esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Experiments
### Performance Testing and Results Analysis
As can be seen from 'Mean' in the last line of Table 8, the IMOGWO algorithm is superior to the other three compared algorithms in \(GD\), \(IGD\), and \(\Omega\) indicators. For the \(SP\) indicator, the IMOGWO is slightly worse than the NSGA-II algorithm. Table 9 summarizes the results of the Wilcoxon symbolic rank test with three factories, from which it can be seen that, all relevant p-values are less than 0.05 for \(GD\), \(IGD\), and \(\Omega\) indicators. For \(SP\) indicators, the p-values are less than 0.05 for all except when IMOGWO compares with the MOGWO algorithm aiming at the 01 problem, and when IMOGWO compares with MOGWO, the NSGA-II algorithm aims at 11 problem. It can be concluded that the proposed IMOGWO is significantly superior to the other compared algorithms at 95% confidence level for \(GD\), \(IGD\), and \(\Omega\) indicators. For \(SP\) indicators, there is no significant difference between IMOGWO and MOGWO (for 01 and 11 problems), NSGA-II (for 11 problem), but, compared with the MODGWO algorithm, IMOGWO is significantly superior to it. The box-plots of the four evaluation indicators in Figure 6 further verify the conclusion. Figure 5: Box-plots of four evaluation indicators for two factories’ test cases. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**IMOGWO**} & \multicolumn{2}{c}{**MODGWO**} & \multicolumn{2}{c}{**MOGWO**} & \multicolumn{2}{c}{**NSGA-II**} \\ \cline{2-10}
**Test Example** & \multicolumn{2}{c}{**Avg**} & \multicolumn{2}{c}{**Min**} & \multicolumn{2}{c}{**Avg**} & \multicolumn{2}{c}{**Min**} & \multicolumn{2}{c}{**Avg**} & \multicolumn{2}{c}{**Min**} & \multicolumn{2}{c}{**Avg**} & \multicolumn{2}{c}{**Min**} \\  & & \multicolumn{2}{c}{**(Max)**} & & \multicolumn{2}{c}{**(Max)**} & & \multicolumn{2}{c}{**(Max)**} & & \multicolumn{2}{c}{**(Max)**} \\ \hline Problem & _SP_
## 5 Analysis of Link Up/Down Dynamics
### Link Available Time  
Assume the initial distance between the source vehicle and a group member is \(r\) and consider two mobile nodes, a source vehicle and a group member, with the velocity vectors \(\mathbf{v}_{1}\) and \(\mathbf{v}_{2}\), respectively, which implies that the relative velocity vector is \(\mathbf{v}_{r}=\mathbf{v}_{1}-\mathbf{v}_{2}\) at a given time \(t\). After time \(\triangle t\), a group member may move out of the transmission range of the source vehicle. Referring to the derivation in [33], the probability that the link available time is less than \(\triangle t\) is given by:  
\[P_{lat}(r,\psi;\triangle t)=\int_{0}^{2\pi}\int_{v_{\mu}}^{\infty}f_{v_{r}}(v _{r},\theta_{r})dv_{r}d\theta_{r} \tag{14}\]where \(\psi\) is an angle between the one-hop link between a source vehicle and a group member, the X-axis, \(v_{\mu}=\mu(r,\theta_{r})/\triangle t\), \(f_{v}(v_{r},\theta_{r})\) is the polar form of the relative velocity probability density function and \(\mu(r,\theta_{r})\) is a function of the initial distance \(r\) between the source vehicle and a group member and the angle \(\theta_{r}\) between the relative moving direction of the group member and the line connecting these two vehicles. Therefore, through the averaged analysis, the mean link available time distribution between two arbitrarily chosen vehicles yields:  
\[\bar{P}_{lat}(\triangle t)=\int_{0}^{2\pi}\int_{0}^{R_{\mathcal{M}}}P_{lat}(r, \psi;\triangle t)rdrd\psi \tag{15}\]  
which is the averaged probability that the link is available for less than \(\triangle t\) seconds. The frequency of topology change and the link up/down dynamics is further discussed and illustrated via simulation in Section 6.
# Towards Mobile Federated Learning with Unreliable Participants and Selective Aggregation
## 3 Proposed Federated Learning Infrastructure
### Training Parameters  
For our FL implementation, there were 4 controllable variables that allowed the testing of various training scenarios.  
The first parameter (**num_clients**) represented the total number of clients participating in the federated solution. The dataset was divided among these clients so that each client received a predetermined number of dataset elements.  
