esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 OMWW as Replacer of Synthetic Additives  
The strong demand for adequate nutrition is accompanied by the concern for environmental pollution with a considerable emphasis on the recovery and recycling of food by-products and wastes [9].  
Several studies have focused on replacing synthetic additives with natural substances, mainly derived from plants and agro-industry by-products [123; 124] with promising results. The addition of such substances not only inhibits the growth of pathogens but also prolongs the shelf life of food products. OMWWs are added as such or as extracts, concentrated and stabilized and, in some cases, microencapsulated. Specifically, encapsulation protects them from degradation due to different factors reducing the amount of compounds required to be efficient and controlling their release into the food matrix [125].  
Besides therapeutic benefits, biophenols present in OMWW have been explored for their antimicrobial, antifungal, and antiviral properties. [PERSON] et al. [14] reported thatthe phenolic fraction of OMWW shows antibacterial activity against several species, particularly _S. aureus, Bacillus subtilis_, _Escherichia coli_, and _Pseudomonas aeruginosa_. However, the antimicrobial activity was found to be higher when the whole phenolic content is used, compared with the activity of the single phenolic compound [126]. In particular, [PERSON] et al. [127] showed that natural OMWW extracts exhibited a higher antimicrobial activity compared with the three individual biophenols (quercetin, hydroxytyrosol, and oleuppein), suggesting a synergic effect among molecules. In most cases, to inhibit the growth of target strains, the effective tested dose was 1000 ug/mL. In addition, it has been shown that individual phenolic compounds, used at low concentrations, were not able to inhibit the growth of _E. coli_, _Klebsiella pneumoniae_, _S. aureus_, and _Staphylococcus pyogenes_, while whole OMWW was effective in inhibition of both Gram-positive and Gram-negative bacteria [121]. Other authors, however, reported that the bactericidal and fungicidal activities of OMWW are mainly related to the content of phenolic monomers, such as hydroxytyrosol and tyrosol [128]. Hydroxytyrosol was found to also be active against foodborne pathogens such as _Listeria monocytogenes_, _S. aureus_, Salmonella Enterica, and _Yersinia_ spp. [129] and against beneficial microorganisms, such as _L. acidophilus_ and _Bifidobacterium bifidum_. In addition, [PERSON] et al. [130] confirmed the bactericidal effect of phenol extract purified from OMWWs. In particular, _S. aureus_ and _L. monocytogenes_ showed the lowest level of resistance (minimum bactericidal concentration MBC = 1.5-3.0 mg/mL) while Gram-negative bacteria (e.g., Salmonella Typhimurium and _Pseudomonas_ spp.) showed higher resistance, with MBC values ranging from 6 to 12 mg/mL. In the same study, among the tested starter species, the growth of _Staphylococcus xylosus_ and _L. curvatus_ was drastically reduced (at concentrations of 0.75 and 1.5 mg/mL MBC, respectively).
# Broadcasting in Stars of Cliques and Path-Connected Cliques +
## 4 Decision Algorithm for the Broadcast Time of Star of Cliques
### Algorithm Correctness  
**Lemma 5** (Greedy Choice).: _Let \(G_{k}\) be a star of cliques where the originator is the universal vertex \(u\) and \(l_{1}\geq l_{2}\geq\cdots\geq l_{k}\geq 1\), where \(l_{i}\) is the number of vertices in clique \(C_{i}\) (excluding vertex \(u\)) for all \(1\leq i\leq k\). Then, there is an optimal broadcast scheme where the largest clique \(C_{1}\) is informed in the first time unit._  
**Lemma 6** (Optimal Substructure).: _Let \(G_{k}\) be a star of cliques where the originator is the universal vertex \(u\) and the set \(S_{j}=\{C_{1}^{\prime},\ldots,C_{k}^{\prime}\}\) be the unaddressed vertices in cliques \(C_{1},\ldots,C_{k}\)respectively, at time unit \(j\). Then, an ordering of \(S_{j}\), which is an optimal broadcast scheme from the universal vertex \(u\), must also include an optimal broadcast scheme for \(S_{j+1}\)._  
Consider the star of cliques illustrated in Figure 1, which consists of three cliques with sizes \(10\), \(9\), and \(5\), respectively, and a universal vertex denoted by \(u\). Based on Lemmata 2 and 3, the broadcast time \(b(u,G_{k})\) has a lower bound of five time units and an upper bound of six time units. By applying the procedure described in Algorithm 3, it is possible to design a broadcast scheme that achieves the lower bound. This can be carried out by prioritizing the early notification of \(C_{1}\), which allows the universal vertex to allocate additional broadcasts to \(C_{2}\) and \(C_{3}\). Specifically, vertex \(u\) broadcasts to \(C_{1}\) at time \(1\), to \(C_{2}\) at times \(2\) and \(4\), and to \(C_{3}\) at times \(3\) and \(5\), ensuring that all vertices in the graph are informed within the lower bound of the five time units.
