esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results of Aerosol Model and the Optical Properties Inter-Comparison
### Evaluation of the VIIRS_EDR Aerosol Model Type
All these results indicate that the M_VIIRS_EDR selected more weakly absorbing aerosol models in the NCP sites. It is worth to noting that with more frequencies of clean and dust aerosol models (higher SSA values) used in the VIIRS_EDR retrieval, the VIIRS_EDR AOD should have an underestimation if the surface reflectance characterization in VIIRS_EDR algorithm is perfect. The fact that the VIIRS_EDR AOD in Beijing has a high bias reflects that other factors (e.g., surface reflectance characterization or cloud contamination) are important error sources in the VIIRS_EDR algorithm, and their effects on aerosol retrievals override the effects from non-ideality in aerosol model types. This can be an interesting topic for future studies. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Sensor**} & \multicolumn{4}{c}{**SSA at 670 nm**} \\ \cline{3-6}  & & **Spring** & **Summer** & **Autumn** & **Winter** \\ \hline \multirow{3}{*}{**Beijing**} & CE318 & 0.94 \(\pm\) 0.02 & 0.96 \(\pm\) 0.03 & 0.95 \(\pm\) 0.03 & 0.91 \(\pm\) 0.04 \\  & VIIRS\_EDR & 0.96 \(\pm\) 0.02 & 0.96 \(\pm\) 0.03 & 0.97 \(\pm\) 0.02 & 0.98 \(\pm\) 0.01 \\  & VIIRS\_ML & \(\sim\)0.9 & \(\sim\)0.95 & \(\sim\)0.95 & \(\sim\)0.9 \\ \hline \multirow{3}{*}{**XiangHe**} & CE318 & 0.91 \(\pm\) 0.04 & 0.95 \(\pm\) 0.03 & 0.92 \(\pm\) 0.04 & 0.89 \(\pm\) 0.04 \\  & VIIRS\_EDR & 0.96 \(\pm\) 0.02 & 0.95 \(\pm\) 0.03 & 0.96 \(\pm\) 0.03 & 0.97 \(\pm\) 0.01 \\  & VIIRS\_ML & \(\sim\)0.9 & \(\sim\)0.95 & \(\sim\)0.95 & \(\sim\)0.9 \\ \hline \multirow{3}{*}{**Xinglong**} & CE318 & 0.93 \(\pm\) 0.03 & 0.92 \(\pm\) 0.03 & 0.92 \(\pm\) 0.03 & 0.96 \(\pm\) 0.03 \\  & VIIRS\_EDR & 0.96 \(\pm\) 0.02 & 0.94 \(\pm\) 0.02 & 0.95 \(\pm\) 0.03 & 0.97 \(\pm\) 0.00 \\ \cline{1-1}  & VIIRS\_ML & \(\sim\)0.9 & \(\sim\)0.95 & \(\sim\)0.95 & \(\sim\)0.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The seasonal values of SSA at 670 nm from CE318 inversion, VIIRS_EDR and VIIRS_ML during 2 May 2012–31 March 2014. Figure 3: The accuracy rate of the aerosol model used in: the VIIRS_EDR (**a**,**c**); and the VIIRS_ML (**b**,**d**). The accuracy rate stands for the ratio of the number of accurate model type used in the VIIRS to all the matchups between the VIIRS and CE318. The accuracy rate of each model is the ratio of the number of accurate model to the matchups of its corresponded model type.
