esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation and Industrial Application
### Signal Denoising
MSE of the WTD-FOA was smaller than that of WTD-GA while the PVE was contrary, which indicated the two methods had no optimal solution, both in global and local. The WTD-IFOA overcame the disadvantage and contributed a superior scheme. The MSE of WTD-IFOA was decreased about 35.36% compared with SST, and the PVE decreased about 9.40%. As optimal threshold was obtained through the iterative process, the last four methods were much more time-consuming. Among these approaches, TFB cost the most time due to its complex calculation. The optimization process of WTD-GA was much more complex than the other intelligent solutions from the table. Moreover, the WTD-IFOA, respectively, saved 26.96% and 12.47% time compared with WTD-GA and WTD-FOA because of its stronger addressing ability. In order to research the denoising performance at different SNR, a further comparison was made and the average value of five simulation results are shown in Figure 9. Four noisy signal with SNR = 5 dB, 10 dB, 15 dB and 20 dB were synthetized and handled. Then the MSE, PVE and CT of the denoising processes were presented in the figure. It can be roughly obtained that the three evaluation parameters decreased with the SNR. As the threshold value of each level and wavelet coefficients were determined directly by Equations (3) and (4) in SST, the denoising process could be finished in a short time, while the TFB based on gradient descent algorithm was time-consuming in different SNR as its complex calculation process. Denoising methods using optimization algorithm had a comprehensive denoising performance. The computation time was decreased sharply compared with TFB while \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**IndexMethod** & **SST** & **TFB** & **WTD-GA** & **WTD-FOA** & **WTD-IFOA** \\ \hline MSE (\(\times 10^{-4}\)) & 12.19 & 9.61 & 11.78 & 8.53 & 7.88 \\ PVE (\%) & 10.56 & 7.39 & 2.18 & 2.28 & 1.16 \\ CT (s) & 0.87 & 24.61 & 11.24 & 9.38 & 8.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the five methods (SNR = 5 dB). it still cost more time than SST. In detail, the WTD-GA did not obviously vary from WTD-FOA in PVE, while it had a distinct weakness in MSE and CT compared with FOA-based methods. It could be seen from the simulation results that the TFB, WTD-GA and WTD-FOA fell into local extreme during the parameters optimization. Moreover, the WTD-IFOA exhibited obvious superiority in the denoising effect, which revealed its terrific global and local ability compared to the other methods in different SNR.
## 6 NP Immunomodulators  
### Bacterial-Derived Probiotics  
Probiotics are defined as \"live microorganisms which when administered in adequate amounts confer a health benefit on the host\" [55]. They can consist of commensal bacteria of the healthy gut microbiome which are administered as food and, in some cases, are well- characterised immunomodulators. Over the last decade, the efficacy of _G. mellonella_ as an alternative in vivo model for the preclinical assessment of putative probiotic immunomodulators has been demonstrated. _Lactobacillus rhamnus_ (_L. rhamnus_) is an example of a probiotic which exerts its immunomodulatory effect by stimulating pro-inflammatory mammalian (murine) cytokine levels [56]. In the same study, the prophylactic inoculation of larvae (\(n=16\)) with _L rhamnus_ strain ATCC 7469 increased haemocyte number (1.5-fold versus negative control) and reduced pathogenic fungal (_C. albicans_) load following experimental challenge 24 h later (\(p<0.0001\)). In another study, the supernatant from a different L. _rhammosus_ strain (ATCC9595) was shown to cause an 80% increase in the survival of larvae (\(n=16\); \(p>0.0001\) versus negative control) challenged with the same lethal fungal inoculum, which was attributed, at least in part, to a 2.1-fold increase in haemocytes [57].  
