esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Performance Evaluation and Optimization Theory for Thermal Microscope Imaging Systems
## 4 Analysis
### Evaluation Model Simulation Analysis
#### 4.2.1 Simulation Analysis of MRTD in the IPD  
According to the physical limitations of the mechanism of an IPD, IPD is suitable for working at relatively low-temperature conditions. Assuming a background operating temperature of 200 K and a target emissivity difference of 0.01, the simulated MRTD (_f_) curves of IPD in different bands are shown in Figure 7. To further analyze the difference  
Figure 6: Relationship between system temperature resolution and \(D_{NA}\).  
in temperature resolution of MRTD in different bands, a simulation experiment of MRTD with background temperature \(T_{B}\) was completed. To further analyze the difference in temperature resolution of IPD in different bands, a simulation experiment of MRTD with background temperature \(T_{B}\) was completed (as shown in Figure 8).  
As shown in Figure 7, MRTD increases with increasing spatial frequency, which means that when the target to be measured has a certain temperature difference, the higher the spatial frequency, the lower the temperature resolution of the system, which means that the target to be measured is more difficult to be resolved.  
Figure 8: Relationship between background temperature \(T_{B}\) and MRTD (IPD).  
Figure 7: Relationship between spatial frequency \(f\) and MRTD (IPD).  
As shown in Figure 8, the MRTD increases with increasing background temperature, indicating that even under the condition of constant spatial frequency the increase of the background temperature will also increase the MRTD, resulting in a decrease in the temperature resolution of the system, meaning that for targets with a certain temperature difference, the higher the background temperature, the harder it is to be discriminated. From the above analysis of Figure 8, we find that to make the system have better performance, besides optimizing the system from the perspective of detector type, band selection, and optical design, the operating environment temperature of the system is also an important factor, and based on the newly established MRTD model, we can choose appropriate background temperature for different types of systems.  
Combined Figures 7 and 8 show that the expressions of MRTD about \(f\) and \(T_{B}\) are monotonically increasing functions. As the spatial frequency \(f\) and background temperature \(T_{B}\) increase, MRTD also tends to increase monotonically. The temperature resolution ability of the IPD under the medium-wave is better than the long-wave. For example, when the spatial frequency \(f=0.2 cyc\times mrad^{-1}\), the MRTD of the IPD in the long-wave and medium-wave is 711.6 \(mK\) and 320.4 \(mK\). When \(T_{B}=200\) K, the MRTD of the ideal photon detector in the long-wave and medium-wave is 740.8 \(mK\) and 333.6 \(mK\).
# Semi-Direct Point-Line Visual Inertial Odometry for MAVs
## 3 Proposed System Implementation
### Back-End Optimization
#### 3.3.2 Improved Marginalization
The Hessian matrix after the marginalization of \(\mathbf{L}_{p_{1}}\) is shown in Figure 8.  
\(\mathbf{L}_{p_{1}}\) will be marginalized after the marginalization of \(\mathbf{L}_{p_{1}}\), which requires calculating \(\mathbf{H}_{mp_{1}}^{\mathbf{L}_{p_{2}}T}\mathbf{H}_{lr_{p_{2}}}^{-1}\mathbf{ H}_{mp_{1}}^{\mathbf{L}_{p_{2}}}\), and the computational complexity is approximately \(\mathcal{O}_{p_{2}}=\mathcal{O}(3^{3}+3^{2}\times 36+3\times 36^{2})\). The remaining \(\mathbf{L}_{i_{1}}\) and \(\mathbf{L}_{i_{2}}\) were marginalized in the same way, with a computational complexity of \(\mathcal{O}_{l_{1}}=\mathcal{O}_{l_{2}}=\mathcal{O}(6^{3}+6^{2}\times 51+6 \times 51^{2})\). The Hessian matrix after the marginalization of uncorrelated states is shown in Figure 9. Then, correlated states that contain \(\mathbf{P}_{1}\) and \(\mathbf{b}_{1}\) in \(\mathbf{x}_{m}\) were marginalized, by calculating \(\mathbf{A}_{p}-\mathbf{A}_{mp}^{T}\mathbf{A}_{m}^{-1}\mathbf{A}_{mp}\), with a computational complexity of \(\mathcal{O}_{r}=\mathcal{O}(15^{3}+15^{2}\times 36+15\times 36^{2})\). Therefore, the total computational complexity will be \(\mathcal{O}=\mathcal{O}_{p_{1}}+\mathcal{O}_{p_{2}}+\mathcal{O}_{l_{1}}+ \mathcal{O}_{l_{2}}+\mathcal{O}_{r}\). Figure 8: The Hessian matrix after marginalization of \(\mathbf{L}_{p_{1}}\). Figure 9: The Hessian matrix after the marginalization of all landmarks. Through the above analysis, it is obvious that, compared with the basic marginalization method corresponding to Equation (25), the improved marginalization method greatly reduces the amount of computation.
