esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State-Space Network (SSN) Superstructure for Diesel Conditioning Pathways
The colored arrows represent candidate flow paths evaluated by the algorithm. To enable structured and stable design of the conditioning paths in this study, the following assumptions are made:  
1. Steady-State Operation: The diesel conditioning paths are assumed to operate under steady-state conditions, without dynamic adjustments over time or in response to changes in oxidant concentration in the storage tank. This simplifies operation and ensures model stability. 2. Focus on Oxidative Stability: The primary degradation concern is assumed to be excessive oxidative instability. The conditioning process is therefore designed specifically to improve this property, while other factors affecting fuel quality are neglected. 3. Uniform Additive Distribution: It is assumed that the diesel entering the system already contains antioxidants at a constant and homogeneously distributed volume fraction. This assumption simplifies the modeling and analysis of additive effectiveness. 4. Pump Capacity Limit: The maximum flow rate of each transport pump is fixed. The total feed flow rate is constrained to remain below the pump's capacity to avoid overloading and ensure safe operation. 5. One-Way Interface Constraints: Each inlet or outlet of a diesel tank can function either as a feed port or a discharge port, but not both simultaneously. This assumption ensures directional clarity and avoids flow ambiguity. 6. Minimum Equipment Availability: At least one feed port, one discharge port, and one conditioning unit must be present to ensure that the system is operable and the conditioning process can proceed successfully. 7. Steady, Incompressible, Single-Phase Flow: The diesel-antioxidant mixture is assumed to behave as a steady-state, incompressible, single-phase liquid. Multiphase effects such as evaporation, gas entrainment, or cavitation are not considered. As a result, fluid density is treated as constant, and mass conservation is equivalently expressed as a balance of volumetric flow rates, which simplifies the modeling process and enhances numerical stability. In conclusion, the SSN superstructure for emergency diesel conditioning enumerates all valid flow paths among pumps, conditioning units, and pipelines, based on steady-state assumptions. It enables the optimization of conditioning scheduling (minimizing time, maximizing quality) and provides a structured, flexible framework for enhancing both the reliability and efficiency of critical emergency fuel systems. Leveraging recent SSN-based optimization developments, the proposed approach offers a robust solution for improving emergency diesel treatment performance in nuclear and other high-stakes applications.
# Verifiable (2, \(n\)) Image Secret Sharing Scheme Using Sudoku Matrix
## 1 Introduction
To achieve a flexible secret sharing scheme, we encode the shares by using simultaneous algebraic equations, in which the parameters are a hybrid of the secret pixels and the corresponding authentication codes. Later, the shares are embedded into the cover images. The cover images can be different individually. Any two cover images can be combined together to completely restore the secret image. If the pixels in the cover image are missing or have been modified, an authentication mechanism indicates the untrue locations. During the embedding process, the cover image is only slightly modified due to the concept of Sudoku. Therefore, it is difficult to identify the difference between the original cover image and the hidden image, which helps protect the secret image from detection by malicious users. When compared with [PERSON] et al. 's method [34; 35], as summarized in Section 4, the proposed approach achieves superior performance in terms of visual quality, hiding capacity, and authentication ability. The structure of this paper is organized as follows. First, we briefly present the Sudoku matrix in Section 2. Next, we describe our proposed scheme in Section 3, followed by the experimental results and analysis in Section 4. Finally, our conclusions are summarized in Section 5.
# Enhancing Driving Safety of Personal Mobility Vehicles Using On-Board Technologies
## 5 Conclusions  
This study integrates information and communication technologies (ICT) into personal mobility devices, such as electric wheelchairs, to enhance the quality of life of vulnerable populations, including the elderly, visually impaired, and youth. The proposed object recognition system employs sensors and cameras to enhance navigation safety by detecting obstacles in real time and providing immediate Text-to-Speech (TTS) guidance. The YOLOv8 model demonstrated robust performance in recognizing various obstacles in diverse driving environments, including pedestrians, vehicles, and stationary objects. However, our experiments revealed limitations in terms of detecting essential objects, such as pedestrians and stationary obstacles (e.g., benches and streetlights). These limitations were more pronounced in environments with multiple overlapping objects, which reduced the confidence levels. Addressing these issues is critical for improving the safety and usability of the proposed system for wheelchair users.  
