esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Headspace Solid-Phase Microextraction (HS-SPME)  
The headspace extraction was performed by manual SPME holder equipped with polydimethylsiloxane/divinylbenzene (PDMS/DVB) fiber and divinylbenzene/carboxylate/ polydimethylsiloxane (DVB/CAR/PDMS) fiber obtained from Supelco Co. (Bellefonte, PA, USA). Each fibre was conditioned before the extraction (according to Supelco Co. instructions). For HS-SPME, previously prepared samples of fresh (1 g; F-CB) and dried _C. bursa_ (0.7 g; D-CB) were placed separately in 5 mL glass vials and hermetically sealed with polytetrafluoroethylene (PTFE)/silicone septa. The vials were maintained in a water bath at 60 \({}^{\circ}\)C during the equilibration (15 min) and extraction (45 min). After sampling, the SPME fiber was withdrawn into the needle, removed from the  
Figure 1: _Codium bursa_ (Olivi) C. Agardh: (**a**) fresh sample (F-CB), (**b**) freeze-dried sample (FD-CB).  
vial, and inserted into the injector (250 \({}^{\circ}\)C) of the GC-FID and GC-MS for 6 min. The extracted volatiles were thermally desorbed directly to the GC column. HS-SPME of F-CB was performed within 24 h after the collection. HS-SPME was done in duplicate.
# Design and Analysis of an Inductive Coupling System for the Early Detection of Heart Failure
## 5 Conclusions  
Heart failure can be predicted based on a number of different factors, such as the expansion and contraction of the heart as well as the quantity of fluid that is present in the heart. It was found that a change in the reflection coefficient at the resonant frequency of the transceiver coil represented a change in distance between the chambers of the heart. Although the fluids present in the heart can be determined using this method, the expansion and contraction of the heart are considered to be the most critical aspect of this research. In the experimental results, the \(5_{33}\) with a 50 mm distance between the implanted coils demonstrated a reflection coefficient of \(-10.66\) dB at resonant frequency whilst the \(S_{33}\) with a 10 mm spacing between the implanted coils exhibited a reflection coefficient of \(-10.79\) dB at the resonant frequency, and this shift in the value clearly indicated that the variation in the implanted coils' distance apart, with pork skin placed in between, influenced the reflection coefficient of the transceiver coil at the resonant frequency.  
In the future, a magneto-resistive sensor (TMR) can be employed to detect the position of the implanted coils. This TMR sensor can be placed between the transceiver coil and the implanted coils and can also be used to evaluate coil misalignment due to the simplicity of the computation procedure by measuring the mutual inductance [13; 31]. This variation in the mutual inductance between the transceiver and receiver coils will provide the distance between the coils more efficiently. The mutual inductance increases with an increased distance between receiver coils since the coils will be aligned perpendicularly to the transceiver when the distance is varied. The mutual inductance suddenly drops down when the receiver coils surpass the transceiver coil's boundary. Hence, this idea will be implemented in the near future by involving ESP32 WROOM-32E MCU and the MIT app inventor, which provides data to a handheld device via Bluetooth 2.45 GHz.  
Conceptualization, J.R.C.; methodology, R.K.V. and K.M.; simulation, J.L.; writing--original draft, R.K.V. and K.M.; review, J.R.C., R.K.V., V.G. and K.Y.; supervision, J.R.C.; project administration, J.R.C.; funding acquisition, J.R.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R11A3065961) and BK21 Four project funded by the Ministry of Education, Korea (No. 4199990113966).  
Not applicable.  
The authors declare no conflict of interest.
# Sampling-Based Path Planning Algorithm for a Plug & Produce Environment
## 2 Implementation of Path Planning Algorithms
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Algorithm Implemented** & **Characteristics of Each Algorithm Evaluated** \\ \hline RRT & RRT efficiently searches nonconvex, high-dimensional spaces, and builds the tree randomly by creating \(q_{rand}\) nodes. This is a non-optimal active sampling-based algorithm. \\ \hline Adaptive RRT & Adaptive RRT efficiently searches nonconvex, high-dimensional spaces, and builds the tree randomly by creating \(q_{rand}\) nodes, and has an extra collision check module which adapts the sampling value according to the constraints present in the environment. This is also a non-optimal active sampling-based algorithm. \\ \hline PRM + Dijkstra & PRM works in two phases: the construction phase and the query phase. During the construction phase, the road map is built for the entire environment, and during the query phase, the start and goal configurations relate to the build road map and are later implemented with the Dijkstra algorithm to find the shortest path from the map for the start and goal point in the configuration space. \\ \hline RRT\({}^{*}\) & The RRT\({}^{*}\) algorithm efficiently builds the tree in the entire search space of nonconvex and high-dimensional space. After building the tree, it finds the asymptotically optimal path from the starting position of the configuration to the goal position of the configuration by a best parent search in the optimal step. \\ \hline Adaptive RRT\({}^{*}\) & Adaptive RRT\({}^{*}\) algorithms create the path like RRT\({}^{*}\), but have an extra collision check module during more constraints present in the environment. Due to this extra collision the module should be checked. An adaptive RRT\({}^{*}\) algorithm changes its sampling value and finds the \(q_{mid}\) when the obstacle presents between \(q_{near}\) and \(q_{nearest}\). Then, it should be checked for the collision between \(q_{near}\) and \(q_{mid}\). Therefore, this creates a path efficiently, even if the narrow path exists and can accommodate the tool size in the gap. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Algorithms evaluated and their respective characteristics.
