esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Formulation
### Logistic-\(\beta\) Construction Timing Sub-Model
#### 2.2.3 Construction Timing  
Let \(S_{T}\) denote the network scale at time \(T\), then the function is as Equation (14):  
\[S_{T}=S_{0}+\sum_{i=1}^{n}l_{i},i\in N \tag{14}\]  
where \(S_{0}\) is the network scale at \(T=0\), \(T\) is the construction planning period of the URT, and \(l_{i}\) is the construction mileage.  
As shown in Figure 2, the line mileage is open range. To solve this problem, a proximity substitution point is set: \(P_{0}^{\prime}=(0,S_{0}^{\prime})\), \(P_{T}^{\prime}=(T,S_{T}^{\prime})\). Let \(\beta\) denote the closeness between the logistic curve calibrated by the proximity substitution point and the ideal logistic curve, as shown in Equation (15).  
\[\beta=(S_{T}-S_{0}^{\prime})/S_{T}=S_{T}^{\prime}/S_{T},\beta\in(0,1) \tag{15}\]  
The larger the value of \(\beta\) is, the closer the network growth curve will be to the ideal logistic growth curve. The URT network growth curve scale is defined as shown in Equation (16):  
\[s=f(t)=\begin{cases}S_{T}/(1+\beta/(1-\beta)e^{\frac{2\ln(1-\beta)/\beta}{T} \cdot t})&S_{0}=0,\\ S_{T}/(1+(S_{T}-S_{0})/S_{0}e^{\frac{\ln S_{0}(1-\beta)/(S_{T}-S_{0})}{T}\cdot t })&S_{0}>0.\end{cases}\quad t\in[0,T]\enspace,\beta\in(0,1) \tag{16}\]  
where \(s\) is the cumulative operation scale of the URT, \(t\) is the cumulative construction time of the URT, \(S_{T}\) is the total network scale (km), \(S_{0}\) is the length of constructed lines, \(T\) is the construction planning period of the URT, and \(\beta\) is the resolution of the growth curve.  
Figure 2: The influence of parameters \(A\), \(B\), and \(K\) on the Logistic curve. (**a**) The influence of the parameter \(A\); (**b**) the influence of the parameter \(B\); (**c**) the influence of the parameter \(K\).  
In the light of the construction sequence of the URT \(U_{o}=\{u_{o1},u_{o2},\cdots,u_{om}\}\) (see Equation (12)), the reasonable growth curve of URT network scale is \(S=f(t)\), and the data of line length \(L_{o}=\{l_{o1},l_{o2},\cdots,l_{om}\}\) and the construction end time of each line \(t_{oi}^{end}\) can be obtained as shown in Equation (17):  
\[t_{oi}^{end}=f^{-1}(S_{i})=f^{-1}(S_{0}+\sum_{j=1}^{i}l_{oj}),i\in N \tag{17}\]  
The reasonable construction period is then:  
\[T_{o}=\{T_{o1},T_{o2},\cdots,T_{on}\},\forall T_{oi}\in T_{o},t_{oi}^{end}-T_{oi }\geq 0,i\in N \tag{18}\]  
The construction starting time of each line \(t_{oi}^{begin}\) is then as shown as Equation (19):  
\[t_{oi}^{begin}=f^{-1}(S_{0}+\sum_{j=1}^{i}l_{oj})-T_{oi},i\in N \tag{19}\]
## 4 Conclusions  
In this study, the effects of curing time and fillers, including SFs and CFs, and CB, on the mechanical properties of CGCMs under uniaxial loading were demonstrated using AE. The following conclusions can be reached.  
1. CB enhances the compressive strength of the samples by 10.84% and 13.85% at 7 and 14 days of curing, respectively. However, it leads to a slight reduction in compressive strength, decreasing by 3.27%, at 28 days of curing. SFs increase the compressive strength of the samples by 4.16% at 14 days of curing but reduce it by 8.45% and 13.69% at 7 and 28 days, respectively. CFs negatively impact compressive strength, decreasing it by 21.11%, 17.94%, and 24.08% at 7, 14, and 28 days, respectively. The compressive strength of samples reinforced with CB, SFs, and CFs increases by 14.2%, 17.14%, and 26.5%, respectively, from 7 to 14 days, but shows only minor changes beyond 14 days of curing. In contrast, the compressive strength of plain cemented paste continues to increase steadily, by 11.2% and 10.1%, over time.
