esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup and Results
### Measurement System
#### 3.1.1 RF Front End  
The RF front end consists of four components which include:  
* Receiving dipole antenna
* RF bandpass filters
* Low noise amplifier (LNA)
* RF peak envelope detector  
A dipole receiving antenna has been used to receive the PD signal that is emitted from the dielectric material. The dipole antenna has the vertical polarization with the frequency range from  
Figure 8: Overview of the supervisory system.  
Figure 7: Block diagram of a PD signal measurement radiometer sensor system [34].  
20 MHz to 1 GHz with an omnidirectional response. Once received by the antenna, the signal is then passed to the RF filters. The experimental study suggests that PD signal bandwidth remains between 50-800 MHz, the used passbands have a frequency range from 30 to 75 MHz and 255 to 320 MHz [34; 35]. To remove unwanted signals that could be present in the monitored signal from various sources such as TV, FM, digital radio and private radio, RF filters have been used at these two bands as shown in RF front-end part of Figure 7.  
The function of the LNA is to increase the sensitivity of the sensor by providing a fixed gain value of 16.5 dB. The use of RF filters and LNA enables RF front end part of the system to generate the frequency response in the range of 30 to 75 MHz and 255 to 320 MHz with the noise figure value from 5-7 dB and passband gain value of 12-14 dB. By doing this, a 20-m range is obtained from the PD source which is far above the minimum set requirement of 10 m. The reason to set the minimum 10-m range is to make the system cost effective and practically usable for continuous PD monitoring and localization. The function of the envelope detector is to reduce the signal bandwidth by removing the RF component and leaving envelope only.
## 5 Current Status and Limitations of Data Interoperability with Landsat and Sentinel  
Since 2015, a number of researchers have worked to combine Landsat and Sentinel-2 data for particular applications, and more efforts to do so are emerging on a regular basis. In this section we provide the perspective from several applications research groups on the current status and limitations of interoperability between Landsat and Sentinel-2, and the expectations emerging from the broader international Earth observation community. These perspectives, in conjunction with the calibration results above, inform the recommendations for the calibration community provided in Section 6 below.
## 2 Related Work  
### Privacy Protection Methods in Federated Learning  
With the research on FL gradually developing, the security and privacy protection issues have attracted wide attention. Privacy protection methods in FL are mainly divided into three categories: secure multi-party computation (SMC), differential privacy, and homomorphic encryption.  
The key to SMC is zero knowledge, where both parties of communication know nothing but input and output. While this concept is idealized, its practical implementation often necessitates intricate protocols, leading to potential inefficiencies. [PERSON] et al. [21] proposed a scheme to mitigate the impact of unreliable users and protect the privacy of all users' information through secure two-party computation (2 PC). [PERSON] et al. [22] trained models with two semi-honest servers via the SMC framework, and they also designed a 3 PC framework [23] with an honest majority. But when the servers collude together, the security mechanism will be destroyed.  
Differential privacy is a privacy-preserving method, which masks the sensitive attributes of data by adding noise. [PERSON] et al. [24] proposed a scheme, SecProbe, which uses noise to perturb the loss function of deep neural network models. [PERSON] et al. [25] did clients' contributions by introducing differential privacy to protect local data in FL. However, adding noise inevitably has an impact on data availability. The solutions using differential privacy often involve a trade-off between utility and privacy [26].  
Homomorphic encryption protects data privacy by encrypting the parameters exchanged between clients and servers in FL. This enables the server to calculate the ciphertext of the parameters directly, which can achieve the same effect as the aggregation of plaintext. [PERSON] et al. [27] proposed a scheme to protect model parameter information using additively homomorphic encryption in collaborative deep learning. [PERSON] et al. [28] designed an algorithm that enables the server to aggregate the local model parameters which are encrypted using different keys. In practice, evaluating nonlinear functions in FL with additively homomorphic encryption requires polynomial approximation, which also leads to a trade-off between accuracy and privacy [29].  
