esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Analysis of iPyroxy Block
### Absorption Value  
The absorption characteristics of the iPyrovy block via UV-Vis spectroscopy is measured for the fabricated block. UV light was applied to the sample, and the resultant spectra were carefully analyzed to ascertain the material's absorption characteristics. The iPyrovy block was produced using a precise 3:2 ratio of iron pyrite to epoxy resin, followed by a 24 h curing duration to guarantee its structural stability. The UV-Vis measurement indicated an absorption value of 0.875% for the iPyrovy block, correlating to a band gap energy of 1.3 eV (Figure 4). This discovery demonstrates the effective engagement with UV energy, establishing the material as appropriate for applications requiring selective absorption and emission of electromagnetic radiation. The absorption value of 0.875% indicates the material's moderate absorption in the UV spectrum, facilitating its use in optoelectronics where these properties enhance energy transfer and signal transmission. The UV spectroscopic analysis of the iPyrovy block reveals a somewhat sluggish absorption of UV light within the examined spectrum, as seen by the documented absorption value.
# Analysis and Assessment of a Brushless DC Outrunner Motor for Agriculture Drones Using JMAG
###### Abstract  
Designing propulsion systems for agricultural drones involves a repetitive process that is both expensive and time-intensive. At the same time, conducting comprehensive experimental tests demands specialized equipment and strict safety protocols. In this work, the design and assessment of the propulsion system (propeller, motor, and battery) for large-sized drones in agricultural applications are conducted using numerical methods. To properly predict and validate the performance of a brushless direct current motor, a three half-bridge inverter circuit, featuring a trapezoidal commutation, is implemented and constructed. First, the propeller is studied using the finite volume method, obtaining a maximum variation of 6.32% for thrust and 10.1% for torque. Additionally, an electromagnetic analysis on a commercial brushless direct current motor (BLDC) using JMAG software from JSQL corporation (JMAG designer 23.2, Cd.Obregon, Mexico) resulted in 4.43% deviation from experimental electrical measurements. The selected propulsion system is implemented in a 30 kg drone, where motor performance is evaluated for two instants of time in a typical agriculture trajectory. The findings demonstrate that numerical methods provide valuable insights in large-sized unmanned aerial vehicle (UAV) design, decreasing the experimental tests conducted and accelerating implementation time.  
BLDC; JMAG; CFD; power electronics; agriculture applications +
Footnote †: journal: Applied system  
_Article_
## 3 Results and Discussion
### Main Complexes Formed with Microcystins with High Affinity Scores
#### 3.1.3 Nuclear Receptors and Genotoxicity Potential  
The alpha receptor of the thyroid hormone (TR\(\alpha\)) was the only nuclear receptor that interacted with the four MC variants, but with affinities of less than \(-10.0\) kcal/mol. This receptor acts as a repressor or activator of transcription, and is involved in heart rate regulation [56]. In contrast, MC-LR and MC-LY presented interactions with 131 receptors, especially with retinoic acid (RA), RXR-alpha receptor, and nuclear receptor coactivator, which stimulated transcriptional activity and recruitment of general hormone-mediatedtranscription factors [57]. A recent study has demonstrated a linear relationship between the content of RAs and genotoxicity developed by exposure to cyanobacterial extracts [14].  
Figure 4: Three-dimensional representations of angiotensin-I-converting enzyme (ACE)–microcystin complexes. Microcystin-LR (**A**), MC-LY (**B**), MC-LA (**C**) and MC-RR (**D**) appear in blue, red, yellow, and orange, respectively.  
Figure 3: Interaction of MC-LA with TLR8 (A and B chains) (**A**), and TLR4 (A and D chains) (**B**). Figures on the right display atomic level views showing interactions between contact residues on the binding site and the ligand, as predicted by Ligand Scout. Atoms are colored with the CPK scheme.  
Human transthyretin (TTR), a thyroxine receptor responsible for the major transport of thyroxine in various tissues and associated with the accumulation of abnormal amyloid protein deposits in the brain, has been reported to be altered by MCs [57]. The presence of MC-LR has been associated with a lower expression of TTR, and thus, a reduction in thyroxine levels after acute exposure [58]. The affinity between TTR and MC-LA is similar to that of MC-LR (\(-10.8\) and \(-10.1\) kcal/mol, respectively, Table 1). Therefore, we suggest that the effects are similar between both theoretical complexes.  
