esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple Thermal Parameter Inversion for Concrete Dams Using an Integrated Surrogate Model
## 3 Case Study
### Discussion  
Previous studies have already demonstrated the significance of thermal parameter inversion on the dynamic simulation of temperature fields during the construction period. However, it has been a challenge to reconcile the efficiency and accuracy of the inversion analysis. To solve this problem, this article proposed a thermal parameter inversion method using uniform design, ISM, and Jaya optimization algorithm. This approach is applied to the concrete thermal parameters inversion for two strength grades of an extra-high arch dam. Its effectiveness was verified by evaluating the ISM's predictive capability, inversion accuracy, and inversion efficiency.  
Introducing the SBO method in thermal parameter inversion aims to enhance the efficiency of the process, particularly concerning intricate temperature analysis models. Consequently, the proposed methodology in this paper holds a stronger advantage in computational efficiency compared to direct inversion methods based on GA [11], DE [12], PSO [13], and WOA [14]. Furthermore, compared to the inversion methods based on individual surrogate models in Ref [20; 21; 22], the proposed method sacrifices a small amount of computational efficiency and does not have an absolute advantage in inversion accuracy. However, it enhances the robustness of the surrogate model and avoids the negative effects of inappropriate model selection. For example, in this study, although the accuracy of the inversion results based on the Kriging model is close to that of the ISM, as shown in Table 5, the problem is that we do not know beforehand whether using Kriging would obtain better results. The ISM, to some extent, avoids this issue.  
In summary, the method proposed in this paper advances the balance between inversion accuracy and efficiency compared to traditional methods. It provides a simple and effective strategy for obtaining the thermal properties of concrete accurately and efficiently. Additionally, this method has potential applications for the thermal analysis of massive concrete structures, such as bridge piers and pumping stations.
## 5 Discussion
### Analysis of the Landslide Risk in the Study Area  
The results regarding the landslide risk depend on the spatial distribution of both the landslide hazard and vulnerability [14]. To ensure high accuracy in the LRA study, the landslide hazard mapping for the study area was ultimately based on the DFR-RF model outputs. Figure 7 shows that the AUC value of the RF model in this study is relatively high, and its performance is superior to that of the AHP model. In previous studies on  
Figure 10: Landslide risk map of the study area.  
landslide hazards [16; 20; 48], different models often show varied performance in prediction. The prediction performance of the same model also varies significantly in LHA studies in different regions. This is closely related to the landslide inventory, the selection of predisposing factors, and the internal logic of the model itself [11]. Therefore, due to the above uncertainties, it is necessary to adopt different prediction models in the assessment of landslide hazards and risks, such as the AHP model and RF model. Among them, the model with the best prediction performance should be selected to calculate the final LHIs. However, most LRA studies only use a single evaluation model to obtain landslide hazard data [6; 13; 14], and the results often have much uncertainty. Additionally, by comparing Figures 7d and 10, it can be found that many zones with high landslide hazards are classified as those with low or very low landslide risks. In the landslide hazard calculation results of the DFR-RF model, there are 726, 1183, and 1339 evaluation units with very high, high, and moderate landslide hazards, respectively. However, the evaluation units with very high, high, and moderate landslide risks amount to only 17, 110, and 750, respectively. The zones with very high and high landslide risks are mainly distributed along the roads on the southern side of Aoyitake Town. In addition, regions that are distant from roads and densely populated towns generally exhibit lower vulnerability and landslide risks. This phenomenon is particularly common in sparsely populated regions [14].
## 3 Test Results and Discussion  
### Binder Properties  
#### 3.1.1 Particle Size Distribution  
The particle size distribution (PSD) of the three calcined clay samples, limestone, and cement are presented in Figure 3a,b. D\({}_{10}\), D\({}_{50}\), and D\({}_{90}\) are defined as sizes in the PSD where 10%, 50%, and 90% of the total volume of material in the sample are accumulated, respectively [25]. D\({}_{50}\) is the median size of the distribution of each material. All four powders have finer particles than the PC, ensuring sufficient physico-chemical interaction as a composite binder. The EXW is the finest of the three clay samples and is closer to that of the limestone. EXW has an average particle size of 5.45 \(\upmu\)m, while the limestone has 5.2 \(\upmu\)m. the specific surface area distribution of the binders can be found in the Appendix A (Figure A1) EXW particle sizes seem to be widely spread across 1.1-54.9 \(\upmu\)m, while the MFN, BFN, and limestone particles seem to contain greater size ranges (37.1, 51.8, 54.9, 13.7, respectively).  
