esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Crack Orientation on Fatigue Life of Reinforced Concrete Bridge Decks
## 5 Conclusions  
The effect of crack orientation on the fatigue life of RC decks is investigated by using the multi-scale simulation and the pseudo-cracking method with respect to the most sensitive crack direction. On the basis of large numbers of reviewed crack patterns, the following conclusions are drawn.  
1. The crack orientation is highly associated with the coupled flexure-shear mode of failure, where pre-cracks tend to arrest the ensuing propagation of post-shear cracks. As a result, enhanced fatigue life is obtained.
2. The pre-crack arrest mechanism enables bridge maintenance managers to conduct a fair assessment of RC decks that present a rough appearance but have mechanically enhanced fatigue life.
3. The cracks that approximate the deck's longitudinal or transverse direction are understood to most extend the remaining fatigue life of RC decks.
4. The central zone of the loading path is found to be the location where the crack arrest mechanism is effective, and the remaining fatigue life is elevated.
5. During inspections, careful attention shall be paid to cracks at the corners of RC decks since the stopping mechanism that arrests diagonal cracking does not effectively function there.  
[PERSON] conducted and analyzed the numerical studied cases; [PERSON] and [PERSON] supervised the analytical process and developed the simulation program; [PERSON] and [PERSON] wrote the paper.  
This study was financially supported by the Council for Science, Technology, and Innovation, \"Cross-ministerial Strategic Innovation Promotion Program (SIP), Infrastructure Maintenance, Renovation, and Management\" granted by the Japan Science and Technology Agency (JST).  
Figure 22: Hazard map for the locations of higher risk cracking.  
**Acknowledgments:** The authors extend their appreciation to [PERSON] of Yokohama National University and The University of Tokyo, for his valuable advice and encouragement to bridge multiple disciplines in engineering.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Field Tests on Eco-Friendly Railway Precast Concrete Slab
## 2 B2S Panel  
This study adopts one of the conventional reinforced concrete (RC) slab designs used by the Seoul Metro subway. Figure 1 shows a 3D rendering image of the B2S panel. Its dimensions were 2.75 m (width) \(\times\) 1.225 m (length) \(\times\) 0.17 m (thickness). An elastic fastening system was used to connect the rails to the slab panel. The B2S reinforced concrete adopted by the Seoul Metro subway system required us to use the minimum compressive strength of 50 MPa concrete in order to meet its design criteria [19]. Their standard was established to minimize the settlement of the track structure, especially the subsoil. The B2S track system had a higher track stiffness than a ballasted track structure with prestressed concrete sleepers. In addition, [PERSON] et al. reported that the installed B2S track structure system in the Seoul Metro subway reduces the noise and vibration level as compared with the ballasted track structure [20].  
Given the geometric design, two types of B2S panels were fabricated with (1) a conventional concrete (minimum 28-day compressive strength, \(f^{c}\) = 50 MPa) and (2) the eco-friendly concrete (minimum 28-day compressive strength, \(f^{c}\) = 70 MPa). Hereafter, the first panel with a conventional concrete is named \"Conventional B2S panel,\" and the second panel with the eco-friendly concrete is named \"Eco-friendly B2S panel.\"  
Figure 2 shows the pictures of the fabrication process of the B2S panels: (1) #3 and #4 rebars were assembled in a steel formwork (see Figure 2a), (2) concrete was poured into the formwork, (3) concrete was cured at the ambient temperature, and finally, Figure 2d shows the prototype of two eco-friendly B2S panels. Normally the conventional concrete takes at least seven days to reach the design strength (50 MPa). However, the eco-friendly concrete can reach a strength of 50 MPa within one day. In order to develop the high-early strength concrete, the eco-friendly concrete used the Portland type III cement and GGBF slag together as the cement-binder. The water to cement-binder ratio was 0.35. In addition, it is worthwhile to note that the self-weight of the eco-friendly concrete was at least 10% heavier than the conventional concrete, since the density of the EAF slag (3.4 g/cm\({}^{3}\)) is heavier than natural fine aggregate (2.6 g/cm\({}^{3}\)). The more detail of the eco-friendly concrete can be found in previous studies [16; 17].  
