esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Strengths, Limitations, and Future Research Directions
### Limitations
#### 6.2.3 Performance Optimization  
CNNs can suffer from reduced performance and increased memory consumption when applied to high-resolution images or large-scale datasets, as they require processing of local receptive fields at multiple spatial scales, leading to increased computational requirements. Compared to CNNs, ViTs are computationally expensive, especially as the image size increases or model depth increases, which may limit their use in certain resource-constrained environments. Reduced computational complexity can sometimes result in decreased robustness, as models may not have the ability to learn complex features that can help generalize well to adversarial examples.
# Sustainable Support Material for Overhang Printing in 3D Concrete Printing Technology
## 2 Experimental Details  
### Materials  
The paste of the support material is formulated with class F fly ash and finely ground recycled glass powder. The absence of Portland cement, the main constituent responsible for the hydration chemical reaction of the mixtures, allows the material to remain in a soft state. The soft material facilitates the ease of removing the support from the main structure after the material has cured sufficiently to be self-supported in the overhang sections. The class F fly ash particles were found to have rounded shapes, while recycled glass powder particles have irregular shapes with sharp edges, as shown in the scanning electron microscope (SEM) images in Figure 6. The particle size distribution of the two powder materials, as measured by the particle size analyzer (Mastersizer 2000 by Malvern Analytical), is shown in Figure 7.  
The paste in the mixtures consists of fly ash and recycled glass powder mixed with water. The water/paste ratio was kept consistent at 0.3 for all the mixtures. The effect of the fly ash powder and recycled glass powder content in the paste was studied by the variation of the percentages of each powder type in the paste. The recycled glass content varied from 40% to 70% in the paste, while fly ash powder made up the remaining portion of the paste. Natural sand, at a ratio of 0.8 by weight to the paste, was also added to achieve higher yield stress and shape retention of the mixtures. The mix designs of the mixtures examined in this study are summarized in Table 1.  
Figure 6: SEM Images of (**a**) recycled glass powder and (**b**) fly ash powder.
The relatively low cost of LC-MS instruments has contributed to their widespread use in the analysis of phytoestrogens with single quadrupole (Q) that is more economically available in Latin America. MS instruments, such as Q, time-of-flight (TOF), Q-TOF, triple quadrupole (QqQ) and others, have ensured more known and unknown phytoestrogens being accurately identified. However, most LC-MS methods have focused on specific phytoestrogens to be analyzed per run [10; 11]. Furthermore, a validated LC-MS method for the simultaneous quantification of phytoestrogens in food and biofluids has not been established. Even though these are complex and different matrices, with different phytoestrogen metabolites and with a wide variation in concentrations (i.e., serum levels lower compared to food and urine), quantification can be achieved with a comprehensive LC-MS method. In the present study, we established and validated a high-performance liquid chromatography diode-array detector electrospray ionization mass spectrometry (HPLC-DAD-ESI-MS) method for the quantification of sixteen phytoestrogens (biochanin A, seciosolariciresinol, matairesinol, enterodiol, enterolactone, equol, quercetin, genistein, glycitein, luteolin, naringenin, kaempfer, lormonontein, daidzein, resveratrol and coumestrol), suitable for the analysis of food, serum and urine. The novelty of this method is the inclusion of a wide variety of phytoestrogens with high oestrogen-like activity present in food, beverages and related metabolites in human biofluids. The method was successfully applied to commonly eaten food items from North Mexico, as well as to urine and serum samples from healthy women. This method is flexible and suitable for phytoestrogens' quantification, and could be used to analyze human samples in large-scale epidemiological studies investigating the association between phytoestrogen exposure and human health. This study will contribute to the development of food databases in Latin-American countries with the inclusion of regional food items, which could represent an important dietary source of phytoestrogens.
