esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Inverse Problem
### Decentralized Fuzzy Adaptive PID Control Method
#### 3.3.3 Weighting and Synthesizing Scheme  
\(\Delta u_{i}\) is the compensation for the boundary of all nodes at an unknown boundary when only the \(i\)th measured temperature is considered. Actually, the value of all nodes at the unknown boundary has a different effect on the temperature of all measured points. So, the whole measured temperature should be considered when compensating for the unknown boundary implementation. The weighting and synthesizing scheme are performed by Equation (13):  
\[\left[\begin{array}{c}\Delta y_{1}\\ \Delta y_{2}\\ \vdots\\ \Delta y_{N}\end{array}\right]=\left[\begin{array}{ccccc}\alpha_{1,1}& \alpha_{1,2}&\ldots&\alpha_{1,M}\\ \alpha_{2,1}&\alpha_{2,2}&\ldots&\alpha_{2,M}\\ \vdots&\vdots&\ldots&\vdots\\ \alpha_{N,1}&\alpha_{N,2}&\ldots&\alpha_{N,M}\end{array}\right]\left[ \begin{array}{c}\Delta u_{1}\\ \Delta u_{2}\\ \vdots\\ \Delta u_{M}\end{array}\right] \tag{13}\]  
where \(\alpha_{j,i}\) is the weighting factor, which reflects the influence of \(j\)th discrete point of \(f(x)\) on the \(i\)th temperature at the measured point; the larger \(\alpha_{j,i}\) is, the greater the influence. Based on the normal distribution weighting method, the weighting coefficient \(\alpha_{j,i}\) is determined by Equation (14):  
\[\alpha_{j,i}=\frac{\exp[-(\frac{[x_{i}-x]}{\varnothing})^{2}]}{\sum_{i}^{M} \exp[-(\frac{[x_{i}-x]}{\varnothing})^{2}]},j=1,2,\ldots,N \tag{14}\]  
where \(x_{i}\), \(x_{j}\) are the \(x\)-axis values of the inversion and measurement points, and \(\varPhi\) is the variance coefficient of normal distribution. The larger \(\varPhi\) is, the smoother the distribution curve of the weighting coefficient is, the more measurement information is effectively used in the inversion process, but the slower the inversion speed is; the smaller \(\varPhi\) is, the more centralized the distribution of the weighting coefficient is, the less measurement information is effectively used, but the faster the inversion speed is.
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 2 Basic Principles of FL over Wireless Networks  
In this section, we provide a brief overview of the fundamental principles governing FL in a general wireless network, with a focus on understanding the associated energy consumption model during the FL process. FL is broadly classified into two main categories: centralized federated learning (CFL) and decentralized federated learning (DFL). These approaches differ in how they coordinate and aggregate model updates from participating devices, aligning closely with specific wireless network architectures based on their communication patterns and infrastructure requirements.  
The overarching concept of FL, encompassing both CFL and DFL, is to collaboratively derive a global model, denoted as \(\mathbf{q}\), by aggregating contributions from all participating local models. The primary objective of FL is to determine the optimal parameters \(\mathbf{q}\) for the global model by minimizing the global loss function, expressed as  
\[\min_{\mathbf{q}}F(\mathbf{q})\triangleq\sum_{k=1}^{K}\frac{D_{k}}{D}F_{k}( \mathbf{q}), \tag{1}\]  
where \(F_{k}(\mathbf{q})=\sum_{i=1}^{D_{k}}\mathrm{f}(\mathbf{q},x_{k,i},y_{k,i})\) represents the local loss function for the \(k\)-th client over its local dataset \(\mathbf{D}_{k}\) with size \(D_{k}\), and \(D=\sum_{k=1}^{K}D_{k}\) denotes the total dataset size across all participating devices. Here, \(\mathrm{f}(\mathbf{q},x_{k,i},y_{k,i})\) denotes the loss function for an individual data sample pair \((x_{k,i},y_{k,i})\) from the local dataset \(\mathbf{D}_{k}\). To solve the optimization problem (1) for both CFL and DFL, we introduce the foundational FL algorithm, federated averaging (FedAvg), initially proposed in [31].
# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 6 Conclusions
### Limitations and Future Work  
While MPFM-VC demonstrated superior performance in both clean and noisy voice conversion scenarios, several limitations remain to be addressed in future work.  
First, although the use of flow matching provides a non-autoregressive and parallelizable generation scheme, the overall inference pipeline--particularly the multi-dimensional feature perception module--still introduces non-negligible computational overhead. This may pose challenges for deployment in resource-constrained or real-time streaming applications, especially when dealing with longer input sequences or high-resolution acoustic representations. We hypothesize that a knowledge distillation approach--similar to that used in ComSpeech [34]--could be leveraged to approximate the one-step solution of the ODE, thereby enhancing inference efficiency.  
