esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Dermatological Clinical Evaluation Results  
Within the scope of the study, the average values for the degrees of orange peel appearance, both before and after the training sessions as assessed through dermatological clinical evaluations of the participants, were presented in Table 4. Regarding the obtained data, a decrease in the degree of orange peel appearance was observed in all participants who wore microencapsulated products. In contrast, participants in the control group showed either no change or a decrease in cellulite levels. This outcome indicates that while exercise contributes to the reduction in cellulite appearance, the use of microencapsulated fabrics examined in this study also plays a significant role in diminishing the visibility of cellulite.  
To determine whether there was a statistically significant interaction among garment group, fabric type, and time considering the dermatological clinical evaluation results by Repeated Measures ANOVA, only the time \(\times\) group interaction in the dermatological evaluation showed a significant difference (\(p\) = 0.034). Therefore, the results obtained from the dermatological evaluations before and after the activity for both the control group and the microencapsulated product group were analyzed using the Wilcoxon Signed Rank Test (Table 7).  
In the microencapsulated group, all nine participants who performed the training procedure showed negative values in the appearance of orange peel skin, indicating a reduction in cellulite. In the control group, which also consisted of nine participants who performed the training, two showed negative values, one showed a positive value, and six showed no change in their cellulite levels.  
Moreover, to evaluate whether the difference between dermatological evaluations before and after the activity for all fabric types was statistically significant, the Wilcoxon Signed Rank Test was applied to each fabric type. The results indicated no significant difference in the dermatological clinical evaluation values before and after the activity for volunteers wearing different fabrics (F1: \(p\) = 0.194; F2: \(p\) = 0.059; F3: \(p\) = 0.066).  
The low thermal permeability of the fabric accelerates the breakdown of microcapsules designed to reduce the appearance of orange peel skin and contributes positively to the elimination of cellulite tissue. While high thermal permeability (low thermal resistance) is desirable in everyday sportswear to facilitate the transfer of increased body heat, the aim for the microencapsulated garments studied here is to reduce the appearance of orange peel skin. Therefore, retaining heat on the body's surface is a desired feature for these garments. In this context, one of the volunteers wearing the fabric with the highest thermal resistance (Fabric F3) experienced a two-grade reduction in the appearance of orange peel skin. Generally, since the thermal resistance values of the fabrics are similar, a greater reduction in the appearance of orange peel skin was observed in volunteers wearing microencapsulated products compared to the control group.
## 3 The Proposed Approach
### Patch Generation and Validation  
In the patch generation phase, we use the beam search strategy [3, 4], which is widely used in NMT, to generate a large number of candidate patches. The results of a recent study [10] show that 93% of developers are only willing to review up to 10 patches. To be consistent with this practical limitation, we select the top 10 patches with the highest sequence probability as candidate patches from the list of candidate patches generated in the beam search. In the next part of the experiment, we setup the following validation process: for each bug, we configure each APR method to generate 10 candidate patches (consistent with the practical limitation of patch review [10]). Subsequently, we execute the full developer-written test suite on these candidate patches. We labeled the first patch that passed all the test cases as a plausible patch, and then manually verified the true correctness of these plausible patches (excluding overfitting patches). The experiments eventually measure the fixing capability of different APR approaches by using the number of bugs for which each approach can produce manually verified correct patches as the core evaluation metric [10].
## 1 Introduction
In these fields, operators also seek to balance product quality, energy efficiency, and uninterrupted operation, making IMOAHLO a valuable decision-support tool. The main contributions of this paper are summarized as follows:1. A mathematical model was constructed based on a comprehensive consideration of the multiple constraints that need to be balanced in production, modeling a formulation scheme as a complex multi-objective optimization problem. This model addresses the core requirements of the RHF blending process and fills the gap in systematic modeling within this field. 2. An IMOAHLO algorithm was proposed which integrates neighborhood search and adaptive mechanisms based on the traditional Human Learning Optimization (HLO) algorithm, balancing exploration and exploitation efficiency, and enhancing its local search capability as well as adaptability to production constraints. 3. Experiments based on real-world data from a steel plant demonstrated that the IMOAHLO algorithm significantly outperforms three other optimization algorithms across multiple evaluation metrics. It achieves the goal of optimizing the element content in blended materials, improves production stability, and reduces the frequency of manual intervention, thereby indicating the algorithm's intelligent decision-making capability in industrial application scenarios. The rest of the paper is organized as follows: Section 2 introduces the current state of research on formulation strategy in the blending system and the development of the HLO algorithm. Section 3 describes the mathematical model for the formulation scheme optimization. Section 4 provides the proposed algorithm. Section 5 gives the extensive experiments and comparisons based on real-world cases. Finally, Section 6 concludes the paper.
