esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Such velocity directly affect the cutoff frequency since it linearly depends on the relative velocity between the receiver, the ionosphere and the GPS satellite (e.g., References [27; 29; 33]). The choice of the best cutoff frequency is crucial for high accuracy positioning. This is because diffraction is stochastic so the degradation due to ionospheric scintillations cannot be corrected or mitigated. On the other hand, since the refraction is deterministic, phase fluctuations, which are typically produced by ionospheric irregularities at wave number much smaller than the Fresnel scale, can be corrected by using multi-frequency measurements. However, such correction is beyond the scope of this work that is focused on the scientific understanding of the scintillation events. In this paper, we report and discuss how the peculiar conditions triggered by the 2017 September storm resulted in amplitude scintillations. Following a multi-instrumental approach that combines information derived from ground-based and space-based observations, we investigated the complex spatial-temporal context in which the selected events occurred, also identifying the physical conditions that likely favor the occurrence of amplitude scintillations at high latitudes. The paper is structured as follows. Section 2 provides an overview of all the different types of data here considered and their preprocessing; Section 3 describes the analyzed geomagnetic storm and Section 4 the observed scintillation events. In Section 5, the large amount of information and data described in previous sections is interpreted and discussed separately for each GNSS receiver. Finally, Section 6 summarizes the results.
## 2 Numerical Analysis Model
### Turbulence Model  
Numerical simulation techniques of turbulent flow are typically divided into DNS (direct numerical simulation), LES (Large Eddy Simulation), and RANS (Reynolds averaged Navier-Stokes simulation) [25]. DNS resolves all range of spatial and temporal scales of the turbulence. Because the grid and time scales of DNS need to be less than the Kolmogorov scale, that is, the smallest dissipative scales, the DNS requires fine grids and small time intervals. LES solves spatially filtered Navier-Stokes equations on coarser grids. Thus, the LES does not resolve the entire scales of the turbulent flow. LES resolves only large scales of the turbulent flow. Small scales of the turbulent flow are modeled using sub-grid scale (SGS) models by [PERSON] [26]. RANS solves the time averaged Navier-Stokes equations and models all the scales of the turbulence using turbulence models such as \(k\)-\(\varepsilon\), \(k\)-\(w\), and SST.  
Assuming that computing cost of the RANS is equal to be one, that of the DNS and LES increases as the cube and square of Reynolds number, respectively. Since Reynolds number based on the rotor tip speed and rotor tip radius of the present wind turbine is 2.25 \(\times\) 10\({}^{8}\), the DNS and LES are not feasible methods. For the current wind turbine  
Figure 3: Whole computational domain for computational fluid dynamics (CFD) analysis.  
analysis, the RANS simulation is preferably selected because it is very practical and affordable engineering solutions with good knowledge of the turbulence [27; 28].  
The turbulence model for the RANS equations computes the Reynolds stresses tensor from turbulent fluctuations in fluid momentum. The turbulence models like the \(k\)-\(\varepsilon\), \(k\)-\(w\), and SST have become industry standard models and are commonly used for most types of engineering problems, although \(k\)-\(\varepsilon\) model has the weakness in cases of large adverse pressure gradient and \(k\)-\(w\) model is too sensitive to the inlet free-stream turbulence properties [29]. For current work, the SST model was selected because it uses the \(k\)-\(\varepsilon\) model in the free-stream and the \(k\)-\(w\) model in the viscous sub-layer [30]. The minimum grid scale on the rotor surface was set to 0.05 mm to capture the laminar to turbulent flow transition with SST turbulence model. Additionally, the high-resolution option for advection and turbulence was selected for the numerical scheme in the CFX setup, and the second order backward Euler scheme was taken for transient term.  
The normalized time, \(\Delta t^{*}\), interval of the simulation was set to 0.0323, which was normalized by the rotor tip speed and rotor tip radius as below.  
\[\Delta t^{*}=\Delta t\times\frac{V_{\theta-tip}}{R} \tag{2}\]  
where \(\Delta t\) is the time interval, \(V_{\theta-tip}\) is the rotor tip speed, and \(R\) is the rotor tip radius. In addition, the normalized time for one revolution of the rotor is equal to 6.276.
