esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality Assessment of Photogrammetric Methods--A Workflow for Reproducible UAS Orthomosaics
## Materials and Methods
### Image Georeferencing  
Very high resolution orthomosaics such as those resulting from UAV flights require precise positioning to avoid the introduction of complex errors in the image processing [33]. The standard GNSS receivers that are built in cameras do not provide sufficient accuracy. There are two alternative strategies for georeferencing the UAS products: direct georeferencing of the images with a RTK on the UAV or the use of GCPs. Direct georeferencing requires the accurate time synchronization between the RTK device and the camera, which has been reported as a major source of error [33; 34; 35].  
The use of GCP implies that the study area is accessible in order to install visible ground markers before the flight and precisely measure their position. Ideally, the GCP should be equally distributed over the study area to avoid distortions during processing [33]. During the orthomosaic processing, the ground markers need to be interactively identified in the images. Despite these drawbacks, georeferencing through GCP with general-purpose GNSS receiving systems--that are nowadays standard equipment for surveying--is still far more widespread and potentially more cost-effective than the direct georeferencing with RTK [34].  
In any case, GCPs are also required for the independent validation of the referencing accuracy during the processing [24] and therefore essential for a proper accuracy assessment. The geolocation accuracy is usually given as the checkpoint root mean squared error (checkpoint error, Equation (1)) that quantifies the distance between the position of the measured GCP (XYZgcp) and the estimated positions of these coordinates in the photogrammetric processing (XYZest). It can be calculated for each direction individually.  
\[\textit{Checkpointer}=\sqrt{\textit{mean}\left(\left(XYZ_{gcp}-XYZ_{est} \right)^{2}\right)} \tag{1}\]
# GPON PLOAMd Message Analysis Using Supervised Neural Networks
## 4 PLOAMd Analysis Using Various Recurrent Neural Networks and Results Discussion
### Learning Process  
The learning data preparation is described in Section 3. The dataset is shuffled with predefined constant seed to guarantee the same data order every time. Ten-fold cross-validation is used to evaluate performance of models correctly [55]. This process splits the dataset into ten equally big groups. Each group is used as the validation dataset for a separate instance of the inspected model, while the remaining groups are used as the learning dataset. Based on the training history of all models, the average and standard deviation are evaluated for each model metric.  
The design model input is a three-dimensional array because it is a list of sequences. Each model is learned using the Adam optimizer and updated every 50 sequences (i.e., the batch size is set to 50). Based on the learning history, it is estimated whether the model is capable of pattern recognition. The learning process of each model takes 200 epochs (the number of epochs is high enough to evaluate the accuracy and the loss in a under-fitted, good fit, and over-fitted model) to check the loss and accuracy evolution over learning epochs and whether the learning and validation loss increase, decrease, or remain constant.  
The model with RNN layers is able to recognize learning samples very well, but it presents a problem in learning generalized patterns in the sequences, as shown by the validation loss and validation accuracy in Figure 2. The validation accuracy slowly raises and after few epoch it remains stable at accuracy of 74%. A rapid drop in the learning loss indicates that the model memorizes the input without generalized patterns, thus the validation loss raises slightly over epochs.  
The model with LSTM layers exhibits sufficient accuracy in distinguishing standard and non-standard/corrupted data sequences for both the learning and validation datasets, as shown in Figure 3. The LSTM layers are able to learn patterns in the communication and decide whether they meet the GPON standard. On the other hand, this model is the most complex among the tested models and requires the most resources to learn.  
The last evaluated model is based on layers with GRU cells. Similar to LSTM, the model starts to be perfectly fitted after approximately 75 learning epochs. More interestingly, the model accuracy for the validation dataset is slightly higher compared to the LSTM model, as shown in Figure 4.
