esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Invention Classification
#### 4.2.1 Fuel Storage  
Patents in this category focus on addressing the dual challenges of safely storing hydrogen onboard aircraft and integrating storage systems into airframe structures without compromising performance or safety. These innovations span cryogenic, pressurized, solid-state, and hybrid storage methods. Several incorporate advanced materials, thermal management, and modularity. Many designs use composite or double-walled vessels with embedded sensors for structural health monitoring and hydrogen leak detection. Cryogenic tanks feature layered insulation, hydrogen stabilizing materials, and aerodynamic shaping to minimize fuel boil-off and improve efficiency. Structural integration is a recurring theme in this category. For instance, some patents claim fuel tanks that double as load-bearing elements in the fuselage or wing assemblies to enable weight savings and optimal center-of-gravity management. Some systems include smart ballast or inert gas pressurization to enhance flight stability and safety. Other patents in this category emphasize modular, swappable tanks for rapid refueling or maintenance. Collectively, these storage solutions are claimed to enable reliable, efficient hydrogen containment across a range of aircraft architectures.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Procedure** & **2024** & **2023** & **2022** & **2021** & **2020** & **2019** & **2018** & **Total** \\ \hline Claims & 5,465,182 & 5,324,681 & 5,408,479 & 5,238,192 & 5,940,592 & 5,841,994 & 5,036,914 & 38,256,034 \\ Patents & 373,858 & 350,097 & 360,419 & 348,470 & 435,901 & 392,750 & 341,384 & 2,602,879 \\ AND kw & 778 & 712 & 530 & 496 & 537 & 572 & 534 & 4159 \\ OR kw & 91 & 62 & 24 & 19 & 21 & 22 & 18 & 257 \\ -Duplicate & 91 & 62 & 24 & 19 & 21 & 22 & 18 & 257 \\ -Similar & 91 & 60 & 23 & 19 & 20 & 22 & 18 & 253 \\ Relevant & 68 & 43 & 16 & 9 & 11 & 10 & 9 & 166 \\ \hline \end{tabular}
\end{table}
Table 2: Outcome of the data extraction, data cleaning, and relevance screening.  
Two illustrative patents in this category highlight advanced strategies for integrating hydrogen fuel storage into aircraft architecture. [PERSON] et al. (2024) developed a cryogenic liquid hydrogen tank embedded within the fuselage [43]. It features optimized extraction angles, multi-layer insulation, and strategic tank placement to maintain center-of-gravity balance throughout the flight. This design enhances thermal retention and enables direct fuel delivery to power systems, minimizing line losses. [PERSON] et al. (2024) advanced this concept by incorporating vertically oriented cylindrical tanks into a blended wing body airframe [44]. These permanent tanks not only reduce structural redundancies by bearing internal loads but also maximize available internal volume without compromising aerodynamics. Together, these patents exemplify the structural-functional integration trend that supports scalable, efficient hydrogen aviation.
# Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS
## 2 Materials and Methods
### Test Data Generation
#### 2.6.2 Data Generation Methods  
The generation of these data is carried out through a combination of automated simulations and data generation tools:  
* Automated Simulations: Specialized tools simulate network traffic and cyberattacks, generating data indistinguishable from actual network activities. This provides a realistic scenario for incident response training and exercises.
* Synthetic Log Generators: These are used to create security event logs that mimic those natural systems generated in response to normal and suspicious activities. This includes simulating temporal patterns and correlations between events from different systems.
* Attack and Defense Scenarios: Using artificial intelligence tools such as OpenAI GPT-3, narrative attack scenarios are generated and then translated into data sequences that mimic the behavior of attackers and defenders in various cybersecurity situations [40].
* Threat Emulation: Specialized tools are implemented that emulate malicious behavior within the network to generate threat data. This includes using software that simulates network attacks and generating malware payloads in a secure environment.
* Traffic Generation Tools: Applications that generate network traffic, such as HTTP, FTP, and database traffic, create a dynamic and realistic network environment.
* Anomaly Injection: Anomalies and irregular patterns are deliberately introduced into the data to test the robustness and sensitivity of monitoring and detection tools.  
