esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### NEE, Heat and Water Vapor Fluxes  
We expected that there would be a relationship between CO\({}_{2}\) exchange and energy and water budget components because stomata activities are directly related to heat and water accessibility. We expected, therefore, that correlations between CO\({}_{2}\) exchange and evapotranspiration in the dry climate of interior Siberia would be particularly significant. These relationships were complex and not readily apparent during any of the entire growing season; two-thirds of the determination coefficients were, on average, rather high, R\({}^{2}\) > 0.5, explaining at least 50% of the daily NEE variation (Tables 2 and 3).  
In the dry larch forest with limited moisture, LE explained 70% of the NEE variation in the dry summer of 2004, while in the wet summers of 2007 and 2008, when it was likely that evaporation was unlimited, it explained only 10-20%. In the pine forest, LE explained 80% of the NEE variation in the dry summer of 1998. Even in wet summers, heat components R\({}_{\rm n}\) and H explained only half of the NEE variation in both forests. This fact confirmed the assumption that in the dry climate of interior Siberia, water primarily governs ecosystem processes.  
In addition, the non-linear (a parabolic-type) relationship between NEE and the Bowen ratio (\(\beta\)) was established in both ecosystems with R\({}^{2}\) = 0.8 (\(p\) < 0.001) in the pine forest and R\({}^{2}\) = 0.3 (\(p\) < 0.001) in the larch forest (Figure 7). The maximal CO\({}_{2}\) exchange, \(-\)(200-270) mol d\({}^{-1}\) in the pine forest and \(-\)(130-200) mol d\({}^{-1}\) in the larch forest, were related to a range of \(\beta\)\(\approx\) 1-2.5. Under lower \(\beta\), \(\beta\) < 1, cool and wet conditions, CO\({}_{2}\) exchange decreased in both ecosystems and could become positive, turning the system into a CO\({}_{2}\) source. Under high \(\beta\), \(\beta\) > 2.5, dry and hot conditions, CO\({}_{2}\)-exchange also decreased, likely because stomata closed under high sensible heat and water vapor deficit which are trade-offs between water loss and C gain in the process of photosynthesis.
# Optimization Design for Sparse Planar Array in Satellite Communications
## 2 Sparse Planar Array Optimization Design Model  
The primary objective of optimizing sparse planar arrays is to obtain an antenna pattern that satisfies expectations. The characteristics of the antenna pattern are primarily determined by factors such as amplitude and phase excitation, number of elements, array layout, array geometry, and array material. Optimization parameters include peak sidelobe level, antenna directivity and gain, main lobe width, and zero position. Satellite communication scenarios necessitate the consideration of additional indicators such as array scanning angle and polarization.  
This section focuses on the mathematical underpinnings of array antennas, mathematical model of the sparse plane array pattern, optimal design model based on minimum peak sidelobe level, and antenna pattern reconstruction model, as well as key issues to be addressed in optimizing sparse planar arrays.
