esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method Application
### Pintion Cutter--Slotting Case
#### 3.2.1 Analytical Modeling  
The reference systems are defined, in correlation with Figure 8, as follows:  
- \(x_{0}y_{0}z_{0}\) is a fixed system solidary with the \(z_{0}\)-axis of the pinion cutter model;  
- \(x_{1}y_{1}z_{1}\) is a fixed system attached to the \(z_{1}\)-axis of the generated gear;  
- \(x_{2}y_{2}z_{2}\) is a fixed system attached to the \(z_{2}\)-axis of the generated gear rim.  
Figure 8: Running centrodes and reference systems [27].  
The fixed systems have parallel axes that are oriented in the same direction. The distances between the \(z_{0}\), \(z_{1}\), and \(z_{2}\) axes are defined by the \(A_{12\text{-}1}\) and \(A_{12\text{-}2}\) quantities, respectively. At the same time, mobile reference systems are also defined:  
* \(\xi\eta\zeta\), originally overlapped with \(x_{0}y_{0}z_{0}\), with the center in \(O_{1}\), in solidarity with the centrode of the pinion cutter--\(C\).
* \(X_{1}Y_{1}Z_{1}\), originally overlapped with the \(x_{1}y_{1}z_{1}\) system, where \(Z_{1}\) is the gear axis.
* \(X_{2}Y_{2}Z_{2}\), originally overlapped with the \(x_{2}y_{2}z_{2}\) system, where \(Z_{2}\) is the gear rim axis. Thus, the running condition is as follows:  
\[R_{rt}\cdot\varphi=R_{rp1}\cdot\varphi_{1}. \tag{9}\]  
The matrix equation of the relative motion of the pinion cutter, solidary with the system \(\xi\eta\zeta\), with respect to \(X_{1}Y_{1}Z_{1}\), is as follows:  
\[X_{1}=\omega_{3}(-\varphi_{1})\cdot\bigg{[}\omega_{3}^{T}\cdot\xi+\begin{pmatrix} A_{12-1}-k\cdot\varphi_{1}\\ 0\end{pmatrix}\bigg{]}. \tag{10}\]  
In Equations (9) and (10), \(\varphi\) and \(\varphi_{1}\) are angular parameters in the rotational motion of the pinion cutter and the generated workpiece.  
Transforming and replacing and, at the same time, considering that the relationship between the angular parameters \(\varphi\) and \(\varphi_{1}\) is given by the rolling condition of the pinion cutter and workpiece centrodes, the increment \(\Delta\varphi_{1}\) was determined as the size of the workpiece rotation angle corresponding to a double stroke of the tool:  
\[\Delta\varphi_{1}=s_{c}\cdot\frac{n_{cd}}{R_{rp1}}, \tag{11}\]  
where \(s_{c}\) is the circular feed, \(n_{cd}\) is the number of double strokes of the pinion cutter and \(Rrp_{1}\) is the rolling radius of the workpiece centrode.
## 3 Model Development and Analysis  
### Global Solar Radiation  
A surface can be irradiated by the following three sources:  
* Direct irradiation (\(\rm{G_{D}}\))
* Diffuse irradiation (\(\rm{G_{d\theta}}\))
* Irradiation reflected from the ground (\(\rm{G_{R}}\))  
The total irradiation on a surface is therefore given by  
\[\rm{G_{t}}=\rm{G_{D}}+\rm{G_{d\theta}}+\rm{G_{R}} \tag{2}\]  
To calculate the beam energy falling on a surface of any orientation, it is necessary to convert the incoming beam flux from the direction of the sun to the direction normal to the surface.  
Direct irradiation on a surface of any orientation can be deduced from the direct irradiation on a surface normal to the sun's rays called direct normal irradiance (DNI) denoted by (\(\rm{G_{ND}}\)).  
\[\rm{G_{D}}=\rm{G_{ND}cos}\theta \tag{3}\]  
\[\rm{G_{ND}}=\rm{G_{d}}\times\frac{\rm{A}}{\rm{exp}\Big{(}\frac{\rm{B}}{\rm{ sin}\beta}\Big{)}} \tag{4}\]  
A: apparent solar irradiation at air mass equal to zero (\(\rm{W/m^{2}}\))  
B: atmospheric extinction coefficient, the radiant energy absorption by the particles and gases in the sky.  
