esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips
## 4 User Experience Model
### Experience in Different Route Segments  
The satisfaction of the decision-maker in a trip segment varies according to segment accessibility. Thus, to measure this satisfaction, the general time for a given segment is:  
\[T(s)=S(s)t_{s}+\epsilon, \tag{10}\]  
where \(S(s)\) measures user satisfaction in a given transportation mode. The variable \(\epsilon\) describes the error due to non-observable components, such as weather conditions. Here, we consider that this error is normally distributed, which causes a reduced impact on the final values according to the Random Utility Theory [18].  
To define \(S\), we use In-Vehicle Time (IVT) multipliers that estimate impressions of a user in a given transport mode as compared to in-vehicle impressions. For example, a given user may consider 5 min of walking to be as unpleasant as 10 min in a vehicle. In  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(n_{r}\) & **0** & **1** & **2** & **3** & **4** & **5** \\ \hline \(\lambda_{r}\) [\%] & 0\% & 2.72\% & 7.39\% & 20.09\% & 54.60\% & 148.41\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Transfer mode impact for \(a=1\).  
this case, the IVT multiplier would be \(10/5=2\). There are different ways of estimating IVT values, which usually consider surveying passengers. We consider the characteristics of the segment \(s\) and impressions of the level of service of the decision-maker \(w\) to measure the satisfaction \(S\).  
\[S(s)=w(s)P(s). \tag{11}\]  
\(P(s)\in[0,1]\) is a depreciation factor to describe the acceptability of traveling a segment \(s\). Functions \(P\) and \(w\) depend on the scenario, and their selection is discussed in Section 4.4.
# Evaluation of Proximal Contact Tightness and Contact Area of Posterior Composite Resin Restorations
###### Abstract  
The purpose of this study is to evaluate the influence of the matrix system on proximal contact tightness (PCT) of posterior composite resin restorations. Standardized class II cavities on 180 first lower molar dendrimer model teeth (Frasaco GmbH, Tettrang, Germany) were prepared. Three groups were formed considering the matrix system: Group-IM: Ivory matrix (Hahnenkraft GmbH, Konigsbach-Stein, Germany), Group-OM: Omni matrix (Ultradent, South Jordan, UT, USA), and Group-PM: Palodent V3 sectional matrix (Dentsply, Charlotte, NC, USA). Teeth were restored with resin composite mounted in a manikin head to simulate the clinical environment. Proximal contact tightness (PCT) was measured using a custom-made portable dental pressure meter (PDPM), and the validation of the PCT results was performed with a histogram analysis acquired from bite-wing radiography. All data were statistically analyzed by ANOVA and _t_-test in SPSS software (v.27.0) (_p_ < 0.05). PM group showed statistically tighter contacts on both mesial (PCTm: 228.28 \(\pm\) 59.17 N) and distal surfaces (PCTd: 254.91 \(\pm\) 65.69 N) (_p_ > 0.05). Mesial contacts were found to be significantly tighter than distal contacts among all (_p_ < 0.05). According to the histogram results, only in the PM group, the difference between the mesial and distal areas is significant (_p_ < 0.05). Histogram results confirmed that the tightest contact values were achieved in the PM group, followed by the OM and IM groups, respectively (_p_ < 0.05). The use of sectional matrix systems and separation rings is more effective in creating tighter contact than conventional matrix systems. The use of anatomical wedges will help to create a more natural approximal contour and narrow contact area.  