# Digital Holographic Positioning Sensor for a Small Deployable Space Telescope
## 2 Opto-Mechanical Concept of the Telescope
We should also emphasize that some functions of the opto-mechanical design, as limiting of the side illumination, are not considered here. However, some internal baffle can be placed in the opening of the support ring \(5\). This baffle should limit the area of the underlying Earth surface, which illuminates the detector, to the nominal field of view. Then it should be possible to find a small region that can be used as a dark background for the positioning system image. Figure 4: Opto-mechanical concept of the deplorable telescope fitting in a 1U CubeSat: (**A**) stowed position; (**B**) fully deployed position. Figure 3: Comparison of a deployable primary mirror case with a monolithic one: (**A**) optical designs compared in scale (1—deployable telescope, 2—1U unit front side, 3—telescope with monolithic mirror), (**B**) PSF with the deployable mirror, (**C**) PSF with a monolithic mirror. We perform an estimate tolerance analysis for the optical system in order to understand the precision requirements for the positioning sensor. We use the Marechal criterion, which requires the RMS wavefront error to be \(<\)\(\lambda\)/14, and consider only the center of the field of view. The reference wavelength is 633 nm. We take into account the vertex radius and high order shape error of the mirrors, the distances between the components and their tip/tilt and decenters. The analysis relies on the Monte Carlo method with 200 runs. It is assumed that the Marechal criterion should be met with a 90% probability. The results are presented in Table 1. As the results show, the system is sensitive to the rotations of the primary mirror segments. The main goal of the positioning sensor is to measure the position of the primary mirror segments and the secondary mirror with high accuracy during the deployment. The positioning sensor output can be used to control the deployment process via a feedback loop to reach the required alignment. For further analysis, we assume that the most important degrees of freedom are those, used in the deployment process, i.e., rotation of each of the primary mirror segments with respect to its joint attachment point on ring 5 and the axial position of the secondary mirror 2. For the segments of primary mirror, we convert the tilt into a linear misplacement of its edge, since the transverse component is negligible due to the small projection angle. Particularly, the tilt value from Table 1 corresponds to the axial displacement of 8 \(\upmu\)m. We assume that due to the mirror surface quality and the telescope structure stiffness, all other contributors are minor. We should emphasize that this work presents a concept of a position sensor and its goals do not include exploration of the entire errors budget, including thermal effects, vibrations, noise, etc., or creation of a finalized telescope design directly suitable for a certain mission.
# Damage Identification of Stay Cables Based on a Small Amount of Deflection Monitoring Data
## 4 Analysis of Influencing Factors of Cable Damage Identification
### Influence of Noise on Damage Identification
#### 4.1.2 Influence of Noise on Damage Degree Identification  
The deflection difference of the girder at the anchorage points of cable M7 and M16 after adding noise (instead of the measured deflection difference) is used as the basis to identify the damage degree of the cables. The damage index is re-identified according to the damage degree identification method, and the damage identification results of single-cable and double-cable can be obtained. The results show that the difference between the recognized damage index and the actual damage index (the preset damage index) is very small (the maximum error is 1.2%). It can be seen that the addition of noise will not affect the accuracy of identifying cable damage degree based on the Kriging proxy model.
## 2 FMCW LiDAR
### FMCW LiDAR Key Parameters
#### 2.3.3 Angular Resolution  
The angular resolution of FMCW LiDAR refers to the ability of the LiDAR to discriminate targets at the same distance \(R\) but at different relative angles in a single measurement [16]. According to the Rayleigh criterion of a Gaussian beam, the beam divergence angle \(\theta\) is affected by the aperture \(d\) of the collimator at the transmitter end and the wavelength \(\lambda\) of the optical carrier wave, which satisfies \(\theta=\frac{1.27\lambda}{d}\). The distinguishable radial distance \(S_{A}\) can be expressed as follows when the measurement distance is \(R\):  
\[S_{A}\geq 2R\cdot sin\frac{\theta}{2}. \tag{11}\]  
In the equation, \(S_{A}\) is the radial distance angular resolution, \(R\) is the measurement distance, and \(\theta\) indicates the beam divergence angle. With a fixed operating wavelength, common methods to improve angular resolution include increasing the optical apertured of the receiving system or reducing the beam divergence angle \(\theta\). The angular resolution of FMCW LiDAR depends not only on the characteristics of the light source but also on the optical design of the receiving system. In the receiving system, the beam divergence angle \(\theta\) is constrained by the aperture \(d\). A larger aperture helps to reduce the beam divergence angle, thereby enhancing the angular resolution. Therefore, FMCW LiDAR used for imaging is often combined with synthetic aperture techniques. By conducting multiple measurements of a target at different positions and applying correlation algorithms, the virtual aperture is enlarged to improve angular resolution [17].
## 3 Results and Discussion  
### Fabrication of OCGAs  
Figure 2 shows the detailed fabrication process of OCGA. The liquid DE and GO aqueous solution were stirred into Pickering emulsion. The microcells with a shell-core structure were prepared by the self-assembly of GO nanosheets at interface between DE and water, and the formation mechanism of this process had been explained in our previous paper [23]. Then, the emulsion was freeze-dried into GO aerogels, and the microcells were opened by DE vapor ejection in a vacuum environment [26; 27]. Finally, the OCGA was obtained after chemical reduction using N\({}_{2}\)H\({}_{4}\)-H\({}_{2}\)O vapor. Through the above process, a series of OCGAs was prepared by adjusting the concentration of GO solution, the volume ratio of GO solution to DE and the rotational speed.