The second parameter represented the number of rounds that the training should take (**num_rounds**), and the third parameter represented the total number of local training rounds on each selected client's device (**epochs**). The fourth and last parameter was the batch size (**batch_size**). Data were loaded per batch, so it was important to choose a value that did not allow too little data to be loaded at once, as this would degrade training performance by not utilizing the full processing power of the client devices. However, it was also important not to select a value that was too high, as this could lead to data overload on the clients.  
After the initial definition of **num_clients** and **num_rounds**, in each communication round (a communication round in federated learning represents the server-client and client-server data exchange associated to the training performed at each client), training on the client's devices with the local dataset took place, according to the number of **epochs** and the **batch_size**. Then, the server aggregated the individual models by weighting the corresponding parameters into one global model.  
Below, we present the training parameters chosen in this work. The values for **num_rounds** were set empirically, since for the chosen dataset (see Section 5), we achieved an accuracy after 100 rounds that was approximately equal to the maximum achieved by [PERSON] et al. [26]. The **num_clients** was limited by the resources available for Android deployment in our work. The values of **epochs** and **batch_size** were fixed to eliminate their influence on the results and to allow a comparison of different configurations under the same test conditions.  
```
num_rounds={10,30,500,100} num_clients={2,4,8} epochs=5 batch_size=32
```
###### Abstract
This study presents closed-form azimuth and elevation angle solutions for microphone arrays with co-centered and orthogonally placed microphone pairs using TDoA times. The obtained solution provides accurate angle and quadrant estimation in a single step using the same microphone array geometry as in the far-field approach. Additionally, in our simulation studies, it is observed that the zero-angle error in the far-field approach can be reduced to near-zero values with the proposed method with appropriate microphone aperture and more accurate results are produced against TDoA measurement errors. The success of our method is demonstrated with the experimental results. The paper is organized as follows: In Section 2, we review the existing DoA methods based on the far-field approach and explain the multilateration method. In Section 3, we explain the observation that forms the basis of the solution we obtained. Then, we obtain the arrival angle detection method we propose from the multilateration equations. In Section 4, we report the simulation results of the proposed method with the existing methods. In Section 5, we report the experimental study and the results obtained. In the final section, we discuss the success of the proposed method compared to the existing methods and conclude the paper.
# Oral Health and Cognitive Decline: A Systematic Review of the Periodontitis-Alzheimer's Connection
## 1 Introduction
### Pathophysiological Mechanisms Connecting Periodontal Disease to Alzheimer's Disease
#### 1.2.2 Microbial Invasion and Direct Effects  
The oral cavity serves as a reservoir for pathogenic bacteria, which can gain access to systemic circulation through periodontal pockets and bacteremia [142; 143; 144; 145; 146; 147; 148; 149]. _Porphyomonas gingivalis_ and its gingipains have been detected in the brain tissue and cerebrospinal fluid of AD patients, suggesting a direct microbial invasion. These pathogens can disrupt the integrity of the BBB, facilitate the aggregation of amyloid-\(\upbeta\) (A\(\upbeta\)) peptides, and induce tau hyperphosphorylation, all of which are key features of AD pathology [150; 151; 152; 153; 154; 155; 156; 157; 158; 159].
# Machine Learning-Based Monitoring of DC-DC Converters in Photovoltaic Applications
## 2 Materials and Methods
### Photovoltaic Source  
The energy source considered in this paper is a 230 W solar panel with 60 multicrystalline cells TW230P60-FA by Tianwei New Energy [28]. The main electrical characteristics of the panel are extracted from its datasheet and reported in Table 1, where \(V_{MPP}\) and \(I_{MPP}\) are the maximum power point voltage and current, respectively, \(V_{OC}\) is the open-circuit voltage, and \(I_{SC}\) is the short-circuit current.  
Based on these characteristics, it is possible to implement an equivalent circuit model in a Simulink environment for the panel and extract the voltage-current curves as the solar irradiance and the working temperature change. Figure 1a,b shows these curves obtained for different values of irradiance and temperature.  
Obviously, the input current and voltage depend on the environmental conditions, and this is reflected in the internal electrical quantities of the DC-DC converter. Since the measurements extracted from the DC-DC converter for evaluating its state of health are sensitive to the changes in the input current and voltage, those measurements are sensitive  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(V_{MPP}\) & \(I_{MPP}\) & \(V_{OC}\) & \(I_{SC}\) & \(\mathbf{\pi_{T}}\) & \(N\)_Cell_ \\ \hline
29.4 V & 7.82 A & 37.3 V & 8.22 A & 0.06\%/\({}^{\circ}\)C & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the photovoltaic panel at the Standard Test Condition.  
