esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.2 NMR Relaxometry  
All core plugs were analyzed using a 1H low-field (2 MHz) NMR unit--Geospec 2-53 (Oxford Instruments Inc., Abinden, UK). The parameters obtained included the T\({}_{2}\) relaxation spectrum, saturation profile, and T\({}_{1}\)-T\({}_{2}\) fluid identification maps. The core samples of BF were dried until the sample weight stabilized, saturated with decane, and additionally resaturated with a model of formation brine with consecutive NMR studies of the core sample at each stage of the experiment. The NMR results are interpreted out in accordance with existing methods of fluid identification [43; 44; 26; 45]. Those methods were adapted for tight oil rock samples for the estimation of immobile and mobile fluid interpretation by separating residual water, oil, and effective oil saturation. In this work, a decane was utilized as a hydrocarbon liquid, and a brine model was prepared with deionized water and sodium chloride at a  
\begin{table}
\begin{tabular}{c c c} \hline
**Experiment** & **Sample** & **Objective** \\ \hline Porosity and permeability by gas & Cylinders, \(30\times 30\) mm & Change in porosity and permeability under different temperature exposure \\ \hline NMR relaxometry & Cylinders, \(30\times 30\) mm & Evaluation of the change in saturation by NMR \\ \hline Rock-Eval pyrolysis & Core powder, 30 mg & Study of the OM \\ SEM & Core chip, \(3\times 3\) mm & transformation \\ \hline \end{tabular}
\end{table}
Table 2: Test design strategy.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample ID.** & **Oil Field** & **Lithology** & **CT Zone** & **Exposed Temperature (°C)** \\ \hline
1 & Oil Field 1 & Siliceous rocks & 2.1 & 301 \\ \hline
2 & & 2.2 & 369 \\ \hline
3 & & 3.1 & 430 \\ \hline
10 & & 6.1 & 207 \\ \hline
12 & & 4.1 & 463 \\ \hline
13 & Oil Field 2 & Mixed argillaceous-siliceous rocks & 2.2 & 368 \\ \hline
14 & & 3.4 & 461 \\ \hline
15 & & 4.2 & 422 \\ \hline
16 & Oil Field 3 & Siliceous rocks & 2.1 & 300 \\ \hline
18 & & 3.1 & 430 \\ \hline n1, n2, n3, n4, n5 & Oil Field 4 & Mixed argillaceous-siliceous rocks & n/a & n/a \\ \hline \end{tabular}
\end{table}
Table 3: List of the investigated samples.  
concentration of 20 g/L. The processing of data was accomplished by the commercial software package by Green Imaging Technologies Inc. (GIT, Fredericton, NB, Canada) Advanced v.7.5.1 [46].
## 2 Predictive Equations and Machine Learning Models
### Machine Learning Methodologies
#### 2.3.1 Gradient Boosting Methods  
Gradient boosting algorithms, such as XGBoost, CatBoost, LightGBM and extra trees regressor, are based on the combination of individual decision tree predictions to generate a strong learning algorithm. The algorithms produce a sequence of decision trees, with the goal of every new decision tree being a correction for the prediction errors made by the preceding decision trees. The combination of decision tree predictions to generate a strong learner is described in Equation (2), where \(\mathbf{x}\) is a data point, \(\mathrm{N}\) is the predictive model's total number of decision trees, \(\mathrm{f_{i}(\mathbf{x})}\) is the prediction of an individual decision tree with index \(\mathrm{i}\) and \(\mathrm{f(\mathbf{x})}=\hat{\mathrm{y}}\) is the model output:  
\[\hat{\mathrm{y}}=\mathrm{f(\mathbf{x})}=\sum_{\mathrm{i=1}}^{\mathrm{N}} \mathrm{f_{i}(\mathbf{x})} \tag{2}\]  
\[\mathrm{L}=\sum_{\mathrm{k=1}}^{\mathrm{n}}\mathrm{d}(\hat{\mathrm{y}}_{ \mathrm{k}},\mathrm{y_{k}}) \tag{3}\]  
During each tree iteration, the gradient boosting method aims to minimize the loss function \(\mathrm{L}\), given in Equation (3), where \(\mathrm{d}\) is a function measuring the distance between the target value \(\mathrm{y_{k}}\) and the prediction \(\hat{\mathrm{y}_{k}}\) and \(\mathrm{n}\) is the total number of data points in the training set.
