esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Air-Puff Stimulation  
The air pulses were generated by the rapid movement of a piston, driven by a solenoid motor (Figure 1A). The activation of these pulses was synchronized with OCT acquisition. By adjusting the capacitor's discharging voltage (C in Figure 1A)), we customized the spatio-temporal characteristics of the air pulses.  
Figure 1: (**A**) Scheme of air-puff chamber and the set-up used for air-puff characterization. C—capacitor, Ps1—reference pressure sensor for air-puff chamber, (**B**) Profiles of the selected air puff types (so called Type 1—high-amplitude, Type 2—medium-amplitude, Type 3—low-amplitude air puff) measured with the reference pressure sensor.  
Three distinct settings for high-, medium-, and low-amplitude stimuli were selected (70 V, 55 V, and 45 V, respectively) and are referred to throughout the article as Type 1, Type 2, and Type 3 air-puffs, respectively. The generated air pulses were characterized using a pressure sensor (Ps1; SenSym, Milpitas, CA, USA) incorporated into the air-puff chamber (Figure 1A). Ten readouts from the Ps1 sensor were used to extract both the peak pressure and the duration of the air pulse temporal profiles (Figure 1B). This duration was defined as the time span during which the pressure magnitude exceeded 10% of the peak value. The mean and standard deviations of the amplitude and duration of the air puff were calculated. The sensor Ps1 was also calibrated by an external force sensor, which allowed for expressing the pressure signal from Ps1 in force units. The procedure of calibration was described in an earlier study [11].  
We used an MPX5050D pressure sensor (NXP Freescale Semiconductor Inc., Austin, TX, USA) to characterize the spatial profile of each tested air-puff configuration (Figure 1A). The sensor was moved along the Z axis to determine axial air-puff pressure amplitude profiles. The translation range was 18 mm, and the measurements were taken in 1 mm steps. Then, the sensor was placed at the distance of 10 mm in front of the air-puff nozzle, which corresponded to the position of the corneal apex in ex vivo measurements. To investigate the lateral (X and Y axis) distribution of the air pulse, the sensor was moved along the X and Y axes in 0.5 mm increments, covering a range of \(\pm\)2 mm distance around the center. Five measurements were completed in each sensor position.
# Fuzzy PID Control of the Three-Degree-of-Freedom Parallel Mechanism Based on Genetic Algorithm
## 2 Model of the Three-Translation PM
### Coordinate System Establishment  
Each driven arm of the PM was a parallelogram, and the distance between the two sliding rods was unchanged; therefore, the structure could be simplified (see Figure 2). Three points, L1, L2, and L3, were the vertices of the equilateral triangle, and R was its circumscribed circle radius. The angle between it and the X-axis was \(\alpha_{i}\) (\(i=1,2,3\)) with the circle center as the origin, the direction pointing to the circle center as the X-axis, and the normal plane direction of the regular triangle as the Z-axis. Three points, \(N_{1}\), \(N_{2}\), and \(N_{3}\), were the vertices of an equilateral triangle whose circumscribed circle radius was r.  
The circle center was taken as the origin, and the three coordinate axes at the initial position were parallel to the basic coordinate system. Dynamic coordinate system O\({}^{\prime}\)-X\({}^{\prime}\)Y\({}^{\prime}\)Z\({}^{\prime}\) was established. \(l_{b}\) is the length of the active arm \(L_{i}M_{i}\), and \(l_{ai}\) is the length of the slave arm \(M_{i}N_{i}\). \(\theta_{i}\) is the angle between the static platform and the active arm as well as the input parameter, and \(i=1\), \(2\), and \(3\). The center coordinate of the moving platform is O\({}^{\prime}\)(x,y,z), where x, y, and z are output parameters.  
Figure 1: PM structure.  
Figure 2: Established coordinate system.
