esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Microplastic Content and Grain Size of the Sediment  
During the 2020 sampling campaign, both fine-grained and coarse-grained sediment samples were collected at each sampling site to reveal the relationship between the MP content of the sediment and its grain size. Considering all samples, the MP content is not related to the sample's particle composition (\(d_{50}\)), as the R\({}^{2}\) was below 0.1 (Figure 7).  
Some weak correlations emerged when the samples were classified based on their locations along the Tisza and their geomorphological position. In section S1, the fine-grained samples contained 39% more MPs than the coarse-grained samples collected at the same site (Figure 7). However, the difference between the two sediment types was reversed and gradually increased with the flow direction, i.e., fine-grained samples contained less and less MPs than the coarse samples. Thus, in section S2, the fine-grained samples contained 17% less MP than in the coarse samples, then the difference increased to 43% (S3) and 51% (S4) further downstream.  
It was also evaluated whether the MP content changes with flow direction in samples with different particle compositions to the same extent (Figure 7). The fine-grained samples contained more and more MPs toward section S3, but the enrichment of the MP decreased, as it was 19% between section S1 and S2 and just 6% between sections S2 and S3. Further downstream, instead of enrichment, the sediments contained less and less MP by 16% (S4/S3) and 60% (S5/S4). Contrary to fine-grained sediments, there was a steady increase in MP in the coarse sediments from section to section, though the enrichment was getting smaller: by 99% (S2/S1), then 54% (S3/S2) and finally only by 2% (S4/S3).  
The MP content in coarse- and fine-grained samples from the same geomorphological forms was also evaluated. On the point-bars, the MP content of the coarse material was higher by 34-55% than at the same site in the fine-grained samples (Figure 8). The difference was particularly pronounced in the Middle Tisza (S3-S4) samples containing the most MP particles. In the case of the point-bars' coarse material, there was only a weak correlation between the MP content and the coarseness of the grains. However, in the case of side-bars or sediment sheets, no correlation was found between the MP content of the coarse- or fine-grained samples. In the side-bars and sediment sheets of the Upper Tisza, the fine-grained material contained more MP (by 30%) than the coarse material, but in the Middle Tisza, the situation was the opposite, so the sandy samples contained more MP by 39-44%.  
Figure 7: (**A**) Relationship between the grain size (\(d_{50}\)) of the samples and their microplastic content (item/dry kg). (**B**) Mean microplastic content (item/dry kg) of the fine-grained and coarse-grained sediments collected from the various sections of the Tisza in 2020.
## 3 Experimental Results and Analysis  
### Testing for Meaningful Signals with Respect to Noise (O1)  
As described in Section 2.2, the DMA testing provided a conventional, yet rigorous means of testing whether the objective O1 is achievable. The procedure P1 involved a relatively high vibration frequency peak of 100 Hz and low vibration amplitude of 25 \(\upmu\)m defined in the DMA operational settings. Two type-A blanc and two -A slabs were initially tested in dynamic three-point bending see Figure 2(a). Typical responses of one type-A and one -A blanc in form of electrical signals (in V) versus time are presented in Figure 2(b). Obviously, based on the time plot alone, no quantitative distinction (for instance, differences on signal variance or mean) between signals from type-A and -A blanc slabs may be noted. This behavior is not unreasonable, because the external mechanical loading cannot generate significant electrical power by means of the magnetoelastic phenomenon. Consequently, when similar passive techniques (i.e., no excitation on the reception coil) are utilized, one should at best expect qualitative rather than quantitative changes in the signals recorded. In other words, the spectral characteristics of such signals should be analyzed.  
Figure 5 presents the power spectra corresponding to response signals from two type-A blanc and two A slabs showing clear differences. The insets show significant spikes at around 740 and 1220 Hz for signals from the two type-A slabs (red and green lines, respectively). At these frequencies, at least, there is a visible contribution from the M-sensor, superseding the effects of other phenomena (noise and so on). The latter show up as an output response signal on the coil even when a type-A blanc slab is tested.  
