esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Method
### Review Process  
Using a comprehensive search of online journals, 814 records were located, but after removing similar or duplicate publications (using EndNote X9 software (Clarivate Analytics, Philadelphia, PA, USA)), only 272 unique studies remained. Further screening based on abstracts and titles resulted in the disqualification of an additional 198 articles. Two experienced researchers (E.R.C., D.N.O.) manually analyzed the remaining studies and excluded any studies that did not meet the predetermined exclusion criteria. The same two authors then examined the selected studies in full text and excluded any that lacked relevant information for the review. Any disagreements were resolved with the assistance of a third experienced reviewer (I.N.Z.) selected from among the authors. Ultimately, 39 RCTs and SMDs that met the necessary inclusion and exclusion criteria were chosen for the review (Figure 1).  
Figure 1: PRISMA flowchart of the methodology for the conducted study.
# Globally Scheduling Volunteer Computing
## 5 Implementation and Status  
Science United was launched in 2017. Currently (August 2021) it has about 4100 active volunteers and 5800 computers, of which 4100 have usable GPUs. Averaged over the last month, these computers process 160,000 jobs per day and have a throughput of 400 TeraFLOPS.  
The scheduling policy described in Section 4 has evolved over time and has been in its current form for two years. It seems to satisfy the goals listed in Section 4.6: project throughputs are stable and reflect shares, device starvation is rare, and no volunteers have complained about preferences not being respected.  
Figures 3 and 4 show recent throughput histories for CPU and GPU, respectively. Projects such as Rosetta@home appear only in the CPU graph because they have no GPU app versions.  
Figure 3: CPU throughput of the top 10 projects in 2020. The top two projects, which do COVID research, have larger allocation shares.  
Science United is implemented in PHP. It uses a MySQL database to store volunteer and project information, accounting data, and so on. The Science United source code is distributed under the LGPL v3 license and is available on Github.  
SU required some modifications to the BOINC client, such as the starvation monitoring described in Section 4.5. These changes were introduced in the 7.16 client release.
# The Influence of Copper Oxide Particle Size on the Properties of Epoxy Resin
## 1 Introduction
Copper's ability to generate toxic hydroxyl radicals, which can disrupt bacterial cell membranes, contributes to its effectiveness in combating microbial growth [24]. The primary objective of this investigation was to explore the viability of manufacturing and employing epoxy-resin-based composites that are modified with copper oxide particles. The variable component in the examined materials was the size of the reinforcement particles, ranging from 10 nm to 10 \(\upmu\)m. As part of the evaluation process, basic physical and mechanical properties were determined using assessments such as impact tests, and strength properties were assessed through static tensile tests and static bending tests. Accelerated aging tests were conducted to determine the influence of environmental conditions on the properties of the composites. Additionally, scanning electron microscopy was used to capture microscopic images of the materials produced, and the thermal conductivity of the composites was measured.
## 3 Materials and Methods  
### Research Objective and Methodology  
The purpose of this research was to compare a human operators' inspection abilities and performance with those of advanced technologies including image processing (IP), artificial intelligence (AI) and 3D scanning. The study comprised three human-technology comparisons for three different inspection types, namely: piece-part inspection, borescope inspection and visual-tactile inspection. We introduced the term 'inspection agent' when referring to both the human operator and technology. The performance of the inspection agent was measured in form of inspection accuracy, assessor agreement (consistency) and inspection time. The results were statistically analysed and compared against each other. The assessment of the inspection abilities was done using a weighted factor analysis and included additional criteria such as technology readiness level, agility, flexibility, interoperability, automation, standardisation, documentation and compliance.
## 3 Results and Findings
### Results of Empirical Study
#### 3.2.3 Descriptive Statistics  
In this section, the data of the questionnaire descriptive statistics for each construct item are presented. Mean, standard deviation, skewness, and kurtosis were calculated for all items of the respective construct, as shown in Table 6. The CRM factors of the respondents were estimated by eleven questions followed by a 5-point Likert scale. This scale extends from \"strongly agree\" (1) to \"strongly disagree\" (5). The mean score of the eleven CRM factors was between 1.86 (0.853) and 2.82 (1.308).
## 3 Detailed Description of the Existing Protocols
### Gedar  
In this subsection, GEDAR protocol is discussed with its pictorial description [23]. GEDAR performs geo opportunistic routing with DA. Wherever a sensor node discovers a void  
Figure 6: System model of IETR.  
node in its range, it adaptively amends its depth. Afterward, it again discovers its neighbor nodes in its range. If it found the next potential node, it advances the DP; otherwise, it again amends its depth. However, continuous DA leads the UWSN towards an energy hole. This energy hole dissipates high EC. At this end, high EC results in maximum energy depletion, which results in a void hole. If the cost of a mobile node is somehow reduced, the high E2E delay affects the network performance. The induction of void node in the UWSN increases backward transmissions, which also increases the E2E delay. These aforementioned problems encourage the researchers to enhance this protocol.  
