esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Discussion  
Based on the results of the experiment, we suggest the following strategies for controlling and reducing rumor propagation in the workplace:  
* According to the main effects plot in Figure 4, the persuasive constant implies that the behavior and opinions of the majority are essential for the rumor spreading process. People tend to consider the opinions of their peers when they hear about a new rumor. Thus, the opinions of the majority may change the way we think about new information in society. As shown in the previous section, if the persuasive-constant increases to a = 0.692 (or the converted actual value is \(N^{0.7115}\)), the percentage of people who are infected by the rumor will actually decrease.
* Besides, environmental bias in the workplace also has a significant impact (10%) on rumor spreading when kept at a low level. Traditional style workplaces, as opposed to open spaces, contribute somewhat to the spread of rumors. Information coming from authority figures within the organization also effectively reduces the spread of rumors.
* Providing counseling of leaders has the strongest effect on employee response to rumors. In other words, leader counseling significantly reduces the spread of rumors. When the value of  
Figure 6: (**a**) Contour plot of persuasive-constant and environmental bias; (**b**) environmental bias and counseling of leader.  
Figure 7: (**a**) Residual vs. prediction; (**b**) prediction vs. response plot.  
leader counseling was at a low level (L = \(-\)0.621), the percentage of infected people was 25.8%. According to [PERSON] et al. [(43)], if leaders have strong influence and are highly connected to their employees, they can significantly impact their opinions with rumor propagation or even dominate them [(44)]. We found that if leaders or supervisors monitor the spread of rumors early on, they can reduce the percentage of those infected to approximately 18% of the cohort size instead of 32%.
* In addition, influencers can inspire potential followers to believe them, so the managers should concentrate on these influencers immediately when they begin to recognize any rumor outbreaks. If the organization has a small percentage of influencers, they can easily be stopped through counseling. In contrast, if the propagator is the leader or influencer, the rumor will likely disperse more quickly throughout the company, which may lead to tragic consequences.
* Previous scholars have found that people are less persuaded by a propagator who is far away from them as opposed to nearby [(45)]. However, according to our findings, rumors are spread more quickly on social media than via the traditional face-to-face method [(46)], but this has also led to a decrease in the spreading of rumors.
# Dual-Path Beat Tracking: Combining Temporal Convolutional Networks and Transformers in Parallel
## 2 Background
The shift toward integrating both local and global temporal modeling, along with innovations in attention mechanisms and minimal post-processing, positions modern beat tracking as a critical technology for applications in MIR, real-time performance, and multimedia synchronization.
## 3 Results and Discussion
### Scanning Electron Microscopy (SEM) Analysis  
Using SEM, yeast cells' rehydration process with different media (PTWAir, PTWN, CW) were observed. Figure 2 show a cluster of cells of the yeast strains used for rehydration. According to the literature, visualization of treated cells by scanning electron microscopy (SEM) should reveal changes in cell morphology and in the cell wall. Numerous changes were also observed when the yeast was treated in suspension. Under these conditions, some of the treated cells took on a shrunken and crumbled appearance, and the severity and frequency of changes depend on the surrounding environment [55; 56]. Taking into account the scientific reports, we took SEM pictures to check whether the produced plasma-treated water would contribute to significant changes in the cell structure during rehydration. In the microscopic image, we looked for significant changes in the structure of the membrane and the cell wall, which may be noticeable in the form of breaks in the structure or shrinkage of cells. This observation showed no differences between the analyzed samples. During yeast rehydration at excessively high temperatures, cells may be damaged, causing membrane leakage and the loss of internal dissolved substances. During oxidation, the formation of free radicals may occur that could result in damage to proteins, lipids and nucleic acids, damage to the mitochondria and leakage of the membrane [57]. Hence, the rehydration medium used did not change the cell structure of the yeast strains analyzed.  
