esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Proposed System Model  
The secondary users or fusion center decisions in spectrum sensing are modeled by formulating these decisions as a binary hypotheses test \(\mathcal{H}_{0}\) and \(\mathcal{H}_{1}\), where \(\mathcal{H}_{0}\) and \(\mathcal{H}_{1}\) are the hypotheses that the sensed PU band is vacant or not, respectively. Detection performances of spectrum sensing, or the performances of these decisions, are usually analyzed via the probabilities of false alarm and detection. The probability of false alarm represents the chances of having an occupation decision favoring \(\mathcal{H}_{1}\) when the sensed PU band is in fact under \(\mathcal{H}_{0}\), i.e., \(P_{\mathrm{a}}=\Pr(\mathcal{H}_{1}|\mathcal{H}_{0})\), which is also the probability of the test statistic \(T\) exceeds a given decision threshold, here denoted as \(\gamma\), under \(\mathcal{H}_{0}\), that is \(P_{\mathrm{fa}}=\Pr(T>\gamma|\mathcal{H}_{0})\). The probability of detection means the chances of having an occupation decision favoring \(\mathcal{H}_{1}\) when the sensed PU band is, in fact, under \(\mathcal{H}_{1}\), i.e., \(P_{\mathrm{d}}=\Pr(\mathcal{H}_{1}|\mathcal{H}_{1})\), which is also the probability of having \(T>\gamma\) under \(\mathcal{H}_{1}\), that is, \(P_{\mathrm{d}}=\Pr(T>\gamma|\mathcal{H}_{1})\). These probabilities are combined by using the receiver operating characteristic (ROC) metric, which maps \(P_{\mathrm{fa}}\) and \(P_{\mathrm{d}}\) under the variation of \(\gamma\) for a given detector. Another metric is the area under the ROC curve, AUC.  
Let us consider the above-mentioned binary hypotheses. Given a PU transmitter using one antenna and a multiantenna SUs containing \(U\) SUs with \(L\) antennas each, the \(n\)-th received signal sample at the \(l\)-th antenna of \(u\)-th SU, with \(n=1,2,\ldots,N,l=1,2,\ldots,L\), and \(u=1,2,\ldots,U\), can be written as:  
\[y_{l,u}(n)=\begin{cases}\eta_{l,u}(n)&;\mathcal{H}_{0}\\ \sum_{z=0}^{Z-1}h_{l,u}(z)s(n-z)+\eta_{l,u}(n)&;\mathcal{H}_{1}\end{cases} \tag{1}\]  
for a given sensing instant under hypothesis \(\mathcal{H}_{0}\) or \(\mathcal{H}_{1}\), respectively. Variable \(\eta_{l,u}(n)\) in (1), \(\eta_{l,u}(n)\sim\mathcal{N}(0,\sigma_{lu}^{2})\), represents the \(n\)-th complex zero_mean\(\sigma_{lu}^{2}\)-variance AWGN sample received at the \(l\)-th antenna of \(u\)-th SU. Notice that the noise variances are possibly different not only among SUs, which is a common situation in practice [20], but also possibly different at each antenna of the \(u\)-th SU, meaning that the antennas of \(u\)-th SU are assumed to be uncalibrated and, therefore, expected to cause performance degradation [24]. The \(n\)-th binary phase-shift keying (BPSK) PU signal sample is written as \(s(n)\) and the operation given by the sum in (1) evaluates the convolution of the PU signal with the impulse response of sensing channel formed between the PU transmitter and the \(l\)-th antenna of \(u\)-th SU. When \(Z\) equals 1, the sensing channels are characterized as slow-flat Rayleigh ones, and when \(Z\) is greater than 1, they are slow-frequency-selective Rayleigh ones [11].
## 3 Lipase Characteristics and Engineering  
### Structural Characteristics of Lipases  
Knowing and understanding the structure of an enzyme is of key importance not only to improve enzyme production, but also mainly to provide information for protein engineering through site directed mutagenesis. Regarding lipase structural features, they consist of a catalytically active core domain (the \(\alpha\)/\(\beta\)-hydrolase fold), similar to other hydrolases, containing the catalytic triad and the oxygenion hole. They might also contain additional structural modules, such as a lid or flap-like structure. Most of the lipases possess an active core domain predominantly composed of eight parallel \(\beta\) strands forming a super-helically twisted central \(\beta\) sheet surrounded by a varying number of \(\alpha\) helices [103; 104]. However, the number, as well as the organization of \(\beta\) strands, may still diversify. For instance, _Bacillus subtilis_ lipase lacked the \(\beta 1\) and \(\beta 2\) strands in the canonical fold [105]. In addition, the first X-ray structure of a triglyceride lipase (_Mucor miehei_) was reported by [PERSON] et al. [106]. Figure 2 illustrates the three-dimensional structure of two microbial lipases with the lid in open and closed conformation.  
