esa-sceva/satcom-chunk-collection huggingface dataset:
# Stomatal and Non-Stomatal Turbulent Deposition Flux of Ozone to a Managed Peatland
## 2 Materials and Methods
### Instrumentation  
The eddy covariance setup consisted of three main components. First, a CSAT3 (Campbell Scientific Logan, UT, USA) ultrasonic anemometer was employed to measure the 3D wind vector and the sonic temperature. It was mounted at a height of \(z=2.42\) m above ground level and oriented to the southwest. Second, a LI-7200 CO\({}_{2}\)/H\({}_{2}\)O enclosed-path gas analyzer (LI-COR, Inc., Lincoln, NE,USA) measured the CO\({}_{2}\) and the water vapor concentration. Its intake tube was 0.5 m long and had an inner diameter of 5.3 mm. The inlet was mounted 4 cm to the north and 22 cm above the center of the ultrasonic anemometer. Third, a CLD 88 O3 (ECO PHYSICS AG, Durnten, Switzerland) ozone analyzer was used to measure the mixing ratio of O\({}_{3}\) (\(\chi_{O_{3}}\)) with the gas phase chemiluminescence method after O\({}_{3}\) reacted with NO, which was offered in surplus. The instrument has a measurement range from 0.5 ppb to 5000 ppb and the lag time within the analyzer is below 1 second. The inlet of a 16 mm inner diameter Teflon tube was located 13 cm to the northeast of the center of the ultrasonic anemometer. The data acquisition frequency for all instruments described so far was 5 Hz. Whenever needed, ozone mixing ratios (\(\chi_{O_{3}}\), in units ppb) were converted to concentrations (\(c_{O_{3}}\), in units of \(\mu\)g m\({}^{-3}\) or nmol m\({}^{-3}\)) by using actual air temperature (\(T_{air}\)) and pressure data.  
Further, low-frequency measurements were made for air temperature (Tair) and relative air humidity (rH) with an HMP45A (Vaisala, Helsinki, Finnland) within a radiation shield at 2 m above ground. A four-channel CNR 4 radiometer (Kipp & Zonen B.V., Delft, Netherlands) was employed for the measurement of longwave and shortwave radiation (upwelling and downwelling, respectively), and a Young Typ 61302 V sensor measured the air pressure. These measurements were collected at 1 Hz and aggregated to 10-min averages. The photograph in Figure 1 (left) shows the instrumented field site.
# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 2 E-Nose System
### Principle of Odor Sensors  
At the heart of the e-nose technology lies the fundamental principle of odor sensors, mirroring the intricate mechanisms of the human olfactory system. In analogy to the human nose's olfactory receptors, the e-nose employs an electronic sensor array to detect even minute traces of diverse chemicals present in the ambient air [54]. This technological counterpart parallels the human olfactory system, where receptors in the nose identify volatile molecules, initiating the transmission of signals to the olfactory bulb. Here, the odor is transformed into a recognizable stimulus, ultimately interpreted by the brain [54].  
In the e-nose realm, the symbiotic relationship between hardware and software components becomes paramount. The software aspect serves as the system's \"brain\", while the hardware component, represented by an array of sensors, emulates the function of \"olfactory receptors\". This array engages with volatile compounds, generating distinct signal patterns that are subsequently subjected to processing and classification using digital signatures associated with specific chemicals, culminating in precise odor identification [54].  
Comparatively, while the human olfactory system relies on approximately 100 million sensors for odor perception, the e-nose achieves this feat through its sensor array. This divergence leads to the replacement of the olfactory bulb's recognition role with a computer equipped with advanced algorithms, enabling feature extraction and pattern recognition [54].  
A diverse range of sensor materials contribute to the e-nose's efficacy, encompassing options such as semiconducting metal oxides, MOX sensors (Honeywell, Charlotte, NC, USA), field-effect metal-oxide-semiconductor transistor sensors (MOSFET) (Metoree, Tokyo, Japan), mass-sensitive sensors (JLM Innovation, Tubingen, Germany), conductive organic polymers (CP), solid electrolyte sensors (SES) (SES AI, Woburn, MA, USA), and optic fiber sensors (Omega Engineering, Inc., Norwalk, CT, USA) [21]. Among these, MOX sensors, extensively employed in commercial applications, operate based on the alteration of oxide conductivity upon exposure to oxidant or reducing gases, triggering surface-based reduction and oxidation reactions [49].
