esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data
#### 2.2.3 Digital Elevation Model  
Given the complex topography of the territory, and to be consistent with S2 imagery, we used a DEM at 20 m resolution. It was created by merging the Digital Terrain Models (DTMs) available on the geoportals of Piedmont (5 m resolution, [[https://www.geoportale.p](https://www.geoportale.p)]([https://www.geoportale.p](https://www.geoportale.p)) iemonte.it/geonetwork/srv/ita/catalog.search#/metadata/r_piemon:224 de2 ac-023e-441c -9 ae0-ea493b217a8e, accessed on 5 February 2021) and Aosta Valley (2 m resolution, [[https://geoportale.regione.vda.it/download/dtm/](https://geoportale.regione.vda.it/download/dtm/)]([https://geoportale.regione.vda.it/download/dtm/](https://geoportale.regione.vda.it/download/dtm/)), accessed on 5 February 2021) Regions and the European Digital Elevation Model version 1.1 (EU-DEM, 25 m resolution, [[https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1](https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1)]([https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1](https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1)), accessed on 5 February 2021), to fill the remaining study area.
# A Novel Methodology for Planning Urban Road Safety Interventions
## 4 Validation of the Proposed Methodology via Case Study Application
### Cost-Benefit Analysis of Safety Interventions in Parma
#### 4.4.1 Critical Point A  
The iRAP model was used to estimate the potential annual reduction in fatalities and serious injuries for two intervention options at critical point A. The measure of \(FSI_{saved}\) highlights the risk reduction compared to doing nothing (Opt.0), considering traffic volumes in each direction (see Table 2).  
For each proposed intervention, the reduction in social cost (SC) was estimated using unit costs established in Italy for fatalities-F ($1,812,989.00) and serious injuries-SI ($467,159.00) in road accidents. The total benefit of these interventions was calculated by summing the benefits across both traffic directions (see Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{[n\({}^{\circ}\)/Year]} & \multicolumn{2}{c}{**Fatalities**} & \multicolumn{2}{c}{**Serious Injuries**} & \multicolumn{2}{c}{**FSI**} & \multicolumn{2}{c}{**FSI\({}_{saved}\)**} \\ \cline{2-10}  & **East** & **West** & **East** & **West** & **East** & **West** & **East** & **West** \\ \hline Opt.0 & 0.402 & 0.026 & 2.213 & 0.147 & 2.615 & 0.174 & - & - \\ Opt.1 & 0.072 & 0.015 & 0.398 & 0.087 & 0.471 & 0.102 & 2.144 & 0.071 \\ Opt.2 & 0.137 & 0.022 & 0.754 & 0.126 & 0.891 & 0.148 & 1.724 & 0.025 \\ \hline \hline \end{tabular}
\end{table}
Table 2: FSI saved for the east and west directions.  
Figure 20: Accidents between motor vehicles at the signalized intersection, outputted by GIS.  
The cost determined in this manner (for both critical points A and B) represents only the construction cost within the economic framework. However, it excludes the additional funds that are part of the real cost. Among the main components of the additional funds are technical expenses, which, typically in Italy, range between 12 and 15% of the construction cost, as well as VAT (in Italy is equal to 22%). Therefore, a multiplicative factor (1.35) is introduced to account for 'other costs, VAT included', which provides an accurate estimate of the total cost in the economic framework (real cost). Since a CBA (cost-benefit analysis) methodology is being proposed, it is essential that costs and benefits are fully comparable (homogeneous).
# Automating Visual Blockage Classification of Culverts with Deep Learning
## 3 Dataset
### Visual Hydrology-Lab Dataset (VHD)  
This dataset consisted of simulated images of culverts captured from a controlled hydrology lab experiment. A comprehensive in-lab investigation of blockage of carried out by performing series of experiments using scaled physical models of culverts under multiple flooding conditions. Experiments were recorded using two high definition (HD) cameras with a different view of the culvert and images of culverts in blockage and clear condition were extracted, referred to as \"Visual Hydrology-Lab Dataset (VHD)\". VHD consisted of a diversity of images including four different culvert configurations (i.e., single circular, double circular, single box, double box), different blockage types (i.e., urban, vegetative, mixed), different simulated lighting conditions, different camera viewpoints, and different flood levels controlled by inlet water discharge. Limitations of the dataset included reflections from the water surface and flume walls, identical background and scaling, and clear water. For this investigation, in total, 1630 images were used with 1526 images from the \"blocked\" class while 104 images from the \"clear\" class. Figure 2 shows the sample images of each class from VHD.
