esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Split Tensile Strength  
The split tensile strength results for plain concrete and PA fiber-reinforced concrete are presented in Figure 10. The data indicate that the addition of PA fibers enhances the split tensile strength of plain concrete, with the highest increase observed at fiber volume fractions of 0.5% (13.33%). However, the strength improvement diminishes as the fiber  
Figure 7: UPV for cubes with powder glass and PA fibers (6 cubes/mix).  
volume fraction increases, which may be due to segregation issues arising from inadequate compaction of mixtures with higher fiber volumes. These results are consistent with those of [PERSON] et al. [75], where the split tensile strength of M-sand concrete (C2) reinforced with 0.5% sisal fiber exhibits an improvement in strength when compared to conventional concrete (C1). Specifically, at 7 days, the split tensile strength of C2 with 0.5% sisal fiber increases by 29.41% compared to C1, while at 28 days, the strength of C2 with 0.5% sisal fiber increases by 24% compared to C1. It has been observed that the strength of the composite increases almost linearly with the volume percentage of fibers when segregation is prevented [76]. This effect can be partly attributed to the NaOH treatment of PA fibers, which can enhance their interaction with the cement matrix [77]. Treatment with NaOH can increase the surface roughness of the fibers, improving their mechanical interlocking with the matrix [77]. This, in turn, can promote better fiber distribution and orientation within the matrix, leading to improved mechanical properties such as increased compressive strength and decreased pulse velocity. Furthermore, NaOH treatment can also alter the chemical composition of the fiber surface, facilitating better chemical bonding between the fibers and the matrix, thus further contributing to the superior performance of the fibers in cement-based materials [78]. The addition of 10% powder glass without PA fibers resulted in similar outcomes in terms of both compressive strength and split tensile strength. This suggests that the presence of powder glass may have compensated for the lack of fibers in terms of enhancing the material's strength and resistance to splitting forces.
## 4 Discussion
### Water sorption and Solubility
We failed to reject the fourth null hypothesis for all materials except for Zendura. We rejected the fifth null hypothesis for all materials except for Essix Plus. The optimal orthodontic retainer must have low water absorption capabilities [29]. The water sorption of all evaluated materials was within the ISO specification, which should not exceed \(32\)\(\upmu\)g/mm\({}^{3}\)[25]. Moreover, the water solubility of all evaluated materials was within the ISO specification, which should not exceed \(5\)\(\upmu\)g/mm\({}^{3}\)[25]. However, PU-based material (Zendura) performed the poorest in terms of water sorption compared to all tested materials in all groups. Essix Plus had the highest water solubility in Groups 1 and 2, and Zendura was the highest in Group 3. Water sorption and solubility results can be attributed to the dissimilar polymer molecular weight, chemical composition, density, and crystallinity of the examined materials, which led to different water molecule penetration into the polymeric material. Variations in the water sorption and solubility characteristics of the evaluated PETG polymers could be attributed to differences in their molecular weight (which would go undetected by FTIR spectroscopy) and different additives used during manufacturing. Depending on the nature of a polymer material and some other operating parameters (temperature or relative humidity), the water uptake and swelling phenomena could lead to initiating and developing mechanical degradation processes, with direct consequences in undesirable changes of the physio-chemical properties of the material [19; 20; 21]. Thus, an increased water absorption capacity seems to be strongly related to a higher potential degradation of such a material. In fact, the water absorption tests provide a crucial indicator of the material's interaction with the oral environment, as saliva is composed of more than 99% water [30].
# Data Collection in Areas without Infrastructure Using LoRa Technology and a Quadrotor
## 2 System Description
* Proposing an economical and easy-to-build alternative for measuring, storing, packaging, and sending information in areas without infrastructure. * Establishing a methodology for collecting and uploading data collected at monitoring stations using a UAV.