## 2 Climatic Actions on Structures
### Classical Extreme Value Theory  
The classical extreme value theory provides a rigorous framework for the analysis of climate extremes and their return period. The cornerstone of extreme values theory is the extremal types of theorem [12], providing the convergence of the distribution of maxima to one of the three limiting distributions usually referred as Gumbel or extreme value type I distribution [13; 14], Frechet or extreme value type II distribution [15], and Weibull or extreme value type III distribution [16]. As known, the three extremes values distributions behave differently, but they can be grouped into a single family of three-parameter distributions, the generalized extreme value distribution (GEV) or Fisher-Tippett distribution [17], characterized by the following cumulative distribution function:  
\[F(x<X)=\exp\left\{-\left[1+\xi\left(\frac{x-\mu}{\sigma}\right)\right]^{-\frac {1}{\xi}}\right\}, \tag{1}\]  
defined on the set \(\{z:1+\xi((x-\mu)/\sigma)>0\}\), where the extreme value parameters location \(\mu\), scale \(\sigma\), and shape \(\xi\) satisfy the inequalities: \(-\infty<\mu<\infty\); \(\sigma>0\); \(-\infty<\xi<\infty\). The type II (Frechet) and type III (Weibull) classes of extreme value distributions correspond respectively to the cases \(\xi>0\) and \(\xi<0\), and the subset of the GEV family with \(\xi=0\) can be interpreted as the limit of Equation (1) where \(\xi\to 0\), corresponding to the type I (Gumbel) with cumulative distribution function  
\[F(x<X)=\exp\left\{-\exp\left[-\left(\frac{x-\mu}{\sigma}\right)\right]\right\} \quad\mu\in\mathbb{R};\ \sigma>0, \tag{2}\]  
where the location parameter \(\mu\) specifies the center of the distribution and the scale parameter \(\sigma\) determines the size of deviations around the location parameter.  
Extreme value type I distribution is often assumed as the limiting distribution for maxima in the definition of characteristic values of climatic actions \(c_{k}\)[18], which can be determined by means of the formula  
\[c_{k}=\mu+\sigma\cdot\{-\ln[-\ln(1-p)]\}, \tag{3}\]  
where \(p\) is the probability of exceedance in one year, which, according to EN1990 [3], is taken as \(p=0.02\).
## 4 Results and Analysis
### Influence of Solution Cavities Characteristics on the Wellbore Stability  
#### 4.3.1 Influence of Solution Cavities Size on the Wellbore Stability  
Figures 20 and 21 present the variations in stress and displacement around the wellbore for various solution cavity diameters (\(D\)). These figures reveal a consistent trend: stress and displacement at different locations around the wellbore gradually increase with an increase in \(D\). For instance, when \(D\) is 152 mm, the stress and displacement values at point A are measured as 201.03 MPa and 3.52 mm, respectively. However, with \(D\) increased to 914 mm, the stress and displacement at point A rise to 256.32 MPa and 4.03 mm, respectively. This represents a significant increase of 28.01% in stress and 14.48% in displacement compared to the previous values.  
This phenomenon can be attributed to the lower mechanical strength of the fillings within the solution cavities compared to the surrounding rock. In such cases, stress tends to concentrate on the solution cavities wall due to the limited supporting capacity of the fillings, as exemplified by the conditions observed at point A in Figure 20. Furthermore, this stress concentration amplifies with the enlargement of \(D\), leading to increased deformation in the solution cavities area, as shown in Figure 21.  
