esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 3 Methodology
### U-Net Architecture
#### 3.2.1 U-Net Encoder with Hierarchical Convolutional Blocks  
The encoder path comprises a series of convolutional blocks, each containing convolutional layers that utilize batch normalization and ReLU activation (Figure 4). As these blocks proceed, they incrementally increase the filter count while decreasing spatial dimensions using max-pooling, which helps extract increasingly abstract hierarchical fea  
Figure 3: Architecture of the proposed CCT-U-ViT model.  
tures. Skip connections from each encoder stage preserve high-resolution spatial details for subsequent reconstruction.  
The encoder uses convolutional layers to extract features. The general form of the convolution operation is:  
\[F\_out=sigmoid(W*X+b) \tag{1}\]  
where: \(F\_out\) is the output feature map, \(W\) is the convolution filter (kernel), \(X\) is the input feature map, \(b\) is the bias term, \(\sigma\) represents the activation function (ReLU).  
Max pooling reduces spatial resolution:  
\[X\_pool=max\_pooling(X,f) \tag{2}\]  
where \(f\) is the filter size.
## 4 Results and Discussions
### Relationship between the Inzari Formation's Mechanical and Physical Characteristics  
To investigate any potential relationships between the various samples of the Inzari Formation, their mechanical and physical characteristics were displayed against one another. A significant reduction in the strength of rocks occurs when their void capacity increases. A notable mechanical effect can result from a slight change in the volume of pores [21]. From (Figures 15-17), and Table 6, we noted that it is typical and predicted for there to be a decrease in porosity and the quantity of water absorbed as the compressive strength rises and an increase in Schmidt hammer as the compressive strength rises.  
Figure 14: Core sample loading for the unconfined tensile strength (UTS).  
Figure 15: Relationship between UCS (Unconfined Compressive Strength) and porosity. The circles represent individual data points, indicating measured UCS values at corresponding porosity levels. The dashed line represents a trend line or linear regression line, showing the overall trend in the relationship between UCS and porosity, with UCS generally decreasing as porosity increases.  
Figure 16: Relationship between UCS (Unconfined Compressive Strength) and water absorption (%). The circles represent individual data points, showing measured UCS values at corresponding water absorption levels. The dashed line represents a trend line or linear regression line, indicating the general trend where UCS tends to decrease as water absorption increases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Number** & **Cube Sample** & **UCS (MPa)** & **Water Absorption (\%)** & **Specific Gravity** \\ \hline Pi1 & Pi1 (a) & 23 & 0.50 & 2.666 \\ \hline \multirow{3}{*}{Pi2} & Pi2 (a) & 35 & 0.37 & 2.674 \\ \cline{2-5}  & Pi2 (b) & 36 & 0.27 & 2.677 \\ \cline{2-5}  & Pi2 (c) & 71 & 0.33 & 2.680 \\ \hline \multirow{3}{*}{Pi3} & Pi3 (a) & 63 & 0.21 & 2.683 \\ \cline{2-5}  & Pi3 (b) & 75 & 0.20 & 2.688 \\ \cline{2-5}  & Pi3 (c) & 69 & 0.18 & 2.687 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between UCS, water absorption, and specific gravity.  
Figure 17: Relationship between UCS (Unconfined Compressive Strength) and Schmidt hammer values. The circles represent individual data points, indicating measured UCS values at corresponding Schmidt hammer readings. The dashed line represents a trend line or linear regression line, showing a positive correlation where UCS tends to increase with higher Schmidt hammer values.
## 3 Methods and Results
### ISS and LEO Dosimetry  
The optimal radiation monitoring instrument should be sensitive to as much environment radiation as possible and have good charge, energy, and LET (linear energy transfer) resolution to identify different particles and to assign appropriate values of quality factors. Differences in dose were measured between different locations within the same time period,  
Figure 1: Dose in Si as a function of spherical Al shielding as calculated by SHIELDOSE-2Q for the entire juice mission [29].  
reflecting differences in shielding [30]. For fixed-oriented spacecraft such as the ISS (International Space Station), differences in the directionality of the primary radiation field will also lead to large differences in the measured dose throughout the habitable volume. Two types of dosimeters are commonly used: passive and active. Passive dosimeters, which include thermoluminescent, optically stimulated, and radiophotoluminescent dosimeters, accumulate the total radiation dose over time. In contrast, active dosimeters, which employ semiconductor materials such as silicon diodes or metal-oxide-semiconductor field-effect transistors, provide real-time measurements of radiation exposure. This information is critical for radiation protection, as it enables the development of effective countermeasures to minimize radiation exposure and establish safe exposure limits for astronauts and equipment. The use of dosimeters on the ISS has provided invaluable data on the expected radiation dose in LEOs, facilitating the development of strategies to mitigate the risks of ionizing radiation in space. Personal dosimetry is also necessary for each astronaut, and area dosimetry is often carried out at different locations within the spacecraft.
