esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Study of In Vitro Release of Eugenol and Release Kinetic  
The in vitro release test of eugenol followed the method used by [PERSON] et al. [22] and [PERSON] et al. [23]. The sample (40 mg) was placed in an Erlenmeyer flask containing 10 mL of 60% phosphate-buffered saline (pH 7.4) and 40% ethanol. Agitation was carried out using a shaker incubator at a temperature of 27-30 \({}^{\circ}\)C with a speed of 75 rpm. At the appointed time intervals, 5 mL of the sample was sucked out for analysis and replaced with 5 mL of the buffer phosphate solution. The sample was transferred into a tube for further filtration using 0.45 \(\upmu\)m PVDF and put into a 2 mL vial for the measurement process by HPLC. The cumulative percentage of the amount of active compound/essential oil released from the spray-dried powder was obtained by dividing the cumulative amount of essential oil released at any given time interval (Mt) by the initial weight of the encapsulated essential oil (M0) (Equation (3)):  
\[\text{Release Cumulative Percentage}=\sum\
olimits_{t=0}^{t}\frac{Mt}{M0} \times 100\% \tag{4}\]  
\[\frac{Mt}{M0}=\text{kt}^{n} \tag{5}\]  
The release mechanism and release kinetics of eugenol from the spray-dried powder were investigated using the Korsmeyer-Peppas kinetic model [24] (Equation (4)). Mt is the amount of eugenol released at a given time, M0 is the initial amount of eugenol contained in the spray-dried powder or the maximum amount of eugenol that can be released from the spray-dried powder, t is the release time, k is the kinetic constant for the system, and n is the release characteristic and determines the release mechanism.
# A W-Band Communication and Sensing Convergence System Enabled by Single OFDM Waveform
## 5 Experimental Setup and Results
### Range Measurement  
In Figure 7(a), range measurement is implemented when a static target is placed 0.605 m from the receiver. The target is detected with an error of 0.005 m, as mentioned in Table 3. Based on Figure 7(a), we can also observe multiple reflections originating from the target and wall; TS1 and TS2 indicate first and second reflections from the target, whereas W is the wall's reflection. Here, the second reflection refers to a signal that is reflected twice from the target and eventually detected by the receiver since the receiver reflects a portion of strong signal. Then, we perform the measurements for two targets placed at 0.6 m and 0.65 m, and Figure 7(b) shows that the closely placed targets are distinguishable from each other at their locations. Similarly, Figure 7(c) provides the measurement results for two targets standing at 1.313 m and 1.548 m, respectively. The change in the received power is due to a higher FSPL.  
Figure 6: Block diagram of the measurement setup, showing different stages of processes in the digital and analog domains at the transmitter \(T_{X}\) and the receiver \(R_{X}\).  
Figure 7: Experimental setup for the measurements. (a) Photo of the setup to measure range of two static targets. (b) Range-speed measurement setup of a static target and a target mounted on a belt capable to move with adjustable speed up to 5 m/s. (c) Data link arrangement.
# Review of Energy Management Systems in Microgrids
## 2 Energy Management Systems in Microgrids
### Model Predictive Control-Based Energy Management Systems  
Model predictive control-based energy management systems aim to predict the microgrid's controllable load to implement an effective energy management strategy. The system performs better in terms of fewer power outages, a lower maximum demand, and an improved load factor because of its predictive control capability [31]. Energy trading with the main grid, optimizing the use of renewable energy resources, battery and electrical vehicle management, and other measures to guarantee system stability and profitability are all part of the microgrid's economic operation.  
Optimal control schemes in the microgrid are learned from data, making them stand out as model-independent or data-driven calculations. By using learning-based techniques, and having an open system model, it is possible to reduce resistance, increase the scalability of the energy management system, and reduce costs. To release accurate models and permissions for a microgrid, model-based energy management systems depend on domain expertise. This means that while this method produces high improvement rates, it is neither scalable nor transferable. However, improvements in microgrids might force a redesign that results in noticeably higher maintenance costs [32; 33].
