esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Selective Encryption of DICOM Images
## 3 The Proposed Approach
### Approach I: Multi-Region Selective Encryption Approach for Mammography DICOM Images
#### 3.1.3 ROI Data Distribution and Thresholds' Estimation  
As detailed in Appendix A, optimal regions' sizes that guarantee minimum encryption time in comparison with Naive AES encryption time were calculated based on the proposed Linear System (LSE). Equations (A8)-(A10) show the regions' sizes (number of pixels for each region), but it does not specify which pixels. To identify the pixels for each region, ROB pixels were removed based on the two-region adaptive pixel thresholding approach that was presented in [25]. Then, using pixel intensity distributions of ROI; pixel thresholds that specify each region were estimated as follows:  
1. Calculate region probability: \[P_{ri}=\frac{S_{ri}}{S_{ori}}\] where \(S_{ri}\) is region \(i\) size and \(P_{ri}\) is region \(i\) probability.
2. Sort ROI pixels in descending order (based on pixel intensities).
3. Allocate pixels' intensities that have probability equal to the region probability (\(R_{3}\)), starting at the minimum value of pixels' intensities, until the region probability is reached. Then, assign the region threshold to the maximum reached pixel intensity (\(th_{1}\)). Next, after removing pixels corresponding to \(R_{3}\), \(th_{2}\) can be determined based on \(R_{2}\) probability in the same manner as \(R_{3}\). Henceforth, thresholds were determined based on the following order \(K_{3}\to K_{2}\to K_{1}\).
4. Segment ROI into multi-region, using estimated pixels' thresholds, where regions are defined as shown below: \[\begin{array}{c}R_{3}=ROI(p_{x})\leq th_{1}\\ R_{2}=(th_{2}>ROI(p_{x})>th_{1})\\ R_{1}=ROI(p_{x})\geq th_{2}\end{array}\] where \(R_{1}\) is region one and it represents the most informative pixels around the mean value of ROI pixel data. \(R_{2}\) and \(R_{3}\) are regions two and three, respectively. \(th_{1}\) and \(th_{2}\) are the first and the second thresholds estimated in step 3, and \(ROI(p_{x})\) is pixel \(x\) intensity that belongs to the ROI.
# Numerical Simulation of Seismic-Wave Propagation in Specific Layered Geological Structures
## 5 Conclusions  
This research introduces a new numerical simulation method for simulating the seismic-wave propagation in geological structures during earthquakes by using the TNT energy release process. This new approach allows for the modeling of energy discharge in seismic sources in a manner analogous to TNT explosions, providing new insights into the dynamics of seismic waves in geological settings. The feasibility and accuracy of this model have been demonstrated by comparing simulated results with actual seismic data from the 2012 Northeast China earthquake.  
The capability of the simulation to reproduce the seismic-wave propagation process within varied geological structures, including water and layered geological configurations, has been validated. This facilitates a deeper comprehension of wave propagation and attenuation laws in these settings. The results presented in Section 3.2 substantiate the suitability of the geological parameters employed and their efficacy in accurately simulating seismic-wave propagation.  
Furthermore, an equivalent blast load model was established through numerical simulations, thereby facilitating an exhaustive examination of seismic-wave propagation in singular media within shallow geologies. Uncovering the propagation characteristics and attenuation laws of these seismic waves has strengthened the foundation for future exploration of seismic-wave propagation in shallow, complex geological structures.  
However, it should be noted that the simulation model in this study is based on the assumption that the geological structure of the medium is homogeneous, which is  
Figure 16: Pressure distributions of granite at different times.  
Figure 15: Pressure variation at different distances from the loading pressure.  
limited to simulating the wave propagation within a homogeneous geological structure or obtaining the approximate results for the complex geological structure. To face more complex geological conditions, many influencing factors, such as the inhomogeneity of the geological structure and complex boundary conditions, need to be considered in the simulation model in future work.  
