esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### In Vitro Biological Results  
#### 3.5.1 PNIPAM/Magnetite NPs Biocompatibility Evaluation  
The biocompatibility of the PNIPAM/magnetite NPs was evaluated in direct contact with the MCF7 breast cancer cells in order to validate their lack of toxicity and, consequently, their prospective use as drug delivery systems. For this, various dilutions of pristine PNIPAM/magnetite NPs ranging from 25 mg/mL to 50 mg/mL were applied on MCF7 monolayers for 24 h, and then the viability of the cells was evaluated by MTT assay and compared with the viability of untreated MCF7 cells. The obtained results are illustrated in Figure 6 and show that independent of the NPs dose, they did not impact the MCF7 cells' viability after 24 h of exposure in any way.  
Figure 5: Doxorubicin release profiles from PNIPAM/magnetite nanoparticles in PBS at pH 7.45 and 37 °C (precipitation temperatures 30, 34, and 38 °C).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Precipitation Temperature (°C)** & **Sample Name** & **Release Efficiency (R.E. \%)** \\ \hline \multirow{4}{*}{30} & PNIPAM 0.25\% & 43 \(\pm\) 5 \\  & PNIPAM 0.5\% & 49 \(\pm\) 5 \\  & PNIPAM 1\% & 70 \(\pm\) 7 \\  & PNIPAM 2\% & 79 \(\pm\) 8 \\  & PNIPAM 4\% & 87 \(\pm\) 8 \\ \hline \multirow{4}{*}{34} & PNIPAM 0.25\% & 36 \(\pm\) 5 \\  & PNIPAM 0.5\% & 43 \(\pm\) 5 \\  & PNIPAM 1\% & 48 \(\pm\) 5 \\  & PNIPAM 2\% & 53 \(\pm\) 5 \\  & PNIPAM 4\% & 64 \(\pm\) 6 \\  & PNIPAM 0.25\% & 41 \(\pm\) 5 \\  & PNIPAM 0.5\% & 46 \(\pm\) 5 \\
38 & PNIPAM 1\% & 50 \(\pm\) 4 \\  & PNIPAM 2\% & 54 \(\pm\) 5 \\  & PNIPAM 4\% & 63 \(\pm\) 6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Release efficiency of doxorubicin in PNIPAM/magnetite nanoparticles.  
Considering that the MCF7 cells' viability did not decrease by the exposure of the pristine PNIPAM/magnetic NPs, we concluded that the NPs are not cytotoxic on MCF7 cells and, therefore, were further used to encapsulate DOX for its controlled release in cancer cells.
# Microencapsulation of Polyphenols and Their Application in Food Technology
## 4 Microencapsulation of Polyphenols
### Encapsulation Carriers
#### 4.2.4 Biopolymers  
In addition, biopolymers, such as chitosan, are increasingly being utilised in microencapsulation due to their biocompatibility, biodegradability, and functional versatility. For example, chitosan, derived from chitin, a natural polymer found in the shells of crustaceans, is particularly noted for its ability to form strong, stable films that can protect polyphenols from degradation [156]. Moreover, chitosan is a positively charged polysaccharide that readily interacts with negatively charged polyphenols through electrostatic interactions. This interaction allows for efficient encapsulation and enhanced stability in aqueous environments [170]. Additionally, chitosan encapsulation offers controlled release properties, as the polymer is sensitive to pH changes, enabling the release of polyphenols in specific areas of the gastrointestinal tract. Notably, chitosan has also been shown to possess antimicrobial properties, which can further enhance the shelf life and safety of encapsulated polyphenols [171; 172].
