esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Influence of Ground Stress on Fracture Propagation
#### 4.2.2 Effect of the Difference in the Horizontal Ground Stress  
To study the effect of the horizontal ground stress on fracture propagation, the difference in the horizontal ground stress was varied from 2 MPa in Case 1 to 5 MPa in Case 3, while the other parameters were kept unchanged.  
Figure 17 shows the fracture shape in Case 3, in which the horizontal ground stress difference is 5 MPa when \(t=111\) s and \(t=140\) s. In this case, the hydraulic fracture in Well 3 interacts with the natural fracture at \(t=111\) s and expands upward by passing through the natural fracture after encountering it, rather than propagating along the natural fracture as in Case 1. The hydraulic fracture in Well 2 propagates downward due to fracture interference. Fracture interference causes the expansion of fractures in Well 1 toward the direction of the maximum horizontal ground stress.  
Figure 16: Temporal variation in the fluid loss in Cases 1 and 2.  
Figure 18 shows the pressure at the bottom hole of Well 3 and the hydraulic fracture area of all the wells in Case 3. Compared with Case 1, the area of the fracture observed in Case 3 continues to steadily increase around \(t\) = 90 s, whereas it rapidly increases at this time in Case 1. The pressure change in Well 3 in Case 3 is not significant compared with that in Case 1 at \(t\) = 90 s. This is because the natural fracture has not been clearly opened. This phenomenon is in accordance with the fracture shape shown in Figure 18.  
When the difference in the horizontal ground stress changes from 2 MPa to 5 MPa, the fracture propagation is dominated by the vertical ground stress of the reservoir, and after the hydraulic fractures intersect with the natural fractures, they expand upward and cross through the natural fractures. Simultaneously, the hydraulic fractures in the adjacent intermediate injection wells spread downward due to the fracture interference.
## 2 Current Status of Development of Geophysical Exploration Techniques
### Exploration Techniques Based on Apparent Resistivity Parameters
#### 2.1.4. Resistivity Computed Tomography Method  
The resistivity computed tomography method [28] and various other computed tomography methods are based on the principle of laminar imaging, which analyzes the differences in the geological conditions between survey areas by measuring the apparent resistivity distribution between boreholes [29]. This technique mainly adopts an array electrode system to process the data of the measured apparent resistivity by 2D or 3D inversion to obtain a distribution image of the true resistivity [30], which reflects the location of underground anomalies and hidden dangers with high accuracy and evident detection effect. However, the common resistivity method is limited in engineering applications in terms of the spatial effect and depth corresponding to the proposed section recording point of the apparent resistivity. [PERSON] et al. [28] proposed a computed tomography exploration technique based on the monopole cross-hole DC resistivity method by laying electrodes  
Figure 4: Principle of measurement of well fluid resistivity.  
in parallel boreholes, and the change in the resistivity during hydraulic fracturing was measured by conducting a concrete model test, proving the feasibility of the technique. [PERSON] et al. [31] established a geoelectric model of subsurface cavities, and the resistivity response characteristics and laws of the resistivity computed tomography method were analyzed and summarized for aerated, water-filled, and partially water-filled cavities. The analysis results proved the effectiveness of the resistivity computed tomography method. The resistivity computed tomography method is a lossy detection technique, requiring multiple boreholes with depths up to the bottom of the dike, and it must satisfy the condition that the dike leakage channel passes between the boreholes [32].
###### Abstract  
The influence of joint roughness and inclination on the dynamic mechanical properties of rocks is a prominent research area. In order to investigate the effects of joint roughness and inclination on the dynamic mechanical properties of jointed rocks, impact tests were conducted using a split Hopkinson pressure bar (SHPB) apparatus for prefabricated serrated joint cement mortar specimens with varying joint roughness and inclination. The failure mode, stress wave propagation characteristics, peak stress, and stress wave energy transfer law under impact load were analyzed. The results indicate that both joint inclinations and joint roughness coefficients (JRC) have a strong influence on the failure mode, peak stress, and stress wave energy transfer of jointed specimens. The jointed specimens exhibit three distinct failure modes under impact load, namely splitting tensile failure, shear slip failure, and compound failure of splitting tensile and shear. The peak stress initially decreases then increases with the increase in joint inclination angle, namely a V-shape variation, while it gradually increases with JRC increasing from 0 to 20. Jointed specimens exhibit the lowest peak stress at an inclination angle of 45\({}^{\circ}\) and JRC of 0. The variation of transmitted energy coefficient is similar to the peak stress, while the variation of reflected energy coefficient is opposite to the peak stress. At joint inclination angles of 0\({}^{\circ}\) or 90\({}^{\circ}\), the reflected energy coefficient increases with the increase of JRC from 0 to 20, while the transmitted energy decreases. However, when the joint inclination angle is in the range of 30\({}^{\circ}\) to 60\({}^{\circ}\), the reflected energy coefficient gradually decreases with JRC increasing, while the transmitted energy coefficient gradually increases.  