## 3 Numerical Investigation
### The Effect of Initial Values of Structural Parameters  
The influence of the initial values of structural parameters on the convergence is investigated in this section. The structural responses are assumed to be contaminated by 10% noise. The time durations from 2 s to 20 s with 900 sample points are used for the identification. The initial values of the structural parameters are set to be 5 times, 10 times, and 15 times larger than the actual ones, respectively. The identification results are given in Table 3. It can be observed that the maximum identification error for the stiffness is only 1.08%, even though the initial guesses are 15 times larger than the actual ones. To further investigate the effect of the initial values on the iteration procedure, the comparison of the identified stiffness with different initial values is given in Figure 9. Similarly, only the identified \(k_{5}\) is plotted in Figure 9 as an example. Obviously, the identified values are stably and accurately converged to the actual ones for the aforementioned three cases. Similar results can be obtained for the remaining parameters.  
Figure 8: Identified ground acceleration using 900 sample points.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Structural Parameters**} & \multicolumn{2}{c}{**5 Times**} & \multicolumn{2}{c}{**10 Times**} & \multicolumn{2}{c}{**15 Times**} \\ \cline{2-7}  & **Identified** & **Error (\%)** & **Identified** & **Error (\%)** & **Identified** & **Error (\%)** \\ \hline \(k_{1}\) & \(4.758\times 10^{5}\) & 0.88 & \(4.751\times 10^{5}\) & 1.02 & \(4.748\times 10^{5}\) & 1.08 \\ \(k_{2}\) & \(5.954\times 10^{5}\) & 0.66 & \(5.948\times 10^{5}\) & 0.87 & \(5.944\times 10^{5}\) & 0.93 \\ \(k_{3}\) & \(5.956\times 10^{5}\) & 0.73 & \(5.950\times 10^{5}\) & 0.83 & \(5.946\times 10^{5}\) & 0.90 \\ \(k_{4}\) & \(5.957\times 10^{5}\) & 0.71 & \(5.951\times 10^{5}\) & 0.81 & \(5.947\times 10^{5}\) & 0.88 \\ \(k_{5}\) & \(5.964\times 10^{5}\) & 0.60 & \(5.960\times 10^{5}\) & 0.67 & \(5.954\times 10^{5}\) & 0.77 \\ \(\alpha\) & 0.638 & 6.33 & 0.642 & 7.00 & 0.648 & 8.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The identified parameters with different initial values.  
Figure 9: Comparison of the identified \(k_{5}\) with different initial values: (**a**) 5 times; (**b**) 10 times; (**c**) 15 times.  
Similarly, the unknown ground acceleration can be also identified. The comparison results for the case of 15 times initial values are plotted in Figure 10. It is clear that satisfactory identification results can be obtained.
## 2 3D Shape Measurement Methods
### Coordinate Transformation  
The measurement or evaluation results in this system are projected onto the object surface from a mapping projector. In this case, the projected image should be produced with deformation according to the 3D shape of the object because the position, orientation, and image size of the mapping projector  
Figure 2: Relation between the point on the reference plane and the phase of the projected grating: (**a**) Relation between the point on the reference plane and the phase of the projected grating using the measurement projector and (**b**) relation between the point on the reference plane and the projected grating phase using the mapping projector.  
and the camera are different. This study proposes a method to generate the mapping image using a coordinate transformation table in a short time. Figure 3 shows the change in the coordinates of the measured and projected images. The measurement image is obtained using the measurement projector, as shown in Figure 3a. The projection image projected from the mapping projector is produced using a coordinate transformation of the measurement image according to the projection destination shape, as shown in Figure 3b.  
