esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Rollover Stability and an Anti-Rollover Warning System for the Vibroseis Truck
## 5 Simulation Analysis of the Anti-Rollover Warning System for Vibroseis Truck
### Comparative Analysis between Flat-Road Steering and Slope Steering Process for the Vibroseis Truck  
The contrasting curves of critical steering speed and warning index between flat road steering and slope steering are shown in Figures 17 and 18, respectively. The values of the two critical steering speeds gradually decreased with the increase of time, tending to finally stabilize. If the speed and steering angle were the same, and the slope angle was different, the flat road and the slope angle were \(0^{\circ}\) and \(15^{\circ}\), respectively, causing the critical steering speed on the slope to be greater than the critical steering speed on the flat road. The warning index \(A\) behaved as the speed. The index \(A\) for the slope was the first to reach its critical value of 1, which indicated that the rollover stability and security in the slope steering process were low; in other words, the slope steering process was prone to rollover accidents. Therefore, in the driving process on the slope, the driver should pay more attention to the control of speed and steering angle to improve the stability and safety of the vibroseis truck.  
Figure 16: Sound–light alarm of the vibroseis truck in the slope steering process.  
Figure 17: Comparison of the critical steering speed between flat-road steering and slope steering.  
Figure 15: Time–amplitude curve of the warning index \(A\) in the slope steering process.
# Chlorobenzene Removal Using DBD Coupled with CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) Catalyst
###### Abstract  
The removal of chlorobenzene using a dielectric barrier discharge (DBD) reactor coupled with CuO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) catalysts was investigated in this paper. The coupling of CuO enhanced the chlorobenzene degradation and complete oxidation ability of the DBD reactor, especially under low voltage conditions. The characterization of catalyst was carried out to understand the interaction between catalyst and plasma discharge. The effects of flow rate and discharge power on the degradation of chlorobenzene and the interaction between these parameters were analyzed using the response surface model (RSM). The analysis of variance was applied to evaluate the significance of the independent variables and their interactions. The results show that the interactions between flow rate and discharge power are not negligible for the degradation of chlorobenzene. Moreover, based on the analysis of byproducts, 4-chlorophenol was discriminated as the important intermediate of chlorobenzene degradation, and the speculative decomposition mechanism of chlorobenzene is explored.  
plasma catalysis; dielectric barrier discharge (DBD); chlorobenzene; chlorinated volatile organic compounds (CVOCs) +
Footnote †: journal: Applied Sciences
# Multicriteria Optimization of Lower Limb Exoskeleton Mechanism
## 3 The Method of Kinematic Synthesis  
### Analytical Solution on 4 Synthesis Parameters  
Unknown variables are:  
\(x_{1}:=x_{p},x_{2}:=y_{p},x_{3}:=\xi^{0},x_{4}:=\eta^{0}\).  
The conditions \(\frac{\partial S}{\partial x_{j}}=0,j=\overline{1,4}\) lead to the following equations involving four unknowns \(x_{1},x_{2},x_{3},x_{4}\):  
