esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Acquisition and Processing
### Rayleigh Wave Ellipticity Curve  
The estimation of the RWE curves has been done using the RayDec code [20], applying the same parameters of analysis for the four selected zones of Oued-Fodda city. That is, ambient vibration recordings of at least 20 min of length have been selected first and then, divided into two equal windows of 10 min. The individual RWE curves have been estimated for each window in 0.1-20 Hz frequency range, and subsequently the average RWE curve has been obtained for each zone, with the corresponding standard deviation (\(\sigma\)).
# Deep Eutectic Solvent Pretreatment and Green Separation of Lignocellulose
## 4 Effect of DES on Sugar Conversion of Substrate  
It is found that the reducing sugar yields from the pretreated cellulose-enriched solid residue differ from those of the untreated biomass [130]. Untreated biomass contains both cellulose and hemicellulose. However, after pretreatment with DES, the biomass generally no longer contains hemicellulose. Figure 3 completely shows the enzymatic hydrolysis process of lignocellulosic isolated after DES pretreatment to produce glucose. It is worth noting that DES treatment removes lignin and hemicellulose in biomass, which significantly improves the enzymatic hydrolysis rate of cellulose enriched residue after treatment.  
Table 2 shows the changes in enzymolysis glucose yield of lignocellulosic biomass substrates before and after pretreatment with different types of DES. It is evident that each DES pretreatment significantly improves the substrate enzymatic hydrolysis efficiency between (174-883)% with different types of cellulase [16; 131; 132; 133; 56; 134]. It is worth noting that adding appropriate metal chloride salts or benzene sulfonic acid to DES as an auxiliary solvent can further significantly improve the glucose yield compared to conventional binary DES. [PERSON] et al. added 1.6 wt% p-TsOH to soybean straw as pretreatment in BTEAC/glycerol system, which increased the yield of substrate glucose hydrolysis from 9.17% to 90.18% [131]. These findings are interpreted as the disruption of the complex structure of biomass, degradation of hemicellulose, and removal of lignin after pretreatment with DES. This pretreatment strategy resulted in the destruction of the hemicellulose-cellulose binding protein matrix, exposing more enzyme attack sites, and improving the accessibility of substrate enzymes [72; 133].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Substrate** & **DES** & **Condition** & **Untreated Glucose Yield (\%)** & **DES-Treated Glucose Yield (\%)** & **Increase Rate (100\%)** & **Ref.** \\ \hline \multirow{3}{*}{Switchgrass} & \multirow{3}{*}{ChCl: lactic acid 1:2} & Solid loading 1.5 wt\%, 20 mg protein/g solid, & \multirow{3}{*}{13.61} & \multirow{3}{*}{75.0} & \multirow{3}{*}{4.51} & \multirow{3}{*}{[56]} \\  & & CTe2:HTEC2 = 1.9 (vol\%) & & & & \\ \hline Industrial xylose residue & \multirow{3}{*}{ChCl: monoethanolamine 1:6} & Solid loading, 5 wt\%, 10 FPU/g CTe2 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effects of pretreatment of lignocellulosic biomass with DES pretreatment system on reducing glucose yield.  
Figure 3: Schematic diagram of enzymatic hydrolysis of cellulose enrichment residue after DES pretreatment.
# Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories
## 3 Results and Discussion
### Estimation of Regional Methane Emissions  
#### 3.3.1 Total Regional Emissions  
Regional methane emissions for fourteen regions are shown in Figure 3. These regions were described by [PERSON] et al. (2010) and modified in our study by combining Taiwan with China (removing it from central Eurasia and Japan).  
Figure 2: (**a**) Monthly variation in global total methane emissions (tot) and anthropogenic emissions (an), (**b**) monthly variation in wetland emissions (vss) and anthropogenic emissions. S0: simulation with EDGAR v4.3.2 and S1: simulation with EDGAR v4.3.2 scaled to UNFCCC reports.  
