esa-sceva/satcom-chunk-collection huggingface dataset:
# Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment
## 3 Method  
In recent years, several advances have been made in the development of SAR-based flood extent mapping techniques (e.g., [14; 16; 21; 22]), new methods have been set up for deriving water levels from SAR, combining digital elevation (DEM) data ([10; 23]), and in ways to integrate this added-value data in prediction models, either in near-real time through assimilation or subsequent recalibration (e.g., [15; 18; 25]). For a comprehensive review of relevant flood remote-sensing research activities, readers can refer to [29]. The proposed study aims to go beyond flood extent mapping calibration through SAR images with a view to showing the potential of these images in spatio-temporal floodwater depth characterization in extremely flat areas with complex topography (e.g., levees, banks, bridges, etc.), while also considering the impact of the timing of SAR image acquisition.  
The proposed methodological framework, proposed in Figure 2, utilized to build the event scenarios for the Bradano case study and to monitor the spatio-temporal dynamic of floodplain drainage is described in this section. In particular, the section presents the flood mapping algorithms to extract flood extent layouts using SAR images (Section 3.1) that are, later, combined with a DEM in order to extract water-depth maps in a flat terrain with complex topography (Section 3.2). Subsequently, using these hazard-derived SAR and DEM images, acquired respectively near the flood peak and in the recession stage of the flood event, the processes adopted to constrain (at different floodplain points and in different time instants) a 2D hydraulic model (presented in Section 3.3), limiting the uncertainty of roughness parameters, are proposed in Section 3.4.  
The methodology set out in this paper can be extended to reliable flood damage assessment for flood emergency management by increasing process automation levels. Operational products that are under development for accurate and timely flood risk assessment can address the growing need for knowledge transfer from science to practice through an open, transparent approach that is reliable, extensible, and participative ([30; 31; 32]).  
Figure 2: Schematic flow diagram of the proposed methodological framework.
## 4 Experiments
### Pedestrian Interference Recognition Experiment  
To illustrate the effectiveness of selecting the pedestrian distance, azimuth angle, head observation angle, and action type classification as the input variables for interference analysis, we used the distance between pedestrians, as presented by [PERSON] et al., to evaluate the pedestrian interference as a comparison input variable [8]. We also selected the distance and angle between the pedestrians, as proposed by [PERSON] et al., to evaluate the pedestrian interference as a comparison [5]. The calculations were performed with the network used in the pedestrian interaction evaluation feature module described above. To account for the influence of the dropout layer on the final results, we repeated the training and testing 50 times during each experiment and finally recorded the maximum, minimum, and average values of the Precision, Recall, F1-score, and Accuracy. The results are shown in Table 4.  
The recognition results with only the distance between pedestrians as the input variable had the lowest values of all four evaluation indicators. In comparison, the recognition results with the pedestrian distances plus the angle between the two significantly improved the evaluation indicators. Compared to the above methods, the recognition results for our proposed method were significantly better than the results of the other two methods. Some of the experimental results are shown in Figure 10.  
Figure 10: Pedestrian interference recognition experiments: (**a**) calculated result had no behavioral interference between the two people, and (**b**) calculated result had behavioral interference between the two people.
# Lactic Acid Modified Natural Rubber-Bacterial Cellulose Composites
## 3 Results
### Mechanical Properties  
The Young's modulus, tensile strength, and elongation at break of dry films of NR, BC, NRBC, and ANRBC were determined (Figure 7). The values were expressed as mean \(\pm\)SD from five samples (n = 5) and statistically analyzed by Student's \(t\)-test for two-sample assuming equal variances in Microsoft office 2010. The differences were considered statistically significance at the level of \(p<0.05\). The Young's modulus and tensile strength of both NRBC and ANRBC films were enhanced with an increase in the BC fiber content. Maximal Young's modulus and tensile strength were obtained with BC loading at 80 wt.%. The lactic acid modification significantly improved Young's modulus (\(p<0.05\)) and tensile strength (\(p<0.05\)) of ANRBC at 80 wt.% BC loading content as compared to NRBC. The tensile strength of ANRBC80 was approximately 209 times and 1.6 times those of NR and NRBC80 films, respectively. On the other hand, the elongation at break of both NRBC and ANRBC were reduced with an increase in the BC fiber content. Among the NRBC composites, NRBC20 had the highest elongation at break at 15.4%, which was about 0.13 of the NR film. With lactic acid modification, the elongation at break of ANRBC20 was improved to \(\approx\)1.3 of NRBC20.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & \(\mathbf{T_{max}}\) (\({}^{\circ}\)C) & \(\mathbf{T_{m}}\) (\({}^{\circ}\)C) & \(\mathbf{T_{g}}\) (\({}^{\circ}\)C) \\ \hline NR & 350 & 267 & \(-\)64 \\ BC & 301 & 149 & - \\ NRBC20 & 370 & 220 & \(-\)63 \\ ANRBC20 & 367 & 280 & \(-\)62 \\ NRBC50 & 335 & 176 & \(-\)64 \\ ANRBC50 & 329 & 186 & \(-\)63 \\ NRBC80 & 334 & 167 & \(-\)66 \\ ANRBC80 & 331 & 180 & \(-\)65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermal properties of NR, BC, NRBC, and ANRBC films.  
