esa-sceva/satcom-chunk-collection huggingface dataset:
# Plant-Derived Phytobiotics as Emerging Alternatives to Antibiotics Against Foodborne Pathogens
### Saponins  
Saponins are a diverse group of naturally occurring plant glycosides with significant ecological, biological, and pharmacological roles. They are amphipathic molecules composed of a hydrophobic aglycone (triterpenoid or steroid) linked to oligosaccharide moieties. The presence of a hydrophobic aglycone backbone in combination with hydrophilic sugar residues gives them an amphipathic character, which is reflected in their ability to foam and emulsify [81; 82]. Saponins play an important role in plant defense, repelling insects and herbivores, and participating in allelopathy by inhibiting the growth of competing species. In addition, they are involved in the regulation of growth and development processes and in ecological interactions, such as plant-microbe or plant-plant relationships, affecting the balance of the ecosystem [83; 84; 85; 86; 87]. Saponins also exhibit significant antimicrobial properties, which include activity against pathogenic bacteria, fungi, and protozoa. For example, saponins from _Medicago_ plants show high activity against Gram-positive bacteria, especially against _Bacillus_ and _Staphylococcus_ species, while saponins from quinoa are mainly effective against Gram-negative bacteria such as _Salmonella enteritidis_ and _Pseudomonas aeruginosa_. The antimicrobial effects are attributed to their ability to interact with and permeabilize cell membranes due to their amphiphilic nature. The antimicrobial action of saponins is primarily due to their ability to disrupt cell membranes. This disruption leads to the leakage of cell contents, ultimately causing cell death [88; 89]. The most important classes include triterpenoid saponins and steroidal saponins. Triterpenoid saponins are commonly found in dicotyledonus plants [81; 82]. Examples of these compounds include ginseng present in ginseng and soy saponins found in soybeans [82; 90]. These compounds exhibit numerous biological properties, including anti-inflammatory, anticancer, and immunomodulatory effects. Steroidal saponins predominate in monocotyledonous plants and include, among others, diosgenin extracted from yam (_Dioscorea_ spp.) and timosaponin found in _Anemarhena asplodeloides_. They exhibit beneficial pharmacological effects, including supporting cardiovascular function [81; 82; 91].
## 2 Materials and Methods
### Thermal Performance of the Insulating Envelope and Energy Simulation  
#### 2.4.1 Thermal Inertia  
To evaluate the impact of the rice husk fiber material on thermal inertia, a component of the Skaylark 250 system, specifically the block M version 0.1, was selected (Figure 4). The thermal inertia of the material was analyzed using the Ubakus online tool, which adheres to the procedures outlined in the DIN 4108 [43] and DIN EN ISO 6946 [44] standards. These standards define methods for assessing thermal resistance and energy efficiency in building systems.  
Ubakus employs finite element methods to calculate thermal resistance and transmittance, offering a reliable approach for evaluating and optimizing the thermal performance of the construction materials. For thermal capacity calculations, the tool determines the temperature profile within the material for two internal temperatures differing by 1 \({}^{\circ}\)C under static conditions (t\(\rightarrow\)\(\infty\)). From these profiles, the average temperature difference (dT) is calculated for each layer, and the stored heat (dQ) is computed using the formula:  
\[\text{dQ}=\text{dT}\cdot\text{density}\cdot\text{specific heat capacity}\cdot\text{thickness} \tag{1}\]  
The key thermal parameters considered included time lag (the delay between external and internal temperature peaks), decrement factor (the ratio of temperature amplitudes), the thermal capacity of the inner layers, and the heat storage capacity. A 24 h simulation with sinusoidal external temperature variations ranging from 15 \({}^{\circ}\)C to 35 \({}^{\circ}\)C provided temporal temperature profiles, enabling a comprehensive analysis of the material's thermal damping properties [25; 29]. For the thermal inertia assessment, rice husk fiber was analyzed and  
Figure 4: Details of block M wall element.  
compared with recycled PUR, as both materials are suitable for application through the blowing technique.
