esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficient Data Transmission for Sensors with Wireless Charging
## 3 Transmission in Limited \(K\) Periods
### The General Problem with \(C=m\)  
In the general case where \(C=mE\), we can get a similar result as the problem with \(C=2E\).  
Lemma 4: _When packet size \(s_{i}\leq(m-1)E\), transmitting this packet does not result in energy overflowing._  
Corollary 2: _Energy overflowing happens only when transmitting packet of size \(s_{i}>(m-1)E\)._  
One can use Algorithm 2 and Algorithm 3 to solve the problem with \(C=mE\). The energy utilization is shown in Theorem 3.  
Theorem 3: _The energy utilization of Algorithm 2 is at least \((1-\frac{1}{2m})(OPT-E)\)._  
Proof: Let OPT be the energy used in the optimal solution. The perfect solution is that \(OPT=KE\).  
Similar to the proof of Theorem 2, when all the packets are shorter than \((m-1)E\), transmitting these packets does not result in energy overflowing. Therefore, even if the energy charged in the last period is not used, the energy utilization \(OPT-E>(1-\frac{1}{2m})(OPT-E)\).  
We now consider the situation where there are packets longer than \((m-1)E\). Because the energy overflowing happens only when transmitting packet with size \(s_{i}>(m-1)E\), the worst case is that energy overflow happens as many times as possible. Let the energy left in the battery be \(x\). When transmitting a packet with size \(s_{i}>x+(m-1)E\), \(x\) units of energy overflow. After transmitting this packet, the energy left in the battery is \(y=mE-s_{i}<E-x\). When transmitting a packet with \(s_{i+1}>y+(m-1)E\), then \(y\) units of energy overflow. It can be found that totally \(x+y<E\) energy overflows when transmitting these two packets. Therefore, it indicates that transmitting two larger packets continuously may result in at most \(E\) units of energy overflow. That is, at most \(E\) units of energy overflow in \(2m\) periods of wireless charging, i.e., the ratio is \(\frac{2m-1}{2m}=1-\frac{1}{2m}\). In the first \(2m\lfloor\frac{k}{2m}\rfloor\) periods, at most \(\lfloor\frac{k}{2m}\rfloor E\) units of energy overflow. The energy utilization in the first\(2m\lfloor\frac{K}{2m}\rfloor\) periods is \((1-\frac{1}{2m})\cdot 2m\lfloor\frac{K}{2m}\rfloor E\). Similar to the analysis in Theorem 2, we consider the last \((K\mod 2m)\leq 2m-1\) periods.  
Similar to the analysis in Theorem 2, if \((K\mod 2m)\leq m\), at most \(E\) units of energy are unused. This derives that the energy utilization is at least \((1-\frac{1}{2m})(OPT-E)\).  
If \(m<(K\mod 2m)\leq 2m-1\), at most \(\frac{E}{2}+E\) units of energy are unused. This derives that the energy utilization is at least \((1-\frac{1}{2m})(OPT-E)\).  
This completes the proof.
## 1 Introduction
### Prior Work on Sinter-Joining Processes for Powder Metallurgical Manufacturing
#### 1.2.1 Influence of the Initial Contact on the Diffusion Process  
During quality control of sinter-bonded components, the bond strength is key, besides characteristics such as shape accuracy and surface properties. Several researchers have found that the most significant influence on a successful sinter joint of compatible materials is the initial contact of the joining surfaces of the green parts before the sintering process [24; 25]. [PERSON] and [PERSON] described the sinter-joining process as the joining process least tolerant of misfit of the joining surfaces [25]. Furthermore, this influence has been explicitly investigated by [PERSON] [21], among others, for the effects on the achievable joint quality of MIM components. It is shown that the initial contact depends on the shape of the joining surface, the accuracy of the manufacturing process as well as the arrangement of the green parts before the joining process. Shape deviations of the components weaken the achievable joint strength. This is confirmed by [PERSON] et al. [22] in the field of diffusion bonding. The large influence of the initial contact on joint quality is due to the contact of the particles required for material diffusion. This influence is further confirmed in literature on the application of MIM sinter joining, 2C-MIM (two-component MIM) and sinter brazing [19; 20; 25; 26; 27; 28].  
