esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulations
### Simulation Setup
#### 5.1.3 Comparison Methods  
Some FL algorithms assume that the FL server understands some prior knowledge, such as the number of samples residing on each client or the distribution of samples in each client, and design corresponding client selection or model aggregation methods to improve the model accuracy or accelerate the training process. However, we argue that this prior knowledge may not be available or accurate and violates data privacy, which is one of the major motivations for applying FL. The proposed DecantFed does not require such prior knowledge, and in order to achieve fair comparisons, two FL baselines, i.e., FedAvg [3] and FedProx [20], that also do not require such prior knowledge are used to compare the performance with DecantFed. Here, FedAvg selects all the clients in each global iteration without setting up a deadline. Also, FedAvg performs uniform local model training, meaning that the number of data samples to train a local model is the same for all the clients. On the other hand, FedProx performs synchronous FL by setting up a deadline  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Background noise \(N_{0}\) & \(-94\) dBm \\ Bandwidth (\(b\)) & 1 MHz \\ Client transmission power (\(p\)) & 0.1 watt \\ Size of the local model (\(s\)) & 100 kbit \\ Client \(i\) CPU frequency & \(f_{i}\in U(0.1,1)\times 10^{9}\) Hz \\ Number of CPU cycles required for & \(C_{i}\in U(1,5)\times 10^{7}\) \\ training one sample on client \(i\) & \\ Number of local samples \(|\mathcal{D}_{i}|\) & Dirichlet distribution \\ Number of local epochs & Various, dynamic local training \\ Number of local batch size & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
\(\tau\) and selects only a few clients who can upload their local models before the deadline to participate in the model training. Also, FedProx will dynamically allocate workloads to different clients based on their computing capacities. Three FL algorithms are summarized in Table 2. To compare the performance of different FL algorithms, an evaluation will be conducted on their convergence rate and model test accuracy. Since the duration of a global iteration may vary for the three FL algorithms, the convergence rates will be measured over both time and global iterations.
# Effect of Aggregate Size on Recycled Aggregate Concrete under Equivalent Mortar Volume Mix Design
## 3 Results and Discussion
### Hardened Properties
#### 3.2.3 Elastic Modulus  
The elastic modulus of the concrete mixes at 7, 28, and 56 days are given in Figure 7. Except for RAC1-AP-50 at 28 days, all concrete mixes showed higher values of elastic modulus than the control mix at 28 and 56 days. The RAC1-AP series had an elastic modulus in the range of 99-107% of NAC1, and the RAC1-OA series was in the range of 107-112%. Compared with the control concrete (NAC2), the 28-day modulus of the RAC2-AP series with high-quality RA was 6% and 9% higher at 25% and 50% mixing rates, respectively, and the RAC2-OA series was 12% and 20% higher at the corresponding replacement levels.  
Interestingly, in terms of compressive and splitting tensile strength, the control mixture at 56 days showed higher strength than the RAC1 series, whereas, in terms of elastic modulus, all RAC1 series mixes were higher than the control mixtures. This is because  
Figure 6: Correlation between the compressive strength and splitting tensile strength of recycled aggregate concretes.  
Figure 7: Elastic modulus development with age.  
the elastic modulus increases as the volume fraction of the aggregate increases [59]. In many studies, it has been reported that the compressive strength of RAC designed with the EMV mix design was lower than that of control concrete, but the elastic modulus was higher [18; 27; 28; 60].  
The correlation between compressive strength (\(f_{c}\)) and elastic modulus (\(E_{c}\)) is plotted in Figure 8. In addition, Equation (3) for predicting the elastic modulus depending on the compressive strength of concrete provided in ACI 318-14 is shown in Figure 8.  
