esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Data Processing and Results
### Projection of Future Coordination Degree
#### 5.7.1 Linear Regression with Polynomial Features  
Figure 23 shows the results of the training and testing performance using \(R^{2}\) using our dataset \(O\). As can be seen, the high training performance does not translate to high testing performance at all. All but one region has a training \(R^{2}\) greater than 0.9. However, 12 regions have negative testing \(R^{2}\). Only seven regions score \(R^{2}\) greater than 0.8. This result means that one would have to be cautious when interpreting the projected coordination degrees, particularly when enacting development policies.  
Figure 23: LRPF training \(R^{2}\) and testing \(R^{2}\) for the coordination degrees using our dataset \(O\).  
In contrast, the training performance and testing performance for the coordination degrees using the reference dataset \(R\) are far worse as shown in Figure 24. Eight regions have very low training \(R^{2}\). Only six regions have training \(R^{2}\) higher than 0.9. Furthermore, only two regions have positive testing \(R^{2}\). Again, our findings demonstrate that using inappropriate predictors would render unreliable results.  
For completeness, we show the actual coordination degree and the predicted value for two regions, Beijing and Sichuan, using our dataset \(O\) in Figure 25. Beijing has reasonably good project performance, while Sichuan has negative \(R^{2}\) value. The insets are zoomed-in for the five-year projection.  
Figure 26 shows the actual coordination degree and the predicted value for two regions, Hainan and Sichuan, using the reference dataset \(R\). Hainan has the best project performance using dataset \(R\), while Sichuan has a negative \(R^{2}\) value. As can be seen, the regression errors are much larger than those using our dataset \(O\).  
Besides \(R^{2}\), the regression and projection performance can also be evaluated in terms of mean absolute error (MAE) and root mean squared error (RMSE). For completeness, we show the performance evaluation in terms of MAE and RMSE for both our dataset \(O\) and the reference dataset \(R\) in Figures 27-30.  
Figure 24: LRPF training \(R^{2}\) and testing \(R^{2}\) for the coordination degrees using the reference dataset \(R\).  
Figure 25: The actual data and the LRPF-predicted values using our dataset \(O\) for Beijing and Sichuan.  
Figure 28: LRPF training MAE and testing MAE for the coordination degrees using the reference dataset \(R\).  
Figure 26: The actual data and the LRPF-predicted values using the reference dataset \(R\) for Hainan and Sichuan.  
Figure 27: LRPF training MAE and testing MAE for the coordination degrees using our dataset \(O\).
###### Abstract  
In this study, semi-continuous anaerobic digestion of lignin-rich steam-exploded _Ludwigia grandiflora_ (Lignin = 25.22% \(\pm\) 4.6% total solids) was performed to understand better the effect of steam explosion on the substrate solubilisation and inhibitors formation during the process. Steam explosion pretreatment was performed at 180 \({}^{\circ}\)C for 30 min at a severity factor of 3.8 to enhance the biogas yield of the lignocellulosic biomass. The semi-continuous anaerobic digestion was performed in a continuously stirred tank reactor for 98 days at an initial hydraulic retention time of 30 days and an organic loading rate of 0.9 g-VS L\({}^{-1}\) day\({}^{-1}\). The performed steam explosion pretreatment caused biomass solubilisation, resulting in enhanced biogas production during the process. During the anaerobic digestion process, the average biogas yield was 265 mL g-VS\({}^{-1}\), and the pH throughout the operation was in the optimum range of 6.5-8.2. Due to fluctuations in the biogas yield, the hydraulic retention time and organic loading rate were changed on day 42 (50 days and 0.5 g-VS L\({}^{-1}\) day\({}^{-1}\)) and on day 49 (40 days and 0.7 g-VS L\({}^{-1}\) day\({}^{-1}\)), and 1 M of NaOH was added to the liquid fraction of the steam-exploded _L grandiflora_ during the latter part of the operation to maintain the stability in the reactor. Therefore, the steam explosion pretreatment helped in the degradation of _L grandiflora_ by breaking the lignocellulosic resource. In addition, changes in the operating conditions of the anaerobic digestion led to an increase in the biogas production towards the end of the process, leading to the stability in the CSTR.  