Figure 7: Mechanical properties, **(A)** Young’s modulus, **(B)** tensile strength and **(C)** elongation at break of NR, BC, NRBC, and ANRBC films. The values are expressed as mean \(\pm\) SD (n = 5).
## 3 Materials and Methods
### Feature Extraction from Final Multi-Modal Data  
To extract features from the final multi-modal data, tokenization and word embedding were performed using the Word2 Vec technique. This technique captures semantic links between words, enhancing the performance of hybrid deep learning (DL) models on the extracted text data. For the image data, multiple feature extraction approaches were employed, including CNN features, DL pre-trained models using transfer learning, and the patch-embedding approach. Transfer learning allows the use of knowledge from previously trained models to improve categorization, while the patch-embedding approach divides images into smaller patches for sequential processing.
# Performance Analysis of DF Relay-Assisted D2D Communication in a 5G mmWave Network
## 2 System Model
### Path Loss Model  
The communication between the D2D users in the mmWave band is affected by several environmental factors that cause interference. Therefore, in order to accurately portray the impact of various interference factors on the mmWave network, a path loss model is mentioned in the system model. According to the NYUSIM channel model [20], the path loss model considered for the free-space path loss (FSPL) is as follows,  
\[FSPL(f,1m)(dB) =20\text{log}_{10}\frac{(4\pi f_{c}\times 10^{9})}{c} \tag{1}\] \[=32.4(dB)+20\text{log}_{10}f_{c}\]  
where \(f_{c}\) is the carrier frequency and c is the speed of light. \(FSPL(f,1m)\) denotes the free-space path loss in dB with a transmitter-receiver (T-R) distance of 1 m. For high-penetration losses, a large-scale path loss model was considered for a 1m reference distance as per NYUSIM [20], which is shown as follows,  
\[PL(f,d)(dB) =FSPL(f,1m)(dB)+10\text{log}_{10}(d) \tag{2}\] \[+AT(dB)+\chi_{\sigma}\]  
where \(d\) is the separation distance (in 3D) between the T-R and \(\alpha\) and \(\chi_{\sigma}\) denote the path loss exponent and the log-normal shadow fading having zero mean with \(\sigma\) the standard deviation (in dB). \(AT\) represents the interference due to atmospheric absorption. The channel gain (Rician fading channel) \(h\) between the two D2D users can be represented as [21],  
\[h=10^{-PL(f,d)(dB)/10} \tag{3}\]  
\(h\) is dependent on the distance between the T-R and is independently and identically exponentially distributed with a mean \(\mu^{-1}\).
## 5 Performance Analysis and Results  
The effectiveness of the generative model is examined in this chapter, with particular emphasis placed on the ability of the VA to minimize errors and hallucinations while maximizing response accuracy. The testing explored multiple response generation methods: predictions without context, contextual predictions utilizing the RAG method, and learning from one or more examples, including one-shot or few-shot learning. Each approach presents unique strengths and limitations. Responses deemed correct for one-shot and few-shot learning are derived from a curated set of accurate answers and contextually relevant literature. These have been selected to give the model reliable patterns for responding to similar inquiries. The tendency for hallucinations to be produced by the model was revealed through context-free predictions, mainly when complex or nuanced queries were interpreted without supporting data. The addition of contextual information through the RAG method significantly reduced these errors, affirming the importance of context for generating relevant, accurate responses. Furthermore, additional promise was shown by the learning method from examples, particularly in the enhancement of response accuracy where relevant context was sparse.