## 2 Background
### ECC over \(GF(2^{m})\)  
Projective coordinates represent the affine \(x\) and \(y\) coordinates of a point \(P\) over \(GF(2^{m})\) field in a triplet, i.e., \(X:Y:Z\). Therefore, for a \(GF(2^{m})\) field, a Lopez Dahab projective form of the ECC curve is defined by Equation (1), where the variables \(X\), \(Y\), and \(Z\) are the Lopez Dahab projective elements of point \(P(X:Y:Z)\), \(Z\
eq 0\), \(a\) and \(b\) are the curve constants, and \(b\
eq 0\):  
\[E:Y^{2}+XYZ=X^{3}Z+aX^{2}Z^{2}+bZ^{4}. \tag{1}\]  
To implement Equation (1), it is necessary to utilize PA and PD operations for evaluating the PM operation of ECC. Let us illustrate ECC's PA, PD, and PM operations through an example: suppose we have two points (\(P\) and \(Q\)) on an ECC curve. Performing a PA results in \(R=P+Q\), where \(R\) represents the resultant point. Similarly, adding two identical points on the ECC curve, denoted as \(P+P=2P\), corresponds to a PD operation. By employing the definitions of PA and PD operations in ECC, the PM operation involves the addition of \(k\) instances of PA and PD on the specified ECC curve. This can be computed using Equation (2):  
\[Q=k\cdot P=\underbrace{P+P+\cdots+P}_{P\text{ is added }k\text{ times}}. \tag{2}\]  
Here, \(P\) is an initial point; \(k\) is a scalar multiplier; and \(Q\) denotes a resultant point in Equation (2). Several PM algorithms exist in literature to implement Equation (2). A comparison of hardware implementations of several PM algorithms is described in [2]. The research presented in [2] suggests that a Montgomery PM algorithm is more practical for implementing side-channel resistant ECC. This algorithm exhibits similarities in instructions used for both PA and PD computations. Therefore, in this study, Algorithm 1, a Montgomery PM algorithm, is employed to develop a hardware implementation of ECC that offers robust side-channel protection. Algorithm 1 bears an initial point \(P\) and a binary sequence denoting a scalar multiplier \(k\) as inputs. Its output is the final point \(Q\) with \(x\) and \(y\) coordinates. The first line of Algorithm 1 determines the operations for converting between affine and projective (Lopez Dahab) coordinates. Similarly, the \(for\) loop runs the PM operation in the Lopez Dahab coordinates. The \(if\) and \(else\) statements used inside the \(for\) loop direct to PA andPD operations, respectively. Particularly, instructions from \(Inst_{1}\) to \(Inst_{7}\) computes PA, whereas \(Inst_{8}\) to \(Inst_{14}\) perform PD. The choice between the execution of \(if\) and \(else\) instructions depends on the value of inspected one bit scalar multiplier. The final two lines of Algorithm 1 consist of instructions for converting the Lopez Dahab projective coordinates back to affine coordinates.
## 3 Results
### Comparison of Maximum Force Among Aligners  
The aim was to verify whether the maximum force differs statistically significantly between the aligners. First, the normality of force distribution within groups was tested to determine whether to use the parametric ANOVA or the non-parametric Kruskal-Wallis test. The Shapiro-Wilk test indicated non-normality (\(p\)-value \(<\) 0.05) for some groups; therefore, the non-parametric Kruskal-Wallis test was chosen. Although the Kruskal-Wallis test was not significant at the 0.05 level, it reached significance at the 0.1 level, prompting further analysis with pairwise post-hoc tests. The post-hoc pairwise analysis identified aligner pairs with significant differences (\(p\)-value \(<\) 0.05) in terms of maximum force:  
* [noitemsep]
* Scalloped margin aligners had significantly lower values (\(p\) = 0.029) compared to Noxi aligners with high margins and horizontal and vertical gradients;
* Scalloped margin aligners also exhibited significantly lower values (\(p\) = 0.008) compared to F22 aligners;
* Noxi aligners with zenith margin and uniform gradients demonstrated significantly lower values (\(p\) = 0.040) compared to F22 aligners;
* At the 0.1 level, Noxi aligners with high margins and uniform gradients showed significantly lower values (\(p\) = 0.086) compared to F22 aligners.  
These results are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample 1** & \multirow{2}{*}{**Median**} & **25 th** & **75 th** & **Statistic Test** & **Std. Error** & **Std. Statistic Test** & **Sig.** \\
**Sample 2** & & **Percentile** & **Percentile** & **Statistic Test** & **Std. Error** & **Std. Statistic Test** & **Sig.** \\ \hline AS & 7.3 & 2 & 13 & 6.5 & 10.435 & 0.623 & 0.533 \\ NUZ & 9.3 & 3.5 & 14.5 & & & & \\ \hline AS & 7.3 & 2 & 13 & 9.944 & 10.435 & 0.953 & 0.341 \\ NUA & 10 & 2 & 16.5 & & & & \\ \hline AS & 7.3 & 2 & 13 & 15.278 & 10.435 & 1.464 & 0.143 \\ NOVZ & 9.3 & 6 & 16.5 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pairwise comparisons of maximum force among aligners. Aligners: NUZ (zenith margin with uniform gradient); NUA (high margin with uniform gradient); NOVZ (zenith margin with both horizontal and vertical gradients); NOVA (high margin with both horizontal and vertical gradients); F22 (F22 aligner); AS (scalloped aligner) The significance level is 0.50. (*: \(<\) 0.50, **: \(<\)0.05).  