In contrast to MIM, properties of green parts in the binder jetting process differ not only in strength, but also primarily in the expression of surfaces, because the binder-jetting process, unlike MIM, is tool-less, and surfaces are created free in the powder bed instead of being an impression of a smooth tool surface [12; 29]. Thus, although binder jetting allows for the flexible production of a wide variety of shapes, these have significantly rougher surfaces. The surface properties of binder jetting green parts are also subject to other factors, such as the orientation of the relevant surface in the printing process and the process control in the depowdering process [30]. Furthermore, a wide variety of phenomena can lead to a deviation from the components' nominal geometry in the form of warpage, such as negative pressure during the curing process, deviation in the stacking of the individual layers or binder bleeding [31; 32].
## 2 Selection Method of Wavelet Parameters
### Time-Frequency Resolution and Edge Effect  
A previous study has shown that the time-frequency resolution of the CMOR wavelet transform depends on the full width at half maximum (FWHM) of the window function \(w(t)\)[28]. Based on the FWHM, the time resolution \(\Delta t\) is shown in Figure 1c. According to Equation (7), the following equation can be derived:  
\[\frac{w(\beta)}{w\left(\beta+\frac{\Delta t}{2}\right)}=\frac{1}{e^{-\frac{( \Delta t_{f})^{2}}{4\beta^{2}f_{b}}}}=2 \tag{9}\]  
Therefore, the \(\Delta t\) is derived as:  
\[\Delta t=\frac{2\sqrt{\ln 2}f_{c}\sqrt{f_{b}}}{f_{k}} \tag{10}\]  
The FT of the wavelet function \(\psi_{\alpha,\ \beta}(t)\) is derived as follows:  
\[\Psi(f)=\left\langle e^{j2\pi f(t-\beta)},\psi_{\alpha,\beta}(t)\right\rangle= \left\langle e^{j2\pi(f-f_{k})(t-\beta)},w(t)\right\rangle=\sqrt{\frac{f_{c}} {f_{k}}}e^{-\frac{(\pi f_{c})^{2}f_{b}}{f_{k}^{2}}(f-f_{k})^{2}} \tag{11}\]  
Obviously, \(\Psi(f)\) is a Gaussian function. The frequency resolution \(\Delta f\) is the FWHM of \(\Psi(f)\). According to Equation (11), the \(\Delta f\) is:  
\[\Delta f=\frac{2\sqrt{\ln 2}f_{k}}{\pi f_{c}\sqrt{f_{b}}} \tag{12}\]  
According to Equations (10)-(12), we can find that \(\Delta t\Delta f=\frac{4\ln 2}{\pi}>\frac{1}{4\pi}\), which satisfies the limit of the Heisenberg uncertainty principle.  
As can be seen from Figure 1c, the window function \(w(t)\) can be completely multiplied by the signal \(u(t)\) when \(\beta\) is located in the middle instant of \(u(t)\) (the orange line). However, it only partially multiplied when \(\beta\) is located in the instant of signal appearance (\(t=0.0\) s, namely the green line) or disappearance (\(t=1.0\) s, namely the red line). That is the reason inducing the edge effect of CMOR wavelet transform. From Figure 1c, the total time of edge effect is \(\delta t=6\sigma\). Therefore, according to Equation (7), \(\delta t\) can be expressed as:  
\[\delta t=\frac{3\sqrt{2}f_{c}\sqrt{f_{b}}}{f_{k}} \tag{13}\]  
It can be found from Equations (10) and (13) that the total time of edge effect is obviously proportional to the time resolution.
## 2 Materials and Methods
### Nanofluid Preparation  
The NP were dispersed in deionized water (DIW) at a predetermined concentration using a magnetic stirrer at 500 rpm for 30 min. To loosen the agglomerates in the nanopowder and disperse it, probe sonication was applied using an ultrasonic processor. Sonication was performed for 120 min (50% amplitude and 0.5 pulse) with breaks every 15 min to avoid overheating. [PERSON] et al. [23] observed that silica nanofluids prepared by dispersing the NP in DIW using an ultrasonic probe proved to be the most effective technique. Three types of nanofluids were prepared at varying concentrations of NP:  
1. Silica dispersed in DIW.
2. Sulfonated silica dispersed in DIW (functionalized).
3. Silica dispersed in DIW with MPTMS stabilizing fluid.  
Two types of NP were used in this study: firstly, silica acquired from Sigma-Aldrich and surface modified NP. The modification procedure is described in the following section. The nanofluids of silica in DIW with MPTMS stabilizing fluid were prepared by dispersing the desired concentration of silica in DIW via ultrasonication. Thereafter, 1 g of MPTMS was added per 100 mL of the nanofluid under vigorous stirring. To avoid confusion in this text between NP and MPTMS concentration, NP concentration is always stated in g/L units and MPTMS concentration is always stated in g/100 mL units.