The DA in GEDAR is shown in Figure 7. In this figure, the source node transmits the DP to its forwarder node, but, at its \(3^{rd}\) hop, the respective node has no next potential forwarder node. Therefore, this node needs to adjust its depth to find the next forwarder neighbor node (to forward its DP). After its DA, this node successfully forwards the data towards the sink node (using multi-hop transmission).
# Effective Risk Detection for Natural Gas Pipelines Using Low-Resolution Satellite Images
## 2 Risks of Natural Gas Pipelines
Finally, the PlanetScope API offers 8-band multispectral images dating back to 2020, which aligns with the time interval covered by our dataset [13]. However, we leave the utilization of 8-band data for future work. It is important to note that the dataset and helicopter flight reports are proprietary and thus cannot be made public. After establishing the data-gathering process, we next introduce the ChangeFormer model for CD.
## 3 Results and Discussion
### Wear Mechanisms
Figure 7b shows that plastic deformation and scratches were formed on the worn surface of the as-cast Al-5 Si-1 Cu-0.5 Mg alloy at a sliding speed of 0.565 m/s and the load of 60 N, which could suggest the abrasion wear mechanism. The same wear mechanism was also observed on the worn surfaces of the SiCp/Al-5 Si-1 Cu-0.5 Mg and the SiCp/Al-5 Si-1 Cu-0.5 Mg alloy with T6 treatment at the same sliding conditions. The plastic deformation of the as-cast Al-5 Si-1 Cu-0.5 Mg alloy was the most seriously and the oxides were the minimum, owing to the high plastic deformation in contact zone and no oxides on the surface. Moreover, the existence of SiCp particles and Al\({}_{2}\)Cu phase could help to protect the worn surfaces at a sliding of 0.565 m/s. The dominant wear mechanism of the SiCp/Al-5 Si-1 Cu-0.5 Mg alloys with and without T6 treatment was plastic deformation, oxidation and abrasion wear at sliding speed of 0.565 m/s and load of 60 N (as Figure 7d,f shown). Figure 12 shows the SEM micrographs of the debris of the SiCp/Al-5 Si-1 Cu-0.5 Mg alloy with T6 treatment and the EDS analysis of point C. As shown in Table 4, O and Fe content were 10.48 and 33.88 wt %, respectively. The bulk wear debris was generated by the development of plastic strain and crack propagation in the subsurface of the composite. It indicated that delamination wear also occurred during the sliding process except for the wear mechanisms mentioned. With the increase of applied loads, a large plastic strain appeared on the wear surface, and the wear rate increased (as Figure 5 shown). Figure 13a-c show the SEM images of the SiCp/Al-5 Si-1 Cu-0.5 Mg composite with T6 samples under different loads. Numerous narrow scratches paralleling the sliding direction and few white powders can be distinguished on the wear suface at a load of 15 N (as shown in Figure 13a). Thus, abrasion wear was the dominant wear mechanism, and oxidation also caused some wear. When compared with Figure 13a, more white powder and deeper and wider scratches were shown in Figure 13b. This indicated that the wear intensity increased. Oxidation wear and abrasion were considered to be the dominant wear mechanisms. Some bulk wear debris were observed and a rugged flat surface appeared indistinctly on the wear surface (as shown in Figure 13c). This indicated that the plastic deformation was present on the wear surface. Meanwhile, the oxidation wear and abrasive wear was still observed. Figure 13d shows the SEM images of edging morphology of the as-cast Al-5 Si-1 Cu-0.5 Mg pin sample at 90 N. The edging shape of the pin sample along the sliding direction was irregular due to the pressure of plastic deformation that was caused by the high applied load [32]. In other words, the wear resistance of the Al-5 Si-1 Cu-0.5 Mg alloy was poor.
## 2 Materials and Methods
### Design of the Device
#### 2.1.4 Calibration Method  
The thermistors were calibrated using conventional non-expensive methods by accessing the sensors temperature resistance curves [31]. The procedure was based on Ohm's Law and the Hoge-2 equation [32]. Only the MC65 thermistor is described since the JS8746A was calibrated the same way. The procedure is summarized in Figure A2 (Appendix B).  