Figure 3: Cybersecurity test environment architecture integrating AWS and OpenAI (source: authors’ elaboration).
# Calculation of Blasting Damage Zone Radius of Different Charge Structures in Burnt Rock
## 2 Materials and Methods
### Explosion Stress of Uncoupled Charge  
When the charge structure adopts a cylindrical uncoupled charge, the air between the explosive and the hole wall is compressed into an air shock wave after the detonation of the explosive, which, in turn, exerts a force on the rock.  
The initial pressure \(P_{m}\) during the expansion is  
\[P_{m}=\frac{1}{2}P_{H}=\frac{1}{8}\rho_{0}D_{p}^{2} \tag{4}\]  
In this formula, \(P_{H}\)--blasting wave pressure, Pa; \(\rho_{0}\)--explosive density, kg/m\({}^{3}\); and \(D_{p}\)--explosive detonation velocity, m/s.  
Therefore, when the charge structure is radially uncoupled, the initial pressure value generated after the explosion of the explosive is  
\[P=P_{m}\bigg{(}\frac{V_{c}}{V_{b}}\bigg{)}^{3}=\frac{1}{8}\rho_{0}D_{p}^{2} \bigg{(}\frac{V_{c}}{V_{b}}\bigg{)}^{3} \tag{5}\]  
In this formula, \(V_{c}\)--explosive volume, m\({}^{3}\), and \(V_{b}\)--borehole volume, m\({}^{3}\).  
For the uncoupled charge structure, the actual pressure on the hole wall is about 8-11 times the initial pressure due to the combined action of the air compression wave formed by the explosion and the explosion shock wave [30]. \(n\) can be used to indicate the increase in pressure; then, the actual impact pressure on the hole wall is  
\[P=nP_{m}\bigg{(}\frac{V_{c}}{V_{b}}\bigg{)}^{3}=\frac{1}{8}n\rho_{0}D_{p}^{2} \bigg{(}\frac{V_{c}}{V_{b}}\bigg{)}^{3} \tag{6}\]  
When \(V_{c}=\frac{1}{4}\pi d_{c}^{2}\) and \(V_{b}=\frac{1}{4}\pi d_{b}^{2}\) in the above formula, the formula can be used to calculate the pressure on the hole wall when the uncoupled charge is loaded. In addition, the charge density needs to be converted to account for uncoupling (air gap): \(\rho=\rho_{0}\frac{l_{e}}{l_{b}}\). In summary, when the uncoupled charge structure is used, the pressure on the hole wall is  
\[P=\frac{1}{8}n\rho_{0}D_{p}^{2}\bigg{(}\frac{d_{c}}{d_{b}}\bigg{)}^{6}\bigg{(} \frac{l_{e}}{l_{b}}\bigg{)}^{3} \tag{7}\]  
In this formula, \(d_{c}\)--charge diameter, \(d_{b}\)--blasthole diameter, \(l_{e}\)--blasting hole length and \(l_{b}\)--charge length (all units are m).
# Marine Gelatine from Rest Raw Materials
## 2 Properties and Applications of Marine-Derived Gelatine  
Gelatine is a water-soluble protein produced by partial hydrolysis of collagen, a ubiquitous constituent of bones, cartilage tissues, and skins of animals [7]. Collagen is the principal protein constituent found in skin, cartilage, blood vessel walls, and teeth. Together with other proteins, such as elastin and proteoglycans, it builds extracellular matrix around cells in various tissues. The collagen molecule is shaped in form of a triple helix, formed by three \(\alpha\)-chains positioned in a three-dimensional structure which allows efficient intramolecular hydrogen bonding [6]. Although all of the 20 natural amino acids can be found in collagen, it is particularly rich in glycine, proline, and hydroxproline. Tri-peptides which build the main portion of collagen have frequent repetitions of the sequence--Gly-Pro-X or Gly-X-Hyp and the distribution of polar and non-polar amino acid residues at the X position determines the order of aggregation of the molecule. Polypeptide chains undergo a total or partial separation during denaturation of collagen, due to weakening and destruction of hydrogen bonds, which causes a loss of the original triple-helix conformation. This results in polymers adopting a coiled form different than the original protein structure [17]. The proportion and ratio of total amino acids influences the gelatine stability, and this property may vary depending on the source of collagen. The current manufacturing process of gelatine includes pre-treatment of raw animal materials (hides, bones, etc.) with dilute acid or alkali, which results in partial denaturation of collagen due to hydrogen bonds cleavage in its structure [18]. It is known that various marine processing by-products, such as fish skin, bones, scales, surimi production discharge waste, and squid skin represent a good source of collagen for gelatin production [19; 20; 17; 21].  