Figure 1: (**a**) Principal component analysis—plot of component weights. (**b**) Principal component analysis showing the qualitative characteristics of the _Saccharomyces cerevisiae_ (SafBrew T58, US-05, S33) and _Saccharomyces pastoriamus_ (SafLager W30/70) yeast strain after rehydration with plasma-treated water.
# Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks
## 4 Simulation Environment and Performance Evaluation
### Simulation Results
#### 4.2.2 Average End-To-End Delay  
End-to-end delay is the duration between the instant the packet is generated by a node and the instant this packet is received by the sink node. Figure 7 shows the average end-to-end delay based on different traffic patterns. The average end-to-end delay is calculated over the total received packets only. This means that these results do not include the end-to-end delay of packets that are lost in the network.  
Figure 6(a) shows that ADUP outperforms AODV on average end-to-end delay for upstream traffic. This is mainly due to the fact that ADUP can quickly adapt to mobility and dynamically update parents set according to the movement of nodes, which reduces time interval before sending a packet. However, AODV needs to build routes before transmission. This process is more frequent in mobility, since link failure happens more often, which results in longer delays before sending a packet with AODV. Compared with ADUP and AODV, the average end-to-end delay of Flooding increases exponentially when the number of nodes increases. The reason is that Flooding broadcasts packets to the entire network which causes network congestion. Congestion causes packets to be delayed at each hop until they reach the sink. It is worth noting that, although ADUP and AODV achieve similar average end-to-end, ADUP was able to reach that delay for a higher packet-delivery ratio.  
Figure 6(b) shows that the average end-to-end delay of ADUP outperforms that of AODV and Flooding for downstream traffic. This is mainly due to the fact that upstream traffic helps ADUP quickly update route information. Packets sent by the sink are relayed to the destination in a timely manner. The same as upstream traffic, the average end-to-end delay of Flooding on downstream increases exponentially as well. Network congestion caused by upstream traffic is also the reason.
## 3 Methodology
### Preprocessing Raw Data
#### 3.1.1 Down-Sampling  
The raw EEG data were down-sampled from 500 Hz to 256 Hz to accelerate the processing and reduce the computational complexity.
## 4 Description of the Application Problem and Employed Multi-Fidelity Models
### Comparison between Low-Fidelity (LF) and High-Fidelity (HF) Condensation Prediction  
To assess the difference in performance between the 0D model (LF) and the 3D CFD model (HF), two cases are selected from the numerical campaign that will be described in Section 5.2.1: one delivering low condensation (Figure 5) and another providing more significant condensation (Figure 6). The results predicted by these models are included in Table 2.  
The working points of Table 2 present the same air mass flow rate, but point B features a much greater EGR (humid stream) mass flow rate than point A, which is the main reason for the increase in the 0D condensation mass flow rate by 130%. The high-fidelity model also predicts an increase in condensation mass flow rate when shifting from case A to case B, but a much greater one (590%; see Table 2). Whereas the LF 0D model only considers the global psychrometric information, the HF 3D CFD model also considers the local flow features.  
Figure 5: Low condensation case  
Figure 6: High condensation case  
The different predictions of the LF and HF models can therefore be explained based on the flow patterns depicted in Figures 5 and 6. Figure 5 presents a low EGR mass flow rate, which results in a wall-jet flow configuration (see [PERSON] et al. [72]), while Figure 6, with a greater EGR rate, displays a flow pattern in the threshold between a deflecting-jet and an impinging-jet [72]. The 0D model provides an upper bound for the condensation mass flow rate, as it considers a perfect mixture between the streams. The different deviation between the 3D and the 0D models depends on the flow mixing, as the 3D CFD model predicted, as condensation is strongly correlated with mixing [66]. The mixing intensity (and thus condensation) is much greater for the high condensation case (Figure 6) due to the high penetration of the EGR stream than for the low condensation case (Figure 5). In the framework of multi-fidelity modeling [73], the 0D case could be useful to provide the psychometric information of the operating conditions, being a LF model with little computational effort. However, a comprehensive set of expensive 3D CFD simulations will still be required. The examples analyzed in this section show how the deviation between the LF and HF models can differ when shifting the working point.  