anaerobic digestion; steam explosion; severity factor; lignin accumulation; biogas production; methane yield +
Footnote †: journal: Applied Sciences  
1
## 1 Introduction
Whilst the former approach [17] approximates the FO differentiation with the use of a rational function derived based on the Pade approximation, the latter approach [18] approximates the FO derivatives linearly between time-steps. Both techniques, therefore, provide efficient ADE schemes with straightforward implementation within the FDTD algorithm. In the approach [19], FDTD is successfully applied to the more general Raicu medium. The proposed method solves [PERSON]'s equations directly in the time domain with the use of a Riemann-Liouville FO derivative. The obtained results prove the accuracy of this technique in the ultra-wideband frequency range. In [20], a general FDTD scheme, able to incorporate the model of the medium based on the general [PERSON] polynomial series approximation, is presented. Its applicability is further considered in several examples demonstrating its usefulness for understanding a variety of electromagnetic phenomena involving media with power-law frequency dispersion. The aim of this paper is to present the FDTD method for simulation of wave propagation in the media described by the TF constitutive relations [21]. The considered medium is analysed based on [PERSON]'s equations. In [22], an analysis of Poynting's vector and propagation velocity in such a medium is performed. The causality for a plane wave propagating in the considered medium is confirmed by verification of the Kramers-Kronig relations. In [23], the uniqueness of solutions and the reciprocity are proved. In both papers, the analysis is supported by the results of numerical simulations. These are performed using the frequency-domain simulation method, employing the Fourier and Hilbert transformations, or using the time-domain method, which is based on [PERSON]'s function computations. In this paper, we investigate an alternative simulation method in an attempt to replicate the existing results and evaluate the applicability of FDTD for the considered media with the TF constitutive relations. The novelty of the paper relies on formulation of FDTD for TF Maxwell's equations, and its evaluation in a code. Furthermore, the stability condition is derived for the proposed FDTD method based on a numerical-dispersion relation. The paper begins with a short introduction to the fractional-derivative definition and its approximation used throughout the paper. In Section 3, [PERSON]'s equations are derived based on the TF constitutive relations. In Section 4, the FDTD method is formulated for the media described by the TF constitutive relations, and its stability analysis is performed. Long derivations of dispersion relations and stability conditions are collected in Appendices A-C. In Section 5, the obtained numerical results are presented in reference to the existing frequency-domain method presented in the literature. In Section 6, an analysis of the computational complexity of the developed method is performed. Finally, the conclusions are drawn in Section 7.
## 2 Structural Design of Semi-Active Vibration Damping Seat Based on Eddy Currents  
### Eddy Current Damping Seat Model  
Electromagnetic eddy current dampers come in various forms. Based on their structural design, they can generally be classified into cylindrical, disc-shaped, and planar (plate) types [23]. In this paper, a vibration-damping seat model equipped with a plate-type electromagnetic eddy current damper is proposed. The main structure of the automobile seat for vibration damping includes a lower support plate, an upper support plate, an eddy current damping module, springs, and the like. The eddy current damping module therein includes copper plates, electromagnets, concentrating flux plates, and the like. The schematic diagram of the structure is shown in Figure 1.  
In the eddy current damping module, the electromagnet is used as the primary, which consists of eight pieces set to be fixed on both sides of two concentrating flux plates, arranged as an array of two rows at the top and bottom in the opposite magnetic field direction for forming a magnetic circuit to increase the magnetic induction strength. The air gap is set to 2 mm to reduce magnetic leakage. As the secondary in the eddy current damping module, three copper plates are arranged in the middle and on both sides of the  
Figure 1: The structure schematic diagram of the eddy current damping seat model.  
electromagnet group. The eddy current damping module arrangement with the magnetic field direction of the electromagnet array is shown in Figure 2.
## 2 Naturally Occurring Coumarins and Their Role in Plants
### Classification of Naturally Occurring Coumarins and Their Role in Plant Protection
#### 2.2.5 Pyranocoumarins  
Pyranocoumarins are rare secondary metabolites of plants that contain a pyran core condensed with coumarin. These substances are distributed widely among the plant families Umbelliferae and Rutaceae. Although pyranocoumarins in the plant are very poorly studied, some studies indicate their protective role against phytopathogenic organisms [109]. However, pyranocoumarin isolated from the Rutaceae tree (Staurantus perforatus), xanthyletin, has shown significant phytotoxic effect on seed germination and root growth of _Amarathus hypochondriacus_ (Amaranthaceae) [110]. Among the pyranocoumarins isolated from the roots of Ferulago campestris (Apiaceae), aegelinol and grandivittin have been shown to have cytotoxic properties [111]. Pyranocoumarin seselin isolated from _Clausen anisata_ (Rutaceae) leaves acts as an antifeedant against _Lucilia cuprina_ larvae [112].
