esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Regarding future missions, ESA is planning to launch the ROSE-L system as part of its Copernicus Programme [34], and the United States and India Space Agencies (NASA & ISRO) are working on the NISAR mission, scheduled for 2023 [35]. Both platforms will carry an L-band SAR instrument designed to acquire InSAR data. Among their applications, forest mapping, and characterization will be one of the most important ones. The purpose of this paper is to contribute to the existing knowledge on InSAR analysis for forestry monitoring, with an emphasis on the analysis and characterization of the interferometric coherence for short temporal baseline L-band spaceborne systems. The analyses presented in this work refer to the amplitude of the InSAR coherence only, namely \(|\gamma|\). To achieve this goal, this work presents an assessment of the InSAR coherence over forests based on the analysis of data from the L-band Argentinian SAOCOM-1 and the C-band European Sentinel-1 constellations, as a first step towards understanding the suitability of SAOCOM-1 data for forest monitoring and mapping. It is worth noting that since November 2021, the SAOCOM-1B satellite is in operation, allowing us to form 8-day InSAR pairs in combination with SAOCOM-1A images. This work presents, for the first time, the analysis of these 8-day temporal baselines, L-band, orbital interferograms, with its focus on forest monitoring. In addition, collocated GEDI data is considered to perform a preliminary analysis of the InSAR coherence dependency on forest canopy height. All these analyses are intended to generate a proper basis and criteria for interferometric pairs selection in future research involving forest canopy height retrieval. The article is organized as follows. Section 2 introduces the basics of InSAR, and, in particular, the concepts of interferometric coherence and coherence decorrelation, as well as the study sites and the data sources considered in this study. Section 3 refers to the processing results, and presents a detailed assessment of the InSAR coherence, considering different acquisition parameters and target characteristics. Section 4 presents further analyses of the results to understand the advantages and limitations of the proposed approach, and Section 5 concludes the article.
## 3 A Taxonomy of Measuring Methods, Techniques, and Sensors in Physiological-Based DMSs
### Bioimpedance Measurement (BIM)
#### 3.4.1 Contact BIM Measurement  
In 2023, [PERSON] et al. [70] published detailed information about cuffless BP measurement in real time with a ring device (Figure 4a). The development and validation of the ring using the 4-electrode bioimpedance measurement method are presented. The device measures impedance changes related to blood volume in the finger arteries. This approach overcomes the sensitivity of the PPG method to skin color and tissue structure. Different electrode configurations have been experimented with to achieve maximum accuracy in BP measurement. Machine learning was used to process the impedance data to predict BP in real time. The authors reported high accuracy achieved, with an error of 0.4 mmHg relative to reference values. The study was conducted in a laboratory setting and does not demonstrate what the results would be if the ring was used on a moving body part, such as a finger while driving. Nevertheless, this method provides opportunities for continuous physiological measurement, as the rings are easy to wear, are not heavy compared to other smart wearables, and ensure stable contact between the sensors and the skin at all times.
## 4 Results
### Compare the Downscaled SIF with Direct Resampling Results  
To demonstrate the superiority of the RF-based downscaled method, this study conducted a comparison between the downscaled SIF and direct resampling results (Figure 6). GOSIF was resampled to 1 km resolution using nearest neighbour and bilinear methods. The RF-based downscaled, nearest neighbour and bilinear methods SIF results had a similar change trend to the original GOSIF data. The nearest neighbour resampling method significantly altered the texture information of the original image. The bilinear method resulted in smoother images but introduced modifications to the pixel values. The RF algorithm exhibited remarkable robustness against adverse factors, such as noise and outliers. Moreover, it possessed the capability to handle both discrete and continuous data effortlessly, eliminating the necessity for dataset standardization. In contrast, the downscaled SIF result obtained through the RF method exhibited more spatial details compared to the direct resampling methods.