\[\sum_{i=1}^{N}\left(\delta_{i}^{\xi}\cdot\frac{\partial\delta_{i}^{\xi}}{ \partial x_{j}}+\delta_{i}^{\eta}\cdot\frac{\partial\delta_{i}^{\eta}}{ \partial x_{j}}\right)=0,\quad j=\overline{1,4}. \tag{10}\]  
Namely,  
\[\sum_{i=1}^{N}\delta_{i}^{\xi}\cos\theta_{i}+\sum_{i=1}^{N}\delta_{i}^{\eta} \sin\theta_{i}=0 \tag{11}\]  
\[-\sum_{i=1}^{N}\delta_{i}^{\xi}\sin\theta_{i}+\sum_{i=1}^{N}\delta_{i}^{\eta} \cos\theta_{i}=0 \tag{12}\]\[\sum_{i=1}^{N}\delta_{i}^{\xi} =0 \tag{13}\] \[\sum_{i=1}^{N}\delta_{i}^{\eta} =0. \tag{14}\]  
Let us introduce notations:  
\[k :=\sum_{i=1}^{N}\cos\theta_{i} \tag{15a}\] \[m :=\sum_{i=1}^{N}\sin\theta_{i}\] (15b) \[b_{1} :=\sum_{i=1}^{N}\biggl{(}-\xi_{E_{i}}+L\cdot\frac{i-1}{N-1} \biggr{)}\cos\theta_{i}-\sum_{i=1}^{N}\eta_{E_{i}}\sin\theta_{i}\] (15c) \[b_{2} :=\sum_{i=1}^{N}\biggl{(}\xi_{E_{i}}-L\cdot\frac{i-1}{N-1} \biggr{)}\sin\theta_{i}-\sum_{i=1}^{N}\eta_{E_{i}}\cos\theta_{i}\] (15d) \[b_{3} :=\sum_{i=1}^{N}\biggl{(}-\xi_{E_{i}}+L\cdot\frac{i-1}{N-1} \biggr{)}\] (15e) \[b_{4} :=\sum_{i=1}^{N}(-\eta_{E_{i}}). \tag{15f}\]  
Then we come to the system of linear equations expressed as follows:  
\[Nx_{1}-kx_{3}-mx_{4} =b_{1} \tag{16a}\] \[Nx_{2}+mx_{3}-kx_{4} =b_{2}\] (16b) \[kx_{1}-mx_{2}-Nx_{3} =b_{3}\] (16c) \[mx_{1}+kx_{2}-Nx_{4} =b_{4}. \tag{16d}\]  
The only solution of this system in case \(k^{2}+m^{2}\
eq N^{2}\) is  
\[x_{1} =\frac{-Nb_{1}+kb_{3}+mb_{4}}{k^{2}+m^{2}-N^{2}} \tag{17}\] \[x_{2} =\frac{-Nb_{2}-mb_{3}+kb_{4}}{k^{2}+m^{2}-N^{2}}\] (18) \[x_{3} =\frac{Nb_{3}-kb_{1}+mb_{2}}{k^{2}+m^{2}-N^{2}}\] (19) \[x_{4} =\frac{Nb_{4}-mb_{1}-kb_{2}}{k^{2}+m^{2}-N^{2}}. \tag{20}\]  
The necessary conditions also serve as sufficient conditions [36] for attaining the minimum of the function \(S\): the matrix  
\[\frac{d^{2}S}{d\vec{x}^{2}}=\begin{bmatrix}N&0&-k&-m\\ 0&N&m&-k\\ -k&m&N&0\\ -m&-k&0&N\end{bmatrix}\]is positive-definite (in case \(k^{2}+m^{2}\
eq N^{2}\)), since the determinants of all corner minors are non-negative [37]:  
\[\begin{split}\Delta_{1}&=N>0,\quad\Delta_{2}=N^{2}>0, \quad\Delta_{3}=N^{3}+N(k^{2}+m^{2})>0,\\ \Delta_{4}&=(k^{2}+m^{2}-N^{2})^{2}>0.\end{split} \tag{21}\]
# Recent Advances in Additive Manufacturing of Fibre-Reinforced Materials: A Comprehensive Review
## 2 Techniques and Materials
### Prepreg Extrusion  
This technology involves the principle of continuous fibre fabrication (CFF) [30] and utilises pre-impregnated fibres, coated with a thermoset or thermoplastic material, to ensure uniform distribution of the coating element. In general, there are two printing heads. The first one is dedicated to the extrusion of the thermoplastic material, which constructs the external layers and the path in which the reinforcing fibre will be accommodated. The second nozzle is dedicated to the extrusion of the fibre compound, which is pre-impregnated with a thermoset. The nozzle extrudes the continuous fibre to fill the designated path constructed from the first nozzle. The partially cured resin in the prepregs reduces the need for extensive postprocessing, which speeds up production cycles. The main manufacturing companies that use this technology are Markforged and Ingersoll Machine Tools. The materials used are PLA, PC, PETG, and PA, while the fibres can be glass, carbon, aramid, and Markforged ONYX, a specialized composite of micro carbon fibre-filled nylon [31; 32].