2. The damage process of cemented paste can be classified into three or four stages according to the variations in stress growth rate. The post-cracking behavior in compressive tests of specimens reinforced with CFs or SFs exhibits notable toughness, while the reference samples and those reinforced with CB show a sharp decline at the end of stage III. Additionally, the duration of stage IV for CF- and SF-reinforced samples decreases significantly by 91% and 45%, respectively, as the curing age increases from 7 to 28 days.
3. The reinforcing effect of additives can be detected by the AE method, and the AE characteristics vary according to the types of fillers. The cumulative count growth rate of plain and CB-reinforced samples is lower at first and then becomes higher, while the cumulative count growth rate of CF- and SF-reinforced samples is almost linear during the loading process. AE events at stage III can effectively realize the identification of the reinforcing effects of CFs and SFs.
4. From stages I to III, shear cracks are the predominant crack type, with their average proportion across all samples decreasing from 86.61% to 76.61%. In stage IV, the primary failure mode shifts to tensile cracks, which account for 77.62% of the total. Additionally, the presence of CB and CFs positively influences resistance to tensile cracking, while SFs effectively mitigate shear cracking.
5. Adding CB results in a denser and more stable hydration product structure, whereas CFs and SFs contribute to a more porous structure with increased cracks.  
This research was financially supported by the Nature Science Foundation of China (52304162), the Chinese Central Government-Guided Special Fund for Regional Scientific and Technological Development (ZYYD2024 JD16), and the Major Science and Technology Projects of Xinjiang Uygur Autonomous Region (2012A01002).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The author declares no conflicts of interest. All authors have read and agreed to the published version of the manuscript.
# Solar Filament Detection Based on an Improved Deep Learning Model
## 3 Hybrid CNN-Transformer Model
### Transformer  
In recent years, Transformer [27], which uses Transformer layers instead of convolutional operations, has become a popular deep learning model. The structure of the Transformer layer is illustrated in the Figure 5. It requires vectors as input, and focuses more on the relationships among the input vectors through Multi-Head Self-Attention (MSA), Multi-Layer Perceptron (MLP), and Layer Normalization (LN).  
Transformer is capable of capturing dependencies between various positions in the input sequence, thereby establishing a global receptive field and exhibiting strong global information extraction capabilities. Vision Transformer [28] applied the Transformer to the field of computer vision. It achieved promising results, inspiring a large number of computer vision models based on the Transformer. However, when processing images, the Transformer only captures features with relatively large grain size. This results in the loss of much local detail information.  
Figure 4: A set of full-disk H\(\alpha\) image and corresponding filament annotation. On the (**left**) is the full-disk H\(\alpha\) image, and on the (**right**) is the annotation.
## 2 Related Works
### Existing Publisher-Subscriber Approach
#### 2.1.2 Scalability and Efficiency Issues  
PS brokers for IoT data can face significant scalability issues, primarily due to the huge volume and velocity of data generated by numerous connected devices. These models often struggle to process and transmit data in real time, creating performance bottlenecks as the number of devices increases [21; 29]. Blockchain's decentralized and immutable ledger has been shown to efficiently handle large-scale IoT data by distributing the workload across a network of nodes, thus preventing bottlenecks and enhancing system resilience and performance. For example, the REEDS system (Revocable End-to-End Encrypted Message Distribution System) proposed in [30] utilizes blockchain's decentralized nature to provide secure and efficient data storage and processing, ensuring data integrity and reducing latency through automated transactions and data verification via smart contracts. Other works mentioned [31] focus on implementing blockchain technology for various IoT-based applications to improve the security and scalability issues of IoT. Although we do not focus on the scalability issues in this paper, previous studies suggest that the scalability-related challenges are the same whether blockchain is used or not.
