esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 High-Efficiency Wideband Envelope Amplifier
### Slow Envelope Power Supply
Figure 13 shows the L-C low-pass filter response, together with the spectra of the \(v_{PWM}\) control signal. It provides a flat amplitude response within the bandwidth of the slow envelope (\(v_{SER}\)), while switching frequency components are attenuated and the output capacitor, \(C_{F}\), provides the high-frequency current components (\(i_{SE}\)) of the filter output slow envelope signal (\(v_{SE}\)). A signal corresponding to the envelope of a 4C-CH VDL-T signal was used to test the SEPS in combination with the WLEA. The time-domain output voltage waveforms are shown in Figure 14 for a peak power of \(P_{EOPK}\) = 125 W and a mean power of \(P_{EO}\) = 24 W (PAPR = 7.2 dB) from a main supply voltage of \(V_{S}\) = 34 V. The voltage ripple at the SEPS output (\(v_{SE}\)) is due to the strong high-frequency \(i_{SE}\) currents and the impedance presented by the output L-C filter capacitor, \(C_{F}\), at these frequencies, but these nonlinearities are filtered out by the WLEA and do not affect the linearity of its output (\(v_{E}\)), as seen in Figure 14b. Figure 12: Spectra of the envelope of a 4C-CH VDL-T (located at 120 MHz, 123 MHz, 128 MHz, and 130 MHz) signal. Reference envelope (\(v_{ER}\)), bandwidth-reduced envelope through a moving peak process (\(v_{MPK}\)), and slow envelope resulting (\(v_{SER}\)) from smoothing \(v_{MPK}\) using a moving mean filter. For hybrid EAs (regardless of whether they are made up of serial or parallel combinations of linear and switchmode power stages), the power efficiency depends on the bandwidth of the envelope signal: the wider the bandwidth is, the lower the efficiency. This is because the switching amplifier (SEPS in this case) can only cover the low-frequency components, and, particularly in this case, this effect is noticeable due to the large amount of energy of the high-frequency envelope components (as seen in the PSD plotted in Figure 12), which are totally dependent on being amplified by the WLEA. Therefore, although the SEPS provides very high power efficiency (over 90%), the overall efficiency of the EA remains around \(\eta_{EA}=52\%\) (for the envelope of the wideband 4C-CH VDL-T test signal), which, although at first glance may seem to be a discreet value, is quite a satisfactory figure considering the demanding nature of the envelope (\(v_{E}\)) of the test signal (which has a PAPR = 7.23 dB) in terms of its bandwidth and spectral distribution.
## 3 Security Issues in Public, Private and Hybrid Clouds  
While cloud models provide rapid and cost-effective access to business technology, not all of these services provide the same degree of flexibility or security control. In most organizations, data protection levels vary depending on the use of technology [21].  
Public clouds (or external clouds) describe Cloud computing in the traditional mainstream sense, whereby resources are dynamically provisioned on a fine-grained, self-service basis over the Internet, via web applications or web services, from an off-site, third-party provider who shares resources and bills on a fine-grained, utility-computing basis. In a public cloud, security management day-to-day operations are relegated to the third party vendor, who is responsible for the public cloud service offering [22].  
Private clouds differ from public clouds in that the network, computing, and storage infrastructure associated with private clouds is dedicated to a single organization and is not shared with any other organizations (_i.e._, the cloud is dedicated to a single organizational tenant). The security management and day-to-day operation of hosts are relegated to internal \(\Pi\) or to a third party with contractual SLAs. By virtue of this direct governance model, a customer of a private cloud should have a high degree of control and oversight of the physical and logical security aspects of the private cloud infrastructure [22].  
A hybrid cloud environment consisting of multiple internal and/or external providers is a possible deployment for organizations. With a hybrid cloud, organizations might run non-core applications in a public cloud, while maintaining core applications and sensitive data in-house in a private cloud [22].  
Providing security in a private cloud and a public cloud is easier, comparing with a hybrid cloud since commonly a private cloud or a public cloud only has one service provider in the cloud. Providing security in a hybrid cloud consisting of multiple service providers is much more difficult especially for key distribution and mutual authentication. Also for users to access the services in a cloud, a user digital identity is needed for the servers of the cloud to manage the access control. While in the whole cloud, there are many different kinds of clouds and each of them has its own identity management system. Thus a user who wants to access services from different clouds needs to have multiple digital identities from different clouds, which will lead to inconvenience for users. Using federated identity management, each user will have his unique digital identity and with this identity, he/she can access different services from different clouds [23].