# Anisotropy Corrected FMC/TFM Based Phased Array Ultrasonic Imaging in an Austenitic Buttering Layer
## 3 Experimental Details  
### Specimen Details  
Buttering of 200 mm diameter 10 Cr steel was carried out by hot-wire TIG welding process using Alloy 617 filler wire (ERNiCrCoMo-1) [4]. The total thickness of the buttering layer was about 25 mm. A sample containing only the buttering layer of about 120 mm length, 25 mm width, and 24 mm thickness was extracted, and the surfaces were made plane parallel for the angle-dependent velocity measurement using the TT mode. Similarly, a sample containing only 10 Cr ferritic steel was also extracted for reference.  
Side drilled holes (SDHs) of 2 mm diameter were machined at depths of 7 mm, 15 mm, and 26 mm (near the buttering layer-ferritic steel interface) as reference defects. The photograph of the reference block is shown in Figure 3. The locations of the SDHs in the reference block are also marked in Figure 3.  
Figure 3: Photograph of the reference block used in the present study. The depths of the side drill holes (SDHs) and their relative distances are given in mm.  
Figure 2: Schematic showing the phased array trough-transmission (TT) setup used for measuring angle-dependent anisotropic ultrasonic velocity.
###### Abstract  
This study aims to analyze the bending behavior of polylactic acid (PLA) structures made by fusion deposition modeling (FDM) technology. The investigation analyzed chiral structures such as lozenge and clepsydra, as well as geometries with wavy patterns such as roller and Es, in addition to a honeycomb structure. All geometries have a relative density of 50%. After being subjected to three-point bending tests, the capacity to spring back with respect to the bending angle and the shape recovery of the structures were measured. The roller and lozenge structures demonstrated the best performance, with shape recovery assessed through three consecutive hot water immersion cycles. The lozenge structure exhibits 25% higher energy absorption than the roller, but the latter ensures better replicability and shape stability. Additionally, the roller absorbs 15% less energy than the lozenge, which experiences a 27% decrease in absorption between the first and second cycle. This work provides new insights into the bending-based energy absorption and recovery behavior of PLA metamaterials, relevant for applications in adaptive and energy-dissipating systems.  
metamaterial; additive manufacturing; bending test; shape recovery; cyclic loading Article
# Satellite Quantum Communications When Man-in-the-Middle Attacks Are Excluded
## 4 Materials and Methods  
The decoy state BB84 equations used are those published in reference [14], and the BBM92 equations are those from reference [26].  
The equation for PPM PKD is derived from the Devetak-Winter bound for collective attack for a simplified coherent one way (COW) PPM scheme with decoys from Appendix B of reference [27]. All parts due to decoy are set to neutral, and an error correction term is added to the equation. The final key rate is thus  
\[R(\mu_{opt})=g(\zeta)\times\frac{t}{(1-t)}\times\eta\times(1-H_{2}(QBER)\times f _{E})\times f_{source} \tag{1}\]  
where \(g(\zeta)\) is a privacy amplification term corresponding to the optimized mean photon number \(\mu_{opt}=\frac{\zeta}{1-t}\). \(\eta\) is the detection efficiency, \(f_{source}\) is the source frequency and \(t\) is the transmissivity.
# Biomimicry and the Built Environment, Learning from Nature's Solutions
## 2 Biomimicry in Architecture
### Potentials and Benefits of Biomimicry in Architecture and Design
#### 2.2.3 Building Envelope (Heating, Cooling, and Lighting)  
There are infinite sources of inspiration from nature that can be utilized in different design and construction technologies and contribute to the effective algorithm, method, material, processes, structure, tool, mechanism, and systems. Living organisms have unique integration geometries and techniques that enable them to adapt themselves to harsh-diverse environments easily. Similarly, buildings nowadays use specific methods to adapt well to their surrounding environments and minimize the adverse impact on the environment.  
Designing the building envelope is among the important methods. The building envelope, also known as the third skin, is 'an extended buffer between the building and the exterior environment'. The first human shelters and settlements consisted of cloths or natural caves. Later, these shelters were built with raw materials, and nowadays, we see communities where houses are built to protect one another and thus create a single unit with an external wall. However, with the development of individual buildings, the optimized envelop design and multi-layered construction have increased. Past services were mainly attached to the envelope and provided isolated solutions while neglecting the building features and improving the constant need for maintenance.
## 5 Integrated System Testing
### Test Scenario 1
#### 5.1.2 Test Results  
The results for Scenario 1 are shown in Figure 16. The figure shows real-time tracking of the temperature, humidity, pressure, and battery power of Sensor 1. The test ran from 1.00 a.m., 23 September 2016 to 11.00 a.m. 24 September 2016, during which time 96 data entries were collected. The timestamps confirm that data was collected in accordance with pre-set timings. Therefore, Scenario 1 was marked 'pass'.
