esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 CapsNet-GA Method for Electromagnetic Characteristic Fitting
### Determine the Objective Function  
The ultimate goal of the genetic algorithm is that the RCS distribution characteristics meet the requirements of the objective function; that is, the comprehensive error is the smallest. The comprehensive error is mainly the RCS peak error, the angle error corresponding to the RCS peak point, and the RCS average error in the non-peak area. Therefore, the first layer of the objective function is first established as:  
\[F(x_{1},x_{2},x_{3})=\frac{\sqrt{x_{1}{}^{2}+x_{2}{}^{2}+x_{3}{}^{2}}}{3} \tag{6}\]  
Figure 4: Schematic diagram of the CapsNet structure.  
where \(F\) represents the comprehensive error of the RCS distribution characteristic, and \(F\) is required;  
\(x_{1}\) represents the RCS peak error;  
\(x_{2}\) represents the angle error corresponding to the RCS peak error;  
\(x_{3}\) represents the RCS average error in the non-peak region.  
Then, the first layer objective function is decomposed to obtain the second layer sub-objective function:  
\[\left\{\begin{array}{l}x_{1}=\left|\Delta\sigma_{\max-\mathrm{optimize}}- \Delta\sigma_{\max-\mathrm{goal}}\right|\\ x_{2}=\left|\Delta\phi\right|_{\sigma_{\max}-\mathrm{optimize}}-\Delta\phi \right|_{\sigma_{\max-\mathrm{goal}-\mathrm{optimize}}}\\ x_{3}=\left|\Delta\overline{\sigma}\right|_{\mathrm{Non-\sigma_{\max}} \mathrm{region-optimize}}-\Delta\overline{\sigma}\right|_{\mathrm{Non- \sigma_{\max}}\mathrm{region-optimize}}\end{array}\right| \tag{7}\]  
where \(\Delta\sigma_{\max}\) represents the RCS peak;  
\(\Delta\phi\left|{}_{\sigma_{\max}}\right.\) represents the angle corresponding to the RCS peak;  
\(\Delta\overline{\sigma}\left|{}_{\mathrm{Non-\sigma_{\max}}\mathrm{region}}\right.\) represents the average value of the RCS in the non-peak region.  
The optimization target values of the three sub-objective functions \(x_{1}\), \(x_{2}\), and \(x_{3}\) are all 1; that is, the error of the RCS peak and the average value of the RCS in the non-peak region do not exceed 1 dB, and the angle error corresponding to the RCS peak does not exceed 1\({}^{\circ}\).
## 3 Test Site and Field Testing
### Introduction to the Coal Mine  
The Datong First Coal Mine is a high gas coal mine located in the southern Qianjiang District, Chongqing, China (shown in Figure 4). The approved production capacity is 1.5 million tons/year. The three minable coal seams (M6, M7, and M8) have average thicknesses of 0.74 m, 1.10 m, and 2.85 m, respectively. In addition, folds and faults are present in this mine. The Yutiao anticline (the main geologic structure) is located in the middle of the mine, and the trend is 50\({}^{\circ}\) to 60\({}^{\circ}\) to the northeast. The F35 reverse fault is the largest fault, and it decreases significantly with increasing depth and forms some smaller faults at depth.  
Figure 4: Location of the Datong First Coal Mine.  
The target coal seam in the test is M7, with an average dip angle of 8\({}^{\circ}\). The gas content of the M7 coal seam is 19.41 m\({}^{3}\)/t, and the original gas pressure is 5.73 MPa. There have been 235 CGO accidents in this mine, and more than 90% of the accidents occurred in the M7 coal seam [28].
