esa-sceva/satcom-chunk-collection huggingface dataset:
# Explaining and Measuring Social-Ecological Pathways: The Case of Global Changes and Water Security
## 4 Earth Observations for Water Security: The Data Cube
### The Data Cube to Make Earth-Observation-Based Research Replicable over Time and Space
Besides Landsat, the Sentinels program, operated by the European Space Agency (ESA), forms another valuable EO data source. Sentinels are part of a satellite constellation for the operational need of the European EO program called Copernicus [84; 85]. These two exhaustive sources favor establishing common, reliable and precise measurements that minimize the risk of missing data over time and space. Among the major benefits of EODC is that it makes data analysis easier, facilitating data access and distribution and lowering the technical barriers for managing and processing many EO data (Figure 4). Traditionally, satellite imagery is downloaded, processed and provided to users on a custom basis. This process serves a single purpose each time analyzing data locally (e.g., desktop computer) and downloading data on scene-based file. With the paradigm shift advanced by EODC information becomes available more rapidly, the burden of data preparation and usage is drastically reduced [14; 86]. Moreover, the data quality is enhanced as ARD are homogenous. They are combined in a consistent workflow offering pre-processed data with the same procedure along the entire time-series. Similarly, it makes easier the cross-checking with other databases because data can be accessed through interoperable Application Programming Interfaces (APIs). This allows seamlessly integrating data coming from various sources into a single analysis in a very efficient and effective manner. Consequently, the widespread availability of EO data, the advent of ARD, and the large processing capabilities provided by high performance computing (e.g., clouds, clusters) allow envisioning supporting policy frameworks such as the SDGs [87]. EODC can provide the long baseline required to determine trends, define present, and inform future, and thus provide a good fit between the analytical scope of SES evolution and empirical tests.
## 3 Model Architecture
### Input Vector  
Based on the physical model presented in Section 3.1, we identified the factors that influence the output power. For a certain array, the efficiency and the size are fixed, as included in the historical data. However, the solar radiation and ambient temperature change with time periodically. Therefore, we choose time, solar radiation and ambient temperature as the input parameters. The input data is obtained from a numerical weather predilection (NWP) model, which uses mathematical models of the atmosphere and oceans to predict the weather on current weather conditions [22]. The input vector is:  
\[\mathbf{x}_{i}=\left[\begin{array}{ccc}time&tem&R\end{array}\right]^{T} \tag{12}\]  
where \(tem\) is the ambient temperature, and \(R\) is the amount of solar radiation reaching the part of the Earth's surface near the photovoltaic cells.
## 4 Discussion
### Penetrometer Tests
#### 4.2.2 Effect of Biopolymer Concentration  
_Medium-grained sand._ Penetrometer test results showed that doubling the concentration significantly increased the penetration resistance of most biopolymers tested (Figure 7 and Table 4). This trend corresponds with previous research (Tables A7 and A8) [14; 17; 28; 33]. As stated by [PERSON] et al. [14], this is because higher biopolymer concentrations increase the thickness of the inter-particle bonds forming between the coated sand particles, which causes the crust strength to increase. Doubling the concentration of WS and WPC only slightly increased the penetration resistance (Figure 7). The minor effect of WPC suggests that it generally has limited potential to enhance soil properties. Regarding WS, it is likely that its high viscosity limited the infiltration depth, leading to the presence of more biopolymer molecules in the pores than required to form a stable 3D network. Thus, for WS doubling, the concentration likely exceeded a threshold concentration, beyond which only minor increases in crust strength occur. This corresponds with findings of [PERSON] et al. [16], who observed that applications of sodium alginate and pectin only increased penetration resistance up to 2 or 3 wt%.  
_Fine-grained silica sand._ Doubling the concentration increased the penetration resistance of most biopolymers tested (Figure 8), similar to the findings on medium-grained sand (Figure 7) and the published literature (Tables A7 and A8). WS and XG exhibit a different trend, as doubling their concentration resulted in decreased penetration resistances. This can be attributed to the high viscosities of their respective solutions, which limited infiltration depth and crust thickness, causing the crust to endure less load. For WS, the low infiltration and high local biopolymer concentration even caused the crust to curl up because of tension forces that occurred as the swollen biopolymers dehydrated and shrank (Figure 10).  
