esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Raman Analysis
### Raman Spectra Analysis of TiO\({}_{2}\)-Peg  
The Raman active vibrational modes of TiO\({}_{2}\) localized in the range of the 400-600 cm\({}^{-1}\) band in TiO\({}_{2}\)-PEG did not show any appreciable shift, indicating that upon the addition of PEG, the structure of TiO\({}_{2}\) is not affected (Figure 4c). This confirms that the crystalline phase of TiO\({}_{2}\) NPs is preserved in the functionalization process. It should also be mentioned that the intensities in the region between 1000-3200 cm\({}^{-1}\) have been amplified, because this zone is less intensive compared to the TiO\({}_{2}\) band (the ratio of intensities between the bands of TiO\({}_{2}\) and the bands that arise due to the functionalization with PEG is \(\frac{T_{TiO_{2}}}{T_{TiO_{2}-PEG}}\)= 50).  
On the other hand, due to the functionalization of TiO\({}_{2}\)'s surface with PEG, we can observe a broad peak at around 1000-1200 cm\({}^{-1}\) attributable to C-C and C-O stretching vibrations. Two sharp peaks between 2800-3000 cm\({}^{-1}\) are also observed, corresponding to C-H stretching vibration; these peaks confirm the addition of PEG with TiO\({}_{2}\) (Figure 4c). The prominent peak localized in the range of 2800-3000 cm\({}^{-1}\) corresponds to the stretching vibrations of the methylene group C-H of TiO\({}_{2}\)-PEG found at 2883 cm\({}^{-1}\); this band is shifted about 3 cm\({}^{-1}\) from its original position (at 2886 cm\({}^{-1}\) in pure PEG). This slight shift suggests the formation of hydrogen bridge bonds, these bonds have arisen due to the Van Der Waals interaction between the OH groups of TiO\({}_{2}\) and the hydroxyl group of PEG [28; 32; 34; 35]. These results demonstrate that the surface modification of TiO\({}_{2}\) NPs by PEG is highly influenced by the intermolecular interactions between these two materials. We can observe that the vibration of C-C, C-O, and C-H groups (located in the range 1000-1200 cm\({}^{-1}\)) are as prominent as in the IR and Raman spectra.
# Single EX-CCCII-Based First-Order Versatile Active Filter
## 2 Circuit Description
### Current-Mode First-Order Versatile Active Filter  
Figure 3 presents the proposed first-order universal filter with the current mode. The filter uses only one active EX-CCCII block and one grounded capacitor, thus achieving a minimum number of active and passive elements. The input current signal of the current filter is fed to the low-impedance terminal x of the EX-CCCII and the output currents are obtained from the high-impedance terminals of the EX-CCCII. Thus, the proposed filter has low-input and high-output impedances, which is desirable for current-mode circuits. The circuit uses a grounded capacitor, which is ideal for integrated circuits and easily compensates for the stray capacitance. Using (1) and nodal analysis, the output currents \(I_{01}\), \(I_{02}\), \(I_{03}\), and \(I_{04}\) can be expressed as  
\[\text{HPF}\qquad\quad I_{01}=-I_{02}=\bigg{(}\frac{sC_{1}R_{x1}}{sC_{1}R_{x1}+ 1}\bigg{)}I_{ln} \tag{3}\]  
\[\text{LPF}\qquad\quad I_{03}=-I_{04}=\bigg{(}\frac{1}{sC_{1}R_{x1}+1}\bigg{)}I _{in} \tag{4}\]  
\[\text{APF}+\qquad\quad I_{AP+}=I_{02}+I_{03}=\bigg{(}\frac{1-sC_{1}R_{x1}}{1+ sC_{1}R_{x1}}\bigg{)}I_{ln}=-\bigg{(}\frac{sC_{1}R_{x1}-1}{sC_{1}R_{x1}+1} \bigg{)}I_{ln} \tag{5}\]  
\[\text{APF}\qquad\quad I_{AP-}=I_{01}+I_{04}=\bigg{(}\frac{sC_{1}R_{x1}-1}{sC_{ 1}R_{x1}+1}\bigg{)}I_{in} \tag{6}\]  
Figure 3: Proposed current-mode first-order versatile active filter using EX-CCCII.  
Figure 2: CMOS structure of the EX-CCCII.  
The circuit offers first-order non-inverting and inverting transfer functions of the HPF, LPF, APF; thus, six transfer functions can be obtained in one circuit, where APF+ is the phase lag APF (non-inverting APF), and APF\(-\) is the phase lead APF (inverting APF).  
