esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistics  
Means and standard deviations were calculated for all technical indicators across difficulty levels. A mixed linear model with subjects as a random effect was first used, but no significant subject-related effects were found. Therefore, one-way ANOVA (SPSS Inc., Chicago, IL, USA) was used to analyze the technical characteristics at different difficulty levels. The model treated difficulty level as the independent variable and technical indicators as dependent variables. The significance level \(\alpha\) was set at 0.05. When the main effect of difficulty level reached statistical significance (\(p<0.05\)), post hoc pairwise comparisons were performed using [PERSON]'s Honestly Significant Difference (HSD) test to identify specific differences between difficulty tiers. Homogeneity of variance was verified using [PERSON]'s test prior to ANOVA. Effect sizes were quantified using partial eta-squared (\(\eta_{\text{p}}^{2}\)) values to assess the proportion of variance in technical indicators explained by difficulty level, with values of 0.01, 0.06, and 0.14 interpreted as small, moderate, and large effects, respectively.
###### Abstract  
Polymer nanocomposites containing nanometals became a subject of interest due to their bactericidal properties. Different polysaccharides have been used as matrices for nanosilver and nanogold synthesis. In this study, we present a novel, environmentally friendly method for the preparation of sodium alginate/nanosilver/graphene oxide (GOX) and sodium alginate/nanogold/graphene oxide GOX nanocomposites and their characteristics. The formation of approximately 10-20 nm ball-shaped Ag and Au nanoparticles was confirmed by UV-vis spectroscopy, scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) spectra. The incorporation of GOX sheets within the ALG matrix improved the thermal stability of the nanocomposites film, which was measured using the differential scanning calorimetry (DSC). We also estimated the molecular weights of polysaccharide chains of the matrix with the size exclusion chromatography coupled with multiangle laser light scattering and refracometric detectors (HPSEC-MALLS-RI). The composites were more prone to enzymatic hydrolysis. The strongest bacteriostatic activity was observed for the sample containing nanosilver.  
Keywords: polysaccharides; sodium alginate; nanocomposites; graphene oxide; nanosilver; nanogold +
Footnote †: journal: Applied Sciences
# Atenea Exams, an IMS LTI Application to Solve Scalability Problems: A Study Case
## 3 Solution and Results
### A solution to Cope with Scalability Issues in Atenea
#### 3.2.2 The Selected Architecture  
The UPC chose option 2: a single high-performance instance. This choice was copied by many other institutions, some such as the University of Barcelona which with the technical support of UPCNet deployed its virtual Campus for 80,000 users.  
Independent Moodle servers called Atenea Labs were made available to teachers who requested them to accommodate teaching innovation activities with experimental Moodle modules. The servers at Atenea Labs were not initially connected to the UPC's user authentication system, and user registrations for student courses must be made manually. However, one of the modules tested at Atenea Labs, the Wiki module developed by [PERSON], [PERSON], [PERSON] and other FIB students, became part of the official distribution of Moodle from version 2.0 in 2010.  
The architecture chosen in a single instance turns out to be a good option and together with a good execution by UPCNet, the Moodle-based Atenea platform has been providing services for 16 years without major incidents.  
In the autumn term of the 2019-2020 academic year, the Atenea system supports approximately:  
* 60,000 daily user sessions
* Peaks of 6000 simultaneous sessions (with historical peaks of 9000 concurrent sessions)
###### Abstract
It should also be mentioned that another configuration of scanning the breast in the horizontal plane using FASCI (full-angle spatial compound imaging) allows the best results of 3D breast imaging in terms of visualization of quality of coronal sections (Figure 1c). However, when using a single B-mode ultrasound scanner with an ultrasound probe, it is necessary to rotate the probe around and move along the water-submerged breast, significantly prolonging and complicating the diagnostic scanning procedure [41; 42]. It is also possible to use multiple B-mode probes together with separate probes and beamforming systems [43; 44] or a specially designed multi-element ultrasonic ring array with an ultrasound tomography scanner system, which can switch the sectors of the reflection imaging array. The sectoral images are then automatically reconstructed using the CI method or the synthetic aperture focusing technique (SAFT) [41; 45; 46]. However, such methods are very complicated, and the devices that use them are specialized and very expensive medical diagnostic equipment. The research presented in this paper is focused on the evaluation of the capabilities of different types of standard B-mode ultrasound probes used in MACUI to acquire breast sections. Each of these probes uses a different method of steering the ultrasound beam, which also involves different folding of the imaging lines using RF signals and different sizes of the imaging area, which in turn is important in the CI method. Studies in this context have not been presented in the literature before and are essential in terms of the universal applications and the wider use of this method in clinical practice. The probes that are used in the work are linear, convex, and phased array probes and are highly available standard probes used in HHUS imaging.