# Physical and Chemical Methods to Assess Performance of TPO-Modified Asphalt Binder
## 4 Discussion
### NMR Testing Comparison  
Since aging has an impact on the chemical microstructure environment in the blends, the \(T_{1}\) relaxation times of unaged, short-term aged, and long-term aged blends were used to determine asphalt aging effects as seen in Figure 15. Pure TPO has a primary relaxation time around 0.27 s and a secondary relaxation time around 0.57 s. The impact of TPO on binder blends was most visible from changes in the secondary relaxation times of the unaged 2% and 8% TPO blends. The unaged 2% TPO sample had an increase in physical stiffness and resulted in a water vapor peak in the FTIR spectra. The unaged and short-term aged 8% TPO samples were less stiff due to the TPO present in the blend. This impact of the TPO in the 2% and 8% binder blends was seen as a secondary relaxation time around 0.27 s which is close to the characteristic relaxation time of pure TPO. While the secondary relaxation times are similar, the effect is different. This difference can be explained by the primary relaxation time.  
The primary relaxation time of the 2% sample was closer to the 0.57 s characteristic peak of the TPO. This less mobile chemical environment indicated that the 2% blend would be stiffer, while the 8% sample did not share this increase in primary relaxation time. The 4% sample had similar physical and chemical properties to the original PG 64-22 binder (0% TPO).  
Aging also influences relaxation times due to the changing chemical microstructure environment. The primary relaxation time of the original binder sample increased as it aged which was consistent with the increase in physical stiffness. NMR relaxometry relies on the mobility of the asphalt matrix. It takes more time for the relaxation of hydrogen nuclear spins when the binder is stiffer and less mobile. Therefore, a longer relaxation time results in a \"stiffer\" blend. The original short-term aged sample had the longest primary relaxation time while the 8% TPO blend had the shortest. Furthermore, the long-term aged samples had similar primary relaxation times which were echoed by their similar physical stiffness and carbonyl indices.  
Figure 14: Carbonyl indices for aged and unaged samples.
## 5 Discussion
### Application Domains  
On the one hand, the applications in agriculture include the following domains:  
* Monitoring of livestock and fish farming: this involves the monitoring of the environmental parameters (basins, water quality, etc.) of the animals as well as the internal (health) parameters of the animals themselves.
* Monitoring of crops and soil for smart precision agriculture (moisture, humidity, temperature, pest control, etc.).
* Monitoring of meteorological parameters for agriculture.
* Smart irrigation systems.
* Detection of animal intrusion in fields.  
On the other hand, the works with applications in environment include the following domains:  
* Monitoring of water quality.
* Monitoring of air quality (unwanted gaseous substance emission and particulate matter).
* Monitoring of meteorological parameters such as rainfall, wind speed, sunshine and air humidity.
* Monitoring of solar street lamps.
# Comparison of Genotoxicity and Pulmonary Toxicity Study of Modified SiO2 Nanomaterials
## 3 Results
### CA Test  
The LC 30 values of SA, mSA, SB, and mSB were 1.387, 0.778, 0.353, and 1.569 mg/mL, respectively. These figures were based on cytotoxicity tests (data not shown). First, all the test articles were tested at concentrations of LC 30 in the absence of a rat S9 metabolic activation system. The results revealed that there was no significant difference in the frequencies of chromosomal aberrations in the SA, mSA, or mSB groups compared to the negative control. However, the SB-treated CHO-K1 cells showed significantly increased frequencies of chromosomal aberrations when compared to the control group. Because the frequencies of chromosomal aberrations in the SB group were increased by about 3.7-fold from that of the negative control, the results of the CA test in the SB group with rat S9 metabolism were positive (Table 6). Second, in the presence of a rat S9 metabolic activation system, the results revealed no significant difference in the frequencies of chromosomal aberrations in the treatment groups compared to the negative control (Table 7). Overall, in the CA test, SA, mSA, and mSB revealed negative results with or without rat S9 metabolism. However, SB without rat S9 metabolism showed genotoxicity to CHO-K1 cells, indicating the genotoxicity of SB to CHO-K1 cells might be reduced with rat S9 metabolism.