Figure 1: A 3D rending image of a ballasted track to slab track (B2S) panel.
## 1 Introduction
The goal of the research conducted in this paper is to present the methodology for optimizing the monolith length (spacing between the contraction joints) to reduce the impact of the thermal processes on the increase in TTS in concrete and thus reducing cracking, and degradation of durability and safety of CGD [30; 31]. In this research, in the calculation models developed by the authors, which simulate the construction process using the block method, a phase semi-coupled TSA was performed, taking into account the following thermal boundary conditions: at the interface between the blocks and the rock mass, at the interface between the old and newly poured blocks, at the surfaces of blocks that are in contact with air and water, as well as temperature levels of the surrounding rock mass and fresh concrete mix. The analysis is conducted over a long-term period that entails the construction, reservoir filling, and exploitation process of the structure. Two different monolith lengths were considered (20.0 m and 15.0 m). TTS analysis, performed on the calculation model, takes into account all the relevant thermal processes, and self-weight of the structure, but disregards other actions which would disturb the realistic TTS field (hydrostatic pressure and the uplift forces). As the result of the analysis, the temperature fields (TF), the stress fields (SF), as well as the zones in which the concrete tensile resistance in the dam's axis direction is exceeded, are shown for characteristic construction and exploitation phases. TF results show sensitivity to the cement hydration process [32] and external environment conditions during construction and exploitation. SF results show the sensitivity of the structure to the process of shrinkage of concrete caused by a sudden or gradual cooling process, as well as to the change of the monolith length, whereupon the reduction of the zones in which the concrete tensile resistance is exceeded resulting from the reduction of the monolith length is noticed. The results of the research point out the significance of the process of optimization of the monolith length and the possibility of advancing the presented methodology by varying the arrangement and dynamics of pouring the concrete blocks, considering the cooling and concrete curing processes during construction [33, 34] and connecting with a monitoring system in the construction and exploitation phase [35, 36].
# Adaptive Metrics for Adaptive Samples
## 1 Introduction  
### From Points to Topology  
Both surface reconstruction and homology inference are algorithmic problems that take points as input and produce a topological representation of the underlying space from which the points were drawn. In surface reconstruction, one often wants a homeomorphic reconstruction in the form of a triangulation, whereas in homology inference, it suffices to compute the homology groups. Although similar in many respects, the general trend is that with weaker conditions on the input (i.e., noisier samples), one can only hope for weaker guarantees on the output (homology rather than homeomorphism). There is one aspect of these theories that directly contradicts this trend: many surface reconstruction algorithms are able to work with an adaptive sample, while most homology inference algorithms require a uniform sample. Here and throughout, we use \"uniform\" in the Hausdorff sense, not the statistical sense. An adaptive sample has a density that adapts to some local sizing function. Thus, areas that require higher fidelity will have higher density (and a smaller scale), while areas that can get by with less fidelity will have lower density (and larger scale).  
There have been some notable works that have bridged this gap between surface reconstruction and homology inference for adaptive samples. Most theoretically guaranteed surface reconstruction algorithms assume an input that is sufficiently dense with respect to the distance to the medial axis, a kind of skeleton describing the complement of the underlying shape. [PERSON] et al. [1] introduced the conformal alpha shape filtration as a way to build triangulations at different scales that have local connectivity related to the local feature size. Although their stated goal was surface reconstruction, the work employed many of the methods of homology inference. [PERSON] and [PERSON] [2, 3] gave a more direct generalization of methods in surface reconstruction with adaptive samples to homology inference, achieving some guarantees for smooth manifolds assuming both upper and lower bounds on the density. [PERSON] et al. [4] gave a homology inference algorithm for manifold data thatattempts to sample a subset of the medial axis in order to approximate the local feature size. This work was the main motivation for the current paper, and we adopted their notation of \(X\) for the space and \(L\) for the approximation to the complement space. We extend these works by providing guaranteed homology inference for a much more general class of samples and spaces; we do not require the space to be a manifold or the sample to adapt to the medial axis.
