esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Model Description
### Multispecies Transport Model  
The diffusion coefficients of cations, anions, and neutral species in a solution have different values, and the diffusive flux of a selected ion, \(i\), considering concentration and electrical potential gradients and the chemical activity effects, can be expressed by the Nernst-Planck equation as follows [22; 25]  
\[J_{i}=-D_{w,i}\bigg{[}\frac{\partial\ln(\gamma_{i})}{\partial\ln(c_{i})}+1 \bigg{]}\frac{\partial c_{i}}{\partial x}+D_{w,i}z_{i}c_{i}\frac{\sum_{j=1}^{n }D_{w,j}z_{j}\big{[}\frac{\partial\ln(\gamma_{j})}{\partial\ln(c_{j})}+1 \big{]}\frac{\partial c_{j}}{\partial x}}{\sum_{j=1}^{n}D_{w,j}z_{j}^{2}c_{j}} \tag{13}\]  
where \(D_{w,i}\) is its diffusion coefficient, \(\gamma_{i}\) is its activity coefficient, \(c_{i}\) is its concentration, and \(z_{i}\) is its valance; the subscript \(j\) is introduced to show that these species are for the potential term. The diffusion coefficient of ions in a porous medium, \(D_{p}\), is related to \(D_{w,i}\), porosity, \(\Phi\), and tortuosity, \(\tau\), by [PERSON]'s law  
\[D_{p}=D_{w,i}\Phi^{\tau} \tag{14}\]  
The multi-component diffusion calculation was performed using the built-in model in PHREEQC with the keyword of \"-multi_d\". The length of a single pore in the hardened body is subdivided into several immobile cells, and one-dimensional multi-species transport takes place from the exposure  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **C\({}_{3}\)S** & **C\({}_{2}\)S** & **C\({}_{3}\)A** & **C\({}_{4}\)AF** \\ \hline K\({}_{1}\) & 1.5 & 0.5 & 1 & 0.37 \\ N\({}_{1}\) & 0.7 & 1 & 0.85 & 0.7 \\ K\({}_{2}\) & 0.05 & 0.006 & 0.04 & 0.015 \\ K\({}_{3}\) & 1.1 & 0.2 & 1 & 0.4 \\ N\({}_{3}\) & 3.3 & 5 & 3.2 & 3.7 \\ H & 1.8 & 1.35 & 1.60 & 1.45 \\ E\({}_{a}\) (J/mol) & 41,570 & 20,785 & 54,040 & 34,087 \\ \hline \end{tabular}
\end{table}
Table 10: Parameters adapted to calculate the hydration degree of the clinker minerals as a function of time [27; 40].  
solution to pore water. In the simulation, the transport model was applied with the phase-equilibrium model to simulate ionic profile and chemical reaction as a result of ionic ingress. The multicomponent diffusion model is described in detail in our previous studies [25; 26].
## 3 Results
#### 3.1.2 \"Fan-Shaped\" Layout  
The \"fan-shaped\" layout was an intuitive arrangement initially considered. The ultimate goal was to develop a detector capable of resolving moisture anomalies in grain piles along the x, y, and z axes. However, as evident from Figure 5, the \"fan-shaped\" layout clearly lacked resolution capability along the y-axis. Although the regions near the transmitter, where electromagnetic field channels were dense, allowed for more accurate localization, the y-axis resolution deteriorated significantly at greater distances from the transmitter. This resulted in elongated voxel representations of the anomalous regions in the localization results. Given these limitations, it became necessary to improve the transmitter layout to achieve the desired resolution capabilities across all three axes.
# Mapping with Pleiades--End-to-End Workflow
## 2 Related Work
### Digital Surface Model Generation
#### 2.2.6 Post-Processing  
Appropriate post-processing is always of interest, also at different stages of the presented workflow. For instance, occluded regions may be filled with an interpolation technique that filters towards the ground level. This avoids blurring of 3D breaklines while still gathering a high spatial reconstruction coverage. A post-processing option can also be seen in the so-called peak filter that segments images into radiometrically connected regions [61; 89]. Small or isolated regions can then be discarded.
