esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Sensitivity Analysis and Design Optimization of Parameters
### Result Analysis for a Single Index
#### 5.1.2 Influence of Parameters on the Stress Variation Coefficient  
The influence of parameters on the stress variation coefficient is shown in Figure 8. From Figure 8, it can be obtained that the stress variation coefficient decreases with the increase of the bifurcation diaphragm thickness and increases with the growth of the box girder height. The influence of the ramp radius and supporting condition are relatively insignificant. The increase of the bifurcation diaphragm thickness enhances the torsional stiffness, which improves the ability to resist torsion and distortion and reduces the uneven stress distribution. Increasing the box girder height increases the flexural rigidity and torsional stiffness of the bridge, simultaneously. The vertical bending stress in the total vertical stress quickly decreases, while distortion and warping stress reduce insignificantly [18]. This makes the impact of distortion and warping more significant. Therefore, the stress distribution is seriously uneven and the stress variation coefficient becomes larger. Based on the range of \(R\) values, it can be found that the influence of factors on the stress variation coefficient is: bifurcation diaphragm stiffness > box girder height > ramp radius > supporting condition. Bifurcation diaphragm stiffness and box girder height are the first two factors affecting the uneven stress distribution. The smaller the stress variation coefficient is, the more uniform the stress distribution. The optimal parameter combination for the stress distribution index is: a bifurcation diaphragm thickness of 2 m and box girder height of 1.6 m, while the ramp radius and supporting condition can adopt reasonable values due to their negligible influence.  
Figure 8: Influence of varying parameters on the stress variation coefficient.  
Figure 7: Influence of varying parameters on maximum stress.
# Study on the Anti-Interference Performance of Substrate-Free PEDOT:PSS ECG Electrodes
## 1 Introduction
The results were analyzed to determine how each factor affects skin-electrode impedance and ECG. The results reveal that unstable skin-electrode contact leads to impedance fluctuations, resulting in ECG waveform distortion and interference. While electrode size affects the impedance value, its impact on the ECG signal is small. Clothing friction, however, significantly affects the dynamic impedance, causing ECG interference. Furthermore, different breathing frequencies, although not influencing impedance, result in ECG interference, which may be related to other physiological electrical signals, especially electromyography.
# Parallel Reservoir Simulation with OpenACC and Domain Decomposition
## 4 Results and Discussion
### Evaluating and Discussing the GPU-Aided Domain Decomposition Method  
In this section, we evaluated the acceleration effect about the proposed above GPU-aided domain decomposition method. Here the variables were the number of subdomains and the number of overlapping layers from ASM. The total number of node was set to 3000, the sum time of the Jacobian matrix assembly and the linear solver was as the experimental result. In addition the method which only adopted OpenACC but not DDM was set as the baseline. Due to the limitation of the node number, the number of the subdomains was set to 9 and 25, the corresponding results were shown in Figures 6 and 7 respectively. As a consequence, the other variable was set to related values, specifically, the number of overlapping layers was set ranging from 3, 5 to 7 in Figure 6 and from 2, 4, 6, 8, 10 to 12 in Figure 7. In these pictures, \"O-number\" was the number of overlapping layer where \"O\" representd the overlapping layer and \"number\" was the value, \"NUDD\" means that the GDDM was not used.  
As shown in Figure 6, the domain was divided into 9 subdomains, the number of overlapping layers were set as mentioned above. With the number of overlapping layers increasing, the runtime gradually reduced. When the overlapping layer was set to 3, the runtime was more than the baseline method, then when it was set to 7, the GDDM implemented a 1.4 times acceleration effect. We thought that when the number of the overlapping layers was set small, the local solution in subdomain contained large deviation compared to global solution, many iterative process would be performed to complete the convergence, with the number rising, the problem was solved. In Figure 7 the previous problem occurred again, when the number increased, the runtime was reduced by degrees. When the overlapping layer reached 10, the runtime was minimal, after that the runtime started to rise, therefore as the number of overlapping layers was set to 10, the GDDM achieved the best acceleration effect, 1.6 times faster than the baseline.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Section Name** & **Amount of Original Code** & **Amount of Increased Code** & **The Increased Code Ratio (\%)** \\ \hline
**Jacobian matrix assembly** & 1703 & 307 & 18 \\
**Linear equation solver** & 2073 & 520 & 25.1 \\
**Sum of the two parts** & 3776 & 827 & 21.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluating the workload of porting the application.
