esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Nanomedicine to Enhance the Therapeutic Effectiveness of BC
### Nanoparticle-Mediated Drug Delivery
#### 7.1.2 Solid Lipid Nanoparticles  
Recently, solid lipid nanoparticles (SLNs) based on glyceryl monostearate were employed to encapsulate methotrexate for breast cancer treatment [101]. The methotrexate NPs hindered the cell cycle's advancement into the S phase. Compared to standard methotrexate, the methotrexate NPs demonstrated enhanced cellular absorption. Moreover, these nanoparticles curtailed the movement and growth of cancer cells and stimulated cell death both in vitro and in vivo. Cytotoxicity assessments showed promising tumor suppression and biocompatibility with minimal side effects [101]. Similar results were obtained by [PERSON] et al. when SLNs were loaded with curcumin to become biocompatible nanocarriers for reducing P-glycoprotein expression, enhancing the effectiveness of doxorubicin against resistant TNBC tumors and using smaller, harmless doses of CURC. Previous P-glycoprotein inhibitors have been ineffective due to their lack of specificity, high toxicity, and interference with the natural functions of P-glycoprotein in healthy tissues. SLNs have proven to be both biocompatible and safe. This finding is crucial since doxorubicin-based chemotherapy is a primary treatment for TNBC. Unfortunately, this breast cancer subtype is less responsive to doxorubicin because of the prevalent presence of P-glycoprotein [102]. Furthermore, psoralen-loaded polymeric lipid nanoparticles demonstrated enhanced antimetastatic and antitumor effects of paclitaxel in both in vitro and in vivo studies against TNBC [103].
# Computer Vision Measurement of Pointer Meter Readings Based on Inverse Perspective Mapping
## 3 Experiments and Analysis  
Experiments were carried out to evaluate the effectiveness of the presented method. Table 1 listed the primary devices in the experiment. In the actual measurement, a low-resolution camera was adopted to save costs. A short-focus lens was employed for minimizing the measured space to avoid the impact of object distance for measurement. In the experiment, the image of the pointer meter was \(1292\times 964\) pixels captured by a camera. A chessboard pattern with \(30\times 30\) corner points was applied for calibrating the intrinsic parameters of the camera (in the Appendix A for the detail). A Backlight was typically used during the measurements to make sure high image quality.  
To ensure identical parameters in the measurement, lens settings, illumination intensity and relative distance between the camera and the pointer meter should remain unchanged. Besides, two different sizes of pointer meters were used for measurement, as shown in Figure 4. One of pointer meters with a radius of 622 mm was measured separately. Then other pointer meters with a radius of 406 mm were measured.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Name** & **Type** & **Performance** \\ \hline
1 & CCD camera & JAI CCD camera & Resolution: \(1376\times 1024\) \\
2 & Lens & M0814-MP & Focal length: 25 mm \\
3 & Model plane & NANO CBC 75 MM-2.0 & Precision of grid: 1 \(\upmu\)m \\
4 & Backlight & CCS LFL-200 & Color: red \\
5 & Pointer meter1 & Pressure gauge & Range: 0–40 MPa; Radius: 650 mm \\
6 & Pointer meter2 & Oxygen pressure gauge & Range: 0–25 MPa; Radius: 400 mm \\  & & & Range: 0–2.5 MPa; Radius: 400 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main experimental devices.  
Figure 3: Result of the detected the end point \(e(x_{e},y_{e})\) of pointer, the rotation center \(c(x_{c},y_{c})\) and all the midpoints of scales in the pointer meter.
# Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm
## 3 Solution Methodology
### Generalized Normal Distribution Optimization Algorithm
#### 3.1.2 Global Exploration  
In combinatorial optimization, global exploration means using advanced evolution rules to explore the solution space and find promissory solution regions that are likely to harbor the global optimal solution [29]. In the GNDO approach, based on the recommendations of [27], the global exploration stage has three main components, which are combined in Equation (20).  
