esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Algorithm Design
### Attitude Controller Design
#### 3.3.2 Sun-Aligned Phase  
After completing angular rate damping, the satellite enters the sun-aligned phase. During this phase, the satellite is controlled to converge to the state that the optical axis of the sun sensor is aligned with the solar vector by using the measurement data of the sun sensor.  
Because 3-axis MTQs can only output the control torque perpendicular to the geomagnetic field vector, the attitude maneuvering ability of the satellite is insufficient. Therefore, at the beginning of the sun-aligned phase, the control law of the initial damping phase continues to be used to search the sun slowly while damping the angular rate of the satellite, until the sun enters the sun sensor's field of view.  
When the sun enters the field of view of the sun sensor, motivated by [32], the desired control torque \(\mathbf{T}_{c2}\in\Re^{3}\) outputted by the controller is designed as follows:  
\[\mathbf{T}_{c2}=\mathbf{J}\left(K_{2}\mathbf{e}_{s}^{b}\times\mathbf{S}^{b}+K_{3}\mathbf{S}_{prev} ^{b}\times\mathbf{S}^{b}-K_{4}\mathbf{S}^{b}\times\mathbf{\omega}_{bi}^{b}\times\mathbf{S}^{b} \right)+\mathbf{\omega}_{bi}^{b}\times\left(\mathbf{J}\mathbf{\omega}_{bi}^{b}\right), \tag{15}\]  
where \(K_{2}\), \(K_{3}\), and \(K_{4}\) are the control coefficients, and all of them are greater than zero; \(\mathbf{e}_{s}^{b}\) represents the unit vector along the optical axis of the sun sensor, which is determined by the installation angle of the sun sensor; \(\mathbf{S}^{b}\) is the unit vector pointing from the satellite to the sun measured by the sun sensor at the current sampling time, and \(\mathbf{S}_{prev}^{b}\) is the unit vector pointing from the satellite to the sun measured by the sun sensor at the previous sampling time. The main objective of the control torque \(\mathbf{T}_{c2}\) is to align the optical axis of the sun sensor with the sun. The first part of \(\mathbf{T}_{c2}\) is used to make sure that \(\mathbf{e}_{s}^{b}\) is aligned with \(\mathbf{S}^{b}\). The second part and the third part are used to dampen the angular rate component perpendicular to \(\mathbf{S}^{b}\). The fourth part is used to eliminate the effects of angular rate coupling of different axes. It should be noted that if the field of view of the sun sensor is larger than \(90^{\circ}\), the first part of \(\mathbf{T}_{c2}\) can be modified to:  
\[\left\{\begin{array}{ll}K_{2}\mathbf{e}_{s}^{b}\times\mathbf{S}^{b}&,&if\ \ 0^{\circ}\leq\beta\leq 90^{\circ}\\ K_{2}\frac{\mathbf{e}_{s}^{b}\times\mathbf{S}^{b}}{\left\|\mathbf{e}_{s}^{b}\times\mathbf{S}^{ b}\right\|}&,&else\end{array}\right., \tag{16}\]  
where \(\beta\) is the angle between the vectors \(\mathbf{e}_{s}^{b}\) and \(\mathbf{S}^{b}\).  
Then, the desired control torque \(\mathbf{T}_{c2}\) will be outputted by the 3-axis MTQs. When the angle between the vectors \(\mathbf{e}_{s}^{b}\) and \(\mathbf{S}^{b}\) is close to \(0^{\circ}\), the controller enters the next phase, namely the spin-up phase.
## 2 Materials and Methods
### Analytical Methods  
The DCF concentration was analyzed using reversed-phase High Performance Liquid Chromatography (HPLC) (Shimadzu LC-10 AT, Shimadzu, Kyoto, Japan) with a diode array UV-Vis detector (model SPD-M10 AVP, Kyoto, Japan). The analytical column was a Phenomenex Luna C18 (2) (250 \(\times\) 4.6 mm, 5 \(\upmu\)m, Torrance, CA, USA) and the detection was made at 280 nm. The mobile phase was a mixture of a 0.2 M aqueous solution of formic acid and acetonitrile (40:60) with an injected sample volume of 20 \(\upmu\)L.  
The removal efficiency (R%) of DCF was is calculated according to Equation (1) [33]:  
\[\mathrm{R\%}=\frac{(\mathrm{C}_{0}-\mathrm{C}_{\mathrm{t}})}{\mathrm{C}_{ \mathrm{t}}}\times 100 \tag{1}\]  
where \(\mathrm{C}_{0}\) is the initial concentration of DCF and \(\mathrm{C}_{\mathrm{t}}\) is the concentration of the treated DCF.  