# A Laser Irradiation Method for Controlling _Pieris rapae_ Larvae
## 3 Results
#### 3.1.3 Laser Opening Time  
The laser irradiation tests showed that the antifeedant percentages and mortality rates associated with laser opening time increased in an irradiation time-dependent manner (Figure 2c,g). The highest killing effect was observed at 72 h posttreatment with all laser opening times, and antifeedant percentages of 79.12%, 87.42%, 91.99% and 95.07% were detected at 0.5 s, 1 s, 2 s and 3 s, respectively (Figure 2c). A mortality rate of 100% was found at 72 h posttreatment with all laser opening times (Figure 2g). However, 76.67 \(\pm\) 5.77% of the larvae left the irradiation area within 3 s, which indicated that the laser opening time was too long (data not shown). Moreover, no markedly significant difference was found between the laser opening times of 2 s and 3 s at all posttreatment times (2 s vs. 3 s: antifeedant percentage, 24 h, \(F\) = 2.819, df = 1, 4, \(p\) = 0.168; 48 h, \(F\) = 5.186, df = 1, 4, \(p\) = 0.085; 72 h, \(F\) = 5.375, df = 1, 4, \(p\) = 0.081; mortality rate: 24 h, \(F\) = 8.000, df = 1, 4, \(p\) = 0.047; 48 h, Same, all 100%). Therefore, the laser opening time range was adjusted to 0.5-2.5 s.
## 3 Results
### Performance Evaluation of Shared Fiber Topology
This difference behavior between the two QKD detector setups is reflected in Figure 10a,b. Figure 10: SKR as a function of fiber length, for the P2 MP shared fiber link serving \(N=4\), 16 or 64 users, where the quantum channel at 1550 nm is multiplexed with the classical signals at 1310 nm for both downlink and uplink, for (**a**) InGaAs; and (**b**) upconverted CMOS-based QKD setups. The horizontal lines with different styles correspond to the AES limits for key refresh times equal to 5.14 min (0.83 bps), 1 min (4.3 bps) and 1.4 s (183 bps), respectively.
# Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application
## 4 Web of Objects Based Contextual Data Quality Assessment Model
### Data Quality Assessment for WoO Enabled Applications  
To perform contextual data quality assessment over the CVOs data, the communication between the WoO enabled ASC and TMC healthcare application model and data quality layer functions (see Figure 8 for WoO and data quality layer to layer communications) have been done through the specially designed API interfaces. These API interfaces have been developed with HTTP REST protocol and the communication with CVOs repositories has been done through the secure SPARQL endpoints. When the CVO (i.e., stress monitoring and mood monitoring CVO) has been composed from the relevant VOs data, then the CVOs data quality analytic request has been sent through HTTP REST interface protocol to the data quality assessment layer. In order to analyze the contextual data quality as requested by the WoO healthcare application; initially, the contextual assessment deep learning models have been trained at the assessment analytics server based on the CVOs' historical data. For the future CVOs data event only the new incoming data have been validated through contextual assessment functions. The implemented contextual assessment model's sequence diagram is shown in Figure 15.  
As shown in Figure 15, the physiological sensor data has been received from users at the VOs/CVOs ontology server by publishing and subscribing (HTTP REST) interfaces. Then this data has been checked and harmonized with VOs ontologies. Later based on the service request, the CVOs are composed of the VOs data and analyzed through contextual data quality assessment functions. In this contextual data quality, metric parameters are applied at the data quality assessment layer. The communication between the VOs/CVOs ontology servers and the assessment analytic server is done through HTTP REST interfaces. The data quality assessment server performs various operations before analyzing the CVOs data with deep learning models. These operations include data querying, exploring, matching and sampling. After the contextual analytics, the data quality result metadata along with CVOs data have been stored at the object management server for further using it in the services.  