# A Physical-Layer Watermarking Scheme Based on 5G NR
## 2 Watermark System Model on 5G NR  
This paper mainly studies the physical-layer uplink shared channel (PUSCH) in a 5G system. In a 4G LTE system, to reduce the peak-to-average ratio of the system, the multiple access mode used by the PUSCH is SC-FDMA instead of OFDMA, whereas in a 5G system, these two multiple access modes are applied simultaneously [17]. The signals in the PUSCH can be divided into users' signals and the DMRS. According to 5G protocol, the base sequence format of the DMRS is different according to the multiple access mode and the number of subcarriers, which can be divided into three categories: the base sequence applied to OFDMA, the base sequence applied to SC-FDMA with no fewer than 36 subcarriers, and the base sequence applied to SC-FDMA with no fewer than 36 subcarriers [18]. In the third case, this paper designs a watermarking scheme and assumes that the modulation mode of the user's signal is 16-ary quadrature amplitude modulation (16 QAM). The system model after embedding the watermark is shown in Figure 1.  
In Figure 1, the black box represents the signal generation process specified by the existing 5G protocol, and the red box represents the embedding and extraction process of the watermarking scheme designed in this paper. It is worth noting that the design of this watermarking scheme was applied to the existing 5G NR system, but to minimize the coupling with the existing system, this watermarking scheme is independent of the existing 5G protocol stack. Therefore, in the watermarking scheme, the processes of channel coding and scrambling of user bits are not involved (scrambling sequence involves higher layer configuration, which is unknown in this watermarking scheme).  
In the communication system, the watermark is first generated by the sender and embedded in the transmitted signal, and then the receiver extracts the watermark from the transmitted signal. At the same time, the receiver also regenerates a watermark and compares the generated watermark with the extracted watermark to complete the authentication process.  
Figure 1: Watermark system model on 5G NR. The black box represents the signal generation process specified by the existing 5G protocol, and the red box represents the embedding and extraction process of the watermarking scheme designed in this paper. In the model, FDMA means frequency division multiple access and DMRS means demodulation reference signal.
# Evaluation on Radiometric Capability of Chinese Optical Satellite Sensors
## 2 Materials and Methods
### Methods  
The procedure in this paper for evaluating radiometric capability of Chinese optical satellite sensors using MODIS is shown in Figure 2 and it includes three parts: (1) calculating the TOA reflectance; (2) spectral matching to eliminate the influence of different spectral response; (3) evaluating the radiometric capability of the sensors using the long-term series TOA reflectance between sensors and MODIS after spectral matching.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Satellite**} & \multirow{2}{*}{**Sensor**} & \multirow{2}{*}{**Band**} & \multirow{2}{*}{**Spatial Range (mm)**} & \multirow{2}{*}{**Resisult (mm)**} & \multirow{2}{*}{**Resisult Period (Days)**} & \multirow{2}{*}{**Arbit (km)**} & \multirow{2}{*}{**Local Equator- (km)**} & \multirow{2}{*}{**Field of View Angle (\({}^{\circ}\))**} \\ \cline{6-9}  & & & & 430-520 & 30 & & & \\ \cline{3-9} HJ-1A/B & \multirow{2}{*}{\
\begin{tabular}{c} CCD1 \\ and \\ CCD2 \\ \end{tabular} } & Green & 520-600 & 30 & & & & & \\ \cline{3-9} CCD2 & \multirow{2}{*}{
\begin{tabular}{c} CCD1 \\ and \\ \end{tabular} } & Red & 630-690 & 30 & & 2 days for A and & 649 & 10:30 a.m. in descending node & 31 \\ \cline{3-9}  & & Near infrared & 700-900 & 30 & & & & \\ \hline \multirow{3}{*}{FY-3} & \multirow{3}{*}{MERSI} & Blue & 445-495 & 250 & & & & \\ \cline{3-9}  & & Green & 525-575 & 250 & & & & \\ \cline{3-9}  & & Red & 630-678 & 250 & & & & \\ \cline{3-9}  & & Near infrared & 838-993 & 250 & & & & \\ \hline \multirow{3}{*}{FY-3} & \multirow{3}{*}{VIRR} & Blue & 431-490 & 1100 & & & & \\ \cline{3-9}  & & Green & 518-581 & 1100 & & & & \\ \cline{3-9}  & & Red & 573-684 & 1100 & & & & \\ \cline{3-9}  & & Near infrared & 820-998 & 1100 & & & & \\ \hline \multirow{3}{*}{Terra/Aqua} & \multirow{3}{*}{MODS} & Blue & 459–479 & 500 & & & & \\ \cline{3-9}  & & Green & 545-565 & 500 & & & & \\ \cline{3-9}  & & Red & 620-670 & 250 & & & & \\ \cline{3-9}  & & Near infrared & 841-876 & 250 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The primary characteristics of participating bands of CCDs, MERSIs and VIRRs.