## 2 The Analysis of SDP's Characteristics
In conclusion, the selection of different lag coefficients \(l\) and aperture gains \(\zeta\) influences the topological structure of the SDP image. Keeping one influencing parameter constant and only optimizing the other overlooks the interaction between the two, resulting in a solution that is only relatively optimal, limiting the application effect of the SDP transformation. Therefore, adaptively selecting the optimal combination of parameters \(l\) and \(\zeta\) for the SDP transformation is extremely important, facilitating a better representation of the signal through the SDP image.
## 3 Experimental Results and Discussions
### Analysis of Signal Waveform Quality  
To qualitatively analyze the signal distortion in this dual-hop data-transmission system, a BERT is used to send a 23\({}^{1}-\)1 pseudo-random binary sequence (PRBS) at 1 Gbps and a real-time oscilloscope is used to capture the waveform, simultaneously. Figure 5 shows the signals at the test points before Tx.1, after Rx.1, Amp.2, and Rx.2, respectively. Furthermore, the peak-to-peak-voltage (Vpp) is also noted. Figure 5a,b shows that the OOK signal suffers distortion and overlaps introduced noise as it passes through the 2 m link. To obtain an ideal signal strength to drive Tx.2, the electrical signal generated by Rx.1 is amplified. The amplifier (DR-AN-20-MO, iXblue, BesanCon, France) provides a tunable gain of 22 dB, which amplifies the electrical signal from 137.6 mV to 4.8 V. This voltage serves as the optimum modulation depth. The waveform is closer to a square wave after the amplifier, mainly because of the influence of oscilloscope noise superimposed on the final waveform instead of the improvement of the signal-to-noise ratio (SNR), as shown in Figure 5b,c. Then, the signal passes through the second 2-m link. Figure 5c,d shows that the OOK signal suffers similar distortion and overlaps introduced noise as it passes through the second 2-m link.  
Figure 4: The spectrum of the package 100-\(\upmu\)m micro-LED.  
Figure 5: Signal observed on oscilloscope after (**a**) Tx.1, (**b**) Rx.1, (**c**) amplifier, (**d**) Rx.2.
## 3 Results and Discussion
### Validation of the Developed Algorithm
#### 3.1.1 Validation of the Delamination Model  
Initially, the model was validated for both undamaged and delaminated laminates, considering only the linear components of the strain. Although [PERSON] et al. [16] provided a detailed validation, some aspects are presented here for clarity. A numerical simulation was first performed on a laminated composite cantilever square plate made of woven glass-epoxy, with dimensions of 150 mm \(\times\) 150 mm and a thickness of 1.6 mm, for both healthy and delaminated conditions. The material properties for the lamina are defined as \(E_{11}\) = 6.125 GPa, \(E_{22}\) = 5.421 GPa, \(G_{12}\) = 2.79 GPa, \(G_{23}\) = 1.395 GPa, \(G_{13}\) = 2.79 GPa, \(v_{12}\) = 0.17, and \(\rho\) = 1400 kg/m\({}^{3}\). The first six natural frequencies are computed and presented in Table 1. The results from the current model show good agreement with the experimental and numerical data provided by [PERSON] et al. [39]. In the second case study, a laminated composite cantilever plate made of graphite-epoxy with dimensions of 250 mm \(\times\) 250 mm and a thickness of 2.12 mm, following a stacking sequence of \((0^{\circ}/90^{\circ}/45^{\circ}/90^{\circ})s\), is analyzed for both intact and delaminated conditions. The material properties used are \(E1\) = 132 GPa, \(E_{2}\) = 5.35 GPa, \(G_{12}\) = 2.79 GPa, \(G_{13}\) = 2.79 GPa, \(G_{23}\) = 2.79 GPa, \(v_{12}\) = 0.291, and \(\rho\) = 1446.2 kg/m\({}^{3}\). The natural frequencies of the first six modes are calculated and listed in Table 2. The outcomes match well with those obtained using the First-order Shear Deformation Theory (FSDT) [40] and the Layerwise theory [41]. Previous studies have shown that Layerwise theory provides results for natural frequencies, deflections, stresses, and buckling loads that more closely match exact 3D elasticity solutions than First-Order Shear Deformation Theory (FSDT). The present 3D degenerated element theory offers the same nodal degrees of freedom as FSDT, making it computationally faster than Layerwise theory due to fewer global degrees of freedom, while still achieving greater accuracy than FSDT (see Table 2). In most cases, this theory also yields better results than Higher-order Shear Deformation Theory (HSDT) [39], as shown in Table 1. For undamaged plates, the natural frequencies for most modes are closer to experimental results in this study compared to HSDT. However, this trend is less consistent for delaminated plates. There are several reasons for this discrepancy. Firstly, each theory uses different approaches to model delamination numerically, leading to variations in outcomes. Secondly, experiments involving delaminated plates may not yield results as precise as expected due to multiple uncertainties, such as challenges in fabricating samples with accurately sized and positioned delaminations and potential noise in experimental measurements. Therefore, some variation between numerical and experimental results isexpected. Overall, the present theory demonstrates acceptable accuracy in predicting the natural frequencies of damaged plates.