Methodology, Z.G.; Software, Z.G.; Formal analysis, K.L.; Investigation, C.H.; Writing--original draft, C.H.; Writing--review & editing, K.L. and X.W.; Supervision, X.W.; Funding acquisition, K.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant Nos. 12272391 and 12232020).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
## 3 Major Bioactive Compounds and Potential Applications
### Alkaloids  
Alkaloids are a structurally diverse group of nitrogen-containing secondary metabolites, produced by selected marine microalgae, particularly dinoflagellates and cyanobacteria. Though less common than other bioactive classes, alkaloids exhibit potent neurotoxic, anticancer, and antimicrobial effects, and have attracted interest for pharmaceutical, cosmetic, and functional food applications [41].  
A major representative is saxtoxin, a tricyclic guanidinium alkaloid synthesised by _Alexandrium_, _Gymodinium_, and _Pyrodinium_ dinoflagellates, as well as cyanobacteria like _Anabaena_ and _Cylindrospernopsis_. Saxtoxin and its analogues (e.g., neosaxitoxin, gonyau-toxins) block voltage-gated sodium channels (Nav), interrupting nerve signal transmission. Although these toxins are responsible for paralytic shellfish poisoning, controlled doses of neosaxitoxin have demonstrated long-lasting anaesthetic effects, making it a promising non-opioid analgesic with nerve-block duration up to 48 h [42].  
Beyond neurotoxins, marine cyanobacteria produce indole alkaloids such as curacin A, lyngbyatoxin A, and dolastatin-10, noted for their anticancer potential. These compounds disrupt tubulin polymerisation, inhibit cell division, and induce apoptosis. For instance, dolastatin-10 exhibits cytotoxicity at IC\({}_{50}\) values of 0.5-1.2 nM in leukaemia and carcinoma cells and serves as a template for Food and Drug Administration (FDA) -approved antibody-drug conjugates [43].  
Ecologically, alkaloid biosynthesis may serve as a defence mechanism against predators or to maintain ionic homeostasis under stress. For example, elevated Na\({}^{+}\) and pH levels have been shown to upregulate saxtoxin production and export, pointing to its role in cellular ion regulation [44].  
Alkaloids also have relevance in cosmetic applications, with indole derivatives demonstrating antioxidant, antimicrobial, and anti-photoaging effects that support their use in skincare and UV-protective formulations [45].  
In the functional food sector, trace alkaloid components in edible cyanobacteria like Spirulina may contribute to immune modulation and microbial balance in the gut. However, due to their bioactivity, rigorous safety assessments are essential to ensure consumer protection [46].  
In summary, alkaloids from marine microalgae are biochemically potent molecules with diverse therapeutic prospects. Their ability to act on specific cellular targets, such as ion channels or the cytoskeleton, positions them as valuable leads for drug development in neurology, oncology, and anaestbiology. Advances in biosynthetic gene discovery and heterologous expression systems will be key to expanding their accessibility and industrial scalability [47].
# Advancing Model Explainability: Visual Concept Knowledge Distillation for Concept Bottleneck Models
## 3 Methodology
### Loss Function  
Logit-level KD utilizes the intermediate layer logits from the auxiliary classifiers, as described in [43]. In our study, we introduce the concept of an auxiliary bottleneck. As depicted in Figure 2, the intermediate layers of the teacher and student models are connected to their respective auxiliary task predictors. Subsequently, the KL divergence is calculated between the output distributions of these predictors to quantify the discrepancy between the teacher and student models at the auxiliary bottleneck level.  
The total logit-level KD loss in a CBM is computed as  
\[\mathcal{L}_{logit}=\alpha\cdot(\mathcal{L}_{KL}+\mathcal{L}_{Aux-KL})+(1-\alpha) \cdot\mathcal{L}_{CE}, \tag{6}\]  
where \(\mathcal{L}_{KD}\) represents the KL divergence loss between the final output logits of the teacher and student models, \(\mathcal{L}_{Aux-KL}\) represents the KL divergence loss computed using auxiliary classifiers at the intermediate layers, and \(\mathcal{L}_{CE}\) is the cross-entropy loss. The coefficient \(\alpha\) is utilized to balance the contributions of these loss terms.  