# Dynamic Response Threshold Model for Self-Organized Task Allocation in a Swarm of Foraging Robots
## 4 Discussion
### Different Energy Consumption Due to Obstacle Avoidance  
To explore the influence of the energy consumption per OAE (\(E_{a}\)) on foraging efficiency, we ran experiments with \(E_{a}=20\) and keeping the other parameters the same as in the comparison experiments.  
As shown in Figure 12, the DRTM (\(V_{f}=3.06\)) maintains a number of food tokens that is closer to the desired value compared to the ARTM (\(V_{f}=5.18\)). The total number of food tokens collected by the swarm using the DRTM is 189.14 and the net food is 20.39; the corresponding values for the ARTM are 189.32 and 15.99, respectively. According to Equation (9), the DRTM (\(E_{e}=10.78\%\)) consumes less energy than the ARTM does (\(E_{e}=8.45\%\)), so the DRTM gives rise to more efficient foraging.  
Figure 13 plots the number of OAEs for both the ARTM (\(M_{T}=1152.26\)) and DRTM (\(M_{T}=912.06\)). It is obvious that the average number of OAEs is less for the DRTM (\(M_{V}=45.60\)) compared to the ARTM (\(M_{V}=57.61\)). We conclude that the DRTM copes effectively with changing traffic conditions.  
Increasing the energy consumption per OAE (\(E_{a}\)) decreases the energy efficiency and increases the ADF. To maintain the number of food tokens at the desired level, the swarm allocates many more robots to foraging. This produces many more OAEs, reducing the foraging efficiency further. Therefore, compared with the comparison experiments, increasing the energy consumption per OAE decreases the foraging efficiency.
# A Method for Energy Balance and Data Transmission Optimal Routing in Wireless Sensor Networks
## 5 Simulation
### Simulation of Relationship between Node Density and Lifetime  
The density of nodes affects the effect of energy balance. We simulated the relationship between node density and system lifetime by simulating different number of nodes in the same size region. The simulation results are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Initial energy / J & 0.5 \\ Packet length / bit & 4000 \\ Energy consumption of transmitting and receiving & 50 \\ circuits / nl-bit\({}^{-1}\) & 10 \\ \(\varepsilon_{\text{kg}}\) / pJ-bit\({}^{-1}\).m\({}^{-2}\) & 0.0013 \\ \(E_{\text{da}}\) / nJ-bit\({}^{-1}\) & 5 \\ Energy transfer voltage (\(u\)) / V & 5 \\ Energy transfer current (\(i\)) / mA & 10 \\ Power line resistance (\(\rho\)) / \(\Omega\).m\({}^{-1}\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 8: _Cont._  
The abscissa of each of the graphs in Figure 8 is round, representing the lifetime of the node. The ordinate is the number of alive nodes at each round. The simulation is stopped when the number of surviving nodes is less than 10%. The lifetimes of LEACH and EEUC vary little with node density, while the effect of CH-leach deteriorates as node density increases. In our proposed approach, all nodes work together until the energy is exhausted, and all nodes have the same lifetime and are much longer than other protocols. Figure 9 shows the comparison of the average lifetime of all nodes.  
From the simulation results in the above figure, it can be seen that the lifetime of our scheme is longer than other protocols under different node densities. The average lifetime is 2.4, 2.9, and 1.8 times that of LEACH, EEUC, and CH-leach, respectively.
_Applied_  
_sciences_  
_Article_  
**Low-Phase-Noise Oscillator Using a High-Q\({}_{\text{L}}\) Resonator with Split-Ring Structure and Open-Loaded T-Type Stub**  
**for a Tumor-Location-Tracking Sensor**  
**[PERSON] 1,2,t**, [PERSON] 1,2,3,4,e**, **, [PERSON] 5,6,* and [PERSON] 1,7,t**  
1 Medical Devices R&D Center, Gachon University Gil Medical Center, Inchoon 21565, Korea;  
[EMAIL_ADDRESS] (K.-C.Y.); [EMAIL_ADDRESS] (B.-S.K.)  