## 4 Results and Discussion
### Practical Applications of NAS in GANs
#### 4.5.2 Data Augmentation for Limited Datasets  
In many domains, the availability of large datasets is a significant bottleneck. NAS-GAN approaches can produce diverse, high-quality synthetic images that mirror the statistical properties of the original data, thereby augmenting training datasets. For instance, a study on data augmentation using GANs demonstrated that augmenting CT scan datasets can improve diagnostic performance [58]. Such methods are particularly beneficial in rare disease scenarios where only a few examples are available [17].
# Analysis of Application Parameters of Hydraulic Slotting Technology in Jointed Coal Reservoirs
## 3 Optimization of Slotting Parameters  
In the process of continuous water jet impact on coal and rock, a large number of cracks are produced, and this greatly improves the permeability of the coal seam. Theoretically, as the pump pressure increases, the slotting effect improves. However, during field applications, various problems may appear if the pump pressure is high. The rotation slotting speed and pump pressure are the typical hydraulic slotting application parameters. Experimental testing is an effective method for investigating field applications and determining the appropriate pump pressure. The size of the slot and the crack development in the coal were investigated using computerized tomography (CT) scanning technology. The specimen was made as a briquette to eliminate the influence of natural cracks (pre-existing cleats) on the results. The strength of the specimen was low. When the water jet impact is oblique, the impact force of the water jet will damage the specimen. Eventually a wedge-shaped pit will form instead of a slot. Therefore, we did not research the effect of incident angle in this experiment, and the water jet incident angle was set at 90\({}^{\circ}\).
# Programmable Organic Chipless RFID Tags Inkjet Printed on Paper Substrates
## 2 The Tags and the Reader  
As previously mentioned, near-field chipless RFID systems comprise the reader and the tag. The sensitive part of the reader consists of a microstrip transmission line loaded with a square-shaped split ring resonator (SRR) in bandpass configuration [57; 58], as can be seen in Figure 1. When the tag, consisting of a set of identical resonators to that of the reader but oppositely oriented, is located in close proximity to the sensitive part of the reader, coupling between the resonators arises, and the overall response is shifted down. A large excursion of the transmission coefficient at the design frequency generates a high dynamic range at that frequency, i.e., a high difference between two transmission coefficient values. In Figure 2, the unloaded reader allows the signal to be transmitted (equal to the '1' logic state); meanwhile, the reader loaded with the tag resonator blocks and reflects back the signal (equals to the '0' logic state). Note that the simulated response depicted in Figure 2 is a general solution of a tag formed with one resonator element and considering a Rogers substrate with characteristics shown in this figure caption.
# Monitoring Changes in the Enhanced Vegetation Index to Inform the Management of Forests
## 2 Materials and Methods
### Data
#### 2.2.3 Climate and Other Data Sources  
We extracted 30 climate-related variables from ClimateNA (version 7.31, [50]) for the period 1998-2021 and converted them to gridded data (i.e., raster formatted data). We linked these gridded data to our response data (i.e., EVI breaks, see Section 2.3.1 below) by matching pixel ID and year. Then, we created time-lagged versions of these variables by keeping the pixel ID match but modifying the year match by 1, 2, and 3 years (1-, 2-, and 3-year temporal lags). We applied variance inflation factors (VIF, threshold: VIF \(<5\)) to the full set of climate variables, keeping only six climate variables that were not collinear: winter degree days above \(5\,\mathrm{\SIUnitSymbolCelsius}\) (winter DD5) with a 1-year lag, winter DD5 with a 3-year lag, summer climate moisture index (summer CMI), summer CMI with a 1-year lag, summer CMI with a 2-year lag, and summer CMI with a 3-year lag. Warm winters and hot dry summers can reduce water availability and increase evapotranspiration, which in turn affect tree growth, health, and the provisioning of forest ecosystem services [51; 52; 5].  