Figure 22 presents the relationship between the equivalent drilling fluid densities of collapse pressure (\(\rho_{\rm c}\)) and solution cavities diameter (\(D\)). The figure reveals an initial linear increase in \(\rho_{\rm c}\) with the growth of \(D\), and \(\rho_{\rm c}\) for the situation of \(D\) = 609 mm is increased by 14.12% compared with the situation of \(D\) = 152 mm. However, when \(D\) exceeds 609 mm, there is no significant change in \(\rho_{\rm c}\). This phenomenon can be ascribed to the fact that when the size of the solution cavities reaches a certain value, for example, \(D\) = 609 mm, it may cause wellbore collapse and damage to the surrounding rock mass, thereby forming a substantial cavity. As shown in Figure 21, the area (area B) with a displacement larger than 3.5 mm for \(D\) = 609 mm increases sharply compared to that for \(D\) = 457 mm. In this case, the main factor affecting wellbore stability would be the damage to the rock between the solution cavity and the borehole, and this cavity may become large enough to minimize the  
Figure 21: Displacement nephogram around the wellbore for different solution cavities diameter: (a) \(D\) = 152 mm; (b) \(D\) = 304 mm; (c) \(D\) = 457 mm; (d) \(D\) = 609 mm; (e) \(D\) = 762 mm; (f) \(D\) = 914 mm.  
Figure 20: Stress nephogram around the wellbore for different solution cavities diameter: (a) \(D\) = 152 mm; (b) \(D\) = 304 mm; (c) \(D\) = 457 mm; (d) \(D\) = 609 mm; (e) \(D\) = 762 mm; (f) \(D\) = 914 mm.  
impact of further increases in solution cavity size on \(\rho_{\rm c}\). Therefore, the linear relationship between the diameter of solution cavities and \(\rho_{\rm c}\) rises to 609 mm and then becomes flat.
## 4 Experiments and Discussion
### Parameter Selection
For setting the values of \(L_{f}\) and \(L_{c}\), the crack classification performance was given priority, and the computational burden can be reduced by some efficient computing platforms (e.g., graphic processing units). With this consideration, \(L_{f}\) in the ELM feature representation was set to be 2000, and \(L_{c}\) in the ELM classification was set to be 6000.
# Pgds-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images
## 4 Results
### Improved Models' Comparison on the MS-COCO 2017 Dataset  
Our proposed method demonstrates excellent performance in object detection for fisheye images. To further validate the broad applicability of the proposed method, the MS-COCO 2017 dataset is used for experiments. Compared with the original YOLOv8s model, the improved models exhibit more excellent object detection performance. Among them, the YOLOv8s model introducing MPGD and APGD modules reduces the feature loss in the downsampling stage and performs better in detecting smaller objects. The YOLOv8s model introducing the C2 fs and SPPFSE modules pays more attention to the informative channel features and performs better on larger objects. The PGDS-YOLOv8s model combines the advantages of the proposed modules to show the best detection performance. Table 4 shows that several evaluation performance metrics of the PGDS-YOLOv8s model on the MS-COCO 2017 dataset perform very well. Among them, AP improved by 1.4%, AP\({}_{50}\) improved by 1.7%, AP\({}_{75}\) improved by 1.2%, AP\({}_{S}\) improved by 2.5%, AP\({}_{M}\) improved by 1.1%, and AP\({}_{L}\) improved by 1.7%. The experiments demonstrate that the proposed model still enhances object detection for images on the MS-COCO 2017 dataset.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Methods** & **Precision (\%)** & **Recall (\%)** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS] (\%)** & **FPS** \\ \hline YOLOv8s & 86.6 & 79.9 & 87.1 & 57.7 & 189 \\ LCat & 83.2 & 78.6 & 84.6 & 48.9 & 127 \\ SCat & 84.9 & 82.1 & 87.0 & 55.2 & 119 \\ SLCat & 82.4 & 78.8 & 84.9 & 48.7 & 123 \\ YOLOv8s + ECDAB & 85.0 & 83.0 & 87.8 & 58.1 & 185 \\ YOLOv8s + SphereConv & 84.9 & 82.2 & 87.6 & 57.9 & 175 \\ YOLOv8s + MPGD+APGD & 87.4 & 81.1 & 88.7 & 60.2 & 208 \\ YOLOv8s + C2 fs + SPPFSE & 85.5 & 83.7 & 89.0 & 60.5 & 169 \\ PGDS-YOLOv8s & 86.4 & 82.9 & 89.0 & 59.9 & 179 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with several advanced methods on the UAV-360 dataset.
## 3 Results and Discussion
### Hydrological Modeling  
Monthly stream records of 30 years (1976-2006) were used for model evaluation. Out of the 30 years records, 18 years (1981-1998) was used for the calibration while 8 years (1999-2006) were used for the validation with 5 years warm up period in each. In both calibration and validation periods, the monthly observed and simulated discharges from the model were compared. Results of R\({}^{2}\), NSE, and PBIAS during the calibration and validation periods were 0.74, 0.71, and 4.21; and 0.71, 0.69, and 5.32, respectively. These results indicate a satisfactory simulation, as the values are greater than 0.5 [51]. The observed and simulated flows matched well during the calibration and validation periods as shown in Figure 6. However, there was under-prediction of some peak flows with much higher values for some months.  