# Novel Equalization Techniques for Space Division Multiplexing Based on Stokes Space Update Rule
## 3 Equalizers for SDM  
Commonly, the coherent detection is divided into three modules: optical front-end, analogue-to-digital converter (ADC), and the DSP module [1]. The optical front-end is responsible for the down-conversion of the optical signal into the electrical domain, which is subsequently converted into the digital domain by the ADC. The DSP module compensates for the signal impairments and obtains the information stream modulated over the signal.  
In this paper, we focus on the DSP module, revising the single-mode equalization techniques by reviewing different error functions: least mean square (LMS) and Stokes space algorithm (SSA). We also explain the multi-mode generalization: \(2M\times 2M\), including the novel \(2M\times 2M\) SSA-MIMO algorithm.
## 3 Compaction Effect Analysis
### Particle Motion Statistics and Analysis  
Compaction primarily involves the rearrangement of particles as they approach each other and smaller particles enter the voids of larger particles through motion [25; 26]. The primary particle movements associated with deformation are sliding and rotation [27; 28]. Here, the particle displacement and rotation angle represent sliding and rotation amounts, respectively.  
Figure 5 illustrates the average particle displacement and rotation angle as a function of load cycles for different mixing degrees. In terms of the duration of loading, both metrics gradually increased over time and closely aligned with the settlement trend shown in Figure 3. During the rapid densification phase (\(t<25\)T), significant increases occurred, indicating substantial particle movements. As stabilization began (\(t>25\)T), the values plateaued, reflecting the stabilization of particle movements.  
Figure 4: Variations in void ratio, final void ratio, and minimum void ratio.  
Figure 3: Changes in void ratio of particle system with load period.  
In terms of the effects of mixing degree, displacements and rotations did not exhibit clear trends during the early stages of loading (\(t<10\)T), but divergence became apparent at the later stages (\(t>10\)T). However, while the final void ratios systematically decreased with higher mixing degrees, displacements monotonically increased while rotations decreased. This suggested that particle movements during compaction did not necessarily promote densification. It is likely that more particles undergo reciprocal or even upward displacements, while rotations are biased towards compaction to achieve greater homogeneity.
Additionally, understanding the seasonality and magnitudes of fluxes from native prairie over an extended region helps to better assess the impacts on the mesoscale atmospheric environment as the dynamics of the land surface processes and lower atmosphere are tightly coupled [23]. Thus, it is necessary to investigate the variability in CO\({}_{2}\) fluxes and ET and to improve our understanding of the key drivers for those variations during non-growing seasons for major land use types. Satellite remote sensing products have been used for phenology applications due to their ability to capture phenology patterns continuously and retrospectively [24]. However, most studies focused on growing seasons to characterize land surface phenology and investigate the sensitivity of remotely sensed vegetation indices of tallgrass prairie to environmental conditions or management practices [25, 26, 27]. As a result, satellite remote sensing products have not been validated satisfactorily for tallgrass prairie during dormant seasons. Based on eddy fluxes and satellite-derived vegetation indices, growing seasons of tallgrass prairie in the U.S. Southern Plains generally begin and end at approximately DOY 100 and 300, respectively [28]. Comparative studies of the vegetation phenology and dynamics of CO\({}_{2}\) fluxes and ET using multiple EC systems during the dormant season (i.e., from November to March) in differently managed tallgrass prairie from different landscape positions under the same climatic regime are scarce. To address this knowledge gap, a long-term integrated Grassland-LivestOck Burning Experiment (iGLOBE) was established with a cluster of six EC systems on differently managed (i.e., different burning and grazing regimes) native tallgrass prairie located in different landscape (e.g., upland, intermediate, and lowland) positions [9]. These pastures are also part of the GRL-FLUXNET (a network of eddy flux towers at the United States Department of Agriculture Agricultural Research Service (USDA-ARS), Grazinglands ResearchLaboratory, El Reno, OK, USA) and SP-LTAR (Southern Plains Long-Term Agroecosystem Research) sites. The major objective of this current study was to compare vegetation phenology (vegetation indices derived from satellite remote sensing observations) and the dynamics of EC-measured CO\({}_{2}\) fluxes and ET during dormant seasons (November-March) in multiple native tallgrass prairie pastures. We hypothesized that vegetation phenology, CO\({}_{2}\) fluxes, and ET would show no fluctuations during dormant seasons across sites and years even with differences in landscape positions and land/livestock managements. Investigating the dynamics of CO\({}_{2}\) fluxes and ET during the dormant season in differently managed tallgrass prairie is critical for improving our understanding of the dynamics of CO\({}_{2}\) fluxes and ET in tallgrass prairie to develop resilient forage-livestock systems.