Application of the Proposed Method to the Problem of Volume Condensation Due to Mixing: Results and Discussion
# Research on Vibration Comfort of Non-Motorized Lane Riding Based on Three-Axis Acceleration
## 4 Experimental Results Analysis
### Multifactor Logistic Regression Analysis
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Variable** & **B** & **S.E.** & **WaldX2** & **OR (95\%CI)** & \(p\) \\ \hline With or without non-motorized lanes & & & & 1.000 & \\ Yes (control) & 0.244 & 0.122 & 4.000 & 1.276 (1.005–1.621) & 0.046 \\ No & 0.179 & 0.081 & 4.884 & 1.196 (1.020–1.402) & 0.027 \\ Riding speed & & & & 1.000 & \\ Low (control) & & & & 1.000 & \\ Medium & 0.298 & 0.147 & 4.110 & 1.347 (1.010–1.797) & 0.043 \\ High & 0.334 & 0.167 & 4.000 & 1.397 (1.007–1.937) & 0.046 \\ Number of roadway articulated & & & & & \\ protrusions & & & & 1.000 & \\
0 (control) & & & & 1.000 & \\
1–5 & 0.100 & 0.047 & 4.527 & 1.105 (1.008–1.212) & 0.033 \\
6–10 & 0.231 & 0.113 & 4.179 & 1.260 (1.010–1.572) & 0.041 \\ With or without on-street parking area & & & & 1.000 & \\ No (control) & & & & 1.000 & \\ Yes & 0.102 & 0.048 & 4.516 & 1.107 (1.008–1.217) & 0.034 \\ Type of non-motorized vehicle & & & & 1.000 & \\ Bicycle (control) & & & & 1.000 & \\ Electric bicycle & 0.334 & 0.101 & 10.936 & 1.397 (1.146–1.702) & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Multifactor logistic regression analysis of factors affecting riding vibration comfort. to speeds as high as 40 km/h. Therefore, stricter traffic control of non-motorized vehicles and a prohibition on the riding of illegally modified electric bicycles are essential.
# NBTI and Power Reduction Using an Input Vector Control and Supply Voltage Assignment Method
## 2 Preliminaries
### Cell-Based Leakage Power Model  
In recent years, as the supply/threshold voltage for CMOS circuits continues to scale down, leakage power is becoming a significant fraction of total power dissipation. In current CMOS technologies, there are three main sources for leakage current: the source/drain junction current, the gate direct tunneling current, and the sub-threshold current [3]. Among these leakage current sources, the sub-threshold current \(I_{sub}\) is substantially larger than the other leakage current components. \(I_{sub}\) is due to the diffusion current of the minority carriers in the channel for an metal oxide semiconductor (MOS) device operating in weak inversion mode, and can be calculated as follows [3]:  
\[I_{sub}=K_{1}\bigg{(}1-e^{-\frac{V_{th}}{V_{th}}}\bigg{)}e^{\frac{(V_{gs}-V_{ th}+\eta V_{th})}{\lambda V_{th}}} \tag{8}\]  
where \(K_{1}\) and \(\lambda\) are technology dependent parameters, and \(\eta\) is the drain-induced barrier lowering coefficient. Equation (8) shows that \(I_{sub}\) is dependent on the \(V_{th}\) of the transistor, and the \(V_{th}\) increase induced by the NBTI effect can decrease the leakage current for the gate. The leakage power change induced by NBTI over time is not considered in this paper, and we extract the leakage power for each gate with all possible input vectors at the starting time of the circuit, which is the maximum value. Then, these leakage power values are stored in a look-up table for a later ILP formulation process.  