sectional matrix; resin composite; friction testing; contact analysis; X-ray image analysis +
Footnote †: journal: Applied Sciences
## 3 Results
### Evaluation of Antiproliferative Activity  
Antiproliferative effect of _Cistus ladanifer_ extracts was evaluated on 22 Rv1, HepG2, and _MDA_-MB-231 cell lines. The viability and cytotoxicity index percentages were plotted against various extract concentrations using the MTT technique, which demonstrated time- and dose-dependent effects (Figures 4 and 5). On the three tested cell lines, hexanic, dichloromethane, ethyl acetate, and ethanolic extracts exerted an antiproliferative effect after 72 h of treatment. IC\({}_{50}\) values for the four extracts are shown in Table 3. In HepG2 cells, the antiproliferative effect of the four extracts exhibited moderate cytotoxicity, and the dichloromethane extract seems to be more active on this line scoring an IC\({}_{50}\) of 31.54 \(\pm\) 0.242 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. Ethyl acetate, hexanic, and ethanolic extracts reduced viability of this line with IC\({}_{50}\) values of 97.74 \(\pm\) 0.148; 109.6 \(\pm\) 0.166 and 180.5 \(\pm\) 0.64 \(\mathrm{\SIUnitSymbolMicro g}\)/mL respectively. In both 22 Rv1 and _MDA_-MB-231 cell lines, the hexanic extract exhibited higher antiproliferative activity compared to the other extracts scoring values of IC\({}_{50}\) of 11.32 \(\pm\) 2.126 \(\mathrm{\SIUnitSymbolMicro g}\)/mL and 82.4 \(\pm\) 1.124 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, respectively. In 22 Rv1 cell line and in a dose-dependent manner, dichloromethane, ethyl acetate, and ethanolic extracts inhibit 22 Rv1 cell growth with IC\({}_{50}\) ranging from 45.96 \(\pm\) 0.125 to 61.47 \(\pm\) 0.551 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. In the human  
Figure 3: Reducing power of Ethyl Acetate (E.A), Ethanolic (ETOH), Dichloromethane (Dichloro), and Hexanic extracts of _Cistus ladanifer_ (mean \(\pm\) SD, n = 3 experiments, ns: not significant, \(p\) values; **: \(p\) < 0.01, *** \(p\) < 0.001).  
breast cell line MDA-MB-231, ethanolic, dichloromethane, and ethyl acetate presented an important antiproliferative activity in a dose-dependent manner growth with IC\({}_{50}\) ranging from 144.255 \(\pm\) 12.43 to 290.33 \(\pm\) 68.63 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL. Interestingly, 22 Rv1 line was considerably affected by the four extracts, compared to the two cell lines HepG2 line and _MDA_-MB-231. However, there is no significant difference observed between the extracts. (\(p>0.05\)).  
Figure 4: Antiproliferative activity of _Cistus ladanifer_ extracts using the MTT assay for 22 Rv1, HepG2 and MDA-MB-231 cell lines.
# Epigenetic Signatures of Dental Stem Cells: Insights into DNA Methylation and Noncoding RNAs
## 3 Results  
### High-Quality Methylation Probe Selection  
A comprehensive quality control pipeline was applied to retain only high-confidence CpG probes from the starting 485,577 probes, based on detection \(p\)-values (485,482), exclusion of sex chromosome probes (473,835), removal of probes affected by SNPs or cross-reactivity (412,997), filtering of probes with missing data (412,997), and extreme methylation values (246,824). The analysis of these values through principal component analysis showed that two components can capture 100% of the variance in methylation profiles across the three samples. The separation observed in the PCA space (Figure S1) suggests that all three samples exhibit distinct methylation profiles.  
A balanced subset of probes that captures most of the biologically relevant variability, while reducing dimensionality and avoiding saturation by uninformative probes, was obtained using the elbow method. A total of 17,175 probes, \(\sim\)4% of the full 450K array, were found to be the most informative and variable probes across the three samples (Figure S2). Probes beyond this point show, in fact, diminishing variance, likely contributing noise or background methylation.
# RobustQuote: Using Reference Images for Adversarial Robustness
## 2 Related Works
### Adversarial Training  
**Ad**versarial training **(AT)**[5] injects adversarial examples into the training dataset \(\mathcal{D}\) to improve the robustness of the model against adversarial attacks. Given a neural network \(F_{\theta}(\cdot)\) parameterized by \(\theta\) and the input natural image \(X\), \(x\in\mathbb{R}^{N\times D}\) are the corresponding internal features computed within \(F_{\theta}(X)\), where \(N\) is the number of tokens and \(D\) is the dimension of token features. We also define \(y\) as the ground-truth label and \(\hat{y}=\arg\max F_{\theta}(X)\) as the predicted category with respect to the input image. Then, AT can be formulated as a min-max robust optimization problem as follows:  
\[\min_{\theta}\mathbb{E}_{(X,y)\sim\mathcal{D}}[\max_{\delta}J(F_{\theta}(X+ \delta),y)], \tag{1}\]  
where \(J(\cdot,\cdot)\) is the loss function, and \(\delta\) is the adversarial perturbation that is bounded within the \(l_{p}\) norm constraint of \(\left\|\delta\right\|_{p}\leq\epsilon\).  
