esa-sceva/satcom-chunk-collection huggingface dataset:
# Anti-Breast Cancer Activity of Essential Oil:
## 3 Result
### Bioactivity of Essential Oils as Anti-Breast Cancer Agents
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**No** & **Plant Name** & **Compounds** & **Methods** & **Activities** & **Ref.** \\ \hline
13 & _Nigela sativa_ & Thymoquinone (p-benzoquinones) & In vitro using MCF-7 cells & _Nigela sativa_ essential oil significantly reduced breast cancer cell survival (MCF-7). The nucleo-cytoplasmic morphological features of NSEO-NE-treated cells were cell membrane blistering, cytoplasmic vaccination, chromatin marginalization, and nuclear fragmentation. The results demonstrated that NSEO-NE induces apoptosis in MCF-7 cells. & The loss of viability was due to the induction of apoptosis as demonstrated by the Annexin V-propidium iodide and caspase-3/7 activity assays/test. & [102] \\ \hline
15 & _Hypericum perforatum_ & Sesquiterpenes & MCF-7 cells & The loss of viability was due to the induction of apoptosis as demonstrated by the Annexin V-propidium iodide and caspase-3/7 activity assays/test. & [103] \\ \hline
16 & _Pinus corainensis_ & Monoterpens & MDA-MB-231 cells (TNBC) & The essential oil of Hypericum perforatum also showed anticancer activity against MCF-7 cells. The IC\({}_{50}\) values of essential oil, MTX, and MTX essential oil were 0.78, 6.25, and 0.195 \(\upmu\)g/mL, respectively. However, Hypericum perforatum essential oil was found to be non-cytotoxic for MDBK cells. & [104] \\ \hline
17 & _Chenopodium ambrosioides_ L. & Monoterpenses & MCF-7 cells & Inhibits the growth of MCF-7 cells within 24 h (\(p<0.05\)), which is consistent with the results of fluorescent staining of live/dead cells. & [106] \\ \hline
18 & _Boswelia sacra_ & Triterpenes & MDA-MB-231 cells (TNBC) & Induces cancer cell death, prevent the formation of cellular tissue (MDA-MB-231) cells in Matrigel, and cause multicellular tumor spheroid damage (T47D cells), and regulates molecules involved in apoptosis, signal transduction, and cell cycle development. & [107] \\ \hline
20 & _Syzigium aromaticum_ & Diterpenes & BSLT (brine shrimp lethality test) method & Tests on BSLT and MTT showed essential oils had the highest cytotoxic effect, followed by ethanol and water extracts. The LD\({}_{50}\) concentration of essential oil in 24 h of BSLT was 37 \(\upmu\)g/mL. & [108, 109] \\ \hline
21 & _Laurus nobilis_ L. & Monoterpenes & MCF-7 and T47D cells & Shows a strong antiproliferative activity for both leaves and fruit; however, the fruit remained more potent against both breast cancer cell models (MCF7 and T47D). & [110] \\ \hline
22 & _Lycopus lucidus_ & & & The essential oil can induce apoptosis of carcinoma cell lines and decrease the level of intracellular GSH. & [111] \\ \hline
23 & _Cordia africana_ & Sesquiterpenes & MCF-7 cells & Has an apoptotic mechanism with IC\({}_{50}\) inhibition of 4.55 \(\upmu\)g/mL. & [112] \\ \hline
24 & _Lemon volatile oil_ & Monoterpenes & MCF-7 cells & Oil derived from lime leaves shows cytotoxic activity against breast cancer cells & [113] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 4 Throughput Maximization and Resource Allocation Algorithms
### Selected Papers  
Regarding the technical challenges of converged optical and mmWave radio networks, the selected papers in Table 3 focus on solving the beamforming optimization and interference mitigation for throughput maximization. From the ANM perspective, the selected throughput optimization algorithms reveal that both centralized and distributed management systems are used in solving dynamic bandwidth allocation (DBA) problems. However, there is a shift towards distributed throughput optimization solutions, especially with the involvement of different network stakeholders in management decisions. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Four Objectives** & \multicolumn{2}{c}{**Three Objectives**} & \multicolumn{2}{c}{**Two Objectives**} & \multicolumn{2}{c}{**One Objective**} \\ \hline User number & 10 & Transmit power & 4 & Coverage radius & 3 & Antenna number & 3 \\ \hline Traffic load & 9 & Delay & 3 & Bandwidth & 3 & Queue length & 1 \\ \hline Number of APs & 8 & User location & 3 & Rate of requests & 3 & Operator number & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Distribution of evaluation criteria. The integration of massive MIMO and beamforming solutions for throughput optimization in optical and mmWave radio networks leads to solving the multiple objective resource allocation algorithms joint with power allocation [51] or beam selection [55] optimization solutions. In [51], an algorithm called volume adjustable backhaul constrained water-filling dynamic programming method is developed to maximize the down
## 3 Model Architecture
### Cybersecurity Resilience Capacity (RC) Model  
The RC model quantifies cybersecurity resilience as a dynamic, time-varying metric, translating static risk assessments into continuous resilience trajectories. The model is formulated as follows:  
\[RC(t)=\frac{P(t)\cdot R^{1.2}(t)+A(t)\cdot L^{0.8}(t)}{1+\sigma\cdot\Delta T} \tag{1}\]  
where \(RC(t)\) represents the overall resilience capacity at time \(t\), and the variables \(P(t)\), \(R(t)\), \(A(t)\), and \(L(t)\) denote the quantified capability scores of the system in the four dimensions: Prevention, Destruction Resistance, Adaptive Recovery, and Evolutionary Learning, respectively. These scores are not arbitrary values but are derived through a comprehensive aggregation of specific, measurable indicators within the P-R-A-L framework, as detailed in Section 4. For instance,  
* \(P(t)\) is computed as a weighted aggregation of sub-indicators such as threat intelligence accuracy, risk prediction coverage, situational awareness latency, proactive defense effectiveness, and supply chain security maturity.
* \(R(t)\) is derived from indicators like redundancy robustness, fault tolerance under attack, and heterogeneous architecture diversity.
* \(A(t)\) aggregates metrics such as mean time to recovery (MTTR), recovery automation rate, and post-incident functionality restoration ratio.
* \(L(t)\) combines indicators like incident learning rate, policy adaptation frequency, and autonomous strategy optimization effectiveness.  
This structured approach ensures that the theoretical model is directly tied to practical, quantifiable assessments, facilitating real-world implementation.
## 3 System Model and Definitions  
Traffic flow predicting utilizes historical traffic observation data to make predictions about how the traffic flow will vary in the future. Given a road system, it is characterized by \(G=(V,E,A)\), where \(V=\left\{\
u_{1},\
u_{2},\cdots\
u_{N}\right\}\) is the collection of all observation points in the traffic road system, wherein v represents each observation point (i.e., sensor), \(E\) is the set of sides connecting two nodes in \(V\), and the edges' weights correspond to the actual distance of each node. The adjacency matrix \(A\in\mathbb{R}^{N\times N}\) symbolizes the connection relationships of each edge in graph \(G\). Assuming the given historical traffic sequence is represented as \(X_{G}^{(t-T+1)},X_{G}^{(t-T+2)},\cdots X_{G}^{(t)}\), the predicted future traffic sequence can be represented as \(X_{G}^{(t+1)},X_{G}^{(t+2)},\cdots X_{G}^{(t+T)}\), where \(X_{G}^{(t)}\in R^{C\times N\times T}\) represents the characteristic attributes of all nodes in graph \(G\) at time step \(t\), with \(T\) representing the length of the specified historical time sequence, and \(C\) representing the quantity of feature channels. \(T^{\prime}\) symbolizes the length of the time sequence to be forecasted. The following is a definition of the traffic flow forecast problem:  
\[\left[X_{G}^{(t-T+1)},X_{G}^{(t-T+2)},\cdots X_{G}^{(t)}\right] \xrightarrow{f_{\theta}}\left[X_{G}^{(t+1)},X_{G}^{(t+2)},\cdots X_{G}^{(t+T^{ \prime})}\right] \tag{1}\]  
where \(f_{\theta}\) presents a learnable mapping function based on parameter matrix \(\theta\), which maps historical sequences to predicted sequences.