## 4 Experimental Results and Analysis
### Results and Analysis
#### 4.3.4 Automatic Reconstruction of the BIM
This was due to the presence of pipeline obstructions in the corners during the data collection process, leading to voids in the original data. During the BIM reconstruction process, these voids resulted in certain inaccuracies when aligning with the original point cloud data. Upon analyzing the areas with larger errors, it was found that this deviation was caused by the thickness of certain wall decorations during the finishing process, leading to discrepancies in the design. To provide a more intuitive representation of the deviation between the BIM model and the actual point cloud, this study used the ICP algorithm to visualize the errors, as shown in Figure 21.  
Based on the comprehensive analysis of the experiments, the method proposed in this study demonstrated its feasibility in terms of its accuracy. The feature-enhanced point transformer algorithm achieved an accuracy of 71.3% mIoU, surpassing traditional segmentation algorithms. The average error when reconstructing a BIM was effectively controlled within 2 mm, indicating the high precision of the reconstruction results. In practical engineering applications, to leverage the point cloud data collected, the BIM reconstruction method proposed in this study, based on the feature-enhanced point transformer and octree, enabled a rapid and automated indoor BIM reconstruction. This contributed valuable data support for the application of BIMs for the operation and management of existing buildings.
## 3 Proposed Model  
### Model Architecture  
We build a speech enhancement model based on U-Net structure as shown in Figure 2. Our model directly enhances noisy speech in time-domain.  
In this work, to pay attention to the speech contextual information and prevent the detailed features from being lost during down-sampling or up-sampling, we construct time-domain dilated residual blocks as shown in Res Block in Figure 2 for down-sampling and up-sampling in the encoder and decoder. When down-sampling or up-sampling, the input speech feature map is first fed a dilated convolution, in which kernel size is 8, stride is 1 and dilated factor is 2, and then a Batch normalization (BN) and rectified linear unit (ReLU) is performed. The number of filters of dilated convolution is equal to \(\mathrm{C_{i}}\) or \(\mathrm{H_{i}}\). For example, as shown in Figure 2, in the Res_Block_2 of the encoder, the input channel of dilated convolution is 64, and the output channel (i.e., the number of filters) is 128. In the Res_Block_4 of the decoder, the input channel of dilated convolution is 128 and the output channel (i.e., the number of filters) is 64. Since the gated liner unit (GLU) [28] will split the input tensor along the specified dimension, we double channels of the speech feature map by one-dimensional convolution (1D-Conv), in which kernel size is 1, stride is 1 and the number of filters is 2 \(\times\)\(\mathrm{C_{i}}\) or 2 \(\times\)\(\mathrm{H_{i}}\). As shown in Figure 2, in the Res_Block_2 of the encoder, the input channel of 1D-Conv on the left is 128 and the number of filters is 256. In the Res_Block_4 of the decoder, the input channel of 1D-Conv on the right is 64 and the number of filters is 128. Then, the output of 1D-Conv is activated by GLU along the channel axis and followed by a BN. Dilated convolution can expand receptive fields and aggregate context in speech enhancement. BN prevents gradient vanishing or exploding problems. Moreover, the channel of original input is adjusted by 1D-Conv, in which kernel size and stride are both 1, and the number of filters is \(\mathrm{C_{i}}\) or \(\mathrm{H_{i}}\), and then BN is performed. The 1D-Conv ensures that the dimensions of the outputs on both sides are equal to perform an addition operation (denoted by a circled plus sign in Figure 2). Finally, the speech feature map is fed to a convolution (Conv) or transposed convolution (Trans-Conv), in which kernel size is 8, stride is 4 and the number of filters is \(\text{C}_{\text{i}}\) or \(\text{H}_{\text{i}}\), and then activated by ReLU to obtain the output of the encoding layer or decoding layer. For instance, in Figure 2, in the second down-sampling, the number of input channels and filters in Conv are both 128, and in the fourth up-sampling, the number of input channels and filters in Trans-Conv are both 64. Only one block of the down-sampling and up-sampling operations is shown separately in Figure 2, except for the number of channels (i.e., \(\text{C}_{\text{i}}\) and \(\text{H}_{\text{i}}\)), the other layers use identical sampling operations.
# Biosurfactants' Potential Role in Combating COVID-19 and Similar Future Microbial Threats
## 2 Approaches That Can Use Biosurfactants in Outbreak Prevention and Management
### Applications in Diagnostics
#### 2.2.2 Contrasting Agents  
Microbubbles synthesized with biosurfactants can offer an option for the synthesis or the development of non-invasive, low-cost, and highly specific diagnostics. They can be applied in diagnoses using molecular imaging. For the diagnosis of specific diseases, the bubble surface can be chemically modified or conjugated to a disease-specific ligand. When retained in the targeted tissue and detected with ultrasound by using it as a contrast, it can be a means for specific and sensitive diagnosis in early disease detection and progression [67; 68].