# Fewer Dimensions for Higher Thermal Performance: A Review on 2D Nanofluids
## 5 Life Cycle Assessment
### Tungsten Disulfide  
Regarding the LCA analysis of WS\({}_{2}\) nanomaterials, a study was conducted by Bobba et al. [177] considering the EU-FP7 project AddNano to evaluate the EI of the gas phase reaction in the production of WS\({}_{2}\) nanoparticles. The authors performed a preliminary review of the published literature and highlighted many data gaps because of the little or no communication between stakeholders and the lack of published LCA studies and results related to nanomaterials in general with regards to the main stages of the LCA of nanoparticles. A from-cradle-to-gate LCA of the industrial processing of WS\({}_{2}\) nanoparticles was carried out. The obtained results confirmed that the production of WS\({}_{2}\) nanoparticles is very energy consuming, and the tungsten trioxide is the raw material with the greatest contribution. It should be clarified that the EI evaluation referred to the synthesis of one gram of nanosized WS\({}_{2}\). The production of WS\({}_{2}\) was found to be a strongly energy-consuming procedure. The LCIA CML1992 study revealed that the contribution of electricity in the preparation stage of WS\({}_{2}\) was, in every mode, higher than 26%, with 26.1% human toxicity and approximately 31% greenhouse gas impact. It was found that the impacts derived from the scrubbing solution and gas mixture were very low. Indeed, regarding the CML1992 and ReCiPe2008 impact categories, the overall impact of these products never surpassed 3.84%, with the highest value related to the human toxicity of 1.97E-05 HC for 1 g of WS\({}_{2}\) and 8.64% with the highest value associated to the human toxicity of 2.57E-04 kg 1,4-DB eq. for 1 g of WS\({}_{2}\). Considering the electricity consumption, it was found that the contribution of electricity to the entire production stage of WS\({}_{2}\) was very high for all considered impact categories. The authors conducted the LCA analysis based on data gathered from the industrial sector and, hence, the results are in reference to large-scale production processes, averting scale-up simplifications. Due to the scarce number of published LCA studies on nanomaterials, with the available studies often fragmented, it is very complex to gather data to better understand LCA approaches and to apply the corresponding outcomes for developing new and more accurate analyses. Furthermore, the available datasets and environmental assessments of the nanomaterials are usually based on data from laboratory pilot experiments and not on large-scale industrial implementation processes. The conducted review of the published LCA data emphasized an urgent need for the implementation of datasets and the improvement of communication between the various nanotechnology sectors and between producers and customers. In this regard, the LCA analysis concerning the production of WS\({}_{2}\) nanomaterials was consistent with the published LCIA results.
# Design and SAR Analysis of a Dual Band Wearable Antenna for WLAN Applications
## 3 Results
### On-Body Analysis
#### 3.2.2 Directivity and Gain Patterns  
The directivity and gain patterns of the designed conventional antenna, without a body and when mounted on different body parts, are shown in Figures 21 and 22, respectively. These figures demonstrate that among all conditions, antenna to be placed over a flat body phantom is the most suitable choice in terms of side and back lobes. For rounded body parts, the side and back lobes decrease dramatically as the bending radius increases,  
Figure 19: Off-body gain patterns of the conventional dual band antenna at (**a**) 2.5 GHz and (**b**) 5.2 GHz.  
Figure 20: On-body return loss of conventional dual band antenna.  
and the radiation patterns approach that of the flat body condition. Table 5 presents a comparison on the basis of maximum gain and directivity between the conventional antenna without a body and the antenna with a child's arm with a radius of 40 mm (worst case). A small amount of reduction at the lower resonance frequencies has been observed in both the maximum gain and directivity when the designed antenna is worn on a child arm (worst case).
## 3 Experiments and Analysis  
### Dataset Construction  
In this article, a JPG image dataset of WTB surface damage is constructed. The images of the dataset were obtained from the WTB surface damage image collected by the team using a high-definition UAV at a wind farm in Liaoning, China. After obtaining the original images collected by the high-definition UAV, the collected original images were cropped according to a uniform image size to meet the requirements of image classification. Then, image enhancement was performed on the unclear images. Finally, the training and test sets were split and built after data augmentation using a relatively modest number of damage types.  
Figure 8: Structure diagram of CBAM and E_CBAM. (**a**) CBAM. (**b**) E_CBAM.  
The raw image data consist of 1074 images, which contained both normal WTB surface images and WTB surface damage images. Figure 9 illustrates the deterioration in the form of cracks as well as surface shedding.  