## 3 Methodology
### Obtaining the Local ET,\(LE_{loc}\)
#### 3.1.1 Calculation of \(r_{a-loc}\)  
The measurement of the special \(r_{a-loc}\) was through a laboratory experiment. In the experiment, the observational object was bare soil with a saturated soil water content. Parameters for the calculation including the radiometric soil surface temperature (\(T_{s}\)), actual surface vapor pressure (\(e_{a}\)) and evaporation (\(LE\)), were measured with no wind. The formula used for deriving \(r_{a-loc}\) is written as:  
\[r_{a-loc}=\frac{\rho C_{p}\left(e_{s}-e_{a}\right)}{\gamma LE}-r_{s} \tag{6}\]  
where \(\gamma\) is the psychometric constant (Pa/K), \(r_{s}\) (s/m) is the surface water vapor resistance and equals zero when the soil is fully saturated, \(e_{s}\) (Pa) is the surface saturated vapor pressure, and \(LE\) (g) is the evaporation from the soil and measured by the weighing method.  
In the experiment, we set five different initial air conditions with different \(T_{\mathrm{a}}\) and \(RH\), and we calculated \(r_{a-loc}\) once per hour from 08:00 to 18:00 at the heights of 1 m and 0.5 m, respectively. Tables 1 and 2 show the calculation results of \(r_{a-loc}\) at the heights of 0.5 m and 1 m. In the tables, \(T_{\mathrm{a}}\) is the air temperature, \(RH\) is the air relative humidity, and av. is the average value of
## 2 Theory and Analysis
### The Classification and Identification Method
#### 2.2.3 Roadmap of Radiated Emission Spectrum Identification  
The classification results of the small data sample described above can be used to obtain the dissimilarity data of various test data under three kinds of component representations. The Euclidean distance and the modified Jaccard similarity coefficient are used to evaluate the dissimilarity of the spectrum between the standard equipment and the equipment to be identified. If the group of the spectrum to be verified is similar to the standard group, the conclusion can be drawn that the three dissimilarity coefficients \(S_{1}\), \(S_{2}\), and \(S_{3}\) are all clustered around 0 based on Equations (3), (10), and (15). Therefore, a series of the data of some standard equipment can be pre-tested. By calculating the \(S_{1}\), \(S_{2}\), and \(S_{3}\) values of these pieces of equipment of the same type, the degree of dispersion can be described. Here, it is assumed that a piece of equipment has been pre-tested for N times of the spectrum. The dissimilarity between all pre-test groups can be expressed by a matrix:  
\[\mathbf{S}_{l}=\begin{bmatrix}S_{l}^{11}&\cdots&S_{l}^{1n}&\cdots&S_{l}^{1N}\\ \vdots&\ddots&\vdots&&\vdots\\ S_{l}^{m1}&\cdots&S_{l}^{mn}&\cdots&S_{l}^{mN}\\ \vdots&&\vdots&\ddots&\vdots\\ S_{l}^{N1}&\cdots&S_{l}^{Nn}&\cdots&S_{l}^{NN}\end{bmatrix},(l=1,2,3) \tag{18}\]The largest element in the matrix \(\mathbf{S}_{l}\) can be used as the threshold \(S_{l}^{max}\) to confirm whether it is such equipment. When a new group of unclassified spectrum data is obtained again, the dissimilarity data of three kinds of components between the unknown equipment and all known equipment can be calculated according to the previously mentioned method. Since the \(S_{l}\) of similar equipment is clustered around 0, when a certain type of component datum is significantly higher than the threshold \(S_{l}^{max}\), the possibility that it is this type of equipment can be ruled out. By comparing and excluding the equipment's spectrum with those of different types of standard equipment, the type of equipment can be finally determined by referring to the three types of radiation emission components.  
In the next section, the effectiveness of this method for classification and identification based on radiation emission components is demonstrated in detail with two examples.
## 5 Experimental Setups and Procedures
### Setup I Experimental Procedure and Observations  
#### 5.2.1 Experiment Procedure  
The experimental procedure is performed by transferring a certain amount of data from the sending to the receiving nodes using the MPTCP protocol. The data are generated using the _ipref_ traffic generation tool, which is also used to measure the data transfer throughput. The downlink energy consumption is measured during data transfer using an energy meter, as depicted in Figure 2. Each experiment is repeated for around 20 to 30 samples to obtain accurate statistics of the average throughput and energy consumption. The experiment is performed with a varying number of receiving nodes.
# Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
## 2 Materials and Methods
### Eddy Covariance Flux Data  
The eddy covariance (EC) measurement system at Lompolojankka included a USA-1 three-dimensional sonic anemometer (METEK GmbH, Elmshorn, Germany) and a closed-path LI-7000 CO\({}_{2}\)/H\({}_{2}\)O infrared gas analyzer (LI-COR, Inc., Lincoln, NE, USA). At Silikaneva and Mycklemossen a USA-1 anemometer was used with an LI-7200 (LI-COR Inc., USA) gas analyzer. At Degero, a 1012R3 Solent (Gill Instruments, Lymington, UK) anemometer was used with an IRGA LI-6262 (LI-COR Inc., USA) gas analyzer and the Abisko-Stordalen site used a uSonic-3 Class A (METEK GmbH, Germany) anemometer with an LI-7200 gas analyzer.  
From the high frequency (10 Hz) EC data, 30-min CO\({}_{2}\) fluxes were derived taking into account the corrections following [35]. At the ICOS Sweden sites, the open source EddyPro(r) Eddy Covariance Processing Software, version 6.0 (LI-COR Biosciences, Lincoln, NE, USA), was used following the ICOS protocol for data processing [36; 37]. The processing procedures at Lompolojankka and Siikaneva have been presented in [PERSON] et al. [31] and [PERSON] et al. [10], respectively. More details on the data processing at Mycklemossen are given in the Supplementary Materials. The processed EC fluxes were partitioned into GPP and ER and gap-filled using standard methods with an exponential temperature response for respiration [38] and a saturating light response for GPP [39].  
Daily averages of the fluxes were calculated from the gap-filled 30-min CO\({}_{2}\) flux data acquired from the sites and the time series were smoothed using a spline function in TIMESAT (see Table S1 for the spline parameters). We applied the same spline parameters as used for the LST data (see Section 2.3) to reduce the high-frequency (daily) variations in the flux data that could not be captured by the remote-sensing data due to its coarser temporal resolution. The smoothing of the CO\({}_{2}\) flux data did not have a substantial effect on the seasonal shape of the fluxes or the annual NEE balance (see Section 3.3).  
Figure 1: Locations of the peatland EC flux measurement sites.
## 2 Materials and Methods
### OCT Data Acquisition and Image Processing
#### 2.3.1 Determination of the Sample Margins  
The margins of both joining partners were determined for every B-scan by simple thresholding since they are already clearly visible as bright lines in the raw data. After improving the contrast using the \"_enhance contrast_\" function with default parameters (leading to a mean greyscale of 138.7 \(\pm\) 3.5 and a standard deviation of 53.8 \(\pm\) 1.2 of the histogram), the image was converted to binary, where grayscale values between 230 and 255 were set to white (255) and all others were set to black (0). After removing the remaining noise using the \"_remove outliers_\" function with a block size of 10 pixels in the horizontal direction and one pixel in the vertical direction (chosen because the margins are horizontal as well), and one pixel standard deviation, the obtained binary image contained the margins of both the upper and the lower joining partner, and the data were transferred as CSV to MATLAB. In MATLAB, the centroid of the pixels of the upper surface was calculated for every B-scan. After removing outliers, a 2 nd-degree polynomial was fitted, since the C-scan is slightly curved due to a lack of image field correction and the sample might be bent. The value of the polynomial function was used as the starting point for measuring the weld seam within a B-scan.  
Figure 3: (**a**) Volumetric view (C-scan) of the unprocessed data; (**b**) _yz_-slice (B-scan) of the data; (**c**) processed image of the slice with the weld seam measurements; (**d**) volumetric view of the completely processed data. The view shows the margins of the joining partners, the joining zone in the middle and the adjacent weld seam. Base area: 0.7 \(\times\) 0.7 mm\({}^{2}\). Material: Ultramid B3S. Processing parameters: Rayleigh length = 0.3 mm, \(\lambda\) = 1940 nm, \(\alpha\) = 0.8 1/mm, \(v\) = 200 mm/s, \(E\) = 0.22 J/mm.
## 2 Materials and Methods
### Set-Up of Experiments
#### 2.4.2 Data Normalization and Splitting  
Data normalization followed balancing procedures. Image datasets underwent normalization utilizing mean and standard deviation values recommended explicitly by PyTorch documentation for VGG-16 implementations. Conversely, extracted feature datasets were normalized through min-max scaling, an optimal approach compatible with the majority of selected models, facilitating efficient model convergence and accurate representation of data points within the model's operational range.  