The inverse model-estimated corrections of total emissions were found to vary among regions, with the most significant changes taking place in Russia and India. The total methane emissions optimized by inversion for Russia increased by 27% from 36.2 to 46 Tg CH\({}_{4}\) yr\({}^{-1}\) when the prior anthropogenic emissions were adjusted from EDGAR to UNFCCC values (cases S0 to S1), and the inversion adjusted the emissions downwards. The reduction in the total prior methane emissions for India was 29%, and the posterior emissions decreased from 44.8 to 32.5 Tg CH\({}_{4}\) yr\({}^{-1}\) from case S0 to S1. Temperate South America showed 12% higher posterior emissions (from 12.8 to 14.3 Tg CH\({}_{4}\) yr\({}^{-1}\)) and boreal North America increased by 6% (from 21.4 to 22.7 Tg CH\({}_{4}\) yr\({}^{-1}\)), while the estimates for central North America decreased by 6% (from 11.2 to 10.5 Tg CH\({}_{4}\) yr\({}^{-1}\)). The differences in the optimized total methane emissions for contiguous USA, Oceania, Southern Africa, Southeast Asia, and Europe were less than 3% between case S0 and S1. The optimized total methane emissions of contiguous USA slightly increased from 42.9 to 43.4 Tg CH\({}_{4}\) yr\({}^{-1}\) by implementing the UNFCCC scale but were still lower than the estimation by [PERSON] et al. (2013) (47.2 Tg CH\({}_{4}\) yr\({}^{-1}\)) and [PERSON] et al. (2013) (52.4 Tg CH\({}_{4}\) yr\({}^{-1}\)). The optimized total methane emissions for Europe decreased from 26.2 to 25.5 Tg CH\({}_{4}\) yr\({}^{-1}\), and these values were 6%-7% higher compared with the prior fluxes in both cases. The results are in line with the estimation (26.8 Tg CH\({}_{4}\) yr\({}^{-1}\)) from the multi-model experiment for the EU-28 for 2006-2012 ([PERSON] et al., 2013). For boreal North America and China, the inverse model lowered the total methane emissions in both cases. The optimized total methane emissions for China decreased from 53.7 to 50.5 Tg CH\({}_{4}\) yr\({}^{-1}\), which is higher than the total methane emissions of China estimated as 38.6 Tg CH\({}_{4}\) yr\({}^{-1}\) in 2007 by [PERSON] et al. (2016). Other inverse modeling studies have also inferred smaller emissions than the EDGAR estimate for China (China, 2014; [PERSON] et al., 2016).
# Energy Consumption Prediction for Fused Deposition Modelling 3D Printing Using Machine Learning
## 4 Methodology
### Overview of Machine Learning Algorithms
#### 4.1.5 Gaussian Process Regressor (GPR)  
The Gaussian process regressor formula is given by (13) with the input \(X=[x_{1},x_{2},\ldots,x_{n}]\), the output \(Y=[y_{1},y_{2},\ldots,y_{n}]\), and for any element \(\{(x_{i},y_{i})\mid i=1,\ldots,n\}\),\(y_{i}=f(x_{i})\). The main idea of GPR is to apply a Gaussian process (GP) to represent f(x), which we will call latent variables. Input \(X\) allows us to index these latent variables so that \(\{f(x_{1}),\ldots,f(x_{k})\}\) is a Gaussian distribution correlated between all elements.  
\[\text{m(x)}=\text{E(f(x))} \tag{11}\]  
m(x) is the mean of f(x) and, for the covariance, is described by the following equation:  
\[k\big{(}x,x^{\prime}\big{)}=E(f(x)-m(x))\big{(}f\big{(}x^{\prime}\big{)}-m \big{(}x^{\prime}\big{)}\big{)} \tag{12}\]  
\(\text{E( )}\) represents the expectation. The function of the mean is especially important when there is a region with unobserved inputs with a value of zero.  
