esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principles
### Principle of Conventional NM-CAP Receiver with Cascaded Matched Filter and LMS  
In conventional CAP demodulation, the received signal r(t) first passes through a matched filter that separately performs matched filtering on the I/Q signals, effectively isolating the I and Q components from the received signal. The impulse response of the matched filter is the same as the PSF used at the transmitter, ensuring optimal signal recovery. The transfer function of the matched filter, h(t), can be expressed as follows:  
\[\rm h(t)=g(t), \tag{5}\]  
The matched filters for the received signal can be expressed as follows:  
\[\rm m_{n}^{I}(t)=h(t)cos(2\pi v_{n}t),\ m_{n}^{Q}(t)=h(t)sin(2\pi v_{n}t), \tag{6}\]  
The outputs of the received signal after passing through the matched filters are as follows:  
\[\rm r_{n}^{I}(t)=r(t)\otimes m_{n}^{I}(t),\ r_{n}^{Q}(t)=r(t)\otimes m_{n}^{Q} (t), \tag{7}\]  
After the I/Q signals undergo matched filtering and down-sampling, they are combined into complex symbols. Despite matched filter processing, the signal may still suffer from ISI and IBI. To further mitigate ISI and IBI caused by the channel, an LMS equalizer processes the matched filter output, adjusting the filter coefficients to minimize ISI and IBI. The LMS equalizer updates the filter coefficients using gradient descent to reduce the mean squared error (MSE). The update equation is as follows:  
\[\rm w(t+1)=w(t)+\mu e(t)x(t), \tag{8}\]  
where w(t) represents the filter coefficients at the current time, \(\mu\) is the step size factor controlling the update speed, e(t) is the current error signal, and x(t) is the input signal. Finally, the system performs a symbol decision on the equalized signal. By analyzing the amplitude and phase of the I/Q signals, the signal is mapped back to the corresponding QAM symbol.
## 3 Mathematical Model
### Passenger Dynamic Demand Analysis  
#### 3.2.1 Passenger Ticket-purchasing process  
In real life, many stochastic processes are Markov processes, such as the daily sales situation, which should be studied with respect to commercial activities, the number of people waiting at the station, the number of people infected with infectious diseases, etc., which can be regarded as Markov processes. A Markov process is an important method for studying discrete event dynamic systems, and its mathematical basis is the theory of stochastic processes [35]. The Poisson process is a kind of simple stochastic process with continuous time and a discrete state, which has been widely used in queuing theory and service systems. The ticket-purchasing process of passengers during the pre-sale period can be regarded as the arrival process of several ticket-purchasing requests, which is a stochastic process and fits the concept of the Poisson process. The following describes the ticket-purchasing process through the Poisson process. N\({}_{\text{t}}\) represents the total number of passengers' ticket-purchasing requests in the period \([0,t]\). \(\lambda=E(\text{N}_{\text{t}})/t\) represents the intensity of this Poisson process. However, the passengers' ticket-purchasing requests are unevenly distributed in the time dimension, i.e., the Poisson intensity \(\lambda\) is not a fixed constant. Therefore, the condition of a non-homogeneous Poisson process is satisfied; that is, the non-homogeneous Poisson process is caused by the non-stationary intensity of the exponential distribution of time between passenger requests (as a function of time), primarily. Moreover, a passenger may purchase several tickets in each purchase process during the pre-sale period, and the number of tickets cannot be described only through the non-homogeneous Poisson process. Therefore, the compound non-homogeneous Poisson process is introduced to describe the passengers' ticket-purchasing process, which is defined as follows. Assume that \(\{\text{N}_{t}\,,\text{t}\geq 0\}\) is a non-homogeneous Poisson process with intensity \(\lambda(\text{t})>0\). \(\{\text{Y}_{k}\,,\text{k}=1,2\dots\}\) is a group of stochastic variables representing the number of tickets purchased per arrival. \(\{\text{N}_{t}\,,\text{t}\geq 0\}\) and \(\{\text{Y}_{k}\,,\text{k}=1,2\dots\}\) are independent of each other. Make  
\[\text{X}(\text{t})=\sum_{k=1}^{\text{N}(\text{t})}\text{Y}_{k}\,,\ \text{t}\geq 0 \tag{1}\]
###### Abstract  
The present study evaluated the protective effects that are induced by fluoride and Nd:YAG laser irradiation to control dentine erosion. One hundred and twenty bovine dentine slabs were eroded with citric acid twice a day for a total period of 15 days. At day 10, the samples were randomly assigned into eight groups: C (Control); F (Fluoride); L1 (Nd:YAG laser, 1 W, 100 mJ, 79.5 J-cm\({}^{-2}\)); L2 (Nd:YAG laser, 0.7 W, 70 mJ, 55.7 J-cm\({}^{-2}\)); L3 (Nd:YAG laser, 0.5 W, 50 mJ, 39.7 J-cm\({}^{-2}\)); F + L1; F + L2; and, F + L3. Optical Coherence Tomography images were collected from the surface of the samples in order to monitor the alterations that were induced by erosion and the effectiveness of each treatment using the Optical Attenuation Coefficient (OAC). OAC values that were calculated for samples from groups C, F, L1, L3, FL1, and FL3 indicated no protective effects against the progression of erosive lesions. Samples from L2 and FL2 groups presented OAC values, indicating that laser irradiation under this condition did not induce the formation of craters on dentine tissue and promoted sufficient chemical and morphological changes to avoid the progression of dentine erosion.  
