esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Spectrophotometric Analysis of LE and DM  
#### 3.5.1 Samples Treatment  
The frozen remaining water after EO distillation was freeze-dried at \(-\) 54 \({}^{\circ}\)C and 0.05 mbar, through a BUCHI Lyovapot(tm) L-200 freeze-dryer (Buchi Labortechnik AG, Flawil, Switzerland). The lyophilized extracts (LE) were ground in a mortar and the obtained powders were maintained at 4 \({}^{\circ}\)C until further analyses.
## 5 Conclusions and Future Works  
In this paper, we tested two ML models' architecture to build classifiers for adversarial attack detection and isolation. We designed a set of scenarios that utilize a leave-one-out and cross-validation methodology to investigate whether it is possible to distinguish the occurrence of the attack and the attack type based on diagnostic attributes.  
The results show that the method works and is suitable for attack detection--it is possible to build a classifier, operating on diagnostic attributes, that can detect adversarial attacks with a balanced accuracy greater than 0.9. This method works also for detecting attacks that were not included in the training data set.  
For attack isolation, we obtained poorer performance of classifiers--the balanced accuracy varied from 0.66 to 0.80. The conducted research indicated the most important  
Figure 4: Mean rank of feature importance across classifiers for attack isolation.  
features used by trained classifiers: the balanced accuracy of the diagnosed data set, uncertainty, and neighborhood size.  
Future works can be divided into three distinctive work streams:  
* Increasing the quality of classifiers, by training them on larger representations of known attack methods and data sets. New data sets and new adversarial examples will become available as a direct result of implementation activities performed by the QED Software company and will focus on real-life examples of attacks and data in attack-prone environments.
* Testing different hierarchical approaches to the classifier construction--chaining attack detection classifiers with attack identification ones.
* Combining the resulting classifiers with external input sources explaining changes in data characteristics. This includes methods for concept drift detection, anomaly detection, and expert reasoning.  
Conceptualization, P.B. and L.W.; methodology, P.B. and L.W.; software, L.W.; validation, P.B. and L.W.; formal analysis, L.W.; investigation, L.W.; resources, P.B.; data curation, L.W.; writing--original draft preparation, P.B. and L.W.; writing--review and editing, P.B. and L.W.; visualization, L.W.; supervision, P.B.; project administration, P.B.; funding acquisition, P.B. and L.W. All authors have read and agreed to the published version of the manuscript.  
The work was partially financed within the statutory research project of Institute of Innovative Technologies EMAG (Lukasiewicz Research Network) and partially from the funds of QED Software.  
Not applicable.  
All data utilized in this study were publicly available on [[https://www.openml.org/](https://www.openml.org/)]([https://www.openml.org/](https://www.openml.org/)).  
The authors declare no conflict of interest.
# A Model-Free Online Learning Control for Attitude Tracking of Quadrotors
## 2 Model Description and Problem Statement  
### Notations  
Let \(\mathbb{R}\) denote the set of real numbers, \(\mathbb{R}^{m\times n}\) denotes the set of \(m\) by \(n\) real matrices, and \(\mathbf{I}_{n}\in\mathbb{R}^{n\times n}\) denote an \(n\) by \(n\) identity matrix. For a matrix \(\mathbf{A}\in\mathbb{R}^{m\times n}\), \(\mathbf{A}^{T}\) denotes its transpose, \(\|\mathbf{A}\|=\sqrt{\lambda_{\max}\big{(}\mathbf{A}^{T}\mathbf{A}\big{)}}\) denotes its 2-norm, where \(\lambda_{\max}(\mathbf{M})\) denotes the largest eigenvalue of a symmetric matrix \(\mathbf{M}\). For any vector \(\mathbf{v}=\left[v_{1},v_{2},\ldots,v_{n}\right]^{T}\in\mathbb{R}^{n\times 1}\), \(\|\mathbf{v}\|=\sqrt{\mathbf{v}^{T}\mathbf{v}}\) denotes its Euclidean norm. The operator \((\mathbf{v})^{\wedge}\) for a vector \(\mathbf{v}=[v_{1},v_{2},v_{3}]^{T}\in\mathbb{R}^{3}\) denotes a skew-symmetric matrix:  
