esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Experimental Results  
The results of a test advertisement conducted from 15 April to 30 April 2022 are presented in Table 1. After running the advert for a \(t\) period of time (chosen individually by the marketer), the marketer analyzes the obtained results and, if necessary, forms better advertising content for a more effective target group. In the designed system, we considered a type of video content (Figure 11) where the generated image is used for the initial thumbnail before the video advert starts. The system includes a previously developed targeting model based on decision trees and distribution of the target group [3], which allows using tree branches to make changes to the advertising campaign based on attributes that depend on the target function. Moreover, this model makes it possible to quickly clean and filter the data. To verify the results of modeling based on decision trees, repeated modeling of targeted advertising was carried out, where \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Advert Version** & **Content** & **Target Audience** & **Results** & **Reach** & **Views** & **Cost per Result** \\ \hline Version 0 & “Women’s handbags & All of Ukraine, all age & & & \\ (initial) & for everyone” & groups and genders & 85 & 1236 & 2365 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of test advertisement (15 April 2022–30 April 2022). Figure 10: Menu for creating the advertising post where: (**a**) creating an advertising post, (**b**) setting the advert parameters, (**c**) attaching the generated image. the following target group for advertising was selected: women of the age groups 18-25, 35-40, 35-45, 40-45, and 45-50 (see Figure 11). Note that the marketer can, after receiving the results, conduct a repeat advertising campaign and check their quality. In rerun ads, the cost per conversion for video ads dropped from USD 0.75 to USD 0.15 for the period 1-15 May 2022, which is about 20% better than the original test ad. The average value of advertising effectiveness (click rate) CTR increased from 3.6 to 6.6, i.e., 83% better. Moreover, the target audience is chosen, and the content is created using two more methods: (i) the method of setting the advertising context and the target audience based on learning associative rules [43] and (ii) the intelligent method of creating advertising content based on semantic analysis. To implement the methods, surveys were conducted for 56 buyers of the product selected at the stage of intelligent selection of a competitive product. A set of rules was formed based on the developed method of setting the advertising context and the target audience, as well as the learning of associative rules (Figure 12). This made it possible to obtain advertising content (Table 2), which compares the effectiveness of the advertising content created on the basis of learning associative rules in the period from 1 May 2022 to 15 May 2022, with all variants of content with rules (see Figure 12). Based on the latter, the marketer creates an ad including the old initial Version 0. Figure 11: Selection of target audience based on decision trees.
# A Novel Method for Assessing Regional Tendon Stiffness and Its Significance
###### Abstract  
Elastography can be used to estimate the regional shear modulus of a tendon. This can advance our knowledge on the impact of patellar alignment and regional patellar tendon stiffness. This is important as patellar tendon abnormality is mainly found in the medial portion of the tendon in subjects with proximal patellar tendonopathy. This paper aims to assess the effect of patellar displacement on differential modulation on the shear modulus of the patellar tendon. Shear modulus is captured on the medial and lateral half of the patella tendon using the Axiplorer(r) ultrasound unit in conjunction with a 4-15 MHz, 50 mm linear transducer with the patellar being positioned in its resting, medio- and laterally displaced positions on 40 adults (19 females, 21 males). When the patellar is displaced laterally, the shear modulus is significantly increased at the medial half in both genders but decreased at the lateral half only in females. Conclusions: Elastography detects changes in regional tendon stiffness associated with alteration in patellar positions. The modulation on the shear modulus is gender and region specific.  
Keywords:supersonic shear imaging; shear modulus; tendon stiffness; patellar positions; patellar tendon +
Footnote †: journal: Applied Sciences
## 3 General Opportunities, Challenges, and Applications
### Opportunities  
Federated learning allows model training across multiple devices while keeping data local. This decentralized approach offers major benefits in data privacy and training efficiency, making FL valuable for various industries. Research highlights FL's importance in machine learning and data science [11; 12]. Experts like [PERSON] in [13] stress the need to address issues like data handling, communication costs, and security.
## 3 Influencing Factors and Data Preprocessing
### Data Preprocessing  
Because the magnitudes of the selected predictors are not the same, the min-max normalization method was used to normalize the sample matrix, with values of 0.4 and 0.6 for fully mechanized mining and fully mechanized top caving, respectively [10]. The formula is as follows:  
\[y_{i}=\frac{x_{i}-x_{\rm min}}{x_{max}-x_{\rm min}} \tag{1}\]where \(y_{i}\) is the normalized data, \(I=1\), 2..., \(n\), and \(x_{min}\) and \(x_{max}\) are the minimum and maximum values of the factors, respectively.  