The Gaussian process regressor is given by:  
\[f(x)\sim GP(m(x),k\big{(}x,x^{\prime}\big{)}) \tag{13}\]  
with:  
\[y_{i}=f(x_{i})+\varepsilon \tag{14}\]  
\(\varepsilon\sim N\big{(}0,\sigma^{2}\big{)}\) and \(\sigma^{2}\) are the variance of the noise.  
For the covariance, there are some specific parameters that are independent and give the distribution of f(X) and named hyperparameters \(\theta_{i}\), and it usually uses the exponential squared Covariance (15).  
\[k\big{(}\mathbf{x},\mathbf{x}^{\prime}\big{)}=\theta_{1}\exp\left(-\frac{\|\mathbf{x }-\mathbf{x}^{\prime}\|^{2}}{2\theta_{2}}\right) \tag{15}\]  
Note that the covariance function decreases rapidly at input pairs \(\mathbf{x}\) and \(\mathbf{x}^{\prime}\), which are distant, which expresses a low correlation between f(\(\mathbf{x}\)) and f(\(\mathbf{x}^{\prime}\)). Therefore, \(\theta_{1}\) is a hyperparameter that represents the maximum correlation and \(\theta_{2}\) is a positive value defining the rate of decline for the correlation of points that are not close to each other.
# Review of Indoor Positioning: Radio Wave Technology
## 3 Radio Signals-Based Positioning
### Cellular Technology  
Cellular wireless signals such as 2G, 3G, 4G, and 5G (millimetre wave technology) have been used for localization systems [69; 92; 146; 147; 148; 149; 150; 151]. Also, the cellular implementations aim to give effective coverage in the indoor environment. Specifically, the new LTE signals have a large bandwidth, a structure and a synchronization frame that can create them, which are well matched for location determinations [152]. In 4G LTE web systems, the RSRP and RSRQ values are used to observe the signal strength. The RSRP is termed and received as a signal indicator. The RSRP and RSRQ are defined in the 3 rd Generation Partnership Project (3 GPP) typical design [92; 153]. Normally, 3 GPP LTE divides between the frequency division duplexing mode and the time-division duplexing mode. The time-division duplexing mode uses the same frequency while the frequency division duplexing mode uses two dissimilar frequency ranges for uplink and downlink.  
The LTE downlink physical layer is constructed in accordance with the OFDM modulation. The LTE signal-based positioning systems consider a number of aspects. These signals should rather occur within the downlink with no operator demand, thus no precise operation of the system is required, preventing further cost and system traffic. The LTE signal would be excluded for a base position, therefore the signals from various base positions working on a similar frequency range can be divided. Furthermore, the bandwidth of the communicated signal would be exploited in the network bandwidth to give a frequency impulse response with better resolution [152].  
The radio signal scatterings for base stations on various radio channels change with positions. Thus, a radio channel combination can support escape from misclassification instead of depending on one radio channel. In addition, the cellular signals are obtained by smartphones without extra cost. Furthermore, coarse location data can be obtained from cellular networks, although its precision is lacking for most indoor applications. Moreover, 2G cellular signals only apply averaged RSSI that is less crude, as it consists of power associating with thermal noise, serving cells, and co-channel cells [91]. The propagation channel actually disturbs the accuracy of an LTE-based localization system, which is unsuitable for location approximation in a distributed antenna system (DAS) [154]. Moreover, the LTE commercial systems are not developed for TDOA-based positioning system due to the non-compromise of positioning reference signals (PRS) and non-synchronized base stations [155].
# Demonstration of Triband Multi-Focal Imaging with Optical Coherence Tomography
## 3 Registration and Fusion of Imaging Data  
The measurements from each band were combined to generate a single image with extended DoF. Because each band has its own delay location, axial resolution, and axial sampling (i.e., pixel spacing), a registration and fusion procedure was developed using a polyurethane foam sponge as a reference sample. To normalize the axial sampling, we empirically determined the depth range of each band's individual images, and from this derived the appropriate zero-padding to use in subsequent DFTs of each band to result in equal axial spacing. Here we note that a separate numerical dispersion compensation was applied to each band to remove any dispersion mismatch between the sample and reference arms. The axial location of a common structure was measured in each band to align the axial positions of each dataset. Finally, we merged the in-focus imaging data from each band at that band's native axial resolution, resulting in an image with slightly non-uniform axial resolution. It would also be possible to generate an image with a constant axial resolution by downgrading the axial resolutions of the 1060 nm and 1310 nm bands to match that of the 1550 nm.  