# Estimation of Wind Turbine Blade Icing Volume Based on Binocular Vision
## 2 Principle and Method  
### Stereo Matching  
Due to the indistinct features and weak textures of ice accretion on wind turbine blades, traditional Census transform algorithms that rely on the central pixel of the window are susceptible to noise interference in weakly textured and complex textured regions, leading to reduced matching accuracy. To address these issues, this paper proposes an improved stereo matching algorithm.  
The proposed algorithm primarily consists of four steps: cost computation, cost aggregation, disparity computation, and disparity optimization. Figure 1 illustrates the flowchart of the proposed algorithm. The specific steps are as follows:  
1. Initial Cost Computation: The initial matching cost is obtained by combining an improved Census transform with gradient and color information.
2. Cross-Based Cost Aggregation: We adopt the adaptive window method based on cross-based regions from reference [24] and introduce a unidirectional dynamic programming cost aggregation algorithm for cost aggregation.
3. Disparity Computation: The initial disparity map is computed using the Winner-Take-All (WTA) method.
4. Disparity Optimization: Multiple methods are employed to optimize the initial disparity map.
# Total Polypphenols and Antioxidant Properties of Selected Fresh and Dried Herbs and Spices
## 2 Materials and Methods
### Methods  
70% methanol extracts of the tested spices and herbs were prepared for the analyses, according to [PERSON] et al. [31]. For this purpose, 5 g of the sample (weighed on a Radwag brand analytical balance) were shaken in 80 mL of methanol for 2 h at room temperature, in the absence of light, in an Elpan laboratory shaker (type 357). Then, the solutions were filtered and the obtained extracts were stored in plastic containers at a temperature of \(-22\)\({}^{\circ}\)C.  
Total polyphenols were determined by the Folin-Ciocalteu method in a 70% methanol extract according to [PERSON] and [PERSON] [32]. The colored compounds, formed as a result of the reaction between phenolic compounds and the product components present in the extract, were determined spectrophotometrically (using a Rayleigh UV-1800 spectrophotometer) at a wavelength of 760 nm. The standard calibration curve was obtained using several concentrations of chlorogenic acid. Total polyphenols content expressed as mg of chlorogenic acid equivalent (CAE) per 100 g of fresh (FW) or dried mass (DM) samples.  
The antioxidant activity was carried out using the stable free radical ABTS++ according to [PERSON] et al. [33]. The amount of the colored radical ABTS++ (2,2'-azino-bis (3-ethylobenzthaloline-6-sulphonic acid), which remained after the reaction with the antioxidant compounds of the extract (in 70% methanol), was determined spectrophotometrically (Rayleigh UV-1800 spectrophotometer) at a wavelength of 734 nm. Results are expressed in umol Trolox equivalents (TE) per 1 g of fresh (FW) or dried mass (DM). Calibration curve was constructed using Trolox over a concentration range of 10-800 um.  
Antioxidant activity was also determined using a stable free radical DPPH (1,1-diphenyl-2-picrylhydrazyl) [31]. The crystalline DPPH radical was dissolved in 99% methanol and left for 1 h without light. Before each analysis, the radical solution was diluted so that the mixture contained 4 mg of DPPH in 100 mL of methanol. In order to perform the determination, 0.2 mL of test extracts were collected into test tubes and made up to 1.5 mL by adding 70% methanol. In addition, 3 mL of diluted DPPH solution was added successively, mixed (vortex), and then the absorbance (wavelength 516 nm) was measured against methanol. Subsequent measurements were carried out at intervals of 1 min for 10 min from the addition of the radical. The % RSA antioxidant activity is presented as the degree of free radical scavenging:  
\[\%\ \text{RSA}=[(\text{E}_{1}-\text{E}_{2})/\text{E}_{1}]\times 100\]  
E\({}_{1}\)--absorbance in 0 min, E\({}_{2}\)--absorbance in 10 min.