Furthermore, in view of Figure 5, note that the slab excitation takes place at frequencies up to 100 Hz, yet the slab response shows mainly frequency content (harmonics) at higher frequencies (740 and 1220 Hz). The explanation may be two-fold. First, Figure 5 suggests that, at low frequencies (up to 200 Hz), a type-A slab actually produces a signal which has insignificant frequency content, barely distinguishable from parasitic noise. At higher frequencies and especially at 740, 1220 Hz, or more, the magnetoelastic strip seems to be generating a signal with more significant frequency content. This lack of sensitivity might be alleviated if a more efficient (and, thus, expensive) coil was used, but this would compromise one of the main goals of this work, namely performing damage diagnosis via conventional, affordable solutions. Second, there is the possibility of the vibrating slab behaving in a nonlinear manner. External loading of the slab at a given frequency produces a response signal with a shift of that frequency (and its harmonics), see Ch. 5 in [27]. Even though it is not trivial to state which explanation is valid, the fact is that, at a specific frequency band, the M-sensor can provide a signal qualitatively different than that recorded by vibrating type-A blanc slabs, thus achieving objective O1.
## 1 Introduction
On the other hand, there is a growing demand among certain consumer groups for food products without synthetic additives. Consequently, active packaging could be a promising alternative for effectively preventing the deterioration of fat-based products [17]. In fact, films incorporating active substances have the ability to slow down or inhibit lipid oxidation by regulating the release of antioxidants into the food product [18]. For instance, high-density polyethylene active package containing rosemary extract powder effectively delayed the oxidation of sunflower oil [18]. Polyethylene terephthalate bottles enriched with titanium dioxide and zinc oxide nanoparticles efficiently acted as antimicrobial packaging for mayonnaise sauce [19]. Furthermore, [PERSON] et al. [20] reported that the oxidation of butter wrapped in chitosan film sachets enriched with _Santalum album_ essential oil was delayed compared to butter stored without film or with chitosan film without the active additive. Thus, it can be assumed that both natural and synthetic packaging materials reinforced with active substances significantly extend the shelf life of high-fat food products. Recently, black cumin oil has been used to increase the oxidative stability of mayonnaise [21], but no data have been found regarding the application of black cumin cake extract (BCCE) as a direct additive or as an active component of the packaging material of mayonnaise to inhibit its oxidation processes. Moreover, the scientific literature has presented little information on studies to determine the shelf life of new vegan mayonnaises with functional additives. However, the evaluation of the shelf life of these products is crucial in the fat industry because it ensures the quality and safety with which the oil-based product will be distributed. Thus, the main objective of this research was to estimate the changes in the antioxidant capacity (AC) and oxidation stability of egg-free mayonnaises with natural antioxidants present in BCCE, synthesized octyl caffeine (OC), and contacted with active film incorporating BCCE during storage for up to 4 weeks. The antioxidant effect of BCCE and OC on the antioxidant properties, oxidative status, and shelf life of aquafaba-based mayonnaise was investigated. The AC of prepared mayonnaise samples was compared with the antioxidant potential of commercial plant-based mayonnaise (CPBM). The modified QUick, Easy, New, CHEap, and Reproducible-2,2-diphenyl-1-picrylhydrazyl (QUENCHER-DPPH) and QUick, Easy, New, CHEap, and Reproducible-2,2\({}^{\prime}\)-azino-bis(3-ethylbenzothazoline-6-sulfonic acid) (QUENCHER-ABTS) procedures were applied for AC evaluation. However, the amounts of primary and secondary oxidation products and free fatty acids in all studied mayonnaise samples were analyzed using the official methods. Moreover, principal component analysis (PCA) was applied for grouping the studied mayonnaise samples, their characterization, and detecting differences.
# Enhanced Speech Emotion Recognition Using Conditional-DCGAN-Based Data Augmentation
## 2 Related Works
### Research on Speech Emotion Recognition
#### 2.2.3 Bidirectional Long Short-Term Memory  
Bi-LSTM (Bidirectional Long Short-Term Memory) is an extension of LSTM, where two separate LSTMs process the sequence in both forward and backward directions, utilizing both past and future information to learn the bidirectional context of sequential data. This approach is particularly useful in applications where bidirectional information is important, such as natural language processing (NLP), speech recognition, and time-series prediction. Bi-LSTM has also been widely used in speech emotion recognition, as shown by the experiments conducted by [PERSON] et al. [29] and [PERSON] et al. [30].  
