esa-sceva/satcom-chunk-collection huggingface dataset:
# Antibiofilm Potential of Natural Essential Oils
## 4 ABF Activity of EOs and Their Components
### EOs Compounds as Antimicrobial Agents  
#### 4.2.1 Phenolic Compounds  
Phenolic compounds (PCs) are widespread secondary metabolites with strong biological activity [43]. The phenolic components of EOs (carvacrol, eugenol, and thymol) (Figure 2) affect the cytoplasmic membrane, electron flow, proton forces, active transport, and coagulation of cell contents. EOs are complex mixtures of different active compounds that exert additive, antagonistic, or synergistic effects on each other. The identification of the mechanism of EO action depends on its chemical composition [23]. ABF activity has also been demonstrated for eugenol, which was found, among others, in clove oil. It can inactivate enzymes, react with the cell membrane, and disrupt the functionality of genetic material, energy production, and synthesis of structural components. Also, it inhibits BF formation in the intermediate and maturation phases, and eliminates BF formed by a group of microorganisms (_S. aureus_, _E. coli_, _P. aeruginosa_, and _C. albicans_). In addition, eugenol shows a concentration-dependent ABF activity in single and mixed BFs formed by drug-resistant strains (_C. albicans_ and _S. mutans_) [44], and has been shown to be active against planktonic cells and BF of _Vibrio parahaemolyticus_, a widespread foodborne pathogen that can cause severe gastroenteritis [45]. Studies by [PERSON] et al. [46] have indicated that eugenol at a concentration of 0.005% (\(v/v\)) significantly inhibits BF formation by _E. coli_ O157:H7 (EHEC) without suppressing the growth of planktonic cells. EHEC forms BFs tolerant to AM agents and is a causative agent of serious health problems worldwide. Similar effects were also observed for eugenol derivatives: isoeigenenol, 2-methoxy-4-propylenol, and 4-ethylguaiacol. These findings indicate that the C-1 hydroxyl unit and the C-2 methoxy unit, as well as the C-4 alkyl or alkane chain in the benzene ring of eugenol, play an important role in the ABF activity. Transcription analysis has shown that eugenol downregulated 17 of the 28 genes analyzed, including those required for BF formation (csgABDFG, fimCDH, espD, esC, esC, and tri). In the nematode _Caenorhabditis elegans_ model, clove oil and eugenol attenuated the virulence of EHEC [46]. The complexity of the structure of individual EOs components may be one of the main reasons for their effective AM and ABF activity and, ultimately, the effective therapeutic effect of EOs.
## 2 Materials and Methods  
The well crystallized single-phase Bi\({}_{2}\)FeMnO\({}_{6}\) film was deposited on a silicon substrate using a pulsed laser deposition method (PLD) at 923 K with 500-600 mTorr dynamic oxygen. The film thickness was about 150 nm. The reflection-type absorption spectra were measured in UV-3600, (Shimadzu Corporation, Kyoto, Kyoto Prefecture, Japan), with a relative type suite. A reflection type pump probe set up was employed here which was described in detail in formal lectures [17]. The light source was provided by a commercial mode-locked Ti:sapphire laser (Spitfire Pro, Spectra-Physics, CA, USA) running at the repetition rate of 1 kHz, the pulse width of 120 fs, and the center wavelength of 800 nm. The pump beam with photo energy of 3.1 eV was obtained from a frequency-doubled fundamental beam (800 nm) in a 1 mm \(\beta\)-BaB\({}_{2}\)O\({}_{4}\) crystal. The average pump fluence was about 6 mJ/cm\({}^{2}\) which is 30 times larger than that of the probe. Both of the pump and probe beams were focused on the surface of the sample with a spot diameter about 200 \(\upmu\)m. The sample was mounted in a closed-cycle liquid-He cryostat in vacuum chamber with four optically accessible windows.  