According to the selected predictors, the measured heights of WFFZ in the mining of several Jurassic coal seams in the Ordos Basin were collected, The Inner Mongolia-Shaanxi border area mining area was taken as the research object, the mining parameters of 18 working faces were analyzed in detail. The mining methods mainly include fully mechanized mining and fully-mechanized top coal caving. The thickness ratio of hard rock and soft rock in bedrock ranges from 1.45 to 3.56, the mining height ranges from 2.75 to 10.1 m, and the burial depth ranges from 248 to 692 m. The length of the working face is between 90 and 360 m, and the measured height of the WFFZ is between 45.6 and 245.51 m. The five selected influencing factors and the measured height data of the WFFZ were normalized, respectively, and the processed data are between 0 and 1, and the deep learning sample data and normalized values were established (Table 1).
# Wheel Flats in the Dynamic Behavior of Ballasted and Slab Railway Tracks
## 2 Dynamic Models for Coupled Vehicle-Track Systems
### Track Model  
#### 2.1.1 Ballasted Track  
A ballasted rail track is composed of the rails, pads, sleepers, ballast, and sub-ballast placed on a foundation. In the 2D calculations, the rail was modeled by Euler-Bernoulli beam elements discretely supported on sleepers. The Timoshenko beam element may also be used, but according to several studies, this type of element does not influence the track response for the frequency range of the analysis. In the present study, the pad placed between the rail and the sleeper was represented by a spring in parallel with a dashpot with linear behavior, which is a simplified approach, as pads and granular materials present nonlinear material behavior. This nonlinearity was not contemplated in this research, because combining the material nonlinearity with the dynamic calculations increases the complexity of the problem, and it is very time consuming. The sleepers, usually spaced on ballasted tracks at 0.60 m, were simulated by punctual masses. In 2D ballasted tracks models, the granular materials may be represented by discrete or continuous models. Discrete modeling has been adopted by several authors such as [20; 21; 22; 23], among others. Continuous models of the granular layers are defined by finite plane stress elements to simulate the longitudinal continuity of the layers. This approach was used by [24; 25; 26; 27; 28]. In the present research, the continuous model as presented in Figure 1 was used, because the continuity of granular layers plays an important role in the dynamic behavior of the track.  
In Figure 1, as well as in the whole paper, the parameters are: \(E\), elasticity modulus; \(I\), the moment of inertia of the cross-section of the rail; \(\
u\), Poisson ratio; \(A_{\rm t}\), area of the cross-section of the rail; \(m_{\rm r}\), mass of the rail per meter; \(k_{\rm p}\), stiffness of the pad; \(c_{\rm p}\), damping of the pad; \(m\), sleepers' mass; \(e\), spacing between consecutive sleepers; \(h_{\rm b}\), thickness of the ballast layer; \(h_{\rm sb}\), thickness of the sub-ballast layer; \(k_{\rm f}\), stiffness of the foundation; \(c_{\rm f}\), damping of the foundation.
## 3 Results and Discussion
### GridMask Data Augmentation Results
The combined use of these techniques minimizes false detections and strengthens the recognition of diverse behaviors across various environmental conditions, resulting in clear and well-defined object detections. It not only demonstrates the efficiency of the combined augmentation strategies but also highlights the potential of our proposed model to reliably capture subtle cattle behaviors, offering a promising tool for applications in precision livestock farming. [PERSON] et al. [8] utilized tail-mounted accelerometers to classify cattle behavior, achieving a _sensitivity (recall)_ and _precision_ of over 86.7%. [PERSON] et al. [9] classified cattle behavior using accelerometer necklaces, achieving an average F1 score of 0.82. However, our study significantly differs from these approaches as we only used 2D RGB camera data and did not attach invasive sensors to the cattle. This approach not only simplifies the data collection process but also potentially reduces stress on the cattle. This study achieved a 93% _mAP_, demonstrating the efficiency of using 2D RGB camera-based observations for high-accuracy cattle behavior detection. This study proposed a cattle behavior detection model based on YOLOv7-E6E. Cattle behavior data were collected using 2D RGB cameras, and the model performance was enhanced by training it on the original data augmented through AutoAugment. Additionally, training on data augmented through GridMask decreased _precision_ by 0.5% compared to the original dataset but increased the _recall_ and _mAP_ by 9.2% and 4.8%, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Behavior** & _Precision (\%)_ & _Recall (\%)_ & _mAP (\%)_ \\ \hline Resting & 92.4 & 93.8 & 95.0 \\ Communication & 82.1 & 86.7 & 90.1 \\ Feeding & 84.5 & 77.4 & 83.2 \\ Drinking & 96.2 & 98.8 & 98.9 \\ Eating & 83.8 & 97.5 & 97.7 \\ All & 87.8 & 90.8 & 93.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of the cattle behavior detection model trained on data augmented using AutoAugment and GridMask.