###### Abstract  
In order to differentiate the extra virgin olive oils (EVOO) of different origin of purchase, such as monovarietal Italian EVOO with protected denomination of origin (PDO) and commercial-blended EVOO purchased in supermarkets, a number of samples was subjected to the analysis of volatile aroma compounds by both targeted gas chromatography/mass spectrometry (GC-MS) and untargeted profiling by comprehensive two-dimensional gas chromatography/mass spectrometry (GC\(\times\)GC-TOF-MS), analysis of phenols by targeted high-performance liquid chromatography/mass spectrometry (HPLC-DAD-ESI/MS), and quantitative descriptive sensory analysis. Monovarietal PDO EVOOs were characterized by notably higher amounts of positive LOX-derived C6 and C5 volatile compounds, which corresponded to the higher intensities of all the assessed positive fruity and green odor sensory attributes. Commercial-blended EVOOs had larger quantities of generally undesirable esters, alcohols, acids, and aldehydes, which coincided with the occurrence of sensory defects in many samples. Many minor volatile compounds that were identified by GC\(\times\)GC-TOF-MS were found to differentiate each of the two investigated groups. The differences between the groups with respect to phenols and taste characteristics were evident, but less pronounced. The results that were obtained in this study have undoubtedly confirmed the existence of the large heterogeneity of oils that are sold declared as EVOO. It was shown that GC-MS, GC\(\times\)GC-TOF-MS, and HPLC-DAD-ESI/MS analyses have complementary outputs, and that their use in combination has advantages in supporting the results of sensory analysis and objectively differentiating these groups of EVOO.  
extra virgin olive oil; volatiles aroma compounds; phenols; sensory attributes +
Footnote †: journal: molecules
# Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
## 2 Literature Review
### Bone Quality and Common Imaging Technique  
Bones provide the primary support for our body and form our shape. Bone formation mainly constitutes the cells and bone matrix. The bone matrix itself consists of inorganic components, which include about 69% hydroxyapatite and 22% organic components of collagen as the main constituents [6]. From a physiological point of view, bones consist of supporting cells called osteoblasts and osteocytes, and remodeling cells called osteoclasts. These components are responsible for the dynamic processes that comprise the modeling and remodeling processes inside the bone.  
Bones are characterized by stiffness, hardness, and a degree of dynamic processes within the bone, such as modeling and remodeling processes. Based on these characteristics, bone can be categorized into two components: cortical bone and trabecular bone. The cortical bone is dense and surrounds the marrow space, whereas the trabecular bone is a honeycomb-like tissue consisting of interspersed trabecular plates and rods [6]. Bone quality and quantity are commonly assessed to determine the characteristics of bone geometry and microarchitecture using different modalities of non-destructive methods, such as CT scanning, high-resolution peripheral quantitative CT (HR-pQCT) scanning, high-resolution MRI, and micro-CT scanning.  
In the last decade, CT scan machines have been widely proposed as an alternative technology for evaluating bone quality. The device uses a cone-type source and a 2D digital array detector to scan 3D volumes in a single scan. The scanner has the convenience of performing slice scans (up to 64 rows can be obtained at one time) [7]. With this CT scan technology, the examination time is shortened, and the distortion effect due to the internal movement of the patient during the scan can be reduced.  
The quality of the CT scan image is controlled by using the field of view (FOV). If a larger FOV is set, the image quality decreases, the contrast between different tissues is reduced, and the image becomes more scattered (artifacts) [7; 8; 9]. The quality of the CT scan image also depends on the size of the detector as a voxel area, which is independent of the slice thickness [10].  
Some researchers have investigated studies on the loss of bone quality using CT scans by looking at CT scans on spinal weakening compared to readings from other methods such as DEXA. An illustration of the detection of weakened bone quality in the spine using a CT scan technique is shown in Figure 1. In their research, the bone quality was determined from the HU obtained from CT scan data, which was then compared with the standard DEXA method, as shown in Figure 1D.
