esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Total Phenolic Content of Leptadenia pyrotechnica Stem  
The effect of microwave heating and conventional heating on the total phenolic content (TPC) of \(L\). _pyrotechnica_ stem is shown in Figure 2. Generally, the microwave-treated samples exhibited a higher TPC than the oven-treated samples. It was observed that microwave treatment for 8 min showed the highest TPC (10.68 mg GAE/g DW), followed by microwave treatment for 10 min (8.98 mg GAE/g DW) and hot-air heating for 120 min (7.84 mg GAE/g DW). The control sample of \(L\)_pyrotechnica_ showed the lowest TPC value of 4.46 mg/g DW (\(p\) < 0.05).  
Figure 1: HPLC chromatogram of phenolic standards.  
Figure 2: Effect of microwave heating (MW) and hot-air heating (HA) methods on the total phenolic content (TPC) of _Leptadenia pyrotechnica_ stem samples. The bars for the treatments bearing different letters are significantly different from each other at \(p\) < 0.05.  
Our results follow those of a previous study of microwaved-dried coriander leaves. The microwave drying (500 W, 430 s) yielded a higher total polyphenol content (48.44 mg GAE/g DW) as compared to that (26.64 mg GAE/g DW) obtained on oven drying (60 \({}^{\circ}\)C, 160 min) the coriander leaves [20]. In another study, microwave-dried blueberry leaves showed a higher TPC than oven-dried leaf samples [21]. The highest TPC was recorded in the _Strobilanthes crispus_ tea leaves developed from microwave-dried leaves [22]. On the contrary, the total phenolic content of date fruit flesh decreased after microwave and conventional oven drying compared to the control sample [23]. Microwave, vacuum, and oven drying were applied to celery slices, and it was noticed that the dried samples had 38.51-75.34% less TPC than fresh samples [24]. However, degradation, auto-oxidation, and polymerization can occur during drying, which might explain the decrease in the total phenol contents [25]. Moreover, the heating and drying processes encourage the release of phenolic compounds from the plant matrix, which happens after the breakdown of cellular constituents [26; 27].
## 2 Control Strategies Description
### Model Description
#### 2.6.1 Grid Data  
The performance evaluation of the considered control strategies and their combinations are tested by means of simulations on an existing LV grid, which is shown in Figure 3. This is an LV network in Suha, Slovenia, consisting of 78 nodes spread over several feeders. The MV/LV transformer of Dyn type has a nominal power of 250 kVA, short circuit voltage of 4% while the no load losses are 325 W and 3250 W, respectively. The primary and secondary nominal voltages are 20 kV and 0.4 kV, respectively. The voltages at the secondary side are set to be 1.05 p.u., which is a typical setting used by the DSO in order to avoid undervoltages to the most distant customers when high loading conditions are present.
# The Impact of Internal Structure Changes on the Damping Properties of 3D-Printed Composite Material
## 7 Conclusions  
The article examined the impact of changes in the internal structure of composite specimens on their damping capabilities. It is important to acknowledge that the experimental methods used provide only indicative values; however, they offer advantages such as speed, simplicity and the ability to perform measurements in almost any environment. To ensure more accurate results, it is preferable to use non-contact vibration sensing, which eliminates errors caused by the mass of physical sensors. Despite differences in results from various facilities, similar conclusions can be drawn that correlate with findings from other researchers.  
Our measurements indicate that the orientation of reinforcement fibers significantly affects the damping and mechanical properties. The main finding is that specimens with circumferential fibers, or the presence of longitudinal fibers combined with different fiber orientations in the center of the specimen, significantly reduced the damping of the structure. From a material point of view, the glass- and Kevlar-fiber-reinforced specimens show better damping capabilities compared to the carbon-fiber-reinforced specimens but, on the contrary, the mechanical properties are lower.  