# Explanation of Air Quality Data Using Takagi-Sugeno Fuzzy Inference System
## 1 Introduction  
As stated in _Directive (EU) 2024/2881 of the European Parliament and of the Council on ambient air quality and cleaner air for Europe_ (see [1]), scientific evidence shows that sulphur dioxide, nitrogen dioxide and oxides of nitrogen, particulate matter (\(PM_{10}\) and \(PM_{2.5}\)), benzene, carbon monoxide, arsenic, cadmium, lead, nickel, some polycyclic aromatic hydrocarbons and ozone are responsible for several significant negative effects on human health and are linked to several non-communicable diseases, adverse health conditions and increased mortality. The impact on human health and the environment occurs via concentrations in ambient air and via deposition. To minimise harmful effects on human health, particularly in vulnerable groups and sensitive populations, and the environment, limit values should be set for the concentrations of sulphur dioxide, nitrogen dioxide, particulate matter (\(PM_{10}\) and \(PM_{2.5}\)), benzene, carbon monoxide, arsenic, cadmium, lead, nickel and polycyclic aromatic hydrocarbons in ambient air. The members of the council shall, in an easily understandable manner, establish and make publicly available an air quality index covering hourly updates on, at least, sulphur dioxide, nitrogen dioxide, particulate matter (\(PM_{10}\) and \(PM_{2.5}\)) and ozone.  
Particulate matter \((PM)\) represents a complex mixture of small, solid particles and liquid droplets in the air. Two basic types of \(PM\) are defined--\(PM_{10}\) and \(PM_{2.5}\). In this article, \(PM_{10}\) values are selected as the main indicator of air quality. \(PM_{10}\) is composed of particles with a diameter of 10 microns or less. It is a mixture of materials that can include soot, metals, salt and dust. Major sources include vehicles, wood burning, wild or open fires, industry, dust from construction sites, gravel pits, agriculture and open landfills [2]. Exposure to high concentrations of \(PM_{10}\) can result in a number of health impacts, ranging from coughing and wheezing to asthma attacks and bronchitis, high blood pressure, heart attacks, strokes and premature death [3].  
The EU directive (see [1]) stipulates limit values for the protection of human health. A distinction is made between long-term exposure, expressed as annual average concentration values, and short-term exposure, expressed as hourly or daily average concentration values. For hourly and daily average concentrations, the maximum number of such exceedances per year is set. In the case of \(PM_{10}\), the annual average concentration limit value is 40 \(\upmu\)g/m\({}^{3}\). On the other hand, a limit value of 50 \(\upmu\)g/m\({}^{3}\) per day cannot be exceeded more than 35 times per calendar year.
## 3 Attacks in Blockchain
### Detecting and Mitigating Malicious Blockchain Attacks
#### 3.3.3 Man-in-the-Middle (MITM) Attack  
Man-in-the-middle (MITM) attacks involve intercepting and manipulating communication between nodes in a blockchain network. These attacks can compromise data integrity, steal sensitive information, and undermine trust within the network. Detection Techniques
* Network Monitoring: Detects unusual activity, such as rogue nodes or unexpected data transmissions [74]. * Consensus Checks: Identifies discrepancies in transaction data and halts suspicious processes [75]. * Digital Signatures: Ensures data integrity and authenticates transaction senders, preventing tampering [76]. * Reputation Systems: Tracks node behavior to identify and flag potentially malicious nodes [77]. Mitigation Strategies
* Encryption: Ensures that data transmitted between nodes remain secure and incomprehensible to attackers [78]. * Multi-Factor Authentication (MFA): Protects private keys using techniques such as biometric authentication and one-time passwords [79]. * Consensus Mechanisms: Requires multiple nodes to verify and approve transactions, making it difficult for attackers to manipulate data [80]. * Identity Management: Authenticates and authorizes nodes before allowing them to join the network using tools like a public key infrastructure and digital certificates [81]. * Real-Time Monitoring: Combines network and transaction monitoring to detect and mitigate potential MITM attacks [82]. Effective detection and mitigation of MITM attacks safeguard blockchain networks by ensuring data confidentiality, integrity, and authenticity. Table 2 summarizes the findings.
