esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### LANDSAT Images and Topographic Correction  
Three LANDSAT 8 Operational Land Imager (OLI) images were acquired from the USGS database. Their acquisition dates correspond to three distinct seasons in 2017: an image from 17 February for winter, 8 May for spring, and 25 June for summer. The year 2017 was chosen rather than the concurrent 2018-2019 survey dates because it was the only year that guaranteed cloud-free images in all survey seasons. These images are provided pre-processed in surface reflectance at ground level using the LEDAPS procedure [47] and orthorectified. The Ouled Hanneche forest is located in a mountainous region with steep relief; it was deemed necessary to perform a topographic correction of the images' reflectances. The objective of the topographic correction or illumination correction is to compensate for differences in solar illumination (irradiance) between locations with different slopes and exposures to obtain the values of luminance or reflectance that the sensor would have acquired if the observed surface was perfectly flat [48]. This radiometric normalization procedure is unavoidable when the images are acquired during winter when the low zenithal solar angle can cause serious shadow effects [49]. Among the different categories of topographic correction methods, those that present the best compromise between the complexity of implementation and the quality of the results obtained are the semi-empirical methods [50]. They are based first on the calculation of the cosine of the solar incidence angle representing the illumination conditions for each pixel and calculated from the acquisition of the solar and topographic angles (slope and exposure) estimated from an ALOS AW3D30 Digital Surface Model (DSM) [48]. In our study, we applied the semi-empirical SCS + C method [51]. It combines the modeling of the geometry of the \"sun-canopy-sensor\" (SCS) complex with a corrective factor specific to each spectral band (c-correction) relating the reflectance to the illumination conditions. In addition, to improve the correction, we chose to vary parameter C according to stratification by slope classes [52]. The topographic correction formula is as follows:  
\[\rho_{SCSC,A}=\rho_{\lambda}\frac{\cos\beta\cos\theta_{s}+c_{\lambda}}{\cos \gamma_{i}+c_{\lambda}}\]  
where \(\rho_{SCSC,A}\) is the ground reflectance for the \(\lambda\)-band corrected by the SCS + C method, \(\rho_{\lambda}\) is the uncorrected ground reflectance for the \(\lambda\) band, \(\beta\) is the slope computed from the DSM, \(\theta_{s}\) is the solar zenith angle at the date of image acquisition, and \(c_{\lambda}\) is the empirical coefficient of the \(\lambda\) band, calculated as the ratio of the intercept and slope of the illumination cosine regression (cos \(\gamma_{i}\)) by the ground reflectance of each band. The coefficient \(c_{\lambda}\) is determined individually for each regular \(5^{\circ}\) slope interval [52; 50].
## 3 Results and Discussion
Pearson correlation results for detection distance and mean humidity during the trials was negative and significant (r = -0.33, 2-tailed p \(<\) 0.0001) and was significant and positively related to both mean temperature (r = 0.35, 2-tailed p \(<\) 0.0001) and mean wind speed (r = 0.27, 2-tailed p = 0.0005). The greater the wind speed, the greater the detection distance. Given our understanding of the processes necessary for a dog to detect tortoise odor, we know that higher temperatures will cause more evaporation and higher winds will transport the odor farther. This analysis is complicated by the inter-relationships among T, RH and wind speed. As expected, when T increased wind speeds increased and RH dropped (Table 2). It is likely that there was insufficient variation in climatic conditions during this study (e.g. a day with high RH and high temperature) to fully separate the influence of these parameters. RH is more challenging to interpret; since we do not know exactly what the tortoise odor compounds are we cannot be sure how RH influences their behavior. The results of the detection distance analysis showed that dogs detected surface tortoises from distances \(\geq\) 60 m and \(\leq\) 1m. Detection distance was independent of size or sex of tortoise and whether or not the tortoise had epoxy, glue, or a transmitter. The dogs were able to learn tortoise scent through extraneous anthropogenic scent and located them from long distances regardless of the tortoise's level of human handling. This is an important factor because dogs would be expected to encounter tortoises with and without transmitters in the field during actual surveys. Based on our results there is no reason to expect detection distance bias in surveys using dogs from tortoises with or without hardware or prior human handling. Dogs can detect as well as differentiate tortoise odor from all other non-tortoise scent. In fact, the dogs demonstrated that they could differentiate live tortoises from scat and from tortoise urine, as the dogs never alerted on tortoise scat or urine during the trials. Few carcass were \begin{table}
\begin{tabular}{|l|l|l|l|} \hline  & Wind speed & temperature & humidity \\ \hline Wind speed & 1.0000000 & 0.4238679 & -0.4888721 \\ \hline Temperature & 0.4238679 & 1.0000000 & -0.9172985 \\ \hline Humidity & -0.4888721 & -0.9172985 & 1.0000000 \\ \hline \end{tabular}
\end{table} 
Table 2: Correlation coefficients for wind speed (m/s), temperature (C) and humidity. encountered during the study and dogs did not alert on the few encountered, nor did the dogs indicate falsely on other flora or fauna.