# Energy Consumption Prediction for Fused Deposition Modelling 3D Printing Using Machine Learning
## 4 Methodology
### Overview of Machine Learning Algorithms
#### 4.1.6 Elastic Net Regressor  
The elastic net regression model was introduced by [34] in order to solve the criticisms on the lasso, whose set of selection variables may be too dependent on the data and therefore unstable. The solution is to combine the regression penalties of ridge regression and lasso to exploit the best aspects of both methods. Elastic net aims to minimize Function (16):  
\[L_{\text{enet}}\ (\hat{\beta})=\frac{\sum\limits_{i-1}^{n}\big{(}y_{i}-x_{i}^{ \prime}\hat{\beta}\big{)}^{2}}{2n}+\lambda\left(\frac{1-\alpha}{2}\sum\limits _{j=1}^{m}\hat{\beta}_{j}^{2}+\alpha\sum\limits_{j=1}^{m}\bigl{|}\hat{\beta}_{ j}\bigr{|}\right) \tag{16}\]  
with:\(\hat{\boldsymbol{\gamma}}=\hat{\beta}_{0}+\mathbf{x}_{1}\hat{\beta}_{1}+\cdots+ \mathbf{x}_{n}\hat{\beta}_{n}\) is the predicted value for the input X of n elements.  
\(\hat{\beta}_{i}\) is the predicted coefficient.  
\(0\ \leq\ \alpha\ \leq 1\) and \(\lambda\geq 0\)  
where \(\alpha\) is the mixing parameter between ridge regression (\(\alpha=0\)) and the lasso (\(\alpha=1\)). Here, \(\alpha\) determines the influence of ridge regression relative to lasso.  
There are two parameters to adjust: \(\lambda\) (penalty) and \(\alpha\).  
Elastic net must minimize the following function [34]:  
\[EN(\beta)=\sum\limits_{i=1}^{n}\left(y_{i}-x_{i}^{\top}\beta\right)^{2}+ \lambda_{1}\sum\limits_{j=1}^{p}\bigl{|}\beta_{j}\bigr{|}+\lambda_{2}\sum \limits_{j=1}^{p}\bigl{|}\beta_{j}\bigr{|}^{2} \tag{17}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are the lasso and ridge regression penalties, respectively.  
Elastic net combines the advantages of lasso and ridge, which are shrinkage and sparsity, respectively.
## 3 Results and Discussion
### Dust Emission and Distribution Characteristics during Coal Charging
#### 3.3.2 Distribution Characteristics of Coal Dust  
The position and velocity distribution of dust particles escaping from the coal charging process were simulated with new equipment by the FLUENT software. The simulation results are shown in Figure 11. Figure 11a represents the process of filling the cylinder with coal. Figure 11b is the process of filling the chamber with coal as the cylinder is lifted, 1 and 2, respectively, represent the dust emission measurement point near the coke oven charging hole and the dust emission measurement point in the coking chamber. According to the simulation results, when the cylinder was not lifted, coal particle dust was mainly concentrated in the cylinder, but when the coal was filled into the cylinder, dust particles were expelled from the gap between the cylinders due to air resistance. This was the same as the results obtained from the test points in the experimental process. When the cylinder began to lift, the coal continuously flowed out of the cylinder blanking port, then diffused and flowed along the length of the coking chamber.
