esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization of the Enzymes  
To determine the optimum temperature, 50 mM Tris-HCl (pH 8.0) buffer was preheated to test temperatures, at which the substrate and the enzyme were added. Absorbance was measured using a spectrophotometer. To measure thermostability, the enzyme was heated at 30, 40, 50, and 60 \({}^{\circ}\)C for 0, 5, 10, 20, 30, and 60 min prior to substrate addition, and enzyme activity was then measured at indicated times using the standard assay condition. The half-life at each temperature was determined graphically by calculating the time showing 50% of the relative activity. To confirm the optimum pH, enzyme activities were measured using 50 mM Universal buffer (boric acid/citric acid/trisodiumorthophosphate) for pH from 6.0 to 12.0. The molar absorption coefficients of each pH were used as described in a previous study [29].  
Substrate specificity was analyzed using p-NP esters, such as p-NP acetate (C2), p-NP butyrate (C4), p-NP caproate (C6), p-NP octanoate (C8), p-NP decanoate (C10), p-NP laurate (C12), p-NP myristate (C14), and p-NP palmitate (C16). Kinetic experiments were performed using five different substrate concentrations: 0.1, 0.2, 0.5, 1.0, and 2.0 mM of C4. The K\({}_{\text{m}}\) and V\({}_{\text{max}}\) values were determined by a Lineweaver-Burk plot using Excel 2016 software.  
The ion effect on enzyme activity was investigated by adding K\({}^{+}\), Na\({}^{+}\), Mg\({}^{2+}\), Mn\({}^{2+}\), Ca\({}^{2+}\), Cu\({}^{2+}\), Co\({}^{2+}\), Ba\({}^{2+}\), Fe\({}^{2+}\), and Zn\({}^{2+}\) ions at 2 and 5 mM concentrations, after dialysis of the enzyme sample against 50 mM Tris-HCl (pH 8.0) for 6 h with two buffer changes. The inhibitor effects of EDTA and PMSF were analyzed at 1 mM concentration. Enzyme stability against organic solvents was measured using methanol, isopropanol, and acetonitrile at concentrations of 5% and 30%. Stability against surfactants was measured by adding sodium dodecyl sulfate (SDS) and Triton X-100 at concentrations of 1%. Because \(p\)-nitrophenyl butyrate as the substrate was dissolved in isopropanol, 1% isopropanol was included in the control.  
The lipid hydrolysis activity of the enzyme was confirmed using a pH shift assay [37]. In 20 mM Tris-HCl (pH 8.0), oils (fish oil and olive oil) and glyceryl triesters (glyceryl trietnotate, and glyceryl trioteate) were added at a concentration of 1%, and then 0.1% phenol red was added. Absorbance was measured using the kinetic mode of a spectrophotometer at 25 \({}^{\circ}\)C and 560 nm for 180 min every 30 min to determine the amount of substrate remaining. To confirm the enantioselectivity of the enzyme, (\(R\))-methyl-3-hydroxy-2-methyl-propionate and (\(S\))-methyl-3-hydroxy-methyl-propionate were added at a concentration of 1% as a substrate [29].
## 3 Methodology  
The objective of the proposed method is to obtain spatially continuous NMRI products by fusing optical remote sensing VWC-related indices. Specific process of the point-surface fusion model used in this study is described below, and a flowchart of the method is shown in Figure 2.  
**(1) Data processing and dataset selection**. Firstly, we removed outliers from the dataset and unified the temporal and spatial resolutions to 16 days and 500 m. Then, we analyzed the correlation between the variables and select the best auxiliary dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Resolution** & **Product** & **Period** \\ \hline
**NMRI [37]** & Daily/site-based & PBO H2O & 2007.01.01–2016.12.31 \\
**NDVI [6]** & 16 day/500 m & MOD13A1 & 2007.01.01–2013.12.31 \\
**NDWI [9]** & 8 day/500 m & MOD09A1 (bands 2,5) & 2007.01.01–2013.12.31 \\
**NDII [10]** & 8 day/500 m & MOD09A1 (bands 2,6) & 2007.01.01–2013.12.31 \\
**GPP [42]** & 8 day/500 m & MOD17A2H & 2007.01.01–2013.12.31 \\
**LAI [43]** & 8 day/500 m & MCD15A2H & 2007.01.01–2013.12.31 \\
**Precipitation [46]** & Daily/25 km & TRMM\_3B42 RT\_Daily & 2007.01.01–2013.12.31 \\ \hline \hline \end{tabular}  
Footnote 1: Footnote: NMRI (Normalized Microwave Reflection Index), NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), NDII (Normalized Difference Infrared Index), GPP (gross primary productivity), LAI (leaf area index).  