The pH measurements were performed using an Elmetron CX-461 multimeter (Warsaw, Poland) designed for accurate measurements of pH, which had been previously calibrated with buffered solutions.  
Dissolved oxygen was measured using a Mettler Toledo DO meter purchased from Sigma Aldrich (Poznan, Poland).  
Figure 1: Schematic diagram of experimental setup for diclofenac solution nanobubbling and irradiation.  
Total carbon (TC) and inorganic carbon (IC) were analyzed using a TOC analyzer multi N/C 3100 (Analytik Jena GmbH+Co. KG, Jena, Germany), with a combustion temperature at 820 \({}^{\circ}\)C.  
A Dionex ICS-5000 SP chromatograph (ThermoFisher Scientific, Frederick, MD, USA) was applied to analyze cations and anions. A Dionex Ion PaCTM AS23 + AG23 (4 \(\times\) 250 mm) column and Dionex Ion PaCTM CS12A + CG12A (4 \(\times\) 250 mm) were used to examine anions and cations, respectively. Dionex seven anion standard II and Dionex six cation standard II were used to make the calibration curves. Mobile phase 4.5 mM Na2 CO3 + 0.8 mM NaHCO3 and mobile phase 20 mM methanesulfonic acid at a flow rate of 1 mL/min were used for the analysis of anions and cations, respectively, with a sample injection volume of 25 \(\mu\)L.  
Chemical oxygen demand (COD) was determined according to Method 5220 C of the Standard Methods for the Examination of Water and Wastewater using the Tittimetric Method [34]. COD measures the oxygen equivalent to the organic matter content of a sample that is susceptible to chemical oxidation by potassium dichromate.  
The degradation products of DCF were identified using Liquid Chromatography-Mass Spectrometry (LC-MS), using a C18 reverse-phase column. The detailed experimental conditions are given in the Appendix A. The chemical identity of each identified degradation product was confirmed by accurate mass determination on an ImpactII ESI-QqTOF mass spectrometer (details are given in the Appendix B).
# Efficient Key Exchange Using Identity-Based Encryption in Multipath TCP Environment
## 5 Experimental Evaluation of Proposed Model
### Security Complexity of IBE  
The chosen-cipher text attack (CCA) is considered a standard acceptable attack model, in which attackers can gain access to plaintexts corresponding to the chosen cipher text for asymmetric encryption schemes. Hence, IBE needs to be proven as secure under the  
Figure 13: Experimental setup for testing the proposed work.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Attack**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{\begin{tabular}{c} **Proposed** \\ **Solution** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **SCMTCP** \\ **[33]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Secure and** \\ **Lightweight** \\ **Subflow** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **MPTCP** \\ **[34]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **MPTLS** \\ **[47]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Hash** \\ **Chain**[20]** \\ \end{tabular} } & \multirow{2}{*}{**MPTCP** [17]**} \\  & & & & & **Scheme** [23] & & & \\ \hline \hline \multirow{3}{*}{\begin{tabular}{c} **Session** \\ hijacking \\ using \\ ADD.ADDR \\ Vulnerability \\ \end{tabular} } & \begin{tabular}{c} **Off Path** \\ **Active** \\ **attack/Partial** \\ **Time on Path** \\ **Active attack** \\ \end{tabular} & Y & Y & Y & Y & N & N \\ \hline \hline \
\begin{tabular}{c} **Eavesdrop** \\ in the initial \\ handshake \\ \end{tabular} &
\begin{tabular}{c} **On Path** \\ **Attack** \\ \end{tabular} & Y & Y & N & Y & Y & N & N \\ \hline \hline \end{tabular}  
Keys: Y: Yes-Offers Security, N: No-Doesn’t offer Security.  
\end{table}
Table 5: Comparative evaluation of existing security solutions against attack vector.  
chosen-cipher text attack (CCA) model, which can be further considered adaptive (IDA-ID-CCA) or non-adaptive (IDA-CCA). The basic indent (IBE) proposed by [PERSON] and [PERSON] [25] was not a secured chosen-cipher text, but the FullIndent proposed by [PERSON] is a secured chosen-cipher text IBE, as the security of this IBE scheme is dependent on the bilinear Diffie-Hellman assumption (BDH). In the proposed model SKEXMTCP, the FullIndent version of secure IBE is used for encryption, which is secure in random oracle against the chosen-cipher text attack by assuming that BDH is hard in groups [25].