## 3 Materials and Methods
### Spectrophotometric Analysis of LE and DM
#### 3.5.2 Total Polyphenols Content  
The total polyphenols content (TPC) in LE was measured by applying the Folin-Ciocalteu method [64], with little variation. Briefly, 0.5 mL of aqueous solutions of LE (1 mg/mL), after being added to 2.5 mL of Folin-Ciocalteu reagent solution (diluted 10 times in water) and 7 mL of 7.5% Na\({}_{2}\)CO\({}_{3}\) solution, were stored for 2 h at room temperature in the dark. The spectrophotometric assay was carried out with a Cary 8454 UV-Vis (Agilent Technologies, Woburn, MA, USA) at 735 nm. The calibration curve of gallic acid was employed in order to determine the TPC, which was reported as the mean of two measurements, and indicated as mg of gallic acid equivalents (GAE) per g of dry extract (DE). The TPC was evaluated also for the dried detrepenated material (DM). In this case, 1 g of DM was extracted in an ultrasound bath with 10 mL of a 50% ethanol aqueous solution, and 0.5 mL of the supernatant was subjected to the spectrophotometric test, following the same previous procedure. The results were expressed as mg GAE per g of dry hemp (DW).
# Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network
## 5 Performance Analysis
### Feasibility Comparison  
In order to ensure the effectiveness of the proposed approach, we compared it to some key approaches from the literature. The findings are summarized in Table 8.  
From our literature review leading to the above comparison analysis, we noticed that most studies have focused on the key obstacles of establishing a green cellular network that included solar PV and wind turbines as well as a backup system. Furthermore, many of them did not analyze the system for heterogeneous networks. To the best of our knowledge, we are the first to develop a techno-economic and energy efficiency analysis integrating solar PV/BG system with electrical grid system for heterogeneous networks, in addition to the proposed sleep algorithm for IoT applications. Additionally, the suggested system was found to have only have a net present cost of about USD 21,445 and an energy-generating cost of USD 0.379/kWh for the macro-base station. Furthermore, a steady decline in per-unit energy generation cost has been found in response to higher system bandwidth, resulting in lower carbon footprints.  
Figure 28: Percentage of power consumption v.s packet arrival rate (Assumption 2).  
Figure 29: Overall packet delay vs. packet arrival rate (Assumption 2).
## 3 Materials and Methods
### Mechanical Characterization of the Constituent Materials  
The studied materials included solid bricks and natural hydraulic lime (NHL) mortar. The bricks consisted of fired-clay extruded units with dimensions 200 mm \(\times\) 95 mm \(\times\) 65 mm. The mortar was prepared with NHL 3.5 (following the classification in EN 459-1 [25]), which was supplied by SECIL, and sand from a nearby quarry with a proportion binder/aggregate of 1:2.5 in volume. The quantity of water for the preparation of the mortar was chosen to obtain 145-155 mm in the flow table test [26], which is a recommended value for conservation mortars [27].  
The tests performed for the mechanical characterization of bricks included: elastic modulus, following the standard EN 12390-13 for hardened concrete [28]; uniaxial compression, according to EN 772-1 [29]; and three-point bending tests, as specified in ASTM C67/C67M [30]. Similarly, the mechanical characterization tests on mortar specimens included: elastic modulus, according to EN 12390-13 [28]; uniaxial compression and three-point bending, following EN 1015-11 [31]. Molded mortar specimens were prepared with the same mixture used for the construction of the walls. The mortar specimens remained in the same location as the walls to ensure analogous curing conditions, namely, temperature and relative humidity. The average results obtained from the mechanical characterization tests on constituent materials are presented in Table 1. In the table, the coefficient of variation (CoV) is given between parenthesis, expressed in percentage.
## 4 Methods
### Optimization of LSSVM by Particle Swarm Algorithm  
It can be seen from Equations (7) and (8) that \(\gamma\) and \(\sigma\) are the critical parameters in determining the weight of the error vector in the LSSVM model [45]. Thus, improving the ability to learn and generalize is vital when applying the LSSVM model to predict nonlinear data with a small sample. In order to improve the prediction accuracy, this paper introduced the PSO algorithm into the LSSVM. Figure 4 shows the optimization flow of the hyperparameters (\(\gamma\) and \(\sigma\)) in the LSSVM model using PSO. The root-mean-squared error (RMSE) was chosen as the fitness function for the optimization of the two hyperparameters \(\gamma\) and \(\sigma\):  
\[\text{fit}(\gamma,\sigma)=\sqrt{\frac{1}{\alpha}{\sum\limits_{i=1}^{n}{(y_{i}- \hat{y}_{i})}^{2}}} \tag{11}\]  
Among them, \(n\) is the number of samples; \(y_{i}\) is the actual value of the sample; \(\hat{y}_{i}\) is the predicted value of the sample.  