\[E_{c}=4700\sqrt{f_{c}} \tag{3}\]  
The R\({}^{2}\) values of RAC-AP and RAC-OA were both above 0.9, indicating that there were very strong correlations between compressive strength and elastic modulus. This could have a strong practical benefit for the prediction of elastic modulus based on compressive strength, since the strength test is standard, while the elastic modulus test is more technical and time-consuming. In the RACs used in this study, higher elastic modulus values than the prediction model were observed. However, according to the trend line, this equation might overestimate the modulus at a lower strength. At the same value of compressive strength, the RAC-OA series exhibited a higher elastic modulus than the RAC-AP series.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Copper sulfide Films
The dissociation of thiourea in the basic conditions (provide \(S^{2-}\) ions) and the formation of divalent sulfide ions are represented in Equations (13) and (14), respectively. Lastly, the formations of Cu\({}_{2}\)S films (a few nanometers) were found in the substrates. \[\text{CuSO}_{4}+2\text{NH}_{4}\text{OH}\rightarrow\text{Cu}\left(\text{OH} \right)_{2}+2\text{NH}_{4}^{+}+\text{SO}_{4}^{2-} \tag{10}\]  
\[\text{Cu}\left(\text{OH}\right)_{2}+4\text{NH}_{4}^{+}\to 2\text{H}^{+}+2 \text{H}_{2}\text{O}+\left[\text{Cu}\left(\text{NH}_{3}\right)_{4}\right]^{2+} \tag{11}\]  
\[\left(\text{CH}_{2}\right)_{6}\text{N}_{4}+6\text{H}_{2}\text{O}\to 6\text{HCHO}+4 \text{NH}_{3} \tag{12}\]  
\[\text{CSNH}_{2}\text{NH}_{2}+\text{OH}^{-}\rightarrow\text{H}_{2}\text{O}+ \text{HS}^{-}+\text{CH}_{2}\text{N}_{2} \tag{13}\]  
\[\text{HS}_{2}+\text{OH}^{-}\rightarrow\text{H}_{2}\text{O}+\text{S}^{2-} \tag{14}\]  
\[\left[\text{Cu}\left(\text{NH}_{3}\right)_{4}\right]^{2+}+\text{S}^{2-} \rightarrow\text{Cu}_{2}\text{S}+4\text{NH}_{3} \tag{15}\]  
Raman spectra and XPS studies showed very strong bands at 472 cm\({}^{-1}\) and no other impurities in these samples. The maximum specific capacitance, specific energy, and specific power for the films prepared by using ammonia were 765 F/g, 25.1 Wh/kg, and 12.2 kW/kg, respectively. In this work, thin films produced by using ammonia had better stability over the 2000 cycles when compared to hexamethylenettermine (93%), based on the high-resolution transmission electron microscopy (HRTEM) results, a thicker structure (d-spacing value = 3.012 A), and a nanotube structure (d-spacing value = 3.105 A) by using ammonia and hexamethylenettermine, respectively.
## 5 Conclusions  
Wind-vortex-induced vibration was considered, and an NES was used for vibration reduction. The coupling dynamic equation of a deep-water jacket pipe-NES was established. Based on the multi-scale mixed harmonic balance method, the nonlinear dynamic characteristics of the coupling system and the vibration reduction effect of an NES were investigated, and suggestions for parameter design were proposed.  
Figure 12: Comparison of pipe response of different parameters, (**a**) response (\(\kappa=367.38\), \(\rho_{2}=0.02\)), (**b**) response (\(\kappa=24.425\), \(\rho_{2}=0.02\)).  
1. The approximate analytical solution of the coupled dynamic equation was solved by using the multi-scale mixed harmonic balance method. The stability of the trivial solution of the system was analyzed according to the Routh-Hurwitz criterion. The correctness of the theoretical derivation process was verified by comparison with numerical solutions.
2. The nonlinear stiffness has a great influence on the nonlinear motion of the pipe. When the nonlinear stiffness increases to a certain extent, saddle-node bifurcation and Hopf bifurcation appear, and the motion form of the pipe is more complex, while the vibration amplitude of the pipe is greatly suppressed. Therefore, the vortex-induced resonance of the pipe can be effectively suppressed by reasonably selecting the nonlinear stiffness of an NES.
3. For a system with nonlinear motion, an NES damping mainly affects the range of the unstable region of the pipe. The larger the damping, the smaller the unstable region of the amplitude-frequency response curve. For a system without nonlinear motion, increasing an NES damping will decrease the response amplitude of the pipe near the resonance region. A reasonable combination of nonlinear stiffness and damping should be considered.  
The deep-water jacket pipe had a rigid body and only one degree of freedom in this study, and its elasticity should be further studied. In addition, this paper only considers one NES damper, and there will be multiple NESs in practice, which requires further study.  