Generally, the finer the SCMs, the better their reactivity [26]. Therefore, the clay samples finer than the PC are expected to show efficient reactivity with the hydration by-products (Ca(OH)\({}_{2}\)). In addition, the limestone, being the finest of the lot, is expected to have adequate filler/hydrating effects, improving the density of the matrix [15; 27], which can imply fewer air voids and better durability. These will enhance the physico-chemical interaction of the limestone and calci  
Figure 3: Particle size distribution of the binders: (**a**) cumulative finer volume, (**b**) percentage distribution of the sizes.
## 2 Methods and Materials
### Data Processing
#### 2.4.2 Fatigue Crack Growth Data Processing  
Figure 6 shows the overview of DIC and IRT data processing for a FCG test. Since the AE data processing is essentially the same for both tensile and fatigue crack growth tests, it was excluded from the figure for brevity (see Figure 3 for an overview instead). IRT analysis is divided into three primary steps: (i) locating the crack tip, (ii) finding cameras motion and defining a reference point (feature on the surface) to determine the global position of the crack, and (iii) motion compensation, and data smoothing for spatial and temporal calculations. The DIC method utilises a similar approach for finding global crack properties, e.g., the crack length. Details of processing of FCG DIC and IRT data are provided in Sections 2.4.3 and 2.4.4, respectively.  
Figure 6: _Cont._  
The motion of the camera or the specimen during mechanical testing is inevitable and this needs to be compensated by data processing. The corrective procedure is essentially the same for both DIC and IRT images (with only minor differences between these two), and is, therefore, outlined here. Once two images of interest, which are displaced with respect to each other, are selected, Features from Accelerated Segment Test (FAST) [111] are obtained using Matlab routines [112]. If preferred, ROI from which the features have been extracted can be applied. This is of interest when only a part of the image represents rigid body motion. Further, the features are extracted, and matched, which allows to estimate the geometrical transform between images. Displacements described by this transformation matrix might be caused by the motion of the camera or the specimen. In case of IRT images, they were high-pass filtered and re-scaled before the FAST features were found. Figure 7 shows superposition of two IRT images with two different pseudo colours. Since the camera was moved horizontally between the two to follow the growing crack, there is a displacement between them. ROI was reduced to the left half of the image, to exclude the crack visible on the right-hand side. FAST features are marked with the red circles on image 1, and the corresponding green crosses on image 2. The yellow line between the matched points shows the displacement between them, indicating that the motion between the images was dominated by the translation along the horizontal axis.  
Figure 6: Flowchart illustrating processing of IRT and DIC data for a FCG test.  
Figure 7: Illustration of FAST points used to find camera motion between two successive recordings.
## 1 What is the type of hologram?
### Diagnosis of Distortions of Digital Holograms
#### 5.4.1 Quantization Errors  
Aside from the aliasing artifacts discussed in Section 5.2, PSRs can also be used to analyze flaws that occurred upon processing. Probably the most frequent distortion encountered by DH is quantization. May it be for reasons of limited analog-digital converter precision in the recording sensors, limited bit-depth in spatial light modulators, or reasons of a limited storage capacity, quantization is frequently applied. Multiple works have been published on the effects of quantization in DH, e.g., [49; 50; 51], and in general it must be said that the perceptual quality impairment of quantization depends strongly on the hologram type, its diffraction regime, the pixel pitch, the specific holographic display setup, and especially the distance of the scene to the hologram plane. Thus for each setup type and set of hologram parameters a perceptual quality evaluation is currently required.  
One independent way to assess the severity of the effects of quantization on the hologram is by visualizing its phase space footprints as done in Figure 10 for the object of Figure 9 using the half the dynamic range to enhance contrast. Multiple bit-depths were used with a uniform quantizer applied to real and imaginary parts independently. Clearly, we can see the increase of quantization noise, which is typically modeled as white noise per pixel. White noise affects all frequencies and since it is delocalized, we expect a drastic worsening of the visible signal to noise ratio, such as in Figure 10. At the chosen contrast almost no artifacts are visible with 5_bit_ per channel. While the analysis of quantization artifacts is important, PSRs can be used to analyze arbitrarily complicated post-processing chains of DH.  
Figure 10: Top: Phase space visualization of the error introduced in Figure 9c by uniform quantization with various bit-depths per real/imaginary part. In brackets the signal-to-noise ratios of the quantized holograms are provided. Bottom: Corresponding back-propagations using the same parameters as in Figure 9a. The dynamic range was halved to enhance contrast.
