esa-sceva/satcom-chunk-collection huggingface dataset:
# Line Beam Scanning-Based Ultra-Fast THz Imaging Platform
## 1 Introduction  
In order to contain the increasing threat of terrorism, public security screening systems, especially at airports and railway stations, are becoming increasingly significant. Classical security screening systems include metal scanners for people and X-ray detectors for luggage and have been widely used [1; 2; 3; 4]. Unfortunately, these traditional techniques are no longer capable of fulfilling the demands due to their low detection efficiency and high false alarm rate; therefore, developing rapid and precise security screening system is required [5; 6].  
Recently developed X-ray imaging can easily detect contraband, even concealed under cloths due to the strong X-ray absorption by metals [7; 8; 9]. However, considering its high photon energy and ionizing potential, it is harmful to human bodies and only suited for luggage detections. Different from X-ray imaging, THz imaging can also provide target details in another perspective [10; 11; 12; 13; 14; 15; 16]: After penetrating the cloths, THz wave is often absorbed by the human body but strongly reflected by metals; therefore, the received reflected THz wave marks the metallic objects [17]. Compared to X-ray, THz wave has a much lower frequency band, which is nonionizing and poses no known health risks [18; 19; 20; 21; 22], indicating that THz imaging is especially suitable for security screening for both people and luggage, though there is a lack of efficient sources and detectors [23; 24; 25]. The currently proposed THz imaging systems include two fundamental tactics based on synthetic aperture radar (SAR) [26; 27] and multiple-input multiple-output (MIMO) [28; 29; 30; 31], respectively. SAR-based THz imaging suffersfrom slow imaging efficiency for each scan, limiting their applications in high-speed security screening. In order to accelerate the efficiency, MIMO-based THz imaging can reduce the scanning time using parallel measurement with an entire array of transmitters and receivers, illustrating that MIMO-based THz imaging is preferred for high-speed security screening platform construction. Here, in order to further accelerate the THz imaging speed, as well as to simplify the system, a line beam scanning-based ultra-fast THz imaging platform is proposed. Its optical system is rather simple, only composed of a source/sensor chip for THz wave emitting and receiving, a cylindrical reflector for wavefront reshaping, and a scanning mirror for line beam scanning. To assemble the optical system, as well as to control the mirror for line beam scanning, a lightweight mechanical system is designed and fabricated. The designed THz imaging platform realizes human body scanning within 0.17 sec in a high resolution of 12 mm and easily distinguishes metallic objects, it is believed the proposed line scanning-based ultra-fast THz imaging platform is a potential tool for fast safe public screening applications.
## 5 Conclusions  
In this work, a new type of MR damper having a specific pole shape function (MRD-PSF in short) was proposed and its effectiveness was demonstrated through computer simulations. After explaining the operating principle of the proposed damper with the schematic configuration, the field-dependent damping forces (or coefficients) characteristics are analyzed considering the main orifice and the bypass orifice. The activation of the main orifice can alter the magnitude of the damping coefficient, while the activation of the bypass orifice can change the position of the SBP. Subsequently, a certain desired damping force imposed in a wide stroke (or piston velocity) range can be realized by controlling input currents to be applied to the main orifice and bypass orifice, respectively. In other words, the bilinear damping characteristics required in real operation of vehicle suspension systems to achieve high ride comfort and steering stability can be realized by activating two independent parameters of the main orifice and bypass orifice in the open-loop control manner.  
It is finally remarked that to validate the modelling and simulation shown in this work, the prototype fabrication of the MRD-PSF is ongoing. Its damping force characterization will be empirically evaluated and its application to the vehicle suspension system will be carried out in the future. In addition, control performance of a vehicle suspension system equipped with the proposed MR damper is to be carried out in the future to enhance both ride comfort and road holding property.  
Conceptualization, D.-S.Y. and B.-G.K.; methodology, B.-G.K, software, A.S.T., writing-review and editing, G.-W.K. and T.S., supervision, S.-B.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare that they have no conflict of interest regarding the publication of this article.  