Conceptualization, M.S. and J.M.; methodology, P.M.; software, M.V.; validation, M.S. and J.M.; formal analysis, J.F. and M.V.; investigation, P.M.; resources, J.M.; data curation, M.S.; writing--original draft preparation, P.M.; writing--review and editing, M.V.; visualization, J.F.; supervision, M.S.; project administration, P.M.; funding acquisition, M.S. and M.V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by KEGA grant project, 005 ZU-4/2024.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon request.  
The authors would like to thank their colleagues from the University of Ostrava, Department of Applied Mechanics, for their help and for providing the laboratory and experimental equipment.
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 5 Conclusions  
This study presents a multi-path hybrid MBMQA routing scheme with a back-pressure algorithm strategy for the IoT 5G network based on D2D communication. The proposed routing scheme consists of multiple parameters, including energy consumption, queue length size, and mobility of the devices. In order to determine the optimal paths that provide maximum network performance, back-pressure forwards data packets toward the devices with low queue backlogs while maintaining network stability. The MBMQA routing scheme ensures that multiple stable routes can be formed from source to destination without over-exhausting any single device's battery by enhancing the conventional routing schemes. Moreover, the MARS decision metric is utilized to evaluate the device selection criterion in an optimal route based on mobility, battery, and queue length size of the devices. Simulation results indicated that MBMQA outperforms its MEQSA-OLSRv2 and MP-OLSRv2 counterparts in terms of packet delivery ratio, average end-to-end delay, throughput, packet drop, energy consumption, and energy cost, especially for high mobility scenarios. In addition, the MBMQA scheme mitigated the number of dropped packets and delivered a higher number of data packets at a lower energy cost per packet, which resulted in higher energy efficiency. In the future, with the help of the resource allocation method, a higher system data rate can be achieved, along with the security aspects. Therefore, D2D communication is one of the critical technologies in the 5G network, and further research in the heterogeneous network domain is imperative.  
Conceptualization, V.T., M.N.H. and R.S.S.; methodology, V.T.; software, M.N.H. and R.S.S.; validation, K.D.; formal analysis, S.S., K.D. and M.N.H.; investigation, D.N.K.J. resources, D.N.K.J. and S.S.; data curation, V.T., K.D. and E.H.; writing--original draft preparation, V.T., M.N.H. and E.H.; writing--review and editing, V.T., S.S. and E.H.; visualization, D.N.K.J.; supervision, K.D. and D.N.K.J.; project administration, M.N.H. and E.H.; funding acquisition, S.S. and R.S.S. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the CEU-Cooperativa de Ensino Universitario, Portugal and the competitiveness enhancement program of National Research Tomsk Polytechnic University, Russia.  
The authors declare that there is no conflict of interest regarding the publication of this paper.
## Experiments
### Ablation Study
#### 4.4.2 Interaction Embedding Graph  
The proposed IE-Graph models both intra- and inter-subjects relationships within a unified graph structure. To illustrate the broad applicability and validity of proposed IE-Graph, we select two GCN-based methods, GCN-LSTM and ST-GCN, as the baselines which trained from scratch for ablation study. As shown in Table 5, the accuracy of ST-GCN w/ IE-Graph improves by 5.2% compared to one w/o IE-Graph, and GC-LSTM w/ IE-Graph improved by 5.5%. Although the number of corresponding parameters has increased by 10%, we believe the performance improvement is more impressive. This result shows that the proposed IE-Graph adequately extracts the interaction information between subjects and enables the interaction recognition accuracy of the model to be improved. In addition, IE-Graph has wide applicability to most GCN-based methods.  
Another interesting phenomenon is that when the baseline uses IG, the recognition accuracy of all actions lags behind mutual actions. We believe that since mutual actions have richer information, the design of IG is more conducive to information embedding. For non-mutual actions, the IG is more like adding a new feature space. Therefore, although the accuracy of all-category action has been improved, the proportion of improvement lags behind mutual action.