Regarding the genotoxic potential of MCs, the high affinity found between the four variants with DNA topoisomerases type IIA, (TOP2A) could explain the reduction in the DNA repair system linked to the presence of these toxins [10]. The similar degree of interaction between the different congeners with TOP2A and LIMK1, together with the damage generated by the presence of ROS, suggests that the different variants affect the integrity of the genetic material and cellular homeostasis in the same way. Probably, they induce loss of the control of cellular functions, causing both tumorigenic and carcinogenic activity. The formation of the MC-LY complex with CYP3A4, which presented the lowest binding energy among the complexes found (\(-11.9\) kcal/mol), could also be involved in DNA damage (Figure 5). This can be related to the fact that MCs generate a decrease in the levels of several members of the CYP family, leading to the formation of toxic metabolites, which may be involved in different processes of DNA damage and in the occurrence of ROS [10,59].
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 4 Discussion
### Future Aspects
#### 4.2.3 Enhanced Data Analytics  
Future AIoT-driven solar energy systems will harness advanced data analytics to enhance decision-making, optimize energy output, and enable predictive maintenance. Intelligent analytics, including self-learning algorithms and real-time anomaly detection, will allow PV systems to identify inefficiencies, detect faults, and adapt dynamically to environmental changes. Big data techniques will integrate vast streams of structured and unstructured data--from IoT sensors, weather forecasts, and grid interactions--boosting the accuracy of energy forecasting. GenAI will play a key role by generating synthetic datasets, simulating complex operational scenarios, and improving fault prediction in data-scarce settings. Automated feature selection and dimensionality reduction methods, such as VAEs and PCA, will streamline data pipelines, enhancing model efficiency and scalability. Moreover, cloud-edge hybrid analytics will distribute workloads efficiently, with edge devices handling time-sensitive data and cloud infrastructure performing deeper, long-term analysis. Together, these advancements will drive more proactive, adaptive, and intelligent AIoT-based solar energy management.
# UV Light Application as a Mean for Disinfection Applied in the Dairy Industry
## 5 Application Areas
### Air Disinfection  
For the food processing sector, clean and fresh air is needed. In the fields of processing, packing, cooling, transportation, and ripening, UV technology may be used to prevent the dissemination by successfully inactivating the airborne pathogenic microorganisms [10]. A disinfection system to control the multiplication of fungus on objects inside manufacturing industries which has relatively high moisture, for instance, filters of recirculating air conditioners, was commercially launched. As molds generate spores and mycotoxins which can be distributed with the aid of an air conditioner throughout the entire manufacturing space and ultimately resulting in significant health concerns for both employees and products. This system is effective as it does not produce ozone. Moreover, UV-C light treatment can be applied to inactivate bacteriophages that are widespread in the dairy industry [73]. The low-pressure mercury vapor lamps are efficiently used as the source of UV light. This method's effectiveness relies upon region volume and UV light power [10].
# A New Approach for Deepfake Detection with the Choquet Fuzzy Integral
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline
**Study** & **Method** & **Dataset** & **AUC** \\ \hline \multirow{4}{*}{DDA (Model) 1} & \multirow{4}{*}{XceptionNet} & DFDC-P & 78.64 \\ \cline{3-4}  & & CELEB-DF & 77.45 \\ \cline{3-4}  & & FF-DF & 95.50 \\ \cline{3-4}  & & DF-TIMIT & 73.75 \\ \hline \multirow{4}{*}{DDA (Model) 2} & \multirow{4}{*}{EfficientNet + XceptionNet} & DFDC-P & 86.5 \\ \cline{3-4}  & & CELEB-DF & 75.2 \\ \cline{3-4}  & & FF-DF & 95.3 \\ \cline{3-4}  & & DF-TIMIT & 74.1 \\ \hline \multirow{4}{*}{DDA (Model) 3} & \multirow{4}{*}{EfficientNet} & DFDC-P & 87.98 \\ \cline{3-4}  & & CELEB-DF & 72.09 \\ \cline{3-4}  & & FF-DF & 93.4 \\ \cline{3-4}  & & DF-TIMIT & 63.47 \\ \hline \multirow{4}{*}{[39]} & MLP & \multirow{4}{*}{FF-DF} & 86.6 \\ \cline{3-4}  & LogReg & & 82.3 \\ \hline \multirow{4}{*}{[28]} & \multirow{4}{*}{iCaps-Dfake} & CELEB-DF & 96.9 \\ \cline{3-4}  & & DFDC-P & 87.8 \\ \hline \multirow{4}{*}{[53]} & \multirow{4}{*}{CapsuleNet} & FF-DF & 94.52 \\ \cline{3-4}  & & CELEB-DF & 99.14 \\ \hline \multirow{4}{*}{[54]} & \multirow{4}{*}{XceptionNet variant} & FF-DF (LQ) & 98.1 \\ \cline{3-4}  & & ZJU & 99.4 \\ \cline{3-4}  & & CEW & 99.69 \\ \hline \multirow{4}{*}{[56]} & \multirow{4}{*}{OF + RNN + CNN combinations} & FF-DF & 91.0 \\ \cline{3-4}  & & DFDC & 68.0 \\ \cline{3-4}  & & CELEB-DF & 83.0 \\ \hline \multirow{4}{*}{[57]} & Combination of 6 different ResNet versions & \multirow{4}{*}{Google + FF-DF} & 95.5 \\ \cline{3-4}  & (50, 50V2, 101, 101V2, 152, and 152V2) & & \\ \hline \multirow{4}{*}{[58]} & DFC—Combination of 7 different classifiers & \multirow{4}{*}{FF-DF} & \multirow{4}{*}{100.0} \\  & (XceptionNet, InceptionV3, Inception v2, & \\ \cline{3-4}  & & MobilNet, ResNet101, DenseNet121, and & \\ \cline{3-4}  & DenseNet169) & & \\ \hline \multirow{4}{*}{[59]} & Combination of EfficientNet B4, B4 ST, B4 Att, & FF-DF & 94.4 \\ \cline{3-4}  & B4 AttST & DFDC & 87.8 \\ \hline \multirow{4}{*}{[60]} & Combination of 3 different classifiers & \multirow{4}{*}{DFDC} & 97.5 \\ \cline{3-4}  & (Xception, Xception with Attention, and & \\ \cline{3-4}  & Efficient-NetB3 with Attention) & CELEB-DF & 98.4 \\ \hline \multirow{4}{*}{[61]} & MLP—Combination of 6 different classifiers & \multirow{4}{*}{FF-DF} & \multirow{4}{*}{98.4} \\ \cline{3-4}  & (EfficientNetAttB4 ST, AttB4, B4 ST, B4, & \\ \cline{3-4}  & Xception, and ResNet) & \\ \hline \multirow{4}{*}{Proposed Method} & \multirow{4}{*}{Choquet fuzzy integral} & DFDC-P & 95.3 \\ \cline{3-4}  & & CELEB-DF & 92.6 \\ \cline{3-4}  & & FF-DF & 99.8 \\ \cline{3-4}  & & DF-TIMIT & 89.7 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison of the proposed method with other methods.
## 2 Materials and Methods
### Experimental Treatments
#### 2.3.3 Sensor-Based N Fertilization (N 18-N 28)  
Eleven sensor-based treatments (N 18-N 28) were tested, differing in target yields and N application adjustments. The core sensor-based treatment (N 18) followed the N fertilization algorithm exactly as it is intended for practical application [12; 30]. The other treatments were designed to evaluate the performance of this core algorithm--specifically, whether it achieves the full yield potential or requires adjustments. For the second and third applications (BBCH 32 and BBCH 39), a handheld multispectral sensor was used to calculate the vegetation index REIP (Red Edge Inflection Point), and the application of an N fertilization algorithm according to [PERSON] [12; 30] (see [PERSON] et al. for more details [7]). To test different scenarios, variations were introduced as follows:  
* Sensor-determined N applications were adjusted (N 19 to N 22) to explore the flexibility of the system.
* The first N application was varied (N 23, N 24) to test the sensitivity of the algorithm to initial fertilization levels.
* Target yields were adjusted (N 25, N 26) to evaluate performance under modified yield expectations.
* The total N supply was limited (N 27, N 28) to assess efficiency under reduced N availability.  
For reduced N treatments, target yields were adjusted based on the assumption that a 2% N reduction decreases yield by 1%. The new target yield was calculated as follows: New target yield = Original target yield \(\times\) (1 \(-\) (% N reduction \(\div\) 2)).
## 5 Innovative Approaches for Enzyme Immobilization
### Nanomaterial-Based Enzyme immobilization
#### 5.1.4 Carbon Nanotubes and Graphene-Based Enzyme Immobilization  
The immobilization of enzymes onto nanomaterials, such as carbon nanotubes (CNTs) and graphene, has garnered attention for enhancing enzyme stability, activity, and reusability [138; 139]. Graphene, a 2 Dsp\({}^{2}\)-hybridized carbon material, offers exceptional mechanical, electrical, and chemical properties at low cost.  
Both CNTs and graphene provide large surface areas, chemical stability, and superior electrical conductivity, making them ideal for biosensing and catalysis. Enzyme immobilization on these materials improves orientation, substrate interactions, and longevity while reducing diffusion barriers, benefiting industrial and medicinal applications. Recent progress in the development of biosensors, biofuel cells, and catalytic systems highlights the growing role of carbon-based nanomaterials in boosting enzyme performance [139; 140].  