## 2 Materials and Methods
### Crop Row Recognition and Navigation Line Extraction
#### 2.2.3 Crop Row Centerline Fitting  
To accurately identify soybean and maize rows within the main crop regions and enable path guidance, this study proposes a least-squares-based centerline fitting method. This approach is used to extract the centerline of each crop row and subsequently generate a navigation line. The core objective of the method is to minimize the error between the point cloud data and the fitted curve, thereby producing an optimal linear model that reflects the structural characteristics of the crop rows. The corresponding coordinate system is illustrated in Figure 7.  
Given the linear distribution characteristics of crop rows in the soybean-maize strip intercropping system, a linear function was selected to fit the point cloud data of the main crop row regions. The fitting model is expressed as follows:  
\[y=Ax+B \tag{9}\]  
Figure 6: Crop row region framing.  
Figure 7: Extraction of feature crop rows.  
In the equation, \(y\) and \(x\) represent the coordinates of the points along the \(Y_{\rm w}\)-axis and \(X_{\rm w}\)-axis, respectively. \(k\) and \(b\) are the model parameters to be estimated, corresponding to the slope and intercept of the fitted line, respectively.  
Based on the proposed model, the centerlines of soybean and maize rows were extracted by fitting the point cloud data within their respective main stem regions. As shown in Figure 8, the colored points represent valid point cloud data within the crop row region, and the fitted lines denote the extracted crop centerlines. The results indicate that the fitted lines accurately passed through the core point clusters, reflecting the actual distribution patterns of the rows and providing a reliable basis for subsequent path planning and navigation control.  
To quantitatively assess the fitting performance, we employed the coefficient of determination (\(R^{2}\)) and root mean square error (RMSE) as evaluation metrics. The experimental results showed an average \(R^{2}\) value of 0.973 and an average RMSE of 0.023 m, indicating high fitting accuracy and robustness.
# Numerical Study of Viscoplastic Flows Using a Multigrid Initialization Algorithm
## 2 Numerical Methodology
### Convergence Criteria  
Once the solver iteration is completed and the primitive variables corrected, convergence may be monitored by the residual for each of the discretization equations. The discretized equation for general flow variable \(\phi\) at control volume \(i\) can be expressed as follows:  
\[(a_{p}\phi_{p})_{i}=\left(\sum_{nb}\!\!a_{nb}\phi_{nb}\right)_{i}+(b_{\phi})_{i} \tag{38}\]  
The final solution will not satisfy Equation (38) exactly at all cells in the mesh, but after \(k\) iterations, there will be a difference between the left and right hand sides. The absolute value of this difference at mesh cell \(i\) is termed the local residual \((R_{\phi})_{i}^{(k)}\):  
\[(R_{\phi})_{i}^{(k)}=\left|(a_{p}\phi_{p})_{i}^{(k)}-\left(\sum_{nb}\!\!a_{nb }\phi_{nb}\right)_{i}^{(k)}-(b_{\phi})_{i}^{(k)}\right| \tag{39}\]  
To get an indication of the convergence behavior across the whole flow field, we define the global residual \(R_{\phi}^{(k)}\), which is the sum of the local residuals over all \(N\) control volumes within the computational domain. After \(k\) iterations we have:  
\[R_{\phi}^{(k)}=\sum_{i=1}^{N}(R_{\phi})_{i}^{(k)}=\sum_{i=1}^{N}\!\left|(a_{p} \phi_{p})_{i}^{(k)}-\left(\sum_{nb}\!\!a_{nb}\phi_{nb}\right)_{i}^{(k)}-(b_{ \phi})_{i}^{(k)}\right| \tag{40}\]  
Inspection of Equation (40) shows that the magnitude of the global residual \(R_{\phi}\) decreases as we get closer to the final solution, since the size of the local residuals should decrease in a converging sequence. Thus, it would seem that \(R_{\phi}\) might be a satisfactory single number indicator of convergence. However, the global residual will be larger in simulations where the flow variable \(\phi\) has a larger magnitude, so we would need to specify different truncation values for \(R_{\phi}\). This can be resolved if we use a global residual that is  
Figure 3: Illustration of different multigrid cycle strategies: (**a**) V-cycle, (**b**) W-cycle.  