## 1 Introduction
LSCF based composite cathodes have been widely studied with SDC-carbonate (SDCC) electrolyte materials to improve its oxygen-ion conduction and structural stability at low operating temperatures. For instance, [PERSON] et al. reported single cell performance based on SDC-carbonate (SDCC) and LSCF composite cathode and found that the composite cathode exhibited power densities as high as 117.9 and 120.4 mW/cm\({}^{2}\) at an operation temperature of 550 \({}^{\circ}\)C [28]. In addition, [PERSON] et al. [29] also proposed that different powder mixtures of SDCC influenced the surface area and thermal expansion coefficient of the prepared composite cathode powder. However, studies on the effect ofSDCC composite electrolyte content on the electronic network over the LSCF cathode surface are still very limited. Therefore, this study aims to analyze the effect of SDCC composite electrolyte content on the in-plane electronic conducting behavior at the surface of the LSCF cathode at low operating temperatures (400-650 \({}^{\circ}\)C). The composite cathode is prepared by mixing LSCF with SDCC composite electrolytes at different weight percentages (i.e., 70:30 wt %, 60:40 wt %, and 50:50 wt %) to determine the effect on the overall electrochemical performance under real fuel cell operating conditions. The phase, microstructure, particle size distribution, BET specific surface area, and electrochemical performance of the LSCF-SDCC composite cathodes pellets were deeply analyzed in this study.
## 5 Simulation Setup
### Numerical Solution of the Ideal Camera Operation Space  
With known parameters from the camera and robot arm specifications, all possible locations of the camera center \(C(X_{\rm C}^{V},\)\(Y_{\rm C}^{V},\)\(Z_{\rm C}^{V})\) inside the camera operational space can be derived from the following steps:  
1. Calculate \(\overline{Z}_{Vmax}\) and \(\overline{Z}_{Vmin}\) by Equation (77) and mesh grid \(Z_{\rm C}^{V}\) in \((\overline{Z}_{Vmin},\)\(\overline{Z}_{Vmax})\);
2. Take the mesh value \(Z_{\rm C}^{V}\) and calculate \(\overline{Y}_{Vmax}\) and \(\overline{Y}_{Vmin}\) by Equations (82) and (83) and mesh grid \(Y_{\rm C}^{V}\) in \((\overline{Y}_{Vmin},\)\(\overline{Y}_{Vmax})\);
3. Take the mesh \(Y_{\rm C}^{V}\) and calculate \(\overline{X}_{Vmax}\) and \(\overline{X}_{Vmin}\) by Equations (85) and (86) and mesh grid \(X_{\rm C}^{V}\) in \((\overline{X}_{Vmin},\)\(\overline{X}_{Vmax})\).  
Each above computed \(C(X_{\rm C}^{V},\)\(Y_{\rm C}^{V},\)\(Z_{\rm C}^{V})\) is inside the camera operation space, and these three steps completely account for all the geometric constraints defined above. The number of mesh points (nodes available for searching) depends on the mesh grid size.
## 3 Results and Discussion  
### Tolerance of Yeast Strains to _p_-Coumaric Acid  
Previous works suggested that _p_-coumaric acid can be used as a substrate for resveratrol production in engineered microorganisms expressing 4 CL and STS [14; 20]. However, these studies also suggested the potential toxicity of this phenolic compound on _Escherichia coli_ and _S. cerevisiae_. In order to explore the antimicrobial effect of _p_-coumaric acid on recombinant lines, the broth microdilution method using rezasurin as an indicator of cell viability was performed (Figure 2). According to these results, all the strains had minimum inhibitory concentrations (MIC) over 100 mg L\({}^{-1}\)_p_-coumaric acid, similar MIC values were reported for some strains of _E. coli_[21]. Interestingly, MIC values for all the assayed strains had statistically significant differences (_p_ < 0.01), which may suggest that the genetic material contained in those strains influenced the resistance to _p_-coumaric acid. This resistance could be associated to the enzymatic activity of Pa4 CL1 and STS inserted in the recombinant strains [22]. Interestingly, the lowest MIC observed was for wild type W303 (_p_ < 0.01), which supports the idea that the insertion of genetic material may be favoring _p_-coumaric acid uptake.  
Figure 2: Minimum inhibitory concentration (MIC) for wild and recombinant strains of _S. cerevisiae_ treated with _p_-coumaric acid. Diverse letters indicate statistically significant differences (_p_ < 0.01).
## 3 Results
### Postprocess Distortion Measurements  
The global distortion that occurred in patterns 1\(\sim\)6 are measured by CMM and plotted as shown in Figure 12. The results show that there is a difference in quantitative values, but the tendency of out-of-plane deformation is similar for all of the patterns except for pattern 2. In particular, it can be seen that longitudinal bending is dominant in pattern 1 and angular distortion in transverse direction is predominant in pattern 2. The top view, front view and the side view of the pattern 1 specimen results are shown in Figure 13.  
