esa-sceva/satcom-chunk-collection huggingface dataset:
# A Unified Theory for 3D Gear and Thread Metrology
## 3 3D Model of Helical Machine Elements
### Involute Gears  
Flanks of an involute gear are generated by a line tangent to the helix  
\[H(\psi)=\begin{pmatrix}r_{b}\cos(\varphi_{b}+\psi)\\ r_{b}\sin(\varphi_{b}+\psi)\\ \texttt{hand}\cdot\frac{1}{c}\psi\end{pmatrix} \tag{18}\]  
with  
\[c=\frac{\tan\beta_{b}}{r_{b}} \tag{19}\]  
through the point \((r_{b}\cos(\varphi_{b}),r_{b}\sin(\varphi_{b}),0)\)[37, 38]. Here, \(\beta_{b}\) denotes the base helix angle and \(r_{b}\) the base circle radius of the gear [24]. This implies that  
\[m=\texttt{hand}\cdot\frac{1}{cr_{b}}=\texttt{hand}\cdot\frac{1}{\tan\beta_{b}}, \tag{20}\]  
and hence from Equation (10) it follows that  
\[\vartheta_{\mathcal{P}}(r) = \frac{1}{r_{b}}\sqrt{r^{2}-r_{b}^{2}}-\arccos\left(\frac{r_{b}}{r}\right) \tag{21}\] \[= \text{inv}\Big{(}\arccos\left(\frac{r_{b}}{r}\right)\Big{)}\] (22) \[= \text{inv}(\alpha_{t}(r)), \tag{23}\]  
where \(\text{inv}(\alpha)=\tan(\alpha)-\alpha\) is the involute function and \(\alpha_{t}(r)=\arccos(\frac{\gamma_{b}}{r})\) the transverse pressure angle [24]. The flank surface is thus  
\[S_{\mathcal{P}}(r,z)=\begin{pmatrix}r\cos(\varphi_{b}+\texttt{hand}\cdot cz+ \texttt{flank}\cdot\text{inv}(\alpha_{t}(r)))\\ r\sin(\varphi_{b}+\texttt{hand}\cdot cz+\texttt{flank}\cdot\text{inv}(\alpha_{t }(r)))\\ z\end{pmatrix}. \tag{24}\]Note that in the case of gears the limit \(c\to 0\) results in spur gears. However, the flanks are then of course no longer ruled generalized helicoids.
# Durability Issues and Corrosion of Structural Materials and Systems in Farm Environment
## 4 Mitigation of Corrosion
### Sulfur Concrete  
Apart from fiber-reinforced polymers and additives (such as fly ash), the risk of corrosion and environmental degradation of concrete in agricultural environments can be mitigated by sulfur concrete [27], which is modified form of concrete which uses sulfur as a binder. Material characterization experiments have shown the superiority of sulfur concrete compared to traditional Portland cement concrete--the superiority is evident from the physic-mechanical and chemical properties. In addition, sulfur has a strong binding ability for all elements except for noble gases [27]. Therefore, it has the ability to form robust bonds with concrete and prevent corrosion and degradation by aqueous solutions. On the downside, sulfur concrete is only widely used in North America. In addition, the material is susceptible to corrosion, considering that sulfur is soluble in non-polar solvents. In addition, the durability of sulfur is dependent on the type of allotrope used-- there are at least 31 different S-allotropes [27]. According to research, the best S-allotrope for the formation of sulfur concrete is monoclinic because it is stable below 95 \({}^{\circ}\)C, which is within the ambient temperatures in farm environments. However, the durability of sulfur concrete is also influenced by the addition of talc, alumina, fly ash, and micro silica, as shown in Figure 6. Sulfur concrete infused with alumina had the least apparent porosity (-1.3 to 1.5), which reduced the dissolution effect of HCl, H\({}_{2}\)SO\({}_{4}\), and other corrosive agents [27].
## 4 Results and Discussion
### Experimental Results
In turn, this leads to differing skills for recording the small distinctions between KOA severity classifications. Thus, the applied CNN model shows varied performance in the different severity grades of KOA. * The image enhancement technique (image sharpening) improved the classification performance of five-class KOA severity grade, which helps to overcome the limitations of the earlier implementation of CNN with less accuracy and lower performance and produces robust and superior performance in comparison with earlier work.
