esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Chemicals and Reagents  
All chemical reagents were purchased from Sigma-Aldrich (St. Louis, MO, USA) unless otherwise noted. Dulbecco's modified Eagle's medium (DMEM), fetal bovine serum (FBS), and penicillin-streptomycin were purchased from WelGENE (Gyeongsan, Republic of Korea), and horse serum was purchased from Invitrogen (Grand Island, NY, USA). Hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT), 4\({}^{\prime}\),6-diamidino-2-phenylindole (DAPI), propidium iodide (PI), and 5,5\({}^{\prime}\),6,6\({}^{\prime}\)-tetrachlor-1,1\({}^{\prime}\),3,3\({}^{\prime}\)-tetraethyl-benzimidazoylcarbocyanine iodide (JC-1) were purchased from Sigma-Aldrich. The Annexin V-FITC Apoptosis Detection Kit and 2\({}^{\prime}\),7\({}^{\prime}\)-dichlor fluorescein diacetate (DCF-DA) were purchased from R&D Systems Inc. (Minneapolis, MN, USA) and Molecular Probes, Inc. (Eugene, OR, USA), respectively. Antibodies against H2A histone family member X (H2 AX, #2595), phosphorylated (p-) H2 AX (\(\gamma\)-H2 AX, Ser139, #2577), and p-AMPK\(\alpha\) (Thr172, #2535), acetyl-CoA carboxylase (ACC, #3662), and p-ACC (Ser79, #3661) were purchased from Cell Signaling Technology, Inc. (Danvers, MA, USA). \(\beta\)-actin (sc-47778), Bcl-2 (B-cell lymphoma 2, sc-492), Bcl-2-associated X protein (Bax, sc-70405), pro-caspase-3 (sc-7272), poly(ADP-ribose) polymerase (PARP, sc-8007), cytochrome \(c\) (sc-13560), AMPK\(\alpha\)1/ (sc-74461), Nrf (sc-722), Kelch-like ECH-associated protein 1 (Keap1, sc-15246), HO-1 (sc-390991), myogenic differentiation 1 (MyoD, sc-304), and myogenin(sc-52903) were obtained from Santa Cruz Biotechnology, Inc. (Dallas, TX, USA), and cytochrome \(c\) oxidase subunit IV (COX IV, ab14744) and p-Nrf2 (Ser40, ab180844) were purchased from Abcam, Inc. (Cambridge, MA, USA).
## 3 Materials and Methodologies  
### Research Development  
In the study, the researchers applied the resampling model in DEA to establish a systematic forecast and evaluation approach. Figure 1 outlines how to access resampling in DEA through detailed steps to search for alliance partners. The steps involved in data collection and input-output selection constitute the initial work of the study. DEA is a linear programming method. It evaluates the efficiency of DMUs through a structure containing multiple inputs and outputs.  
The DEA-Solver software is used for the calculation in the third step. The resampling model is applied to the calculation in Step 4. The correlation coefficient test is applied to check if the values between the input and output are correlated with one another in Step 5. If a negative coefficient appears, the value is deleted, and the process returns to Step 2 to recalculate until the requirement is met. In Step 6, the research uses the Super-SBM model to assess past performance in 2015-2019. Afterward, forecasting using the \"Resampling\" model is applied to predict data for the years 2020-2024. The results of the prediction are used in the DEA model to measure the efficiency of each DMU during and after the alliance.  
The purpose of Step 7 is to calculate the past performance and compare the target company and the other 19 competitors. This is done by applying the resampling model to real data and by ranking the DMUs according to the efficiency acquired. Step 8 was conducted to establish a new alliance by combining the target DMU1 with the other 19 DMUs. After the merger, the new company is compared with the existing company. Suggestions are provided on the basis of the results of the analysis in this step, but they do not necessarily assume feasibility and proceed to further analysis in Step 9. In this step, the researcher carefully studies the candidate companies from different aspects to determine possible alliance methods.