Figure 6: Simulated and observed monthly discharge for Bernam river basin during calibration and validation periods.
# An Investigation of Optically Very Thin Ice Clouds from Ground-Based ARM Raman Lidars
## 3 Methods  
To examine the optically very thin ice clouds (\(\tau<0.01\)), all transparent profiles from the RLs are considered without discarding profiles when CALIPSO is not passing over. All transparent profiles from the CALIPSO within a 5\({}^{\circ}\) latitude by 5\({}^{\circ}\) longitude box centered over the SGP and TWP sites are considered without discarding profiles when the RL is not operational. Discarding these profiles produces nearly identical results to those presented here. Overall, the total number of transparent profiles considered are 334,675 for the RL over SGP, 100,640 for CALIPSO over SGP, 25,958 for the RL over TWP and 100,652 for CALIPSO over TWP in this analysis (see Table 1).  
Ice cloud column optical depths are constructed as integrating all ice cloud particulate extinction within a profile, expressed as  
\[\tau_{i,c}=\ \int_{z_{blue}}^{z_{top}}\alpha_{i}(z)dz \tag{1}\]  
where \(\tau_{i,c}\) is the ice cloud column optical depth, \(\alpha_{i}\) is ice cloud particulate extinction in km\({}^{-1}\), and \(z_{top}\) (\(z_{base}\)) is the highest (lowest) ice cloud top (base) height of the atmospheric profile.  
Ice clouds with column optical depths below 0.01 are analyzed to determine the nearby profile conditions. For all transparent ice cloud profiles with ice cloud column optical depths below 0.01, the consecutive profiles are grouped, and the profiles directly preceding and succeeding the group in time/space are checked. If the ice cloud column optical depth is above 0.01 for either the preceding or succeeding profile, but the profile on the other side is clear sky, this group of optically very thin ice cloud profiles are considered as a \"cloud edge.\" If both the preceding and succeeding profiles have an ice cloud column optical depth above 0.01, the group of optically very thin ice clouds are considered as  
Figure 3: Cumulative distribution functions (CDFs) of ice cloud column optical depths for transparent ice cloud profiles from CALIPSO (red) and RL with CALIPSO-like extinction retrieval resolution (i.e., 8 min/60 m at SGP and 24 min/60 m at the Tropical Western Pacific (TWP) site) (black) for the (**a**) SGP and the (**b**) TWP sites. The percentages of the distribution below the ice cloud column optical depth of 0.01 (cyan line) are shown as “x” and the percentage values are given on the left. Mean and median values are given in the parentheses. Data for the RL at the SGP site are from August 2008 to August 2016 and at the TWP site from December 2010 to January 2015. Data for CALIPSO for both the SGP and TWP sites are from June 2006 to August 2016.  
\"in-between clouds.\" If both the preceding and succeeding profiles are clear sky, the group of optically very thin ice clouds are considered as \"isolated.\" The group that does not belong to the above three categories is not considered here, which are related to the preceding/succeeding profiles that have no measurements or are attenuated. They represent only about 10% of the optically very thin ice cloud profiles from both the RL and CALIPSO.
## 4 Experiment and Evaluation
### Experiments and Analysis Based on Simulation Platform Data  
To demonstrate the efficiency of the CNN-LSTM network model, two alternative networks are selected in this paper to compare the performance levels:  
1. ResNet-50 ResNet50 and FCN: This network utilizes the ResNet50 architecture for feature extraction. The resulting feature vectors are subsequently input into a fully connected network (FCN) equipped with a Softmax layer to predict driving decisions for parking.
2. ResNet50, LSTM, and FCN: This network uses CNN-extracted visual features fused separately with LSTM-extracted coded sequence features in the feature fusion layer, and the FCN layer in the feature fusion layer is responsible for predicting parking driving decisions.  
To measure the accuracy of the end-to-end intelligent parking driving model output driving decisions, the last 10% of the data samples of the dataset (the validation data collected by the parking process will not be included in the training) are selected as the validation set. This is because using 10% of the randomly selected data as the validation set would be very similar to the training set, resulting in a low generalization capability of the trained model.  