## 3 Methods
### Statistical Methods
#### 3.2.1 Autoregressive Integrated Moving Average (ARIMA)  
ARIMA models are used for time series forecasting by capturing linear dependencies in data. An ARIMA model is denoted as \(ARIMA(p,d,q)\), where \(p\) is the number of autoregressive terms, \(d\) is the number of non-seasonal differences, and \(q\) represents the number of lagged forecast errors [24]. The ARIMA model is represented as  
\[\Phi(B)Y_{t}=\theta(B)\epsilon_{t} \tag{6}\]  
where \(\Phi(B)\) is the autoregressive polynomial, \(\theta(B)\) is the moving average polynomial, and \(\epsilon_{t}\) is the error term.
## 2 Satellite Atmospheric Observing System Design  
We developed our end-to-end simulation framework for designing an atmospheric observing system, using a systematic approach given in Rodgers [18]. We first present a simple visualisation of this atmospheric observing system design (AOSD) process in the form of a schematic block diagram in Figure 2; then, we use this as a guideline to build our EIPS framework. It is very important that any end-to-end simulation system should encompass all the physical processes and factors related to the design of a satellite instrument.  
A generic end-to-end atmospheric remote sensing simulation system should comprise at least three main components:  
1. _Sensor modelling_: this should take account of all the instrument system parameters that can affect the measurement of the signal such as: spectral response function (SRF), detector noise etc.,
2. _Atmospheric radiative transfer modelling_: modelling of the propagation of EM radiation through the earth's atmosphere,
3. _Inversion algorithm_: an algorithm is required to retrieve the geophysical variables of interest and to test their accuracy and sensitivity to other factors.
## 3 Results
### 10 km Tests  
#### 3.2.1 Physiological variables  
The physiological data showed no significant differences between shoe conditions. (Figure 2a-c). For rel. VO2, the range was between 42.0 \(\pm\) 5.4 mL/min/kg for S2 and 42.3 \(\pm\) 5.3 mL/min/kg for S1. Similar trends were seen in RE and HR. S2 showed the best values for RE (207.8 \(\pm\) 13.1 mL/kg/km) and for HR (162.1 \(\pm\) 14.4 bpm), and S1 the worst values (209.5 \(\pm\) 12.8 mL/kg/km), respectively (164.6 \(\pm\) 16.6 bpm).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Rel.** & **70\% of rel.** & **v\% of rel.** & **HR\% of rel.** \\  & **VO2 max** & **v\_max [km/h]** & **[bpm]** & **VO2 max** & **VO2 max** & **HR\% of rel.** \\  & **[mL/kg/min]** & & **[mL/kg/min]** & **[km/h]** & **VO2 max** [bpm]** \\ \hline Mean & 59.3 & 18.5 & 185.5 & 41.5 & 12.2 & 155.2 \\ \(\pm\) & \(\pm\) & \(\pm\) & \(\pm\) & \(\pm\) & \(\pm\) \\ SD & 7.8 & 2.1 & 10.6 & 5.4 & 1.6 & 12.6 \\ \hline \end{tabular}
\end{table}
Table 2: Group mean \(\pm\) standard deviation of the exhaustion test for physiological variables (all subjects).  
Figure 2: Comparison of shoe conditions. Panel (**a**): relative oxygen consumption (rel. \(\dot{\mathrm{VO}}\)\({}_{2}\)); Panel (**b**): running economy (RE), and Panel (**c**): heart rate (HR). No statistical differences were found for any variables between shoe conditions.