## 4 Discussion
### Effect of Magnetic Field on the Corrosion Mechanism of API 5L X65
Again, as mentioned above, when a N-S magnetic field is applied, the corrosion rate is increased to 1.11 mmpy, which is almost 60% more than the no magnetic field condition, and, when a N-N magnetic field is applied, the corrosion rate increased to 1.54 mmpy, which is 120% higher than the no magnetic field condition. The corrosion process due to a magnetic field does not change when a flow is introduced to the system. It only aggravates the corrosion process, causing more corrosion by the dissolution of ions on the surface. Similar to what is observed at 1 m/s, the corrosion rate is higher at 2 m/s with 0.78 mmpy in the no magnetic field condition, 1.47 mmpy for the N-S magnetic field, which is almost 50% higher than the no magnetic field condition, and 2.39 mmpy for the N-N magnetic field, which is 200% higher than the no magnetic field condition. Hence, it is clear that the presence of a magnetic field increases the corrosion rate of the samples irrespective of any kind of environment. As discussed above, the magnetohydrodynamic mechanism plays an important role in increasing the corrosion rate. However, in this case, as there is no impressed current in the corrosion process, the Lorentz force is weak and can be neglected [50]. Hence, the complete corrosion process is caused by the MFGF. The MFGF presence in the electrochemical system, when an external magnetic field is applied, causes the movement of anions and cations. For a ferromagnetic material such as API 5L X 65, the dissolution of ions by the anodic reaction results in the formation of paramagnetic ions. By applying a magnetic field to these paramagnetic ions, the MFGF causes the concentration of paramagnetic ions on the surface of the metal [21; 34; 51]. The MFGF, which has the same direction as the movement of paramagnetic ions, results in the dissolution of the protective film on the surface, and, consequently, the surface corrosion increases. This phenomenon of the breakage of the protective layer can be observed from Nyquist plots in Figure 5 and Table 3. For zero velocity, the R\({}_{\text{f}}\) value at A1 is observed as 490.3 \(\Omega\)cm\({}^{2}\), which then reduces to 373.8 \(\Omega\)cm\({}^{2}\) as soon as a a N-S magnetic field is applied. It again reduces to 100.3 \(\Omega\)cm\({}^{2}\) when a N-N magnetic field is applied, depicting that the MFGF causes the dissolution of the protective layer. The reason that the N-N magnetic field condition results in the highest corrosion  
Figure 8: Corrosion rate of API 5L X65 samples at different magnetic field and different flow velocities. rate out of all the cases is that, for a N-N condition, there is no cancellation of the magnetic fluxes by which aggravation will occur from both of the directions, causing concentration of ions from both the sides. However, in the case of N-S condition, there is a cancellation of the magnetic fluxes between the two different magnets, causing decreased concentration of ions on the surface.
## 5 Enhancement of Dental Imaging Protocols and Applications
### Imaging-Guided Diagnosis and Treatment Planning
#### 5.2.4 Forensic Dentistry  
In forensic dentistry, radiographic imaging supports personal identification, bite mark analysis, and age estimation. Dental radiographs are uniquely individual and serve as reliable records in disaster victim identification and criminal investigations. CBCT enhances visualization of dental structures, restorations, and morphological features, thereby improving identification accuracy [37].  
High-resolution imaging is also essential for bite mark comparisons in cases of abuse or assault, while radiographs documenting tooth development stages are widely used for age estimation [38]. The integration of AI further strengthens forensic evaluations by automating anomaly detection, enhancing reproducibility, and accelerating data analysis, establishing it as a powerful asset in modern forensic science [39]. However, standardized validation protocols and cross-jurisdictional comparisons are currently lacking, making broader forensic integration premature.
# How Hydrogen (H\({}_{2}\)) Can Support Food Security: From Farm to Fork
## 2 H\({}_{2}\) Use on the Farm  
Global losses of food productivity as a result of increasing burdens (e.g., climate change, pollution and soil erosion) are estimated to be in the region of USD 170 billion annually [16]. Ergo, there is a pressing requirement for additional, effective, means of preserving the nutritional content, preventing the spoilage and prolonging the shelf-life of fresh produce. In this regard, there is an extensive body of research attesting to the benefits of adding H\({}_{2}\) into the feedwater of seeds, seedlings and maturing plants, wherein H\({}_{2}\), supplied as HRW, is demonstrated to improve crop viability and yield [17; 18]. The antioxidant activity of H\({}_{2}\) is proposed to be the primary mode of action, involving the upregulation of glutathione and sulphur metabolism [19], enhanced antioxidant gene expression [18] and  
Figure 1: Potential methods of utilizing hydrogen in the agri-food industry. Images created by the author using an artificial intelligence platform (wonderal.app). Mode: Photo. Prompts: HRW/HNW Pre-Harvest Irrigation ’Rice field irrigation’. Soil supplement ’Adding calcium powder to soil’. Post-Harvest ’Washing apples on farm’. H\({}_{2}\) Gas. Drying ’Drying fruit industry’. Packaging ’Dry packaging of food for retail’.  
promoting hormone synthesis [20]. Such cytoprotective actions proffer resistance to both biotic and abiotic stressors, including drought [21], heavy metal toxicity [17; 22], increased salinity [23], temperature variation [18; 24] and viral infection [25].  
