esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Transcriptome Analysis of Tobacco Leaves Treated with Moderately High Temperature
_DXR_ genes in tobacco chloroplasts significantly increases the solanesol content in tobacco leaves [51]. Transient expression of potato _DXR_ genes in _Nicotiana benthamiana_ significantly increases the solanesol content [13]. In the present study, MHT treatment led to significant upregulation of the transcription level of _NtDXk_ in tobacco leaves, and significant downregulation of the transcription level of _NtDXS_ at 3, 6, 9, and 12 DAT (Figure 5). Notably, NtDXS has little effect on the rapid biosynthesis of solanesol in tobacco under MHT treatment. By contrast, NtDXR catalyzes the committed step of plastidial isoprenoid-precursor biosynthesis and plays a vital role in the rapid biosynthesis of solanesol in tobacco exposed to MHT. In the MEP pathway, GGPS catalyzes the condensation of three IPP molecules with one DMAPP molecule to form one GGPP molecule; SPS catalyzes the synthesis of SPP from IPP, DMAPP, geranyl diphosphate (GPP), FPP, and GPPP [6; 39]. Notably, SPP is the direct precursor of solanesol biosynthesis [2; 5; 6]. Transient expression of potato _GGPS_ genes significantly increased the solanesol content of _N. benthamiana_, and co-expression of potato _SPS_ genes with _DXS_, _DXR_, _IPI_, and _GGPS_ genes significantly increased the solanesol content of _N. benthamiana_[13]. Overexpression of the tomato _SPS_ gene in tobacco significantly increased the solanesol content in mature leaves [25]. In the present study, MHT treatment led to a significant upregulation of the transcription levels of _NUGGPS_ and _NtSPS_ in tobacco leaves at 3, 6, 9, and 12 DAT (Figure 5). Thus, as the key enzymes in the MEP pathway, NtGGPS and NtSPS play a vital role in the rapid biosynthesis of solanesol in tobacco under MHT treatment.
## 5 Energy Balance and Error Analysis  
### Energy Balance  
To complete the model verification step, it was necessary to determine the amount of energy involved during the PCM melting and solidification phases, for various loadings, in order to ensure energy conversation during these storage and retrieval cycles. This determination entailed integrating the heat flux (both experimental and numerical) with respect to time.  
These results indicate a very slight difference (less than 1%) between the energy calculated from the experimental flux measurements during the melting phases and that  
Figure 19: Evolution of the flux exchanged between the plate and the sample (partial melting).  
Figure 20: Evolution of the flux exchanged between the plate and the sample (partial solidification).  
obtained during the solidification phases, for each ramp (Table 6). This difference is probably due either to measurement errors during the tests or to the supercooling, which stochastically influences the energy storage/release, especially during the solidification phase. Several studies have reported that such supercooling appears unpredictably and depends on various factors. According to [PERSON] et al. [59], supercooling essentially depends on the purity of the PCM. Indeed, impurities in the material will act as primers to accelerate the crystallization phenomenon. In addition, other authors, including [PERSON] [60] and [PERSON] [61], have shown that all heating/cooling cycles imposed on the material during its lifetime (nature, repetitions) constitute the thermal history of the PCM, which in turn has a direct influence on the degree of supercooling. All these key factors act on the PCM supercooling and thus cause differences between the energy balances in melting and solidification for each thermal stress cycle.  
Let's also note a difference of roughly 2.4% when performing the energy balance from the heat fluxes calculated numerically by the hybrid binary-Gumbel model (Table 7). This difference is likely due to inaccuracies related to the optimization of the numerical model parameters. Indeed, errors in experimental measurements of heat flux or temperature lead to inaccuracies when characterizing the thermophysical parameters of the PCM. Therefore, an error analysis is performed in order to evaluate the impact of these measurement errors.
# Free-Hand Input and Interaction in Virtual Reality Using a Custom Force-Based Digital Thimble
## 4 Evaluation Protocol
### Experimental Devices  
An Oculus Touch Controller (Figure 2) was used as the baseline condition in the evaluations since it is the most common input device used in VR. The embedded sensors of the controller can detect users' hand position and orientation within a virtual space. The controller includes three physical buttons, one thumbstick, one thumbrest, and a trigger for interacting with virtual objects. In the evaluations, the controller is used to point at objects, using raycasting, and then the trigger is pulled to confirm selection. The Unity3D Oculus integration was used to translate the controller's horizontal and vertical movements into the cursor's movements along the \(x\) and \(y\) axes, respectively. Movements along the \(z\) axis (depth) were disregarded for simplicity.  