The same group found that inoculation with another species belonging to the _Lactobacillus_ genus, _L. paracasei_ was superior to _L. rhamnus_ in eliciting a greater level of protection in the experimental candidiasis _G. mellonella_ model with an almost 2-fold increase in survival rate (27% versus 14% survival 120 h post-challenge) [58]. The optimal inoculum (10\({}^{7}\) cells/larvae) of _L. paracasei_ when administered alone demonstrated a 2-fold increase in haemocyte number and a significant increase in AMP gene expression (the anti-fungal peptides galiomicin (6.67-fold) and gallerymicin (1.68-fold)) immediately post-infection, compared to the negative control. The accumulation of evidence gathered in these studies seems to point to the probiotic fungicidal prophylactic effect being due to their ability to stimulate the host immune system rather than any direct anti-fungal effect.  
_G. mellonella_ has been shown to be an effective model to study the immunomodulating properties of probiotic food supplements and as a screen for new immunomodulatorydynamics strains and secretory components. This model could also contribute to the discovery of other genera of immunomodulating probiotics, thus extending the knowledge in the prevention of disease.
# Automatic Definition of Traffic Analysis Zones Based on Big Data
## 5 Process Flow  
### Sorting to Coordinates  
In the first step, the application loads the following libraries and modules: matplotlib, numpy, scipy, and sklearn. The first module starts by reading the data. The json file is loaded, from which the \"jams\" part is extracted, and then, records the individual congestion signals as separate lines. The data are then generated in a way that Python can process them properly, and corrections are made if necessary. The module then organizes the data into coordinates. Each congestion is associated with a \"revisions\" category, which contains the precise location of the congestion (based on GPS coordinates). The location essentially represents a segment, with its coordinates sequentially linked to form the congestion. Congestion can persist for a longer period, indicated by the \"created at\" timestamp. Based on this, multiple points at different times are associated with a congestion identifier.  
\begin{table}
\begin{tabular}{l l l} \hline
**ID** & **Name** & **Meaning** \\ \hline
01 & uuid & Congestion identifier \\
02 & country & Name of the country \\
03 & type & Congestion \\
04 & turn\_type & Type of turn (right, left, straight, none) \\
05 & publish\_date & Congestion start date \\
06 & last\_seen & Last detection of congestion \\
07 & city & Name of the city \\
08 & street & Name of the road \\
09 & start\_node & Start of congestion (nearest road/node/-) \\
10 & end\_node & End of congestion (nearest road/node/-) \\
11 & road\_type & Type of the road \\
12 & alert\_uuid & Alert identifier (if any) \\
13 & revisions & \\
13.1 & level & Congestion severity (0—free flow, 5—completely stopped traffic) \\
13.2 & length & Congestion length [m] \\
13.3 & speed & Average speed [m/s] \\
13.4 & delay & Delay due to congestion (compared to free flow) [s] \\
13.5 & line & GPS coordinates of the congestion section \\
13.6 & created\_at & Timestamp \\ \hline \end{tabular}
\end{table}
Table 1: Automatically generated data types.
## 2 Materials and Methods
### Laboratory Data Analysis  
#### 2.3.1 Signal Pre-Processing  
Figure 2 shows example waveforms and spectrograms of oral airflow, intraoral pressure, acoustic microphone, and accelerometer signals, which were calibrated to units of milliliters per second (mL/s), centimeters of water (cm H\({}_{2}\)O), pascals (Pa), and vibration acceleration (cm/s\({}^{2}\)), respectively. Slope and intercept calibration terms were applied to each uncalibrated voltage signal. For oral airflow, a line was drawn through three points with known airflow volume velocity as output by an airflow calibration unit (Model MCU-4; Glottal Enterprises): 500 mL/s outward flow, zero flow, and 500 mL/s inward flow. For intraoral pressure, a line was drawn through five points with known pressure produced by advancing a syringe through a closed-loop system: 0, 5, 10, 15, and 20 cm H\({}_{2}\)O, as measured by a calibrated pressure gauge (Model PC-1; Glottal Enterprises). For the acoustic microphone signal, a line was drawn through multiple points with measured RMS levels in Pa (Model NL-20; RION Corporation, Tokyo, Japan) produced by a synthesized harmonic complex at multiple intensity levels. Finally, each ACC sensor was calibrated in units of cm/s\({}^{2}\) by applying a chirp signal with known amplitude and 10-5000 Hz bandwidth using an electrodynamic vibration exciter (Mini-Shaker Type 4810, Bruel & Kjaer) and a reference accelerometer (Model 4533-B, Bruel & Kjaer, Naerum, Denmark) placed on a vibration isolation table (BT-2024, Newport Corp., Irvine, CA, USA). The ACC signal (up-sampled to 20 kHz) was aligned with the other recorded signals in the laboratory by maximizing the cross-correlation between the ACC signal and the microphone signal.  