## 2 Related Works
### Non-Homogeneous Negative TIS  
The problem of data heterogeneity is common in federated learning [30]; e.g., data have different feature distributions but the same label (domain shift [31]). Data heterogeneity induces the \"catastrophic forgetting\" problem for classification models; the global parameters might not be optimal simultaneously on data with different distributions. The model gradually \"forgets\" the knowledge learned from previous tasks during the continuous learning process [25]. Indeed, the data heterogeneity problem also occurs in TIS prediction. Negative TISs are often located in different regions in an mRNA sequence, and the contextual information of these TISs is very different. To our best knowledge, only a few works have paid attention to the non-homogeneous property of negative TISs in genomic sequences. TriTISA [32] is a method for detecting TISs in microbial genomes, which classifies all candidate TISs into three categories based on evolutionary properties, e.g., positive TISs, negative TISs upstream of positive TISs, and negative TISs downstream of positive TISs. Then, TriTISA characterizes them using Markov models. The work [33] extended TriTISA to genome sequences. This method divides all TISs into different groups, e.g., introns, UTRs, and exons. Then, different classifiers are trained for each group, and the trained classifiers are combined for the final TIS prediction.  
Our work differs from the aforementioned works in two aspects. First, these methods divide candidate TISs according to the regions where they are located. However, this kind of division strategy is rough; e.g., the negative TISs in the same CDS may be located in different reading frames. In contrast, our proposed method divides the candidate TISs according to the predicted reading frame where they are located. Second, these methods divide the candidate TISs by using labeled data. However, in the test process, the labeled information of the test data is lacking. These methods must combine the prediction results of all the local classifiers, which inevitably induces prediction noise from different classifiers, whereas, in our proposed method, a local classifier is assigned to each sample in an adaptive manner according to predicted frame information.
# First Lidar Campaign in the Industrial Sites of Volta Redonda-RJ and Lorena-SP, Brazil
## 2 The Campaign Sites and Instrumentations
### Campaign Sites--Paratha Valley Region  
Volta Redonda is a medium size city with about 260,000 inhabitants, located at the Paraiba valley region, 141 km from the city of Rio de Janeiro, in the state of Rio de Janeiro. It grew up around one of the largest steel producers in Latin America, the Presidente Vargas Steelworks, which is the most important Companhia Siderugica Nacional (CSN) manufacturing unit and one of the state's main sources of revenue. This steelworks alone, with its two blast furnaces, produces 12.8 thousand tons of pig iron per day. In addition, CSN has a cement plant at the Presidente Vargas unit, with a capacity of 4.3 million tons per year.  
Besides, other metallurgical factories were established in the region in order to supply CSN demand, forming a great industrial complex that contributes to local atmospheric pollution and makes imperative a meticulous control of air quality [41; 42].  
Halfway between the two most important Brazilian cities, Rio de Janeiro and Sao Paulo, and approximately 133 km from Volta Redonda, at the borders of Presidente Dutra Highway (PDH), is located Lorena, a medium size city with about 90,000 inhabitants, which belongs to the state of Sao Paulo.  
Similarly to Volta Redonda city, Lorena, immersed in the metropolitan region of the Paraiba valley, is surrounded by many kinds of industries, as well as by other large urban areas that contribute to the emission of particulate matter into the atmosphere.  
The first lidar campaign in the Paraiba valley was carried out in Lorena and Volta Redonda cities, whose geolocations are marked in red color in the map of the region of the Paraiba valley, see Figure 1a. The Rio de Janeiro and Sao Paulo megacities are also marked, as well as other important regions along the PDH.
## 2 Materials and Methods
### Theoretical Modeling
#### 2.2.8 Localized Molecular Agitation (Focused IR)  
Some perfusion systems may include IR diodes that emit in a controlled manner to maintain particle movement at a microscopic level without mechanically disturbing the solution (unlike physical agitation).  
Nucleation is the process by which the molecules of a substance begin to arrange themselves in an ordered network, forming small crystal \"embryos\" (called crystal nuclei). If these nuclei become stable enough, they grow and lead to the complete crystallization of the substance.  