Inner maximization usually uses the projected gradient descent (**PGD**) attack method [5] to find the adversarial perturbation \(\delta\), and the outer minimization updates parameter \(\theta\) to minimize \(J(\cdot,\cdot)\) with respect to the adversarial example \(X+\delta\) via a default optimizer such as stochastic gradient descent (**SGD**) with momentum. Recently, adversarial regularization techniques have been proposed to further improve robustness, such as adversarial logit pairing (**ALP**) [23] and TRADES [24]. Simply speaking, these methods regularize the feature distance between natural and adversarial samples.
# An Improvement of a Mapping Method Based on Ant Colony Algorithm Applied to Smart Cities
###### Abstract  
The ant colony algorithm has been widely used in the field of data analysis of smart cities. However, the research of the traditional ant colony algorithm is more focused on one-to-one scenarios and there is insufficient research on many-to-one scenarios. Therefore, for the many-to-one topology mapping problem, this paper proposes a mapping method based on the ant colony algorithm. The design purpose of the mapping algorithm is to study the optimal mapping scheme, which can effectively reduce the cost of solving the problem. The core of the mapping algorithm is to design the objective function of the algorithm optimization. The commonly used optimization objective function and evaluation index is the average hop count; the average hop count is the most important indicator to measure the entire system. The smaller the average hop count, the less the pulse data needs to be forwarded, which can reduce the communication pressure of the system, reduce congestion, reduce the energy consumption caused by communication, and reduce the delay from the generation of pulse data to the response, etc. Therefore, this paper chooses the average hop count as the optimization objective and reduces the average hop count by designing a mapping algorithm. Through the simulation and verification of the improved ant colony algorithm in the scenario of many-to-one topology mapping, it is concluded that the final convergence result and convergence speed of the improved ant colony algorithm are significantly better than those of the traditional ant colony algorithm.  
ant colony algorithm; many-to-one topology; average hop count
## 3 Test Results and Analyses
### Embedment Depth Sensitivity Analysis  
The load-displacement curves for different depth-to-diameter ratios are shown in Figure 7. (1) In the early stage of loading, the load is relatively small, and the soil around the anchor helix begins to compress. The displacement of the anchor helix gradually increases with the applied tension, showing a linear relationship between load and displacement, indicating elastic deformation. (2) As the tension force on the anchor helix further increases, its displacement also increases, and the rupture of the surface of the soil in front of the plate begins to develop. The relationship between load and displacement becomes approximately nonlinear. (3) When the load on the anchor helix reaches the ultimate uplift bearing capacity, the displacement continues to increase, but the load remains constant. At this point, the slope of the curve stabilizes, indicating that the anchor helix is in an unstable state.  
The four graphs in Figure 7 display a consistent pattern, showing that the uplift bearing capacity of the helical anchors gradually increases with the depth-to-diameter ratio. This occurs because deeper embedment depth increases the contact area between the anchor and the soil, thereby enhancing lateral resistance and friction, which in turn increases the uplift bearing capacity. However, for the four different diameter ratios in Figure 7, the uplift bearing capacity of the helical anchors decreases from 0.1 kN to 0.025 kN as the diameter ratio increases. Therefore, it can be concluded that the diameter ratio significantly impacts the uplift bearing capacity of the helical anchors. This is because, as the diameter of the anchor increases while the diameter of the anchor helix remains unchanged, the  
Figure 7: Load–displacement curve of different depth-to-diameter ratios. (**a**) \(D_{S}\)/\(D_{H}\) = 0.22 (**b**) \(D_{S}\)/\(D_{H}\) = 0.26 (c) \(D_{S}\)/\(D_{H}\) = 0.33 (**d**) \(D_{S}\)/\(D_{H}\) = 0.43.  
support area of the anchor helix decreases. This reduction in support area diminishes the end bearing force of the anchor helix, thereby reducing the uplift bearing capacity of the helical anchor.
# Characterization of HVSR and VRSR in the Loess Plateau of China Based on Strong-Motion Data
## 4 Results and Discussion
### Characterization of the Fourier Amplitude Spectrum
#### 4.1.2 Characterization of the Fourier Amplitude Spectra at Different Epicentral Distances  
The epicentral distances (\(R\)) were grouped into four ranges: 0-20 km, 20-50 km, 50-200 km, and \(\geq\)200 km. For each group, the Fourier amplitude spectra of the EW, NS, and UD components were calculated, and the mean values of the Fourier amplitude spectra for all components were obtained. The mean Fourier amplitude spectral curves are shown in Figure 8. The results of \(F_{max}\) and \(T_{f}\) across different ranges of epicentral distance are listed in Table 2.  