\[v_{i}^{t}=x_{i}^{t}+\beta\times(|\lambda_{3}|\times v_{1})+(1-\beta)\times(| \lambda_{4}|\times v_{2}), \tag{20}\]  
where \(\beta\times(|\lambda_{3}|\times v_{1})\) has information regarding the local solution region, and \((1-\beta)\times(|\lambda_{4}|\times v_{2})\) has information of the global solution space. Note that \(\lambda_{3}\) and \(\lambda_{4}\) represent random numbers obtained using a normal distribution, \(\beta\) corresponds to a randomly chosen adjusting parameter between 0 and 1, and \(v_{1}\) and \(v_{2}\) are also two trail vectors.  
\[v_{1}=\begin{cases}x_{i}^{t}-x_{j}^{t}&\text{if}\quad A_{f}(x_{i}^{t})<A_{f} \Big{(}x_{j}^{t}\Big{)}\\ x_{j}^{t}-x_{i}^{t}&\text{otherwise}\end{cases} \tag{21}\]  
\[v_{2}=\begin{cases}x_{k}^{t}-x_{k}^{t}&\text{if}\quad A_{f}\big{(}x_{k}^{t})<A_ {f}\big{(}x_{m}^{t}\big{)}\\ x_{m}^{t}-x_{k}^{t}&\text{otherwise}\end{cases} \tag{22}\]  
Note that in Equations (21) and (22), the subscripts \(j\), \(k\), and \(m\) represent integer numbers associated with three positions of the solutions in the current population. The only restriction applicable to these numbers is that they must be different from each other and the analyzed individual \(i\).  
Before deciding whether the potential solution individual \(v_{i}^{t}\) will be part of the next population, each one of the elements must be checked in order to ensure the feasibility of the solution, i.e.,  
\[v_{i,l}^{t}=\begin{cases}v_{i,l}^{t}&\text{if}\quad x_{l}^{\min}\leq v_{i,l}^ {t}\leq x_{l}^{\max}\\ v_{i,l}^{t}=x_{l}^{\min}+\lambda_{5}\big{(}x_{l}^{\max}-x_{l}^{\min}\big{)}& \text{otherwise}.\end{cases} \tag{23}\]  
where \(x_{j}^{\min}\) and \(x_{j}^{\max}\) are the lower and upper admissible limits of the variable \(j\), respectively. Note that, due to the restriction implied by the integer nature of the decision variables associated with the nodes where the PV sources are to be located, the first \(N_{pp}^{anq}\) is rounded to its nearest integer value. In order to choose the next solution individual, the following rule is applied:  
\[x_{i}^{t+1}=\begin{cases}v_{i}^{t}&\text{if}\quad A_{f}\big{(}v_{i}^{t}\big{)} <A_{f}\big{(}x_{i}^{t}\big{)}\\ x_{i}^{t}&\text{otherwise}\end{cases} \tag{24}\]
# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 2 Motivation  
The emergence of deepfake technology has made it more difficult to distinguish between real and fake audio. Deepfake audio is a new take on the impersonation tactics that have been utilised for a long time in social engineering and phishing attacks. Partial fake speech is a new challenge that needs to be addressed and detected. It can be performed using existing attacks that are based on entirely fake audio. Here are some of the motivations for PF voice fraud:
# Prediction System for Prostate Cancer Recurrence Using Machine Learning
## 2 Materials and Methods  
### BCR Prediction Data  
The data used in this study was obtained from the KPCR database which included 7394 patients with prostate cancer. This database included Electronic Medical Records (EMRs) data collected from the six major medical institutions in South Korea between May 2001 and December 2014 (Figure 1). The study protocol was approved by the institutional review board of the Catholic University of Korea (IRB No. MC16 RIMI0107).  
Data standardization and quality control were implemented to ensure data integrity, and exclusion criteria were created and applied to the database to refine the data used in the BCR prediction.  
Our data subset was created from the main KPCR database using the following processes (Figure 2):  
* There were 7394 potentially relevant records in the KPCR database
* 2280 records, including those of non-Koreans (51), those who did not have a full follow-up period of at least one year (633), those who did not have a neoclassical prostatectomy (80), those who performed pre-supplementary therapy (230), and those for whom critical data were missing (1286) were excluded
* We identified 5114 individual records as being statistically usable and relevant
* We classified the 5114 patient data items into BCR (1207) and Non-BCR (3907) groups to identify the characteristics of each group.  
Figure 1: The Process of Building a Korean Prostate Cancer Registry (KPCR) Database Based on Electronic Medical Records (EMRs).  