In the model's training, the particle velocity was updated by Equation (1), and the particle position was updated by Equation (2). The individual and global fitness were updated by calculating and finding the minimum RMSE value in each step. The minimum RMSE was recorded in the iterative process to find the corresponding optimal hyperparameters. The time cost of the algorithm was appropriately improved, while the prediction accuracy of the algorithm was enhanced.
# An Improved Galerkin Framework for Solving Unsteady High-Reynolds Navier-Stokes Equations
## 4 Numerical Experiments  
### Lid-Driven Cavity Flow (\(Re=1\))  
To first verify the fundamental accuracy and convergence rate of the implementation, a standard example needs to be found. However, in the case of high Reynolds number, the academic community has not found its standard exact solution. Therefore, the exact solution for \(\text{Re}=1\) is provided as  
\[u_{1}(x,y,t) =\frac{1}{10}(6+4\cos(4t))\cdot 16\sin^{2}(\pi x)\cdot y(1-y)(1-2y),\] \[u_{2}(x,y,t) =\frac{1}{10}(6+4\cos(4t))\cdot(-8\pi)\sin(2\pi x)\cdot(y(1-y))^ {2},\] \[p(x,y,t) =\frac{1}{10}(6+4\cos(4t))\cdot\sin(\pi x)\cos(\pi y).\]  
The initial and boundary conditions are derived from this exact solution. Figure 2 shows the comparison between the numerical solution and the exact solution at \(T=0.03\), while Tables 1 and 2 provide a quantitative error analysis. The results confirm that the numerical solution accurately matches the analytical solution, and the error magnitudes are consistent with the theoretical convergence rates of the Taylor-Hood element.  
The error of each time step is shown in Table 1 below.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Time Step** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline \(\|\mathbf{u}_{h}\|_{0}\) & 0.08\% & 0.08\% & 0.08\% & 0.08\% & 0.08\% & 0.08\% & 0.08\% & 0.08\% & 0.08\% & 0.08\% \\ \(\frac{\|\mathbf{u}_{h}\|_{0}}{\|\mathbf{u}_{h}\|_{0}}\) & 0.93\% & 0.92\% & 0.93\% & 0.93\% & 0.93\% & 0.94\% & 0.93\% & 0.94\% & 0.93\% \\ \(\frac{\|p-P_{h}\|_{0}}{\|p\|_{0}}\) & 3.70\% & 1.25\% & 3.96\% & 1.41\% & 4.12\% & 1.54\% & 4.24\% & 1.66\% & 4.32\% & 1.76\% \\ \(\frac{\|\mathbf{u}_{h}\|_{1}+\|p-P_{h}\|_{0}}{\|\mathbf{u}_{h}\|_{1}+\|p\|_{0}}\) & 1.11\% & 0.94\% & 1.13\% & 0.96\% & 1.14\% & 0.97\% & 1.15\% & 0.98\% & 1.16\% & 0.98\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The error of each time step in the case of \(\text{Re}=1\).
# Reconstruction of Preclinical PET Images via Chebyshev Polynomial Approximation of the Sinogram
## 1 Introduction
We compared our reconstructions over FBP via three image metrics, namely percentage standard deviation, recovery coefficient, and spill-over ratio. Our results indicate that Chebyshev polynomials of the first kind provide analytical PET reconstructions that are comparable to FBP, potentially yielding an alternative to standard preclinical analytic reconstruction methods.