## 5 Performance Evaluation
### Errors from Different Locations  
The LRA method is a positioning algorithm based on MC4L devices that we proposed previously. It obtains the corresponding relationship between irradiated areas and real distance using a logarithmic regression algorithm. Since lasers have different scattering effects on different target surfaces, deviations will occur in the process of positioning the laser irradiation point. Therefore, the LRA method will have an uneven distribution of measurement errors. Although LRA, LBP-CNNs, and LBP-FFT-CNNs algorithms are all based on MC4L, the LBP-CNNs method we proposed not only leads in error but also has more stable error control. [PERSON] adopts a stereo camera to reduce errors, but it also makes errors vary greatly in different situations. The average errors of LRA, LBP-CNNs, and LBP-FFT-CNNs are 2.4 cm, 1.2 cm, and 0.9 cm. Compared with the previous two models, the LBP-FFT-CNNs model is improved by 62.5% and 24%, respectively. To evaluate the stability of model prediction and verify whether the model performs consistently at different distances, the PSI utilized is shown in Equation (34). Figure 11 shows errors at different distances from target objects.  
\[PSI=\sum_{i=1}^{n}\left(A_{i}-E_{i}\right)\times\ln\left(A_{i}\,/\,E_{i}\right) \tag{34}\]  
Figure 10: Indoor positioning error comparison based on the different depth estimation model.  
Here, \(A_{i}\) and \(E_{i}\) represent the actual distribution and expected distribution at the current location, respectively. When \(PSI<0.1\), it means there is little change, and the model is basically stable. When \(0.1\leq PSI<0.2\), it means there is a slight change, which requires further observation. However, when \(PSI\geq 0.2\), it indicates a significant change, indicating that the model may fail, or data may have a large drift. The PSI values of LBP-FFT-CNNs, LBP-CNNs, and LRA are 0.017, 0.019, and 0.02, respectively. Although their value is all less than 0.1, the LBP-FFT-CNNs model is more stable.
## 4 Results
### Systematic Analysis
#### 4.2.1 Q4
In practical applications, these systems have been used to process work orders automatically [45], coordinate maintenance and operational information [65], and enhance OEE through rule engines and MES platforms [67]. Supporting these digital infrastructures, CPSs help to close the loop of digital intelligence and physical assets [50]. Through the integration of sensors, SCADA systems, cloud platforms, and AI models, CPSs support dynamic, computerized maintenance responses [58]. Even with decreased-scale implementations, such as those based on the combination of MEMS sensors with FFT analysis, CPSs are effective in enabling local monitoring [66]. Despite their potential, some studies still refer to IISs only conceptually, without reporting actual deployment [68]. Alongside these technologies, HMIs, particularly when enhanced with augmented reality (AR), enable a considerable amount of operator autonomy and real-time decision-making [69]. Through AR applications, procedures may be walked through interactively by a person wearing intelligent glasses or using tablet devices, improving inspection accuracy and making process training easier [69]. Concurrently, cloud-based dashboards and mobile platforms are gaining popularity as means to facilitate proactive TPM monitoring [70]. Complementing these tools, digital twins represent a high-potential yet still underutilized technology. When integrated with reinforcement learning, they enable real-time, simulation-based planning, making predictive maintenance more precise and responsive [59]. To synthesize the various technologies discussed throughout this section, Figure 7 below provides an overview of all the technologies contributing to the digitalization of TPM. It outlines their respective roles and key components, as identified in the literature, offering a comprehensive mapping of how each technological element supports the transformation of traditional TPM practices in the context of Industry 4.0.
## 5 Results/Findings
### Assessment of Structural Model (Inner Model)
This evaluation is determined through a PLS algorithm with removing one exogenous latent construct from the model to generate the \(R^{2}\) excluded value. A similar procedure is repeated by returning the removed predictor in the model to generate another \(R^{2}\) included value. These recorded changes in \(R^{2}\) values were subsequently used to calculate the effect size (\(f^{2}\)). In accordance with criterion suggested by [PERSON] [66], the effect size is less than 0.02 (0.02 = small, 0.15= medium, 0.35 = high). The effect size for continuance commitment is 0.399; the effect size for on-the-job embeddedness is 0.233 and the effect size for off-the-job embeddedness is 0.253. By implication, a research model with higher Q\({}^{2}\) values is assumed to be a sound predictive relevance. The blindfolding procedure is utilized in PLS-SEM to determine the predictive relevance of a model. To be precise, to assess the study model's predictive relevance, a cross-validated redundancy measure (Q\({}^{2}\)) was used [64]. The Q\({}^{2}\) is a standard used to determine how well the excluded cases data is predicted by a model. It is considered that a study model with Q\({}^{2}\) statistics (s) greater than zero has predictive relevance. More predictive relevance is also suggested by a research model with greater positive Q\({}^{2}\) values. The results of the cross-validated redundancy test Q\({}^{2}\) for the present analysis are listed in Table 6.  