## Nomenclature and Abbreviations  
\begin{tabular}{l l}
**Nomenclature** & \\ \(A\) & Total heat transfer surface area of the heat sink (m\({}^{2}\)) \\ \(Amp\) & Peak-to-peak amplitude (m) \\ \(A\) & Vibrational amplitude (\(Amp/2\)) (m) \\ \(b\) & Fin spacing (m) \\ \(c_{p}\) & Constant specific heat (J/(kg K)) \\ \(f\) & Vibrational frequency (Hz) \\ \(h\) & Height of the fins (m) \\ \(k_{fin}\) & Thermal conductivity of the fin material (W/(m K)) \\ \(k_{fluid}\) & Thermal conductivity of the fluid (W/(m K)) \\ \(L_{fin}\) & Length of the fins (m) \\ \(Nu\) & Nusselt number of heat sink at the normal condition with fin effect \\ \(\overline{Nu}\) & Time-averaged Nusselt number \\ \(Nu_{A}\) & Area-averaged Nusselt number \\ \(Nu_{ideal}\) & Ideal Nusselt number \\ \(p\) & Hydrostatic pressure (Pa) \\ \(Pr\) & Prandtl number \\ \(Re\) & Reynolds number (\(\rho Ulb/\mu\)) \\ \(T\) & Temperature (K) \\ \(t\) & Time (s) \\ \(t_{fin}\) & Thickness of the fin (m) \\ \(U\) & Inlet velocity (m/s) \\ \(u\), \(v\), \(w\) & Velocities along the x, y, and z directions, respectively (m/s) \\ \(x\), \(y\), \(z\) & Cartesian coordinates (m) \\ \(Y\) & Displacement at any time (m) \\
**Greek Symbols** & \\ \(\mu\) & Fluid viscosity (Pa.s) \\ \(\rho\) & Fluid density (Kg/m\({}^{3}\)) \\ \(\tau\) & Cycle time (s) \\ \(\Delta t\) & Time step (s) \\ \(\alpha\) & Thermal diffusivity of the fin material (m\({}^{2}\)/s) \\
**Abbreviations** & \\ \(PR_{Re}\) & Percentage reduction in Reynolds number \\ \end{tabular}
## 3 Proposed System
### Hardware Modules  
The embedded system utilizes an infrared camera as the primary data sensor, and 940 nm wavelength infrared fill-lights are installed in proximity to the camera to ensure sufficient captured-image brightness. The embedded system processor adopts a big.LITTLE framework, comprising two Cortex-A72 cores and four Cortex-A53 cores. The system operates on a Linux platform and transmits results information through an Ethernet interface, as shown in Figure 4.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 7 Selected Properties of HCFs  
In this section we present the following unique properties of hollow-core fibers important for some telecom, datacom, and similar applications, but not described before:dimensions and their definitions (Section 7.1), polarization mode dispersion (Section 7.2), temperature and strain dependence of latency (Section 7.3), and backscattering (Section 7.4).
## 4 Conclusions  
Based on SPAC theory, this study integrated multi-source information data such as meteorology, remote sensing, and reanalysis, to construct a comprehensive drought index. Through static and dynamic hazard assessment models, we analyzed the spatial and temporal distribution characteristics of drought disaster and hazard during the growing season (May to September) in NEC and also verified the validity of the CDI and hazard index. The following conclusions were obtained:  
1. Compared with a single drought index, the CDI index for drought monitoring has the advantages of a broad spatial range, long time range, and high accuracy and can reflect agricultural drought well.
2. The growing season in NEC showed a trend of becoming drier from 1982 to 2020, and the hazard of agricultural drought also showed a non-significant (\(p<0.05\)) increasing trend. However, the trends of the drought index, the impact scope of drought events, and the hazard of agricultural drought all turned around 2000, and the drought hazard was highly significant (\(p<0.001\)) and decreased from 2000 to 2020.
3. The frequency of drought disasters was the highest and the hazard was the highest in May. The best level of climatic yield anomalies in maize was explained by the drought hazard in August (R\({}^{2}\) = 0.28). High hazard levels were mostly distributed in the farmland and grassland areas located in the central and western parts of the study area.  
Figure 12: Linear relationship between climatic yield anomalies and drought hazard during the growing season for maize in NEC.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs15010057/s1](https://www.mdpi.com/article/10.3390/rs15010057/s1)]([https://www.mdpi.com/article/10.3390/rs15010057/s1](https://www.mdpi.com/article/10.3390/rs15010057/s1)), Figure S1. Linear relationship between climatic yield anomalies and drought hazard during the growing season (May to September) for soybean in Heilongjiang Province.  