Data splitting represented another critical step. Employing a K-Fold cross-validation [54] approach with 10 folds maintained the stratification integrity of the dataset. For scikit-learn-compatible models, K-Fold cross-validation automatically partitioned data into training and testing subsets, thereby streamlining the validation process. PyTorch-based models required an initial division into separate training and testing sets, subsequently applying K-Fold cross-validation solely to the training subset. This method facilitated the establishment of balanced training and validation subsets while maintaining the integrity of the testing dataset, allowing for effective model performance assessment without data leakage.
## 2 Materials and Methods  
### Participants  
Appropriate informed consent was obtained for each subject enrolled in the study for participation, scientific use of the data and publication (Local Ethics Committee, Universita Azienda Ospedaliera di Padova, trial n\({}^{\circ}\) 46039, registration date 29 July 2019).  
Twenty male children with FXS phenotype (mean (\(\pm\)SD) age and BMI, respectively, of 10.00 (\(\pm\)3.74) years and 18.57 (\(\pm\)3.46) kg/m\({}^{2}\)) were evaluated in a routine clinical setting at the Department of Women's and Children's Health, University of Padua; thirteen of the FXS children carried a full mutation of the FMR1 gene (FXS-FM; age of 10.69 (\(\pm\)3.67) years, BMI of 18.82 (\(\pm\)4.09) kg/m\({}^{2}\)), five of them carried a methylation mosaicism (FXS-M; age of 9.00 (\(\pm\)3.35) years, BMI of 18.70 (\(\pm\)2.34) kg/m\({}^{2}\)), two carried a premutation (FXS-P) and were affected with a phenotype compatible with FXS. Sixteen controls with typical neurodevelopment (CS; mean (\(\pm\)SD) age of 10.22 (\(\pm\)3.19) years, BMI of 22.84 (\(\pm\)3.93) kg/m\({}^{2}\)) were evaluated at the BioMovLab (University of Padua, Padova, Italy).  
Subjects' numerosity was defined based on power analysis [14] carried out on a previously published dataset [7] considering the value of the envelope peak as a variable; a number of 11 subjects was found to be sufficient for our analysis, and our sample consisted of 18 FXS children and 16 controls.  
FXS subjects were enrolled according to the following inclusion criteria:  
* Molecularly documented full mutation of the FMR1 gene with expansions of more than 200 CGG repeats and methylation of the promoter and repeated sequence; possible size and/or methylation mosaicism.
* Ability to walk independently.
* Absence of documented orthopaedic comorbidities affecting the lower limbs within 12 months from the beginning of the study.
* Absence of documented neurological disorders. BMI- and age-matched subjects with typical neurodevelopment were enrolled as controls for the study according to the following inclusion criteria:
* Ability to walk independently.
* Absence of documented lower-limb injures within 12 months of the beginning of the study.
* Absence of documented neurological disorders.
# Biological Decontamination by Microplasma
## 2 Materials and Methods
### Microbiology Assay  
Three bacterial strains were used to determine the inhibitory capacity of the microplasma: _Escherichia coli_ (ATCC: 25922), _Pseudomonas aeruginosa_ (ATCC: 27853), and _Staphylococcus aureus_ (ATCC: 25923). The microbiological assays were conducted according to EUCAST protocols [37; 38; 39]. The 24 h samples were adjusted to \(10^{-5}\) dilution and spread with a sterile swab on 10 cm \(\mathcal{O}\) Petri dishes containing Nutrient Agar. Two replicates were used for each treatment and each bacterial strain, from which the average was calculated, and one for the untreated control. The average of these data were expressed as an arithmetic mean \(\pm\) standard deviation (SD). The volume capacity of the swab is of 200 \(\mu\)L, and we estimated a volume of \(\sim\)50 \(\mu\)L to be dispersed on the surface of the Petri dish. Two experimental designs were approached. The dishes were left to rest for 30 min; then, the microplasma device was applied for 5 min at \(-1.5\) and \(-1.7\) kV. The plates were then incubated for 24 h at 35 \({}^{\circ}\)C. Based on the best inhibitory efficiency, the next experimental design was employed. Here, using the same bacterial density, the strains were spread on the agar plates and left to grow for 24 h, after which the plasma was applied at \(-1.7\) kV, for 5 min. The plates were incubated for another 24 h post treatment and the results were quantified.  
The approximate \(D\) values in Joule, for \(\sim\)50 \(\mu\)L of bacterial suspension, were calculated as:  
\[D=\left(\frac{\text{Treatment time }t}{\log_{10}\text{Initial Concentration }-\log_{10}\text{Concentration after treatment time }t}\right)\ast\text{Discharge Power} \tag{4}\]