In general, gelatine is used in the food, pharmaceutical, and photography industry for a number of applications including jelly production, encapsulation, and fruit juice clarification, dairy processing, soup manufacture, photography and others. Typical applications of gelatine depend on the gelatine type, and some are shown in Table 1[22]. Its great versatility enables use in both the food and pharmaceutical industry. In the food industry, gelatine is considered an essential ingredient, and can also be considered a \"clean label\" product, since  
* Gelatine is not chemically modified and has no, possibly harmful, by-products of chemical modification
* It does not contain and is not made of any genetically modified organisms
* Is not a food additive and therefore does not require an E-number
* It is considered Generally Recognised As Safe (GRAS)
* It does not cause any known allergies
* It has been consumed for more than 2000 years and is known for generations [23]  
Figure 1: Boarfish (_Capros aper_) [9].
## 4 Discussion
Structural rearrangements of limonene, sabinene, gamma-terpinene, and beta-pinene influenced by heat and oxidation would lead to p-cymene and related compounds of the p-menthadiene skeleton, such as terpinolene and beta-phellandrene [46]. The bicyclogermacrene could be partially transformed to spathulenol. A mechanism for the transformation of bicyclogermacrene into spathulenol seems to be a possible reaction sequence which may explain the transformation by autoxidation [50; 51; 52]. The chemical changes and artifact formation in (_Citrus aurantium_L. var. _cyathifera_Y. [PERSON]) cold-pressed peel oil upon storage at 20, 5, and 21 \({}^{\circ}\)C for 3, 6, and 12 months were investigated by [PERSON] et al. Notable decreases of germacrene D, mycrene, linalyl acetate, and limonene occurred. Prominent increases of cis-carveol, trans-beta-farnesene, trans-p-2,8-menthadien-1-ol, linalool, and beta-caryophyllene were found. Thirty-four artifact compounds were formed upon 12 months at 20 \({}^{\circ}\)C. The artifacts consisted of alcohols, carbonyl compounds, esters, epoxides, and hydrocarbons. The prominent artifact compounds were (\(\pm\))-carvone, trans,trans-farsely acetate, sabinene hydrate, 1-octen-3-ol, cis,cis-farnesyl acetate, and dihydroracved acetate [51]. Changes in 44 compounds of _Citrus junes_Sieb. ex Tanaka steam-distilled peel oil and possible artifacts that accrue during storage at 25 \({}^{\circ}\)C were investigated by [PERSON], et al. Total monoterpene hydrocarbons decreased markedly, with major losses of limonene and gamma-terpinene and notable increases in p-cymene, as well as alcohols [52]. Beta-elemene cyclization and structural rearrangement could lead to beta-selinene, which upon oxidation could form eudesmol derivatives [53]. The increase in the relative percentages of the sesquiterpene alcohols could to some extent be attributed to possible reactions with the germacrene hydrocarbons. It is suggested that the possible formation of the tricyclic sesquiterpene alcohols, globul, viridiflorol, and spathulenol, as artifacts in the oil could be attributed to oxidation of the germacrene type hydrocarbons, aromadendrene, allo-aromadendrene, and bicyclogermacrene to tricyclic sesquiterpenes [50].
