esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Description
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**KT (min)**} & \multirow{2}{*}{**“library**} & \multirow{2}{*}{**CAS \#**} & **U \(\rightarrow\) \(M\) \(\downarrow\) \#** & **U \(\rightarrow\) \#2** & **U \(\rightarrow\) \#3** & **U \(\rightarrow\) \#4** & **Ion** \\  & & & & **Math \%** & **CAS \#** & **(day \%** & **day \%** & **day \%** & **day \%** & **day \%** \\ \hline \multicolumn{10}{c}{**MS Detector Response, Peak Area Counts (PACM), Arbitrary Units**} \\ \hline
1 & Acetone & 3.24 & 68 & 67-64-1 & 358,744 & 380,600 & 474,903 & 430,899 & 430(100), 580(400), 420(100), 395 \\ \hline
2 & 2,4+Headermal & 5.33 & 81 & 110-62-3 & 131,308 & 1,653,133 & 725,418 & 810(000), 96(400), 580(300), 65(100) \\ \hline
3 & Pentural & 5.56 & 88 & 110-62-3 & 1,344,963 & 1,653,133 & 725,418 & 1,945,524 & 440(100), 416(60), 580(57), 430(400) \\ \hline
4 & Toluene & 7.11 & 85 & 108-88-3 & 803,174 & 893,198 & 1,315,663 & 91(1000), 92(600), 650(510) \\ \hline
5 & Hexant & 8.06 & 95 & 66-25-1 & 242,377/673 & 29,383,184 & 11,078,905 & 157,770,078 & 564(100), 416(60), 570(540), 390(300), 340(300) \\ \hline
6 & (\(\mathcal{Z}_{2}\)-Pureureure & 8.80 & 63 & 646-08 & & & & & & \\ \hline
7 & 1-Pentanol & 9.37 & 72 & 71-41-0 & 6,742,800 & 6,744,690 & 9,929,407 & 55(1000), 420(100), 415(700), 390(35), 5730(300) \\ \hline
8 & Pentune, 1-chloro- & 9.4 & **99** & 543-99- & 4,209,417 & & & & & \\ \hline
9 & 2-Butylfuran & 10.21 & 68 & 4466-24-4 & & 1,463,055 & 1,005,109 & 81(000), 41(500), 50(602), 124(52), 532(50) \\ \hline
10 & 2-Butylfuran & 10.54 & 86 & 110-43-0 & 2,818,994 & 6,796,498 & 9903,958 & 5,096,800 & 43(100), 805(75), 173(110) \\ \hline
11 & \(\mathcal{u}\)-Pione & 10.88 & 91 & 80-6-8 & 16,646,791/8 & 46,903,540 & 9,096,994 & 25,095(63) & 93(000), 919(200), 293(700), 690(402), 410(25) \\ \hline
12 & 1-Butyloon, 3-methyl-y, acetate & 10.97 & 76 & 1229-292 & & 1,560,406 & & & & \\ \hline \multicolumn{10}{c}{**Note: U + \(M\)\(I\) + \(P1\) (day) = Carpet + sub-flooring + cat urine + \(M\). _litens_ + Product 1 (day 0). U + \(M\)\(I\) + \(P2\) (day 0) = Carpet + sub-flooring + cat urine + \(M\). _litens_ + Product 2 (day 0). U + \(M\)\(I\) + \(P3\) (day 0) Carpet + sub-flooring + cat urine + \(M\). _litens_ + Product 3 (day 0). U + \(M\)\(I\) + \(P4\) (day 0) = Carpet + sub-flooring + cat urine + \(M\). _litens_ + Product 4 (day 0). \"library spectral match \(\pm\)60\%**=” **bolded.**} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Example of a summary of volatiles emitted on day zero from carpet + subflooring + cat urine + \(M\). _litens_ + Product 1, carpet + subflooring + cat urine + \(M\). _litens_ + Product 2, carpet + subflooring + cat urine + \(M\). _litens_ + Product 3, carpet + subflooring + cat urine + \(M\). _litens_ + Product 4. Complete data is summarized in Table S3 (Supplementary Materials).
## 5 Simulation Results
### Classification Results  
Four classifiers were used: BP neural network, grey relation analysis, random forest, and K-nearest neighbor. The training dataset contained 25,600 samples. From \(-\)5 dB to 10 dB, there were 200 samples per signal under each SNR. The testing dataset contained 25,600 samples. From \(-\)5 dB to 10 dB, there were 200 samples per signal under each SNR. The parameters of the BP neural network were as follows: the number of hidden layers was 120. The training time was 500. The training accuracy was 0.04, and the learning rate was 0.1. The number of trees grown of the random forest was 300.  