\end{table}
Table 1: Indices used in the study.
# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
## 3 Result and Discussion  
### Dataset Setup  
The dataset employed consisted of three components: training data, testing data, and validation data. These components encompassed two datasets: the received signal data set, denoted as \(Y\), and the transmitted data set, denoted as \(X\), in accordance with the principles of communication systems. We collected and simulated a total of 100,000 datasets.
## 4 Simulation and Discussion
### Comparison of Test Results
#### 4.4.1 Comparison of PPI Network Topology Related Algorithms  
Figure 10 shows the comparison between the IYEPDNN algorithm and PPI network topology-related essential protein recognition algorithms in the DIP dataset. Figure 11 shows the comparison in the GAVIN dataset. With the decrease in the accuracy of candidate essential proteins, the number of essential proteins identified by the PPI network topology correlation algorithm increases linearly. The BC method in Figure 10 is relatively poor, while the LAC and NC methods are effective. Among the first 61 candidate essential proteins, the correct identification accuracy of NC is \(32/61>50\%\), while the correct identification accuracy of other PPI network topological correlation algorithms is \(24/61=39.34\%\). LAC identified 552 of the 1273 candidate essential proteins, with an accuracy of \(552/1273<44\%\). The linear growth rate of PPI network topological correlation algorithms in DIP datasets is lower than \((552-29)\) /\((1273-61)=43.15\%\). In Figure 11, the EC method is relatively poor and the LAC method is relatively good. Of the first 19 candidate essential proteins, EC identifies only 6 and LAC identifies 14. EC identifies only 125 of the 464 candidate essential proteins and LAC 254. The linear growth rate of the PPI network topological correlation algorithms in the GAVIN datasets is lower than \((254-14)\) /\((464-19)=53.69\%\). The GAVIN dataset is superior to the DIP dataset in basic protein recognition accuracy. It can be seen from Figures 10 and 11 that the number of essential proteins recognized  
by the IYEPDNN algorithm is much higher than that of the PPI network topological correlation algorithm. In the DIP dataset, the linear growth rate of the IYEPDNN algorithm is \((1122-42)\)\(/(1273-61)=89.10\%\). The linear growth rate of the IYEPDNN algorithm in the GAVIN dataset is \((421-18)\)\(/(464-19)=91.01\%\). At the same time, it can be seen that the number of essential proteins identified by the IYEPDNN algorithm is twice that of the PPI network topological correlation algorithm after 10% candidate essential proteins in the DIP dataset. The number of essential proteins identified by the IYEPDNN algorithm is \(1.5\) times higher than the PPI network topological correlation algorithm after 10% candidate essential proteins in the GAVIN dataset.
# Experimental Study on a Granular Material-Filled Lining in a High Ground-Stress Soft-Rock Tunnel
## 1 Introduction
Scholars have conducted in-depth research into the different filling materials of compressible layers. [PERSON] et al. [31] considered foamed concrete the most promising filling material for compressible layers. [PERSON] et al. [9; 10] presented a compressible ceramsite layer behind the pipe-sheet lining wall to achieve yielding. [PERSON] et al. [32] proposed a support technology solution combining dynamic pressure relief and active pressure in U-shaped steel-reinforced brackets and foam concrete filling combined with prestressing anchor cables. Yielding capacity is limited if a compressible layer is applied to achieve yielding support. After a certain compression level, it is more challenging for the lining structure to continue to exert pressure. If the lining structure stress is not released in time, it increases, and the support effect is unsatisfactory. A U-shaped shrinkable steel bracket prevents warpage damage to the steel arch by allowing movable joints to slide and cause shrinkage of the steel arch section. However, the clamping force of the sliding joints is not large enough to provide support resistance in the initial support of the steel arch. If the clamping force is too high, then it will not work when the steel arch is damaged. Some researchers [33; 34; 35; 36] have also conducted numerical simulations related to yielding-pressure support for tunnel lining. The above research shows that rigid support technology is used more for point or line reinforcement, emphasizing solid support and sturdy top. However, this is inadequate for supporting high ground-stress soft rocks with large deformations over long periods. Existing flexible support technology can only create controllable support forces in point or line form. However, it cannot produce controllable and homogenized surface support within a wide range of free faces. Based on this, we propose a lining filled with granular material, which provides yielding-pressure and relief-pressure support technology with a compressible buffer layer at the reserved deformation layer. Granular material mobility forms a controllable and homogenized surface support force. The compressibility of the filling material absorbs the pressure generated by the deformation of the surrounding rock, which reduces the yielding pressure. Using the automatic discharge of granular material releases the surrounding rock pressure, which has a better pressure relief effect so that the load on the lining structure is no greater than the set threshold. Therefore, the safety and stability of the lining structure can be guaranteed through the yielding pressure and relief pressure of the lining. This technology provides an innovative way to support soft-rock tunnels with high ground stress.