_Comparison of polysaccharides and proteins._ On average, protein-treated samples exhibited larger incremental increases in penetration resistance than polysaccharide-treated samples upon doubling the concentration (Figures 7 and 8). Thereby, on medium-grained sand, some protein treatments only formed stable crusts at 2 wt%, while most tested polysaccharides formed stable crusts at 1 wt% on both soils (XG = 0.25 wt%). This suggests that for the tested polysaccharides, doubling the concentration likely surpassed a threshold beyond which only small increases in crust strength occur. As a result, proteins must be applied at higher concentrations to achieve comparable penetration resistances as polysaccharide-induced crusts on medium-grained sand.
## 3 Experiments and Results
### CoM Estimation  
The CoM estimation based on the data obtained from Figure 3 yielded the results shown in Table 3. Considering the data specified in the Robotiq 3-Finger Adaptive Gripper datasheet and the length of the mount we designed, the CoM along the \(z\)-axis is approximately 105 mm, while the estimated result is 104.4 mm. The difference in the CoM results for the \(x\)- and \(y\)-axes is due to the fact that our calibration aimed to estimate the wrench acting on the sensor. Therefore, the calibration was performed to predict the CoM in a way that minimizes the effect of the end tool on the sensor.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Kinematics** & **Calibration Result [\({}^{\circ}\)]** \\ \hline \(\alpha\) [\({}^{\circ}\)] & - & 1.38 \\ \(\beta\) [\({}^{\circ}\)] & - & 4.88 \\ screw angle [\({}^{\circ}\)] & 4.8 & 5.07 \\ \hline \end{tabular}
\end{table}
Table 2: The result of the incline estimation. Since the kinematically computed angles do not indicate the axis of rotation, they are expressed using the screw axis. Also, kinematic angles are used as the approximation value to verify if the calibration result is reasonable.  
Figure 4: The mobile base on an inclined surface. The incline angle can be calculated geometrically using the wheelbase of the mobile robot and the height of the step.  
Figure 3: The 10 sampling poses used for calibration throughout this paper. These postures are selected by rotating the wrist angle in the orientation that generates the highest torque, allowing for various orientations.
# EGSDK-Net: Edge-Guided Stepwise Dual Kernel Update Network for Panoptic Segmentation
## 4 Experiments
### Comparison Experiment  
Table 1 presents the quantitative comparison results of EGSDK-Net with SOTA methods [5; 10; 11; 12; 13] on the Cityscapes dataset [14]. For a fair comparison, we retrain the main reference method of EGSDK-Net in the current experimental environment, while using the official data for the other methods. According to the experimental results, EGSDK-Net demonstrates a clear leading advantage in the comprehensive evaluation of panoptic segmentation. It achieves a PQ of 60.6, leading RT-K-Net [5], the second-ranked method, by 2.19%, while other methods do not even reach a PQ of 59. In the evaluation of \"thing\" classes, EGSDK-Net ranks second overall, only slightly behind [PERSON] et al. [13] (51.9 vs. 52.1). Notably, the retrained RT-K-Net [5] performs poorly in the \"thing\" classes, surpassing only MGNet [10]. Although the improvement over RT-K-Net [5] is not very significant, we must point out that the results of RT-K-Net [5] in the \"stuff\" classes already reach a relatively high value. EGSDK-Net surpassing it numerically is sufficient to confirm its outstanding performance. This result is attributed not only to the strong RTFormer backbone and the optimizations provided by RT-K-Net [5], but also to the enhancements in the kernel update strategy within EGSDK-Net.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Backbone** & \(PQ\) & \(PQ_{th}\) & \(PQ_{st}\) & **GPU** \\ \hline MGNet [10] & ResNet-18 & 55.7 & 45.3 & 63.1 & TitanRTX \\ K-Net [2] & ResNet50-FPN & 56.9 & 46.0 & 64.8 & - \\ Petrovai and Nedevschi [12] & VoVNet2-39 & 57.3 & 50.4 & 62.4 & V100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison results between our method and the state-of-the-art panoptic segmentation methods on the Cityscapes validation set. The best results are shown in **boldface** and the second-best results are underlined.