A comparison of the deflection values at the center of the substrate along the length direction is shown in Figure 14. In the case of patterns 3\(\sim\)6 applied by combining short raster pattern and long raster pattern, it is shown that the amount of deflection is within the result range of Pattern 1 and Pattern 2. The results of pattern 2, 4, and 6, in which the first layer was applied as a short raster pattern, were shown to have a small deflection when a short raster pattern was applied at a lower layer. Likewise, pattern 1, 3, and 5 in which the first layer was applied as a long raster pattern, showed the same tendency. From these results, it can be seen that the design of the first layer pattern has a dominant influence on the longitudinal bending deflection that occurs.  
Figure 11: Maximum in situ temperature recorded in thermocouple 2 at each layer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Maximum Temperature (\({}^{\circ}\)C)** & **Pattern** \\ \hline
550\(\sim\)600 & Pattern 3, Pattern 5 \\
500\(\sim\)550 & Pattern 2, Pattern 4 \\
450\(\sim\)500 & Pattern 1, Pattern 6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall maximum temperature recorded in thermocouple 2.
# Methacrylate Polymer Monoliths for Separation Applications
## 2 Methacrylate Polymer Monoliths  
Methacrylate is a term for any derivative of methacrylic acid, such as glycidyl methacrylate and methyl methacrylate, which are common monomer precursors, while Poly Methyl Methacrylate (PMMA) is a common monolith material (see Figure 1). As a monolith material, PMMA is presented as a linear structure. Glycidyl methacrylate is a very commonly used monomer as it contains an epoxy group allowing a wide variety of functionalizations [3; 15; 25; 26; 27; 28; 29; 30; 31] and is also used as a grafting agent [32]. Other methacrylate monomers include methyl methacrylate [33], ethyl methacrylate [34], butyl methacrylate [35; 36; 37], and octadecyl methacrylate [38]. Methacrylic acid [39; 40; 41] and hydroxyethyl acrylates [42; 43] have also been investigated in this role. The latter are used in the polymer mixture to tailor the hydrophilicity of the resulting stationary phase.  
Methacrylates are generally moderately polar due to the presence of carbonyl and ester bonds, however, they are more commonly used in reverse phase chromatography. As a result of the mixed polar/non polar groups and ease of preparation/functionalization, methacrylate monoliths have been prepared for a number of different methods of chromatographic separation, such as ion exchange [44], Hydrophilic Interaction Liquid Chromatography (HILIC) [45], and Reverse Phase [38]. This includes application for the separation of proteins and peptides [27], polymer separations [46; 47] as well as alkyl benzenes and other small polar molecules [48; 49; 50].
# Solar Radio Emissions and Ultralight Dark Matter
## 5 Detection
\[v_{\theta}=v_{0}\frac{b}{r},\ \ v_{r}=\sqrt{v_{0}^{2}-v_{\theta}^{2}+\frac{2 GM_{ \odot}}{r}},\ \ \ v^{2}=v_{r}^{2}+v_{\theta}^{2}. \tag{28}\]5 The projected sensitivity of the parameter space of the dark photon dark matter based on the radio telescopes SKA phase 1 and LOFAR, with the observation time assumed to be 1 and 100 h, respectively. The plot also shows the existing constraints from CMB [86; 8], the cavity experiment WISPDMX [87], and the multiple haloscope-type experiments [100; 101; 102; 103; 104]. Figure adapted from [67]. In addition to the terrestrial facilities such as SKA and LOFAR discussed above, space radio probes are also powerful tools for searching for ultralight dark matter signals from resonant conversion. The STEREO observatory [49; 127] consists of two nearly identical satellites, observing the Sun in the 1 AU orbit. It carries a low-frequency radio receiver operating in the frequency range 2.6-150 kHz and a higher-frequency receiver operating in the frequency range 125-16 MHz. Furthermore, the Parker solar probe [50; 69] provides new opportunities for observing the conversion signal with the perihelion reaching \(\sim\)10\(R_{\odot}\) from the Sun's centre. In fact, the Parker satellite is the first spacecraft that can fly into the solar corona. The best advantage for us is that the Parker satellite can measure in situ the resonant-conversion events in the solar-wind plasma as shown in Figure 1b, thanks to its motion between the Sun and 1 AU. The Parker satellite carries a low-frequency radio receiver operating in the frequency range 10-1.7 MHz and a higher-frequency radio receiver operating in the frequency range 1.3-19.2 MHz. We expect that the Parker satellite has a strong performance in searching for resonant-conversion signals in these frequency ranges. The analysis of space probes such as the Parker solar probe and STEREO to search for ultralight dark matter is currently in progress by the authors of this review.