# Reconstruction of the Subsurface of Al-Hassa Oasis Using Gravity Geophysical Data
## 3 Gravity Survey
#### Gravity Data Processing
The dipping direction of the anomaly sources can also be estimated, which dips towards the negative tilt angles. The tilt-depth method [44] can be utilized to analyze the variations in the depths of the anomaly sources. Given vertical geologic contacts or faults, the depths at which the upper surfaces of the sources are buried can be found by measuring half the physical distance between the corresponding contours of \(\pm 45^{\circ}\). To examine the distribution of rock densities beneath the surface, we generated three-dimensional models using the VOXI Earth Modeling tool within the Oasis montaj software. This software mainly uses the Cartesian cut cell method [42; 43] for the inversion process. This approach utilizes a Cartesian voxel comprising prismatic elements, which diminishes computational complexity and saves computation time, while also enabling highly accurate geometric depiction of geological surfaces. By integrating the method into inversion and forward calculations, well-known artifacts that arise from representing topography with prisms of fixed vertical extent are eliminated. To highlight the subsurface basins where tectonic features largely influence fluid circulation, we studied the 3D distribution of the negative density contrast bodies (density lower than the average crustal density of 2.67 g/cm\({}^{3}\) that we used).
## 4 Conclusions  
In this study, a new Windows-based program, GPSQC with MS GUIs, was developed to improve GPS positioning precision through the efficient quality control of GPS observations. It was applied to actual GPS sensing data, and the following conclusions were found.  
(a) Eight quality control parameters (_a_, \(b\), _DOP_, _MP1_, _MP2_, _ion_, _iod_ and _cyc_) that represent the GPS observation quality were calculated using RINEX-formatted GPS sensing data.  
(b) The GPS data quality results of the IGS station (SUWN) for a five-day duration that were calculated using the GPSQC program was compared with the positioning results from the precise GPS analysis software (GIPSY/OASIS-II) to analyze the correlation between the GPS data quality and positioning precision. The results showed that a lower positioning precision led to a lower GPS data quality and that the quality control of GPS sensing data using the proposed GPSQC program is suitable for GPS quality control.  
(c) Quality control parameters, their allowable range, and the percentage (%) of content that is related to the quality of GPS observations are provided in time-series graphs and summary reports in PDF format, and general GPS users can control the GPS data quality more easily and intuitively. This is more convenient than the existing quality control software packages, such as TEQC and GQC because of the user-friendly environment.  
(d) Because the quality of GPS sensing data is immediately checked during the GPS observation, the positioning precision from the GPS observations can be checked to determine whether it meets the precision required for the appropriate GPS survey purpose before post-processing of GPS data. Therefore, the decision can be made quickly in the field regarding whether re-observation or additional observation for precise GPS positioning is required, and time and cost can be saved by eliminating unnecessary GPS observation.  
However, further study is required on the correlation between the allowable range of the quality control parameter and its percentage of content for achieving the desired precision to ensure more efficient and economic GPS data quality control. In addition, the Total Electron Content (TEC) factor, which is a main error source of GPS observations [23], also needs to be studied.