**Author Contributions:** Conceptualization, K.L. and J.Z.; methodology, K.L. and G.R.; data, B.S. and Q.S.; validation, S.W., C.L., Y.Y. and Y.G.; writing--original draft preparation, K.L.; writing--review and editing, J.Z.; project administration, J.Z.; funding acquisition, J.Z. and C.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by the National K&D Program of China (2019 YED1002201), the National Natural Science Foundation of China (U21A2040), the National Natural Science Foundation of China (41877520), the National Natural Science Foundation of China (42077443). Industrial technology research and development Project of Development and Reform Commission of Jilin Province (2021C044-5), the Key Scientific and Technology Research and Development Program of Jilin Province (20200403065 SF), and the Construction Project of the Science and Technology Innovation Center (20210502008 ZP).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Materials and Methods  
### Materials and Mixtures  
The cement used was Portland composite cement (PCC) with 30 MPa of resistance (PCC 30R). Physicochemical requirements are stipulated in the Mexican regulation NMX-C-414 and are equivalent to ordinary Portland cement Type III established in ASTM C150; PCC 30R was chosen because it is commonly used and widely applied in the concrete industry in Mexico. Natural coarse aggregates (NCA) came from a local river, considering a maximum particle size of 25.4 mm, and the natural fine aggregates (NFA) were siliceous river sand. RA came from the crushing of demolished concrete pavements in the city with the characteristics shown in Table 1.  
The RA consisted of natural aggregate plus adhered mortar, in turn containing cement paste and the original sand. The detailed characterization of the aggregates used and the production process was reported in a previous publication [18].  
Physical properties of the aggregates are shown in Table 2, where it can be observed that the RCA and the RFA present an absorption of 5 and 6 times greater than that of the NCA and NFA, respectively. It can also be observed that the density of the recycled aggregates decreases, which is due to the porous mortar adhered to them.  
\begin{table}
\begin{tabular}{c c} \hline Absorption (\%) & 6.91 \\ \hline Density g/cm\({}^{3}\) & 2.36 \\ \hline Porosity \% & 17.87 \\ \hline Compressive strength (MPa) & 28.15 \\ \hline \end{tabular}
\end{table}
Table 1: Physical properties of old concrete.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Density (g/cm\({}^{3}\))** & **Absorption \%** & **Fineness Modulus** & **Maximum Size (mm)** \\ \hline NCA & 2.56 & 1.19 & - & 25.4 \\ \hline NFA & 2.59 & 1.88 & 2.53 & 4.76 \\ \hline RCA & 2.38 & 6.27 & - & 25.4 \\ \hline RFA & 2.28 & 11.86 & 3.38 & 4.76 \\ \hline \end{tabular}
\end{table}
Table 2: Physical characteristics of the aggregates.  
The values of the void ratio, porosity, and therefore hydraulic conductivity, in the case of RA are higher than a natural aggregate [19].  
The particle size distribution of the recycled and natural aggregates are shown in Figures 2 and 3.  
Ten mixtures were designed with a w/c ratio of 0.5 and with the proportions shown in Table 3. Mixture 1 (NAT) and mixture 10 (RA-100) are reference mixtures, the first one is of conventional concrete (it does not contain RA) while the second one does not contain natural (or conventional) aggregates, that is, it is made with 100% RCA and RFA. In mixtures 2 to 5, NCA was replaced by the  
Figure 3: Particle size distribution of fine aggregates.  
Figure 2: Particle size distribution of coarse aggregates.  
RCA in percentages of 20, 30, 60, and 100 (RCA-20, RCA-30, RCA-60, and RCA-100); in mixtures 6 to 9, NFA was replaced by RFA in percentages of 20, 30, 60, and 100 (RFA-20, RFA-30, RFA-60, and RFA-100).
