esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reputation-Based Collaborative User Recruitment Algorithm in Edge-Aided Mobile Crowdsensing
## 4 Reputation-Based Collaborative User Recruitment Algorithm
### Reputation-Based User Recruitment  
In our MCS system, we assume that in urban scenarios, there are plentiful mobile users with intelligent devices randomly distributed in the city. They move at a constant speed \(v\) to roam around the task locations. After the cloud platform \(P\) publishes the tasks \(\theta_{total}\) from some applications or the requesters, these mobile users \(U\) volunteer to take part in these tasks, and they will choose what kinds of tasks to participate in according to the information of the tasks. Hence, for each task \(\theta_{j}\), the cloud platform \(P\) will select the users who are close to the task in time and space and whose costs are less than the reward as the candidates. Then it will form a subset of users \(S_{Can}\) for the task \(\theta_{j}\) and choose the most appropriate user to complete the task.  
Then the reputation module will evaluate the performance of the users in \(S_{Can}\) and update their past reputation scores \(\sigma_{u_{i}}(t^{-})\). The higher value \(\sigma_{u_{i}}(t^{-})\) means that the users \(u_{i}\) have outstanding performance in the previous tasks with higher quality, which leads to a higher possibility of being recruited for the next task. Through Equation (6), we focus on the performance of the last five tasks the user participates in to attain the past reputation score. Additionally, we consider other real-time factors, spatial and temporal constraints. We calculate the user instantaneous reputation score \(\sigma_{u_{i}}^{inst}(t)\), which is related to locations and sensing radius. Finally, the cloud platform can attain the user \(u_{i}\) reputation score \(\sigma_{u_{i}}(t)\) for the task \(\theta_{j}\) at time \(t\).  
In order to maximize profits, we propose a Reputation-Based User Recruitment (RBUR). The cloud platform \(P\) prefers to recruit users who can complete the tasks efficiently and achieve the maximum coverage qualitatively but require less reward for their costs. Therefore, we establish a fitness function that is based on the user reputation and cost for the task, which is given as follows:  
\[f_{u}=\lambda\sigma_{u_{i}}(t)+(1-\lambda)\left(1-\frac{C_{u_{i}}^{\theta_{j} }}{R\omega_{\theta_{j}}}\right), \tag{13}\]where \(\lambda\) represents the balancing parameter ranging from 0 to 1, and \(C_{u_{i}}^{\theta_{j}}\leq R\omega_{\theta_{j}}\). The fitness function \(f_{u}\) can provide strong guarantees by adopting the user reputation method with the goal of improving the task completion rate and achieving higher coverage quality, meanwhile maintaining the proper tradeoff between the cost to the user and the reward of the task.
## 3 Results and Discussion  
### Mass Concentrations of PM and Meteorological Condition  
Figure 2 depicted the temporal variations of the mass concentrations of PM1, PM2.5 and PM10 in the winter of 2017. The daily mean mass concentrations were 63.20 (range of 15.05 to 146.79) \(\upmu\)g\(\cdot\)m\({}^{-3}\) for PM1, 119.63 (range of 19.62 to 248.06) \(\upmu\)g\(\cdot\)m\({}^{-3}\) for PM2.5 and 211.95 (range of 73.34 to 466.41) \(\upmu\)g\(\cdot\)m\({}^{-3}\) for PM10, respectively, which decreased by approximately 50% compared with those reported in the wintertime of 2015 and 2016 (224 and 223\(\upmu\)g\(\cdot\)m\({}^{-3}\), respectively). Nevertheless, 69% of the sampling days were still recognized as polluted days, with a daily average PM2.5 exceeding 75 \(\upmu\)g\(\cdot\)m\({}^{-3}\), and there were nine days with a daily average PM2.5 exceeding 150 \(\upmu\)g\(\cdot\)m\({}^{-3}\). This indicated it was still heavily polluted and necessary to further mitigate in the future.  
It has been widely accepted that specific meteorological conditions facilitate haze formation, such as high relative humidity (RH), low wind speed (WS) and temperature inversion condition, especially in wintertime in north China [33]. High RH is favored by atmospheric chemical reactions in the dry season, and then accelerates the PM\({}_{2.5}\) retention in low WS or temperature inversion conditions [34]. Accordingly, in the present study, the mean RH and WS in the periods of PM\({}_{2.5}\) exceeding 150 were 65% and 1.1 m\(\cdot\)s\({}^{-1}\), respectively (Figure S1). This is consistent to other studies in Zibo (WS of 1.1 m\(\cdot\)s\({}^{-1}\) and a RH of 55%) [35] and Zhengzhou (2.0 m\(\cdot\)s\({}^{-1}\) and the RH of 50-65%) [36] in the North China Plain. This suggested that meteorological conditions were important factors to contribute to heavy haze formation.