There are two main types of nucleation:  
Homogeneous nucleation occurs spontaneously throughout the solution, without impurities or surfaces.  
Heterogeneous nucleation occurs on impurities, on the walls of the container, or on suspended particles.  
Infrared light is a form of electromagnetic radiation, i.e., a stream of photons with a certain energy. Each photon has an energy defined by the following equation:  
\[\mathrm{E=h\;X\;v=\frac{hXC}{\lambda}} \tag{10}\]  
E = photon energy,  
h = [PERSON]'s constant (\(\sim\)6.626 \(\times\) 10\({}^{-34}\) J-s),  
v = wave frequency,  
\(\lambda\) = wavelength (700 nm-1 mm for IR),  
c = speed of light.  
Molecules have chemical bonds that act as mass-spring systems: the atoms are the masses, and the chemical bond is the elastic spring. They have natural frequencies of vibration (like a swing or a guitar string).  
When IR radiation has exactly the natural frequency of vibration of a chemical bond, the photons are absorbed, and their energy is converted into molecular vibration.  
Resonance: the key to molecular agitation.  
It is like when you push a child in a swing at the right time, each small impulse adds energy, and the movement increases.  
The phenomenon of resonance: if the frequency of IR radiation corresponds to the natural frequency of a molecular vibration (e.g., stretching of the C-H bond (covalent bond between a carbon (C) atom and a hydrogen (H) atom), that bond will vibrate more intensely, thus the agitation occurs.  
IR radiation causes various types of intramolecular vibrations: vibration type Description, Stretching Bond stretching/compression, bending. Change in angle between bonds, twisting around the molecule's axis. The increase in diffusion can be quantitatively described by the Einstein-Stokes equation:  
\[D=\frac{KbT}{6\pi\mathrm{nr}} \tag{11}\]  
D is the diffusion coefficient.  
K b is the Boltzmann constant.  
T is the absolute temperature.  
\(\eta\) is the dynamic viscosity of the medium.  
r is the hydrodynamic radius of the diffusing particle.  
According to this relation, a rise in temperature \(T\) results in a proportional increase in the diffusion coefficient \(D\), provided that viscosity and particle size remain relatively constant. Consequently, IR irradiation indirectly promotes the homogenization of multi-component systems by facilitating the redistribution of molecular species through enhanced diffusional dynamics.
# Fractal Cardioid Slot Antenna for Super Wideband Applications
## 2 Proposed Antenna and Parametric Sweep
### Effects of the Iteration Factor  
When designing the antenna, it was determined that the parameter \(a_{1}\) and the overall dimensions of the antenna (\(W\times L\)) affect the position of the lowest resonant frequency  
Figure 2: Simulated reflection coefficients for the fractal antenna geometries with different iteration orders. (**a**) 0 th iteration order; (**b**) 1 th iteration order; (**c**) 2 nd iteration order; (**d**) 3 rd iteration order.  
meet more antenna requirements than in case with a fixed IF. In this manner, the antenna characteristics can be even further improved.
