esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### GC/MS Profiling of Volatiles Emitted by Z1-04-02  
The ability to suppress fungal growth may be the result of antifungal metabolites and/or VOCs emitted by the Z1-04-02 strain, and therefore, solid-phase microextraction (SPME) was conducted to trap volatiles and tentatively identify them through gas chromatography-mass spectrometry (GC/MS). GC/MS profiling revealed four terpene compounds, namely 2,6-dimethyl-1,3,6-heptatriene (C\({}_{9}\)H\({}_{14}\)), (1R)-(-)-myrrtenal (C\({}_{10}\)H\({}_{14}\)O), 3,3-dimethyl-6-methylencyclohexene (C\({}_{9}\)H\({}_{14}\)), and 7-propylidene-bicyclo[4.1.0]heptane (C\({}_{10}\)H\({}_{16}\)), in volatiles of Z1-04-02. Details of each compound are presented in Table 1, and the major peaks of the mass spectra are presented in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**RT (min)** & **Compounds** & **\%Match** & **Formula** & **MW** & **Peak Area** \\ \hline
6.41 & 2,6-Dimethyl-1,3,6-heptatriene & 89 & C\({}_{9}\)H\({}_{14}\) & 122 & 76,249 \\
6.80 & (1R)-(-)-Myrrtenal & 87 & C\({}_{10}\)H\({}_{14}\)O & 150 & 44,937 \\
11.02 & 3,3-dimethyl-6-methylencyclohexene & 86 & C\({}_{9}\)H\({}_{14}\) & 122 & 36,671 \\
11.17 & 7-propylidene-bicyclo[4.1.0]heptane & 89 & C\({}_{10}\)H\({}_{16}\) & 136 & 23,318 \\ \hline \end{tabular}
\end{table}
Table 1: Volatile compounds produced by _Streptomyces_ sp. Z1-04-02, identified through GC/MS analysis.  
Figure 3: Effect of temperature on the antifungal ability of the cell-free culture filtrate (CF) incubated at 37 \({}^{\circ}\)C (**A**), 100 \({}^{\circ}\)C (**B**), and the percentage inhibition of the cell-free CF (**C**). Different letters indicate significant differences according to [PERSON]’s test (\(p<0.05\)).
## 2 Materials and Methods
### Integration of Dynamic Equations
\begin{array}{l}if\ \left(\lambda_{1}\right)_{n+1}\leq\ 0\rightarrow\left(k_{3} \right)_{n+1}=k_{cont};\ if\ \left(\lambda_{1}\right)_{n+1}\ >\ 0\ \rightarrow\ \left(k_{3}\right)_{n+1}=0,\ \left(\lambda_{1}\right)_{n+1}=0\\ if\ \left(\lambda_{2}\right)_{n+1}\leq\ 0\ \rightarrow\ \left(k_{4}\right)_{n+1}=k_{ cont};\ if\ \left(\lambda_{2}\right)_{n+1}>\ 0\ \rightarrow\ \left(k_{4}\right)_{n+1}=0,\ \left(\lambda_{2}\right)_{n+1}=0\\ \end{array}  \tag{22}\]  
According to Equation (22), it takes into account the possibility of contact and take-off by considering the impact between the catenary and the pantograph. Thus, for catenary 1, if the contact force is negative or equal to \(0\ \big{(}\left(\lambda_{1}\right)_{n+1}\ \leq\ 0\big{)}\), then there is contact, and the stiffness matrix is configured according to this condition for the next step. If the contact force is positive, no contact is considered, the contact force is 0, and the stiffness matrix is set for the next step with that condition. For the second catenary, it is the same procedure (\(\left(\lambda_{2}\right)_{n+1}\ \leq\ 0\)). It is evident that, when the pantograph does not circulate through the common transition area, some contact elements are deactivated, and the corresponding force will be annulled. In addition, when circulating through the common transition area, the two contact elements are activated, and take-offs and couplings can be detected in all cases. To initialize the integration algorithm, it is necessary to provide a value for the coordinates and their derivatives, and for the contact forces in the zero instant. It is assumed that, at the initial instant, the velocities and accelerations in the coordinates are zero:  
\[\dot{q}_{0}=\dot{q}_{-\frac{1}{2}}=\ddot{q}_{0}=0 \tag{23}\]  
Substituting these values into Equation (18) results in a linear system that can be resolved for \(q_{0}\) and \(\lambda_{0}\), as follows:  
\[\left( \begin{array}{cc}K_{o}&\varnothing_{0}^{t}\\ \varnothing_{0}&0\end{array} \right)\left( \begin{array}{c}q_{0}\\ \lambda_{0}\end{array} \right)=\left( \begin{array}{c}R_{0}\\ 0\end{array} \right) \tag{24}\]  
Equations (19)-(22), which are based on the method of explicit integration of central differences, make it possible to obtain the system variables directly by simply treating the non-linearities caused by take-offs and the effect on the overlapping, thus adapting the different variable elements of the dynamic equation at each integration step.