## 3 Laboratory Study
### Testing  
#### 3.3.1 Brookfield Rotational Viscosity (BRV) Test  
In this study, the influence of Kraft lignin, as an additive, on the viscosity of bitumen was evaluated, as the fine powder nature of lignin can significantly modify this property of bitumen.  
The viscosity of bitumen samples was determined using a Brookfield Rotational Viscometer (BRV) device, which characterizes the bitumen for workability during pumping, mixing with aggregate, and compaction of hot mix asphalt. To perform the BRV test, a cylindrical spindle was rotated inside a container filled with bitumen at a specified speed. To determine the viscosity of bitumen, the torque needed to maintain a constant rotational speed of the spindle was measured. A Brookfield DV2T viscometer produced by Ametek-Brookfield was used in this study. The BRV test was conducted following the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Details** & **Kraft Lignin (wt \%) 1** & **Blending Protocol** & **Code** \\ \hline Virgin Bitumen (VB) & 0 & — & VB \\  & & MM & VB-MM \\  & & HSM & VB-HSM \\ \hline Bitumen with added Kraft & & & \\ lignin (L) & 5 & MM & 5L-MM \\  & & HSM & 5L-HSM \\ \cline{2-4}  & 10 & MM & 10L-MM \\  & & HSM & 10L-HSM \\ \cline{2-4}  & 20 & MM & 20L-MM \\  & & HSM & 20L-HSM \\ \hline \hline \end{tabular}  
* The lignin content is a percentage of the total weight of the lignin-modified bitumen (lignin + bitumen).  
\end{table}
Table 4: Details of specimens tested in the study.  
Figure 3: Blending Kraft lignin and bitumen using Mechanical Mixer (MM) and High-Shear Mixer (HSM) in the oil bath.  
test procedures described in the LC 25-007 Quebec test method, at 135 and 165 \({}^{\circ}\)C for all samples. The bitumen samples were poured into a cylindrical container and placed in the device at a temperature of 135 followed by 165 \({}^{\circ}\)C with a conditioning time of 15 min for each temperature. At each temperature, nine (9) readings of viscosity were recorded at 1 min intervals.
# A Multi-Scale Approach for Modelling Airborne Transport of Mucosalivary Fluid
## 2 Governing Equations
### Lagrangian Phase  
The velocity and the position of the particles laden into the computational domain are the results of the momentum and trajectory equations. In this paper we adopt a two-way coupling between carrier fluid and Lagrangian phase. Thus, non-collisional spherical particles are scrutinized. Saliva droplets, considered in this paper, exhibit a diameters' range such that pressure, virtual mass and Brownian forces can be neglected (20; 21). Furthermore, the Weber number calculated for the droplets' velocities considered in this work, is such that no secondary break-up phenomena occur. For the above reasons is possible to study each discrete particle including: gravity, aerodynamic drag and buoyancy. Aerodynamic drag coefficient is obtained from standard Putnam correlation for spheres (22). By contrast, the convective heat transfer coefficient and the mass transfer one are obtained from two different approaches summarized in Table 1. Specifically, Re\({}_{d}\) is the Reynolds number evaluated on the particles' diameter, while Pr and Sc represent Prandtl and Schmidt numbers, respectively.  
The droplets' initial diameter is assessed through a Rosin-Rammler distribution (25):  
\[f=\frac{n}{\overline{D}_{p}}\bigg{(}\frac{D_{p,i}}{\overline{D}_{p}}\bigg{)}^{ n-1}\exp\bigg{[}-\bigg{(}\frac{D_{p,i}}{\overline{D}_{p}}\bigg{)}^{n}\bigg{]}; \tag{2}\]  
the parameters required by Equation (2) are calibrated on the basis of [PERSON] et al. (26) experimental data (27). Specifically, \(n\) was fixed equal to 8 while average parcels' diameter, \(\overline{D}_{p}\) was set equal to o 80 \(\upmu\)m. In Equation (2) \(D_{p,i}\) represents the \(i\)-th particle's diameter. Lastly, the minimum diameter of the injected parcels is 10 \(\upmu\)m, while the maximum one is 280 \(\upmu\)m.
