esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Application Case  
Furthermore, a comparison of NO\({}_{2}\) VCD from EMI and TROPOMI was made by processing roughly one month data over the Beijing area. Figure 12 shows the daily variation of the mean NO\({}_{2}\) VCD in Beijing in January 2019. OMI data is not included in this comparison due to the serious influence of \"row anomaly\". There are four NO\({}_{2}\) pollution events in January that were caught by both EMI and TROPOMI with a correlation coefficient of 0.96, as shown in the shaded areas. Overall, the variation of EMI NO\({}_{2}\) VCD is larger than TROPOMI, and on the heavily polluted days, EMI NO\({}_{2}\) VCD is mostly higher than TROPOMI, and the largest discrepancy is up to \(3.27\times 10^{15}\) molec cm\({}^{-2}\) on 14 January. On the contrary, EMI NO\({}_{2}\) VCD is lower than TROPOMI on days with little NO\({}_{2}\) pollution, and the smallest discrepancy is \(8\times 10^{12}\) molec cm\({}^{-2}\) on 15 January. The difference between EMI NO\({}_{2}\) VCD and TROPOMI ranges from 0.2% to 44%.  
Figure 11: TROpospheric NO\({}_{2}\) VCD results of **(a)** OMI, **(b)** EMI, and **(c)** TROPOMI (top panel) and comparisons of EMI and OMI **(d)** and TROPOMI **(e)** over the North China Plain region on 22 January, 2019.  
Figure 12: Daily variation of the mean NO2 VCD of EMI and TROPOMI in the Beijing area (115.5–117.5 E; 39–41 N) in January 2019.
## 4 Experiment
### The 3D Foot Model Measurement  
After obtaining the 3D foot model, we could use the measurement tool provided by Meshlab to obtain the key parameters of the foot model as grid units. In this paper, the Ground Control Point (GCP) method was used to convert the coordinates of the point cloud model into world coordinates by using the actual size. The foot, and the magazine of a known size as a calibration object, were modeled together, the relationship between the 3D coordinate system and the world coordinate system was obtained by the conversion relation between the actual size of the calibration object and in the 3D model.  
We used the measurement tool provided by the Meshlab to measure the foot length and foot width as a grid unit, then converted them to millimeters in the real world using calibration objects. The parameters measured by hand were used as calibration values, and the foot data obtained by foot scanner as a contrast, to compute measurement errors.  
We take the average value of the parameters in the unit of grid in the 3D coordinate system by multiple measurements--the foot length is 0.892023, and the foot width is 0.307358, and the magazine width is 0.7550 grid. Previously, it was known that the size of the magazine width is 210 mm in the actual world, so we can calculate the foot parameters as follows:  
The left of the equal sign is the object size in the actual world, and the right of the equal sign is the 3D model size in the Meshlab:  
\[\frac{\text{The foot width (object size)}}{\text{The magazine width (object size)}}=\frac{\text{The foot width (3D model size)}}{\text{The magazine width (3D model size)}} \tag{11}\]  
In our experiment, the parameters above the formula are:  
\[\frac{\text{The foot length (object size)}}{\text{210 mm}}=\frac{\text{0.892023 grid}}{\text{0.7550.grid}} \tag{12}\]  
So we can find that the foot width is 85.49 mm, and we can also find that the foot width is 248.11 mm in the same way.  
In this paper, the AIRAJ digital caliper with an accuracy of 0.01 mm was used. The parameters measured by this digital caliper were used as the calibration value, and we obtained the foot length and foot width were 249.20 mm and 84.42 mm, respectively. In this paper, the santi-foot scanner is used, as shown in Figure 1c. The right foot of the volunteer was scanned for comparison, and the length and width of the foot are 248.50 mm and 84.90 mm, respectively. The results are compared as follows (Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline
**Measurement Category** & **Foot Length/Error (mm)** & **Foot Width/Error (mm)** \\ \hline Handcraft & 249.20 & 84.42 \\ Foot-scanner & 248.50/(0.70) & 84.90/(0.48) \\
3D foot reconstruction & 248.11/(1.09) & 85.49/(1.07) \\ \hline \end{tabular}
\end{table}
Table 1: The results of different measurements.  
Figure 7: The 3D reconstruction foot model.