Finally, the dynamic power of the circuit can be calculated using Equation (9):  
\[P_{\text{dyn}}=\frac{1}{2}f\sum_{i=1}^{N}\alpha_{i}C_{i}V_{dd}^{2} \tag{9}\]  
where \(f\) is the clock frequency, \(\alpha_{i}\) is the switching probability of gate \(i\), \(C_{i}\) is the capacitance load of gate \(i\), and \(N\) is the total number of gates in the circuit [10].
###### Abstract  
The remote sensing of chlorophyll \(a\) concentration from ocean color satellites has been an essential variable quantifying phytoplankton in the past decades, yet estimation of accessory pigments from ocean color remote sensing data has remained largely elusive. In this study, we validated the concentrations of multiple pigments (Cpigs) retrieved from in situ and MEdium Resolution Imaging Spectrometer (MERIS) measured remote sensing reflectance (\(R_{\text{rs}}(\lambda)\)) in the global oceans. A multi-pigment inversion model (MuPI) was used to semi-analytically retrieve Cpigs from \(R_{\text{rs}}(\lambda)\). With a set of globally optimized parameters, the accuracy of the retrievals obtained with MuPI is quite promising. Compared with High-Performance Liquid Chromatography (HPLC) measurements near Bermuda, the concentrations of chlorophyll \(a\), \(b\), \(c\) ([Chl-a], [Chl-b], [Chl-c]), photoprotective carotenoids ([PPC]), and photosynthetic carotenoids ([PSC]) can be retrieved from MERIS data with a mean unbiased absolute percentage difference of 38%, 78%, 65%, 36%, and 47%, respectively. The advantage of the MuPI approach is the simultaneous retrievals of [Chl-a] and the accessory pigments [Chl-b], [Chl-c], [PPC], [PSC] from MERIS \(R_{\text{rs}}(\lambda)\) based on a closure between the input and output \(R_{\text{rs}}(\lambda)\) spectra. These results can greatly expand scientific studies of ocean biology and biogeochemistry of the global oceans that are not possible when the only available information is [Chl-a].  
Keywords: phytoplankton pigments; ocean color; remote sensing; MERIS; global oceans +
Footnote †: journal: Accepted: 6 September 2018; Published: 19 December 2018
# Inter-Beam Co-Channel Downlink and Uplink Interference for 5G New Radio in mm-Wave Bands +
## 4 Assumptions and Scenarios of Simulation Studies
Single antenna beam patterns of the UE and gNodeB for direction \(\Phi_{0}=0^{\circ}\) and \(\Phi_{0}=30^{\circ}\) are illustrated in Figure 4[32]. In the gNodeB, we used a vertical patch as an antenna array with a size \(12\times 8\) of elements, whereas the UE antenna consists of a single element. The simulations were carried out for an urban macro (UMa) environment that is characterized by a normal delay profile with the rms delay spread equal to 266 ns [14]. To model the channel transmission properties, we adopted the TDLs with the 3 GPP standard [14], i.e., TDL-D and TDL-B for LOS and NLOS conditions, respectively. To estimate \(p_{S,I}(\theta,\phi)\), we used the averaging PASs obtained in 3600 Monte-Carlo simulations. In each Monte-Carlo run, the PAS was obtained based on the generation of 10 random propagation paths for each time-cluster (ellipsoid). Figure 5 presents averaged PAS examples of the UE-S and UE-I in the azimuth plane for the UL scenario, \(D_{S}=D_{I}=100\) m, \(\Delta\alpha=30^{\circ}\), LOS and NLOS conditions. The presented results show the diversity of \(p_{S}(\theta,\phi)\) and \(p_{I}(\theta,\phi)\) both due to the relationship between the beam lobes and surface areas under the graphs that correspond to the received powers, \(P_{S}\) and \(P_{I}\), respectively. This fact indicates the dependence of the received power on the main lobe orientation of the gNodeB beam pattern in relation to the UE-I. This directly influences the determined SIR value. We observe the same situation in the DL scenario. A detailed analysis of the simulation test results is described in the next section.