## 3 Extraction of Bistatic RCS from Passive Radar Measurements
### Inversion of the Bistatic Radar Equation  
Let us indicate with \(P_{S}(t)\) the target signal power at trajectory time \(t\) and with \(P_{N}(t)\) the corresponding noise power. Let us also assume that the noise power \(\vec{P_{N}}(t)\) can always be estimated from the RDM by averaging in a noise-only region, and that this value is then used to normalize the entire RDM. Since a passive radar system does not have control over the transmitted signal (and consequently on the quality of the corresponding received direct signal), the noise level \(P_{N}\) (or \(\vec{P_{N}}\)) is also a function of the trajectory time \(t\). The signal power measured in the resulting RDM at the target position \(P_{tgt}(t)\) will then be the sum of the target signal power \(P_{S}(t)\) plus the noise power \(P_{N}(t)\) divided by the estimated noise power \(\vec{P_{N}}(t)\). That is,  
\[P_{tgt}(t)=\frac{P_{S}(t)+P_{N}(t)}{\vec{P_{N}}(t)}\simeq SNR(t)+1, \tag{4}\]  
assuming \(\vec{P_{N}}(t)\simeq P_{N}(t)\). By recalling the bistatic radar equation, the SNR over trajectory time \(t\) can be written as:  
\[SNR(t)=\frac{P_{tx}G_{tx}G_{rx}\lambda^{2}\,\sigma(t)\,\,N_{\text{int}}(t)}{( 4\pi)^{3}R_{tx}^{2}(t)R_{rx}^{2}(t)P_{N}(t)L}, \tag{5}\]  
where \(P_{tx}\) is the transmitted signal power, \(G_{tx}\) and \(G_{rx}\) are respectively the transmitter and receiver gains in the directions of the target, \(\lambda\) is the carrier wavelength, \(\sigma(t)\) is the true bistatic radar cross section, \(N_{\text{int}}(t)\) indicates all processing gain including: (i) the coherent Doppler integration over \(M_{S}\) DVB-T symbols; (ii) the compression gain of the RpF; and (iii) the incoherent integration over \(K_{c,j}(t)\) RDM cells. \(R_{tx}(t)\) and \(R_{rx}(t)\) are the one-way ranges from the target to the transmitter and to the receiver, respectively. Finally, \(L\) depicts all losses at the receiver. Please note that in general the terms, \(G_{tx}\) and \(G_{rx}\) also depend on the trajectory time \(t\).  
By plugging (5) into (4) and by compensating for the time-varying range terms, the following quantity that is directly proportional to the target RCS is obtained.  
\[\tilde{\sigma}(t)=\gamma\cdot\sigma(t)=\left(P_{tgt}(t)-1\right)\cdot R_{tx}^ {2}(t)R_{rx}^{2}(t) \tag{6}\]  
where \(\gamma\) accounts for all nonvarying terms in (5).
## 3 Results and Discussion
### Performance Modeling
Although all three independent variables interacted with each other through double or triple interactions, these interactions did not significantly impact the system as shown in Table 9.  
Footnote 3: The statistical analysis results in Minitab for the \(\text{y}_{\text{Vf}}\) model. As seen from Table 10, while the predictor of \(\text{X}_{2}\)3 entered the \(\lambda\) model in the first step and could be explained by 41.93% of the system, the predictor of \(\text{X}_{3}\)3 entered the model in the second step and affected approximately 33% of the system. Although the interactions that entered the model in the third and fourth steps were not as effective as the seed rate and seed falling angle on the system, they were included in the model equation in terms of \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Step No**} & **Model** & **Model** & **Standard** & **R2 Variation** \\  & **Predictors** & **Coefficient** & **Deviation** & **(\%)** \\ \hline - & Model Constant & 2.020 & - & - \\
1 & \(\text{X}_{2}\)3 & 2.84 & 5.55 & 41.93 \\
2 & \(\text{X}_{3}\)3 & \(-\)3.28 & 3.65 & 75.37 \\
3 & \(\text{X}_{1}\)\(\text{X}_{3}\) & \(-\)2.29 & 3.36 & 79.47 \\
4 & \(\text{X}_{1}\)\(\text{X}_{2}\)\(\text{X}_{3}\) & 2.29 & 3.03 & 83.56 \\
5 & \(\text{X}_{1}\) & 1.67 & 2.69 & 87.26 \\
6 & \(\text{X}_{1}\)\(\text{X}_{2}\)\(\text{X}_{3}\) & \(-\)1.34 & 2.50 & 89.22 \\
7 & \(\text{X}_{2}\)2 & \(-\)1.10 & 2.34 & 90.72 \\
8 & \(\text{X}_{3}\)2 & \(-\)0.75 & 2.26 & 91.51 \\
9 & \(\text{X}_{3}\) & 1.66 & 2.18 & 92.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The statistical analysis results in Minitab for the \(\text{y}_{\lambda}\) model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Step No**} & **Model** & **Model** & **Standard** & **R2 Variation** \\  & **Predictors** & **Coefficient** & **Deviation** & **(\%)** \\ \hline - & Model Constant & 0.5507 & - & - \\
1 & \(\text{X}_{1}\)2 & \(-\)0.576 & 0.753 & 31.82 \\
2 & \(\text{X}_{2}\) & \(-\)0.565 & 0.589 & 59.00 \\
3 & \(\text{X}_{1}\)3 & \(-\)0.365 & 0.462 & 75.17 \\
4 & \(\text{X}_{1}\)\(\text{X}_{3}\) & 0.387 & 0.390 & 82.64 \\
5 & \(\text{X}_{1}\)2 & \(X_{3}\) & 0.313 & 0.334 & 87.53 \\
6 & \(\text{X}_{2}\)2 & \(-\)0.158 & 0.305 & 89.79 \\