## 2 Related Works  
### Listening Comprehension  
Listening comprehension has been studied extensively in people studying a second language. Often, the level of language proficiency is measured using internationally recognized standardized tests, such as TOEFL (Test of English as a Foreign Language) and IELTS (International English Language Testing System). As this type of test evaluates listening comprehension, research has been conducted to observe the effects of varying certain components of the test on performance. For instance, by means of TOEFL-like evaluations flat audio has been compared with audio that includes visual material related to the interlocutors. Findings suggest that video content favors student performance on these types of tests [25,28].  
Another study involved Korean high school students and university students who possess knowledge of a second language (English). The main differentiator between these two groups was their level of English. Each group had to perform comprehension tests, after which researchers compared the results for both groups. Flat audio lessons were compared with audiovisual lessons in both Korean and English. It was found that visual content does contribute to better performance for the English test in individuals with less proficiency in the language (in this case, high school participants) but that the presence of images does not affect comprehension in the native language. Among the qualitative observations, it was highlighted that the participants felt that the level of effort was higher in comparison to that of taking the flat audio lesson [27], despite facing less difficulty in the test associated with the audiovisual lesson.  
Following the idea that visuals favor the listening comprehension of foreign language learners, another study explored the relevance of visual content and its effect on listening comprehension. Three versions of the same lesson were compared--flat audio, audio with video containing related content and audio with video containing irrelevant content. Surprisingly, results showed that listening with video formats helped participants perform better on the comprehension test regardless of content, with no major differences detected between the two versions [33].  
In another piece of research, researchers address the optimization of listening comprehension supported by visual elements for foreign language learners. The objective was to determine the ideal order in which to present visuals. Three proposals were tested--flat audio playback, the presentation of images before audio playback and the presentation of images during audio playback. The study concluded that the best results in comprehension tests are achieved when showing visual elements before introducing audio [34].  
These effects seem to relate top-down processing strategies when using audiovisual and bottom-up strategies when the task is limited to audio [35]. In other words, the visual content gives background content instead of requiring users to identify keywords.
## 3 Results and Discussion
### Nanoparticles Reactivity Characterization  
PdNPs promises to be useful as catalysts in various reaction types as a Suzuki's reaction [63] or dye degradation [7]. This characteristic is important since it shows their reactivity in an environment that surrounds them. PdNPs works as an electron acceptor in the presence of H\({}_{2}\) in a catalytic reaction [64]. These reaction mechanisms could be involved in the toxicity mechanisms with different cell types. It has been explained that a Fenton-type reaction is involved in the toxicity mechanism of NPs. This catalytic reaction has also been used in organic pollutant degradation [65]. As a part of PdNPs-PVP-Q characterization, dye degradation was evaluated (Figure 6). The PdNPs-PVP-Q showed catalytic activity in the MO and 4N (Figure 6a,b, respectively) but not in MB. The decolorization changes were observed immediately after Na\({}_{2}\)BH\({}_{4}\) addition. The degradation of 4N has been reported by PdNPs synthesized using protein ferritin from _Pyrococcus furios_ as a stabilizer [66]. The degradation of MB at a low rate due to a large kinetic energy barrier has been reported [52]. Many factors influence MB degradation, such as pH and the presence of amine groups [67]. Additionally, the photolytic activity  
Figure 5: Fourier transformed infrared spectroscopy (FT-IR). The transmittance is shown for all components involved in functionalization Q, PVP, PolarPdNPs-PVP-Q.  
degradation of MB by PdNPs has been demonstrated [7]. Dye water contamination is an important pollution problem [68], and NPs represent an alternative to a solution to this. Different NPs used for wastewater have been used as an alternative for dye degradation [68; 69].  
Quercetin Liberation  
The capacity of nanoparticles as carriers has been widely reported [70; 71]. In this work, we studied the ability of NPs to release quercetin at different pH levels (Figure 7). PdNPs-Q and PdNPs-PVP-Q were employed (orange and gray bars in Figure 7, respectively). Both nanoparticles were effective in quercetin liberation, but PdNPs-PVP were the most efficient.  
Figure 6: PdNPs-PVP-Q Catalytic activity. (**a**). 4-nitrophenol, (**b**). methyl orange, and (**c**). methyl blue. The components involved in functionalization: quercetin, PVP, PdNPs-PVP-Q, dye.  
pH was shown to affect this liberation. No influence of time was observed. The quercetin concentration was the same at 6 and 12 h (Figure 8A,B). However, pH 7.5 allowed for the liberation of almost twice the concentration of quercetin than at pH 6.5 (Figure 8B). pH influence in molecule liberation from NPs has been shown before [11; 15]. Rhodamine B liberation of silica-alginate nanoparticles was more efficient at a pH of 7.5 than 2.5 [72]. Quercetin has been used as a load molecule for ZnONPs; however, in these nanoparticles, more efficient quercetin liberation was presented at a pH of 6.5 [62]. In contrast, [PERSON] et al. [73] showed that in silica, NPs loaded with folic acid and quercetin, a pH of 4.4 was more efficient for quercetin liberation.
