esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Transmission lines periodically loaded with bends of different sharpness have been proposed in this paper for the realization of leaky wave antennas. The results of our simulation, mathematical modeling, as well as experimentation suggest that transmission lines periodically modulated with sharp bends exhibit both left-hand and right-hand scanning behaviors. On the other hand, transmission lines periodically loaded with smooth bends exhibit right-hand scanning behavior only. The proposed methodology has been successfully used to realize two leaky antennas, with the one modulated with sharp bends for composite right-hand/left-hand (CRLH) scanning and the other one modulated with smooth bends for right-hand scanning. The peak efficiencies for both fabricated leaky antennas reached 90 percent along with wide band impedance matching and high gain in the operating frequency bands of our choice. The proposed technique has opened up fundamentally new methodologies for leaky wave designs for applications including sensor and frequency scanning applications.  
[PERSON] conceived of the idea and did experiments; [PERSON] supervised the work; [PERSON] did the revisions and analyzed the paper; [PERSON] helped in the experiments and the writing; [PERSON] performed the numerical modeling.  
This work was supported by the National Natural Science Foundation of China under Grant No. 61871375, U1505251, the National Key R&D Program of China under Grant No. 2018 YFC2001002, and the Shenzhen Basic Research Project under Grant No. JCYJ20180507182231907.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CRLH & Composite right-hand/left-hand \\ LWA & Leaky wave antennas \\ SIW & Substrate integrated waveguide \\ \end{tabular}
## 2 Measurement Method and Experimental Setup Design
### Line Stabilization Algorithm  
As it was stated earlier, long-time signal accumulation, which is necessary for high spectral resolution and measurement accuracy in the ICOS cell, is only possible with proper laser frequency stabilization. Thus, line stabilization mode is necessary if spectrum averaging is enabled.  
Common laser temperature control is based on thermistor input, which is not precise enough and does not represent actual laser temperature, rather some intermediate inert value, therefore spectrum averaging in this mode leads to a line profile deformation, decreasing the accuracy of the model fitting. To solve this problem, we implemented a laser temperature controller based on its spectral information. The algorithm is aimed at spectral line center search in the photodiode output signal, shown in Figure 7a. The error between the actual and desired line center coordinates is then given to the PI controller for laser TEC control, leading to a spectral image fixation.  
Because of limited computing resources and a small number of points (less than 100) per line profile, a simplified algorithm was implemented. Firstly, a predefined part of the spectrum is sliced out and background radiation is deducted, as shown in Figure 7b. Secondly, a baseline is calculated using 15 edge points for later normalization, which could be seen in Figure 7c. After line normalization, the spectrum is deducted from 1 and squared to get a positive bell for the center of mass calculation, seen in Figure 7d. Using Formula (3), a line center is obtained and error between actual and desired centers enters the laser TEC PI controller.  
\[Line\;center=\frac{\sum_{i}i\times Spectrum_{i}}{\sum_{i}Spectrum_{i}} \tag{3}\]  
A simulation of a line center search algorithm gave the accuracy of 0.1-0.2 of the interval between consecutive measurements, whereas the real implementation occurred to 4-fold less accurate. Such precision drop is mainly caused by an optical interference on a spectrum and PI controller precision, nevertheless, providing a temperature-based controller is applied, we observe the 1-2 intervals accuracy with noticeable long-time spectral lines position drift. As for the rough conversion to a physical value, 1 spectral interval equals to 0.001 cm\({}^{-1}\). It is also interesting to note that the second-order least-squares method for line center approximation showed worse results than the center of mass algorithm in simulation.  
Figure 7: Real-time line center search algorithm visualization. (**a**) simulation of photodiode data for algorithm verification; (**b**) slice of original data with deducted background radiation; (**c**) line after normalization; (**d**) final result for the center of mass calculation.
# Planar UWB Monopole Antenna with Tri-Band Rejection Characteristics at 3.5/5.5/8 GHz
## 5 Comparison with Other UWB Antennas with Band-Notched Function  
In Table 3, we compare our proposed UWB antenna with triple band-notched function with other UWB antennas with band-notched function in the literature. In their designs, some antennas were too large and some antennas did not reject the full WiMAX or WLAN bands. In contrast with other antennas, our proposed antenna has a moderate size and rejects complete WiMAX, WLAN, and X-band satellite applications operating near 8 GHz.