## 2 MOFs for Fuel-Cell Applications  
### MOFs as Electrode Application  
The potential for supplying sustainable clean energy for Fuel-cell development through devices with capabilities to both store and deliver energy upon demand is accelerating efforts to engineer desirable MOF structures. However, these devices still seek technological improvement as usable electrocatalysts for the electrochemical reduction of oxygen as the base reaction (ORR) [45; 46; 47]. The durability and catalytic activity of electrocatalysts is pivotal for the long-term performance of the Fuel-cell system. Cost limitations combined with the precious metal shortage is a huge barrier to the commercialization of fuel despite their relatively low overpotential. However, the efficient usage of particles in nanoscale architectures provides a route for the expedition of metal sites at the surface and their dispersion in abundancy. There is a high potential for developing fuel cells in the future with these materials, especially those incorporating single-atom catalysts [48; 49]. As has been established by the in-depth characterization of these species and theoretical studies, it has been demonstrated that the M-Nx type of single-atom catalyst (SAC) shows functional importance in the ORR/OER reactions in H\({}_{2}\)/O\({}_{2}\) fuel cells [40; 50]. It has been identified that a MOF template synthesis method is a promising way of achieving the controlled synthesis of SACs through the pyrolysis of precursors. In addition, those nanostructures that emerge from MOF-derived SACs have excellent active-site support and excellent dispersion properties when applied to electrocatalytic processes. High absorbent properties that persist and are likely better controlled in self-removable supports deliver porous, hollow, structurally defined cavities [51; 52; 53; 54]. According to the results of our study, MOF-designed electrocatalysts for fuel cells are capable of improving their working durability and have the potential to replace precious metals as costly conventional materials. The performances of known metal catalysts are summarized in Table 1.  
Figure 3: The scheme showing the criteria for selection of MOF for electrocatalyst and electrolyte modification for PEMFCs.
## 5 Discussion
### The Role of the Yield Strain Rate  
In MBM, the yield strain rate \(\dot{\gamma}_{y}\) defined the fluid behavior in the regime of un-yield and yield zones. If the yield strain rate was zero, the material returned to Bingham fluid. As the yield strain rate became higher, the plug zone became wider. The fluid was harder to transfer from un-yield to yield phases. The behavior of the tailings was not monotone when \(\dot{\gamma}_{y}\) increased. There were two kinds of behavior of the tailings, presented in Figure 15. In the regime of \(0.0\leq\dot{\gamma}_{y}\leq 0.3\) s\({}^{-1}\), the result shows that when \(\dot{\gamma}_{y}\) increased, the inundation widths were narrower and the inundation lengths were longer. In the regime of \(0.3\leq\dot{\gamma}_{y}\leq 0.6\) s\({}^{-1}\), when \(\dot{\gamma}_{y}\) increased, not only were the inundation widths narrower, the inundation lengths were also shorter as well.  
Rheological properties of hyper-concentration are generally formulated as a function of the fluid concentration. [PERSON] (1986) recommended empirical formulas with the exponential relationships for yield stress and viscosity at large concentrations of fines. The typical values of coefficients for different types of mud, clay, and lahar are presented in Table 2. Kaolinite and Typical soils are utilized to describe the features of BM and MBM in this section. Eighteen numerical cases, including nine Bingham cases and nine modified Bi-viscosity cases with different concentration \(C_{v}\), are performed. The yield shear stress and the viscosity are presented in Table 3. The yield strain rate is specified as \(\dot{\gamma}_{y}=0.0\) s\({}^{-1}\) for the Bingham cases and \(\dot{\gamma}_{y}=0.2\) s\({}^{-1}\) for the modified Bi-viscosity cases. The goal of this analysis is to find a material, which has a similar property to the tailings in FGT66. A similar inundation profile is the key. Figure 16 shows the simulation results with different concentration \(C_{v}\) by BM and MBM. The deposited boundary of Kaolinite at \(C_{v}=0.5\) (red line in Figure 16b) has the best fit to the MBM result of FGT66 (red line in Figure 15). It is due to the similarity of \(\tau_{y}\) between two materials: Kaolinite \(\tau_{y}=1580\) Pa and the gypsum tailings \(\tau_{y}=1500\) Pa.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & **Yield Stress in Pa: \(\tau_{y}=a10^{\text{pC}_{v}}\)** \\  & & & **Viscosity in Pa s: \(\mu_{B}=0.001\times 10^{\text{pC}_{v}}\)** \\ \hline
**Material** & **Liquid Limit \(C_{v}\)** & **a** & **b** & **c** \\ \hline Bentonite & 0.05–0.2 & 0.002 & 100 & 100 \\ Sensitive clays & 0.35–0.6 & 0.3 & 10 & 5 \\ Kaolinite & 0.4–0.5 & 0.05 & 9 & 8 \\ Typical soils & 0.65–0.8 & 0.005 & 7.5 & 8 \\ Granular material & - & - & 2 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coefficients of the yield stress (yield shear stress) and viscosity relationships [PERSON] (1986).  