7 & \(\text{X}_{1}\) & 0.321 & 0.279 & 91.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The statistical analysis results in Minitab for the \(\text{y}_{\text{Vf}}\) model. their tendency to increase the R\({}^{2}\) value. The terms that entered into the equation from the fifth step onwards affected the system with a decreasing rate as R\({}^{2}\) increased. The resulting model equations are valid for the system under the specified constraint values, and it should be noted that the predictability of the models may decrease with different values. 1.01 ms\({}^{-1}\)\(\leq\) Forward speed \(\leq\) 3.01 ms\({}^{-1}\)  
6.6 kg ha\({}^{-1}\)\(\leq\) Seed rate \(\leq\) 23.4 kg ha\({}^{-1}\)  
1.36\({}^{\circ}\)\(\leq\) Seed falling angle \(\leq\) 68.64\({}^{\circ}\)
# Compressive Sampling with Multiple Bit Spread Spectrum-Based Data Hiding
## 1 Introduction
Then, the outputs of SVD, i.e., \(\mathbf{U}\), \(\mathbf{S}\), and \(\mathbf{V}\), are truncated at a specific rank. We transform the truncated singular matrix \(\mathbf{S}_{r}\) to compressed domain \(\mathbf{Y}\) via an over-complete dictionary containing SS-based data hiding \(\mathbf{A}\). Thus, the matrices ready to be transmitted to the detector are \(\mathbf{U}_{r}\), \(\mathbf{Y}\), and \(\mathbf{V}_{r}\). Then, in the receiver, firstly, we detect dictionary \(\mathbf{A}\) containing the hidden data. We can extract the hidden data from the detected dictionary. Not only can we take back the hidden data, but also, we can get the reconstructed signal to the original domain. Note that the process on the receiver needs only the compressed domain signal, such as \(\mathbf{U}_{r}\), \(\mathbf{Y}\), and \(\mathbf{V}_{r}\). There is no dictionary, and original data are needed for data detection and signal reconstruction. We organize the rest of this paper as follows. Section 2 describes the sparsity of the singular value and CS technique for the audio compression. Section 3 explains the mathematics model and derivation of audio watermarking including the embedding, the extraction, the audio reconstruction process, and the effect of the noisy environment in this proposed method. Section 4 discusses the result of the simulation, while Section 5 reports the conclusion of this paper.
# A Payload Based Malicious HTTP Traffic Detection Method Using Transfer Semi-Supervised Learning
## 4 Experiment
### Experiment 1: The Evaluation of Different Payload Segmentation Strategies  
In this experiment, we tried to evaluate the effectiveness of the proposed payload segmentation approach (abbreviated as **SegAsWord**) by comparing it with the following two payload segmentation approaches, which were utilized in previous work.  
(1) **SegAsChar**: It corresponded to the character-level payload segmentation approach [21]. (2) **SegAsNGram**: It corresponded to the approach that segments payloads based on the character-level N-gram algorithm [9]. Here, we set \(N=3\).  
Here, strategy A was used for evaluation and TextCNN is used as the classifier. The experiment results are shown in Table 2. First, SegAsNGram outperforms SegAsChar. It was intuitive that N-gram fragments could better capture the payload semantics than single characters did. For example, if there was a 3-gram fragment \"sel\", it was probably a SQL injection payload with the keyword \"select\", while it was almost impossible to draw a reasonable conclusion if only a single character \"s\" was observed. However, although single characters could not capture the payload semantics, the performance of SegAsChar was not that bad. It is because that the convolution operation of TextCNN processed the adjacent h characters in a filter, which could achieve a similar effect with character-level N-gram. Second, it was also intuitive that SegAsWord outperformed SegAsNGram, because the segmented keywords could capture the payload semantics in an explicit manner.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **SegAsChar** & **SegAsNGram** & **SegAsWord** \\ \hline Precision & 0.7522 & 0.8134 & 0.8667 \\ Recall & 0.7893 & 0.8253 & 0.8722 \\ F1-Measure & 0.7703 & 0.8193 & 0.8694 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The evaluation of the payload segmentation approaches.
## 2 Extraction and Clean-Up of PPCPs in Water
### Online SPE  
SPE can be used offline (independently from the further chromatographic analysis) or online (directly connected to the chromatographic system). However, there are few difference in the components of the techniques between the two formats, with the exception of the valves system used to connect SPE online with the determination technique (commonly any type of HPLC-MS). In both, the main factors that affected the results of these technique were the formats and sorbents of stationary phase (cartridge) and the solvent(s) used for activation, washing and elution. Online SPE can be coupled to both, LC or GC. However, the preferred technique is online SPE liquid chromatography tandem mass spectrometry (SPE-LC-MS/MS), PPCPs and the SPE eluents are much more compatible with the mobile phase of LC than with that of GC.  