As described in previous work [44], vowel segments were defined by processing the microphone signal using Praat version 6.0.30 [95]. Figure 2A illustrates an example segmentation of the vowel and silent segments. Figure 2B displays a zoomed-in version of the signals with boundaries defined for each intraoral pressure plateau between the vowel segments. Reference estimates of Ps were computed for each vowel segment by the average of the peak amplitudes of the intraoral pressure plateaus preceding and following each vowel segment.  
Figure 1: Data acquisition setups for (**a**) laboratory recordings of acoustic microphone (MIC), electroglottography (EGG), accelerometer (ACC), high-bandwidth oral airflow (FLO), and intraoral pressure (PRE); and (**b**) infield recording of the accelerometer sensor connected to a smartphone either placed in a belt holster or in a pocket. Reprinted with permission from Ref. [94]. Copyright 2013 IEEE.
## 4 Results and Discussions
### Results for Ahmedabad Link  
Figure 3 shows the day-to-day comparison between the maximum power received (out of the received powers for all 11 sets) and the received power for the lowest (first) set of transmit and receive beam elevation angles, for morning and evening times, in summer and winter seasons, for the Ahmedabad link. It was found that the maximum power was received from beam sets other than the lowest beam set (elevation angle 1) at multiple instances. For morning times, the maximum power was received from elevated beams (elevation angles 2 to 11) for 15% of the days in the year, mostly from beam sets 2 to 5. The maximum power received was greater than the power received for the lowest elevation beam set by more than 1 dB on 5% of the days and was even more than 3 dB on a few occasions. The largest difference between the maximum power and the lowest elevation beam power in the morning time, when considered for all of the available days, was found to be 4.12 dB in the month of May. For the evening time, the occasions when the maximum power was received from elevated beams increased to 21% of the days in the year. Days when the difference between maximum power and lowest elevation beam power was more than 1 dB, increased to 8% in evening time and the instances of a more than 3 dB difference doubled. The maximum difference between the maximum power and the lowest elevation was found to be 6.05 dB in the month of June. Comparing the results obtained for the summer and winter seasons, the average received power was higher in the summer seasons. The maximum power in the summer was received mostly from elevated beams, especially in the evening. The maximum power was received from almost all beams (elevation angles 1 to 10) on different occasions in the year, except from the beam with an elevation angle of 11.  