## 3 Results and Discussion
### Comparative Analysis of SHF and SSF Configurations for 2,3-BDO Production from Carrot Discard
In a direct comparison using the SHF configuration, _B. amyloliquefaciens_ was the more effective 2,3-BDO producer, achieving a significantly higher concentration (16.7 vs. 10.9 g/L), yield (0.33 vs. 0.17 g/g), and productivity (0.298 vs. 0.236 g/L-h) than  
Figure 3: Kinetics of carrot discard fermentations in SHF and SSF configurations by _B. licheniformis_ (continuous lines) and _B. amyloliquefaciens_ (dashed lines). Glucose consumption (**a**), and production of 2,3-butanediol (**b**), acetoin (**c**), and ethanol (**d**). B. licheniformis (p < 0.05). In contrast, _B. licheniformis_ demonstrated a remarkable capacity for acetoin synthesis, producing a 3-fold higher concentration than _B. amyloliquefaciens_ (24.2 vs. 8.0 g/L). These divergent product profiles on CD align with the findings from the semi-defined media (Section 3.1). A crucial finding for process viability is that these results were achieved using untreated CD, circumventing the need for costly pretreatment and thereby enhancing the potential profitability of this valorization route. The optimal results obtained in this study--notably a 2,3-BDO titer of 16.7 g/L, a yield of 0.33 g/g, and a productivity of 0.298 g/L.h--are highly competitive when contextualized with the existing literature. Lower 2,3-BDO levels (5.2-5.9 g/L) were achieved by [PERSON] et al. [31] from aqueous solutions of pepper, pineapple, and cabbage waste, using _B. licheniformis_ YNP5-TSU. _Bacillus_ strain (_B. subtilis_ LOCK 1086) was also employed in the fermentation of apple pomace enzymatic hydrolyses, resulting in lower 2,3-BDO concentrations (12.8 g/L) than those reported in this work [32]. Not very different 2,3-BDO production (18.8 g/L), but much lower acetoin levels (2.1 g/L) were also reported from carrot discard enzymatic hydrolysate, but using _P. polymyna_ DSM 365 [3]. [PERSON] et al. [33] also stated similar or even much lower 2,3-BDO concentrations (16.5, 10.7, and 5 g/L) than those found in this study from enzymatic hydrolyses of apple pomace, dried sugar beet pulp, and potato pulp, respectively, by _B. amyloliquefaciens_ TUL 308. [PERSON] et al. [34] obtained much lower 2,3-BDO concentration, yield, and productivity (3.7 g/L, 0.14 g/g, and 0.10 g/L-h, respectively) than those achieved in this study from brewer's spent grain in the co-culture fermentation by _Paenibacillus polymyna_ DSM 365 and _Rhodococcus_ sp. _B. licheniformis_ NCIMB 8059 was also used for 2,3-BDO production from enzymatic apple pomace hydrolysate, yielding only 11.6 g/L 2,3-BDO [35]. Slightly lower 2,3-BDO yields (0.29-0.30 g/g) than those achieved in this work were reported by [PERSON] et al. [22] from spirit-based distillers' grain (SDG) by _B. amyloliquefaciens_ B10-127.
## 3 Construction of Iterative Methods
### Iterative Method III
Let \(\mathbf{A}\) be the discrete approximation of linear differential operator \(\mathrm{L}(\cdot)\), \(\mathrm{f}(\mathbf{y})=[\mathrm{f}(\mathrm{y}_{1}),\mathrm{f}(\mathrm{y}_{2}),\cdots,\mathrm{f}(\mathrm{y}_{\mathrm{m}})]^{\mathrm{T}}\) and \(\mathrm{w}(\mathbf{x})=[\mathrm{w}(\mathrm{x}_{1}),\mathrm{w}(\mathrm{x}_{2}),\cdots,\mathrm{w}(\mathrm{x}_{\mathrm{m}})]^{\mathrm{T}}\). The nonlinear problem Equation (24) can be written in the form of a system of nonlinear equations:  
\[\mathbf{F}(\mathbf{y})=\mathbf{A}\,\mathbf{y}+\mathrm{f}(\mathbf{y})-\mathrm{ w}(\mathbf{x})=\mathbf{0} \tag{25}\]  
It should be noticed that there are discretizations of Equation (24) that do not correspond to Equation (25). Here, we explicitly assume that the numerical methods used for approximating Equation (24) lead to a representation as in Equation (25). The higher order Frechet derivatives of Equation (25) can be computed as:  
\[ \begin{split}&\mathbf{F}^{\prime}(\mathbf{y})=\mathbf{A}+\mathrm{ diag}\big{(}\mathrm{f}^{\prime}(\mathbf{y})\big{)}\\ &\mathbf{F}^{\prime\prime}(\mathbf{y})=\mathrm{diag}\big{(} \mathrm{f}^{\prime\prime}(\mathbf{y})\big{)}\\ &\mathbf{F}^{\prime\prime\prime}(\mathbf{y})=\mathrm{diag}\big{(} \mathrm{f}^{\prime\prime\prime}(\mathbf{y})\big{)}\end{split}  \tag{26}\]  