VICO-KD is calculated solely at the logit level, and the overall loss function is as follows:  
\[\mathcal{L}=\mathcal{L}_{logit}+w\cdot\mathcal{L}_{VC}, \tag{7}\]  
where \(\mathcal{L}_{VC}\) represents the loss term for transferring visual concept knowledge from the teacher to the student model, and \(w\) is a weight parameter that regulates the intensity of this transfer. By minimizing \(\mathcal{L}_{VC}\), the student CBM learns to align its visual concept activation with the teacher CBM.
# Sustainable Fire Protection: Reducing Carbon Footprint with Advanced Coating Technologies
## 2 Materials and Methods
### Comparison with Other Systems  
#### 2.3.1 Intumescent Paints  
This section presents a comparison between cork paint (MFR) and intumescent paints [13] in terms of adhesion, required thickness, thermal conductivity, and corrosion protection--key aspects for the protection of metallic structures.  
Adhesion:  
* Cork paint (MFR): According to ISO 4624 (Paints and Varnishes--Pull-Off Test for Adhesion) [11] MFR demonstrated adhesion greater than 5 MPa on metallic surfaces. This level of adhesion ensures coating durability, even in adverse conditions.
* Intumescent paints: Intumescent paints typically exhibit adhesion in the range of 2-4 MPa, depending on the type of paint and surface preparation. Although this value is adequate for many applications, it may not be sufficient in environments where maximum coating durability is required.  
Required Thickness:  
* Cork paint (MFR): The typical application thickness of MFR ranges between 3000 and 5000 microns (3-5 mm). This thickness allows for effective protection with a minimal impact on the weight added to the structure.
* Intumescent paints: To achieve an equivalent level of fire protection, intumescent paints require greater thicknesses, generally between 4 mm and 10 mm or more. This significantly increases the weight and can affect structural integrity over the long term.  
Thermal Conductivity:  
* Cork paint (MFR): Cork is known for its low thermal conductivity, with values between 0.037 and 0.040 W/m-K. This not only contributes to fire resistance, but also provides effective thermal insulation, crucial for the protection of metallic structures.  
Figure 1: Pull-off test.  
* Intumescent paints: While intumescent paints offer thermal insulation during a fire, their thermal conductivity in normal conditions is generally higher (0.1-0.3 W/m\(\cdot\)K), making them less effective in preventing heat transfer in normal conditions. Corrosion Protection:
* Cork paint (MFR): Studies have shown that cork-based coatings can reduce corrosion rates on steel surfaces by up to 60% compared to untreated surfaces, according to salt spray tests (Progress in Organic Coatings, 2020) [14].
* Intumescent paints: Intumescent paints do not provide corrosion protection on their own, requiring the application of additional anti-corrosive coatings to ensure the steel is protected, thus increasing the complexity and cost of the coating system.  
This comparison highlights the advantages of cork paint (MFR) over intumescent paints in applications that require not only fire protection but also superior adhesion, minimal thickness, effective thermal insulation, and comprehensive corrosion protection.
# Computationally Efficient Robust Color Image Watermarking Using Fast Walsh Hadamard Transform
## 4 Performance Evaluation and Simulation Results
### Robustness Analysis  
FWHT is resilient to low quality compression compared to other common transforms like DCT and DWT. Lena image, of size 512 \(\times\) 512 shown in Figure 4 below is watermarked with a watermark of size 32 \(\times\) 32. Figure 5 below shows that as the compression quality decreases, the extracted watermark is getting distorted and the BER at the extractor increases. Compared to the other algorithms, the proposed algorithm is able to withstand low quality compression up to a quality factor of 15. Comparison of robustness of the proposed algorithm and DCT has been done in the case of various attacks as shown in Table 2. The results reinforce the fact that proposed technique is more robust to all the tested attacks compared to the similar DCT algorithm.  