## 4 Discussion
Therefore, this paper selected fuzzy entropy, approximate entropy, sample entropy and spectral entropy as feature extraction methods. Seen from the analysis results, it can clearly obtain an improvement in detection of driver fatigue via use of combined entropy features. However, in the analysis of EEG signals, many types of entropy had been broadly applied recently. Of special note, one review introduced the application of various entropies for automated diagnosis of epilepsy using EEG signals [40]; the authors used a total of 13 entropy features such as approximate entropy, fuzzy entropy, sample entropy, Renyi's entropy, spectral entropy, permutation entropy, wavelet entropy and so on to discriminate normal, interictal and ictal EEG signals of epilepsy, finding that most of the entropy features are best suitable for the classification of epileptic EEG signals. This study concluded that Renyi's entropy, sample entropy, spectral entropy and permutation entropy are highly discriminative features to classify normal, interictal and ictal EEG signals. The fact that many types of entropy were applied in that paper [40] indicated that in future work we can use and fuse more different entropies to analyze different fatigue states such as normal, alert, and fatigued. Therefore, due to the fact that different entropy features have their own advantages, we will study the influence of other entropies and their different combination on the classification results for detecting driver fatigue in the future.
# Overset Grid Assembler and Flow Solver with Adaptive Spatial Load Balancing
## 6 Governing Equations  
The integral form of Euler equations is  
\[\frac{\partial}{\partial t}\int_{V}\mathbf{U}\,\mathrm{d}V+\int_{A}\mathbf{F} \,\mathrm{d}A=0 \tag{1}\]  
where \(V\) and \(A\) are the volume and area of a cell and \(\mathbf{U}\) and \(\mathbf{F}\) are the vectors of conserved variables and interface fluxes:  
\[\mathbf{U}=\begin{bmatrix}\rho\\ \rho u\\ \rho v\\ E\end{bmatrix},\quad\mathbf{F}=\begin{bmatrix}\rho u\\ \rho u^{2}+p\\ \rho uv\\ \rho uw\\ u(E+p)\end{bmatrix} \tag{2}\]  
\(u\) is the velocity component in face-normal direction, \(v\) and \(w\) are tangential velocity components, \(E\) is total energy, and \(p\) is static pressure. Euler equations are used in x-split form [18] and, therefore, velocity components are aligned in Cartesian coordinates such that \(u\), \(v\), and \(w\) are parallel to the \(x\)-, \(y\)-, and \(z\)-axis.  