A commercial input device popular in the Asian market, the AOKID Creative Finger Mouse (Figure 2), was also included as it has the form factor closest to that of the Digital Thimble. Both devices are worn on the finger, allowing users to perform other tasks (e.g., typing on a keyboard) while wearing them. Users wear the device on either their index or middle finger, with the optical sensor facing the open side of the finger. Like conventional mice, the finger mouse has a left and a right button, which are pressed with the thumb. To select a target, users bend the finger to place the tip of the device on the surface, move the hand to reposition the cursor, and then press the left button to confirm selection. As with the controller, the movements of the mouse were mapped to the cursor's movements using the Unity3D mouse input API. It is important to note that while the use of traditional mice has been thoroughly investigated in VR, the finger mouse has not yet been evaluated in this context.
## 3 Results and Discussion
### Photo-Fenton
#### 3.1.4 Chelating Agents' Assessment  
Finding the best chelating agent is currently one of the focuses of the scientific community researching the photo-Fenton process at circumneutral pH. Recently, the interest in using NTA to keep iron in the solution has grown since it can form strong ligands with Fe\({}^{3+}\), remaining dissolved at pH 7 and has a lower cost than EDDS [11; 46]: the NTA (analytical compound) cost is 90% less than EDDS. Thus, the comparison between EDDS and NTA was also addressed.  
The 500 W MP lamp with the optimal reagents' dosages was selected to study them. The results showed a considerable difference in removal rates between EDDS and NTA. Using EDDS, the 80% removal of indigo carmine was achieved in 20 min, while, with NTA, 30 min were needed (Figure 4c). It means an increase in relative contact time from 0.4 minutes when EDDS was used to 0.6 minutes with NTA. Iron dissolved in both experiments remained constant until the target removal was achieved. It can be explained by the higher photoactivity of EDDS, which makes its decomposition faster, generating a higher amount of Fe\({}^{2+}\) available to react and generate **O**H [47].
## 3 Results and Discussion
### DPPH Radical Scavenging Activity
#### 3.3.2 The Role of TQ in BCSO Radical Scavenging Activity  
As can be seen in Figure 5a, during the accelerated storage test performed at 60 \({}^{\circ}\)C, the IC50 value was increased linearly with the heating time up to 18 days for both the \"open\" and \"closed\" samples. However, the growth rate for the \"open\" samples was clearly higher. In the final stage of the heating, after 18 days, the growth rate was decreased, maintaining the IC50 value at a constant level until the end of the test. Our studies indicate that the antioxidants contained in BCSO responsible for the observed antiradical properties were not resistant to elevated temperatures and were subject to degradation during oil heating. Additionally, the presence of oxygen enhanced this process.  
Even though, for the \"open\" samples, the trend of the changes in antiradical properties mostly reflects the loss of TQ in BSCO and a statistically significant relationship between the IC50 value and TQ amount was observed (\(r=-0.9729\)), the changes in IC50 do not completely correlated with the variations in the TQ content in the \"closed\" samples (\(r=0.0986\)). Namely, for the covered samples, IC50 increased gradually with storage time, while the alteration in TQ concentration was not statistically significant, beyond the first and last period (Figure 4b). Therefore, the obtained results indicating that TQ did not play a decisive role in the observed DPPH radicals scavenging of BCSO. It is probably due to the TQ low capacity to react as a hydrogen or electron donating antioxidant and, therefore, it exhibited low DPPH reactivity [42], although the free radical scavenging potential of TQ was clearly exhibited in [47]. Simultaneously, TQ is a significant hydroxyl radical and hydrogen peroxide scavenger, and may be converted by enzymatic reduction into thymohydroquinone, which exerts a highly radical-scavenging capacity [42; 48].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**IC50 [ug/mL]**} & \multirow{2}{*}{**Reference**} \\  & **This Study** & & **Literature** \\ \hline Trolox & 5.60 \(\pm\) 0.22 & 3.70 \(\pm\) 0.08 & [41] \\ Tocopherol & 2.21 \(\pm\) 0.12 & & \\  & & 125.65 \(\pm\) 0.76 & [42] \\ TQ & 72.31 \(\pm\) 0.26 & 121.56 \(\pm\) 0.66 & [44] \\  & & 211.0 \(\pm\) 0.0 & [43] \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{BCSO} & \multirow{2}{*}{4.13 \(\pm\) 0.36} & 2.30 \(\pm\) 0.02 & [5] \\  & & 0.46 \(\pm\) 0.00 & [43] \\  & & 4.02 \(\pm\) 0.04 & [18] \\ Methanolic extract of BCSO & 5.07 \(\pm\) 0.38 & 11.26 \(\pm\) 0.93 (seeds) & [44] \\ \hline \hline \end{tabular} The data in the table are presented as the means \(\pm\) standard deviation (SD).  