Keywords:jointed rock mass; joint inclination; joint roughness; energy +
Footnote †: journal: Applied Physics Letters
# SCCDNet: A Pixel-Level Crack Segmentation Network
## 2 Methods
### Decoder Module  
The function of the Decoder module is to resize the low-resolution feature map obtained by the Encoder to the same resolution as the input image to achieve pixel-level segmentation of the image. As shown in Figure 3, the dotted box contains the structure diagram of the Decoder. As can be seen from the figure, the Decoder and Encoder can be divided into five parts according to the spatial resolution of the feature map, and each part is composed of two depth wise separable convolutions, which can minimize the complexity of the model compared to conventional convolutions.  
Figure 2: Structure of SSE module. \(\mathbf{F}_{tr}\) refers to any matrix transformation in the network, \(\mathbf{F}_{sq}\) refers to the Squeeze operator in the SSE module, \(\mathbf{F}_{ex}\) refers to the Excitation operator, \(\mathbf{F}_{sc}\) refers to channel-wise multiplication.  
In addition to the \(14\times 14\times 256\) feature map obtained at the bottom of the module, the input of the Decoder module also includes the result of the feature maps of different spatial resolutions in the Encoder module after being recalibrated by the SSE module. For example, the input of the first part of the Decoder consists of two parts, the first part of the feature map represented by the blue rectangle comes from the output of the SSE5 module, and the size is 28 \(\times\) 28 \(\times\) 8D; The other part of the feature map represented by the red rectangle comes from the feature map of the lowest resolution after 2 \(\times\) 2 deconvolution, whose size is 28 \(\times\) 28 \(\times\) 8D. After concatenating two feature maps of the same size, two depth separable convolutions are performed to obtain the output feature map of the first part of the Decoder module.  
In order to further alleviate the problem of gradient disappearance as the network depth increases, we establish short connections in the Decoder module. For instance, in addition to the output of the SSE1 module, we use the remaining five output feature maps of the Decoder module as the input of the fifth part of the module. This strategy not only considers all the output feature maps of the Decoder, but also considers that of the Encoder through the SSE module, and will not weaken the features obtained by the shallow network as the depth of the model increases.
## 4 Active Load Alleviation Performance
### Static Aeroelastic Analysis
#### 4.1.3 Simulation Results  
A steady level flight condition is considered at sea level with Mach number \(M_{\infty}=0.41\). A static pull up manoeuvre with load factor \(N_{z}=2.5\) is used to evaluate the controller performance. The results in terms of structural loads are presented in Figure 19 considering the out-of-plane bending moment and normalizing the value with respect to the value at wing root.  
A reduction in the structural loads was obtained, with a limited reduction at wing root, and a very significant reduction at wing tip. However, since retrofitting winglets to  
Figure 19: Symmetric manoeuvre. Span-wise distribution of bending moment.  
existing aircraft leads to increased structural loads at the outboard section of the wing, a controller capable to reduce structural loads at the outer wing sections is of particular interest, even if it provides only a limited alleviation in the inboard wing sections.
# A Semantic Framework for Decision Making in Forest Fire Emergencies
## 5 Semantic Reasoning for Decision Support  
In the context of wildfire events, the presence of a semantic reasoning approach becomes essential for effectively handling diverse knowledge and heterogeneous data. The development of a forest fire decision support system that is based on the semantic web is crucial to achieving its potential and enhancing the quality, safety, and efficiency of wildfire management. Semantic reasoning is a valuable component of the ONTO-SAFE framework as it aims to execute semantic queries on top of the ontology in order to retrieve alerts and recommendations that fulfill the criteria and return the results in an understandable format for decision-makers.  
In our previous work [9], we introduced an initial ontology-based reasoning module designed to facilitate wildfire event management. We described how the proposed ontology addresses the specific requirements of end-users, supported by examples. In addition, we demonstrated the extraction of valuable location-specific information on wildfire incidents, including details such as the closest hospital and nearest water source in relation to the  
Figure 6: Recommendation activates communication to avoid prescribed burning plans to a specific location.  
fire. Moreover, we presented the ability of the semantic reasoning module to extract new, enriched information, such as adding a proper mission in an incident. The decision makers are supported to send the proper equipment for this mission by the combination of data from in situ cameras and sensors.  
