esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling of IES
### HCNG-CCS-P2G IES Mathematical Model
#### 2.1.2 DHB Strategy  
In the HCNG model, a constraint should be set for the hydrogen blending ratio \(r(t)\). Based on whether the blending ratio changes continuously over time or in discrete time phases, the DHB strategy is further divided into two dynamic adjustment strategies: Continuous Hydrogen Blending (CHB) and Time-phased Hydrogen Blending (THB).  
The hydrogen blending ratio in CHB changes continuously over time, providing greater flexibility. Therefore, CHB only imposes a range limitation on \(r(t)\) and solves it as an optimization variable. The range limitation expression is as follows:  
\[0\leq r_{\text{c}}(t)\leq 20\% \tag{7}\]  
where, \(r_{\text{c}}(t)\) is the hydrogen blending ratio considering CHB.  
In the optimized hydrogen blending ratio, CHB clusters near two specific values during distinct time intervals, exhibiting step characteristics. Furthermore, Reference [20] demonstrated that during the hydrogen blending process, hydrogen fluctuations should be minimized, and hydrogen supply should occur as intermittently as possible. Based on these considerations, the hydrogen blending ratio \(r(t)\) obtained through CHB optimization is binarized. Specifically, the average value of the 24-h hydrogen blending ratio optimization plan is used as the threshold: for periods below the average, no hydrogen blending is applied (i.e., the blending ratio is zero); for periods above the average, the blending ratio is set to the maximum value (i.e., 20%).  
The step characteristics of the hydrogen blending ratio directly reflect the dynamic coupling and economic efficiency of the HCNG-CCS-P2G system. Section 5.2.3 will use simulations to verify the compatibility of this tendency with the THB strategy. Its mathematical model is as follows:  
\[\overline{r_{\text{c}}(t)}=\frac{1}{T}\sum_{t=1}^{T}r_{\text{c}}(t) \tag{8}\]  
\[r_{\text{t}}(t)=\begin{cases}0,&r_{\text{c}}(t)\leq\overline{r_{\text{c}}(t) }\\ 20\%,&r_{\text{c}}(t)>\overline{r_{\text{c}}(t)}\end{cases} \tag{9}\]  
where, \(\overline{r_{\text{c}}(t)}\) is the average value of the CHB hydrogen blending ratio optimization result and \(r_{\text{t}}(t)\) is the hydrogen blending ratio considering THB.
# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 3 Introduction to Signal Quality Index for Photoplethysmographic Signals
### Manual annotation  
Manual annotation involves human intervention in the process. As with any manual annotation, this process takes time and is difficult. It is also subject to errors from annotators (noise in labels).  
The annotation of PPG signals differs from the annotation of objects in images [18] as follows.  
* A PPG signal is a physiological signal, and evaluating its metrological quality needs the opinion of experts in the field. Consequently, the process of annotation can be expensive, and the annotation of large datasets is difficult.
* with a set of rules that the PPG signal must comply with;
- with another physiological signal not affected by the perturbation.  
Other than subjective or guided annotation, a third way to manually annotate the quality has been proposed. This method consists in including artificial perturbations during the recording of the PPG. For example, the luminosity can be changed during the recording of an iPPG signal, or a patient may be asked to move his/her hand at a given time (Figure 4) during the recording of the cPPG. Then, the PPG signal corresponding to the occurrence of the perturbation is labelled as low-quality. The impact of the perturbation on the signal may still depend on the intensity of the artifact induced. Table 3 proposes a summary of the different methods of annotation.  
Figure 4: Example of annotation done during a controlled experiment. Here, a patient is wearing a PPG sensor and is asked to not move during a given period. Then, at a specific time, the patient is asked to artificially create a perturbation (here, a motion artifact with up–down movement). The signal recorded during this period of perturbation is automatically annotated as “bad-quality”.
# A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams
## 2 Distance Learning
### State of the Art
#### 2.1.1 Students
This regards the inclusion of students' everyday environments into the learning scenario, as well as the integration, into a unique framework, of daily learning collaborators, contents and services and digital augmentation of the physical objects of the students' real life.