# A 40 Mb/s VLC System Reusing an Existing Large LED Panel in an Indoor Office Environment
## 2 Experimental Setup
### MultiCAP for the Downlink Visible Light Communication
The MultiCAP signal is generated by summing up (4), which is expressed by:  
\[s(t)=\sum_{n=1}^{N}s_{n}(t). \tag{5}\]  
In our demonstration system, the MultiCAP signal is generated in MATLAB and then uploaded to the AWG, which produces a corresponding waveform at a sampling frequency of \(f_{T}\) to modulate the LED panel's intensity. At the receiver, demodulation is carried out in the reverse order of the modulation. After the flicker removal and resampling operations, the signal \(y(t)\), which is captured by the OSC at a sample frequency of \(f_{R}\), turns to the signal \(s^{\prime}(t)\), which is ready for demodulation. The signal \(s^{\prime}(t)\) is first applied to \(N\) pairs of match filters with the pulse impulse response of \(g_{\text{In}}(-t)\) and \(g_{\text{Qn}}(-t)\), \(n=1,\cdots,N\), and then down-sampled to recover the transmitted I/Q symbols. Following QAM constellation demapping, \(N\) streams of symbols \(\{D_{n}^{\prime}\}\) are obtained and compared with the transmitted data \(\{D_{n}\}\) to calculate the symbol error rate (SER) or the bit error rate (BER). The selection of the QAM order \(k_{n}\) is realised using a simple bit-loading algorithm, which iteratively searches for the largest \(k_{n}\) that is capable of supporting a BER below the 7% forward error correction (FEC) limit of \(3.8\times 10^{-3}\). In our work, the determination of the bit-loading pattern was only carried out at the longest distance (2 m), which has the worst SNR profile and it can then support shorter distance with better BER performance. Note that the flicker removal is to mitigate the baseline wander that is caused by the current ripple of the LED driver and it will be detailed in the next section. Additionally, since the nominal frequency of \(f_{R}\) is not identical to \(f_{T}\), a resampling process is applied to match the underlying sampling rate with that of the reference transmitted data. Using the demodulation algorithms that are described above, the recovered data stream is compared with the transmitted data to determine the BER. Table 2 lists the key parameters adopted in this work.
## 4 Experiments and Results
### Experimental Results and Analysis
#### 4.5.3 Comparison of Visual Segmentation Results  
To visually illustrate the effectiveness of MLNet in DR lesion segmentation, this paper selects five representative DR images and corresponding ground truth and presents the visual results of five different DR lesion segmentation methods, including MLNet, as shown in Figure 5.  