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 4 Results  
### Spectral Analyses  
Concerning the Goethite spectrum (Figure 3), two diagnostic iron absorption features (at \(\sim\)673 nm and \(\sim\)924 nm) and one diagnostic reflectance peak at \(\sim\)750 nm can be observed [43]. After that, in the SWIR region, we can observe two generic OH/water absorption features at \(\sim\)1401 nm and \(\sim\)1915 nm. Hematite differs from Goethite by a tiny difference in one of the iron absorption bands that appears at \(\sim\)859 nm [44; 45].
## 3 Methodologies
### Smote  
It is well known that the data quality is optimal when the number of positive samples equals the number of negative samples in the training set. Therefore, when there is an imbalance between positive and negative samples, some strategies need to be adopted to alter the sample distribution, transforming the imbalanced samples into a relatively balanced state. Data resampling algorithms primarily include oversampling and undersampling [60]. The goal of oversampling is to increase the number of minority class samples to balance the class distribution in the dataset. As such, oversampling is more suitable for situations with small sample sizes compared to undersampling. The imbalance of the samples generated in oversampling and the variation in the oversampling rate can have a certain impact on the training results of the final machine learning model [61].  
SMOTE (Synthetic Minority Over-sampling Technique) is an improved approach based on random oversampling algorithms [62]. Since random oversampling increases the number of minority class samples by simply duplicating samples, it can lead to overfitting of the model, meaning that the model learns overly specific information that is not generalizable. The basic idea of the SMOTE algorithm is to analyze the minority class samples and  
Figure 7: SOM schematic diagram.  
artificially add new samples to the dataset based on the minority class, as shown in Figure 8. This method creates several \"synthetic\" samples for each minority class sample by drawing line segments between k-nearest neighbor minority class samples. It then generates synthetic samples by computing the vector difference between the minority class feature vector and its nearest neighbor minority class feature vector. This difference is multiplied by a random number between 0 and 1 and added back to the minority class feature vector. This results in a random point being selected along the line segment between two minority class features, thereby increasing the number of minority class samples. Therefore, SMOTE expands the decision region of the minority class, making it more generalizable [63].
## 3 Aseismic Properties
### Aseismic Characteristics of Historical Buildings  
#### 3.2.1 Single Houses  
The configuration of the building, the construction design and the architectural form have a significant influence on its behaviour in an earthquake. Experience has shown that simple structures have the greatest chance of surviving. This type of building is easy to build, maintain and repair. Its response to an earthquake can be relatively easily determined and analysed. If the building is symmetrical, compact and not too elongated, it will behave much better in an earthquake. On the other hand, if a building does not possess such properties, it may be exposed to torsion and various influences at its ends. Corner damage often occurs at the corners of buildings due to the stress concentrations that occur at the intersection of perpendicular walls. Instability of corner sections often occurs because two sides of the corner are unrestrained. Therefore, the corner section is free to collapse outward from the building.  
In the Bay of Kotor, the buildings are, as a rule, simple, mostly rectangular, symmetrical, compact and with relatively uniform dimensions of the base and other proportions.  
The most representative single houses of the bay have the so-called belvedere room in the attic, which is the prominent central part of the facade with a pair of window shafts. Despite the original trend that was later chiefly abandoned, to connect this part of the facade with the rest of the structure using wood beams--tie rods and iron anchors (_ital. capochiave_), during the earthquakes, a concentration of stress would occur, causing cracks along the horizontal joint with even the case of the whole belvedere structure collapsing at the point of discontinuity, that is, the place where the wall facade canvas narrows (Figures 8 and 9). Cracking and collapse of the front and back facades during earthquakes is the most frequent failure of the system caused by this fragility [9].  
Figure 7: The Church of St. Mary dome is more solidly attached with the substructures, walls and lateral and transverse arches than the Cathedral of St. Tryphon.  