The pole frequency of the filters can be given by  
\[\omega_{o}=\frac{1}{C_{1}R_{x1}} \tag{7}\]  
The pole frequency of all filtering functions can be electronically controlled by \(R_{x1}\) via the bias current \(I_{set}\).
# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
## 2 Proposed Methods  
This section introduces a speech recognition method to address the challenges of small-sample, multi-class speech recognition by integrating XTTS speech generation technology [6] with deep learning models for data augmentation and classification to enhance system performance. The proposed approach comprises the following steps. Initially, we employ XTTS with the AISHELL-3 dataset [14] to generate synthetic speech. By integrating this synthetic speech with authentic human speech, we enrich the training dataset, thereby enhancing the model's learning capabilities. Next, we apply volume normalization, voice activity detection (VAD), and MFCC feature extraction to standardize the input features and reduce non-speech interference. Subsequently, we design two classification architectures--CNN-based and CNN-Transformer-based -- to process the extracted features. Figure 1 shows the function blocks of the proposed recognition algorithm. The following subsections elaborate on the methodology, detailing how speech generation, data preprocessing, and classification techniques collectively improve the accuracy and generalization of insult speech recognition.  
Figure 1: Function blocks of the proposed recognition algorithm.
# Solvent-Dispersible Nanostructured MIMI: An Experimental and Computational Study
## 1 Introduction  
Nanofabrication technologies [1; 2; 3] have attracted researchers to explore the field of plasmonic modes [4; 5; 6; 7], such as localized surface plasmons (LSPs) [8; 9] and coupled plasmonic systems [10; 11; 12], which strongly increase an interacting electromagnetic field [13; 14]. These features are generally shown by suitably coupling a metal with a dielectric, while, more recently, metal-insulator-metal-insulator structures (MIMI) have provided encouraging results. MIMI is a new architecture in which an insulator is enclosed by two metal claddings, highlighting potential attractive features [15; 16; 17; 18; 19] which depend on the thickness and composition of the surfaces that compose it [20; 21; 22]. On the MIMI structures are based metamaterials, that constitute the last frontier in the search for new materials, possessing properties not found in nature [23; 24; 25].  
A further step forward is represented by three-dimensional MIMI systems composed of nanoparticle films that can achieve major advances compared to multilayered planar devices; their electronic and optical properties are encouraging for numerous applications such as third-generation solar cells, light-emitting diodes, field-effect transistors [26; 27; 28; 29]. Ultimately, the performance of these devices is based on the modulation of the energy levels and optical properties of the nanoparticles by regulating their shape, size, surface and chemical composition [30; 31; 32; 33; 34].  
With respect to the available MIMI devices, which consist of a solid array [18; 35], the aim of this work was to obtain a MIMI nanoparticle in colloidal dispersion. Among the many advantages of having a potential metamaterial in colloidal dispersion, there is their in vivo use for the detection of particular analytes present in biological fluids, while the current detection systems based on metamaterials require the collection of these fluids [36]. Recent advances in nanofabrication have encouraged the development of new plasmonic biosensors that can go beyond the limits of conventional plasmonic sensors [37; 38; 39]. [PERSON] et al. demonstrated an improvement in biosensing technology using a plasmonic metamaterial based on a porous nanorods layer [40]. Furthermore, the possibility of having nanosized metamaterials allows the functionalization of these nanoplatforms, favoring extreme versatility.  
To obtain solvent-dispersible nanoparticle MIMI, a synthetic protocol based on a seed-mediated growth approach [41; 42; 43; 44; 45] is presented, to obtain a nano-matryoshka array by the alternation of gold and silica layers.  
A numerical simulation based on a Finite Element Method (FEM) analysis was conducted on each member of the rod-core MIMI family (samples a, b, c, d) to evaluate the absorption and extinction cross section of a single nano-object, displaying a good agreement with the experimental results.