# Farm Monitoring System with Drones and Optical Camera Communication
## 3 Proposed Scheme
### Variable Definition
#### 3.2.2 Variables  
The variables used in the proposed model are summarized in Table 1. Let \(\mathcal{I}\) denote the set of sensor nodes and \(i,j\) denote the identifier for them. The position of the \(i\)th sensor node in the global coordinate system is defined as \((x_{i},y_{i},z_{i})\). For simplicity, a sensor node is approximated as a sphere with radius \(r_{i}\). Let \((u_{i},v_{i})\) denote the coordinates of the center of the \(i\)th sensor node in the image plane. The horizontal and vertical resolutions of the image are \(l_{h}\) and \(l_{v}\), respectively. The horizontal and vertical angles of view of the camera are denoted as \(\phi_{h}\) and \(\phi_{v}\). The focal length of the camera is described as \(f\). The size of the image sensor is defined as \(\rho\).  
Figure 1: Conceptual system architecture (Orange arrows: drone trajectory)  
Figure 2: Block diagram of proposed system.
# Improving Throughput of Mobile Sensors via Certificateless Signature Supporting Batch Verification
## 4 Performance Analyses  
### Computational Costs  
The efficiency performance of the scheme was evaluated by comparing it with [PERSON]'s CLS [13] through simulation experiments. We use \(T_{add},T_{P},T_{mul},T_{e}\) to represent the time of a point addition, a pairing operation, a scalar multiplication in the elliptic curve group, and an exponential operation in the \(G_{T}\) group. \(G\) and \(Z_{p}\) represent the elliptic curve group and the group of integers that are modular to a prime number \(p\) without an explicit statement. The experimental environment and the results are shown as Table 1 and Table 2, respectively:
## 3 Results and Discussion
### Dynamics Studies  
Dynamic analysis was performed for measurements with optimal process parameters, that is, a bed height of 3 cm, flow rate for adsorption of Ag\({}^{+}\) at 2 cm\({}^{3}\)/min or for adsorption of Cu\({}^{2+}\) at 3 cm\({}^{3}\)/min, and initial concentrations of silver or copper ion solutions of 5000 mg/dm\({}^{3}\). For kinetic analysis of the adsorption process in the column, four kinetic models were used: Bohart-Adams, [PERSON], Yoon-Nelson and Clark. Table 7 presents values of the coefficients of equations and the degree of correspondence between the theoretical approximation and the experimental data. Figure 11 shows the course of curves and experimental points with the nonlinear regression of models. The [PERSON] model gave the best predictions of experimental behaviour for the adsorption of both silver and copper ions. Apparently, only slight differences from the other models were observed and the compatibility of models with experimental parameters was similar. On this basis, by analyzing only model parameters, it is difficult to obtain relevant information from the adsorption mechanism and the nature of the process. To choose or develop a suitable model, accuracy and convenience should be considered simultaneously. The dynamics of copper ion adsorption in a fixed-bed column system were investigated by [PERSON] et al. According to [PERSON] et al., the column experimental data were well-fitted with both the [PERSON] and the Yoon-Nelson models, which also suggested slight differences in models results [25]. The experimental research confirmed that the [PERSON]\(-\)[PERSON], [PERSON], and Yoon and Nelson models were equivalent mathematically: the breakthrough curves that describe the experimental points overlap [40].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Isotherm**} & \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Adsorbed Metal Ions**} \\ \cline{3-4}  & & \(Ag^{+}\) & \(Cu^{2+}\) \\ \hline \multirow{4}{*}{Bohart–Adams} & k\({}_{\text{BA}}\left(\text{dm}^{3}\text{min}^{-1}\text{ mg}^{-1}\right)\) & 0.0071 \(\pm\) 0.0003 & 0.021 \(\pm\) 0.004 \\  & N\({}_{0}\) (mg \(l^{-1}\)) & 0.51 \(\pm\) 0.005 & 0.50 \(\pm\) 0.04 \\  & R\({}_{\text{adjusted}}^{2}\) & 0.9729 & 0.9548 \\  & F & 649.8 & 277.2 \\ \hline \multirow{4}{*}{Clark} & A\({}_{\text{c}}\) & \(2.7\pm 0.3\) & \(2.3\pm 0.5\) \\  & r (min\({}^{-1}\)) & 0.015 \(\pm\) 0.001 & 0.12 \(\pm\) 0.0009 \\ \cline{1-1}  & R\({}_{\text{adjusted}}^{2}\) & 0.9943 & 0.9766 \\ \cline{1-1}  & F & 531.1 & 121.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The Bohart–[PERSON], [PERSON], Yoon–[PERSON] and [PERSON] model parameters using nonlinear regression analysis.  