# Detection of RINEX-2 Files With Mixed GPS L2P(Y)/L2C Carrier Phase Observations
## 3 Detection Algorithms
### Algorithm 2: Carrier Phase Widelane in Baseline Mode  
This algorithm uses widelane fractional ambiguities of the carrier phase WL linear combination (1) and is not influenced by any biases affecting code measurements. In order to keep residual ionospheric effects small, IGS global ionosphere maps are used to reduce ionospheric delays. Since remaining ionospheric errors are still too large, this detection algorithm is used in baseline mode, reducing residual ionospheric delays further by differencing. While working in baseline mode, which is only able to determine differences in L2 biases, it is essential to know whether the second station is affected by the L2 quarter cycle problem.  
The WL linear combination is influenced by orbit errors, which makes it necessary to use precise IGS orbits. Also, tropospheric zenith delays need to be estimated. The analysis of fractional ambiguities is done similarly to Algorithm 1. Experiences show that Algorithm 2 produces reliable results for baseline lengths of up to several 100 km. If, however, large residual ionospheric errors exist, no definite results are obtained.
# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 4 Our Proposed Architecture for Smart Solar Plant
### Perceptual Layer  
Depending upon the granularity of monitoring, solar plant monitoring can be performed at module, array, inverter, and battery levels. The end devices comprise the compute board, sensors, and communication module. Salient features to be considered in the design and development of end devices are:  
* A low-cost system by using off-the-shelf hardware and open-source software.
* Power optimization for minimum human intervention.
* Flexibility from modification perspective [16; 20].
* Scalability and independence from solar panel layout and configuration.  
Data are collected from the physical world in the form of analog signals through different sensors. These signals (representing electro-physical properties) are converted to digital values and transmitted wirelessly to edge nodes.  
Figure 3: This figure illustrates a layered architecture for smart solar plant monitoring, integrating cloud, edge, and IoT technologies. It shows the hierarchical data flow from IoT sensors through the edge layer to the cloud for further analysis. The IoT layer consists of module-level end devices, while the edge layer consists of a compute board that receives data from these devices and processes data from various environmental sensors as well. Data collected at this layer is prepared for upward transmission to the cloud, where it undergoes further processing for detailed analytics.
## 3 Materials and Methods
### RSRQ Measurements  
Experimental values for RSRQ have been obtained and are shown in Figure 11 following the same pattern as in the Figures 6 and 9. A spatial correlation has been performed and the results are included in Table 9. In contrast to the results obtained for RSRP and RSSI, the spatial correlation does not indicate a high level of consistency between related measurements for the same provider. Furthermore, the values for spatial correlation are significantly lower.  
The average RSRQ for each position was calculated for each provider. The results are shown in Figure 12. Furthermore, the confidence interval for a confidence of 0.8 was calculated and included in the figure. As in the case of the previous assessments, the reduction of the width of the confidence interval due to the higher number of measurements is obvious when comparing Figure 12a to Figure 12b.  
Figure 11: RSRQ as a function of distance (solid lines: raw data; dots: resampled, interpolated data). Gray, dotted horizontal lines indicate ranges according to Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & **Risk of** & \\
**Measurement** & **Provider** & **Excellent** & **Good** & **Fair to** & **Disconnect** & **Range** \\  & & & & **Poor** & **or No** & **Analysis** \\  & & & & & **Signal** & \\ \hline (a) & A & 1983 m & 431 m & 587 m & - & 3001 m \\ (b) & A & 1737 m & 1264 m & - & - & 3001 m \\ (c) & A & 1294 m & 1380 m & 327 m & - & 3001 m \\ (d) & B & 1515 m & 599 m & 887 m & - & 3001 m \\ (e) & B & 1767 m & 828 m & 406 m & - & 3001 m \\ \hline \hline \end{tabular}
\end{table}
Table 8: Provider-based assessment RSSI.  
The measurement shown in Figure 11 has been evaluated based on the criteria in Table 4 and the result is presented in Table 10. Similar to the result for RSSI, the evaluation for RSRQ indicates at least fair to poor coverage for the full range for both providers. Furthermore, the results do not indicate a clearly superior provider.