The size of the original images is 5472 \(\times\) 3684 pixels. However, the input was too large for the training and testing of this network, which causes an excessive computational burden, and contains excessive and irrelevant background. However, if the image size is too small, the difference between the image is inconspicuous after subsequent data augmentation. This results in the decline of the training effect and affects the final experimental index of the network. Therefore, for the determination of image size, we referred to numerous sample set sizes commonly used for CNNs, as shown in Table 1.  
Considering the random region clipping of the images during the subsequent data augmentation, the original image was scaled down to 336 \(\times\) 336 pixels. Then, image enhancement was performed for images that were not sharp enough due to interference from objective factors such as shooting angle and lighting conditions. A total of two image enhancement methods were applied, including histogram equalization and median filtering. Specifically, histogram equalization enhances the contrast between the damage location and its surroundings, while median filtering eliminates the noise in the image. Next, data augmentation was performed, mainly using OpenCV as an image processing tool. First, the image was randomly cropped with a crop frame of 224 \(\times\) 224 pixels in size, then clockwise, counterclockwise rotation, and mirror flip were carried out. Finally, random salt-and-pepper noise was added.  
After data augmentation, the number of images reached 4729, among which the number of WTB for normal, cracks, and surface shedding images were 1738, 1449, and 1542, respectively. Finally, the above three kinds of images were divided into the training set and the testing set according to the quantity ratio of 7:3, and the construction of the WTB surface damage dataset was finally completed.
# Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data
## 3 Data Collection and Model Equation Development
### Development of Adjustment Factor Estimation Criteria
#### 3.4.5 The Adjustment Factor (\(\mathrm{f_{SD}}\))  
Pertaining to the Number of Traffic Breaks  
The adjustment factor for the number of traffic breaks (\(\mathrm{V_{SD}}\)) is obtained via the same process. The larger the number of traffic break facilities is, the lower the travel speed is. The slope estimated by the regression equation is \(-2.13\) and the constant is 62.53. In the following equation, the constant (\(\mathrm{y_{SD}}\)) for the x-axis is 29.4. When the estimated regression line is projected to be 80 km, the constant for the x-axis (\(\delta_{\mathrm{SD}}\)) is 37.6. Therefore, when the number of traffic break facilities per km is approximately, the travel speed theoretically becomes zero:  
\[85\mathrm{-percential\ speed}=62.53-2.13\times\mathrm{V_{SD}},\]  
\[\delta_{\mathrm{SD}}=80/\alpha_{\mathrm{SD}}=80/2.13=37.6.\]  
Figure 15: Adjustment factor development (**a**) and standardization for the existence (**b**) of the level of parking.  
between each variable and V85, but the effect of each adjustment coefficient may be different in the combined model (Equation (10)). It is necessary to verify the weight calculation for each coefficient. The fact that the weight of the adjustment coefficient calculation criterion is close to 1 means that the linear assumption in the development of the calculation criterion is satisfied in the speed limit model equation.  
For weight verification, if a weight (\(\mathrm{w_{i}}\)) is assumed in Equation (10) and a natural logarithm is taken, verification through multiple linear regression analysis is possible. To calculate the weight, the above equation is converted into a log function form according to the variables of this study, as shown in Equation (11):  
\[\mathrm{ln(RSL/MSSL)=(w_{fc}\times ln[1-0.13\times(V_{FC}-1)/2])+(w_{CD}\times ln [0.82+0.18V_{CD}])+(w_{PL}\times ln[1-0.16\times(V_{PL}-1)/2])}\\ +(w_{AD}\times ln[1-V_{AD}/222.2])+(w_{sd}\times ln[1-V_{SD}/37.6]). \tag{11}\]  
Multiple linear regression model coefficients were estimated with \(\mathrm{ln(RSL/80)}\) as the dependent variable and \(\mathrm{ln(f_{i})}\) as the independent variable. The F-value and coefficient of determination (R2) were analyzed to examine the significance of the independent variable at the 0.05 level, find the correlation between variables, and determine whether the model is useful.  
As a result of the multiple regression analysis, we determined that the model fit was high, with a coefficient of determination of 1.0, as shown in Table 12.  
Since the regression coefficient value is close to 1 for all variables, it can be judged that the linear hypothesis applied when developing the calculation criteria is correct. Multi-collinearity between variables is not a problem because the variance extension coefficient is also less than 1.