# Influence of Knee Angle on Hametring/Quadriceps Strength Ratio in Male Soccer Players
## 4 Discussion
[36] determined that, regarding the conventional ratio, taking a value of 0.658 as a cut-off point has greater sensitivity (and therefore fewer false negatives) than a value of 0.47. However, taking 0.47 as the cut-off point has a higher specificity than taking a value of 0.658 (therefore, there will be fewer false positives), so it is difficult to determine what cut-off point to set. When interpreting the strength values of the hamstring musculature and the H/Q ratio, it is necessary to take into account the level at which the athlete we are evaluating competes, since it has been indicated that second-division soccer players have a lower concentric hamstring strength peak (measured at 60\({}^{\circ}\)/s) than first-division soccer players and, therefore, a lower conventional H/Q ratio at 60\({}^{\circ}\)/s (0.59 (0.1) versus 0.62 (0.1)) [33]. In the present study, third-division players were measured whose mean conventional H/Q ratio at 60\({}^{\circ}\)/s resembles second-division players. [PERSON] et al. [26] stated that with the strength values obtained using the dynamometer, more than one-third of hamstring injuries could be reduced. The results of their study concluded that hamstring injuries can be predicted 36.9% of the time if the concentric H/Q ratio at 180\({}^{\circ}\)/s is less than 0.47. They also state that if the concentric hamstring strength at 60\({}^{\circ}\)/s is 15% lower than the contralateral side, injury can be prevented 34% of the time. Additionally, [PERSON] et al. [9] reported that one-third of hamstring injuries could be reduced by finding muscle imbalances between the hamstring and quadriceps musculature. Isokinetic knee dynamometric assessment in soccer players is usually performed in a seated position [37], but in the studies of [PERSON] et al. [16] and [PERSON] et al. [37], the conventional H/Q ratio was found to be different according to the hip position. It seems interesting, beyond the conventional H/Q ratio, to analyze in detail the strength graph of the hamstring and quadriceps muscles to obtain ratios at different angles for predicting more injuries, as shown with hand grip strength [38]. It will be necessary to continue this line of research by relating the H/Q ratios at 15\({}^{\circ}\) and 30\({}^{\circ}\) knee flexion with future muscle injuries, establishing new cut-off points for predicting injuries with greater accuracy and reducing the incidence of hamstring injuries.
# Approaching Electroencephalographic Pathological Spikes in Terms of Solitons
## 6 Solitons' Generation  
Due to their inability to form spontaneously from noisy and/or dissipative systems, solitons' generation relies on the occurrence of external perturbations (Figure 11) [23]. Artificial solitons can be engineered via different techniques in a wide range of media. A steady train of water waves with a well-defined shape can be produced in a shallow water regime by driving a piston at one end of the tank. The solitary waves may either ricochet back and forth or be absorbed at the other end with a porous material acting as an artificial beach [6,44]. In contrast to homogeneous media in which stable multi-peaked solitons do not exist, localized \"lumps\" of light can be produced in slightly nonlinear diffractive media such as glass, photonic lattices and optical fibers [33,56] by manipulating the light in miniaturized semiconductor chips [57]. In nonlinear silica optical fiber systems, the spreading due to dispersion and the intensity-dependent refraction due to nonlinearity are extremely balanced to generate solitons traveling in the waveguide for long distances [34]. In this case, the production of compact soliton beams is driven by femtosecond pulses oriented towards the transverse dimension [5], allowing ultrafast switching rates of several terahertz. Yet, solitons made of repulsively interacting atoms have been produced in microcavities and Bose-Einstein condensates [58]. Every soliton generated at each boundary of the box propagates in a uniform background density, colliding with one another in a way that can be controlled [59].  
Therefore, a model that predicts soliton propagation in the mammalian central nervous system must carefully describe how solitary waves could be produced in the electromagnetic milieu produced by the neural tissue.
