esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 2 Rubber Toughening
### Factors Influencing Rubber Toughening
#### 2.2.1 Rubber Content  
Rubber content is a primary factor influencing the toughening effect. Generally, the toughening effect increases with rubber content up to a certain point, after which it decreases. [PERSON] et al. [29] investigated the effect of rubber content on the fracture behavior of toughened epoxy resins. To alter the concentration without changing other morphological characteristics, they dispersed preformed acrylic particles in liquid epoxy resin monomer to prepare the material. The study indicated that toughness increased with rubber content, reaching a maximum value between 12.5 phr and 25 phr, and remained relatively constant. However, further increases in rubber concentration led to a gradual decrease in toughness, with crack propagation behavior transitioning from unstable to stable.  
Similarly, [PERSON] et al. [30] incorporated varying amounts of acrylonitrile-butadiene-styrene (ABS) copolymer with 16% acrylonitrile content into bisphenol-A-type epoxy resin cured with multi-amino imidazoline to enhance toughness. Their research showed that impact strength and fracture toughness (K_IC) reached maximum values when the ATBN content was 12.5 phr. Therefore, for a specific rubber-toughened epoxy resin system, there exists an optimal rubber content that maximizes the toughening effect.
## 8 Conclusions and Suggestions  
This article outlines the various modulation techniques and their suitability for wireless data transmission for bio-medical implanted devices. The different modulation schemes show different types of bandwidth data transmission and frequencies. The ASK modulation is based on on/off keying, multiplication of the binary message and the carrier signal used for low power consumption, while the FSK mode is the most suitable digital modulation, which sends binary data with two different frequencies and the resultant modulated signal is represented by binary 1 and 0. PSK is the largest class of digital modulation applied in complex bio-medical implanted devices by coupling inductive link to transmit both power and data between coils using the same carrier frequency. The review investigated and observed that the quality of the implanted devices and their reliability depends on the modulation scheme in terms of data transmission, operating frequency and power consumption. The main challenges in existing biomedical devices are the data transfer rated, suitable modulation and carrier frequency for wireless transmission, size, cost and design. Highly efficient systems with minimum error, and enhanced encoding of information to protect the data acquired from the human body must be designed. Thus, choosing a suitable modulating technique is very important to achieve high data transfer rates according to the implanted applications. This study explained the existing implantable biomedical devices, their characteristics, challenges and problems in aiming to develop low power consumption, efficient energy transfer, highest data rate, efficient power amplifiers, small size and low-cost, reliable future biomedical implantable devices and their modulation techniques. To achieve the aforesaid aims, advanced techniques like artificial intelligence can be use to provide a stable RF signal and offers flexibility for updating the data to the implanted devices using automatic frequency control programs. This research will lead the increasing need to develop new and improved implanted and wireless telemetry devices.
# Novel Pointing and Stabilizing Manipulator for Optical Space Payloads
## 4 Simulation Validation  
The proposed algorithms and the conclusions are verified through simulation. The virtual prototype built in ADAMS is shown in Figure 5. The pointing and stabilizing manipulator is installed on a support module weighted \(9.13\times 10^{3}\) kg. The flexibility of the solar arrays is considered to simulate the influence of the support module modes. The optical payload is a laser communication weighted \(14.89\) kg, and the flexibility of its secondary mirror truss is considered to simulate the influence of payload modes. In addition, all the coarse rods and diaphragm springs were flexible parts.
# Towards Understanding the Function of Aegerolysins
## 2 Aegerolysins
### Fungal Aegerolysins from Ascomycotta--Eurotimyetes
#### 2.2.1. Aegerolysins from _Aspergillus fumigatus_  
_Neosartorya fumigatus_ [PERSON], [PERSON], and [PERSON], 2008 (_Aspergillus fumigatus_ Fresenius 1863) (_Eurotiales_), is a ubiquitous saprotrophic fungus that plays an important role in recycling carbon and nitrogen on Earth, but can also be a deadly opportunistic primary and opportunistic pathogen [22]. It is a trimorphic fungus with vegetative mycelium that contributes to the decomposition of soil organic matter, asexual conidia responsible for the aerial dispersal of the species, and dormant ascospores that ensure the long-term survival of the organism. Its natural ecological niche is the soil, where it survives and grows on organic debris. Although this fungal species is not the most abundant in the world, it is one of the most widespread fungal species with airborne conidia. Similar variations were found outdoors and in hospital, with consistently lower numbers indoor. Plant debris in the form of compost piles and stacks of high-moisture hay and straw bales that are self-heating, produce large numbers of spores that can be released into the air, resulting in high but localized numbers when disturbed. The widespread dispersal of decaying leaves after leaf fall is a potential source of smaller spore concentrations, but these are dispersed over a much larger area [23].  