###### Abstract
The stability of the implicit methods is fundamentally better; therefore, they are considered superior and commonly used by many scientists to solve these and similar equations [16; 17; 18]. However, in each time step, it is required to solve a system of algebraic equations for which the process is not easily parallelizable. Calculations can be very slow with large amounts of memory. This is common in multiple dimensions of space when the matrix has a huge size and is non-tridiagonal. Moreover, with implicit methods, it is much harder to follow the trend toward increasing parallelization. So, it is shown that explicit methods can be more efficient even if a small time-step size has to be applied [19]. It is worth noting that many clever combinations of the explicit and the implicit approaches, e.g., semi-explicit or semi-implicit methods, are also proposed [20; 21; 22; 23]. Nevertheless, they do not resolve the above-mentioned dilemma of the explicit and implicit methods. Regarding the above information, it is not baseless to believe that explicit methods, especially if they have enhanced stability properties (see [24; 25; 26; 27; 28; 29; 30; 31] for examples), have an increasing comparative advantage over the long term. A couple of years ago, we initiated the development of new explicit methods, which are unconditionally stable, at least for the linear diffusion or diffusion-reaction equation. Our original publications (see, e.g., [32; 33; 34]) investigated the new methods theoretically and tested them with simple analytical as well as with numerical reference solutions. We have shown that they can provide quite accurate results significantly faster than the widely used methods, including MATLAB ODE solvers. In [35], we tested 14 methods in the case of a space-dependent diffusion coefficient. Most of these algorithms had been proposed by us previously as explicit and stable schemes, but we also included Dufort-Frankel and other known methods. However, as far as we know, until now no one tested any of these algorithms when the diffusion coefficient was both space and time dependent. Moreover, we do not know any work in which unconditionally stable explicit methods with an order larger than one are used to construct adaptive time-step size solvers. The rest of this work is organized as follows. In Section 2, we analytically solve the investigated equations and present the results. Section 3 describes the discretization procedure and the numerical schemes used. In Section 4, the tests are performed for two sets of parameters to see how the methods perform in different situations. Then, in Section 5, the LNe-type methods are organized into adaptive time-step size solvers and tested against popular Runge-Kutta solvers. Finally, in Section 6, we summarize our conclusions and mention future research directions.
# Feature-Based Approach to Change Detection of Small Objects from High-Resolution Satellite Images
## 2 Feature Point-Based Change Detection Method
### Pixel-Based Change Analysis  
After the pre-processing step, as shown in Figure 1, a pixel-based change detection process is carried out in parallel with feature extraction and matching. For generation of a pixel-based change magnitude map, we use the Multivariate Alteration Detection (MAD) algorithm [48; 49; 50; 51], which is a well-known pixel change analyzer. Here, we restate its principle for the completeness of this paper. The _MAD_ generates transformed images \(W\) and \(P\) from images \(X\) and \(Y\) having \(n\) spectral bands as in Equation (1).  
\[W=a^{t}X=a_{1}X_{1}+\cdots+a_{n}X_{n} \tag{1}\]  
\[P=b^{t}Y=b_{1}Y_{1}+\cdots+b_{n}Y_{n} \tag{2}\]  
where \(X_{1}\) to \(X_{n}\) and \(Y_{1}\) to \(Y_{n}\) are the brightness values of each band in images \(X\) and \(Y\), respectively, and \(a_{1}\) to \(a_{n}\) and \(b_{1}\) to \(b_{n}\) are the transformation coefficients for images \(X\) and \(Y\), respectively. The transformation coefficients are calculated by the covariance among the original images \(X\) and \(Y\) and the correlation between the transformed images \(W\) and \(P\) to maximize the deviation between two images [48; 49]. The _MAD_ calculates changes as the difference between transformed images \(W\) and \(P\), as shown in Equation (3). The _MAD_ is invariant to changes in gain and offset settings in measuring devices and to linear radiometric and atmospheric correction schemes [49].  
\[MAD=W-P \tag{3}\]  
The changed area can be highlighted and detected by maximizing the deviation. For this paper, The _MAD_ was applied to the green, red, and near infra-red bands of the Kompsat-3A images.  
After change magnitude generation, changed pixels are determined by thresholding the change magnitude. Determination of the optimal threshold value is of great importance for accuracy and performance in change detection. In this paper, our focus is on proposing feature-based change detection and checking its performance with respect to pixel-based approaches. Therefore, we used various threshold values repeatedly, and checked the performance of change detection per threshold. For a given threshold, a pixel-based change map was generated and delivered for the next process of feature-based change analysis.