Figure 16: Three examples of desired damping characteristics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Parameter**} \\ \cline{2-5}
**Type** & \(c_{1}\) [Ns/m] & \(V_{b}\) [m/s] & \(I_{M}\) [A] & \(I_{B}\) [A] \\ \hline Case 1 & 11000 & 0.1 & 2.33 & 1.56 \\ Case 2 & 8000 & 0.05 & 0.27 & 1.24 \\ Case 3 & 3000 & 0.3 & 0.53 & 0.54 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Required input current \(I_{M}\) and \(I_{B}\) for implementing some desired \(c_{1}\) and \(V_{b}\).
# Interpretation for Variational Autoencoder Used to Generate Financial Synthetic Tabular Data
## 3 Materials and Methods
### Sensitivity Analysis  
The essence of a sensitivity analysis is taking first-order partial derivatives of outputs with respect to inputs in a neural network. First-order partial derivative, measuring changes in an output due to changes in an input, is also known as sensitivity:  
\[s_{uv}(x_{n})=\frac{\partial y_{u}}{\partial x_{v}}\;, \tag{1}\]  
where \(x_{n}\) represents the nth data sample in the dataset; and \(s_{uv}\) represents the sensitivity of the nth neuron in the output layer (\(y_{u}\)) with respect to the vth neuron in the input layer (\(x_{v}\)) [19]. The notation used throughout the rest of the paper is outlined in Table 1.  
Step 1 (Sensitivity):  
To adapt a sensitivity analysis to fit into an encoder of a VAE, the sensitivity can be modified as:  
\[s_{hm,\mu}(x_{n})=\frac{\partial\mu_{h}}{\partial x_{m}}\;, \tag{2}\]  
\[s_{hm,\;\sigma}(x_{n})=\frac{\partial\sigma_{h}}{\partial x_{m}}\;, \tag{3}\]  
where \(h\) is the index of means and standard deviations in the latent space of a VAE; \(m\) is the index of input features; \(s_{hm,\mu}(x_{n})\) refers to the sensitivity of \(h\)th mean in the latent space of a VAE (\(\mu_{h}\)) with respect to \(m\)th input feature (\(x_{m}\)) for the data sample \(x_{n}\); and \(s_{hm,\sigma}(x_{n})\) refers to the sensitivity of \(h\)th standard deviation in the latent space of a VAE (\(\sigma_{h}\)) with respect to \(m\)th input feature (\(x_{m}\)) for the data sample \(x_{n}\). Step 2 (Local Feature Importance):  
A local interpretation of each input feature refers to the case that only a single data sample in the dataset is studied. In this paper, we determine the effects of each input feature on the latent representation for a specific data sample by considering both the overall sensitivity of mean and the overall sensitivity of standard deviation with respect to each input feature:  
\[ \begin{array}{ll}s_{m}(x_{n})&=s_{m,\mu}(x_{n})+s_{m,\sigma}(x_{n})\\ &=\sum\limits_{h=1}^{H}\left|s_{hm,\;\mu}(x_{n})\right|+\sum\limits_{h=1}^{H} \left|s_{hm,\;\sigma}(x_{n})\right|\;,\end{array}  \tag{4}\]  
where \(s_{m}(x_{n})\) represents the sensitivity of the latent space to input feature \(x_{m}\) for data sample \(x_{n}\) and \(H\) is the total number of means or standard deviations in the latent space  
Figure 1: Architecture of the Variational Autoencoder (VAE). of a VAE. Taking the absolute values of \(s_{hm,\,\mu}(x_{n})\) and \(s_{hm,\,\sigma}(x_{n})\) is designed to prevent cancellation of positive sensitivity values with negative ones. Step 3 (Global Feature Importance):  
In order to obtain a global interpretation, the mean squared sensitivity introduced by [PERSON] et al. in 1994 is modified to fit a VAE model [24]:  
\[S_{hm,\mu}^{sq}=\sqrt{\frac{\sum_{n=1}^{N}(s_{hm,\,\mu}(x_{n}))^{2}}{N}}\, \tag{5}\]  
\[S_{hm,\,\sigma}^{sq}=\sqrt{\frac{\sum_{n=1}^{N}(s_{hm,\,\sigma}(x_{n}))^{2}}{N }}\, \tag{6}\]
## 1 Introduction
Several studies have provided a comparative analyses of the relationships between fluorescence emissions at O\({}_{2}\)A and O\({}_{2}\)B bands, revealing also the relationships of the emissions to canopy photosynthesis [22; 23; 24]. Complexity in diurnal SIF dynamics in natural stands arises from nonhomogeneous canopies, which can create a highly dynamic light environment that influences photosynthesis and NPQ within the canopy [25]. At certain solar angles, shaded leaves may be abruptly exposed to higher light intensities, e.g., sunflecks, causing more photosystem II (PSII) reaction centers to be occupied while NPQ concomitantly increases to offset the absorption of excess light energy. As a result, canopy structure, plant physiology, and solar geometry interactively impact the emission of SIF, and such interaction depends on canopy architectures. Solar zenith and azimuth angles change the distribution of light within the canopy and thereby affect the detected fluorescence signals. The ratio