We developed many databases and servers in order to perform data quality assessment of the WoO enabled healthcare applications. We implemented the VOs and CVOs repository by using the apache Jena triple store. This database contains VOs and CVOs ontologies along with its data and metadata. We developed the data quality assessment analytic server database on OpenTSDB, which is suitable for the scalability and massive amount of analytic data assessment. We developed the objects management database in which the object management server stores the contextual analyzed CVOs results for further using it in the services.  
Figure 15: Sequence diagrams for contextual data quality assessment of semantic data applications.
# Clinical Validation of a New Enhanced Stent Imaging Method
## 3 Results  
### Patient and Lesion Characteristics  
#### 3.1.1 Qualitative Analysis  
The qualitative analysis included 157 lesions with a total of 249 ESI sequences from 140 patients. The mean patient age was 64.7 \(\pm\) 10.8 years, and 72.1% (\(n\) = 101) were men. Calcifications were reported on angiography in 72% (\(n\) = 113) of the cases. A total of 170 stents were placed, of which, 140 (82.5%) were drug-eluting stents. Post-dilatation was performed in 68% of the cases; of these, 92% used a non-compliant balloon. The lesions were deemed highly calcified for 13 lesions (8%), moderately calcified for 40 (25%), slightly calcified for 60 (38%), and free of calcifications for 44 (28%). The baseline clinical, lesion, and stent characteristics of the study population are outlined in Table 1. Out of the 157 treated lesions, 26 (17%) were ST elevation myocardial infarctions (STEMI) and 46 (29%) non-ST elevation myocardial infarctions (NSTEMI). The indication to perform PCIs for the other patients was angina pectoris or arrhythmia, with ischemia proven non-invasively or after measurement of a fractional flow reserve. There were 18 total occlusions (11%). The lesions  
Figure 1: On the top, comparison between the original angiographic image (**left**), StentBoost (**middle**) and StentEnhancer (**right**) and the region where the noise was computed (red square) and the signal (blue region of interest around the stent). Bottom, original frame (**left**) and StentBoost (**middle**) and StentEnhancer (**right**) results where an underexpansion can be detected from the important calcifications outside of the stent. SNR in original image is 2.8, and, respectively, 5.7 and 7.7 for the SB and SE images (in area around stent).  
were localized in 34% of the cases in the right coronary artery (RCA), in 24% in the left circumflex artery (LCx), in 39% in the left anterior descending artery (LAD), and in 5% in the left main coronary artery.
## 2 Methods  
### Datasets  
Vaihingen dataset [31]: It contains 33 images, each measuring 2494 \(\times\) 2064 pixels. With a 9 cm spatial resolution, each image in the collection includes red, green, and near-infrared channels. Additionally, each image includes a digital surface model that is registered with the image data. The imagery is classified into six distinct categories, namely impervious surfaces, buildings, low vegetation, trees, cars, and background.  
Potsdam dataset [31]: It includes 38 images with a size of 6000 \(\times\) 6000 pixels. The sample distance from the ground is 5 cm. Three bands--red, green, and near-infrared--make up the 8-bit TIFF file format used for remote-sensing images. The additional data contain the digital surface model data. The Potsdam dataset has six categories as well.  
LoveDA dataset [32]: It contains 5987 images with RGB channels with 1024 \(\times\) 1024 pixels, and is from the images constructed from Nanjing, Changzhou, and Wuhan using Google Earth data. There is a 0.3 m ground sampling distance. Because every research area has a unique development approach, the ratio of rural to urban areas varies. The National Bureau of Statistics' urban and rural zoning codes were used to guide the collection of data for both urban and rural areas. We chose nine heavily populated urban regions from areas with affluent economies. Undeveloped areas were the source of the remaining nine rural areas.