Therefore, the phase value of the projector can be obtained from the measurement coordinates. The coordinates \((i_{p},j_{p})\) of the image for mapping can be determined from Equations (1) and (2) once the phase value of the projector is determined. The phase value of the mapping projector in the \(x\) direction after the coordinate transformation is denoted as \(\varPhi_{ip}\), while that in the \(y\) direction is denoted by \(\varPhi_{ip}\). The grating pitches in the \(x\) and \(y\) directions projected by the projector are _Pip_ and _Pjp_, respectively.  
\[i_{p}=\frac{P_{ip}}{2\pi}\varPhi_{ip} \tag{1}\]  
\[j_{p}=\frac{P_{jp}}{2\pi}\varPhi_{jp} \tag{2}\]  
The phase values of the projector are transformed into the coordinate values of the projected image based on Equations (1) and (2). A table of 3D and projected coordinates is then created. The mapping coordinates are obtained by the coordinate transformation of the \(z\) coordinate of the measurement value at each pixel. Subsequently, mapping the measurement and analysis results to the correct position on the object becomes possible. In this way, the coordinate values can be obtained at a high speed during 3 DM-PM. The high-speed projection mapping can be realized using this table. Figure 4 shows the relationship between the \(z\) coordinate and the projection coordinate \((i_{p},j_{p})\) in 1 px of the camera.  
Figure 4: Transformation table.  
Figure 3: Coordinate position change of the measurement and projection images: (**a**) Measurement image and (**b**) projection image.
## 4 Detailed Proposed Technology and Test Results
### Development Plan for Vision-Based AI Algorithms Enabling Maintenance and Continuous Management
#### 4.3.2 Minimizing the Development Period and Investment Cost of Algorithm Development Plan  
Deep learning techniques are broadly classified into three categories, based on their detection performance and resource utilization: image classification, object detection, and segmentation [17]. The accuracy of the analysis increases in the order of classification, object detection, and segmentation, but the processing resources required also increase with the increase in the amount of data to be processed. Additionally, as the accuracy increases, the cost and labeling period for the training data also increase, which are crucial issues in industrial sites where there are frequent changes in the inspection items. Extensive research is being conducted on auto-labeling to address this issue [18; 19; 20]. Although this demonstrates a certain level of performance that can be achieved in industrial sites, separate confirmation is required since even one or two mislabeled data points can significantly impact the algorithm's performance. In this study, classification was applied to the data format used in manual assembly processes for inspecting the automobile parts, considering cost and data acquisition time. Since the classification technique exhibits a lower accuracy than other techniques, it must be improved. To improve the inspection accuracy, we focused on implementing image pre-processing algorithms that can obtain high-quality images and improve the algorithm performance.  
Figure 8: Comparison of results between training from scratch and transfer learning using OK (20 images) and NG (20 images) training data with the resnet101 model.  
Figure 7: Inspection part unit cropping images for reuse of car assembly part types and learning images.
# Characterization of the Aroma of an Instant White Tea Dried by Freeze Drying
## 2 Results and Discussion
### GC-MS-O and OAV Analyses of Arona-Active Volatiles in the SDE Extract
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No** & **R1** & **Volatiles** & **Aroma Description 1  & **FD Factor 2 ** & **Threshold (ug/L) 3 ** & **OAV** & **Aroma Description 5 ** \\ \hline
1 & 1031 & 3-hexanol & roasted & 3.7 & 4 & NF6 ** & - & fruity, alcoholic \\
2 & 1072 & 2-hexanoate & floral, sweet & 4.7 & 16 & NF7  & - & fruity \\
3 & 1075 & \(\alpha\)-berinol & green & 4.7 & 64 & 450  & 94 & green, embryoreous \\
4 & 1442 & \(cis\)-linalo oxide & fruity & 3.0 & 4 & 320  & 28 & floral, woody \\
5 & 1460 & \(trans\)-linalo oxide & fruity, floral & 1.7 & 4 & 320  & 22 & floral \\
6 & 1535 & \(\mathrm{linalo}\) & floral & 5.0 & 64 & 6  & 21451 & citrus, floral, sweet \\
7 & 1599 & \(\mathrm{hortin}\) & green, woody & 2.3 & 4 & 110  & 637 ** & 637 ** & 65 & floral, none, woody \\
8 & 1655 & \(\mathrm{benzeneacetaldehyde}\) & sweet, honey & 4.7 & 16 & 4 ** & 9505 & sweet, floral, honey-like \\
9 & 1687 & \(\mathrm{safarnal}\) & green, woody & 3.7 & 16 & NF7 ** & - & woody \\
10 & 1735 & \(\alpha\)-berinol & green & 1.3 & 330 ** & 111 & floral \\
11 & 1832 & \(trans\)-\(\beta\)-danesaceno & sweet & 5.0 & 64 & 0.0013 ** & 4546692 & honey, sweet \\
12 & 1859 & \(\mathrm{safarnal}\) & sweet, floral & 2.3 & 4 & 40 ** & 1616 & green, floral \\
13 & 1876 & \(\mathrm{benzyl alcohol}\) & floral & 2.0 & 4 & 10000 ** & 18 & floral, rose-like \\
14 & 1909 & \(\mathrm{phenylethyl alcohol}\) & floral & 1.2 & 1 & 1000 ** & 19 & floral, rose-like \\
15 & 1930 & \(trans\)-\(\beta\)-ionone & woody & 1.4 & 1 & 7 ** & 29 & floral, woody \\
16 & 2350 & \(\mathrm{in}\) & \(\mathrm{in}\) & \(\mathrm{in}\) & \(\mathrm{in}\) & \(\mathrm{in}\) & \(\mathrm{in}\) & \(\mathrm{in}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Gas chromatography-mass spectrometry-olfactometry (GC-MS-O) and odor active value (OAV) analyses of aroma-active compounds in the SDE extract.