# Analysis and Prioritization of the Floating Photovoltaic System Potential for Reservoirs in Korea
## 4 Results
Therefore, the top 10 reservoirs with high economic efficiency were also analyzed for an installed 100-kW floating PV (476 units of PV panel with 0.21-kW capacity). For a floating PV installed in proportion to the area of the reservoir, the analysis shows that maximum power production corresponds to a PV installed in Yedang reservoir, located in Chungcheongnam-do. In this case, reservoirs suitable for floating PV installations are distributed nationwide. On the other hand, in terms of economic efficiency, reservoirs of Jeoltanam-do with high solar irradiance were found to be advantageous. Figure 10: Boundary data and satellite images of top 10 reservoirs with the highest power production: (**a**) Yedang; (**b**) Seomjin; (**c**) Najuho; (**d**) Topjeong; (**e**) Togyo; (**f**) Miho; (**g**) Gosam; (**h**)Bulgop; (**i**) Yidong; (**j**) Deokdong. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Installation area to reservoir area ratio (\%)** & 10 & 20 & 30 & 40 & 50 & 60 & 70 & 80 & 90 & 100 \\ \hline
**Number of reservoirs satisfying 100 kW** & 1134 & 1201 & 1218 & 1225 & 1220 & 1234 & 1236 & 1226 & 1226 & 1236 \\ Maximum capacity (MW) & 75.85 & 151.70 & 227.55 & 303.40 & 379.25 & 455.10 & 530.95 & 606.80 & 682.65 & 758.50 \\ Total capacity (MW) & 2102.89 & 4216.33 & 6326.63 & 8436.33 & 10,545.97 & 12,655.63 & 14,765.12 & 16,874.42 & 18,983.73 & 21,093.03 \\
**Total power production (GW/year)** & 2031.94 & 5878.65 & 8820.95 & 11,762.40 & 14,703.79 & 17,645.20 & 20,586.38 & 23,527.30 & 20,468.21 & 29,409.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of floating photovoltaic system installations according to the ratio of the installation area for all reservoirs satisfying the water depth requirements. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Number of** \\ **Reservions** \\ \end{tabular}  & \ \begin{tabular}{c} **Mean** \\ **Water** \\ **Depth (nm)** \\ \end{tabular}  &
\begin{tabular}{c} **Mean** \\ **Measure (km\({}^{2}\))** \\ **(km\({
# Comparative Study of Blue Light with Ultraviolet (UVC) Radiation on Betacoronavirus 1
## 1 Introduction
The 222 nm, however, can cause both genome and protein damage because structural proteins more readily absorb 222 nm in contrast to 254 nm [15]. In terms of the shorter wavelength (200 to 230 nm) range, 222 nm is proven to be more effective than 210 and 230 nm [15]. Visible blue light (400-470 nm), especially 405 nm, has shown to have germicidal properties. Unlike UVC, blue light is considered safe for human exposure [16] and has no degrative effect on material [17]. A past study [17] conducted a comparative analysis of the degrative effects of UVC and 405 nm on endoscope tube material. The UVC caused substantial photodegradation damage, whereas 405 nm showed no notable detrimental effect [17] on the endoscope tube material. Low blue light doses have shown inactivation for bacteria such as _Clostridium_ spp. and _Listeria_ spp. [18; 19], fungal species such as _Saccharomyces_ spp. and _Candida_ spp. [19], and a higher dose has shown inactivation for viruses such as _feline calovirus_ (FCV) 30 [20] and viral _ hemorrhagic septicemia virus_ (VHSV) [21]. The inactivation mechanism of blue light is by absorbing the light via photosensizers such as porphyrins and reacting with oxygen or other cell components [22; 23]. This reaction produces reactive oxygen species (ROS), which cause oxidative damage [22; 23]. The nonselective nature of ROS can cause direct damage to biomolecules, such as proteins, lipids, and nucleic acids, which are essential constituents of bacteria, fungi, and viruses [22; 23]. As the virus lacks porphyrins, the virucidal property of blue light is highly speculated. Most of the research conducted used porphyrins to inactivate the virus in media. In this study, the media had no porphyrins, and the test was conducted without the use of external photosensizers. A recent