# Building Geometry Simplification for Improving Mesh Quality of Numerical Analysis Model
## 4 Automatic Geometry Simplification Method for FVM  
The building geometry simplification method proposed in this study consists of preprocess, face classification, and polyhedron generation steps (Figure 2). This method evaluates the similarity between surfaces to classify the surfaces to be removed. When the target model is composed of multiple solid objects, such as internal spaces or structures of the building, there may be numerical errors, such as small gaps between the solids. This can cause simplification errors and increase the number of surfaces, adversely affecting the performance of the algorithm. Considering this, a single solid object is created by uniting the target solids (Figure 2b). The similarity between surfaces is evaluated by the  
Figure 1: Non-orthogonal and skew cells; \(P\) and \(Q\) are the centers of each cell, \(f_{c}\) is the face centroid, \(f_{i}\) is the face interpolated value, and \(\overrightarrow{n}_{f}\) is a normal of face \(f\).  
difference in the angle between the surfaces, as well as the distance. For this purpose, the surface of the target model should consist of a flat surface (i.e., a face). Therefore, when there is a curved surface in the target model, the model has to be approximated in the form of a polyhedron composed of faces. In the face classification step, faces are classified into major faces (solid lines in Figure 2c) for composing the surface of the simplified model, and minor faces (dotted lines) to be removed from among the surfaces. To fill the empty spaces where minor faces have been removed, solids are generated using the boundaries of stretched major faces, and solids that are similar to the original geometry are selected (Figure 2d). The selected solids are united to obtain a simplified building geometry (Figure 2e).
## 2 Background
### Overview of AR Placement Methods
#### 2.1.1 Marker-Based Approaches  
Marker-based approaches use a designated marker to activate the AR experience. They can be divided into hyperlink and vision-based methods.  
* Hyperlink methods  
A hyperlink method connects physical objects to web-based material via graphic tags or automatic identification technologies, such as the radio frequency identification (RFID) system. There are two types of subgroups: direct and indirect URL discovery methods [26]. Direct methods use active emitters of identifiers, whereas indirect methods use passive devices to provide identifiers for consequent active sensors. For further details, we recommend the publications of [26; 27].  
Figure 1: Taxonomy of AR placement methods.  
* Vision-Based methods  
A vision-based method is a field of computer vision that allows detecting markers on physical objects. In marker-based AR, a marker must activate an augmentation. Markers can be physical objects in the real world or paper-based patterns that are easily recognized and processed by cameras. Markers are visually independent of their surroundings. Additional data can be calculated, such as object orientation, color, size, and shape [28]. Computer vision-based recognition identifies objects by detecting features in an image. Scale-invariant feature transform (SIFT), speeded up robust features (SURF), oriented FAST, and rotated BRIEF are some of the most commonly used computer vision algorithms for object detection [29]. Important AR libraries in vision-based technologies include ARKit, ARcore, and Vuforia [24]. We note that although vision-based methods are more complicated than sensor-based approaches, they are accurate and reliable [30].
## 5 Results and Discussion
### Multivariate Models  
The LSTM multivariate model considers four time series data strings for classifying the input instance. Running ten iterations with the model gave an average accuracy of 30.7%. Figure 10 and Table 10 show the LSTM confusion matrix and performance table, respectively. It can be noted that the model identifies all the test cases as jamming faults. This, in turn, leads to high false-positive cases of jamming faults and simultaneously high false negatives for OC and IOC. From the matrix for the second case, it can be seen that the model labels all of the test instances as IOC, thereby generating a high number of false-positive cases for the same, which also contributes to a high number of false-negative cases for OC and jamming.  
The performance of the TSF multivariate model is discussed next. The average accuracy of the model is 34.3%, 50% being the highest and 23.3% the lowest. It can be gathered from the confusion matrices that the model is highly susceptible to misclassify OC faults, generating several false-negative alerts for IOC and OC faults. Figure 11 and Table 11 display the model confusion matrix and performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**Average Accuracy**} & \multicolumn{2}{c}{**30.7\%**} \\ \hline \multicolumn{2}{c}{**Average Precision**} & \multicolumn{2}{c}{**Average Recall**} & \multicolumn{2}{c}{**Average F1-Score**} \\ \hline OC & 0\% & 0\% & 0\% \\ IOC & 10.7\% & 40.0\% & 16.9\% \\ Jamming & 20.0\% & 60.0\% & 30.0\% \\ \hline \end{tabular}
\end{table}
Table 10: LSTM Multivariate Performance.  