# Techno-Economic Assessment of an Innovative Small-Scale Solar-Biomass Hybrid Power Plant
## 3 Results and Discussion
### Levelized Cost of Energy
As mentioned in the analysis of Figure 11, the first conclusion drawn from comparing Figures 12 and 13 is the increase in LCOE resulting from the establishment of limits on biomass contribution. However, unlike Figures 12 and 13 highlights that in contexts with limitations on biomass consumption, the BFF = 50% configuration was more cost-effective. This is justified by the fact that the limitation on biomass contribution results in periods of shutdown for the hybrid plant. In this situation, the lower investment and operating costs associated with the BFF = 50% configuration compensated for the lower annual electricity production, making this configuration more attractive. Finally, it can be observed that for the BFF = 50% case, the optimal LCOE was achieved with SM = 1.5, as previously indicated for cases with the same biomass contribution limit. Consequently, it is concluded that the LCOE values obtained for a 2 MW plant are higher than the cEUR 23.8/kWh feed-in tariff applied in similar situations [47]. However, an increase in plant capacity has a positive impact on the corresponding LCOE results. Figure 14 shows the LCOE (cEUR/kWh) as a function of the gross power of the plant for a 50% biomass contribution limit and SM = 1.5. The obtained results demonstrated that increasing the plant capacity allowed for the minimization of the LCOE, reaching typical values for biomass plants. Specifically, it was determined that the aforementioned target value of cEUR 23.8/kWh was achieved with a plant size of 13.5 MW in the case of BFF = 100% and with 5.4 MW in the case of BFF = 50%. Lastly, it was necessary to compare the annual biomass consumption of the previous alternatives to the annual production within a 25 km radius of the plant. In the case of the 13.5 MW plant with BFF = 100% and SM = 1.5, approximately 16.9% of the biomass produced in the plant's vicinity was utilized. On the other hand, for the 5.4 MW plant with BFF = 50% and SM = 1.5, the biomass consumption accounted for approximately 7.8% of the total annual production. Figure 13: LCOE (cEUR/kWh) as a function of SM for a 50% biomass contribution limit for the two considered values of BFF.
# Satellite Attitude Determination and Map Projection Based on Robust Image Matching
## 5 Discussion
### Combination with Sequential Observations  
The number of repetitions in RANSAC or PROSAC could also be reduced by referencing the satellite attitude derived from a prior observation in a given observation sequence. In this approach, feature pairs are eliminated if their \(\theta\) magnitudes exceed the threshold of the prior observation attitude. To test this approach, we used an observation image obtained 8 s before the observation of Kyusyu on 26 September 2015. Following UNIFOMR-1's polar orbit from north to south in the descending phase [3], this sequential image is located north of Figure 4c (Figure 8). By referencing the satellite attitude from the prior observation, we confirmed that the remaining matched pairs for Figure 4c without any repetition exactly coincided with the pairs extracted from the RANSAC approach. The rotation angle between two observations can be determined from the \(\mathbf{\hat{M}^{\prime}}\) difference, which expresses the temporal variation in the satellite attitude during the 8 s between the observations (Table 6). This comparison provides additional information for the satellite control.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**RANSAC (Expected)**} & \multicolumn{3}{c}{**RANSAC (Actual)**} & \multicolumn{3}{c}{**PROSAC (Actual)**} \\ \cline{2-10}  & **Mean** & **99.9\% Guarantee** & **Mean** & **SD** & **Max** & **Min** & **Mean** & **SD** & **Max** & **Min** \\ \hline (a) Kanto, Japan & 3.3 & 19 & 3.5 & 3.1 & 25 & 1 & 6.1 & 5.8 & 24 & 2 \\ (b) Yosemite, US & 4.3 & 26 & 4.9 & 4.7 & 28 & 1 & 3.6 & 1.8 & 11 & 2 \\ (c) Kyushu, Japan & 145 & 956 & 139 & 139 & 1058 & 1 & 28.2 & 16.2 & 86 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the expected and the actual repetition numbers with RANSAC and the actual repetition numbers with PROSAC for rejecting outliers embedded in the proposed framework. The “expected” column shows statistical expectations based on the analysis presented in Appendix B and the “actual” column shows actual values from 1000 trials. SD represents the standard deviation of repetition numbers.