# Detecting Deepfake Voice Using Explainable Deep Learning Techniques
## 3 Preliminaries and Dataset
### Dataset  
For the general interpretation of the detection model, two datasets with exclusive characteristics were used. The first set of experiments was conducted upon the ASVspoof 2021 Logical Access dataset. ASVspoof consists of 2580 bona fide user speech data collected from 107 speakers and the corresponding 22,800 synthesized speech data generated using 19 synthesizers. We labeled the bona fide user voice and synthesized voice as the 'human voice' and 'deepfake voice', respectively. With various human speakers and randomly sampled transcripts for text inputs of deepfake voice generation, the experimental environment was able to represent near real-world behavior.  
For the constrained environment, paired data for both human and deepfake voices was required. We used the IJSpeech dataset consisting of 13,100 transcripts and speeches and generated the corresponding deepfake speeches. With the IJSpeech dataset, we used Tacotron, an attention-based sequence-to-sequence TTS generator to train the generation model [20]. From the complete dataset, we selected 8076 speeches and prepared the corresponding synthesized speeches. IJSpeech speech and synthesized speech were, respectively, labeled and trained as 'human voice' and 'deepfake voice'.
# Reality-Virtuality Technologies in the Field of Materials Science and Engineering
## 2 Methodology
### Planning  
During the planning stage, the motivation, objectives, and research questions of the systematic search were defined. The motivation for this systematic search arose from the need to have a global image of how RVTs are used in the field of MSE. Obtaining this impression through the scientific works published in the last decade, it is possible to favor the opening of new lines of research that allow the development of new tools based on RVTs (or updating the existing ones), which can be used in new fields and with purposes other than those pursued to date, in addition to facilitating the choice of technology to be used. To meet this need, three main objectives were established that must be achieved. First, the aim was to have a list of relevant works in which the general operation of the tools was briefly described. Second, we intended to discover the magnitude of the number of academic papers in which the use of RVTs is linked to MSE, as well as their typology (e.g., conference papers), and what proportion of these papers includes some type of empirical study. Third, we sought to find out to which areas of MSE the tools developed in the academic field are circumscribed and to which sectors (e.g., education) they are directed. Fourth, we sought to discover the degree of implementation of the different types of RVTs and what properties have been exploited and reported to date.  
To achieve the three established objectives, this systematic search aimed to answer the following questions:  
1. How do RVT-based tools work and what specific purpose do they pursue?
2. How many scientific works have been published in the last decade explicitly relating to RVTs and MSE, and have they been indexed in Web of Science, Scopus, or IEEE Xplore?In what type of document have these works been published--that is, how many have been published in the form of articles, conference papers, reviews, or book chapters, and how many contain an empirical study (empirical data collection and analysis thereof)?  
In which areas of MSE are the described tools circumscribed?  
To which sectors are the described tools directed?  
What types of RVTs are used in the described tools--that is, how many use immersive VR, non-immersive VR, AR, or MR?  
What properties of the RVTs are exploited in the described tools--that is, how many exploit the improvement of spatial vision, the possibility of interacting with virtual elements, or the simulation of real gadgets?
# Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications
## 3 Reconstruction Algorithm
### Bayesian Algorithm  
The Bayesian reconstruction algorithm considers the time correlation of signals to provide better reconstruction accuracy than that of other reconstruction algorithms, especially when signal time correlation is strong.  
Common Bayesian algorithms include the expectation-maximization [57], Bayesian compressive-sensing [58], sparse Bayesian learning (SBL) [59] and multiple SBL (MSBL) [60] algorithms. The SBL or MSBL algorithm differs from the \(l_{1}\)-norm convex-optimization algorithm. The global minimization of \(l_{1}\)-norm convex optimization is usually not the sparsest solution, while the global minimum of SBL or MSBL is sparsest and less than those of typical algorithms (for example, FOCUSS). In a conventional SBL framework, \(x\) meets Gaussian prior distribution:  
\[p(x\mid\alpha)=\prod_{i=1}^{n}p(x_{i}\mid\alpha_{i}), \tag{29}\]  
where \(p(x_{i}\mid\alpha_{i})=\mathrm{N}(x_{i}\mid 0,\alpha_{i}^{-1})\), and \(\alpha\triangleq\{\alpha_{i}\}\) are non-negative hyperparameters. Equation (29) shows that, when \(\alpha_{i}\) tends to infinity, corresponding coefficient \(x_{i}\) turns to zero.  