# Evaluating the Effect of Intensity Standardisation
## 2 Materials and Methods
### Equipping FSL-SIENA with Intensity Standardisation  
We quantified atrophy using FSL-SIENA [13]. This tool measures brain edge displacement between scans acquired at baseline and follow-up visits as a surrogate measure of cerebral atrophy. Briefly, the processing pipeline consists of skull stripping, registration, tissue segmentation and brain edge displacement analysis. Among these steps, intensity variations have been found to affect registration [21] and segmentation [14]. Thus, in this work, we evaluate the effect of standardising intensities in FSL-SIENA in two separate experiments: when standardisation occurs before registration (Figure 1(a)) and after registration (Figure 1(b)). Given that FSL-BET (default skull stripping tool) may not perform well in Alzheimer's disease patients [22], we used ROBEX instead due to its improved effectiveness [23; 24].
# Acquisition and Processing of UAV Fault Data Based on Time Line Modeling Method
## 3 The Design of Simulation Environments and Simulation Tasks
### Simulation of Engine Faults  
(1) Simulation parameter configuration when simulating engine failure  
In the simulation environment, SIM_ENGINE_MUL was set to 0 to simulate the failure of the drone's engine. Since parameters such as wind force and wind speed have little effect on engine failure, the wind speed is always 0 during the experiment. Some parameters of the simulation environment when simulating engine failure are shown in Table 3.  
(2) The Simulation of Accelerometer Fail  
The running trajectory of the UAV under QGroundControl is shown in Figure 13. Engine failure occurred from point A to point B and from point C to point D. In the simulation environment when the drone's engine failed, the drone fell from the air to the ground. If the fault is recoverable, the UAV can be launched from the ground to perform the flight mission again. The UAV will quickly take off from the ground to continue the mission.  
In order to understand the impact of power loss on the state of the UAV, this article extracts some information for analysis. As shown in Figure 14a, when the TimeStamp was about 330,000 Ms, the power of the UAV was lost, and its GPS speed was 0. After the power was restored, the UAV took off quickly and continued to perform flight missions. From Figure 14b,c, when the TimeStamp was about 320,000 Ms, before the UAV's power was lost, its circular surface angle and acceleration were abnormal and then became 0. As shown in Figure 14d, when the TimeStamp was about 330,000 Ms, the drone's pitch and roll angles were abnormal and became 0 after landing. That is because the UAV fell to the ground from a high altitude in free fall after losing power, and its roll angle showed irregular changes. After landing, the UAV was still, so the angle did not change.  
Figure 13: The flight trajectory of the UAV in the state of engine power loss.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline SIM\_WIND\_T\_ALT & 60.00000 & Full wind height \\ FS\_EKF\_THRESH & 0/1 & Disable/enable fail-safe mechanism \\ SIM\_ENGINE\_MUL & 1/0 & Clear/simulate engine failure \\ SIM\_BATT\_VOLTAGE & 12.6 & Simulate ambient battery voltage \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter settings in the engine failure simulation.
## 3 Results
### Influence of T127 on the Photocycle Kinetics  
As the channel conductance was drastically reduced by the T127 replacement but the absorption spectrum of dark-state \(Cr\)ChR2 was hardly affected, we performed time-resolved UV/Vis spectroscopy (Figure 7). Excitation by a nanosecond laser pulse led to the depletion of the dark state of \(Cr\)ChR2, which was reflected by a loss of absorption at 470 nm (blue traces in Figure 7). The P\({}_{1}\)[500] state rose at times beyond the resolution of our experiment, but the decay was resolved at 530 nm (green traces). The rise of the blue-shifted P\({}_{2}\)[380] state with a deprotonated retinal Schiff base was recorded at 380 nm (red traces). Rise and decay of the succeeding P\({}_{3}\)[530] intermediate were observed at 530 nm and the latter correlated with channel closure in \(Cr\)ChR2 [34]. The kinetics of the desensitized state P\({}_{4}\)[480] were also observed at 530 nm.  