Figure 10: Confusion Matrix for LSTM Model: (a) Case 1; (b) Case 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**Average Accuracy**} & \multicolumn{2}{c}{**72.3\%**} \\ \hline \multicolumn{2}{c}{**Average Precision**} & \multicolumn{2}{c}{**Average Recall**} & \multicolumn{2}{c}{**Average F1-Score**} \\ \hline OC & 62.4\% & 83.1\% & 70.8\% \\ IOC & 100\% & 89.8\% & 94.4\% \\ Jamming & 61.6\% & 42.1\% & 49.3\% \\ \hline \end{tabular}
\end{table}
Table 9: k-NN Univariate Performance.
# Controlling Remotely Operated Vehicles with Deterministic Artificial Intelligence
## 2 Materials and Methods
### Control Output  
Each time the user specified yaw set point changes a new \"primary\" trajectory, \(q_{1}(t)\), is generated between the current vehicle state, and the specified set point and 0 velocity. Then, on each time step, a \"secondary\" trajectory \(q_{2}(t)\) is generated between the current vehicle position, and the position and velocity of the primary trajectory a short time, \(\Delta t\), in the future, i.e., if the current position and velocity at time \(t_{k}\) are \(x_{k}\) and \(\dot{x}_{k}\), then the \"secondary\" trajectory, \(q_{2}(t)\), is generated between \(x_{k}\) and \(\dot{x}_{k}\), and \(q_{1}(t_{k}+\Delta t)\) and \(\dot{q}_{1}(t_{k}+\Delta t)\). This keeps the vehicle on the specified \"primary\" trajectory, driving it back to the trajectory if it is perturbed. The \(\Delta t\) used to generate the \"secondary\" trajectory is a tunable parameter. For the system simulated in this paper, a value of 0.25 s was chosen.  
The velocity and acceleration trajectories calculated for \(q_{2}(t)\) using Equations (12) and (13), and the estimated model parameters calculated from (3) are then used to calculate the controller output for each time step. First, calculate the desired yaw velocity, \(r_{d}\), from Equation (12), then calculate the desired yaw acceleration, \(\dot{r}_{d}\), from Equation (13). Next, substitute \(r_{d}\) and \(\dot{r}_{d}\) for \(r\) and \(\dot{r}\), respectively, in \(\Phi\) to yield the desired state vector \(\Phi_{d}\). Then using the most recent estimate for the model parameters \(\hat{\Theta}\) calculated from (3) the prescribed yaw torque is  
\[\tau_{d}=\Phi_{d}\hat{\Theta} \tag{14}\]  
Finally, the yaw torque is related to the control, \(u_{\psi}\), by the \(A_{0}\) coefficient via  
\[u_{psi}=\frac{\tau_{d}}{A_{0}} \tag{15}\]
## 4 The Bassnet Model  
The BassNet model architecture and information flow is shown in Figure 1. The architecture is designed to model the relationship between a mono mix of one or more input tracks (henceforth referred to as the mix), and a mono output track containing the bass. The mix and bass are represented as CQT magnitude spectrograms (see Section 4.1), denoted \(x\) and \(y\), respectively. Although the architecture involves convolution along both the frequency and the time axes, the inputs to the model are fixed length windows of the spectrograms, rather than the spectrograms of the entire tracks.  
The relationship between \(x\) and \(y\) is encoded in a latent variable \(z\), as shown in the upper half of the diagram in Figure 1. The latent variable can be of arbitrary dimensionality, but, in the present work, we use a fixed dimensionality of two, so that it can be conveniently presented to the user without dimensionality reduction. Although the \(x\) and \(y\) variables convey windows of multiple timesteps, the corresponding \(z\) is just a single coordinate in the latent space. As such, the value of \(z\) (i.e., a pair of real-valued scalars) describes the constellation of spectro-temporal relationships between \(x\) and \(y\), not just spectral relationships at a single timestep.  
Because the modeling of the relationship between the mix and the bass is done while using CQT magnitude spectra, rather than using waveforms, we need an extra sonification step to convert the model outputs into an audio track. We do this by inferring three low-level audio descriptors from the generated magnitude spectrogram \(\hat{y}\), from which a bass track is then synthesized. This process corresponds to the lower right part of the diagram. The lower left part depicts the extraction of descriptors from the data in order to serve as ground truth in the training phase.  
In the rest of this section, we describe the various aspects of the architecture in more detail. We start with a brief discussion of the input representation (Section 4.1). Subsequently, we give an overview of the audio descriptors for the bass track (Section 4.2). We then focus on the GAE part of the architecture, and formulate a training objective to produce temporally stable latent representations (Section 4.4).