# Bio-Based Polymers with Potential for Biodegradability
## 3 Poly(lactic acid) (PLA) and Related Polymers
### Biodegradation of PLA  
Biodegradation of plastics is a complex process involving multiple steps and pathways [122]. The initial degradation step is for PLA to be broken down into monomers or low molecular weight oligomers, where the ester bonds are cleaved hydrodlytically [123]. Reduction in molecular weight into smaller water soluble fragments is required to facilitate uptake into microorganisms [124], PLA becomes water soluble when the molecular weight (\(M_{\text{n}}\)) is below 20,000 g/mol [124]. The initial degradation into smaller fragments is a rate limiting step in biodegradation processes [125]. After uptake into the microorganisms, subsequent metabolic activity breaks the polymers down into metabolic end products such as carbon dioxide and water, while a portion of the carbon is converted into biomass [126].  
As [PERSON] et al. pointed out, environmental degradation might be an equally appropriate name for the overall mechanism of PLA biodegradation because abiotic and biotic processes occur simultaneously [119]. Environmental composting of PLA proceeds efficiently under adequate conditions due to the combined effects of hydrolysis and microbial activity [127]. Furthermore, elevated temperatures encountered during composting accelerate the hydrolysis process of PLA, especially when temperatures exceed 50 \({}^{\circ}\)C [128]. Extracellular enzymes released by specific microorganisms can cause cleavage of PLA chains, which may contribute to the degradation process. However, some studies have shown that extracellular enzymes do not significantly accelerate depolymerization [125].  
The reaction mechanism for the hydrolysis of polyesters is bimolecular nucleophilic substitution reaction (\(S_{\text{N}}2\)), which may be catalyzed with either acids or bases [129]. A schematic of acid-catalyzed hydrolytic cleavage of PLA chains is shown in Figure 2[124]. Furthermore, the reaction rates for hydrolytic degradation are dependent on a number of factors including: temperature [130], size and shape of samples [131], molecular weight [129], crystallinity [132], and relative humidity [133].  
Degradation of PLA and other aliphatic polyesters biomedical devices inside animal or human tissue is also controlled by hydrolytic mechanisms [134]. Biodegradable devices degrade slowly over time and eventually become absorbed by the body [135]. Many factors affecting hydrolytic degradation of polyesters in the environment (e.g., morphology, crystallinity, sample size, molecular weight) also affect hydrolytic degradation inside of body tissues. However, other factors, like sterilization and site of implantation, can play a role in degradation of biomedical devices [136].  
Figure 2: Acid-catalyzed hydrolysis of poly(lactic acid) (PLA).
## 4 Deconvolution
### Minimum Entropy Deconvolution  
Minimum entropy deconvolution (MED) has carved out a significant niche in the deconvolution landscape. Rooted in information theory concepts, MED seeks to find a deconvolution filter that minimizes the entropy of the output signal. This approach has proven invaluable in scenarios where extracting hidden or obscured features in signals is crucial. In the following subsections, we will delve into the definition of MED, explore its wide-ranging applications, and critically assess its strengths and weaknesses.