# MPC and PSO Based Control Methodology for Path Tracking of 4 WS4 WD Vehicles
## 4 Simulation  
### Simulation Setup  
The working process of the vehicle control system is illustrated in Figure 6. The dashed box at the top shows the operation of the vehicle dynamic model. Based on the actual inputs (i.e., \(T_{i}\) and \(\delta_{i}\)) as well as the vehicle velocity states \(\mathbf{vel}\), the tire model can generate the wheel forces, which is then used in driving unit model. The driving unit model considers the wheel inertia and gives the actual forces acting on the vehicle body. The dynamic states of the vehicle such as positions and velocities are simulated by running the proposed vehicle body dynamic model in Matlab (R2017a, MathWorks, Natick, MA, United States) using a Runge-Kutta method based solver with a time step of 8.33 \(\upmu\)s.  
In the simulation, both external disturbances and state measurement noises are involved in validating the robustness of the proposed control methodology. The measured states are compared with the reference profiles to generate the error states. Using the method proposed in Section 3.3, the search boundaries of the drive forces and steering angles are obtained. Meanwhile, the error states are transfered to the offset model. Virtual inputs calculated by the MPC algorithm are delivered to the control allocation modual. Then, the actual control inputs including drive forces and steering angles are generated and substituted into the dynamic model for the next iteration. The dashed box at the bottom represents the main controller. PSO controller can be substituted in place of the MPC controller.  
The PSO and MPC controllers are applied to drive the simulated vehicle to track the reference path shown in Figure 7a. In the simulation, the varying terrain conditions are considered to verify the validity and robustness of each controller. As presented in Figure 7b, the reference path is divided into ten sections, which have different tire stiffnesses \(k_{li}\) and \(k_{Li}\) acting on each wheel. To simulate the terrain disturbances, the disturbances \(F_{gi}\) applied to all four wheels were always in the vehicle longitudinal direction. As shown in Figure 8, the disturbances are modeled as step signals and their magnitudes are generated randomly to show the uncertainties.  
Figure 6: Flowchart of the vehicle control system.  
As a comparison, a kinematic model based control method is applied to drive the same vehicle. According to relative kinematic path tracking research [8; 19], the side slip is the main disturbance that leads to the unpredicted tracking errors. Using the dynamic model based observer, the side slip of the vehicle can be predicted with an error of 10% to 30%. In this simulation, a random side slip velocity that is less than 10% of its longitudinal velocity is added in the kinematic model.
###### Abstract  
High rates of childhood vaccination defaulting remain a significant barrier to achieving full vaccination coverage in sub-Saharan Africa, contributing to preventable morbidity and mortality. This study evaluated the utility of machine learning algorithms for predicting childhood vaccination defaulters in Ghana, addressing the limitations of traditional statistical methods when handling complex, high-dimensional health data. Using a merged dataset from two malaria vaccine pilot surveys, we engineered novel temporal features, including vaccination timing windows and birth seasonality. Six algorithms, namely logistic regression, support vector machine, random forest, gradient boosting machine, extreme gradient boosting, and artificial neural networks, were compared. Models were trained and validated on both original and synthetically balanced and augmented data. The results showed higher performance across the ensemble tree classifiers. The random forest and extreme gradient boosting models reported the highest F1 scores (0.92) and AUCs (0.95) on augmented unseen data. The key predictors identified include timely receipt of birth and week six vaccines, the child's age, household wealth index, and maternal education. The findings demonstrate that robust machine learning frameworks, combined with temporal and contextual feature engineering, can improve defaulter risk prediction accuracy. Integrating such models into routine immunization programs could enable data-driven targeting of high-risk groups, supporting policymakers in strategies to close vaccination coverage gaps.  
**Keywords:** childhood vaccination; defaulters; machine learning; predictive modelling; ensemble classifiers; data augmentation; SMOTE; feature engineering; Ghana
###### Abstract  
In this work, the performance of a new ethylene-vinyl acetate-based low temperature encapsulation method, conceived to protect perovskite samples from UV irradiation in ambient conditions, has been analyzed. To this purpose, perovskite samples consisting of a set of MAPb\({}_{3}\) (CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3}\)) films and MAPb\({}_{3}\) with an ETL layer were deposited over glass substrates by spincoating techniques and encapsulated using the new method. The samples were subjected to an UV lamp or to full solar irradiation in ambient conditions, with a relative humidity of 60-80%. Microscope imaging, spectroscopic ellipsometry and Fourier-transform infrared spectroscopy (FTIR) techniques were applied to analyze the samples. The obtained results indicate UV energy is responsible for the degradation of the perovskite layer. Thus, the cut-UV characteristics of the EVA encapsulate acts as an efficient barrier, allowing the laminated samples to remain stable above 350 h under full solar irradiation compared with non-encapsulated samples. In addition, the FTIR results reveal perovskite degradation caused by UV light. To extend the study to encompass whole PSCs, simulations were carried out using the software SCAPS-1D, where the non-encapsulated devices present a short-circuit current reduction after exposure to UV irradiation, while the encapsulated ones maintained their efficiency.  