# Classification Performance of Thresholding Methods in the Mahalanobis-Taguchi System
## 2 The Concept of Mahalanobis Distance (MD)
### Probabilistic Thresholding Method  
Ref. [13] introduced a probabilistic thresholding method (PTM) in their study to evaluate the classification performance of MTS grounded by [PERSON]'s theorem. [PERSON]. [32] used PTM based on [PERSON]'s theorem in his work to reduce solder paste inspection process in a Surface-Mount Technology (SMT) assembly using MTS. [PERSON]'s theorem is useful to estimate the probability of getting a value that deviates from the mean by less than some degree of standard deviation, especially when the probability distribution of the dataset is unknown. The optimal threshold (\(MD_{T}\)) can be calculated with the following formula:  
\[MD_{T}=\overline{MD}+\sqrt[2]{\frac{1}{1+\lambda-\omega}}\times\ s_{MD} \tag{14}\]  
where:  
* \(\overline{MD}\) is the average of the MDs of the normal group,
* \(s_{MD}\) is the standard deviation of the MDs of the normal group,
* \(\lambda\) is a small parameter or the confidence level (typically 5% or 0.05)
* \(\omega\) is the percentage of the normal examples whose MDs are smaller than the minimum MD of the remainder abnormal examples and do not overlap with the abnormal MDs.  
Ref. [32] provides the method to determine \(\omega\) as illustrated in Figure 3 for a simple example. The 10 blue boxes represent normal samples on their MD scales while the 7 orange boxes represent abnormal samples with two boxes of respective samples being overlapped to each other. Thus, \(\omega\) is obtained by taking the percentage of the non-overlapped normal boxes over the total of normal boxes which is in this case, 7 divided by 10 equivalents to 70% or 0.7 on a zero-to-one scale.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 3 Standalone Remote Sensing (RS) Technologies
### Vision-Based (Imaging) Technologies
#### 3.7.1 Photo/Videogrammetry
Then, for any new query image, the location/camera pose of the input image would be retrieved by using a feature-based matching with the base 3D point cloud map generated earlier. Finally, by aligning the parameters of the 3D point cloud with a geo-referenced BIM model, global localization of the frames can be estimated.
## 3 Numerical and Experiment Results of Metamaterials  
To show the validity and application of the present periodic metamaterials in attenuating longitudinal wave, this section presents several numerical studies and experiment results of periodic structures. The band gap phenomena of straight and curved beams with cylindrical metamaterials, conical metamaterials, and combining metamaterials are investigated. The effects of the geometries on the band gap phenomena are also investigated. Bars are made of aluminum 2011 ([PERSON]'s modulus: 66 GPa, Density: 2823 kg/m\({}^{3}\), Poisson's ratio: 0.3), and the cylindrical and the conical metamaterials are made of aluminum 7075 ([PERSON]'s modulus: 71 GPa, density: 2810 kg/m\({}^{3}\), Poisson's ratio: 0.33). The theoretical and experimental studies are carried out with aluminum as it is easy to make specimens. However, the present studies can be applied for general materials.
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 3 Related Work
### USRP/GNU Radio and RP3 Testbeds
#### 3.2.2. USRP/GNU Radio with RP3 Testbeds
In this paper, the testbed consists of: (a) nine USRP/GNU radio nodes equipped with RP3 to handle the computational process, whereby a node is either a source, intermediate, or destination node; and (b) a PC performs the functions of a MC BS, such as establishing a backbone route. PUs interferes with the routes established between a source node and a destination node in a random manner. As shown in Table 2, our testbed extends existing testbeds to provide separate processing units (i.e., RP3), has a higher number of nodes (i.e., 9), has the highest number of D2D links (i.e., 8), and considers heterogeneous network entities. Hence, a source node, being a SU, must select a route with more white spaces. Although the focus of [14; 29; 38; 39; 40; 41; 42] are mainly the capability of RP3 and their compatibility with USRP, this paper uses a testbed with separate processing units in the investigation of a route selection mechanism in the presence of PUs' activities for achieving an improved measurement accuracy of network performance (i.e., packet delivery ratio and end-to-end delay). In short, compared to existing testbeds in the literature, our testbed provides communication among a larger number of heterogeneous nodes between MC BS and D2D nodes, contributing to a larger number of D2D links in the network. Experiments using the testbed were conducted in the laboratory environment, and so there are two types of noise: (a) noise within the operating frequency range (or interference) from 850 MHz to 960 MHz covering the TV bands of very high frequency (VHF) and ultra-high frequency (UHF), and this noise has been taken into consideration; and (b) noise below or beyond the operating frequencies, including the ambient noise, which has minimal effect to packet transmission. For the preceding type of noise, the received signal at each node is passed through Gaussian minimum shift keying (GMSK) demodulator and converted into data packets. GMSK ensures the signal has a sharp cut-off with no overshoot impulse responses and is closest to the Gaussian shaped response. For the latter type of noise, GMSK implements phase frequency shifting, unlike most noise which is mainly amplitude-based. This helps the received signals to become immune to ambient noise. GMSK has been used to mitigate ambient noise in the literature [15; 28; 29; 30; 31; 33; 39; 43]. To avoid PUs' activities, it is assumed that the central controller has a record of PUs' activities and it prioritizes the available routes for the source node. However, in case of PUs' reappearance, the source node must select other available routes.