scaled to take out the magnitude of \(\phi\). Thus, we define the normalized global residual \(\overline{R_{\phi}}\) for flow variable \(\phi\) after \(k\) iterations as follows:  
\[\overline{R_{\phi}}^{(k)}=R_{\phi}^{(k)}/F_{R}^{\phi} \tag{41}\]  
where :  
\[F_{R}^{\phi}=\sum_{i=1}^{N}|(a_{p}\phi_{p})_{i}^{(k)}| \tag{42}\]
## 4 Simulation Verification
### Simulation of Single-Exposure Holographic Interferometry  
Figure 5 illustrates the workflow for recording and detection in single-exposure holographic interferometry:  
Procedure:  
(1) Model construction: This involves creating pre- and post-deformation objects. The following parameters are also input: wavelength, wavenumber, diffraction distance, and object plane size.  
The lens focal length f = 0.1 m. The deformation phase field U\({}_{\rm 0 YP}\) (varying from 0 to 4\(\pi\)) simulates glass deformation. Wavenumber k = 9.93 \(\times\) 10\({}^{6}\) m\({}^{-1}\) is used for light field propagation calculation. The imaging distance Z\({}_{\rm i}\) = 0.151 cm, and the image plane size L\({}_{\rm i}\) = 0.0097 m. Lens phase modulation eliminates wavefront curvature.  
(2) Pre-deformation hologram: The diffraction distribution of the object wave on the holographic plane is computed using the triple fast Fourier transform (T-FFT) [20] algorithm. The hologram is generated via interference with the reference beam.  
(3) Post-deformation diffraction: The diffraction distribution of the deformed object wave is calculated by using T-FFT.  
(4) Imaging lens configuration: The lens parameters are input to determine imaging distance.  
(5) Reconstruction and interference:  
a. The hologram is illuminated with either the reference beam or the deformed object beam.  
b. Reconstruction is carried out using the single fast Fourier transform (S-FFT) algorithm.  
c. Reconstructed fields are superimposed for interference detection.  
Figure 5a: Diffraction pattern of object wave before deformation. The central rectangular region (256 \(\times\) 256 pixels) exhibits a Gaussian distribution, with Fresnel diffraction rings appearing at the edges. No interference fringes are present (pure object wave intensity distribution).  
Figure 5b: Hologram before deformation, showing clear tilted interference fringes. Spectrum broadening occurs in the central rectangular region.  
Figure 5c: Diffraction pattern of deformed object wave. Intensity distribution in the central region is distorted, diffraction ring structure is disrupted, and no clear interference fringes exist.  
Figure 5d: Reconstructed image using reference wave, showing a bright central rectangular image (+1 diffraction order), a conjugate image (\(-1\) diffraction order), and a central zero-order diffraction spot.  
Figure 5e: Reconstructed image illuminated by deformed object wave. The image appears blurred, shifted, with multiple images (due to wavefront mismatch of illumination light) and reduced contrast; the object wave change causes image degradation.  
Figure 5f: Interference fringes obtained by simultaneous illumination with reference wave and deformed object wave. Dense interference fringes appear in the central region, with local fringe breakage (at phase jump locations), visualizing the optical path difference distribution caused by deformation. Although simple to implement, the single-exposure method is limited by low fringe contrast, noise sensitivity, and lack of directional detection, failing to meet high-precision deformation detection requirements.