# Efficient Machine Learning-Based Prediction of Solar Irradiance Using Multi-Site Data
## 1 Introduction
### Motivation, Contributions, and Organization
This model aims to predict solar irradiance effectively across different sites with similar configurations. Our methodology represents a novel approach as it transitions from localized prediction models to a more generalizable framework. The results of this study could surpass the limitations of site-specific models, offering a versatile solution for solar irradiance prediction. The remainder of the paper is structured as follows. Section 2.1 details the dataset, the designed framework, the machine learning models utilized, the evaluation strategies adopted, and the steps taken for exploratory data analysis. Section 3 presents findings from six stations, evaluating the performance of various machine learning models across different time shifts and forecasting horizons. This section also employs explainable AI (XAI) techniques to interpret the results, offering valuable insights into model behavior and the significance of features. Lastly, Section 4 summarizes the key findings, highlights the study's contributions, and identifies promising directions for future research and practical implementations.
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 4 Analysis of Key Parameter Dependencies, Limitations, and Impacts  
### Dependencies and Limitations of Key Parameters  
To generate realistic voltage notch waveforms in simulations, it is crucial to design, size, and model key parameters to closely reflect real-life LV network conditions. This requires analyzing the dependencies between parameters and considering their limitations, alongside external factors like standard limits, construction details, technological limitations, and technical constraints. Key points and dependencies observed in the conceptual diagram of Figure 7 are described as follows:  
* \(S_{sc}\): Determined by external factors such as upstream network characteristics provided by the distribution network operator.
* \(S_{r}\): Limited by the ratio \(S_{r}/S_{sc}\) to ensure proper operation.
* \(X_{com}\): Sized according to specified expressions to prevent excessive voltage notches.
* RC-snubber: Designed based on \(X_{com}\), \(-di/dt\), and \(U_{rm}\) of the thyristors.
* \(\alpha\): Constrained by the minimum allowed or target power factor (PF) at the PCC.
* \(U_{D}\) and \(U_{D}\): Depend on \(\alpha\), \(U_{s}\), and other parameters.
* \(P_{D}\) and \(R_{D}\): Computed from \(I_{D}\) and \(U_{D}\) values, with \(L_{D}\) derived from \(P_{D}\).
* Linear Loads: The maximum apparent power (\(P_{ll}+jQ_{ll}\)) is constrained by the maximum allowable voltage drop at the PCC, which depends on \(S_{sc}\) and \(S_{r}\). The \(PF\) of the LV installation depends on both \(\alpha\) and the \(PF\) of linear loads.
* \(Q_{cb}\): Improves \(PF\) and allows for a broader range of \(\alpha\). The maximum \(Q_{cb}\) is constrained by the voltage rise at the PCC, typically managed between 2% and 3%.
* Detuned capacitor bank: \(L_{cb}\) depends on \(Q_{cb}\) and the design \(f_{t}\), while \(R_{cb}\) depends on \(Q_{cb}\) and \(QF\).
* Non-detuned capacitor bank: \(P_{cb}\) is expressed as a percentage of \(Q_{cb}\).  
Other network parameters, such as background voltage unbalance and distortion, also impact notch waveforms and are therefore considered in the simulations. However, their dependency on the other key parameters is assumed negligible.
###### Abstract  
This study aimed to investigate the potential use of carbon nanotubes (CNTs), which are an innovative construction material preferred by many researchers. Long-term microstructure enhancement and on-site application are major reasons to conduct research on CNT-cement composites; thus, a study on mechanical properties as well as the thermal conductivity of CNT-cement composites was carried out. As the CNT content increased, the thermal conductivity of CNT-cement composites was also enhanced. In addition, a couple of microstructure analyses such as isothermal calorimetry, thermal gravimetric analysis and SEM-EDS (Scanning Electron Microscope-Energy Dispersive X-ray Spectroscopy) for observing hydration reaction rate and types of hydration products were conducted to establish the advantage of CNT use in cement composites. Strength development of CNT-cement composites at early ages was slow, although eventually CNTs containing water developed equivalent level of strengths at last as internal curing effects.  
Keywords:CNT-cement composites; nano-materials; strengths; thermal conductivity; microstructures +
Footnote †: journal: Applied Sciences
## 6 Performance Evaluations
### Simulation Results  
We compare the network lifetime, packet miss ratio, end-to-end delay, packet delivery ratio, energy imbalance factor, and time complexity of our proposal with the SMRP [15] and EERP [23] to assess its feasibility and effectiveness. Unlike the EERP, since our proposal takes into consideration the environmental impact as one of the main metrics in the routing process of the real-time data, all subsequent experiments are carried out with two scenarios since our solution takes the environmental effect into account as one of the key criteria in both the route determination of the mobile sink and the routing mechanism of the urgent data.  