Out-of-plane bending of walls is a very frequent mechanism. Horizontal inertia actions are transferred from the floor structures, which should act as rigid horizontal diaphragms, into the bearing walls, causing shearing and bending effects, and from the bearing walls into the foundation system. Additionally, due to the distributed mass of wall elements, distributed inertia forces are induced, resulting in out-of-plane bending of walls.  
The possibility of overturning of large parts of walls, facades and bell towers is certainly the most critical hazard in this kind of structure. Small tensile strength of masonry favors separation of external walls from the main body of the building, progressively turning them into freestanding walls.
# Non-Mypopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks
## 4 Simulation and Analysis  
This section reports on numerical experiments to assess the satisfaction of the PCL-indexability conditions and the effectiveness of the proposed Whittle (MP) index policy in the scalar-state case.  
The proposed US-MP index policy will be compared with the two myopic baselines, i.e., the TEC index policy and the myopic policy, in the multi-dimensional state cases. Since the dynamic state \(\mathbf{x}_{n,t}\) belongs to \(\mathbb{R}^{4}\), we conclude that \(L=4\). Based on [29], in the multi-dimensional state case, the _TEC index_ policy focuses on the current cost and defines the index by  
\[\lambda^{\text{TEC}}(\mathbf{P})=d\frac{\text{tr}(\mathbf{P})}{L}. \tag{48}\]  
The TEC index represents the magnitude of the current covariance in each target. The computational complexity of the TEC index derivation is \(O(NL)\).  
The myopic policy focuses on higher-cost targets at the immediate subsequent moment and calculates the one-step prediction cost under the tracking action 1 and the detection probability \(p_{\text{d}}\), with the indices being given by  
\[\lambda^{\text{myopic}}=\frac{d\big{[}p_{\text{d}}\text{tr}(\psi^{1}(\mathbf{ P},\mathbf{\hat{x}},\mathbf{u}^{1}))+(1-p_{\text{d}})\text{tr}(\phi^{0}(\mathbf{P},\mathbf{u}^{1})) \big{]}}{L}. \tag{49}\]  
The computational complexity for \(\lambda^{\text{myopic}}\) is \(O(2 NL^{2})\). The baseline policies exhibit lower computational complexity but, in general, fail to take into consideration the long-term effects of the employed actions.  
The performance metrics include the discounted cost in (13) and weighted RMSE (WRMSE), which is defined in (50).  
\[\text{WRMSE}_{t}\triangleq\sqrt{\frac{1}{N_{\text{mc}}}\sum_{m_{\epsilon}=1} ^{N_{\text{mc}}}\sum_{n=1}^{N}\frac{d_{n}}{|d|}\big{[}(\hat{x}_{n,t}(m_{c})-x _{n,t})^{2}+(\hat{y}_{n,t}(m_{c})-y_{n,t})^{2}\big{]}}, \tag{50}\]  
where \((\hat{x}_{n,t}(m_{c}),\hat{y}_{n,t}(m_{c}))\) is the estimated location of target \(n\) in \(m_{c}\)-th Monte-Carlo simulation. \(N_{\text{mc}}\) is the total number of Monte-Carlo trials, \(d\triangleq(d_{1},\dots,d_{N})\) and \(|\mathbf{d}|\) denotes the sum of weights. Then, the average WRMSE (AWRMSE) is defined as  
\[\text{AWRMSE}\triangleq\frac{1}{T}\sum_{t=1}^{T}\text{WRMSE}_{t}. \tag{51}\]
## 4 Command and Control
### Components Communication  
The Message broker is the technological mean through which all the components send and receive application-specific data delivered with a timestamp synchronized with respect to the time of a common NTP server local to the network.  
Two aspects are relevant for flexible integration: the definition of a message exchange protocol and its structure, which allow one to easily integrate sensors and security applications in the architecture, and a method for an accurate and efficient video tracking hand-over from two different types of video systems.
