esa-sceva/satcom-chunk-collection huggingface dataset:
# Organic Crystals for THz Photonics
## 2 Second-Order Organic Nonlinear Optical (NLO) Crystals
### Optical and NLO Properties of Organic Crystals  
Figure 4 shows the crystal structures and the dominant sample morphology of some of the most well investigated organic NLO crystals for THz-wave generation during the last decade: the stilbazolium salts DAST and DSTMS (4-\(N\),\(N\)-dimethylamino-4\({}^{\prime}\)-\(N^{\prime}\)-methyl-stilbazolium 2,4,6-trimethylbenzenesulfonate), the non-ionic configurationally locked polyene crystal OH1 (2-(3-(4-hydroxystyryl)-5,5-dimethylcyclohex-2-enylidene)malononitrile) and the quinolinium salt HMQ-TMS (2-(4-hydroxy-3-methoxystyryl)-1-methylquinolinium 2,4,6-trimethylbenzenesulfonate). The relevant crystallographic properties and the microscopic/macroscopic NLO properties of  
these crystals are listed in Table 1, which includes another widely investigated material BNA (\(N\)-Benzyl-2-methyl-4-nitroaniline). Examples of refractive index dispersion and electro-optic coefficient dispersion in the optical range are shown in Figures 5 and 6. In the following subsections, we discuss these properties in more detail for the selected ionic and non-ionic organic NLO crystals.
## 4 Cluster Formation Algorithm Evaluation
### Number of Clusters and Average Cluster-Size  
The average number of clusters, N\({}_{\mathrm{C}}\), and the average cluster-size (\(\mu_{\mathrm{Csize}}\)) in a tier/network for different node densities with several overlapping scales are shown in Figures 5 and 6. Increasing the node density does not only cause an increment in the number of clusters but also yields more overlapping areas among FoVs and thus raises the cluster-size. However, the overlapping scale (\(\gamma\)) also impacts in the cluster membership selection process. The overlapping scale determines the minimum region that is required to be overlapped between the FoV of each node belonging to a given cluster and the FoV of the first member of that cluster. So, \(\gamma\) determines the minimum intersection part of FoV of each member with the first member of an established cluster. Lower overlapping scales obligate less overlapping areas for cluster membership and increase the domain covered by a given cluster since more nodes will be conforming to the membership rule. Increasing the overlapping scale restricts node membership because of higher required overlapping areas between FoVs of nodes. Thus, higher overlapping scales result in lower cluster-sizes, less MCCD and thus higher number of clusters.  
Sparse networks have low average cluster-size, \(\mu_{\mathrm{Csize}}\), because sparse deployments result in low overlapping areas. Moreover, high values of \(\gamma\) also will produce low \(\mu_{\mathrm{Csize}}\). The result will be lower potential for node coordination. On the other hand, dense wireless multimedia sensor networks can particularly benefit from higher cluster sizes and thus more potential for node coordination.  
Finally, Figure 7 shows the cumulative probability function for the cluster-size in the network for different node densities assuming an overlapping scale of \(\gamma=0.5\). For example, in a network consisting of 250 nodes, 28% of clusters have a single member which does not have enough overlapping with others to satisfy the overlapping scale, 32% of clusters have a cluster size of 2, 21% of 3, 12% of 4 and 7% of them consisting of more than four members.  
Figure 5: Average number of clusters (N\({}_{\rm C}\)).  
Figure 6: Average cluster-size (\(\mu_{\rm Csize}\)) for different number of nodes and overlapping scales.