# Energy Management for a Power-Split Plug-In Hybrid Electric Vehicle Based on Reinforcement Learning
###### Abstract  
This paper proposes an energy management strategy for a power-split plug-in hybrid electric vehicle (PHEV) based on reinforcement learning (RL). Firstly, a control-oriented power-split PHEV model is built, and then the RL method is employed based on the Markov Decision Process (MDP) to find the optimal solution according to the built model. During the strategy search, several different standard driving schedules are chosen, and the transfer probability of the power demand is derived based on the Markov chain. Accordingly, the optimal control strategy is found by the Q-learning (QL) algorithm, which can decide suitable energy allocation between the gasoline engine and the battery pack. Simulation results indicate that the RL-based control strategy could not only lessen fuel consumption under different driving cycles, but also limit the maximum discharge power of battery, compared with the charging depletion/charging sustaining (CD/CS) method and the equivalent consumption minimization strategy (ECMS).  
energy management strategy; Markov decision process (MDP); plug-in hybrid electric vehicles (PHEVs); Q-learning (QL); reinforcement learning (RL) +
Footnote †: journal: Applied Sciences
## 4 Case Study
### Architecture Configuration
#### 4.2.1 Hardware Layout Optimization  
Firstly, the networking of all resources is realized, in which the WIFI-based industrial Internet was built in the _Component Center_, and the existing Internet and Internat can be used outside. The centralized server was placed in the computer room, and two distributed servers for deploying M\({}^{2}\)ES Apps were added to it. Additionally, some clients were deployed where needed. The hardware layout and photos can be seen in Figure 8.
## 3 Detection Approaches Review
(e.g., accuracy, precision, and recall of the proposed technique or method on the addressed problem, or data on the significance of an effect, for example data on the significance of the effect of emotion on keystroke latency). For searching for works, different databases were used, which were Google Scholar and Web Of Science. Intensive search was performed on both of them for finding relevant works for the aim of this literature review. \begin{table}
\begin{tabular}{c l l} \hline
**Reference** & **Metics** & **Values** \\ \hline
[10] & Accuracy, precision, recall and F1 & 0.6778-0.7961, 0.628-0.8905, 0.5931-0.9231 and 0.5549-0.8591 \\ \hline
[11] & Precision and recall & 0.56–0.79 and 0.67–0.80 \\ \hline
[12] & Precision, recall and F1 & Single-domain approach: 0.622-0.792, 0.414-0.661 and 0.697-0.702 \\ \hline  & \multicolumn{1}{c}{} & Cross-domain approach: 0.565-0.678, 0.273-0.435 and 0.36–0.518 \\ \hline  & \multicolumn{1}{c}{} & Accuracy precision, recall and F1 & Multi-aspect sentence labeling: 0.477-0.83, 0.126-0.969, 0.179-1 and 0.148-0.887 \\ \hline
[13] & L1 error, \(\rho\)aspect, \(\rho\)review and MAP \#10 & Multi-aspect rating prediction with indirect supervision: 0.238-0.645, \(-\)0.149-0.715, 0.454-0.866 \\ \hline  & \multicolumn{1}{c}{} & Supervised multi-aspect rating prediction: 0.554-1.071 \\ \hline  & \multicolumn{1}{c}{} & Explicit feature extraction task: 93–0.95\% and 73-80\% \\ \hline  & \multicolumn{1}{c}{} & Finding word semantic orientation: 72–88\% and 55-83\% \\ \hline
[14] & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{3-3}  & \multicolumn{1}{c}{} & Extracting opinion phrases: 79\% and 76\% \\ \hline  & \multicolumn{1}{c}{} & Extracting opinion phrase polarity: 86\% and 89\% \\ \hline
[15] & \multicolumn{1}{c}{} & Precision and recall & 0.75–0.955 and 0.2–0.286 \\ \hline
[16] & \multicolumn{1}{c}{} & Precision, recall and F1 & 0.761–0.918, 0.37–0.82, and 0.498–0.837 \\ \hline  & \multicolumn{1}{c}{} & Using visual features: 0.49–0.83 \\ \hline
[17] & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & Using visual and text features: 0.48–0.88 \\ \hline
[18] & \multicolumn{1}{c}{} & Precision, recall, accuracy and F1 & 0.691–0.702, 0.729-0.905, 0.667–0.783 and 0.722–0.846 \\ \hline
[19] & \multicolumn{1}{c}{} & Accuracy & 0.68–0.82 \\ \hline
[20] & \multicolumn{1}{c}{} & Accuracy & 0.452–0.851 \\ \hline