The result is presented in Figure 2. The depth-encoded maximum intensity projection (MIP) was created generating a Hue-Saturation-Value image with the depth of the MIP signal mapped to Hue (\([0,0.4]\)), the MIP signal (after normalizing to the range \([0,1]\)) mapped to Value, and the Saturation set uniformly to 1.0. The projections applied to individual imaging bands are shown in Figure 2a-c. The magenta arrows mark locations in which the sponge fibers are in focus. The projection of the extended-focus dataset is shown in Figure 2d and demonstrate higher resolution imaging across the entirety of the 1 mm depth range.
# Borrow from Source Models: Efficient Infrared Object Detection with Limited Examples
## 2 Related Work
### Object Detection  
Current object detection frameworks can be divided into two groups: anchor-based methods such as Faster RCNN [8], SSD [9], and YOLOv3 [18] and anchor-free methods represented by CenterNet [19], CornorNet [20], ExtremeNet [21], and FCOS [22]. Anchor-based methods firstly define a series of rectangle bounding boxes, called anchors, as proposal candidates. Then, all potential object detections are enumerated exhaustively according to proposed anchors. Finally, additional Non-Maximum Suppression (NMS) [24] is used to remove duplicated locations for the same instance. To avoid the redundant design of anchors and lessen the computation burden, anchor-free methods regard the detection problem as a keypoint estimation without pre-defined anchors. For example, CenterNet [19] predicts the center point of an object and then regresses to other properties such as object size. Although those algorithms achieve remarkable performance, they are mainly driven by extensive public training data and focus on detecting the targets in standard visual RGB images. For infrared detection, the lack of large-scale labeled thermal images hinders the power of detectors based on CNN. Researchers cope with this problem from two aspects: one is finetuning a pre-trained model [14; 15], the other is introducing corresponding RGB images as supplements [1; 16]. The first strategy hardly makes full use of the information from the RGB domain, and the sophisticated structures in the second method are difficult to be performed. Different from two solutions, our SMG not only  
Figure 1: The overall framework of SMG, which mainly consists of two parts: a source model to provide source knowledge and a target network to predict infrared detection results. Red arrows indicate the backpropagation pathways.  
leverages existing RGB models as the guidance for infrared detectors but also is easily plugged in any modern detection framework.
## 3 The Proposed Model
### Derivation of Routing Process  
The major objective of the HMSO-EARS approach is to minimize energy utilization and maximize the lifespan of the network of each SN [27]. Consider \(h_{1}\) as an objective function so that the next-hop relay node with larger residual energy is chosen to route the data and thus the network lifespan can be maximized, for example, \(h_{1}\) is maximization. Consider \(h_{2}\) as an additional main function that is the minimal distance to next-hop to \(BS\). Consider \(h_{3}\) as the third main function so that the relay node selects the next-hop relay node with less node degree. The proposed model executes an objective function for the maximization of network lifetime and minimization of energy, given as follows:  
\[b_{ij}=\begin{cases}1&if\text{ }next-hop(\ )=CH_{j},\forall i,j1\leq i,j\leq m \\ 0&Otherwise\end{cases} \tag{18}\]  
\[Minimize\ F=\frac{1}{h_{1}}\times\beta_{1}+h_{2}\times\beta_{2}+h_{2}\times \beta_{3} \tag{19}\]  
Subjected to,  
\[dis\big{(}N_{i},N_{j}\big{)}\times\leq d_{\max}RNe\{C+BS\} \tag{20}\]  
\[\sum_{j=1}^{m}b_{ij}=1\text{ }and\ 1\
eq j \tag{21}\]  
\[0<\beta_{1},\beta_{2},\beta_{3}<1 \tag{22}\]