# Electronic and Transport Properties of Epitaxial Graphene on SiC and 3C-SiC/Si: A Review
## 5 Fine-Tuning of Transport Properties
### Intercalation
The same group also demonstrated the possibility to tune the charge density modulation in the graphene by controlling the oxidation parameters. The starting sample had an n-type sheet charge of \(1.4\times 10^{13}\) cm\({}^{-2}\) corresponding to \(\mathbf{E_{\mathrm{F}}}\) of 500 meV below \(\mathbf{E_{\mathrm{D}}}\). After the first O-intercalation step, the sheet density changed to \(4.6\times 10^{12}\) cm\({}^{-2}\) (n-type) and \(\mathbf{E_{\mathrm{F}}}\) of 300 meV. The second O-intercalation step yielded \(1.9\times 10^{12}\) cm\({}^{-2}\) (n-type) and \(\mathbf{E_{\mathrm{F}}}\) of 200 meV. Quasi-free-standing bilayer graphene (QFSBLG) with p-type doping of \(10^{13}\) cm\({}^{-2}\) was achieved by [PERSON] et al. via oxygen intercalation by annealing the monolayer graphene on SiC(0001) at 600 \({}^{\circ}\)C in the air [138]. The most advanced intercalation may be magnesium intercalation of monolayer EG on SiC (0001) creating QFSBLG [43]. For this case, ARPES measurements demonstrate a very high n-type concentration of \(2\times 10^{14}\) cm\({}^{-2}\) at an \(\mathbf{E_{\mathrm{F}}}\) of 720 meV above \(\mathbf{E_{\mathrm{D}}}\) and the creation of a band gap of 0.36 eV. The Fermi velocity was measured to be \(0.97\times 10^{6}\) ms\({}^{-1}\), which is only slightly different from MLG. This may indicate that the graphene lattice is only slightly perturbed by the magnesium intercalant. [PERSON] et al. [135] obtained a charge-neutral QFSG on SiC (0001) using fluorine from a fluorinated fullerene source, which was stable under ambient conditions, whereas resistant to temperatures up to 1200 \({}^{\circ}\)C. [PERSON] et al. [136] demonstrated buffer-layer decoupling using gold intercalation of EG on SiC (0001). The formation of an n-doped or a p-doped graphene layer can be controlled by varying the gold coverage about one-third or one monolayer, respectively.
## 3 Network Model and Protocol Overview
### Protocol Overview  
We propose an expected area-based real-time routing (EAR2) protocol for supporting the real-time routing of data with a desired time deadline \(T_{\mathit{setdeadline}}\) from a source node to a mobile sink as shown in Figure 1. EAR2 calculates the expected area (EA) of the mobile sink based on its location, speed, and direction information. Then, it provides real-time data routing from the source node to the mobile sink by guaranteeing that \(T_{\mathit{setdeadline}}\) is smaller than the total time of the forwarding time \(T_{\mathit{forwarding}}\) from the source node to the single flooding point \(P\) nearest to the \(EA\) and the flooding time \(T_{\mathit{f flooding}}\) within the \(EA\). First, we present a method for calculating the expected area (EA) of a mobile sink in Section 4.1. We next present a method for calculating the \(T_{\mathit{f flooding}}\) and \(T_{\mathit{forwarding}}\) in Section 4.2.
## 2 Communication Challenges and Service Types  
### Communication Challenges  
The communication demands of the Hyperloop differ from the existing rail transportation dramatically, bringing about some new challenges on the train-to-ground communication system. (1) Special channel characterization. If adopting the public cellular network solution, the Doppler shift varies from the maximum to the minimum value rapidly as the Hyperloop travels across the trackside antenna at an ultra-high-speed, resulting in the drastic variation of the received signal [25]. Not to mention the severe penetration attenuation derived from the steel tube and the metal train body. (2) Except for the severe Doppler effect, another challenge caused by the high velocity is the frequent handover. Furthermore, all passengers insides the vactrain body move simultaneously, which leads to a phenomenon of signaling storm when dealing with a group handover. (3) Various types of communication services with strict demands. Different from rail transportation, the Hyperloop needs more types of mission-critical services with stringent requirements to guarantee the safe operation at such an ultra-high-speed. In addition, the passengers expect to enjoy in-journey communication services of good QoS, which requires a sufficient data rate. As such, the coexistence of both mission-critical and non-critical traffic calls for a novel resource allocation algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Term** & **Definition** & **Term** & **Definition** \\ \hline HSR & High-Speed Railway & uRLLC & ultra-Reliable Low Latency Communication \\ GSM-R & Global System for Mobile & eMBB & enhanced Mobile Broadband \\ LTE-R & Long Term Evolution-Railway & 5G & The 5 th generation \\ LOS & Line-of-Sight & CBTC & Communication Based Train Control \\ CU & Centralized Unit & SIL & System \\ DU & Distributed Unit & WDM & Wavelength Division Multiplexing \\ AAU & Active Antenna Unit & MIMO & Multi-Input and Multi-Output \\ DAS & Distributed Antenna System & CIR & Channel Impulse Response \\ LWS & Leaky Waveguide System & PL & Path Loss \\ LCX & Leaky Coaxial Cable & C-RAN & Cloud Radio Access Network \\ QoS & Quality of Service & NFV & Network Functions Virtualization \\ QoE & Quality of Experience & HARQ & Hybrid Automatic Repeat reQuest \\ KPI & Key Performance Indicator & MEC & Mobile Edge Computing \\ BER & Bit Error Rate & PSO & Particle Swarm Optimization \\ PRB & Physical Resource Block & QAM & Quadrature Amplitude Modulation \\ BBU & Baseband Unit & RRU & Remote Radio Unit \\ \hline \end{tabular}
\end{table}
Table 1: Abbreviations and definitions.  