## 4 Discussion
In the case of the STR and the Ts\(-\)Ta, the initial threshold taken was the median pixel value of the variable, which was based on the experience from the previous studies in the region [46; 47; 52]. These initial thresholds and the related assumptions should be field tested further to ascertain the strengths and weaknesses of this approach in different landscapes and at different times. This study used a simple threshold method for classification. However, for future studies, it is desirable to employ other approaches such as machine learning over larger areas. The classification method used in this study assumed that the main source of surface moisture was irrigation and that rainfall did not have any significant impact. As stated earlier, the summers in the study area are generally dry. The long-term average of rainfall during the three months of summer is 101.8 mm (Figure 2). The study period (December 2020 to February 2021) was, however, drier, with a total of 80.6 mm. In cases of high rainfall situations, there will be a requirement for season-wide vegetation as well as STR analysis of the known irrigated and dryland farms vis-a-vis rainfall records to make the desired adjustments to the thresholding method. This study used a single image as a representative of the summer season. This is in accordance with a previous study in the irrigation regions of Victoria, Australia, which found the use of a single'mid-season' image adequate to assess maximum crop cover because of the strong temporal stability in NDVI response [53]. However, for further studies, there is scope to test temporal aggregates of image-based variables from multiple sources including Landsat-8/9 and Sentinel-2. This study is a formal affirmation of using the STR as a substitute for Ts-based measures for irrigated landcover classification. The basis of this affirmation is the formal comparison of the optical approach with the thermal approach implemented in a key irrigation district. This opens the opportunity of utilizing the widely available optical sensors for irrigated landcover investigations.
## 3 Optimization Based on GA
### Establishment of Constraints  
(1) Constraints of transmission angle  
Transmission angle is one of the most important criterions of mechanical transmission efficiency. To ensure the transmission efficiency of the steering cylinder during steering, it is required that the minimum value of the transmission angle be greater than 10 degrees [22]:  
\[J(1)=(a+b_{\min})-170^{\lx@math@degree}\leq 0, \tag{5}\]  
\[J(2)=(a-b_{\max})-10^{\lx@math@degree}\geq 0. \tag{6}\]  
(2) Constraints based on the structure of cylinders  
In the design and manufacturing process, it is necessary to fully consider whether the designed structural dimensions meet the actual production requirements and commonality of cylinders. To meet the specification requirements of cylinders, it is essential to make the size and ratio meet the following conditions [2]:  
\[J(3)=A^{\prime}C_{\max}+\varepsilon-(l_{\max}+s)\leq 0, \tag{7}\]  
\[J(4)=B^{\prime}D_{\min}-(s+\varepsilon)\geq 0, \tag{8}\]  
\[1.3\leq\frac{l_{\max}+s}{s}\leq 1.6, \tag{9}\]  
\(l_{\max}\): The maximum stroke of the steering cylinder;  
\(s\): The minimum installation distance of the steering cylinder;  
\(\varepsilon\): Reserve allowance for stroke, which can prevent the rigid impact between the rod and block of the cylinder.  
(3) Constraints about triangle  
It is well-known that the prerequisite for the triangle establishment is that the sum of any two sides is greater than the third:  
\[J(5)=CO+A^{\prime}O_{\max}-(l_{\max}+s+\varepsilon)\geq 0, \tag{10}\]  
\[J(6)=s+\varepsilon+OB^{\prime}_{\max}-OD\geq 0. \tag{11}\]  
(4) Constraints on steering resistance moment  
The steering resistance moment of pivot steering is usually 2-3 times that of driving. Thus, the pivot steering torque is taken as the calculated load. The following empirical formula comes from the test curve [23]:  
\[M_{r_{0}}=\frac{Gf^{n}}{\eta}(0.1L+0.6)(0.3b^{2}+0.1b+2.6), \tag{12}\]  
\(\eta\): Steering mechanism efficiency [1], \(\eta=0.9\);  
\(G\): Vertical load on the tire;  
\(f\): Friction coefficient, \(f=0.1\)-\(0.5\);  
\(L\): The wheelbase of the vehicle.  