According to Figure 5, a visual analysis of the segmentation results provides further insights. In the first row, for the case with a single type of lesion cluster, MLNet, Swin-T, and VTA accurately capture the lesion areas, but MLNet can segment EX-type subtle lesions more precisely. In the second row, for the case with irregular distributions of lesions in multiple sizes and types, MLNet can segment all sizes of lesions and accurately identify the lesion types compared to other methods. This capability sets MLNet apart from other methods in accurately representing the diverse characteristics of lesions. In the third row, for the case of lesion clusters with a rectangular-like shape and scattered lesions, all methods except HEDNet can capture the rough shape of the lesion cluster. However, MLNet achieves a more refined segmentation result, accurately delineates the EX-type lesion, and better captures other subtle lesions. In the fourth row, for the case with concentrated lesions of multiple sizes and types, MLNet outperforms other methods in accurately capturing lesions of all sizes and types. This highlights MLNet's robustness in handling complex lesion distributions and accurately delineating the diverse range of lesions. In the fifth row, for the case with sporadically distributed single-type lesions, only MLNet correctly segments MA-type lesions without any segmentation errors. This further demonstrates MLNet's ability to accurately capture specific lesion types even in challenging scenarios.  
In conclusion, based on the visual comparison of the segmentation results, MLNet demonstrates superior segmentation performance for various distributions of lesions, particularly for subtle lesions. This is attributed to the effective combination of the MSAB, the LPB, and the unique loss function designed in this paper. These components enable MLNet to capture the multi-scale lesion characteristics in retinal fundus images and exhibit a deep perception of subtle lesions, thereby enhancing its segmentation capability.  
Figure 5: Visualization of different methods’ segmentation results.
## 4 Conclusions  
This study proposes the production of PVA/_Lithospermum erythrorhizon_ extract nanofibers membranes (LENMs), and successfully optimizes the LE extraction process. The optimal extraction concentration of 53.8% and optimal amount of 1.07 mg/mL are presented when the extraction temperature is \(-\)10 \({}^{\circ}\)C. The test results of a cell viability assay show that 0.7 \(\upmu\)M SK significantly improves the viability of fibroblasts to 136.8%. PENM-9 is the optimal product, and the optimal electrospinning parameters were found to be an electric field of 1.5 kV/cm and a flow rate of 1.0 mL/h. Moreover, the average diameter of LENM-2 is smaller than that of PENM-9 by 13.9%. Based on the results of in vitro drug release measurement, LENM-2 also has the maximum drug release amount of 79%, lasting 48 h. Finally, the results of the cell viability assay show that LENM-1, LENM-2, and LENM-3 all significantly enhance the viability of fibroblasts, and the optimal viability of fibroblasts  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Shikonin**} & \multirow{2}{*}{**Total Released SK (48 h, \%)**} & \multirow{2}{*}{**Drug Release at 60 min (\%)**} \\ \cline{2-2} \cline{5-5}  & **EE \%** & & & \\ \hline LENM-1 & 92.5 \(\pm\) 9.5 & 0.15 \(\pm\) 0.02 & 71 \(\pm\) 7.9 & 36 \(\pm\) 3.1 \\ LENM-2 & 89.2 \(\pm\) 2.2 & 0.21 \(\pm\) 0.01 & 79 \(\pm\) 5.5 \({}^{*}\) & 47 \(\pm\) 3.4 \\ LENM-3 & 66.0 \(\pm\) 3.5 & 0.22 \(\pm\) 0.01 & 61 \(\pm\) 3.6 \({}^{*}\) & 43 \(\pm\) 3.1 \\ \hline \end{tabular} Note: Data shown as mean \(\pm\) SD. * \(p<0.05\) vs. LENMs group.  
\end{table}
Table 4: Summary of encapsulation efficiency (EE) and drug-loading content (DLC) of LENMs.  
Figure 5: The cell viability of LENMs. * \(p<0.05\) compared with the control group.  
reaches up to 157.5% in LENM-1. LENMs were considered and proven to have the potential to effectively accelerate wound healing and tissue regeneration for future clinical use.  
The authors would especially like to thank the Ministry of Science and Technology of Taiwan for financially supporting this research under Contract MOST 105-2221-E-166-006. [PERSON] and [PERSON] contributed equally to this work.  