## 3 Proposed Method and Implementation
### Dynamic Analysis Process  
This method is mainly based on the analysis of the behavior of the malware and it is different to the static method in the fact that a simulated environment is set up (virtual machine, sandbox,  
Figure 3: Interactive DisAssembler (IDA Pro) dissembling an executable file.  
Figure 2: Top 10 packers from 8637 packers.  
simulator, etc.) to run the malware in order to extract different malware behaviors. Dynamic analysis is more effective compared to static analysis and does not require the executable file to be disassembled. It discloses the malware programs' natural behavior that is more resilient to static analysis.  
A dynamic analysis method to detect malicious software has been developed by [PERSON] et al. [40]. Their system detects malware by observing the network patterns of applications and the same research claims that there is pattern similarity of network traffic patterns of different applications with the same functions. A web-based android malware detection and classification system has been proposed [41] where they developed an auto-trigger view identification in addition to a droidbox structure. Android malicious software detection is outside the scope of this paper. In this research, Cuckoo SandBox has been used as it provides information on the file system, registry keys, and network traffic monitoring in a controlled environment and produces a well-formed report from Jason format. For the API call list from the malware analysis behavior, the call sequence has been considered using e-behaviors, i.e., file behavior, registry behavior, process behavior, and network behaviors. The Windows application programming interface (API) from the Microsoft Developer Network (MSDN) has been used to map and operate a match.
## 3 Analysis of Lightning Strike Effects on Patch-Repaired Composite
### Optimizing Aluminum Coating Applications for Repaired Laminate
#### 3.2.1 Impact of Aluminum Coating on Damage Reduction in Patch-Repaired Laminate  
A 0.01 mm thick aluminum coating was locally applied to the geometric center of the structure along the x-axis direction, measuring 100 cm in length and 30 cm in width, as shown in Figure 9. This targeted application covered the junction, and was anticipated to enhance surface electrical conductivity. Under a 5 kA peak current, no ablation damage was observed on the surface or within the structure. This indicates that the high electrical conductivity of aluminum provides conductive pathways, potentially mitigating breaks in current flow caused by misaligned fibers at the junction, thereby enabling the structure to uniformly disperse the current and heat generated by lightning strikes.  
To assess the lightning strike protection of a structure subjected to high-intensity currents, this study amplified the peak current to 20 times and 40 times the initial test value, respectively, applying simulated currents of 100 kA and 200 kA. As depicted in Figure 10, the surface damage area was significantly reduced. Furthermore, no ablation damage was observed in the second ply, neither in the patch nor the laminate. The electrical conductivity of aluminum alloy is three orders of magnitude higher than that of the laminate, enabling more efficient conduction and thus reducing the current density within the laminate. Figure 10a shows a symmetrical diamond-shaped damage area formed by the uniform expansion of ablation damage around the lightning strike entry point due to the aluminum coating. This phenomenon can be attributed to the isotropic conductivity of aluminum. However, when the current was increased to 200 kA, as shown in Figure 10b, the damage initially extended to the junction between the patch and the laminated structure,  
Figure 9: Patch repair assembly diagram for composite laminates with 30 mm wide aluminum coating.  
further propagating towards the main body of the laminate along this direction, forming an approximate rectangular ablation area. This suggests that under high current densities, the aluminum coating may reach its current-carrying capacity limit.  
Analysis of the damage results demonstrated that the application of a localized aluminum coating on the surface significantly enhanced the structure's lightning protection performance, reducing damage areas by 78.6% under 100 kA, where the damage areas were reduced by 1.85 cm\({}^{2}\) to 4.91 cm\({}^{2}\) compared to uncoated controls. However, the protective strategy may introduce substantial weight when applied on a larger scale, potentially adversely affecting structural performance. Therefore, future research endeavors should explore more optimized coating strategies.
# Boosting Safety Protocol Compliance with Eye-Tracking in Chemical Plants
## 2 Materials and Methods
### Experiment Environment
#### 2.3.6 Human-Computer Interaction (HCI)  
The human-computer interaction (HCI) interface was designed to enhance user engagement by providing real-time feedback and adaptive interaction during training. The interface utilized multimodal sensory inputs, including visual, auditory, and tactile feedback, to improve situational awareness and safety compliance. By integrating HCI elements into the training workflow, the system dynamically adjusted guidance and feedback based on participants' gaze behavior, ensuring that users received immediate corrective prompts when deviating from key safety areas.  