The need to meet new user requirements led us to expand the semantic reasoning component and implement additional functionalities. The following subsections present the extension of the ONTO-SAFE's semantic reasoning in the field of decision support for weather and forecast anomalies and for the prescribed burning process.
# Minimizing Energy and Computation in Long-Running Software
## 3 Minimizing Computation Time and Energy  
When we include both the time and energy needed to create each checkpoint, and assuming a fixed number of instructions \(y\) executed between successive checkpoints, we can obtain the total cost of the program up to and including the last instruction executed at \(Y=yN\) as:  
\[G_{N}(y) = NB_{0}+\sum_{i=1}^{N}iyB_{1}+NC(y), \tag{9}\] \[= NB_{0}+C(y)+\frac{N(N+1)}{2}yB_{1}. \tag{10}\]  
The optimum checkpoint interval \(y^{*}\) is then the value of \(y\) that minimizes \(\kappa_{N}(y)\), the overall cost per unit work that is accomplished, i.e., \(G_{N}(y)\) divided by \(Y=Ny\) which is the total number of useful instructions executed over this time:  
\[\kappa_{N}(y)\equiv\frac{G_{N}(y)}{Ny}\] \[=\frac{B_{0}+C(y)}{y}+(\frac{Y}{y}+1)\frac{B_{1}}{2}\] \[=\frac{B_{0}+\frac{B_{1}Y}{2}+C(y)}{y}+\frac{B_{1}}{2}. \tag{11}\]  
Therefore, to seek the optimum value of \(y\), we compute the following derivative and set it to zero:  
\[\frac{d\kappa_{N}}{dy}=\frac{y\frac{dC(y)}{dy}-(B_{0}+\frac{B_{1}Y}{2}+C(y))}{ y^{2}}, \tag{12}\]  
so that the optimum value of \(y\) is:  
\[y^{*}=\frac{B_{0}+\frac{B_{1}Y}{2}+C(y^{*})}{\frac{dC(y)}{dy}|_{ y=y^{*}}}=\] \[\frac{B_{0}+\frac{B_{1}Y}{2}+b_{0}[a^{-y^{*}}-1]+\frac{c+b_{1}}{1 -a}[a^{-y^{*}}-1]-b_{1}y^{*}}{-\ln a.[\frac{b_{0}}{a^{y^{*}}}+\frac{c+b_{1}}{a ^{y^{*}}(1-a)}]-b_{1}},\] \[or\;-\frac{y^{*}lna+1}{a^{y^{*}}}=\frac{B_{0}+\frac{B_{1}Y}{2}}{ b_{0}+\frac{c+b_{1}}{1-a}}-1. \tag{13}\]  
Defining \(B=B_{0}+\frac{B_{1}Y}{2}\) and  
\[A=b_{0}+\frac{c+b_{1}}{1-a}, \tag{14}\]  
we have:  
\[-\frac{y^{*}\ln a+1}{a^{y^{*}}}=\frac{B_{0}+\frac{B_{1}Y}{2}}{b_ {0}+\frac{c+b_{1}}{1-a}}-1,\] \[or\;\ln(a^{-y^{*}}.e^{-1})[e^{-1}a^{-y^{*}}]=\] \[-(y^{*}\ln a+1)e^{-(y^{*}\ln a+1)}=\frac{B-A}{e.A}, \tag{15}\]  
To verify that \(y^{*}\) is the minimum value, we compute:  
\[\frac{d^{2}\kappa_{N}(y)}{dy^{2}}=\frac{y^{3}C^{\prime\prime}(y)-2y(yC^{ \prime}(y)-B-C(y))}{y^{4}}, \tag{16}\]where \(C^{\prime}(y)\), \(C^{\prime\prime}(y)\) denote the first and second derivatives of \(C(y)\) with respect to \(y\), and \(B=B_{0}+B_{1}Y\). Since at \(y^{*}\) we have \(y^{*}C^{\prime}(y^{*})=B+C(y^{*})\), we can write:  
\[\frac{d^{2}\kappa_{N}(y)}{dy^{2}}|_{y=y^{*}}=\frac{C^{\prime\prime}(y)}{y}|_{y= y^{*}}, \tag{17}\]  
and we need to examine the sign of \(C^{\prime\prime}(y^{*})\). Starting from (8) we have:  
\[C^{\prime\prime}(y)=a^{-y}(\ln a)^{2}[b_{0}+\frac{c+b_{1}}{1-a}]-a^{-y}\ln a \frac{c+b_{1}}{(1-a)^{2}}, \tag{18}\]  
which is positive, so that \(y^{*}\) is indeed the value of \(y\) at the minimum.