To enhance system performance, future research will optimize pedestrian detection algorithms and improve the accuracy of stationary obstacle recognition. Combining datasets, such as the KITTI Dataset and AI HUB's autonomous wheelchair sensor data, will further refine the detection capabilities under diverse conditions. Additionally, integrating a multi-ultrasonic sensor array and infrared sensors will address the challenges of detecting soft or irregular objects such as fabrics or rubber. Future research will also enhance inference speed and accuracy under adverse weather and nighttime conditions to ensure practical usability. These enhancements deliver safer and more reliable navigation for personal mobility devices, ultimately improving the quality of life of diverse social groups. Future work includes extending the system's applicability to other equipment, such as agricultural machinery.  
Conceptualization, M.C. Funding acquisition, M.C. Investigation and methodology, M.C., T.A.D. and E.C. Project administration, M.C. Resources, M.C. Supervision, M.C. Writing of the original draft, E.C. and T.A.D. Writing of the review and editing, M.C. Software, E.C., T.A.D. and M.C. Validation, E.C., T.A.D. and M.C. Formal analysis, E.C. and M.C. Data curation, E.C., T.A.D.  
and M.C. Visualization, E.C., T.A.D. and M.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Innovative Human Resource Development for Local Intellectualization program through the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (IITP-2025-RS-2020-II201462).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed at the corresponding author.  
The authors declare no conflicts of interest.
## 5 Conclusions  
### Summary  
This study mainly uses industrial Internet of Things technology, big data processing technology, and edge computing to realize real-time detection, processing, and analysis of production data, equipment status, and production progress of intelligent production lines. The underlying system uses the OPC UA protocol to realize the standardized collection of heterogeneous equipment data, and the edge side performs preliminary analysis and processing of key data, which improves the real-time performance and response efficiency of data processing. The processed data are efficiently transmitted through the Kafka message queue and are persistently stored and backed up by the PostgreSQL database. Finally, the real-time monitoring and tracking of the entire production process is realized through the front-end visual interface, providing data support for production process optimization and decision-making.  
The current testbed deploys Kafka nodes based on virtual machines (VMs), which can simulate the industrial network environment to a certain extent, but does not fully reflect the complexities of real industrial deployments, such as equipment performance differences, network congestion, and system heterogeneity. Therefore, future research will further deploy this framework in actual industrial sites to verify its stability and practicability in complex environments.
## 5 Performance Analysis for Fixed-Gain Relaying
### Ergodic Capacity  
Substituting (31) into (38), the ergodic capacity for fixed-gain relay can be expressed as:  
\[\begin{split}\mathcal{C}^{F}&=A_{1}\frac{\alpha_{t}}{ \ln 2}\int_{0}^{\infty}\frac{1}{1+\alpha_{t}\gamma}\text{exp}\bigg{(}-\frac{ \gamma}{\gamma_{RF}}\bigg{)}\\ &\times G_{2t,2t+1}^{2t+1,t}\bigg{[}\frac{C\gamma\varphi_{m}^{ \prime}}{\gamma_{RF}\gamma_{FSO}}\big{|}\begin{matrix}B_{1}\\ B_{2}\end{matrix}\bigg{]}d\gamma\end{split} \tag{44}\]  
With the aid of [33] (Equations (10) and (11)) and the integral of EGBMGF [36] (Equation (20)), the ergodic capacity for fixed-gain relay can be written as:  
\[\mathcal{C}^{F}=A_{1}\frac{\alpha_{t}\tilde{\gamma}_{RF}}{\ln 2}\times C_{1,0 1,1:2t,2t+1}^{1,0,1:1:2t+1,t}\begin{bmatrix}1&0\\ -0\end{bmatrix}\begin{matrix}B_{1}\\ B_{2}\end{matrix}\big{|}\alpha_{t}\tilde{\gamma}_{RF},\frac{\mathcal{C}\varphi_{ m}^{\prime}}{\tilde{\gamma}_{FSO}}\bigg{]} \tag{45}\]
# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 3 Results
### The Relationship between Water Level and Water Area  
Figure 15 shows the interannual and seasonal fluctuations in the water area obtained using WaterUNet and the histogram for Poyang Lake from October 2014 to March 2021. Similar to the water level, the water area also fluctuated regularly from 2014 to 2021. The similar fluctuation pattern of the water area can indirectly prove that a 5-7-day revisit cycle is sufficient for monitoring water body changes for Poyang Lake. Figure 15 also shows that the highest water area occurred in July every year as the water level reached the highest level. Under the combined action of Yangtze River flood and the five upstream rivers flood, the water level of Poyang Lake was always at a high water level, resulting in the longest duration when the water area was higher than 2500 km\({}^{2}\). Although the water level in 2020 was higher than that in 2016, the water area in 2020 was not larger because the water level had exceeded the warning level and was also affected by the scope of the study area.  