## 2 Materials and Methods
### Daily EVI2 Trajectories of Satellite Pixels  
The eight ICOS sites are covered by seven Sentinel-2 MSI tiles. A total of 1312 available Sentinel-2 images acquired in 2016 and 2017 for the seven tiles were downloaded from the ESA Copernicus Sentinel's Scientific Data Hub [57]. The Sen2 Cor processor (version 2.5.5) was applied for atmospheric correction to obtain Level-2A surface reflectance and scene classification layer (SCL) [58]. EVI2 of each Sentinel-2 pixel (10m resolution) was calculated by red reflectance (\(\rho RED\)) and near-infrared reflectance (\(\rho NIR\)) using the following equation:  
\[EVI2=2.5\times\frac{\rho NIR-\rho RED}{\rho NIR+\ 2.4\times\rho RED+1}. \tag{1}\]  
Through the SCL, pixels recorded as vegetation and soil were marked as high quality, whereas those recorded as cloud, snow, and water, were marked as low quality. Two different spatial resolution MODIS EVI2 (250 m and 500 m) datasets for 2016 and 2017 were generated from MODIS/Terra MOD9Q1 (250 m) and MOD09A1 (500 m) [59] to be comparable to Sentinel-2 EVI2. Each MODIS pixel's quality flag was created from the binary MODIS quality (QA) data.  
Figure 2: An example of the contribution contours of eddy covariance flux measurement. The black dot represents the location of the flux tower. The red curves represent the flux contribution of the area. The background image (green) shows Sentinel-2 EVI2 at the flux tower site SE-Lnn on day of year 182 in 2017. The 80% flux footprint contribution contour was used to define the footprint area of the site.  
For generating daily EVI2 time-series from Sentinel-2 data and MODIS data, we applied a time-series fitting algorithm, box constrained separable least squares fitting to double logistic model functions [60], so that the datasets could be compared. This is a time-series fitting method that reduces noise, gap-falls the data, and generates output a regular time step (Figure 3). The inputs of the method were EVI2 time-series and quality flags. Only highest quality data were used in the function fitting. To avoid errors due to unequal time sampling of MODIS [61], the actual acquisition dates of the MODIS observations were used as a time vector of the MODIS EVI2 time-series. The default procedure and settings as described by [PERSON] et al. [60] were applied to produce daily EVI2 time-series. The fitting method is similar to the commonly used double logistic model functions [62] but with the following three differences: (1) the box constrained method applies a constant base level, which was estimated from a 5% low threshold of the clear observation histogram for the full time-series; (2) through a shape prior, i.e., a common season estimated for the full time-series through which the box constrained method improves the reconstruction of the seasons also in cases when the amount of observation data is insufficient; and (3) where the observation data was sufficiently large over a growing season, the estimation of parameters of the double logistic function for the season was constrained within a set of ranges ('box') in the box constrained method.
# Tropical Cyclone Winds from WindSat, AMSR2, and SMAP: Comparison with the HWRF Model
## 4 Results  
This section conducts a detailed statistical comparison between the satellite TC-winds and modeled HWRF winds for 19 storms in various ocean basins between 2017 and 2020. These comparisons were made after the HWRF winds were resampled to the same spatial resolution as the satellites using the Gaussian weighting method detailed in Section 3.1 and temporally interpolated to the same time as the satellite.
## 2 Mesoscopic Parameter Calibration and Basic Theory
### Specim Preparation and Mesoscopic Parameter Calibration
To ensure that the numerical model maintains static equilibrium during the loading process, the displacement loading rate of the numerical model is taken as 0.08 m/s. In PFC, it is a crucial step to calibrate the mesoscopic parameters by performing a laboratory compression test on the standard specimens. The macroscopic mechanical properties of specimens are determined by the values of the mesoscopic parameters between the particles. A few references [39; 40] reveal that the value of the compressive strength (\(\overline{\sigma_{c}}\)) and the tensile strength (\(\overline{\tau_{c}}\)) will affect the failure mode and the type of micro-crack, as such the friction coefficient between the particles has less influence on the significant parameters such as initiation stress, peak strength, and elastic modulus of specimens. According to the characteristics of cement mortar material, the parameters obtained by numerical simulation are consistent with the physical experimental parameters of the complete standard specimen by adjusting the mesoscopic parameters. The specific parameters are listed in Table 2. Since cement mortar is a brittle material inducing complex crack types under compressive loads, so it is essential to control the type of micro-crack by continuously adjusting the values of \(\overline{\sigma_{c}}/\overline{\tau_{c}}\), to make sure that the failure modes of the specimens obtained by numerical simulation are in a good agreement with the laboratory test results. The specific failure modes are shown in Figure 2, and the final mesoscopic calibration parameters are given in Table 3. \begin{table}
\begin{tabular}{c c c} \hline
**Properties** & **Specimens for Laboratory Tests** & **Specimens for Numerical Simulation** \\ \hline Density \(\rho\) (g/cm\({}^{3}\)) & 2.38 & 2.38 \\ Young’s modulus E (GPa) & 11.63 & 11.95 \\ Poisson’s ratio \(\
u\) & 0.20 & 0.21 \\ Uniaxial compressive strength \(\sigma_{c}\) & 58.25 & 57.30 \\ (MPa) & & \\ \hline \end{tabular}
\end{table} 
Table 2: Physico-mechanical parameters of intact cement mortar specimens for laboratory tests and numerical simulations. Figure 1: Numerical model for pre-existing parallel flaws layout (\(\alpha\) = 30\({}^{\circ}\), Unit: mm).