Studies show that enzymes immobilized on 3D graphene, CNTs, graphene oxide, and multi-walled carbon nanotubes (MWCNTs) displayed enhanced efficiency and stability [141; 142]. The CNT/3 DG hybrid shows particular promise for biofuel cell production and biosensor development, offering extended enzyme lifespan, high stability, catalytic efficiency, and selectivity.
## Materials and Methods
### Co-Localization of Podocin, Podocalyxin, and CD80  
Expression of Podocin, Podocalyxin, and CD80 was identified by indirect immunofluorescence assay (IF). Podocytes were washed with PBS and fixed with 4% paraformaldehydeidehyde at 4 \({}^{\circ}\)C for 5 min. Then, were permeabilized with Triton 1X (Triton 0.1% with 1% sodium citrate in PBS) for 2 min, and washes three times with PBS. After, the nonspecific binding sites were blocked with 20% fetal bovine serum in PBS for 30 min and incubated for 1 h with anti-Podocin antibody (rabbit anti-human polyclonal IgG, Podocalyxin-like 1 (3D3) mAbs-IgG\({}_{1}\) mouse or anti-CD80 (B7.1) antibody Goat anti-human polyclonal IgG diluted 1:100 in PBS. After washes with PBS, the presence of bound antibodies was detected by Goat anti-rabbit IgG FITC, Goat anti-mouse IgG\({}_{1}\) TR, and Donkey anti-goat IgG FITC, staining. Podocytes were counterstained with 4\({}^{\prime}\),6-diamidino-2-phenylindole (DAPI) for 30 min, after washing with methanol and methanol-PBS were mounted in PBS/glycerol (3/7) for be photographed at 40X in a microscope (Olympus BX-40). The images were analyzed using Image-Pro Plus software, version 7.0. (Media Cybernetics, Rockville, MD, USA) and the intensity of signal was expressed in pixels. Podocin and CD80 were identified by emitting fluorescence in green, and Podocalixin red color, with the contrast of the DAPI was determined the cellular localization. The analysis was conducted independently by two researchers.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Groups**} & \multirow{2}{*}{**Gender**} & \multirow{2}{*}{\begin{tabular}{c} **Age** \\ **(years)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **pH** \\ **Urine** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **PTU** \\ **(mg/m2/h)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Cholesterol** \\ **Levels** \\ **(mg/dL)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Triglyceride** \\ **Levels** \\ **(mg/dL)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Serum** \\ **Albumin** \\ **(mg/dL)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Serum** \\ **Creatinine** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **GFR** \\ **(mL/m)** \\ \end{tabular} } \\ \hline INS &
\begin{tabular}{c} M = 58.33\% \\ F = 41.66\% \\ M = 60\% \\ F = 40\% \\ F = 40\% \\ \end{tabular} & 4.8 & 5.75 & 9.43 & 227.83 & 165.58 & 3.2 & 0.327 & 232.5 \\ \hline \multicolumn{2}{c}{INS: Idiopathic Nephrotic Syndrome, HC: Healthy Subjects, PTU: Proteinuria, GFR: Glomerular Filtration Tests.} \\ \end{tabular}
\end{table}
Table 1: Clinical and biochemical characteristic of the study population.
## 2 Architectures of 5G and 6G Networks
To mitigate associated risks, advanced security measures are implemented at both the edge and core network layers, including anomaly detection systems and robust authentication protocols. Figure 2: Comprehensive security framework for 6G networks addressing network slicing, AI/ML integration into SONs, quantum safe encryption, and IoT protection. These four pillars are explicitly mapped to a layered defense strategy, adapted from the security model and comparable characteristics proposed in [22; 23]. Table 1 presents a comparative analysis of this work --structured around the aforementioned pillars--against the existing literature on ML-based security in 5G/6G networks. The comparison emphasizes scope, techniques, focus, regulatory insights, and unique contributions. Afterwards, to operationalize these concepts, we propose a three-layered security architecture depicted in Figure 3, incorporating state-of-the-art techniques such as intelligent reflecting surfaces, blockchain-anchored policy enforcement, quantum-safe APIs, and runtime AI-driven attestation. Our proposed three-layered architecture reinforces channel-level confidentiality at the physical layer through beam-forming, intelligent reflecting surfaces, and physical key generation to neutralize eavesdropping and jamming. At the connection layer, we integrate SDN/NFV telemetry with blockchain-anchored policy enforcement to contain slice-to-slice spill-over and thwart signalling-plane DoS. Finally, the service layer is protected by quantum-safe, zero-trust APIs--interfaces that enforce verification of every interaction regardless of origin, based on strict identity and policy authentication--and AI-driven runtime attestation that mitigate deepfake, malware, and insider threats across XR, autonomous driving, and tactile internet workloads. The key innovations of our approach compared to conventional machine learning intrusion detection and prevention systems (ML-IDPS) are as follows:  
* **Layer-Oriented Telemetry Mapping:** Directly binds physical-layer metrics (e.g., Received Signal Strength Indicator (RSSI), Channel Quality Indicator (CQI)) with SDN flow records to enhance context-aware anomaly detection. * **Shapley Additive Explanation (SHAP)-Driven Drift Monitoring at the Edge:** Incorporates SHAP to monitor model drift, allowing proactive alerts before performance degrades. * **Quantum-Safe APIs and Zero-Trust Enforcement:** Implements CRYSTALS-Kyber for key encapsulation in zero-trust API interactions, ensuring confidentiality and authentication by default.