demineralization; dental erosion; optical coherence tomography; Nd:YAG laser; fluoride; optical attenuation coefficient; OCT; dentine erosion Article
## 3 Numerical Examples  
Bi-coupled periodic multi-component beams with five kinds of unit cell, including two binary, two ternary and one quaternary unit cells, have been considered in all the numerical examples to validate our proposed MRRM, to illustrate the advantages of our MRRM compared to TMM, to demonstrate the general dispersion characteristics of bending waves in bi-coupled periodic multi-component beams, and to study the influences of various construction factors of the unit cell on the band structures. The configurations of these five kinds of unit cells are listed in Table 1, with the parameters of all the four involved materials provided in Table 2. In all the following calculations, the Timoshenko beam theory and the shear coefficient \(5/6\) are used for all of the constituent beams in the unit cells. Nevertheless, in Section 3.1 the Euler-Bernoulli beam theory is also exploited to model the structural member for the sake of observing the effect of beam theories on the dispersion curves. Except for the different geometric parameters of the exemplified periodic beams in Sections 3.2 and 3.4.1, the cross-sectional area and the cross-sectional inertia moment of all constituent beams in the unit cell are given as \(0.1\) m\({}^{2}\) and \(2.083\times 10^{-3}\) m\({}^{4}\) for the exemplified periodic beams in all the other sections of this part.  
Adopting the aforementioned parameters, the comprehensive dispersion curves of the five exemplified bi-coupled periodic multi-component beams are calculated by our proposed MRRM, containing the frequency-attenuation constant, the frequency-phase constant,the frequency-wavelength, the frequency-phase velocity dispersion curves that are in group called as the frequency-related spectra and the phase velocity-phase constant, the phase velocity-wavelength, and the phase velocity-frequency dispersion curves that are called the phase velocity-related dispersion curves in group. For the convenience of plotting these dispersion curves and also for the ease of discussing the corresponding dispersion characteristics, the engineering frequency \(f=\omega/(2\pi)\), the dimensionless wavenumber (phase/attenuation constant) \(ql/\pi\), the dimensionless wavelength \(\lambda/l\) and the dimensionless phase velocity \(c/c_{1 Rf}\) are used with \(c_{1 Rf}=\sqrt{E_{Rf}/\rho_{Rf}}\) as the wave speed of longitudinal mode in the referred constituent beam. If the unit cell of the considered periodic beam contains a concrete beam member, then it is taken as the referred beam. Otherwise, the aluminum beam is deemed as the referred beam.
## 8 Introduction to Proprietary and Standards-based Solutions
### Proprietary Technologies
#### 8.1.4 Qowisio  
Qowisio is an ultra-narrowband, dual-mode technology for LPWAN applications. It is compatible with LoRa and provides technological choices and flexibility to the end-users [23; 24]. It offers connectivity as a service to the end-users by providing the end devices, deploying the network infrastructure, developing customized applications, and hosting them at a backend cloud. Qowisio has a full range of intelligent devices supporting different applications such as asset management, perimeter control, motion detection, lighting, environment parameter monitoring, energy and power monitoring, tracking, and several others.