Moreover, to further advance research on enhancing speaker similarity and robustness within the flow-matching framework, we believe that a recent study on flow matching [53] offers valuable insights. This work proposes guiding the training of the flow-matching vector field using an auxiliary speech representation. Inspired by this, we suggest that such a strategy could be adapted to continuously guide the content encoder, effectively disentangling speaker-related information while enriching the completeness of content representations. This, in turn, has the potential to significantly improve the robustness and speaker similarity of voice conversion systems.  
In future work, we also plan to explore lightweight alternatives to reduce model complexity, improve streaming compatibility, and investigate more efficient flow-based generation methods for low-latency deployment.  
Y.W.: methodology, software, validation, formal analysis, investigation, resources, and data curation; Y.W. and X.H.: writing--original draft preparation; Y.W., X.H., S.L., T.Z., and Y.C.: writing--review and editing; Y.W.: visualization; X.H.: supervision and project administration. All authors have read and agreed to the published version of the manuscript.  
This research study received no external funding.  
Not applicable.  
Data Availability Statement: The original contributions presented in this study are contained in this paper. Further inquiries (including but not limited to trained models and code) can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results
### PMMA (Templated)  
In total, 4 of the 41 studies provided data for PMMA (templated) in relation to infection complication risk as per the selection criteria. Implant number varied between 16 and 31 implants.  
Infection rates varied between 3.70% and 9.68%, with an average infection rate of 6.86%, a standard deviation of 2.14% and a relative risk of 0.65.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{(**a**)} \\ \hline
**Paper** & \multicolumn{3}{c}{**Number of Implants**} & **Number of Infections** & **Average Infection Rate (\%)** \\ \hline  
Total: & 102 & 7 & 6.86 \\ Standard Deviation: & - & - & 2.14 \\ Relative Risk: & 0.65 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: (**a**) PMMA (Prefabricated); (**b**) PMMA (Hand-formed); (**c**) PMMA (Templated). PMMA: polymethylmethacrylate.  
PMMA implant manufacture produces surface textures ranging from smooth to coarse and can have varying degrees of porosity from 0% to 40% [32]. As such, the ISF ranking assigned to PMMA is 1. PMMA is a bioinert material and is shown to have very little natural support for attachment and ingrowth of tissue [32], however small holes may be bored into the implant to provide pathways for tissue growth [24]. Whilst these holes can allow some tissue connection through the implant, they are not considered to be an extensive and integral component of the PMMA implant. When we consider, in conjunction, that PMMA does not actively provide any native support for tissue ingrowth, the TAB value is considered as 0.
###### Abstract  
As the square steel tube in the tension zone is always the weakest part of moment-resisting joints, modified blind bolts (Hollo-Bolts) and a locally strengthened steel tube in the panel zone were adopted to enhance the joint performance. Cyclic loading tests were carried out on eight anchored blind-bolted extended end-plate joints between square concrete-filled steel tube (CFST) columns and steel beams. The test parameters included the end-plate thickness, steel tube wall thickness, beam section size, local strengthening connection method, blind bolt anchorage method, and stiffeners. The failure mode, hysteretic behavior, stiffness, strength, ductility, strength degradation, stiffness degradation, and energy dissipation capacity of the joints were studied and analyzed. The test results showed that the application of anchored blind bolts and a locally strengthened steel tube can fully utilize the bolt strength and significantly improve the joint performance, especially in terms of strength and strength degradation. The test observations revealed three typical failure modes for the joints, and the failure mode depended on the weakest component. In addition, the local reinforcement of C-channel and change in the anchorage method had a limited effect on the initial stiffness. Greater end-plate thickness and the use of stiffeners significantly increased the joint stiffness and decreased the rate of stiffness degradation. The use of stiffeners also significantly enhanced the ductility and energy dissipation by moving plastic hinge outward from the joints. Finally, finite element analysis (FEA) models were developed and validated against the experimental results, and the stress distribution and force transfer pattern were investigated.  
concrete-filled steel tube (CFST); blind bolts; extended end-plate connections; cyclic (seismic) behavior; finite element analysis +
Footnote †: journal: Applied Soft. 2020, 10, 904; doi:10.3390/app10030904
## 3 Results  
### Impedance Analysis  
The initial analysis revealed a significant impedance difference between the IRE and NIRE groups (Figure 1D). The NIRE group showed an impedance variation of 246 Ohm before and after pulsing (\(p\) < 0.001), whereas the IRE group exhibited a change of 233 Ohm (\(p\) < 0.001). Significantly, the impedance in the NIRE group was greater by 13.5 Ohm compared to the IRE group (\(p\) < 0.01).