Conceptualization, L.Y., L.L. and Y.Y.; methodology, L.Y. and C.L.; software, Y.Y., Y.C. and L.L.; validation, L.L., Y.Y. and L.Y.; formal analysis, L.Y.; investigation, L.L. and C.L.; resources, Y.C.; data curation, Y.Y. and C.L.; writing--original draft preparation, L.L., Y.Y. and Y.C.; writing--review and editing, Y.Y.; supervision, L.Y. and C.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications
## 5 System Implementation and Experiment Results
### RSU Controller and Signal Controller Integration  
The RSU controller connects to a signal controller and periodically reads the traffic signal plan which contains the information of signal state, phase separation, steps, seconds, and control strategy. The RSU controller can send commands to the signal controller such as extending the green period or cutting off the red period. The RSU controller periodically broadcasts the real-time signal plan via 802.11p or Bluetooth/Wi-Fi interface, OBU or smart phone App in the radio signal coverage range can receive the signal plan data and reacts to the signal plan. For example, vehicles can take action before the signal change and performs eco-driving or re-routing.  
An integration validation experiment is performed using an EVSP scenario, as shown in Figure 15, where a LED signal simulation board is attached to the signal controller as simulating the control output of the designed control algorithm. We simulate an EV approaching the intersection, and the message broadcast by OBU is collected by RSU and displayed on the screen (lower left part), and the EVSP control algorithm sends the command to a signal controller to execute the whole EVSP processes (lower right part).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Distance** & **Signal Transmission Strength (dB)** & **Signal Receive Strength (RSSI)** \\ \hline
25 m & 12 & –79 \\
50 m & 15 & –77 \\
80 m & 15 & –86 \\
100 m & 15 & –89 \\
150 m & 15 & –88 \\
200 m & 18 & –92 \\
265 m & 22 & –93 \\
300 m & 22 & –92 \\
400 m & 25 & –87 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Communication distance test of 802.11p module.  
Figure 15: Testing EVSP functions in the lab.  
Some module verifications in the RSU are performed including the state transition test, the host RSU determination algorithm, the control verification of the signal in the EVSP state, and the function of judging the departure of the emergency vehicle. The function verification in the OBU includes the control of GPS and 802.11p modules, and continuously broadcasts information at a fixed frequency (1 Hz). We also did a detailed test on the function of HostRSU determination test, including the long-time processing mechanism, the distance threshold judgment mechanism, the phase angle matching mechanism, and the multiple emergency vehicle request determination tests.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 6 Antibacterial Effect of Chitosan Nanofibers
### Susceptibility of Different Bacterial Strains to Chitosan
The results suggest that the protonated amino groups in CNFs were dominantly responsible for the inhibition of _E. coli_[144]. With the aim of reproducing real food systems, the mechanism of action of CNFs against food spoilage bacteria such as _Escherichia coli_ (_E. coli_) and _Listeria innocua_, as well as pathogenic _Staphylococcus aureus_ (_S. aureus_) and _Salmonella typhimurium_, were investigated under the same environmental conditions [145]. After 60 min of exposure, 99.9% of Gram-negative _E. coli_ were inactivated compared to 180 min for Gram-positive _L. innocua_ and 240 min for _S. aureus_. As for _S. typhimurium_, only a decrease of 99% could be marked. The presence of lipopolysaccharides containing phosphate and carboxylic groups is considered the major reason for the increased hydrophilicity and negative surface charge density of Gram-negative bacteria [145], giving the cell wall a highly polar character and leading to a stronger affinity for chitosan than Gram-positive bacteria, which is the main responsible factor of the bactericidal effect. However, the results revealed that the antibacterial activity of CNFs is not Gram-dependent but rather strain-specific. Accordingly, as shown in Figure 4, chitosan's strain sensitivity can be classified in the following order: _E. coli_ > _L. innocua_ > _S. aureus_ > _S. typhimurium_. Nevertheless, the results (Figure 7) show that Gram-positive _L. innocua_, which is supposed to be less hydrophilic, was more susceptible than Gram-negative _S. typhimurium_. The bacteria were then classified between pathogenic and innocuous (harmless). It was observed that the most susceptible strains _E. coli_ and _L. innocua_ were both innocuous, while the most resistant _S. aureus_ and _S. typhimurium_ were pathogenic. The conclusion was that chitosan nanofibers have little influence on pathogenic bacteria regardless of whether they are Gram-positive or Gram-negative. However, they can inhibit the growth of innocuous bacteria depending on the hydrophilicity and surface charge density, so the greater influence, in this case, would be on the more hydrophilic Gram-negative innocuous bacteria _E. coli_, followed by the Gram-positive innocuous bacteria _L. innocua_[146].