The steering torque should satisfy the following relationship:  
\[2M_{r_{0}}-M=0. \tag{13}\](5) Constraints based on the frame of the AORV  
During the actual design process, in order to prevent structural interference and ensure the feasibility of hydraulic cylinders layout, it is important for designers to consider the limits and impact from the structure of the frame, which plays an essential role on the layout of the cylinder position. As a result, there are many boundary constraints related to the frame, which are shown as follows:  
\[\begin{array}{l}c_{1}\leq CQ\leq c_{2}\\ c_{3}\leq QO\leq c_{4}\\ c_{5}\leq AP\leq c_{6}\\ c_{7}\leq PO\leq c_{8}\end{array} \tag{14}\]  
where \(c_{1},c_{2},c_{3},c_{4}\) are constant values related to the frame structure.
## 3 Results and Discussion
### Garson Sensitivity Analysis
Figure 7: Regression curve between simulated and experimental data for drying rate: (**a**) ANN model with hyperbolic tangent activation function; (**b**) ANN model with logistic sigmoid activation function. connection. The superscripts \"\(i\)\", \"\(h\)\", and \"\(o\)\" refer to the input, hidden, and output layers, respectively, and the subscripts \"\(k\)\", \"\(m\)\", and \"\(n\)\" refer to the input, hidden, and output neurons, respectively. Figure 8 presents the results of the sensitivity analysis. It was determined that, except for the wind velocity, all the variables significantly impacted the drying velocity. In quantitative terms, the percentage of relevance was as follows: solar radiation (\(G_{T}\)) (17.6%), ambient temperature (\(T_{a}\)) (16.2%), relative humidity (RH) (15.1%), wind velocity (\(w_{v}\)) (0.3%), drying chamber inlet temperature (\(T_{i}\)) (17.4%), drying chamber outlet temperature (\(T_{o}\)) (17.7%), and fan voltage (\(V_{f}\)) (15.7%). From these results, it can be seen that, due to it being a forced convection system, the effect of natural wind circulation was diminished. On the other hand, the wind velocity had a very low influence because there was not much variation in the database during the experiment (See Table 3), and the drying system was closed. The major influence regarding the wind velocity was represented by losses via convection on the surface, thus presenting a lower influence in the drying process. Likewise, the other parameters presented a similar significance level, differing by less than 2%. The fan voltage was shown to have an important impact, validating the application of control strategies as it is the only manipulable element. Derived from the decoupling of wind velocity by forced ventilation, the other input variables were uniformly distributed, with an average contribution value of 16.61%. This means that they were strongly linked, due to the energetic interactions of the heat transport phenomena that occur in the drying process, such as the supply of heat by solar radiation to the solar collector. This heat is further injected into the air stream, raising its temperature to generate a gradient and provide the hot air flow, needed to remove any excess of water.