A schematic of its training process is shown in Figure 10.
## 3 Results and Discussions  
### X-rays Diffraction (XRD) Analyses  
The crystallinity and phases of gold nanostructured in samples Au-20 and Au-PVP-20 were probed with Bruker D\({}_{8}\) DISCOVER Advance in Bragg-Brentano mode with Cu K\(\alpha\) radiation (40 KV, 1.54 A, and 40 mA) over 20 angle ranging from 10\({}^{\circ}\) and 70\({}^{\circ}\). The samples for XRD analysis were prepared by drop-casting colloids separately on glass strips followed by drying at room temperature. The XRD data were compared with the JCPDS data card 04-0784 for gold, as depicted by graph (a) in Figure 4. The patterns of Au-20 and Au-PVP-20 appeared to be identical, as shown by graphs (b) and (c), respectively, in Figure 4. The Bragg's reflections peaks of both samples appeared at 38.24\({}^{\circ}\), 44.3\({}^{\circ}\), and 64.7\({}^{\circ}\), corresponding to (111), (200), and (220) planes, respectively, indicating the FCC lattice.  
The maximum intensity peak at 38.24\({}^{\circ}\) for both samples indicated the preferential growth in the (111) direction, which is in agreement with previously reported results [30].  
Figure 4: XRD pattern of (a) the JCPDS data card 04-0784 for gold; (b) Au-20; (c) Au-PVP-20.  
Figure 3: HA activity of AIV/H9N2 after mixing the Au-20 and Au-PVP-20 formulation for a 20-min microplasma discharge time. (a) HA activity of Au-20; (b) HA activity of Au-PVP-20.  
However, a decrease in (111) peak intensity and a slight increase in the intensity of (200) and (220) directions in the case of Au-PVP-20 was observed [30]. This increase in intensity might have been due to increased stacking in the (200) and (220) planes because of the reorientation of the (111) planes in the dielectric polymeric medium of a relatively higher refractive index after PVP capping [31]. This change in intensities is also attributable to the changes in the shape of the nanostructures, as evidenced by the SEM micrographs in Figure 5. Here PVP has demonstrated a shape-directing character of AuNPs metal cores when it adsorbed to gold colloids [32].
# Techno-Economic Analysis of a Solar Thermal Plant for Large-Scale Water Pasteurization
## 3 Plant Model Design
### Treatment Unit  
The main treatment unit consists of a hot water storage tank which accommodates the heating coil employed for pasteurization purposes. Given that the solar irradiance varies during the day while the thermal power required for the water treatment is constant, the unit is designed to also serve as a thermal energy storage system for the excess solar thermal energy. The energy balance for this unit yields the thermal power available for pasteurization \(\Phi_{w}\) as  
\[\Phi_{sf,u}+\Phi_{gb,u}=\left(\frac{dE_{i,tank}}{dt}\right)_{tu}+\Phi_{hl,tu}+ \Phi_{w}, \tag{17}\]  
where  
\[\Phi_{sf,u} =\eta_{sf,pipe}\,\epsilon_{sf,coil}\,\Phi_{sf} \tag{18}\] \[\Phi_{gb,u} =\eta_{gb,pipe}\,\epsilon_{gb,coil}\,\Phi_{gb} \tag{19}\]  
are respectively the useful heat power from the solar field and auxiliary gas burner, which are reduced by the thermal losses from the pipe lines through the efficiency \(\eta_{pipe}\) and by the heat exchange efficiency \(\epsilon_{coil}\) of the coils. The time-derivative of the internal energy \(E_{i,tank}\) accounts for the heat storage in the tank (treatment unit). \(\Phi_{hl,tu}\) quantifies the thermal losses from the tank towards the external environment, which are evaluated as  
\[\Phi_{hl,tu}=UA_{tank}\,(\bar{T}_{tank}-T_{env}), \tag{20}\]  
being \(U\) the overall thermal transmittance between the inner tank and the surrounding ambient, \(A_{tank}\) the external surface of the tank, \(\bar{T}_{tank}\) the mean temperature of water inside the tank and \(T_{env}\) the environment temperature. Considering a tank made of concrete with a 100 mm thick rockwood layer insulation, the mean value of the thermal transmittance has been estimated as \(U=0.31\) W m\({}^{-2}\) K\({}^{-1}\).