# Local Ecological Knowledge (LEK) Can Guide Decision-Making in Inland Fisheries Management
## 2 Materials and Methods
### Questionnaire's Structure  
A questionnaire comprising 43 questions, categorized into three sections, was developed and used. The first section focused on demographics, and delved into various aspects of the fishers' personal information. This included their age, place of birth, father's employment, marital status, educational qualification, and income. In the second section, which explored involvement in fishing, questions were tailored to gather detailed insights into responders' fishing participation. This covered the initiation of their fishing engagement, vessel characteristics, sources of information on fishing-related matters, fishing effort, technical aspects of fishing methods (utilizing the fishing tactics typology by [18; 20; 21]), seasonality of fishing activity, frequency and duration of fishing, recording of commercial and discarded catches, identification of areas with specific fishing interest, fishing equipment, target species sizes, and more. The final section documented the opinions of the fishers on the state of the stocks and the ecosystem, and the adequacy of existing legislation. It also provided an opportunity for the fishers to propose management interventions and share their ecological knowledge. Specifically, the questionnaire addressed their view on the challenges of their employment, environmental issues in the lake, overfishing of stocks, existing fishing legislation, and suggestions for lake management. It also explored the potential for necessary interventions to support the sustainable development of the area, the prospects of the sector, and the possibility of developing secondary activities such as fishing tourism, to supplement fishers' income and preserve the local population and traditional fishing practices. Additionally, three questions were dedicated to exploring the ecological knowledge of the fishers. Specifically, they were asked to name other non-commercial fish species that inhabit the lake as well as their awareness of the reproductive period and the size of the first sexual maturity of commercial species (here the length of the smallest mature specimen).  
The questionnaire underwent a pre-test with a test group consisting of two professional fishers who fish in another inland Greek freshwater body. The objective was to enhance the clarity of certain questions.
# Determinants of Y-Shaped Agility Test in Basketball Players
## 3 Results  
Test results used in this study and the coefficient of variation are shown in Table 1. The reaction time tests reflect the sensory component, sprints, and change of direction speed test, and jump tests reflect the motor component of the Y-shaped agility test.  
The Y-shaped agility performance significantly correlated with times in the 5 m-long sprint and 20 m-long sprint (r = 0.795** and r = 0.676*, respectively) and RSI in the drop jump (r = \(-\)0.619*). The time in the Y-shaped agility test did not correlate with any other variables measured; however, the relationship between the time in the Y-shaped agility test and simple reaction time indicated a large effect size (r = 0.566) (Table 2).  
The simple linear regression analysis revealed that sprint speed for 5 m (F\({}_{(1,9)}\) = 15.439, \(p\) = 0.003) followed by sprint speed for 20 m (F\({}_{(1,9)}\) = 7.593, \(p\) = 0.022), and RSI (F\({}_{(1,9)}\) = 5.579, \(p\) = 0.042) were significant predictors of Y-shaped agility performance. The results of the regression analysis are displayed in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Test** & **Mean \(\pm\) SD** & **CV** \\ \hline
5 m sprint time (s) & \(1.1\pm 0.1\) & 9.1\% \\ \hline
20 m sprint time (s) & 3.2 \(\pm\) 0.1 & 3.1\% \\ \hline Time in the Y-shaped agility test (s) & \(2.0\pm 0.1\) & 5.0\% \\ \hline Time in the 505 Agility test (s) & \(2.3\pm 0.1\) & 4.3\% \\ \hline Simple reaction time (ms) & \(298.1\pm 11.5\) & 3.9\% \\ \hline Two-choice reaction time (ms) & \(425.6\pm 24.2\) & 5.7\% \\ \hline Reaction time to four visual stimuli (s) & \(26.3\pm 0.6\) & 2.3\% \\ \hline Squat jump height (cm) & \(34.3\pm 4.8\) & 14.0\% \\ \hline Countermovement jump height (cm) & \(40.8\pm 4.2\) & 10.3\% \\ \hline RSI in the drop jump (cm.ms\({}^{-1}\)) & \(1.6\pm 0.3\) & 17.2\% \\ \hline \end{tabular} Note. SD—standard deviation, CV—coefficient of variation.  