As depicted in Table 6, the researcher was able to show the sum of the squared observations (SSO), the amount of the squared prediction errors (SSE) as well as the predictive relevance (1-SSE/SSO). The cross-validated redundancies (Q\({}^{2}\)) for that of the endogenous variables (Continuance Commitment, Intention to Stay, On-the-Job Embeddedness and Off-the-Job Embeddedness) are 0.402, 0.366, 0.399 and 0.420. These values reflect the model's adequate predictive capabilities considering the recommendations of [PERSON] and [PERSON] [67], which indicated that the Q\({}^{2}\) values should be greater than zero. \begin{table}
\begin{tabular}{c c} \hline \hline
**Constructs** & **Q\({}^{2}\) (=1-SSE/SSO)** \\ \hline Continuance Commitment & 0.402 \\ Intention to Stay & 0.366 \\ On-the-Job Embeddedness & 0.399 \\ Off-the-Job Embeddedness & 0.420 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Construct Cross Validity Redundancy.
# An Alternative Procedure for the Description of Seismic Intensity Parameter-Based Damage Potential
## 4 Conclusions  
The present study investigated the relationship between seismic intensity parameters and structural damage potential using a novel correlation-based statistical approach, resulting in the following findings:  
1. Spectral-based and energy-related IMs correlated strongly with observed damage indices, reinforcing their relevance in damage assessment models;
2. Velocity-based IMs, particularly Peak Ground Velocity (PGV) and Sustained Maximum Velocity, were also significant predictors;
3. The multiple regression analysis confirmed that a limited set of IMs could effectively predict the structural damage grade, reducing the complexity of damage estimation without compromising accuracy and computational efficiency;
4. The introduction of correlation-based weighting, in the form of exponentiation, enhanced prediction accuracy and damage grade classification;
5. Both Forward Stepwise Selection-generated models presented the best overall performance regarding prediction accuracy and damage grade classification compared to their Ordinary Least Squares and Backward Stepwise Selection counterparts.  
The proposed methodology enhances seismic damage assessment and provides a practical statistical framework for damage grade approximations.  
Conceptualization, A.E.; methodology, E.C., I.E.K., K.E.B., and A.E.; software, E.C., I.E.K., K.E.B., and A.E.; validation, E.C., I.E.K., and K.E.B.; formal analysis, E.C., I.E.K., K.E.B., and A.E.; investigation, E.C., I.E.K., K.E.B., and A.E.; resources E.C., I.E.K., K.E.B., and A.E.; data curation, E.C., I.E.K., K.E.B., and A.E.; writing--original draft preparation, E.C. and K.E.B.; writing--review and editing, I.E.K. and A.E.; visualization, E.C., I.E.K., K.E.B., and A.E.; supervision, A.E.; project administration, I.E.K. and K.E.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Discussion
### Sources of Error and Uncertainty in GWR Downscaling Models  
The GWR precipitation downscaling model calibrated with rain gauge data has achieved good results in the Three-River Headwaters region, but there are still some uncertainties and errors that limit the improvement of the quality of downscaled precipitation data. Spatial precipitation downscaling assumes that the regression relationship between precipitation and auxiliary variables constructed under coarse-scale conditions is still applicable at a fine scale. However, there are regional and seasonal deviations in the original satellite precipitation data [29; 116], which may seriously interfere with the relationship between precipitation at coarse-resolution scales and auxiliary environmental variables, and further affect the precision of precipitation at the fine scale. For example, in the ERA5 data selected in this paper, there is a serious overestimation of the precipitation data, which limits its quality improvement in the downscaling process to a certain extent. At the same time, when performing residual correction on some downscaled precipitation data in this study, the results show that the correction effect has a negative optimization phenomenon [33], which indicates that the model residual correction after downscaling should be discussed. According to this paper, we speculate that it may be the influence of the spatial density and residual spatial structure of satellite precipitation data.  