# A Novel Data Forwarding Strategy for a Drone Delay Tolerant Network with Range Extension
## 4 Heuristic Flight Path Planning (HFPP)
By completing the selection process, we see that these drones only exchange two packets with destination \(d_{2}\) and \(d_{6}\) that results 2.5 km and 3.5 km less travel for drones \(n_{1}\) and \(n_{2}\) respectively. ```
Input: \(G\), \(L_{max}\), \(d_{cons}\), \(\overline{\text{T}}_{max}\)// \(d_{cons}\) and \(\overline{\text{T}}_{max}\) represent destination and delivery deadline of consignment Output: \(M=m_{0}\), \(m_{1}\), \(m_{2}\)... \(m_{q}\), \(m_{0}=null\), where \(m_{0}\in B\), \(m_{i\
eq 0}\in D\)
1 begin
2 int \(A_{n}=0\); //counter for number of registered destinations added to the list of deliveries
3 int \(\omega_{max}=0\); \(flag=0\); \(\mathbf{\mathbf{N}}=-1\); //\(\omega_{max}\) and \(\mathbf{N}\) are the maximum weight and the index of registered destinations respectively
4 float \(cost=0\);
5 boolean \(mark=[false\), \(false\), \(\ldots]\), \(remove=[false\), \(false\), \(\ldots]\);
6\(M=M\cup m_{0}\cup d_{\text{cons}}\); \(A_{n}++\);
7 while\(A_{n}<|G|\)do
8 for\(k=0\) to \(|G|\)do
9 if\(mark(k)\)&& \(\omega_{i}>\omega_{max}\)then
10\(\mathbf{N}=k\); \(\omega_{max}=\omega_{k}\); \(flag=1\);
11 endif
12 endfor
13 if!flag then
14 break;
15 endif
16\(mark(\mathbf{N})=true\); \(M=M\cup\mathbf{N}\); \(A_{n}++\);
17\(cost=TSP(M)\);
18 if\(\delta<cost/s\)then
19 if\(cost\leq L_{max}\)then
20 for\(k=0\) to \(|M|\)do
21 if\(\left[\overline{\text{T}}_{k}<\frac{h_{i}\cdot w_{k}}{s}\right.\)&&\(mark(k)\)\(\left|\left|\left[\frac{h_{i}\cdot w_{k}}{s}<\frac{h_{j}\cdot w_{k}}{s}\right]\right| \right.\)\(\left|\left[\overline{\text{T}}_{\text{cons}}<\frac{h_{k}\cdot w_{\text{cons}}}{s}\right]\right.\)
22\(remove(k)=true\); \(mark(k)=false\);
23\(M=M-k\); \(A_{n}--\);
24 endif
25 endfor
26 endif
27 if\(cost>L_{max}\)then
28\(c_{n} Nearby=nearby(C,M)\);
29\(M=M\cup c_{n}urby\);
30 goto line 17
31 else
32\(M=M-\mathbf{N}\); \(A_{n}--\);
33\(flag=0\); \(\omega_{max}=0\);
34 goto line 8
35 endif
36 endwhile
```  
**Algorithm 1** Heuristic Flight Path Planning (HFPP)  
We should also point out that the algorithm calculates the maximum length that a drone can fly. Hence, if a drone wishes to deliver a packet but it may run out of battery, the algorithm tries to find the nearby charging station with respect to the current flight path. Hence, the drone will get the chance to be charge for a short period to deliver more data packets. In our algorithm, we only include the charging station along as part of the route only if the distance to charging station with respect to the original flight path is not more than 1 km.