###### Abstract  
Very-low-frequency electromagnetic waves have low propagation loss, slow attenuation, a stable phase and amplitude in the Earth ionosphere waveguide cavity, and are widely used in VLF communication and navigation, ionospheric heating, global lightning distribution inversion, and other fields. Studying the transmission characteristics of very-low-frequency (VLF) signals in the ionosphere is of great significance in spaceborne VLF communication technology. The existing research on ionospheric transmission characteristics using the finite-difference time domain (FDTD) algorithm is mostly based on high-frequency pulse signals, and the propagation model is relatively rough, resulting in certain calculation errors. To this end, a time-domain finite-difference algorithm model based on a uniaxial anisotropic perfectly matched layer (UPML) boundary in a spherical coordinate system was established, effectively solving the reflection problem existing in PEC boundary. The algorithm was used to numerically calculate the field-strength attenuation of VLF waves in the ionosphere. The simulation results showed that in the VLF frequency band, reducing the frequency is beneficial for electromagnetic waves to penetrate the ionosphere. Although the attenuation trend in the VLF waves is roughly the same during the day and night, the attenuation during the day is significantly greater than that at night, and this was compared and analyzed with traditional algorithms to verify the accuracy of the algorithm.  
Keywords:FDTD; VLF electromagnetic; ionosphere; accuracy +
Footnote †: journal: Applied Optics
## 2 Data Preprocessing
### Data Preprocessing  
To address overfitting in the training process, this study employs data augmentation techniques tailored to waveform line plots generated from microseismic time-series data. The specific procedures are as follows:  
First, images like Figure 1 are subjected to random scaling and cropping. Each 224 \(\times\) 224 waveform line plot--derived from time-series data where the \(x\)-axis represents a 0-2000 ms duration (sampling rate: 1000 Hz) and the \(y\)-axis amplitude is normalized to the range [\(-\)1, 1] via min-max scaling--is resized to 256 \(\times\) 256, followed by cropping a 224 \(\times\) 224 region from the center and converting it into a tensor. Scaling is restricted to \(\pm\)10% of the original dimensions to preserve fine details such as the abrupt amplitude changes of P-waves and S-waves.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Label**} & **Earthquake** & \multirow{2}{*}{**Label**} & **Earthquake** & \multirow{2}{*}{**Label**} & **Earthquake** \\  & **Magnitude** & & **Magnitude** & & **Magnitude** \\ \hline
1 & 0.0–0.1 & 11 & 1.0–1.1 & 21 & 2.0–2.1 \\
2 & 0.1–0.2 & 12 & 1.1–1.2 & 22 & 2.1–2.2 \\
3 & 0.2–0.3 & 13 & 1.2–1.3 & 23 & 2.2–2.3 \\
4 & 0.3–0.4 & 14 & 1.3–1.4 & 24 & 2.3–2.4 \\
5 & 0.4–0.5 & 15 & 1.4–1.5 & 25 & 2.4–2.5 \\
6 & 0.5–0.6 & 16 & 1.5–1.6 & 26 & 2.5–2.6 \\
7 & 0.6–0.7 & 17 & 1.6–1.7 & 27 & 2.6–2.7 \\
8 & 0.7–0.8 & 18 & 1.7–1.8 & 28 & 2.7–2.8 \\
9 & 0.8–0.9 & 19 & 1.8–1.9 & 29 & 2.8–2.9 \\
10 & 0.9–1.0 & 20 & 1.9–2.0 & 30 & 2.9–3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Magnitude classification and labels.  
Next, waveform-specific image enhancements are applied, including horizontal flipping, which simulates symmetric variations in a time-series distribution while maintaining the proportional mapping of \(x\)-axis time intervals; rotation within a \(\pm 5^{\circ}\) range, to avoid distorting the temporal sequence integrity of waveform phases; and Gaussian noise injection (with a signal-to-noise ratio \(\geq 30\) dB), to mimic residual high-frequency noise that remains uneliminated by the preprocessing soft thresholding algorithm.  
Batch normalization is adopted to stabilize feature learning, with attention modules appended after the nonlinear activation of each convolutional layer. Composed of channel and spatial attention submodules, these modules recalibrate feature weights across channel and spatial dimensions--specifically emphasizing critical waveform segments (e.g., phase arrival times corresponding to \(x\)-axis time points) and discriminative amplitude features (within the \(y\)-axis range \([-1,1]\)) while suppressing irrelevant noise. During testing, images undergo identical preprocessing but no augmentation. Training with augmented data ensures the model's robustness against rotational or flipping variations in unseen data, thereby effectively mitigating overfitting.