After obtaining both the water area and water level data, we chose 284 pairs of water area and water level data acquired on the same day and analyzed the relationship between them (Figure 16). Firstly, we calculated the correlation coefficient between the water area and water level, which was up to 0.92, showing a good correlation between them. Then, the quadratic polynomial fitting equation was obtained as \(y=-21.796x^{2}+975.334x-8150.724\), and the fitting \(R^{2}\) reached 0.88. The quadratic polynomial can sufficiently express the relationship between the water level and water area; thus, the expression can be used to estimate the water area or water level of Poyang Lake when the water level or water area data are invalid. From Figure 16, we can also see that, when the water level reaches over 20 m, the water area does not increase significantly due to the dikes around Poyang Lake (Zakha  
Figure 14: The IF distribution maps for different months from 2014 to 2021. The numbers in the brackets are the observation times for that month across all years.  
the limitation of the extent of the study area. As shown in Figure 1, Poyang gauging station is located on the east bank of Poyang Lake, so the relationship can be improved by using the water level from the gauging station located in the central part of Poyang Lake.
# LSTM Attention-Driven Similarity Learning for Effective Bug Localization
## 4 Our Approach
### Top-K Code Similarity  
BM25 [41] is a widely used ranking function for evaluating the relevance of a query to a document. It serves as a scoring algorithm commonly employed in search engines and recommendation systems. BM25 is based on the bag-of-words concept, which calculates how frequently a query term appears in a document and assigns a relevance score using the principles of term frequency (tf) and inverse document frequency (idf) while also accounting for document length.  
The BM25 score is calculated using the following equation:  
\[Score(D,Q)=\sum_{i=1}^{n}IDF(qi)*\frac{f(qi,D)*(k_{1}+1)}{f(qi,D)+k_{1}*\left(1 -b+b*\frac{|D|}{avgdl}\right)} \tag{1}\]  
The _Score_(_D_,_Q_) represents the BM25 score of query \(Q\) for document \(D\). \(Q\) is the set of query terms consisting of \(q_{1}\), \(q_{2}\), \(q_{3}\), \(\ldots\), \(q_{n}\). The term \(\
ot\!\!q_{i}\), \(D\)) indicates the frequency of word \(q_{i}\) in document \(D\). The hyperparameters \(k_{1}\) and \(b\) control the term frequency scaling and the document length normalization, respectively. \(|D|\) denotes the length of document \(D\), and avgdl is the average length of all documents.  
To present the application of BM25 in bug localization, Table 2 provides examples of similar code files identified within the Birt project. For each target file, the top-K most similar code files were determined based on their BM25 scores.  
For example, when analyzing the target file MongoDBDataSetWizardPage.java, the most similar code file identified was OlapQueryUtil.java, with a similarity score of 36.86. The second and third most similar files were QueryProperties.java (36.06) and MongoBDriver.java (35.66), respectively.  
In this study, the BM25 algorithm was employed to calculate the top-K code similarity, enabling the identification of code files that are closely related to a given bug report. This ranking and similarity analysis provided a robust foundation for training the LSTM-Attention model and further improving bug localization performance.