Figure 7: Maximum force between aligners in adult model with aligned adult (AA), adult model with dental crowding (AF), adult model with small teeth and diastemas (AD), and early mixed dentition model (DM). Aligners: NUZ (zenith margin with uniform gradient); NUA (high margin with uniform gradient); NOVZ (zenith margin with both horizontal and vertical gradients); NOVA (high margin with both horizontal and vertical gradients); F22 (F22 aligner); AS (scalloped aligner).
## 4 Results  
It is important to note that the real-world videos analyzed in this pilot study are not annotated with ground truth activity labels. Therefore, it was not possible to compute standard evaluation metrics such as per-class precision, recall, or confusion matrices. Nevertheless, the model's performance was previously validated using annotated datasets (e.g., UP-Fall and UR-Fall), where F1-scores above 92% were achieved [7]. The encouraging results of this exploratory study motivate the future development of our own annotated dataset for public transport scenarios, which will allow for a complete quantitative evaluation.  
The results show that the Random Forest-based model achieves an accuracy above 90% in activity classification. Specific patterns in the passengers' poses were identified, which could be associated with difficulties in accessing the bus. This information could be used to improve public transportation design and policies. Among the videos captured at bus stops in Valparaiso, three main scenarios were identified: passengers getting off the bus, passengers running to catch the bus, and pedestrians waiting for the bus. The mentioned scenarios, along with the results of pose estimation and activity prediction, are presented below. All videos were recorded at 60 fps and processed through AlphaPose to estimate the poses of individuals, followed by activity prediction using the ML-RF model.
## 4 Results
### Output Data Comparison Analysis by Failure Type in the 365 W, 220 W, and 35 W Panels  
As the output of the PV panels varies by size, failure types were generated on a total of 50 panels composed of 365 W, 220 W, and 35 W PV panels to compare the output data. Figure 5 illustrates failure images of a 365 W panel by failure type, while Figures 6 and 7 illustrate failure images of a 220 W panel and a 35 W panel, respectively. Figure 8 shows combined thermal images for each output. The important factor here is to analyze the changes in the output in each failure type and propose a method for increasing the efficiency of the power generation facility by prioritizing replacement of the PV panels having the lowest output among the failure types.  
Figure 9 compares the output values of each PV panel by failure type. In the figure, (a) to (f) indicate the following failure types: (a) hotspot, (b) diode failure, (c) connector breakage, (d) panel breakage, (e) busbar, and (f) overheating of panel cells. The failure type yielding the lowest panel output was analyzed as the (c) connector breakage, followed by (d) panel breakage, (b) diode failure, (e) busbar, (f) overheating of panel cells, and (a) hotspot. When the connector breakage was detected through PV panel imaging, the corresponding panel was analyzed as the panel requiring a priority in replacement. It is inferred that the panel that requires immediate replacement can be derived based on the images obtained using a thermal imaging camera and the panel output data.  
Figure 8: Thermal images of the 365 W (left), 220 W (middle), and 35 W (right) panels after different failure types: (a) hotspot, (b) diode failure, (c) connector breakage, (d) panel breakage, (e) busbar, and (f) overheating of panel cells.  
Figure 9: Output comparison of each PV panel by failure type.
# Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data
## 2 Materials and Methods
### Methods
Statistical analyses for modeling NEE applying Sentinel-1 VV and VH data were done in STATISTICA and EXCEL software.
## 4 Performance Analysis
### PTP with RSTP  
The standard PTP with RSTP recovery mechanism starts by detecting the Bridge Max Age time-out duration that detects the reception of the periodical Hello messages from the other nodes [13]. Considering that, in the case of link failure occurrence in the network, the failure node broadcasts a Bridge Protocol Data Units (BPDU) frame through all the ports after the above-mentioned duration, this specific frame notifies the remaining nodes that the topology has been changed. Subsequently, the root nodes that received this specific frame will broadcast a BPDU frame, which contains a Proposal Agreement for re-establishing the RTSP tree. Eventually, the nodes that receive the Proposal Agreement notification will replay with a BPDU frame, which contains an Agreement Acknowledgment towards the root of that level of the tree.  