## 2 Materials and Methods  
### Materials and Mixture Proportions  
To investigate the effect of the rheological properties of fresh UHPFRC on the fiber distribution and the mechanical properties of hardened UHPFRC, four types of UHPFRC mix proportions were considered, where the proportions were designed to obtain different rheological properties by adding different dosages of WRA. The mix proportions of UHPFRC are listed in Table 1. The water-to-binder ratio of the UHPFRC mix design was set as 0.2. Type I Portland cement and undersified silica fume were used as the cementitious material. Quartz sand was used as a fine aggregate. It has a grain size distribution with a maximum diameter of 0.5 mm and a density of 2.62 g/cm\({}^{3}\). The filler used for improving the strength and workability had a mean grain size of approximately 4 \(\upmu\)m, density of 2.62 g/cm\({}^{3}\), and crystalline SiO\({}_{2}\) over 98%. Two types of straight steel fibers with lengths of 16.3 mm and 19.5 mm were incorporated. The tensile strength, density, and diameter of the steel fibers were 2,500 MPa, 7.5 g/cm\({}^{3}\), and 0.2 mm, respectively. The amount of fiber with a length of 16.3 mm was 1 vol\(\%\) and the amount of fiber with a length of 19.5 mm was also 1 vol\(\%\). A polycarboxylate-based superplasticizer was used for the WRA. Its density was 1.01 g/cm\({}^{3}\) and the solid content was 30\(\%\). Four different amounts of WRA were applied for the mix design. The weight percentage of solid content of WRA to cement was designed to be 1.2\(\%\), 1.8\(\%\), 2.4\(\%\), and 3.0\(\%\) for Mix 1 to Mix 4. Unit water content was decreased as the amount of WRA was increased in the mix design.
# Cheating Detection in Online Exams Using Deep Learning and Machine Learning
## 2 Materials and Methods
### Dataset and Data Cleaning  
The dataset used in this study was obtained within the scope of the study titled \"Development of a New Model for Detection of Unwanted Behavior in Online Exams\", which was approved by the decision numbered 2022/15-19 and E.3165 of the Bitlis Eren University Ethics Principles and Ethics Committee (the Ethics Committee decision is given in Appendix A). The dataset consists of 221 student exam data, supervised and unsupervised, using real examand artificial exam types consisting of 15 multiple-choice questions conducted through the Advancity Learning Management System (ALMS), Bitlis Eren University distance education system for the Basic Information Technologies course. The online exam was administered synchronously and asynchronously. Synchronous exams were administered in computer laboratories with an invigilator, while asynchronous exams were administered unsupervised at any time of the day. The data of the participants who took the exam were transferred to the Phyton/Pandas library in Excel format by the researchers.  
Datasets obtained often contain missing values, making data cleaning a crucial step in the process. Multiple methods can be used to address missing values. These can include techniques such as coding the missing value with a certain value, using the average of other values for that feature, or assigning the most appropriate value based on the existing distribution. However, since demographic variables such as department, grade, and gender were registered in the system, no missing data were encountered in this study. Nevertheless, the absence of responses to questions caused significant issues in detecting question times, so missing data completion was not pursued in terms of data integrity. Before preprocessing, a total of 221 exam records were obtained, but due to missing responses, the records of participants with incomplete data were removed in Python to ensure data integrity. As a result, 162 exam records were obtained through the data cleaning method.