study also showed the inactivation of SARS-CoV-2 and influenza A H1N1 without using a photosensitizer [24]. Nevertheless, mechanisms by which blue light causes nonselective damage to the virus is still unknown. A future investigation is required to understand how blue light causes inactivation. The dose requirement varies with wavelengths and pathogens. The wavelengths below 240 nm can more readily generate ozone [25] and, thus, should be a deciding factor for dose determination. The dose of 222 nm used was 3 mJ/cm\({}^{2}\) for this study. Whereas for 254 and 270/280 nm, a slightly higher dose of 10 mJ/cm\({}^{2}\) was used. For 405 nm, a dose of 17,280 mJ/cm\({}^{2}\) was used. The dose chosen for 405 nm was considerably low, safe, and can be generated from commercially available products. The primary goal of this study was to investigate blue light's (405 nm) viral efficacy.
## 4 Theoretical Optimization of Converter Inductor
### Frequency and Ripple Optimization for One Inductor Value
#### 4.1.3 Losses in the Differential Mode Filter  
In Section 3 the modelling technique to derive the losses in the differential mode filter with and without \(L_{filter}\) was introduced. \(L_{filter}\) inductor values are chosen for the XGL6060 component series and capacitors are paralleled 10 uF ceramic capacitors. In order to account for capacitor derating with respect to the voltage rating, six 10 uF ceramic capacitors are used to implement a 20 uF \(C_{x}\) capacitor (at 20 V). Figure 17 below presents the evolution of the losses inside the DM filter with respect to the frequency for the operating point defined above and for main inductor values \(L\) = 47 uH. In this particular case, the value for the filter inductor \(L_{filter}\) is adapted with respect to the ripple current flowing across the main inductor \(L\). This is the reason why there are different slopes in Figure 17. As can be observed in Figure 17, the filter losses are also decreasing with respect to the frequency.  
Figure 16: Switching losses made by the four transistors depending on the switching frequency in a converter using two 47 uH main inductors.  
Figure 17: Losses caused by the two filtering inductors depending on the switching frequency in a converter which \(2\times 47\) uH main inductors.  
Increasing the frequency leads to reduced ripple current which in turn leads to reduced HF losses in the filter.
## 5 Conclusions  
To solve problems in traditional tire vulcanization technology, e.g., high energy consumption, low vulcanization efficiency, and poor manufacturing accuracy, this work proposed a direct-pressure vulcanization technology with the steel internal mold heated by electromagnetic induction heating. Through simulation and trial-production of a certain tire size, the following conclusions can be drawn.  
1. Based on the same process temperature, the new technology reduced vulcanization energy consumption by 86%.
2. The new technology breaks the interlocking of internal temperature and pressure. Through finite element simulation, the traditional process was improved and curing efficiency was increased by 22.5%, with the internal temperature of vulcanization raised from 185 to 205 \({}^{\circ}\)C.
3. The uniformity and balance of the tire was improved, with both RFV and couple unbalance mass reduced.
4. The temperature difference between the upper and lower sidewall of the raw tire was reduced from 11 to 3 \({}^{\circ}\)C, and the uniformity of degree of curing of sidewalls was clearly improved.
5. Physical properties of the rubber parts of the finished tire were improved, which was especially significant for the sidewalls.  
The current internal mold structure was suitable for tires with an expansion ratio smaller than 0.45. Further development is needed to expand the size series, which have a larger radial expansion ratio. Moreover, the influence of internal pressure generated from the internal mold on the vulcanization quality of tires remains to be extensively studied.  