# A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
## 2 Related Works: EEG Circuit Design Analog Front-End Architectures  
Modern CMOS technology has greatly favored the development and advancement of the analog front-end for EEG applications. Ref. [33] designed using the TSMC 90 nm CMOS process a compact EEG-signal amplifier employing 90 nm MOS capacitors that has been reported to attain high CMRR and gain but in an area-saving design. Another design [34] used an LNA and HPF with a 3 rd-order Butterworth filter to achieve high power efficiency, which is extremely vital for wearable EEG devices. A 4-channel AFE was created in [35] to be versatile in electrode compatibility and also reduce noise. The design in [36] focused on EEG signal acquisition for epilepsy detection. The AFE in [37] provides mode-selectable gains for local field potential and action potential detection. Wireless multi-channel EEG devices [38; 39; 40] have not only improved portability and patient comfort but have also shown efficiency and a low power profile through Bluetooth. Some of these innovations include a present-reused DDA AFE [41] and a low-noise chopper instrument amplifier [42]; both designs achieve high CMRR and PSRR, which are important for the acquisition of good EEG signals. Some other works in the field of EEG signal acquisition and monitoring applications include Neonates-Specific EEG System [43], wireless EEG monitoring circuit with compact amplifiers and signal conditioning modules [44], Single-Chip EEG Signal Sampling Circuits [45], and BCI-Specific EEG [46], etc. Following this overview of EEG circuits, a special extended analysis will be conducted on three distinct EEG circuit designs, in particular, Case \"1\" [47], Case \"2\" [48] and Case \"3\" [49], elaborating on their topology and electronic characteristics. These Cases are used by the authors of this work as a basis for comparison with existing designs that have been published in the peer-reviewed bibliography. A more detailed presentation of these circuits is given in the main text. Furthermore, additional information for an in-depth understanding of each Case's specific requirements and specifications essential for efficient EEG signal acquisition, simulation and experimental results and comparison with the EEG Generic Circuit Design is outlined in Supplementary Materials \"SB\" (see Sections SB.1-SB.3), in order to validate the efficacy of these circuits, highlighting their potential impact on advancing EEG technology. For further information on [43; 44; 45; 46], see Supplementary Materials Section SB.4.
## 3 Proposed Design
### Application Traffic Patterns Identification
#### 3.1.2 Electromyography (EMG)  
EMG helps to understand the muscle function with the help of electrical signals generated by the muscles. Surface electrodes are placed on the skin and these electrodes can receive electrical signals when a muscle performs some movement like contracting. Hence, EMG is helpful for measuring muscle activity and used in specific bio-medical activities.  
From Table 1, if we consider sampling rate 150 Hz and the sampling resolution 12 bits then SIT = 1/150 = 6 ms; MPDS = 150 \(\times\) 12= 1.8 Kbps = 225 bytes/s; using Equation (1) the DNS after 6 ms = (225 \(\times\) 6)/1000 = 1.3 bytes; using Equation (2) in case of EMG node, DNS after 6 ms including minimum header size = 1.3 + 9 + 6 = 16.3 bytes = 131 bits; using Equation (3), total data rate required by EMG packet = 150 \(\times\) 131 = 19.6 Kbps.
# A Variable-Order Dynamic Constitutive Model for Clay Based on the Fractional Calculus
## 6 Model Validation and Sensitivity Analysis
### Comparison of Models  
To date, many dynamic constitutive models for soil have been proposed by scholars, and these models have been adapted for various situations. These include the exponential model initially proposed by [PERSON], a hyperbolic model, an improved exponential model which considers the effect of stress levels, and an improved hyperbolic model [14; 51; 54; 55]. On the whole, the ability of such models to fit the accumulative strain curve has become better and better. Equations for the models described above are given by Equations (49) to (52).  