###### Abstract  
Pathogenic _Escherichia coli_ cannot be killed by most antibiotics (including colisti, a last-resort drug) due to the rapid development of antibiotic resistance. A highly conserved prokaryotic mitotic protein, filamenting temperature-sensitive protein Z (FtsZ) with GTPase activity, plays a key role in cell division and has become a promising target for screening novel antibacterial agents. In this study, the amplified _ftsZ_ gene was inserted into cloning/expression vectors and recombinantly produced in _E. coli_; the recombinant FtsZ protein was purified by the Ni\({}^{2+}\)-NTA affinity column and then was used to screen for natural antibacterial agents. The results showed that the _ftsZ_ gene with a size of 1170 bp was successfully amplified from _E. coli_ and inserted into the pET-28a expression vector. After induction with 0.2 mM isopropyl \(\beta\)-D-1-thiogalactopyranoside (IPTC), FtsZ was expressed in _E. coli_ BL21 as inclusion bodies. After purification, the recombinant FtsZ protein showed GTPase activity. The highest GTPase activity (0.998 nmol/mL/min) of FtsZ was observed at a GTP concentration of 1.25 mM. Several alkaloids were screened by a constructed model of FtsZ inhibitors. Sanguinarine chloride exhibited higher antibacterial activity against _E. coli_ and _Salmonella enteritidis_ (with minimum inhibitory concentrations (MICs) of 0.04-0.16 mg/mL and minimum bactericidal concentrations (MBCs) of 0.16-0.32 mg/mL) than tetrandrine (0.16-0.32 mg/mL) and berberine hydrochloride (0.32-0.64 mg/mL). Berberine hydrochloride prevented FtsZ polymerization in a concentration-dependent manner and bound to FtsZ protein by hydrogen bonding interaction. This study suggested that the FtsZ-based _E. coli_ screening model could be exploited for the development of novel antibacterial agents for clinical applications.  
FtsZ; expression; purification; GTPase; alkaloid; antibacterial activity Article
# Verifiable Threshold Multi-Party Fully Homomorphic Encryption from Share Resharing
###### Abstract  
Threshold multi-party fully homomorphic encryption (TMFHE) schemes enable efficient computation to be performed on sensitive data while maintaining privacy. These schemes allow a subset of parties to perform threshold decryption of evaluation results via a distributed protocol without the need for a trusted dealer, and provide a degree of fault tolerance against a set of corrupted parties. However, existing TMFHE schemes can only provide correctness and security against honest-but-curious parties. We construct a compact TMFHE scheme based on the Learning with Errors (LWE) problem. The scheme applies Shamir secret sharing and share resharing to support an arbitrary t-out-of-N threshold access structure, and enables non-interactive reconstruction of secret key shares using additive shares derived from the current set of online participants. Furthermore, the scheme implements commitment and non-interactive zero-knowledge (NIZK) proof techniques to verify the TMFHE operations. Finally, our experiments demonstrate that the proposed scheme achieves active security against malicious adversaries. It overcomes the limitation of existing TMFHE schemes that can only guarantee correct computation under passive semi-honest adversaries.  
multi-party fully homomorphic encryption; share resharing; commitments; non-interactive zero-knowledge proofs; malicious adversary Article
## 4 Numerical Examples
### Performance Indicators
#### 4.2.2 Effective Optimal Ratio (EOR)  
Clustering trends in the optimization process may create many redundant data for DOEs. In the training process of the surrogate model, sampling points in the same or similar locations make little contribution to increasing the accuracy of the model. Therefore, to quantify the effective training samples acquired from the optimization process, the effective optimal ratio (\(EOR\)) is used.  
\[EOR=\frac{\left|N_{\text{eop}}\right|}{Np_{\text{end}}} \tag{21}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **2\(d\)** & **3\(d\)** \\ \hline \(Np_{\text{end}}\) & 100 & 400 \\ \(\varepsilon\) & 0.05 & 0.05 \\ \(MFE\) & 10,000 & 50,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Setting of basic parameters.  
where \(N_{\text{eop}}\) is the quantity of effective particles in the resulting solution set. Points that clustered together according to the distance to the reference set were considered as one effective optimal point in our study.  