Figure 10: Breakthrough curves for different flow rates of feed solution (C\({}_{0}\) = 4000 mg/dm\({}^{3}\), h = 6 cm): (a) Ag\({}^{+}\) adsorption, (b) Cu\({}^{2+}\) adsorption.
# Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia
## 5 Discussion
The distribution of training points is shown in the map at the HLS website ([PERSON], 2007). If this is the explanation, then it suggests that caution should be exercised before using a global adjustment for this purpose, and that, at the very least, such an adjustment should be tested for a particular region before accepting it as suitable. A good test of this hypothesis would be to use regional subsets of the global Hyperion spectra dataset to fit adjustments per region, and test how compatible they are. This is similar to the technique used by [PERSON] (2007) to test the variability across the Australian region. Another possible explanation for the difference between the adjustments fitted here and those fitted by HLS may be that they are based on different methods for transforming to surface reflectance. As discussed above, the most important factor is to use the same method for the two instruments, so that it does not add any apparent difference between instruments. However, if the adjustment factors fitted for transforming Sentinel-2 to Landsat are strongly dependent on the nature of the surface reflectance transformation used, this would also suggest that caution is required before using the HLS coefficients on any other data than that used by their project. In particular, when ESA settles on a standard surface reflectance algorithm for their Level 2 product, it may be unwise to apply the HLS coefficients to transform it to match Landsat-8. In order to test this hypothesis, it seems likely that careful experimentation with a number of different surface reflectance approaches would be required.
# SMILE: Segmentation-Based Centroid Matching for Image Rectification via Aligning Epipolar Lines
## 3 Methodology
### Selecting Object Pairs
The x-coordinate of the centroid, \(cx\), is calculated as  
\[cx=\frac{M_{10}}{M_{00}} \tag{5}\]  
The y-coordinate of the centroid, \(cy\), is similarly calculated as  
\[cy=\frac{M_{01}}{M_{00}} \tag{6}\]  
The process of selecting object pairs from two images is shown in Algorithm 1. ```
Two images \(I_{1}\) and \(I_{2}\) Result: Best matching object pairs for each image \(I_{i}\) (\(i=1,2\)) do  Detect objects using YOLOv9-seg model for each detected object \(k\)do  Extract segmentation mask \(M_{i}^{k}\)  Calculate moments \(M_{00}\), \(M_{10}\), \(M_{01}\)  Calculate centroid \(C_{i}^{k}=(M_{10}/M_{00},M_{01}/M_{00})\) endfor  Store centroids \(C_{i}\) and object classes \(L_{i}\) endfor  Initialize \(best\_pairs=None\), \(\theta_{min}=\infty\) for each pair of objects \((a,b)\) in \(I_{1}\) and \((c,d)\) in \(I_{2}\)do if\(L_{1}^{a}=L_{2}^{c}\) and \(L_{1}^{b}=L_{2}^{d}\) and positions are similar then  Calculate rotation angles \(\theta_{1},\theta_{2}\) if\(\max(|\theta_{1}|,|\theta_{2}|)<\theta_{min}\)then  Update \(\theta_{min}\) and \(best\_pairs\) endif endif endfor return\(best\_pairs\)
```  
**Algorithm 1** Object selection
# Comparing EEG-Based Epilepsy Diagnosis Using Neural Networks and Wavelet Transform
## 3 Methods and Materials  
### Data and Materials  
To test the proposed method in this study, epilepsy data from the Bonn University database was used. This database includes 5 different recording models, each one with 500 pieces of 100 points as shown in Figure 1 (a) non-epileptic recording with open eyes, (b) non-epileptic recording with closed eyes, (c) recordings of EEG from the hippocampal formation in the hemisphere opposite the epileptogenic zone, (d) EEG recordings of the epileptogenic zone, and finally (e) a collection of epileptic seizure activity recorded from the hippocampal focus.  
Figure 1: EEG patterns of different recording models which (**a**) is a non-epileptic recording with open eyes, (**b**) non-epileptic recording with closed eyes, (**c**) recordings of EEG from the hippocampal formation in the hemisphere opposite the epileptogenic zone, (**d**) comprised EEG recordings of the epileptogenic zone, and finally (**e**) a collection of epileptic seizure activity recorded from the hippocampal focus.  