\[\mathbf{v}^{\wedge}=\begin{bmatrix}0&-v_{3}&v_{2}\\ v_{3}&0&-v_{1}\\ -v_{2}&v_{1}&0\end{bmatrix} \tag{1}\]  
In attitude control, we denote the inertial frame as \(\mathcal{I}\), and the UAV fixed body frame as \(\mathcal{B}\). The attitude of a quadrotor is defined by the state of \(\mathcal{B}\) relative to \(\mathcal{I}\). Unit-quaternion \(\mathbf{q}=\big{[}s,\mathbf{v}^{T}\big{]}^{T}\in\mathbb{R}^{4\times 1}\) is used to present the attitude of quadrotors, where \(s\) is the scalar part and \(\mathbf{v}\in\mathbb{R}^{3\times 1}\) is the vector part; moreover, \(\|\mathbf{q}\|=1\). Furthermore, \(\mathbf{R}(\mathbf{q})=\big{(}\mathbf{I}_{3}+2\mathbf{v}^{\wedge}+2\mathbf{v}^{\wedge}\mathbf{v}^{ \wedge}\big{)}\in\mathbb{R}^{3\times 3}\) denotes the rotation matrix from \(\mathcal{B}\) to \(\mathcal{I}\).
# An Analysis of Bubble Migration in Horizontal Thermo-Capillarity Using the VOF Modeling
## 4 Results and Discussion
### Numerical Verification  
First, the numerical results of our model are benchmarked against the theoretical solution for verification. The capillary length \(L_{c}\) is essential for the surface tension effect and is defined as follows:  
\[L_{c}=\sqrt{\frac{\sigma}{\mathbf{g}(\rho_{l}-\rho_{v})}}\ =3.23\times 10^{-2}\ \text{m} \tag{9}\]  
where \(\sigma\) is the surface tension, \(\mathbf{g}\) is the gravity acceleration, and \(\rho_{l}\) and \(\rho_{v}\) represent the density of liquid and vapor. The theoretical thermos-capillary migration velocity \(V\) of a bubble/drop in a vertical temperature gradient was first given by [PERSON] [1]  
\[V=\frac{2}{3}\Big{[}\mu\gamma^{\prime}RT_{C}-\big{(}\rho-\rho^{\prime}\big{)} \mathbf{g}R^{2}\big{(}\mu+\mu^{\prime}\big{)}\Big{]}\big{(}3\mu+2\mu^{\prime} \big{)}^{-1}, \tag{10}\]  
where \(R\) is the bubble radius, \(T_{c}=3T_{1}/(2+(h^{\prime}/h))\), \(\rho\) and \(\rho^{\prime}\) are the densities and viscosities of fluid outside the bubble, respectively, \(\rho^{\prime}\) and \(\mu^{\prime}\) are the densities and viscosities inside the bubble, and \(\gamma^{\prime}\) is the temperature coefficient of surface tension. [PERSON] also performed experiments on bubble migration under thermo-capillary force aboard the NASA Space shuttle in orbit [2]. The thermo-capillary migration velocity of a drop \(V\) in reduced gravity is  
\[V=-\frac{2|\
abla T_{\infty}|\sigma_{T}R}{\mu(2+3\pi)(2+\beta)}\, \tag{11}\]  
where \(\
abla T_{\infty}\) is the temperature gradient, \(\sigma_{T}\) is the rate of the change of the interfacial tension with temperature, \(\alpha\) is the viscosity ratio of inside and outside the bubble, and \(\beta\) is the thermal conductivity ratio of inside and outside the bubble. The size of the computational model is 0.12 m \(\times\) 0.04 m (See Figure 2). Initially, the vapor bubble with a radius \(R=0.005\) m is positioned near the bottom wall. The gradient of temperature between two walls \(\
abla T\) is set as 833.3 K/m. Using Equation (11), the migration velocity is calculated as \(V=0.15625\) m/s. Figure 2 shows the transient bubble position with zero gravity. As time t increases, it is found that the temperature contour tends to show linear distribution, and the bubble moves toward the hot (left boundary) wall and remains at the same height under no gravity. Our simulation result shows that the migration velocity is approximately 0.142 m/s which is smaller than the theoretical solution, but it falls in the same order which shows a qualitative agreement. However, [PERSON]'s experiment [2] also showed a smaller migration velocity compared with the theoretical results. According to [PERSON]'s ([PERSON], Goldstein, and [PERSON]) model [1], the deformation of the bubble requires large enough kinetic energy, and the bubble deformation is not seen with \(V_{real}<V_{YGB}\), which is in good agreement.
