esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
With the aim of improving the understanding of water exchanges in medium-scale catchments of northern China, the spatiotemporal characteristics of rainfall and several key water cycle elements e.g., soil moisture, evapotranspiration and generated runoff, were investigated using a fully coupled atmospheric-hydrorologic modeling system by integrating the Weather Research and Forecasting model (WRF) and its terrestrial hydrologic component WRF-Hydro (referred to as the fully coupled WRF/WRF-Hydro). The stand-alone WRF model (referred to as WRF-only) is also used as a comparison with the fully coupled system, which was expected to produce more realistic simulations, especially rainfall, by allowing the redistribution of surface and subsurface water across the land surface. Six storm events were sorted by different spatial and temporal distribution types, and categorical and continuous indices were used to distinguish the applicability in space and time between WRF-only and the fully coupled WRF/WRF-Hydro. The temporal indices showed that the coupled WRF-Hydro could improve the time homogeneous precipitation, but for the time inhomogeneous precipitation, it might produce a larger false alarm than WRF-only, especially for the flash storm that occurred in July, 2012. The spatial indices showed a lower mean bias error in the coupled system, and presented an enhanced simulation of both space homogeneous and inhomogeneous storm events than WRF-only. In comparison with WRF-only, the fully coupled WRF/WRF-Hydro had a closer to the observations particularly in and around the storm centers. The redistributions fluctuation of spatial precipitation in the fully coupled system was highly correlated with soil moisture, and a low initial soil moisture could lead to a large spatial fluctuated range. Generally, the fully coupled system produced slightly less runoff than WRF-only, but more frequent infiltration and larger soil moisture. While terrestrial hydrologic elements differed with relatively small amounts in the average of the two catchments between WRF-only and the fully coupled WRF/WRF-Hydro, the spatial distribution of elements in the water cycle before and after coupling with WRF-Hydro was not consistent. The soil moisture, runoff and precipitation in the fully coupled system had a similar spatial trend, but evapotranspiration did not always display the same.  
fully coupled atmospheric-hydrorologic modeling system; spatiotemporal homogeneousness; rainfall events; Weather Research and Forecasting (WRF) model; WRF-Hydro 2020, 12, 1209; doi:10.3390/w12041209
## 4 Conclusions  
This paper discusses the flexibility of a recurrent neural network to automatically compute a reverberated audio signal. The algorithm models the reverberation-affected signal in the frequency domain by analysing the spectrum on a logarithmic scale. The study examines three different recurrent network structures and compares the modeling of the reverberated signal when each octave of the spectrum is divided into a different number of bands. Using a model trained for one mid-octave band (No. 7) and tested as a model for applying the reverberation effect to the remaining 11 bands, it was found that even a half-tone change in the spectrum should be analyzed separately. To ensure a good prediction of the full spectrum of the reverberated signal, we need to train a separate one-dimensional RNN model for each band. This can be defined as a limitation of our proposed method.  
The BiLSTM-based RNN has shown more stable results in part of the frequency spectrum. Considering that all models were trained using the same audio recordings, it can be concluded that this type of RNN is more flexible in adapting to frequency changes related to room reverberation. Future work may explore methods for consolidating these models or refining the architecture to achieve more efficient and scalable solutions for modeling reverberated audio across different frequency bands.  