The catalytic mechanism of lipases is based on a catalytic triad made up of a nucleophilic amino acid (Ser), an acid (Asp or Glu), and a His. The catalytic triad containing Asp is known in enzymes, such as proteases. Interestingly, however, different from the other lipases and serine proteases, the catalytic triad of a _G. candidum_ lipase is Ser-His-Giu, with glutamic acid replacing the common aspartate [107]. The central nucleophile residue is located within a conserved Glu-X-Ser-X-Glu motif [108]. The oxyanion hole is another important component involved in the catalytic efficiency of this enzyme, since it helps in stabilizing the transition state in the catalysis. During the catalytic process, a negatively charged tetrahedral intermediate is generated, and the oxygen ion thus formed is stabilized by the oxyanion hole amino acids. The oxyanion hole residues play an essential role in stabilizing this oxygen by hydrogen bonding. The catalytic region of lipases has one of the oxyanion hole's residues positioned adjacent to the nucleophile amino acid serine, while the second residue is located between the \(\beta\)3 strand and \(\alpha\) helix [104]. Lipases also possess a lid or flap-like structure, which is composed of one or more \(\alpha\) helices of variable length. The binding pocket of lipases is present on the central \(\beta\) sheet, which can be a hydrophobic, crevice-like binding site located near the protein surface or funnel-like or tunnel-like binding sites. The lid domain involves specific interactions with the substrate and controls the inactive/active-form enzyme equilibrium [33].
# Parameter Estimation for Some Probability Distributions Used in Hydrology
## 3 Methods
### Methods of Parameter Estimation
#### 3.2.5 Frechet Distribution (F3)  
For estimation with MOM, parameter \(\alpha\) has the following approximate forms depending on \(\mathrm{C}_{\mathrm{s}}\):  
Rational approximation form, for \(\mathrm{C}_{\mathrm{s}}>1.14\):  
\[\alpha=\frac{1+0.73874734\cdot\mathrm{C}_{\mathrm{s}}+0.37485038\cdot\mathrm{C }_{\mathrm{s}}^{2}+0.103516119\cdot\mathrm{C}_{\mathrm{s}}^{3}}{-0.277593723+0. 116805709\cdot\mathrm{C}_{\mathrm{s}}+0.071360121\cdot\mathrm{C}_{\mathrm{s}} ^{2}+0.035014727\cdot\mathrm{C}_{\mathrm{s}}^{3}} \tag{77}\]  
\[\beta=\frac{\sigma}{\sqrt{\Gamma\left(1-\frac{2}{\alpha}\right)}-\Gamma\left( 1-\frac{1}{\alpha}\right)^{2}} \tag{78}\]  
\[\gamma=\mu-\beta\cdot\Gamma\left(1-\frac{1}{\alpha}\right) \tag{79}\]For estimation with the L-moments, parameter \(\alpha\) has the following approximate forms depending on L-skewness, \(|\tau_{3}|\geq 0.17\):  
Approximation 1, a rational function, adopted from GEV approximation [11]:  
\[\alpha=\frac{1}{\left(\begin{array}{c}-7.817740\cdot\left(\frac{2}{3+\tau_{3}} -\frac{\ln(2)}{\ln(3)}\right)-2.930462\cdot\left(\frac{2}{3+\tau_{3}}-\frac{ \ln(2)}{\ln(3)}\right)^{2}-\\ 13.641492\cdot\left(\frac{2}{3+\tau_{3}}-\frac{\ln(2)}{\ln(3)}\right)^{3}-17.20 6675\cdot\left(\frac{2}{3+\tau_{3}}-\frac{\ln(2)}{\ln(3)}\right)^{4}\end{array}\right)} \tag{80}\]  
a better approximation, named here approximation 2, also a rational function form but characterized by smaller relative errors in the range of 0.6-1 of the L-skewness:  
\[\alpha=\frac{1+0.441588375\cdot|\tau_{3}|-0.071007671\cdot\tau_{3}^{2}+0.01563 4368\cdot|\tau_{3}|^{3}}{-0.283759107+1.669931462\cdot|\tau_{3}|} \tag{81}\]  
\[\beta=\frac{\mathrm{L}_{2}}{\Gamma\left(-\frac{1}{\alpha}\right)\cdot\left(1- 2^{-\frac{1}{\alpha}}\right)} \tag{82}\]  
\[\gamma=\mathrm{L}_{1}-\beta\cdot\Gamma\left(1-\frac{1}{\alpha}\right) \tag{83}\]
## 5 Experiment Results
### Experiment Results
#### 5.3.2 Ablation Experiment  
The ablation experiment separates three key components of the FEG-AE methods and then uses them separately. The result is shown in Table 2. Using a forecasting network to reconstruct the liquid level in mold sequence can greatly improve precision. But because anomalies last a long time, anomaly data occupies a relatively large portion of all data. During unsupervised training, the forecasting network does not know which data is normal. Therefore, if an error repeatedly appears in the training set, it will still mistake it for normal data. The error extraction network acts as a regular