where \(\mathrm{diag}(\cdot)\) represents the diagonal matrix, which keeps the input vector as its main diagonal. The iterative method Equation (23) for weakly nonlinear systems can be written as:  
\[ \begin{cases}&\mathbf{u}_{0}=\text{Initial guess}\\ &\mathbf{B}=\mathbf{A}+\mathrm{diag}\big{(}\mathrm{f}^{\prime}(\mathbf{u }_{0})\big{)}\\ &\mathbf{B}\,\boldsymbol{\varphi}_{1}=\mathbf{A}\,\mathbf{u}_{0}+\mathrm{ f}(\mathbf{u}_{0})-\mathrm{w}(\mathbf{x})\\ &\mathbf{u}_{1}=\mathbf{u}_{0}-\boldsymbol{\varphi}_{1}\\ &\mathbf{B}\,\boldsymbol{\varphi}_{2}=\mathbf{A}\,\mathbf{u}_{1}+\mathrm{ f}(\mathbf{u}_{1})-\mathrm{w}(\mathbf{x})\\ &\mathbf{B}\,\boldsymbol{\varphi}_{3}=\mathbf{A}\,\boldsymbol{\varphi}_{2}+ \mathrm{f}^{\prime}(\mathbf{u}_{1})\odot\boldsymbol{\varphi}_{2}\\ &\mathbf{B}\,\boldsymbol{\varphi}_{4}=\mathbf{A}\,\boldsymbol{\varphi}_{3}+ \mathrm{f}^{\prime}(\mathbf{u}_{1})\odot\boldsymbol{\varphi}_{3}\\ &\mathbf{B}\,\boldsymbol{\varphi}_{5}=\mathrm{f}^{\prime\prime}(\mathbf{u}_{1 })\odot\boldsymbol{\varphi}_{2}^{2}\\ &\mathbf{B}\,\boldsymbol{\varphi}_{6}=\mathrm{f}^{\prime\prime}(\mathbf{u}_{1 })\odot\boldsymbol{\varphi}_{2}\odot\boldsymbol{\varphi}_{3}\\ &\mathbf{u}_{2}=\mathbf{u}_{1}+\beta_{1}\,\boldsymbol{\varphi}_{2}+\beta_{2} \,\boldsymbol{\varphi}_{3}+\beta_{3}\,\boldsymbol{\varphi}_{4}+\beta_{4}\, \boldsymbol{\varphi}_{5}+\beta_{5}\,\boldsymbol{\varphi}_{6}\end{cases}  \tag{27}\]  
where \(\odot\) is element-wise multiplication between two vectors.
## 2 Measurements and Quality Control Processes
### Daily Data  
Figure 11 shows the temporal evolution of the daily downward and upward longwave irradiation. As can be seen, during the winter and spring season a strong variability occurs. The maximum daily value of \(L_{dn}\) is 35.2 MJ m\({}^{-2}\) while the respective minimum value is 17.4 MJ m\({}^{-2}\). The maximum value of \(L_{up}\) is 48.3 MJ m\({}^{-2}\) and the minimum value is 26.3 MJ m\({}^{-2}\). Figure 10: Monthly boxplots of **(a)** downward (\(L_{dn}\)) and **(b)** upward (\(L_{up}\)) longwave irradiances at Athalassa. The monthly statistics of daily downward and upward longwave irradiation is shown in Table 5, which presents the following statistical parameters: number of observations (N), arithmetic mean (Mean), standard deviation (StDev), coefficient of variation in percent (CV(%)), minimum (Min), the first and third quartiles (Q1 and Q3), and median and maximum (Max). Generally, the median values are close to the mean values. The variability of \(L_{up}\) is lower than \(L_{dn}\), since its coefficient of variation (CV%) ranges from 2% to 5%. \(L_{dn}\) has higher variability during the winter and spring months. The monthly mean daily values of all radiation components are plotted in Figure 12. Longwave irradiation shows the highest mean daily values comparing to the other shortwave radiation components. \(L_{up}\) has the highest mean daily values from all the radiation components. The higher values of both longwave irradiation are observed during the summer months due to higher air and ground surface temperatures. As a result, the net longwave irradiation is always negative ranging from \(-\)5.9 to \(-\)12.1 MJ/m\({}^{2}\). The net longwave irradiation represents the radiative cooling potential. On the other hand, the net shortwave irradiation (\(R_{ns}\)) ranges from 6.6 to 22.4 MJ/m\({}^{2}\). Therefore, the net daily irradiation is positive ranging from 1.3 to 10.5 MJ/m\({}^{2}\). The pattern of \(R_{n}\) follows the same as the \(R_{ns}\), i.e., higher values during the summer season. The variability of the net short, long and allwave radiation components is presented in Figure 13. The highest variability is observed during the winter and spring months as it is indicated by the length of the boxplots. Figure 14 shows the CDF curves of the daily downward and upward longwave irradiation. As can be seen in both cases in 60% of the days the \(L_{dn}\) is lower than 28.3 MJ m\({}^{-2}\) and \(L_{up}\) is lower than 40 MJ m\({}^{-2}\). Figure 11: Daily downward and upward longwave irradiation at Athalassa, during the period 2016–2017. Figure 12: Monthly mean daily longwave and shortwave irradiation components.