## 3 Security Aspects Regarding Wireless Body Area Framework
### Data Transmission
#### 3.3.1 Ascon  
ASCON is a lightweight cryptographic standard with authenticated encryption with associated data (AEAD) designed for efficient implementation in various constrained environments, including IoT devices, embedded systems, and low-power devices like those commonly found in WBANs [28]. In 2023, NIST declared ASCON the US standard for lightweight cryptography. It is notable for its simplicity, security, and speed, making it suitable for resource-constrained environments [29]. It provides both authenticated encryption (ensuring confidentiality and integrity) and the ability to handle associated data, which are additional pieces of information not directly encrypted but are authenticated alongside the encrypted message.
# MC-EVM: A Movement-Compensated EVM Algorithm with Face Detection for Remote Pulse Monitoring
## 2 Related Works
### Artificial Intelligence and Face Detection
Attempts to improve the accuracy of SSD models were introduced in [44] to enhance the model by performing a concatenation over feature maps, and in [45; 46] by combining/fusing the feature maps of the output layers of the model to improve the detection of small objects. Finally, a review of the right model architecture to use for independent application areas and platforms depending on the best balance between speed, memory, and accuracy was introduced in [47]. A considerable number of research papers were introduced on the use of convolutional neural networks and Tensorflow for face detection. A CNN model based on Tensorflow was introduced in [30] for face recognition where the experimental results showed an improved recognition accuracy in complex and unsupervised environments. A framework was introduced in [31] to use the pre-trained model \"FaceNet\" and a triplet loss technique to maximize/minimize the distance between the anchors and negative/positive detections; this uses the Labeled Faces in Wild (LFW) dataset to check the performance of face recognition. In [32], the authors used Tensorflow and Keras to introduce a CNN-based approach for face detection, and the performance of face recognition was evaluated using a custom dataset. Furthermore, a face detection and face tracking solution were proposed in [48] for a class activation map of deep features for thermal images. Other research papers proposed solutions for face emotion classification such as in [49; 50]. In addition, a review of the list of used techniques for face detection was introduced in [51]. Finally, some other research was conducted in order to detect few-shot surface defects using the Enhanced Multiview Attention Model with Random Interpolation Resize (ERNet). This technique helps in the detection of potential defects with limited data and to improve industrial products quality for real-time defect detection [52]. ERNet could be a good alternative to detect defects in the input video of the EVM process. After checking the performance, detection speed, and low computational capacity of smartphone devices, we decided to use both the SSD model and the EfficientDet-lite0 to train our model to check their performance in real-time while using the EVM process. In our solution, we propose using these trained models to compensate for the movements of users while carrying out a pulse measurement using the EVM process. This can be achieved by cropping the detected face and providing it to the EVM to proceed with the magnification process.
## Citrus Leaf Volatiles as Affected by Developmental Stage and Genetic Type  
**Muhammad Azam, Qian Jiang, Bo Zhang, Changjie Xu * and Kunsong Chen**  
Laboratory of Fruit Quality Biology/The State Agriculture Ministry Laboratory of Horticultural  
Plant Growth, Development and Quality Improvement, Zhejiang University, Zijingang Campus,  
Hangzhou 310058, China; E-Mails: azam@zju.edu.cn (M.A.); twinssky1987@163.com (Q.J.);  
bozhang@zju.edu.cn (B.Z.); akun@zju.edu.cn (K.C.)  