For non-Cartesian unstructured meshes, to calculate the interface flux, conserved variables are rotated to face-normal coordinate system with transformation matrix:  
\[\mathbf{T}=\begin{bmatrix}1&0&0&0&0\\ 0&\cos\theta^{(y)}\cos\theta^{(z)}&\cos\theta^{(y)}\sin\theta^{(z)}&\sin \theta^{(y)}&0\\ 0&-\sin\theta^{(z)}&\cos\theta^{(z)}&0&0\\ 0&-\sin\theta^{(y)}\cos\theta^{(z)}&-\sin\theta^{(y)}\sin\theta^{(z)}&\cos \theta^{(y)}&0\\ 0&0&0&0&1\end{bmatrix} \tag{3}\]  
where \(\theta^{(y)}\) and \(\theta^{(z)}\) are found from components of face-normal \([nx,ny,nz]^{T}\):  
\[\begin{split} n_{x}&=\cos\theta^{(y)}\cos\theta^{(z)}\\ n_{y}&=\cos\theta^{(y)}\sin\theta^{(z)}\\ n_{z}&=\sin\theta^{(y)}\end{split} \tag{4}\]  
The Riemann problem is approximately solved at each cell interface with HLLC solver [18] in order to compute interface flux. When providing left and right states to the Riemann solver, the interface velocities are corrected by subtracting the rotated mesh velocity \([b_{x},b_{y},b_{z}]^{T}\) from the rotated velocity such that  
\[\begin{split} u&=u-b_{x}\\ v&=v-b_{y}\\ w&=w-b_{z}\end{split} \tag{5}\]  
Discretized equations are solved with dual time stepping approach with  
\[\frac{V}{\Delta\tau}(\mathbf{U}^{\mathrm{s}+1}-\mathbf{U}^{\mathrm{s}})=- \sum_{f}^{\mathrm{nface}}\mathbf{F}(\mathbf{U}^{\mathrm{s}})A_{f}-\frac{V}{2 \Delta t}(3\mathbf{U}^{\mathrm{s}}-4\mathbf{U}^{n}+\mathbf{U}^{n-1}) \tag{6}\]where superscripts \(s\) and \(n\) denote states in pseudo and real times. The parameter \(\Delta t\) is real time step and \(\Delta\tau\) is pseudo time step calculated from  
\[\Delta\tau=\text{CFI}\frac{V}{\sum_{f}^{\text{face}}\text{max}(|\lambda|)A} \tag{7}\]  
where \(\text{max}(|\lambda|)\) is the maximum absolute eigenvalue among all faces of a cell. Equation (6) is solved until steady state for maximum 100 pseudo time steps. When the left hand side of the equation vanishes, the original unsteady equation recovers. Interface flux \(\mathbf{F}\) is evaluated explicitly as a function of conservative variable vector in previous pseudo time step, that is \(\mathbf{U}^{s}\).
# An AUV-Aided Cross-Layer Mobile Data Gathering Protocol for Underwater Sensor Networks +
## 4 Description of Our Proposed Cross-Layer Mobile Data-Gathering Protocol
### Overview  
The purpose of our proposed cross-layer protocol is to enhance the communication efficiency as well as to reduce the energy consumption by integrating functionalities of two layers in a distributed manner. To this end, CLMD allows the data packets to be efficiently delivered to the destination by sharing the underwater acoustic medium and aggregating data packets in a group of cluster heads to be visited by an AUV in the periodic tours.  
CLMD includes four phases to operate: neighbor discovery, distributed clustering, AUV discovery, and normal operational phase, as illustrated in Figure 1. The beginning time of each phase has been set for all sensors in order to start and end together. A guard time has also been applied to avoid the effect of clock drafts that may occur over a long period of time.  
In CLMD, in a distributed manner, a subset of sensors is selected as Cluster Heads (CHs) based on their priority considering the number of covering sensors, the closeness to the sink, and the residual energy of the sensor. Afterwards, an AUV tour is planned to visit CHs in a certain order for data collection.  
The tour length should be minimized to reduce the end-to-end delay. The cluster heads are also reselected at each operational phase to prolong the network lifetime. A node with higher residual energy has a high chance to be selected as CH at the clustering phase.
## 2 Materials and Methods  
### Study Area and Sample Collection  
The study area is located in the Yuecheng district of Shaoxing City, Zhejiang Province, China (Figure 1). The site is located on the east coast of China and belongs to a subtropical monsoon climate zone with a mild, humid, and rainy climate. As a famous city with a history of \(>\)2500 years, Shaoxing is densely covered by rivers and lakes, and the history of canal construction is well known, both domestically and internationally. Shaoxing City has more than 30 rivers with \(>\)50 km\({}^{2}\) drainage area and 23 lakes with \(>\)1 km\({}^{2}\) water surface area. Shaoxing, a millennium-old city renowned for its waterways and often referred to as the \"Venice of the East,\" is notable for its unique concentration of different types of water bodies within a single urban area. Particularly, the ancient canals, which have been in existence for over a thousand years, serve as a testament to Shaoxing's development and transformation as a water-centric city. From the perspective of population size and urban development scale, Shaoxing, as a prefecture-level city, represents an intermediate tier between major metropolitan areas and provincial capitals. Therefore, considering both its distinctive hydrological features and urban characteristics, Shaoxing City serves as a typical representative of medium-sized cities in eastern China.  