The confusion matrices of the eight signals under 0 dB are given in Figure 5.  
The simulation results indicated that the BP neural network and random forest classifiers performed better at SNR = 0 dB. For eight different kinds of communication modulation signals, the 4 ASK, 2 FSK, 16 QAM, and 32 QAM had better recognition rates than the others. The recognition rate of 8 FSK was the worst. The classification errors of 8 FSK were the largest among the eight different signals.  
Similarly, in order to analyze the recognition effect under high SNR, the confusion matrices of four classifiers under 10 dB are given in Figure 6.  
As shown in Figure 6, among the eight different signals, 8 FSK was again the worst recognition rate. At 10 dB, the recognition rate of the random forest classifier was the best. The BP neural network had a great ability to distinguish most signals, but the recognition rate of 8 FSK was not very good, the number of correct classifications was 0. The recognition rates of the grey correlation and K-nearest neighbor classifiers were poor, but the K-nearest neighbor classifier was better than the grey correlation classifier. On the whole, compared with 0 dB, the recognition rate was significantly improved at 10 dB SNR.  
Figure 5: Confusion matrices at 0 dB signal-to-noise ratio (SNR).  
Figure 7 shows the overall recognition rate curves under different SNRs. It indicates that the recognition rate of the four different classifiers was about 45% at SNR = \(-\)5 dB. With increasing SNR, the recognition rate increased continuously. It was stable at SNR = 8 dB. The BP neural network, grey relational, and K-nearest neighbor classifiers stabilized at 85%, but the random forest classifier stabilized at 96%. In addition, the recognition rate of the random forest classifier was better than the others in the whole SNR range. Therefore, the recognition performance of the random forest classifier was optimal.  
Figure 6: Confusion matrices at 10 dB SNR.  
Figure 7: Recognition rate curve under different SNRs.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 3 Grid Operations
### Metering, Billing, and Customer Service in Operation  
The integration of digital technologies, especially smart meters, is reshaping metering, billing, and customer service in distribution power systems, marking a shift towards more efficient, reliable, and customer-oriented operations. Study [82] underlines the intricate analysis enabled by smart meter data, from load forecasting to detecting non-technical losses (NTLs), emphasizing the role of smart meters in enhancing power network operations. Additionally, Ref. [64] delves into direct load control features within smart meters, improving demand management and customer engagement through remote connectivity services.  
The research in [197] introduces an IoT-based framework for real-time power quality measurement, advancing metering technology by providing immediate insights into power metrics. Study [198] explores advanced metering infrastructures leveraging real-time data for identifying technical losses and preventing unauthorized energy consumption, showcasing the vast potential of digital tools for optimizing energy distribution.  
Furthermore, Ref. [38] discusses the transformative impact of big data analytics on SGs, linking operational enhancements in metering and billing to improvements in customer service. This connection highlights the crucial role of digital innovations in driving more streamlined, responsive, and sustainable power distribution systems.  
The adoption of digital tools across metering, billing, and customer interactions signals a significant move towards modernizing power grids, enhancing functionality from smart metering to IoT-based monitoring and big data analytics. These advancements collectively drive operational excellence and consumer satisfaction. Despite challenges like privacy and cybersecurity, ongoing research is dedicated to addressing these issues, with the goal of boosting consumer engagement and refining grid optimization for an automated, customer-centric energy landscape.
# Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna
## 1 Introduction
In recent years, deep convolutional networks have made breakthroughs in many areas such as hand gesture recognition [15]. In general, it consists of a convolutional layer, normalized layer, pooling layer and activation function in deep convolutional networks. Moreover, the different sizes of convolutional kernels are used to extract local spatial information. The commonly used framework of deep convolutional networks is ResNet [16] and DenseNet [17]. This paper applies the deep convolutional networks to ADS-B signal separation. By learning the feature relationship between the overlapping and separated signals, the ADS-B overlapped signals are better separated. The single antenna ADS-B signal separation problem is similar to the speech separation problem. In speech separation based on deep learning methods, the framework including the encoder, separation and decoder is commonly used. The separation model can be divided into three approaches depending on the network types. The first approach [18; 19; 20] uses 1D convolution network with different size kernels to extract separation mask. However, the long temporal information cannot be effectively exploited. The second approach [21] uses the Recurrent Neural Network (RNN) to capture temporal features. However, the sequence is too long, and the RNN cannot be used to process the signal sequence directly. Generally, the long sequence is divided into many segments and generate the matrix. Therefore, spatial information in matrix is ignored and it has high complexity using two RNNs. The third approach [22] utilizes the transformer framework to extract mask features. However, it has higher complexity. Compared with RNN and the transformer-based framework, Conv-TasNet has low computational complexity. Thus, we improve the Conv-TasNet for ADS-B signal separation in this paper. ADS-B signal separation based on deep learning is rarely focused due to lacking actual dataset. In order to solve this problem, a large semi-real ADS-B dataset (SR-ADSB) with two signals overlapping is created. The real ADS-B messages collected around Tianjin Airport are used to form overlapped signals by combining different signal parameters such as SNR, carrier frequency and relative time delay. Moreover, a multi-scale convolutional separation network is proposed to solve the problem that the Conv-TasNet cannot effectively capture long temporal information. In the separation network, the multi-scale temporal information is extracted by using the convolutional layer with different convolutional strides, and the deconvolution is used to restore the multi-scale under- sampled signal. Finally, the different temporal scale features are fused to generate the separation mask. The proposed method is verified on the SR-ADSB dataset and the average decoding accuracy on the test set is 90.34% and the BER is 0.27%.
## 4 On-Board Unit Implementation
### Hardware Setup  
Figure 4 shows the hardware components of the experimental OBU. The OBU is composed of a general-purpose computer (i.e., a laptop) and several external hardware devices, as it can be observed. The external hardware devices are a 4G/5G cellular modem, a GPS/GNSS receiver, a Controller Area Network (CAN) bus to USB adapter, and a DC/DC converter. The 4G/5G cellular modem provides V2N connectivity to the outside world. The modem is connected to the computer while using a USB to mPCI adapter. The GPS receiver provides the real-time positioning of the vehicle and, at the same time, the OBU leverages the GPS-PPS signal to run a GPS-based time service while using Network Time Protocol (NTP). In doing so, the OBU keeps the internal clock synchronised with the backend applications of the ACCA service. The CAN bus to USB adapter establishes the connectivity with in-vehicle sensors and actuators network. The DC/DC converter provides the uninterrupted power supply to all pf the components of the OBU.
## 6 Experimentation
### Performance Measures  
To provide a sufficient comparison, contour tracking control of the LMs-driven X-Y-Y stage using 2 PID control system as shown in Figure 3 was tested firstly. Besides, an auto-tuning 2 PID (A2 PID) contour tracking control system was implemented for further examination. In the A2 PID control system, the control parameters \(K_{P}\), \(T_{I}\), \(T_{D}\), \(\alpha\), and \(\beta\) of each 2 PID controller were dynamically tuned by the IABC algorithm, while the control parameter \(K_{cix}\) and \(K_{ciy}\) of each contour error compensator was set as zero.  
To evaluate the quality of the contouring control scheme, the following two performance indices including the sum of the absolute values of the tracking errors \(P_{e}\) and the sum of the absolute values of the contour error \(P_{e}\) are defined as follows:  
\[P_{e}=\frac{1}{R}\bigg{[}\sum_{k=1}^{R}\big{|}e_{\pi}(k)\big{|}+\big{|}e_{y1}(k )\big{|}+\big{|}e_{y2}(k)\big{|}\bigg{]}, \tag{39}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Term** & **\(x\)-Axis** & **\(y_{1}\)-Axis \& \(y_{2}\)-Axis** \\ \hline Continuous force (N) & 99 & 29 \\ Continuous current (rms) & 2.6 & 1.6 \\ Peak force (1 s) (N) & 395 & 116 \\ Peak current (1 s) (rms) & 10.4 & 6.4 \\ Width of stator (mm) & 160 & 94 \\ Length of stator / Dimension (mm) & 60 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the LMs driven X-Y-Y stage.  
Figure 5: Experimental setup of the LMs driven X-Y-Y stage.  
\[P_{\varepsilon}=\frac{1}{R}\underset{k=1}{\overset{R}{\sum}}\Big{[}\big{|} \varepsilon_{1}(k)\big{|}+\big{|}\varepsilon_{2}(k)\big{|}\Big{|}, \tag{40}\]  
where \(k\) and \(R\) denote the iteration index and the total iteration number during the control process, respectively. In this study, the sampling frequency of the data acquisition system was set as 10 Hz for performance measures. Thus, the time interval between each \(k\) as shown in (39) and (40) was 0.1 s. As such, 10 s of data were measured to verify the superior tracking and contouring performance of the proposed ACC2 PID method even under extreme contouring tasks.