Conceptualization and methodology, all authors; validation, M.T.; analysis, A.S.; writing--original draft preparation, M.T. and T.S.; supervision, A.S.; writing--review and editing, all authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**RNN** & **Bin Number** & **SSE (103)** & **RSS (Mean)** & **R-Squared** & **RMSE (Mean)** \\ \hline \multirow{8}{*}{GRU} & 1 & 32.56 & 3.69 & **0.96** & 0.0241 \\  & 2 & 68.70 & 4.73 & 0.92 & 0.0365 \\  & 3 & 86.63 & 5.16 & 0.92 & 0.0430 \\  & 4 & 145.46 & 5.60 & 0.87 & 0.0527 \\  & 5 & 73.08 & 5.49 & 0.92 & 0.0409 \\  & 6 & 89.77 & 4.85 & 0.78 & 0.0405 \\  & 7 & 95.12 & 3.90 & 0.58 & 0.0418 \\  & 8 & 24.23 & 2.95 & 0.94 & 0.0216 \\  & 9 & 57.30 & 2.43 & **0.96** & **0.0164** \\  & 10 & N/A & 32.44 & N/A & 0.6615 \\  & 11 & N/A & 31.48 & N/A & 0.6381 \\  & 12 & 124.86 & 2.69 & 0.95 & 0.0210 \\ \hline \end{tabular}
\end{table}
Table 5: _Cont._
## 3 Results and Discussion
### Fourier Transform Infrared Spectroscopy Analysis  
Fourier transform infrared (FTIR) spectroscopy was applied to determine the possible functional groups in the compositions. The FTIR spectra of the irradiated samples are depicted in Figure 3. All the three samples were composed of C-H (884.34 cm\({}^{-1}\)), C-O (1127.35 cm\({}^{-1}\)), C-C/C-N (1314.46 cm\({}^{-1}\)), C=C (2158 cm\({}^{-1}\)) and O-H (3668.94 cm\({}^{-1}\)). C-N stretching confirmed the existence of a N atom in the composition and formed a weak bond with a carbon atom. No sharp peak was observed in 15% Fe/N-rGO, indicating the weaker bond between the two atoms. Furthermore, 10% Fe/N-rGO and 20% Fe/N-rGO exhibited a slightly sharp peak. The C-N bond in 15% Fe/N-rGO almost disappeared. Previous studies revealed the C-O stretching peak between 1028 and 1308 cm\({}^{-1}\), the C-H stretching peak between 500 and 1000 cm\({}^{-1}\) and the O-H stretching peak after 3420 cm\({}^{-1}\). The peaks of C-O and O-H respectively found at \(\sim\)1127.35 cm\({}^{-1}\) and 3668.94 cm\({}^{-1}\) could be ascribed to the deformation of rGO [38; 39].
# Volatile Organic Compounds in Breads Prepared with Different Sourdoughs
## 2 Materials and Methods
### Physicochemical Analysis  
The pH values of sourdough and dough bread were measured after calibration with a basic pH meter 20 (Crison Instruments, Barcelona, Spain) following the procedure of [PERSON] et al. [16].  
The titratable acidity (TA) was determined in sourdoughs using the method described by [PERSON] and [PERSON] [1]. Briefly, 100 mL of acetone/water (5:95 \(v/v\)) was added to 10 g of sourdough and homogenized. Subsequently, 2 drops of phenolphthalein were added, and the initial pH was measured. The pH was then titrated using a 0.1 N NaOH solution until reaching a pH of 8.5 (corresponding to the change in phenolphthalein to pink). The total titratable acidity was expressed as mL of NaOH/10 g of sample.  
Moisture content was evaluated in bread by the gravimetric method following the procedure based on the official method G.U 189/1967 [17]. The samples were oven-dried at 105 \({}^{\circ}\)C for 12 h and accurately weighed until a constant weight was reached. Three measurements were performed for each sample. The moisture content was expressed as g/100 g of bread.  
\begin{table}
\begin{tabular}{c c c} \hline
**Samples** & **Wheat Flour Used** & **Microbial Cultures** \\ \hline S1 sourdough & Wheat flour 00 and Manitoba flour (4:1) & Bacteria/Yeast in 1.5 ratio (selected cultures) \\ S2 sourdough & Wheat flour 00 and Manitoba flour (4:1) & Bacteria/Yeast in 3.3 ratio (selected cultures) \\ S3 sourdough & Wheat flour 00 and wheat flour 0 (4:1) & Bacteria/Yeast in 3.3 ratio (selected cultures) \\ S4 sourdough & Wheat flour 00 and Manitoba flour (4:1) & Yeast beer (natural cultures) \\ \hline \end{tabular}
\end{table}
Table 1: Sourdough samples.  