perovskite; UV light; degradation; absorption; FTIR; encapsulated; ethylene-vinyl acetate +
Footnote †: journal: Applied Sciences
## 2 State of the Art  
Smart WHMS definitely represent a very hot research topic, due to the fact that they may play a crucial role in present and future society. As a result, many research projects and prototypes have been developed during the last years and major breakthroughs in enabler technologies have been achieved in order to meet the specific requirements of these systems, as well as to face the challenges they present [3, 7, 8, 9, 10, 11, 12, 13, 14, 15].  
This section first discusses the typical communications architectures (based on a reference one) and the most relevant communications technologies used in this kind of systems. Next, a comprehensive survey on research projects, prototypes, and systems developed during the last years in this area is presented.
# Survey on Wireless Technology Trade-Offs for the Industrial Internet of Things
###### Abstract  
Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, connecting mobile robots and autonomous transport vehicles, as well as localization and tracking of assets. All these opportunities already inspired the development of many wireless technologies in an effort to fully enable Industry 4.0. However, different technologies significantly differ in performance and capabilities, none being capable of supporting all industrial use cases. When designing a network solution, one must be aware of the capabilities and the trade-offs that prospective technologies have. This paper evaluates the technologies potentially suitable for IWSAN solutions covering an entire industrial site with limited infrastructure cost and discusses their trade-offs in an effort to provide information for choosing the most suitable technology for the use case of interest. The comparative discussion presented in this paper aims to enable engineers to choose the most suitable wireless technology for their specific IWSAN deployment.  
Industrial Internet of Things (IIoT); LoRa; IEEE 802.11 ah; WiFi HaLow; Time Slotted Channel Hopping (TSCH); Narrowband IoT (NB-IoT); Bluetooth Low Energy (BLE); BLE Long Range; WirelessHART; ISA100.11a
## 5 Water Treatment Employing Bioadsorbents
### Bioadsorbents Based on Herbage and Forage
Other agricultural byproducts (raw wheat straw and corn stalks) were evaluated for the adsorption of these heavy metals, obtaining higher q\({}_{\text{m}}\) values for cadmium (0.089 mmol/g) and nickel (0.051 mmol/g) using raw wheat straw [73]. The adsorption of chromium and lead from synthetic water by applying mixed tea waste obtained from a coffee shop showed a maximum Cr(VI) removal of approximately 97% after 180 min of contact time at pH 2 [93]. This synthetic water consisted of tap water with trace concentrations of suspended organic and inorganic solids added to resemble municipal wastewater. Sulfonate-treated tea waste was an effective adsorbent for Cr(VI), exhibiting a removal efficiency of 96% and reaching equilibrium in the first 30 min [91]. [PERSON] et al. [40] identified the optimal conditions for calcined cereal byproducts to achieve maximum chromium removal efficiency (90.3%) at pH values ranging from 6 to 8 and a high initial metal concentration (132 mg/L). In a similar vein, [PERSON] et al. [16] studied the optimal conditions for the retention of lead employing rice straw, concluding that when increasing the pH from 3.5 to 6 the removal of the metal exceeded 90%. Date palm leaves, SCB, and black tea were effective herbage adsorbents for capturing Pb(II) in aqueous solutions, with the former retaining more than 98% of the metal at a pH of 5.5-6.0 [41, 86] and the black tea showing a 94% removal and q\({}_{\text{m}}\) of 65 mg/g [90].