## 1 Introduction  
Computational strategies such as Computational Fluid Dynamic (CFD) are usually applied to simulate fluids (gas and liquids) to obtain different physical variables such as pressure, velocity, mass rate, turbulence energy, temperature, turbulence intensity, vorticity, and others. Based-on CFD applications work over a computational domain using partial differential equations (PDE) and ordinary differential equations (ODE) to describe the relationship among different physical variables to understand the characteristics of fluid flow. The specific applications of the method can be very diverse, depending on the objectives of the analysis, the required accuracy and other factors, such as computing times (which can be high, namely, from hours to days) [1]. The main applications of CFD encompass aerodynamic analyses, multi-phase flows, transport, compressible flows, phenomena and chemical processes involving transfer heat phenomenon, dissipative phenomenon, rotary, mixing of different fluids and chemical reactions [1; 2; 3; 4; 5].  
Furthermore, specific CFD software can be difficult to handle and, sometimes, unintuitive for students without previous experience. Carrying out processing tasks using CFD can become a long, hard and intense work when it is performed for specific applications [1], for example, optimization [3; 6], model calibration [7], sensitivity analysis and consequence analysis [8]).  
As it is established in [9], within the CFD context, the Finite Volume Method (FVM) is a specific numerical iterative technique that involves partial differential equations (mainly representing conservation laws) applied over differential volumes. This discretization process is similar to the Finite Element Method (FEM) [9]. The meshing procedure is highly important in the FVM method because it strongly affects the accuracy and stability of the flow predictions [10]. The meshing consists of the generation of a grid over the fluid computational domain. This grid can be generated for the computational model with different typologies (mainly structured, non-structured and hybrid) being a quality mesh essential for a quality simulation [11]. Through the mesh, the discretization of the volume is obtained and the partial differential equations are discretized into algebraic equations by integrating them over each discrete volume element. As a result, a discrete number of algebraic equations are established over a finite number of volumes from application of the meshing process to the whole study volume. These algebraic equations are solved as an algebraic equation system to calculate and compute the values of the dependent variable of each discrete volume element. An adequate mesh design is critical, since the accuracy of the results of the simulation process highly depends on how well the equation system or the mathematical model captures the flow physics [1].
## 4 Security Analysis and Simulation Verification  
To achieve anonymous security authentication between two parties, two-way authentication and user anonymity between protocol units A and B must first be fulfilled. Simultaneously, attacks on known session-specific temporary information must be prevented when the temporary information of protocol entities A and B is leaked to achieve secure key negotiation when the temporary session information of two parties is leaked. Therefore, in this section, formal analysis methods are used to demonstrate the security of the two-way authentication, user anonymity, and resistance to attacks on known session-specific temporary information. Informal analysis methods are used to demonstrate the security of other properties that are easier to achieve. In addition, the simulation verification tool ProVerif is used to verify the authentication process and security of the session key of the proposed protocol.