The water activity (\(\mathrm{a_{w}}\)) was measured in bread using an Aqua Lab instrument (METER Group, Inc., Pullman, WA, USA). Three measurements were performed for each sample.
## 4 Conclusions  
In conclusion, a THz PC waveguide with a \(\mathrm{ilac\_shaped}\)-shaped defect is demonstrated in this work. The reflection and transmission characteristics of the proposed waveguide have been analyzed both in simulation and experiment. The waveguide spectral response is further optimized by changing the distance of the \(\mathrm{ilac\_shaped}\)-shaped resonant cavities. The designed waveguide performs a resonant reflection in the region of 0.2 to 0.3 THz and a low-pass transmission in the 6G mobile communication window. These results have demonstrated that the waveguide performance can be controlled by the gap distance. For  
Figure 5: Characteristics of the THz PC waveguide. Influence of the gap distance (\(g\)) on (**a**) the resonant frequency \(f_{R}\), (**b**) Q factor, (**c**) the reflection loss (at \(f=\)\(f_{R}\)), (**d**) the bandwidth (with FWHM and \(-60\) dB bandwidth, respectively), and (**e**) the transmission loss with \(f=0.1\) THz, \(f=0.2\) THz, and \(f=0.3\) THz, respectively.  
some special occasions, the gap distance can be treated in order to evaluate the pros and cons of this device. In addition, the proposed waveguide has many advantages, including a designable structure, low-cost and easy fabrication, and operation in a low-THz frequency. Although its reflection and transmission loss are larger than other types of waveguides, the performance of the proposed waveguide will be optimized with the development of SLA technology. It holds great promise for extensive applications in communication, sensing, and imaging.  
Conceptualization, J.S. and Y.D.; methodology, J.S., Y.D. and X.L. ([PERSON]); software, H.B., X.L. ([PERSON]), and W.F. ([PERSON]); validation, L.T., W.F. ([PERSON]), and X.Y.; writing--original draft preparation, J.S. and Y.D.; writing--review and editing, J.S., X.Y. and P.N.; visualization, W.F. ([PERSON]); supervision, P.N. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This work is sponsored by the National Natural Science Foundation of China (61905177, 61901297, 61903273, and 81802118); the Natural Science Foundation of Tianjin City (19)QCNJC01400); the Open Research Fund of National Mobile Communications Research Laboratory, Southeast University (2022D12); and the Open Fund of IPOC, BUPT (IPOC2021B02).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Model and Methods
### Description of the TL-CLUE Model
The MODIS LSP product is derived from the time series of the two-band Enhanced Vegetation Index (EVI2) calculated from MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). EVI2 with no blue band was developed in 2008 [66] and is less susceptible to atmospheric interference, such as aerosols and clouds than some other vegetation indices. Previous studies confirmed that the MODIS LSP product has an excellent performance in monitoring land surface phenology [67; 68], so has been widely used for various direct or indirect studies relative to phenology. In addition, the MODIS LSP phenology has the same spatial resolution as the MOIS CI, which is useful for matching spatial characteristics. Therefore, the OYC was divided into a leaf-off season (before Greenup and after Dormancy), leaf-on season (from Greenup to Dormancy), leaf-scattering season (from Greenup to MidGreenup and from MidGreendown to Dormancy), and leaf-gathering season (from MidGreenup to MidGreendown (Table 2) according