Figure 2: Patient Flow Chart.  
We found that follow-up periods ranged between 0 and 157 months, and out of the total of 7394 patients, 70% or more had a follow-up period of less than five years. Therefore, we decided to predict BCR likelihood at three and five years after radical prostate cancer surgery.  
Factors affecting BCR have been reported to include age, initial PSA, clinical T stage, pathology Gleason score sum, pathology T stage, surgical margin, perineural invasion, seminal vesicles, extracapsular extension, and lymphovascular invasion [16; 17; 18; 19]. We extracted these factors from the 5114 analytical data items and generated a training data set for predicting three- and five-year BCR after radical prostate cancer surgery.
# Multi-Authority Revocable Access Control Method Based on CP-ABE in NDN
## 6 Summary  
Information caching technology in named data networks improves the efficiency of data distribution. However, information caching decouples data publishers from data, causing data in storage nodes to face threats issues because of the lack of security controls. The security issues of NDN content affect NDN applications and deployment. For the purpose of solving the access control problem of cached content in the named data network NDN, this paper constructs a proxy-assisted access control scheme. The multi-authorization revocable access control method based on CP-ABE implements secure and effective access control of data in the NDN network. Because of the partial decryption on the NDN node, the decryption burden of the consumer client in the solution is reduced, and the method implements effective user and attribute revocation, and ensures forward security and backward security against collusion attacks. Finally, the performance and security analysis of the scheme of this paper proves that the scheme is safe and efficient.  
[PERSON] and [PERSON] contributed to the conception of the study, [PERSON] collected important background information, performed the experimental analyses and wrote the manuscript. All authors read and approved the final manuscript.  
This work was supported in part by the joint funds of National Natural Science Foundation of China and Civil Aviation Administration of China (U1933108), the Key Program of Natural Science Foundation of Tianjin (I7 CZD)C30900), the Scientific Research Project of Tianjin Municipal Education Commission (2019 KJ117), and the Fundamental Research Funds for the Central Universities of China (3122018D34007, 3122018C003).  
The authors declare no conflict of interest.
# A Topological Approach to Enhancing Consistency in Machine Learning via Recurrent Neural Networks
## 4 Experiments
### Analysis in 3D-Space
#### 4.3.1 Continuous Functional Mapping  
To test the proposed consistency in Equation (4), we generated random paths in 3D-Space using the domain \(x\in(0,1)\), \(y\in(0.5,1)\) and \(z\in(0.5,1)\). For each axis--\(x,y\) and \(z\)--20 random points are generated and uniformly distributed to form input paths. These paths are then mapped to the output paths, indicated by the red line in Figure 6, using Equation (9). Following this method, 1000 random input paths were generated and separated into training and test sets, with dimensions set to \((800,20,3)\), and \((200,20,3)\), respectively. (Here, the coordinates of the given vectors are interpreted as in the 1D case.) The mapped paths corresponding to these input paths are used as labels for the training and testing sets.  
\[F:R^{3}\to R^{3},\ F(x,y,z)=(x^{2}-yz,1-x^{2}(y+z),4 xyz) \tag{9}\]  
The same two different NN architectures are trained for 1000 epochs in 3D space using the Adam optimizer and MSE as the loss function. The GRU-built architecture resulted in an MSE of \(1.8\times 10^{-3}\), while the ConsRNN-built architecture achieved an MSE of \(3.3\times 10^{-3}\) on the test set. These architectures are also tested on incremented paths of \(10^{2}\), \(10^{3}\), and \(10^{4}\) to quantitatively observe the results, as shown in Figure 6. As observed from the results in Figure 6, while the number of increments increases, the predictions of the GRU-built architecture move away from the ground truth data. On the other hand, the estimates from the ConsRNN-built architecture remain consistent.  
Figure 5: Consistency analysis results in 2D-Space with the transformation of input path through the objective non-smooth continuous function.
## 8 A Unified Framework for Cognitive Radio and Cognitive Radar
### Logistic Regression  
In contrast to BN, which models the dependency explicitly, logistic regression models the dependency in an implicit and linear manner and provides a direct prediction of spectrum activity. The advantage of logistic regression is that it is simple and can give the probability of prediction. Mathematically, logistic regression can be written as,  
\[\log\left(\frac{p\left(S_{i}=1\right)}{p\left(S_{i}=0\right)}\right)=\sum_{k= 1}^{N}\beta_{k}f\left(S_{i-k}\right) \tag{52}\]  
where \(\beta_{k}s\) are regression coefficients to be estimated from training data. \(f\) is a function of state.  