## 3 Analysis of SCSRR-Based DGS MIMO Antennas with Different Feed Networks
### Coaxial Probe Feed  
To design a two-element MIMO antenna, the antenna design equations mentioned in Section 2 were used. The geometry of the proposed MIMO antennas with a coaxial probe feed is presented in Figure 9. The MIMO antenna was equipped with an SCSRR-based DGS (see dimensions Table 3), as illustrated in Figure 10. Their design and dimensions were determined from the equations detailed in Section 2.  
Figure 11 illustrates the ANSYS HFSS simulation results in terms of isolation (\(S_{21}/S_{12}\)) and return loss (\(S_{11}/S_{22}\)) of MIMO antenna with and without an SCSRR. The resonance frequency of the MIMO antenna without an SCSRR-based DGS had strong coupling, with an isolation value of 21 dB due to surface wave excitation. On the other hand, the MIMO antenna with anSCSRR-based DGS had suppressed surface waves, and simulations show that the peak isolation increased to 58 dB which is 31 dB higher than the without SCSRR-based DGS.  
The 3D gain patterns of the coaxial probe feed MIMO antennas with SCSRR-based DGS are illustrated in Figure 12. The distribution of surface currents on the ground plane with one antenna excited and the other terminated with a 50 \(\Omega\) impedance is shown in Figure 13. According to Figure 13b, the suppression of the space wave by virtue of the bandgap filtering is clearly observed. In addition to this, the high concentration of the surface currents can observed in the loaded antenna, which resulted in only a slight shift in the resonance frequency of the patches, as depicted in Figure 11. The comparison of the antenna performance parameters with and without SCSRR-based DGS structures is provided in Table 4. The DGS-based antennas yielded relatively higher bandwidth in  
Figure 10: Coaxial probe feed MIMO antenna model with SCSRR-based DGS. Dimensions of antenna: L\({}_{sub}\) = 36 mm, W\({}_{sub}\) = 58 mm, L\({}_{p}\) = 16 mm, W\({}_{p}\) = 12.55 mm, d\({}_{1}\) = 26 mm. Note: Dimensions of SCSRR are listed in Table 3.  
Figure 9: Coaxial probe feed MIMO antenna model without SCSRR-based DGS. Dimensions of antenna: L\({}_{sub}\) = 36 mm, W\({}_{sub}\) = 58 mm, L\({}_{p}\) = 16 mm, W\({}_{p}\) = 12.55 mm, d\({}_{1}\) = 26 mm.  
contrast to antennas without the DGS. The bandwidth enhancement effect was attributable to DGS structures' property of decreasing the slope of the imaginary part of the input impedance of the antenna. However, the peak gain of antennas incorporating DGS was found to be lowered by up to 0.8 dBi, since the DGS structures increased the back lobe radiation. Yet again, the DGS-based antennas ascertained a gain of 6 dBi at 5.8 GHz. Furthermore, the peak isolation values of DGS-based antennas were improved from 16.5 dB to 58 dB over a bandwidth of 180 MHz.  
Figure 11: S-parameters of coaxial probe feed MIMO antennas with SCSRR-based DGS.  
Figure 12: The 3D gain patterns of coaxial probe feed MIMO antennas with SCSRR-based DGS.  
Figure 13: Current distribution of coaxial probe feed MIMO antennas with and without SCSRR-based DGS. **(a)** Without SCSRR-based DGS and **(b)** With SCSRR-based DGS.