## 2 Theoretical Aspects of Lamb Waves  
### Basic Propagation Theory of Lamb Waves  
Unlike Rayleigh waves or surface acoustic waves that propagate near a free surface with a penetration depth comparable to the wavelength, guided waves are elastic waves that propagate within thin plates or shell-like structures and are always confined within the boundaries of these structures, hence the name \"guided waves\". The propagation theory of ultrasonic-guided waves was first described by the mathematician [PERSON], and a comprehensive theory on such waves was subsequently published. These types of waves are known for their ability to propagate over long distances with minimal energy loss, highlighting their ideal and applicability in non-destructive health monitoring applications, especially in the ultrasonic testing of structures. Guided waves can be divided into two distinguishable types, namely shear horizontal waves and Lamb waves; the latter are also known as guided plate waves because they are guided by the free parallel upper and lower surfaces of the plate.  
The difference between the two lies in the particle motion of the propagating waves. Shear horizontal waves are horizontally polarized, with the shear particle motion confined to a horizontal plane perpendicular to the direction of wave propagation (parallel to the plate surface), while Lamb waves are vertically polarized, with elliptical particle motion confined to a plane determined by the direction of wave propagation and the normal to the plate. Both types of waves can be divided into symmetric and antisymmetric waves.  
Apart from the fundamental symmetric mode of the shear horizontal wave, all other wave modes exhibit dispersive properties, meaning that the group velocity depends on the frequency of the propagating wave. In shear horizontal waves, the fundamental symmetric mode can be found at all frequencies, and each mode has a critical frequency \(W_{cr}\) at which it appears concurrently with the preceding mode. On the other hand, in Lamb waves, and for low-frequency thickness coefficients \(f.d\), two basic modes \(S_{0}\) and \(A_{0}\) can be observed. As this coefficient increases, other modes begin to appear and propagate simultaneously, depending on the dispersion curve of the material being processed. When \(f.d\rightarrow\infty\), as the thickness is greater than one wavelength, symmetric and antisymmetric modes degenerate into Rayleigh waves, as shown in Figure 1. Conversely, when \(f.d\to 0\), \(S_{0}\) mode disappears into an axial or longitudinal wave, and \(A_{0}\) mode disappears into a plate bending wave. The appeal of low-frequency research lies in the lower dispersion of the fundamental modes and the reduced complexity of the problem due to the ability to excite specific modes \(S_{0}\) and \(A_{0}\), making it attractive for study.
## 3 The Proposed SP-IoUAV Model
### Intrusion Detection System (IDS)
#### 3.3.1 Data Collector  
The data collector is a critical component of our proposed IDS model, responsible for efficiently collecting encrypted data from the privacy-preserving ensemble. This ensemble, consisting of federated learning, differential privacy, and secure multi-party computation techniques, securely aggregates data from multiple UAVs while preserving individual data privacy. Using advanced cryptographic protocols, the data collector receives the encrypted dataset \(E_{i}\) from the privacy-preserving ensemble, where \(E_{i}\) represents the collective data from multiple UAVs. Each UAV's data are encrypted using its respective secret key \(key_{i}\) before transmission to the data collector. The encryption process can be represented as:  
\[E_{i}=\text{Encrypt}(D_{i},key_{i}) \tag{8}\]  
where \(\text{Encrypt}(\cdot)\) denotes the encryption function using the UAV's secret key, and \(D_{i}\) is the dataset collectively aggregated from multiple UAVs.  