Following the traditional SBL algorithm, [PERSON] et al. demonstrated a new method to recover block sparse signals whose block sparse structure was completely unknown [61]. They introduced a pattern-coupled hierarchical Gaussian prior model that could characterize not only coefficient sparseness, but also the statistical dependence of the adjacent signal coefficients. As discussed in [62], the two-layer Gaussian-inverse gamma hierarchical prior led to a learning process that tended to approach most coefficients that were considered uncorrelated, and only retained very few correlation coefficients to interpret the data. The prior of each coefficient involved hyperparameters of its own and its immediate neighbor.
## 2 Materials and System  
The architecture of the multi-crane visual sorting system with cloud PLC in 5G network is shown in Figure 1, which consists of three layers, including the device layer, the transport layer, and the computing layer. The data of all devices are transmitted by a 5G network, and the commands from the cloud PLC also are sent to control the devices by the 5G network.  
There are three types of devices in the device layer, including a multi-crane, a conveyor belt, and the cameras. The movement of the multi-crane and the conveyor belt are controlled by local PLCs that receive the commands from the cloud PLC. In order to locate and recognize the materials on the conveyor belt in real-time, the images or the video are transmitted by a 5G network to the cloud, in which an AI server is deployed that integrates deep learning methods to process data.  
In the transport layer, a 5G system consists of an access network, a bearer network, and a core network. We deploy two 5G base stations as the access network. The bearer network is as a bridge to transmit the signal between the access network and the core network. In order to meet the requirement of high reliability and low latency about reliable low latency communication (URLLC) [32] as one typical application of 5G, some function of the core network sink to the edge. We can deploy cloud PLC in the edge or in the cloud.  
In the computing layer, we design the cloud PLC to coordinatively control remote devices and cooperate with the high-performance server to support many applications that need a large amount of computing, like CV applications and precise location.  
For the multi-crane visual sorting system, the first step is to locate the position and obtain the world coordinates of the materials. Second, the class of all materials are recognized from the original image. The process results are transmitted to the cloud PLC by the 5G network. Third, the cloud PLC sends commands to the local PLC that controls the multi-crane according to the dynamic scheduling method. In this article, we focus on three critical technologies that are the location module, the recognition module, and the control module, the details of which are introduced as follows.
# Void Avoidance Opportunistic Routing Protocol for Underwater Wireless Sensor Networks
## 1 Introduction
Our novel protocol is a reactive loop-free OR protocol proposed to enhance EEDOR by addressing the void area problem based on the hop count discovery of the source node. This approach improves the packet delivery ratio by avoiding paths that have a large number of hops that increase the packet error rate as a result of packet collisions. The rest of this paper is organized as follows. In Section 2, some of the well-known related works in the area of void avoidance are reviewed. The details of our protocol are presented in Section 3. The simulation results of different scenarios are presented in Section 4. Finally, the conclusion of our study is provided in Section 5.
## 3 Results and Discussion
### Specific Consumption of BETs, FCETs, and ICETs-CNG  
The truck and tour data described in Sections 2.4, 2.5, 3.1 and 3.2 were used to determine the consumption of BETs and FCETs at the current state of the art. Together with the expected technology developments from Section 2.6, this allowed model-based predictions for the medium-term period up to 2030 and the long-term period up to 2050. We predicted technological progress not only for the new drive technologies BET and FCET but also for the ICET-CNG. Although the latter is considerably more advanced in its development than BETs and FCETs, there is further potential for sustainable improvement, as evidenced by the recent progress made by DAF with its XG generation (see Table 9).  
The results are summarized in Figures 10 and 11. Specific consumption figures are usually given in 1/100 km for liquid fuels, in kg/100 km for gaseous fuels, and in kWh/100 km for electric drives. For the sake of comparability, we have converted the various dimensions to kWh/100 km (H\({}_{2}\): 33.30 kWh/kg, CNG: 13.36 kWh/kg). The great savings potential in the transition from ICET-CNG\({}_{2024}\) to the new BET and FCET technologies is evident. The BET\({}_{2050}\) and the FCET\({}_{2050}\) will only consume 34.4% and 41.6%, respectively, of the energy required by the ICET-CNG\({}_{2024}\). The higher energy requirement of the FCET compared to the BET is due to a longer drive train. However, at 17%, the difference between BET\({}_{2050}\) and FCET\({}_{2050}\) is less than the underlying efficiency loss of the fuel cell (\(\eta^{\text{FC}}_{2050}=75\%\), as outlined in Figure 7 of [31]). This is caused by the significantly higher weight of the BET (see Table 10 and Figure 9). The resulting additional consumption is caused by the fact that acceleration and gravitational losses increase with mass.  