Figure 1: Voltage–Current curves of the photovoltaic panel; (**a**) curves obtained with fixed temperature (25 \({}^{\circ}\)C) as the irradiance varies, (**b**) curves obtained with fixed irradiance (1000 W/m\({}^{2}\)) as the temperature varies.  
to the environmental conditions of the PV device as well. This can create confusion during the classification of malfunctions because the monitoring system must be able to discriminate the variations introduced by the aging of the components from those caused by changes in solar irradiance and working temperature.  
To avoid this problem, a simple solution could be to add the values of the irradiance and temperature to the set of measurements processed by the classifier. However, these quantities are not easily measurable from a practical point of view, and this solution makes the training stage more complex by requiring a very large dataset. In this paper, a graphical method is proposed for choosing the time-domain measurements less sensitive to variations in solar irradiance and temperature.
# A Multi-Mode Recognition Method for Broadband Oscillation Based on Compressed Sensing and EEMD
## 2 Signal Sampling and Reconstruction Using Compressed Sensing
### Reconstructing a Signal Based on the Subspace Pursuit (SP) Algorithm  
The SP algorithm refers to backtracking sequential coding theory [21], and its basic principle is to re-estimate the credibility of all candidates at each iteration. The focus of the SP algorithm is to determine the generation method of \(k\) column vectors in subspace. After determining the correct position of subspace, a non-zero sparse approximation of the signal can be calculated by a pseudo-inverse process [22]. The flow of the SP algorithm is as follows:  
1. [label=(0)]
2. Inputting the compressed signal \(y\) and sensing matrix \(\Theta\).
3. Initializing the residual \(r_{0}=y\), candidate vector \(C_{0}=\phi\), and index set \(\gamma_{0}=\phi\), with iteration number \(i=1\). \(\phi\) represents the relevant atoms in the sensing matrix.
4. Calculating the candidate vector: \(C_{i}=\text{abs}[\Theta^{\top}\times r_{0}]\). \(C_{i}\) represents the absolute value of the inner product of the sensing matrix \(\Theta\) and residual \(r_{0}\).
5. Finding the position labels of the \(K\) largest elements in the candidate vector \(C_{i}\) and updating the index set \(\gamma_{t}=\text{sup}(C_{i-1}|_{K})\cup\Gamma\), where the position label is \(\Gamma=\text{sup}(C_{i}|_{K})\).
6. Updating the estimated value: \(x_{i}=\Theta_{\gamma_{t}}^{\dagger}\times y\). \(\Theta_{\gamma_{t}}^{\dagger}\) represents the pseudo-inverse of the updated sensing matrix \(\Theta_{\gamma_{t}}\).
7. Selecting the optimal \(K\) sparse elements, \(x_{i}=\left(x_{i}\right)^{k}\), and updating the residual \(r_{i}=y\ -\ \Theta\times x_{i}\).
8. If \(\left\|r_{i-1}\right\|_{2}<\left\|r_{i}\right\|_{2}\) or \(i<K\), \(\hat{x}=x_{i}\), the loop ends; otherwise, \(i=i+1\), and we return to Step (3).
9. Outputting the x reconstruction estimate \(\hat{x}\), residual \(r\).
## 2 Literature Review
### Fast Fourier Transform (FFT)  
Signals in the time domain are normally irregular and difficult to observe. Fourier transform solves the problem because such a transform converts the time domain information to the frequency domain and makes it more convenient to observe the irregular features. However, before the rapid development of computer technology, the application of Fourier was limited because the calculation was time-consuming. With the achievement of computer technology, FFT becomes one of the most widely used techniques in signal processing. [PERSON] and [PERSON] [12] studied infrared spectroscopy signals during the scanning of the biological specimen to detect cancer. The frequency domain information then represented changing patterns in the tissue indicating the possible presence of cancer. FFT can be applied in the field of electrostatics; [PERSON] et al. [13] implemented FFT to evaluate the multipole expansion.  
Quality of the analysis outcome depends on the materials and components. [PERSON] et al. [14] explored the application of FFT in the chatter detection during titanium alloys machining process, titanium alloys which are regarded as one of the most difficult metals to be machined because the material renders the tool blunt and broken. The research analysed the frequencies occurring during cutting. By eliminating the harmonics, more generic signals related to chatter were identified. However, the method is tedious and time consuming. [PERSON] et al. [15] reviewed the effect of instantaneous frequency in enhancing the signal-to-noise ration of the incoming signals. Fault conditions are then defined according to certain undesirable frequencies being detected.  