# Intelligent Transportation System Technologies, Challenges and Security
## 3 The Impact of Innovative Technologies on Intelligence Transportation Systems
### Cloud Computing  
Cloud computing is a general term for information services that provide computing resources that are shared among users and are always available to computers and other devices on the internet. Cloud computing is predicted to transform the automotive industry, transportation, and services. The development of cloud computing technologies and the IoT will help solve traffic problems such as congestion and vehicle safety. In recent years, researchers have proposed cloud models such as ITS-cloud for ITS to improve vehicle-to-vehicle communication and road safety [34].
## 2 Optimization Problem Formulation
### Sensitivity Analysis  
There are several methods to obtain the sensitivity of the design variables, such as those pointed out by [6; 7; 18]. Thus, without changing its removal heuristic, according to [13], it can be expressed as follows:  
\[E_{i}(x)=E_{min}+x_{i}^{p}(E_{0}-E_{min}) \tag{3}\]  
with \(E_{min}\) being the modulus of elasticity for the \"empty\" material, the value \(E_{min}=10^{-9}\) used to avoid the singularity in the stiffness matrix, \(K(x)\), \(E_{0}\) is the modulus of elasticity for the \"solid\" material. Therefore, the stiffness matrix using Equation (3) can be written as  
\[K(x)=\sum_{i=1}^{n}\Big{[}E_{min}+x_{i}^{p}(E_{0}-E_{min})\Big{]}k_{i}^{0} \tag{4}\]Deriving the equilibrium equation \(K(x)u(x)=F\) and making some mathematical manipulations:  
\[\frac{\partial u(x)}{\partial x_{i}}=-K^{-1}(x)\frac{\partial K(x)}{\partial x_{ i}}u(x) \tag{5}\]  
The derivative of the objective function, Equation (1), in relation to \(x_{i}\), after applying Equation (5) and using the expression \(F^{T}=u^{T}(x)K(x)\), we obtain, which is given by  
\[\frac{\partial\mathcal{C}(x)}{\partial x_{i}}=-u^{T}(x)\frac{\partial K(x)}{ \partial x_{i}}u(x) \tag{6}\]  
Substituting the derivative of Equation (4) in relation to \(\mathrm{x_{i}}\) in Equation (6), the sensitivity of the objective function can be rewritten as follows:  
\[\frac{\partial\mathcal{C}(x)}{\partial x_{i}}=-u^{T}(x)\Big{[}p(x_{i})^{P-1}( E_{0}-E_{min})k_{i}^{0}\Big{]}u(x) \tag{7}\]  
The sensitivity of the cost function with respect to the design variables, Equation (7), is obtained using the finite difference method. The sensitivity expression is valid for the four methods described in this paper. However, with different physical interpretations since SIMP and SERA have the element density as a design variable, while ESO and SESO use the domain element instead.
# An Explainable Deep Semantic Coding for Binary-Classification-Oriented Communication
## 3 Deep Variational Semantic Coding
### Parameterized Chernoff Information
#### 3.2.1 Estimation of Renyi Divergence  
Let \(P_{1}(x)\), \(P_{2}(x)\) be two discrete probability distributions defined on the same finite or countable space. Assume that \(P_{1}\) is absolutely continuous with respect to \(P_{2}\), i.e., for every event \(x\), \(P_{1}(x)=0\) whenever \(P_{2}(x)=0\). For parameter \(a\in(0,1)\cup(1,\infty)\), the Renyi divergence of order \(\alpha\) is defined as  
\[R_{\alpha}(P_{1}\|P_{2})=\frac{1}{\alpha-1}\log\sum_{x}P_{1}^{\alpha}(x)P_{2} ^{1-\alpha}(x). \tag{20}\]  
According to Donsker-Varadhan Renyi variational formula [34], Renyi divergence can be reformulated as  
\[R_{\alpha}(P_{1}\|P_{2})=\sup_{T_{\psi}}\Bigg{\{}\frac{1}{\alpha-1}\log\sum_{ x}P_{1}(x)e^{(\alpha-1)T_{\psi}(x)}-\frac{1}{\alpha}\log\sum_{x}P_{2}(x)e^{ \alpha T_{\psi}(x)}\Bigg{\}}, \tag{21}\]  
where \(T_{\psi}\) is a discriminator function parameterized by \(\psi\), and is trained with respect to the objective  
\[T_{\psi^{*}}=\arg\min_{T_{\psi}}-\frac{1}{\alpha-1}\log\sum_{x}P_{1}(x)e^{( \alpha-1)T_{\psi}(x)}+\frac{1}{\alpha}\log\sum_{x}P_{2}(x)e^{\alpha T_{\psi}( x)}. \tag{22}\]  