By altering the doping ratio, x, between 0 and 1, the band gap for BiFe\({}_{\text{x}}\)Mn\({}_{1\text{-x}}\)O\({}_{3}\) could be modulated from 1.2-2.6 eV [12]. Figure 1a shows the reflection type UV-Vis spectrum of Bi\({}_{2}\)FeMnO\({}_{6}\)/Si film. It could be seen from Figure 1 that two distinct reflectivity dips occur at 1.1 eV and 1.5 eV, respectively. The 1.1 eV dip arises from the band gap transition of silicon substrate, and the higher energy dip at 1.5 eV corresponds to the on-site Mn\({}^{3+}\) d-d charge transfer (CT) gap at room temperature, which is consistent with reported values of the band gap at x = 0.5 in the literature [9; 12]. The other slight deviations correspond to the mechanical error for changing optical gate. Figure 1b shows the transition processes of electrons in the corresponding materials by 1.55 eV pumping and 3.1 eV pumping, respectively. In perovskite manganite the 1.55 eV transition is determined to be the e\({}_{\text{g}}\)-t\({}_{\text{2g}}\) transition which is \(\Gamma^{3}\rightarrow\Gamma^{5}\) between the hybridized O\({}^{2-}(2p)\)-Mn\({}^{3+}(3d)\) states and the Mn\({}^{3+}\) states [16; 19; 20; 21].
## 4 HydroSAR Product Portfolio and Product Algorithms
### Input Data  
Input data for HydroSAR are shown in Figure 3 and consist of SAR data, digital elevation model (DEM) information, and ancillary information such as hydrobasin and agriculture reference data. Data used for product validation are presented in Section 7.  
SAR data used in HydroSAR consists of NASA-managed Sentinel-1 SAR data holdings at the Distributed Active Archive Center (DAAC) of NASA's Alaska Satellite Facility (ASF). The Sentinel-1 C-band SAR constellation has been providing free and open, globally observed SAR data since 2014 and observing all global land masses at high sampling rates. Sentinel-1 provides complete coverage of the HKH once every 8-10 days. Data are typically processed and delivered to the ASF's DAAC within 6 h after image acquisition. With its global acquisition strategy, its cloud-free imaging capabilities, and its comparably low latency data delivery, Sentinel-1 provides an excellent basis for HydroSAR's inundation mapping goals.  
The DEM data used in this project consisted of the 30 m resolution Copernicus GLO-30 digital elevation model [32]. This freely-accessible resource is based on acquisitions of the TanDEM-X mission, a constellation of two satellites flown with the goal to generate a global DEM [33]. GLO-30 is a validated DEM [34] and provides consistent-quality topographic information across our area of interest. The HydroSAR processor accesses the GLO-30 DEM directly in AWS through its Registry of Open Data program.  
Beyond these data types, We used ancillary information about watersheds and land cover in some of our processing routines (see Section 4.2.1). Hydrobasin information was accessed from the HydroSHEDS database [35], which offers a suite of global digital data layers in support of hydro-ecological research and applications worldwide. The landcover information used in HydroSAR originated from the Copernicus Global Land Monitoring Service [36].  
Figure 3: Portfolio of flood hazard products created by HydroSAR to support the analysis of flood status and impact in the HKH. Input data are shown on the left. The green column shows the intermediary data layers needed to generate the final visual and quantitative HydroSAR products shown on the right side of the figure. Lines indicate workflows and dataset dependencies. The HydroSAR agriculture extent product AG30 is not discussed in this paper but is included for completeness.
# Fuzzy Model Predictive Control for Unmanned Helicopter
## 2 Materials and Methods  
### Control Plant Description  
The proposed control system was designed for the ARCHER unmanned helicopter. This is a small-scale rotorcraft, which was designed and built at the Warsaw University of Technology in 2018. Its name stands for Autonomous Reconfigurable Compound Helicopter for Education and Research [27]. The ARCHER was originally built in the classical configuration with a main rotor and tail rotor for flight control. Apart from this, the main purpose of the described aerial vehicle is to be easily modified by attaching to its main structure additional control effectors or aerodynamical surfaces, to expand its application range. In Figure 1, the ARCHER helicopter with an additional pushing propeller is presented.  
For the purpose of this research, ARCHER is considered in its standard form, where the main carrier module consists of the fuselage, propulsion and power distribution system, main rotor, tail rotor, and rigid landing gear. Both rotors are powered by separate electric motors, which enables them to rotate at individual angular velocities independently, without using advanced powertrain construction. The ARCHER uses a see-saw rotor hub, which is a popular solution in small helicopters. The main rotor blades are designed using NACA23012 airfoils and the tail rotor blades are shaped using symmetrical NACA0015 airfoils. ARCHER's main rotor rotates in the clockwise direction. The basic geometrical and mass characteristics of ARCHER helicopter are presented in the table below (Table 1).