Figure 3: _Cont._
## 3 Results
### GPP Validation
#### 3.1.2 Seasonal GPP  
The tower-based GPP measurements (Flux_Tower GPP) were compared with the results achieved by the four algorithms (_i.e._, MOD_NCEP GPP, MOD_Tower GPP, NCEP_GLASS GPP, and Tower_GLASS GPP) over each season (Figures 4-7 and Table 3). Overall, the four algorithms effectively estimated the Flux_Tower GPP in the spring, autumn and winter. In particular, the algorithms had the best estimating effectiveness for the winter (the values of R\({}^{2}\) ranged from 0.87 to 0.92). However, the overall estimating effectiveness of the four algorithms was poor for the summer (values of R\({}^{2}\) ranged from 0.36 to 0.52). The details are as follows:  
1. [leftmargin=*]
2. Spring (March-May): The estimation performances of MOD_NCEP GPP (R\({}^{2}\) = 0.64) and Tower_GLASS GPP (R\({}^{2}\) = 0.65) were excellent (Figure 4, Table 3). The MOD_NCEP GPP, MOD_Tower GPP and Tower_GLASS GPP underestimated the Flux_Tower GPP to varying degrees (\(-39\) gC\(\cdot\) m\({}^{-2}\cdot\) 3-month\({}^{-1}\), \(-122\) gC\(\cdot\) m\({}^{-2}\cdot\) 3-month\({}^{-1}\) and \(-80\) gC\(\cdot\) m\({}^{-2}\cdot\) 3-month\({}^{-1}\), respectively), primarily because of the considerable underestimation for QYZ and YC (Figure 4a,b,d). In contrast, NCEP_GLASS GPP overestimated the Flux_Tower GPP (40 gC\(\cdot\) m\({}^{-2}\cdot\) 3-month\({}^{-1}\)) because the measurements for DHS were considerably overestimated (Figure 4c). 3. Summer (June-August): None of the four algorithms estimated the Flux_Tower GPP effectively (Figure 5, Table 3). The main reason that the Flux_Tower GPP were seriously
## 2 Material and Methods
### Optimization of the Method of Safrole Extraction from Cowpea Beans  
Safrole was extracted from cowpea beans by HS-SPME-GC/FID. The optimization of this method was divided into two steps: (i) univariate optimization of the fiber to be used, and (ii) multivariate optimization of the extraction time and temperature. At the first stage, four types of fibers produced by Supelco (Bellefort, PA, USA) were tested: 7-um poly(dimethylsiloxane) (PDMS); 100-um poly(dimethylsiloxane) (PDMS), 85-um polyacrylate (PA), and 50/30-um divinylbenzene/carboxen/poly(dimethylsiloxane) (DVB/CAR/ PDMS) fiber. The tests were performed in triplicate, and all fibers were preconditioned as instructed by the manufacturer.  
Biopsticide-free cowpea-bean samples (5.0 g) were weighed with an analytical balance inside 44-milliliter amber glass vials. The samples were fortified with safrole at 0.19 \(\upmu\)g kg\({}^{-1}\), using a mixture volume of 2 mL kg\({}^{-1}\) of cowpea beans, diluted in methanol. Once fortified, they were vortexed for 1 min and let repose for 30 min before the safrole extraction. Next, the flasks with the fortified samples were heated in a water bath at 30 \({}^{\circ}\)C with magnetic stirring, and the fibers were exposed for 3 min in the headspace. After the analyte adsorption period, the fibers were collected from the vial and immediately inserted and exposed into the chromatograph injector. The thermic desorption time was 3.0 min, after which the fiber was removed from the injector. In the second optimization stage, a complete 2\({}^{2}\) factorial design (Table 1) was used to evaluate the behavior of the independent factors: (i) temperature (30 or 60 \({}^{\circ}\)C) and (ii) extraction time (3 or 10 min), at the upper (+) and lower (\(-\)) levels, in eight trials. These analyzes were performed in duplicate using the fiber that presented the best chromatographic response in the first optimization step.  
Figure 1: HS-SPME scheme for safrole analysis in cowpea beans samples. Source: Modified from [PERSON] et al. [48] and [PERSON] et al. [14].