2 Department of Biomedical Engineering, College of Medicine, Gachon University, Incheon 21565, Korea  
3 Department of Biomedical Engineering, College of Health Science, Gachon University, Incheon 21936, Korea  
4 Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon 21565, Korea  
5 Department of Gastroenterology, College of Medicine, Gachon University, Incheon 21565, Korea  
6 Department of Gastroenterology, Gachon University Gil Hospital, Incheon 21565, Korea  
7 Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul 08826, Korea  
*Correspondence: [EMAIL_ADDRESS] (K.-G.K.); [EMAIL_ADDRESS] (J.-W.C.); Tel.: +82-10-3393-4544 (K.-G.K.); +82-10-9004-1604 (J.-W.C.)  
+[PERSON] and [PERSON] contributed equally to this work and are co-first (lead) authors.  
**Abstract: Sensors in the medical field to detect specific tissues, such as radars, must provide accurate signals from frequency generators. In order to supply an accurate frequency signal, the oscillator must have a low phase noise. Therefore, the resonator used in the oscillator must provide a high Q\({}_{\text{L}}\). Therefore, in this paper, we have proposed a low-phase-noise X-band oscillator that used a resonator with a high value of Q\({}_{\text{L}}\) as a sensor for tissue-locating applications. The resonator had a split-ring structure and consisted of an open-loaded, T-type stub with a high-Q\({}_{\text{L}}\); such high-Q\({}_{\text{L}}\) levels were enabled by controlling the length of the open-circuit in the T-type stub. This led to the generation of only low-phase noise in the proposed oscillator. Experimental results showed that, at an operating frequency of 10.08 GHz, the output power was 18.66 dBm, the second harmonic suppression was \(-\)34.40 dBc, and the phase noise was \(-\)138.13 dBc/Hz at an offset of 100 kHz. This proposed oscillator can be used as a sensor to detect the location of tissues during laparoscopic surgery.**  
**Keywords: high loaded quality factor, low phase noise; split-ring structure; open-loaded T-type stub; tissue location sensor**
# GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System
## 6 Experimental Analysis under Inducing Spoofing Attack
### Rural  
For the rural scene shown in Figure 5a, the segment from 450 s to 485 s of the data set is exposed to a GNSS inducing spoofing (a 0.5 m distance is superimposed at each GNSS localization moment). Figure 14 shows the absolute position errors of GNSS, VINS-Fusion, PTC-SLM (forwarding method only) and PTC-SLM after GNSS inducing spoofing attacks. Figure 15 shows the trajectory of the four methods. Their maximum value (Max), average value (Mean), median value (Median), minimum value (Min), rmse and std of the absolute position errors of the four methods are shown in Table 7.  
It can be seen that the GNSS localization results are directly affected by GNSS inducing spoofing, and the absolute position error of GNSS method grows gradually as the GNSS inducing spoofing attacks occur as shown in the blue curve in Figure 14. The error on trajectory is shown in Figure 15a. The rmse of GNSS increased to 24.497 m. The localization result of VINS-Fusion will gradually deviate from the normal track and the absolute  
Figure 14: The absolute position error of the four methods in rural scene.  
Figure 15: Trajectories of the four methods in rural scene. (**a**) GNSS, (**b**) VINS-Fusion, (**c**) PTC-SLM-Forward, (**d**) PTC-SLM.  
position error is shown in the brown curve in Figure 14, and the error on the trajectory is shown in Figure 15b. The rmse of VINS-Fusion is 21.191 m.  
The PTC-SLM-Forward method did not identify GNSS inducing spoofing attacks in time but only when the forwarding spoofing deviation \(\psi_{i}\) satisfy the constraints of \(\psi_{i}>E_{d}\). At this time, the method determines that forwarding spoofing has occurred. The absolute position error is shown in the green curve in Figure 14, and the error on the trajectory is shown in Figure 15c. It can be seen that the localization trajectory offset is smaller than the GNSS and VINS-Fusion methods, but larger than the PTC-SLM method. The rmse of PTC-SLM-Forward is 8.508 m, with an improvement of 65.269% compared with the GNSS method. The PTC-SLM method can detect GNSS inducing spoofing attacks in time to suppress the impact of GNSS inducing spoofing. The absolute position error is shown in the red curve in Figure 16, and the error on the trajectory is shown in Figure 17d. It can be seen that the localization trajectory offset is the smallest and the rmse of PTC-SLM is 2.870 m, with an improvement of 88.284% compared with the GNSS method.