## 3 Results
### Characterization of MG-Loaded Mesoporous Silica  
#### 3.2.1 Textural Properties  
The textural properties of the obtained silica systems were characterized by N\({}_{2}\)-sorption measurements. The uptake and release of drug molecules generally depend on the specific surface area, total pore volume and pore diameter of the mesoporous silica materials [27; 30; 41]. Basically, the reason for using such measurements is to investigate any modification of the textural properties of the porous silica material. Figure 1a,b shows the recorded isotherms and the corresponding pore size distribution curves for the necked mesoporous SBA-15 silica (C), APTES-functionalized mesoporous SBA-15 silica (Cf), and magnolol-loaded APTES-functionalized mesoporous SBA-15 silica material (Cf-MG).  
According to the International Union of Pure and Applied Chemistry (IUPAC) classification, all four adsorption-desorption isotherms are of type IV, with a well-defined capillary condensation stage in which the relative pressure of 0.6-0.8 occurs, and a steep hysteresis loop characteristic to mesoporous materials with pore sizes of 7-8 nm. It is easy to observe that functionalization has influenced both the specific surface area and pore diameter. The presence of magnolol molecules in the SBA-15 cylindrical pores is proved by the decrease of the adsorbed nitrogen volume, of the total pore volume and of the specific surface area.  
As can be seen, APTES and magnolol do not actually introduce a marked change in the shape of the isotherm or of the hysteresis loop. The functionalization step and the further loading of magnolol conduct for the pore narrowing noted in the pore size distribution patterns.  
The pore size distribution curves report on pore size homogeneity and are used for evaluating the diameters of the pores by applying the BJH model. Figure 1a shows that all porous systems are characterized by a narrow pore size distribution, with the mean pore diameter of 8.3 for the necked SBA-15 silica and only 7.2 nm for the functionalized magnolol-loaded silica. As expected, 65% of the specific surface area was lost in the case of magnolol-loaded APTES-functionalized mesoporous SBA-15 silica material, while, with the APTES-functionalized mesoporous SBA-15 silica, the loss is 55% and for the magnolol-loaded unfunctionalized silica C-MG, 47%. A 47% reduction of the total pore volume occurred for sample Cf and 55% for the Cf-MG system, while, in the case of sample C-MG, the reduction was of about 25% only. All calculated textural features are presented in Table 1.
# Xanthone Derivatives and Their Potential Usage in the Treatment of Telangiectasia and Rosacea
## 2 Xanthone Derivatives and Their Vascular Activity
### Sealing Blood Vessel Activity
#### 2.1.4 Diabetic Vascular Complications  
Telangiectasia can be also caused by diabetic vascular changes, often followed by microangiopathy.  
Diabetic vascular complications represent a significant cause of mortality and morbidity in individuals with diabetes, particularly those with type 2 diabetes. These complications are marked by endothelial dysfunction, with impaired NO production playing a central role. NO is crucially synthesized in vascular endothelial cells through the activation of endothelial nitric oxide synthase (eNOS). However, in diabetes, eNOS activity may be compromised due to the accumulation of ceramide. Recent studies have highlighted the potential anti-diabetic properties of \(\alpha\)-magnostin [37].  
Research demonstrates that \(\alpha\)-magnostin ameliorates impaired endothelium-dependent vasodilation (EDV) in diabetic animals by modulating the aSMase/ceramide pathway and enhancing the eNOS/NO pathway in aortic tissues. Additionally, \(\alpha\)-magnostin inhibited the elevated aSMase/ceramide pathway and restored EDV in isolated mouse aortas exposed to high-glucose conditions. Moreover, \(\alpha\)-magnostin increased eNOS phosphorylation and NO production in aortic tissues treated with high glucose. Furthermore, \(\alpha\)-magnostin normalized the activation of the aSMase/ceramide pathway and improved the eNOS/NO pathway in endothelial cells exposed to high glucose concentrations. Overall, studies revealed that \(\alpha\)-magnostin regulates the eNOS/NO pathway and enhances EDV in diabetic mice by inhibiting aSMase activity and reducing endogenous ceramide accumulation. These results provide novel insights into the therapeutic potential of \(\alpha\)-magnostin in alleviating endothelial dysfunction associated with diabetes. Additionally, these findings expand upon previous research demonstrating the beneficial effects of inhibiting ceramide synthesis on arterial function in diabetic animals and reversing NO levels in endothelial cells exposed to high glucose [38; 39].  