Herein, 32 surface water samples from four typical urban water types (Canal, Lake, Pond, and River) were collected in 500 mL polyethylene plastic bottles. Sampling was conducted in June 2019, coinciding with the wet season in Shaoxing, when water levels across various urban water bodies were abundant. This period facilitated field sample collection and provided optimal conditions for analyzing multiple water quality parameters. The water quality parameters were measured on-site, and the remaining chemical indicators were quickly transported back to the laboratory for analysis. In total, 32 surface sediments were collected using a grab surface sediment collector. In each of the above sampling stations, the sediments at three parallel sampling points were selected to be uniformly mixed, representing the sampling station. The samples were returned immediately frozen at 20 \({}^{\circ}\)C and subjected to freeze-drying, then ground using a 200-mesh nylon mesh screen and stored in polyethylene plastic bags under sealed conditions for further experimental analysis.
## 4 Discussion
### Limitations and Direction of Improvements  
There was only one flux tower in the study area. Whether its data can cover the whole study area remains to be studied. Since there was no field measurement of NPP, the results of this study were only verified and compared with MODIS NPP and other research results. Other studies [15; 47; 48] have found that LAI was an important input data of the model, and its accuracy would affect some components of the model, including photosynthesis and vegetation respiration. Therefore, high-precision LAI data was a necessary condition for reliable simulation results. In the future, the accuracy of the results of this study can be improved by improving the above aspects.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 5 The Cross-Layer Design Method
### Intelligent Algorithm-Based Methods
#### 5.2.1 Fuzzy Logic-Based Routing Protocols  
Fuzzy logic is a kind of science based on multiple-valued logic. Fuzzy logic utilizes a fuzzy set to represent fuzzy thinking, fuzzy language, and fuzzy control laws. In 1965, the mathematician [PERSON] first proposed the principle of fuzzy sets [23]. The essential of fuzzy mathematics and fuzzy logic is to provide accuracy descriptors for fuzzy objects. Fuzzy logic uses mathematical methods to imitate the actions of the human brain, such as the reasoning mode, uncertainty analysis, _etc._ By using fuzzy sets and fuzzy rules to reason the transitivity limitations or qualitative experiences, fuzzy logic algorithms can imitate the human brain to achieve fuzzy logic judgment and resolve issues which cannot be addressed by traditional methods. Fuzzy logic is good at dealing with issues in which the limitations are not clear.  
Many underwater sensor network routing protocols have introduced fuzzy logic algorithms into the protocol design [74; 75; 76; 77; 78]. In [74], the power efficient routing (PER) protocol includes two modules: the forwarding node selector and the forwarding tree trimming mechanism. The forwarding node selector uses a fuzzy logic inference system and decision trees to choose the next hop relay node. Three parameters are used as the inputs of the fuzzy logic inference system: the transmission range, the include angle, and the residual energy. Different from [74], the algorithms in [75; 78] are cluster-based fuzzy logic routing algorithms. In [75], the authors propose a cluster-based fuzzy logic routing algorithm with even load distribution for large scale networks. This algorithm is similar to LEACH; the difference is that this protocol does not include TDMA scheduling formation in the initial setup phase. Based on these, the proposed protocol can prolong the network lifetime by an average 40% to 50% more than LEACH. In [78], taking the residual energy, the distance to sink node, the node density, the load, and the link quality into account, the authors propose a fuzzy logic-based clustering and aggregation technique for underwater acoustic sensor networks. The work in [77] is a grid-based fuzzy logic optimized routing protocol. In [77], the network is divided into different virtual grids. Each grid has only one active node. The fuzzy logic system is used to select the active node. In [76], the authors use the distance to the sink, the residual energy, and the link quality of the potential neighbors near the sink as the inputs of the fuzzy logic system to choose the best routing path, which has more balanced energy performance than the traditional routing protocols.
# Kinematic Analysis and Verification of a New 5-DOF Parallel Mechanism
## 4 Workspace Analysis
### Monte Carlo Method  
Monte Carlo method is based on the idea of random sampling to solve mathematical problems. The flowchart of solving mechanism workspace by Monte Carlo method is shown in Figure 12. The analysis process is as follows:  
Figure 12: The flowchart of solving mechanism workspace by Monte Carlo method.  
The random function is used to traverse the value in the initially set workspace.
2. These values are introduced into the inverse kinematics solution Equation (11) to obtain the variable values of each kinematic pair.
3. Record the corresponding traverse points of each motion pair in the workspace. These recorded traverse points are the points that the geometric center of the moving platform can reach, and finally constitute the three-dimensional point cloud map of the workspace.