As illustrated in Figure 8 and Table 2, the epicentral distance has a pronounced impact on the Fourier amplitude spectra of the three components. Notably, a clear anomaly appears when the distance exceeds 200 km, likely due to the dominance of records from the \(M_{\rm S}8.0(M_{\rm wg}7.9)\) Wenchuan earthquake in this range. The far-field effects of such a large event tend to produce atypical patterns. Additionally, for any given epicentral distance range, the horizontal components (EW and NS) not only exhibit greater \(F_{max}\) values but also have larger corresponding \(T_{f}\) compared to the vertical (UD) component.  
Figure 7: Mean Fourier amplitude spectra for various magnitude groupings in the Loess Plateau region.
# Simulation of Aerosol Evolution within Background Pollution for Nucleated Vehicle Exhaust via TEMOM
## 3 Simulation Configuration
### Initial Conditions Calculation  
The initial boundary condition numerical is calculated from research on the atmosphere of Beijing [31]. Table 1 is the atmospheric background aerosol data under pollution and non-pollution conditions in Beijing over three years. The first three initial moments were calculated by Equation (21):  
\[M_{m}=NV_{g}^{m}\exp\left(m^{2}\frac{w_{g}^{2}}{2}\right) \tag{21}\]  
where \(w_{g}=3\ln\delta_{g}\). The calculation results are Table 2. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Mode** & \multicolumn{3}{c}{**Nucleation**} & \multicolumn{3}{c}{**Aitken**} & \multicolumn{3}{c}{**Accumulation**} \\ \hline  & **Ni** & **GMD** & \(\delta\) & **Ni** & **GMD** & \(\delta\) & **Ni** & **GMD** & \(\delta\) \\ \hline
2004 nonpolluted & 16 & 15.5 & 1.80 & 27 & 60.4 & 1.87 & 3 & 200 & 1.70 \\
2004 polluted & 14 & 17.0 & 1.90 & 19 & 80.9 & 1.93 & 7 & 245 & 1.58 \\
2005 nonpolluted & 7 & 14.4 & 2.00 & 20 & 58.9 & 2.00 & 5 & 174 & 1.71 \\
2005 polluted & 6 & 18.0 & 2.00 & 17 & 75.8 & 1.99 & 8 & 251 & 1.59 \\
2006 nonpolluted & 9 & 14.3 & 1.92 & 15 & 61.8 & 2.00 & 2 & 225 & 1.64 \\
2006 polluted & 5 & 18.7 & 1.89 & 15 & 84.6 & 1.96 & 6 & 246 & 1.54 \\ \hline \hline \end{tabular} Where Ni is mode particle number concentration (\(\times 10^{3}\) cm\({}^{-3}\)); GMD is geometric mean diameter of the mode (nm); and \(\delta\) is standard deviation of the mode.  
\end{table} 
Table 1: Average lognormal fit parameters of the particle number size distributions and meteorological factors on polluted and nonpolluted days in the summers of 2004, 2005, and 2006. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Condition** & **Nucleation** & **Aitken** & **Accumulation** & **Sum** & \begin{tabular}{c} **Non-** \\ **Dimensional** \\ \end{tabular}
# Devising Mobile Sensing and Actuation Infrastructure with Drones
## 4 Implementing and Evaluating DaaG
### Implementation
#### 4.2.2 DaaG Software  
There is an Apache server running on DaaG, to handle requests from the nodes. Alongside the Apache server, MySQL DB and PHP scripts are used to enable easier manipulation of the server. Nodes in our implementation make HTTP requests to send and receive data. DaaG mimics the HTTP requests with a DTN-like communication style is needed. Normal communications are made using typical TCP communication.  
In the implementation scenario, DaaG has to collect the light intensity value from the sensors, determine which area needs to be monitored and calculate the angle of the actuators to watch a certain area. Algorithm 4 shows the procedure of DaaG operation designed for the implemented scenario. In the algorithm, \(S\) is the set of sensors, \(A\) is the set of actuators, \(H\) is the desired heading for the actuators, \(getIntensityDiff(s_{i})\) is the function returning the intensity difference of node \(s_{i}\) and \(calculateActuatorDirection(a_{j},s_{i})\) is the function that returns the heading direction of the given actuator \(a_{j}\) to the sensor \(s_{i}\). The process of the algorithm is as follows. The drone visits all sensor nodes. While visiting all nodes, the drone receives light condition data and generates light condition change data. After the data collection, the drone calculates the command, which makes the actuator change direction. After the commands are generated, the drone visits the actuators and rotates their cameras toward the sensor with the highest light condition change data.  