## 4 Correspondence of Vortex Structures and Vortex-Induced Pressures
### Relation between Vortex Structures and VIPs at the Stable Stage of VIV
The vortex on the leeward inclined web of the lower surface is also moving backward, which causes the positive VIPs in this area to become negative. In conclusion, the vortex close to the surface of the box girder will lead to positive VIPs in the corresponding region. The backward movement of the vortex will produce negative VIPs on the surface of the box girder. The absolute value of negative VIPs decreases as the vortex departs the surface of the box girder, while the absolute value of positive VIPs increases, potentially causing the VIPs to change from negative to positive. Positive VIPs will diminish and the absolute value of negative VIPs will increase if the vortex remains stationary on the surface of the box girder, and positive VIPs will change to negative VIPs. Figure 9: Distributed VIPs and vortex structures during one cycle of the stable stage of VIV. (**a**) 0 moment. (**b**) \(\pi/4\) moment. (**c**) \(\pi/2\) moment (**d**) \(3\pi/4\) moment. (**e**) \(\pi\) moment. (**f**) \(5\pi/4\) moment. (**g**) \(3\pi/2\) moment. (**h**) \(7\pi/4\) moment. In addition, it can be seen that the huge vortex on the upper surface is formed by a large number of small vortex structures on the leading edge of the box girder.
# Terahertz Broadband Adjustable Absorber Based on VO2 Multiple Ring Structure
## 3 Simulation Results  
Firstly, the Figure 2a exhibits the best absorption state of the absorber when the conductivity of VO\({}_{2}\) is 2 \(\times\) 10\({}^{5}\) S/m. It can be seen from the result that under the condition of vertical incidence, the bandwidth greater than 90% absorption efficiency is as high as 4.26 THz and the frequency covers 3.01 THz to 7.27 THz. To verify the effect of polarization on the incident electromagnetic wave, the absorptivity under different polarization angles is simulated in Figure 2b. The absorption performance of the absorber remains consistent and confirms that the absorber is hardly affected by the polarization angle.  
Next, the absorption and reflection spectrum can change dynamically accordingly to the conductivity variety of VO\({}_{2}\). In Figure 3, when the conductivities of VO\({}_{2}\) change from 200 S/m to 2 \(\times\) 10\({}^{5}\) S/m, the absorption and reflection rate varies between 4% and 100%. At this time, the central frequency of the spectrum remains almost invariant at 5.17 THz, but the absorption peak has changed from the single absorption peak to double absorption peaks, with the increasing absorption bandwidth. The variety of the dielectric constant of VO\({}_{2}\), which affects the overall absorption efficiency with the change of conductivities, can be taken into consideration to explain this phenomenon. It can be seen from Figure 4 that the imaginary part of the dielectric constant is much greater than the real part, which is the main factor of the absorption effect. With the increase in the conductivities, the imaginary part arises rapidly, thereby improving the overall absorption efficiency of the absorber. The results show that when the conductivities of VO\({}_{2}\) are actively controlled by temperature, the absorber has tunable absorption characteristics. In addition, the properties of VO\({}_{2}\) can also be adjusted by electricity [43] or heat [39] to realize the dynamic tuning of the working band  
Figure 1: Unit cell of the structure in (**a**) top view and (**b**) three-dimensional schematic.
## 3 Experimental Results
### Analysis and Comparison of Experimental Results
#### 3.2.4 Equivalent Damping Ratio Per Load Cycle  
Figures 10 and 11 show the equivalent damping ratio \(\xi_{eq}\) and the energy dissipation E\({}_{D}\) energy of the specimens per load cycle, as it varies with the drift ratio. The energy dissipation is defined as the area of the cyclic curve at the second loading cycle. The equivalent damping ratio \(\xi_{eq}\) can be calculated as  
\[\xi_{eq}=\frac{E_{D}}{4\pi E_{So}} \tag{4}\]  
where \(E_{So}\) is the maximum strain energy.  