Current privacy protection methods often compromise efficiency or accuracy. However, we offer personalized privacy protection for FL clients through hierarchical access control, representing a groundbreaking design. Clients and servers are categorized into different levels based on privacy requirements and security. The management of knowledge flow among varying security levels is executed through cryptography. Importantly, this solution does not compromise data availability or model accuracy.
# Fast Planar Detection System Using a GPU-Based 3D Hough Transform for LiDAR Point Clouds
## 4 Experiments and Analysis
### Multiple Fraction Integration  
Compared with dense point cloud data, which were evenly distributed and possess good point features, an LiDAR point cloud tended to be sparse and inhomogeneous, and valid point features were hard to extract. Considering that the LiDAR acquired environment data along the scan line, the point cloud had a higher density on horizontal scan lines. However, in the vertical direction, the spacing between the horizontal scan lines increased as the distance from the scanning centre rose, rendering point clouds in the vertical direction sparse and unevenly distributed. Therefore, the Hough transform method could be extensively used in plane detection from dense point clouds and in obtaining accurate detection results. However, given that the points on the horizontal scan lines were superior in numbers in sparse LiDAR point clouds, imprecise results, such as horizontal plane detection (Figure 9), would frequently emerge and lead to adverse effects on the detection of vertical walls. Therefore, the traditional Hough transform method was not applicable to plane detection of the LiDAR point cloud.  
Thus, the fraction-to-fraction Hough Transform method was employed in this study to detect plane surfaces in the environment, rather than the Hough Transform directly applied to obtain point clouds in the entire area. The collected point clouds were firstly divided into several fractions in different regions with a defined number of points. Then, the proposed parallel 3 DHT method was applied to one fraction in a single frame until all fractions have been processed. After all the Hough space buffers were established, peak cells in different buffers were extracted out using a 3D connected component labelling algorithm. The parameters of the multiple detected planar slides would be obtained, through which our detection targets, including the vertical walls in the sensed environment, could be merged and displayed. To merge the planar slides detected in different fractions, which belong to a same plane surface, a threshold \(\kappa\) was utilized to judge the parameter differences between several sub planes. In our application, the threshold \(\kappa\) was set as 3. If the \(\kappa\) is high, some non-coplanar planes were merged into one plane. If the \(\kappa\) was small, some coplanar planes couldn't be merged. Until all the sub planes' parameters were measured by the threshold, the merge process was finished with the detection result of target planes.  
Figure 9: Error in the plane detection result of the parallel three-dimensional Hough transform (3 DHT).
# Depth-Extrapolation-Based True-Amplitude Full-Wave-Equation Migration from Topography
## 2 Methods
### Estimation of the Vertical Partial Derivative of the Surface Wavefield  
Among the existing methods, the most used method to provide the vertical partial derivative has been proposed by [PERSON] and [PERSON] (2012). They assume that the velocity near the surface is laterally uniform. Under this assumption, we can calculate the vertical partial derivative from the wavefield value of the surface. In reality, the surface conditions on land are usually more complex, such as severe lateral velocity variation and complex terrain. In order to achieve full-wave-equation depth extrapolation under complex topography, we propose a new method to calculate the vertical partial derivative of the surface wavefield.  
The energy of the seismic wave is emitted from the local source, and the wavefront always has a certain degree of curvature. However, at a sufficient distance from the source, the wavefront is flat enough so that the plane wave approximation is locally correct.  
The seismic wave starts from the source and reaches the ground after being reflected by the reflectors. Figure 1 shows a microscopic part of the surface: point A is on the surface, point B is very close to point A. The distance between two points is \(r\). The angle between the raypath and the horizontal surface is \(\theta\). From the plane wave equation we can get the following:  
\[U_{A}=U_{B}e^{-jkr} \tag{1}\]  
where \(U\) is the wavefield, \(k=w/V\), and \(V\) is the P-wave velocity. From Equation (1) we can obtain  
\[U_{B}=U_{A}e^{ikr} \tag{2}\]  
The wavefield gradient can be obtained from Equation (2),  
\[\frac{dU_{B}}{dr}=jkU_{A}e^{ikr} \tag{3}\]  
When \(r\) approaches 0, we have  
\[\frac{dU}{dr}=jkU_{A} \tag{4}\]  
Since  
\[r=\sqrt{x^{2}+z^{2}}=z/\sin(\theta) \tag{5}\]  
where \(X\) and \(Z\) are the horizontal and vertical coordinates.  