## 4 Results and Discussions
### Wavelet Coherence Analysis of Daily Global Solar Radiation with other Meteorological Factors
Figure 6: Wavelet coherent power spectrum between daily global solar radiation and other meteorological factors, including (**a**) RAD-SSD, (**b**) RAD-TEM, (**c**) RAD-PRS, (**d**) RAD-WIN, (**e**) RAD-RHU and (**f**) RAD-PRE (where the abbreviations RAD, SSD, TEM, PRS, WIN, RHU, and PRE, respectively, represent global solar radiation, sunshine duration, air temperature, atmospheric pressure, wind speed, humidity, and precipitation). According to the wavelet coherence spectrum analysis between the global solar radiation and six other meteorological factors in Harbin over the last ten years, global solar radiation and the other six series have strong correlations over the annual period, but their dominant relationships are different. Global solar radiation and sunshine time show good phase consistency across the whole time period, in which the correlation between the two meteorological factors is the strongest. In the annual period, global solar radiation slightly ahead of atmospheric temperature and precipitation, and shows a complete negative correlation with atmospheric pressure. Furthermore, wind speed leads global solar radiation by 1 to 3 months, while the relationship between global solar radiation and air humidity is more intricate, with no apparent dependency. In addition, both air humidity and precipitation are approximately negatively correlated with global solar radiation over shorter time periods, which is completely different from the correlation shown in the annual period.
# Diversity of Potential (Bio)Technological Applications of Amino Acid-Based Ionic Liquids
### General Structures, Synthesis, and Key Properties
In addition, they exhibit higher thermal stability than [Gly][NO3] but lower than [P444][Gly] [25]. * _Ionic conductivity_ According to the results, [N444][AAs] showed conductivity values in the range of 0.16-0.54 ms cm\({}^{-1}\), which were much higher than those of similar ILs, such as [Emim][AAs] (\(\kappa\) = 9.1 \(\times\) 10\({}^{-6}\) to 0.65 ms cm\({}^{-1}\)) [22]. For the series tested, the authors found that the ionic conductivity is inversely proportional to the viscosity and is governed by the van der Waals interactions and the H-bonding ability of the ILs [22]. * _Chirality_ [AAOEt][NO\({}_{3}\)] (except [ProOEt][NO\({}_{3}\)]) have nearly the same specific rotation value as their precursors, while [AAE][Sac] undergo partial racemization. Chirality is preserved and remains the same for 3 months similar to the chirality of AAOEt-ILs, which are used as catalysts in the Diels-Alder cycloaddition of cyclopentadiene to methyl acrylate. AAOEt shows the same selectivity as [Bmim][BF\({}_{4}\)]. [AAOEt][SA] show better and more stereoselectivity than [AAOEt][NO\({}_{3}\)] [18]. The optical properties of [Ch][AAs] are consistent within the family and slightly higher than other common ILs. The refractive indices of all measured [Ch][AAs] lie in the range of 1.49-1.54 and are similar to many common organic solvents. There is no apparent correlation with other measured physical properties or the identity of the anion. The optical rotation of some enantiomerically pure [Ch][AAs] has also been reported. These are quite different from those of pure amino acids, as has been seen with other chiral ILs [23].
## 3 Results and Discussion
### Vickers Hardness  
One of the main characteristics of metallic carbides is hardness. Table 5 summarizes the average values of Vickers hardness obtained for each sample at different milling times.  
Table 5 shows how hardness increased when the grinding time was increased, between 25 and 50 h, and the hardness percent change was 27.4%. From 50 to 75 h, it grew up to 87.04% and from 75 to 125 h, a value of 10.05% was observed. The augmentation in hardness diminished with decreasing crystal size. There are refinements of the microstructure and a high density of defects like dislocations in the powder processed by MA [7]. According to the literature, the typical carbide hardness for Mo\({}_{2}\)C shows values from 15.5 to 24. 5 GPa and for W\({}_{2}\)C, the hardness value is about 22 GPa [39]. In Table 5, the samples milled at 25 and 50 h showed hardness below these values. At 75 h of milling, hardness  
Figure 9: (**a**) High-resolution transmission electron microscopy (HRTEM) micrograph of a powder particle of the nanostructured MoWC system at 150 h of milling, (**b**) d-spacing measurement with the Digital Micrograph (D.M.) software, (**c**) Inverse fast Fourier transform (IFFT) image of the selected area, (**d**) simulated fast Fourier transform (FFT), and (**e**) FFT mask.  
was similar to that of Mo\({}_{2}\)C. At 150 h of milling, hardness was higher than that one of the individual carbides (Mo\({}_{2}\)C and W\({}_{2}\)C), which showed that a new type of carbide in a solid solution was obtained as discussed previously. The X'pert HighScore Plus software was used in order to calculate crystal size from the X-ray spectrum data. In short, hardness increased with grinding time, while crystal size decreased. The microhardness values were higher than those reported in literature [7].