# Bio-Inspired Real-Time Prediction of Human Locomotion for Exoskeletal Robot Control
###### Abstract  
Human motion detection is of fundamental importance for control of human-robot coupled systems such as exoskeletal robots. Inertial measurement units have been widely used for this purpose, although delay is a major challenge for inertial measurement unit-based motion capture systems. In this paper, we use previous and current inertial measurement unit readings to predict human locomotion based on their kinematic properties. Human locomotion is a synergetic process of the musculoskeletal system characterized by smoothness, high nonlinearity, and quasi-periodicity. [PERSON] reconstruction method can well characterize quasi-periodicity and nonlinear systems. With [PERSON] reconstruction framework, we developed improving methods, including Gaussian coefficient weighting and offset correction (which is based on the smoothness of human locomotion), Kalman fusion with complementary joint data prediction and united source of historical embedding generation (which is synergy-inspired), and Kalman fusion with the Newton-based method with a velocity and acceleration high-gain observer (also based on smoothness). After thorough analysis of the parameters and the effect of these improving techniques, we propose a novel prediction method that possesses the combined advantages of parameter robustness, high accuracy, trajectory smoothness, zero dead time, and adaptability to irregularities. The proposed methods were tested and validated by experiments, and the real-time applicability in a human locomotion capture system was also validated.  
real-time prediction; Takens' reconstruction method; human locomotion; synergy; data fusion +
Footnote †: journal: Applied Sciences
# Atomic Force Spectroscopy on Ionic Liquids
## 5 Structure of the Interface Layer of Ionic Liquids Revealed by AFS
### Influence of Water
[113] investigated the formation of the mica/liquid interface when an ionic liquid, here [C\({}_{8}\)MIM][Cl], was subsequently added to water. As is shown in Figure 15, force-separation histograms were constructed for different concentrations of the ionic liquid in the mixture. While at a concentration of 1 mM, no dense interface structure was present, the jumps in the force curves setting in at 50 mM indicate the formation of a dense hydrophobic molecular surface layer. At higher concentrations, the amount and position of the jumps was found to be modified, revealing that at first the formation and adsorption of micellar structures occurs and, finally, a dense multilayer interface evolves. This also impacts the compression of the layer calculated as force/distance slopes. At low concentrations of the ionic liquid, a value of 3.2 nN/A is obtained, which corresponds to a dense surface layer of single molecules. This compression is weakened at an intermediate concentration, indicating a mixture of separated molecules and micelles, while at a high concentration, the compression was found to be the highest at 4.4 nN/A, reflecting the dense micellar layering. Figure 15: AFS investigation of the interface between mica and mixtures of water with different concentrations of [C\({}_{8}\)MIM][Cl]. The force/distance slopes (m) corresponding to the stiffness of each layer are shown as blue dotted lines and the schematic structural interpretations are depicted as insets. Adapted with permission from [PERSON] et al. [113]. Copyright 2018, American Chemical Society.
# A Seabed Real-Time Sensing System for In-Situ Long-Term Multi-Parameter Observation Applications
## System Design of SRSS/ILMO
### Remote Monitoring System Design  
The remote monitoring system is the monitoring center of SRSS/ILMO, which is composed of the monitoring client software system, the front-end server software system, and the database system. The system composition is shown in Figure 7. The communication function between the monitoring client software and the front-end server software is realized through the internet. The communication between the front-end server software and the Beidou satellite module is standard UART serial communication. The remote monitoring system provides the user's operation interface to realize the  
Figure 5: The seabed observation controller.  
Figure 6: The relay transmission buoy controller.  
transmission of the control command and working parameters, the recovery of the observation data, and working status information.  
The data storage system adopts a distributed design, including the two-level structure of the central server and the observation equipment server. It realizes system information management, data storage, and database backup maintenance of the monitoring software, and enhances the security of data storage and the efficiency of the query operation.