\(\rm{\beta}\): Solar elevation (altitude) angle  
\[\rm{G_{d\theta}}=\rm{G_{d}}\times\rm{F_{sur-sky}} \tag{5}\]  
Figure 3: Solar hybrid system.  
\(\mathrm{G_{d}}=\mathrm{Diffuse}\) irradiation on a horizontal surface  
\[\mathrm{G_{d}}=\mathrm{CG_{ND}} \tag{6}\]  
\[\mathrm{F_{sur-sky}}=\frac{1+\cos\alpha}{2} \tag{7}\]  
The amount of radiation received due to reflection from the ground, \(\mathrm{G_{R}}\)  
\[\mathrm{G_{R}}=\mathrm{G_{th}}\times\rho_{g}\times F_{sur-g} \tag{8}\]  
\[F_{\mathrm{sur-g}}=\frac{1-\cos\alpha}{2} \tag{9}\]  
A, B, and C are constants whose values keep changing every month, as shown in Table 4. These values depend on the dust and water-vapor content of the atmosphere and on the changing in the earth--sun distance during the year. As the earth's axis is tilted relative to its orbital plane by 23.45\({}^{\circ}\), the positive angle indicates the earth is tilted towards the north, whereas the negative indicates towards the south, and zero when the axis is not tilted relative to its reference plane.
## 1 Introduction  
Atrial fibrillation (AF) is the most common arrhythmia associated with high morbidity and thrombo-embolism mortality [1]. At age 40 and older, AF lifetime risks increase for both men and women [2]. Previous studies have suggested that reduced flow velocity, i.e., flow stasis, in the left atria (LA) and LA appendage among subjects with paroxysmal AF in sinus rhythm can be an independent predictor of thrombus formation and stroke [3, 4, 5]. The complex 3-dimensional nature of LA flow can be explored using four-dimensional flow (4D-flow) by magnetic resonance imaging (MRI), which has proved to effectively assess LA stasis [6, 7, 8]. Furthermore, 4D-flow facilitates the visualization of flow patterns [9, 10], vortex formation [8], and other advanced hemodynamic biomarkers [9, 10]. In patients where rate control and anti-arrhythmic drugs are insufficient, pulmonary vein (PV) ablation therapy is used to maintain sinus rhythm. Therefore, the assessment of LA stasis changes due to ablation therapy requires further exploration.  
This study aims to demonstrate that LA stasis, derived from 4D-flow, is a useful biomarker of LA recovery in patients with AF undergoing PV ablation. Our hypothesis is that LA recovery will be associated with a reduction in LA stasis.
# COX2-Inhibitory and Cytotoxic Activities of Phytoconstituents of _Matricaria chamomilla_ L.
## 3 Results
### Cytotoxicity against Tumor Cells In Vitro  
Since there is a mechanistic link between inflammation and carcinogenesis [52], we were interested in exploring the COX2-inhibitory activity of the selected compounds. The cytotoxicity of the selected compounds towards 60 cell lines of different tumor origins (leukemia, melanoma, brain tumors, carcinoma of the colon, ovary, breast, kidney, lung, or prostate) determined by a sulforhodamine B assay was mined in the NCI database (dtp.cancer.gov; accessed on 1 July 2023). The responsiveness of these cell lines is plotted as mean log\({}_{10}\)IC\({}_{50}\) values for each tumor type in Figure 5. \(\beta\)-Sitosterol and daucosterol were minimally active against the tumor cell lines, while apigenin, myricetin, and farnesol were most active (Figure 5A). Celecoxib, the control drug, was the most active. \(\beta\)-Amyrin was not included in the NCI database. Therefore, we detected the cytotoxicity of this compound in CCRF-CEM cells which are also included in the NCI cell line panel and found that this compound did not affect these tumor cells. Since we previously investigated apigenin and farnesol in the NCI cell line panel [53; 54; 55; 56], we focused on myricetin in the present study. The inhibitory activity of myricetin against the tumor cells shown in Figure 5B demonstrated that this compound was most active against colon and lung cancer cell lines. For comparison, celecoxib as a control drug was most active against leukemia and prostate cancer cell lines (Figure 5C).  