# Quantifying Soil Moisture Impacts on Water Use Efficiency in Terrestrial Ecosystems of China
## 3 Results
### WUE Variability Caused by SM  
In this study, the absolute value of the percentage change in the _WUE_ (\(\Delta\textit{WUE}\)) was used as an index to analyze the _WUE_ variability. Figure 5a shows that the variation of _SM_ led to significant _WUE_ variability, especially in northeast and northwest China. At the growing season scale, the average \(\Delta\textit{WUE}\) due to the variation in _SM_ ranged from \(0\%\) to \(190.86\%\), with an average value of \(28.02\%\) (Figure 5a). For different climatic zones (Figure 5c), _SM_ in the CTMP had the most significant effect on _WUE_, causing an average variation in _WUE_ of approximately \(53.90\%\). In contrast, _SM_ in the PTMP had the least impact on _WUE_, causing only, approximately, a \(11.96\%\) variability in _WUE_. For the other climatic zones, _ANUIE_ caused by _SM_ variation in MTMP, WTMP, NST, MT, SST, MST, and PSF were \(38.93\%\), \(27.75\%\), \(23.04\%\), \(18.39\%\), \(14.78\%\), \(12.84\%\), and \(12.30\%\), respectively. The spatial trend of the maximum _WUE_ variability (\(\Delta\textit{WUE}_{\text{max}}\)) was similar to the variability of the _ANUIE_ (Figure 5b). It also had the greatest change in northeast and northwest China. Furthermore, the \(\Delta\textit{WUE}_{\text{max}}\) ranged from \(0\%\) to \(758.78\%\), with an average value of \(109.29\%\). These results indicated that the maximum change in _SM_ over the past \(30\) years doubled the variability in _WUE_. In different climatic zones, the trend of change in _ANUIE_max was the same as that for _ANUIE_, but the value of \(\Delta\textit{WUE}_{\text{max}}\) was larger than that of _ANUIE_. \(\Delta\textit{WUE}_{\text{max}}\) was generally \(3.7\)-\(4.4\) times greater than _ANUIE_ (Figure 5c).  
The feedback relationship between _SM_ and _WUE_ was affected by the climatic factors and vegetation cover. The ratio of vegetation cover between forest and non-forest was also an essential factor affecting the feedback relationship between _WUE_ and _SM_ (Figure 5d). The lower the forest cover, the more significant the change in _WUE_ caused by changes in _SM_. As the forest cover increased, the variability of _WUE_ caused by changes in _SM_ rapidly decreased. When the cover ratio of forest to non-forest vegetation was higher than one, the variability of the _WUE_ caused by changes in the _SM_ tended to stabilize. The _WUE_ of the forest ecosystem had strong resistance to variation in soil water. In contrast, the _WUE_ of non-forest vegetation was more sensitive to the changes in _SM_.
# Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
## 2 Proposed Methodology
### CNN and LSTM Hybrid Structure  
In this study, a hybrid model that integrates CNN and LSTM networks to extract features from pipeline data was used. This section outlines the individual components of CNN and LSTM and then describes their combined structure, specifically designed for the feature extraction task.
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 3 Satellite Measurement of Precipitation
### Science and Technology Advances
#### 3.1.2 Precipitation Products  
The quantitative investigation of the water cycle requires global precipitation products at the appropriate time scales. Several products are available and are used for global and regional studies and for the comparison/evaluation of model output. Since the TRMM era, global precipitation estimates from satellite have become more quantitative, thus filling gaps, especially in the tropics. Most datasetsare nowadays gauge-corrected to ensure the necessary calibration; the most used for climate studies is the Global Precipitation Climatology Project (GPCP, [117]) dataset, which is the longest satellite precipitation record (40 years) based on IR and MW data. Mean values, variations, and trends during the satellite era have been computed from this dataset [118]. Global IR-based datasets cover reasonably long time spans, such as the Climate Hazards Center's Infrared Precipitation with Stations (CHIRPS, [119]) and the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP, [120]). Among the merged IR/MW or MW-only products, it's worth noting the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN, [121]), the CPC MORPHing technique (CMORPH, [122]), and the Global Satellite Mapping of Precipitation (GSMaP, [123]). Africa has always been an important target of precipitation estimation, given its relatively poor gauge and radar coverage; the same is true for the tropics, given their importance for global precipitation. Datasets over Africa include the Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT, [124]), the Africa Rainfall Climatology 2.0 (ARC2, [125]), and the Tropical Amount of Rainfall with Estimation of ERrors (TAPEER, [126]). The launch of GPM has initiated a community effort to transition from the TRMM Multi-satellite Precipitation Analysis (TMPA, [127]) to the Integrated Multi-satellite Retrievals for GPM (IMERG, [128]).  