## 2 Materials and Methods
### Structural Characterization of Isolated Lignin Samples (GPC, DFRC, 2 DHSQCNMR)  
The recovered lignin fractions were analyzed by various qualitative analyses such as gel permeation chromatography (GPC), derivatization followed by reductive cleavage (DFRC), and 2D heteronuclear single quantum coherence nuclear magnetic resonance (HSQC NMR) spectrum by following the methods according to our previous studies [37]. Briefly, for GPC analysis, to determine the weight average (Mw) and number average (Mn) molecular weights of the lignin samples, the isolated lignin fraction was firstly put to acetylation by dissolving it in a 1:1 (\(v\):\(\%\)) of pyridine and acetic anhydride solution. It was periodically shaken after every 30 min while maintaining a temperature of 105 \({}^{\circ}\)C for almost 2 h in the oven. Afterwards, it was washed and freeze dried and 20 mg of this acetylated lignin sample was dissolved in 2 mL of tetrahydrofuran (THF) followed by analyzing it on a GPC, Agilent 1200, USA (equipped with an ultraviolet detector (UV) on a PL-gel 5 mm Mixed-c. column. This was first calibrated with the standards of polystyreneand THF was run as the mobile phase at a flow rate of 1 mL/min. After running all the samples, the GPC chromatograms were also drawn. To this end, after doing the standards calibration, the graphs were drawn between the peak intensity and the time intervals. As a typical rule of size exclusion, the higher molecular weight curve is detected earlier followed by the successive less molecular weight curves. Similarly, in our case, MWL peak was shown earlier on GPC chromatogram having 10,660 molecular weight followed by DA lignin with 4718 molecular weight. Lastly the ODA lignin peak was detected as having the lowest molecular weight (4358). For the DFRC analysis, 20 mg of lignin was dissolved in 5 mL of (AcOH + AcBr (4:1)) solution and kept at 50 \({}^{\circ}\)C for 1 h with periodic shaking. Afterwards it was dried and the residue was mixed with 2 mL of dioxane/acetic acid/water (5:4:1, \(v/v/v\)) solution followed by zinc dust (50 mg) addition. After 1 h, an internal standard tetracosane together with 20 mL of each of CH\({}_{2}\)Cl\({}_{2}\) and saturated NH\({}_{4}\)Cl were added and the pH of this whole mixture was set to around 2 with HCl and the phases were separated precisely. The organic phase was dried again on rotary evaporator and then finally subjected to acetylation and were quantified by GC-MS (Agilent 7890B USA). For 2D-HSQC-NMR, the lignin samples were prepared using 20 mg of lignin and dissolving it in 0.75 mL DMSO-D6 and then the analysis data was acquired with a Bruker AVANCE 600 spectrometer (Bruker, Germany).
# A Sustainable Electrochemical-Based Solution for Removing Acetamiprid from Water
## 2 Materials and Methods
### Electrolytic System and Analytical Procedures  
Two anodes were used in the present study and were provided by De Nora (Brazil, TiO\({}_{2}\)-RuO\({}_{2}\)-IrO\({}_{2}\)) and the Electrochemistry and Environmental Engineering laboratory at the Universidad de Castilla-La Mancha (Spain, BDD). A titanium plate was used as the cathode. Both electrodes had a geometrical area of about 18 cm\({}^{2}\). The ACT electrochemical oxidation experiments (500 mL of solution), under magnetic stirring conditions, were carried out in a single-sharing electrochemical cell, applying different \(j\) (30, 60, 90, and 120 mA cm\({}^{-2}\)) with a power supply (model MLP-3303, 3A/5V, Minipa) in a supporting electrolyte of 0.5 mol L\({}^{-1}\) Na\({}_{2}\)SO\({}_{4}\). During the electrolysis, samples of 10 mL were collected to perform a complete wavelength scan from 200 to 800 nm (Varian Cary 50 Conc UV-Visible Spectrophotometer) and COD analysis. The detection wavelength of ACT was 245 nm, which is the peak value reported in the literature by [PERSON] et al. (2020) when working with the degradation of standard ACT. COD was performed using a set of HANNA kits' reagent bottles following the Environmental Protection Agency (EPA) method for determining COD in the medium range (0-1500 ppm, Ref. HI93754B-25) [20]. Three measurements were taken, and the average value with a standard deviation of <3% was recorded. pH conditions were monitored using a pH meter (TECNAL, model PG2000).  
Additionally, water quality was assessed according to the international standard DIN EN ISO 7887 (2012-04) as reported elsewhere, where the examination and determination of color is conducted. The solution coloration was analyzed by estimating the parameter DFZ (Deutsche Farb Zah) (m\({}^{-1}\)) from the solution. The absorbance at characteristic wavelengths (\(\lambda\) = 436 nm, \(\lambda\) = 525 nm, and \(\lambda\) = 620 nm) was taken from the UV-vis spectrum between 200 and 800 nm. DFZ estimation allows complex case evaluation of effluent coloration by considering these characteristic wavelengths, and then DFZy was calculated according to Equation (1) following the method DIN EN 7884:2012 [21].  
\[\text{DFZ}_{\text{y}}=100\times\left(\text{E}_{\text{y}}/\text{d}\right) \tag{1}\]  
where E\({}_{\text{y}}\) is the absorbance at a y wavelength, and d is the cell path length in cm.