## 3 Results and Discussion
### VOCs Profile of Honey  
Ninety-seven volatile compounds were identified and semi-quantified via HS SPME GC-MS analysis in the five monofloral Italian legume honey belonging to distinct chemical classes: terpenoids and norisoprenoids (26 compounds), alcohols (14), aldehydes (12), acids (11), esters (9), phenols (6), ketones (5), furans (5), pyranones (3), benzene derivates (2), lactones (2), and sulfur compounds (2). The recognized 97 volatile metabolites, the abbreviation code, the experimental and literature Kovats index, and the identification methods are listed in Table S1. T. SPME GC-MS semi-quantitative data were subjected to one-way ANOVA to investigate the effect of the five honey types on the identified VOCs. Table 3 summarizes the results, showing the 97 volatiles and their semi-quantitative data (RPA%), evaluated as the percent ratio relative to the peak area of 1-octanol, used as IS, obtained via SPME GC-MS analysis. The total number of compounds detected for each legume honey varied among samples, which were 64 for astragalus honey, 60 for sainfoin and indigo honey, 53 for alfalfa, and 45 for carob honey. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Metabolites** & **Code** & **Carob** & **Alfalfa** & **Sainfoin** & **Astragalus** & **Indigo** & \(p\) \\ \hline
**Esters** & & & & & & & \\ Ethyl butanoate & **E1** & nd & nd & 3.69 & nd & nd & *** \\ Ethyl 3-methylbutanoate & **E2** & nd & nd & 1.03 & nd & nd & *** \\ Ethyl hexanoate & **E3** & nd & nd & 4.34 & nd & nd & *** \\ Ethyl octanoate & **E4** & nd & nd & 6.76 & nd & nd & *** \\ Ethyl benzoate & **E5** & nd & nd & 2.65 & nd & nd & *** \\ Methyl salicylate & **E7** & 4.64 & nd & nd & 10.15 & nd & *** \\ Ethyl phenylacetate & **E8** & nd & nd & 7.32 & nd & nd & *** \\ Methyl 3,5-dimethoxybenzoate & **E9** & nd & nd & 1.95 & 0.09 & nd & *** \\ \hline
**Aldehydes** & & & & & & & \\
2-Methyl- 2 butenal & **Ald1** & nd & 18.93 & 6.63 & 5.15 & 4.60 & *** \\
3-Methyl- 2 butenal & **Ald2** & nd & 12.61 & nd & nd & nd & *** \\ Octanal & **Ald3** & 10.7 & nd & 5.67 & 7.11 & 3.06 & ** \\ Nonanal & **Ald4** & 8.65 & 14.24 & 18.80 & 18.09 & 45.47 & *** \\
2-Fururtal & **Ald5** & nd & 79.83 & 25.21 & 27.32 & 48.16 & ** \\ Benzaldehyde & **Ald6** & 25.97 & 81.29 & 58.47 & 30.90 & 75.86 & * \\
5-Methyl-2-furfurtal & **Ald7** & nd & 5.35 & 0.99 & 25.11 & 20.81 & * \\ Benzene acetaldehyde & **Ald8** & 4.30 & 36.61 & 85.88 & 83.16 & 7.65 & * \\
5-Formylfurfurtal & **Ald9** & nd & _7.77_ & 16.77 & 20.00 & 10.0 & + \\
4-Methylo y benzaldehyde & **Ald10** & nd & _2.04_ & nd & nd & *** \\
3-Phenylpropanol & **Ald11** & nd & 1.85 & 0.00 & 0.00 & 8.89 & *** \\
5-Hydrooxymethyl-2-furfurtal (5 HMF) & **Ald12** & nd & 4.75 & 4.79 & 207.96 & 154.77 & ** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: VOCs identified in the five legume honeys.
## 2 Seawater Channel Modelling Elements
### Scattering Phase Function Formats
#### 2.7.5 Fournier-Forand Scattering Phase Function  
The FF-SPF [82] is another model commonly used in oceanic studies to describe the angular distribution of light scattering by particles suspended in water. This phase function is especially relevant for forward scattering applications, such as underwater imaging and visibility simulations. It is a parametric model based on empirical measurements and observations of light scattering in natural waters.  
fitted to empirical data from measurements in the specific water body of interest to use the FF-SPF. Once the coefficients are determined, you can use the phase function to simulate light scattering underwater.  
FF-SPF developed the analytic version of the phase function for a collection of particles with a hyperbolic particle size distribution and every particle scattering employing the anomalous diffraction theory.  
Equation (39) was used to visualise the FF-SPF family of curves in Figure 6. As we mentioned previously, we can notice that the HE-SPF has a nearly similar profile to the FF-SPF over small values of \(\theta_{s}\).
## 3 Results
### Corrosion Resistance  
In order to conduct thermal aging in an atmosphere saturated with water vapor, an amorphous steel ribbon coated with a TEOS/BTSE binder, and dried and annealed, was placed in a climatic chamber. After continuous exposure to atmospheric factors, the samples were taken out for testing and the results are presented in Figure 2.  
It can be seen that, after a week of aging, corrosion has started at several points in the untreated steel ribbon area, while the binder-coated part remains clean without visible corrosion. More significant changes can be observed in the case of the sample aged for three weeks. A clear difference can be seen between the coated areas (unaffected clear surface) and the untreated areas (large number of corrosion spots). This effect indicates that the binder used improves the corrosion resistance of the amorphous steel ribbon; hence, both the service or shelf life of the treated ribbon can be extended, and, as a result, the electrical device using the bonded ribbon will be more resistant to the corrosion phenomenon.