## 4 Experiments and Results  
This section will construct a real environmental experimental dataset based on the actual industrial control environment of an oilfield station to validate the proposed method. First, this section conducts a comprehensive analysis of the proposed method from two aspects: the effectiveness of DF and the performance of the anomaly detection model, testing the effectiveness of the proposed improvements. Second, this section compares and analyzes the proposed method with the more advanced existing methods in the field of industrial control device anomaly detection, further verifying the superior performance of the proposed method.
# Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising
## 2 Review on Time-Series Forecasting Studies
### Statistical Model  
A statistical model is a mathematical model that can be used for time-series forecasting [96]. It is classified into two categories of regression and stochastic models.
## 3 Algorithms Descriptions
### State-of-the-Art (Earliest Finish Time) EFT-ILS  
In [11], the authors introduce an ILS, which will be called Earliest Finish Time (EFT)-ILS (in this paper), see Algorithm 4. EFT-ILS consists of two phases; the first explores random feasible execution orders of the tasks' graph from lines 3 to 10. After each new ordering \(o^{\prime}\), the algorithm assigns the machines to the tasks that produce their Earliest Finish Time (EFT) (see line 5 and Algorithm 5). For heavy computational instances, we suggest using an external stopping criterion as CPU time, see line 10. The second phase initializes an ILS described in Section 3.1, using the best order \(o_{best}\) and solution \(s_{best}\), found by the first phase, as initial solution. The next subsection details the Local Search and perturbation processes.  
```
0:\(G=(T,C)\), computational costs \(P_{i,j}\).
0:\(s_{best},o_{best}\)
1:\(s^{\prime}\leftarrow\) Random initial solution
2:\(s_{best}\gets s^{\prime}\)
3:for\(i=1\) to \(MaxIterations\)do
4:\(o^{\prime}\leftarrow\) Random DAG \(G\) topological order
5:\(s^{\prime}\gets EFT(o^{\prime})\)
6:if\(f(s^{\prime})<f(s_{best})\)then
7:\(s_{best}\gets s^{\prime}\)
8:\(o_{best}\gets o^{\prime}\)
9:endif
10:endfor\(\triangleright\) If the external stopping criterion is reach stop the for loop
11:\(ILS(s_{best},o_{best})\)
```  
**Algorithm 4** Earliest Finish Time-Iterated Local Search.
# Common Gabor Features for Image Watermarking Identification
## 2 Gabor Features  
[PERSON] [23] developed Gabor filtering for image textural analysis. Because of their spatial localization, orientation selectivity, and frequency characteristic, Gabor filters have been frequently employed in a variety of image analysis and classification applications [22]. They are complicated band-limited filters that have optimal localization in both the spatial and frequency domains. In the spatial domain, the family of 2D Gabor filters can be formulated (1) as having:  
\[\begin{split} G_{\theta,f,\sigma 1,\sigma 2}(x,y)&=exp \left[\frac{-1}{2}\left(\frac{x^{2}}{\sigma_{1}^{2}}+\frac{y^{2}}{\sigma_{2}^{ 2}}\right)\right]\;\cos(2\pi fx^{\prime}+\varphi)\\ x^{\prime}&=x\;\sin\theta+y\;\cos\theta\\ y^{\prime}&=x\;\cos\theta+y\;\sin\theta\end{split} \tag{1}\]  
where:  
\(f\) = the spatial frequency of the wave at an angle \(\theta\) with the \(x\) axis;  
\(\sigma 1\) and \(\sigma 2\) = the standard deviations of the 2D Gaussian envelope;  
\(\varphi\) = the phase.  
The textural abnormality in an image is caused by inserting any watermark into a host image. Therefore, the texture analysis may be able to quickly detect these watermarks. Gabor filters have been frequently employed in a variety of image analyses and classification applications. Therefore, Gabor filtering plays a unique role in the image watermarking classification by detecting textural abnormalities within the host image that are harder to identify with the human eye. Across various orientations and scales, two common Gabor features [24; 25], namely mean squared energy and mean amplitude, are extracted.  
To further understand the Gabor, we applied the Gabor filter to the image in Figure 4 and extracted certain features.  
In order to compute Gabor features at varied scales and orientations, Figure 5 displays the 2D Gabor filters with varied eight orientations and five scale [26].  
Figure 4: An Image example.  