## 4 Methodology
### Proposed Framework  
The proposed process for text label extraction is based on four main steps. The first step is image acquisition, which involves capturing the image of the label using a camera attached to a controlled environment. The second step is detection and image processing, where the label image is extracted from the captured image and processed to reduce noise and correct the image angle. The third step involves information extraction from the label, which is divided into two parts: the image decoding of barcodes and QR codes, and Optical Character Recognition (OCR). Finally, the fourth step involves post-processing of the OCR output. This step involves processing the text information obtained from OCRin a structured manner that can be easily used as structured data. Figure 4 illustrates an overview of the framework employed in this study.
## 4 Reinforcement Learning-Based Joint Optimization Strategy for Flight Path and Resource Allocation
### Overall Algorithm Flow  
Combining Algorithm 1 and Algorithm 2, we propose a joint optimization strategy for flight path and resource allocation based on RL, as shown in Algorithm 3. In each time slot, the algorithm first runs Algorithm 1 based on the remaining throughput of GPFs to obtain the current coordinates of APs for that time slot (Step 2b). Subsequently, each GPF obtains the state of the current time slot. Based on the trained energy efficiency estimation matrix, Algorithm 2 is executed to select the optimal resource allocation strategy (Step 2c). The specific flow is illustrated in Figure 2.  
The concept of the proposed algorithm draws on the Monte Carlo method and the single-step Temporal Difference method. It updates the value at a single moment and selects actions through a soft greedy strategy to balance exploration and optimization. It only requires a certain sampling sequence, that is, from the environment. Using the sequence of states, actions, and benefits sampled, we can iterate to the optimal strategy.  
Figure 2: Reinforcement learning-based joint optimization strategy for flight path and resource allocation—process flowchart.  
```
(1) Input: GPFs set \(\mathcal{N}\), APs set \(\mathcal{M}\), each algorithm parameter, Environmental state information, GPFs' workload \(\mu\), the upper limit of transmission power of GPFs \(p_{max}\). Output: Optimal Flight Path strategy \(\mathcal{Q}\) and optimal device association and power control strategy \(\omega_{s},\omega_{m},\mathcal{P}\). (2) for each training episodes \(N_{\text{epi}}\) do  for each time slot \(t\) do  Based the GPFs' workload and position obtained from Algorithm 2 in time slot \(t\), decide the Flight Path strategy \(\mathcal{Q}(t)\) according to Algorithm 1.  Based on Flight Path strategy \(\mathcal{Q}(t)\) obtained from Algorithm 1 in time slot \(t\), the device association and power control strategy \(\omega_{s},\omega_{m},\mathcal{P}\) is decided using Algorithm 2.  Update Environmental state information. \(t=t+1\).  end for  until \(t>T\). (3) \(N_{\text{epi}}=N_{\text{epi}}-1\). (4) end for (5) until \(N_{\text{epi}}\leq 0\). (6) Return \(\mathcal{Q},\omega_{s},\omega_{m},\mathcal{P}\).
```  
**Algorithm 3** A Joint Optimization Strategy for Flight Path and Resource Allocation Based on RL
# From Complex Modulus E* to Creep Compliance D(t): Experimental and Modeling Study
###### Abstract  
Creep compliance (D(t)) is a very important input for the thermal cracking resistance in the Mechanistic-Empirical Pavement Design Guide (MEPDG). The aim of the work presented here is to predict the results of creep compliance D(t) from the result of complex modulus E*(\(\omega\)). The work plan is divided in two main parts: an experimental part consisting of creep tests, and a modeling part. Three configurations were compared together, namely direct tensile, direct compression and indirect tensile tests. The modelling part consists of using a 2S2P1D model coupled to Kopelman approximation to switch from the frequency domain to the time domain. Additionally, 2S2P1D was used to calibrate the generalized Kelvin-Voigt model and get the creep compliance directly from E* results. The experimental results show that D(t) from direct tensile and direct compression are the same in the viscoelastic domain and are greater than D(t) from the indirect tensile test. The indirect tensile test (IDT) seems to be very difficult to achieve compared to the other two variants. The converted results using the 2S2P1D model coupled to Kopelman approximation and the results from the GKV model describe the experimental points very well.  