\end{table}
Table 1: Descriptive statistics of basketball players test performance.  
\begin{table}
\begin{tabular}{c c c} \hline
**Y-Shaped Agility Performance** & \(\mathbf{r}\) & \(\mathbf{p}\) \\ \hline
5 m sprint time & 0.795 & 0.003 \\ \hline
20 m sprint time & 0.676 & 0.022 \\ \hline Time in the 505 Agility test & 0.280 & 0.404 \\ \hline Simple reaction time & 0.566 & 0.069 \\ \hline Two-choice reaction time & \(-\)0.350 & 0.292 \\ \hline Reaction time to four visual stimuli & 0.437 & 0.179 \\ \hline Squat jump height & 0.103 & 0.764 \\ \hline Countermovement jump height & 0.007 & 0.985 \\ \hline RSI in the drop jump & \(-\)0.619 & 0.042 \\ \hline \end{tabular} Note. \(r\)—Pearson coefficient of correlation, \(p\)—probability value.  
\end{table}
Table 2: Relationship between Y-shaped agility performance and sprint, change of direction speed, reaction speed, and muscle strength parameters in basketball players.  
Based on simple linear regression analysis and coefficients of determination, Y-shaped agility performance was determined by sprint speed for 5 m and 20 m (63.2% and 45.7%, respectively) and reactive strength in the drop jump (38.3%).
## 2 Materials and Methods
### Anonymization and Information Loss Estimation
#### 2.2.4 Information Loss
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **\(QI\) Set** & \(QI\) **ID** & \(k\) & \(GIL\) & \(DM\) & \(C_{AVG}\) \\ \hline S0 & [] & 0 & 1 & 0 & 0 & 0 \\ \hline S2.1 & [AGE, SEX] & 2 & 2 & 0.394 & 346,873,092 & 492.353 \\ \hline S1.2 & [AGE, SEX] & 2 & 3 & 0.394 & 346,873,108 & 331.017 \\ \hline S2.3 & [AGE, SEX] & 2 & 5 & 0.394 & 346,873,168 & 202.034 \\ \hline S2.4 & [AGE, SEX] & 2 & 10 & 0.394 & 346,873,396 & 102.789 \\ \hline S2.5 & [AGE, SEX] & 2 & 15 & 0.394 & 346,875,014 & 70.378 \\ \hline S2.6 & [AGE, SEX] & 2 & 20 & 0.394 & 346,878,574 & 54.25 \\ \hline S2.7 & [AGE, SEX] & 2 & 30 & 0.394 & 346,884,568 & 37.558 \\ \hline S3a.1 & [AGE, SEX, OUTCOME] & 3a & 2 & 0.352 & 254,366,790 & 209.25 \\ \hline S3a.2 & [AGE, SEX, OUTCOME] & 3a & 3 & 0.352 & 254,366,928 & 144.133 \\ \hline S3a.3 & [AGE, SEX, OUTCOME] & 3a & 5 & 0.352 & 254,367,482 & 91.547 \\ \hline S3a.4 & [AGE, SEX, OUTCOME] & 3a & 10 & 0.353 & 254,371,334 & 50.509 \\ \hline S3a.5 & [AGE, SEX, OUTCOME] & 3a & 15 & 0.353 & 254,378,570 & 36 \\ \hline S3a.6 & [AGE, SEX, OUTCOME] & 3a & 20 & 0.354 & 254,353,268 & 28.721 \\ \hline S3a.7 & [AGE, SEX, OUTCOME] & 3a & 30 & 0.355 & 254,372,510 & 20.134 \\ \hline S3b.1 & [AGE, SEX, CURAD\_DAYS] & 3b & 2 & 0.214 & 75,443,862 & 21.275 \\ \hline S3b.2 & [AGE, SEX, CURAD\_DAYS] & 3b & 3 & 0.215 & 75,448,478 & 16.502 \\ \hline S3b.3 & [AGE, SEX, CURAD\_DAYS] & 3b & 5 & 0.216 & 75,461,502 & 11.83 \\ \hline S3b.4 & [AGE, SEX, CURAD\_DAYS] & 3b & 10 & 0.219 & 75,530,606 & 7.73 \\ \hline S3b.5 & [AGE, SEX, CURAD\_DAYS] & 3b & 15 & 0.219 & 75,530,606 & 7.73 \\ \hline S3b.6 & [AGE, SEX, CURAD\_DAYS] & 3b & 20 & 0.222 & 75,820,294 & 5.208 \\ \hline S3b.7 & [AGE, SEX, CURAD\_DAYS] & 3b & 30 & 0.225 & 76,159,906 & 4.099 \\ \hline S4.1 & [AGE, SEX, CURAD\_DAYS, PREVAD\_DAYS] & 4 & 2 & 0.085 & 7,964,110 & 3.789 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The different dataset versions that were generated from the anonymization process, the corresponding anonymization parameter values and the obtained values of the information loss metrics.