* Author to whom correspondence should be addressed; E-Mail: chjxu@zju.edu.cn;  
Tel.: +86-571-8898-2289; Fax: +86-571-8898-2224.  
_Received: 23 April 2013; in revised form: 19 August 2013 / Accepted: 21 August 2013 / Published: 29 August 2013_  
**Abstract:** Major volatiles from young and mature leaves of different citrus types were analyzed by headspace-solid phase microextraction (HS-SPME)-GC-MS. A total of 123 components were identified form nine citrus cultivars, including nine aldehydes, 19 monoterpene hydrocarbons, 27 oxygenated monoterpenes, 43 sesquiterpene hydrocarbons, eight oxygenated sesquiterpenes, two ketones, six esters and nine miscellaneous. Young leaves produced higher amounts of volatiles than mature leaves in most cultivars. The percentage of aldehyde and monoterpene hydrocarbons increased, whilst oxygenated monoterpenes and sesquiterpenes compounds decreased during leaf development. Linalool was the most abundant compound in young leaves, whereas limonene was the chief component in mature ones. Notably, linalool content decreased, while limonene increased, during leaf development in most cultivars. Leaf volatiles were also affected by genetic types. A most abundant volatile in one or several genotypes can be absent in another one(s), such as limonene in young leaves of lemon _vs._ Satsuma mandarin and \(\beta\)-terpinene in mature leaves of three genotypes _vs._ the other four. Compositional data was subjected to multivariate statistical analysis, and variations in leaf volatiles were identified and clustered into six groups. This research determining the relationship between production of major volatiles from different citrus varieties and leaf stages could be of use for industrial and culinary purposes.  
**Keywords:** volatiles; young leaf; mature leaf; citrus types; GC-MS +
Footnote †: journal: J. Mol. Sci. **2013**, _14_
## 2 System Modeling
### System State-Space Form  
Constructing the state variables \(x=\ [x_{1};x_{2};x_{3}]=\left[\theta;\dot{\theta};A_{1}P_{1}-A_{2}P_{2}\right]\), the system state-space form by using (5) and (10) is  
\[\left\{\begin{array}{l}\dot{x}_{1}\ =\ x_{2}\\ \dot{J}\dot{x}_{2}\ =\ \frac{\partial x_{s}}{\partial x_{1}}x_{3}-mgd_{4}\cos( \beta_{0}+x_{1})-A_{f}S_{f}(x_{2})-Bx_{2}+\Delta(t)\\ \dot{x}_{3}\ =\ \varphi_{1}u-\varphi_{2}+q(t)\end{array}\right. \tag{12}\]  
in which  
\[\left\{\begin{array}{l}\varphi_{1}\ =\ \beta_{e}k_{t}(\frac{A_{1}}{V_{1}}s_{1}+ \frac{A_{2}}{V_{2}}s_{2})\\ \varphi_{2}\ =\ \beta_{e}(\frac{A_{1}^{2}}{V_{1}}+\frac{A_{2}^{2}}{V_{2}}) \frac{\partial x_{s}}{\partial x_{1}}x_{2}+\beta_{e}(\frac{A_{1}}{V_{1}}+ \frac{A_{2}}{V_{2}})C(P_{1}-P_{2})\\ q(t)\ =\ \beta_{e}\Big{[}\frac{A_{1}}{V_{1}}q_{1}(t)+\frac{A_{2}}{V_{2}}q_{2}(t) \Big{]}\end{array}\right. \tag{13}\]  
We attempt to let the tracking error converge to zero by using the developed control approach on basis of two assumptions:  
**Assumption 1**.: _The terms \(\Delta(t)\) and \(q(t)\) in (12) satisfy._  
\[|\Delta(t)|\leq c_{1},|q(t)|\leq c_{2} \tag{14}\]  
_where \(c_{i}\) (\(i=1,2\)) are known positive constants._  
**Assumption 2**.: _The reference trajectory \(x_{1d}\in\mathbb{C}^{3}\) and is bounded._
## 3 Research Methodology
### Hybrid Feature Selection (HFS)
#### 3.3.2 Support Vector Machine (SVM)  
SVMs can solve linear and nonlinear problems and provide power classification results [53]. The most important advantage of SVM is that it can control overlearning and high dimensionality and decrease computational complexity and local extremum [42]. Introducing a kernel function can facilitate the solving of nonlinear problems. Types of kernel function include linear, polynomial, and sigmoid functions. The radial basis function (RBF), presented in Equation (5), is one of the most popular kernel functions because it requires only one parameter, \(\delta\), which is a free parameter with a significant effect on classification accuracy and has a lower complexity, compared with other functions [41; 54]. Another essential parameter in SVM problems is \(C\), which is the penalty factor and shows the cost of misclassification. According to the significance of \(C\) and \(\delta\) on the result of SVM, they need to be optimized to obtain the desired accuracy, which can be accomplished using GA optimization.  