Figure 4: (a) Lena Image (b) 32 \(\times\) 32 watermark.  
Figure 5: Compression Quality Vs BER for proposed technique (FWHT) and for similar DCT technique.  
Figure 3: (a) Cover Image (b) Watermark (c) Decoder output.
# Effect of Ion Release on Color Stability of Zirconia: A Comparative Study
## 3 Results  
Colorimetric Results of Zirconia Test Pieces with Different Thicknesses at Different Immersion Times  
The analysis of color difference (\(\Delta\)E) based on L*a*b* and LCH systems across different thicknesses and immersion times demonstrated distinct material-dependent variations, as shown in Figure 1. In DD specimens, \(\Delta\)E values remained relatively low and stable across all thickness groups (1.0 mm to 2.5 mm), with only modest increases observed at longer immersion times (3 and 5 min). Notably, the 2.5 mm DD specimens consistently exhibited the smallest color differences.  
Figure 1: Colorimetric results (\(\Delta\)E) of TT (**a**) and DD (**b**) zirconia specimens assessed across different thicknesses and immersion durations, using L*a*b* and LCH systems. TT: TT ONE Multilayer; DD: cubeX\({}^{2}\) ML.  
In contrast, TT specimens exhibited higher \(\Delta\)E values across all immersion times, particularly in thinner specimens (1.0 mm and 1.5 mm). Prolonged immersion (5 min) amplified color deviations in TT specimens, whereas increased thickness (2.0 mm and 2.5 mm) led to a general reduction in \(\Delta\)E values. Consistent trends were observed when comparing Lab* and LCH calculations: thicker specimens showed better color stability in both zirconia brands, with DD exhibiting overall superior stability compared to TT.
# Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning Models
## 3 Theoretical Modeling  
In this section we present the algorithms used for the application of ML in the above-stated regression problem. In general, several families of ML models can be used for solving regression problems, such as linear regression, decision trees, and neural networks (NNs). The proper selection of the algorithm, which depends on the nature of the examined application and the particularities of the available data, is crucial as it can significantly affect the accuracy of the final predictions. In this study, the basis of all utilized models is a decision tree.  
Before presenting the utilized algorithms in detail, it is essential to justify the use of tree-based models over other standard alternatives, such as linear regression and NNs [32; 33]. First, linear regression models mostly apply to problems where data relationships are linear, thus rendering them unsuitable for the examined application. Second, NNs, and particularly deep networks, are data-hungry in nature, typically requiring more data than decision trees, both to be trained and tuned. Thus, in our case, the relatively small number of available EE renovation action records becomes a limiting factor towards the adoption of such networks. On the contrary, decision trees can effectively handle data with non-linear relationships, require less data to be properly trained, and involve a significantly smaller number of parameters that are easier to estimate [32; 34].  
In the following subsections, the algorithms used, namely random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), are described. These tree-based algorithms exploit ensembling techniques like bagging and boosting to improve generalization and provide more accurate predictions.
## 1 Introduction
For the purpose of further improving the accuracy of residual stresses evaluation by LCR waves, another three factors have been studied in this paper. First, the distances between the transmitter and the receivers have been discussed from sound field and signal processing perspectives. Second, the frequency and diameter of the transducer have been analyzed. Finally, three different frequencies of transducers were used to evaluate the residual stresses throughout the 304 stainless steel plate thickness.