## 3 Materials and Methods
### Synthesis
#### 3.3.4 Synthesis of [3-(1H-Benzo[d]imidazol-2-yl)-1H-indol-1-yl]phenylmethananone (**3 bd**)  
To a solution of 2-(1H-indol-3-yl)-1\(H\)-benzo[d]imidazole (**3a**) (1.0 mmol, 0.233 g) and Et\({}_{3}\)N (4.5 mmol, 630 \(\mu\)L) in N,\(N\)-dimethylacetamide (4.0 mL), benzoyl chloride (4.0 mmol, 465 \(\mu\)L) was added. The reaction mixture was heated at 150 \({}^{\circ}\)C for 4 h (TLC control). Then, the reaction mixture was poured into H\({}_{2}\)O (50 mL) and extracted with EtOAc (3 \(\times\) 20 mL). The combined organic layers were washed with H\({}_{2}\)O (3 \(\times\) 10 mL) and brine (10 mL), dried with Na\({}_{2}\)SO\({}_{4}\), and concentrated to dryness. The product was purified via column chromatography on silica gel using the mixture of petroleum ether/EtOAc (gradient chromatography from 30:1 to 1:1) as the eluent. There was a yield of 0.118 g (36%) and it was a brown oil. \({}^{1}\)H NMR (400 MHz, DMSO-\(d_{6}\)) \(\delta\) = 12.78 (brs, 1H) 8.79-8.75 (m, 1H), 8.39-8.36 (m, 1H), 8.34 (s, 1H), 7.90-7.88 (m, 2H), 7.70-7.67 (m, 2H), 7.64-7.60 (m, 2H), 7.53-7.48 (m, 3H), and 7.23-7.18 (m, 2H) ppm; \({}^{13}\)C[\({}^{1}\)H] NMR (100 MHz, DMSO-\(d_{6}\)) \(\delta\) = 168.2, 146.8, 135.9, 133.6, 132.2, 129.2, 129.1, 128.8, 128.4, 128.2, 127.9, 127.5, 125.9, 125.4, 124.4,3.5 Synthesis of 3-(1\(H\)-Benzo[\(d\)]imidazol-2-yl)-1-ethyl-1\(H\)-indole-2-carbaldehyde (**3 bf**)  
To a solution of 2-(1-ethyl-1\(H\)-indol-3-yl)-1\(H\)-benzo[\(d\)]imidazole (**3b**) (0.4 mmol, 0.104 g) in dry THF (40 mL) cooled at -78 \({}^{\circ}\)C, \(n\)-butyllithium (0.6 mmol, 240 \(\upmu\)L, 2.5 M in hexanes) was added dropwise. After 1 h of stirring at -78 \({}^{\circ}\)C, DMF (1.6 mmol, 124 \(\upmu\)L) was added dropwise. After 4h at -78 \({}^{\circ}\)C, the reaction mixture was quenched by addition of saturated aqueous NH\({}_{4}\)Cl solution (20 mL). The aqueous phase was extracted with EtOAc (3 \(\times\) 10 mL) and the organic phase was washed with H\({}_{2}\)O (3 \(\times\) 5 mL) and brine (5 mL), dried with Na\({}_{2}\)SO\({}_{4}\), and concentrated. The product was purified via column chromatography on silica gel using the mixture of petroleum ether/EtOAc (gradient chromatography from 30:1 to 10:1) as the eluent. There was a yield of 0.018 g (16%) and it was a yellow oil. \({}^{1}\)H NMR (400 MHz, DMSO-\(d_{6}\)) \(\delta\) = 10.49 (s, 1H), 8.25-8.23 (m, 1H), 7.78-7.76 (m, 1H), 7.71-7.67 (m, 2H), 7.56-7.52 (m, 1H), 7.37-7.33 (m, 1H), 7.29-7.25 (m, 2H), 4.71 (q, \(J\) = 7.2 Hz, 2H), and 1.36 (t, \(J\) = 7.2 Hz, 3H) ppm; \({}^{13}\)C[\({}^{1}\)H] NMR (100 MHz, DMSO-\(d_{6}\)) \(\delta\) = 185.2, 146.2, 138.5, 132.3, 127.8, 124.9, 123.3, 122.7, 122.5, 117.7, 115.7 (br.s), 111.7, 40.1, and 16.0 ppm. HRMS (ESI) calcd for C\({}_{18}\)H\({}_{16}\)N\({}_{3}\)O [M+H]\({}^{+}\) 290.1288; found 290.1287.