The HCI system functioned through a structured gaze tracking and feedback loop, as illustrated in Figure 6. Initially, the system identified the user's current gaze area by analyzing eye-tracking data collected in real-time. This gaze area was then compared against predefined working gaze zones within the VR environment. If the participant's gaze deviated outside the designated safety-critical area, the system triggered a warning notification, alerting the user to the deviation. Subsequently, corrective feedback was provided, prompting the user to return their focus to the correct operational zone, thereby reinforcing safe working habits.  
Through this interactive feedback mechanism, the HCI system ensured that participants maintained continuous focus on essential safety elements such as control panels, hazard indicators, and operational guidelines. The adaptability of the system allowed for real-time adjustments based on task complexity and user performance, making the training experience more responsive and personalized.  
By integrating these advanced technologies, the training platform offers a robust framework for assessing and improving industrial safety performance. The combination of VR-based simulation and real-time gaze tracking fosters a data-driven approach to safety training, ensuring that participants develop the necessary skills to operate efficiently and accurately in high-risk chemical environments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device** & **Specification** & **Value** \\ \hline CPU & Intel i7 11 th Gen & 3.9 GHz \\ GPU & NVIDIA RTX 3080 & 10 GB VRAM \\ Memory & RAM & 16 GB \\ Latency & Processing Latency & \(\leq\)15 ms \\ Software & XR and Data Processing & Unity 3D 2022.3.38f1c1, \\  & & HTC Pro SDK \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hardware specifications table.
# An IoT-Based System for Efficient Detection of Cotton Pest
## 3 Results and Discussion
### Simulation Results  
As a proof of concept, a simulation-based case study was also carried out. For this, an IoT-based multi-agent discrete event simulator called CupCarbon [32] was used. It is a smart city and IoT wireless sensor network (SCI-WSN)-based simulator. One of the key features of this simulator is its integration capability with real-time maps and real devices. Therefore, a real agricultural field using a Google map (satellite) was used.  
Figure 10 shows three types of nodes: (A) sensing node, (B) base station, and (C) mobile node, which were used in the simulation scenario. Here, the mobile node mimics cotton pests, who are randomly moved into an agriculture field using a random mobility model.  
The sensing node was simulated with the same sensing capabilities as the proposed nodes in Section 2.3 and Table 3 shows the general simulation parameters.
## 3 Methodology
### Power Flow Analysis
#### 3.1.2 BIBC, BCBV, and DLF Matrices  
The _BIBC_ matrix establishes the link between the current injected at various nodes and the currents flowing through the network branches [43]. It essentially encodes the network's connectivity, capturing how current inputs at the nodes translate into currents along the lines. Mathematically, this can be expressed as follows:  
\[I_{b}=BIBC\cdot I_{inj} \tag{3}\]  
where \(I_{inj}\) is the vector of node current injections, and \(I_{b}\) is the resulting vector of branch currents.  
In a radial configuration, the _BIBC_ matrix has a lower triangular form, reflecting the hierarchical, tree-like structure of the network. Each element \(BIBC_{ij}\) is set to 1 if node \(j\) is located upstream of node \(i\), and 0 otherwise.  
Complementary to the _BIBC_, the _BCBV_ matrix describes how branch currents influence voltage drops across the network nodes [43]. This matrix inherently integrates the impedance characteristics of the network's branches, converting the branch current vector (\(I_{b}\)) into a vector of node voltage drops (\(\Delta V\)). The transformation can be represented by the following:  
\[\Delta V=BCBV\cdot I_{b} \tag{4}\]  
The construction of the _BCBV_ matrix depends directly on the impedance values of each branch. For a network composed of \(n\) nodes, the matrix will have a dimensionality of \((n-1)\times(n-1)\) corresponding to the \(n-1\) branches typically present in a radial layout.  