Once the optimal discriminator network \(T_{\psi^{*}}\) is obtained, the Renyi divergence can be approximated as  
\[R_{\alpha}(P_{1}\|P_{2})=\frac{1}{\alpha-1}\log\sum_{x}P_{1}(x)e^{(\alpha-1) T_{\psi^{*}}(x)}-\frac{1}{\alpha}\log\sum_{x}P_{2}(x)e^{\alpha T_{\psi^{*}}(x)}. \tag{23}\]
## 4 Experiments and Result
### State Space, Action Space, and Reward Function  
In the environment of the described case, the state space of the reinforcement learning model is defined by a tuple representing a vector, as shown in (4):  
\[\mathcal{S}=\left([u_{1},u_{2},\ldots,u_{n}],[s_{a},s_{a-1},\ldots,s_{a-l}]\right) \tag{4}\]  
where \(u_{i}\) is the number of semiproducts of the i-th version waiting in the buffer \((\langle 0,q\rangle)\), \(s_{a}\) is the currently processed version of the product, \(s_{a-1\ldots l}\) is the processed versions preceding the current one, and \(l\) is the vector length of preceding versions.  
In the model, the action can be understood as the control of the order of product allocation to the production cell at the current moment, and the action space A is the number of the version selected for processing in particular, being a discrete parameter:  
\[\mathcal{A}=(p_{i}) \tag{5}\]  
where \(p_{i}\) is the \(i\)-th version of semiproduct.  
The reward function \(r(A)\) is set as shown in (6), which indicates the reward received by the agent after executing each joint scheduling action:  
\[r(A)=\left(\frac{min(t_{s_{i},(1\ldots n)}))}{t_{s_{i},s_{i-1}}}\right)^{2}. \tag{6}\]  
It takes values <0.1> and is the enhanced result of the ratio of the minimum execution time to the actual processing time (in response to the action taken by the agent). The reinforcement in relation to (3) is intended to give a greater reward to solutions close to the maximum and a greater penalty for choosing actions that cause large time differences.
# Detecting Fake Reviews in Google Maps--A Case Study
## 3 Creating a Dataset of Google Maps Reviews
### Database Validation  
We conducted a validation procedure to ensure the correctness of annotation in our dataset. We decided to follow the reviewer-centric analysis. First, we wanted to double-check if the accounts indicated algorithmically as fake were also fake in experts' opinions. Similarly, we wanted to check if the accounts presumed to be genuine, which were selected randomly, were genuine indeed in the eyes of invited experts. It is worth noting that the algorithm selecting genuine accounts did not evaluate their genuineness in any way, but simply relied on the presumption that most of the users commenting on Google Maps places were real. However, theoretically, a fake review might have been randomly selected.  
To validate our database, we invited opinions from seven experts with experience in cyber security and Internet media. Each was presented with a questionnaire with the basic data for 442 accounts in random order, and they were asked to decide whether they found them genuine (scored as '\(+1\)') or fake (scored as '\(-1\)'); \"don't know\" as an answer was also allowed (scored '0'). The experts were able to see basic data about the account (the account name, the local guide level, the number of reviews), data about the first five reviews of the account (the type of object assigned by Google Maps, rating, the review content), and a map showing the location of the reviewed places, captioned with the object type. If an account published fewer than five reviews, all the available reviews were displayed.  
After the assessment, the scores were summed up. Their distribution is shown in Figure 1 (please note the logarithmic scale of the \(Y\) axis). One can see that agreement between the raters was high, which resulted in low values between \(-2\) and \(+2\) scores and high extreme values (\(\pm 7\)). Additionally, the correlation between the raters' opinions and the initial labels was very high.  