# Automated Personalized Loudness Control for Multi-Track Recordings
## 4 Discussion  
Our comprehensive analysis yielded compelling results: both GA and CNN displayed a remarkable ability to not only improve upon the raw, unprocessed tracks, but also to meet--and occasionally exceed--the quality of mixes engineered by humans. This was evidenced across a training set of 15 songs and a validation set of 5 complex songs, where the automated algorithms consistently achieved scores close to or surpassing those of the human-engineered mix. The findings from our study are not an indicator that the role of the audio engineer is being replaced by algorithms, but rather that these technologies are emerging as sophisticated assistants in the mixing process. The GA and CNN demonstrate an ability to lay the groundwork according to an engineer's preferences, effectively setting the stage for them to concentrate on the more creative and subjective aspects of mixing. This symbiosis could see engineers leveraging AI (Artificial Intelligence) to handle initial and time-consuming tasks, such as balancing levels and applying basic EQ settings, thereby freeing up more time for them to delve into the artistic nuances that truly define a mix. Despite the promising outcomes, our results are not without limitations. The current algorithms, while proficient in technical execution, may not yet fully replicate the creative and intuitive aspects of human mixing--the 'art' that goes beyond the'science'. Moreover, the listening tests were confined to specific genres and mixing styles, which may not represent the full spectrum of music production. Future research should explore the incorporation of creative and stylistic decision-making into the algorithms to capture the artistry of human engineers more faithfully. Additionally, expanding the dataset to include a wider variety of musical genres and engineering styles could provide a more comprehensive understanding of the capabilities of the algorithms. Investigating user-friendly interfaces for these technologies could also facilitate their adoption by professionals in the field, enhancing the collaborative potential between humans and AI in audio engineering. As we look to the future, our research will not rest at the threshold of leveling algorithms. We plan to expand this personalized approach to encompass other critical aspects of mixing, including equalization, compression and saturation. By extending the scope of our algorithm to these domains, we aim to offer an even more comprehensive and intuitive mixing tool, one that further blurs the lines between professional studio quality and accessible home recording setups.
# A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition
## 4 Experimental Evaluations  
The proposed model HPCB outperforms its peers on three benchmark datasets described in Section 4.1.
# Reverse Engineering and Topology Optimization for Weight-Reduction of a Bell-Crank
## 3 Materials and Methods
### Bell Crank Model Validation
#### 3.4.1 Finite Element Mesh and Material Properties  
The element size of the bell crank was set at 3 mm, and the 10-node quadratic tetrahedron element was chosen as the element type.  
Figure 4 shows the meshed bell crank, which contains 20,827 elements and 35,803 nodes to the solid section. The mechanical properties of AW-6082-T6 aluminum alloy were assigned to the bell crank model [27,28]. The material properties used for the bell crank are as follows: Yield strength, \(f_{y}=310\) MPa, density, \(\rho=2.7\times 10^{-9}\) kg/mm\({}^{3}\), Young's Modulus, E = 70 GPa and Poisson's ratio, \(\
u=0.33\).
# Performance Optimization Design of Diagonal Flow Fan Based on Ensemble of Surrogates Model
## 2 Research Object and Method  
### Research Object  
A small diagonal flow fan was studied in this research. The main structure is shown in Figure 1. Its flow components are mainly composed of a collector, moving blade, guide vane, and diffuser, in which the impeller is the only working element. The structural parameters were as follows: Impeller inlet outer diameter \(D_{in}\) = 175 mm, impeller outlet outer diameter \(D_{out}\) = 235 mm, blade number Z = 10, guide vane blade number 9, rotation speed n = 2000 r/min, and flow rate Q = 19.4 m\({}^{3}\)/min at 25 \({}^{\circ}\)C.
# A Review of MEMS Scale Piezoelectric Energy Harvester
## 2 Research Progress of PEH
### Research and Improvement of Piezoelectric Modes  
The PEHs depend on the direct piezoelectric effect to convert vibration energy to electrical energy. According to the relationship between the stress and polarization direction of the piezoelectric material, the piezoelectric modes of the PEHs can be divided into \(d_{31}\), \(d_{33}\) and \(d_{15}\)[46; 47; 48]. The piezoelectric material is exposed to bending stress when the PEH is operated in \(d_{31}\) and \(d_{33}\) mode. By contrast, the piezoelectric material is exposed to torsion stress when the PEH is operated in \(d_{15}\) mode.
## 3 Tracking Loops Error Characterization
### Thermal Noise  
The thermal noise in the PLL is related with the induced noise of electronic parts that compose a receiver. It is determined by the PLL bandwidth, carrier to noise ratio density, predetection integration time and carrier wavelength. The 1-sigma thermal noise error can be expressed as follows [21; 22]:  
\[\begin{split}\sigma_{T}&=\frac{360}{2\pi}\sqrt{ \frac{B_{PLL}}{c\,/\,n_{0}}\bigg{(}1+\frac{1}{2\cdot T_{PLL}\cdot c\,/\,n_{ 0}}\bigg{)}}\quad\big{(}^{\circ}\big{)}\\ &=\frac{\lambda_{c}}{2\pi}\sqrt{\frac{B_{PLL}}{c\,/\,n_{0}} \bigg{(}1+\frac{1}{2\cdot T_{PLL}\cdot c\,/\,n_{0}}\bigg{)}}\quad\big{(}m \big{)}\end{split} \tag{1}\]where \(\lambda_{c}\) is the carrier wavelength (m). \(B_{PLL}\) is the PLL bandwidth (Hz). \(T_{PLL}\) is the predetection integration time (s). \(c\) / \(n_{0}\) is the carrier to noise power expressed as a ratio \(10^{(C/N_{0})/10}\) (C/N0 expressed in [dB-Hz]).