Therefore, FFT is a broad spectrum analysis technique that does not easily point to specific symptom in the signal stream. To enhance its ability to determine more exactly when the fault occurs, FFT was applied with continuous wavelet transform to detect fault in the rotor-stator system [16]. This problem presents difficulties in fault trend diagnostics requirements, making it a feasible but rather tedious method to be used. Its advantage is simplicity of detection method, especially for signals that come from a sine-based oscillatory environment.  
Fault diagnostics in single time or frequency domain has its limits as time and frequency information are both crucial in fault diagnostics.
# Quality Analysis of Unmanned Aerial Vehicle Images Using a Resolution Target
## 4 Results  
Based on the resolution target, we analyzed the quality of the images obtained by the UAVs and the manned aircraft. The results were divided into the outcomes of the GSD and MTF analyses as follows:  
1. To analyze the effect of camera performance on the quality of the UAV's photos and outcomes, we set the flight height to be almost identical at 150 m, and the overlap at P = 60% and at Q = 70-75%. To compare the camera performance, we indicated the name of each camera model.
2. Using the FC 6310 and iXM-100 sensors, we captured the images at different heights to analyze the effect of flight height on the quality of the UAV's images and outcomes.
3. The GSD and MTF of the manned aircraft images from the UltraCAM Eagle M2 sensor and of the UAV images from the four sensor types were analyzed and compared.
# Integration of ERT, IP and SP Methods in Hard Rock Engineering
## 4 Results and Discussion
### Detection of Faults  
Low resistive zones (resistivity less than 1000 \(\Omega\)m) were interpreted either as a weathered layer or the fracture/fault. A total of ten main fractures/faults (F1-F10) were delineated over the entire investigated site (Figure 4). These faults were interpreted based on the hydrogeological information of the study area, the deep weathered zones and an overall model generated by the integration of all ERT models (Figure 4). The identified fractures/faults provide the most unsuitable foundation rock for engineering design. Along profile A, one fault (F1) was detected at 220 m distance. Profile B revealed four faults, such as F2 at 30 m, F3 at 190 m, F4 at 280 m and F5 at 400 m distance. Profile C evaluated four faults, such as F3 at 50 m, F4 at 210 m, F6 at 270 m and F5 at 400 m distance. Profile D revealed four faults, such as F2 at 130 m distance, F4 at 350 m, F6 at 450 m, and F5 at 530 m distance. Profile E detected three faults, such as F2 at 120 m, F4 at 310 m and F5 at 470 m distance. Three faults were identified along profile F, such as F2 at 40 m distance, F7 at  
Figure 3: (**a–j**) 2D resistivity models of ERT (resistivity increasing from black to red on a color scale) along all geophysical profiles generated by the inversion program of RES2 DINV. The zones marked by pink color on the top of each ERT model suggest suitable places for engineering structures, whereas the zones of white color indicate unsuitable locations for engineering structures.  
290 m, and F5 at 440 m distance. Along profile G, one fault (F3) was identified at a distance of 120 m. One fault F8 was revealed along profile H in the north at 350 m distance. Along profile I, five faults were detected, such as F2 at 70 m, F9 at 250 m, F7 at 300 m, F10 at 390 m, and F5 at 470 m distance. Similarly, four faults were evaluated along profile J, such as F2 at 70 m, F4 at 270 m, F7 at 370 m and F5 at 450 m distance. F1 with the northeast-southwest direction was identified along profile A in the south. The overall orientation of faults F2-F10 is from northwest to southeast. F2 and F5 are the longest faults with about 700 m in length. F1, F6, F8, F9 and F10 are the shortest faults with lengths of about 150 m. Whereas F3, F4 and F7 are about 300-550 m long. The fractures/ faults were identified by low values of resistivity (Figure 2a), IP (Figure 2c) and SP (Figure 2d).
## 6 Conclusions  
This study discussed the sources and the extraction methodologies for obtaining ros-marinic acid (RA) and ulvan, starting with its sources, continuing to discuss its extraction methodologies, bionanosystems and its production, before analyzing its final applications (Figure 1). The resulting evidence demonstrated the high potentialities of both rosmarinic acid (RA) and ulvan as useful functional agents in several applications, ranging from biomedical, pharmacologic, cosmetic, and food packaging, as well as personal care and coatings.  