of fluorescence peaks is found to be sensitive to nitrogen content, water status, and temperature stress in plants [26] and, hence, have attracted attention in similar comparative studies [22; 23]. The possibility of employing the ratio of fluorescence estimated at the oxygen bands (i.e., 687 nm and 760 nm) for photosynthesis detection was studied by [PERSON] et al. [27]; the ratio is more frequently used to make comparisons with foliar characteristics between days. The current limited knowledge of how optical features correlate with photosynthesis precludes the generalization of any measured data to large areas and across long time periods. Integrated data, mainly those that map seasonal variations of fluorescence at fine temporal resolution, are scarce. The growing network of flux towers is capable of hosting tower-mounted spectrometers and, thus, allows intensive and extensive measuring fluorescence seasonality. The results of such measurements can be used to further test the potentials and limitations of spectral measurements for monitoring ecosystem functioning. In this study, we make comparisons between on-site estimated fluorescence at O\({}_{2}\)A and O\({}_{2}\)B oxygen bands; we show how they are influenced by canopy structure, NDVI, and APAR, and we demonstrate how these fluorescence emissions are related to canopy Gross Primary Productivity. This work uses a dataset already presented in [PERSON] et al. [28] but further extends analysis with a consideration of fluorescence emissions dynamics at the O\({}_{2}\)B band. We show also diurnal trends in fluorescence emissions. The data originate from automated spectroradiometers that were placed above evergreen spruce and deciduous beech forests to study the dynamic changes in fluorescence along with the changes in NDVI and GPP. We aimed to explore how the changes in optical parameters were correlated with changes in photosynthetic CO\({}_{2}\) uptake and environmental factors.
## 3 MLE Parameter Estimation Model  
In the case of an unknown prior distribution of estimated parameters, MLE provides the parameter estimation with the smallest error variance when the parameters to be estimated is symmetric and unimodal distributions. In terms of the linear discrete-time system, linear KF is the optimal filter [28]. Therefore, the MLE combined with KF can make an accurate estimation of the parameters in the carrier loop, which can improve the tracking loop performance. The MLE is used to nonlinearly estimate the carrier loop parameters of interest, and then KF is applied to smooth the estimated parameters from MLE. Therefore, the proposed algorithm of combining MLE with KF is the optimal solution for the TC-OFDM carrier frequency accurately tracking in the weak TC-OFDM signal environment.  
Figure 2: Conventional carrier loop structure.
# An Integrated Algorithm for Extracting Terrain Feature-Point Clusters Based on DEM Data
## 4 Conclusions  
Terrain feature-point clusters determine the overall structure and characteristics of the terrain. They play a vital role in terrain representation and reconstruction, hydrological analysis of watersheds and other scientific studies and engineering applications. This work proposes an integrated extraction method for terrain feature-point clusters. Firstly, this method combines the morphological characteristics and runoff simulation to determine the best flow accumulation threshold quantitatively, and extracts the ridgelines based on this threshold. Then, the integrated terrain feature clusters are extracted using space segmentation and spatial analysis.  
Experiments on six typical sample areas in Shaanxi Province show that the terrain feature-point clusters extracted by the proposed method are not only accurate in spatial position, but also have a good coupling relationship with each other, which is consistent with geomorphological and hydrological cognition. The point clusters' attribute statistics show that there is an exponential dependency \(y=ae^{bx}\) and a linear dependency \(y=cx+d\) of the mountain control area size, and of the mountain undulation, respectively, from the HDPS. Meanwhile, the fitting coefficients have a great correlation with the landform types. The fitting coefficient \(a\) decreases with the decrease of terrain undulation as a whole, while the coefficients \(b\) and \(d\) increase. In addition, there is a good correspondence between the spatial distribution trend of point cluster attributes and the geomorphological zoning boundaries of Shaanxi Province.  