The IP specimen shows greater \(\xi_{eq}\) values than the SSP specimen at drift ratios of 1.5% and 2.0% because the integrated member contributes to energy dissipation. The stud-reinforced specimens show low \(\xi_{eq}\) values, because inelastic deformation did not occur at these drift ratios. Above a drift ratio of 3.0%, all the specimens show similar \(\xi_{eq}\) values. However, the energy dissipation \(E_{D}\) is the highest in the IP-IS specimen and the lowest in the SSP specimen, as shown in Figure 11. This is because the  
Figure 10: Variation in the equivalent damping ratio per load cycle with drift ratio.  
Figure 9: Comparison of the average envelope curves of the specimens.  
study connected to the integrated member only in the hold-downs did not behave in combination with the integrated member, as shown in Figure 7d.
# Dynamic Functional Principal Components for Testing Causality
## 2 Linear Causality with Two Functional Time Series  
### Definition  
Let \(\mathcal{H}\) be a separable Hilbert space, with its inner product \(\langle\cdot,\cdot\rangle\) and the norm associated \(\|h\|=\sqrt{\langle h,h\rangle}\), \(\forall h\in\mathcal{H}\). We take \(\mathcal{H}=L^{2}[0,1]\) for simplicity. Thereafter, for every functional time series \(\{Z_{t}\}_{t\in\mathbb{Z}}\) valued in \(\mathcal{H}\), we make the assumptions that \(\mathbb{E}\big{[}\|Z_{t}\|^{2}\big{]}<+\infty\) and \(\mathbb{E}[Z_{t}]=0\), \(\forall t\in\mathbb{Z}\); moreover, we assume that \(\{Z_{t}\}_{t\in\mathbb{Z}}\) is strongly stationary. Let us recall the two definitions: strongly and weakly stationary. We say that series of random functions \((Z_{t})\) are _strongly stationary_ if for any \(h\in\mathbb{Z}\), \(k\in\mathbb{N}\) and any sequence of indices \(t_{1},\ldots,t_{k}\), \((Z_{t_{1}},\ldots,Z_{t_{k}})\) and \((Z_{t+t_{1}},\ldots,Z_{t+t_{k}})\) are identically distributed. We say that \(\{Z_{t}\}_{t\in\mathbb{Z}}\) is _weakly stationary_ if for all \(t\) we have:  
(i) \(Z_{t}\) verifies \(\mathbb{E}\big{[}\|Z_{t}\|^{2}\big{]}<+\infty\),  
(ii) \(\mathbb{E}Z_{t}=\mathbb{E}Z_{0}\),  
(iii) \(\forall h\in\mathbb{Z},\forall(u,v)\in\left[0,1\right]^{2}\), we have \(\text{cov}(Z_{t+h}(u),Z_{t}(v))=\text{cov}(Z_{h}(u),Z_{0}(v))\).  
Let us introduce a key concept in functional data analysis about the second-order property of the process. To understand why this operator is a key concept, we recommend reading [19]. We define the operator of covariance \(\Gamma_{Z}\) of the stationary functional time series \(Z_{t}\) by:  
\[\Gamma_{Z}(U)=\mathbb{E}[\langle Z,U\rangle Z],\quad\forall U\in\mathcal{H}, \tag{1}\]  
where we omit the index \(t\) of time, due to stationarity. It is well known that this operator is a self-adjoint, positive, nuclear operator.  
Let \(X_{t}\) and \(Y_{t}\) be two stationary functional time series valued in \(\mathcal{H}\). Let \(U_{t}\) be the information accumulated since time \(t-1\), and \(U_{t}-X_{t}\) denote all this information without the series \(X_{t}\). Define the predictive error series by  
\[\varepsilon_{t}(Y|U)=Y_{t}-\hat{Y}_{t}(U),\]  
where \(\hat{Y}_{t}(U)\) is the best linear predictor of \(Y_{t}\) using the information \(U_{t}\). Then, we have the following definition.  
**Definition 1** (Causality).: _We say that \(X\) is causing \(Y\) if \(\Gamma_{\varepsilon(Y|U-X)}-\Gamma_{\varepsilon(Y|U)}\) is a positive definite operator._  
This definition is a natural extension of the idea of causality studied by [PERSON] [5]. In fact, we replace the variance of the time series by the covariance operator of the functional time series. In addition, in order to compare covariance operator, we assume that a symmetric operator \(A\) is greater than a symmetric operator \(B\) if the difference \(A-B\) is a positive definite operator. This is a theoretical definition because, in general, we do not have access to the information \(U_{t}\), so we must be careful for the conclusions in practice.