More recently, the idea emerged to take full advantage of the complementary nature of gauge, radar, satellite, and reanalysis data. The Multi-Source Weighted-Ensemble Precipitation (MSWEP) merges gauges, satellite, and reanalysis data. It is now available at 0.25\({}^{\circ}\)[129] and 0.1\({}^{\circ}\)[130] spatial resolution, and is widely used for water cycle closure experiments. Another experimental merged dataset has been developed over China at 1-km resolution, and is based on high-density gauge observations, high-resolution weather radar quantitative precipitation estimation (QPE), and seamless satellite-based precipitation estimates [131].
# Studies on the Electrochemical Behavior of Sulfite on Incoloy 800 in a Neutral Environment
## 1 Introduction
investigated the anodic oxidation of sulfite in 0.1 M Na2 SO4 on lead dioxide placed on platinum, where linear voltammetry tests have revealed that sulfite is oxidized on the anode using lead dioxide [36]. Graphite is one of the most extensively investigated anode materials for sulfite oxidation, particularly in neutral and basic conditions [37; 38]. According to [PERSON] et al., the process of sulfite oxidation on a graphite rotating disk anode involves the generation of sulfite radicals in the first stage, followed by their oxidation to sulfate via reactions (1) and (2). [PERSON] et al. investigated the electrochemical behavior of sulfite in neutral medium on a graphite anode to determine the link between sulfite content and the kinetic parameters that define the sulfite oxidation process [39]. [PERSON] et al. employed chronoamperometric and chronocoulometric techniques to determine the degree of sulfite conversion to sulfate [34]. [PERSON] and [PERSON] demonstrated that in order to produce consistent results from the oxidation of sulfites on graphite, a surface renewal approach is required, as the graphite surface degrades throughout the anodic process [40]. This is why graphite is unsuitable for industrial-scale sulfite oxidation procedures. The main aim of the studies presented in this paper is to determine the kinetic parameters that characterize the anodic oxidation of sulfites to sulfates on Incoloy 800 in a neutral 1 mol L\({}^{-1}\) solution. As well, the mechanism of the anodic process is discussed based on cyclic voltammetry and electrochemical impedance spectroscopy.
## 4 Results
### Dynamic versus Static Measurements Results and Validation
#### 4.2.2 Resulting PAT 4-Quadrant Characteristic  
In the case of the PAT, an accentuated hysteresis, specific to S-shaped characteristic curves, is noticed for high rotational speed values. To this end, considering the speed, the discharge and the power factors (Equations (7)-(9)), the resulting S-shaped 4-quadrants characteristic curves of the PAT measured through the dynamic method are provided in Figure 10. Focusing on the discharge-speed characteristic, all the measurements are clustered on only one S-shaped curve. Indeed, this is explained by the fact that the machine has not been designed with any distributor, its characteristic being similar to the one of a pump-turbine at fixed guide vanes opening angle. However, on a power-speed representation, depending on the recirculating pumps' speed, several S-shaped characteristics and hysteresis are noticed. Concerning the hydraulic configuration of the test rig for these 4-quadrants measurements, no special adjustment has been applied, the machine running smoothly between the turbine and reverse-pump modes, and vice versa.  
\[\mathrm{N_{ED}}=\frac{\mathrm{N}\times\mathrm{D_{ref}}}{60\times\sqrt{\mathrm{ E}}}\;[-] \tag{7}\]  
\[\mathrm{Q_{ED}}\;=\frac{\mathrm{Q}}{\mathrm{D_{ref}^{2}}\sqrt{\mathrm{E}}}\;[-] \tag{8}\]  
\[\mathrm{P_{ED}^{*}}\;=\;\frac{\mathrm{P_{meo}}\;/\;\mathrm{elec}}{\rho\times \mathrm{D_{ref}^{2}}\mathrm{E}^{1.5}}\;[-] \tag{9}\]
## 3 Results And Discussion
### Calculation Results of Flow Noise  
According to the acoustic solution theory, sound sources can be divided into three types: monopole, dipole, and quadrupole sources. In the flow field radiation noise, the noise caused by turbulent phenomena such as boundary layer and vortex development in the flow field is a quadrupole noise source, and the vehicle wall pressure pulsation caused by turbulent activity is a dipole noise source. Monopole and dipole noises are surface sources, whereas quadrupole noise is a volume source. Some studies reported that when the flow velocity is low, the surface-source noise can be mainly considered,  
Figure 4: Vortex structure.  