Conceptualization, W.Y. and J.Z.; methodology, B.W. and Q.D.; software, X.L.; validation, X.L., J.Z. and J.T.; investigation, J.Z.; resources, H.Y; data curation, X.L.; writing--original draft preparation, J.Z.; writing--review and editing, J.Z. and L.C.; visualization, B.W.; supervision, X.L.; project administration, J.T. and H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research and Development Program of Shandong Province, China (2019 JZZY010450).  
This work was sponsored by National Engineering Laboratory for Tire Design and Manufacturing. Furthermore, the authors would like to acknowledge researchers of Greatoo Intelligent Equipment Inc. who assist us in the mold processing. In addition, we are grateful to [PERSON] and [PERSON] for the equipment debugging.  
The authors declare no conflict of interest.
###### Abstract  
Multi- system and multi-frequency are two key factors that determine the performance of precise point positioning. Both multi-frequency and multi-system lead to new biases, which are not solved systematically. This paper concentrates on mathematical models of biases, influences of these biases, and positioning performance analysis of different observation models. The biases comprise the inter-frequency clock bias in multi-frequency and the inter-system clock bias in multi-system. The former is the residual differential code biases (DCBs) from receiver clock and satellite clock and usually occurs at the third frequency, the latter is the deviation of the receiver clock errors in different systems. Unified mathematical models of the biases are presented by analyzing the general formula of observation equations. The influences of these biases are validated by experiments with corresponding observation models. Subsequently, the experiments, which are based on the data at five globally distributed stations in Multi-Global Navigation Satellite System (GNSS) Experiment (MGEX) on day of year 100, 2018, assess positioning performance of different observation models with combination of frequencies (dual-frequency or triple- frequency) and systems (BeiDou Navigation Satellite System (BDS) or Global Positioning System (GPS)). The results show that the performances of triple-frequency models are almost as the same level as the dual-frequency models. They provide scientific support for the triple-frequency ambiguity-fixed solution which has a better convergence characteristic than dual-frequency ambiguity-fixed solution. Furthermore, the biases are expressed as an unified form that gives an important and valuable reference for future research on multi-frequency and multi-system precise point positioning.  
inter-frequency clock bias; inter-system clock bias; multi-frequency; multi-system; precise point positioning +
Footnote †: journal: Sensors
## 3 Materials and Methods
### Remote Sensing ET Model (PT-JPL-Thermal)  
The PT-JPL-thermal model is described in [PERSON] et al. [42], which is a modified version of the PT-JPL model in [PERSON] et al. [41] using thermal remote sensing data. In this work, the PT-JPL-thermal model was spatially distributed over the study area for a 10-year period at a daily time-scale. Daily estimations provided by the model were further aggregated per month to be comparable to the other evaluated models' temporal-scale. The PT-JPL-thermal model retrieves actual evapotranspiration (\(ET\)) in mm/day by estimating actual canopy transpiration (\(ET_{c}\)) and soil evaporation (\(ET_{s}\)) as two layers (Equation (1)). The quantity of water intercepted by the canopy was not considered in the formulation of the model as seen in [PERSON] et al. [42].  
\[ET = ET_{c}+ET_{s} \tag{1}\]  
Potential crop evapotranspiration \(ETp\) is downscaled to actual \(ET\) by considering biophysical limitations from the canopy and the soil. \(ETp\) depends on net radiation (\(R_{n}\)) and soil heat flux (\(G\)). \(G\) was considered negligible at the daily scale, which was outlined in [PERSON] et al. [41] for monthly time steps (see [PERSON] et al. [50] for further expansion on \(G\)). \(R_{n}\) is the sum of longwave (\(R_{L}\)) and shortwave  
Figure 1: Map of the study area including (i) irrigated areas: mixed-irrigation areas (BXII District and left bank) and rice fields, (ii) natural ecosystems in the Dófana National Park: wetland, shrubland, and coniferous forest.  
radiation (\(R_{S}\)) where incoming longwave radiation (\(R_{L_{inst}}^{\downarrow}\)) depends on the air temperature at the time of the MODIS overpass (\(T_{air_{MODIS-time}}\)) and outgoing \(R_{L_{inst}}^{\uparrow}\) relies on surface emissivity (\(\varepsilon_{S}\)) and _LST_. _LST_, \(\varepsilon_{S}\) and \(T_{air_{MODIS-time}}\) were retrieved from daily Aqua MYD11A1 for each pixel, replacing the _LST_, \(\varepsilon_{S}\) and \(T_{air_{MODIS-time}}\) by Terra MOD11A1 values only in case of no data due to clouds or other factors. The \(\varepsilon_{S}\) was calculated as the average of the two thermal channels from the MODIS sensor.  