## 3 Test Methods and Procedures
### Direct Shear Test  
The two bentonite sand mixtures were tested for direct shear in accordance with ASTM D 3080, Standard Test Method for Direct Shear Test of Soils Under Consolidated drained Conditions [27]. The optimum moisture content of the tested samples was selected at three different levels: wet of optimum moisture content, optimum moisture content, and  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Mixture** & **Moisture State** & **Actual wc (\%)** & **Actual Ywet** & **Actual Ydry** & **Measured Cohesion** & **Measured Friction Angle** \\ \hline \multirow{3}{*}{5\% B} & DOMC & 9.88 & 1.820 & 1.656 & 2.2 & 38.6 \\ \cline{2-6}  & OMC & 13.85 & 1.961 & 1.723 & 15.2 & 33.5 \\ \cline{2-6}  & WOMC & 17.33 & 1.895 & 1.615 & 6.5 & 36.4 \\ \hline \multirow{3}{*}{10\% B} & DOMC & 10.63 & 1.869 & 1.689 & 22.3 & 33.4 \\ \cline{2-6}  & OMC & 13.61 & 1.979 & 1.742 & 30 & 33.5 \\ \cline{2-6}  & WOMC & 18.68 & 1.887 & 1.590 & 18 & 34 \\ \hline \hline \end{tabular} Note: wc = water content, \(\Upsilon_{\text{wet}}\) = wet unit weight, \(\Upsilon_{\text{tt}}\) = dry unit weight, \(\varphi\) = friction angle. C = cohesion.  
\end{table}
Table 2: Compaction and shear strength properties of tested samples of clay–sand mixtures.  
dry of optimum moisture content. The compaction energy applied was the same as the standard proctor test. The resulting dry densities for the 5% bentonite mixtures were: 1.656, 1.723, and 1.615 gm/cm\({}^{3}\) at moisture contents of 9.88, 13.85, and 17.33, respectively. The resulting dry densities for the 10% bentonite mixtures were: 1.689, 1.742, and 1.590 gm/cm\({}^{3}\) at moisture contents of 10.63, 13.61, and 18.68, respectively.  
The specimen size was prepared to fit a 10 cm by 10 cm shear box mold placed in a motorized apparatus running at a slow strain rate equal to 0.12 mm/min. The vertical stresses applied were 50 kPa, 100, kPa, and 200 kPa.
## 3 Results
### The Operation of the IFCS  
The results show that the IFCS could effectively lower the water level during the flood season, especially in the EDL (East Dongting Lake) area (see Table 4). However, the operation of the IFCS has great effect on the water level reduction and the water level could be even higher than the natural condition if the wrong operation is chosen. Therefore, we built another model to explore the effect of the gate operation on the water level reduction. The 1996 type flood was chosen, as there was only one big flood peak in this year (Figure 8) and IFCS was only adopted in the East Dongting Lake (see Figure 9) to avoid the operation in each lake affecting  
Figure 10 presents the simulated fluctuation of the water level at Chenglingji station to show the effect of the operation on the water level reduction during the flood period. Before the gate is opened, there is a period (11-18 July) when the calculated water level is higher than that in the natural condition as the floodwater is restricted in the main channel after building the dike in the EDL. The water level begins to decline after the gate opens due to the excess floodwater flowing into the inner lake. For case 1, the operation water level was set as equal to the safe water level, with a corresponding peak water level reduction of 0.22 m. When the operation water level of both gates decreases by 0.02 m (Case 2), the reduction of the water level is more efficient, and the peak value is decreased by 0.28 m. However, instead, the reduction of peak water level decreases to 0.19 m when we continue to decrease the operation water level by 0.02 m (Case 3).
# Finite Difference Algorithm on Non-Uniform Meshes for Modeling 2D Magnetotelluric Responses
## 4 Numerical Results  
### Comparison of FD Results and Analytical Solutions  
The two-layered model is used as a test example for the comparison of FD numerical solution and analytical solution. It is supposed that the 1D geo-electric model includes two resistivity layers. The thickness of the top layer is 1 km, and its resistivity value is assumed to be 10 ohm-m. For the bottom layer, its thickness and resistivity value are assumed to be 1 km and 100 ohm-m. The computational size of the model is set as 20 km \(\times\) 2 km.  
Figure 3: FD solution of magnetic field in homogeneous half-space model. (**a**) Numerical solution of real component; (**b**) Numerical solution of imaginary component; (**c**) Analytical solution of real component; (**d**) Analytical solution of imaginary component; (**e**) Relative error of real component; (**f**) Relative error of imaginary component.  