The database in the proposed framework provides a storage for knowledge, policy and other data. The decision maker in Figure 34 chooses a policy for execution based on the information provided by the cognizer and the knowledge from the database. Partially Observable Markov Decision Process (POMDP) can be used for the decision process [140]. POMDP models the interaction procedure of an agent with outside world. The solution of POMDP is the optimal policy for choosing actions. Solving a POMDP is not easy. The first detailed algorithms for finding exact solutions of POMDP were introduced in [141]. There exists some software tools for solving POMDPs, such as pomdp-solve [142], MADP [143], ZMDP [144], APPL [145], and Perseus [146]. Among them, APPL is the fastest one in most cases [145].  
The idea of cognitive radar was put forward in [147]. The framework shown in Figure 34 can also be applied to cognitive radar. The major difference between cognitive radio and cognitive radar is the implementation of executer. For cognitive radio, a traditional wireless communication device or SDR can be used as the executer. While for cognitive radar, the executor can be the current radar system.
## 2 Methodology
### Numerical Model  
Theories on anisotropic hyper-elastic material of the arterial layers were developed by many research teams and have been applied in the field of computational biomechanics [19; 20; 21; 22; 23; 24]. In particular, the Fung [22] and Holzapfel [19; 20] models have been widely used in various simulations of the arterial wall. In the Fung model [22], the underlying assumption is based on a strain formulation that the preferred material directions are initially aligned with an orthogonal coordinate system in the reference configuration. These directions may become non-orthogonal only after the deformation. In the Holzapfel model [19; 20], unlike the strain-based formulation, the invariant-based formulation of fiber directions need not be orthogonal during the initial configuration. Therefore, a constitutive law on the description of mechanical responses in the arterial tissues has been proposed, and the arterial walls were considered as a thin-walled circular cylindrical tube consisting of layers. In the present study, the Holzapfel model [19; 20] was applied to the numerical simulation, and the material constants according to age were calculated. Moreover, the formulae for material constants including the age were proposed. **Hyper-elastic materials. ** In hyper-elastic materials, the relationship between stress and strain are determined on the basis of the strain energy density function (\(\psi\)), which is defined according to a  
Figure 1: Failure (**a**) stress and (**b**) stretch distribution, according to age. deformation gradient or strain tensor. The derivative of the strain energy density function of a strain component provides its corresponding stress component [24]:  
\[S_{ij}\ =\ \frac{\partial\psi}{\partial\epsilon_{ij}} \tag{1}\]  
where \(\mathbf{S}\) is the second Piola-Kirchhoff stress tensor and \(\epsilon\) is the Lagrangian strain tensor, which is defined as follows:  
\[\epsilon_{ij}\ =\ \frac{1}{2}\big{(}C_{ij}-\delta_{ij}\big{)} \tag{2}\]  
In Equation (2), \(\mathbf{C}\) is the right Cauchy-Green deformation tensor (\(\mathbf{C}=\mathbf{F}^{\mathrm{T}}\mathbf{F}\)), and \(\mathbf{F}\) is the deformation gradient that can be presented in terms of the displacement vector \(\mathbf{u}\) (\(\mathbf{F}=\
abla\mathbf{u}+\mathbf{I}\)). Local geometry deformation of a material element is explained on the basis of the deformation gradient, and the strain energy function is commonly defined as a function of the deformation gradient. Actually, the function is expressed as the right Cauchy-Green deformation tensor. Therefore, the relationship of the second Piola-Kirchhoff with the Cauchy stress tensor is expressed as follows [24]:  
\[\mathbf{S}\ =\ J\mathbf{F}^{-1}\sigma\mathbf{F}^{-T} \tag{3}\]  
where \(J=\) det \(\mathbf{F}\). Therefore, the Cauchy stress tensor is defined according to the following Equations (1)-(3):  
\[\sigma\ =\ 2J^{-1}\mathbf{F}\frac{\partial\psi}{\partial\mathbf{C}}\mathbf{F}^{T} \tag{4}\]  
In isotropic materials, the strain energy density function is dependent on the right Cauchy-Green deformation tensor based on its invariants.