Figure 5: Davitin A (1,8-dihydroxy-2,5,6-trimethoxyxanthone) structure.  
Figure 6: Structure of \(\gamma\)-magnostin (1,3,6,7-tetrahydrooxy-2,8-bis(3-methylbut-2-en-1-yl)-xanthone).
# Modeling of Key Specifications for RF Amplifiers Using the Extreme Learning Machine
## 4 Modeling Results and Discussion
### 50-450 MHz CMOS LNA  
#### 4.2.1 S21  
The modeling results of S21 of LNA are shown in Figure 7. Figure 7a,b show the modeling results with simulation data and without simulation data. First, in terms of the temperature characteristics of S21, S21 of LNA decreases with the increase in temperature. This phenomenon is mainly because the rise in temperature causes the carrier mobility to decrease and thus the trans-conductivity to decrease. It can also be noted that when the temperature change is the same at different frequencies, the magnitude of S21 change is not the same. For example, when the temperature increases from \(-40\)\({}^{\circ}\)C to \(90\)\({}^{\circ}\)C, the S21 distribution at 70 MHz and 450 MHz decreases by 3.54 dB and 3.02 dB. This also shows that the carrier mobility is temperature-dependent and frequency-dependent. Second, in terms of the model's accuracy, it is evident that the model with simulation data agrees well with the actual measurement results, while the model without simulation data has a significant deviation from the actual measurement results. This indicates that a better modeling result can be achieved using the simulation data with the same number of measurement points and a smaller number of measurement points.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Number and Distribution of Measured**} & \multicolumn{2}{c}{**MSE**} \\ \multicolumn{2}{c}{**Temperature Points**} & \multicolumn{2}{c}{**} \\ \hline
**No.** & **Temperature (\({}^{\circ}\)C)** & **This Work** & **Ref. [19]** \\ \hline
6 & \(-40\); \(-10\); \(15\); \(40\); \(65\); \(90\) & \(8.5972\times 10^{-4}\) & \(9.6521\times 10^{-2}\) \\ \hline
6 & \(-40\); \(-20\); \(0\); \(70\); \(80\); \(90\) & \(8.6113\times 10^{-4}\) & \(2.8317\times 10^{-1}\) \\ \hline
7 & \(-40\); \(-20\); \(0\); \(25\); \(50\); \(70\); \(90\) & \(8.3821\times 10^{-4}\) & \(9.4621\times 10^{-2}\) \\ \hline
7 & \(-40\); \(-30\); \(-20\); \(50\); \(60\); \(75\); \(90\) & \(8.4981\times 10^{-4}\) & \(2.5237\times 10^{-1}\) \\ \hline
8 & \(-40\); \(-30\); \(-10\); \(10\); \(30\); \(50\); \(70\); \(90\) & \(8.1681\times 10^{-5}\) & \(9.2386\times 10^{-3}\) \\ \hline
8 & \(-40\); \(-35\); \(-5\); \(15\); \(20\); \(25\); \(85\); \(90\) & \(8.2234\times 10^{-5}\) & \(2.3025\times 10^{-2}\) \\ \hline
9 & \(-40\); \(-25\); \(-10\); \(5\); \(25\); \(45\); \(60\); \(75\); \(90\) & \(7.8475\times 10^{-5}\) & \(9.0274\times 10^{-3}\) \\ \hline
9 & \(-40\); \(-5\); \(10\); \(15\); \(35\); \(60\); \(65\); \(85\); \(90\) & \(8.0521\times 10^{-5}\) & \(2.0679\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 7: S21 of the LNA: (a) with simulation; (b) without simulation.