Exponential model [14]:  
\[\varepsilon_{d}(t)=aN^{b} \tag{49}\]  
Hyperbolic mode [54]:  
\[\varepsilon_{d}(t)=\frac{N}{a+bN} \tag{50}\]Improved exponential model [55]:  
\[\varepsilon_{d}(t)=a\bigg{(}\frac{\sigma_{d}}{\sigma_{s}}\bigg{)}^{m}N^{b} \tag{51}\]  
Improved hyperbolic model [51]:  
\[\varepsilon_{d}(t)=d\Big{(}\delta^{N}-1\Big{)}+\frac{bN^{m}}{1+cN^{m}} \tag{52}\]  
where \(N\) represents circulation times, and \(t=NT\), where \(T\) is the period of cyclic load, and \(a\), \(b\), \(c\), \(m\) are all fitting parameters. \(\delta\) expresses the state of soil, \(d\) is the form factor of accumulative strain, and the fitting results of different models are plotted in Figure 15.  
It can be seen from Figure 15 that the prediction abilities of the dynamic constitutive model presented in this paper and the improved hyperbolic model are significantly higher than those of the exponential model, improved exponential model and hyperbolic model. By comparing the fitting results of the new dynamic constitutive model and the improved hyperbolic model for the accelerating stage, we find that the new dynamic constitutive model is almost completely consistent with the experimental data, which indicates that the viscoplastic body can well describe the deformation characteristics of clay in the accelerating stage.
# Smart Airport Cybersecurity: Threat Mitigation and Cyber Resilience Controls +
## 3 Theoretical Framework
[19] presented use cases of attacks in airports and explained which prevention methodology can be implemented, in order to improve the security level with the integration of several security tools, services and fields. [PERSON] et al. [20] focused on analyzing Denial of Service (DoS) attacks that occur in airports and especially in their automation systems by describing how attacks are launched along with effective countermeasures. Moreover, U.S. Department of Homeland Security [21] published a report which analyzed botnets and other automated, distributed threats, pointing out that such types of attack are a global problem nowadays. Finally, SESAR research addressed cybersecurity issues in Airport Operations Centers including a comprehensive maturity model to approach to cyber-security within European ATM and to develop a comprehensive response to cyber-threats [22]. Although threats to smart airport's cyber security apply to broad categories of assets (such as communication networks, servers and control systems, internal/sensitive information, authentication and access control systems), the majority of researchers focus on one or two scenarios of attack, while addressing cybersecurity issues in airports. To the best of our knowledge, no one has presented a complete scenario analysis of malicious attacks that may happen in smart airports, concerning IoT technologies and smart applications, including mitigation actions, resilience measures and impact effects on the information security triad (Confidentiality-Integrity-Availability: CIA).
# CDSTAEP: Cross-Domain Spatial-Temporal Association Learning for Abnormal Events Prediction
## 3 Preliminaries and Problem Definition
### Curvature Space  
Curvature space, also known as differential manifold \(\mathcal{M}\), is the extension of the concept of curves and surfaces in three-dimensional Euclidean space to high-dimensional space, which has the characteristics of local flatness and overall bend. According to the difference of curvature \(\kappa(x)\) everywhere in the space, the curvature space can be divided into constant curvature space and mixed-curvature space. A mixed-curvature space means a space in which the curvature \(\kappa(x)\) is unevenly distributed throughout the space, and the geometric properties of points \(x\) are different. If the curvature \(\kappa(x)\) everywhere in the curvature space satisfies a uniform distribution, then the curvature space \((\mathcal{M},g)\) is a constant curvature space. According to the positive and negative values of space curvature, it can be divided into Euclidean space with curvature \(\kappa=0\), hyperbolic space withcurvature \(\kappa<0\), and spherical space with curvature \(\kappa>0\). An overview of the various curvature spatial properties is shown in Table 3.  
A Riemannian space of constant curvature is defined [42] as follows:  
\[\mathcal{M}=\begin{cases}\mathbb{E}^{n}=\mathbb{R}^{n},&\text{if }k=0\\ \mathbb{H}^{n}_{k}=\Big{\{}x\in\mathbb{R}^{n}:||x||=\sqrt{x,x}=-\sqrt{-k}\Big{\}},&\text{if }k<0\\ \mathbb{S}^{n}_{k}=\Big{\{}x\in\mathbb{R}^{n}:||x||_{2}=\langle x,x_{2} \rangle=\sqrt{k}\Big{\}},&\text{if }k>0\end{cases} \tag{2}\]  
where \(\langle\cdot,\cdot\rangle_{2}\) is the standard Euclidean inner product, \(\langle x,x\rangle_{\mathcal{L}}\) is the Lorentz inner product, and \(||\cdot||\) means the norm.