[PERSON] et al. [29] extracted 72-dimensional features, including chroma and MFCC, from the Emo-DB [13], SAVEE [31], and CASIA [32] datasets and used two parallel Bi-LSTM models. The results showed that Emo-DB achieved WA of 86.44% and UAR of 84.65%, SAVEE achieved WA of 62.49% and UAR of 56.50%, and CASIA achieved WA of 58.25% and UAR of 79.67%. Additionally, [PERSON] et al. [30] proposed an LSTM-Transformer model using MFCC as input and tested it on the RAVDESS [14] and Emo-DB datasets. The model achieved WA of 77.33% and UAR of 75.62% for RAVDESS and WA of 87.72% and UAR of 85.55% for Emo-DB.
## 6 Future Outlook and Recommendations
### Holistic Policy and Ethical Frameworks  
Maximizing benefits while mitigating risks requires a unified policy approach. Regulatory bodies, accreditation agencies, and local governments should coordinate guidelines on data protection, algorithmic transparency, and teacher certification in AI tools. Policies must be flexible enough to accommodate variations in school budgets and technical expertise. Internationally recognized principles rooted in fairness, accountability, and human-centric design can offer scaffolding to ensure that advanced telecommunications serve, rather than overshadow, fundamental educational goals. Given the rapid evolution of AI and 5G technologies, education policy-makers should base decisions on empirical data rather than solely aspirational visions. Longitudinal studies can capture shifts in student engagement, teacher attitudes, and system performance over multiple academic cycles. In parallel, cost-benefit analyses at local and district levels will clarify when it is economically viable to scale pilot programs. Evidence-based frameworks also reduce the risks of underutilized tools or hasty technology retirements which fail to deliver anticipated learning gains.
# Empowering Pedestrian Safety: Unveiling a Lightweight Scheme for Improved Vehicle-Pedestrian Safety
## 5 Limitations  
The study primarily focuses on a set of predefined pedestrian behaviors, such as typical walking patterns. However, the real-world variability in pedestrian actions, sudden changes in direction, and interactions with the environment may not be fully captured.  
Figure 11: Design parameters in current V2P systems.  
Figure 10: Classification of V2P system design approaches.  
A broader exploration of diverse pedestrian behaviors, including unpredictable movements, is essential for a more comprehensive assessment. The proposed scheme relies on the assumption that pedestrians actively engage with their smartphones during road activities. This assumption may not align with real-world scenarios, where distractions or varying levels of smartphone usage might impact the effectiveness of V2P communication. The scheme may not thoroughly explore potential privacy concerns associated with collecting and communicating data from pedestrians' smartphones. Balancing the need for safety with individual privacy rights is a delicate aspect that should be carefully considered in the deployment of V2P communication systems.
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 5 Detailed System Description
### User Joins the System
#### 5.1.2 Creation of ARMs  
Once the UIP has been constructed, the marked interests that have been associated with the user's activity may be used to create requests for relevant adverts while the remaining entries are ignored. The client begins by generating a cryptographic key \(K_{0}^{user}\), which is then used to create multiple one-time keys \(K_{i}^{user}\), which are produced with the use of a hash chain:  
\[K_{i}^{user}=h(K_{i-1}^{user}) \tag{1}\]  
The client goes through the UIP and recovers the Interest-ID for each of the marked entries. For each of the marked interests, the client then creates an Ad Request Message (ARM), which contains the equivalent Interest-ID and a new \(K_{i}^{user}\). The ARMs are then encrypted with a public key that comes pre-installed in the client and is known as the System Encryption Key (\(EK_{System}\)). Finally, the resulting encrypted ARMs, as well as a copy of each \(K_{i}^{user}\) are stored for future use. Note that this operation does not need to take place in real time. We can therefore significantly preserve resources by performing it while the device is idle and preferably connected to a power source.  
\[ARM_{(ARM-ID)}=E_{EK_{System}}\Big{[}interest-ID,K_{i}^{user}\Big{]} \tag{2}\]
## 6 Strengths, Limitations, and Future Research Directions
### Strengths
#### 6.1.5 Explainability and Interpretability  
CNNs produce feature maps that can be visualized, making it possible to interpret the behaviour of the model by visualizing the learned features or activations in different layers. They capture local features in images, such as edges or textures, which can lead to interpretable features that are visually meaningful and can provide insight into how the model is processing the input images. ViTs, on the other hand, are designed to capture global contextual information, making them potentially more interpretable in tasks that require an understanding of long-range dependencies or global context. They have a hierarchical structure with self-attention heads that can be visualized and interpreted individually, providing insights into how different heads attend to different features or regions in the input images.