## 2 Materials and Methods
### Positive Matrix Factorization (PMF)
#### 2.3.2 Input Data Pre-Treatment and Model Runs  
The available data from the measurement campaigns were 16 trace elements (Al, As, Ba, Cd, Co, Cr, Cs, Cu, Fe, Mn, Ni, Pb, Sr, Tl, V and Zn), 6 water-soluble anions (Cl\({}^{-}\), NO\({}_{3}\)\({}^{-}\), NO\({}_{2}\)\({}^{-}\), Br\({}^{-}\), PO\({}_{4}\)\({}^{3-}\) and SO\({}_{4}\)\({}^{2-}\)) and elemental and organic carbon (EC, OC). As, Ba, Co, Cs, Ni, Sr, Tl, V, Zn, NO\({}_{2}\)\({}^{-}\), Br\({}^{-}\) and PO\({}_{4}\)\({}^{3-}\) were set as \"bad\" species and excluded from the analysis either due to the high percentage of missing values or their very low signal/noise ratios (<0.2). NO\({}_{3}\)\({}^{-}\), Cu and Cr were set as \"weak species\" due to their low signal/noise ratios (S/N) or/and poorly scaled residuals (d-matrix). PM\({}_{10}\) mass concentration was set as the total variable. Concentration data lower than the detection limit were substituted with one-half of the detection limit, while missing data were substituted with the median value for the specific species [57]. Similarly, uncertainty for data below the detection limit was set equal to 5/6 of the detection limit, while uncertainty for missing data was set equal to 4 times the median concentration. The modeling extra uncertainty was adjusted to 14%.  
Therefore, an input matrix of 50 samples and 12 chemical species was inserted in the model, while 30 runs were performed for each testing case, for obtaining Q-value stability. All runs converged, and Q values ranged between \(\pm\)1.2%. The Q-robust value was lower than 1.5 times the Q-true value for each case, signifying that the impact of the outliers on Q value was not considerable. The optimal number of factors was determined by examining the Q values for PMF solutions resulting from a range of the \"number of factors\" values without excluding the solution's physical validity. A range of solutions was examined with different numbers of factors (3-10), but six was the maximum number of factors corresponding to meaningful sources. In fact, when the PMF factors number increased from the optimal, a number of factor profiles were split to profiles with no physical meaning, while the rotational instability of the solution increased significantly. Finally, bootstrap and displacement runs were used for estimating the effect of random errors and the rotational ambiguity of a dataset. In our study, two constraints needed to be applied with the limitation of dQ% being kept at the lowest value of 0.5%: (i) Al: pull up maximally in \"resuspension\" factor, and (ii) SO\({}_{4}\)\({}^{2-}\): pull up maximally in \"secondary sulfate\" factor. Compared to the initial results, no substantial changes to either the constrained profiles or the constrained contributions were observed, amplifying the accuracy of the selected solution.
## 4 Results and Validation  
In this section, surface area time series based on our new approach are presented and validated. Since no in situ measurements for surface areas are available, we compare our results with water level time series as both have a linear or monotone relationship depending on the surrounding topography. Primarily, we are use water level time series from in situ stations for validation. But in remote areas where no in situ data is available, we are use water level time series from satellite altimetry which are available at the DAHITI ([[http://dahiti.dgfi.tum.de](http://dahiti.dgfi.tum.de)]([http://dahiti.dgfi.tum.de](http://dahiti.dgfi.tum.de))). Since a global validation is not possible, we focus on 32 selected study areas with different characteristics introduced in Section 4.1. Then, three of the study areas (Ray Roberts, Poco da Cruz, Tharthar) are presented and validated in detail followed by a quality assessment of all 32 investigated inland water bodies.
## 5 Numerical Simulation and Validation Analysis
Furthermore, under uniform random disturbances, the LADRC-SMC scheme reduces the peak contact-force error rate by more than tenfold while ensuring effective tracking of the joint angles. Ensuring consistency between the parameters and interference conditions of the comparison scheme is a prerequisite for analyzing the comparison conclusion. The experimental results of the LADRC-SMC scheme demonstrate that innovative research combining different control methods is beneficial for exploring the advantages of their combination. The LADR-SMC ensures system stability, robustness, and disturbance rejection while achieving system tracking. Compared with the SMC, the LADR-SMC effectively addresses the issue of poor contact-force tracking under uniform random disturbances and guarantees joint-angle tracking (Compare References [16; 19] in the introduction). However, performance degradation may occur in the LADR-SMC in practical scenarios owing to system delays. To investigate and address this issue, we conduct a simulation comparison of the third set of schemes in Section 5.3. Figure 11: Comparison of contact-force tracking error between LADRC-SMC and SMC schemes under random disturbance. Figure 10: Contact-force tracking of LADRC-SMC and SMC schemes under random disturbance.