The P\({}_{2}\)[380] state was formed faster in the T127A variant (Figure 7, top panel) and decayed slower than in the WT (Figure 7, bottom panel), i.e., the Schiff base deprotonated at an earlier stage and was reprotonated later. The kinetics of P\({}_{2}\)[380] rise in T127A had a half-life of t\({}_{1/2}\) = 0.7 \(\upmu\)s, one order of magnitude faster than the rise of the P\({}_{2}\)[380] state in WT. The decay of this blue-shifted intermediate in T127A, with a t\({}_{1/2}\) = 9 ms, was more than four times slower than in WT. The decay kinetics of the P\({}_{2}\)[380] state were altered at the expense of the P\({}_{3}\)[530] intermediate, which rose later and was significantly reduced in transient amplitude. This observation tallied the reduced channel conductance based on the correlation of the lifetimes of the open state and the P\({}_{3}\)[530] state. The decay of the non-conductive P\({}_{4}\)[480] state was not influenced by the T127A exchange. The less invasive replacement of threonine by serine in the T127S variant left the photocycle kinetics of \(Cr\)ChR2 unchanged (Figure 7, middle panel). Thus, we inferred that the hydroxyl group at position 127 was essential to a WT-like photoreaction, with a high accumulation of the late red-shifted intermediate P\({}_{3}\)[530].  
Figure 6: Two-electrode voltage-clamp (TEVC) recordings of \(Cr\)ChR2-WT, T127A, and T127S variants expressed in _Xenopus_ oocytes. (**A**) Light-induced currents at \(-\)60 mV and \(+\)60 mV holding potential. The traces for the T127A variant (red) were scaled by a factor of 10 to compare to the WT (black). (**B**) Histogram of the current amplitudes and closing kinetics. Currents were normalized to the WT at \(-\)60 mV. Bars represent the mean and the s.e.m. (n = 14 for WT, n = 29 for T127A variant, and n = 18 for T127S variant). (**C**) Time-resolved currents from T127A in HEK293 cells. Raw data of an average of eight traces at \(-\)80 mV to \(+\)40 mV in steps of 40 mV. The red curve shows a single exponential fit of the current decay with a time constant of 10 ms.
# Design of Robust Sensing Matrix for UAV Images Encryption and Compression
## 3 Proposed LESM Sensing Matrix Design
### The Algorithm of Optimal CS System  
With the new structure of SRE described in Section 3.1 and the novel model for sensing matrix design detailed in Section 3.2, the systematic framework of compressed sensing is concluded in Algorithm 1. This new CS framework with the proposed sensing matrix LESM can provide better performance to the images with large SRE.  
abla}_{opt}\) and the robust sensing matrix \(\mathbf{\Phi}_{opt}\), the test on UAV images can be executed as the procedure shown at the bottom of Figure 1. Firstly, the images are taken by the UAV. Secondly, the images are encrypted and compressed by the designed sensing matrix \(\mathbf{\Phi}_{opt}\), and then the low dimension measurements are obtained. Thirdly, the incomprehensible measurements are transmitted with high confidentiality. At the decoding stage, the measurements are recovered successfully by adopting the above optimal dictionary \(\mathbf{\
abla}_{opt}\) and the robust sensing matrix \(\mathbf{\Phi}_{opt}\).  
**Stage 1**: Dictionary learning and SRE decreasing (**NSSRE** structure showed in Figure 1 top left):  
_Input_: The training sample sequence \(\mathbf{X}\in\Re^{N\times Q}\), the initial DCT dictionary [39]\(\mathbf{\
abla}_{0}\in\Re^{N\times L}\).  
**Initialization**: The parameter \(\Delta\mathbf{\hat{S}}^{[1]}\in\Re^{L\times Q}\) is initialized as zeros matrix, and set \(J=1\).  
**Start**:  
_Step 1_: Obtain the sparse representation pair \(\{\mathbf{\
abla}_{opt},\mathbf{A}^{[1]}\}\) from the training samples \(\mathbf{X}\) by adopting the KSVD algorithm [34].  
_Step 2_: Calculate the SRE \(\mathbf{E}=\mathbf{X}-\mathbf{\
abla}_{opt}\mathbf{A}^{[1]}\), the residual \(\mathbf{E}\) begins with: \(\mathbf{\hat{E}}^{[1]}=\mathbf{E}\).  
**Repeat**_Steps 3_-5 until \(J>C-1\):  
_Step 3_: Calculate \(\mathbf{A}^{[J+1]}\) by using the OMP algorithm [16] in the residual error \(\mathbf{E}^{[J]}\) and the optimal dictionary \(\mathbf{\
abla}_{opt}\).  