# Position Accuracy and Distributed Beamforming Performance in WSNs: A Simulation Study
## 4 The Number of Sensors and Cramer-Rao Bound  
The number of sensor nodes significantly influences the system's performance, affecting energy efficiency and the resulting beam pattern. Increasing the number of nodes generally leads to a narrower main beam and a reduction in the number of side lobes, improving the directivity of the beam [6,18,19]. Figure 5 illustrates this effect by comparing the beampatterns of two linear arrays with four and eight elements.  
Furthermore, the number of sensors can significantly impact the system's accuracy. To evaluate the theoretical limits of position estimation accuracy, we employ the Cramer-Rao Bound (CRB) [20]. The CRB provides a lower bound on the variance of any unbiased estimator, offering a valuable benchmark for assessing the achievable accuracy in the presence of noise and uncertainties. While other bounds exist, the CRB is widely recognized for its effectiveness and unbiased nature in estimating the precision of parameter estimation, as it is directly derived from the Fisher Information Matrix (FIM) [20].  
In our case, we are concerned with the system's performance when there is an error in the position of the sensors, which leads to an error in the computed phase shift (5) significantly affecting the Bit Error Rate (BER) performance, especially when fewer sensors are contributing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Frequency (\(f\)) & 433 & MHz \\ \hline Propagation Speed (\(c\)) & \(3\times 10^{8}\) & m/s \\ \hline Signal-to-Noise Ratio (\(SNR\)) & \([-20;20]\) & dB \\ \hline Number of Sensors (\(N\)) & 40 & - \\ \hline Error Standard Deviation (\(\sigma\)) & \([0,0.025,\ldots 0.1]\times\sqrt{\lambda}\) & m \\ \hline Field side & 100 & m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 4: Schematic of simulation testbed for position of sensors error.  
We can use the FIM to find the lower bound on the phase shift resulting from errors in the sensors' positions. The FIM can be obtained from the gradient, \(\
abla\phi\), of the phase shift Formula (5) with respect to the coordinates of the sensors' positions, as given by  
\[\
abla\phi=\frac{2\pi}{\lambda}\Big{(}\tilde{S}_{(x,y,z)}-D_{(x,y,z)}\Big{)} \oslash||\tilde{S}_{(x,y,z)}-D_{(x,y,z)}|| \tag{9}\]  
where \(\tilde{S}_{(x,y,z)}\) is the matrix of sensor position coordinates, with one sensor per row; the row vector \(D_{(x,y,z)}\) represents the true coordinates of the drone receiver; \(||\tilde{S}_{(x,y,z)}-D_{(x,y,z)}||\) is the vector of distances of each sensor to the drone; and \(\oslash\) denotes element-wise Hadamard division. The FIM matrix is thus given by  
\[FIM=\
abla\phi\odot\
abla\phi^{\prime} \tag{10}\]  
where \((^{\prime})\) denotes the transpose operation and \((\odot)\) represents the Hadamard dot product between the gradients.
## 3 Results
### Opportunities to Improve Model Pharmacies: Perspectives of the Pharmacists
#### 3.3.2 Role of Pharmacist-in-Charge  
Given the shortage of Grade A pharmacists, it has been explored whether Grade B pharmacists could fill in for Grade A pharmacists when necessary. However, the majority of the Grade A pharmacists indicated that Grade B pharmacists study a three-year diploma course, whose curriculum has comparatively low standards compared to the four-year bachelor's course studied by a Grade A pharmacist. As a result, there is insufficient coverage of pharmaceutical topics in the course curriculum for the Grade B pharmacists. Examples include managing the extensive inventory of medicines and overseeing pharmacy operations, which would require a comprehensive knowledge of pharmacology, which Grade B pharmacists cannot learn from their limited diploma courses. Further, the current operational guidelines for the model pharmacies do not permit Grade B pharmacists to take on the role of a pharmacist-in-charge. As such, Grade B pharmacists are not equipped with adequate knowledge and training to assume the role of a pharmacist-in-charge in a model pharmacy, which is the expected role of a Grade A pharmacist. Therefore, the Grade A pharmacists discouraged the promotion of the role of a Grade B pharmacist to the role of a Grade A pharmacist, and instead advised that the number of graduate pharmacists be increased to facilitate the operations of a high number of model pharmacies in order to improve quality of pharmacy practices in Bangladesh.  