In summary, the point cluster extraction results of the proposed method are consistent with geomorphological cognition, and their attributes have important geomorphological significance, which can serve for geomorphological zoning and mapping. However, due to the limitations of DEM data accuracy and topographic undulation size, the terrain feature-point clusters are relatively poorly depicted in plain areas, which needs further research.  
Funding acquisition, M.L.; methodology, J.H. and M.L.; writing--original draft, J.H.; validation, L.B. and J.D.; writing--review and editing, M.L. and B.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number: 41871324).  
The authors appreciate the USGS for the free deliveries of the SRTM DEM data. Meanwhile, the authors appreciate [PERSON] for his valuable suggestions on this paper's revision.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
###### Abstract  
The present study investigates the potential of using fecal ash as an adsorbent and demonstrates a self-sustaining, optimized approach for urea recovery from wastewater streams. Fecal ash was prepared by heating synthetic feces to 500 \({}^{\circ}\)C and then processing it as an adsorbent for urea adsorption from synthetic urine. Since this adsorption approach based on fecal ash is a promising alternative for wastewater treatment, it increases the process' self-sustainability. Adsorption experiments with varying fecal ash loadings, initial urine concentrations, and adsorption temperatures were conducted, and the acquired data were applied to determine the adsorption kinetics. These three process parameters and their interactions served as the input vectors for the artificial neural network model, with the percentage urea adsorption onto fecal ash serving as the output. The Levenberg-Marquardt (TRAININLM) and Bayesian regularization (TRAININBR) techniques with mean square error (MSE) were trained and tested for predicting percentage adsorption. TRAINBR was demonstrated in our study to be an ideal match for improving urea adsorption, with an accuracy of R = 0.9982 and a convergence time of seven seconds. The ideal conditions for maximum urea adsorption were determined to be a high starting concentration of 13.5 g.L\({}^{-1}\); a low temperature of 30 \({}^{\circ}\)C, and a loading of 1.0 g of adsorbent. For urea, the improved settings resulted in maximum adsorption of 92.8%.  
optimization algorithm; closed-loop sanitation; human waste management; Bayesian regularization; Levenberg-Marquardt algorithm; adsorption +
Footnote †: journal: Applied Sciences
## 2 Methodology of SSI-COV  
Stochastic subspace algorithms are methods used in signal processing and dynamic system analyses to extract relevant information from observed data. These algorithms focus on matrix decomposition and the identification of subspaces that capture fundamental characteristics of the data to identify the dominant subspaces that may correspond to vibration modes in structural systems [26; 27].  
One of the key tools in this approach is the Hankel matrix, which provides a structured representation of the system's dynamics by organizing the output data into past and future observations. This matrix is pivotal for identifying the system's modal characteristics,as it encapsulates both the temporal correlations and the dynamic interactions of the measured outputs.  
The Stochastic Subspace with Covariance [28; 29] algorithm uses covariance information between the different measured signals to identify the relevant subspaces that capture the system's dynamic characteristics. This is especially useful when the system contains noise, as it separates the signal of interest from the present noise. Additionally, SSI-COV can be used when the forces within the system are unknown, meaning that it can be applied to structures under operational conditions without the need for applying known test forces.
## 3 Results and Discussions
### Polarized Optical Microscopy Observation  
The overall crystallization kinetics and crystal growth kinetics of the composites were obtained using DSC method above. However, the impact of GO and GO-H202 on the crystalline morphology of the samples was still unknown. To understand fully, polarized optical microscopy obervation during isothermal crystallization process was performed. The samples were heated at 200 \({}^{\circ}\)C for 5 min and then rapidly cooled to 130 \({}^{\circ}\)C for isothermal crystallization.  