###### Abstract  
Nonlinear effects in the radio front-end can degrade communication quality and system performance. In this paper we present a new design technique for reconfigurable antennas that minimizes the nonlinear distortion and maximizes power efficiency through the minimization of the coupling between the internal switching ports and the external feeding ports. As a nonlinear design and validation instance, we present the nonlinear characterization up to 50 GHz of a PIN diode commonly used as a switch for reconfigurable devices in the microwave band. Nonlinear models are extracted through X-parameter measurements supported by accurate calibration and de-embedding procedures. Nonlinear switch models are validated by S-parameter measurements in the low power signal regime and by harmonic measurements in the large-signal regime and are further used to predict the measured nonlinearities of a reconfigurable antenna. These models have the desired particularity of being integrated straightforwardly in the internal multi-port method formulation, which is used and extended to account for the power induced on the switching elements. A new figure of merit for the design of reconfigurable antennas is introduced--the power margin, that is, the power difference between the fed port and the switching elements, which combined with the nonlinear load models directly translates into nonlinearities and power-efficiency-related metrics. Therefore, beyond traditional antenna aspects such as port match, gain, and beam orientation, switch power criteria are included in the design methodology. Guidelines for the design of reconfigurable antennas and parasitic layers of minimum nonlinearity are provided as well as the inherent trade-offs. A particular antenna design suitable for 5G communications in the 3.5 GHz band is presented according to these guidelines, in which the specific switching states for a set of target performance metrics are obtained via a balancing of the available figures of merit with multi-objective separation criteria, which enables good control of the various design trade-offs. Average Error Vector Magnitude (EVM) and power efficiency improvement of 12 and 6 dB, respectively, are obtained with the application of this design approach. In summary, this paper introduces a new framework for the nonlinear modeling and design of reconfigurable antennas and provides a set of general-purpose tools applicable in cases beyond those used as examples and validation in this work. Additionally, the use of these models and guidelines is presented, demonstrating one of the most appealing advantages of the reconfigurable parasitic layer approach, their low nonlinearity.  
reconfigurable antennas; reconfigurable parasitic layers; antenna optimization; antenna design; nonlinear characterization; behavioral modelling; x-parameters; PIN diode +
Footnote †: journal: Sensors
## 1 Introduction  
The research on metal oxide semiconductors (MOS) has drawn researchers' attention for several years. Due to its various advantages: visible-light transparency, great uniformity, high mobility and lower processing temperature, MOS materials have been applied on an active matrix display field to replace amorphous silicon (a-Si) [1, 2]. Amorphous indium-gallium-zinc oxide (a-IGZO) is the most conventional material [3, 4], however, the low content in the earth and high cost of gallium and indium limit the application of a-IGZO [5, 6]. Therefore, new MOS materials without indium have attracted researchers' interest [7, 8, 9, 10], and a non-indium MOS material (AZTO etc.) is a new choice to solve the problem of a-IGZO [11, 12].  
Before this work, there were no reports about Zr-AZTO work as an active layer of a TFT device. In this paper, the Zr-doped AZTO (Zr-AZTO) thin film and TFT devices were prepared by co-sputtering, and how Zr element in the film influences the electrical properties of devices was investigated. X-ray photoelectron spectroscopy (XPS, Thermo ESCALAB 250 Xi) was used to ascertain the Zr content and the O 1 s peak which could represent the carrier concentration. With the discharge power of ZrO\({}_{2}\) increasing, the oxygen vacancy of Zr-AZTO films decreases, which means the discharge power of ZrO\({}_{2}\) target can adjust oxygen vacancy of Zr-AZTO films and the high concentration of oxygen vacancy was obtained with a low discharge power of ZrO\({}_{2}\). The XRD measurement (PANalytical Empyrean, Cu X-ray target) shows that the Zr-AZTO films under different conditions are amorphous, which means the Zr-AZTO can be used on large-scale display because of its good uniformity. The localized states in the active layer which could affect the charge carrier mobility of TFTs were evaluated by \(\upmu\)-PCD. The \(\upmu\)-PCD shows that the Zr-AZTO has low density of localized states when prepared under a low discharge power of the ZrO\({}_{2}\) target. With high concentration of oxygen vacancy, low localized states, and an amorphous structure, the Zr-AZTO has the potential to apply on TFT devices.