## 2 Materials and Methods  
### Experimental Setup for MM-OCT  
All experiments were carried out with a multimodal imaging system for MM-OCT, laser speckle reflectometry and light microscopy, which was presented recently by our group [6]. In brief, the setup shown in Figure 1 consists of a conventional epi-fluorescence microscope (Leica DMRB, Leica Microsystems GmbH, Wetzlar, Germany) for bright-field transmission and multi-color fluorescence microscopy, an integrated MM-OCT probe including an electromagnet for magnetic field generation, and a dark-field laser illumination for laser speckle imaging. The setup allows parallel visualization of the same cells by means of two-dimensional (2D) light microscopy and 3D OCT, as well as investigation of sample motion by means of laser speckle variance analysis. Laser illumination of the sample is generated by a fiber-coupled laser diode at 638 nm (Lasiris PTL-500-635-1-2.0, Coherent Inc., Santa Clara, CA, USA) with an output power of 1 mW. Light microscopy and laser speckle images are detected by a video camera (1.3 megapixel monochrome CMOS, Sumix SMX-M71M, Sumix, Oceanside, CA, USA) mounted on the microscope. The microscope-integrated OCT probe is fiber-coupled to a self-developed spectral domain OCT system operating at 880 nm [10]. This system is equipped with a superluminescent diode (Exalos EXS8810-2411, Exalos AG, Schlieren, Switzerland) with a center wavelength of 876 nm and a spectral bandwidth of 64 nm (FWHM), and a linear-in-wavenumber spectrometer. In summary, the system allows OCT imaging at an axial depth scan (A-scan) rate of 11.9 kHz with an axial resolution of 6.7 \(\upmu\)m in air, an optical imaging depth of 3 mm and a sensitivity of \(-\)102 dB.  
The schemagnet for magnetic field generation (Intertec Components GmbH, ITS-MS 5030 12 VDC, Hallbergmoos, Germany) is attached to the distal end of the OCT probe with parts of the OCT imaging optics incorporated into a center bore of the solenoid's ferromagnetic core. With a working distance of 6 mm and an imaging numerical aperture (NA) of 0.1 yielding a theoretical lateral resolution of 3.3 \(\upmu\)m, this MM-OCT probe allows observation of single cells in a three-dimensional tissue-like environment. The field of view of the OCT probe in the focal plane is 1.5 mm in diameter.  
The solenoid coil (18.5 \(\Omega\)) is usually driven in pulsed operation mode by a self-developed capacitive-discharge pulse generator synchronized to the OCT system. Typically, the magnetic pulse length is set to 80 ms, which corresponds to the acquisition time of two tomographic images (B-scans), and the pulse repetition rate is set to 1 Hz. In the current setup, the maximum charging voltage of the capacitor is 120 V yielding a peak magnetic flux density of 0.2 T and an axial field gradient of 18 T/m in the sample region.
# Mobility-Multihoming Duality
## 2 Combined Mobility and Multihoming
### Locator Identifier Separation Protocol (LISP)  
The Locator Identifier Separation Protocol (LISP) [7] is a network-based solution to realise an identifier-locator split architecture using a routing approach, with direct support for multihoming. LISP defines an End-system Identifier (EID) as a name that acts as an identity for a host and a Routing Locator (RLOC), which acts as a network name for a host. In LISP, both EID values and RLOC values are each an IPv6 address (128 bits), i.e., the semantics of an IPv6 address are overloaded in the communication stack. There is work in progress to define a mobility function for LISP--_EID Mobility_[2]. This allows EID values to'move' and establish different bindings--mappings--to RLOC values, i.e., effectively move from network to network. In principle, it should be possible for the harmonious operation of both mobility and multihoming for a network site and also possibly for an individual host.  
However, the mobility management is complex. It requires a control plane involving a number of additional entities under the administration of a network provider [2]. These include a Map Server, which would be discovered from a Map Resolver, plus Ingress Tunnel Routers (ITRs) and Egress Tunnel Routers (ETRs) to enable the tunnelling mechanism, which is central to the operation of LISP.  
Our approach requires no additional network entities, and it has a much simpler control plane.