# Automated Defect Detection Using Threshold Value Classification Based on Thermographic Inspection
## 2 Theory  
### Image Processing  
Lock-in infrared thermography (LIT) is a technique in which a heat source in the form of a harmonic function is incident on an object and the response signal generated at this time is processed to obtain changes in phase and amplitude [8; 24]. Many previous studies on LIT techniques have been conducted [25; 26; 27; 28; 29]. Therefore, image processing for automatic defect detection was performed based on this technique.  
Figure 1 shows the overall process of this study. The study process is as follows:  
1. Step 1: 2D thermal images were acquired using two halogen lamps for heat source excitation. Then, phase and amplitude images were acquired by applying the lock-in signal processing technique;
2. Step 2: Contrast evaluation was performed to analyze the optimum frequency of phase and amplitude images at the excitation frequency set in this study;
3. Step 3: Filtering (mean, median, NLmean, Gaussian) for the first de-noising was applied, and the signal-to-noise ratio (SNR) was calculated to perform comparative analysis with non-filtering images;
4. Step 4: Utilizing a grayscale-based histogram to find the optimal threshold value that can be classified as 'class 1' and 'class 2' for the binary image;
5. Step 5: There was still noise in the binary image; therefore, the second de-noising was performed. After tracing the boundary line of the defect in the image, the metric equation was introduced to analyze the automatic defect detection based on the threshold value.  
The main core of the image processing performed in this study is the binarization image processing, which is discussed in the next section.
## 3 Results and Discussion
### Microbiological Stability of the Beverages Obtained in the Repeated 24-h Subcultures  
Considering that the four beverages from the repeated 24-h fermentation had the best visual appearance, their microbiological and chemical stability under refrigerated storage at 4 \({}^{\circ}\)C was analyzed over 28 days. Regarding the microbiological analysis, it can be noted that the LAB counts in the four subcultures decreased slightly at mean rates of 0.050, 0.016, 0.020, and 0.090 log CFU/mL/day reaching final levels of 7.28, 8.48, 8.49, and 8.42 log CFU/mL, respectively, in the four subcultures by day 28 (Figure 8). The AAB counts also decreased slightly, with mean rates of 0.016, 0.021, 0.013, and 0.015 log CFU/mL/day during storage, resulting in final counts of 8.42, 8.40, 8.69, and 8.34 log CFU/mL, respectively, by day 28 (Figure 8). In the case of yeasts, the counts decreased at mean rates of 0.003, 0.007, 0.025, and 0.007 log CFU/mL/day, with final counts of 6.13, 6.08, 6.57, and 6.08 log CFU/mL, respectively, on the 28 th day in the four subcultures (Figure 8).  
These decreases could be associated to the slight decrease in pH values (Figure 9) and the slight increase in the production of LA, AA, and EtOH (Figure 10), which probably affected the viability of the LAB, AAB, and yeasts [102]. This increase in product formation paralleled the consumption of lactose and proteins (Figure 9), which were consumed during storage at 4 \({}^{\circ}\)C at rates considerably lower than those during the corresponding 24-h subcultures (Figures 1 and 9).  
A similar trend in LAB viability, pH decrease, and protein consumption was observed previously in kefir [102] or yogurt [103] samples stored under refrigeration at 4 \({}^{\circ}\)C for 28 days. [PERSON] et al. [104] also observed a decrease in the lactobacilli and lactococci counts during the first 14 days of storage at 5 \(\pm\) 1 \({}^{\circ}\)C, which then stabilized until the end of the storage period (28 days). In contrast, the AAB and yeast counts, as well as the pH and lactose content, did not show significant variation during storage.  
However, the bacterial counts of the three microbial groups by day 28 always surpassed the minimal number of probiotic cells needed to observe a probiotic effect on the host [102]. Additionally, it is expected that a high number of these cells can reach the intestine and colonize it, as it has been suggested that the food matrix (milk) can protect these cells against gastrointestinal conditions [102; 105].  
Figure 8: Evolution of the lactic acid bacteria (LAB), acetic acid bacteria (AAB), and yeasts in fermented beverages from the four subcultures of fermentation I during refrigerated storage at 4 \({}^{\circ}\)C.