## Data and Methods  
### Study Sites and Flux Tower Data  
We used flux and meteorological data from the latest FLUXNET synthesis dataset, the FLUXNET2015 database. This database was first released in December 2015 with more site-years of data added in 2016. It provides standardized and high-quality flux and meteorological data for a total of 221 sites across the globe ([[http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/data-processing/](http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/data-processing/)]([http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/data-processing/](http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/data-processing/))). Over the past decade or so, there have been two major data synthesis activities initiated by the FLUXNET research community, leading to two synthesis datasets: La Thuile and FLUXNET2015. The FLUXNET2015 dataset has up to 15 years of overlap between EC data and MODIS data products, while the La Thuile dataset has only up to 5 years of overlap with the MODIS data record. The new dataset also has better quality than its predecessor. We therefore used the FLXUNET2015 dataset in our study. The flux and meteorological data in the dataset were gap filled, and the NEE measurements were partitioned into GPP and ecosystem respiration using standardized procedures [33].  
In our study, we selected 121 Tier-1 sites that are open and free for scientific research (Figure 1). The site code, site name, latitude, longitude, vegetation type, and duration of measurements, and references are provided in Table S1. Although these sites are distributed across the globe with latitudinal distribution and encompass the following ten biomes: evergreen needleleaf forests (ENF), evergreen broadleaf forests (EBF), deciduous broadleaf forests (DBF), mixed forests (MF), closed and open shrublands (COSH), woody savannas (WSA), savannas (SAV), grasslands (GRA), wetlands (WET), and croplands (CRO), there are actually no significant latitudinal impacts on GPP and VIs (Figure S1). The GPP data (GPP-DT-CUT-REF) partitioned from NEE using the daytime partitioning approach [34] and meteorological data were used in our analysis.
## 2 Measurement of Complete Geometric Errors  
Most geometric errors are generated from the manufacturing and assembly stages. As the basis and premise of precision production, the measurement and control of geometric errors are of important significance. The measurement of geometric errors for a multi-axis machine tool relies on a variety of measurement methods, techniques and tools. Its control process aims to establish a transfer and mapping model from geometric errors to machining errors, and to research its quantitative transfer process.  
Traditional measurement methods generally ignore small geometric angle errors. The resultant geometric error items are thus incomplete. Furthermore, the calculation and compensation accuracy of machining errors is lower. Due to the coupling between geometric error items, the coupling and decoupling measuring method is proposed in this paper to obtain complete geometric error items.
## 4 Findings and Discussion
### Discussion
#### 4.4.3 Need for ICV-Based Dataset  
Although, XAI adoption for ICV is still at the infant stage, the AI-based approaches witnessed the use of inappropriate datasets for model evaluation. Since the ICV or IoV has unique features, datasets reflecting the reality of the connected vehicles should be advocated if the goal of XAI will be achieved.
## 2 Materials and Methods
### Settings for Instrument Response Functions and SNR  
To explore the impact of different spectral resolutions, the full width at half maximum (FWHM) of the SRF was adjusted accordingly. Based on the spectral resolution of most hyperspectral imagers, spectral resolutions of 2 nm, 5 nm, 10 nm, and 15 nm were selected, respectively. Critical sampling was applied, with the sampling frequency set to FWHM/1.0, as implemented in the AVIRIS system [36]. In addition, the SRF was described by a Gaussian shape [12; 37].  
Image noise typically comprises two primary components: photon noise (PN) and constant noise (CN). PN is generally proportional to the square root of the radiance, while CN remains independent of radiance. At a given radiance level, the total noise is predominantly determined by PN, resulting in noise that scales proportionally with the square root of the radiance [21]. In this study, an idealized scenario without surface variability was simulated, eliminating the necessity to distinguish measurement noise from surface contributions.  
Therefore, the noise, expressed in terms of radiance, was multiplied by a random number from a Gaussian distribution with a mean of 0 and a standard deviation of 1. This scaled noise was then added to the simulated radiance at each wavelength for each pixel. The experiment was repeated 1000 times. Noise corresponding to SNR of 100, 200, 400, 600, and 800 was added to the simulated radiance.