Hence, the total delay of the standard PTP with RSTP (\(D_{PTP-RSTP}\)) for detecting and recovering the failure occurrence in the network can be determined as follows:  
\[D_{PTP-RSTP}=2\times(D_{TR}+D_{PG})+BD_{MAX}, \tag{1}\]  
where  
\[BD_{MAX}=2\times H_{PCK}. \tag{2}\]  
\(BD_{MAX}\) refers to the Bridge max age of detecting the failure and \(H_{PCK}\) refers to the duration of non-receiving a Hello packet from the connected nodes, which indicates the link failure occurrence in the same network segment. Usually, the \(H_{PCK}\) detection duration ranges between hundreds of milliseconds to a few seconds [14]. In addition, \(D_{TR}\) and \(D_{PG}\) refer to transmission delay and propagation delay, respectively. Since these delays consume a negligible amount of time, the total delay of PTP with RSTP can be approximated as follows:  
\[D_{PTP-RSTP}\cong 2\times H_{PCK}. \tag{3}\]  
For the sample PTP network shown in Figure 2, the delay of PTP with RSTP using typical \(H_{PCK}=2\) s is:  
\[\text{D}_{PTP-RSTP}\cong 2\times 2=4\,\text{(sec)}. \tag{4}\]
# Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models
## 3 Methodology
### Experimental Setup
#### 3.3.2 Input Setup  
The input images were resized to 224 \(\times\) 224 \(\times\) 3 pixels. The images were re-scaled in the range [0, 1]. Image augmentation was applied to the images and included operations, such as shear, zoom, and horizontal flipping.
## 3 Methodology
### Integration of Machine Learning in Simulation  
Figure 7 illustrates the adopted system architecture for this study, integrating the ML-DProSa with the Unity simulation environment. Python serves as the platform for executing ML-DProSA tasks, while Unity is utilized for simulation purposes. Since these environments do not natively interoperate, we establish communication between them using a socket-based server connection. In this setup, the server initiated in Python awaits a client connection, representing the simulation environment.  
Upon initiating the simulation, it tries to establish a connection with the server's address. Once the connection is successfully established, the client transmits data strings, which are subsequently decoded by the server.  
There are specific instructions sent by the client that could be handled by the server. Upon successful decoding, the server invokes ML-DProSA to execute the specified instruction. The specific instructions that the server can decode are as follows:  
1. Perform cluster: The server invokes ML-DProSA to perform specific clustering method to the contents of a specific directory. This directory information is also embedded in the data string transmitted by the simulation.
2. Perform sort: The server calls ML-DProSA to execute a sorting operation based on the string received from the simulation, subsequently returning the sorted string.
3. Perform normal: This instruction prompts the server to perform a pseudo-sorting operation, where it refrains from executing any sorting and simply returns the original string received from the simulation.
# Static and Moving Target Imaging Using Harmonic Radar
## 4 Synthetic Aperture Radar (SAR) Imaging
### Moving Aperture SAR Imaging
#### 4.2.3 SAR Imaging Results  
SAR images were generated using the step frequency data and the back-projection algorithm [66]. More details on harmonic SAR processing can be found in [71]. Linear and nonlinear SAR data were collected on both targets present in the scene using both the linear and the nonlinear radar. The mixer placed at position A and the corner reflector placed at position B (See Table 2 for location coordinates). The linear and harmonic SAR images are presented in Figure 15.  
Figure 14: Photographs of the targets used for SAR imaging: (**a**) Linear target; (**b**) Nonlinear target.  
Figure 15: SAR results from the nonlinear target at position A and the linear target at position B: (**a**) Linear SAR image; (**b**) Harmonic SAR image.  
The linear response shows the corner reflector and mixer at the correct locations, while the nonlinear radar only shows the mixer and suppresses the corner reflector. Although the corner reflector was present in the scene while the nonlinear data were collected, it does not appear in the nonlinear SAR image in Figure 15b. This clearly demonstrates the advantage of nonlinear radar in detecting only nonlinear targets. The antenna coupling also shows a strong response in the linear SAR image, and it stays in the same range bin regardless of aperture position; thus, it is observed along the entire aperture, \(x=-1.5\) m \(=\)\(-5\) ft to \(x=+1.5\) m \(=+5\) ft.  
Figure 16 clearly delineates different phenomenological mechanisms that more clearly expound upon the conclusions drawn above.