## 3 Experimental Results
### Experimental Results of the Second Part of the Dataset
The final model's right-side trajectories are smoother, while the left-side trajectories remain uneven due to visibility issues, consistent with the findings in light clothing. Standing arm adduction in light clothing shows a mean error of 0.1325 m (SD = 0.0375 m) at the right elbow and 0.1803 m (SD = 0.0496 m) at the right wrist. When performing arm retraction in dark clothing, the errors increase to a mean of 0.1637 m (SD = 0.0489 m) at the right elbow and 0.2027 m (SD = 0.0564 m) at the right wrist. For arm adduction while standing in light clothing, the right elbow has a mean error of 0.1089 m (SD = 0.0406 m) and the right wrist has a mean error of 0.0927 m (SD = 0.0552 m). In dark clothing, these errors are 0.1051 m (SD = 0.0596 m) for the right elbow and 0.1251 m (SD = 0.0830 m) for the right wrist. Figure 23 shows 3D results for bending both arms. The front and side views reveal depth errors, but the final model (pink) improves alignment. The right leg, however, is inaccurately predicted, demonstrating challenges in handling complex multi-limb movements. Figure 24 illustrates depth coordinate changes for bending both arms in light clothing. Depth trajectories are smoother, though discrepancies on the left side arise due to visibility issues in the second stream, impacting the final result's accuracy. Figure 21: Depth coordinate changes during upper arm adduction in light clothing: (**a**)—right elbow joint; (**b**)—right wrist joint. Figure 22: Depth coordinate changes during upper arm adduction in dark clothing: (**a**)—right elbow joint; (**b**)—right wrist joint. Finally, Figure 25 shows depth coordinate changes for bending both arms in dark clothing. The trends are similar to those for light clothing tests but with increased depth er  
Figure 13:0 A 3D human skeleton of the flexion of both upper arms. Figure 13:1 Depth coordinate changes during the bending of both upper arms in light clothing: (**a**)—right elbow joint; (**b**)—right wrist joint; (**c**)—left elbow joint; (**d**)—left wrist joint. rors. The right-side trajectories are smoother, while the left-side remains uneven, consistent with issues observed in light clothing. For standing bicep curls, light clothing results in mean errors of 0.1041 m (SD = 0.0365 m) at the right elbow and 0.1275 m (SD = 0.0553 m) at the right wrist, with corresponding values for the left elbow and left wrist being 0.0850 m (SD = 0.0293 m) and 0.0750 m (SD = 0.0295 m), respectively. In dark clothing, these errors increase to 0.1275 m (SD = 0.0495 m) at the right elbow and 0.1477 m (SD = 0.0651 m) at the right wrist, while for the left elbow and left wrist, the means are 0.0917 m (SD = 0.0376 m) and 0.0817 m (SD = 0.0385 m), respectively.
## 2 Methods
### The UAV Model  
The considered UAV belongs to the family of Mini and Light Tactical UAV, with a MTOW (maximum take-off weight) ranging from 25 to 50 kg. The UAVs of this class are designed to integrate multiple payloads with different capabilities, e.g., EO/IR sensors, multi/hyperspectral cameras, LiDAR, transmitters, and radars. Flexibility is ensured by the fuselage modularity and by the possibility of changing the onboard systems configuration to achieve an optimised aircraft balance.  
In this work, we consider a simplified geometric model of the UAV, as plotted in Figure 1, where small-scale geometric details and the propeller are omitted. The motivation is two-fold: such a simplified geometry, while remaining representative of the actual aircraft and retaining its essential qualitative features and dimensional characteristics, is free from intellectual property constraints; moreover, the lack of small-scale details allows some savings of computational effort. The simplified UAV is 2.4 m long and it has a span \(b=3.6\) m. It has a swept wing with a chord length of 0.3 m at the root with winglets at the tips of 0.22 m and dihedral angle of 21.5\({}^{\circ}\). The considered reference surface area is \(S=1\) m\({}^{2}\). The UAV is characterised by a reverse V tail made by a symmetric four-digit NACA airfoil with a span of 1.05 m and a chord of 0.23 m. The tail is directly connected to the lower surface of the wing by two booms of 1.05 m with a circular cross-sectional area of radius 0.015 m. The fuselage is 1.41 m long and its cross-section originates from a rectangular shape, 0.29 m high and 0.23 m wide, with rounded edges. The drone cruise speed is 22 m/s, leading to a chord-based Reynolds number \(Re_{\infty}=5\times 10^{5}\). The UAV weight of 25 kg and the cruise speed of 22 m/s, together with the geometrical information mentioned above, imply a lift coefficient in cruise of \(C_{L}=0.8322\).