Then, as illustrated in Figure 6, we convolved each filter with the image to generate 40 (8 \(\times\) 5 = 40) alternative representations (response matrices) of the same image.  
Finally, mean squared energy and mean amplitude are extracted as feature vectors from those response matrices in order to obtain feature vector [27]. The squared value of each matrix value in a response matrix was summed together to calculate mean squared energy. The sum of absolute values of each matrix value in a response matrix was used to calculate the mean amplitude. [27] is recommended to read to learn more about Gabor features.
## 2 Methodologies of Developing Fragility Curves
### Empirical Cumulative Distribution Function (CDF)  
The implementation of IDA method results to EDPs values that correspond to each seismic excitation for a given value of IM. Therefore, stripes of discrete EDPs values are generated at increasing levels of IM. The probability an EDP to exceed a particular performance level for a given value IM is estimated as the sum of those events which exceed the capacity \(\hat{\text{C}}\) over the total number of seismic excitations:  
\[\text{P}\big{[}\text{EDP}\ \geq\hat{\text{C}}\big{]}=\frac{\text{m}}{\text{n}} \tag{2}\]  
where m is the number of events which exceed a particular limit state (capacity level) \(\hat{\text{C}}\) at the level of IM, and n is the total number of seismic excitations.  
In Figure 1, the IDA curves for n ground motions and the corresponding fragility curve based on the empirical CDF method are presented. In Figure 1a, dots represent the structural response at a particular value of IM, while the vertical solid line depicts the capacity \(\hat{\text{C}}\) at the performance level of the assessment. The red dots refer to the m events where the demand exceeds the capacity at the particular level of IM, while the blue dots refer to (n-m) events for which demand does not exceed capacity. The discrete probability data points (Figure 1b) at the performance level of \(\hat{\text{C}}\) can be evaluated based on Equation (2). Therefore, the fragility curve based on the empirical CDF can be developed by connecting the discrete points with linear segments. [PERSON] et al. (2019) utilized the discrete probability data points to validate the fragility curves developed through PSDM for different values of separation gap distance.  
Figure 1: (**a**) Set of IDA curves and stripes of EDP at discrete values of IM, (**b**) Fragility curve based on empirical cumulative distribution function (CDF).
## 2 Materials and Methods
### Surface Characterization  
The surfaces of the specimens (Ti, CTO 700, and CTO 800) were characterized precisely by the following techniques. Surface micromorphology was observed using scanning electron microscopy (SEM) (JSM-6500F, JEOL, Tokyo, Japan); elemental composition was examined by energy-dispersive X-ray (EDX) spectroscopy (JSM-6500F, JEOL). The surface roughness was calculated by a three-dimensional non-contact surface roughness tester (Talysurf CCI6000, Taylor Hobson, Leicester, UK) and a Gaussian filter was performed with the cut-off length of 50 \(\times\) 50 \(\upmu\)m. Hydrophilicity was defined on the basis of the contact angle of a 1 \(\upmu\)L H\({}_{2}\)O droplet onto the test surfaces, measured by portable contact angle meter (PCA-1, KYOWA, Saitama, Japan).
## 2 SGP System Description  
The Sunlight Intensity based Global Positioning System (SGPS) is able to localize outdoor objects by its earth coordinates (longitude and latitude) using only light intensity information.  
The system described takes into account stationary objects. This means that the object has to be in the same position during the whole day (or at least during the daylight time). Due to the accuracy of the system and due to the fact that the sunlight intensity can be considered constant in small areas, small movements are possible. In this case the term _small_ depends on the accuracy of the system, a few kilometers.
## 3 Results
### Liquid Structure
#### 3.1.3 Angular Distribution Function  
The angular distribution function (ADF) of Cl-A-Cl (A = Li, Na, K) from A-2 to A-8 at 1100 K is shown in Figure 5. Here, \"bond\" does not mean a real chemical bond, but just an atom pair within some distance. In the ternary melts, the Cl-A-Cl bond angles were distributed between 60\({}^{\circ}\) and 150\({}^{\circ}\), all showing a broad peak and a smearing phenomenon, which indicates that the coordination bonds are orientated randomly.  