\end{table}
Table 2: The calculated IC50 for BCSO, and methanolic extract of BCSO, TQ, and control samples.
## 3 Measuring Process and Results  
### Measuring the Experimental Conditions  
The DOE used in the experiment is shown in Figure 7, the base equation was Equation (4), and the designated parameters are listed in Table 1. The diameter was 22 mm, with a structure of 10 stepped teeth. The average tooth height was 1.3912 \(\upmu\)m, the step angle was about 12.4\({}^{\circ}\), and the base profile curvature was up to 31\({}^{\circ}\). According to the geometric characteristics of the curve-based DOE to be measured, the confocal probe was selected with a numerical aperture of 0.99, the maximum object/degree was \(\pm\)42.5\({}^{\circ}\), the measurement range was 130 \(\upmu\)m, the axial resolution was 8 nm, and the accuracy was 35 nm, which can meet the scanning measurement of each step structure and complete base contour.  
\[\begin{array}{l}F(r)=\frac{\sigma^{2}}{1+\sqrt{1-(1+k)^{2}r^{2}}}+a_{4}r^{4} +a_{6}r^{6}+a_{8}r^{8}+a_{10}r^{10}\\ r^{2}=x^{2}+y^{2}\\ c=1/R_{0}\end{array} \tag{4}\]  
In the equation, \(c\) is the base curvature at the vertex, \(k\) is a conic constant, \(r\) is the radial coordinate measured perpendicularly from the optical axis, and \(a_{i}\) are the high-order aspheric terms.  
Before measuring the DOE, the accuracy of the confocal probe should be verified. Herein, a standard step of 10 \(\upmu\)m (nominal 9.976 \(\upmu\)m) was repeatedly measured, and the measurement results are shown in Figure 8. The average measurement accuracy of the probe shown was 34.2 nm, according to the results listed in Table 2, indicating that the probe had good measurement accuracy and satisfied the measurement requirements of the DOE.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tooth Number** & **Pitch (mm)** & **Tooth Height (\(\upmu\)m)** \\ \hline
1 & 1.290 & 1.390 \\
2 & 0.993 & 1.390 \\
3 & 0.847 & 1.389 \\
4 & 0.760 & 1.390 \\
5 & 0.704 & 1.391 \\
6 & 0.671 & 1.390 \\
7 & 0.656 & 1.390 \\
8 & 0.659 & 1.391 \\
9 & 0.690 & 1.393 \\
10 & 0.760 & 1.398 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The nominal tooth profile information of a DOE.  
Figure 7: Feature size of a DOE.
## 5 Discussion  
As mentioned in Section 3.2, in a real transmission line system, it is difficult to detect whether the UAV is located below or above the cables without additional detection mechanisms. This is only possible in Figure 14 because the SIL calculated with a static equivalent current and was only used in the context of the tests.  
In this paper, transmission lines were modeled as perfectly straight cables, which may not be the case in real-world circumstances. Transmission lines, for example, hang downward, thus forming more of a curved line. Even though the radius of this curve is very large, the influence on navigation accuracy should be investigated in a further work. In UAV indoor navigation applications, this problem does not occur because the cables are shorter and can be routed to fit the straight line model.  
Furthermore, the magnetic field contributions of the other two phases of the three-phase system were neglected, since they are typically at least 2 m away from the phase under consideration. This should also be investigated in more detail in a further study in the context of transmission line navigation.  
Finally, the magnetometers should be as far away as possible from ferromagnetic materials that may be in the UAV; otherwise, they may affect the sensor readings.