# Remediation Opportunities for Arsenic-Contaminated Gold Mine Waste
## 4 Phytoremediation
### Co-Application of Soil Amendments
#### 4.2.2 Phosphate and Organic Amendments  
[PERSON] et al. [16] investigated the use of phosphate as a soil amendment to improve phytoextraction using crop species suited to the Mediterranean climate and _P. vittata_. Their results showed that phosphate was successful at improving As bioaccumulation, and plants showed no signs of phytotoxicity; however, biomass did not increase. A similar study by [PERSON] et al. [133] reported that phosphate increased As phytoextraction and improved biomass for _Brassica_ species under spiked As concentrations of 0-75 mg/kg. [PERSON] et al. [16] used soil collected from the field, an industrial area, with average As concentrations of 25-2595 mg/kg as a result of chemical plant productions. These As concentrations are considerably higher than the [PERSON] et al. [133] study, and the use of As-rich field soil (non-spiked soil) introduced other soil As characteristics (e.g., speciation) that influence mobility and toxicity. For example, in Canada at Giant Mine, the dominant As species present was arsenic trioxide, which is the most bioavailable form of As for humans [21; 134]. Additionally, a study by [PERSON] et al. [135] investigated As uptake by _P. vittata_ with chromatated-copper-arsenate (CCA)-contaminated soil (135 mg/kg) and As spiked soil (126 mg/kg). The study included soil amendments, which were phosphate rock, municipal solid waste, and biosolid compost. [PERSON] et al. [135] stated that phosphate enhanced As bioaccumulation and removed 8.27% of As from CCA soil and 14.4% from the spiked soil. Compost amendment improved As bioaccumulation in CCA soil but decreased in spiked soil, and it was concluded that soil characteristics influenced the compost treatment [135]. Phosphate and compost treatment increased As leaching, but this was aided by _P. vittata_ As uptake. A study by [PERSON] et al. [55] investigated the use of co-application of phosphate and lime for As bioaccumulation, also using _P. vittata_. This study used soil collected from mining and smelting locations in Cornwall, England, and reported that there was no increase in As bioaccumulation. It was also stated that natural As in legacy mine waste soils may not be as easily bioavailable as other sources of As, e.g., industry, CCA, and spiked, because of soil properties. There is a need to assess phytoremediation techniques and soil amendments using mine waste soil collected from the field as results vary depending on the As source and soil characteristics.
# Detection of Preferential Water Flow by Electrical Resistivity Tomography and Self-Potential Method
###### Abstract  
This study explores the hydrogeological conditions of a landslide-prone hillslope in the Upper Mosel valley, Luxembourg. The investigation program included the monitoring of piezometer wells, hydrogeological field tests, analysis of drillcore records, and geophysical surveys. Monitoring and field testing in some of the observation wells indicated very pronounced preferential flow. Electrical resistivity tomography (ERT) and self-potential geophysical methods were employed in the study area for exploration of the morphology of preferential flowpaths. Possible signals associated with flowing groundwater in the subsurface were detected; however, they were diffusively spread over a relatively large zone, which did not allow for the determination of an exact morphology of the conduit. Analysis of drillcore records indicated that flowpaths are caused by the dissolution of thin gypsum interlayers in marks. For better understanding of the site's hydrogeological settings, a 3D hydrogeological model was compiled. By applying different subsurface flow mechanisms, a hydrogeological model with thin, laterally extending flowpaths embedded in a porous media matrix showed the best correspondence with field observations. Simulated groundwater heads in a preferential flow conduit exactly corresponded with the observed heads in the piezometer wells. This study illustrates how hydrogeological monitoring and geophysical surveys in conjunction with the newest hydrogeological models allow for better conceptualization and parametrization of preferential flow.  