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 3 Method
### Output Calculation
#### 3.5.2 Heart Rate Calculation  
Heart rate is calculated by averaging all IBIs over a time window, and computing the inverse of it. That is, \(\text{HR}_{w}=1/\overline{\text{IBI}}_{w}\), where \(\overline{\text{IBI}}_{w}\) is the mean of all inter-beat intervals that fall within the time window \(w\). This gives the heart rate in Hertz (assuming IBIs in seconds), and multiplying by 60 gives us the heart rate in beats-per-minute. The choice of this time window can be based on the user's requirement (e.g., instantaneous HR, long-term HR).
## 3 Results
### Parameter Correlations
#### 3.3.2 Ventilation-Acoustic Relationships  
The correlation between air velocity (often associated with ventilation) and acoustic comfort is relatively low, with a correlation coefficient of 0.076. This suggests that the relationship between air velocity and acoustic comfort is weak, meaning that variations in air movement are not strongly associated with changes in perceived noise levels or acoustical comfort in the analyzed studies.  
One possible reason for this weak correlation is that ventilation and noise may be influenced by different factors within indoor environments. While air velocity is primarily influenced by HVAC systems, natural ventilation, and building design, acoustic comfort tends to be more dependent on the materials used in construction, the presence of sound-proofing, and the sources of noise within the environment (e.g., equipment, traffic, and human activities). Therefore, improving one of these parameters does not necessarily lead to improvements in the other.  
Despite the weak correlation, it is important to note that in specific contexts--such as noisy environments--ventilation strategies that reduce noise (e.g., using quieter HVAC systems or acoustic treatments in ducts) might have a more noticeable effect on both air quality and acoustic comfort. However, overall, the low correlation between air velocity and acoustic comfort indicates that these two parameters are influenced by independent factors in most indoor settings.  
In conclusion, while there is a slight relationship between ventilation (air velocity) and acoustic comfort, the weak correlation suggests that strategies to optimize one parameter may not necessarily lead to improvements in the other. Further research could explore specific scenarios where these factors might interact more significantly, especially in environments where both high noise levels and ventilation are crucial for occupant well-being.
# Cross-Feature Transfer Learning for Efficient Tensor Program Generation
## 4 Evaluation
### Evaluation of Heterogeneous Transfer Learning
A limitation inherent in our proposed methodology is its challenge in effectively transferring knowledge across hardware architectures of dissimilar classes, such as attempting knowledge transfer from one CPU class to a GPU class. This constraint becomes especially notable in situations where the dataset available for a specific hardware class is limited, leading to substantial time requirements for data collection and model training. The consequences of insufficient data may manifest in the autotuner's performance, potentially resulting in suboptimal convergence. Emphasizing the significance of robust datasets for each hardware class becomes crucial to ensure the effectiveness of the knowledge transfer process and the subsequent autotuning performance. Additionally, exploring research avenues in few-shot learning methods for hardware-aware tuning could provide valuable insights into mitigating this limitation. Table 10 presents the assessment outcomes of the proposed tuner compared to the TenSet XGB Tuner based on Top-k scores. The table outlines each tuner's top-1 and top-5 accuracy metrics across two target hardware platforms: H100 and A64 FX. In the case of  
Figure 3: Comparing Training Convergence of Tuners. (**a**) XGBoost, (**b**) MLP, (**c**) LightGBM, and (**d**) Our Tuner, experimented on Nvidia RTX 2080. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Target Hardware**} & \multirow{2}{*}{**Network**} & \multicolumn{2}{c}{**Without Transfer Tuning**} & \multicolumn{2}{c}{**With Transfer Tuning**} \\ \cline{3-6}  & & **Time-to-Tune** & **Mean Inference Time** & **Time-to-Tune** & **Mean Inference Time** \\ \hline \multirow{2}{*}{CPU} & Inception\_v3 & 614 & 75.27 & 61 & 73.80 \\  & MobileNet\_v3 & 236 & 5.48 & 71 & 5.57 \\  & ResNet\_50 & 128 & 11.93 & 86 & 12.12 \\ \hline \multirow{2}{*}{GPU} & Inception\_v3 & 2510 & 28.72 & 191 & 28.73 \\  & MobileNet\_v3 & 1092 & 1.72 & 136 & 1.75 \\  & ResNet\_50 & 817 & 3.79 & 226 & 3.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Evaluation of Proposed Tuner: Variation In Tuning Time And Inference Time. our proposed tuner, it was trained to utilize all available hardware datasets, excluding the specific hardware under examination. Subsequently, an evaluation was performed for both tuners. The network architecture employed for this evaluation was ResNet_50. Our tuner exhibited comparable performance to the XGB tuner, which was trained for the underlying hardware. Our tuner, conversely, leveraged learning schedules derived from a similar architecture. This quantitative analysis underscores the proficiency of our tuner in transfer learning, demonstrating competitive performance across the specified hardware configurations.