# Multi-Source Image Fusion Based on BEMD and Region Sharpness Guidance Region Overlapping Algorithm
## 5 Experimental Results
### Qualitative Evaluation
To sum up, the proposed algorithm still outperforms the other algorithms in several aspects for all image sets. Therefore, from the perspective of objective evaluation indicators, the proposed method has a good processing effect on images with high resolution and rich image information. In addition to the six transform domain methods of LP, RP, DWT, DTCWT, CVT, and NST, we provide a deep convolutional neural network [22] method to simulate and compare with our method, as shown in Table 6. For the data comparison between the deep convolutional neural network and the proposed algorithm for four datasets (Table 6), the proposed method is superior to the deep convolutional neural network method in five indicators in the color multi-focus data set. For indicators in gray multi-focus sets, medical multi-modal data sets, and infrared and visible data sets, except for MI indicators in infrared and visible images, the algorithm proposed in this paper is generally superior to the deep convolutional neural network algorithm, especially in terms of time indicators, and the time fluctuation of the deep convolutional neural network algorithm on different data sets is large. The proposed algorithm consumes less time and has less fluctuation in different data sets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **MI** & **FMI** & **PSNR** & **SD** & \(\mathbf{Q}^{\text{Y}}\) & \(\mathbf{Q}^{\text{VIF}}\) & **TIME** \\ \hline LP & 0.4047 & 0.8052 & 28.4983 & 8.4960 & 0.8295 & 0.9444 & 0.0061 \\ \hline RP & 0.3667 & 0.8562 & 27.6723 & 8.3892 & 0.6932 & 0.8668 & 0.0370 \\ \hline DWT & 0.3844 & 0.8923 & 28.4818 & 8.4584 & 0.7630 & 0.7499 & 0.0633 \\ \hline DTCWT & 0.3756 & 0.9013 & 28.4693 & 8.3869 & 0.8079 & 0.7787 & 0.1444 \\ \hline CVT & 0.3513 & 0.8988 & 28.4634 & 8.3969 & 0.7668 & 0.7252 & 0.8411 \\ \hline NST & 0.3864 & 0.9025 & 28.4964 & 8.4097 & 0.8275 & 0.8329 & 2.3144 \\ \hline Ours & 0.5300 & 0.8991 & 29.3041 & 8.9991 & 0.8328 & 0.9826 & 1.0568 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison results of different algorithms in infrared and visual image sets.
## 2 Materials and Methods  
### Chemicals  
The chemicals 1,1-Diphenyl-2-picrylhydrazyl (DPPH), 2,2\({}^{\prime}\)-azino-bis (3-ethylbenzothazoline-6-sulfonic acid) (ABTS), ammonium molybdate, Folin-Ciocalteu reagent, ferric chloride (FeCl\({}_{3}\)), potassium ferricyanide K\({}_{3}\)[Fe(CN)\({}_{6}\)], and trichloroacetic acid (TCA), and the standards ascorbic acid, gallic acid, and quercetin, and all other standards (Table S1) were provided by Sigma-Aldrich (Steinheim, Germany). The solvents (ethanol and methanol) were provided by Sigma-Aldrich (St. Louis, MO, USA). All used reagents and solvents were of analytical grade.2.2. Raw Materials  
The rice flour, chickpea flour, soy flour, potato starch, corn starch, tapioca starch, guar gum, and xanthan gum used in this study were provided by the REGIMA company (SNC REGIMA BOUFETTA & CIE-Setif, Setif city, Algeria). Rice flour had a moisture content of 14.22%, a protein content of 9.42%, a fat content of 0.06%, and 1.66% ash. Corn flour had 12.88% moisture, 9.77% protein, 1.14% fat, and 1.40% ash. Chickpea flour had 9.30% moisture, 20.5% protein, 3.87% fat, and 3.14% ash. Soy flour contained 10.04% moisture, 39.2% protein, 21.4% fat, and 4.78% ash. Acorn flour (purchased from NaturAlim company, Algers, Algeria) had 8.20% moisture, 9.94% protein, 0.82% fat, and 0.48% ash. Millet (purchased from a local market in Constantine, Algeria) was ground on a Bomann brand grinder set to high speeds (28,000 RPM; 1300 W) to obtain flours. Millet flour contain 11.43% moisture, 12.5% protein, 4.8% fat, and 2.8% ash, while chestnut flour contain 8.35% moisture, 6.1% protein, 3.43% fat, and 2.34% ash. Quinoa (provided by Eurl Earth Grains company, Algers, Algeria) was ground, and the flour had 12.09% moisture, 13.00% protein, and 6.50% fat. The chestnut was purchased from a local market. The process for obtaining chestnut flour involves the meticulous peeling and sun-drying of chestnut kernels. The kernels were then crushed and sieved through a 250 um mesh to obtain flour. Stevia powder, used as a sugar substitute, was produced by grinding and sifting dried stevia leaves using a 250 um sieve. These leaves are donated by NaturAlim company, Algers, Algeria.  
The other ingredients utilized in the cookies' manufacture included baking powder, fresh eggs, margarine, and salt, which were purchased from the local market.