By combining these two matrices, a more comprehensive tool known as the Distribution Load Flow (_DLF_) matrix can be derived. This composite matrix provides a streamlined way to calculate voltage drops across nodes directly from current injections, eliminating the need for intermediate calculations involving branch currents. The _DLF_ matrix is defined by the following:  
\[DLF=BCBV\cdot BIBC \tag{5}\]  
Thus, the node voltage drop vector can be directly obtained as follows:  
\[\Delta V=DLF\cdot I_{inj} \tag{6}\]  
A notable feature of the _BIBC_, _BCBV_, and consequently the _DLF_ matrix is their stability: they remain constant as long as the network topology does not change. This constancy is particularly advantageous for repeated load flow analysis, as it allows for efficient calculations without needing to recompute these matrices unless there are structural changes in the system.
# Experimental and Empirical Studies to Evaluate Liquefaction Resistance of Partially Saturated Sands
## 1 Introduction
Previous element and physical modeling tests showed that several parameters such as the degree of saturation (\(S_{r}\)), initial effective confining stress (\(\sigma_{c}^{\prime}\)), and back pressure (\(u_{0}\)) affect the co- and post-liquefaction behavior of partially saturated soils. The excess pore pressure generation, ground surface, and structural settlement are significantly reduced as the degree of saturation of soils is lowered [30; 31; 32; 33]. Through element tests, the liquefaction resistance of partially saturated soils was shown to increase with a decreasing degree of saturation [34; 35; 36; 37; 38; 39; 40]. The observations based on cyclic element tests also indicated that not only the degree of saturation is of importance, but the effects of initial effective confining or vertical stress and back pressure also must be accounted for [17; 41]. Partially saturated soils under a higher confining stress and lower back pressure appear to show greater resistance to liquefaction. As mentioned previously, the presence of gas bubbles in the pore spaces plays an important role in soil liquefaction, and a true assessment of this aspect is important in geotechnical earthquake engineering practice. The current study discusses the most important parameters that govern the liquefaction behavior of partially saturated sands with occluded gas bubbles (e.g., \(S_{r}\geq 80\%\)). Moreover, an empirical framework was adopted to obtain a quick and reasonable estimation of the liquefaction-resistance ratio in terms of the bulk modulus, maximum volumetric strain, and excess pore-pressure ratio. To achieve these goals, a series of 60 stress-controlled undrained dynamic triaxial tests were conducted on nearly/fully saturated and partially saturated sand specimens prepared with a chemical composition of sodium percarbonate. The test parameters (e.g., degree of saturation, initial effective confining stress, back pressure, and relative density) were changed. Subsequently, the predictions of the proposed empirical methodology were compared with the experimental data obtained from this study and collected from the published literature. This study is expected to provide significant insights that may enhance the use of this particular induced-partial-saturation method in design practice.
## 2 Wireless Sensor Networks
In order to diminish the effects of node failures, multi-path routing schemes have been developed on top of these algorithms, but the resource demands are quite high. Collaborative signal and information processing over a network is an important area of research and is related to distributed information fusion. Processing data from more sensors generally results in better performance but also requires more communication resources especially in terms of energy. Similarly, less information is lost when communicating information at a lower level, but it requires more bandwidth. Fusion approaches range from simple rules of picking the best result to model-based techniques that consider how the information is generated. Here the efficient and intelligent usage of battery becomes also a most challenging issue: again there is a trade-off between redundancy and energy constraint [9]. Typically, WSNs use static sinks and multi-hop forwarding to collect data from sensor nodes. In this architecture the nodes close to the sink tend to consume more energy in relaying data from other nodes. In particular, the use of DSN even with mobile sensor requires algorithms capable of assuring a proper coverage [10] and the use of mobile sinks to significantly extend the lifetime of the sensor network [11]. In addition, parameters detected by one node have to be associated with variables detected by nodes of different kind to avoid duplication and enable fusion. Optimal data association is computationally expensive and requires significant bandwidth for communication [12]. To face this problems several approaches have been proposed by employing mobile sinks and by improving the cooperation between nodes [13] and by iterative partitioning and hierarchical clustering based mechanisms [14], by using suitably defined protocols to reduce network traffic [15]. Thus, modern multi-platform sensor networks can provide a new kind of instrument that enables us to observe and interact with physical phenomena in real time at a fidelity that was previously unexpected, as described in the next section.