###### Abstract  
The seismic behavior of pile-supported structures is influenced by complex interactions between inertial force and kinematic force mainly drawn by soil properties and superstructure characteristics. This study aims to investigate the combined effects of inertial and kinematic interaction on the dynamic response of pile foundations under seismic loading. To achieve this, three-dimensional numerical simulations were conducted using FLAC3D, based on a bridge substructure model. A total of twelve analysis cases were developed by varying input seismic motion levels, soil relative densities, and pile cap masses. The results demonstrate that kinematic force effects become more dominant in dense soils as seismic intensity increases, resulting in greater velocity responses and internal forces in the pile cap. Meanwhile, inertial forces from heavier superstructures interacted with kinematic force effects in a resistive manner, particularly under embedded pile cap conditions. The displacement of pile foundations remained within serviceable limits in all cases, although structural demands would be elevated under certain conditions. These findings confirm the significance of accounting for both inertial and kinematic effects in seismic design and highlight the importance of site-specific soil conditions.  
Keywords:group pile-supported structure; seismic response; soil-structure interaction; kinematic force; inertial force; soil relative density Article
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Analysis of Covert Storage Patterns
#### 4.2.6 Reserved/Unused (PS12)  
Pattern  
The hidden information is embedded in unused or reserved header elements. As specified in TS 38.323 [10], the value of the reserved header fields in the PDCP has to be ignored by the receiving entity. Since the header of the PDCP contains five reserved bits, this is the most suitable of all analyzed patterns for a covert channel within the PDCP header.
## Results and Discussion
### SPME/GC-Flame Ionization Detector Analysis  
In the present section, the relative differences of ethanol in ground beef when \"_isipouro_\" was applied compared to controls over storage time were monitored. Considering that, in the developed antimicrobial active packaging based on \"_tsipouro_\", ethanol would migrate to ground beef through two mechanisms, diffusion and evaporation, 2 types of sampling took place, i.e., from the surface and the bottom side of each ground beef piece (Section 2.12).  
With regards to GC data, the detected ethanol in samples packaged with \"_tsipouro_\" was ca. 3 to 6-fold higher than the respective values of ethanol in controls (\(p<0.05\)), regardless of temperature, storage day, and sampling side (Figure 7). In controls, the detected ethanol was likely the result of microbial metabolic activity. No significant differences (\(p\geq 0.05\)) in ethanol content were observed between samples from the surface and bottom of ground beef, refuting the potential hypothesis that migration through diffusion could reveal higher ethanol content compared to evaporation, due to the direct contact between the antimicrobial (on the pad) and ground beef. On the contrary, the results potentially indicated that recorded ethanol during storage may be the combined result of both migration mechanisms, leading eventually to a gradual ethanol equilibrium in the packaging headspace during storage (Figure 7). On samples packaged with \"_tsipouro_\" pads, the latter equilibrium took place earlier as the storage temperature increased from 0 to 12 \({}^{\circ}\)C, since the evaporation rate of ethanol increased. specifically, the ethanol equilibrium was recorded close to the suggested chilling temperatures (0 \({}^{\circ}\)C and/or 4 \({}^{\circ}\)C), in ground beef samples either from the top surface or the bottom, while an initial increase was rapidly followed by equilibrium close to abuse temperatures (8 and 12 \({}^{\circ}\)C). However, it should be also taken into account that the suggested ground beef with \"_tsipouro_\" is a product that will be consumed after thermal treatment, thus any residual perception of ethanol through odour or via taste after cooking would be negligible.  
Figure 6: Mean results of buttery and acidic odour of ground beef packaged with absorbent pads supplemented with a total volume of 50 mL. “_tsipouro_” (40 mL under the ground beef piece and 10 mL under the labelling, facing the headspace) under 80% O\({}_{2}\): 20% CO\({}_{2}\) during isothermal storage at 0, 4, 8, and 12 \({}^{\circ}\)C. Untreated samples served as controls. The hedonic scale was set from 1 to 3 with 0.5 intervals. Score of 2 was the rejection threshold.