The length of each recording is 23.6 s and the sampling frequency is 173.6 Hz. In this study, models B and D were used both with and without wavelet transform decomposition at a level of 8. The details of the database are summarized in Table 1. The database is open-access and available on the website of Bonn University.
# Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
## 3 Results  
### Relationships between GPP, ER and Remote Sensing Variables  
Daily mean EVI2 from Sentinel-2 showed a clear relationship with flux tower derived GPP, R\({}^{2}\) = 0.61 for all sites and years together (Figure 2a). Stronger relationships were found when studying each site and year separately, and differences in the relationship between GPP and EVI2 during the spring and autumn were also clearer at some sites (Figure S1a). These results indicate that it would be possible to use different regression models for the spring green-up and the autumn senescence, as the relationships seemed to be logarithmic and exponential, respectively, instead of linear. However, since it was not possible to define general functional relationships that could be used across all sites, we used linear relationships between EVI2 and GPP. In addition to EVI2, we used LST and W\({}_{s}\) as proxies for the environmental variables controlling GPP, reducing the tendency to underestimate high GPP and increasing the strength of the relationship (R\({}^{2}\) = 0.72, Figure 2b).  
There was also a strong exponential relationship between ER and LST, R\({}^{2}\) = 0.63 at all sites, and years together (Figure 3). Similarly to the relationship between EVI2 and GPP, some sites showed clear seasonal variation in the ER-LST relationship between the spring and autumn (Figure S1b).  
Figure 2: (**a**) Linear regression for EC-derived mean daily GPP against daily EVI2; (**b**) linear regression for EC-derived mean daily GPP against the product of EVI2, moisture scalar W\({}_{s}\), and daytime LST. All the sites and available years were included.
## 3 Results
### Quantitative Image Analysis  
We evaluate the PSNR and SSIM on the 4667 CT images from 50 test cases. The number of samples for calculating PSNR and SSIM was 4667. The PSNR and SSIM were 29.2 \(\pm\) 1.49 and 0.75 \(\pm\) 0.04 for the sparse-sampling images before denoising, 48.5 \(\pm\) 2.69 and 0.99 \(\pm\) 0.01 for deeper U-net, and 37.3 \(\pm\) 1.97 and 0.93 \(\pm\) 0.02 for RED-CNN.
## 4 Results
### SHM Plate with Stringer
#### 4.1.1 Comparison of _SHM Plate_ and _SHM Plate_ with Omega Stringer  
To study the influence of the omega stringer to the ultrasound signals the _SHM plate_ without and the _SHM plate_ with stringer are analysed. Both plates are made of the same composite layup as described in Section 2.1. Figure 9 presents the comparison between the measurements for \(f_{c}\) = 260 kHz of both plates. Addtional results are provided in the datasets available at the OGW website. In this example, the first transducer \(T_{1}\) is the actuator and all other transducers \(T_{2}\)-\(T_{12}\) serve as receivers. The influence of the stringer can be clearly identified especially for receivers \(T_{7}\)-\(T_{12}\) that are located on the opposite side of the stringer. The amplitude of the sensor signals at the excitation side are larger, and smaller for the opposing side for the _SHM plate_ with stringer. This is due to reflections at the stringer.  
\begin{table}
\begin{tabular}{c c c} \hline
**Damage ID** & **Coordinate System (Structure/Camera [SLDV Built-In])** \\  & **x-Coordinate (mm)** & **y-Coordinate (mm)** \\ \hline Transducer & 250/165 & 250/\(-\)65 \\ Damage & 125/40 & 125/\(-\)190 \\ Measurement area & 0–275 & 0–275 \\ Omega stringer & 0–500 & contact area (top) 410–435 \\  & & contact area (bottom) 320–345 \\ \hline \end{tabular}
\end{table}
Table 6: Coordinates of the measurement setup (SLDV system) of the _wave field_ plate. Please note that the SLDV system has two coordinate systems, one related to the structure and one related to the built-in camera.  
\begin{table}
\begin{tabular}{c c c} \hline
**Signal Type** & **Parameter** & **Value** \\ \hline Tone burst & Center frequencies & 30, 40, 50, 100, 150, 200 kHz \\  & Number of cycles & 5 \\  & Windowing function & Hann \\  & Amplitude & \(\pm\)150 V \\ \hline Chirp & Center frequencies & 20–200 kHz \\  & Chirp duration & 0.366 ms \\  & Amplitude & \(\pm\)150 V \\ \hline \end{tabular}
\end{table}
Table 7: Excitation signal parameters (SLDV system).