## 4 Energy Management Strategies
### Energy Management Protocols
#### 4.3.2 Energy Harvesting Aware Protocols  
* **Objective:** Incorporate energy-harvesting capabilities into routing and data management strategies.
* **Examples:** Protocols that adjust energy consumption based on the availability of harvested energy [79, 80, 81].
# The Privacy Flag Observatory: A Crowdsourcing Tool for Real Time Privacy Threats Evaluation
## 4 Implementation and Operation of the Observatory  
The Privacy Flag Observatory evaluates mobile applications and services with respect to threats related to three distinct categories: _Confidentiality_, _Security_ and _Privacy of Data_. In Figure 3, we see the initial page of the platform's website.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Threat Descriptor** \\ \hline \multirow{2}{*}{**Name**} & **Does the Website Use Certificate Pinning? (HTTP Public Key Pinning)** \\ \hline \multirow{4}{*}{**Threat**} & **Website Impersonation** \\ \hline \multirow{8}{*}{High-level Description} & HTTP Public Key Pinning (HPKP) is a security mechanism which allows HTTPS websites to defend against impersonation attacks in which attackers deploy misusses of fraudulent certificates. For example, attackers might compromise a certificate authority (i.e., the entity that issues soft authentication certificates for websites) and then misissue certificates for any domain. To defend against this risk, the web server can provide a list of “pinned” public key hashes. Thus, on subsequent connections, web browsers expect that server to use one or more of those public keys in certificate chain. \\ \hline \multirow{2}{*}{Threat Category} & Confidentiality of Communications \\ \hline \multirow{4}{*}{Implementation Details} & This threat is implemented as a backend script that takes the URL as input and reads the HTTP headers. If the “public-key-pins” header exists, the “max-age” is well configured (i.e., greater than 30 s) and there is a valid SHA256 hash for the public key, then the script returns true, otherwise, it returns false. \\ \hline \multirow{2}{*}{Return Value} & True/False \\ \hline \hline \end{tabular}
\end{table}
Table 1: An example Thread Descriptor.  
In what follows, we will discuss these categories and the operation of the observatory.
## 2 Proposed EPP-Based Beam Scanning Approaches
### Test case (5): \(R=0.5\)  
In this case, the cosine wave half period is chosen to be greater than the array length, \(H=9.87\)\(\lambda\), and the array length equal \(L_{h}=4.935\)\(\lambda\), hence \(R=0.5\). Accordingly, after applying the EPPs of the synthesized array elements for \(A_{c}\) changing from positive and the cosine wave amplitude is changing from positive to negative and its magnitude is changing from \(A_{c}=0\) to \(A_{c}=2\)\(\lambda\), the array main beam tilted from the broadside direction of \(\theta^{\circ}=90^{\circ}\) to \(\theta^{\circ}=65.97^{\circ}\) with minimal changes in the HPBW that is increased by \(3.02^{\circ}\) at \(\theta^{\circ}=65.97^{\circ}\) compared to the broadside direction, while the SLL is increased from \(-29.79\) dB to \(-27.35\) dB as the main beam is titled away from the broadside direction to \(\theta^{\circ}=65.97^{\circ}\). The achieved maximum scanning range is \(-24.03^{\circ}\leq\theta_{r}\leq 24.03^{\circ}\) around the broadside direction. The relation between the amplitude changes of the cosine waveform and the scanning angle, HPBW, SLL, and maximum scanning range for the eight-element ULAA at a compression ratio \(R=0.5\) are listed in Table 13. The polar plot of the synthesized radiation pattern using the SLL/EPP-BSA approach at compression ratio \(R=0.5\) and \(A_{c}=2\)\(\lambda\) is shown in Figure 34.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cosine Wave Amplitude \(A_{c}\)** & **Main Beam Direction \(\theta_{o}\), in \({}^{\circ}\)** & **SLL (dB)** & **HPBW in \({}^{\circ}\)** & **Maximum Scanning Range** \\ \hline