preferential flow; resistivity tomography; self-potential mapping; hydrogeological modeling 202114224. [[https://doi.org/10.3390/app11094224](https://doi.org/10.3390/app11094224)]([https://doi.org/10.3390/app11094224](https://doi.org/10.3390/app11094224))
## 2 Methods
### Wavepacket Dynamics
In AI-MCE, the molecular wavepacket, \(|\Psi(t)\rangle\), is expanded in terms of Ehrenfest functions with dynamically coupled expansion coefficients \(D_{k}(t)\),  
\[|\Psi(t)\rangle=\sum_{k=1}^{N}D_{k}(t)|\psi_{k}(t)\rangle, \tag{4}\]  
where each Ehrenfest function, \(|\psi_{k}(t)\rangle\), consists of a Gaussian nuclear coherent state, \(|\bar{\mathbf{z}}_{k}(t)\rangle\), distributed across \(N_{s}\) electronic states, \(|\phi_{k}^{i}\rangle\), with the amplitudes \(a_{k}^{i}(t)\),  
\[|\psi_{k}(t)\rangle=\left[\sum_{i=1}^{N_{s}}a_{k}^{i}(t)|\phi_{k}^{i}\rangle \right]|\bar{\mathbf{z}}_{k}(t)\rangle. \tag{5}\]  
The coherent state, \(|\bar{\mathbf{z}}_{k}(t)\rangle\), is a product of 3\(\times\)(number of atoms) one-dimensional Gaussian coherent states, shown here for \(x_{\alpha}\), the \(x\)-component of the coordinate of atom \(\alpha\),  
\[\langle x_{\alpha}|\bar{\mathbf{z}}_{k}(t)\rangle=\left(\frac{\gamma_{\alpha} }{\pi}\right)^{\frac{1}{4}}\exp\left(-\frac{\gamma_{\alpha}}{2}\left(x_{ \alpha}-Q_{\alpha x}^{k}\right)^{2}+\frac{1}{\hbar}P_{\alpha x}^{k}\left(x_{ \alpha}-Q_{\alpha x}^{k}\right)+\frac{\imath P_{\alpha x}^{k}Q_{\alpha x}^{k} }{2\hbar}\right), \tag{6}\]  
where the position is \(Q_{\alpha x}^{k}\), the momentum \(P_{\alpha x}^{k}\), and the width parameter \(\gamma_{\alpha}\). The Ehrenfest ansatz can be slow to converge, but, in the context of this article, the difference is mostly technical, as sampling methods have been developed to speed up the convergence of Equation (4) [42; 48]. Each time-step of the propagation requires multiple calls to an ab-initio electronic structure software package to calculate energies for the ground and excited states, the potential gradients, and non-adiabatic couplings. For further details, see, e.g., Refs. [39; 40; 42; 49]. In this article, we use the AI-MCE method to simulate the photoexcited dynamics of the molecules ethylene and CHD.
# Effect of Bioaugmentation on Biogas Yields and Kinetics in Anaerobic Digestion of Sewage Sludge
## 3 Results and Discussion
### Biogas Production  
The results of the investigations are shown in Table 2 (average data are reported). It should be noted that the feed conditions varied through phases (R1-R3 and R4-R5), which was attributed both to the changes in sludge characteristics and the addition of microorganisms from the bioaugmenting mixture. In terms of organic matter removed via anaerobic digestion, regardless of the feedstock, the lowest biogas yield was observed for the removed COD, while the highest for the removed VS. Considering organic substances fed to the reactor, a higher biogas yield was noted in terms of VS and lower regarding TS (Table 2).  
The addition of microorganisms from the bioaugmenting mixture had no significant influence on the biogas production (the differences of means were not statistically significant). However, the biogas yields, as well as daily biogas production, were enhanced compared to the control (particularly for R2 and R3). These occurred despite the progressive decrease in the hydraulic retention time (HRT) from 20 d to 16.7 d that led to the faster washing out of the microorganisms from the semi-flow system. Moreover, similar methane content was noted independently of the mixture dose. It was also shown that bioaugmentation enabled to obtain higher biodegradation efficiency regarding the VS removal within shorter HRT. The above suggests the beneficial bioaugmentation effect. The results achieved could be attributed to the increase in rates of both biogas production and organics decomposition. It was due to the differences between the microbial communities of the bioaugmented digesters and the enhanced activity of microorganisms involved in bioaugumentation of anaerobic digestion. This explanation is consistent with the research by [PERSON] et al. (2016) regarding bioaugmentation with selected strains belonging to _Bacillus_ sp., _Pseudomonas_ sp., and _Actinomycetes_ sp. The slight increase in biogas yields throughout bioaugmentation could result both from the HRT shortening and the TS/VS feedstock dilution by bioaugmenting mixture. The second one followed by the procedure of mixture preparation recommended by ArcheaSolutions Inc. To significantly improve the biogas yields, the higher concentration of the bioaugmenting mixture would be profitable. This could be achieved in the future research using a microfiltration module. The study of [PERSON] et al. (2016) using a novel bacterial strain _Ochroductrum_ sp. POC9 for bioaugmentation of sewage sludge anaerobic digestion revealed much better results than the ones presented here. In that case, the cumulative biogas production increased by 22.06% compared to the control, although the study was conducted in batch mode.