# Signaling in the Tomato Immunity against _Fusarium oxysporum_
## 2 Results  
### Movione Tomato Plants Harbouring the I Resistance Gene Are Immune to Fol Infection  
To characterize the establishment of the ETI, a symptomatology analysis of _Fol_-inoculated Movione (MOV) tomato plants carrying the \(I\) resistance gene was performed. Concurrently, Momor (MOM) tomato plants lacking the \(I\) resistance gene were used to compare the fusarium wilt symptoms. As Figure 1 shows, evident differences in symptom severity were observed between both isogenic lines at 14 days post-inoculation (dpi).  
As Figure 1C,D show, MOV displayed no phenotypical differences between mock- and _Fol_-inoculated plants, confirming the establishment of ETI. On the contrary, _Fol_-infected MOM plants were characterized by a height and weight reduction, yellowing of the leaves, browning of the xylem vessels, and even a total collapse of most plants, therefore confirming their susceptibility to _Fol_ (Figure 1B). Finally, no differences between both non-inoculated isogenic lines were observed (Figure 1A,C).  
To better quantify the differences between infected MOV and MOM plants, several morphological parameters were measured such as height, weight, the ratio between the hypocotyl length and the epicotyl length in reference to the height, as well as the water consumption per plant (Table S1, Supplementary Materials). Infected tomato plants were classified according to their disease index [17] as a medium value out of the score of the symptoms from mild (1) to very severe (4) along the infection (see Materials and Methods).  
As expected, a statistically significant reduction of height, weight, hypocotyl elongation, and symptom severity was observed between both isogenic lines, confirming the resistance of MOV tomato plants to _Fol_ infection.  
To study the possible relationship between the observed symptoms and the presence of the pathogen, _Fol_ levels were analyzed in the hypocotyl of both infected isogenic lines at 7 and 14 dpi by qPCR, being statistically higher in MOM than in MOV plants at any time (Figure S1), and correlating with the symptoms observed in Figure 1.
## 2 Materials and Methods
### Analysis of the Profiles of Potentially Bioactive Peptide Sequences  
The analysis of the protein profiles in the tested legume-derived material was conducted using nanoLC-TIMS-MS/MS. Initially, the samples were freeze-dried (Christ Alpha 1-2 LD plus, Martin Christ Gefiertrocknungsanlagen GmbH, Osterode am Harz, Germany) and ground to a fine powder (7000 rpm for 5 min) using a laboratory mill (Retsch GM200, Retsch GmbH, Haan, Germany). The extraction process followed the method described by (2017). Subsequently, the obtained peptides (2 \(\upmu\)g) were desalted using SPE (Pierce C18 spin columns from Thermo Scientific, Waltham, MA, USA) according to the manufacturer's protocol. After drying, they were resuspended in 20 \(\upmu\)L of 2% acetonitrile (ACN) with 0.1% formic acid (HCOOH) (Sigma-Aldrich, Saint Louis, MO, USA). For the nanoLC-MS/MS analysis, 1 \(\upmu\)L of the sample was injected into the Ultimate3000 nanocapillary chromatography system (Thermo Scientific). Separations were achieved using an Aurora column (25 cm long, 75 \(\upmu\)m ID, C18, IonOptics) with a precolumn PepMap100 C18 5 \(\upmu\)m (5 mm long, 0.3 mm ID, Thermo Scientific). The linear gradient was formed using 0.1% HCOOH in H\({}_{2}\)O (solvent A) and 0.1% HCOOH in ACN (solvent B) at a flow rate of 300 nL/min. The gradient ranged from 2% to 35% B over 90 min, followed by an isocratic hold at 85% B for 7 min. The system was controlled by the Hystar software version 5.0. (Bruker Daltonics, Bremen, Germany), and the chromatographic system was directly coupled to the timsTOPPro 2 (Bruker Daltonics) mass spectrometer. Operating in the positive-ion mode, the instrument conducted scans ranging from 100 to 1700 m/z and ion mobility 1/K0 ranging from 0.6 to 1.6. Fragmentation was performed using the parallel accumulation and serial fragmentation (PASEF) method with standard settings. The acquired mass spectra were analyzed using MaxQuant (version 2.0.1.0). Afterward, the activity of identified amino acid sequences was determined using the method described by [PERSON] et al. (2018) with PeptideRanker (2019), where any peptide predicted to exceed a threshold of 0.5 is considered to exhibit biological activity.