In this study, the concepts and designs for the experiment, all required materials, as well as processing and assessment instruments were provided by [PERSON] and [PERSON]. Data were analyzed, and experimental results were examined by [PERSON] and [PERSON]. The experiment was conducted and the text was composed by [PERSON].  
The authors declare no conflict of interest.
# Path Planning Based on Artificial Potential Field with an Enhanced Virtual Hill Algorithm
## 3 Virtual Hill Concept and Open Path Indicator, _New e\({}_{b}\)_
### Virtual Hill Concept
\(\mathbf{P}_{co}\) is the position vector of the closest obstacle measured discretely by the range sensor. \(t_{i}\) represents a discrete sampling time of the range sensor. Assuming that \(\mathbf{Q}\) is continuously interpolated and differentiable, \(\Psi\) can be written as follows. \[\Psi(t)=\int_{\gamma(t)}d\Psi=\int_{t_{0}}^{t}\frac{d\Psi}{dt}dt=\int_{t_{0}}^ {t}\|\dot{\mathbf{Q}}\|dt\qquad\qquad\text{where}\quad\dot{\mathbf{Q}}=d \mathbf{Q}/dt \tag{8}\]  
\(\Psi(t)\) denotes a distance from \(\mathbf{Q}(t_{0})\) to \(\mathbf{Q}(t)\) along \(\gamma\). The extra force is the negative gradient of extra potential, and it is shown in Equation (9). \[\mathbf{F}_{ext}=-\
abla U_{ext}=k_{e1}\
abla\Psi-k_{e2}\
abla\Big{(}\rho^{2} \Big{)} \tag{9}\]  
This can also be expressed as Equation (10) [34]. \[\mathbf{F}_{ext}=k_{e1}\mathbf{e}_{t}-2k_{e2}\rho\mathbf{e}_{n} \tag{10}\]  
In Figure 2b, \(\mathbf{e}_{t}\) is defined as the unit tangent vector of \(\gamma\), and the derivative of \(\mathbf{Q}\) has the direction of \(\mathbf{e}_{t}\). \[\mathbf{e}_{t}=\frac{\dot{\mathbf{Q}}}{\|\dot{\mathbf{Q}}\|}=\mathbf{e}_{n} \times\mathbf{e}_{b} \tag{11}\]  
\(\mathbf{e}_{t}(t_{0})\) can be obtained by the cross product of the unit normal vector \(\mathbf{e}_{n}\) and unit binormal vector \(\mathbf{e}_{b}\). \[\mathbf{e}_{n}=\frac{\mathbf{P}-\mathbf{P}_{co}}{\|\mathbf{P}-\mathbf{P}_{co }\|}=\frac{\mathbf{P}-\mathbf{P}_{co}}{\rho} \tag{12}\]  
\[\mathbf{e}_{b}=\mathbf{e}_{t}\times\mathbf{e}_{n}=\mathbf{e}_{t}\times\frac{ \mathbf{P}-\mathbf{P}_{co}}{\rho} \tag{13}\]  
\(\mathbf{P}_{co}\) is the closest point from the robot to \(\gamma\), and \(\overline{\mathbf{P}\mathbf{P}_{co}}\) is always vertical to \(\gamma\). The initial direction of \(\gamma\) is determined by \(\mathbf{e}_{t}(t_{0})\). In order for the initial direction of \(\gamma\) to be directed toward goal, \(\mathbf{e}_{b}\) is defined as follows. \[\mathbf{e}_{b}=\frac{\left(\mathbf{P}_{goal}-\mathbf{P}_{to}\right)\times\left( \mathbf{P}(t_{0})-\mathbf{P}_{to}\right)}{\left\|\left(\mathbf{P}_{goal}- \mathbf{P}_{to}\right)\times\left(\mathbf{P}(t_{0})-\mathbf{P}_{to}\right) \right\|}\qquad\quad\text{ where }\quad\mathbf{P}_{to}=\mathbf{P}_{co}(t_{0}) \tag{14}\]  
\(\mathbf{P}(\mathbf{t}_{0})\) is the position vector of the robot when it is trapped in the LM. As shown in Figure 3, \(\mathbf{e}_{b}\) is set to \(\mathbf{K}\) or \(-\mathbf{K}\) at the time of \(t_{0}\) according to the relative positions of the robot, the closest obstacle, and the goal. At the moment the robot is trapped in the LM, \(\mathbf{e}_{t}(t_{0})\), which is the direction for the robot to move, is determined according to the sign of \(\mathbf{e}_{b}\).