In the process of spatial precipitation downscaling, the calibration of rain gauge observations is essential. Studies have shown that the number of rain gauges plays a critical role in the calibration of precipitation downscaling models [31; 117; 118]. GDA and GRA have been widely used and proved to be of great significance in reducing precipitation downscaling errors. The sparse distribution of rain gauges and the high elevation of the region often influence the acquisition of precipitation data, meaning the calibration results of GDA and GRA are lower than expected. The Three-River Headwaters region covers an area of 360,000 square kilometers, with an average elevation of more than 4000 m. Only 29 rain gauge data are available, which greatly increases the difficulty of precipitation downscaling and calibration in this region. At the same time, some of the MODIS cloud attribution data were missing; therefore, the data does not completely cover the entire time period in the study area, which also has a certain impact on the results of the precipitation downscaling model.
# Dark Spot Detection in SAR Images of Oil Spill Using Segnet
## 4 Analysis of Segmentation Experiments
### Overfitting Analysis  
The initial probability of the data in the overfitting experiment was equal. For the first experimental model, the training samples were from the first three SAR oil slick scenes (No.1-No.3). The number of samples from each scene was 720, and the total number of training samples was 2160. The training data of the second experimental model included 720 samples selected from the first SAR oil slick scene (No.1) only. The two models were tested using the same test data, and those 120 test samples here were selected from the SAR oil slick scenes (No.1). The training set of the first model contained 1080 original samples and 1080 noise samples. Accordingly, the training set of the second training model included 360 original samples and 360 noise samples, which have the same distribution as that of the first model. The average values of each parameter based on the first and second models are shown in Table 6, and the average values of the four parameters of the second model were higher  
Figure 17: Four image segmentation standard values under 10 multiplicative noise levels.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Time (s)** & **PA** & **MA** & **MIoU** & **FWIoU** \\ \hline Segnet & \(1.639\times 10^{4}\) & 0.939 & 0.895 & 0.801 & 0.914 \\ FCN8s & \(1.703\times 10^{4}\) & 0.884 & 0.805 & 0.783 & 0.823 \\ SVM & \(1.353\times 10^{2}\) & 0.854 & 0.693 & 0.568 & 0.795 \\ Otsu & \(2.192\times 10^{-3}\) & 0.573 & 0.543 & 0.375 & 0.508 \\ RF & \(1.183\) & 0.867 & 0.735 & 0.612 & 0.814 \\ CART & \(2.023\times 10^{-2}\) & 0.855 & 0.749 & 0.605 & 0.810 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the six segmentation methods.  
than that of the first model. It can be seen that there is indeed an over-fitting phenomenon when the sample space is insufficient.
# HSM4 SSL: Leveraging HSMs for Enhanced Intra-Domain Security +
## 3 HSM4 SSL Architecture
### HSM4 SSL: Low-Level Architecture
#### 3.3.1. HSM4 SSL Application Layer  
The application layer of HSM4 SSL includes software applications running on intra-domain servers. These servers utilize the security services utilized by HSM4 SSL by interacting with the management layer via an API to provide unified and standard access to the available HSMs.  
HSM4 SSL API: To establish a unified access point for software applications to the architecture, we developed RESTful APIs that adhere to the REST architecture. These APIs utilize HTTP requests to communicate with resources identified by URLs, enabling the exchange of information through standard HTTP methods like GET, POST, PUT, and DELETE. The API design process was complemented by conducting a comparative analysis of the currently most-utilized API development tools, which are FastAPI, Flask, Spring, and Django. Figure 4 presents a performance comparison of the aforementioned tools based on the number of RPS that each framework can handle as the number of concurrent users increases from 0 to 40. From the results, Django demonstrates the highest RPS rate across the board. However, FastAPI preserves a consistent RPS rate, suggesting robust performance under a concurrent user load. Further, the latency comparison shown in Figure 5 depicts the latency measured in milliseconds as the number of concurrent users increases from 0 to 40. As the user load rises, all frameworks exhibit an increase in latency. FastAPI shows the lowest increase, suggesting it handles the additional load with minimal latency impact.  