We also used Canada's protected areas boundaries [53] and elevation data (NASA Shuttle Radar Topography Mission Global, 1 arc second \(\approx 30\) m resolution, product: SRTMGL1 v003). A list of key data sources is shown in Table A1 and more details are provided in Appendix A.1.
## 3 Methodology  
The methodology flow chart used in this research is exhibited in Figure 4. Combined with Sentinel C-band monthly composites and percentile composites, interval mean composites and percentile composites of Sentinel-2 were used to identify corn. Moreover, elevation and slope are auxiliary data in the classification. A RF classifier incorporated in the GEE platform was employed to recognize water, urban, forest, corn, and other crops. We integrated pixel-based classification results and object-based segmentation generated by eCognition software and Sentinel-2 cloud-free composites to remove the'salt-and-pepper effect' [61]. Finally, certain field points that were not used to train the model and census data were used to jointly inspect the accuracy.
# Crust and Upper Mantle Structure of Mars Determined from Surface Wave Analysis
###### Abstract  
The crust and upper mantle structure of Mars is determined in the depth range of 0 to 100 km, by means of dispersion analysis and its inversion, which is performed for the surface waves present in the traces of the seismic event: S1094b. From these traces, Love and Rayleigh waves are measured in the period range of 4 to 40 s. This dispersion was calculated with a combination of digital filtering techniques, and later was inverted to obtain both models: isotropic (from 0 to 100 km depth) and anisotropic (from 0 to 15 km depth), which were calculated considering the hypothesis of the surface wave propagation in slightly anisotropic media. The seismic anisotropy determined from 0 to 5 km depth (7% of S-velocity variation and \(\xi\sim\) 1.1) could be associated with the presence of sediments or lava-flow layering, and wide damage zones surrounding the long-term fault networks. For greater depths, the observed anisotropy (17% of S-velocity variation and \(\xi\sim\) 1.4) could be due to the possible presence of volcanic materials and/or the layering of lava flows. Another cause for this anisotropy could be the presence of layered intrusions due to a single or multiple impacts, which could cause internal layering within the crust. Finally, the Moho depth is determined at 50 km as a gradual transition from crust to mantle S-velocities, through an intermediate value (3.90 km/s) determined from 50 to 60 km-depth.  
L +
Footnote †: journal: Applied Sciences
## 4 Results  
### Hydrodynamic Response  
A comparative study of the SFT response using the three different cable configurations defined in Table 1 is presented. The configuration that proves to be the most promising is then used for further investigations.  
The wave and current forces are obtained using the hydrodynamic conditions of Qiandao Lake (Table 2). These forces acting on the SFT are calculated using Morison's equation. During the numerical simulations, the drag forces and inertia forces were considered distributed loadings and were converted to equivalent nodal forces using the work-equivalence method [29]. The time history of these forces per unit length of SFT is shown in Figure 3. The transverse and vertical inertia forces are dominant hydrodynamic forces, while the drag forces are very small.