The accounts which received scores in the range of \(<\)\(-3;3\)\(>\) were further analyzed by a group of three experts. This was also done with cases where an expert left a comment expressing doubts. This analysis involved using additional data, e.g., checking all the available reviews (instead of only the first five), taking the context into account, etc. The final consensus on the genuineness of an account was reached in discussion. As a result, two initial decisions were changed: both from 'fake' to 'genuine'.
# Borehole Condition and Limit Pressure Differential Analysis in Carbonate Reservoirs
## 3 Evaluation of Open-Hole Wellbore Condition in Carbonate Rock  
### Core Mechanical Parameters of Carbonate Reservoir  
Laboratory testing constitutes a direct means of acquiring core mechanical properties. The specimen cores, originating from a depth of 7350 to 7560 m in an oilfield located within the Tarim Basin, exhibit upon visual inspection a distribution of irregular fractures, predominantly well-sealed (Figure 2). During laboratory procedures, when machining standard core columns measuring 25 mm in diameter and 50 mm in length utilizing clear water as the circulating medium, the cores exhibit a propensity to fracture post-water invasion. This observation further attests to the prevalence and high brittleness of these fractures in the absence of confining pressure.  
Figure 1: The effective stress distribution around the borehole of open-hole test in horizontal well.  
Figure 2: Full-size core photos of deep carbonate rocks.  
The native core was machined into standard core specimens, approximately 25 mm in diameter and 50 mm in length, via wire-cutting, followed by conducting compressive strength tests to derive the core's strength and deformation parameters. Figure 3 illustrates the stress-strain curves obtained under varying confining pressure regimes. Under minimal confining pressures, the core exhibits brittle failure. Conversely, as confining pressure escalates, the core demonstrates increased plasticity, with a rise observed in residual strength post-failure.  
Based on the experimental results, the plastic yield strength, peak strength, and residual strength values of the core under differing confining pressure levels have been tabulated (Table 2). These three-strength metrics are depicted collectively in a graphical representation, with separate linear regression analyses applied, as illustrated in Figure 4.  
According to Mohr-Coulomb strength criterion and plastic yield strength data, the core cohesion and internal friction angle are \(C_{y}=10.8\) MPa and \(\phi_{y}=42.6^{\circ}\); calculated by the peak failure strength, the core cohesion and internal friction angle are \(C_{p}=10.8\) MPa  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Confining Pressure (MPa)** & **Plastic Yield Strength (MPa)** & **Peak Strength (MPa)** & **Residual Strength (MPa)** \\ \hline
0 & 49.2 & 52.2 & 24.9 \\
20 & 147.0 & 172.8 & 144.0 \\
30 & 215.1 & 251.8 & 224.7 \\
50 & 305.4 & 357.0 & 331.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Compressive strength test data of deep carbonate rock.  
Figure 4: Linear fitting results of deep carbonate rock strength parameters.  
Figure 3: Test stress–strain curves under different confining pressures.  
and \(\phi_{p}=46.1^{\circ}\); based on the residual strength, the core cohesion and internal friction angle are \(C_{r}=5.2\) MPa and \(\phi_{r}=46.3^{\circ}\), respectively.
# An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments
## 4 Case Study
### Infrastructure Deployment
The medical staff has a set of mobile devices that receive all the data packages from the athletes' smartwatches and the environmental sensors. The raw data are locally stored in an SQLite database. Figure 4: Application scheme. Finally, when the match finishes, this local SQLite database can be sent to the cloud server to build historical user database for further processing and the development of more exhaustive cardiac checks for them. Other scenarios could be designed according to the infrastructure availability and the application's objectives. For example, the coach staff of each team is able to monitor the data and obtain performance statistics for sport-related reasons. That is, monitoring whether an athlete is doing as well as they should be.