# Potential of Liquid-Crystal Materials for Millimeter-Wave Application
## 4 Microstrip-Line-Type Liquid Crystal Phase Shifter
### Basic Performance of Antenna Array
Figure 11 shows the MMW intensity distribution change of the twin array system when a voltage was applied to the LC phase shifter, in which the distance between the antennas was set at 20 mm. Three peaks appeared under the experimental conditions, and slight asymmetry existed without voltage because of some differences between the two units. When voltage was applied to the LC phase shifter at the left-hand side (minus position) in the figure, the wave front was deformed to the left side and it was seen that the main beam shifted to the left side. The maximal shift distance was about 30 mm, which was 150 mm away from the antenna. This change corresponds to anangle change of about 12\({}^{\circ}\); the \(\pm\)12\({}^{\circ}\) beam steering could be achieved by alternatively driving each 40-mm-long LC phase shifter. Since three peaks appeared in the experiment, we focused on the central peak. It was possible to follow the position of each peak, because the position changed continuously as a function of the applied voltage, even if it was quite nonlinear. The position change of the central beam as a function of the applied voltage is shown in Figure 12. A large change occurs between 1 and 2 V before tending to saturate, which is related to the typical LC molecular orientation change in the homogeneously aligned LC cell. Although the driving voltage is very small, up to only a few volts, a precise voltage control around the threshold is essential for precise beam steering. It is also obvious that the position change accompanied a large intensity change. This may cause difficulties in actual usage; thus, a specific driving scheme will be necessary. Figure 11: MMW radiation profile change upon driving an LC phase shifter. Figure 12: Position change of the central beam as a function of the LC deriving voltage.
## 5 Discussion
Characterisation data obtained via the 60\({}^{\circ}\) and 70\({}^{\circ}\) wedged probes for the manual and robotic configurations demonstrated consistency (Table 2). Any variances observed could be attributed to human error, primarily skill-based errors, which further demonstrates the enhanced consistency that can be obtained from automated robotic systems. During the manual experiment, it was observed that the highest peak values fluctuated easily with the slightest shift in movement. The difference in screening speed over the rail was another variable which could have caused measurement discrepancies. However, this was mitigated by adopting a programmed path in the robotic system which moved at a constant slow speed. The robotic system offered this advantage over the manual system. Another successful parameter for which the robotic system was found to be equivalent to the manual configuration was the measurement of internal defect depth/surface distance. Sound path data generated for the localisation of the defects were mostly comparable for both configurations. This similar performance of both experimental set-ups offers significant potential for future testing and the development of detection systems. The literature suggests that the key challenge when using the ultrasound measuring system is the constant need to maintain contact with the couplant and with the rail specimen [27]. However, with the introduction of automation and robotics, the possibility of improper contact was negligible, since the motion system was calibrated and the trajectory was programmed, offering a marginal possibility of error (<0.9 mm). Furthermore, the possibility of human error was also reduced.
## 3 Results  
The proposed 1D-CNN was evaluated in line with three parameters: (1) accuracy percentage, (2) confusion matrix and (3) t-distribution Stochastic Neighbor Embedding (t-SNE) algorithms. t-SNE refers to a 2D representation of the fully connected layer that allows for visual analysis of the classification performed by the network, using both the validation and testing sets.  
The configuration-training-testing cycle described in previous section was run three times by (1) increasing the number of iterations, (2) varying the number of classes and (3) eliminating the most identifiable classes, so as to evaluate accuracy, generalization and versatility of 1D-CNN to identify spindle bearing failures. Results of the three evaluations are presented below.
## 3 Results  
### Zip3 Zinc Uptake Transporters Reside in the Plasma Membrane of Rodent \(\beta\)-Cells  
The immunofluorescence (IF) assay visualized pancreatic islets. RNA knockdown tactics and IF assay results demonstrate that Zip3 transporters are in the plasma membrane in the \(\beta\)-cells isolated from the harvested islets (Figure 1A). The composite pictures were taken at the same setting with 4.89 s exposure time, 1.5 gain, and \(-\)904 offset at \(\times\) 400 magnification. Figure 1A shows both pan-Cadherin protein (green)-containing plasma membrane and Zip3 proteins in blue color. The merged picture of control (SCR or scrambled) \(\beta\)-cells shows brightened spots aligned along the plasma membrane (see arrowheads in Figure 1A), while siRNA-treated cells show faintly stained cells. Western blot assays on fractionated cells localize ZIP3 transporters clearly. As indicated, Zip3 protein presents in the cell membrane, as shown in Figure 1B-D.  