Based on the above analysis, it can be learned that current mobile communication systems such as the GSM-R and LTE-R are incapable of dealing with train-to-ground communication challenges of the Hyperloop. Therefore, we put up with some schemes to investigate on this issue, which will be illustrated in the following sections.
## 1 Introduction
### Contributions
#### 1.1.1 Model Development  
* We propose and develop two deep learning architectures--ResRNN-Net (combining Bi-LSTM, CNN, and attention mechanisms) and MultiScaleFusion-Net (utilizing GRU and multiscale convolution)--designed initially for tabular clinical data.
* Based on the knowledge from single-modality experiments, we introduce a multi-modal deep learning model that integrates tabular features, fetal ultrasound images, and EHG time-series [24] signals into a unified prediction system.
# THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management
## 7 Experiment  
All experiments were conducted in a cluster consisting of 5 nodes, with one master node and 4 data nodes. Each node runs Ubuntu 16.04 LTS on 6 cores Intel Xeon E5-2620 V2 CPU, 16 GB memory and 2 TB disk, nodes are connected through a Gigabit Ethernet switch. THBase is implemented based on Hadoop 2.7.3, HBase 1.3.0, and Redis 3.0.  
The experiment uses the global AIS data (Automatic Identification System) [28] in July 2012 as the dataset. It contains about 1.66 billion messages sent by more than 200,000 ships and is about 200 GB in size. The AIS messages in the dataset have been decoded and sorted in ascending order of time, and each AIS message can be regarded as a trajectory point.  
In THBase, we set the lengths of TI and TP to 2 h and 24 h, respectively, and initialize the first-level grid size of G-index to 1\({}^{\circ}\). According to the reporting interval of AIS, a single trajectory segment can contain up to 3600 trajectory points. We split the dataset into multiple chronological sets, each of which is about 40 GB, and then we insert them into HBase in turn. We test the insert efficiency and query performance when each set is appended. Since the data in the dataset is sorted in ascending order of time, the incremental data will only increase the time range and will have little influence on the density of the inserted data.  
We compare to the performance of THBase with CTDM (Column-Fragmented Traditional Data Model) and SPDM (Segmented-Trajectory Partitioned Data Model) proposed in the literature [10]. CTDM stores the trajectory data of the same MO in one row of an HBase table, and organizes the in-row data in chronological order. It uses a variant of SETI, a global index structure named G-IT (Grid-mapped Interval Trees Index), to index the spatiotemporal attributes of all trajectories. SPDM divides each trajectory into trajectory segments based on a spatiotemporal partitioning mechanism, and encodes the spatiotemporal attributes of trajectories in the rowkeys to provide spatiotemporal query support. In addition, it built a reverse-sort index structure to support the MOID-based query.  
Compared to THBase, the coprocessor mechanism is not appropriate for CTDM and SPDM. The reasons are as follows:  
* The isolation of index and data. Both CTDM and SPDM treat index and data table as independent units, and deploy them on different nodes. Therefore, the coprocessor-based query algorithm is not conducive to the reduction of network overhead.
* The segments of the same MO are distributed on different nodes in SPDM. It is difficult to implement an efficient coprocessor-based shuffling algorithm to merger subtrajectories into whole trajectories.
# A Review on Computer Vision Technology for Physical Exercise Monitoring
## 3 Discussion about Articles
### Muscle Fatigue  
Fatigue is a subjective symptom of malaise and aversion to perform the activity or to objectively impaired performance [79]. It can be assessed by either self-report scales or performance-based measures [80]. Fatigue can be either physical or mental and both types are important to assess due to its high correlation to health-related parameters [81; 82].  
Facial expression is effective in assessing physical and mental fatigue [83; 85]. [PERSON] [84] proposed an approach to measure fatigue by tracking facial features during exercise. The main hypothesis of the study was that, towards a fatiguing state, the points of interest in the image would increase vibration, thus, it could be identified in the power spectral analysis of the signal. This model was tested in maximal and submaximal dumbbell lifting tests, against force measures obtained by a dynamometer. The results showed that the temporal point of interest in the face could be easily found using the method.  