# Structural and Biochemical Characterization of Thioredoxin-2 from _Deinococcus radiotrans_
## Materials and Methods
### Cloning, Expression, and Purification of DrTrxR, DrTrx1, and DrTrx2  
The genes _dr1982_ (_drtrxR_, GenBank accession No. 1797907), _dr0944_ (_drtrx1_, GenBank accession No. 1797257), and _drA0164_ (_drtrx2_, GenBank accession No. 1799587) were inserted into the expression plasmid pET-22b (Novagen, Darmstadt, Germany), following which, the resulting constructs expressed residues 2-325 of DrTrxR, 2-109 of DrTrx1, and 2-142 of DrTrx2 protein with an in-frame non-cleavable N-terminal His\({}_{6}\) tag (MHHHHHH). After verifying the DNA sequence, plasmid DNA was transformed into _E. coli_ strain BL21 (DE3) (Stratagene, La Jolla, CA, USA). The cells were grown to an OD\({}_{600}\) of approximately 0.5 in 1 L of LB medium containing 50 \(\upmu\)g/mL of ampicillin (Duchefa, Haarlem, The Netherlands) at 37 \({}^{\circ}\)C, and expression was induced with 0.1 mM isopropyl-\(\beta\)-D-thiogalactoside (Duchefa, The Netherlands). Following a 16 h induction at 22 \({}^{\circ}\)C, the cells were harvested and resuspended in a final volume of 20 mL of 20 mM Tris-HCl pH 7.5, 200 mM NaCl (buffer A). Then, 2 g of harvested cells were disrupted by sonication using a VP-15s sonicator (Taitec, Koshigaya, Japan). Sonication was performed using the following settings: output power, 8; pulse, 5 s; ON/5 s OFF; temperature, \(\sim\)2 \({}^{\circ}\)C, by floating on ice; and total sonication time, 30 min. Then, cellular debris were discarded via centrifugation at 30,000\(\times\)\(g\) for 30 min at 4 \({}^{\circ}\)C. The resulting supernatant was loaded onto 5 mL of nickel-nitrilotriacetic acid (Ni-NTA) resin (Qiagen, Hilden, Germany) for gravity-flow affinity chromatography. The resin was washed with 50 mL of buffer A containing 30 mM imidazole. DrTrxR, DrTrx1, and DrTrx2 were eluted with 10 mL of buffer A containing 300 mM imidazole. The partially purified proteins were concentrated to 5 mL and subsequently loaded onto a Superdex 75 HR 16/600 column (GE Healthcare, Boston, MA, USA), pre-equilibrated with buffer A using AKTA-Purifier (GE Healthcare, Boston, MA, USA). Equilibration, protein loading, and elution were performed at a flow rate of 1 mL/min. Finally, purified DrTrxR, DrTrx1, and DrTrx2 were concentrated to approximately 10 mg/mL, 11.6 mg/mL, and 5 mg/mL, respectively, with the volume of around 700 \(\upmu\)L for all three proteins.
# PV Sizing for EV Workplace Charging Stations--An Empirical Study in France
## 4 Relationships Necessary for the Application of the Method
### PV Production
#### 4.3.2 Relationship 3: Yearly PV Production versus Peak Power  
There are two manners to obtain the yearly PV energy production from a peak power value for a given location as required to obtain the third relationship.  
* If production profiles are available, integrating the power over a complete year gives the energy produced during this year;
* If production profiles are not available, many free software tools like PVGIS [28] estimate the annual PV production per peak power value.
# Experimental Analysis of Pore and Permeability Characteristics of Coal by Low-Field NMR
## 3 Discussion
### NMR Pore Structure
#### 3.3.1 PSD of NMR
According to the characteristics of the \(T_{2}\) spectra and the PSD curve of transformation (Figure 8), the pores and fissures in the coal were divided into three parts: adsorption-diffusion nanopores with equivalent pore sizes of less than 50 nm, micro-fissures with widths between 50 nm and 2.5 \(\upmu\)m, and fissures with widths larger than 2.5 \(\upmu\)m. In order to verify the reliability of the NMR results, the PSD curve of MIP was given in Figure 8, which was corrected by the coal matrix compression effect. The variation trend of PSD of micro-fissures and fissures by NMR was basically the same as that of MIP, and the corresponding position of the peak was identical, whereas the pore volume of nanopores with NMR was much higher than that with MIP. The difference may be due to signal superposition of hydrogen atoms in gaseous water molecules and methane molecules absorbed in nanopores. For the volume of pores larger than 50 nm, the NMR result was slightly higher than that of MIP. Because coal particles of sizes between 3-6 mm were used for the MIP experiments, some fissures were destroyed in sample crush and preparation. In general, the NMR test considers the differences of pore shapes and can accurately characterize the volume information of pores and fissures in the coal reservoir with pore size more than 20 nm.