# Anticancer Efficacy of Antibacterial Quinobenzothiazazines
## 2 Materials and Methods  
### Chemistry  
The detailed synthesis of alkoxy 5-methyl-12\(H\)-quino[3,4-b][1,4]benzothizinium chlorides **4a-**I and the characterization of all compounds were described recently [53].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & & \\ \hline \multirow{2}{*}{**Comp.**} & \multirow{2}{*}{**R**} & \multicolumn{4}{c}{**IC\({}_{\text{50}}\) [\(\mu\)M]**} \\ \cline{3-6}  & & **SNB-19** & **A549** & **T47D** & **NHDF** \\ \hline
**4a** & 9-OCH\({}_{2}\)CH\({}_{2}\)CH\({}_{3}\) & 8.9 \(\pm\) 0.7 & 5.3 \(\pm\) 1.1 & 26.9 \(\pm\) 3.2 & \textgreater{}100 \\
**4b** & 10-OCH\({}_{2}\)CH\({}_{2}\)CH\({}_{3}\) & 11.4 \(\pm\) 0.8 & 18.4 \(\pm\) 1.2 & 14.7 \(\pm\) 1.1 & 65.5 \(\pm\) 9.7 \\
**4c** & 11-OCH\({}_{2}\)CH\({}_{2}\)CH\({}_{3}\) & 18.9 \(\pm\) 1.4 & 23.1 \(\pm\) 1.8 & 19.2 \(\pm\) 1.2 & \textgreater{}100 \\
**4d** & 9-OCH\({}_{2}\)CH\({}_{2}\) & 15.6 \(\pm\) 0.9 & 6.7 \(\pm\) 0.4 & 7.8 \(\pm\) 09 & \textgreater{}100 \\
**4e** & 10-OCH\({}_{2}\)CH\({}_{2}\) & 15.0 \(\pm\) 1.8 & 19.3 \(\pm\) 1.1 & 15.7 \(\pm\) 0.9 & \textgreater{}100 \\
**4f** & 11-OCH\({}_{2}\)CH\({}_{2}\) & 18.9 \(\pm\) 1.4 & 20.6 \(\pm\) 1.8 & 26.7 \(\pm\) 1.2 & \textgreater{}100 \\
**4g** & 9-OCH\({}_{2}\)CH\({}_{2}\) & 17.2 \(\pm\) 1.3 & 18.0 \(\pm\) 0.9 & 12.9 \(\pm\) 1.2 & \textgreater{}100 \\
**4h** & 10-OCH\({}_{2}\)CH\({}_{2}\) & \(\textless\) 0.5 & 17.7 \(\pm\) 0.8 & 10.7 \(\pm\) 1.1 & \textgreater{}100 \\
**4i** & 11-OCH\({}_{2}\)C=CH & 36.1 \(\pm\) 5.6 & 21.8 \(\pm\) 2.4 & 44.3 \(\pm\) 4.2 & 47.5 \(\pm\) 3.5 \\
**4j** & 9-OCH\({}_{2}\)C\({}_{6}\)H\({}_{5}\) & 11.3 \(\pm\) 0.8 & 14.7 \(\pm\) 0.7 & 21.1 \(\pm\) 1.3 & 37.7 \(\pm\) 3.4 \\
**4k** & 10-OCH\({}_{2}\)C\({}_{6}\)H\({}_{5}\) & 12.0 \(\pm\) 0.9 & 14.5 \(\pm\) 0.6 & 13.5 \(\pm\) 1.1 & \textgreater{}100 \\
**4l** & 11-OCH\({}_{2}\)C\({}_{6}\)H\({}_{5}\) & 6.6 \(\pm\) 0.4 & 14.3 \(\pm\) 07 & 9.3 \(\pm\) 0.6 & \textgreater{}100 \\
**CPT** & \(-\) & 16.7 \(\pm\) 1.3 & 3.0 \(\pm\) 0.2 & 9.0 \(\pm\) 0.7 & 29.9 \(\pm\) 3.3 \\
**DOX** & \(-\) & 1.7 \(\pm\) 0.2 & 1.3 \(\pm\) 0.1 & 0.8 \(\pm\) 0.1 & 5.5 \(\pm\) 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structures of discussed agents **4a-**I and in vitro antiproliferative activity against human cancer cell lines and cell viability of normal human cells (IC\({}_{\text{50}}\) [\(\mu\)M] \(\pm\) SD, n = 6) of investigated agents compared to drugs.