# Intelligent Cyber Attack Detection and Classification for Network-Based Intrusion Detection Systems
## 1 Introduction
A model trained for binary classification with Normal and Attack labels would not be able to recognise which type of attack was performed, only that an anomalous situation it had occurred on the network. This work was developed within the Security of Air Transport Infrastructures of Europe (SATIE) project, as research for a novel approach to investigate the temporal correlation between cyber and physical alerts. The SATIE project will build a holistic, interoperable, and modular security toolkit to be exploited by the next generation of Airport Operation Centres and Security Operation Centres to protect critical air transport infrastructures against combined cyber-physical threats [13]. Our work introduces several novelties, and mainly differs from others presented in Section 2 because, to the best of our knowledge:  
* No work has addressed the target variable _AttackType_ of the CIDDS-001 in order to perform multi-class classification for intrusion detection;
* Only [PERSON] et al. in [14] has addressed network intrusion detection from a sequential perspective by using LSTM. However, the experimental results were achieved for the UNSWNB-15 dataset, which is two years older than the CIDDS-001, contains considerably less data for training and testing, and regards different attack types to those presented in the CIDDS-001 dataset;
* Only in [15], was LSTM used for the CIDDS-001 dataset. Nevertheless, the work does not mention any study about the flow window size and its impact in the model's evaluation metrics. It is also important to highlight that, in [15], only the External Server data was used and the _Class_ label was selected as target value. This analysis is different to the one presented in our work;
* No previous work has established a proper comparison between the two intrusion detection approaches addressed in our work: single-flow and multi-flow;
* Our work was performed in the context of the SATIE project and the methods described in this research will become part of it's modular security toolkit to be used by the next generation of Security Operation Centres to protect critical air transport infrastructures. The paper is organized in multiple sections that can be detailed as follows. Section 2 provides a comparison between the current research and previous works on the CIDDS-001 dataset as well as a description of other implementations of the same methods for other relevant datasets. Section 3 briefly explains the machine learning models that were used, the problem's viewpoints and the considered evaluation metrics. In Section 4, the obtained results are presented and discussed. Section 5 provides a summary of the main conclusions that can be drawn from this research and appoints further research topics that can be addressed.
# Influence of Concrete Strength Class on the Long-Term Static and Dynamic Elastic Moduli of Concrete
## 3 Results and Discussions
### Conversion Equations from Dynamic to Static Modulus of Elasticity  
The assessment of the dynamic modulus of elasticity has the advantage that it involves a non-destructive method, and can be performed, in general, directly on site. Since, for design and technical assessment purposes, the values of the static modulus of elasticity were considered, conversion equations were proposed.  
The considered equations are the ones proposed by [PERSON] [21], Equation (6), the equation proposed by [PERSON] and [PERSON] [22], Equation (7) and the equation available in BS EN 1992-1-1:2004 [23].  
\[\mathrm{E_{c}}=\frac{446.09\times\mathrm{E_{d}^{1.4}}}{\rho_{\mathrm{c}}} \tag{6}\]  
\[\mathrm{E_{c}}=0.83\times\mathrm{E_{d}} \tag{7}\]  
\[\mathrm{E_{c}}=1.25\times\mathrm{E_{d}}-19 \tag{8}\]  
where \(\mathrm{E_{c}}\) is the static modulus of elasticity (GPa), \(\mathrm{E_{d}}\) is the dynamic modulus of elasticity (GPa), and \(\rho_{\mathrm{c}}\) is the density of concrete (kg/m\({}^{3}\)). The obtained results are summarized in Figure 3.  