# Dolphin Sounds-Inspired Covert Underwater Acoustic Communication and Micro-Modem
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Underwater** & **Modulation** & **Areas of Application** & **Characteristics** \\ \hline Teledyne Bentnos & & Remote Monitoring of & Directional or Omni-directional \\ Atm 9 xx [29] & PSK\(\backslash\)MFSK & Oceanographic Sensors & High Data Rate \\  & & Command of AUVs & \\ \hline LinkQuest & & Short to Long Distance Communication & Low Power Consumption \\ UWM series [30] & - & Near-vertical or horizontal environments & Directional or Omni-directional \\  & & Short-range & High Data Rate \\ \hline EvoLogics & & Shallow Waters & Ultra-high-speed \\ S2C M HS [31] & - & Communication and Positioning for AUVs & Smaller and Lighter \\  & & Diver tracking systems & \\ \hline EvoLogics & & Long-range Applications & Low Power Consumption \\ S2C R 7/17W [31] & - & Underwater Observatories & Hemispherical Beam Pattern \\  & & & Long-term Deployment \\ \hline WHOI micro-modem [5] & FH-FSK\(\backslash\)PSK & \
\begin{tabular}{c} UAC \\ Navigation Subsystem \\ \end{tabular} &
\begin{tabular}{c} Variable Rate PSK \\ Low Rate FH-FSK \\ Long Base Line Navigation \\ Narrow-band and Broadband \\ \end{tabular} \\ \hline
[23] & ASK & UWSN &
\begin{tabular}{c} Low Power Consumption \\ Ultrasonic Wave \\ Energy-aware \\ Low Cost \\ \end{tabular} \\ \hline
[24] & ASK & UWSN &
\begin{tabular}{c} Omni-directional Beam Patter \\ Low Power Consumption \\ Low Cost \\ Small Size \\ \end{tabular} \\ \hline
[25] & FSK & \
\begin{tabular}{c} Long-life \\ Short/Medium Range \\ UWSN \\ \end{tabular} &
\begin{tabular}{c} Ultra-low Power Consumption \\ Energy-efficient \\ Low Cost \\ \end{tabular} \\ \hline
[26] & FSK & UWSN & \begin{tabular}{c} High Efficient \\ Low Cost \\ Small Size \\ \end{tabular} \\ \hline Our PBC modem & \begin{tabular}{c} Unconventional \\ mode \\ \end{tabular} & \
\begin{tabular}{c} Short-range \\ Covert Application Between \\ Divers or UUVs \\ \end{tabular} &
\begin{tabular}{c} Covert Acoustic Communication \\ by using Real or Mimetic- \\ Cetacean Sounds \\ Small size \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of developed devices, commercial devices and the Portable Biomimetics-based Covert (PBC) modem. The dolphin and whale call samples used in the communication algorithm are stored in the SD card in advance which makes the selection of communication signals more flexible. For example, in the specific communication process, sound signals will be selected according to the actual situation in order to realize covert communication. Local dolphin calls should be selected for communication in different sea areas, and seasonal dolphin calls should be used for communication in different seasons. These unique features make our PBC modem significantly different from existing developed and commercial modems.
# A Perspective on Emerging Inter-Disciplinary Solutions for the Sustainable Management of Food Waste
## 4 Food Waste Management, Regulations, and Biological Treatment Technologies
### Initiatives Founded on the Discipline of Nanotechnology  
Several researchers have amalgamated nanotechnology with other disciplines to improve anaerobic digestion. Conductive nanomaterials such as magnetite have been shown to improve methanogenesis by facilitating the interspecies electron transfer between acetogenic bacteria and methanogenic archaea, resulting in enhanced biogas outputs [51; 126]. [PERSON] et al. [51] investigated the impact of magnetite nanoparticles (MNPs) on methanogenesis in anaerobic cultures generated from the soil. They observed increased methanogenesis when MNPs were applied in optimized agitated and static cultures.  
Meanwhile, [PERSON] et al. [128] demonstrated the use of nanoparticles for better sludge digestion. By spiking waste sludge with aged, designed nanoparticles and combining silver oxide, titanium dioxide, and zinc oxide, they increased sludge digesting efficiency.  