## 1 Introduction  
Nonverbal communication is defined as interaction without language. Thus, vocal sounds that are not words, such as grunts or singing a wordless note, are nonverbal [1]. The human voice contains a rich source of nonverbal vocal expressive cues which come in different forms such as tone (prosodic expressions), breathing sounds, crying, hums, grunts, laughter, shrieks, and sighs (e.g., paralinguistic cues) [2,3]. Extensive reviews have established that the prosodic expressions of basic emotions--such as anger, fear, happiness, and sadness--are elicited by the acoustic patterns of cues related to pitch, intensity, voice quality, durations, and speed rates [4,5].  
Listeners are able to interpret the emotional content of prosodic variations across languages and cultural differences with accuracy above chance [5, 6, 7, 8, 9]. Studies on speech particularly suggest that the perception of prosodic expressions has a universal component, although further meta-analyses have attested that judges rated expressions from people of their own culture (in-group) more accurately than they did with unfamiliar cultures [4,10]. Interactions with in-group members (people from the same culture) are characterized by a sense of intimacy, familiarity, and trust while interactions with out-group members (people from a different culture) lack these qualities.  
The current study focuses on listener's perception of vocal emotion expressed in synthesized voices based on nonverbal vocal cues. Nonverbal cues play an important role for speakers and listeners to express and recognize emotions. To achieve this goal, we conducted a case study using the Spanish Synthetic Expressive Voices database (SEV) [11] to measure the emotion perception accuracy among the following Asian cultures: Japanese, Chinese, Vietnamese and Malaysians. The effect of two of the prosodic features (intensity and duration) on the perception accuracies was further studied to find the common patterns of emotion perceptions among Asians.
## 3 Results and Discussion
### Metabolism of Carbon Sources  
All the _L. lactic_ isolates were tested for their ability to assimilate 49 carbohydrates and their derivatives using the API 50 CH test (Table 2). After incubation, only 20 chemical compounds were used as media by the selected strains. All isolates of the bacteria were able to ferment glucose, lactose, ribose, fructose, mannose, galactose, N-acetyl-glucosamine, amygdala, arbutin, esculin, salicin, cellobiose, maltose, turnanose, and potassium gluconate. Some differences were found, i.e., the strains from group I were the only bacteria that additionally assimilated glycogen, xylitol and gentiobiose, strains from group I and II additionally assimilated starch, and bacteria from group I and III utilized D-melibiose. No isolates were able to ferment glycerol, erythroid, D- and L-arabinose, D- and L-xylose, adonitol, methyl-\(\delta\)-D-xylopiranoside, sorbose, rhamnose, inositol, dulticl, mannitol, sorbitol, methyl-\(\alpha\)-D-mannopyranoside, methyl-\(\alpha\)-D-glucopyranoside, sacacharose, trehalose, inulin, D-melesitose, raffinose, lysrose, tagatose, D- and L- fucose, D- and L- arabitol, potassium 2-ketogluconate, and potassium 5-ketogluconate. The API CH test, which is a popular method for the identification and biochemical characterisation of bacteria, has often been used by researchers [5; 11; 13; 32]. For example, [PERSON] [5] identified _L. lactis_ and observed that all strains assimilated ribose but not inulin, which confirms our observations. In contrast, the results reported by [PERSON] et al. [32] were partially different from our findings, as the authors observed that the isolates hydrolysed D-mannitol, D-sucrose, D-trehalose, D-melezitose, and D-turnanose, but various patterns of catabolic activity were noted for gentiobiose, D-galactose, glucose, ribose, amygdala, L-arabinose, and D-melibiose. In a study conducted by [PERSON] et al. [11], raw milk and bryndza cheese isolates assimilated 14 carbon sources and, unlike A13 and A14, they assimilated sucrose, but did not assimilate uranose and potassium gluconate, and partially only used starch and gentiobiose. Similar  
Figure 2: Maximum likelihood tree based on 16S rRNA sequences showing the phylogenetic analysis of the studied _L. lactis_ isolates (bolded). Bootstrap values >50% are indicated. The scale bar displays the number of nucleotide substitutions per site. The sequence of _Bacillus subtilis_ was used as an outgroup. to ours and other researcher studies, there was a negative bacterial response to raffinose and inulin [5; 11; 13; 32].