_Step 4_: Calculate the residual error \(\mathbf{\tilde{E}}^{[J+1]}=\mathbf{\tilde{E}}^{[J]}-\mathbf{\
abla}_{opt}\mathbf{A}^{[J+1]}\),  
_Step 5_: Calculate parameter \(\Delta\mathbf{\hat{S}}^{[J+1]}=\Delta\mathbf{\hat{S}}^{[J]}+\mathbf{A}^{[J+1]}\).  
**Output**: The optimal dictionary \(\mathbf{\
abla}_{opt}\), the LSEE \(\Delta\mathbf{E}=\mathbf{\hat{E}}^{[C]}\). The groups of sparse matrices \(\Delta\mathbf{S}=\Delta\mathbf{\hat{S}}^{[C]}\)  
**Stage 2**: Sensing matrix design (**LESM** algorithm shown in Figure 1, top right):  
_Input_: The initial sensing matrix \(\mathbf{\Phi}_{0}\in\Re^{M\times N}\), the optimal dictionary \(\mathbf{\
abla}_{opt}\in\Re^{N\times L}\), the LSEE \(\Delta\mathbf{E}\in\Re^{N\times Q}\).  
_Step 1_: Construct the optimal model presented in (18) to obtain the sensing matrix \(\mathbf{\Phi}_{opt}\) given as (30).  
**Output**: The optimal dictionary \(\mathbf{\
abla}_{opt}\), and the robust sensing matrix \(\mathbf{\Phi}_{opt}\).
## 3 Results
### Agreement of Tear Break-Up Time Measurements  
The E300 system provided significantly higher values for the NIBUT outcome in comparison with the first NIBUT measured with the VX120+ system for right (\(p\) = 0.003), left (\(p\) < 0.001), and both eyes (\(p\) < 0.001). In addition, significantly higher values were also found for NIBUT (E300) in comparison with the NIBUT50% (VX120+) for left (\(p\) = 0.026) and both eyes (\(p\) = 0.042), not being significant for right eyes (\(p\) = 0.52). The NIBUT values obtained with both devices are shown in Table 1.  
Figure 1: Display of the following measurements: (**a**) non-invasive tear break-up time (NIBUT) and (**b**) tear meniscus height (TMH) obtained with the Medmont E300; and (**c**) first NIBUT and 50%NIBUT and (**d**) TMH obtained with the Visionix VX120+.  
No significant correlations were found for any NIBUT outcome between E300 and VX120+ devices. Specifically, no relation was found between the NIBUT (E300) and the first NIBUT (VX120+) for right (Rho = 0.57; \(p\) = 0.11), left (Rho = 0.57; \(p\) = 0.11), and both eyes (Rho = 0.11; \(p\) = 0.42) or between NIBUT (E300) and NIBUT50% (VX120+) for right (Rho = 0.19; \(p\) = 0.32), left (Rho = 0.27; \(p\) = 0.14), and both eyes (Rho = 0.20; \(p\) = 0.12).  
Passing-Bablok regression analyses for the NIBUT measurements between E300 and VX120+ devices for right, left, and both eyes are shown in Figure 2. The regression coefficients (intercept and slope) and their confidence intervals are provided in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Eye** & **NIBUT E300** & **First NIBUT VX120+** & **NIBUT50\% VX120+** \\ \hline Right & 11.85 (4.50/15.00) & 3.10 (3.00/12.85) & 8.55 (8.03/13.35) \\ Left & 14.45 (6.48/15.00) & 3.70 (2.90/6.13) & 8.40 (7.23/8.93) \\ Both & 12.15 (5.28/15.00) & 3.40 (3.00/8.23) & 8.50 (7.70/9.30) \\ \hline \hline \end{tabular} Data are shown as median and interquartile range.  
\end{table}
Table 1: Non-invasive tear break-up time (NIBUT) obtained from Medmont E300 and Visionix VX120+ devices.  
Figure 2: Passing–Bablok regression analyses for the agreement of the non-invasive tear break-up time (NIBUT) obtained from Medmont E300 and Visionix VX120+ devices comparing: (**a**) first NIBUT VX120+ and NIBUT E300 for right eyes, (**b**) left eyes, and (**c**) both eyes; and (**d**) NIBUT50% VX120+ and NIBUT E300 for right eyes, (**e**) left eyes, and (**f**) both eyes.