Figure 4: (**A**). The percentage of the rest activity of COX2 after treatment with apigenin, \(\beta\)-amyrin, \(\beta\)-eudesmol, \(\beta\)-sitosterol, daucosterol, farnesol, and myricetin at 0.1 and 1 \(\mu\)M. (**B**,**C**). Pearson correlation of COX2 rest activity (%) at 0.1 and 1 \(\mu\)M treatment, respectively, vs. low binding energy (kcal/mol). Blue points represent COX2 activity in vitro vs. lowest binding energy (LBE in silico. Red line represents linear regression of blue points.
## 7 Equation Calculation Results and Analysis  
### Mortar Air Void Content Results  
The expressway pavement consisted of an AC13 layer as the surface layer, an AC20 layer as the middle layer and an AC25 layer as the bottom layer, and the mixture's air void contents were 4%, 4.5% and 4.2%, like the commonly used mixture mentioned before. When designing the air void content of the mortar, we first determined the volume in this mortar with a certain mixture type, mixture air void content and mortar NMPS. The air void content of the mortar may larger or smaller than that of the mixture, because the total mortar volume consists of only some part of the whole mixture. For example, the calculation for the mortar with an NMPS of 2.36 mm, the void percent is 48.1%, 40.5% and 35.1%. The \(P_{a}\) values are 33.1%, 40.5% and 35.1%; \(\gamma_{se-r}\) are 2.74, 2.71 and 2.71 g/cm\({}^{3}\); \(P_{b-r}\) are 12.8%, 13.6% and 12.0%; \(\gamma_{b}\) is 1.03 g/cm\({}^{3}\). For example, the air void content of the AC-13 mortar with an NMPS of 2.36 mm is:  
\[VV_{AC-13-2.36}=\frac{4\%\times 48.1\%}{\left(4\%\times 48.1\%+\frac{33.1}{2. 74}+\frac{12.8}{1.03}\right)}\times 100=4.8\;(\%)\]  
Using the same method to calculate the other mortars with different NMPS and mixture types, the results are shown in Figure 8. From Figure 8, the air void content of the AC-13 mortar is smaller than that of AC-20 and AC-25 with a certain NMPS. The air void content of the AC-13 mortar increases with an increasing NMPS.  
With this calculation method, a more accurate air void content of the mortar can be obtained. From Figure 4, the air void content of a 0.6 mm mortar is approximately 1% and that of a 4.75 mm is approximately 8%. Different NMPS have a much smaller effect on the air void content than mortar NMPS. With the increase in the mortar NMPS, the mortar air void content increases at the same time.
# Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
## 4 Proposed Fuzzy Logic Dijkstra-based Routing Algorithm  
The proposed algorithm comprises four main phases: (1) network deployment, (2) cluster formation, (3) CH selection, and (4) data transmission phase. The phases are explained in detail below.
## 2 Materials and Methods
### Methodology
#### 2.3.4 Hot Spot Analysis  
Prior to the implementation of hot spot analysis, the NPP changes of vegetation in Beijing from 2000 to 2020 has used Moran's Index to analyze spatial autocorrelation (Figure 3). The result shows that it has the characteristics of spatial clustering, which can be carried out for spatial cold hot spot analysis.  