to these four threshold dates from the MODIS LSP data. Then, the \(\Omega\)s values of these different seasons for each vegetation type were determined by synthesizing the MODIS CI and some of the published literature on the CI [32; 42; 43; 51; 52] (Table 3). These \(\Omega\)s in the different seasons for different vegetation types were consistent with those of previous studies and realities. To explore seasonal changes (g C m\({}^{-2}\) d\({}^{-1}\)) and annual spatial characteristics (g C m\({}^{-2}\) yr\({}^{-1}\)) of GPP across the North American continent, the eight-day GPP from 2002 to 2020 was simulated by the TL-CLUE model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**V Vegetation**} & \multicolumn{4}{c}{**Seasonal Division**} \\ \cline{2-5}  & **LFS** & **LOS** & **LGS** & **LSS** \\ \hline CRO & (1, 73) and (294, 365) & [73; 294] & [150; 257] & [73; 150] and (257, 294) \\ DBF & (1, 89) and (313, 365) & [89; 313] & [156; 276] & [89; 156] and (276, 313) \\ ENF & (1, 109) and (296, 365) & [109; 296] & [143; 259] & [109; 143] and (259, 296) \\ MF & (1, 89) and (281, 365) & [89; 281] & [146; 257] & [89; 146] and (257, 281) \\ GRA & (1, 89) and (305, 365) & [89; 305] & [146; 256] & [89; 256] and (256, 305) \\ OSH & (1, 97) and (288, 365) & [97; 288] & [156; 249] & [97; 156] and (249, 288) \\ WSA & (1, 33) and (321, 365) & [33; 321] & [86; 265] & [33; 86] and (265, 321) \\ SAV & (1, 81) and (249, 365) & [81; 249] & [152; 192] & [81; 152] and (192, 249) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The OYC of leaf growth is divided into the LFS and LOS, consisting of the LGS and LSS for different vegetation types, labeled according to the MODIS Land Surface Phenology product (MCD12Q2 V061).
# Generalized Beam Theory for Thin-Walled Beams with Curvilinear Open Cross-Sections
## 2 The Beam Model
### Constitutive Law  
It is assumed that the beam is made of an isotropic, homogeneous and linearly elastic material. Moreover, the plane stress state hypothesis is adopted to a thin shell representing any individual beam wall. For thin-walled structures this assumption is realistic and provides reliable results. According to this formulation, the two-dimensional stresses are functions of solely two coordinates \(x\) and \(s\), while all the transverse stresses are negligible:  
\[\sigma_{n}=\tau_{sn}=\tau_{nx}=0 \tag{18}\]  
Thus, the corresponding 3D Hooke's law is simplified to:  
\[\varepsilon_{x}=\frac{1}{E}(\sigma_{x}-\
u\sigma_{s})\qquad\qquad\epsilon_{s} =\frac{1}{E}(\sigma_{s}-\
u\sigma_{x})\qquad\qquad\gamma_{xs}=\frac{\tau_{xs} }{G} \tag{19}\]  
where \(E,G,\
u\) are the material Young's and Kirchhoff's moduli, and Poisson ratio, respectively. Equation (19), when solved in terms of stresses, becomes:  
\[\begin{split}\sigma_{x}=&\frac{E}{1-\
u^{2}}( \varepsilon_{x}+\
u\epsilon_{s})\\ \sigma_{s}=&\frac{E}{1-\
u^{2}}(\varepsilon_{s}+ \
u\epsilon_{x})\\ \tau_{xs}=& G\gamma_{xs}\end{split} \tag{20}\]  
Substituting Equation (12) in Equation (20), the expressions of the individual stress components can be decomposed to the sum of membrane and flexural related terms, as well:  
\[\begin{split}\sigma_{x}&=\sigma_{x}^{M}+\sigma_{x} ^{F}\\ \sigma_{s}&=\sigma_{s}^{M}+\sigma_{s}^{F}\\ \tau_{xs}&=\tau_{xs}^{M}+\tau_{xs}^{F}\end{split} \tag{21}\]  
Finally, the use of Equations (16) and (17) allows one to express the stress components in (21) in terms of the trial functions \((U_{j}(s),V_{j}(s),W_{j}(s))\) and kinematic descriptors \(a_{j}(x),j=1,\ldots,K\). The final expressions are omitted for the sake of brevity.