A wealth of academic studies show that exposing plants to H\({}_{2}\) during the germination, plantation and growth phases can enhance root establishment, increase disease resistance and promote the growth and development of leaves, fruits and seeds. Incorporating H\({}_{2}\) into the feedwater of plant crops is a relatively simple process which can be implemented in various ways. For example, HRW and H\({}_{2}\) gas can also be used as a topical application. However, atmospheric losses due to evaporation make it difficult to envisage how practical this application method would be at scale. HRW and HNW can also be obtained by diffusing H\({}_{2}\) gas (formed through water electrolysis) into nutrient solutions and irrigation systems and by dissolving Mg or H\({}_{2}\) donors (e.g., ammonia borane hollow mesoporous silica nanoparticles (AB@hMSN)) in aqueous solutions. Such methods are already being implemented academically [18; 20; 26] and are beginning to be rolled out commercially.  
The addition of H\({}_{2}\) into soil environments is also noted to improve soil quality through increasing H\({}_{2}\)-oxidising microbial activity and promoting carbon fixation [27; 28; 29], a term referred to as hydrogen fertilisation, although the process is somewhat complex, which may rule out its application in an agricultural setting. Whether this type of application would provide a large-scale, long-term solution to replenishing nutrient-deficient soils will need to be explored.
## 5 Results and Discussion
### Train Support Vector Regression Model  
The objective of the surrogate modelling technique is to discover a function \(E(\boldsymbol{d})_{\text{SVR}}\approx E(\boldsymbol{d})_{\text{FEA}}\) that best predicts the value of \(E(\boldsymbol{d})_{\text{FEA}}\) associated with each value of \(\boldsymbol{d}\). The investigated shape optimization task is a nonlinear regression problem for which supervised machine learning methods could provide an efficient way to handle it. For data regression, support vector regression (\(\epsilon\)-SVR) was introduced in [25], which uses the so-called kernel trick [79] to transform the original nonlinear input data to a higher-dimensional kernel space, where the relation between the inputs and response can be linearly estimated. The goodness of the prediction highly hinges on the kernel function type, which was investigated by us in [32], and the cubic kernel function proved to be the best choice.  
With the use of the training dataset sampled by maximin LHD and the Regression Learner application built into MATLAB, it is possible to tune automatically the hyperparameters of the \(\epsilon\)-SVR model. For small datasets, the usage of the \(k\)-fold cross-validation method is recommended to analyze the model fitting error. The investigation of [PERSON] suggests that \(k=10\) is the optimal value for a general task [80]. The tuning process of the \(\epsilon\)-SVR model was performed for the different sets of training samples, and the root  
Figure 6: The distribution of the selected training data points in the design area using maximin Latin hypercube Design with different numbers of samples.  
mean square error (RMSE) was calculated for the test dataset (128 DP) seen in Table 3. The results showed that the training dataset with the increasing number of samples improved the accuracy of the SVR model. However, after the 45/40 training dataset, there was no further improvement in the accuracy. Therefore, the SVR models tuned with 40 samples and the cubic kernel function will be used in the later processes.  
The predicted response \(E(\boldsymbol{d})_{SVR}\) of the cubic SVR model is plotted against the true response \(E(\boldsymbol{d})_{FEA}\); see Figure 7a. A perfect regression model has a predicted response equal to the true response, so all the points lie on the diagonal line. The vertical distance from the line to any point is the error of the prediction for that point. The selected SVR model predictions are scattered near the diagonal line, which means that the SVR model accurately predicts the nonlinear objective function values.  
Using the trained cubic SVR model, predictions were made for each combination of integer values of design variables. Predicted objective function values are illustrated above the design space according to Figure 4. As a result, it seemed suitable for approaching the values of the nonlinear objective function. The SVR model isoline visualization, which shows valley-shaped behavior similar to the original function, can be seen in Figure 7b.