Hot spot and cold spot analysis is performed to identify the states having the high or low values of cluster spatially by Getis-Ord Gi* statistic. The local sum for a feature in the neighborhood of a grid cell is compared proportionally to the sum of all features using Getis-Ord Gi* statistic which can obtain the Z-score [48]. For statistically significant, the positive and larger Z-score indicated the more intense the clustering of high values (hot spot) and negative and the smaller the Z-score signified the more intense the clustering of low values (cold spot) [49; 50]. The formula is as follows:  
\[G_{i}^{*}(d)=\frac{\sum_{j=1}^{n}W_{ij}(d)X_{j}}{\sum_{j=1}^{n}X_{j}} \tag{15}\]  
Figure 3: Spatial autocorrelation of vegetation NPP in Beijing from 2000 to 2020.  
\[Z(G_{i}^{*})=\frac{G_{i}^{*}-E\big{(}G_{i}^{*}\big{)}}{\sqrt{Var\big{(}G_{i}^{*} \big{)}}} \tag{16}\]  
where \(X_{j}\) is the attribute value for feature \(j\); \(W_{ij}(a)\) is the spatial weight between feature \(i\) and \(j\); \(E\big{(}G_{i}^{*}\big{)}\) and \(Var\big{(}G_{i}^{*}\big{)}\) is the mathematical expectation and coefficient of variation of \(G_{i}^{*}\), respectively. If \(Z\big{(}G_{i}^{*}\big{)}>0\) and significant, it indicates that the value around the research area is higher than the mean value and the area belongs to the hot zone. On the other hand, the value around the research area is low and the area belongs to the cold spot area.
## 5 Discussion
### Limitation  
While this study achieved certain results in evaluating the aging-friendliness of rest facilities in scenic areas, several limitations warrant further discussion.  
Due to research conditions and time limitations, the field observation data were primarily collected during weekends in November and December 2024. Although multi-period monitoring and multi-source data validation ensured representativeness and generalizability, the study did not achieve the long-term continuous monitoring of the scenic area. The acquired data were limited in both temporal and spatial dimensions, lacking coverage of different periods such as peak/off-peak tourist seasons and holidays. Additionally, all observations were conducted under clear weather conditions, without considering the impact of rain, snow, strong winds, extreme heat, or cold on visitors' rest facility usage behaviors. Consequently, the identified aging-friendly facility adequacy rates only reflected conditions during specific timeframes and weather scenarios, potentially failing to fully capture dynamic variations in facility usage across different seasons and climatic conditions.  
Although computer vision algorithms demonstrated high accuracy in facility usage recognition and age classification, several practical challenges remained. Occlusion in complex crowd interactions, specific recognition requirements for special populations (e.g., individuals with disabilities), and variable lighting conditions in outdoor environments all influence recognition accuracy to varying degrees. These technical limitations may introduce biases in actual usage assessments, particularly during peak hours or under adverse weather conditions.  
Due to research constraints, only three rest areas within the scenic site were selected for analysis. While these locations were typical and representative, they did not cover all critical nodes, such as transportation hubs and rest areas near restrooms, where usage patterns may differ. This sampling limitation may affect the generalizability of the findings. Furthermore, the study focused solely on seating facilities, excluding other aging-friendly amenities such as shade structures, accessible pathways, and drinking fountains, thereby limiting the comprehensiveness and systematicity of the conclusions. Additionally, the research primarily examined static facility configurations, with an insufficient analysis of dynamic behavioral patterns such as temporal fluctuations in visitor flow and durations of stays.  
Future research will address these limitations by establishing enhanced long-term monitoring systems, incorporating more precise recognition algorithms, and introducing multidimensional evaluation metrics. These improvements aim to develop a more comprehensive and accurate assessment framework for aging-friendly facilities in scenic areas, thereby providing stronger scientific support for optimizing public spaces in an aging society.
# Efficient Removal Of U(VI) Ions from Aqueous Solutions by Tannic Acid/Graphene Oxide Composites
## 3 Results
### Effect of Solid Content  
Solid content is an important factor for uranium adsorption. The use of a small amount of adsorbent can achieve a considerable adsorption percentage, resulting in a low adsorption cost [15]. The effects of different solid contents of GO and TA/GO are displayed in Figure 5. In comparison to GO, the adsorption percentage of TA/GO was significantly higher at the same solid content (m/V \(<\) 2.0 g/L), indicating the improved uranium adsorption capacity of TA/GO composites because of its higher number of oxygen-containing functional groups. When the solid content of TA/GO increased from 0.2 g-L\({}^{-1}\) to 2.5 g-L\({}^{-1}\), the adsorption percentage of uranium increased from \(\sim\)20% to \(\sim\)96%. It can be ascribed to the increase of the content of available functional groups in TA/GO; therefore, the available adsorption sites for uranium also increased.