First, we had to read the resistance ratio of the thermistor in a range of 0 to 44 degrees of Celsius to choose the category of coefficients according to Table A3. We managed to do this by using a voltage divider and the formula Rt in Table A2. The reference resistance is 10 KOHms with a tolerance of 0.05%. The ADCvalue in the formula corresponds to the readings of the ADS1115 analog to digital converter (15 bits of precision and 1 bit for the sign) and amplifier which we used in order to increase the precision and resolution of the reading since the ADC of the microcontroller is only 10 bits. We calculated the ADC value by using the formula in Table 12. Vcc on gain two is 2.048 as the dataset of the ADS1115 suggests. We amplified the signal by only a gain of two since the output of the thermistor can only range between 0 and less than 2.048 volts. We used the LM4040 to have a reference of 2.048 with a 0.1% tolerance. This addition improved the accuracy and precision of the system since it keeps the voltage reference stable and the values will not fluctuate. The first formula on Table 12 calculates the resistance of the thermistor. The value 10,000 is the reference resistance and 32,767 is \(2^{15}-1\) which is the ADC number of bits.  
The material of the thermistor is type F. For that reason, we used Table 13 to choose which coefficients were appropriate to form the Hoge-2 equation which is the best calibration equation comparing to nine more for the MF501 NTC thermistor according to [PERSON] [33]. In our case the values of the resistor ranged between 0.5 to 3.223 so we used the second row (0.36035 to 3.274), this ratio is close to optimal linearity, according to [PERSON] [34] who states that for a microK-type of instrument, optimum linearity is achieved in the resistance ratio range between 0.2 and 1.2.
## 4 Local Refinement Near Immersed Boundaries Using Algebraic Level Sets
### Brief Review of Algebraic Level Sets
#### 4.1.1 Implicitization of a Parametric Curve  
Given a rational parametric curve \(\mathbf{C}(X(u),Y(u),W(u))\) of degree \(p\) with \(x=\frac{X(u)}{W(u)}\), \(y=\frac{Y(u)}{W(u)}\), we can construct two auxiliary polynomials:  
\[g_{1}(x,u) =W(u)x-X(u)=0 \tag{13a}\] \[g_{2}(y,u) =W(u)y-Y(u)=0 \tag{13b}\]  
The above polynomial equations can be rearranged in descending power of \(u\), as follows:  
\[g_{1}(u) =a_{p}u^{p}+a_{p-1}u^{p-1}+\cdots+a_{1}u+a_{0} \tag{14a}\] \[g_{2}(u) =b_{p}u^{p}+b_{p-1}u^{p-1}+\cdots+b_{1}u+b_{0} \tag{14b}\]  
From the above, the following resultant system can be obtained through algebraic manipulations [43]:  
\[\begin{bmatrix}(a_{p}b_{p-1})&\cdots&(a_{p}b_{0})\\ \vdots&\ddots&\vdots\\ (a_{p}b_{0})&\cdots&(a_{1}b_{0})\end{bmatrix}\begin{pmatrix}u^{p-1}\\ u^{p-2}\\ \vdots\\ 1\end{pmatrix}=\begin{bmatrix}\mathbf{M}^{B}\end{bmatrix}_{p\times p}\begin{pmatrix} u^{p-1}\\ u^{p-2}\\ \vdots\\ 1\end{pmatrix}=0 \tag{15}\]  
where \((a_{i}b_{j})=a_{i}b_{j}-a_{j}b_{i}\), \(\mathbf{M}^{B}\) is a Bezout matrix and is a function of \(x\) and \(y\) with the following important property:  
\[\mathbf{M}^{B}(x,y)=\mathbf{M}^{B}_{x}x+\mathbf{M}^{B}_{y}y+\mathbf{M}^{B}_{w} \tag{16}\]  
where \(\mathbf{M}^{B}_{x},\mathbf{M}^{B}_{y}\) and \(\mathbf{M}^{B}_{w}\) depend on control point coordinates and weights. Therefore, these matrices can be pre-computed for a given rational parametric curve and re-used given any new physical point \(\mathbf{x}\). The determinant, \(\det(\mathbf{M}^{B}(\mathbf{x}))\), is defined as the Bezout resultant. As all allowable parameter values \(u\) for curve \(\mathbf{C}(X(u),Y(u),W(u))\) are roots of the system presented in Equation (15), \(\det(\mathbf{M}^{B}(\mathbf{x}))=0\) provides the equation for the implicit version of the curve. Thus, the algebraic level sets corresponding to a rational parametric curve (e.g., a Bezier curve) are provided by  
\[\Gamma(\mathbf{x})=\det(\mathbf{M}^{B}(\mathbf{x})). \tag{17}\]  
An example of algebraic level sets is shown in Figure 11.