## 2 Materials and Methods
### CFD Simulation
#### 2.2.2 Finite Cell Mesh  
In Figure 6, the finite cell mesh is generated automatically by the ANSYS Fluent Mesh module. It is assumed that the minimal length of the cell edge is equal to \(\mathrm{I_{min}}\) = 0.028 m (value independent of the geometry configuration), and the maximal length of the cell edge is generally greater than \(\mathrm{I_{max}}\) > 6 m, depending on the geometry configuration.  
Such a minimal cell edge length ensures that during the automatic mesh generation of tetrahedral elements, there are no degenerated or highly distorted cells. The existence of such cells makes it very difficult to obtain a convergence solution to the numerical problem. For considered geometry configurations, the number of nodes is over 2.5 million, and the number of cells is over 14 million. Depending on the geometry configuration, the number of nodes and cells varies not significantly.  
To verify the precision of the numerical solutions, which are obtained for the assumed minimal length of the cell edge, the convergence test was performed. In Tables 1 and 2, the total number of nodes, faces, and cells generated for the following minimal lengths of the cell edges is reported: \(\mathrm{I_{min}}\) = 0.056 m, 0.028 m, and 0.014 m. The convergence test was performed for the geometry configuration, for which the angle \(\theta\) = 90\({}^{\circ}\). The overturning moment \(\mathrm{M_{tip}}\) is the moment that is measured concerning the tipping line, which is shown in Figure 7.  
Figure 6: The finite cell mesh: (**a**) basis of the tower; (**b**) tower lattice; (**c**) join of the tower and jib; (**d**) jib lattice.  
Taking into consideration the results presented above in Tables 1 and 2, the assumed value of \(\rm l_{min}\) = 0.028 m is quite reasonable. All other calculations will be performed for this value of \(\rm l_{min}\).  
Finally, it is worth noting that \(\rm l_{e}\) = 0.03 m is the largest element size, which enables automatic finite cell generation without warnings or errors. On the other hand, \(\rm l_{e}\) > 0.02 m leads to an enormous, large number of nodes and cells. Moreover, considering the mentioned limitation connected with element size, the performed convergence test should be rather threaded as a sensitivity analysis.
# Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction
## 2 Methods
### Problem Definition  
The underlying network in which the diffusion occurs was modeled as an undirected simple graph \(G=(V,E)\), where \(V\) denotes the set of nodes and \(E\) denotes the set of edges. Specifically, we mainly focused on static networks whose topology never changes during the diffusion process. The diffusion source \(s^{*}\in V\) represents the only node at which the information originates and it triggers diffusion at some unknown initial time \(t_{0}\). Compared with many previous literature works based on stochastic epidemic processes such as SI, SIS and SIR, a simple diffusion model associated with a time delay along edges was employed in our study.  
The diffusion process is modeled as follows. At time \(t\), each node can be in one of two states: informed and uninformed. An informed node represents the individual who is aware of the information and will propagate it to its neighbors, whereas one uninformed node represents the individual who has not been informed yet. Let \(\Gamma(v)\) denote the neighbors of node \(v\) and suppose that \(v\) is in the uninformed state. After \(v\) receives the information from one of its neighbors for the first time \(t_{v}\), it will change to the informed state and propagate the information. Then, each of its neighbors \(u\in\Gamma(v)\) receives the information from \(v\) at \(t_{v}+\theta_{uv}\), where \(\theta_{uv}\) denotes the diffusion delay associated with edge \(uv\). Specifically, the diffusion delays \(\{\theta_{uv}\}\) along each edges were modeled as an independent and identically distributed random variables that follows a Gaussian distribution \(\mathcal{N}(\mu,\sigma^{2})\). As is often the case in many real situations, such as the spread of a computer virus on the Internet via a cable, the mean diffusion delay \(\mu\) is easy to evaluate, whereas the standard deviation \(\sigma\) is hard to obtain. We further assumed that only \(\mu\) is known, and \(\sigma\) needed to be estimated.  