# Precise Orbit Determination of BDS-2 and BDS-3 Using SLR
## 3 Processed Data and SLR Validation  
### SLR NPs Data of BDS Satellites  
All available SLR NPs observations for BDS-2 and BDS-3 satellites, provided by the Europe Data Centre (EDC) ([[ftp://edc.dgfi.tum.de/pub/slr/data/npt_crd/](ftp://edc.dgfi.tum.de/pub/slr/data/npt_crd/)]([ftp://edc.dgfi.tum.de/pub/slr/data/npt_crd/](ftp://edc.dgfi.tum.de/pub/slr/data/npt_crd/))), have been collected and adopted from 1 January 2019 to 30 June 2019 in our processing, as shown in Figure 2. In the figure, the satellite with the superscript '' refers to the BDS-3 satellites. The BDS-2 MEO C11 owns the most observations among the nine BDS satellites, with the number of daily observations up to 50, while the daily observations for GEO C01 are no more than 10. The SLR observation distribution pattern of IGSO C13 is no worse than that of the four BDS-3 satellites in this time span. It is noticed that almost no SLR observation is provided around 10 February 2019 for all BDS satellites. It happens to be during the Chinese New Year holiday, while the SLR site in Shanghai (SHA) is the only provider of the BDS satellites' orbit predictions for all SLR sites.  
Figure 2: The daily SLR observations of BeiDou Navigation Satellite System (BDS) provided by Europe Data Centre (EDC) since 2019.  
Figure 3 shows the contribution of each SLR site to the daily SLR observations of all BDS satellites. In the figure, the SLR sites with the superscript \"*\" refer to the location of the site in the Southern Hemisphere. Currently, seven SLR sites are active in the Southern Hemisphere, as presented in the website [[https://ilrs.cddis.eosdis.nasa.gov/network/stations/index.html](https://ilrs.cddis.eosdis.nasa.gov/network/stations/index.html)]([https://ilrs.cddis.eosdis.nasa.gov/network/stations/index.html](https://ilrs.cddis.eosdis.nasa.gov/network/stations/index.html)). The Yarragadee (7090) site and Mount Stromlo (7825) site, located in the southwest and southeast of mainland Australia, in addition to the Changchun (7237) site from China, have made outstanding contributions to the SLR observations of BDS satellites. For the other sites, such as 7810 (Zimmerwald, Switzerland), 7839 (Graz, Austria), 7840 (Herstmonceux, United Kingdom), 7845 (Grasse, France), 7941 (Matera, Italy), and 8834 (Wettzell, Germany), their SLR observations are concentrated in the BDS MEO satellites.
## 5 Conclusions  
1. Nickel recovery to the weld metal is improved in unconstrained metal powder application, compared to the application of with pre-alloyed nickel containing powder as reported in previous work;
2. Carbon steel weld metal was alloyed with unconstrained metal powders to 6.9% Ni and 3.7% Al, representing 85% Ni yield and 45% Al yield to the weld metal.
3. The aluminium powder addition ensured sufficient control of the oxygen potential at the molten flux-veld pool interface without interfering with oxygen transfer from the arc plasma to the weld pool, resulting in 247 ppm O in MP6 vs. 499 ppm O when no metal powder was added;
4. Thermodynamic assessment of metal fluoride gas formation reactions indicates that NiF\({}_{2}\)(g) is more likely to form from vaporised Ni, than from NiO formed from Ni oxidation.
5. The aluminothermic reduction of SiO\({}_{2}\) and MnO from the molten flux (slag) heats the weld pool by approximately 53 \({}^{\circ}\)C.
6. Application of unconstrained metal powders in SAW improves overall process productivity because it eliminates the expensive time consuming step of the manufacturing of alloyed wire and alloyed powder because unconstrained metal powders can be applied directly in the SAW process.  
[PERSON] conceptualised the work; [PERSON] and T.C. executed the experiments, interpreted the data, and prepared the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Research Foundation of South Africa, grant number BRIC171211293679.  
The data sets presented in this study are available upon request to the corresponding author.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.