## 4 Methodology
### Training Algorithm
```
1: Initialize policy network \(\pi_{\theta}\) old policy network \(\pi_{\theta old}\) and value network \(V_{\phi}\)
2:for iteration = 1,2,... do
3: # Data collection
4:\(T^{total}\gets 0\)
5:for episode = 1,2,... do
6: # robots are running in parallel
7:for robot = 1,2,... N do
8: Run policy \(\pi_{\theta}\) for \(T_{i}\) time steps, collecting \(\left(O_{i}^{t},a_{i}^{t},r_{i}^{t}\right)\), where \(t\epsilon[0,T_{i}]\)
9: Calculate GAE \(A_{i}^{t}=\sum_{t=0}^{T_{i}}(\gamma\lambda)^{t}\bigg{(}r_{i}^{t}+\gamma V_{ \phi}\left(O_{i}^{t+1}\right)-V_{\phi}\left(O_{i}^{t}\right)\bigg{)}\)
10: Calculate returns \(R_{i}^{t}=A_{i}^{t}+V_{\phi}\left(O_{i}^{t}\right)\)
11:\(T^{total}\gets T^{total}+T_{i}\)
12: Break if \(T_{i}\geq T_{max}\) or a collision happens or each robot reaches a goal
13:endfor
14: Break if \(T^{total}\geq T^{th}N\), and send the data rollout to train the centralized networks
15:endfor
16: # Update policy and value
17: \(\theta old\leftarrow\theta\)
18:for j=1,2... E do
19: # Policy loss
20:\(\left(ratio\right)^{t}=\dfrac{\pi_{\theta}\left(a_{i}^{t}|O_{i}^{t}\right)} {\pi_{\theta old}\left(a_{i}^{t}|O_{i}^{t}\right)}\)
21:\(L^{clip}=\left(\sum_{t=0}^{T^{total}}\min\left(\left(ratio\right)^{t}A_{i}^{t}, clip\left(\left(ratio\right)^{t}A_{i}^{t},1-\epsilon,1+\epsilon\right) \right)\right)/T^{total}\)
22: # Value loss
23:\(L^{v}=\left(\sum_{t=0}^{T^{total}}\left(R_{i}^{t}-V_{\phi}\left(O_{i}^{t}\right) \right)^{2}\right)/T^{total}\)
24: # Entropy loss
25:\(L^{\epsilon}=-\left(\sum_{t=0}^{T^{total}}a_{i}^{t}\log\left(a_{i}^{t}\right) \right)/T^{total}\)
26: # Total Loss
27:\(L=-\left(L^{clip}-c_{1}L^{v}+c_{2}L^{\epsilon}\right)\)
28: Update \(\theta,\phi\) with \(l_{r}\) by the Adam optimizer w.r.t. \(L\)
29:endfor
30:endfor
```  
**Algorithm 1** PPO with a Multi-Robot System
# Optimal Resource Allocation Policies for Multi-User Backscatter Communication Systems
## 1 Introduction
Hence, this paper considers a practical multi-user BackCom system where each tag communicates with the reader in time-division-multiple-access (TDMA) mode. In this paper, both the passive and semi-passive tags are considered. Moreover, a dynamic reader transmission power allocation scheme is proposed to further improve the system performance. The key contributions of this paper are summarized as follows. We consider a multi-user BackCom system including a reader and \(N\) tags for both cases of passive and semi-passive tags. To account for the link reliability of each case, an optimization problem is formulated to maximize the total goodput by jointly optimizing reader transmission power, time allocation, and reflection ratio. For the case of passive tags, by exploiting the priority of allocated reader transmission power, the corresponding non-convex optimization problem is decomposed into \(N\) convex sub-problems. By solving the feasible sub-problems sequentially and comparing their maximum total goodput, we derive the optimal resource allocation policy. In this policy, a part of tags cannot be activated due to the low priorities. The \"activate or not\" decision is discussed. For the case of semi-passive tags, the corresponding problems are also decomposed into \(N\) sub-problems. Each sub-problem is reformulated as a biconvex optimization problem, for which a local optimum point can be found by the proposed block coordinate descent (BCD) [23] based optimization algorithm. Finally, the close-to-optimal resource allocation policy is derived. Note that throughout this paper, \"close-to-optimal\" corresponds to \"locally optimal\". The simulation results are given to confirm the superiority of the proposed resource allocation policies. Moreover, the equal power allocation policy is given as the benchmark. The rest of this paper is organized as follows. In Section 2, we introduce the system model. In Sections 3 and 4, the problem formulation of the passive tags case is formulated, and the optimal solution for this problem is derived, respectively. In Sections 5 and 6, for the case of semi-passive tags, the problem formulation and a close-to-optimal resource allocation policy are given, respectively. Simulation results are presented in Section 7. Finally, Section 8 concludes this paper.