## 3 Numerical Examples
### Cantilever Beam
#### 3.1.2 Sensitivity of Stress Solutions to the Dimension of the Influence Domain  
The size of rectangular influence domain can be defined simplistically by the following equations:  
\[\begin{array}{l}d_{ix}=a_{i}d_{cx}\\ d_{iy}=a_{i}d_{cy}\end{array} \tag{50}\]  
where \(d_{cx}\) and \(d_{cy}\) are the nodal spacing in the x and y directions, and \(a_{i}\) is the dimensionless size of the influence domain. In conventional RPIM and EFG, the stress solutions are affected by the dimension of influence domain. Dimension of influence domain need to be carefully selected in order to get good stress result [36]. \(d_{cx}=2.4\) and \(d_{cy}=1.5\) were used in the numerical examples in this section. The stress solutions obtained from conventional RPIM, conventional EFG, improved RPIM and improved EFG with different dimension of influence domain (\(a_{i}=1.1\), 1.5, 2, 2.5, 3, and 3.5) were computed, respectively. Stress solutions at coordinate position (24 m, 6 m) are shown in Figure 8. Results show that the stress solutions obtained from improved RPIM and improved EFG are both insensitive to the dimension of influence domain. The results also show that there are good agreements among the stress solutions of improved RPIM, improved EFG and the analytical results.  
Figure 8: Investigation on sensitivity of stress solution to dimension of the influence domain.  
Figure 7: Investigation on sensitivity of stress solution to shape parameters.
## 2 Minimally Invasive Therapies for HCC and Their Tissular Effects
### Non-Thermal Ablative Therapies
#### 2.2.1 Percutaneous Ethanol Injection  
Percutaneous Ethanol Injection (PEI) was demonstrated to be an effective method in treating HCC nodules smaller than 3 cm in diameter, with an initial complete response of up to 96% and 5-year survival rates of up to 63% in patients with smaller tumors [69]. The injection is generally performed under local anesthesia with US guidance and is repeated in various areas of the nodule until it appears hyperechoic, due to the microbubbles in the solution [70]. Due to the lack of standardization regarding the number and location of the injections, as well as injected quantity and rate, some variance in effectiveness may be observed.  
After injection, ethanol diffuses into the cells, causing dehydration and protein denaturation leading to coagulation necrosis [71] (Figure 6). The area of necrosis extends around the injection site and should be observed to reach the periphery of the nodule in order to obtain complete ablation. Repeated injections might be necessary in order to improve efficacy, and stronger substances with a higher infiltrative effect are also in use [72]. In patients with liver cirrhosis, ethanol diffuses with ease in the tumor nodule due to it being relatively soft compared to the hardened adjacent liver parenchyma [73]. Ethanol causes endothelial damage and subsequent thrombosis of the feeding vessels, leading to ischemia in the tumor nodule [74]. Animal studies have shown that necrosis can be observed on the needle track when doses higher than 0.3 mL/kg bodyweight are used; also, a fibroblast reaction occurs in the periphery of the injected area, and may be observed after 5 to 7 days [75].  
Figure 5: Overview of the non-thermal ablative therapies and their effects. PEI = percutaneous ethanol injection; IRE = irreversible electroporation; cTACE = conventional transarterial chemoembolization; DEB-TACE = drug eluting beads transarterial chemoembolization; TARE = transarterial radioembolization. DEB = drug eluting beads. \({}^{90}\)Y = Yttrium-90 microshperes. Additional panel in IRE shows disruption of cellular membranes.  
The effectiveness of PEI in causing blood vessel occlusion favored its use alongside other ablative techniques such as RFA, where the heat sink effect is diminished, therefore improving the therapeutic success [74].