(GABA_{A}\) Receptors and ZIP3 Transporter mRNA Levels Increase during Differentiation of SHED into SHED \(\beta\)-Cells  
Real-time qRT-PCR assays were used to analyze maturational changes in the mRNA expression levels of four GABA\({}_{A}\) receptor subunits. Day-10 mRNA levels for GABA\({}_{A}\) c1 receptors (7.2-fold), GABA\({}_{A}\)\(\alpha\)2 (7.6-fold), GABA\({}_{A}\)\(\alpha\)3 (2.3-fold), and GABA\({}_{A}\)\(\alpha\)6 (4.3-fold) receptors were increased relative to 18S ribosomal RNA compared to Day 0 (Figure 2A). The expression levels of these receptors in Day-10 SHED were significantly higher than in Day-5 SHED (\(p<0.0001\) indicated by ** in Figure 2A). The expression levels of ZIP3 mRNA also increased at Day 5 and Day 10 (\(p<0.01\) * in Figure 2B) during differentiation of SHED \(\beta\)-cells. Note that supplementation with 50 uM zinc to the cell medium of Day-10 ICAs increased ZIP3 mRNA levels only slightly (\(p>0.05\)).  
Figure 1: Zip3 protein expressions in rat \(\beta\)-cells. (**A**) Zip3 proteins (see arrowheads) are evident in the cell membrane at \(\times\) 400 magnification. Magnification ruler indicates 5 um. (**B**) Western blot assay results on the whole cells harvested from control (scrambled or SCR) \(\beta\)-cells and the \(\beta\)-cells with siRNA-mediated Zip3 knockdown. (**C**) When fractionated into cytoplasm and plasma membrane portions, Zip3 protein is expressed in the plasma membrane more than the cytoplasm portion. (**D**) Pan-Cadherin protein clearly reveals that Zip3 resides in the membrane. ZIP3 with arrowhead approximates the position of Molecular Weight 34.
# Fatigue Behavior of Prestressed Concrete Beam for Straddle-Type Monorail Tracks
## 2 Experimental Program  
### Materials  
A PC beam was fabricated to represent the typical PC track beam for a straddle-type monorail. The cube compressive strength, tensile strength, and elastic modulus of the concrete were 60 MPa, 3.5 MPa, and 41 GPa, respectively. The reinforcement bars were HRB400, providing yield strength and elastic modulus of 400 MPa and 200 GPa, respectively. The yield strength and elastic modulus of prestressed tendons were 1860 MPa and 195 GPa.
## 5 Results and Discussion
### Cubic Metric
#### 5.1.2 ECMA-368 Signals Case  
Before drawing the BER, the cubic metric of the ECMA-368 standard is plotted. First, in Figures 16 and 17, the cubic metric is plotted to verify that the implementation is working properly.  
It is clear that our implementation allows a good reduction of cubic metric of ECMA-368 signals.  
Figure 16: ECMA-368 cubic metric reduction in the case of QPSK modulation.  
Figure 17: ECMA-368 cubic metric reduction in the case of DCM modulation.
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Combining Prediction Intervals
#### 2.6.1 Simple Averaging PI Combination Method  
This method makes use of the arithmetic means of the prediction limits from the forecasting models. This can be expressed as Equation (27)  
\[L_{Av}=\frac{1}{m}\sum_{t=1}^{m}L_{t}\qquad U_{Av}=\frac{1}{m}\sum_{t=1}^{m}U_ {t} \tag{27}\]  
A robust interval is known to be produced from this fairly simple approach [31].