## 2 Materials and Methods
### FACS Analysis  
To observe the expression of _HLA-A2_ and _EBNA1_ upon DHT treatment in SNU719, we performed FACS analysis. SNU719 cells treated with 500 nM and 5 uM of DHT for 48 h were trypsinized to isolate single cells and washed with FACS buffer (Dulbecco's phosphate-buffered saline [DPBS] containing 0.1% bovine serum antigen [BSA], 2 mM ethylene-diamine-tetraacetic acid [EDTA]). Next, cells were blocked with 1% BSA for 10 min at room temperature (to prevent non-specific binding). Cells were then treated with 10 mg/mL FITC mouse anti-human HLA-A2 antibody (clone BB7.2, BD Biosciences, Franklin Lakes, NJ, USA, #551285), 1 mg/mL EBV-EBNA-1 antibody (clone 1 EB12, Santa Cruz Biotechnology, #sc-81581 AF647) and 10 mg/mL MICA antibody (clone 2C10, Santa Cruz Biotechnology, #sc-23870 PE) for 30 min at 4 \({}^{\circ}\)C. Cell analysis was performed using a MACS Quant Analyzer 10 (Milteny Biotec, Bergisch Gladbach, Germany), with unstained cells used for negative controls.
## 7 Discussion  
### High-Level Experimentation Analysis  
As expected, transfer learning, decaying learning rates, and image augmentation helped to create a better model. Transfer learning had the largest effect on the resulting performance in our high-level experimentation. Transfer learning had the effect of enabling models to be more robust as a result  
Figure 4: ROC curves for all four damage magnitude levels.  
Figure 3: Training and testing cross-entropy loss of final ResNet model from Table 4.  
of the model having already received training on millions of images from the ImageNet dataset, which included images of buildings. In this way, the best performing model was already primed to better distinguish between the shapes, colors, textures, and other features that delineated our samples into their respective classes. For similar reasons, image augmentation also proved to provide significant benefits. As a result of the cropping, zooming, and flipping methods we employed, the model was trained on a slightly different variant of the image at each epoch. For our high-level experimentation, this means that the model was trained on 50 variants of all 1000 training images. This has a large multiplicative effect and, again, greatly adds to the robustness of the model. Lastly, for any model trained with stochastic gradient descent, which most models use [19; 34; 35], decaying learning rates will boost performance, as shown by our experiments. Without decaying learning rates, the stochastic gradient descent process runs a greater risk of taking too large of a step near the minimum solution and consequently stepping over it. This leads to a situation where the minimum can be continually over-stepped and never reached, hence creating a situation where convergence is not reached during training and the resulting model has poorer performance. This becomes even more important as the number of epochs is raised for more sophisticated training.  
Another gap in the literature is the utilization of a classification algorithm with no justification for their choice in selecting it. We avoided this issue in our work as demonstrated in Table 3. Because AlexNet is a predecessor to all the other algorithms we tested, it is not surprising that it had the lowest metrics in all categories. It is worth noting that it does run the fastest, however, so in cases where speed is of the essence, AlexNet still has value. We assume that we could achieve better performance out of both DenseNet and VGG with more individualized tuning, and they are popular options in the literature [13; 27], but in an effort to keep parameters similar between algorithms so we could evaluate the algorithms equally, we did not dig deeply into individualized parameter tuning at this stage in our experimentation. This left us with both GoogleNet and ResNet as our top performers. ResNet did perform better, but it also took significantly longer to train. It is again a case where which algorithm to use will depend on the value placed on speed vs. performance metrics.
# Federated Learning for Edge Computing: A Survey
## 5 Applications
### Healthcare  
As a disruptive method of data privacy, FL holds great promise in healthcare. Every healthcare institute may have a wealth of patient data, but it may not be enough to train its prediction models. At the same time, usually, no medical institute can further share this data for further processing due to privacy and data protection concerns due to health regulations. The combination of FL and disease prediction is one of the promising solutions to break the barriers of analysis in different hospitals and institutions.  
Many studies have also been carried out within the medical domain on protecting patients' health or predicting diseases. [PERSON] et al. [117] investigated differential private learning for electronic health records via FL. Their results found that the performance is comparable to centralized learning. [PERSON] and [PERSON] [118] write on the demonstration and adaptation of FL techniques for healthcare. They introduced and described areas of electronic systems such as drug discovery and disease prediction systems. A community-based FL algorithm was put forth by [PERSON] et al. [119] to forecast the mortality and length of hospital stay. Electronic health records are grouped into communities within each hospital based on standard medical characteristics. Instead of a universal model shared by all hospitals and consequently all patients, each cluster learns and uses a unique ML model customized for each community, improving its performance and efficiency. Using electronic health records' data dispersed across various data sources, FL is also used in [120] to forecast hospitalizations for patients with heart disease during a target year. In [121], a distributed system is designed and implemented to address intrusion detection in medical cyber-physical systems. Processes of private data from patients' devices (e.g., heart rate, blood oxygen saturation, etc.) are locally trained to improve the global model. which is then used to detect harmful activities. Homogeneous patients with similar characteristics are grouped to provide a high-throughput model. Each cluster creates its personalized local and global model.
# Field Trial and Performance Evaluation of Soybean-Based Bio-Fog Seals for Asphalt Rejuvenation
## 2 Materials and Methods
### Experimental Plan
#### 2.4.4 Stiffness and Relaxation  
To assess the impact of bio-rejuvenators on the low-temperature performance of the top layer of the asphalt pavement, a modified Bending Beam Rheometer (BBR) was conducted. This test was chosen for its ability to target the surface of the pavement, the critical zone most affected by bio-rejuvenators and susceptible to cracking. This creep compliance test, typically used for asphalt binders, was adapted to accommodate testing of asphalt mixture beams obtained from the field. Cores extracted from the MnROAD test sections after 7 weeks of the applied treatment were sliced into thin beams, with the top quarter-inch representing the estimated penetration depth of the rejuvenator treatment and tested at the Iowa State University laboratories.  
The BBR testing configuration adopted in this study follows the modifications described by [PERSON] et al. [39] and [PERSON] et al. [40]. This modified configuration utilizes a three-point bending setup where a constant load is applied to the mid-point of the asphalt mixture beam at two critical low temperatures: \(-24\)\({}^{\circ}\)C and \(-12\)\({}^{\circ}\)C. These temperatures represent conditions that can lead to pavement distress through low-temperature cracking. The resulting deflection of the beam under this constant load is measured and is correlated with two key properties that influence the asphalt mixture's behavior at low temperatures. The first property, stiffness, reflects the mixture's resistance to deformation under load. Lower stiffness is generally desirable as it helps mitigate thermal cracking caused by pavement contraction during cold weather. The second property, known as the m-value, represents the rate of change in stiffness over time. A higher m-value indicates a greater ability of the asphalt mixture to relax stresses that accumulate during cold periods. This stress relaxation capability helps reduce the risk of low-temperature cracking, ultimately enhancing the pavement's resistance to damage from severe winter conditions.
## 4 Experiments and Results
### Average Fragment Creation and Save Time by File Size
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System 2** & **Mean (s)** & **\(\sigma\) (s)** & **Min (s)** & **Max (s)** \\ \hline
1 kb & 0.0067 & 0.0078 & 0.0015 & 0.0274 \\ \hline
100 kb & 0.0065 & 0.0066 & 0.0026 & 0.0378 \\ \hline
1 mb & 0.0094 & 0.0087 & 0.0024 & 0.0429 \\ \hline
10 mb & 0.0 Figure 9 illustrates the distribution of average creation and save times for file fragments as a function of their size. The different colored curves correspond to various file sizes, highlighting how processing times change based on file dimensions. On the \(x\)-axis, the time is represented in seconds, while the \(y\)-axis shows the relative frequencies, and the charts depict distributions. These curves provide a clear depiction of the relative frequency of specific processing durations, offering an intuitive understanding of the impact of file size on the time required for fragment creation and storage. In Figure 9, each colored curve indicates a different file size, showing how processing times vary depending on the size of the file. The curves help to visualize the relative frequency with which certain processing times occur, providing an intuitive overview of the effect of file size on the time required to complete the creation and saving process.