## 5 Conclusions  
In this work, we present (Com)\({}^{2}\)INet, a novel communication and computation integrated network architecture, to address the challenges of coordinating multi-dimensional heterogeneous resources for computationally intensive and time-sensitive tasks. By integrating sensing, transmission, and computation into a unified framework, (Com)\({}^{2}\)INet enables efficient, intelligent, and scalable collaborative computing across end-edge-cloud, space-air-ground, and multi-data-center domains. Additionally, to demonstrate its applicability, we present a case study of leveee piping hazard inspection via remote sensing on the (Com)\({}^{2}\)INet platform. Experimental results show that the proposed SC-based SACM approach achieved superior accuracy with an average mIoU of 0.1469, significantly outperforming traditional methods (KM: 0.0423, GMM: 0.0269, FCM: 0.0303). Furthermore, the approach's distributed execution reduced computational latency to 0.2288 s per frame, outperforming monolithic benchmarks (KM: 6.7628 s, GMM: 96.3845 s, FCM: 209.3182 s), demonstrating its suitability for real-time emergency scenarios. For future work, we will investigate an adaptive levee piping hazard inspection mechanism based on federated reinforcement learning, enabling (Com)\({}^{2}\)INet to enable data-sensitive computing services from a long-term system performance perspective. Moreover, large-scale simulations and real-world tests will be conducted to verify the performance of the designed algorithm in high-concurrency scenarios.  
Conceptualization, J.C. and L.G.; methodology, J.C.; software, L.G.; validation, J.C., L.G., Y.W., and K.W.; formal analysis, Y.W.; investigation, K.W.; resources, H.S. ; data curation, Y.W.; writing--original draft preparation, J.C. and L.G.; writing--review and editing, J.C. and L.G.; visualization, H.S.; supervision, Z.W.; project administration, S.Y.; funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.  
Funding: The work was supported by the National Institute of Natural Hazards, Ministry Of Emergency Management of China under grant no. ZDI2024-17, National Key R&D Program of China under grant no. 2024 YFC3013304, National Natural Science Foundation of China under grant no. U23B2026, no. 61971458, and no. 52404225, research grants from the National Institute of Natural Hazards, Ministry of Emergency Management of China under grant no. ZDI2024-29, and the Key Science and Technology Program Project of the Ministry of Emergency Management of China under grant no. 2024 EMST020201. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author due to confidentiality requirements related to levee emergency situations, and further research on the data will be conducted in the future. Conflicts of Interest: The authors declare no conflicts of interest.
## 2 Materials and Methods
### Model Performance Evaluation  
In this study, the performance of all of the developed models in the three periods was evaluated according to the following statistical indices: the coefficient of determination (\(R^{2}\)), index of agreement (IA), root mean square error (RMSE), and mean absolute error (MAE). These performances indicators can be calculated by the following equations:  
\[R^{2}\ =\ \left[\frac{\sum\limits_{i\ =\ 1}^{N}(Y_{o,i}-\overline{Y_{o}})(Y_{m,i }-\overline{Y_{m}})}{\sqrt{\sum\limits_{i\ =\ 1}^{N}(Y_{o,i}-\overline{Y_{o}})^{2}\sum\limits_{i\ =\ 1}^{N}(Y_{m,i}-\overline{Y_{m}})^{2}}}\right]^{2} \tag{1}\]  
\[\text{IA}\ =\ 1-\frac{\sum\limits_{i\ =\ 1}^{N}(Y_{o,i}-Y_{m,i})^{2}}{ \sum\limits_{i\ =\ 1}^{N}(|Y_{o,i}-\overline{Y_{o}}|+|Y_{m,i}-\overline{Y_{o}}|)^{2}} \tag{2}\]  
\[\text{RMSE}\ =\ \sqrt{\frac{1}{N}\sum\limits_{i\ =\ 1}^{N}(Y_{o,i}-Y_{m,i})^{2}} \tag{3}\]  
\[\text{MAE}\ =\ \frac{1}{N}\sum\limits_{i\ =\ 1}^{N}|Y_{o,i}-Y_{m,i}| \tag{4}\]  
where \(Y_{o}\) and \(Y_{m}\) denote the observed and modeled values of daily carbon fluxes, respectively; \(\overline{Y_{o}}\) and \(\overline{Y_{m}}\) are the means of observed and modeled values, respectively; \(N\) is the number of observed values.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 3 Overview of Single-Satellite Navigation and Orbit Determination Methods
### Navigation Data Processing Technology
Therefore, the stability of the algorithm also needs to be considered. In addition, the lunar navigation system has higher real-time requirements, and the calculation amount and convergence speed of the algorithm need to be considered. Therefore, it is necessary to comprehensively consider the estimation accuracy, stability, convergence and other performance parameters of the algorithm, in order to study the corresponding navigation algorithms and achieve the real-time high-precision autonomous navigation of lunar navigation system satellites.