It can be observed that both Equations (6) and (7) give quite accurate results in terms of predicted value of the static modulus of elasticity for Mixes 2 and 3. All empirical prediction equations overestimated the experimental results of Mix 1 by 12.39-24.66%. Equation (8) underestimates the experimental results for Mixes 2 and 3 by 9.54% and 9.85%, respectively.  
Based on the obtained results, it can be concluded that Equations (6) and (7) provide accurate predictions for the values of the static modulus of elasticity in cases of higher concrete strength classes, whereas Equation (8) is better suited for lower concrete strength classes even though it tends to overestimate the experimental results.
## 3 Detection in Receiver
### SVM Detector
#### 3.2.1 SVM Classification  
For the binary classification problem, during the training stage, the goal of SVM is to seek a separation plane which maximizes the margin between the two classes of 1 and 0. Each input training sequence \(\textbf{x}_{i}\in\textbf{R}^{*}\), \(i=1\), 2,..., \(L\) is associated with a binary message \(y_{i}\in\{\)1,0\(\}\) to indicate the desired output. After training is completed, the decision function is constructed via:  
\[f\big{(}x\big{)}=\sum_{i=1}^{L}\alpha_{i}y_{i}K\big{(}\textbf{x},\textbf{x}_{i} \big{)}+b \tag{7}\]  
where L is the number of training sequence, \(\alpha_{i}\) is a Lagrangian constant which contributes to the slope of the separation plane, \(K(\textbf{x},\textbf{x}_{i})=\Psi(\textbf{x}_{i})^{\mathrm{T}}\)\(\Psi(\textbf{x})\) is a kernel function, where \(\Psi(\textbf{x})\) maps the training data vector \(\textbf{x}_{i}\) into the high-dimensional feature space, and b is a bias term of the decision hyperplane.  
Define a coefficient vector **w**, such that:  
\[\textbf{w}=\sum_{i=1}^{L}\alpha_{i}y_{i}\Psi\big{(}\textbf{x}_{i}\big{)} \tag{8}\]  
then the training is completed by solving the following optimization problem:  
\[\begin{split}\min_{\textbf{w}\textbf{H},b\textbf{R},\xi_{i} \in\textbf{R}^{L}}&\frac{1}{2}\Big{\|}\textbf{w}\Big{\|}^{2}+C \sum_{i=1}^{L}\xi_{i},\,\mathrm{s.t.},y_{i}\big{(}\big{(}\textbf{w}\cdot \textbf{x}_{i}\big{)}+b\big{)}\geq 1-\xi_{i},\\ &\xi_{i}\geq 0,i=1,2,\ldots,L.\end{split} \tag{9}\]  
where \(C\) is the trade-off parameter between the training error and the margin of the decision function, and \(\xi_{i}\) is a slack variable to compensate for any non-linearly separable training points.  
The output is a reduced set of those training data, because most training data \(\textbf{x}_{i}\) would have \(\alpha_{i}\) equal to 0. Those training examples which have non-zero \(\alpha_{i}\) are used as the final decision variables, and are called the support vectors (SV) [16].  
Usually, four kernel functions are used in different cases. The RBF kernel non-linearly maps samples into a higher dimensional space, so it can handle the case when the relation between class labels and attributes is nonlinear. Compared to the RBF kernel, the polynomial kernel has more hyper-parameters, which influences the complexity of model selection. In addition, the sigmoid kernel behaves like RBF for certain parameters. Furthermore, there are some situations where the RBF kernel is not suitable. Thus, one may just use the linear kernel which is the simplest one. In this paper, the SVM detector uses two types of kernel functions to compare the performance with each other. The first is the simplest linear kernel, shown as:  
\[K\big{(}x_{i},x_{j}\big{)}=x_{i}^{T}x_{j} \tag{10}\]  
and the second is a more popular radial basis function (RBF) kernel, shown as:\[K\big{(}x_{i},x_{j}\big{)}=\exp\left(-\gamma\big{|}x_{i}-x_{j}\big{|}^{2}\right) \gamma>0 \tag{11}\]  
where \(\gamma\) controls the width of the function.