# A Middleware-Based Approach for Multi-Scale Mobility Simulation
## 3 The Model
### Composing Processes: The \(\oplus\) Operator
#### 3.6.1 Composing Travel Times-Move Functions  
As a general rule, to correct the Move function of a simulation \(\mathcal{S}_{2}\) by a simulation \(\mathcal{S}_{1}\), the middleware modifies the travel times on the arcs traveled by \(\mathcal{S}_{2}\) by the average travel times observed on \(\mathcal{S}_{1}\). This is done by changing the valuations of the relevant arcs in the graph \(G\) input of \(\mathcal{S}_{2}\). This allows \(\mathcal{S}_{2}\), whatever its scale, to apply its traffic model with the corrected travel times and speeds.  
In the case of different \(\omega\) (composed with the \(\circ\) operator) between \(\mathcal{S}_{1}\) and \(\mathcal{S}_{2}\), the middleware sends to \(\mathcal{S}_{2}\) a \(G\) graph with the observed average travel times.  
In the case of different \(\theta\) (composed with the \(\circ\) operator), the middleware executes as many time steps of \(\mathcal{S}_{1}\) until it covers a full time step of \(\mathcal{S}_{2}\) before executing \(\mathcal{S}_{2}\) with the right valuations of the \(G\) graph.  
When we have a difference of spatial representation \(\sigma\) (composed with the \(\bullet\) operator), the composition of the Move functions is performed as follows. An arc in an aggregated spatial representation might represent one or several paths in the detailed spatial representation. To obtain the same travel times for travelers in each simulation, the average travel time of each arc in the aggregated representation has to be the same as the average travel time of its corresponding paths in the detailed representation.  
Figure 3 shows the transition from the detailed network to the aggregated network. We compute the travel time of the aggregated link, as the average of the travel times of its corresponding paths. In the example of Figure 3, we obtain Equations (7) and (8):  
\[T(\overrightarrow{\text{n2*},\text{  
Figure 4 shows the transition from aggregated to detailed representation. Once again, our goal is to obtain a travel time of the aggregated arcs equal to the average travel time of the corresponding paths in the detailed representation.  
We then obtain the system of linear Equations (9)-(11):  
\[T(\overline{n1^{*},n2^{k}})_{agg}= T(\overline{n1^{*},n5})_{det}+T(\overline{n5,n2^{k}})_{det} \tag{9}\] \[T(\overline{n1^{*},n4^{k}})_{agg}= \frac{1}{2}T(\overline{n1^{*},n5})_{det}+\] (10) \[T(\overline{n5,n4^{k}})_{det}+T(\overline{n1^{*},n4^{k}})_{det}\] (11) \[T(\overline{n2^{*},n4^{k}})_{agg}= T(\overline{n2^{*},n4^{k}})_{det} \tag{11}\]  
There is an infinite number of solutions, such as:  
\[T(\overline{n1^{*},n5})_{det}=5\;\text{s},T(\overline{n5,n2^{k}})_{det}=5\; \text{s},T(\overline{n5,n4^{k}})_{det}=10\;\text{s}\] \[T(\overline{n1^{*},n5})_{det}=6\;\text{s},T(\overline{n5,n2^{k}})_ {det}=4\;\text{s},T(\overline{n5,n4^{k}})_{det}=11\;\text{s}\] \[\ldots\]  
Between all the available solutions, we choose the one offering the most homogeneous speeds (travel distance divided by the travel time).