\[K(x,y)=\frac{exp(-|x-y|^{2})}{\delta^{2}} \tag{5}\]
# QuoTa: An Online Quality-Aware Incentive Mechanism for Fast Federated Learning
## 5 Evaluation
### Comparison Algorithm  
1. QuoTa-NT: Remove the computing capability constraints of this algorithm, i.e., the time constraint is not considered when selecting data owners.
2. RRAFL-Variant: As the RRAFL algorithm [2] cannot be directly applied to our scenario, we use RRAFL-Variant as a variant version, which uses the same selection method in terms of reputation/quotation, the model detection method in [2] to detect the local model, and the detected values to update the reputation value of each worker. But, it never considers the training time constraint.
3. RANDOM: The platform randomly selects data owners to participate in training within the budget.
4. SCORE: The bid of each data owner is scored as: \(score_{i}=0.7\times d_{i}+0.3\times C_{i}-b_{i}\), where each term like \(d_{i}\) is normalized, and then the set of data owners with the highest scores are selected within the budget. We add the similar model quality detection method (below a certain reputation threshold can no longer be selected) and the calculation of computing capability using estimated CPU frequency.
5. GREEDY: The server selects the data owner with the lowest bid within the budget and never adopts our model quality detection method.
# Development and Assessment of a 4D Printing Technique for Space Applications
## 2 Materials and Methods
### 4D printing
#### 4.1.1 Shape Memory Effects in 4D Printing  
Shape memory effects (SMEs) play a pivotal role in the functionality of 4D printing, particularly when employing SMPs. SMPs possess the unique ability to recover deformation in response to a stimulus, with thermal energy being the main focus in this context [23]. SMEs in SMPs result from a carefully orchestrated material treatment process, known as a shape memory cycle.  
This cycle involves three main steps:  
1. **Programming:** The polymer is initially in its undeformed state (shape A) and is subjected to thermal energy above a specific transition temperature (\(T_{\text{trans}}\)). This allows the polymer to undergo deformation under an applied force. Upon cooling below \(T_{\text{trans}}\), the deformed state is fixed [24].
2. **Storage:** The polymer, now in its deformed temporary state (shape B), stabilizes the deformation. This stage allows for convenient storage, with the polymer retaining its altered shape [24].
3. **Recovery:** Reheating the polymer above \(T_{\text{trans}}\) reactivates its ability to recover the initial deformation, completing the shape memory cycle [24].  
The underlying polymer structure involves macromolecules with specific configurations. Understanding these configurations is essential for harnessing SMEs effectively.  
In the realm of heat-induced SMPs, three main groups exist [19]: the dual-state mechanism (DSM), dual-component mechanism (DCM), and partial-transition mechanism (PTM). DSM involves a glassy polymer transitioning between rubbery and glassy states, with examples like PMMA and silicone [19]. DCM employs a dual-component system, combining elastic and transition segments within the polymer [19], while PTM relies on the partial heating of the polymer, which supports/enables the SMEs. In the realm of FDM  
Figure 4: Isometric assembly drawing of the programming device **version 1** used in the experiments. With the components: (1) the base lever, (2) the rotating lever, and (3) the slidable hold down mechanism. Outer dimensions: 232 mm \(\times\) 67 mm \(\times\) 16 mm.