The results of the two-layered model are shown in Figure 4. It is obvious that relative errors of the apparent resistivities and the impedance phases were both increased when the frequency was increased, with the maximum error at the longest frequency 1000 Hz and the minimum at the shortest frequency 0.001 Hz. At the highest frequency, the vertical grid spacing size at earth's surface must be small enough so that the linear approximation of the electric field is reasonably close to the exponential decay of the electromagnetic fields. Through the numerical examples, the first vertical mesh size should be suggested approximately 1/3 of the shortest skin depth of the top layer.  
Figure 4: _Cont._
# Design and Depth Control of a Buoyancy-Driven Profiling Float
## 2 System Design
### Variable Buoyancy System  
The VBS included the linear actuator, a driver board, a piston, and an external metal tube. When the linear actuator was pushed outward, the volume of the float became larger, and the static buoyancy increased, causing the float to float up. The volume could vary up to 4.81 \(\times\) 10\({}^{-4}\) m\({}^{3}\), accounting for 5.7% of the maximum volume. The maximum volume change rate was 1.03 \(\times\) 10\({}^{-5}\) m\({}^{3}\)/s.
# Evaluation of Adjustment Effects of Highway Guide Signs Based on the TOPSIS Method
## 4 Construction of Evaluation Index System
### Evaluation Metric Determination
#### 4.3.1 Compliance Indicators  
To address factors affecting the compliance of guide signs, the following evaluation metrics are proposed.  
(1) Conformity rate of distance signs (D\({}_{1}\))  
According to the standards, distance signs should be placed more than 1 km after the end of the acceleration lane of an interchange, with repeated signs spaced at intervals of at least 5 km. This metric is defined as the proportion of distance signs meeting these placement standards relative to the total number of signs.  
\[D_{1}=1-g_{11}/G_{1} \tag{11}\]  
\(D_{1}\) is the conformity rate of distance signs, \(g_{11}\) is the number of distance signs not set according to standards or with incorrect spacing, and \(G_{1}\) is the total number of required distance signs.  
(2) Configuration rate of advance exit signs and confirmation signs (D\({}_{2}\))  
Highways must have advance exit signs before interchanges to alert drivers of upcoming exits, and confirmation signs at the exits to verify the destination. This metric is defined as the proportion of interchanges with complete advance and confirmation signage relative to the total.  
\[D_{2}=1-n_{1}/N \tag{12}\]  
\(D_{2}\) is the configuration rate of advance exit signs and confirmation signs, \(n_{1}\) is the number of interchanges with incomplete signage, and \(N\) is the total number of interchanges.  
(3) Completeness rate of facility guide signs (D\({}_{3}\))  
Facilities along highways include gas stations, service areas, and parking zones. Appropriate guide signs must be placed before reaching these facilities. This metric is defined as the proportion of facilities with corresponding guide signs relative to the total number \(Y\) of facilities.  
\[D_{3}=1-y_{13}/Y \tag{13}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Guide Sign Effectiveness** & **Influencing Factors** \\ \hline \multirow{4}{*}{Readability} & Whether there are obstructions blocking the signs \\ \cline{2-3}  & Condition of the reflective film \\ \cline{2-3}  & Presence of dirt or damage on the sign panel \\ \cline{2-3}  & Whether signs are tilted or misaligned \\ \hline \multirow{4}{*}{Continuity} & Whether information is selected hierarchically \\ \cline{2-3}  & Whether the information meets drivers’ needs \\ \cline{2-3}  & Whether there are interruptions in guidance information \\ \cline{2-3}  & Completeness of advance signs \\ \hline \multirow{4}{*}{Consistency} & Rationality of advance sign information \\ \cline{2-3}  & Accuracy in reaching the destination \\ \cline{1-1} \cline{2-3}  & Effectiveness of route guidance \\ \hline \end{tabular}
\end{table}
Table 6: _Cont._\(D_{3}\) is the completeness rate of facility guide signs. \(y_{13}\) is the number of facilities without guide signs.