## 2 Materials and Methods
### Training and Evaluation Process with the GPSR Algorithm  
The entire flowchart of the training process with GPSR is shown in Figure 5.  
Due to a large number of samples (7157), the original dataset after preprocessing with StandardScaler (only input variables) was divided into a 70:30 ratio: 70% of the dataset was used in the 5-CV and the remaining 30% for the final evaluation.  
The investigated process begins with randomly selecting GPSR hyperparameters (range values defined in Table 3), then the execution of GPSR algorithm with the 5-CV on the training dataset. In the 5-CV [24], there are 5 splits, i.e., the GPSR execution is trained 5 times, and for each time, a new SE is obtained. After each split, the performance metrics are calculated on the training and validation folds. After the process of training is performed, the mean and \(\sigma\) values are calculated and stored. When this step is performed, the obtained SEs are evaluated on the testing dataset, and the performance metrics (mean and \(\sigma\)) values are obtained. For the termination criteria of the entire process, the mean value of \(R^{2}\) must be greater than 0.985. If the value is above 0.985, the process is terminated; otherwise, the process begins from the beginning with a random selection of the GPSR hyperparameters.  
Figure 5: The flowchart of obtaining symbolic expressions using the GPSR algorithm.
## 3 RSS-Based DoA Estimation for Two-Row ESPAR Antenna
### PPCC-MCP Algorithm
As every discretized radiation pattern \(p_{\theta_{m}}^{n}\), where \(m=\{1,\ 2,\ \ldots,\ M\}\), corresponds to discretized values of \(\varphi\) in the same vector \(\varphi\), the values in \(p_{\theta}^{n}\) correspond to those in the vector \(\varphi_{\theta}=\left[\varphi_{\theta_{1}}^{T},\ \varphi_{\theta_{2}}^{T},\cdots,\ \varphi_{\theta_{M}}^{T}\right]^{T}= \left[\varphi^{T},\ \varphi^{T},\cdots,\ \varphi^{T}\right]^{T}\)[24; 28]. In consequence, the estimated DoA angle \(\phi\) is now a value in \(\varphi_{\theta}\) that corresponds to the highest value in \(g_{\theta}\)[28]. It should be underlined, however, that both the number of calibration planes and choice of their \(\theta\) angles highly influence the overall DoA estimation accuracy when RF signals impinging on the antenna arrive from arbitrary \(\theta\) angles [23; 24; 28; 34].
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 8 Evaluation Results
### Micro Benchmarks
#### 8.2.2 Different Subset Size  
It can also be seen from Figure 7 that in \(\text{Silcom}_{3R}\), where the communication rounds remain constant, the server computing time increases in a superlinear manner with the growth of m. In order to reduce computation cost, our tradeoff design \(\text{Silcom}_{trd}\) aims to reduce the \(O(m^{2})\) amount of FSS key computation. From Figure 8, it can be seen that using different subset sizes \(m^{\prime}\) in the two-layer structure effectively reduces the server computation time compared with \(\text{Silcom}_{3R}\). Even in the relatively poor \(m^{\prime}=10\), the server computation time is reduced by five times, and more than ten times in other \(m^{\prime}\) settings. In addition, it should be noted that the computation time does not keep increasing or decreasing with the change of \(m^{\prime}\), because although reducing \(m^{\prime}\) can reduce the cost of  
Figure 7: Server computation time of \(\text{Silcom}_{3R}\) for different amounts of APs.  
computing DDCF keys in the first layer, it will also cause the computing overhead of DDCF keys in the second layer to increase quadratically. Therefore, in order to reduce the overall cost as much as possible, it is necessary to balance the amount of DDCF keys in the two layers.
## 2 Materials and Methods
### Features Extraction and Classification Methods  
In the electronic tongue system, since each sensor response consists of a large number of voltage measurements, features extraction for each response is particularly challenging. In addition, the validity of extracted features would further affect the accuracy of pattern recognition. It is generally believed that the features hidden in the time series include the characteristics of time domain and transformation domain. Time domain features are relatively intuitive, such as the size of the response signal and the location of the mathematical features points. Features in the transform domain are relatively complex, such as the features from frequency domain or matrix decomposition. It should be noted that most of the traditional methods adopted manual features extraction which function similar to the filter. Manual extraction methods make them difficult to adapt to various scenarios and lack of stability.  