A bigger _EOR_ indicates better convergence and more effective candidate sample DOEs.
# Analysis and Design of A PMQR-Type Repetitive Control Scheme for Grid-Connected H6 Inverters
## 3 Analysis and Design of the PMQR-Type RC Scheme
### PMQR-Type RC Scheme Design
 \tag{20}\]  
According to [26], if the frequency of reference input signal \(i_{ref}\left(t\right)\) and disturbance \(u_{g}\left(t\right)\) approaches \(\omega_{l}=2\pi lf\), with \(l=0,1,2,...,L(L=N/2\) for even \(N\) and \(L=(N-1)/2\) for odd \(N\)), then \(z^{N}=1\). Consequently, Equation (20) can be written as:  
\[\left|Q\left(z\right)[1-k_{r}z^{m}S\left(z\right)P_{0}\left(z\right)]\right|<1,z=e^{lyw} \tag{21}\]  
Figure 5: The detailed block diagram of the PMQR-type RC scheme. Suppose \(z=e^{iw}\), \(Q\left(s\right)\) is a constant slightly less than one, which is generally taken as 0.95 in engineering. The low-pass filter \(S\left(z\right)\) and the control plant \(P_{0}\left(z\right)\) can be expressed respectively in the frequency-domain as follows:  
\[S\left(e^{iw}\right) =N_{s}\left(e^{iw}\right)e^{\left[\theta_{s}\left(e^{iw}\right) \right]}\] \[P_{0}\left(e^{iw}\right) =N_{p}\left(e^{iw}\right)e^{\theta_{p}\left(e^{iw}\right)} \tag{22}\]  
where \(N_{s}\left(e^{iw}\right)\) and \(N_{p}\left(e^{iw}\right)\) are the amplitude characteristics of \(S\left(z\right)\) and \(P_{0}\left(z\right)\), respectively. \(\theta_{s}\left(e^{iw}\right)\) and \(\theta_{p}\left(e^{iw}\right)\) are their phase characteristics, respectively. Then, Equation (21) becomes:  
\[\left|1-k_{r}N_{s}\left(e^{iw}\right)N_{p}\left(e^{iw}\right)e^{-j\left[\theta _{s}\left(e^{iw}\right)+\theta_{p}\left(e^{iw}\right)+m\omega\right]}\right|<1 \tag{23}\]  
Obviously, \(k_{rc}>0\), \(N_{s}\left(e^{iw}\right)>0\) and \(N_{p}\left(e^{iw}\right)>0\), transforming Equation (23) through [PERSON]'s formula leads to:  
\[k_{r}N_{s}\left(e^{iw}\right)N_{p}\left(e^{iw}\right)<2 cos\left[\theta_{s} \left(e^{iw}\right)+\theta_{p}\left(e^{iw}\right)+m\omega\right] \tag{24}\]  
Therefore, the inequality is yielded as follows:  
\[\left|\theta_{s}\left(e^{iw}\right)+\theta_{p}\left(e^{iw}\right)+m\omega \right|<\frac{\pi}{2} \tag{25}\]  
\[0<k_{r}<min\frac{2 cos\left[\theta_{s}\left(e^{iw}\right)+\theta_{p}\left(e^ {iw}\right)+m\omega\right]}{N_{s}\left(e^{iw}\right)N_{p}\left(e^{iw}\right)} \tag{26}\]  
Inequalities (25) and (26) offer the design standard of the repetitive controller gain \(k_{r}\) and the phase lead compensator \(z^{m}\) Inequality (25) can be used to design parameter \(m\) due to Inequality (25) only involving one parameter \(m\). Then, the gain \(k_{r}\) can be calculated according to Equation (26).