**Keywords:** creep compliance; complex modulus; 2S2P1D model; direct tensile test; direct compression test; indirect tensile test
# Reviews on Corrugated Diaphrags in Miniature Fiber-Optic Pressure Sensors
## 3 The Mechanical Properties of Diaphrags
### Mechanical Properties of the SCD  
Figure 5 shows the schematic view of the SCD [41]. The edges of the circular diaphragm are clamped, and the central area of the diaphragm is flat with periodic annular corrugations around. \(R_{d},h_{d}\), \(l,s\) and \(H\) are the radius, the thickness, the radial length, the arc length and the corrugation depth of CD, respectively. The profiles of corrugation can be rectangular, trapezoidal, triangular, sinusoidal, and so on. For small deflections, the profiles of corrugation have little effect on the mechanical properties of the diaphragm [43].
# Lifetime Prediction of Single Crystal Nickel-Based Superalloys
###### Abstract  
Single crystal nickel-based superalloys are extensively used in turbine blade applications due to their superior creep resistance compared to their polycrystalline counterparts. With the high creep resistance, high cycle fatigue (HCF) and low cycle fatigue (LCF) become primary failure mechanisms for such applications. This study investigates the fatigue life prediction of CMSX-4 using a combination of crystal plasticity and lifetime assessment models. The constitutive crystal plasticity model simulates the anisotropic, rate-dependent deformation behavior of CMSX-4, while the modified Chaboche damage model is used for lifetime assessment, focusing on cleavage stresses on active slip planes to include anisotropy. Both qualitative and quantitative data obtained from HCF experiments on single crystal superalloys with notched geometry were used for validation of the model. Furthermore, artificial neural networks (ANNs) were employed to enhance the accuracy of lifetime predictions across varying temperatures by analyzing the fatigue curves obtained from the damage model. The integration of crystal plasticity, damage mechanics, and ANNs resulted in an accurate prediction of fatigue life and crack initiation points under complex loading conditions of single crystals superalloys.  
crystal plasticity; artificial neural networks; lifetime assessment modelling; turbine blades 2020  
3 October 2024  
Revised: 25 December 2024  
Accepted: 26 December 2024  
Published: 29 December 2024  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Lifetime Prediction of Single Crystal Nickel-Based Superalloys. _Appl. Sci._**2025**, _15_, 201. [[https://doi.org/10.3390/appr15010201](https://doi.org/10.3390/appr15010201)]([https://doi.org/10.3390/appr15010201](https://doi.org/10.3390/appr15010201))  
**Copyright:** 02 2024 by the authors.  
Liconsec MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
[[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).
## 2 Background: Partial Discharge Inception Field Modeling and \"Three-Leg\" Approach
### A Model for PD Inception  
A simple (even if approximate, being deterministic) general model for PD inception, taken from [15], which is valid for both discharges in gas (due to the orthogonal field component) and discharges on insulation surface/interfaces (due to the tangential field component) is [16]:  
\[E_{i}=\left(E/p\right)_{cr}p\left(1+\frac{B}{\left(pks_{d}\right)^{1/\beta}}\right) \tag{1}\]where \(E_{i}\) is the inception field, \(d\) is the distance from HV to ground (creepage) or defect height (for surface and gas discharges, respectively), and \(p\) is the pressure of the surrounding gas. (\(E\)/\(p\))\({}_{\sigma}\), \(B\), and \(\beta\) are empirical parameters whose values change from internal to surface discharges, as shown in Table 1. \(k_{s}\) is a scale parameter, taking into account the effective streamer length, that becomes = 1 for a uniform field but <1 in the presence of a field gradient. It sets a sort of uniform field volume around the triple point in which partial discharges can occur at an almost uniform field, near to the peak field value. In the case of internal discharges, \(k_{s}\) = 1. If \(k_{s}\) = 1 also for surface discharges, then the classic, standardized concept of creepage keeps working, but for \(k_{s}\) < 1 (which is the most likely case in power modules and high-energy density assets), PD-free insulation system design must resort to (1) since localized PD (at triple points) is a fundamental cause of premature failure.  