# Characteristics of Vibration Velocity Signal Using Liquid Carbon Dioxide Rock-Breaking Technology
## 4 Conclusions  
This study conducted a field test of L-CDRB with a new type of fracturing tube, monitored the internal pressure of the fracturing tube and the site vibration velocity, and analyzed the characteristics of the vibration velocities produced by L-CDRB in comparison with explosive blasting at the same site. The main conclusions are as follows.  
1. These tests resulted in no accidents, such as stuck, flying, or unexploded tubes or hole punches. This result indicates that this new type of fracturing tube is safer in engineering applications.
2. The pressure curve generated by the liquid CO\({}_{2}\) phase change in the fracturing tube was obtained, and the mass of liquid CO\({}_{2}\) was converted into the mass of explosives with the same energy using TNT equivalent as the medium. The calculation revealed that the energy of the CPD-ZLG fracturing tube was 3089.17 kJ, and a single fracturing tube was equivalent to 1.024 kg No. 2 rock emulsion explosive.
3. Based on energy equivalence, attenuation laws were for the vibration velocity of L-CDRB and explosive blasting. The former had a much lower vibration velocity than the latter. The PPV ratio of the two was related to the scaled charge. When the scaled charge was 0.1, the vibration velocity of explosive blasting was 28.5 times that of L-CDRB, and when the scaled charge was 0.2, the ratio was 33 times. This result fully illustrates that L-CDRB technology greatly reduces vibrations, especially in areas close to the blasting center.
4. Analyzed in detail here are the frequency domain characteristics of L-CDRB vibration velocities. The results show that compared with explosive blasting, L-CDRB has a lower dominant frequency of vibration velocities. From the perspective of energy distribution, approximately 60% of the energy is concentrated in the 10-30 Hz range, while explosive blasting produces vibration signal energy mainly concentrated in the 30-60 Hz range. The dominant frequency of vibration is related to the mass of liquid CO\({}_{2}\). As the total charge increases, the dominant frequency gradually increases, and the energy tends to move from a low to a high-frequency band.
# Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images
###### Abstract
Specifically, on the basis of retaining the fine information extracted based on the pixel, the subsequent processing is changed to the image object produced by image segmentation to improve the accuracy. The fusion method not only improves the efficiency, but also obtains higher quality spatial distribution maps of winter wheat. Shandong Province, as one of China's major agricultural provinces, has held the first place for a long time in the added value of agricultural production [22; 23]. It is also a large province for winter wheat cultivation, accounting for about 17% of the national winter wheat cultivation area, which is the second largest after Province Henan [24]. Given the importance of Shandong in winter wheat cultivation and the current status of remote sensing technology in agriculture, it is particularly important to use remote sensing technology to monitor winter wheat cultivation over a large area and multiple time periods [25; 26]. The application of the proposed fusion method in Shandong, a region with complex and highly specialized terrain, helps to identify the deficiencies and limitations of the existing extraction method. This will also promote the further improvement of the method and provide research cases and data support for other related studies. This paper is organized as follows. Section 2 describes the study areas, datasets and methods. Section 3 focuses on the evaluation and analysis of the results. Section 4 presents a further discussion of the study, and some conclusions are given in Section 5.
# A Systematic Review of Osteotomies to Correct Hallux Valgus in the First Metastasis
## 3 Results
### Proximal Metatarsal Osteotomy
#### 3.3.2 Follow-up and Biomechanical Analysis  
Foot pressure measurements were conducted using an EMED-SF platform (EMED-SF Pedography Analyzer (Novel GMBH, Munich, Germany). The pressure platform, measuring 400 \(\times\) 240 (mm), was seamlessly integrated into a 5 m walkway. Patients were instructed to walk barefoot at their usual pace across the platform. Data from three typical steps collected before and after surgery were analyzed using EMED software. Additionally, a control group comprising ten healthy women matched for age walked across the platform at their natural pace three times, and their data were compared ([PERSON] et al., 2020).  
Analysis of the steps involved delineating seven regions of interest: heel, midfoot, lateral, medial forefoot, toes 2-5, and the hallux. Variables utilized in the analysis included area, maximal forces, peak pressures, duration of contact phases, pressure-time and force-time integrals, and the timing of PP and force for each region of interest. The study developed by [PERSON] et al. (2019) concluded that based on the plantar foot pressure data, patients with hallux valgus deformity have a typical medial and CFF loading pattern.  