## 3 Methodology  
Figure 1 shows a PSS concept decision trade-off model based on rough sets and the Shapley value method, which is used to select the optimal concept to trade-off CV-economic expectations for enterprise. The proposed decision analysis framework comprises two parts. In the first part, BWM and EWM are adopted to calculate subjective and objective weights to satisfy CV and economic PSS evaluation criteria, which are then used to construct a comprehensive weighting function and rank the criteria and their importance. In the second part, CV and economic criteria are considered players in constructing a cooperative game comprising players, strategies, and utility. The Shapley value model is adopted to calculate the contribution of each player to the product's overall gain (optimal utility allocation), and the solution whose allocation of utilities is closest to the Shapley value is considered the optimal solution. The aforementioned procedure enables the criteria weighting process to balance CV and economic objectives via the creation of importance ratios during the design phase. This will prevent an excessive focus on PSS performance without considering cost, which would be detrimental to its competitiveness.
# Screen-Cam Robust Image Watermarking with Feature-Based Synchronization
## 5 Experimental Results and Analysis  
We conducted a series of experiments to verify the robustness of the algorithm. Robustness refers to the ability to detect the watermark after the designated class of transformations [53]. Bit Error Ratio (BER) is a commonly used metrics to measure the robustness of watermarking methods. BER is defined as:  
\[\text{BER}(W,w)=\frac{n_{e}}{l} \tag{21}\]  
where \(n_{e}\) is the number of erroneous bits. A lower BER indicates the extracted results are closer to the original watermark information, which means the better robustness. Since the threshold \(T\) for  
Figure 13: Influence of different thresholds on (a) false-positive rate of one LSFR and on true-positive rate between different (b) keys and (c) images.  
watermark detection is set to 8, and the watermark length \(l\) is set to 60 in our method, this means that watermark detection is successful when BER is below 0.1333.  
In Section 5.1, the robustness to common image attacks is discussed. In Section 5.2, the proposed scheme is compared with two state-of-the-art schemes and the performance against screen-cam attack is analyzed in detail. In Section 5.3, considering real-life scenarios, some hypothetical scenarios were designed to verify the robustness of the algorithm.  
The experimental instruments are as follows: The display device in this scenario is a 23-inch monitor with \(1920\times 1080\) pixels. Since the ordinary users' monitors are not accurately corrected, to mimic a real-world scenario, the monitors are not explicitly calibrated. An iPhone X with dual 12 MP pixels is used as the photography equipment. The lens is well focused while shooting, and shooting quality is controlled as much as possible.  
The host images are the eight images in Figure 2. The PSNR values of each square region that contains an LSFR are controlled to be no less than 39 dB in our experiment. Figure 14 shows the corresponding watermarked images generated by the proposed method.
## 3 Proposed Work
### Signal Interference and Noise Ratio (SINR)  
The \"SINR\" is calculated by the ratio of the \"quality of the transmitted signal\" and total \"interference\" and the background noise. The receiver's \(\mathrm{r}_{\mathrm{e}_{i}}\) level of noise and interference, designated by the transmission edge \(\mathrm{e}_{\mathrm{d}_{i}}\), is given as follows:  
\[\mathrm{I}_{f}\ \big{(}\mathrm{e}_{\mathrm{d}_{i}}\ \big{)}=\sum_{m,m=1}G\big{(} \mathrm{T}_{\mathrm{e}_{m},\ \mathrm{r}_{\mathrm{e}_{i}}}\big{)}p(\mathrm{T}_{\mathrm{e}_{m}}\ \big{)}+\lambda_{i} \tag{2}\]  
Here, \(\mathrm{G}\ \big{(}\mathrm{T}_{\mathrm{e}_{m},\ \mathrm{r}_{\mathrm{e}_{i}}}\big{)}\) is the \"path gain between the transmitter\". \(\mathrm{T}_{\mathrm{e}_{m},\ \mathrm{e}_{i}}\) on the current link, m e, and the \"receiver\". \(\mathrm{r}_{\mathrm{e}_{i}}\) on the current edge \(\mathrm{e}_{\mathrm{d}_{i}}\). \(\mathrm{p}\ \big{(}\mathrm{T}_{\mathrm{e}_{m}}\big{)}\) is the capability of transmission by the transmitter's m \(\mathrm{T}_{\mathrm{e}_{m},\ \mathrm{e}_{i}}\) on edge \(e_{m}\), and \(\lambda_{i}\) is the background noise present at the \"receiver node\" \(\mathrm{r}_{\mathrm{e}_{i}}\). Then, the \"SINR\" for the edge is defined as follows:  