LSTM-AE. It achieves a super low precision of 0.111 by mistaking almost every abnormal data point for normal data. Combining the forecasting and error extraction networks and training them using the joint training algorithm in Algorithm 1 significantly improves the precision and F1 score. An error extraction network can extract anomaly data from the forecasting network's training set in the early training stage, therefore keeping the forecasting network free from anomaly data pollution. The dynamic threshold further improves precision and causes high thresholds in some areas. Therefore, the dynamic threshold increases the FN count slightly and thus results in a slightly lower recall.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Methods** & **Precision** & **Recall** & **F1** \\ \hline GRU-AE & 0.111 & 1.000 & 0.230 \\ RAE & 0.500 & 1.000 & 0.667 \\ TadGAN & 0.75 & 0.727 & 0.738 \\ LSTM-AE-ADV & 0.205 & 1.000 & 0.340 \\ FEG-AE & 0.895 & 0.850 & 0.872 \\ \hline \end{tabular}
\end{table}
Table 1: Experiment results by using different baseline methods.
## 4 Results and Discussions
### Contact Load Distribution of DACBB
In the upper half part, the contact load slightly decreases with the increase of angular misalignment. However, in the lower half part, contact load tends to be significantly increased when the misalignment angle increases. Meanwhile, under higher angular misalignment, some rolling balls of upper half part of the right row  
Figure 10: Effect of axial preload on the contact load distribution of DACBB: (**a,b**) contact load distribution of the left and the right rows; (**c,d**) contour map of contact load distribution. do not participant in the load transfer. As a matter of fact, when the inner ring of DACBB tilts an angle relative to the outer ring, the upper half part of the left row tends to be \"compressed\", while the lower half part tends to be \"relaxed\". For the right row, the upper half part tends to be \"relaxed\" and the lower half part tends to be \"compressed\". Accordingly, the contact load distribution will show the behavior described in Figure 11.  
Figure 11: Effect of positive angular misalignment on the contact load distribution of DACBB: (a,b) contact load distribution of the left and the right rows; (**c,d**) contour map of contact load distribution. Figure 12: Effect of negative angular misalignment on the contact load distribution of DACBB: (a,b) contact load distribution of the left and the right rows; (**c,d**) contour map of contact load distribution. Figure 12 shows the influence of negative angular misalignment on the contact load distribution of the left and the right rows of DACBB under combined external loads. From Figure 12, it can be observed that, compared to the results shown in Figure 11, the variation of contact load distribution of each row of DACBB will be reversed when the DACBB is loaded by negative angular misalignment. From the results shown in Figures 11 and 12, it can be seen that the angular misalignment will cause the fluctuation of contact load distribution of each row along the azimuth angle of rolling ball. Meanwhile, under lower angular misalignment, the fluctuation is relatively small, while as the angular misalignment increases, the fluctuation is more sever. As a result, the fluctuation of contact load will lead to the fluctuation of contact stress between ball and raceway and further aggravate uneven distribution of contact stress. This condition is very adverse to the fatigue life of DACBB, and even causes premature failure of DACBB. Therefore, excessive angular misalignment should be avoided as far as possible in the applications of DACBB.
# Control Strategies for Piston Trajectory in Ionic Compressors for Hydrogen Storage
## 4 Conclusions  
Three different piston trajectory control strategies were proposed in this study for an ionic compressor. The control and the compression performances of the compressor when using the different control methods were analyzed using AMESim numerical modeling, based on which the optimal control method was identified considering the system's control error, the hydrogen mass delivered, the energy consumption, and the specific energy consumption. The conclusions are summarized as follows.  