## 2 Research Methodology and Dataset
### Model Performance Evaluation Metrics  
As mentioned earlier, the output of the classification is in a binary form but with three classes of annotation, where white segments of the images represent the delaminated pixels, the black regions represent the sound/non-delaminated, and the background pixels of the bridge decks. Several metrics can be used to evaluate the performance of the proposed architecture. The model's performance is determined by benchmarking the output predictions with the annotated ground truth pixel-wise.  
The performance of the model was evaluated based on the following selected metrics: (i) Global and mean Accuracy (ACC), (ii) Precision/Positive Predictive value (PPV), (iii) F1-score, (iv) True positive rate (TPR)/Recall/Sensitivity, (v) True negative rate (TNR)/ Specificity, (vi) mean Intersection of Union (IOU). The metrics are calculated by the following equations, as shown in Equations (2)-(8):  
\[Global\ Acc.=\text{TP}+\text{TN}/(\text{TP}+\text{FP}+\text{TN}+\text{FN}) \tag{2}\]  
\[Mean\ Acc.=[\text{TP}/(\text{TP}+\text{FP})]/2+[\text{TN}/(\text{TN}+\text{ FN})]/2 \tag{3}\]  
\[Precision/PPV=\text{TP}/(\text{TP}+\text{FP}) \tag{4}\]  
\[F1\text{-}Score=2\text{TP}/(2\text{TP}+\text{FP}+\text{FN}) \tag{5}\]  
\[TPR/Recall=\text{TP}/(\text{TP}+\text{FN}) \tag{6}\]  
\[TNR=\text{TN}/(\text{TN}+\text{FP}) \tag{7}\]  
\[Mean\ IOU=[\text{TP}/(\text{TP}+\text{FP}+\text{FN})]/2+[\text{TN}/(\text{ TN}+\text{FP}+\text{FN})]/2 \tag{8}\]  
where TP refers to a true positive, which is when the model detects delamination correctly, FP refers to a false positive when defective pixels are falsely predicted as sound, TN refers to a true negative when defective pixels are correctly predicted as defective, and FN refers to a false negative when sound pixels are falsely predicted as defected pixels. IOU measures the percentage of the overlapped area between the predicted pixels and the actual pixels (ground truth) over their union. An IOU of 0 implies no overlapping, while a value of one means they are perfectly matched. The model's ACC indicates the correct detection rate with respect to the total number of detections. The PPV and TPR are often used to understand prediction outcomes further. PPV measures what fraction of  
Figure 4: Pixel distribution of FRNB for (**a**) raw annotated dataset (RD), (**b**) background annotated dataset (BD), and (**c**) manually cleaned annotated dataset (MD).  
the detected delamination has been correctly detected given the ground truth, and TPR indicates what fraction of the real damages are correctly detected by the model among the actual defects [47]. Low PPV rates indicate many false defect detections, where many areas are incorrectly classified as delaminated. Models with high PPV results are preferred to minimize false defect detections. Low TPR rates indicate a high number of false negatives or where many of the existing delaminations were missed.