To put up with this bottleneck in the field of e-tongue, we proposed a novel features extraction method, which learns features through deep learning models automatically. The key idea behind our method is to transform the time series into time-frequency map by appropriate strategy so as to make full use of the advantages of CNN in images features extraction and pattern recognition. The structure of the proposed features extraction method is shown in Figure 7 and the algorithm consists of two steps. The first key step is to represent complex time series with features images by the appropriate strategy. The commonly used time-frequency representations of non-stationarysignals are Wigner-Ville Distribution (WVD) [35], short-time Fourier transform (STFT) [36], and Gabor transform [37]. However, considering the characteristics of the transient abruptness of the response signal of the e-tongue system, STFT was adopted to extract the time-frequency characteristics. The second step is to adopt CNN to extract features for learning the details hidden in the time-frequency map and pattern recognition automatically. We innovatively combine STFT with CNN for features extraction to build an automatic features extraction architecture. In other words, the proposed method based on CNN can be considered as a combination of a variety of representative or unknown but effective features extraction methods. Another point we want to emphasize is that the model unifies features extraction and classification steps under a single architecture, which purpose is to improve classification accuracy and the robustness of algorithms.
## 3 Proposed Method
### \(df_{m}\) Estimator Block
In addition, the precision of the estimation depends on the spectral resolution, and this resolution depends on \(T_{b}\). If this parameter is small, the spectral resolution will be bad. To avoid this problem, several peaks are used to estimate this distance. The employed peaks are the peaks between \(f_{\text{min}}\) and \(f_{\text{max}}\). For this reason, the distance between \(f_{\text{min}}\) and \(f_{\text{max}}\) must be enough to contain all the necessary peaks to improve the computation of the distance between peaks. To decrease the noise in the autocorrelation signal and to detect the peaks produced by the motor speed, every sample of the autocorrelation signal is compared to the threshold \(A_{\text{min}}\). If the sample value is lower than \(A_{\text{min}}\), it is set to zero, and if it is higher, it is kept:  
\[A_{\text{th}}[m]=\left\{ \begin{array}{ccc}0&\text{if}&A[m]<A_{\text{min}}\\ A[m]&&\text{otherwise}\end{array} \right. \tag{11}\]  
where \(A_{\text{th}}\) is the updated signal. To detect peaks in the autocorrelation, the last signal is derived according to:  
\[D[m]=A_{\text{th}}[m]-A_{\text{th}}[m-1] \tag{12}\]  
where \(D\) is the derivative of the signal. Then, there is a peak if the derivative is negative and its previous value is positive:  
\[D_{p}[m]=\left\{ \begin{array}{ccc}true&\text{if}&\text{D}[m]<0\text{ and }\text{D}[m-1]>0\\ false&&\text{otherwise}\end{array} \right. \tag{13}\]  
where \(D_{p}\) is a signal with Boolean values that indicates if there is a peak. This peak detection method alone is not sufficient; it detects not only the peaks produced by the motor speed but also the undesired small peaks produced by noise. For this reason, it is necessary to discard the peaks produced by noise. To do this, the distance between peaks is calculated, and then the statistical mode parameter of these distances is obtained. The distance calculated is a discrete measurement and measures the numbers of samples between peaks. Figure 5: Current spectrum autocorrelation obtained according to the proposed method. The mode statistical parameter is used to discard the peaks produced by noise. Every distance calculated is compared to the mode, and if the absolute difference is bigger than the threshold \(d_{a}\), this distance is discarded and deleted from the list. Finally, the mean distance is calculated from the values that have not been discarded. This mean distance is multiplied by the spectral resolution, \(R_{\text{Spectrum}}\):  
\[R_{\text{spectrum}}=1/T_{b} \tag{14}\]  
This new value is the estimation of distance between the frequencies that appear in the current spectrum.