# Hybrid Uncertainty Metrics-Based Privacy-Preserving Alternating Multimodal Representation Learning
## 3 Materials and Methods  
### Overview of Our Proposed PAMRL  
In this study, we propose an enhanced multimodal learning framework, Privacy-Preserving Alternating Multimodal Representation Learning (PAMRL), to address the limitations of prior work, specifically modality laziness, suboptimal uncertainty quantification, and the absence of privacy-preserving mechanisms. Our approach builds upon the Multimodal Learning with Alternating Unimodal Adaptation (MLA) framework by incorporating two key components: a hybrid uncertainty-based dynamic fusion mechanism and a privacy-preserving alternating training strategy. The former integrates entropy and Kullback-Leibler (KL) divergence for a more robust assessment of modality reliability, enhancing cross-modal consistency in noisy or imbalanced scenarios. The latter applies Differential Privacy Stochastic Gradient Descent (DP-SGD) selectively to unimodal encoders to protect sensitive data without compromising cross-modal integration. To evaluate PAMRL's privacy protection, we define a threat model analyzing risks against membership inference attacks (MIAs) for models handling sensitive datasets like CREMA-D and MVSA. In this model, an adversary seeks to infer training data membership (e.g., personal identities in CREMA-D or social media records in MVSA) using model outputs (fused and unimodal predictions, limited gradient information from the shared head) but cannot access training data or internal parameters. This threat model underscores the necessity of PAMRL's privacy mechanisms to mitigate risks like MIAs while maintaining performance. The overall architecture of PAMRL is illustrated in Figure 1. This section provides a high-level overview of our approach, outlining its core components and operational principles, with detailed implementation specifics deferred to the subsequent subsection.
## 2 System Model
### Cognitive Radio Wireless Sensor Network Energy Consumption Model
#### 3.3.4 Transceiver Unit Energy Consumption
\begin{split} E_{CH}(j)=\underbrace{E_{ss}(B_{ss})}_{Events}+ \underbrace{E_{log}\big{(}B_{lg}\big{)}}_{Data}+\underbrace{E_{cs}(n_{z},T_{ cs})}_{\begin{subarray}{c}Channel\\ sensing\end{subarray}}+\sum_{i=1}^{\min_{n}}\left(\underbrace{E_{rx}(B_{ld})}_ {Receiving}+\underbrace{E_{rM}(B_{dt})}_{Data}\right)\\ +\sum_{i=1}^{\min_{n}+1}\left(\underbrace{E_{dp}\big{(}B_{dp} \big{)}}_{Decisions}+\underbrace{E_{ap}\big{(}B_{ap},N_{cy}\big{)}}_{ \begin{subarray}{c}Data\\ aggregation\end{subarray}}\right)+\underbrace{E_{tM}(B_{dt},d_{i,j})}_{ \begin{subarray}{c}Data\\ Transmitting\end{subarray}}\\ =P_{ss}\;x\;T_{ss}\;x\;B_{ss}+B_{lg}(P_{rd}\;+\;P_{wr})+\sum_{m=1 }^{n_{x}}(T_{cs}\;P_{ed}+E_{sp})+\sum_{i=1}^{\min_{n}}(B_{ld}E_{ec}+B_{dt}E_{ec}) \\ +\sum_{i=1}^{\min_{n}+1}\left(\operatorname{E_{dp}\big{(}B_{dp} \big{)}}_{Processing}+\underbrace{E_{ap}\big{(}B_{ap},N_{cy}\big{)}}_{ \begin{subarray}{c}Data\\ aggregation\end{subarray}}\right)+\underbrace{E_{tM}(B_{dt},d_{i,j})}_{ \begin{subarray}{c}Data\\ Transmitting\end{subarray}}\\ =P_{ss}\;x\;T_{ss}\;x\;B_{ss}+B_{lg}(P_{rd}\;+\;P_{wr})+\sum_{m=1}^{n_{x}}(T_ {cs}\;P_{ed}+E_{sp})+\sum_{i=1}^{\min_{n}}(B_{ld}E_{ec}+B_{dt}E_{ec})\\ +\sum_{i=1}^{\min_{n}+1}\left(\operatorname{E_{dp}\big{(}B_{dp} \big{)}}_{Processing}+B_{ap}N_{cy}C_{av}V_{s}^{2}+B_{ap}V_{s}\left(I_{o}e^{\frac{V _{s}}{hpV_{t}}}\right)\left(\frac{N_{cy}}{f}\right)\right)\\ +B_{cd}\left(E_{ec}+E_{am}\;d_{j,Rmax}^{\beta}\right)+B_{pk}P_{ec}+B_{pk} \Big{(}E_{ec}+E_{mp}\;d_{j,g}^{\beta}\Big{)}\end{split}  \tag{30}\]  
Thus, total energy consumption for the entire network is given as:  
\[E_{net}=\sum_{j=1}^{q}\big{(}E_{CH}(j)+\sum_{i=1}^{m_{n,j}}E_{MN}(i,j)\big{)} \tag{31}\]