## 3 Methodology
### Visualization and Localization of TB Manifestations
#### 3.3.2 Grad-CAM  
Grad-CAM is a generalization of class activation maps (CAM). It shows a visual explanation for a decision of all existing DCNN models, whereas CAM itself can only be applied to networks that have a global average pooling layer. The idea behind Grad-CAM is to retrieve the gradient of the prediction probability streaming into the final convolutional layer of a DCNN, import values to each neuron for a decision of a particular task, and indicate the regions of the image that are most significant for classification. The regions with large gradient values are exactly the regions where the prediction score depends mostly on the image. Grad-CAM is applicable to image classification, image captioning, and visual question-answering models [26].  
Here, Grad-CAM was used to visualize the indicative regions of images for TB classification and localization of TB manifestations. First, we retrieved the prediction scores, \(S_{tb}\), for class \(tb\) and the convolutional feature map from the final DCNN layer. \((i,\,j)\) denotes the _ath_ activation feature map at a location \((i,\,j)\). Then, the gradient of the prediction score \(S_{tb}\) with respect to convolutional feature map \(f_{a}(i,\,j)\), i.e., \(\partial S_{tb}/\partial f_{a}(i,\,j)\) was computed. These gradients flowing back were averaged and pooled to obtain the neuron importance \(\beta_{a}^{tb}\).  
\[\beta_{a}^{tb}=\overbrace{\frac{1}{Z}\sum_{i}\sum_{j}}^{\text{ {\it global average pooling}}}\underbrace{\frac{\partial S_{tb}}{\partial f_{a}(i,\,j )}}_{\text{{\it gradients via backprop}}} \tag{3}\]  
where Z is a constant (the number of pixels in the feature map). The class-discriminative localization map \(Grad-CAM_{tb}\in\mathbb{R}^{i\times j}\) was computed via the weighted sum of all feature maps in the final convolutional layer for the expecting class, tb, after applying the nonlinearity function, ReLU--see (4). ReLU was applied to focus only on pixels that have positive weights for class tb and avoid negative weights from impacting class tb because pixels with negative weights are considered to belong to a non-TB class. ReLU is a widely used activation function in neural networks that avoids the vanishing gradient problem and is computationally faster compared to Sigmoid and Tanh, which require exponential calculations.  
\[Grad-CAM_{tb}(i,j)=RELU(\underbrace{\sum_{a}\beta_{a}^{tb}f_{a}(i,j)}_{\text{{ \it linear combination}}}) \tag{4}\]
## 5 Results and Discussion
### Flooding Day
#### 5.1.1 Travel Time  
To evaluate the differences between the flooded and normal simulations, several aspects of travel delays must be considered. A key consideration is the definition of a delay. The simplest explanation could be that a delay is registered for a vehicle if the simulation in a flooded situation takes longer than the normal situation. Nevertheless, it is sensible to establish a threshold that defines the circumstances under which a journey is delayed. The travel-delays threshold can be set as a constant value unit or as a proportionate value. Since Santarem is a small region with short durations and distances of most trips, the proportionate threshold was considered more adequate to be used. Therefore, delays of 10%, 20% and 50% of the original travel time under normal conditions were considered (see Table 4). The 50% increase in travel time was presented as a statistic that describes the system rather than a threshold of travel delays. The proportions of delayed vehicles depend on the selected threshold, but all have registered the highest proportion of delayed vehicles between 12:00 and 13:00--ranging from 20 to 40% (10% and 20% duration increase threshold). This threshold defines the average delay of the affected vehicles as a proportion of the normal situation travel time as well. Considering the delay threshold of 20% increase in travel time, 10% of the vehicles will experience a 50% increase in trip duration. Moreover, about 3% of the vehicles experience a delay of more than 50% of their original trip duration, seeing a 100% increase in travel time on average.  
The overall travel time difference between flooded and normal situations is 20%, calculated as the difference between the sums of all the trips in both conditions. It is worth noting that, while most vehicles face traffic delays, some travel faster than usual. Roads, immediately after a road closure point, have reduced traffic loads, and thus vehicles can move faster. Summation of all trips in a network would not be the optimal strategy because early journeys cannot compensate for individual trip delays. Indeed, some researchers argue that both traffic delays and time gains might be viewed as a loss of work time [40].
# Validation of Smartphone Sway Analysis for Fall Prevention
## 2 Materials and Methods
### Data Analysis  
Processing of the data involved the reading of the measured data into MATLAB R2019b (The MathWorks, Inc., Natick, MA, USA) and using a low pass filter (1 Hz) to eliminate noise. The average path length, total path length, average velocity, Pearson correlation coefficients between plate and application, and mean and standard deviations of the data were calculated.  