After evaluating the performance of various web frameworks, we chose FastAPI for its impressive balance of handling a high number of requests per second and its low latency. FastAPI's performance metrics indicate that it can handle a significant number of requests with minimal delay, which is essential for ensuring a smooth user experience. Prioritizing a lower latency is crucial for our application, as it directly impacts the responsiveness and efficiency of the service.  
Figure 3. HSM4 SSL low-level architecture.
## 4 Results and Discussion
### Aerodynamic Damping
#### 4.3.2 Modal Response in Flow  
Combined with the modal responses in the vacuum that are presented previously, the total damping \(\xi_{tot}\) is calculated using the decay rates extracted from the time history of the blade displacement response in the flow field. The flow field in this section is simulated using a design operation. The response envelope for the blade in flow field is \(Ae^{(-\xi_{tot}\omega_{n}(t-t_{0}))}+A_{\infty}\). Figure 13 shows the responses of the tip leading-edge displacement in the flow field with respect to different Rayleigh damping constants for the first bending modal excitation; the amplitude of excitation force is 0.15 N. The total damping of \(\xi_{tot}\) is 0.0328 and 0.0857, which correspond to the Rayleigh damping constants \(\alpha=0,\ \beta=1.77\times 10^{-5}\) and \(\alpha=4.49,\ \beta=3.67\times 10^{-6}\), respectively. The total damping values are higher than the corresponding non-aerodynamic damping values, which are shown in Figure 9. This indicates that the blade is aeroelastically stable in the first bending mode of the design operation.  
Figure 11: Spectrum analysis for the tip leading-edge displacement.  
Figure 12: Tip leading-edge displacement in a vacuum for the second modal excitation after filtering.  
Figure 10: Tip leading-edge displacement in a vacuum for the second modal excitation.  
The blade tip displacement and its spectrum analysis in the flow field for the second bending modal excitation, which correspond to the Rayleigh damping constant \(\alpha=0,\beta=6\times 10^{-6}\), are shown in Figures 14 and 15, respectively. Obviously, these figures show more significant multi-frequency responses than Figures 10 and 11. Figure 16 shows the filtered displacement response for the second bending modal excitation in the flow field only. The total damping achieved by the response envelope is \(\xi_{tot}\) = 0.0406, which is higher than the corresponding non-aerodynamic damping of \(\xi_{m}\) = 0.0337, which is represented in Figure 12. This indicates that the blade is aeroelastic stable in the second bending mode of the design operation.
## 3 Results and Discussion
### Comparison  
The previous literature reported that a fluorescent competitive detection method based on dummy molecularly imprinted polymers (DMIPs) for melamine was developed [48]. DMIPs were prepared using a traditional molecular imprinting technique, which has some limitations, such as poor binding capacity, incomplete template removal, slow mass transfer and poor selectivity. Compared to conventional MIPs, we synthesized a novel magnetic surface MIP by using Fe\({}_{3}\)O\({}_{4}\)@SiO  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Added (mg/L)** & **Fluorescent Found (mg/L)** & **Recovery, Means \(\pm\) R.S.D (\%, \(n=3\))** & **HPLC-MS/MS** \\ \hline
0.25 & 0.175 & 70.2 \(\pm\) 8.9 & 83.5 \(\pm\) 4.8 \\
0.5 & 0.411 & 82.2 \(\pm\) 12.4 & 82.7 \(\pm\) 4.1 \\
1.0 & 0.853 & 85.3 \(\pm\) 7.6 & 89.6 \(\pm\) 3.3 \\
2.5 & 2.32 & 92.7 \(\pm\) 5.4 & 93.5 \(\pm\) 2.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results for analysis of melamine in milk samples.  
Figure 6: Fluorescence spectra of FITC-Mel with different interfering substances by conducting experiments at the same concentration (1.0 mg/L).  
nanoparticles as a support and magnetic core. The novel MDMIPs have a core-shell sphere structure which enhances the absorption capacity and selectivity for melamine and accelerates the mass transfer. More importantly, most adsorption binding sites were distributed on the surface of MIPs, which indicate a good template removal performance. In addition, MDMIPs possess a high magnetic property that allows for the rapid separation of melamine from the complex sample matrix without any additional operations such as centrifuge.