1. The largest positive errors obtained for the Position-P, Position-S, and Dual-PS control methods were 0.68, 0.32, and 0.11 m/s, respectively.  
Figure 12: The mass of hydrogen delivered and the specific energy consumption after a single compression cycle.  
2. The largest energy consumption for one operation was found to be 2726.54 J when the system was designed using Position-S control, whereas the smallest energy consumption was observed to be 2655.17 J with the Dual-PS control method. Concerning the compressor's isothermal efficiency, the Position-P control method system demonstrated the highest isothermal efficiency at 50.28%. Although the Position-S control system exhibited the lowest isothermal efficiency, no significant difference was observed in the isothermal efficiency when compared to the Dual-PS control method.
3. The maximum mass delivered after a single compression process was 1.14 g, obtained using the Position-S control method, while the minimum was 1.11 g in the system using the Dual-PS control method. In terms of the specific energy consumption, it reached the largest value of 2410.92 J/g with the Position-P control method, while the lowest value was 2389.38 J/g for the system using the Dual-PS control method.
4. The Position-S control method was identified as the optimal solution for the ionic compressor under the designed conditions considering the control precision, the hydrogen mass delivered, and the specific energy consumption.  
Conceptualization, Y.G. and X.P.; methodology, Y.G. and A.D.; software, Y.T.; validation, Y.T. and J.C.; formal analysis, Y.T.; investigation, J.C.; data curation, A.D.; writing--original draft preparation, Y.G.; writing--review and editing, X.P. and A.D.; visualization, Y.G. and Y.T.; supervision, X.P.; funding acquisition, Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, Grant No. 52006174.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could constitute a potential conflict of interest.
# A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
## 3 Materials and Methods: A Novel Proposed EEG Analog Front-End Circuit System
### EEG Sub-Circuit Modeling the Human Body Apart from the Brain and Earth  
This sub-circuit models the relationship between the human body and the Earth, including muscle movements and heart activity, which generate electrical interference in the EEG signal. Resistances are crucial for accurately replicating physiological electrical circuits, both between the head and body and between the body, ground and reference electrode. Physiological sounds, namely complicated muscle movement artifacts, are identified as having a frequency range of 1-200 Hz and an amplitude range of 0.1-100 mV, based on empirical data and study results [24].  
More precisely, the sub-circuit converts muscle noises into voltage sources that occur at various locations throughout the EEG recording process. This is performed in order to compute the mean intensity of the alpha, beta, and theta signals. It mimics biological tissue by using a series of voltage sources and resistors to reduce electrical thermal noise, a phenomenon that is typical in electronic components but absent in actual tissues. According to the empirical results of the experiments related to electrode's voltage measurement during protocols with compound muscle movement, performed in the \"Biomedical Optics & Applied Biophysics Laboratory\" of the School of Electrical and Computer Engineering of the National Technical University of Athens, the voltage sources are configured within the range of 1-80 Hz and 1-50 mV. The aim of these experiments was to capture peak-to-peak (Vpp) raw EEG signals by employing wet electrodes during the performance of intricate muscle movements, such as jumping and walking.  
Furthermore, to simulate the body's grounding phenomenon, the modeling incorporates resistances across various anatomical regions and 300 pC capacitors. In particular, these resistances are modeled as noiseless because, in contrast with their electronic counterparts, those in between tissues do not yield thermal noise in the context of our model. The result is a closed-loop system between the Earth and the EMC model of the human body. In Figure 7, the EEG sub-circuit modeling the human body apart from the brain during protocols with strong compound muscle movement is depicted.
# Optimal Base Station Density of Dense Network:
## Appendix A  
**Proof of Lemma 2.** A user connects to the SmBS tier when \(BP_{\text{m}}R_{\text{m}}^{-\alpha}>P_{\text{M}}R_{\text{M}}^{-\alpha}\), where \(B\) is the bias of the SmBS tier. Thus, we have  
\[A_{\text{m}}=\mathbb{P}\left\{BP_{\text{m}}R_{\text{m}}^{-\alpha}>P_{\text{M} }R_{\text{M}}^{-\alpha}\right\}=\int\limits_{r>0}\underbrace{P\left\{R_{\text {M}}>\left(\frac{P_{\text{M}}}{BP_{\text{m}}}\right)^{\frac{1}{\alpha}}r \right\}}_{\text{A}}f_{R_{\text{m}}}\left(r\right)dr. \tag{1}\]  
Figure 10: Effect of the optimal deployment on the throughput of an SmBS. \(P_{\text{m}}/P_{\text{M}}=0.003\).  