# Discrete-Time Asymptotic Tracking Control System for a Satellite with a Solar Panel
## Appendix B Verification of (112) and (114) for (131)  
In order to verify IS, we substitute (131) into the matrix of (112) and obtain the following:  
\[\left[\begin{array}{cc}A^{d}&B^{d}C_{K}\\ B_{K}C^{d}&A_{K}+B_{K}D^{d}C_{K}\end{array}\right]=\left[\begin{array}{cc}A ^{d}&-B^{d}F&B^{d}(\Gamma+F\Gamma\Gamma)\\ L_{1}C^{d}&A^{d}-L_{1}C^{d}-B^{d}F&L_{1}T^{d}+B^{d}(\Gamma+F\Gamma\Gamma)\\ L_{2}C^{d}&-L_{2}C^{d}&S^{d}+L_{2}T^{d}\end{array}\right]. \tag{154}\]Using the matrices  
\[\mathcal{T}=\left[\begin{array}{ccc}I&0&0\\ -I&I&0\\ 0&0&I\end{array}\right],\quad\mathcal{T}^{-1}=\left[\begin{array}{ccc}I&0&0\\ I&I&0\\ 0&0&I\end{array}\right], \tag{104}\]  
we see that  
\[\begin{split}&\mathcal{T}\left[\begin{array}{ccc}A^{d}&-B^{d}F &B^{d}(\Gamma+F\Pi)\\ L_{1}\mathbb{C}^{d}&A^{d}-L_{1}\mathbb{C}^{d}-B^{d}F&L_{1}T^{d}+B^{d}(\Gamma+F \Pi)\\ L_{2}\mathbb{C}^{d}&-L_{2}\mathbb{C}^{d}&S^{d}+L_{2}T^{d}\end{array}\right] \mathcal{T}^{-1}\\ &=\left[\begin{array}{ccc}A^{d}-B^{d}F&-B^{d}F&B^{d}(\Gamma+F\Pi)\\ 0&A^{d}-L_{1}\mathbb{C}^{d}&L_{1}T^{d}\\ 0&-L_{2}\mathbb{C}^{d}&S^{d}+L_{2}T^{d}\end{array}\right].\end{split} \tag{105}\]  
As the matrices on the right-hand sides of (103) and (105) are similar, then  
\[\begin{split}\sigma(\left[\begin{array}{ccc}A^{d}&B^{d}C_{K} \\ B_{K}\mathbb{C}^{d}&A_{K}+B_{K}D^{d}\mathbb{C}_{K}\end{array}\right])&=& \sigma(\left[\begin{array}{ccc}A^{d}-B^{d}F&-B^{d}F&B^{d}(\Gamma+F\Pi)\\ 0&A^{d}-L_{1}\mathbb{C}^{d}&L_{1}T^{d}\\ 0&-L_{2}\mathbb{C}^{d}&S+L_{2}T^{d}\end{array}\right])\\ &=&\sigma(A^{d}-B^{d}F)\cup\sigma(\left[\begin{array}{ccc}A^{d}-L_{1} \mathbb{C}^{d}&L_{1}T^{d}\\ -L_{2}\mathbb{C}^{d}&S^{d}+L_{2}T^{d}\end{array}\right])\\ &\subset&\mathbb{D}_{1}\,,\end{split} \tag{106}\]  
based on (128) and \(\sigma(A^{d}-B^{d}F)\subset\mathbb{D}_{1}\). In order to verify IMP, we take \(\Sigma=\left[\begin{array}{c}\Pi\\ I\end{array}\right]\) and substitute (131) into (114) to obtain  
\[C_{K}\Sigma=\left[\begin{array}{cc}-F&\Gamma+F\Pi\end{array}\right]\left[ \begin{array}{c}\Pi\\ I\end{array}\right]=\Gamma\,, \tag{107}\]  
and  
\[\begin{split} A_{K}\Sigma&=&\left[\begin{array}{cc}A^{d}-L_{1} \mathbb{C}^{d}-(B^{d}-L_{1}D^{d})F&L_{1}T^{d}+(B^{d}-L_{1}D^{d})(\Gamma+F\Pi) \\ -L_{2}\mathbb{C}^{d}+L_{2}D^{d}F&S^{d}+L_{2}T^{d}-L_{2}D^{d}(\Gamma+F\Pi)\end{array} \right]\left[\begin{array}{c}\Pi\\ I\end{array}\right]\\ &=&\left[\begin{array}{c}A^{d}\Pi+B^{d}\Gamma-L_{1}(\mathbb{C}^{d}\Pi-T^{d} +D^{d}\Gamma)\\ S_{d}-L_{2}(\mathbb{C}^{d}\Pi-T^{d}+D^{d}\Gamma)\end{array}\right]=\left[ \begin{array}{c}\Pi S^{d}\\ S^{d}\end{array}\right]=\Sigma S^{d}\,,\end{split} \tag{108}\]  
where we have used RE.