# DSA-SOLO: Double Split Attention SOLO for Side-Scan Sonar Target Segmentation
## 4 Experimental Results
### Comparative Experiments  
In order to demonstrate the superiority of DSA-SOLO, we performed quantitative and qualitative comparisons on the SCTD dataset and compared with other instance segmentation methods, such as Mask R-CNN [19], YOLACT [23], Polar Mask [24], and SOLO [25].  
Firstly, this paper compares the effects of training losses on the validation set of DSA-SOLO, Mask R-CNN, YOLACT, Polar Mask, and SOLO. As shown in Figure 5, the training loss of DSA-SOLO is significantly lower than other instance segmentation methods, and the training effects (mAP) of DSA-SOLO are the best. Thus, it can be seen that on the SCTD dataset, our proposed DSA-SOLO has better feature extraction ability and better segmentation precision.  
Secondly, to assess the segmentation performance, this paper compares the above methods with the indexes mentioned in Section 4.2, i.e., mAP5.95, mAP5, mAP75, and FPS. We record the quantitative results in Table 3. From Table 3, we can determine that DSA-SOLO achieves the best results in mAP5.95, mAP5, and mAP75. Specifically, for mAP5, DSA-SOLO is 13.3% higher than YOLACT, 9.8% higher than Polar Mask, 6.6% higher than Mask R-CNN, and 5.1% higher than SOLOv2.  
Figure 6 displays the segmentation results of different methods. As we can see in picture (a), the results of Mask R-CNN, YOLACT, Polar Mask, and SOLOv2 are incorrect and incomplete. Our DSA-SOLO can segment the target more accurately. In picture (b), all methods can segment the target accurately except YOLACT, and DSA-SOLO achieved the highest precision compared to other segmentation methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **mAP.5.95** & **mAP.5** & **mAP.75** & **FPS** \\ \hline Mask R-CNN & 37.8\% & 71.8\% & 31.6\% & 7.9 \\ YOLACT & 33.6\% & 65.7\% & 15.3\% & 13.13 \\ Polar Mask & 34.5\% & 68.6\% & 17.6\% & 11.97 \\ SOLOv2 & 40.0\% & 73.3\% & 35.8\% & **18.64** \\ DSA-SOLO & **42.7\%** & **78.4\%** & **43.2\%** & 18.14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative Results of Different Segmentation Models. The bold numbers represent the highest one of each indicator.  
Figure 5: Comparison of segmentation scores, i.e., training loss, and mAP5 between Mask R-CNN, YOLACT, Polar Mask, SOLO, and DSA-SOLO during the training phase. We set the training epoch as 100.
## 4 Materials and Methods
### Data Sources  
#### 4.2.1 Sample Data  
Grassland biomass data were collected mainly from field surveys during the growing season over the past ten years. The sample points were deployed in a representative and random manner, following the classification principles of grassland types, topographic and geomorphological differences, and the selection of sample plots with flat terrain and typical vegetation types based on the spatial distribution characteristics of different grassland types. Each sample plot was 100 \(\times\) 100 m, and a small sample square (1 m \(\times\) 1 m) was placed in each of its four corners and center positions, resulting in five sample squares. The latitude, longitude, elevation, grassland type, and fresh weight at each sampling point were recorded. AGB of grassland was measured and weighed using the flush mowing method, which included collecting samples and drying them in the laboratory at 65 \({}^{\circ}\)C for 48 h to constant weight before measuring their dry weights (accuracy of 0.01 g).
## 3 Design and Construction
### B-Field Collimation
In addition, alignment tolerances and edge inhomogeneity may also degrade performance, so we take a 0.15-inch diameter of collimated B-field as a more realistic minimum estimate.