## 3 Results  
### Participant Flow  
Figure 6 illustrates the participant flow throughout the conducted study, from initial screening to final analysis. Only SUS was administered at a different baseline time point due to the delay in the development of the mHealth app. While the retention rates at baseline and the first follow-up for the CASE-Cancer, PAM, and SUS questionnaires were relatively high (75.9%, 77.1%, and 46.4%, respectively), significant attrition occurred by the end of the study (CASE-Cancer = 40.5%, PAM = 39.1%, and SUS = 64.9%). This attrition led to a reduced sample size for the final analysis, with data included from CASE-Cancer (\(n\) = 75), PAM (\(n\) = 77), and SUS (\(n\) = 27). A total of 41 out of 166 patients (25%) withdrew from the clinical study by the final follow-up. The majority (\(n\) = 31 out of \(n\) = 41, 76% of the total) were female, and withdrawals were more frequent in the breast cancer group (\(n\) = 24 out of \(n\) = 41, 59% of the total). The overall attrition rate for the clinical trial at completion was 25% (\(n\) = 41 out of 166 patients). Attrition rates for the CASE-Cancer, PAM, and SUS questionnaires were 40.47% (\(n\) = 51 out of 126 patients), 40% (\(n\) = 51 out of 128 patients), and 64.93% (\(n\) = 50 out of 126 patients), respectively.  
A comprehensive analysis of the reasons for withdrawal (total \(n\) = 65) (Table 1) revealed that these were primarily related to personal circumstances (\(n\) = 11 out of 65, 17% of the total), technical issues (\(n\) = 10 out of 65, 15% of the total), including smart-bracelet malfunctions and other technical problems, and time constraints associated with participation (\(n\) = 9 out of 65, 14% of the total).
## Materials and Methods
### Determination of Macro- and Microelement Content  
The samples for the determination of macro- and microelements were taken from the batch samples from the above-described two sampling methods. The samples were rinsed with deionized water, and placed in a petri dish and dried in an uncorroded conventional oven at 40 \({}^{\circ}\)C for 3 days. After drying, the samples were packed in labelled mineral-free paper envelopes. The macro- and microelement contents were analysed using inductively coupled optical emission spectrometry (ICP-OES) according to the validated method described by [PERSON] et al. [14]. A radial view Spectro Cios CCD ICP-OES (Spectro Analytical Instruments, Kleve, Germany) was used, and all samples were digested with the closed-tube nitric acid/hydrogen peroxide digestion method as described elsewhere by [PERSON] et al. [14]. About 0.30 g of the dried sample was weighed to 1 mg into 50-mL screw-cap polypropylene tubes, and 2.0 mL of HNO\({}_{3}\) and 0.5 mL of H\({}_{2}\)O\({}_{2}\) were added to initiate the sample digestion. The digestion was done at 125 \({}^{\circ}\)C for 120 min using 72-position DigiPrep digestion blocks (SCP Scientific, Baie D'Urf e, Quebec, Canada). The samples were made to a final volume of 25 mL with 18 M\(\Omega\).cm water. The sample flow rate was 2.0 mL/min, and the total analysis time per sample was approximately 2.5 min. The calibration curves for all elements were created from mixtures of high-purity single-element standard solutions in a 4% (_v/v_) HNO\({}_{3}\) matrix. Background correction was applied to all wavelengths, and spectral interferences were applied where required using software algorithms [14]. A drift correction solution (a mixture of all elements prepared in 4% HNO\({}_{3}\)) was analysed every 25 samples to account for within-run variation in the flows, etc. Additionally, the blank subtraction, the drift correction, and other data processing (mass and volume adjustments) were performed offline [14]. Six different reference materials (RMs) were obtained from the US National Institute of Standards and Technology (NIST, Gaithersburg, MD, USA) and used to establish the quality of the data [14].  