## 4 Proposed Epileptic Seizures Detection Model
### Data Preprocessing  
In this study, the following EEG preprocessing steps were applied in chronological order to prepare the dataset for ML analysis:  
* **Data Import and Inspection**: The UCI Epileptic Seizure Recognition Dataset was loaded using Python (Pandas 2.2.3) and examined using '.head(0', '.tail(0', '.describe(0', and '.info(0' to ensure data integrity;
* **Feature Selection**: The 178 EEG signal features (X1 to X178) were extracted while excluding the subject identifier column. The label column ('y') was retained as the target variable;
* **Segmentation Review**: EEG signals in the dataset are already segmented into 1-s intervals of 178 points, offering consistency in sample size and reducing preprocessing needs;
* **Basic Data Cleaning**: No missing values or corrupted rows were found during inspection; hence no imputation was necessary;
* **SMOTE Balancing**: To address class imbalance, the Synthetic Minority Over-Sampling Technique (SMOTE) was applied to balance the number of instances in each class before training.  
It is important to note that the UCI dataset used in this study consists of pre-processed, artifact-free EEG recordings as confirmed in the dataset documentation. Therefore, additional steps, such as re-referencing, bandpass filtering, artifact rejection, and channel interpolation, were not required or applied. This decision aligns with evaluating classifiers on clean, controlled EEG samples to establish baseline performance. However, future work will include experiments on raw EEG with full clinical preprocessing pipelines.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classes** & **Name** & \
\begin{tabular}{c} **No of** \\ **Samples** \\ \end{tabular} &
\begin{tabular}{c} **Output Classes** \\ **(Labels)** \\ \end{tabular} & \multicolumn{2}{c}{**Description of Classes**} \\ \hline
1 & Ictal & 2300 & 1 & Signals recorded during seizures \\ \hline
2 & Pre-ictal & 2300 & 2 & Signals recorded before the occurrence of a single seizure \\ \hline
3 & Inter-ictal & 2300 & 3 & Signals recorded during the occurrence of consecutive seizures \\ \hline
4 & Healthy (closed eyes) & 2300 & 4 & A healthy subject with closed eyes \\ \hline
5 & Healthy (open eyes) & 2300 & 5 & A healthy subject with open eyes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of EEG Signal classes and their descriptions [1].
## 3 Results and Discussion
### Climate Attribution
The conditions on the frigid Tibetan Plateau are unsuitable for the growth of C4 plants [35]. Consequently, the plateau is dominant by C3 plants [36]. C3 plants are more efficient in vegetative growth than C4 plants in response to the increasing air CO\({}_{2}\)[37]. As a result, GPP increases more rapidly with increased air CO\({}_{2}\) in the Tibetan Plateau than in other regions containing both C3 and C4 plants. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature Continental** & **BCC** & **CMCC** & **MPI** \\ \hline P & 0.8 & 0.72 & 0.77 \\ \hline T & 0.26 & 0.14 & \(-0.1\) \\ \hline Var & 52.20\% & 54.00\% & 66.50\% \\ \hline \multicolumn{3}{c}{**Temperature monsoonal**} \\ \hline P & 0.78 & 0.54 & 0.66 \\ \hline T & 0.06 & 0.35 & 0.52 \\ \hline Var & 55.40\% & 45.90\% & 30.20\% \\ \hline \multicolumn{3}{c}{**Subtypical–tropical monsoonal**} \\ \hline P & 0.64 & 0.51 & \(-0.15\) \\ \hline T & 0.06 & 0.44 & 0.28 \\ \hline Var & 38.10\% & 36.30\% & 13.10\% \\ \hline \multicolumn{3}{c}{**Tibetan Plateau**} \\ \hline P & 0.16 & \(-0.1\) & 0.46 \\ \hline T & 0.38 & 0.75 & 0.75 \\ \hline Var & 15.00\% & 55.70\% & 57.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sensitivity parameters of GPP to climate variables during the historical period of 1980–2013 through the multiple linear regression approach. The final row shows the explained variance of climate to GPP through multiple regression.