# A Novel Nonlinear Magnetic Equivalent Circuit Model for Magnetic Flux Leakage System
## 3 Designing MEC Model for MFL System
### Calculation of the MEC Model Permeances
#### 3.1.3 Calculation of the Winding Leakage Flux Permeance  
In this study, the calculation method used in ref. [24] is selected to determine the permeance of the winding leakage flux \(\Phi_{cal}\). This flux consists of two parts: interior and exterior. The reluctance of the external part of the winding leakage flux can be calculated by Equation (22).  
\[R_{eale}=\frac{d_{w}+\sqrt{w_{e}{}^{2}+2w_{w}w_{e}}}{\mu_{0}w_{e}l} \tag{22}\]  
where \(l=l_{c}+2w_{e}\).  
The permeance for the inner part of the winding leakage flux (\(P_{eali}\)) is shown in Equation (23).  
\[P_{eali}=\frac{\mu_{0}l}{256{w_{w}}^{2}{d_{w}}^{2}}\bigg{[}{k_{2}}^{4}+\sqrt{ 2}{k_{1}}{k_{2}}^{3}+\frac{1}{4}{k_{1}}^{2}{k_{2}}^{2}-\frac{\sqrt{2}}{8}{k_{1 }}^{3}{k_{2}}+\frac{{k_{1}}^{4}}{16}\text{ln}\bigg{(}1+\frac{2\sqrt{2}{k_{2}} }{{k_{1}}}\bigg{)}}\bigg{]} \tag{23}\]  
where  
\[\begin{array}{c}{k_{1}}=|w_{w}-2d_{w}|\\ {k_{2}}=\frac{1}{\sqrt{2}}\text{min}(w_{w},2d_{w})\end{array} \tag{24}\]  
Finally, the permeance of the winding leakage flux is calculated using Equation (25).  
\[P_{cal}=\frac{1}{R_{eale}}+P_{eali} \tag{25}\]
## 4 Performance Evaluation
### Tail Pulling Detection Performance
#### 4.4.1 Experiment Setup  
Fourteen participants participated in this test voluntarily. The instructions were presented automatically on a screen. Participants familiarized with the silicon tail before starting the test. Each gesture instruction was presented for 3 s, followed by a release instruction for 3 s as well. The instructions were:  
* Pull,
* Shake,
* Grasp.  
In total, each instruction was presented five times each, and feedback from the system was disabled.  
Figure 11: Data from one participant’s session showing average pressure used to detect a huge with the device.  
Figure 10: Sensor values for each applied pressure.
# Comparison of Land Cover Categorical Data Stored in OSM and Authoritative Topographic Data
## 4 Results  
### Local CCI Diversity  
The CCI\({}_{\text{L}}\) differs between regions considered, taking the highest values in Ostrowski and Miedzyrzecki (see Table 2), which are both located in Greater Poland. The counties are characterized by a similar urbanization level (53.7 and 52.3, respectively) and land cover structure (see Table 1). Very high statistical dispersion of CCI\({}_{\text{L}}\), as measured by the coefficient of variation, is observed in Shupski and Miedzyrzecki counties, where population density, afforestation and percentage of area covered by agricultural land are similar. However, both counties differ significantly in terms of area, with Shupski county being twice as large.  
The values of the local Compound Correspondence Index in Otwocki and Piaseczno counties are characterized by the highest value of IQR, as well as standard deviation, and thus indicate a high dispersion of local CCI values. Furthermore, mean CCI\({}_{\text{L}}\) takes a value greater than \(\sigma\), which indicates that for most of the hexagonal cells, the CCI\({}_{\text{L}}\) value is lower than the mean value, i.e., the data consistency is relatively high here. The standard deviation of 0.068 and 0.072 in Otwocki and Piaseczno counties is almost twice as high as the lowest value recorded in Sokolski county (0.039). Furthermore, the relatively high diversity of analyzed data is proven by variance, which takes the value of 0.0052, 0.0046 and 0.0039 in Piaseczno, Otwocki, and Sanocki counties, respectively.  
The \(\rm{CCI_{L}}\) values in all analyzed counties reveal clustering, as indicated by the spatial autocorrelation Global Moran's I statistics, with z-score ranges of 21.80 to 14.62 (\(p\)-values \(<\) 0.001) in Piaseczno and Slupski counties, respectively.  