Algorithm 4 outlines the steps performed by the data collector:  
```
Data: Encrypted datasets \(E_{i}\) from the privacy-preserving ensemble Result: Encrypted datasets stored in the data collector for each UAV \(i\)do
1 Receive encrypted dataset \(E_{i}\) from the privacy-preserving ensemble; if Verification of authenticity and integrity of \(E_{i}\) is successfulthen
2 Store the encrypted dataset \(E_{i}\) securely in the data collector's database;
3 end if
4 else
5 Alert: Invalid or tampered encrypted dataset received from UAV \(i\);
6 Terminate: Data collection process for UAV \(i\);
7 end if
8
9 end for
10
11 End
```  
**Algorithm 4** Data Collection Algorithm  
This algorithm securely receives encrypted datasets \(E_{i}\) from the privacy-preserving ensemble, aggregating data from multiple UAVs. It then verifies the authenticity and integrity of the data before securely storing the data in its database. This collaborative approach empowers the intrusion detection system (IDS) to efficiently analyze data from multiple UAV sources while preserving data privacy, enhancing the security and responsiveness of UAV operations within the smart-city environment.
## 5 Experimental Results
### Medium Speed and Large Path Curvature  
The reference path profile used in simulation is a polynomial curve with small curvature radius. Figure 17 shows the desired velocity of the test vehicle.  
Figure 18 shows the desired tire lateral and longitudinal forces calculated by proposed control architecture. The desired lateral force of each tire is about 1400 kN, which is devoted to ensure the vehicles automatic steering in the curve. Figure 19 shows that the reference path can be tracked effectively. It can be seen from Figure 20 that the vehicle slip angle is reduced by the proposed control method, which means better stability performance.  
Figure 17: Desired velocity in medium speed case.  
Figure 18: Desired tire forces from the tire Force Distribution layer: (**a**) Front left tire force; (**b**) front right tire force; (**c**) rear left tire force; and (**d**) rear Right tire force.  
Figure 21a shows the simulation results of changeable weight coefficients in the case of medium vehicle speed and large path curvature. In the path tracking process, there are time periods when the path curvature radius is large, so the weight coefficient \(g_{2},g_{3}\) is large and \(g_{1}\) is relatively small. However, the coefficient \(g_{1}\) is relatively large in the time period of low speed and small path curvature radius. Figure 21b-d show the optimization effect of the proposed control architecture, which further proves the above conclusion.  
Figure 21: Experience results in medium speed and large curvature case: (**a**) Weight coefficients; (**b**) stability margin; (**c**) electrical efficiency; and (**d**) tire energy dissipation.  
Figure 19: Path tracking in the x-y plane.
###### Abstract
It provides a framework for the use of the selection-Gaussian distribution as a prior in data assimilation. This framework is then used for ensemble filtering and smoothing through the selection EnKF (SEnKF) and the selection EnKS (SEnKS) algorithms. In Section 3, a synthetic case study of the diffusion equation, with two distinct test cases, showcases the ability of the proposed approaches to assess a parameter field and the initial state of a dynamic field. Results from the SEnKF and the SEnKS are compared to that of the traditional EnKF and the EnKS, respectively. In Section 4, potential shortcomings are discussed and the results are put into perspective with respect to applicability in more realistic applications. In Section 5, conclusions are presented. In this paper, \(f(\mathbf{y})\) denotes the probability density function (pdf) of a random variable \(\mathbf{y}\), \(\varphi_{\mathbf{n}}(\mathbf{y};\mathbf{\mu},\mathbf{\Sigma})\) denotes the pdf of the Gaussian \(n\)-vector \(\mathbf{y}\) with expectation \(n\)-vector \(\mathbf{\mu}\) and covariance \((n\times n)\)-matrix \(\mathbf{\Sigma}\). Furthermore, \(\Phi_{n}(A;\mathbf{\mu},\mathbf{\Sigma})\) denotes the probability of the aforementioned Gaussian \(n\)-vector \(\mathbf{y}\) to be in \(A\subset\mathbb{R}^{n}\). We also use \(\mathbf{i}_{n}\) to denote the all-ones \(n\)-vector, \(\mathbf{I}_{n}\) to denote the identity \((n\times n)\)-matrix and \(\mathbf{1}(S)\) to denote the indicator function that equals 1 when \(S\) is true and 0 otherwise. We consider log-diffusivity to be an adimensional quantity and it will therefore not be given a unit.