The incremental path length was calculated as the sum of each change in position \(D_{i}\)  
\[D_{i}=\sqrt{{x_{i}}^{2}+{y_{i}}^{2}} \tag{1}\]  
where \(i\) is the time of each \(x\) and \(y\) coordinate of the center of pressure acquired by the force plate and by the smartphone app. The total path length was calculated as the sum of \(D_{i}\).  
The instant velocity was calculated as  
\[V_{i}=|(D_{i}-D_{i-1})/(T_{i}-T_{i-1})| \tag{2}\]  
where \(T_{i}\) is the time of calculated position \(D_{i}\).  
Equations (1) and (2) were used to calculate velocities and path lengths in the medial-lateral (LM) and anterior-posterior (AP) directions as well as absolute path and velocity.  
The typical plots of the results are shown in Figure 2. While there are differences in the absolute values of the calculated COP positions versus time, the general patterns are fairly similar.  
Pearson correlation was used to estimate the validity of the smartphone results as compared to the traditional force plate approach. The Pearson correlation coefficient between each data set as measured by the force plate '\(f\)' and by the smartphone '\(s\)' were calculated by using the standard correlation equation (MATLAB R2019b (The MathWorks, Inc.)):  
\[r=\frac{\Sigma\left(f_{i}-\overline{f}\right)(s_{i}-\overline{s})}{\sqrt{ \Sigma\left(f_{i}-\overline{f}\right)^{2}}\cdot\Sigma(s_{i}-\overline{s})^{2}} \tag{3}\]  
where  
\(r\) = correlation coefficient;  
\(f_{i}\) = values of the force plate data;  
\(\overline{f}\) = mean of the values measured by the force plate;  
\(s_{i}\) = values of the smartphone data;  
\(\overline{s}\) = mean of the values measured by the smartphone;  
\(i\) = index of the measured data.
## 2 Results and Discussion
### Optimization Procedure
As KET and NKET present semi-polar to non-polar characteristics (2.91 < log P < 3.35), it would be expected that successive additions of NaCl or MeOH would probably hinder the microextraction process. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analyte** & **Chemical Structure** & **log P 1** & **pK\({}_{\text{a}}\) 1** & **RT (min)** & **Ions (_m/z_) 2** \\ \hline
**IS** & & - & - & 5.42 & 83, 168, 169 \\ \hline
**NKET** & & 2.91 & 7.48 & 6.73 & 166, 168, 195 \\ \hline
**KET** & & 3.35 & 7.45 & 6.97 & 180, 182, 209 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Chemical structures, octanol-water partition coefficients (log P) and acid dissociation constants (pK\({}_{\text{a}}\)), as well as retention times (RT) and ions (_m/z_) of KET and NKET obtained by LVI-GC-MS(SIM), under optimized instrumental conditions. Figure 1: Effect of polymeric sorbent selectivity (**a**), microliquid desorption (\(\mu\)LD) solvent (**b**) and \(\mu\)LD time (**c**), matrix pH (**d**), polarity (**e**) and ionic strength (**f**), as well as shaking speed (**g**) and microextraction time (**h**) on the enrichment of ketamine (KET) and norketamine (NKET) from aqueous media, obtained by high-throughput bar adsorptive microextraction (HT-BAuE)-\(\mu\)LD/large volume injection-gas chromatography-mass spectrometry operating in the selected ion monitoring mode (LVI-GC-MS(SIM)). The error bars represent the standard deviation for the recovery levels of three replicates for each parameter evaluated. The microextraction devices were designed to be used only one time, once they are inexpensive and in order to avoid carryover effects [17]. Finally, the effect of the shaking speed (Figure 1g) and microextraction equilibrium time (Figure 1h) for the extraction of KET and NKET in aqueous media water was also assayed. The data obtained shows that 1800 rpm is the optimum value to microextract both target analytes by using only 30 min of, with no significant improvements using higher rates. The method development resulted in the following optimized parameters: microextraction devices coated with Strata-X sorbent phase; extraction was performed for 30 min at 1800 rpm (pH 11.0); the \(\upmu\)LD step was performed through cavitation (42 +/- 2.5 kHz, 100 W) for 15 min using 100 \(\upmu\)L of the MeOH containing 1.0 mg L\({}^{-1}\) of IS.