## 3 Results and Discussion
### Model Fitting
In addition, short extraction times are achieved as a result of cavitation, thermal, and physical phenomena induced at the extracted material's surface [29]. Lack of fit was also not significant (\(p>0.05\)) for the models of apigenin, caffeic acid, and luteolin, implying that the models fit the data and each model may give accurate predictions. Significant linear, quadratic, and interaction terms lead to the predictive equations (Equations (2)-(4)) as presented in Table 4. Positive and negative signs of its coefficient in its equation of the quadratic models indicate respectively positive or negative effects on the extraction efficiency of the studied phenolics.
## 3 Results and Discussion
Fatty acids of omega-3 (n-3) and omega-6 (n-6) group have important functions in many biochemical transformations and regulate many physiological activities of the human body. Not all fatty acids can be synthesized by the human body and must be supplied with food. Dietary supplementation of enriched with fatty acids meat can be an alternative to increase the daily intake of this crucial components [31]. The positive effect of addition to pig diet oils rich in polyunsaturated fatty acids (PUFA) on raw meat was also presented in previous studies, e.g., [PERSON] et al. [32] and [PERSON] et al. [33]. However, the higher amount of PUFA can affect meat quality owing to peroxid oxidative changes. Thus, it is recommended to apply to diet also with oil components natural antioxidants, \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multicolumn{3}{c}{**Feeding Groups**} \\ \cline{2-5}  & & **ND** & **SD** & \\ \hline \multirow{2}{*}{Weight losses (\%), \(n=12\)} & 10.1 \(a\) & 10.1 \(a\) & X \\  & 0.85 & 0.8 & sd \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Production yield \\ (\%), \(n=12\) \\ \end{tabular} } & 95.8 \(a\) & 95.7 \(a\) & X \\  & 0.85 & 0.8 & sd \\ \hline \multirow{2}{*}{Shear force (N), \(n=96\)} & 29.5 \(a\) & 27.3 \(a\) & X \\  & 1.67 & 1.66 & sd \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Color parameters \\ n = 60 \\ \end{tabular} } & L & 70.8 \(a\) & 68.8 \(b\) & X \\  & 2.03 & 1.81 & sd \\ \cline{1-1}  & a & 10.5 \(a\) & 11.2 \(b\) & X \\ \cline{1-1}  & 1.15 & 0.93 & sd \\ \cline{1-1}  & b & 3.92 \(b\) & 3.25 \(a\) & X \\ \cline{1-1}  & 0.53 & 0.58 & sd \\ \hline \hline \end{tabular} ND—cured hams produced of raw meat from pigs fed a diet without supplementation; SD—cured ham produced of raw meat from pigs fed a diet supplemented with rapeseed oil and mineral additive. X—mean value; sd—standard deviation; \(a\),\(b\)—different letters indicate significant differences (\(p\leq 0.05\)) between values.  
\end{table} 
Table 3: Weight losses, yield of the process, shear force and color parameters measurement. which have greater potential to prevent oxidative damage and at the same time to increase shelf-life of meat products [34; 35]. Our studies have shown that using feed component (rapeseed oil) rich in polyunsaturated fatty acids lead to production of cured ham beneficial for consumer health due to the ratio of saturated and unsaturated fatty acids. At the same time, the changes of fatty acids content in meat products during storage, were observed only in ND group, which may be related to the additives used in experimental diet. It can be assumed that addition of rapeseed oil, which characterized by high phenolic content (sinapinic acid and other derivatives with antioxidant properties), increases the biologic value of feed and also have a positive influence on raw meat quality [17; 18] and efficiently limited the oxidative changes in fatty acid profile of cured harms. This effect may be accelerated by the addition of mineral additive which also exhibited antioxidant activity in radical scavenging and electrochemical assays [36].