0 & \(90^{\circ}\) & \(-29.79\) dB & \(12.8^{\circ}\) \\ \hline
0.25 \(\lambda\) & \(87.11^{\circ}\) & \(-29.77\) dB & \(12.81^{\circ}\) \\ \hline
0.5 \(\lambda\) & \(84.02^{\circ}\) & \(-29.82\) dB & \(12.94^{\circ}\) \\ \hline
0.75 \(\lambda\) & \(80.92^{\circ}\) & \(-29.67\) dB & \(13.16^{\circ}\) \\ \hline \(\lambda\) & \(77.83^{\circ}\) & \(-29.05\) dB & \(13.48^{\circ}\) \\ \hline
1.25 \(\lambda\) & \(74.74^{\circ}\) & \(-28.06\) dB & \(13.9^{\circ}\) \\ \hline
1.5 \(\lambda\) & \(72.16^{\circ}\) & \(-30.35\) dB & \(14.46^{\circ}\) \\ \hline
1.75 \(\lambda\) & \(69.06^{\circ}\) & \(-28.86\) dB & \(15.18^{\circ}\) \\ \hline
2 \(\lambda\) & \(65.97^{\circ}\) & \(-27.35\) dB & \(15.82^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 13: The relation between the amplitude changes of the cosine waveform and the scanning angle, HPBW, SLL, and maximum scanning range \(\theta_{r}\) for 8-element ULAA at compression ratio \(R=0.5\).  
Figure 33: Polar plot of the synthesized radiation pattern using the SLL/EPP-BSA approach at compression ratio \(R=0.7\) and \(A_{c}=1.5\)\(\lambda\).
# A New Hybrid ConvViT Model for Dangerous Farm Insect Detection
## 3 Materials and Methods  
Agricultural pests are a significant threat affecting crop yields and food security. Agricultural pests cause global crop losses, reduce production efficiency, and increase pesticide use. Traditional methods used to combat agricultural pests are primarily based on chemical interventions. Therefore, accurate detection of pests is essential for sustainable agricultural practices. This study used 1591 images of 15 insect species datasets to detect dangerous farm insects. To increase the classification accuracy in the study, a hybrid ConvViT model was created using CNN and ViT. The experimental results of the created model were compared in detail with EfficientNetB0, DenseNet201, ResNet-50, VGG-16, and ViT.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 5 Sensors Driving Underwater SLAM
### Acoustic-Based SLAM
The availability of dedicated datasets, including mechanical scanning sonar (MSS) data with ground truth localization, has accelerated research in underwater SLAM [65]. Forward-looking sonar has also shown great promise, utilizing factor graph optimization with techniques like SO-CFAR and adaptive thresholding (ADT) for noise filtering and WICP algorithms for feature registration, achieving an 8.52% improvement in RMSE over dead reckoning [66]. As acoustic sensor technologies and SLAM algorithms evolve, these systems address key challenges like data fidelity, real-time processing, and large-area coverage. Table 5 summarizes recent developments, highlighting methods, findings, and limitations driving innovation in acoustic-based underwater SLAM. In summary, acoustic-based SLAM is a robust solution for underwater applications where environmental conditions hinder optical methods. While acoustic sensors may produce less detailed maps and require specialized expertise, their resilience under challenging environments underscores their importance for long-range communication, extensive mapping, and reliable navigation. With ongoing improvements in sensor technology, data  
Figure 10: Typical sonar: (**a**) single-beam sonar; (**b**) side-scan sonar; (**c**) multibeam sonar adopted from [57]. processing, and algorithmic approaches, acoustic SLAM is poised to play an increasingly vital role in underwater exploration and robotics.