A predominance of areas with low and very low differentiation between BDOT10k and OSM (\(\rm{CCI_{L}}\) first and second class) ranging from 83.5% to 85.3% was observed in Slupski and Miedzyrzecki counties. The relative lack of congruence, defined as semi-compliance, with the highest values of 13.1% to 13.3% characterizes Otwocki, Piaseczno and Ostrowski regions. A great diversity in the analyzed datasets (\(\rm{CCI_{L}}\) fourth and fifth class) is noted in Otwocki (9.3%) and Piaseczno (9%) counties. The remaining districts stand out with relatively small noncompliance, amounting in Ostrowski to 7.1%, in Sanocki to 6.6% and in Slupski to 6.3%, with the least in Sokolski (5.6%) and Miedzyrzecki (5.1%) (see Table 3). The level of compliance between the BDOT10k and OSM datasets is shown in Figure 3.
## 3 Experiments and Results
### Comparison with Previous Methods
The forward adaptive module is helpful for the Transformer model to extract the channel and spatial features of speech and further improves the SINRi, SDRi values of speech separation. The speaker enhancement module also improves the subjective evaluation score of separated speech. Similar separation effects employ less parameters than other models. Table 2 compares the PESQ scores of SuperFormer and methods before. The value is generally in the range of \(-0.5\) to \(4.5\). The higher the score, the better the speech quality. In Table 2, the PESQ score of SuperFormer is 3.43, which is 76% of Clean, is 17% higher than DANet-Kmeans, is 18% higher than DANet-Fixed, is 13% higher than ADANet-6-do and is 4% higher than Conv-TasNet. The results show that SuperFormer has a certain improvement in speech quality compared with methods before. However, there is still a large gap from Clean. In addition to SI-SDRi, the SDRi and PESQ, STOI and MOS-LQO indicators are also considered to evaluate speech quality. Table 3 shows the STOI and MOS-LQO scores of SuperFormer. STOI reflects the objective evaluation of human auditory perception on speech intelligibility. The STOI value is between 0 and 1. The larger the value, the higher the speech intelligibility and the more complete and clearer the speech. In order to evaluate the subjective hearing quality, we exploited the objective measurement technology to evaluate the subjective hearing quality, namely MOS-LQO technology. Table 3 shows that although the model proposed in this paper fails to surpass Conv-TasNet in MOS value (substituted by MOS-LQO), it still maintains good speech clarity and integrity. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**PESQ**} \\ \cline{2-7}  & **DANet-Kmeans** & **DANet-Fixed** & **ADANet-6-do** & **Conv-TasNet** & **SuperFormer** & **Clean** \\ \hline WSJ0-2 mix & 2.64 & 2.57 & 2.82 & 3.24 & 3.43 & 4.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PESQ scores on the WSJ0-2 mix dataset. In conclusion, it can be seen from the experimental results that the separation model proposed in this paper can generate excellent Si-SNRi and SDRi on WSJ0-2 mix dataset, and has good PESQ, STOI and MOS-LQO scores. The experiment also proves that the FA module proposed in this paper can effectively improve the channel and spatial adaptability of the model and thus improve the separation effect, and the SE module can learn the speaker's information according to the autocorrelation of the speaker's speech in the mixed speech for further separation enhancement processing. The proposed method can provide a solution to speech separation and speech recognition in multi-speaker scenarios, and has the advantages of better separation effect and less model parameters.
## 1 Introduction
Moreover, it was also reported that the overall far-near perception can be improved by switching between PALs and EDLs according to the desired position of the VSS, though the listener may experience significant discomfort when the loudspeakers are switched. In this study, we aimed to construct a VSS with high precision and less discomfort. As a prerequisite for a smoothly moving VSS in real applications, the VSS is expected to be perceived with high accuracy in each position, independent of the distance. The far-near perception is expected to be improved by combining WFS with PALs and WFS with EDLs; meanwhile, compensation for the difference between PALs and EDLs is needed for a smooth transition. Crossfade processing between PALs and EDLs can be considered as an approach, and its feasibility has been demonstrated in our previous preliminary study [25]. In this paper, we propose a VSS construction based on WFS using crossfade processing with EDLs and PALs. A linear array of PALs and a linear array of EDLs are employed as secondary loudspeakers in WFS. The crossfade processing has two purposes: to smooth the sound pressure transition and to mitigate the difference in frequency characteristics between PALs and EDLs. We carried out objective and subjective evaluation experiments to demonstrate the effectiveness of the proposed method.