Figure 10 shows the morphological evolution of the samples during the isothermal crystallization at 130 \({}^{\circ}\)C. It can be seen that at 5 min after crystallization began, for neat PP, some sporadic nuclei are visible, while for GPP and GHPP, much more amounts of nuclei can be seen, indicating that after the addition of GO or GO-H202, the nucleation rate of the composites is evidently enhanced, which is in accordance with the DSC analysis above; as the crystallization time increases, at 10 min, it can be observed that the spherulites grow continuously; at 15 min, GHPP finished crystallization, and the crystallites occupied the whole image, while for neat PP and GPP, the crystallization was not finished yet.  
Figure 10: _Cont._  
Figure 9: Lauritzen–Hoffman kinetic theory plot for PP, GPP, and GHPP.  
Interestingly, the shape of the crystallites of GHPP is quite different from GPP and neat PP. Considering that the Avrami exponent \(n\) changed after the addition of GO-H202 (Figure 6d), it is proposed that the addition of GO-H202 might change the nucleation and crystal growth manner and further change the crystallite morphology of the composites.
## 4 Results
### Eye Patterns of Decisions  
Table 1 displays the mean and standard deviation of liking and eye-tracking metrics, differentiated by the chosen stimuli and the distractors. Repeated measures ANOVAs  
Figure 4: Correlation matrix between liking and eye-tracking metrics.  
were performed to analyze differences in each metric based on the decision group. Furthermore, post-hoc analysis was conducted for pairwise comparisons between groups (Figure 5). Statistical differences in liking were presented according to the decision group (F(7,1337) = 34.24, \(p\) < 0.001). The chosen stimuli exhibited higher liking in comparison to all distractors, with a substantial gap observed between the chosen stimuli and the rest. Gaze time also showed differences based on the decision group (F(7,1337) = 314.95, \(p\) < 0.001). The chosen stimuli recorded a higher gaze time relative to all distractors. It was noted that groups with very low values displayed an overlap within the confidence intervals of the pairwise analysis. In addition, visits also presented differences (F(7,1337) = 40.79, \(p\) < 0.001). The chosen stimuli exhibited differences only with the 6 th and 7 th distractors. Consequently, in general, there are no differences between the chosen stimuli and the first distractors, but there are noticeable differences with the later distractors. Lastly, statistical differences in the time to first gaze were identified in relation to the decision group (F(7,1337) = 40.19, \(p\) < 0.001). The chosen stimuli showed differences with all distractors except for the first one.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Decision** & **Liking** & **Gaze Time** & **Visits** & **Time to First Gaze** \\ \hline Chosen & 1.639 (1.197) & 2.123 (0.818) & 3.506 (1.583) & 1.9 (1.656) \\ \(1^{\circ}\) dis. & 0.569 (1.452) & 1.657 (0.78) & 3.374 (1.888) & 1.499 (1.497) \\ \(2^{\circ}\) dis. & 0.278 (1.425) & 1.253 (0.661) & 2.947 (1.585) & 1.487 (1.382) \\ \(3^{\circ}\) dis. & 0.09 (1.476) & 0.931 (0.51) & 2.511 (1.342) & 2.097 (1.743) \\ \(4^{\circ}\) dis. & 0.072 (1.524) & 0.781 (0.434) & 2.448 (1.518) & 2.395 (1.889) \\ \(5^{\circ}\) dis. & \(-0.063\) (1.427) & 0.615 (0.386) & 2.257 (1.538) & 2.449 (1.859) \\ \(6^{\circ}\) dis. & \(-0.086\) (1.455) & 0.484 (0.329) & 2.043 (1.385) & 3.002 (1.909) \\ \(7^{\circ}\) dis. & \(-0.205\) (1.562) & 0.374 (0.283) & 1.795 (1.19) & 3.724 (2.069) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean and standard deviation of the liking and eye-gaze metrics by decision.  
Figure 5: Post-hoc analysis of the ANOVAs of liking and eye-tracking metrics by decision. Blue represents the stimuli chosen, while red and grey represent a group with significant and non-significant differences respectively against chosen stimuli.  
The circles and bars in Figure 5 represent the means and confidence intervals for the groups based on [PERSON]'s honestly significant difference procedure. Two group means are significantly different if their intervals are disjoint. Blue represents the stimuli chosen, while red and grey represent a group with significant and non-significant differences respectively against chosen stimuli.