# Adversarial Training for Mitigating Insider-Driven XAI-Based Backdoor Attacks
## 2 Related Work
### Generative Adversarial Networks (GANs)  
Variational autoencoders (VAEs), generative adversarial networks (GANs), and conventional oversampling techniques like SMOTE are well-known strategies for handling data imbalance in challenges with classification. GAN-based methods improve model generalization and robustness by synthesizing realistic samples using adversarial training to learn the underlying distribution of the minority class [9]. Although oversampling techniques, such as SMOTE, offer computational efficiency, they may introduce noise and redundancy that limit their effectiveness, particularly in complex, high-dimensional datasets. Instead, they create synthetic minority class instances by interpolating between neighboring instances [10]. Compared to traditional oversampling, GANs and VAEs can generate more realistic and varied synthetic samples; however, their training challenges and computational demands require careful execution and hyperparameter adjustment.  
Recent research has focused considerable attention on the use of generative adversarial networks (GANs) for the creation of synthetic data. Notably, the methods used for generating tabular data and those used for generating images differ significantly. Recent developments are summarized in this literature overview, highlighting modifications and domain-specific challenges reported in the research.  
In tabular data synthesis [11], GAN-based approaches have been significantly tailored to handle heterogeneous data types, including numerical, binary, and categorical variables. Studies focusing on cybersecurity, healthcare, and fraud detection have applied tabular GANs and their variants to produce synthetic datasets that preserve the underlying statistical properties of the original data [12]. The Conditional Tabular GAN (CTGAN) [13] is one of the most prominent models extensively applied to synthesize realistic tabular datasets. For example, in healthcare, the CTGAN and CopulaGAN have been used to generate synthetic electronic health records that maintain logical relationships and distributional similarities with real datasets, enabling improved predictive modeling and mitigating issues related to data scarcity. Ongoing research continues to reinforce the importance of domain-specific adaptations to meet the varying challenges of synthetic data generation.  
In summary, while both tabular and image data synthesis via GANs share the foundational adversarial training framework, they have evolved to address the unique characteristics of their respective data modalities. Tabular data generation emphasizes statistical properties and the preservation of multivariate relationships, whereas image data generation focuses on spatial feature hierarchies and perceptual realism [14; 15]. This dichotomy illustrates the versatility of GAN architectures and underscores the importance of domain-specific adaptations to meet the varying challenges of synthetic data generation.
## 2 Material and Method
### Evaluation Metric  
Root mean squared error (RMSE), mean absolute percent error (MAPE), and mean absolute error (MAE) are the most commonly metrics used in the evaluation of the accuracy of energy-consumption models [14] and, in particular, studies related to STLF using deep learning [6; 7; 15].  
RMSE is defined as the square root of the mean squared error (MSE) [16] (Equation (2) [17]), that is, the root of the mean square error of the difference between the prediction (\(P_{i}\)) and the real value (\(R_{i}\)), where \(n\) is sample size. As RMSE is more sensitive to more significant errors (outliers) because it squares the difference between the predicted value and the real value. RMSE presents error values in the same dimensions as the analysed variable [16]. It is widely applied in models that use time series [18].  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(P_{i}-R_{i})^{2}} \tag{2}\]  
MAPE is widely used for evaluating prediction models particularly where the quality of the forecast is required and is used in numerous energy-consumption forecasting studies [6; 11; 12; 13; 19]. MAPE is defined in Equation (3) [6; 11; 17] and expresses the accuracy of the error as a percentage. It can be applied in a wide range of contexts, as it is a relatively intuitive interpretation of relative errors; however, it can only be used if the values in the dataset do not equal zero [20].  
\[MAPE\%=\frac{100}{n}\sum_{i=1}^{n}\left|\frac{P_{i}-R_{i}}{R_{i}}\right| \tag{3}\]  
MAE is defined in Equation (4) [12; 17]. Unlike the other metrics presented, MAE depends on the scale of the data. It is not sensitive to outliers, as it treats all errors in the same way. We use it to quantify a model's ability to predict energy consumption.  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|P_{i}-R_{i}\right| \tag{4}\]  
While one or more of RMSE, MAPE, and MAE have been featured in related studies [6; 7], we have chosen to measure all of them for better comprehensiveness and analysis of different aspects of what is being studied [21].