# Efficient Inorganic Stabilization Materials for Chromium and Arsenic Pollution in Water and Soil
## 2 Inorganic Stabilization Materials
### Phosphate Compounds  
Calcium phosphate, aluminum phosphate, and iron phosphate are all recognized as common phosphate compounds, which are stabilized by generating metal phosphate precipitation of Cr and As. Soluble phosphates such as KH\({}_{2}\)PO\({}_{4}\) or H\({}_{3}\)PO\({}_{4}\) are used as sources of phosphate [36]. CaCl\({}_{2}\cdot\)2H\({}_{2}\)O, FeCl\({}_{3}\cdot\)6H\({}_{2}\)O, and (NH\({}_{4}\))\({}_{2}\)HPO\({}_{4}\) were adopted for synthesizing iron-doped hydroxyapatite (Fe-HAP) by [PERSON] et al. [37]. XRD characterization showed that the main components of Fe-HAP were HAP, Fe\({}_{2}\)O\({}_{3}\) and FeOOH, and the characteristic peak of Fe\({}_{3}\)(AsO\({}_{4}\))\({}_{2}\cdot\)6H\({}_{2}\)O appeared after adsorption of As(V) (Figure 4a). The residual state after repair increased from 7% to 18.1% (Figure 4b). The results show that Fe-HAP is a monolayer adsorption and precipitation adsorption process for As(V) (Figure 4c). Fe\({}^{0}\) powder, hydroxyapatite (HAP), and citric acid were ball milled by [PERSON] et al. [38] to obtain iron and phosphate slow-release materials of CA-Fe\({}^{0}\)/HAP. The slow-release generated iron was combined with As, while the rest was formed into iron oxide (hydroxide), which could also combine with As. This raised the residual state of As from 58.01% to 93.82% (Figure 4d). Magnesium ascorbate phosphate (MAP) and phytase were used by [PERSON] et al. [39] to reduce and stabilize Cr(VI). Phosphate stabilizers were applied to soil contaminated with Cr(VI) to remove 99.9% of 100 mg/kg Cr(VI) after 15 days (Figure 4e). Phosphate stabilizers for soil remediation can also meet the needs of plant growth, achieving ecological and stabilization synergistic treatment.
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 3 PPP Models
### GNSS Code and Phase Observations
#### 3.1.4 Narrow-Lane (NL) Code Observation  
The narrow-lane code \(P_{NL}\) can be derived by setting \(\alpha_{NL}=f_{1}/(f_{1}+f_{2})\) and \(\beta_{NL}=f_{2}/(f_{1}+f_{2})\) in (29). The associated wavelength is \(\lambda_{NL}=c/(f_{1}+f_{2})\), equivalent to 10.7 cm for the GPS L1/L2 NL combination. NL code observations offer reduced noise levels compared with the original P1 and P2 codes.
# Influence of Applied Stress on the Ferroelectricity of Thin Zr-Doped HfO\({}_{2}\) Films
## 2 Materials and Methods  
Figure 1a shows the key steps in the preparation of HZO thin film-based ferroelectric capacitors. The process started with an 8-inch Si wafer. After the complete cleaning of organic matter and impurities on the wafer, a 20-nm layer of TiN was deposited as the bottom electrode. Following that, a 10-nm HZO layer was deposited by ALD at 300 \({}^{\circ}\)C. Hf[N(C\({}_{2}\)H\({}_{5}\))CH\({}_{3}\)]\({}_{4}\), Zr[N(C\({}_{2}\)H\({}_{5}\))CH\({}_{3}\)]\({}_{4}\) and H\({}_{2}\)O were used as Hf, Zr, and O sources. The ratio of Hf and Zr was controlled by controlling the cycle of depositing one layer of Hf followed by one layer of Zr, after which the top 20-nm TiN electrode was grown. This was followed by the deposition of the top electrode W. The stress applied to the HZO film can be controlled by controlling the pressure at which the top electrode W grows. The specific theory of stress regulation by W electrodes has been studied in detail by our group, as shown in [30; 31]. It must be noted here that the W electrodes grown at both pressure conditions (323 MPa and 2726 MPa) introduced exotic stresses, but the latter was several orders of magnitude larger relative to the former, and therefore we defined both as stress-free and stressed structures, respectively. In this article, we took this method and used it directly. Finally, the capacitor structure was prepared by annealing at different temperatures. Rapid thermal annealing (RTA) was used for the heat treatment process, where the temperature was rapidly increased to the corresponding temperature in an N2 atmosphere, held for 30 s, and then rapidly cooled to room temperature. The structure of the prepared capacitor with stress is shown in Figure 1b. Figure 1c,d clearly show the HZO capacitance sandwich structure between the two electrodes and the butterfly C-V curve which confirms the ferroelectricity.  
In this paper, capacitance-voltage (C-V) measurements were taken using the 4200 semiconductor parameter analyzer, and all capacitance-based ferroelectricity and endurance characterization measurements were performed based on a TF3000 analyzer.