## 3 Material and Methods
### InSAR Technique and Data
The retrieved InSAR ground velocity maps along the satellites LoS are showed in Figures 3 and 4 for the ascending and descending orbits, respectively. Figure 3: Land velocities (mm/year) estimated in the time span 2015–2020 from InSAR for (**a**) ascending and (**b**) descending orbits. The marked surface deformation observed in the northern part of the area is related to the effects of the volcanic and seismic activity occurred on Etna in December 2018. Thanks to the availability of both the ascending and descending InSAR outcomes, we retrieved the surface movement along the vertical and eastward directions. This can only be done for the pixels common to both the ascending and descending LoS maps. Because of the SAR geometry acquisition, the east-west component of ground motion is proportional to the difference between descending and ascending LoS maps, and it is also a function of the local incidence angles of SAR; the vertical is proportional to the sum of the two datasets, and depends on the local incidence angles, too. Unfortunately, the north  
Figure 4: Vertical land velocities (contour in mm/year) estimated in the time span 2015–2020 from the combination of InSAR ascending and descending orbits, for six investigated coastal zones (see Figure 2 for location). south component of the ground movement has a little effect on the LoS measurements (less than 10% of the north-south movement is captured by SAR); therefore, we cannot estimate it. The analytical expression that we applied to calculate vertical and east-west motions is reported in Dalla Via [99]. Generally, ground movements along the coastal stripes are characterized by subsidence phenomena due to the coastal erosion and soil compaction causing as a consequence the retreat of the coastline [80,100,101]. Moreover, the tectonic regime of the area of interest foresees vertical movements, e.g., rise of marine terraces [58,60,62,65]. Therefore, the horizontal component of the retrieved LoS velocity is negligible in the areas investigated in this study (see also S1--Supplementary Material). InSAR products were validated and calibrated through a comparison with the GNSS data superimposed and projected on the InSAR LoS directions, for both the orbits (see S1--Additional Material for further information). Such decomposition was made pursuant to the InSAR and GNSS data comparison and validation; hence, the reported Up component already include any post-processing performed operation to calibrate InSAR products (see S1--Additional Material for further details).
# A Hybrid Metaheuristic Algorithm for the Efficient Placement of UAVs
###### Abstract  
This work addresses the problem of using Unmanned Aerial Vehicles (UAV) to deploy a wireless aerial relay communications infrastructure for stations scattered on the ground. In our problem, every station in the network must be assigned to a single UAV, which is responsible for handling all data transfer on behalf of the stations that are assigned to it. Consequently, the placement of UAVs is key to achieving both network coverage and the maximization of the aggregate link capacities between UAVs and stations, and among the UAVs themselves. Because the complexity of this problem increases significantly with the number of stations to cover, for a given fixed number \(p\) of available UAVs, we model it as a single allocation \(p\)-hub median optimization problem, and we propose a hybrid metaheuristic algorithm to solve it. A series of numerical experiments illustrate the efficiency of the proposed algorithm against traditional optimization tools, which achieves high-quality results in very short time intervals, thus making it an attractive solution for real-world application scenarios.  
unmanned aerial vehicles; UAV placement; metaheuristics Article
# Textured and Hierarchically Constructed Polymer Micro- and Nanoparticles
## 3 Results
### Hierarchically Constructed Particles of Poly-Arylamid/TPGDA/MMA
#### 3.2.3. Assembling of PMMA on Poly-TPGDA Particles  
Polymer-polymer electrostatic assembly was performed in batch reaction. Composite particles were formed by mixing two types of oppositely charged nano- and microparticles. Positively charged PMMA particles (700 nm size) were added into the tube with well-washed and negatively charged poly-TPGDA microparticles (30-50 \(\upmu\)m). Particles were vortexed for 10 s and incubated at room temperature. Unbound PMMA nanoparticles were removed by washing with deionized water several times. Figure 8 presents examples of PMMA on poly-TPGDA composites.  
Polymer-Polymer Particle Bounding Efficiency Depends on Several Factors  
* First, particle surfaces need to be highly charged in order to form strong electrostatic interactions. Therefore, it is important to functionalize both particle types. PMMA particles carrying the PDADMAC layer had a zeta potential of +35 mV. Poly-TPGDA particles were functionalized by copolymerization with acrylic acid, thus introducing a negative charge. The optimal concentration of acrylic acid in monomer phase was 10%. At lower contents, particle-binding was poor, whereas with 20%, the poly-TPGDA particles were more polydisperse.  
Figure 8: SEM pictures of poly-TPGDA/PMMA assembly produced with different acrylic acid concentrations in monomer phase: **(a)** 10% and **(b)** 20%.  
Figure 7: Optical microscope images of different poly-TPGDA particles produced with different carrier/monomer phase flow rate ratios: **(a)** 50/1 \(\upmu\)L/min, **(b)** 300/1 \(\upmu\)L/min, **(c)** 500/1 \(\upmu\)L/min, and **(d)** 700/1 \(\upmu\)L/min; **(e)** graph of particle size vs. carrier/monomer phase flow rate ratio.  
* Other important factors are the concentration of smaller bound particles and incubation time. In order to allow both types of particles to closely contact, all assembling experiments were performed with a large excess of PMMA particles, and the mixture was incubated for 48 h. After this period, even the smaller particles had settled and bound to the surface of the lager ones (Figure 9).  
These produced composite particles suspended in water were quite stable: they remained unchanged after several months of storage and upon mechanical agitation.