# Ferroelectric Materials: A Novel Pathway for Efficient Solar Water Splitting
## 4 Ferroelectric Materials for Photoelectrochemical (PEC) Water-Splitting Devices
### (4) PbTiO\({}_{3}\)  
PbTiO\({}_{3}\) (PTO) is a visible light active titania-based perovskite oxide that is well-known for its high ferroelectricity. It is a good candidate as a photocatalytic material in water splitting due to its bandgap value of 2.75 eV and suitable band structure, which assists in charge transfer and separation [90; 91; 92]. However, there are a limited number of studies on the application of PTO as a photoelectrode material with demonstrated higher photocatalytic activity for hydrogen evolution, especially in the case of PEC devices.  
Recently, transition metal-doped PTO was investigated as a photocathode material for improving photogenerated electron transfer on the photocathode/electrolyte interface [92; 93]. In particular, the 1 wt % Cu-doped PTO photoelectrode in an aqueous methanol solution under visible light irradiation (\(\lambda\geq\) 400 nm) presented 2.5 times higher photocatalytic performance compared to PTO without Cu loading using the same configuration [92].  
In another report, [PERSON] et al. [93] modified the ITO/PTO photocathode using both Fe (III) doping and grafting, which resulted in a photocurrent increases of up to 220 \(\mu\)A/cm\({}^{2}\) in comparison with 38 \(\mu\)A/cm\({}^{2}\) of pure PTO photocathode. These enhancements were attributed to the change of band positions, which facilitated the transfer of photoinduced charge carriers. A similar phenomenon was observed for a fabricated Ag-Pt bimetallic catalyst on a ferroelectric PTO photocathode surface deposited on ITO quartz glass. As a result, the photocurrent density increased significantly from 60 \(\mu\)A/cm\({}^{2}\) for pure PTO electrode to 202 \(\mu\)A/cm\({}^{2}\) for the Ag-Pt electrode one under 0 V versus SCE, and with a 100 mW/cm\({}^{2}\) Xe lamp illumination [94].  
Due to the superior performance of PTO in comparison with other TiO\({}_{2}\)-based perovskite-type oxide materials (BaTiO\({}_{3}\), SrTiO\({}_{3}\)) and their stability, some innovative synthesis methods have been introduced such as microwave-assisted synthesis [95; 96] and nonhydrolytic sol-gel [97]. A promising  
Figure 7: **(a)** Energy band diagram of Cu\({}_{2}\)O and SrTiO\({}_{3}\) before and after formation of the p–n junction, **(b)** Photocurrent density vs. applied potential curve for (A) pristine Cu\({}_{2}\)O and (C) 343 nm of Cu\({}_{2}\)O/SrTiO\({}_{3}\) heterojunction [88]. Copyright 2014, American Chemical Society.  
and effective PTO photocathode with higher photocurrent efficiency has been achieved using such facile and cost-effective synthesis methods.
## 3 Results  
### Evaluation of the Sample Introduction System  
The determination of \(\sigma_{\text{rel,tol}}\) requires known analyte concentrations entering the MS. Therefore, the sample introduction system has to be carefully characterized. For this purpose, different measurements were performed on both mass spectrometric systems. The suitability of the inlet device was proven by comparing the theoretical and experimental concentrations of toluene, which are easily accessible via standard gas mixtures. A toluene standard solution (10 \(\mu\)L/mL in hexane) was introduced into the system. Different concentrations of toluene inside of the glass chamber were achieved by varying the flow rates of the sample as well as that of nitrogen. A summary of these results for PITOF 1 is given in Figure 3a. The findings for the variation of the nitrogen flow as well as for the sample flow are in good agreement with the theoretical results. The best results were achieved at medium sample and nitrogen flow rates at values of 25 \(\mu\)L/min and 1.3 L/min, respectively. The signal  
Figure 2: (**a**) Experimental setup of the sample introduction system for (semi-)volatile organic compounds; standard solutions, containing known amounts of compounds, are introduced into a nebulizer via syringe pump and sprayed into a heated glass chamber. The evaporated analytes are transferred via a heated transfer line into the ion source of the mass spectrometer. (**b**) Photograph of the introduction system.  
stability and response characteristics of the introduction system for selected analytes are shown in Figure 3b. The variations in signal heights are mainly a result of shot-to-shot fluctuations of the laser beam. VOCs such as toluene and xylene show a rapid response and high signal stability. The time to reach maximal signal heights is approximately 2 s, whereas the time for returning to the baseline was below 10 s. Compounds with a much lower vapor pressure show rise times similar to that of toluene, but longer decline times up to 30 s.