Figure 9: Weight shares of the various truck components on long tour (referring to 2030 values of Table 7; for more details, see Tables S3 and S4 of [41]).  
Figure 10: Specific consumption of a 20-ton truck. (**a**) Short tour. (**b**) Long tour.
## 2 Mathematical Model Analysis
### Mathematical Model of PMSM  
Among different motors, PMSM has the advantages of small size, easy maintenance, high power density, and good speed regulation performance, which is widely used in electric vehicles [23; 24]. Therefore, the PMSM is used for the power train system in this study.  
The electric motor torque equation in the two-phase synchronous rotating coordinate system can be provided as:  
\[T_{\rm e}=P\big{[}\psi\dot{t}_{q}+\big{(}L_{d}-L_{q}\big{)}\dot{t}_{d}\dot{t}_{ q}\big{]} \tag{1}\]  
where subscripts \(d\) and \(q\) are the components of the \(d\) and \(q\) axes, respectively, in the rotating coordinate system; \(i\) is the stator current; \(L\) is the stator inductance; \(T_{\rm e}\) is the electromagnetic torque; \(P\) is the electric motor pole pairs; and \(\psi_{t}\) is the rotor flux.  
For surface-mount PMSM, the inductances of the quadrature and straight shafts of the electric motor are approximately equal. The Equation (1) can be rewritten as:  
\[T_{\rm e}=P\psi\dot{t}_{q} \tag{2}\]  
In the two-phase rotating coordinate system, the mathematical model of the electric motor is decoupled. Thus, for surface-mounted PMSM, \(i_{d}=0\) is often used to achieve decoupling control of the quadrature and straight shafts current.  
The torque balance equation of the PMSM can be deduced as:  
\[T_{\rm e}=\frac{2\pi\eta}{P}\frac{dn}{dt}+2\pi R_{\rm\Omega}\frac{n}{P}+T_{ \rm 1} \tag{3}\]  
According to Equations (2) and (3), the relationship between the motor speed and the current of the quadrature and straight shafts can be obtained.
## 5 Conclusions  
In conclusion, the 3 MT effectively simplifies the traditionally time-intensive, multi-day testing requirements of the Critical Power Model (CPM), offering a viable alternative for measuring aerobic (CP) and anaerobic (W\({}^{\prime}\)) fitness metrics. However, because of the mental and physical demands of the 3 MT, its routine use as a fitness-monitoring tool in many sports settings remains limited. To address this, we explored exponential decay models as a shorter-duration alternative to the 3 MT, with the aim of maintaining accuracy in estimating CP and W\({}^{\prime}\) while minimizing test duration.  
Our findings suggest that reducing the test duration by approximately 50% may provide CP estimates comparable to those obtained from the 3 MT in an isokinetic mode, particularly among cyclists and triathletes. We observed no significant differences in CP estimates at the 90 s mark for BI and the 120 s mark for MONO. However, as W\({}^{\prime}\) values showed a mean difference of 13% (13.18% for BI and 13.35% for MONO, respectively) from 3 MT values, it is essential to acknowledge this variability when considering W\({}^{\prime}\) reliability in the shorter-duration protocol. The Bland-Altman analysis also indicated that, while results are promising, the \(<\)120 s protocol does not perfectly replicate the 3 MT, particularly in terms of W\({}^{\prime}\) accuracy.  
Thus, while the proposed exponential model can serve as a practical, time-efficient method for estimating CP in both men and women, further research is recommended to refine its applicability to W\({}^{\prime}\) estimation across a broader range of athlete types and fixed-resistance settings.  
Conceptualization, M.-C.T. and S.T.; methodology, M.-C.T. and S.T.; software, M.-C.T.; validation, M.-C.T. and S.T.; formal analysis, M.-C.T.; investigation, M.-C.T. and S.T.; resources, M.-C.T., S.T., and M.K.; data curation, M.-C.T.; writing--original draft preparation, M.-C.T.; writing--review and editing, S.T. and M.K.; visualization, M.-C.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Review Ethics Board of University of Toronto (28737 24 April 2013).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The calculated CP and W\({}^{\prime}\) data presented in the study are openly available in GitHub at [[https://github.com/mingchangtsai/exponential3](https://github.com/mingchangtsai/exponential3) MT]([https://github.com/mingchangtsai/exponential3](https://github.com/mingchangtsai/exponential3) MT) (accessed on 8 October 2024).  
The authors wish to thank all the participants for their committed effort and the Ontario Ministry of Health and Long-Term Care for funding this study.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript: