esa-sceva/satcom-chunk-collection huggingface dataset:
## Results  
### LLLT Enhances RAW 264.7 Cell Proliferation, Osteoclast Differentiation, and Bone Resorption  
The effect of LLLT on the survival or proliferation of RAW 264.7 cells was investigated through MTT assays. The RAW 264.7 cells were exposed to LLLT (\(\lambda\) = 810 nm, 80 mW, 80 mA) for 5, 15, or 30 s, then incubated for 3, 6, or 24 h. In the groups cultured for 3 h and 6 h, there was no significant difference in any cells exposed to LLLT, but in the LLLT 30 s irradiation and 24 h incubation group, the cell viability was significantly increased (\(p\) < 0.05). Therefore, we confirmed that exposure to LLLT in RAW 264.7 cells does not negatively affect cell viability, but rather helps cell proliferation (Figure 1).  
Next, we investigated whether LLLT enhances RANKL-induced osteoclast formation in RAW 264.7 cells. To confirm this, RANKL (10 ng/mL) was used to induce differentiation of the cells, and at the same time, LLLT was used for 5, 15, or 30 s, followed by incubation for 5 days. TRAP-positive multinuclear osteoclasts and resorption pit areas were identified using an optical microscope. It was confirmed that the number of TRAP-positive cells significantly increased (Figure 2A,B) in the LLLT 15 s irradiation group (\(p\) < 0.05). However, we observed a significant decrease in TRAP-positive cells in the LLLT 30 s irradiation group.  
These results were also consistent with the pit formation (Figure 2C,D). Therefore, through these results, we confirmed that LLLT irradiation stimulates osteoclast differentiation after 5 to 15 s of treatment.  
Figure 1: Cell proliferation affected by LLLT irradiation in RAW 264.7 cells. Cells were irradiated. LLLT for 5, 15 and 30 s, then incubated for 3, 6 and 24 h. Cell proliferation measurement was conducted by MTT assay. Each value represents the mean \(\pm\) S.D. *\(p\) < 0.05 compared with control.  
Figure 2: Effects of LLLT on RANKL-induced osteoclast and pit formation. (**A**) RAW 264.7 cells were cultured with RANKL (10 ng/mL) for 5 days to induce osteoclast differentiation with treatment with LLLT (5–30 s) to assess its osteoclast effect. (**B**) The number of TRAP-positive multinucleated cells was counted. *\(p\) < 0.05 compared with RANKL alone. (**C**) Resorption pit formation were observed on Corning® Osteo Assay Surface, and (**D**) the pit area was calculated as the absorbed area/total area. *\(p\) < 0.05 compared with RANKL alone.
## 6 Experimental Verification  
The proposed cloud server-assisted remote monitoring and fault identification system for DTPPFs consists of a monitoring system architecture and cloud server software architecture along with the software design of the back-end service layer and functionality design of the front-end application layer. It was successfully applied to monitoring of 25 DTPPFs to provide fault alarms, fault recall, and fault diagnosis while allowing users to view various DTPPF operating states through the internet in real time.  
In our experiments, the monitoring object was the No. 1 medium-frequency furnace in a metal smelting plant in Nanjing City, China. The measured fundamental current and fifth harmonic current in the distribution room at a specific moment are shown in Figure 16.
# A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)
## 5 GeoSOT-AS Based Regional and Time Segment ADS-B Data Extraction Algorithm
### Grid Code Aggregation  
When the finest granularity of grid voxels was set to match the retrieval range, in general, the number of retrieval codes increased as the retrieval range expanded. Here, to avoid the retrieval of excessive finest granularity grid codes, a regional aggregation method was proposed based on grid voxel codes, which uses the prefix consistency of codes for code aggregation to reduce the number of index codes.  
The coding bit operation is introduced, and simply, the prefix extraction function of codes \(Cal\_Sub(Code_{v},i)\) is proposed. To obtain the prefix code before the \(i\)th bit of the encoded interval (Equation (7)):  
\[Cal\_Sub(Code_{v},i)==Code>>(Code_{len}-i) \tag{7}\]  
where the idea of encoding interval extraction is to truncate the encoding by shifting \((Code_{len}-i)\) bits to the right to clear the high bits on the right side, whereas obtaining the first \(i\) bits of the encoding \(Code_{v}\), where \(Code_{len}\) is the original fine-grained encoding bits.  
In general, for the set of mapping codes in the first step, it is only necessary to consider the case of consistent prefix codes in the appropriate code bits to reach the full traversal of filling curves, or full interval coverage of the upper level. Then one can perform simple upward aggregation, via multiple codes with the same code prefix which can be aggregated into one upper-level code. Under these conditions, not only to ensure the consistency of the index range, while avoiding unnecessary code index redundancy, the code aggregation process formula was expressed as follows: \(Code_{init}\) is the encoding prefix for the upcoming aggregation operation (Equations (8) and (9)):  
\[Cell\_Merge=\sum_{i=0}^{n}\partial_{i}*Code(i) \tag{8}\]  
\[\partial_{i}(Code_{v}(i))=\left\{\begin{array}{l}0,Cal\_Sub(Code_{v},i)\
eq Code _{ini},\\ 1,Cal\_Sub(Code_{v},i)=Code_{ini},i=0,1,\cdots,n\end{array}\right. \tag{9}\]
## 5 Discussion  
Mapping landslide susceptibility as a means of the prevention and management of landslides is an important guideline for urban planning, and geological hazard prevention and control [57]. Various approaches have been used to assess landslide susceptibility. Decision trees, SVM and other methods are well established and are considered to be extremely effective in both landslide prevention and other fields. CART, as a classical decision tree, is an easy and straightforward technique to explain. However, it is overly simplistic for capturing the complexities of real-world scenarios [47] and it has limited applications in landslide susceptibility analyses. Nonetheless, improved trees that use CART as the base learner have been shown to be effective for solving complex problems [58]. SVM is a more powerful learning machine, and numerous improvements, such as FR-SVM, the fruit fly optimization algorithm and ensemble learning, have been proposed to enhance its performance [59; 60; 61]. The BLS is a simple and efficient system that was proposed by [PERSON] and has been applied in several fields, but it has not been used in landslide susceptibility analyses to date. The extremely fast training speed, the relatively high reliability and the good performance of the BLS and the integrated BLS in other areas also indicated their potential for landslide applications. In this study, we applied the BLS and the integrated BLS model to evaluate landslide susceptibility, and we compared the performance of the integrated BLS with integrated learning systems that used CART and SVM as the base learners. The evaluation of landslide susceptibility was mapped to provide decision-making guidance for disaster control, urban planning, etc. It may help researchers and decision-makers to make better decisions to reduce the impact of landslide hazards on people's lives.  
Figure 10: ROC curves for the benchmark models: (**a**) training dataset; (**b**) validation dataset.  
In this study, a total of 12 influencing factors were used for predictions based on the results of the evaluated importance of the influences. All three integrated models performed well on the validation dataset, especially the integrated model with BLS as the base learner, which achieved the highest AUC value of 0.889, which represents a significant improvement compared with the integrated models that used CART (AUC = 0.873) and SVM (AUC = 0.846) as the base learners. These findings indicate that the BLS and BLS-based integrated learning methods are effective for evaluating landslide susceptibility. Moreover, the integrated BLS-based model required only slightly more time than the BLS, and the ensemble BLS model can be readily implemented on a pre-trained BLS network [62].
# Computational Investigations and Control of Shock Interference +
## 1 Introduction
Onset of unstart in the full scramjet flow path during flight can occur due to additional factors including pressure perturbations at the isolator exit due to combustion dynamics, distortion of the incoming inlet flow due to an unexpected change in the angle of attack or the ingestion of exhaust gases, and/or changes in wall conditions such as damage to or removal of thermal protection coating. In order to prevent unstart and maintain operability, active and passive control methods have been developed for the mitigation of the onset of scramjet isolator unsstart by preventing any sudden disruption of the flow in the isolator by controlling the effects of shock interference and interaction with  
Figure 1: Schematic of scramjet flow-path and position of the isolator [10]. the boundary layer on the pseudo-shock-train in the isolator. SBLI is shown at the start of the shock-train in Figure 2.  
By controlling the shock-interference interaction with the boundary layer, the pseudo-shock-train in the isolator can be controlled. There are many methods of shock-train control by controlling the SBLI. The active boundary-layer control methods include: boundary-layer suction/bleed, micro-jets, plasma jets, and air vortex generators. Vortex generator implemented upstream of the interaction [16], creates a vortex, energizing the flow, and ultimately delaying the separation. Boundary-layer suction or bleed removes the low-momentum fluid closer to the wall by a mass flow outlet, leaving only higher-momentum fluid within the flow. The higher-momentum fluid is less likely to separate; therefore, flow separation is also delayed in this case, ultimately reducing the adverse consequences of strong SBLI [17]. Boundary-layer suction has been proven by [PERSON] et al. [18] to reduce shock-train oscillations and the length of the shock-train. Passive control methods include: porous cavity, splitter plate, bumps, recirculation cavity, and wall cooling. Theoretically, it has been shown that wall cooling can reduce wall boundary-layer thickness and therefore, can reduce the potential of boundary-layer separation due to SBLI phenomena.
## 3 Results
### Model Evaluation
#### 3.1.2 FGM GPP Estimations Matched with GOSIF and Other GPP Products  
In addition to the flux tower measurements, we further introduced nine global GPP products and remotely sensed GOSIF data to examine the large-scale pattern and temporal dynamics of European GPP estimated by the FGM.  
The FGM effectively simulated the general pattern of GPP along the temperature gradient across Europe (Figure 5a). The mean GPP increased from the boreal to temperate regions and decreased from the temperate regions to the Mediterranean regions. The spatial pattern of the annual mean GPP modeled by the FGM was well correlated with that of the other GPP products (r = 0.61-0.8) (Figure 5b-f) and GOSIF data (r = 0.77) (Figure 5g), although there were regional discrepancies in magnitude.  
The multiyear mean of the annual GPP of Europe estimated by the FGM (9.4 Pg C yr\({}^{-1}\)) was reasonable compared with the other GPP products (5.9 to 9.2 Pg C yr\({}^{-1}\)) (Figure 6a). Moreover, the annual total GPP showed a significant increasing trend from 2001 to 2016 (+0.051 Pg C yr\({}^{-2}\), R\({}^{2}\) = 0.76, \(p\) < 0.01), which was in accordance with the other GPP products (Figure 6a). The annual total GPP across Europe increased from 9.09 Pg C yr\({}^{-1}\) in 2001 to 9.94 Pg C yr\({}^{-1}\) in 2016. In addition, the interannual dynamics of the FGM GPP correlated well with those of the GOSIF GPP, GLASS GPP, VPM GPP, BEPS GPP, and CCW GPP (Figure 6b). These evaluation results indicated that the FGM GPP was reasonable and could be used to further quantify the V\({}_{\text{cmax}}\) change effect on GPP.
## 4 Application
### Non-Clinical Application
#### 4.2.3 Sleep Monitoring  
Continuous monitoring of vital signs, even at night, can be carried out by combining visible RGB cameras and infrared cameras. During the night, for sleep monitoring, the iPPG exploiting infrared cameras will be particularly suitable [PERSON] et al. (2015). HR and RR of sleep apnea patients can also be monitoredusing the Microsoft Kinect sensor [106] and colour magnification. Using three monochrome cameras, [PERSON] et al. [132] proposed a fully automated method to remotely and continuously monitor HR and SpO\({}_{2}\) during sleep. In the proposed method, videos are first pre-processed using Gaussian smoothing using a 2D Gaussian kernel and rigid block segmentation. Then, the pulse signal, extracted using the PBV [79] method, is exploited as a feature to distinguish living and non-living tissue based on similarity mapping. After that, a hybrid method is introduced which combines the iPPG-based subject detection with a tracker to select an ROI. Finally, vital signs are extracted. They used five healthy subjects sleeping in different supine positions, to simulate realistic sleep scenarios. The results showed that the proposed method outperformed the state-of-the-art method (VPS, V-J etc.) for the estimation of oxygen saturation.
## 4 Analysis and Interpretation of Results
### Correlation Links Between Individual Production Sources and Residual Load  
The increasing penetration of renewable energy sources (RES) is fundamentally changing the short-term dynamics of wholesale electricity markets and leading to a change in electricity price volatility. In order to better understand these mechanisms, this section focuses on the analysis of the correlation between the main renewable generation variables--solar generation, wind generation and residual load--and the wholesale electricity price. The correlation analysis is performed on time series with hourly or 15 min resolution over the period 2015 to 2025 and includes six countries with different levels of renewable integration and market interconnectedness. Correlation coefficients are calculated separately for solar generation, wind generation and residual load in relation to electricity prices, allowing a detailed assessment of the impact of each resource. A table with the correlation values for the whole period under study is shown in Table 5. The specific years and their comparison are described in the individual Sections 4.3.1-4.3.6. These outputs provide empirical evidence on how variable generation affects market prices, what role residual load plays in stabilising or in price fluctuations, and what differences exist between countries depending on their structural and regulatory conditions. These insights contribute to a deeper understanding of the challenges associated with RES integration and  
Figure 7: Share of negative price in % in Estonia.  
provide a basis for designing effective strategies to increase the flexibility and stability of energy markets.
## 4 Case Study of Data Collaboration  
This paper is based on spatiotemporal data mining and an improved differential fusion algorithm (DFA) to take the collaboration of UAV video data based on IoT, BIM data, GPS trajectory data and algorithm model as an example (Figure 5) to explain the rationality of the framework.  
Firstly, the error data in the GPS trajectory data are deleted; Secondly, the GPS data are processed, and their longitude and latitude are mapped to a high-precision map by a matching algorithm (through track correction), and the speed of the road is calculated; Then, the spatiotemporal distribution characteristics are mined to extract travel hotspots and peak hours; Next, aiming at the hot intersections, the UAVs are used to capture the traffic flow data in the peak hours, and the UAV video data are processed and analyzed to mine the traffic flow and capture the behavioural characteristics. Finally, visual modelling of the digital twin of target intersections was conducted using UE4 and BIM data.
### Key Contributions  
This study proposes a method for constructing a platform chain optimized for small-scale DATPs. Instead of building the platform chain as a standalone blockchain, the proposed method constructs a platform chain anchored to established blockchains, such as Bitcoin and Ethereum, which serve as reference chains due to their proven stability and reliability.  
The main contributions of the proposed method are as follows:  
* Designing the platform chain as a sidechain of existing blockchains within a private or consortium blockchain framework. This setup leverages the trust and security of established blockchains, while reducing the setup and maintenance costs of the platform chain.
* Enabling seamless integration of the platform chain without requiring any modifications to the reference chain, thereby preserving its autonomy and facilitating the deployment of customized platform blockchains.  
* Recording only the hash values of transactions on the platform chain, while disclosing the actual transaction contents on the dual platform chain, thereby enhancing data privacy and confidentiality.
* Enabling scalable transaction processing by consolidating multiple transactions into a single hash value, thereby enhancing the efficiency and flexibility of the platform.  
These contributions collectively provide a scalable, privacy-aware, and economically viable foundation for digital asset trading on small-scale platforms.  
In lieu of empirical results or simulation-based benchmarks, this study adopts a formal threat analysis to evaluate the robustness and trustworthiness of the proposed framework. This approach is aligned with the design-oriented nature of the work, where system security and integrity are ensured by structural safeguards and role-specific constraints rather than probabilistic performance metrics.  
Since the framework is intended for permissioned blockchain environments with explicit governance mechanisms, well-defined participant roles, and contract-based execution, its effectiveness is best demonstrated through adversarial modeling. Accordingly, the security evaluation is structured to identify potential vulnerabilities and to analyze how each threat is mitigated by the proposed design.
## 2 Finite Element Model Development  
SMRT uses a bottom-contact third rail system, with the third rail being ALSS type. The typical third rail arrangement is shown in Figure 1(a),b. The structural properties of each component in the support structure and the third rail are listed in Table 1.  