## 3 Energy Cost Optimization for Offshore Wind Farms
### Optimization Method
#### 3.1.2 Optimization Procedure  
As regards the model to minimize the COE model, the composite optimization algorithm is designed into two parts: One is the main optimization algorithm, which uses the iterative algorithm to obtain the optimally designed parameters of the wind turbines, while the other is the sub-optimization algorithm, which employs the IPSO algorithm to obtain an optimal turbine layout by comparing the values of the objective function. The main flow chart of the developed algorithm is presented in Figure 2, and the implemented procedures comprise the following two loops.  
(1) The main optimization loop.
* Step 1: Initialize the wind statistics of the offshore windfarm, \(v_{m}\), \(k_{0}\) and \(\alpha\);
* Step 2: Define the ranges of the design parameters \(v_{r}\), \(R\) and initialize the minimum design parameters.
* Step 3: Update the design parameters through their own iterative intervals.
* Step 4: Turn to the IPSO algorithm and obtain the optimal turbine layout and value of the objective function (COE).
* Step 5: Repeat step 3 until all design parameters sets of \(v_{r}\) and \(R\) have been evaluated;
* Step 6: Output, with minimal objective function, the corresponding design parameters and wind turbine layout.  
Figure 2: Flow charts of the proposed COE minimization approach.  
(2) The sub-optimization loop.
* Step 1: Input the wind statistics of the offshore wind farm, \(v_{m}\), \(k_{0}\) and \(\alpha\). Input the wind turbine characteristics parameters, \(v_{c}\), \(v_{r}\), \(v_{f}\), and \(R\) and input the planned installation capacity. Define the grid size of the offshore wind farm.
* Step 2: Initialize the IPSO parameters \(c_{1}\), \(c_{2}\), \(u\), \(w_{max}\), \(w_{min}\), \(t\), T, \(v_{id}\), \(x_{id}\), \(v_{max}\) and \(v_{min}\). Initialize the wind turbine layout: assume the wind turbines are installed in the grid-center and generate a set of numbers which represent the wind turbine layout according to the grids.
* Step 3: According to Equations (16)-(18), calculate the mean wind speed at the installation sites of turbines and then obtain the \(cost_{total}\) and \(AEP_{total}\).
* Step 4: Evaluate the objective function via the question, \"Is the new result of the objective function smaller than the existing one?\" Update the layout corresponding to the smaller one.
* Step 5: Determine whether the procedure termination condition has been met. If yes, turn to Step 8; if no, turn to Step 6.
* Step 6: Update the wind turbine layout by IPSO until there is no duplicate value.
* Step 7: Repeat Step 3 until the procedure termination condition is satisfied.
* Step 8: Output the minimal objective function and the corresponding layout.
# MB-OFDM-UWB Based Wireless Multimedia Sensor Networks for Underground Coalmine: A Survey
## 6 Conclusions  
Since the systems for coalmine safety monitoring, early warning and disaster relief have urgent demands for wireless video surveillance, wireless voice communication, underground environment monitoring and personnel positioning, this paper presented a framework for constructing a wireless multimedia sensor network for underground coalmines that integrates all these functions.  
The framework and related discussion filled the gap of absence of efficient and comprehensive structure for designing WMSNs for coalmine.  
Overall design of WMSNs was presented first, and we discussed in detail the main design challenges, in which MB-OFDM-UWB solution was selected in physical layer. We initially compared the schemes of ZigBee and Wi-Fi with the UWB scheme, and found that UWB can solve both constraints of bandwidth and energy consumption. Then, we further analyzed the several UWB solutions and stress the superiority of MB-OFDM-UWB scheme. The selection of MB-OFDM-UWB wireless transmission solution was based on the characteristics of underground coalmine, and that is different from other schemes for WMSNs which are based on DS-UWB solution. MB-OFDM-UWB transmission solution, together with the characteristics of applications and environments in underground coalmines, was the cornerstone of future research and discussion work. Layer-specific structure was used to discuss the key supporting technologies for underground coalmine WMSNs. Some promising open research areas were also presented, indicating a possible direction for future works.  
This work was supported by the Natural Science Foundation of China under Grant (51474015) and the Key Projects of National Key Research and Development Program (2016 YFC0801806).