Deep learning and thermal imaging were fused to automatically detect in the face exercise-induced fatigue [100]. Different devices captured RGB, near infra-red, and thermal images, while the pre-trained CNN, Alexnet [101], and Visual Geometry Group-16 (VGG16)/VGG19 [102] were the deep learning methods used for the classification of different regions in the face according to fatigued/rested state. The authors found that the Alexnet applied to the region around the mouth showed the best classification of the fatiguing state.
## 3 Experiment and Result Analysis
### Experimental Evaluation Index  
In the experiment, the compression ratio (CR) and information entropy were selected as the evaluation index. In addition, the peak signal to noise ratio (PSNR) is an objective standard for evaluating data compression.  
The entropy feature is a measure of the uncertainty of a random variable [21]. The calculation formula of the average information entropy is  
\[H(X)=-\sum_{i=1}^{n}p(x(i))\log_{2}p(x(i)), \tag{8}\]  
Figure 7: The process of two-dimensional expression of one-dimensional data.  
where \(X\) is the source, \(x(i)\) is the character in the source, \(p(x(i))\) is the probability that the character \(x(i)\) appears in the source \(X\), and the unit of \(H(X)\) is bit/sign.  
Equation (8) is the required sequence \([x(i)]\) as a discrete memoryless source. The method proposed in this paper is a prediction method, whose output is the prediction errors. The prediction error sequences are independent of each other, so it is memoryless. The amount of information in the predicted output sequence can be measured by the average information entropy, which can reflect the compressibility of the sequence.  
The calculation formula of PSNR is:  
\[PSNR=10\bullet\log_{10}\left(\frac{MAX^{2}}{\frac{1}{L}\sum\limits_{i=1}^{L} \left[x(i)-x^{\prime}(i)\right]^{2}}\right), \tag{9}\]  
where \(x\) is the original signal, \(x^{\prime}\) is the signal after adding noise, \(L\) is the length of the signal, \(MAX\) is the maximum value of the signal, and PSNR is measured in dB.  
The calculation formula for the compression ratio is:  
\[M=\frac{S_{in}}{S_{out}}, \tag{10}\]  
where \(S_{in}\) is the byte size before data compression, and \(S_{out}\) is the byte size after data compression.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 2 State of the Art
### Importance of Authentication for Users  
As reported in a paper released in 2014 from the UK government Home Office [13] there were 742,000 cases of mobile phone theft in England and Wales, according to the crime survey from 2012/2013. Even though different ways of protecting mobile devices and their data already exist, a survey conducted in 2014 by ConsumerReports [14] found that approximately 34% of all smartphone users are not even taking simple measurements against device and data theft. However, when buying a new handset, users are aware of a devices security features, ranking it right after battery life as the second most important feature [10].  
In another study presented in 2013 by [PERSON] et al. [15], approximately half of the participants were using locking features to protect their device. It was also revealed that 21% of all participants did use those security features prior, but disabled them because of usability issues [15].
## 4 Hall Thruster Erosion Modeling
[37] reproduced the decrease in thrust observed in the first few hundred hoursof thruster operation and provided evidence to support the notion that low-energy ions (and not neutrals or electrons) are responsible for the majority of wall erosion. Recognizing the limitations of simple sputtering yield models employed by most authors, [PERSON] et al. [87; 88] utilized a molecular dynamics simulation to more accurately model sputtering, and [PERSON] et al. [90] constructed a three-dimensional surface mesh in an effort to recover observed sputtering-induced changes to the Hall thruster wall microstructure. Further plasma discharge model fidelity was attained with the advent of particle-in-cell (PIC) models that solve the equations of motion for super particles representing groups of individual particles. Like fluid modelers, most authors [93; 94; 95; 96; 97; 98] implementing PIC models for erosion calculation relied on simple theoretical fits of experimental data to determine sputtering yield. However, [PERSON] et al. [99] developed a variation of the Yamamura and Tawara model based on physical considerations, and [PERSON] et al. [100] utilized the Huygens wavelet method to predict surface evolution. Although the advent of predictive modeling will require more accurate experimental surface sputtering and near-wall plasma measurements, it will also require higher fidelity modeling approaches. Apart from the exceptions noted above, most efforts have applied increasingly mature plasma models to simple sputtering yield relations. Recovering sputtering-induced changes to surface microstructure and concomitant alterations to near-wall plasma properties will require the application of similarly sophisticated material models. Such an improvement would enable more accurate simulation of the coupled plasma-material sputtering mechanism.