## 3 Proof of security of BB84 against collective attacks
### The Shannon distinguishability  
We shall use \(\mathit{SD}(\alpha,\beta)\) as it is defined in [4; 5] to denote the _Shannon Distinguishability_ between the state (or density matrix) \(\alpha\) and the state (or density matrix) \(\beta\). Consider the following protocol: [PERSON] chooses '\(0\)' or '\(1\)', randomly with equal probability. If [PERSON] chooses '\(0\)', he sends the state \(\alpha\) over to [PERSON]. Otherwise, he sends \(\beta\). \(\mathit{SD}(\alpha,\beta)\) is by definition [PERSON]'s accessible information i.e. the maximum mutual information between [PERSON]'s encoded bit and [PERSON]'s measurement of the state she received. Notice that when \(\alpha\) and \(\beta\) are orthogonal (thus they form a basis), [PERSON] can always distinguish between them, and has information of exactly 1 bit about [PERSON]'s chosen bit. On the other hand, if \(\alpha=\beta\), [PERSON] can never distinguish between those states, and she has 0 bits of information. Important result of the \(\mathit{SD}\) function are summarized in the following lemma:  
**Lemma 2**.: _(a) If \(\widetilde{\rho}_{x}\) is a lift-up of \(\rho_{x}\) (where \(x\in\{0,1\}\)), then \(SD(\rho_{0},\rho_{1})\leq SD(\widetilde{\rho}_{0},\widetilde{\rho}_{1})\); (b) The Shannon Distinguishability of two states can be bounded by half the Trace Norm of their difference: \(\mathit{SD}(\rho_{0},\rho_{1})\leq\frac{1}{2}\operatorname{tr}|\rho_{0}-\rho _{1}|\)_  
Proof.: See [4, Theorem 1 and 2].
# Cyclic Elasto-Plastic Behaviour of 51 CrV4 Steel for Railway Parabolic Leaf Spring Design
## 3 Material and Analysis Procedure  
The calibration of the cyclic behaviour of the steel under investigation, 51 CrV4, was carried out considering two approaches, experimental and computational. In the experimental approach, data collected from cyclic tests were used to estimate kinematic and isotropic hardening parameters using statistical fitting models. The estimated parameters were considered in the computational models in order to compare and validate the Chaboche model for 51 CrV4 spring steel applied to parabolic leaf springs of railway freight wagons.
## 4 Conclusions  
We have carried out simulations for the hard X-ray beamline (10 GeV e-beam) and for the soft X-ray beamline (3.15 GeV e-beam) of Korean XFEL to assess the performance of the multi-electron spike scheme. The simulation results indicate that an isolated attosecond radiation pulse of 0.6 TW power and a 130 attosecond duration can be achieved at a photon energy of 12.4 keV in a \(\sim\)75 m long uniform undulator, which can be further scaled up to 1 TW and 115 attosecond duration in a 75 m long tapered undulator. Similarly, in the soft X-ray case, a radiation pulse of 0.75 TW power and 258 attosecond duration can be achieved at a photon energy of \(\sim\)1 keV (1.25 nm) in a \(\sim\)60 m long uniform undulator, which can be further enhanced up to 1.35 TW and a duration of 230 attosecond via tapering to a few downstream undulator sections within a 60 m long undulator. We noticed that tapering helps in suppressing the amplification of satellite pulses. Only a target pulse (seed pulse) is amplified due to on-resonance amplification. Moreover, the undulator tapering allows one to achieve a two-fold increase in the peak power within the same undulator length. Additional work on the careful optimization of electron beams, lasing domains, and ESASE parameters may further improve the performance of this scheme in SASE FELs. Such high power and short X-ray pulses will be useful in such research fields as bioimaging, mostly for single-molecule imaging [11], as well as ultrafast science.  
This research has been supported in part by the Global Research Laboratory Program [Grant No. 2009-00439], by the Max Planck POSTECH/KOREA Research Initiative Program [2016K1A4A4A01922028], through the National Research Foundation of Korea (NRF) funded by Ministry of Science, ICT Future Planning, and by the Nuclear Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2012027506). [PERSON] acknowledges the support of the Max Planck Center for Attosecond Science (MPC-AS). S.K. and D.E.K. conceived and designed the simulation method; S.K. performed the simulations and analyzed the data; S.K., A.S.L. and D.E.K. wrote the paper.  
The authors declare no conflict of interest.
# Spatiotemporal Variation in Absorption Aerosol Optical Depth over China
## 2 Data and Methodology
### MERRA-2 Data  
The MERRA-2 (Modern-Era Retrospective analysis for Research and Application-2) data reanalysis dataset is produced by NASA's Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4 [43]. At present, the MERRA-2 is the most significant long-term global reanalysis from 1980 to the present, representing the interactions of aerosols with other physical processes in the climate system [44]. A spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.625\({}^{\circ}\) and 72 layers from the surface to around 80 km is provided in the MERRA-2 dataset. MERRA-2 level 3 supplies 1-hourly, 3-hourly, and monthly products in the NASA website [45].