## 3 Materials and Methods
### Data Processing Strategy  
To evaluate the accuracies of abovementioned nine models, IGS ZTD products from 372 to 404 globally distributed monitoring stations were used as references, by which accuracies of nine conventionally used ZTD models or products are evaluated, including saas+norm/GPT/GPT2/GPT2w/GPT3, UNB3, UNB3m, EGNOS and VMF3. In order to analyze the seasonal discrepancies, the data from four 7-day periods in 2019 were selected during spring, summer, autumn, and winter periods. The data time slots were DOY 80-86, DOY 172-178, DOY 266-272, and DOY 356-362 respectively. The corresponding day of year (DOY) and the number of available stations (num) are shown in the Table 4. Figure 1 shows the distribution of global IGS GPS stations. The sampling rate IGS-ZTD produces is 5 min, therefore the model values were computed at every 5 min. For VMF3, the cubic spline interpolation method was used to interpolate the 6 h products to 5 min. Since the Saastamoinen model requires meteorological parameters as inputs, the first step was to calculate the meteorological data by the norm/GPT/GPT2/GPT2w/GPT3 model, and then the ZTD values were calculated by the Saastamoinen model. The GPT model does not provide the water vapor, therefore the same value as that in the norm model was used. Regarding the IGS-ZTD as the \"true value\", and the residual ZTD (dZTD) of each model can be calculated as  
\[dZTD=ZTD_{IGS}^{t}-ZTD_{M}^{t} \tag{8}\]  
where \(ZTD_{IGS}^{t}\) and \(ZTD_{M}^{t}\) are the IGS-ZTD and model-calculated ZTD at the time epoch \(t\), respectively. Mean and Root Mean Square (RMS) values of the dZTD were used to measure the bias and stability of the ZTD models, respectively.  
\[\text{mean}=\frac{1}{N}\sum_{i=1}^{N}(dZTD_{i}) \tag{9}\]  
\[\text{RMS}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(dZTD_{i})^{2}} \tag{10}\]  
where \(N\) is the total number of observations.  
As derived in Section 2, the biases on SPP caused by the dZTD were further calculated by substituting dZTD into Equation (6). The GPS satellite constellation was used, and the elevation cut-off angle was 7 degrees, which is consistent with IGS-ZTD. The GMF [41] and IGS final satellite orbits were used for the calculation.