## 2 Methodology
### Analysis Methods  
To evaluate the performance of the LSM in simulating water and carbon fluxes across different temporal scales, this study employs common evaluation metrics, including the Pearson correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE). The formulas for calculating R, RMSE, and MAE are given in Equations (1)-(3),  
Figure 1: Geographical distribution of FLUXNET sites based on IGBP classification.  
respectively [29]. The Pearson correlation coefficient (R) measures the degree of linear correlation between simulated and observed values, ranging from \(-1\) to \(1\). Values closer to \(1\) indicate a stronger positive correlation, while values closer to \(-1\) indicate a stronger negative correlation. RMSE and MAE quantify the magnitude of the difference between simulated and observed values. Both RMSE and MAE are non-negative, with values closer to \(0\) indicating smaller errors and better agreement between simulations and observations [30; 31].  
\[R=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sqrt{\sum_{i=1 }^{n}\left(x_{i}-\overline{x}\right)^{2}\sum_{i=1}^{n}\left(y_{i}-\overline{y} \right)^{2}}} \tag{1}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_{i}-y_{i})^{2}} \tag{2}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|x_{i}-\overline{y}\right| \tag{3}\]  
where \(x_{i}\) and \(y_{i}\) represent the observed and simulated values, respectively, \(\overline{x}\) and \(\overline{y}\) are the mean values of observations and simulations, and \(n\) is the number of data points.
## 2 Materials and Methods
### Costs  
#### 2.2.1. Desalination Plant Costs  
The desalination plant costs have been decreasing in the past years. Figure 3 presents real commercial costs of sea water RO desalination plants for 2021 excluding installation costs. It has to be highlighted that the cost presented does not include the supporting infrastructure (e.g., transport of water from sea to the desalination plant, connection to a water grid, etc.) since that is heavily dependent on the topography and actual installation places of the various subsystems, nor the brine treatment and disposal, the costs do include pre- and post- treatment subsystems. All systems in the figure include energy recovery.  
Figure 3: Desalination plant cost (Source: Personal communication with [PERSON]—CEO of Watera Hellas, Athens, Greece).  
As visible in Figure 3, there is a very steep decrease in the cost from 100 m\({}^{3}\)/day to 200 m\({}^{3}\)/day with diminishing scale economies as the plants are increasing in capacity. Two different types of pre-treatment technologies (multimedia filtration and ultrafiltration) are presented to highlight that this choice can play an important role in the final cost. In all cases investigated in this paper, multimedia filtration prefiltering is considered.
# Chemotherapy Regimen Optimization Using a Two-Archive Multi-Objective Squirrel Search Algorithm
## 4 Experimental Setup and Results Analysis
### Results and Analysis
#### 4.4.4 Comparison Study with Mathematical Models  
In this subsection, we compared our proposed tumor dynamic model with the existing literature, including a dynamic model of cell cycle-specific drug combination chemotherapy [22] and a two-compartment cell cycle-specific model with a single drug [20; 42], to demonstrate and validate the efficacy of our proposed model. These comparisons were made based on proliferating and quiescent tumor cell reduction proportions and remaining bone marrow cells under aggressive chemotherapy scenarios. The results of these comparisons are listed in Table 8.  
In Table 8, the proposed model of non-cycle-specific and cycle-specific combination chemotherapy demonstrates superior treatment efficacy compared to single-drug and cycle-specific combination chemotherapy under the aggressive chemotherapy scenario. T5 he aggressive regimen from the proposed model achieves reductions in proliferating and quiescent cells of 95.87% and 95.65%, respectively, while resulting in a higher number of remaining bone marrow cells post-chemotherapy. Conversely, results from the literature [20], which utilized only one chemotherapy drug, exhibit the least promising treatment effect among all works. Furthermore, compared to the model of two cycle-specific drugs in combination chemotherapy, the regimen from the proposed model demonstrates significantly greater reductions in tumor cells. Specifically, the reduction proportion of proliferating cells from the proposed model exceeds that of the two cycle-specific drugs regimen by 2.92%, with a corresponding difference of 6.21% for quiescent cells. These findings suggest that both combination chemotherapy and the inclusion of a non-cycle-specific drug can significantly improve treatment efficacy and reduce side effects when compared to single-drug regimens. The validation of our model's performance through this comparison study underscores its potential utility in optimizing chemotherapy regimens for enhanced therapeutic outcomes. In addition to the comparisons conducted in this study, future validation efforts will involve integrating clinical data to further refine and validate our model's predictions against real-world observations.