Figure 2: \(Cont.\)  
A detailed 3D model of a single-span third rail was developed based on the available dimensional information and material properties, as shown in Figure 2c. The single-span third rail model was validated against ANSYS finite element analysis and experimental modal testing. The validated single-span third rail model was then extended to a multi-span third rail model, as shown in Figure 2d. The multi-span model consists of a 35 m long rail, which is supported by eight supporting structures. The distance between two consecutive support structures is maintained at 4.9 m, as per the SMRT standard. As mentioned earlier, the supporting structures were replaced by spring arrangements with equivalent stiffness to simplify the model further. The contact between aluminum rail and stainless-steel conductor rail was defined as bonded. A default messing in Workbench Mechanical was applied, which included element types such as Solid186, CONTA174, Beam188, and COMBIN 14. The element size was selected to be 0.1 m, which resulted in the number of nodes and elements to be 121,004 and 24,546, respectively.  
The equivalent stiffness of the support structures depends on the material and design. Based on the published articles, typical stiffness values for third rail support structures might range from 0.1 MN/m to 20 MN/m [8; 17]. Based on the material properties and design of the SMRT support structure, the equivalent spring stiffness for the supporting structures in three different directions, namely vertical, lateral, and longitudinal, were estimated to be 2.4 MN/m, 0.86 MN/m and 0.8 MN/m, respectively, by applying a known force at the bracket and simulating the structural deflection.
# Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data
## 2 Related Work
A probabilistic model is used to analyze the risk of two frequent types of marine accidents, such as collision and grounding. Their attention is specified upon oil tankers since they pose the highest environmental risks. The probability of vessel colliding is assessed in terms of a minimum-distance-to-collision-based model. The model defines the collision zone using a mathematical ship motion model and considers the traffic flow to be a non-homogeneous process. Similar approach is followed for the probabilistic assessment of grounding. Furthermore, in the work of [15], the authors presented a simulation model that attempts to assess the potential impact of deepening the terminals and ports of Delaware River and Bay areas, by considering multiple parameters such as tide, navigation, terminal and anchorage rules as well as vessel profiles. Concerning risk analysis, a solid review article by [16] emphasized on the catalytic influence of solid scientific basis in qualifying risk measurements. They stated that many of their studied applications and methods lack clarity about foundational issues concerning the scientific method of risk analysis. Definitions for key terminology are often lacking, perspectives are not introduced and little attention is given to the scientific approach underlying the analysis. Furthermore, another important conclusion in their work stressed the fact that even though many articles use probability-based risk analysis, only a minority utilizes factors such as uncertainty. Another review article on the same topic was published by [17] also mentioning the need to obtain both quantitative and qualitative models of risk in maritime transportation. He mentioned several works that are based on Bayesian inference and Bayesian networks, in alignment with the proposed work. In 2010, the article by [18] presented a paper which concerned the risk assessment of maritime industry, using logistic regression and Bayesian Networks. More specifically, a binary logistic regression method was used to provide input, using different data resource in maritime accidents. In the network-learning phase, the writers used a dataset of 130,000 vessels, by combining information about 10,000 lost vessels and 120,000 existing vessels, counting for more than 90% of worldwide commercial tonnage. All of the conditional probabilities and prior probabilities of the nodes of the Bayes Network were obtained through the application of binary logistic regression. Finally, they defined an equation which estimates the occurrence probability of an accident and they presented some instances for the effects of different factors.
## 2 Methods  
By assuming applied loads as an unmeasured white-noise signal, SSI can be defined as an output-only identification method. SSI-DATA algorithm consists of four main steps including:  
1. RQ decomposition;
2. Estimation of state sequences;
3. Finding least square solution; and
4. Kalman filter.  
State-space of a dynamic system is extracted from the product of extended observability matrix and state sequence. The appropriate order of a system is predicted using singular value decomposition (SVD). System order (number of modes) is the only user-defined parameter in subspace identification method and must be selected carefully to obtain meaningful results. Figure 1 shows the configuration for implementation SSI-DATA.