Figure 1: Schematic diagram of the reflected wave at point A on the surface.  
We have  
\[\frac{\partial r}{\partial z}=\frac{1}{\sin(\theta)} \tag{6}\]  
Substituting Equation (6) into Equation (4), we obtain  
\[\frac{\partial U}{\partial z}(x,z=0,\omega)=\frac{jk}{\sin(\theta)}U_{A} \tag{7}\]  
In the field, geophones for reflected wave seismic exploration are generally arranged near the source. So the distance between the source and the geophones can be considered small compared to the distance from the source to the elastic interface which usually reaches up to several kilometers. The angle between the reflected wave and the horizontal surface is close to 90\({}^{\circ}\). Under this assumption, \(\theta=\pi/2\), Equation (7) can be written as  
\[\frac{\partial U}{\partial z}(x,z=0,\omega)=jkU_{A} \tag{8}\]  
According to Equation (8), the vertical partial derivative is calculated locally and trace by trace, hence it can handle surfaces with topography and lateral velocity variations.
## 6 Summary, Future Perspectives and Conclusions
One study in particular demonstrated that VOCs known to be abundant in exhaled breath of CRC patients, could be detected using MOS technology [83]. Application of MOS technology on fecal samples might be the next step in the development of tailor-made disease specific sensors. Although majority of the available studies demonstrate promising results, only a small subset of research groups aimed to validate obtained outcomes in an external cohort. In addition, obtained outcomes are rarely reproduced by other study groups, resulting in a wide variety of volatiles considered to be disease specific. Inter-study variation in study and sampling protocols currently impedes the ability to make reliable comparisons between studies. Therefore, before VOCs could live up to their potential as clinical biomarker, protocols regarding sampling conditions (sampling, transport, and storage) and analytical techniques need to be standardized. This is illustrated by the observation that a broad range of different sampling conditions significantly influence fecal VOC outcome, as described previously. Use of strict protocols on methodology will permit inter-study comparisons, rapidly increasing knowledge about fecal VOCs in both healthy and diseased individuals. In addition, being the waste-product of metabolic processes, detected VOCs may allow for unraveling pathophysiological mechanisms underlying a clinical condition. Next to identification of pathophysiological VOCs, it is first key to determine the spectrum of VOCs still considered to reflect a healthy state  
This leads us to the third hurdle needed to be taken before VOCs could be implemented as clinical biomarker in daily practice. To date, a total of 1840 VOCs have been identified as volatiles deriving from a healthy human body, of which 381 individual VOCs are linked to feces [84]. Presumably, factors including age, gender, body weight, gut microbiota composition, and life style, as well as variation in sampling techniques, all may significantly influence VOC composition. Again, this underlines the need for widespread standardization of sampling techniques and the protocol-driven use of analytical devices, allowing for inter-study comparison. To conclude, a continuously increasing number of studies have attempted to discriminate diseased from healthy individuals based on their fecal VOC profile. Although the majority of these studies demonstrate promising results, outcomes are yet rarely validated or reproduced. Several key issues need to be addressed before (fecal) VOC analysis could live up to its potential as diagnostic or monitoring tool. Especially efforts concerning universal standardization of fecal sampling and methodology, ultimately allowing for inter-study comparison, should receive priority. **Conflicts of Interest:** The authors declare no conflicts of interest.