## 2 Materials  
### Fundamentals of Data Exchange and Extraction  
In recent years, the importance of neutral file-based exchange methods has increased. To ensure continuous, loss-free data flow between different systems, several standards have been developed, such as Drawing Exchange Format (DXF) [18], INP [19], and STEP [20]. To provide semantic interoperability, researchers increasingly use ontologies to exchange geometric and semantic product information consistently, especially in the context of digital twins and the semantic web. For this purpose, different possibilities for creating an ontology are available, such as the open source ontology editor Protege developed by Stanford University [21]. Standardized languages, such as the Web Ontology Language (OWL), are widely used for the creation process [22]. The translation is usually done by mapping the data semantics using an intermediate common model data ontology. Up- and downstream are either system-specific ontologies or neutral interchange formats. However, conversion of the complete content may result in information loss, or in the worst case, direct mapping is not possible [23; 24; 25; 26; 27]. Furthermore, none of the approaches consider directly adapting the contents of a file to establish compatibility between systems, thus minimizing the conversion process and loss. Ontology exchange methods are also difficult to apply when data are not available in an analog ontology form. To intercept a wide range of different partial models, the present work focuses on standardized, neutral file-based exchange since this type of exchange is still standard and widely used [9; 15; 28; 25].  
During data exchange through neutral, system-independent exchange files, the source system exports the file in the first step. Here, a preprocessor converts the system-specific internal binary format to a neutral data format. A postprocessor in the target system converts the file to the internal structure of the target system [29; 30]. Figure 1 shows the process below.  
To manipulate data internally, directly at the file level, we first need to extract it and make it accessible. Using external methods, such as application programming interfaces (APIs), is unsuitable because they do not allow information to be adapted to another target system [31]. There are several approaches in the engineering context in the sub-field of academic research on feature extraction. Some methods are not advanced enough, while others have been replaced with more efficient techniques [29; 31; 32; 33; 34]. In general, graph-based approaches [35; 36; 37], heuristic-based methods [38; 39; 40; 41; 42], volume decomposition, and, most recently, artificial neural networks [33; 43; 44] are currently being used. The present contribution refers to heuristic approaches, as they offer the possibility of extracting items and specifying context-specific compatibility issues precisely. In the course of heuristic-based methods, features are generalized as templates consisting of characteristic rule patterns. If the predefined conditions are matched, then the appropriate pattern is recognized as the respective feature [38; 39; 40; 41; 42].
# Application of Machine Learning Techniques to
## 3 Methodology
### Climate Change Projections and Trend Detection
#### 3.5.1 Mann-Kendall Test (MK Test)  
The MK test [76] was used to evaluate the statistically significant annual seasonal trends in the long term precipitation data. The \"no trend\" in precipitation with time is assumed in the null hypothesis (H\({}_{\text{o}}\)) of the test and vice versa for the alternative hypothesis (H\({}_{\text{a}}\)). The Equations (9)-(12) show the test stats T of the MK test.  
\[T=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}sig\big{(}D_{j}-D_{i}\big{)} \tag{9}\]  
\[sgn\big{(}D_{j}-D_{i}\big{)}=\left\{\begin{array}{cc}+1&if\left(D_{j}-D_{i} \right)>0\\ 0&if\left(D_{j}-D_{i}\right)=0\\ -1&if\left(D_{j}-D_{i}\right)<0\end{array}\right. \tag{10}\]  
\[\sigma(T)=\frac{1}{18}\Bigg{[}n(n-1)(2n+5)-\sum_{p=1}^{q}t_{p}\big{(}t_{p}-1 \big{)}\Big{(}2t_{p}+5\big{)}\Bigg{]} \tag{11}\]  
\[Z=\left\{\begin{array}{cc}\frac{T-1}{\sqrt{\sigma\ (T)}}&if\ T>0\\ 0&if\ T=0\\ \frac{T-1}{\sqrt{\sigma\ (T)}}&if\ T<0\end{array}\right., \tag{12}\]  
where \(D_{i}\) and \(D_{j}\) are the consecutive time-series observations, which are organized chronologically for the length of data (n); \(t_{p}\) represents the data points number for the pth value in a tied group; and q denotes the total number of tied groups \(\sigma\) is the variance. An upward time series trend is represented by a positive \(Z\) value and vice versa for the negative trend. If \(|Z|>Z_{1-\alpha/2}\), the null hypothesis (H\({}_{\text{o}}\)) isrejected which is indicative of a statistically significant trend. The critical value of \(Z_{1-\alpha/2}\) is assumed at the \(p\)-value of 0.05.