## 1 Introduction
A number of different polymerization modes have been used to graft polymer chains on the surfaces like non-living free radical polymerization [13], controlled polymerization techniques such as ionic polymerization [14,15], nitroxide mediated polymerization [13], atom transfer radical polymerization (ATRP) [11,16-18] and reversible addition fragmentation transfer (RAFT) [19-21]_etc_. Conventional polymer systems suffer from unwanted bimolecular termination reactions thereby leading the grafting process to an abrupt end. However, the growth of living polymer chains from the surfaces generated by controlled living polymerization techniques ensures better control over the molecular weight distribution and the amount of grafted polymer. ATRP has been particularly useful technique because of ease of operation, good control on the properties of the grafts, and possibility of polymerization in aqueous phase [3-10,16,17,19,22-39]. As mentioned earlier, environmental responsiveness means reversible changes in the hydrophobicity and hydrophilicity, dimension and density of the polymer chains owing to changes in the intramolecular interactions stimulated by changes in stimulants like temperature, salt concentration, pH, electrical potential _etc_. Out of a number of known polymers known to exhibit this behavior, water soluble poly(\(N\)-isopropylacrylamide) (PNIPAAM) is a very common and extensively studied material. It has a lower critical solution temperature (LCST) of about 32 \({}^{\circ}\)C [40-45] and below this temperature, the chains exhibit chain extended conformations and random coil structure. The intermolecular hydrogen bonding with the water molecules due to the chain extended morphology generates the hydrophilic nature of the chains. The chains transform into more collapsed globular form above the lower critical solution temperature owing to the domination of intramolecular bonding between the CO and NH groups over the external hydrogen bonding. The highly reversible responses of PNIPAAM chains to temperature as well as other stimulants have led to its extensive use for the synthesis of stimulus sensitive surfaces. Due to these characteristics, PNIPAAM chains grown on various surfaces have been used for protein adsorption, responsive gels, biological separations _etc_. [46-56]. The goal of the reported study was to graft environmentally responsible PNIPAAM brushes on the surface of spherical and flat surfaces by using ATRP and to demonstrate the applications of these surfaces. Crosslinked polystyrene particles containing a thin shell of ATRP initiator on the surface were prepared using emulsion polymerization reactions (with and without the use of surfactant) and were subsequently used for the grafting of environmentally responsible PNIPAAM brushes.
## 3 Discussion
### Analysis of Marker Aroma Compounds in Pepper Fruits from Different Samples  
Generally, the special flavor of vegetables, fruits, and tea comes from the marker aroma compounds. (E)-2-hexenal and (Z)-3-hexenal are the most important aromatic compounds with strong green grass and green leaf odor in cherry tomato [36], while lanalool oxide and lanalool are the main components of citrus flower fragrance [37], and hexyl 2-methylbutyrate is the main reason for the \"honey crisp\" apple flavor [34]. The characteristic components (E, E)-2,4-hexagon make Jingshan green tea have a floral and fatty flavor [38]. In Brazil's tabasco pepper, \(\alpha\)-Pinene, hexyl 2-methylbutyrate, hexyl 3-methylbutanoate, and hexyl butanoate are the main contributors to the typical sweet, herbal, and pepper-like flavors [32]. Our research confirmed methyl salicate (Z11) was the main marker aroma compound at the green stage of four pepper varieties and described as green and sweet in the Brazilian tabasco pepper [32], hexanal (Q3) described in cherry tomato as having the smell of freshly cut grass [36], are another characteristic VACs of CTJ and GJ at green stage, however 4-methylpentyl-2-methylbutyrate (Z12) not only accumulated significantly at the green phase of GJ, and was the characteristic volatile of CTJ mature fruit with herbal flavor [32]. Moreover, as one of the main marker volatiles of CTJ and HDL1, 4-methylpentyl 3-methylbutanoate (Z13) may show the same odor as Z12. The study found that the characteristic component trans-2-hexenylisovalerate (Z17) with green and fruit-like odor accumulated significantly at the green stage of HDL2 and breaking stage of GJ, while the hexyl hexanoate (Z27) with an apple fruit flavor is the main marker volatile component in the mature fruit of HDL1, and is usually described as fresh cut grass and vegetable flavor [34]. The above showed that the marker aroma components are different in pepper fruits of different varieties and development stages, which can be used as important indicators to study the flavor quality of pepper. However, how they affect the specific aroma quality of these pepper fruits still needs to be determined. Further sensory evaluation and gas-chromatography-olfactometry (GC-O) methods are needed to study the specific effects of these volatile aroma substances on the aroma of pepper fruit [33; 39].
# Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation
## 2 Materials and Methods
### Methodology
#### 2.2.2 Improved Change Detection Approach  
For the classic detection approach, radar signal change is linked to soil moisture change. It can be written as:  
\[\Delta_{VV}=\alpha\ \Delta_{SSM} \tag{4}\]  
where the soil moisture changes and the radar signal change in VV polarization are expressed in Equations (5) and (6), respectively.  
\[\Delta_{SSM}=SSM_{t}-SSM_{min} \tag{5}\]  
\[\Delta_{VV}=\sigma_{VV}-\sigma_{VVmin} \tag{6}\]  
This relationship is adapted from [35; 78]. It considers as a hypothesis that the difference between two radar signals acquired on two different dates is mainly related to the change in the hydric state of the soil.  
Here, we propose an improved change detection methodology by using a hybrid change detection and empirical approach in which the effect of the vegetation is taken into account thanks to a vegetation-related variable \(V1\). Using this approach, the radar signal change is related to the soil moisture change by the following expression:  
\[\Delta_{VV}=(\alpha-\beta\ V1)\ \Delta_{SSM} \tag{7}\]  
Unlike forward modeling approaches such as the WCM, the radar signal and the soil moisture are introduced as the difference between the radar signal at time t and the minimum signal corresponding to the minimum moisture and the difference between the soil moisture and the minimum moisture value, respectively.  
The main objective of introducing the change as a function of time is to reduce the dependency to other variables affecting the radar signal such as soil roughness, which can be very important, particularly in the context of strong topography or even important spatial changes in microtopography, that change little with time for a given site, in contrast to soil moisture. Two vegetation-related quantities were tested for the \(V1\) parameter: the optical vegetation index \(NDVI\) estimated from Sentinel-2 data, as illustrated in Section 2.3, and the VH/VV ratio, considered to be strongly linked to the dynamics of the vegetation cover. This second option could be particularly interesting in the context of a humid climate with limited optical data.
## 2 Model and Method
### Process of Optimization  
The optimization model includes the input layer, the optimization layer, and the output layer. In the input layer, the parameters of the building, equipment, and economics, the operational strategy, the objective function, and the constraint condition are input into the model. Furthermore, the parameters of optimization, which include the population size (pops), the maximum number of iterations (\(\mathrm{G_{m}}\)), and the probabilities of crossover (\(\mathrm{Pc}\)) and mutation (\(\mathrm{Pm}\)), need to be set. The optimization layer consists of an outer loop, which generates the initial population and new populations of equipment capacities according to the parameters of optimization, and an inner loop, which calculates the annual cost, carbon emissions, and energy consumption of the DES and identifies the elite individual with optimal comprehensive performance [31]. Finally, the optimization results of equipment capacity, the evaluating indicator, and energy flow are output. The flow chart of the DES optimization model is shown in Figure 2.
## 5 Implementation and Performance Analysis  
In this section, simulations are carried out to evaluate the performance of our proposed scheme. We consider a static OFDMA based system for SA, and the wireless channel is modeled including distance-dependent path loss, shadowing fading and multipath Rayleigh fading. There are two component carrier configuration and switching schemes used with the comparative. One is the proposed RT-CCCS scheme, and the other is an equivalent-throughput-based component carrier configuration and switching (Ceq-CCCS) scheme, whose principle can be expressed as follows:  
\[C_{eq}\geq\frac{F}{T_{delay}} \tag{21}\]where \(T_{delay}\) is the delay requirement which is required for SUs' data transmission. The two schemes have the same scheduler structure which is proposed in Section 2. The simulation parameters are summarized in Table 1.  
To facilitate performance analysis, the PUs' arrival rate and service rate in each CC are same, \(\lambda_{pu}\) denotes the PUs' arrival rate and it changes from 0 to 20 users per second, and the PUs' service rate is equal to 50 users per second in each CC. A long simulation run (with duration of 100 seconds) is needed.  
The following measures are used in our study as performance indicators:  
**Average delay time**: the average period from SUs' arrival to complete departure.  
**Number of CCs used**: the number of CCs which are used for SU data transmission in the SA based system.  
**Resource efficiency**: the rate between the resource used for SU data transmission and the resources of all CCs which can be used for SUs' data transmission.