## 1 Introduction
In this paper, we present the characterization of properties (structure, morphology, surface roughness, refractive index, absorption coefficient, optical band gap and Urbach energy) of cobalt-doped and Co/Al-co-doped ZnO thin films deposited by electrospray (ES). The successful implementation of ES as a versatile method of deposition of doped and pure ZnO films at moderate temperatures without postdeposition annealing is demonstrated. Cobalt doping and co-doping of ZnO with Al and Co is achieved simply by mixing appropriate amounts of nontoxic and inexpensive precursors (zinc acetate dehydrate, cobalt acetate and aluminum nitrate) and solvents (de-ionized water and ethanol). The impact of doping and co-doping on the films' properties is revealed and discussed.
# Limitations of Large Language Models in Propaganda Detection Task
## 1 Introduction  
The digital era has ensured people easy access to information coming from various sources, such as social media, messaging applications or online news websites. However, the higher the number of facts and the more diverse the sources are, the harder it becomes to filter what is an objective piece of information. The vast amount of content found online is biased [1], spreads misinformation [2] and disinformation [3], or conveys propagandist overtones [4]. All of these practices can lead to the promotion of particular agendas, manipulation of facts, deliberate spread of false information, skewed reporting or change in audience perceptions.  
This is especially a challenge for online news, which ought to, as any other news outlet, present information objectively. The risks are even higher as the online sphere allows for the rapid and widespread dispersion of unchecked news. Understanding and addressing the aforementioned issues is vital for maintaining the credibility of online news providers for people who seek accurate and impartial reporting.  
In this study, we focused on Polish online news, as we think that under-resourced languages pose even bigger problems in handling these challenges. The research hitherto already tried to study emotional charge in potentially controversial topics and showed thatthere are grounds to claim differences in presenting the emotional load depending on the topic and the news provider [5]. Additionally, an investigation of abilities to specify a news article's establishment and political leaning showed that such a task is not only difficult for recently emerged large language models (LLMs) but also for humans [6]. In this study, we tackled another difficult problem of propaganda detection. The scarce research on it and the shortage of experts in this field, especially in Poland, indicates that this problem needs to be addressed in a time- and cost-efficient way. Therefore, we would like to propose a method that utilizes LLMs for this task and investigate whether it is a noteworthy replacement of human annotators.
# Local Dependency-Enhanced Graph Convolutional Network for Aspect-Based Sentiment Analysis
## 5 Discussion  
### The Influence of the Number of Layers (L)  
Selecting an appropriate number of GCN layers (L) is beneficial for model performance, so we explored the impact of different L values, as shown in Figure 5. The results indicate that when \(L=2\), the Acc and F1 scores are the highest across all datasets.  
The aspect \"food\" has different syntactic distances from the context. With a single GCN layer, each word node aggregates feature information from adjacent word nodes. Therefore, the aspect \"food\" captures feature information only from the context with an \(SD\) of 1. When \(L\) is 1, it is evident that \"the\" and \"is\" do not provide sufficient information. With two GCN layers, \"food\" captures important sentiment features from the context words \"really\", \"not\" and \"delicious\" with an \(SD\) of 2. However, if \(L\) is set to 3 or more, it introduces irrelevant feature information, such as the positive sentiment feature from the word \"romantic\".  
In summary, when \(L=1\), our model fails to capture sufficient aspect-contextual feature information, which may lead to incorrect judgments. When \(L=2\), the aspect \"food\" itself has high semantic importance, and the semantic importance of \"really\", \"not\", and \"delicious\" increases, which benefits our model. However, when \(L=3\) or more, the distribution of semantic importance in the context becomes dispersed, making it difficult for the aspect \"food\" to focus on important information. A visualization of the attention distribution from Layer 1 to Layer 3 is shown in Figure 6.  
Figure 5: Impact of GCN layer, L, on the model effect.  
Figure 6: Visualization of semantic weights for different L.