Assuming \(\ell^{-1}(x)\) as the inverse function of \(\ell(r)\), and substituting \(r\rightarrow\ell^{-1}(x)\), \(z\rightarrow\infty\), we enlarge the area to infinity:  
\[\lim_{z\rightarrow\infty}z^{2}\left(-1+\int\limits_{0}^{z}\frac{2r}{z^{2}}\exp \left(j\omega P_{\mathrm{m}}\ell(r)\right)dr\right)=\int\limits_{0}^{\infty} \left(\ell^{-1}(x)\right)^{2}j\omega P_{\mathrm{m}}\exp\left(j\omega P_{ \mathrm{m}}x\right)dx. \tag{10}\]  
Combining Equations (9) and (10), it has  
\[\mathcal{F}_{I}(\omega)=\exp\left(j\pi\lambda_{\mathrm{m}}\omega P_{\mathrm{m} }\int\limits_{0}^{\infty}\left(\ell^{-1}(x)\right)^{2}\mathrm{e}^{j\omega P_{ \mathrm{m}}x}dx\right). \tag{11}\]  
Substituting \(\ell(r)=x=r^{-\alpha}\) to Equation (11), then we have  
\[\mathcal{F}_{I}(\omega)=\exp\left(j\lambda_{\mathrm{m}}\pi\omega P_{\mathrm{m} }\int\limits_{0}^{\infty}x^{-\frac{2}{\alpha}}\mathrm{e}^{j\omega P_{\mathrm{ m}}x}dx\right). \tag{12}\]  
Considering the complex environment of propagation, we assume \(\alpha=4\)[28]. Due to \(I=P_{\mathrm{m}}x\), we get  
\[f_{I}\left(I\right)=\frac{\mathrm{e}^{-\frac{x^{3}P_{\mathrm{m}}\omega_{ \mathrm{m}}\omega_{\mathrm{m}}^{2}}{\mu}}\pi\lambda_{\mathrm{m}}P_{\mathrm{m}} ^{1/2}}{2I^{3/2}}. \tag{13}\]  
This result is consistent with the result in [34].
# Implementation of PMDL and DRM in OpenSees for Soil-Structure Interaction Analysis
###### Abstract  
It is widely acknowledged that the effects of soil-structure interaction (SSI) can have substantial implications during periods of intense seismic activity; therefore, accurate quantification of these effects is of paramount importance in the design of earthquake-resistant structures. The analysis of SSI is typically conducted using either direct or substructure methods. Both of these approaches involve the use of numerical models with truncated or reduced-order computational domains. To ensure effective truncation, it is crucial to employ boundary representations that are capable of perfectly absorbing outgoing waves and allowing for the consistent application of input motions. At present, such capabilities are not widely available to researchers and practicing engineers. In order to address this issue, this study implemented the Domain Reduction Method (DRM) and Perfectly Matched Discrete Layers (PMDLs) in OpenSees. The accuracy and stability of these implementations were verified through the use of vertical and inclined incident SV waves in a two-dimensional problem. In terms of computational efficiency, PMDLs require a shorter analysis time (e.g., with PMDLs, the analysis concluded in 35 min as compared to 250 min with extended domain method) and less computational power (one processor for PMDLs against 20 processors for the extended domain method) thus offering a balance between accuracy and efficiency. Furthermore, illustrative examples of the aforementioned implemented features are presented, namely the response analysis of single-cell and double-cell tunnels exposed to plane waves inclined at an angle.  
P +
Footnote †: journal: 2024, 14, 8519, [[https://doi.org/10.3390/app14188519](https://doi.org/10.3390/app14188519)]([https://doi.org/10.3390/app14188519](https://doi.org/10.3390/app14188519))
# Research on Networking Protocols for Large-Scale Mobile Ultraviolet Communication Networks
## 2 LSM-UVCN
### Node State Transition
channel resources in a large-scale network, significantly enhancing network performance. However, the non-interfering transmission paths established based on the expired connection matrix may experience path breaks or cross-interference in practical scenarios, leading to a decline in network performance. Therefore, the protocol requires periodic updates to the connection matrix to mitigate the adverse impact of node mobility on network performance.