## 2 Materials and Methods  
For our experiments, we used an AlSi\({}_{9}\)Cu\({}_{3}\) alloy, and the chemical composition must comply with DIN EN 1706 to ensure the consistency and reliability of the results, as the properties and behavior of the alloy can vary depending on its composition.  
The chemical composition of the AlSi\({}_{9}\)Cu\({}_{3}\) alloy we used consisted of approximately 9% silicon (Si) and 3% copper (Cu), and the rest was aluminum (Al) along with trace amounts of other elements such as iron (Fe), manganese (Mn), and magnesium (Mg). The specific composition may vary slightly depending on the manufacturer or specific application requirements. Figure 2 shows the raw casting component that was used for experimentation.  
The Al-Si-Cu alloy is commonly used in the automotive industry due to its excellent casting properties and machinability. The addition of Cu improves machinability and allows for automatic hardening after fast cooling in water. The presence of Mg in the alloy helps improve casting properties. Fast cooling leads to the formation of a supersaturated solid solution \(\alpha\) (Al), which then precipitates with subsequent structure hardening. This alloy is used in the production of various automotive castings such as engine blocks and gear cases [36; 37].  
The process of aging (natural hardening) can significantly impact the material's properties, such as its hardness, strength, and ductility. As a result, it is important to consider the time lag between casting and processing procedures to achieve optimal machining conditions. By allowing sufficient time for the material to age, the machining conditions can be improved, and the service life of the tool can be extended. Table 3 provides information on the physical properties of the casting material, including its density, solidus temperature, and liquidus temperature. Table 4 provides information on the mechanical properties of the casting material, including its ultimate tensile strength, yield point, elongation, and Brinell hardness.  
\begin{table}
\begin{tabular}{c c} \hline The Young’s modulus or Elastic modulus & 72–89 GPa \\ \hline Ultimate tensile strength & 228–330 MPa \\ \hline Yield strength & 97–250 MPa \\ \hline Elongation & 2.5–9\% \\ \hline Hardness & 65–150 BHN \\ \hline \end{tabular}
\end{table}
Table 2: Informative ranges of mechanical properties of Al-Si alloys.  
Figure 2: A non-machined component was used for experimentation.  
The experiments were conducted using the die casting machine CLV 250, which is commonly used in the production of castings for the automotive industry. The use of a die casting machine can have an impact on the properties of the casting, as the high pressure can lead to changes in the microstructure and properties of the material. Therefore, it is important to carefully control the parameters of the die casting process to ensure that the resulting casting meets the desired specifications. The basic parameters of production equipment designed to produce castings are shown in Table 5.
## 2 Theoretical Background
### K-Means  
Classification refers to data labeling into disjoint sets according to a common set of features. Among these, clustering algorithms can be used to determine the natural structures in the data. Clustering algorithms can use more sophisticated properties of the image: in digital imaging, this means that spatial and/or spectral features concerning pixels can be exploited [31].  
K-means is an unsupervised clustering technique that aims at partitioning an input set of \(N\) observations into \(k\) clusters [32]. Let \(\mathcal{X}=\{x_{1},x_{2},\ldots,x_{N}\}\) be a set of vector observations such that \(x_{i}\in\mathbb{R}^{n}\) for \(i=1,2,\ldots,N\). In image segmentation, each component of a vector \(x\) represents a numerical pixel attribute: if segmentation is based on gray-scale intensity alone, the \(n\)-dimensional observation \(x_{i}\in\mathbb{R}^{n}\) degenerates into the scalar value \(x_{i}\in\mathbb{R}\) representing the intensity of the \(i\)-th pixel. The final purpose of k-means is to partition the \(N\) observations into \(k<N\) disjoint cluster sets \(\mathcal{C}=\{\mathcal{C}_{1},\mathcal{C}_{2},\ldots,\mathcal{C}_{k}\}\) such that the sum of the distances from each point in a set to the mean of that set is minimum. Froma mathematical point of view, k-means turns classification into an optimization problem with the following cost function in Equation (5):  
\[\mathcal{J}(\mathcal{X},\mathcal{V})=\operatorname*{argmin}_{c}\Big{(}\sum_{i=1 }^{k}\sum_{x\in\mathcal{C}_{i}}|x-v_{i}|^{2}\Big{)}, \tag{5}\]  
where \(v_{i}\) is the centroid of the samples in the set \(\mathcal{C}_{i}\) for \(i=1,2,\ldots,k\). The function \(\mathcal{J}(\mathcal{X},\mathcal{V})\) has no analytical solution: as a result, k-means proceeds by iteratively finding the minimum of its cost function. In particular, at each iteration \(t\) the centroid values of the prototype set \(\widehat{\mathcal{V}}^{(t)}\)) are updated according to Equation (6).  