There are several additional revelations about attempts to enhance anaerobic digestion systems utilizing nanoparticle oxides of other metals [129] including transition metals, TiO\({}_{2}\), Fe\({}_{2}\)O\({}_{3}\)-TiO\({}_{2}\), and NiO-TiO\({}_{2}\)[98], and various nanoparticles, such as cobalt (Co) and nickel (Ni) nanoparticles [130]. As in many previous concerted attempts, the initial studies did not seek to establish an overview of concurrent optimization of system conditions for dealing with waste management.  
While the use of nanoparticles in anaerobic digestion has for some time been indicated, the process cannot be efficient without optimizing the operation of the biogas plant. Optimization of bioreactors can encompass a wide variety of multidisciplinary features (for example, bioengineering, nanotechnology, and so on), algorithms (artificial intelligence), and biological processes (e.g., omics). From the revelations made throughout this study, it is abundantly clear that more multidisciplinary collaborative efforts are required to improve our knowledge of anaerobic digestion processes and process performance.
# Numerical Simulation of Vacuum Leak Jet and Jet Noise
## 2 Numerical Simulation Setup
As such, the ideal gas state equation is used in this paper to model the density characteristic of compressible gas, satisfying:  
\[\rho=\frac{\mathrm{M}}{\Re}\frac{p}{T} \tag{1}\]  
where \(\rho\) is the density, \(p\) is the absolute pressure, \(T\) is the static temperature, \(\mathrm{M}\) is the average molecular weight of air, and \(\Re\) is the universal gas constant (taken as \(8.31\) J \(\cdot\) mol\({}^{-1}\) \(\cdot\) K\({}^{-1}\)). As the temperature changes are obvious in the vacuum leak, the fluid viscosity change should be considered. In numerical calculation, the Sutherland equation is introduced to model the viscosity:  
\[\mu=\mu_{0}\bigg{(}\frac{T}{T_{0}}\bigg{)}^{\frac{3}{2}}\frac{T_{0}+\mathrm{S }}{T+\mathrm{S}} \tag{2}\]  
where \(\mu\) is the viscosity coefficient, \(\mu_{0}\) is the reference viscosity (taken as \(1.716\times 10^{-5}\) kg \(\cdot\) m\({}^{-1}\) \(\cdot\) s\({}^{-1}\)), \(T\) is the static temperature, \(T_{0}\) is the reference temperature (taken as 273.11 K), and \(\mathrm{S}\) is the Sutherland constant (taken as 110.56 K). Figure 2: Comparison of axial aerodynamic properties under different back pressure. (**a**) velocity; (**b**) pressure; (**c**) density; (**d**) temperature. In terms of numerical calculation, the turbulent characteristics of the jet must be taken into consideration to fully simulate the acoustic characteristics. There are usually three method to calculate a turbulence: Direct Numerical Simulation (DNS), Large Eddy Simulation (LES), and Reynolds Averaged Navier-Stokes (RANS). DNS can obtain the vortex structure on almost every scale by directly solving the N-S equations. But it requires grid number reach Re\({}^{9/4}\), which greatly increases the amount of computation. LES uses the N-S equations to directly solve large-scale eddies and imposes the effect of fine-scale vortices on instantaneous motion through various theoretical models. RANS has a weaker resolution of fluctuation terms than DNS and LES, but it has been widely used in the engineering community because of its reliable calculation accuracy, low computation resource requirement, and widely applicable Reynolds number. Therefore, after comprehensive consideration of computation resource and computation precision, a RANS method is used in this paper. Many scholars have proposed a large number of turbulence models to enclose the equations, among which k-\(\varepsilon\) model has been widely used. [PERSON] [24] applied a k-\(\varepsilon\) model to the computation of supersonic jet mixing noise and successfully predicted the noise source strength. [PERSON] [25] used a modified two-equation standard k-\(\varepsilon\) model to study a circular jet and not only the predicted shock cell structure and radial density profiles, but also the amplitudes of the flapping and helical modes coincide with the experiments very well. Compared with the standard k-\(\varepsilon\) model [26], the Realizable k-\(\varepsilon\) model [27] has higher computation accuracy on jet simulation.