## 2 Materials and Methods
### Fatty Acid Analysis  
Fatty acids in the loin and \"cachaco\" samples were analyzed in the Carcass and Meat Quality Laboratory of ESA-IPB. The total lipids were extracted from 25 g of meat sample according to the Folch procedure [25]. The fatty acid profile was determined using 50 mg of fat. The fatty acids were transesterified according to the method described by [PERSON] et al. [26]; after adding 4 mL of a sodium methoxide solution and vortexing it for five minutes at a time for 15 min at room temperature, 5 mL of H\({}_{2}\)SO\({}_{4}\) solution (in methanol at 50%) was added. Then, we added 2 mL of distilled water and vortexed once more. The organic phase (with the methyl esters of fatty acids) was extracted with 2.35 mL of hexane. The fatty acid methyl esters separation and quantification were performed using a gas chromatograph (GC-Shimadzu 2010 Plus; Shimadzu corporation, Kyoto, Japan) equipped with a flame ionization detector and an automatic sample injector AOC-20i and using a Supelco SP TM-2560 fused silica capillary column (100 m length, 0.25 mm i.d., 0.2 \(\upmu\)m film thickness). The fatty acid contents were calculated using chromatogram peak areas and were expressed as g per 100 g of total fatty acid methyl esters. In addition, the percentage of saturated fatty acids (\(\Sigma\)SFA), monounsaturated fatty acids (\(\Sigma\)MUFA), polyunsaturated fatty acids (\(\Sigma\)PUFA), the ratio PUFA n-6/n-3, and \(\Sigma\)trans were calculated according to [PERSON] et al. [27]. To measure the lipid quality, the index of atherogenicity (IA) and the index of thrombogenicity (IT) were calculated according to [PERSON] and Southgate [28] formulas:  
\[\text{IA}=\frac{\text{C12}:0+4\times\text{ C14}:0+\text{C16}:0}{\sum\text{MUFA}+\text{$\Sigma$PUFA}}\]  
Figure 1: Flow chart of the fabrication of dry-cured loin and “cachaco” of the Bisaro pig (%RH: relative humidity).  
\[\mathrm{IT}=\frac{\mathrm{C14:0+C16:0+C18:0}}{0.5\times\sum\mathrm{MUFA+0.5\times \sum PUFA\ n-6+3\times\sum PUFA\ n-3+\frac{PUFA\ n-3}{PUFA\ n-6}}}\]
# The Effect of Adsorbed Volatile Organic Compounds on an Ultrathin Water Film Measurement
## 3 Results and Discussion
### The Effect of Adsorption of VOCs on SPR Reflectance
The reflectance behavior of the plasma-cleaned sample clearly showed this method of surface cleaning is not a proper method for SPRi, as it can complicate the interpretation of the SPR results. The SPRi of samples cleaned with SCP (sample 4) showed a 4% increase of reflectance in 110 h, as shown in Figure 5b. To estimate the thickness of the adsorbed layer, we assumed a range of refractive indices from 1.33 to 1.4 as the refractive index of adsorbed VOCS. This range of refractive indices represents a variety of organic hydrocarbons [27]. Hence, the reflectance of 4% showed the formation of a 0.85-1 nm adsorbed layer. As adsorption of VOCs is inevitable during the experiment involving ultrathin film (<1 nm) measurement, it is imperative to quantify any uncertainties associated with the adsorption of VOCs. Recently, we have shown the existence of a monolayer thin film between droplets during the dropwise condensation process on a gold-coated glass [5]. The results of our work can shed new light on the mechanism of dropwise condensation, which has been under debate for more than nine decades. Here, we focus on the effect of adsorbed VOCs on a monolayer thin film measurement. The SPRi of dropwise condensation took less than an hour. As a result, the net effect of adsorbed VOCs on the reflectance can be estimated from Figure 5a as 0.0025 (reflectivity unit) or 0.053-0.0625 nm (thickness depends on the index) during the one-hour experiment. It is worth noting that our experiment on the effect of adsorbed VOCs on the SPR reflectance was conducted at lower relative humidity and temperature, as compared to the environmental conditions of the dropwise condensation experiment. It has been shown that the adsorption of VOCs reduces with increasing relative humidity and temperature. Therefore, the net effect of adsorbed VOCs on SPR reflectance during the dropwise condensation experiment should be smaller than 0.0025 (reflectivity unit) or 0.0625 nm [28; 29; 30]. This value is only 30% of the reflectance measured from a monolayer film of water (i.e., the corresponding reflectance is 0.0082) during the dropwise condensation experiment. Quantification of adsorbed VOCs on the SPRi signal helps us to confidently claim that the monolayer film that we have experimentally observed represents an adsorbed water film. Therefore, the adsorbed film exists between droplets during the dropwise condensation. The next questions that needs to be answered by researchers are whether this thin film plays any role in the growth of nucleates and whether this film can affect the overall heat transfer rate. If the answers to these questions are positive, the future models of dropwise condensation need to be revised accordingly.