## 3 Results and Discussion
### Asphalt Binder Preparation  
Based on the TGA results, the initial evaporation temperatures of the PCMs and GPCM were relatively low compared to the base binders (PG58-28 and PG70-28). As a result, the recommended temperatures for the binder preparation using PCM and GPCM  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Initial Evaporation** & **Mass Losses** & **Medium Evaporation** & **Mass Losses** & **Maximum Evaporation** & **Mass Losses** \\  & **Temperature** (\({}^{\circ}\)C) & **\%** & **Temperature** (\({}^{\circ}\)C) & **\%** & **Temperature** (\({}^{\circ}\)C) & **\%** \\ \hline Control & 200 \({}^{\circ}\)C & 0.5\% & 400 \({}^{\circ}\)C & 25\% & \textgreater{}700 & \textgreater{}70\% \\
5\%PCM & 225 \({}^{\circ}\)C & 3\% & 460 \({}^{\circ}\)C & 45\% & \textgreater{}700 & \textgreater{}70\% \\
7\%GPCM & 260 \({}^{\circ}\)C & 3\% & 455 \({}^{\circ}\)C & 50\% & 590 \({}^{\circ}\)C & 10\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Initial, medium, and maximum evaporation temperatures obtained from TGA.  
Figure 8: TGA spectrum of PCM and GPCM.  
should not exceed 90 \({}^{\circ}\)C and 110 \({}^{\circ}\)C, respectively. However, the GPCM was added to the PG70-28 binder at a temperature range of 130 \({}^{\circ}\)C to 150 \({}^{\circ}\)C, as the PG70-28 binder needed a higher mixing temperature to ensure the binder was fluid enough to obtain a homogenous mix. It is worth mentioning that approximately 2.5% mass loss in the GPCM occurred when the mixing temperature reached 150 \({}^{\circ}\)C. Therefore, three different temperature ranges were selected to produce the 5%PCM, 5%GPCM, 7%GPCM, 5%GPCM-SBS, and 7%GPCM-SBS binders, as presented in Table 5. Moreover, after different trials, it was found that a mixing time of 20 min was sufficient to provide a homogenous mixture and well-distributed additives.  
To prepare the modified binders, the base binders were heated to the desired temperature ranges, the additives then were added gradually and mixed using a rotational mixer at 2500 rpm for 20 min in a heating mantle to ensure that the mixing temperature of the binder remained constant.
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 2 Background and Related Works
The study [19] proposes an energy-efficient swarm-intelligence-based clustering (SIC) algorithm, in which the particle fitness function is exploited for intercluster distance, intracluster distance, residual energy, and geographic location. For energy-efficient clustering, cluster heads are selected based on improved particle optimization. In [20], the authors propose a dynamic scale UAV weighted clustering algorithm (DSWCA). By determining the optimal, maximum and minimum values of the cluster scale and make the cluster scale dynamic, the algorithm can achieve superior performance in network load balance. Table 1 summarizes the performance positioning of the discussed clustering schemes in FANETs. Compared with other algorithms, the proposed DTPCH-C scheme comprehensively considers the reliability, energy efficiency, and PDR in the FANET.
## 3 Results
### Results of Individual Studies  
For each study, summary statistics were provided, including the mean gap size and standard deviations, for conventional and digital methods. The digital techniques consistently showed smaller mean gap sizes compared to conventional methods. The overall mean gap for digital techniques was 140 \(\upmu\)m, while conventional methods had an average gap size of 164 \(\upmu\)m. The meta-analysis showed a weighted mean difference (WMD) of 26.29 \(\upmu\)m in favor of digital techniques, with moderate heterogeneity (\(\Omega^{2}=21\%\)). These findings suggest that digital techniques provide better fit accuracy than conventional methods. The detailed results from the individual studies are presented in Table 3 and a forest plot is presented in Figure 2.  
Ultimately, 11 in vitro studies met the inclusion criteria. The study selection process, including the identification, screening, eligibility, and inclusion of studies, is detailed in the PRISMA flow diagram (Figure 1). This section provides a clear overview of the systematic review methodology employed in this meta-analysis, guiding the reader through each stage of the study selection process. The selected studies compared various digital fabrication techniques to conventional lost-wax casting techniques. The overall mean gap size for the conventional method across all the studies was 164.46 \(\upmu\)m, with a standard deviation of 61.05 \(\upmu\)m. In comparison, the digital techniques had a mean gap size of 114.82 \(\upmu\)m, with a standard deviation of 67.55 \(\upmu\)m. The standard deviation reflects the variability in fit accuracy between different fabrication methods. The included studies' characteristics and outcomes are summarized in Tables 2 and 3, respectively.