Let \(O=\{o_{k}\}_{k=1}^{K}\) denote a group of observers whose informed times \(T_{O}=[t_{o_{1}},t_{o_{2}},...,t_{o_{K}}]^{T}\) can be accessed by us. Then, the source localization problem could be described as follows: given the network topology and several observers \(O\), our goal was to find the diffusion source \(s^{*}\) in the network. To simplify the derivation and avoid a non-invertible matrix in our method, we assumed that \(s^{*}\
ot\in O\). There was no loss of generality since one can generalize our method by adding an extra step to calculate the probability of being the source for those \(o_{k}\in O\) with a known initial time \(t_{o_{k}}\). In reality, the rumor source will not report its informed time, since this information will likely expose itself as the first to report the rumor.
# An Improved Federated Learning-Assisted Data Aggregation Scheme for Smart Grids
## 4 Preliminaries
### Threshold Homomorphic Encryption  
Threshold homomorphic encryption is a cryptographic scheme that allows for performing computations on encrypted data without decrypting it. It incorporates the concept of threshold cryptography, where multiple participants collectively hold shares of a secret key [32]. The encryption scheme ensures that a certain threshold number of participants must collaborate to perform operations on the encrypted data. In threshold homomorphic encryption, encrypted values can be combined through specific homomorphic operations,such as addition or multiplication, while ensuring the secrecy of the original data remains intact. The encrypted result of the computation can be decrypted by the participants only when the threshold number of participants collaborate to collectively decrypt it. This cryptographic scheme provides a secure and distributed way to perform computations on sensitive data while preserving privacy. It allows for collaborative computation without revealing individual input and ensures that no single participant has complete access to the decrypted data [33].
## 2 The Ubiquitous Sensor Network Platform  
The vision of providing sensor data for services is not new and there are many initiatives working in that direction. Examples of projects in the environmental monitoring area are the SANY (www.opengeospatial.org/ogc/regions/SANY) (based on OGC standards) and ENVIROFI (www.envirofi.eu/FrequentlyAskedQuestions.aspx) projects. OGC is also working in the direction of extending the current SWE standards towards the IoT (www.opengeospatial.org/projects/groups/sweiotswg). In addition to the OGC SWE framework considered for the USN-Platform presented here, we note that there are a number of other Sensor Web initiatives that can be described as centralized web portals, such as Axeda (www.axeda.com), Xively (xively.com/whats_xively/) and Sensorpedia (www.sensorpedia.com). These Sensor Web portals offer APIs for registering and discovering sensors, uploading sensor data, as well as querying inserted data. Other noteworthy open architectures with the goal to make physical world information available for smart applications have and are being developed in different international projects such as SENSEI (www.sensei-project.eu/), IoT-A (www.iot-a.eu/public/front-page), Artemis SOFIA (www.sofia-project.eu/), Harvard HOURGLASS (www.eecs.harvard.edu/\(\sim\)syrah/hourglass/index.shtml), Global Sensor Networks (GSN) (sourceforge.net/ apps/trac/gsn/), OpenIoT (www.openiot.eu), or SOCRADES (www.socrades.eu/Home/default.html).  
The Ubiquitous Sensor Network (USN) Platform, which was first proposed in [15], is organized conceptually in two layers: a communication layer and a service layer. In the architecture presented here there are two main logical components that correspond to each of these layers, respectively: USN-Gateways, which facilitate the integration of heterogeneous IoT information providers through the Next Generation Networks; and a USN-Enabler, which implements the sensor service layer, based primarily on OGC-SWE specifications. We describe these two components after a brief introduction to NGNs, their function in the USN-Platform, and the benefits for developing future IoT architectures that can be derived from their use.