## 3 Numerical Results and Discussion
### Effect of Random Cumulative Pitch Deviations on Modified HGRS  
#### 3.2.1 Design of Modification Parameters  
It is generally known that the STE fluctuation is usually regarded as an effective standard to evaluate the vibration level of gear pairs. By reducing the material from the tooth top and tooth root, profile modification can change the curve shape and amplitude of transmission errors of a gear pair. When the amplitude of transmission errors is reduced, the vibration of the gear system will become weak. Profile modification has been widely used in the helical gear system [21,33,34]. Thus, it is necessary to understand the effect of random cumulative pitch deviations on the vibration responses of modified HGRS.  
Figure 13 shows the design of profile modification for helical gears. Profile modification parameters include modification curve, amount of modification and length of modification. Both driving and driven gear are modified using tip relief. Based on POA, profile modification parameters are defined by the coordinate system XOY, and the potential maximum length of modification is \(B_{1}B_{2}\), \(e_{\max}\) is the maximum amount of modification, \(L\) is the length of modification.  
Figure 12: Bearing vibration acceleration at different load torques considering random cumulative pitch deviations.  
The modification curve adopts quadratic parabola. The equation of profile modification curve can be written as  
\[\left\{\begin{array}{cc}e_{i}=e_{\max}\Big{(}\frac{x_{i}-L}{L}\Big{)}^{2},&L \geq x_{i}\geq 0\\ 0,&Width-L>x_{i}>L\\ e_{i}=e_{\max}\Big{(}\frac{x_{i}-Width+L}{L}\Big{)}^{2},&Width\geq x_{i}\geq Width -L\end{array}\right. \tag{12}\]  
where \(x_{i}\) is the distance from the contact point \(i\) to the starting position of profile modification, \(e_{i}\) is the amount of modification for contact point \(i\).  
Because the enhanced LTCA model has very high efficiency, aiming to minimize the STE fluctuation, the optimal profile modification parameters can be obtained by brute global optimization method. This method can clearly show that the change law of STE fluctuation with the modification parameters [17]. The optimization scheme of profile modification is given in Figure 14. It can be observed that there is a narrow region for modification parameters (marked by red line) that reduce STE fluctuation significantly. The STE fluctuation in this region is less than 0.5 \(\upmu\)m. From Figure 14, the optimal modification parameters can be obtained very conveniently.  
Figure 14: Brute global optimization of profile modification (unit: \(\upmu\)m).  
Figure 13: Schematic diagram of profile modification for helical gears.
# SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation
## 3 Methodology
### Feature Extractor  
The feature extractor extracts abstract features at various levels from the input image and inputs them into the stacked hourglass module. This process must extract key features that are essential for pose estimation with high performance while minimizing resource consumption, including the computational load. Additionally, the feature extractor should not be model-specific. This study adopts EfficientNet [19], which is a deep learning-based feature extraction model that meets these requirements. EfficientNet is a neural network architecture designed using Neural Architecture Search to balance the size and performance of deep learning models, applying an efficient model scaling method to generate models of various sizes. The model used in this study is EfficientNet-B0, pre-trained on ImageNet-1K [20], which is known for its efficient feature extraction despite its lightweight structure.  
Figure 1: Overall model structure of proposed SMS-Net. Keypoint prediction is performed at the end of each stack.  
Specifically, the feature map generated at the fourth stage of EfficientNet-B0, with dimensions of \(40\times\frac{h}{8}\times\frac{w}{8}\), is processed through \(1\times 1\) convolutions to convert the channel dimension from 40 to 144, followed by up-sampling to generate a final feature map of \(144\times 64\times 64\). The feature map obtained is then passed to the next stage, i.e., the stacked hourglass module. Using the final feature map generated at the last eighth stage of EfficientNet-B0, with a resolution of \(320\times\frac{h}{32}\times\frac{w}{32}\), can cause excessive information loss. Therefore, this study used the feature map generated at the fourth stage, which was revealed to be the best through experiments.