Figure 3: Steady-state calculation results. (**a**) Distribution of sound power level (**b**) Distribution of turbulent kinetic energy.  
and the volume-source noise is neglected. Therefore, most researchers do not consider the quadrupole source when solving and directly use the vehicle wall pulsation pressure as the source excitation for calculation. However, the underwater vehicle is faster. The quadrupole noise ratio may increase in this case, and the flow field results calculated in the previous section may improve. The underwater vehicle has a large wake vortex area, which is filled with large and small vortices, and its generation, development, and breaking inevitably generate noise. Therefore, it can be inferred that the noise calculation in this region cannot ignore the influence of quadrupole noise source generated by the vortex motion. A comparative analysis of the study was performed using wall pulsation pressure and Lighthill volume sound source as the excitation for acoustic calculations. For all the results reported in this paper, the reference sound pressure is taken as \(1\times 10^{-6}\) Pa.
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 4 Experimental Setup  
The current deep neural network-based speaker recognition models are mainly x-vector structures [19]. It learns speaker embeddings using speaker features extracted from a speaker-discriminative network. Several CNN structures such as TDNN, Repvgg [28] and ResNet are mostly used in this approach. Typically, the network is trained to classify speakers in the training set. Then we obtain an utterance-level speaker embedding by aggregating speaker features through the pooling layer of the network.
## 3 Results and Discussion
### Impact of Temperature and Global Radiation on Cultivar-Specific Anthocyanin Profiles
These previous studies have focused on fruit developmental stages and total anthocyanins only [38; 44], or cya-hexose, pel-hexose and pel-rutinose [45]. The current study considered different time intervals up to 15 days prior to harvest and took into account not only the total anthocyanin but all individual anthocyanins evaluated. As expected, pel-3-glc were cultivar-specific, and presented only the last five days before harvest. However, in our study, it was surprising to find environmental effects occurring at an earlier fruit developmental stage affecting the different anthocyanins. For example, in 'Candonga' fruits, global radiation inhibited its synthesis up to 15 days pre-harvest. Furthermore, 'Candonga' was the exclusive cultivar where pel-3-rut, cya-3-mal-glc and pel-3-glc were not found for T\({}_{\text{max}}\) at d10 and d15 before harvest whereas they were partly present in 'Florence', 'Frida' and 'Sonata'. The synthesis of the other individual anthocyanins was affected by temperature and less by global radiation when taking the whole period into account. It is assumed that at this early stage, photosynthesis was enhanced by favorable temperature and radiation conditions and, in that way, precursors of the anthocyanins like carbon skeletons were accumulated. When evaluating primary and secondary metabolites during strawberry development [45], a decrease of diverse sugars was found over time, while anthocyanins increased at the late fruit developmental stages. Herein, the impact of location on anthocyanin profiles of certain cultivars was systematically studied. The obtained results showed that the present work could serve as a useful starting point towards evaluating the latitudinal effects on plant performance and internal fruit quality. However, some further specific studies are required to validate this. In addition, certain optimizations are still required for accounting the seasonal variations in the global radiation and temperatures. These issues will be addressed in detail in our near future research work.
## 2 Materials and Methods  
### Methods  
#### 2.1.1 Chemical Transport Model  
The WRF-Chem [49; 50] Version 4.1.5 [51] was used as the atmospheric chemical transport model of CO and 35 other gases. The chemical and meteorological components of WRF-Chem [49; 50] are simulated simultaneously and fully consistently with each other by using the same mass- and scalar-preserving transport scheme, the same physics schemes for subgrid scale transport, and the same grid cell dimensions. The configure options of the WRF-Chem [49; 50] model are listed in Table 1. The default model for ozone and related chemical tracers and GOCART aerosols (MOZCART) [52; 53; 54], with the Kinetic Preprocessor (KPP) library [55], is used in this study, and the chemical reactions and reaction rates can be found in [52].  