# Open-Set Signal Recognition Based on Transformer and Wasserstein Distance
## 4 The Proposed Algorithm
### Set Distance Metric Sub-Module  
Instead of comparing two datasets with respect to the Euclidean metric, we introduced the popular Wasserstein distance. For the given two datasets \(\{\mathbf{a}_{i},i=1,\cdots,m\}\) and \(\{\mathbf{b}_{j},j=1,\cdots,n\}\), which can be considered as two uniform distributions defined on these two datasets respectively. The corresponding measures can be expressed as:  
\[\mathbf{u}=\sum_{i=1}^{m}\frac{1}{m}\delta_{\mathbf{x}_{i}},\quad\mathbf{v}= \sum_{j=1}^{n}\frac{1}{n}\delta_{\mathbf{y}_{j}},\]  
where \(\delta\) is the function defined in each data sample. The distance between \(\{\mathbf{a}_{i},i=1,\cdots,m\}\) and \(\{\mathbf{b}_{j},j=1,\cdots,n\}\) can be defined as the Wasserstein distance between \(\mathbf{u}\) and \(\mathbf{v}\). We first introduced the formulation of the Kantorovich optimal transport problem as follows:  
\[\mathrm{L}_{\mathbf{C}}(\mathbf{u},\mathbf{v})\ \stackrel{{\text{def.}}}{{=}}\ \min_{\mathbf{p}\in\mathbf{U}(\frac{1}{\mathbf{m}},\frac{1}{\mathbf{n}})} \langle\mathbf{C},\mathbf{p}\rangle\ \stackrel{{\text{def.}}}{{=}}\ \sum_{i,j}\mathbf{C}_{ij}\mathbf{P}_{i,j}, \tag{4}\]  
where, the constraint set \(\mathbf{U}(\frac{1}{\mathbf{m}},\frac{1}{\mathbf{n}})\) is defined as:  
\[\mathbf{U}(\frac{1}{\mathbf{m}},\frac{1}{\mathbf{n}})\ \stackrel{{ \text{def.}}}{{=}}\ \bigg{\{}\mathbf{P}\in\mathbb{R}_{+}^{n\times m}:\mathbf{P} \mathbf{1}_{m}=\frac{1}{\mathbf{m}},\mathbf{P}^{\mathbf{T}}\mathbf{1}_{n}= \frac{1}{\mathbf{n}}\bigg{\}}, \tag{5}\]  
And matrix \(\mathbf{C}\) is the cost matrix while each element \(\mathbf{C}_{i,j}\) is determined by the corresponding data samples (\(\mathbf{a}_{i}\) and \(\mathbf{b}_{j}\)). The [PERSON] optimal transport problem (4) is a linear programming problem with \(n+m\) equality constraints. The Wasserstein distance between \(\{\mathbf{a}_{i},i=1,\cdots,m\}\) and \(\big{\{}\mathbf{b}_{j},j=1,\cdots,n\}\) can further be defined as:  
\[\mathbf{W}(\big{\{}\mathbf{a}_{i}\big{\}},\big{\{}\mathbf{b}_{j}\big{\}}):= \mathrm{L}_{\mathbf{D}^{p}}(\mathbf{u},\mathbf{v}), \tag{6}\]  
where the distance matrix \(\mathbf{D}^{p}\) is used to represent the cost matrix with \(0\leq p\leq 1\). In this paper, the Wasserstein distance is used to improve both the empirical classification risk \(\mathcal{L}_{known}\) and open space risk \(\mathcal{L}_{o}\), i.e.,  
\[\left\{\begin{array}{l}p\Big{(}\mathbf{y}=k\ |\ \mathbf{x},f,\Big{\{}p^{k} \Big{\}}\Big{)}=\frac{e^{\gamma\mathbf{W}\big{(}\mathbf{x},\mathbf{x}^{k} \big{)}}}{\sum_{j=1}^{l}e^{\gamma\mathbf{W}\big{(}\mathbf{x},\mathbf{x}^{k} \big{)}}}\quad\text{in }\mathcal{L}_{known},\\ \mathcal{L}_{o}\Big{(}\theta,\Big{\{}p^{k}\Big{\}},\mathcal{R}\Big{)}=\sum_{k =1}^{\ell}\sum_{\mathcal{D}_{train}\cup\mathcal{D}_{\mathcal{U}}}\Big{\{} \max\Big{(}\mathbf{W}\Big{(}\{\mathbf{x}\},p^{k}\Big{)}-\mathcal{R},0\Big{)} \Big{\}}.\end{array}\right. \tag{7}\]