An expression for \(k_{s}\), based on the extent of field gradient [16], could be given by:  
\[k_{s}=\frac{l(0.9E_{max})^{+}-l(0.9E_{max})^{-}}{d} \tag{2}\]  
being \(E_{max}\) the maximum value of tangential surface field obtained from the electric field profile calculation (if it is not monotone), \(l\) (\(0.9E_{max}\))\({}^{+}\) and \(l\) (\(0.9E_{max}\))\({}^{-}\) are the horizontal distances on the test object surface, where the tangential fields are 90% of \(E_{max}\), considering the negative and positive slopes of the field profile.  
To estimate approximately the partial discharge inception voltage, \(PDIV_{m}\), based on the inception field \(E_{i}\) value computed with (1), the linearity of the Laplace equation can be exploited, starting from the relationship between the applied voltage (\(V_{sim}\)) and the resulting peak field, \(E_{p,sim}\) (computed by a FEM software, 1 st leg, where, e.g., \(V_{sim}\) = 1 kV) [15]:  
\[\frac{V_{sim}}{E_{p,sim}}=\frac{PDIV_{m}}{E_{i}}. \tag{3}\]
# Real-Time Water Level Monitoring Based on GNSS Dual-Antenna Attitude Measurement
## 2 Methodology
### Baseline Vector Calculation
By ignoring the integer property of the integer ambiguity, we can solve for the float solution of the integer ambiguity parameters \(\mathrm{a}\) and other unknown parameters \(\mathrm{b}\) using the least squares method. Their variance covariance array can also be obtained, and the results are denoted as follows:  
\[ \begin{bmatrix}\hat{\mathrm{a}}\\ \hat{\mathrm{b}}\end{bmatrix} ; \begin{bmatrix}\mathrm{Q}_{\ \hat{\mathrm{a}}}&\mathrm{Q}_{\ \hat{\mathrm{b}}\hat{\mathrm{a}}}\\ \mathrm{Q}_{\ \hat{\mathrm{a}}\hat{\mathrm{b}}}&\mathrm{Q}_{\ \hat{\mathrm{b}}}\end{bmatrix}  \tag{15}\]
# Combined MvdXML and Semantic Technologies for Green Construction Code Checking
## 2 Research Background
### Green Construction Code Checking  
Knowledge and rules are two critical parts in the green construction code-checking process. The knowledge related to the green construction code and the project-specific information required by green construction code checking are scattered and fragmented. The rules in the green construction code-checking process should be interpreted systematically and made tractable, whereby complete green construction rule interpretation involves knowledge from human experts in many professional fields. Therefore, the exact requirements for green construction code checking are often much more complex than other standards. Because much information relevant to green construction standard is not available in the BIM model, the IFC standard needs to be physically extended to express the missing information. As not everything in the BIM can be defined at the initial stage, some missing, evolving, or additional information can be inserted into the mvdXML, allowing the mvdXML to include the original IFC information and the extended information.  
Some scholars systematically analyzed code-checking-related systems, concept libraries, query languages, reasoners, and model view definitions [12; 13]. From their analysis, we can see that there are many kinds of rules and concepts supported by the information included in different levels of detail (LOD). In traditional rule checking or code checking, LOD 300 is assumed in general, but green construction code checking needs higher LOD. Before green construction code checking, the BIM model should be modeled as specific assemblies, with complete fabrication, assembly, and detailing information, in addition to precise quantity, size, shape, location, and orientation. Non-geometric information of the model elements can also be attached, which includes model details and elements that represent how building elements interface with various systems and other building elements with model view. Requirements for code checking should match with all kinds of green construction-related information in BIM. In this case, LOD 400 is expected for green construction code checking.