# KDM Security IBE Based on LWE beyond Affine Functions
## 2 Related Works  
We categorized the related works on KDM-IBE schemes into two groups based on the challenge function ensemble: affine and few polynomial functions.  
_KDM-IBE scheme with respect to affine functions._ In 2012, [PERSON] and [PERSON] et al. [2] introduced KDM (key-dependent message) security into the IBE scheme for the first time, proposed the first lattice-based KDM-IBE scheme, and constructed a public key encryption system that satisfied KDM security based on the variant LWE assumption and the all-but-d trapdoor function. Finally, the scheme was proven to be KDM-selective ID CPA (KDM-sID-CPA)-secure under the standard model. However, the noise level with the use of this scheme increased following multiple rounds of encryption, and the efficiency of the scheme was insufficient. Its challenge function ensemble included the affine functions of the secret key, and the security level of the scheme was weak.  
In 2017, [PERSON] and [PERSON] et al. [10] selected a shorter public key based on [2] and used an all-but-d trapdoor function to prove the KDM-sID-CPA security of the scheme under the standard model. They introduced selective opening, or selected to open (SO) security, to obtain a more efficient KDM-IBE scheme with more security attributes; however, the scheme retained a complex structure and the ciphertext was not compact enough. The challenge function ensemble included the affine functions of the secret key, and the security level of the scheme was weak. The abovementioned two schemes, as well as [10; 11; 12; 13; 14; 15], presented this issue. These schemes obtained more secure schemes by improving different security attributes of the KDM-IBE scheme (such as master key and adaptive se  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **AP12 [2]** & **He17 [10]** & **Chen19 [16]** & **Ours** \\ \hline Challenge function & Affine functions & Affine functions & Few polynomial functions & Polynomial functions (even arbitrary functions) \\  & & & PUS+IO (with a complicated construction) & LWE \\ Anti-quantum attack & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) \\ \hline \hline \end{tabular} (\(\surd\) indicates that the function is supported, and \(\times\)\(\times\) indicates that the function is not supported).  
\end{table}
Table 1: Comparison with other major KDM-IBE schemes.  
curity functions); however, they did not solve the problem of the challenge function ensemble consisting of only affine functions, which is a \"weak\" secure property for an encryption scheme.  
_KDM-IBE scheme with respect to a few polynomial functions._ In 2019, [PERSON] et al. [16] constructed a KDM-IBE scheme with respect to polynomial functions based on IO and puncturable unique signatures. However, the scheme was complex in construction and only supported a few polynomial functions; the challenge function was still not advanced enough; and the underlying hard assumptions on which the scheme depended did not function well against quantum attacks.
## 2 Materials and Methods
### Characterization of Plasma Source: Power Measurement Setup  
The power dissipation at different locations inside the discharge were determined from the area underneath the voltage-charge plot according to [56; 57]. A dielectric quartz plate was placed at a distance d from 0 mm to 20 mm towards the tube edge. Behind the quartz, a copper electrode was connected via a capacity of 4.7 nF towards ground. The capacity was determined by measuring the setup capacity as being between 0.2 pF and 0.6 pF with a LCR meter (GW Instek LCR-8110G), rounded towards 1 pF to correct for uncertainty and multiplying with 1000 (\"rule of thumb\") [58]. An identical capacity was connected between the second ring electrode and ground. A voltage probe (PP023, Teledyne Lecroy) was placed each to determine the charge accumulation. Together with the high voltage measurement (P 6015A, Tektronix) and an oscilloscope (Waverunner 640 Zi, Teledyne Lecroy), a setup for power determination at different settings of working gas, distance and applied voltage was realized. Data acquisition was set on 50 averages to account for variances due to the wide range of operation voltage. The evaluation of the acquired data sets was performed via a Matlab script (R2012a Matlab, Mathworks Inc., Carlsbad, CA, USA) performing the polyarea function. A comparison with our previous Python scripts [57] were consistent.