Shortwave radiation was calculated based on local shortwave radiation data and broadband surface albedo from MCD43B3. A conversion factor, \(J\), between instantaneous and daily radiation variables based on the diurnal sinusoidal course of shortwave and net radiation was used [51].  
This specific formulation using MODIS data was expanded from [PERSON] et al. [52] and [PERSON] et al. [53] and to our knowledge it has been formulated herein with these data sources for the first time. To simplify the comprehension of the model, all the equations and variables used to retrieve the _ET_ as the combination of canopy transpiration and soil evaporation are detailed in Table 1.
## 1 Introduction
Through these classic tools, signals received by the sensors could be seen as the co-production of the non-fluorescent signals and fluorescent signals, thus constructing a training set of non-fluorescent spectra and extracting features. Using these features to represent non-fluorescent signals, thus escaping complex expressions of atmospheric situations and transmissions, data-driven algorithms seek to compromise between both physical models and simple experimental methods. The PCA retrieval algorithm considers whole radiance signals as the sum of non-fluorescence and fluorescence and then uses principal components (PCs) [34] to represent complex and hard-to-calculate variables. At the same time, faci \begin{table}
\begin{tabular}{c c c} \hline \hline
**Applications** & **Variables** & **References** \\ \hline \multirow{5}{*}{Photosynthesis estimation} & Absorbed photosynthetic active radiation & [17; 18] \\  & Gross primary productivity & [15; 16] \\  & Light use efficiency & [14; 17; 19] \\  & Seasonal dynamics & [17; 20; 21] \\  & Vegetation type & [22; 23] \\  & Chlorophyll content & [24; 25] \\ \hline \multirow{5}{*}{Stress detection} & Water deficit or drought & [27] \\  & Nitrogen deficit & [26] \\ \cline{1-1}  & Heat & [28] \\ \hline \multirow{2}{*}{Creative applications} & Extreme accidents & [30; 31; 32] \\ \cline{1-1}  & Temperature of canopy & [29] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main applications of SIF in studies. the PCA algorithm could eliminate redundant information and obtain vital features. Thus, the advantage of this method is that it avoids the complex calculation of radiation transmission and improves efficiency, while the disadvantage is that the parameter setting would affect the inversion results. However, there currently exists a lack of in-depth discussion on the parameter selection: the effect of different principal component numbers and band regions on the accuracy of SIF retrieval. Thus, the main target of this research is to analyze the effect of the PC numbers and spectral band regions on the accuracy of SIF retrieval, especially in our study region. Then, PCA methods would benefit and provide a more reliable basis for PCA algorithm parameter settings in SIF retrieval.
# Patient-Tailored Dementia Diagnosis with CNN-Based Brain MRI Classification
## 2 Materials and Methods  
### Data Collection and Preliminary Analysis  
#### 2.1.1 Data Sources  
The data used in this study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database ([[https://adni.loni.usc.edu/](https://adni.loni.usc.edu/)]([https://adni.loni.usc.edu/](https://adni.loni.usc.edu/)), accessed on 15 May 2024) after completing the standard application and approval process [37], including the ADNI Data Use Agreement. All data were provided in de-identified form, per ADNI's ethical and data-sharing policies. Informed consent was obtained from all participants, and ethical approval was granted by the relevant institutional review boards at participating sites.  
While the processed dataset generated for this study cannot be made publicly available due to data use restrictions, researchers interested in accessing the original ADNI data can do so by registering through the official portal and following the established procedures.  
As detailed in Table 1, the data were collected from participants of different ages and genders in the form of anonymized, high-resolution T1-weighted 3D magnetization-prepared rapid gradient-echo (MPRAGE) brain scans obtained from 1.5T MRI units. The participants were assigned into three main research groups as follows, depending on their diagnosis:  
* AD--individuals diagnosed with AD and exhibiting typical signs and symptoms;
* CN--control group of CN subjects exhibiting no signs of dementia;* MCI--individuals with subjective cognitive impairment of varying severity but without any other typical signs of dementia, whose daily activities remained mostly unaffected.