1. First scenario: This is considered with regard to the environmental statistics that are within the normal range; therefore, environmental influences do not affect network performance. This demonstrates the performance of the approaches under normal environmental conditions.
2. Second scenario: It is assumed that the environmental data are outside the normal range. It demonstrates the performance of the approaches under environmental conditions outside the normal range. In the second scenario, all following studies assume the network is impacted by wildfire. Environmental events are considered to occur 400 s after network start-up.  
To be fair, the number of routing paths for each source node in all three routing protocols is set to 1.
## 6 Future Trends and Issues
### Building Integrated Photovoltaics (BIPVs)  
The current global power demand is roughly 15 Tera Watt (\(15\times 10^{4}\) W), or \(10^{4}\) times less than solar power incidents on the earth. The solar energy received in less than an hour is thought to be enough to cover a year's worth of the global energy budget [175]. Therefore, photovoltaic (PV) technology is one of the most attractive options for making efficient use of solar energy. BIPVs are photovoltaic modules that are incorporated into the building envelope, therefore replacing the traditional components of the building envelope. PV modules are used as roofs, facades, and skylights in this application. In comparison to non-integrated systems, BIPVs have a significant benefit because land allocation and stand-alone PV systems are not required [176]. Photovoltaic foils, photovoltaic tiles, photovoltaic modules, and solar cell glazing are some of the different types of BIPV buildings, as shown in Figures 8 and 9, respectively, that use BIPV technology to become energy producers rather than consumers.  
The advancement of BIPV technology and its incorporation into the building envelope provides aesthetic, economic, and technical benefits [175]. BIPV systems can be a powerful and versatile tool to meet the increasing demand for zero energy and zero emission buildings in the near future [177]. BIPVs are capable of delivering electricity at less than the cost of grid-connected electricity to end users at certain peak demand, which may lead to peak shaving without compromising human comfort [178].  
Weather protection, thermal insulation, noise reduction, heating and cooling load reduction, and other benefits are all provided by the BIPV [179; 180]. Utilizing a BIPV semi-transparent arrangement, some of the sunlight can be used for day illumination inside the building [181]. Due to the scarcity of ground area and the abundance of underutilized roof space, rooftop solar PV systems are gaining popularity, resulting in the prediction that the BIPV industry will increase rapidly soon [182]. In addition, feed-in tariffs (FiTs) and other government-sponsored solar energy programs have gained widespread acceptability around the world. Sanyo, Schott Solar, Sharp, and Sun-tech are among the firms developing innovative BIPV technologies for facades, skylights, and windows. FiT implementation, public acceptance, government economic support in the form of subsidies, and technical elements such as power losses and architectural concerns are the most significant impediments to BIPV system adoption [183]. However, a BIPV system's power-generating efficiency is lower than that of stand-alone and BIPV/T systems, but it eliminates the need for an additional power-generation space [184]. Overall, due to its functional qualities, this technology has a promising future in the coming years.
## 3 Numerical Examples  
In this section, solutions to parameter and topology optimization problems are presented to illustrate the efficiency of the NP constraint in obtaining optimum solutions. All programs were written using Matlab (2021b) software. Figure 3 shows the flowchart of the optimization algorithm utilized to solve the example problems. After calculating the objective functions and constraints, the optimization algorithm checks for convergence and updates the design variables accordingly. Two optimization methods were used in the simulations: while the Matlab function 'fmincon' was used to solve the parameter optimization problems by selecting the interior point optimization algorithm for the three examples in this section, the PSO method was also used in the truss system and topology optimization problems.  
Figure 2: Bode gain plots of a mass-spring-damper system with various damping constants (\(\omega_{\text{n}}=1\text{ rad/s}\)). The curves from top to bottom, respectively, correspond to damping values of _0.009_, _0.02_, _0.03_, _0.04_, _0.08_, _0.1_, _0.12_, and _0.13_\(N\)/\(m\)/s.