## 2 Experimental
### Fabrication of PEDOT:PSS Line Pattern  
The nozzle printer (prototype made by Device Eng, Asan-si, Korea) used in the present study consists of a stage, a motored nozzle head, a liquid pump (Havard apparatus-PHD ULTRATMCP, Havard, MA, USA), and a charge-coupled device (CCD) camera (Basler-acA 1300-30 gm, Ahrensburg, Germany), as shown in Figure 1. The stage of the nozzle printer can accommodate a substrate of 100 mm \(\times\) 100 mm size. The CCD camera is located under the stage to capture the coating process through a transparent substrate such as an ITO/glass substrate. The motored nozzle head can be equipped with various nozzle types and move with a maximum speed of 1000 mm/s to the y-axis direction. The used SUS nozzle has an external diameter of 0.23 mm and an internal diameter 0.1 mm,and it is connected to the liquid pump that helps to ensure a constant flow though the nozzle. In this experiment, the nozzle moving speed and flow rate of the coating solution were fixed at 1000 mm/s and 2 mL/min, respectively.  
The PEDOT:PSS (Heraeus CH8000) solution employed in this study is one of the most commonly used HIL materials and has high conductivity, excellent stability, and good transparency in the visible region. It was used to evaluate the printing process and thickness uniformity according to the EtOH volume ratio in aqueous solution. In this study, we prepared ink samples of pristine PEDOT:PSS (0% EtOH) and various diluted-PEDOT:PSS samples mixed with EtOH (10, 20, 30, 40, 50, 60, 70, 80, 90%). The ITO glass substrate was then treated by O\({}_{2}\) plasma to improve its wettability. Finally, we fabricated a PEDOT:PSS line pattern on the ITO substrate with the following nozzle printing conditions and ink formulation:  
* Printing speed of 1000 mm/s
* Ink injection rate of 2 mL/min
* Stage temperature of 24 \({}^{\circ}\)C (\(\pm\)0.5 \({}^{\circ}\)C)
* Atmosphere of 50% (\(\pm\)5%) relative humidity
* PEDOT:PSS baking condition of 100 \({}^{\circ}\)C/30 min after solvent evaporation
# Evaluation of Algal Control Measures in Eutrophic Reservoir Based on Aquatic Ecosystem Models
## 2 Materials and Methods
### Model Setup and Simulation Scenarios
#### 2.5.3 Simulation Scenarios  
The management and control of eutrophication and algae in water sources should consider water usage safety and the ecological risks associated with chemical and microbial methods of algae control, which are challenging to manage. In the prevention and control of algae in water sources such as lakes and reservoirs, the focus should be on reducing nutrient sources, particularly internal sources within the reservoir, and implementing biological and physical algae control measures, such as ecological fish stocking, mollusk breeding, and algae removal equipment [51; 52; 53; 54; 55].  
Consequently, this research project specifically proposes hydrodynamic solutions, a reduction in sediment pollution sources, and ecological stocking measures to address the issue of blue algae bloom in Shanzai Reservoir. It conducts a simulated analysis to evaluate the effectiveness of these control measures.  
This chapter presents a water ecological cycle model, constructed based on the characteristics of the Shanzai Reservoir. It analyzes various simulation scenarios and operating conditions to study their effects on the growth of blue algae in Shanzai Reservoir throughout the year. The research is guided by an ecological perspective of the control system of algae in lakes and reservoirs, and it establishes different simulation scenarios and operating conditions can be used to analyze the impact of different water levels (Table 3), bottom sediment endogenous sources (Table 4), and ecological fish stocking (Table 5) on the growth of blue algae in the Shanzai Reservoir.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **Water Level in Front of the Dam** & **Aquatic Ecological Environment Background** & **Nutrient** \\ \hline
1 & 75 & The inflow is the average of the last five years, including 35 m\({}^{3}\)/s in Huokou and 5 m\({}^{3}\)/s in Rixi. Water quality uses average data of 2022. & The nitrogen and phosphorus loads entering the reservoir include inflow and internal sources of pollution. All parameters are set using the model parameters validated in 2020. \\
3 & 85 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hydrodynamic settings scenario design.