In 1962, the first aegerolysin protein, Asp-hemolysin (**Asp-HS**) was described [107], and in 1975 it was isolated from the filamentous fungus _A. fumigatus_[108]. It was not until 1982 that **Asp-HS** was reported to be hemolytic and involved in the pathogenesis of _A. fumigatus_[109]. The nucleotide sequence of a cDNA encoding _hlyA_ was elucidated in 1994 [110]. **Asp-HS** has been shown to bind specifically to oxidized low-density lipoproteins [111], to have cytotoxic effects on murine macrophages and vascular endothelial cells and to induce cytokine genes [112; 113]. However, wild-type and knock-out _hlyA_ and _hlyA-like_ mutant strains of _A. fumigatus_ have shown that **Asp-HS** production is not important for the progression of invasive alveolar aspergillosis in mice [114]. Instead, **Asp-HS** is thought to play a role in potentiating other virulence mechanisms of the fungus [115].  
Of the two aegerolyins described, **asp-HS** also has a neighboring partner protein gene **asp-HSB**, and **asp-HS-like** does not in strain _A. fumigatus_ Af293 [92]. **asp-HS-like** sequence is also identified as invariant in five other strains, GCA 002234955.1, GCA 002234985.1, A1163, Z5, and var. RP-2014, while for **asp-HS**, variation in one amino acid was observed, and it was absent in var. RP-2014.
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 3 The Causes of Thermal Hazards Associated with Lithium-Ion Batteries
### Thermal Factor  
Besides the overheating caused by physical or electrical factors, thermal failure can also be triggered by external high temperature and overheating. Thermal factor results in a fierce rise of battery temperature, the melting of separator, the decomposition of electrodes/electrolytes and numerous side reactions etc., and finally leads to battery thermal runaway. In other words, thermal abuse is the root cause of battery thermal runaway. It should be noted that both physical and electrical factors also ultimately cause thermal runaway. Further, it can be found that almost all the abusive conditions are accompanied with internal short circuit, i.e., an internal short circuit is the most common feature of thermal runaway. It occurs when the cathode and the anode contact with each other due to the failure of battery separator. Once the internal short circuit is triggered, the electrochemical energy stored inside the battery spontaneously releases, generating large amounts of heat.
## 3 Design of Proposed Intelligent FSO System  
Figure 4 presents the structural layout of the IM-DD FSO transmission system, developed using OptiSystem 22.1 in conjunction with Python version 3.11.4. The proposed setup is employed to evaluate the performance of the proposed eye diagram analysis technique based on deep learning.  
We investigated a 10 Gbps FSO communication system that adopts an On-Off Keying (OOK) modulation scheme. OOK is widely recognized for its applicability in data center interconnect environments due to its simplicity and reduced implementation cost. Compared to more complex modulation formats such as Quadrature Amplitude Modulation (QAM), OOK provides a practical balance between performance and system complexity, making it well suited for high-speed and energy-efficient optical transmission in cost-sensitive applications.
## 2 Methodology
### Back Propagation Neural Network  
A back propagation neural network (BPNN) is a typical kind of feed-forward artificial neural network based on the back propagation algorithm, which is widely applied in many research areas. Compared with conventional statistic methods, one remarkable advantage of the BPNN is that it can approximate any nonlinear continuous function up to any desired accuracy. Generally speaking, a BPNN consists of one input layer, one or more hidden layer and one output layer. In our study, the number of the hidden layers is determined as one, based on [PERSON] et al. [38], so that astandard three-layer \(l\times m\times n\) BPNN structure is developed, as shown in Figure 2. More specifically, the mathematical representation of its training process can be described as follows:  
1. The output of the hidden layer nodes, \(y_{j}^{h}\), can be calculated as: \[y_{j}^{h}=\delta\left(\sum_{i=1}^{l}w_{ji}x_{i}+b_{j}\right)\] (14) where \(w_{ji}\) is the connection weight from the \(i\)th input node to \(j\)th hidden node, \(x_{i}\) means the \(i\)th input data, \(b_{j}\) represents the bias of \(j\)th hidden neuron, and \(\delta(\cdot)\) denotes the nonlinear transfer function of the hidden layer, which usually is a sigmoid function.
2. Then, the output of this neural network, \(y_{k}^{o}\), can be obtained by: \[y_{k}^{o}=\rho\left(\sum_{j=1}^{m}w_{kj}y_{j}^{h}+b_{k}\right)\] (15) where \(w_{kj}\) represents the weight connecting \(j\)th hidden node to \(k\)th output node, \(b_{k}\) is the bias of the \(k\)th output neuron, and \(\rho(\cdot)\) means the output layer's transfer function, which is a linear one by default.