## 5 Results and Verification
### Model Verification
#### 5.3.1 Validation through Comparison between Measured Values and Predicted Values  
Using the Confusion Matrix  
The prediction accuracy of the RORC severity prediction model was determined using data from 40 cases for validation, excluding 403 cases used for development out of the 443 collected cases in total. The result of the decision can be expressed as a confusion  
Figure 5: RORC severity prediction model.  
matrix that can compare the measured and predicted values, as presented in Table 3. The prediction power was high at 90% (\(13+16+9\))/40). From TAN learning, it is seen that the prediction power was improved by linking the variables with a causal relationship between independent variables.  
However, as the 40 data used for verification may not show a distribution that can represent the total accident rate of the entire dataset, we cannot be sure that the model accuracy is high using the chaos matrix alone. In the case of comparative verification with other models, the predictive performance of the model can be comparatively verified through the likelihood ratio test (LRT), root mean square error (RMSE), and the mean absolute error (MAE) [46,47]. However, it is difficult to verify the predictive performance of a single model. When judging the predictive performance of a single TAN model, it is possible to judge more visually and easily through ROC analysis [46].
In recent studies about granular materials, discrete element method (DEM) [27] appears increasingly [28; 29; 30], which provides a new perspective from micro-mechanism to study issues in granular materials. Additionally, the measuring error in laboratory tests can be avoided in DEM and the evolution of fabric can be easily monitored. Therefore, DEM can be a powerful tool for researchers to study the influencing factors of \(K_{0}\) precisely. For instance, [PERSON] [10] studied the effects of confining pressure, soil density, and over-consolidation ratio on \(K_{0}\) values using DEM method. At the same time, the development of microstructure during \(K_{0}\) loading is monitored by using the characteristics of DEM method, to explore the microscopic factors affecting \(K_{0}\) values. [PERSON] [31] generated samples with different void ratios, different initial fabric, and different ways of packing process in DEM simulation. The development of a series of macro and micro parameters, such as fabric, void ratio, coordination number, anisotropy parameter of contact force, and \(K_{0}\) values in oedometric compression process are recorded in detail. Concerning the \(K_{0}\) values, [PERSON] established a simple formula for \(K_{0}\) with the parameters of anisotropy parameters of contact force and fabric. However, the influence of initial fabric anisotropy on \(K_{0}\) values is missing in these DEM modellings. In this paper, we first investigate the effect of initial fabric anisotropy on the \(K_{0}\) values of granular materials. The inherent anisotropic specimens are prepared in DEM simulation and then an oedometer test is performed to determine \(K_{0}\) values. \(K_{0}\) values and evolution of microstructure of specimens with different initial fabric are analyzed and compared. Then, a series of DEM simulations are carried out to analyse the effects of particle shape, initial void ratio, and angle of internal friction between particles on the value of \(K_{0}\) values of granular materials. The results of DEM tests show that the effect of initial fabric anisotropy on the \(K_{0}\) of granular materials is minor. Particle shape, initial void ratio, and internal friction angle all significantly affect \(K_{0}\)-value. Based on these results, the rationality of the existing empirical formulas of \(K_{0}\) is discussed, and a new formula of relatively high accuracy is proposed in this paper.