## 6 Results and Comparison
### Performance Comparison  
For a fair comparison with existing solutions, we synthesized the proposed design using the same Xilinx FPGA devices. The synthesized results are presented in Table 4. The first column of Table 4 lists the specifications of both the reference and proposed solutions, while the second column indicates the platform used. The third column displays the frequency in MHz, and the hardware resources are shown in columns 4 and 5. The time required to perform a single PM operation is listed in column 6 of Table 4, expressed in microseconds (\(\upmu\)s). The last column of the table (column 7) presents the throughput/ratio.
## 3 Simulation Results
### Time-Domain Current Response
#### 3.1.2 Unit Step and Unit Ramp Response  
As concerns the unit step \(u(t)\) and ramp \(r(t)\) response of Phase Conductor \(a\), considerably slower dynamics were obtained with respect to the described current response to an impulsive waveform. The reason behind this different behavior lies in the finite power associated with \(u(t)\) and \(r(t)\), as opposed to \(\delta(t)\). Indeed, in these latter cases, the phase conductor (and soil) currents are bound to the energy continuity principle. Their time-evolution is regulated by the conductors' resistance and inductance values.  
The discussed slower dynamics obtained for the Phase Conductor \(a\) current results in the observed lag of the pipeline and soil response with respect to the impulsive waveform case. While \(\delta(t)\) yields a substantial lag between the soil and pipeline currents, the same feature cannot be observed when \(u(t)\) and \(r(t)\) are considered. This is due to the above-discussed magnetic field characteristic penetration time \(\tau_{p}\), whose value becomes negligible with respect to the soil current dynamics when \(u(t)\) and \(r(t)\) are considered, respectively.  
Finally, the previously discussed inversion of the pipeline and soil current is not observed when the unit step and unit ramp responses are considered. This is due to the nature of \(u(t)\) and \(r(t)\), which results in a monotonic phase conductor current. Furthermore, it can be noticed that both the pipeline and the soil unit ramp responses approach a constant value since the phase conductor current increases linearly due to the applied unit ramp voltage waveform \(r(t)\).
## 1 Introduction
This study focused on producing a landslide susceptibility map of the Reggio Calabria metropolitan area (Italy) by combining GIS techniques and the AHP method. The main objectives of the present work can be synthesized to create a landslide susceptibility map for the province of Reggio Calabria, using the GIS software by means of the weighted combination of various factors, such as the slope, lithology, elevation, rainfall, land use, distance of the road and river. From the obtained map, it is possible to evaluate the landslide susceptibility in the areas that are particularly relevant because of the connections between the internal urban areas and the main towns and services located along the coasts and to analyze the degree of landslide risk of the connecting infrastructures (lifelines). This method could be used in other areas of the Calabria Region for the realization of the landslide susceptibility map in order to be able to use the maps obtained to plan the safety of the slopes or, in particular, identify the main road infrastructures more at landslides risk, comparing the results obtained with the various inventories of landslides that occurred in the past.
# Simulation Study of Thermal-Mechanical Coupling Fretting Wear of Ti-6 Al-4V Alloy
## 1 Introduction
Moreover, all the preceding studies were conducted under constant normal load, and the effect of normal cyclic load was not considered. In practical engineering, the loads in vibration environments are complex and variable. Normal cyclic loading has a non-negligible effect on wear, fatigue, and temperature rise between contact surfaces [22; 23; 24]. [PERSON] et al. [25] studied crack sprouting under nonlinear (normal cyclic loading) loading and analyzed the damage problem under fretting fatigue conditions. [PERSON] et al. [26] investigated the temperature rise distribution between fretting contact surfaces under normal cyclic loading. It was observed that the amplitude of surface temperature rise under normal cyclic loading was lower than that under constant normal loading. Regrettably, there are few studies on fretting wear under normal cyclic loading. In response to the problems of the above study, a numerical method is adopted to simulate wear evolution considering the effect of temperature in this paper. The variable wear coefficient was used to consider the effect of temperature based on the energy dissipation model, and the UMESHMOTION subroutine was created. Then, a coupled temperature-displacement finite element model for calculating fretting wear was developed based on the Ti-6 Al-4V alloy fretting test. As most Ti-alloys [27; 28; 29], Ti-6 Al-4V alloy possesses good high-temperature properties and a high Young's modulus, they were selected for this study. The correctness of the model was verified by comparing its results with the Archard model. The interactions between temperature, stress, and wear are revealed using the model. Finally, the effects of temperature, plastic, number of cycles, and fretting speed and normal cyclic load on wear and temperature rise are explored.