Figure 15: The stoppage boundary profiles of FGT66 predicted by the modified bi-viscosity model (MBM) for sensitivity analysis of yield strain rate \(\dot{\gamma}_{y}\) (unit: s\({}^{-1}\)).
# Factors Affecting the Quality of Bacterial Genomes Assemblies by Canu after Nanopore Sequencing
###### Abstract  
Long-read sequencing (LRS), like Oxford Nanopore Technologies, is usually associated with higher error rates compared to previous generations. Factors affecting the assembly quality are the integrity of DNA, the flowcell efficiency, and, not least all, the raw data processing. Among LRS-intended de novo assemblers, Canu is highly flexible, with its dozens of adjustable parameters. Different Canu parameters were compared for assembling reads of _Salmonella enterica_ ser. Bovis-morbificans (genome size of 4.8 Mbp) from three runs on MinION (N50 651, 805, and 5573). Two of them, with low quality and highly fragmented DNA, were not usable alone for assembly, while they were successfully assembled when combining the reads from all experiments. The best results were obtained by modifying Canu parameters related to the error correction, such as corErrorRate (exclusion of overlaps above a set error rate, set up at 0.40), corMhapSensitivity (the coarse sensitivity level, set to \"high\"), corMinCoverage (set to 0 to correct all reads, regardless the overlaps length), and corOutCoverage (corrects the longest reads up to the imposed coverage, set to 100). This setting produced two contigs corresponding to the complete sequences of the chromosome and a plasmid. The overall results highlight the importance of a tailored bioinformatic analysis.  
flongle; MinION; bacterial genome; _Salmonella enterica_; plasticity; de novo assembly; Canu; options; quality; contigs Article  
Faculty Engineering, 2012
# Inversion-Based Deblending in Common Midpoint Domain Using Time Domain High-Resolution Radon
## 2 Theory
### Radon Transform  
To solve the underdetermined issue seen in the inversion problem, we utilize the hyperbolic Radon transform (HRT) to further constrain the problem. This composite operator, which is made up of both the blending operator and the Radon transform operator, now reformulates the inversion into a sparsely constrained problem. The HRT is a variant of the Radon transform that sums across hyperbolic basis functions; in geophysics, this equation is defined as a function of arrival times and source-receiver offset and interval velocity, as seen in [PERSON] and Claerbout [15]:  
\[m(p,\tau)=\int_{h_{1}}^{h_{2}}d(h,t=\sqrt{\tau^{2}+p^{2}h^{2}})dh. \tag{3}\]  
Here, \(m(p,\tau)\) represents the Radon space data, \(p\) denotes the slowness, \(\tau\) is the two-way travel time, h1 and h2 are the lower and upper offset limits, respectively, and \(d\) is the data space to be transformed. The slowness \(p\) is defined as the inverse of velocity \(1/v\). Mapping based on hyperbolic arrivals allows for a precise and sparse representation of seismic data as seismic signals show up as hyperbolic events on standard seismic surveys. The Radon transform has been employed in various applications, including velocity analysis [15], multiple suppression [16], interpolation in the frequency or time domain [17], and deblending via denoising [8].  
Although the HRT is well suited for seismic data, it has several limitations. Primarily, the transform requires the hyperbolic arrivals to be symmetric and centred in the data space, which is not necessarily the case in real-world data. In reality, the dipping of subsurface structures causes hyperbolic arrivals to shift up-dip, resulting in non-centred arrivals. Symmetry is challenged by two factors: anisotropy and survey conditions. Surface sensor arrays are generally not placed on a perfectly flat surface, leading to deviations from hyperbolic arrivals. This issue can be easily resolved through a trace statics correction, which is part of the standard processing flow. For the anisotropy issue, a modified equation can be used to account for curve stretching in simple cases. The shift from centred locations presents a somewhat complex problem with multiple potential solutions. The first solution involves the implementation of an apex-shifted hyperbolic Radon transform (ASHRT) [18]. In this approach, multiple Radon panels are generated for a single data image, each corresponding to a series of expected shifted apex locations. While this solution is robust and maps well to seismic data, it significantly increases runtimes as there is no shortcut to generating Radon panels, and processing times are multiplied by the number of apexes scanned. The second solution to the centring problem involves transforming the data into the CMP domain, where apexes are centred even for dipping reflections. However, this solution has a major drawback: the CMP domain generally has worse sampling than other data domains, making aliasing a significant concern.