## 2 Feature Extraction Method for Axis Motion Trajectories
### Axis Trajectory Signal Feature Extraction
#### 2.3.2 Distance Data Sequence Calculation  
After selecting the feature points, a series of feature coordinates \(R\ _{t}\) and \(R\ _{t}=[r_{t}(1),r_{t}(2),\cdots,r_{t}(M)]\) are obtained. These feature coordinates form a shape in the two-dimensional plane that approximates the original axis trajectory pattern.  
Taking the coordinate \(r_{t}(1)=(x_{t}(1),y_{t}(1))\), the distance from this point to the origin \(O\) is calculated as follows:  
\[L(1)=\sqrt{x_{t}(1)^{2}+y_{t}(1)^{2}} \tag{5}\]  
where \(L\ (\ 1\ )\) represents the distance between the first coordinate point and the origin \(O\).  
Figure 4: Pattern region division.  
Figure 5: Feature extraction results.  
By traversing all the feature coordinates and performing the calculation in Equation (5), a distance data sequence \(L=[l(1),l(2),\cdots\ l(M)]\) is obtained, as shown in Figure 6.  
After the axis trajectory signal has been purified, denoised, preprocessed, and feature extracted, the original axis trajectory signal is transformed into a concise data sequence that characterizes the geometric features of the axis trajectory.  
The next step is to obtain measured data through a rotor test bench for method validation, as illustrated in Figure 7.
# A Circularly Polarized mmWave Dielectric-Resonator-Antenna Array for Off-Body Communications
## 2 Design Methodology
### Specific Absorption Rate (SAR) of the DRA Array  
The SAR represents the rate at which radio-frequency energy is absorbed by human-body tissue [26], which determines the absorbed energy per mass unit according to the following equation [29].  
\[\text{SAR}=\sigma\;\frac{\text{E}_{i}{}^{2}}{\rho}\;\;\text{W/kg}\]  
where \(\text{E}_{i}\) is the strength of the incident electric field, \(\rho\) is the tissue's mass density in kg/m\({}^{3}\) and \(\sigma\) is the tissue's conductivity in S/m. The specific absorption rate needs to be evaluated to ensure that it is lower than the recommended safety-threshold limit according to the Federal Communications Commission (FCC) and International Commission on Nonionizing Radiation Protection (ICNIRP) standards. The respective SAR thresholds are 1.6 W/kg for any 1 g of tissue and 2 W/kg for any 10 g of tissue, as defined by the FCC and ICNIRP  
Figure 12: Axial ratio of a DRA array placed in free space and next to a body phantom.  
Figure 13: Gain of a DRA array operating in free space and next to a body phantom.  
standards [28]. However, it is important to note that these SAR guidelines are defined for frequencies of up to 10 GHz and 6 GHz for the FCC and ICNIRP, respectively, and have not yet been released for higher frequencies. Since near-field exposure to mmWaves has only became a concern in recent years, these guidelines do not offer any dosimetric information or suggestions that could be applied to near-field exposure to mmWaves [26; 29; 30]. However, it is still important to calculate the SAR, since the antenna operates next to the human body. According to [29], at 28 GHz, the used space between the antenna and the human body can be maintained at 5 mm with the recommended input powers of 15 dBm, 18 dBm, or 20 dBm. Hence, the proposed array was simulated next to equivalent tissues of the skin, fat and muscle, with the characteristics listed in Table 3.  
Figures 15 and 16 illustrate the SAR when the proposed DRA array is placed next to the equivalent three-layer tissue of the human body, where it can be noted that the SAR is well below the required safety threshold for different input-power levels when 1-gram and 10-gram tissues are considered. Table 4 summarizes the maximum achieved SAR levels to confirm that the recommended safety levels were maintained according to the FCC and ICNIRP standards.