Mineral Content Estimation of Lunar Soil of Lunar Highland and Lunar Mare Based on Diagnostic Spectral Characteristic and Partial Least Squares Method  
[PERSON]  
1 School of Geosciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China; [EMAIL_ADDRESS]  
[PERSON]  
2 Hebei Bureau of Coal Geological Exploration, Shijiazhuang 072750, China; [EMAIL_ADDRESS]  
[PERSON]  
3 School of Geosciences, University of Edinburgh, King's Buildings, James Hutton Rd., Edinburgh EH9 3 FE, UK; [EMAIL_ADDRESS]  
[PERSON]  
4 Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100021, China4 Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100021  
and can be used to extract the mineral composition and distribution characteristics of lunar regolith. Visible and near infrared light can reflect the composition information of minerals [7]. In the process of interaction between photons and mineral surfaces, they are either reflected or absorbed. Different wavelengths and crystal structures of minerals produce unique reflection spectra [7,8]. Plagioclase is widely distributed in the lunar sea and lunar highlands. Plagioclase in the lunar soil is poor in sodium and rich in calcium, and has relatively strong absorption near 0.65 \(\upmu\)m and 1.25 \(\upmu\)m, but it is not easy to observe in the lunar soil. This is because the absorption characteristics of plagioclase are weaker than other strong absorption minerals and can easily be concealed. Charge transfer between Fe\({}^{2+}\) and Ti\({}^{4+}\) in agglutinate and volcanic glass will lead to strong absorption in ultraviolet band (300-600 nm), and the shorter the wavelength, the stronger the absorption, which will make the spectra become more \"red\" (spectral reflectance increases with wavelength from ultraviolet to visible light). Fe\({}^{2+}\) in agglutinate and volcanic glass will also lead to absorption features near 1000 nm. The increase of ilmenite will weaken the \"red\" of the spectra and make the absorption features weaker and darker.  
Optical remote sensing is the most effective method to obtain the chemical and mineral composition of lunar regolith by linking the spectral features and mineral compositions of the returned samples of lunar regolith [9]. The spectral features of lunar regolith, such as reflectance, absorption location and absorption depth, can be used in mineral identification. The spectra of Apollo and Luna samples of lunar regolith is the main basis for researching material composition and spectral mechanism of lunar regolith by optical remote sensing, which plays an important role in lunar exploration and research.  
In this paper, the LSCC (Lunar Soil Characterization Consortium) data of lunar regolith is taken as the research object. The partial least squares (PLS) regression model is used to estimate the spectra of lunar regolith measured in RELAB laboratory of Brown University. The mineral contents of plagioclase, pyroxene, olivine, ilmenite, agglutinate and volcanic glass in lunar regolith have been optimized and inversed.
# Design of a Laparoscopic Robot System Based on Spherical Magnetic Field
## 4. **Mathematic Modeling**
### Solution of Force and Torque  
In order to achieve the position and orientation control, as well as the gravity compensation of the rotor, both force and torque acting on the rotor need to be solved. During the solving process, the magnetic forces of the stator to different IPMs of the rotor are not necessarily collinear. Thus, the torque acting on the rotor consists of two parts. One part is created by direction differences of magnetic flux intensity and the magnetic moment of the IPMs. The other part is produced because the magnetic force upon different IPMs are not collinear.  
When calculating the second part of the torque, the computation process can be simplified by setting the rotation center at the hemispherical sphere center of the rotor (i.e., the coordinate origin of the global coordinate). Then, the force and torque of a single coil acting on rotor can be expressed as:  
\[F_{i}^{u}=\sum_{j=1}^{5}\left[\frac{\partial B_{i}^{u}}{\partial x}\frac{ \partial B_{i}^{u}}{\partial y}\frac{\partial B_{i}^{u}}{\partial z}\right]^{ T}M_{j}^{i} \tag{34}\]\[T_{i}^{u}=\sum_{j=1}^{5}\left[M_{j}^{i}\times B_{i}^{u}+L_{j}\times\left[\frac{ \partial B_{i}^{u}}{\partial x}\,,\frac{\partial B_{i}^{u}}{\partial y}\,,\frac{ \partial B_{i}^{u}}{\partial z}\right]^{T}M_{j}^{i}\right] \tag{35}\]  
Among the above expressions, \(L_{j}\) is the arm of the force of IPM \(j\) to the hemispherical center of the rotor, \(\mathbf{M}_{j}^{i}\) is the magnetic moment of IPM \(j\) in coil coordinate \(CS_{i}\). \(F_{i}^{u}\) is the magnetic force of coil \(i\) to the rotor by unit electric current, and \(T_{i}^{u}\) is the magnetic torque of coil \(i\) to the rotor by unit electric current.  
The force and torque acting on the rotor can be expressed as:  
\[F=\sum_{j=1}^{5}R_{j}^{T}F_{j}^{u}I_{j} \tag{36}\]  
\[T=\sum_{j=1}^{5}R_{j}^{T}T_{j}^{u}I_{j} \tag{37}\]