## 3 Results and Discussion
### Isolation and Identification of Fungal and Bacterial Strains  
A total of 20 fungal and 29 bacterial strains, belonging to seven fungal and 13 bacterial species, respectively, were isolated from fermented tea leaves (Table 3). The strains were individually inoculated into tea leaves to determine their ability to synthesize Teadenol A. Teadenol A was detected in tea leaves fermented by _Aspergillus niger_ and _A. tamari_ at 28 \({}^{\circ}\)C for 14 d at concentrations ranging from \(9.85\pm 1.17\) mg/g to 12.93 \(\pm\) 0.38 mg/g. This result was in agreement with the findings of [PERSON], who reported that tea leaves fermented by _Aspergillus_ spp. contain high concentrations of Teadenols [33,34]. Additionally, a method to produce Teadenol A-rich PFPT has been developed by adding _A. niger_ or _A. tamari_ during SSF. Teadenol A has been extracted using ethanol followed by column chromatography.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Position** & **\$C** & **\$H** & **HMBC (\({}^{1}\)H–\({}^{13}\)C)** & **NOE (\({}^{1}\)H–\({}^{1}\)H)** \\ \hline
2 & 73.5 & 4.52 (1H, s) & C-13, 14, 15 & H-3, 4, 13, 15 \\
3 & 72.9 & 4.39 (1H, m) & - & - \\
4 & 25.4 & 2.85 (1H, dd, \(J=4.8,17.4\) Hz) & C-5, 10 & H-2, 3 \\  & & 3.02 (1H, dd, \(J=1.8,17.4\) Hz) & C-2, 3, 5, 10 & H-2, 3 \\
5 & 157.8 & - & - & - \\
6 & 96.7 & 5.91 (1H, d, \(J=2.4\)) & C-5, 8, 10 & H-8 \\
7 & 157.9 & - & - & - \\
8 & 95.9 & 5.78 (1H, d, \(J=2.4\)) & C-6, 9, 10 & H-6 \\
9 & 156.6 & - & - & - \\
10 & 99.3 & - & - & - \\
12 & 148.1 & - & - & - \\
13 & 109.8 & 6.51 (1H, s) & C-2, 12, 16 & H-2, 15 \\
14 & 139.3 & - & - & - \\
15 & 116.6 & 5.19 (1H, s) & C-2, 13 & H-13 \\
16 & 168.3 & 5.27 (1H, s) & C-2, 13 & H-13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Nuclear Magnetic Resonance (NMR) data of compound **1** (in MeOD).  
Figure 5: Chemical structure of 2D NMR correlations of compound 1 (\({}^{\circ}\)HMBC, \({}^{\circ}\)1H - 1H COSY).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Isolates (GenBank** & **Length** & \multicolumn{2}{c}{**Results of ErTaxon**} \\  & **Accession No.)** & **(pp)** & **Closest Match (GenBank Accession No.)** & **Similarity (\%)** \\ \hline
1 & 1-3-b-5 (K149614) & 1373 & _Admonuclear syllosATIONS_ DSM 10346(T) (714908) & 99.854 \\
2 & 2-3-b-2 (K149617) & 1365 & _Admonuclear syllos_ NMRC 152(T) (CP006958) & 99.93 \\
3 & 1-1-b-5 (K14960) & 1385 & _Backillas_ amyloidations subsp. _plartament_ FEB2(T) (CP00050) & 99.928 \\
4 & 3-3-b-3 (KK149598) & 1345 & _Bardella avium_ 1971 (AMI67904) & 99.18 \\
5 & 1-1-b-3 (K149624) & 1385 & _Entroductary satuz_ ICM 6051(T) (AB004744) & 99.35 \\
6 & 1-1-b-4 (K149608) & 1375 & _Entroductary satuz_ ICM 6051(T) (AB00744) & 99.345 \\
7 & 1-1-b-6 (K149614) & 1367 & _Entroductary satuz_ ICM 6051(T) (AB004744) & 99.415 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Identification of bacterial and fungal strains isolated during solid state fermentation (SSF) of PFPT.
## Materials and Methods
### Polarization Method  
Polarization tests were performed with a variable resistor box in the range from 11 M\(\Omega\) to 1 \(\Omega\). For each case, voltage was taken once the cell reached the pseudo-steady state under the correspondent resistor value (typically after 1 min). For each external resistance load, current density was determined as \(I=E/R_{\mathrm{ext}}\) and the power density alternatively as \(P=E^{2}/R_{\mathrm{ext}}\). Both values are normalized to anode capacity (wastewater volume used to feed the MFCs). Polarization and power curves depict voltage and power output, respectively, versus current density measured in polarization tests.