\[\delta\big{(}\mathrm{e}_{\mathrm{d}_{i}}\ \big{)}\ =\ \frac{G\big{(}\mathrm{T}_{\mathrm{e}_{m},\ \mathrm{r}_{\mathrm{e}_{i}}}\big{)}p(\mathrm{T}_{\mathrm{e}_{m}}\ \big{)}}{\mathrm{I}_{f}\ \big{(}\mathrm{e}_{\mathrm{d}_{i}} \big{)}} \tag{3}\]  
According to condition (3) above, when \(\mathrm{I}_{f}(\mathrm{e}_{\mathrm{d}_{i}})\) increases, the transmission power \(p(\mathrm{T}_{\mathrm{e}_{m},\ \mathrm{e}_{i}})\) needs to increase proportionally to maintain the same \"SINR\" value on the connection. However, when \(p(\mathrm{T}_{\mathrm{e}_{m},\ \mathrm{e}_{i}})\) expands, various topology connections might experience more interference. To maintain constant signal strength and communication quality, these connections must strengthen their \"transmission capacity [22]\". This could shorten the system's lifetime and increase the nodes' energy consumption.
## 6 Concluding Remarks  
This study examined the computational simulations of the samples extracted from porcine and healthy human abdominal aorta to investigate the material parameters as a function of age. In addition, the computational simulation results were compared with the uniaxial tensile test results of the porcine specimens to verify the reliability of the numerical simulation technique. The simulations based on age were conducted on circumferentially- and longitudinally-oriented strips, and formulae were proposed to determine the material parameters according to age. The results exhibited the following general trends:  
* According to the uniaxial tensile test results with respect to the loading direction, the circumferentially-oriented strip samples exhibited a maximum tensile strength value of 2.49 MPa, which was 18.3% higher than that for the longitudinally-oriented strip samples, based on the average value of the experimental results. Therefore, the uniaxial tensile stress and stretch in the arterial tissue were identified to be dependent on the fiber orientation.
* In addition, the failure stress and stretch were investigated as a function of age, and the material constants for age were calculated based on the maximum and minimum trend lines. The HGO model was applied to the numerical model for anisotropic hyper-elastic materials during the numerical simulations.
* In the parametric study, the formulae associated with the value of the material constant for the ages of 46-89 years were proposed, and the proportion variance in the dependent variable that was predictable from the independent variable was examined.  
The results obtained in the present study are expected to be applied to study aortic disease, and it is possible to predict the behavior of the soft tissue in aortic disease based on the numerical simulation technique from this study. In addition, in further studies, the uniaxial and biaxial experiments for human aortic tissue will be conducted, and the precise experimental method for the viscoelastic effect will be proposed from the strain rate-dependent tensile test of soft tissue. Moreover, the study for human aortic tissue will be performed based on the experimental results and compared to the clinical data. The material constants for the HGO model will be calculated from the polynomial regression method.  
Conceptualization: U.H., C.-W.L., C.-S.L., and D.-M.R.; methodology: U.H., C.-W.L. C-S.L., and D.-M.R.; software: J.-H.Y., C.-H.S., and D.-M.R.; validation: U.H., J.-H.Y., and C.-S.L.; formal analysis: C.-W.L. and C.-H.S.; investigation: U.H., J.-H.Y. C.-S.L., and D.-M.R.; writing, original draft preparation: U.H., C.-S.L., and D.-M.R.; writing, review and editing: U.H., C.-W.L., C.-S.L., and D.-M.R.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1A6A3A01012664; 2018R1D1A1B07044567).  
The authors declare no conflict of interest. The funders had no role in study design, data collection and analyses, writing of the manuscript, nor in the decision to publish the results.