# Gemini Surfactant as a Template Agent for the Synthesis of More Eco-Friendly Silica Nanocapsules
## 3 Results and Discussion
### Characterization of Silica Nanocapsules
TG profiles are presented in Figure 6. The degradation temperature for both SiNC_CTAB and SiNC_QSB2-12 was found to be 105 \({}^{\circ}\)C, which can be assigned to the process of dehydration of water adsorbed in the interlayer structure [32]. As expected for inorganic silica materials, curves for calcined nanomaterials show no variation in temperature, which suggests good thermal stability. Comparing curves for calcined and as-synthesized nanocapsules, a mass loss of approximately 80% can be observed for the as-synthesized ones. This may be due to the degradation \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Hydrodynamic Size** & \(\zeta\) & **PdI** \\  & **[nm]** & **[mV]** & **PdI** \\ \hline SiNC\_CTAB & \(\begin{array}{c}157.1\pm 18.3\\ 731.2\pm 134.3\end{array}\) & \(32.8\pm 1.1\) & 0.68 \\ \hline SiNC\_QSB2-12 & \(\begin{array}{c}191.6\pm 37.1\\ 662.1\pm 160.0\end{array}\) & \(40.9\pm 6.8\) & 0.56 \\ \hline \hline \end{tabular} \(\zeta\)–Zeta potential; PdI–Polyldispersion index.  
\end{table} 
Table 1: Dynamic Light Scattering (DLS) data for synthesized SiNC_CTAB and SiNC_QSB2-12. Figure 5: Size distribution of silica nanocontainers (SiNC) synthesized with CTAB and QSB2-12. of non-hydrolysed TEOS as well as some residues of cationic surfactants used for the synthesis [14]. These results suggest that replacing CTAB with QSB2-12 will not change the thermogravimetric profile of silica nanocapsules. Textural properties of silica nanocontainers (SiNC_CTAB and SiNC_QSB2-12) were evaluated by adsorption-desorption isotherms N2 at \(-\)196 \({}^{\circ}\)C. In Figure 7a it is possible to observe that both samples show a typical type IV adsorption-desorption, according to IUPAC classification, with symmetric adsorption-desorption pathway isotherm indicating the presence mesoporous materials. A characteristic type H3 hysteresis loop is observed. This type is typical for mesoporous materials with slit-shaped pores [33]. Table 2 presents the main textural parameters of the silica nanocontainers: specific surface area (SBET) and the most frequent diameter of pores (mode of distribution of pore diameter). Data obtained for SiNC_CTAB are in agreement with what is reported in the literature when using diethyl ether as co-solvent [9]. When CTAB is replaced with QSB2-12 a slight increase in pore size was observed. Additionally, changing surfactants during synthesis results in a decrease in surface area from 719 m\({}^{2}\)/g to 603 m\({}^{2}\)/g.
# Federated Distillation Methodology for Label-Based Group Structures
###### Abstract
Therefore, their accuracy is often degraded. When the size of each client group varies, the existing methods significantly undermine the performance of minority client groups. On the other hand, our method performed well regardless of group size by assigning a logit that fits the distribution of the group data. This allowed us to become closer to a fair FL. Guided by an empirical analysis of the esteemed MNIST and CIFAR datasets, we demonstrate that the clustering accuracy via prediction exceeds 90%. We also achieve high accuracy for each client model compared to traditional federated distillation methods in settings where an apparent group structure exists. Performance increased by up to 75%, and the greater the difference in the data distribution between each group, the greater the advantage of our algorithm. We show that our algorithm is effective even when the data is sparse. The main contributions of this paper are:  
* We propose the first federated distillation approach that utilizes the predictions of the clients model on public data to cluster clients. * We show that our approach results in successful clustering even when the boundaries between each client group are unclear. * We demonstrate the effectiveness of our approach under challenging conditions such as insufficient data and ambiguous group boundaries. It also improves the performance of minority groups, bringing us closer to a fairer FL. Our paper is organized as follows. * In Section 2, we review the relevant literature on clustering techniques in federated learning and federated distillation methods. * In Section 3, we formally define the problem and present the proposed clustered federated distillation algorithm using a label-based group structure. * In Section 4, we evaluate the proposed method
* In Section 4.1, we present the setup used in our experiments. * In Section 4.2, we compare the clustering accuracy of our algorithm with existing FL methods using clustering in a label-based group structure. * In Section 4.3, we compare the performance of our method with existing federated distillation methods in a label-based group structure. * In Section 5, we summarize the main results and limitations and suggest directions for future work.