As shown in Figure 1, the WRF-Chem [49; 50] domain covered western Europe and had 99 (west-east) \(\times\) 99 (south-north) grid cells with a spatial resolution of 27 km. In the vertical direction, the atmosphere was decomposed into 36 terrain-following levels from the surface to the upper boundary at 50 hPa.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Options** & **Configuration** \\ \hline Domain Center & 46.982\({}^{\circ}\) N, 3.626\({}^{\circ}\) E \\ Grid Resolution & 27 km \\ nx, ny, nx & 99, 99, 36 \\ Time Step & 150 s \\ Microphysics Process & NSSL 2-moment Scheme [56; 57] \\ Cumulus Parameterization & Kain–Fritsch Scheme [57; 58] \\ Longwave Atmospheric Radiation & RRTMG scheme [57; 59] \\ Shortwave Atmospheric Radiation & RRTMG scheme [57; 59] \\ Planetary Boundary Layer Scheme & YSU [57; 60] \\ Land surface scheme & MMS [57; 61] \\ Chemical Option & MOZCART [52; 53; 54] using KPP library [55] \\ Photolysis Option & Madronich F-TUV photolysis [52; 62] \\ \hline \hline \end{tabular}
\end{table}
Table 1: WRF-Chem configurations.  
Figure 1: The WRF-Chem domain and locations of ICOS ground-CO observation sites used for evaluation. Projection method is Lambert. The center of the domain is at 46.982\({}^{\circ}\) N and 3.626\({}^{\circ}\) E. Dots in different colors represent four different types of ICOS atmosphere stations.
# Improving Streaming Video with Deep Learning-Based Network Throughput Prediction
## 4 Prediction of Network Throughput
### Model Validation  
The prediction is more accurate for traces generated by static terminals compared with the mobile ones; see Figure 7. Furthermore, the traffic from 4G systems obtained a lower NRMSE than the 5G traces. These differences may result from the positive correlation between the prediction errors and variance of the traces. The traces generated by mobile scenarios have higher variation than the static ones, see Table 1. Additionally, the 5G traces, due to higher network bandwidth, achieved higher peak values. The variation of the results is roughly on the same level, except for the 5G mobile scenario where the NRMSEs have a higher range. Compared to a simple moving average, the LSTM predictions achieved lower NRMSE. The difference is particularly notable for the mobile and 5G scenarios. In all scenarios, the best result was obtained by the BdLSTM.  
In Figure 8, we presented a fragment of the M5G trace to visualize the traffic characteristics and the prediction accuracy. The trace has an irregular structure without clearly identifiable patterns. The traffic frequently and abruptly changes its volume, producing oscillations between 2.5 and 5 MB/s. The prediction generally follows the traffic pattern relatively well; however, sometimes, it overestimates the scope of the oscillations, especially in the fragments where the traffic fluctuations have a higher frequency.  
The prediction errors for the exemplary M5G trace have a normal distribution with a zero mean, as shown in Figure 9a. The errors are between \(-1.0\) and \(1.0\) MB/s, and most do not exceed \(0.5\) MB/s. Compared with the case of the simple moving average shown in Figure 9b, its histogram is broader, and consequently, the errors have higher values than for the LSTM prediction.  
Figure 7: NRMSE of the analyzed prediction models for different traces. Every box on the plot represents predictions made on ten overlapping sub-traces every 30 minutes, which were extracted from traces listed in Table 1.
## 3 Method Implementation and Application  
This section presents the method application and its results with respect to the selected case study, which is the development of an electricity pylon for the Abruzzo region, in Italy. We selected this region because it presents different landscape areas, such as the coastal area, the mountains area with very high peaks, and natural reserves, but also important cultural and historical cities; so, the problem of the visual impact of artificial structures on these landscapes is highly relevant. This landscape variety, as well as this cultural and traditional richness, are common to many other European regions and worldwide countries, to which the method can be further applied.  
In the following subsections, we describe how to apply the proposed method to this territory and present the developed outcomes. Our main goal is the development of a structure with an improved visual quality respect to the state-of-the-art, but fully respecting all the requirements, such as regulatory, safety, industrial feasibility, and social components' needs.  
The suspension pylons of power lines may be developed in an advantageous way, making them an element of enrichment of the landscape itself. This is the idea underlying all the pylons previously called \"aesthetic\". However, in the proposed method, we want to take a further step by designing the pylon as an identification element of the landscape where it will be placed.  
The case study addresses the design of a new electricity pylon for a 380-kV line to be installed in the Abruzzo territory. However, it should be noted that its application to a specific case does not limit its generality; indeed, it is easily extendible to global landscapes for which it is possible to identify characteristic shapes.