Each sample was run in duplicate. The macroelements identified in all the genotypes and varieties investigated were calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), phosphorus (P), and sulphur (S). The microelements identified in all the genotypes and varieties investigated were iron (Fe), manganese (Mn), boron (B), copper (Cu), molybdenum (Mo), cobalt (Co), nickel (Ni), zinc (Zn), and aluminium (Al).
# Mod2 VQLS: A Variational Quantum Algorithm for Solving Systems of Linear Equations Modulo 2
## 6 Related Classical and Quantum Methods for Solving Linear Systems
### Quantum Linear Solvers
#### 6.1.2 Specific Differences from VQLS  
There are a few additional differences that distinguish Mod2 VQLS from the VQLS of [PERSON] et al. [9] and subsequent developments. They use a dense encoding, which means that their cost function can be evaluated by executing quantum circuits with a logarithmic number of qubits. This is in contrast to our encoding, which requires a linear number of qubits. With the particular implementation details of the VQLS routine explained in [9] in mind, we see that our encoding has several benefits. For instance, in this context \(\mathcal{A}\) is a unitary operation that can be performed using \(N\) gates, where \(N\) is the number of non-zero entries in the coefficient matrix. By contrast, the \(A\) operator in [9] is a _linear combination_ of \(L\) unitaries, and \(L\) may be exponential in the number of qubits in their circuit. Moreover, the authors do not explain how to achieve such a decomposition in general; however, in Appendix A, they provide some hints for how to proceed when the coefficient matrix is sparse. In this section, they assume access to some oracle \(\mathcal{O}_{A}\) that can effectively query the entries of \(A\). Regardless, it is not clear how to implement this oracle and indeed the authors comment that, \"The gate complexity of both of these strongly depends on the form and precision with which the matrix elements of \(A\) are specified\" ([9], Appendix A).  
Also in [9], the cost function \(\widehat{C}_{G}\) vanishes when the norm of \(|\psi\rangle\) vanishes, making it deficient in the sense that non-solutions to \(Ax\equiv b\mod 2\) correspond to local extrema. By contrast, the variational state \(|\psi(\theta)\rangle\) used here is normalized because \(\mathcal{AV}(\theta)\) is a unitary operation. Thus, there is no need to normalize the cost function, as in Equation (5) of [9] and consider the more complicated so-called _local_ cost functions.
## 1 Introduction
Behavioral therapies, such as Habit Reversal Therapy (HRT) [11; 12], Comprehensive Behavioral Intervention for Tics (CBIT) [11; 12], Massed Negative Practice (MNP) [9; 13] and Relaxation Therapy (RT) [9; 14], are the most commonly studied nonpharmacological treatments for TDs. Overall, these treatments seem to show an efficacy comparable to pharmacological interventions, with sustained post-treatment benefits [15]. Consequently, nonpharmacological treatments are increasingly considered as first-line options for TD management in several countries [10]. Comprehensive systematic reviews of nonpharmacological treatments for children and adolescents with TDs are currently lacking. As the existing literature does not provide evidence on different treatment effects depending on the TD type--TS, CTD, and PTD [7]--it is reasonable to focus on the overall effectiveness of interventions in reducing ice severity. To our knowledge, only a few systematic reviews analyze the efficacy of TD treatments in pediatric age, considering both pharmacological and nonpharmacological (behavioral, physical, and dietary) interventions, either separately or in combination [11; 12]. Although some systematic reviews examined nonpharmacological interventions, specifically behavioral therapies, none specifically focused on children and adolescents [16; 17]; moreover, the research was often limited to TS, rather than comprehensively addressing TDs [17]. Finally, none of these reviews investigated online treatments, despite recent works on online behavioral therapies for TDs in youth [18; 19]. The importance of online interventions for children and adolescents is now established in the treatment of several psychological conditions, accelerated by the COVID-19 global pandemic, which limited their access to traditional face-to-face treatment [11; 20; 21; 22]. Given the growing evidence supporting the use of nonpharmacological treatments for TDs and the gaps in the existing literature, the current review was aimed at assessing the effectiveness of nonpharmacological treatments (both face to face and online) for pediatric TDs, focusing on randomized controlled trials studies. A differential treatment effect on motor tics, vocal tics, and level of impairment was also explored.