# Extension of the Discrete Electron Transport Capabilities of the Geant4-DNA Toolkit to MeV Energies
## 2 Materials and Methods
### Low-Energy Corrections  
The PWBA is known to be a good approximation for electrons with incident energies much larger than the binding energies of target electrons. For liquid water medium, the application of the PWBA to the outer-shell target electrons (with binding energies between \(\sim\)10-30 eV) is well justified for electrons above \(\sim\)1 keV. Since the present model (DNA-Opt4X) is meant to be applicable down to 10 eV, \"low-energy\" corrections to the PWBA must be applied at sub-keV energies. The Mott-Coulomb low-energy corrections included in the default inelastic model (DNA-Opt2) [15] have been already re-assessed and improved in the context of DNA-Opt4 [43; 44]. These improved low-energy corrections are also passed into the present inelastic model (DNA-Opt4X). However, the magnitude of these Mott-Coulomb corrections for particular transitions may be different among the existing DNA-Opt4 and the present DNA-Opt4X. To illustrate this point, we summarize the final expressions. For ionizations, the Mott-Coulomb corrected DCS and TCS read as follows:  
\[\begin{array}{l}\frac{d\sigma_{\text{Mott-Co}}^{(i)}(E;T)}{dE}=\frac{d\sigma _{\text{PWBA}}^{(j)}\left(E;T+B_{j}+U_{j}\right)}{dE}+\frac{d\sigma_{\text{ PWBA}}^{(j)}\left(T+2B_{j}+U_{j}-E;T+B_{j}+U_{j}\right)}{dE}\\ -\sqrt{\frac{d\sigma_{\text{PWBA}}^{(j)}\left(E;T+B_{j}+U_{j}\right)}{dE}} \times\frac{d\sigma_{\text{PWBA}}^{(j)}\left(T+2B_{j}+U_{j}-E;T+B_{j}+U_{j} \right)}{dE},\end{array} \tag{5}\]  
and  
\[\sigma_{\text{Mott-Co}}^{(ioniz)}(T)=\sum_{j}^{ioniz}\int_{E_{min,j}}^{E_{max,j}}\frac{d\sigma_{\text{Mott-Co}}^{(j)}(E;T)}{dE}dE, \tag{6}\]  
with \(U_{j}\) being the electron kinetic energy in the \(j\)-th ionization shell. For excitations, only Coulomb corrections are applied:  
\[\sigma_{\text{Co}}^{(excit)}(T)=\sum_{k}^{excit}\sigma_{\text{PWBA}}^{(k)}(E ;T+2B_{k}). \tag{7}\]  
Evidently, the Mott-Coulomb corrected cross sections, Equations (5)-(7), depend explicitly upon the ELF and its parameterization, which is somewhat different between DNA-Opt4 and DNA-Opt4X. Following the criterion discussed above (see Figure 1), the Mott-Coulomb corrections, Equations (5)-(7), are only applied in the energy Regimes I and II.