# Metamaterial Based Design of Compact UWB/MIMO Monopoles Antenna with Characteristic Mode Analysis
###### Abstract  
In this article, a novel metamaterial inspired UWB/multiple-input-multiple-output (MIMO) antenna is presented. The proposed antenna consists of a circular metallic part which formed the patch and a partial ground plane. Metamaterial structure is loaded at the top side of the patches for bandwidth improvement and mutual coupling reduction. The proposed antenna provides UWB mode of operation from 2.6-12 GHz. The characteristic mode theory is applied to examine each physical mode of the antenna aperture and access its many physical parameters without exciting the antenna. Mode 2 was the dominant mode among the three modes used. Considering the almost inevitable presence of mutual coupling effects within compact multiport antennas, we developed an additional decoupling technique in the form of perturbed stubs, which leads to a mutual coupling reduction of less than 20 dB. Finally, different performance parameters of the system, such as envelope correlation coefficient (ECC), channel capacity loss (CCL), diversity gain, total active reflection coefficient (TARC), mean effective gain (MEG), surface current, and radiation pattern, are presented. A prototype antenna is fabricated and measured for validation.  
metamaterials; mutual coupling; multiple input multiple output; characteristic modes 4.0/. +
Footnote †: journal: Applied Sciences
## 5 A Detailed Case of CPSS-UPTDN
### Prediction Model Construction and Analysis  
According to the processed data, we leverage the deep learning model named stacked autoencoder (SAE) to predict the traffic status and analyze the travel demand of residents, providing data support for decision-making. The model we used and its hyper-parameters are shown in Figure 8. It is composed of a multi-layer sparse autoencoder. The bottom layers are responsible for extracting the features of the data and pre-training the network. The top layer is a predictor that performs regression to predict results.  
The inputs of the model are divided into two parts, one of which receives one-hot labeled cellular data while the other receives the historical patency rate after normalization. The output of the network is the patency rate we predict. By training the network with the dataset, we can obtain an effective prediction model. Using the model with current inputs, we can analyze the crowd distribution in bus routes and predict traffic patency and residents' travel demand more accurately, and then provide the shortest and fastest traffic routes for travelers. Besides, a comparative model that does not consider travel demand is conducted, and then the impact of multi-modal data on model is analyzed.  
Figure 8: The structure of the SAE network.  
Figure 7: (**a**) The travel demand obtained from cellular data of 12 traffic units around TAZ and (**b**) divide it into 10 categories from 8:00 to 20:00.  
Two commonly used performance metrics are adopted to evaluate the results:
* Mean absolute error (MAE): \[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert,\] (1)
* Mean absolute percentage error (MAPE): \[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\frac{\lvert y_{i}-\hat{y}_{i}\rvert}{y_{i}},\] (2) where \(n\) represents the number of samples, \(y_{i}\) is the observed values and \(\hat{y}_{i}\) is the predicted results.  
The historical step of patency rate is set as 5 to predict the status in the next period, and the performance of the two models is demonstrated in Table 1. We conduct the experiments using Keras on a desktop with CPU (Intel i7-8700) and GPU (Nvidia GTX-1060). The results are the average of 10 independent experiments. It can be seen that the time cost is acceptable when hourly travel demand is introduced. The predicting results are shown in Figure 9.  
The performance of prediction improves by 6.12% and 5.79% after appending the population travel demand feature, which reflects the fluctuation of traffic patency around Suz  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
Figure 9: The comparison between SAE model with and without travel demand obtained from cellular data.
# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
###### Abstract  
With the emergence of deep learning, the performance of automatic speech recognition (ASR) systems has remarkably improved. Especially for resource-rich languages such as English and Chinese, commercial usage has been made feasible in a wide range of applications. However, most languages are low-resource languages, presenting three main difficulties for the development of ASR systems: (1) the scarcity of the data; (2) the uncertainty in the writing and pronunciation; (3) the individuality of each language. Uyghur, Kazakh, and Kyrgyz as examples are all low-resource languages, involving clear geographical variation in their pronunciation, and each language possesses its own unique acoustic properties and phonological rules. On the other hand, they all belong to the Altaic language family of the Altaic branch, so they share many commonalities. This paper presents an overview of speech recognition techniques developed for Uyghur, Kazakh, and Kyrgyz, with the purposes of (1) highlighting the techniques that are specifically effective for each language and generally effective for all of them and (2) discovering the important factors in promoting the speech recognition research of low-resource languages, by a comparative study of the development path of these three neighboring languages.  
overview; automatic speech recognition; low-resource; Uyghur; Kazakh; Kyrgyz
## 2 Sampling Design, Sample Quality Control, and Spatial Reference Systems
### Spatial Reference Systems  
To generate spatial predictions (i.e., spatially continuous data) for a region using spatial predictive models, two types of georeferenced data are required: (1) point samples of response and predictive variables; and (2) grid data of predictive variables. Such georeferenced data are often stored according to various spatial reference systems [43]. The spatial reference system used to project or store the spatial information is often assumed to have certain effects on the performance of predictive models; thus, in practice, various spatial reference systems were used to minimize such effects [43]. When a study area is small and within one particular UTM zone, spatial data are often projected using either the UTM zone or an appropriate projection system; when the study area is spanning multiple UTM zones, the existing geographic coordinate system (i.e., WGS84) or another appropriate projection system can be used.  
Although the spatial reference system by which the spatial information is stored is often considered as a potential source of error for spatial predictive modeling, a series of studies demonstrated that the effects of spatial reference systems on the performance of spatial predictive methods (i.e., inverse distance weighting and ordinary kriging) are negligible for areas at various latitudinal locations (up to 70 dd) and spatial scales (i.e., regional and continental) [43; 44; 45]. Therefore, it was recommended that spatial reference system selection and re-projection can be removed for spatial predictive modeling for areas with latitude less than 70 dd, and spatial data can be modeled in WGS84 or the spatial projection system already used for the data. Although new spatial reference systems (e.g., DGGS [46]) may be developed to remedy various limitations of existing ones, the above recommendation may still be applicable as discussed previously on why the effects of spatial reference systems on the predictive accuracy are negligible [43; 44; 45].
## 1 Introduction
Therefore, the difference between calculating the thermal transmittance in steady-state and in transient conditions was evaluated for all the simulated walls. This derives from the fact that most construction materials are porous, having the capacity to store fluids [6]. A higher value of moisture content in a porous material causes a higher value of thermal conductivity and, consequently, a higher value of the wall's thermal transmittance [6], which means that for the same value of area and temperature difference, a higher amount of heat is transmitted through the element. In this study, four types of walls were simulated: (1) a solid brick wall, (2) a rendered solid brick wall, (3) a plastered solid brick wall, and (4) a rendered and plastered solid brick wall. The simulations were computed for lime (L), cement-time (LC), and cement (C) mortars. These three types of mortars were selected as an example of the various moisture transport characteristics of wall finishings and to illustrate the different behaviours they may induce on the performance of specific walls. These assemblies were simulated for the following four climates: Zurich (ZR), Essen (ES), San Francisco (SF), and Lisbon (LX). The changes that the outdoor climate is expected to suffer in the future will differ in accordance with the location [23; 24]. While a general increase in the outdoor temperature is expected, in terms of precipitation, a substantial increase is expected for northern Europe, while a substantial decrease is expected in southern Europe [23; 25]. However, in Lisbon, an increase in extreme rain events associated with strong winds is also expected [26]. Hence, this behaviour is studied in this paper to see if this means that the outdoor moisture will be able to reach deeper thicknesses in the assembly. For this purpose, four future periods of time--near future and far future for RCP 4.5 and 8.5--will be assessed for Lisbon in order to identify the respective WDR spells in accordance with the methodology of EN ISO 15927-3 [27] and BS 8104 [28]. Finally, the hygric effect of the most demanding spells will be assessed using simulation software (WUFI(r)Pro).