1. each ED \(i\) constructs its preference accordingly to (8) and (9);
2. if ED \(i\) prefers \(\delta^{\prime}_{i}\) to \(\delta^{\prime\prime}_{i}\), i.e., the ED \(i\) prefers to receive the packet \(l\) through the direct link connection, \(i\) proposes itself to \(\mathcal{S}^{\prime}\), otherwise the proposal is sent to \(\mathcal{S}^{\prime\prime}\).
3. both \(\mathcal{S}^{\prime}\) and \(\mathcal{S}^{\prime\prime}\) select the minimum values \(i^{\prime}\) and \(i^{\prime\prime}\) within \(\mathcal{D}^{\prime}\) and \(\mathcal{D}^{\prime\prime}\), respectively.
4. Due to the fact that \(i^{\prime}\) represents the first choice of \(\mathcal{S}^{\prime}\) among the received proposals, the SN \(\mathcal{S}\) establishes a direct link towards \(i^{\prime}\);
5. similarly, the SN \(\mathcal{S}\) selects \(i^{\prime\prime}\) as relay node in order to send the packet \(l\) to all the one hop neighbors of \(i^{\prime\prime}\);
6. the \(\mathcal{G}_{d}\) topology is updated as follows: * the ED \(i^{\prime}\) cuts its incident edges; * all the nodes belonging to \(\mathcal{V}_{i^{\prime\prime}}\) cut their incident edges, while preserving the edge connecting themselves with \(i^{\prime\prime}\). In fact, the edges that connect \(i^{\prime\prime}\) to its one hop neighbors are essential to reach all the neighbor EDs and to allow \(i^{\prime\prime}\) to act as a relay.
7. \(\mathcal{D}=\mathcal{D}\setminus\mathcal{V}_{i^{\prime\prime}}\cup\{i^{ \prime\prime},i^{\prime}\}\);
8. each ED \(i\in\mathcal{D}\) updates its preference list;
9. repeat 1-9 until the set \(\mathcal{D}\) is not empty.  
Furthermore, it is dutiful to point out that \(i^{\prime}\) or \(i^{\prime\prime}\) may not exist, but at each round of the algorithm at least one of these terms exists, and if both \(i^{\prime}\) and \(i^{\prime\prime}\) exist, \(i^{\prime}\) uses the direct link communication mode and \(i^{\prime\prime}\) is selected as relay node.
## 4 Feature Extraction
### Time-Frequency Domain Features
#### 4.3.1. Comparison of Features  
The upper of Figure 6a shows the original signal of the non-fault state, where the horizontal axis is the frequency and the vertical axis is magnitude. The lower image of Figure 6a is the zero-order wavelet scattering coefficients, and shows that the numerical values are most frequently between \(-0.05\) and \(0.05\). Figure 6b shows the visualization of Equation (14), and represents the first-order scattering coefficients. The values most frequently occur between \(0.1\) and \(0.7\), and the figure shows the analyzing frequency interval of wavelet scattering, which is 5-22,050 Hz the half of maximum sampling rate. Compared to the other first-order scattering coefficient scattergrams (Figure 7b,e), no obvious feature is seen.  
Figure 5: Cont.  
Figure 5: The features of vibration and PWM signal data: (**a**) average data from the accelerator; (**b**) sample entropy data from the accelerator; (**c**) standard deviation data from the accelerator; (**d**) RMS data from motors; (**e**) average data from motors; (**f**) standard deviation data from motors; (**g**) representation of colors.  
Figure 6: The wavelet scattering features of the non-fault state: (**a**) original signal and zero-order scattering coefficients; (**b**) first-order scattering coefficients scattergram; (**c**) second-order scattering coefficients scattergram.  
Figure 7: The wavelet scattering features of the damaged propeller: (**a**) original signal and zero-order scattering coefficients; (**b**) first-order scattering coefficients scattergram; (**c**) second-order scattering coefficients scattergram. The wavelet scattering features of the loosening of the motor screw: (**d**) original signal and zero-order scattering coefficients; (**e**) first-order scattering coefficients scattergram; (**f**) second-order scattering coefficients scattergram.  
On the contrary, the visualization second-order scattering coefficient (Equation (15)) in Figure 6c provides sufficient characteristics. Compared to the damaged propeller state (Figure 7c) and loosening of the motor screw (Figure 7f), the discrepancy of the scattergrams can be clearly detected by the human eye, in addition to being classified by the machine learning method. Apparently, the second-order scattering coefficient has an excellent ability to find obscure information; this why we used this novel method.
# Machine Selection for Inventory Tracking with a Continuous Intuitionistic Fuzzy Approach
###### Abstract  
Today, businesses are adopting digital transformation strategies to make their production processes more agile, efficient, and sustainable. At the same time, lean manufacturing principles aim to create value by reducing waste in production processes. In this context, it is important that the machine to be selected for inventory tracking can meet both the technological features suitable for digital transformation goals and the operational efficiency criteria required by lean manufacturing. In this study, multi-criteria decision-making methods were used to select the most suitable machine for inventory tracking based on digital transformation and lean manufacturing perspectives. This study applies a framework that integrates the Continuous Intuitionistic Fuzzy Analytic Hierarchy Process (CINFU AHP) and the Continuous Intuitionistic Fuzzy Combinative Distance-Based Assessment (CINFU CODAS) methods to select the most suitable machine for inventory tracking. The framework contributes to lean manufacturing by providing actionable insights and robust sensitivity analyses, ensuring decision-making reliability under fluctuating conditions. The CINFU AHP method determines the relative importance of each criterion by incorporating expert opinions. Six criteria, Speed (C1), Setup Time (C2), Ease to Operate and Move (C3), Ability to Handle Multiple Operations (C4), Maintenance and Energy Cost (C5), and Lifetime (C6), were considered in the study. The most important criteria were C1 and C4, with scores of 0.25 and 0.23, respectively. Following the criteria weighting, the CINFU CODAS method ranks the alternative machines based on their performance across the weighted criteria. Four alternative machines (High-Speed Automated Scanner (A1), Multi-Functional Robotic Arm (A2), Mobile Inventory Tracker (A3), and Cost-Efficient Fixed Inventory Counter (A4)) are evaluated based on the criteria selected. The results indicate that Alternative A1 ranked first because of its superior speed and operational efficiency, while Alternative A3 ranked last due to its high initial cost despite being cost-effective. Finally, a sensitivity analysis further examines the impact of varying criteria weights on the alternative rankings. Quantitative findings demonstrate how the applied CINFU AHP@cODAS methodology influenced the rankings of alternatives and their sensitivity to criteria weights. The results revealed that C1 and C4 were the most essential criteria, and Machine A2 outperformed others under varying weights. Sensitivity results indicate that the changes in criterion weights may affect the alternative ranking.  
cinfu ahp; cinfu codas; MDCM; machine selection; inventory tracking +
Footnote †: journal: Applied sciences  
Article