###### Abstract  
The Italian territory is subject to a high level of hydrogeological instability that periodically results in the loss of lives, buildings and productive activities. Therefore, the recognition of areas susceptible to hydrogeological instability is the basis for preparing countermeasures. In this context, landslide susceptibility in the mid-Adriatic slope was analyzed using a statistical method, the weight of evidence (WoE), which uses information from several independent sources to provide sufficient evidence to predict possible system developments. Only flows, slides, debris flows and mud flows were considered, with a total of 14,927 landslides obtained from the IFFI (Inventory of Franous Phenomena in Italy) database. Seven climatic-environmental factors were used for mapping landslide susceptibility in the study area: slope, aspect, extreme precipitation, normalized difference vegetation index (NDVI), CORINE land cover (CLC), and topographic wetness index (TWI). The introduction of these factors into the model resulted in rasters that allowed calculation by GIS-type software of a susceptibility map. The result was validated by the ROC curve method, using a group of landslides, equal to 20% of the total, not used in the modeling. The performance of the model, i.e., the ability to predict the presence or absence of a landslide movement correctly, was 0.75, indicating a moderately accurate model, which nevertheless appears innovative for two reasons: the first is that it analyzes an inhomogeneous area of more than 9000 km\({}^{2}\), which is very large compared to similar analyses, and the second reason is the causal factors used, which have high weights for some classes despite the heterogeneity of the area. This research has enabled the simultaneous introduction of unconventional factors for landslide susceptibility analysis, which, however, could be successfully used at larger scales in the future.  
landslide; susceptibility; TWI; AUC; NDVI; weight of evidence; WoE
## 3 Results and Discussion
### The Contaminated Water Analysis  
#### 3.2.1 Static Contact Angle Measurement  
Figure 8 shows the static contact angles of sewage water drops on different materials' surfaces when immersed in sewage water. The contact angles of water drops onto the iron casting, steel, PVC, and PE were 63.2\({}^{\circ}\), 57.6\({}^{\circ}\), 48.6\({}^{\circ}\),40.5\({}^{\circ}\), respectively. The contact angles decreased when the 300 Hz vibration was conducted. The results indicated that vibration decreased the contact angles by 30-50%. For different materials, the static contact angle followed the rule that \(\theta_{\text{iron casting}}>\theta_{\text{steel}}>\theta_{\text{PVC}}>\theta_{ \text{PE}}\). The results indicated that different materials showed different affinities with the surface. The sewage water rules were similar to those of the clean water.  
Figure 8: The static contact angles of sewage water drops onto the iron casting, steel, PVC, and PE (**a**) without vibration; (**b**) with 300 Hz vibration.  
Figure 7: The water/solid/bitumen (0.1 wt%) dynamic contact angles of water drops onto the (**a**) iron casting; (**b**) steel; (**c**) PVC; (**d**) PE, with or without 300 Hz vibration.  
Figure 9 shows the static contact angles of sewage water drops onto the different surfaces with or without 300 Hz vibration with different aging time. The results indicated that the static contact angles increased by 100% with the increasing aging time. At the beginning, the contact angles without vibration were higher than those for the 300 Hz vibration. However, the contact angles for the 300 Hz vibration were higher than those without vibration. At the beginning, the contact angles were low, however, when the vibration was increased to 300 Hz, the wettability alteration was enhanced.
# Infrared Thermography to Evaluate Thermal Comfort under Controlled Ambient Conditions
## 3 Results of Phase I: IR Camera Comparison
### Qualitative Analysis
#### 3.2.2 Location of the Hottest Point  
The next step was the detection of the locations of the hottest points measured by the IR cameras. Generally, four different positions were found, as presented in Figure 8a. In the mouth and the eyes, the position of the hottest point often changed between the right and left extremities. IR camera 2 identified the chin as the hottest point in two occurrences. No association was found between the location of the hottest point and the setpoint values, with the positions randomly varying within the face, regardless of the temperature or relative humidity.  
The two IR cameras showed good agreement, identifying the same position in most of the tests. Figure 8b highlights this finding, as a total of 69 scenarios out of 99 present the same location. According to the findings of [PERSON] et al. [13], the inner eye corner is the region with the highest temperature. This was only partially observed in the results, since the largest number of occurrences was in the mouth, followed by the eyes and forehead. This difference could mean that the 10 min period of acclimatization is insufficient to mitigate the effects of the face mask.  
In order to confirm the association between the positions of the hottest points identified by the IR cameras, a chi-squared test was performed. In this test, the null hypothesis was \"H\({}_{0}\): there is no association\" and the significance level applied was \(\alpha\) = 5%. The result was 53.33 with a \(p\)-value < 0.01%, meaning there was an association between the results of the two IR cameras.