RA is a small molecule that can be obtained by many terrestrial plants; hence its production could be increased considering agricultural products and their possible discards and waste. Several extractions methodologies were, up to now, studied that demonstrated the possibility of obtaining RA from several sources with a high yield and desired purity, despite the fact that its production is not yet performed with mature industrial technologies.  
Ulvan is highly available from marine sources and its extraction was also investigated by several authors, but, despite its very interesting potentialities, industrial plants able to produce it in high amounts are not yet available, and this constitutes a current limitation to the wide use of this biopolymer.  
The production of bionanosystems is fundamental for RA and can be incorporated in organic or inorganic substrates, granting high stability and a release control of the functional molecules during its use, thus enhancing its functional properties thanks to the exploitation of nanotechnology. Ulvan can be used as a carrier, or combined with molecules or biopolymer-like chitin derivatives.  
Both RA and ulvan can develop negative charges which are able to determine their capacity to form bionanosystems or hybrid functional materials through nano-assembly by their combination with inorganic or organic systems, characterized by positive charges such as LDH or chitosan. This interesting behavior can certainly allow their use in the production of several functional biomaterials and in biobased products with high performances in a wide range of industrial sectors. The anti-microbial properties shown by both RA and ulvan, combined with their environmentally friendly feature, could be exploited in several products, considering also the COVID-19 pandemic that has currently enhanced this general necessity, thus combining the health and environmental needs of green-minded consumers.  
Conceptualization, S.C., L.P. and M.-B.C.; writing--original draft preparation, S.C., R.P., F.C., B.C. and L.P.; writing--review and editing, M.-B.C., F.C., E.B. and A.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We thank [PERSON] for the design of the macroalgae and polymer (Figure 12). [PERSON] of University of Pisa, is acknowledged for the kind support related to information regarding the bioconomy contest.  
The authors declare no conflict of interest.
## 2 Results and Discussion
### Aromatic Profile Analysis  
The results of the volatile compounds, odor descriptors and thresholds are shown in Table 3. In this study, the olfactory perception thresholds and odor descriptors of compounds in wine were taken from the available literature [14; 15; 16; 17; 18]. Thirty-two compounds, including alcohols (two), aldehydes and ketones (five), terpen (10), acids (two) and esters (13), were identified and quantified in Muscat Hamburg wine. According to the quantitative data, the concentration of total volatiles in the wines ranged from 3614.66 to 6384.97 \(\mu\)g/L. The different winemaking methods significantly (\(p<0.001\)) influenced the concentration of volatiles in the wines. Among the three kinds of wines, the Muscat Hamburg wine prepared by carbonic maceration contained the highest concentration of volatiles (6384.97 \(\mu\)g/L), followed by the wines prepared by classical white-winemaking (4251.65 \(\mu\)g/L) and red-winemaking (3614.66 \(\mu\)g/L) techniques. This result may be due to the occurrence of anaerobic fermentation in the absence of oxygen and saturation with CO\({}_{2}\), and the profile of volatile organic compounds (VOCs) changes significantly. Many studies have reported that carbonic maceration resulted in higher concentrations of VOCs, especially esters and fatty acids [19]. \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Color**} & \multicolumn{2}{c}{**Type of Wine**} & \multirow{2}{*}{**Significance \({}^{\star}\)**} \\ \cline{2-2} \cline{4-4}  & **R** & & **CM** \\ \hline \(L^{\star}\) & 96.41 \(\pm\) 0.34 & 96.79 \(\pm\) 0.42 & — \\ C* & 35.67 \(\pm\) 0.28 & 41.26 \(\pm\) 0.45 & ** \\ \(h\) & 23.68 \(\pm\) 0.12 & 24.14 \(\pm\) 0.25 & — \\ \(a^{\star}\) & 37.65 \(\pm\) 0.59 & 43.25 \(\pm\) 0.45 & ** \\ \(b^{\star}\) & 0.75 \(\pm\) 0.12 & 0.84 \(\pm\) 0.11 & ** \\ \hline \end{tabular} Results are the mean values for the three independent fermentations. \({}^{\star}\) Significant data from red and carbonic macerated Muscat Hamburg wine; —; no significance; \({}^{\star}\)\(p<0.05\); \({}^{\star\star}\)\(p<0.01\).  
\end{table} 
Table 2: Colors of Muscat Hamburg wines from different winemaking technologies.