[PERSON] et al. (2020) studied five fresh-frozen cadaveric below-knee specimens with hallux valgus deformities. These specimens underwent loading up to 400 N on a servo-hydraulic load frame. The study aimed to analyze joint contact characteristics at the first MT joint, employing a Tekscan pressure sensor (Model 6900, 1100 psi; Tekscan Inc., Boston, MA) with varying opening wedge sizes of 3, 5, and 7 mm, both with and without a distal soft tissue release (DSTR). Analysis of variance and post hoc multiple comparisons were utilized to compare contact force, area, and peak contact stress among different groups compared to untreated specimens. The findings revealed that untreated specimens exhibited a mean contact force of 47.7 \(\pm\) 33.5 N. This force increased progressively with the size of the opening wedge and demonstrated statistical significance for a 7 mm opening wedge (129.7 \(\pm\) 62.3 N) and a 7 mm wedge with DSTR (134.8 \(\pm\) 60.5 N).  
The mean peak contact stress for untreated specimens was measured at 2.8 \(\pm\) 1.3 MPa, which escalated incrementally with wedge size to 5.7 \(\pm\) 3.0 MPa for a 7 mm wedge alone and 5.6 \(\pm\) 2.5 MPa for a 7 mm wedge with DSTR. Although contact area increased with corrections, none of these changes reached statistical significance.  
In summary, the study concluded that as the size of the opening wedge increased, so did the loading on the first MT joint. Joint stresses exceeding 4.7 MPa were identified as chondrogenic, potentially increasing the likelihood of arthritic joint development in patients.  
Figure 8: Sketch of an X-ray showing the medial angulation of the first MT and the procedure, comparing the pre- and post-operative periods ([PERSON] et al., 2020).
# Qubit-Based Clock Synchronization for QKD Systems Using a Bayesian Approach
## 5 Conclusions  
In conclusion, we develop a novel probabilistic approach to qubit-based clock synchronization using Bayesian analysis. By exploiting correlations between information [PERSON] shares publicly, such as basis and decoy state choices, and [PERSON]'s detection events, we can find the correct synchronization clock offset without sacrificing any secret key. Additionally, our algorithm is more robust to noise, loss, and clock drift in comparison to other protocols by incorporating all publicly available information using the Bayesian framework. Finally, we demonstrate that our algorithm is successful and robust using a simulated BB84 communication scheme, which confirms that our synchronization metric corresponds to the probability of synchronization, especially in the low-\(\mu\) limit. Our algorithm is applicable to other QKD systems that use other degrees-of-freedom of the photon for which it is possible to divuulge some timing information.  
Conceptualization, R.D.C.; methodology, R.D.C.; software, R.D.C.; validation, R.D.C.; formal analysis, R.D.C.; investigation, R.D.C.; resources, R.D.C.; data curation, R.D.C.; writing--original draft preparation, R.D.C.; writing--review and editing, R.D.C. and D.J.G.; visualization, R.D.C. and D.J.G.; supervision, D.J.G.; project administration, D.J.G.; funding acquisition, R.D.C. and D.J.G. All authors have read and agreed to the published version of the manuscript.  
This material is based on research sponsored by NASA under grant 80 NSSC20K0629 and the Air Force Research Laboratory and the Southwestern Council for Higher Education under agreement FA8650-19-2-9300. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of NASA, the Southwestern Council for Higher Education and the Air Force Research Laboratory (AFRL), or the U.S. Government.  
All code and data used in simulations is publicly available on GitHub: [[https://github.com/roderickdcochran/qubit_based_synchronization](https://github.com/roderickdcochran/qubit_based_synchronization)]([https://github.com/roderickdcochran/qubit_based_synchronization](https://github.com/roderickdcochran/qubit_based_synchronization)) (accessed on 29 July 2021).  
We thank [PERSON] for help in collecting the data on FPGA clock drift. R.D.C. acknowledge discussions of the Bayesian analysis with [PERSON].  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 4: Dependence of string length threshold to achieve 95 percent synchronization confidence on received mean photon number on a logarithmic scale, parameterized by different dark count probabilities.