```
1:enableBeaconing()\(\triangleright\) Run Algorithm 1 in background
2:whileisDaaGOperating() do
3:\(\Delta\ lux_{max}=0\)\(\triangleright\) Initialize variables
4:\(s_{max}=0\)\(\triangleright\) Run Algorithm 2 to collect data
5:\(collectDataFromSensors()\)\(\triangleright\) Run Algorithm 2 to collect data
6:for\(s_{i}\in S\)do
7:\(\Delta\ lux_{i}=getIntensityDiff(s_{i})\)
8:if\(\Delta\ lux_{max}<\Delta\ lux_{i}\)then
9:\(\Delta\ lux_{max}=\Delta\ lux_{i}\)
10:\(s_{max}=s_{i}\)
11:endif
12:endfor
13:for\(a_{j}\in A\)do
14:\(h_{j}=calculateActuatorDirection(a_{j},s_{max})\)
15:endfor
16:\(commandToActuators(H)\)\(\triangleright\) Run Algorithm 3 to control actuators
17:endwhile
```  
**Algorithm 4** DaaG IoT control decision algorithm.
## 4 Applications
### Financial Services  
* Asset management: trade processing and settlement In traditional trade process, each party keeps their own records, which is expensive and risky. After switching to blockchain, the process can be simplified by distributed ledgers and errors can be reduced by encrypting the records. Furthermore, there is no need for intermediaries. Maersk, a Danish shipping company, was reported to aid the process of shipping flowers from the Kenyan port of Mombasa to Rotterdam by using a blockchain-based approach developed with IBM, where all documents were stored in the chain and shared by all participants [57].
* Insurance: claims processing Blockchains can provide a risk-free and transparent process for insurance claims, which are usually facing fragmented and unreliable data, and processed manually. State Farm, a mutual automobile insurance company in the US, announced that it is running a blockchain based test to streamline the subrogation process for auto claims through the first half of 2019. It is hoped that the blockchain solution can help to reduce the time and resource required to complete the subrogation process compared with a regular process [58]. The work in [59] presents a prototype of blockchain based fine-grained transportation insurance. In the prototype, information of drivers and vehicle usage are collected from mobile sensors and stored on an immutable, traceable, transparent  
Figure 5: The statistics of blockchain service providers registered early in 2019 in China.  
distributed ledger owned by different parties. Insurance premium are assessed based on the collected information of vehicles' usage and drivers' behavior, which promote fairness, encourage safer driving style and can be used by insurance companies for marketing.
* Payments: cross-border payments The cross-border payment takes at least days for money to cross the world, and is very costly and easily targeted by money laundering. Blockchain-aided remittance services can help to offer secure, cheap, instant and transparent international payments. In August 2016, SBI Ripple Asia announced that Ripple's blockchain technology will power payments and settlement for a Japanese bank consortium of 15 banks [60]. With the blockchain technology provided by Ripple, domestic and cross-border payments can be settled in real time and cross-border fees can be save up to 90%.
## 2 Theoretical Background  
Tomography is a non-destructive process used to obtain cross-sectional or three-dimensional images of the interior of a given inspected material. This technique is based on radiating the body from different angles, followed by a mathematical procedure called tomographic reconstruction, which is responsible for carrying out the internal reconstruction using the received signals and visualizing the representation of a cross-section or slice of the scanned body.  
This work used ultrasonic signals to obtain tomographic images. Ultrasonic signals are not ionizing waves and do not pose a health risk to people; this makes it, despite its disadvantages, one of the most widely used methods of excitation for inspection in general non-destructive testing. Thus, the use of ultrasonic imaging to assess the quality and degradation of concrete structures by identifying heterogeneities, defects, or cracks is a very attractive solution for the diagnosis of civil infrastructures [22,23]. However, a problem arises when using ultrasonic signals as the excitation method because traditional reconstruction algorithms are designed for excitation using X-ray sources, using the attenuation coefficient. These X-rays do not diffract, allowing them to travel in a straight line, which does not occur when the excitation source uses ultrasonic waves or microwaves; when the wave collides with a heterogeneity, it is scattered in practically all directions and only under certain conditions can we assume that the propagation line of the ultrasonic wave is practically straight. This demonstrates that, in certain cases, classical algorithms are not suitable for ultrasonic reconstruction and that neural networks could compensate for these shortcomings.