# Pollen-Food Allergy Syndrome: Allergens, Clinical Insights, Diagnostic and Therapeutic Challenges
## 7 PFAS Diagnosis
### Exploratory Approaches in Food Allergy Testing
#### 7.5.5 Microbiome Analysis  
Altered compositions of the oral and gut microbiota have been increasingly associated with the development and persistence of food allergies, influencing immune responses and allergic disease trajectories. The gut microbiome plays a pivotal role in immune system development and regulation, with microbial metabolites like short-chain fatty acids (e.g., butyrate) shown to promote regulatory T-cell (Treg) differentiation and tolerance [232; 233]. In food allergy, disruptions to microbial diversity--known as dysbiosis--have been linked to a skewed Th2 immune response, which promotes the production of IgE and the development of allergic sensitization [234].  
Specific microbiome profiles have been observed in food-allergic individuals compared to non-allergic controls. For example, reduced abundances of beneficial genera such as _Prevotella_ and _Bifidobacterium_, alongside an overrepresentation of potentially pro-inflammatory species like _Neisseria_, have been implicated in allergic conditions [235]. These findings highlight the potential for microbiome analysis to identify biomarkers of food allergy and possibly stratify patients based on disease severity or reaction thresholds. Moreover, variations in microbiota composition between high- and low-threshold allergic patients suggest that microbial profiles may influence the clinical manifestation of food allergies [236].  
Despite these promising insights, significant challenges remain in applying microbiome analysis as a diagnostic tool. The complexity and variability of the microbiome, influenced by factors such as diet, age, ethnicity, medication use (e.g., antibiotics), and environmental exposures, make standardization difficult [237]. These variables introduce noise into microbiome studies, complicating the interpretation of findings and reducing reproducibility across populations and settings [238]. Additionally, the interdependent mechanisms between microbiota, host immune responses, and external factors are not yet fully understood, further limiting the clinical utility of microbiome analysis.  
While microbiome analysis offers a fascinating avenue for understanding food allergies and exploring potential therapeutic targets, its role in diagnosis remains exploratory. Future advancements in technology, standardization protocols, and large-scale longitudinal studies will be crucial for unlocking its diagnostic and predictive potential. Until then, microbiome analysis is better suited as an adjunct to traditional diagnostic methods, providing complementary insights rather than serving as a stand-alone tool [217].
## 2 Materials and Methods  
We selected the study period to satisfy multiple criteria: (1) measurable emissions changes due to COVID-19 restrictions; (2) regulatory significance for ozone formation potential (i.e., includes days with ozone close to the current year ozone design values), (3) meteorologically non-anomalous conditions, (4) absence of confounding factors such as wildfires. Google mobility data [16] indicated that the largest effect on transportation occurred in late March and April, with a lessening but continued effect as the summer progressed. In addition, TROPOMI and OMI [17] satellite NO\({}_{2}\) column data indicated that NO\({}_{2}\) columns were lower in June and July than in previous years for the SoCAB, even after considering meteorology and non-COVID related trends [14; 18]. Note that [PERSON] et al. [19] performed a study that utilized surface NO\({}_{2}\) measurements during the COVID-19 shutdown to evaluate the appropriateness of using the satellite NO\({}_{2}\) column to infer NOx emissionst trends. They found the satellite NO\({}_{2}\) columns generally showed a muted response compared to surface measurement due to the background NO\({}_{2}\) contribution. However, for regions with the highest levels of surface NO\({}_{2}\), the satellite data can capture the magnitude of the NOx emissions reductions from March to June 2020. We analyzed the regional meteorology for the period May-August 2020, including the 850 mb temperature (T850), which is the most descriptive parameter for determining the ozone formation potential in the SoCAB. High T850 gives an indication of the strength of the temperature inversion that can trap pollutants near the surface as well as the presence of high temperatures and slow wind speeds, all of which lead to higher ozone formation. We found that May 2020 had much higher T850 than the previous five years, especially May 2019, which was wet and cold, and August 2020 had higher monthly T850 than the previous five years. In addition, August 2020 had an intense wildfire season throughout California [20]. For these reasons we restricted our modeling and analysis to June and July 2020. We also modeled June and July 2019 to perform the dynamic model evaluation.  
The modeling component of this study was accomplished with the WRF meteorological model and the CMAQ photochemical grid air quality model. The WRF and CMAQ 12-km and 4-km modeling domains are displayed in Figure 1a, along with the domain parameters. Figure 1b is a closer look at the SoCAB study region, with ozone monitors indicated and counties labeled. The model projection is Lambert Conic Conformal with latitude of origin = 37.0 N, Central Meridian 120.5 W, and Standard Parallels at 30.0 N and 60.0 N.