With an increasing LiCl addition, the peak values of the bond angle for Cl-Li-Cl shifted from 100\({}^{\circ}\) to 93\({}^{\circ}\), for Cl-Na-Cl, shifted from 88\({}^{\circ}\) to 79\({}^{\circ}\), and for Cl-K-Cl, shifted from 78\({}^{\circ}\) to 68\({}^{\circ}\). The angles become smaller with an increasing LiCl concentration. The corresponding bond angles for pure melts of LiCl, NaCl, and KCl were 98.18\({}^{\circ}\), 87.27\({}^{\circ}\), and 83.64\({}^{\circ}\), respectively [15]. The bond angles of the corresponding pure alkali chloride crystal were mainly distributed in 90\({}^{\circ}\) and 180\({}^{\circ}\)[24] because the three salts in the crystal type were a face-centered cubic structure and sexadentate. Though the long-range order disappears in melts, at the micro-level, there was still some tendency to keep the crystal structure. Considering the coordination numbers of each cation become larger and the bond angles become smaller, we assumed that the deforming in the micro-level structure was larger than in that of the pure [15] and binary melt systems [25]. The ADFs were broader and no peak shifting was observed as the temperature increased (the temperature influence on the ADFs is provided in Appendix A Figure A8).  
Figure 4: Snapshot of A-2 at 1100 K from molecular dynamics (MD) simulation.  
Figure 5: Angular distribution function (ADF) samples of Cl-A-Cl of four typical systems at 1100 K.
# Feasibility Analysis on the Use of Ultrasonic Communications for Body Sensor Networks
## 2 Principles of Ultrasonic Waves  
To use ultrasonic waves for communication, understanding the physical characteristics of ultrasonic waves is necessary. Ultrasonic waves are mechanical waves; the signal attenuation is proportional to the distance and depends on the propagation medium. As a result of this, ultrasonic waves are suitable for short-range communication. From our research, we list three major principles of ultrasonic waves that can cause energy dissipation through the transmission.
## 4 Fatigue Life Prediction of Bridge Cables
### Results and Discussion  
According to the above analysis method, the probabilistic distributions of \(\sigma_{m}\), \(\sigma_{\Delta}\) and \(\sigma_{m}\sigma_{\Delta}\) of cables J13, J12 and J11 are obtained, as shown in Figure 10, where it is observed that these distributions can be represented through normal or lognormal PDFs. Compared with cables J11 and J12, the longer cable J13 may have a slightly smaller mean stress, which is consistent with the calculated and measured results in Figure 7, while it is subjected to larger stress range. Based on the PDFs of \(\sigma_{m}\sigma_{\Delta}\) and the probabilistic distributions of \(Nc\), the probabilistic fatigue lives of the cables J13, J12 and J11 are obtained using Equation (12) from Table 5, and it is observed that the mean lives of cables are 29.11, 34.85 and 44.54 years, respectively. However, the standard deviation of fatigue lives of the three cables are considerable, being 10.32, 13.15 and 17.47 years, respectively, indicating that there are high possibilities for the cables to have a shorter life than designed (_i.e._, 30 years). It is also observed from Figure 10 that longer cables show shorter lifetime, and one reason is that in the Runyang Cable-Stoyed Bridge, longer cables are subjected to larger loads, resulting in larger stress amplitudes, as shown in Figure 10d-f. It is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Meaning of Variable** & **Distribution Type** & **Mean Value** & **COV** & **Source** \\ \hline \(B\) & Fracture parameter & Lognormal & \(1.06\times 10^{-6}\) & 0.63 & [PERSON] [41] \\ \(E\) & Young’s modulus & Lognormal & \(2.0\times 10^{5}\) MPa & 0.05 & [PERSON] _et al._[20] \\ \(a_{0}\) & Initial crack depth & Normal & 0.01 mm & 1.2 & [PERSON] [29]; \\ \(a_{c}\) & Critical crack depth & Normal & 4 mm & 0.2 & MCGHSM [40] \\ \(DLA\) & Dynamic load amplification factor & Normal & 0.057 & 0.8 & AASHTO [39]; \\  & & & & NCHRP [42] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Random variables used in fatigue reliability analyses.  
also worth noting that in material engineering, a specimen with longer length has a shorter lifetime due to the higher probability of having a weaker link [43].