## 2 Methodology
### DualSPHysics  
DualSPHysics [39] is based on Weakly Compressible SPH (WCSPH) with the fluid phase governed by the Navier-Stokes equations, reduced to ordinary differential equations solved in a Lagrangian framework. The conservation of mass and momentum is expressed as [29; 43]:  
\[\frac{\mathrm{d}\rho}{\mathrm{d}t}=-\rho\
abla\cdot\mathbf{v} \tag{1}\]  
\[\frac{\mathrm{d}\mathbf{v}}{\mathrm{d}t}=-\frac{1}{\rho}\
abla p+\mathbf{g}+\mathbf{\Gamma} \tag{2}\]  
where \(\rho\) is the fluid density, \(t\) is time, \(p\) is the pressure, \(\mathbf{v}=\big{(}v_{x},v_{y},v_{z}\big{)}\) is the velocity vector, \(\mathbf{g}=\big{(}g_{x},g_{y},g_{z}\big{)}\) is the gravitational acceleration vector (here \(g_{x}=0,g_{y}=0\), \(g_{z}=-9.81\) m/s\({}^{2}\) is used) and \(\mathbf{\Gamma}\) groups all the dissipative terms that can be defined by using two different formulations, i.e., artificial viscosity [44] or laminar with sub-particle scale turbulence formulations [45].  
SPH discretises every part of the computational domain into sets of particles carrying different properties such as density, pressure, velocity, etc. In general, two steps are defined to apply Equations (1) and (2) to the SPH method, i.e., a kernel approximation and a particle approximation [46]. First, any variable \(f\) of a particle \(a\), located at \(\mathbf{r}_{a}=(x_{a},y_{a},z_{a})\), is represented by an integral at location \(\mathbf{r}=(x,y,z)\)  
\[f(\mathbf{r}_{a})=\int_{\Omega}f(\mathbf{r})W\big{(}\mathbf{r}_{a}-\mathbf{r},h_{p}\big{)} \mathrm{d}\mathbf{r} \tag{3}\]  
with \(\Omega\) as the computation domain, \(W\) as a weighting function called smoothing kernel, which monotonically decreases with distance, and \(h_{p}\) as the smoothing length which determines the size of the kernel support.  
In the second step, the integral in Equation (3) is approximated by interpolating the characteristics of the surrounding particles as:  
\[f(\mathbf{r}_{a})\ \approx\sum_{b}f(\mathbf{r}_{b})\frac{m_{b}}{\rho_{b}}W_{ab} \tag{4}\]  
where the summation is extended to all the particles inside the kernel. In Equation (4), \(W_{ab}=W\big{(}\mathbf{r}_{a}-\mathbf{r}_{b},h_{p}\big{)}\), and \(m_{b}\) and \(\rho_{b}\) are the mass and density of a neighbour particle \(b\) (located at \(\mathbf{r}_{b}=(x_{b},y_{b},z_{b})\)). In addition, any derivative of \(f\) can be expressed as  
\[\
abla f(\mathbf{r}_{a})\ \approx\sum_{b}f(\mathbf{r}_{b})\frac{m_{b}}{\rho_{b}}\
abla_{a}W _{ab} \tag{5}\]where \(\
abla_{a}\) indicates derivation with respect to the coordinates of particle \(a\). Equation (1) is rewritten in the SPH framework for a particle \(a\) as  
\[\frac{\mathrm{d}\rho_{a}}{\mathrm{d}t}=\rho_{a}\,\sum_{b}\frac{m_{b}}{\rho_{b}} \rho_{ab}\cdot\
abla_{a}W_{ab} \tag{6}\]  
A more extensive analysis of the SPH governing equations and model can be found in the reference literature [38; 39; 46; 47; 48].