\"There is no opportunity to replace Grade A pharmacist by Grade B. I guess, there is no opportunity to run a model pharmacy without a Grade A pharmacist in-charge. Otherwise, those pharmacies will not be considered as model pharmacies. One Grade A pharmacist is essential to run a model pharmacy.\" (IDI-A-DH-07)  
Nonetheless, a few Grade A pharmacists acknowledged that the Grade B pharmacists could potentially assume some limited roles of a pharmacist-in-charge in the absence of a Grade A pharmacist, provided that they acquire proper training and gain adequate experience. On the contrary, the Grade B pharmacists expressed confidence in their ability to take on the role of a pharmacist-in-charge, and the majority of them argued that their qualifications, including the length of their studies and the hands-on training received during an internship, adequately prepare them for taking over the role of a Grade A pharmacist. One Grade B pharmacist stated the following:  
\"If you ask about the options of Grade A pharmacists, I should say yes. Grade B pharmacists can manage model pharmacy because they have a 3
## 2 Methodology
### Feature Section Based on Statistical Methods  
For classical statistical methods, the discriminative ability of a feature can be quantitatively measured by its contribution on distinguishing different classes [25; 44].  
The Kendall tau correlation coefficient provides a distribution-free test of independence between two variables. The Kendall tau correlation coefficient of feature \(j\) can be defined as:  
\[\tau_{j}=\frac{n_{c}-n_{d}}{n_{1}\times n_{2}} \tag{5}\]  
where \(n_{c}\) and \(n_{d}\) are the numbers of concordant and discordant pairs, respectively, and \(n_{1}\) and \(n_{2}\) correspond to the number of two classes of samples, respectively. For a pair of data \((x_{ij},y_{i})\) and \((x_{kj},y_{k})\) of feature \(j\), it is a concordant pair when \(sgn(x_{ij}-x_{kj})=sgn(y_{i}-y_{k})\), where \(sgn()\) is the signum function (i.e., \(sgn(x)=-1\) with \(x<0\), \(sgn(x)=0\) with \(x=0\) and \(sgn(x)=1\) with \(x>0\)). Correspondingly, it is a discordant pair when \(sgn(x_{ij}-x_{kj})=-sgn(y_{i}-y_{k})\). The discriminative power of each feature \(j\) is defined as the absolute value of its Kendall tau correlation coefficient.  
The permutation test is a non-parametric test method, which is suitable for the case of a small sample size and unknown sample distribution. Assume that there are two samples \(x_{A}\) and \(x_{B}\), and \(\bar{x}_{A}\) and \(\bar{x}_{B}\) denote the corresponding sample mean, say \(n_{A}\) and \(n_{B}\) are the corresponding sample size. At first, we calculate the observed test statistic \(T_{obs}=\bar{x}_{A}-\bar{x}_{B}\). Then, the two samples are merged and divided into two groups with size \(n_{A}\) and \(n_{B}\). For each division, the difference between the mean values of the two groups is calculated and recorded. The calculated difference set is the accurate distribution of the difference under the null hypothesis. Finally, the ratio of the absolute value of the calculated difference greater than or equal to the absolute value of \(T_{obs}\) is the \(p\)-value based on the two-sided test.  
By the two-sample \(t\)-test, we can also determine whether there are significant differences of each feature. The \(t\)-value of the feature \(j\) can be defined as:  
\[t_{j}=\frac{|\bar{x}_{1}-\bar{x}_{2}|}{\sqrt{\frac{(n_{1}-1)\bar{x}_{1}^{2}+( n_{2}-1)\bar{x}_{2}^{2}}{n_{1}+n_{2}-2}\cdot(\frac{1}{n_{1}}+\frac{1}{n_{2}})}} \tag{6}\]  
where \(\bar{x}_{1}\) and \(\bar{x}_{2}\) are the means of feature \(j\) of patients and health controls (HCs) and \(s_{1}\) and \(s_{2}\) represent the corresponding standard deviations. With the Kendall tau correlation coefficient, permutation test and two-sample \(t\)-test, we can identify features with significant differences.
## 2 Materials and Methods
### Machine Learning Classification Models
#### 2.4.1 Support Vector Machine (SVM)  
Support Vector Machine (SVM) is a supervised machine learning algorithm. SVM performs well not only in processing high-dimensional data but also on small sample data. Its main goal is to find an optimal hyperplane and effectively use this plane to separate different data types. In addition, SVM can also use different kernel functions to cope with linear and nonlinear problems, including linear, polynomial, radial basis functions, and sigmoid functions. According to the training data distribution, the polynomial kernel function is applied to the model in this study.