\[\hat{v}_{i}^{(t)}=\frac{1}{|\mathcal{C}_{i}^{(t)}|}\sum_{x\in\mathcal{C}_{i}}x^ {\prime}, \tag{6}\]  
where \(|\mathcal{C}_{i}^{(t)}|\) is the number of objects belonging to the \(i\)-th at the step \(t\).
# Digital Twin Providing New Opportunities for Value Co-Creation through Supporting Decision-Making
## Appendix A
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Case Details** \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & The availability and reliability of cruise ships is important to ensure schedules are adhered to. Preventative maintenance is a preferred option of both the ship owners and the supplier, however this does not necessary improve ship availability. In order to improve availability, reduced response time and reduced repair time is required. Representation of physical assets (ships) and status, including their location \\  & MRO manager: plan maintenance \\  & Dispatching manager: assign the ships to jobs \\  & Ships and dispatching map \\  & To provide a mimic showing the current position and status of the ship allowing ship-to-shore communications to support remote trouble shooting, and to provide options to the dispatching manager to adjust the dispatch plan. \\  & What is the status of the ship? \\  & What are the best options for MRO? \\  & What if I repair now, vs. later? \\  & When is the ship going to be ready again? \\  & MRO manager \\  & Dispatching manager \\  & Main source of value \\ \hline \hline \end{tabular}   
**Table A9. ** Case 7--server room temperature management. **Aspect** & **Case Details** \\ \hline \multirow{4}{*}{ \begin{tabular}{} \end{tabular}  } & The servers in the server room are critical and must have high availability; temperature management is critical to their reliability and there is a need to improve overall equipment up-time and provide further business stability through equipment risk mitigation. \\  & Representation of physical asset performance and operational processes \\  & FM team: carry out routine system checks, improve maintenance services \\  & Operations: in the event of failure, able to understand key system knowledge (What? Who? How long? Why?) \\  & IT audit team: carry out the system audits to confirm compliance. \\  & Avatars: sensor types (temp, air quality, air flow, humidity, air pressure), redundant \\  & power systems, key technical room equipment (servers, ventilation, coolers), specific \\  & spare parts of tech assets. \\  & The digital twin will provide compliance history for the equipment in the condition as it is operated. It will support the temperature management and audit; it will provide information on failure rates and replacement part information. \\  & What is the status of the assets as they are maintained and operated? \\  & What is the current status of the system? \\  & When do parts reach end-of-life? \\  & What are the availability of spares (stock and supply chain)? \\  & What are the main failures? \\  & What is the mean time to repair? \\  & What is the impact on the server of the failure? \\  & Facility operators \\  & IT team \\  & Facility management contractor \\  & Maintenance technicians \\  & (Operators
## 2 Materials and Methods
### Diachronic Analysis of SWC Implementations  
The temporal evolution of SWC structures' spatial distribution was depicted through diachronic maps, which were compiled from a diverse array of sources. These sources encompassed the interpretation of satellite imagery, analysis of aerial photographs, utilization of Google Earth imagery, and the digitization of data extracted from prior research endeavors. These multifaceted resources were harnessed to construct maps that captured the SWC layout for the years 1980, 1990, 2000, 2010, and 2020, effectively highlighting the dynamic transformation of SWC structures across the study period.