To be functional, online systems require the use of 6 or 10 port valves to automate extraction and connection to the instrument. A schematic illustration of the analytical system is presented in Figure 4. The most common are home-made devices, but there are also commercial systems such as an automated sample processor [82]. These devices can be personalized and adapted to the particular analysis, for example, to obtain online cross, which allows the automatic cross-utilization of two SPE columns to speed up the analysis of pharmaceuticals in different water samples [40].  
The columns for online-SPE mode were characterized by similar polymeric RP sorbents. The main differences, with offline-SPE, were the length of columns (generally 1-2 cm). OASIS HLB (2.1 \(\times\) 30 mm, 10 \(\upmu\)m) [40; 50; 78], MAYI-ODS (10 mm \(\times\) 2.0 mm, 50 \(\upmu\)m) [68], and PLRP columns [39; 82] have been the most widely reported. However, sorbents based on alternative mechanisms, such as the so-called TurboFlow(tm), which mixes size exclusion chromatography with reversed sorbents, has also been reported to determine pharmaceuticals [46]. To this end, three TurboFlow(tm) columns (TFC) connected in series were used, i.e., Cyclone P-C18-P XL-Cyclone MAX in order to achieve a proper extraction and clean-up.  
The online SPE systems present the advantage to provide automatic and efficient sample loading, clean-up, desorption, separation, and detection at the same time, to reduce the sample volume, save time and solvents, prevent sample contamination and PPCP loss, and improve the method performance. The reduction in sample volume that could decrease sensitivity is normally compensated for by the increase in sensitivity as all the analyte retained in the sorbent passes to the chromatographic column.  
The disadvantage of these methods is that they are not very versatile to be adapted to different types and conditions of analysis, because sample pH, injection volume, and valve-switching time needs to be very carefully optimized to ensure appropriate method performance for target PPCPs [39]. Once established, they are more suitable for routine analysis.
## 5 Conclusions and Future Works  
This work analyzes and compares the performance of progressive pattern interleaving (PPI) with random interleaving (RI) in conjunction with two multi-carrier schemes, namely SC-FDMA-IDMA and OFDM-IDMA, in both AWGN and multipath Vehicular A channel environments, aiming for potential non-orthogonal multiple access (NOMA) implementation in 5G/6G communication systems. The utilization of NOMA schemes within 6G and IoT networks is proposed as a promising solution to tackle various challenges, including spectral and energy resource scarcity, the exponential proliferation of devices and traffic, spectral efficiency enhancement, and the augmentation of network capacity to accommodate a large number of users and traffic simultaneously. In this context, NOMA has been integrated with PPI and RI for user discrimination. Upon evaluating the bit error rate (BER) performance of both interleaving techniques across multiple simulation scenarios, it is determined that PPI and RI exhibit comparable performance in AWGN environments across both multi-access schemes. However, in multipath Vehicular A channel environments, SC-FDMA-IDMA demonstrates superiority, while PPI demonstrates equivalent performance to RI with the considered multi-access schemes. The implementation of appropriate channel scheduling algorithms holds potential for enhancing system performance. Comparing the BER performance of PPI with RI under various scenarios leads to the conclusion that PPI performs favorably in terms of BER, with the additional advantage of lower implementation complexity compared to RI within multi-carrier schemes under multipath environments. Hence, it can be suggested that PPI is more appropriate choice for the implementation in a NOMA environment for a multipath channel scenario in future generation of networks. The rationale lies in the fact that, typically, PPI's combination of comparable performance to RI in ideal conditions and effectiveness in challenging multi-path environments, along with its lower implementation complexity, positions it as a more suitable option for NOMA implementation in future network generations, especially in scenarios involving multipath channels.  
As a future work, we will use deep learning to enhance the error performance of uplink NOMA, to serve a great number of devices connected, in different environments. In addition, research and development will be further pursued in optimizing NOMA schemes for 6G and beyond, focusing on aspects such as energy efficiency, connectivity, latency and user fairness.  
Conceptualization, S.D. and V.S.; methodology, S.D. and V.S.; formal analysis, M.K.M.; investigation, M.K.M., J.M.J. and J.L.; writing--original draft preparation, S.D. and V.S.; writing--review and editing, J.M.J. and J.L.; visualization, M.K.M.; supervision, V.S. and J.L. All authors have read and agreed to the published version of the manuscript.  
There was no funding for this project.  
Not applicable.  
Dataset available on request from the authors.  
The authors declare no conflicts of interest.