3. The goal of BPNN is to minimize the error \(E\), namely the mean square error (MSE) by default, which can be measured by: \[E=\frac{1}{N}\sum_{t=1}^{N}\sum_{k=1}^{n}(y_{k}-y_{k}^{o})^{2}\] (16) where \(N\) is the number of input sample, \(y_{k}\) denotes the \(k\)th expected output data.
# Disaster Recognition and Classification Based on Improved ResNet-50 Neural Network
## 3 Results and Discussion
### Practical Deployment of the Disaster Classification System  
To demonstrate the real-world applicability of the proposed disaster classification model, we outline a practical deployment framework that integrates images from diverse disaster scenarios and enables real-time classification support for emergency response operations.  
In a practical setting, the system would receive input images from multiple sources, such as satellite imagery, drone aerial photography, ground-based surveillance cameras, and social media uploads during disaster events. These images would first undergo preprocessing procedures, including resizing, normalization, and noise reduction, to ensure compatibility with the input requirements of the model. Once preprocessed, the images are fed into the improved ResNet-50-based classification model developed in this study. The model rapidly identifies the disaster type--whether earthquake, fire, flood, mudslide,  
Figure 9: **(a) Accuracy curves of the training and validation sets for the improved ResNet-50 model during training; (b) accuracy curves of the training and validation sets for the traditional VGG16 model during training; (c) loss curves of the training and validation sets for the improved ResNet-50 model during training; and (d) loss curves of the training and validation sets for the traditional VGG16 model during training.**  
avalanche, landslide, or land subsidence--with high accuracy. The classification results can then be directly integrated into emergency command platforms, providing decision-makers with immediate insights into the nature of the ongoing disaster. Such integration supports faster resource allocation, optimized rescue planning, and better-informed public warnings. A schematic flow of the practical application system is shown in Figure 10.  
This framework confirms that the proposed model is not only theoretically effective but also practically implementable in real-world disaster management systems. Future work will focus on expanding the model to support real-time processing and incorporating multi-modal data inputs (e.g., combining images with sensor data or textual reports) to further enhance classification accuracy and system robustness under complex disaster scenarios.
# Design, Manufacture and Wind Tunnel Test of a Modular FishBAC Wing with Novel 3D Printed Skins
## 3 FishBAC and Wing Design and Manufacture
### Construction of the Non-Morphing Wing portion  
The primary design goal of this underlying wing structure is to create a rigid, well-defined external geometry that is simple to make and sufficiently strong. The underlying structure is, therefore, designed around machined aluminium 6080-T6 parts. An aluminium box spar of dimensions 19.05 mm \(\times\) 19.05 mm \(\times\) 3.25 mm is centred at the quarter-chord of the airfoil section and acts as the main load bearing member of the structure. To create the desired airfoil geometry, two 3D-printed plastic covers are attached to the box spar to match the NACA 23012 airfoil. These two plastic covers, as well as the leading-edge sections, were 3D printed by Selective Laser Sintering (SLS) additive manufacture technique, using Glass-Filed Nylon 12 (also known as PA 12). To attach the nose leading edge to the front spar, a 15.88 mm \(\times\) 6.25 mm aluminium bar with M4 tapped holes was used to clamp the two together using M4 screws. Additionally, an aluminium channel spar with dimensions 16.01 mm \(\times\) 15.88 mm \(\times\) 1.59 mm was added toward the trailing edge at the chordwise location \(x/c=0.65\) to serve two main purposes: _(i)_ provide additional strength and  
Figure 11: Stitching through the stringers and spine.  
Figure 10: Welded stringers.  
stiffness at the trailing edge where the FishBAC is mounted and _(ii)_ support and react the actuation loads from the actuation mechanism that is mounted to both spars.  
Five fibreglass-filled Nylon 12 parts spanning 199 mm constitute the sections along the wing-span and are printed to add up to the 995 mm wingspan. The actuator and actuation mechanisms (i.e., pulleys, bearings, tensioning mechanism) are mounted to an aluminium frame that is referred to as the 'actuation module'. This actuator bracket is itself mounted to both box and channel spars, ensuring full load transmission between the main load bearing members of the wing. Since there are only three actuation points in this wing, there are only actuation modules at the spanwise Sections 1, 3 and 5. Furthermore, Sections 2 and 4 each have a machined aluminium rib connecting the two spars to add additional strength and stiffness (see Figure 12). To create easy access to the actuation mechanism for any required removal or replacement of components, an access panel was designed into the lower surface of Sections 1, 3 and 5. These are attached to the actuation module by using a series of countersunk screws. A detailed description of the actuation mechanism will be presented in the following subsections.
# Body Area Networks in Healthcare: A Brief State of the Art
## 7 Current Challenges  
The development of new sensors, communication channels, wireless networks, and secure and safe smart integrated components for cyberphysical systems is an area of great interest. According to the European Commission [63], this is a priority in the Horizon 2020 research and innovation programme, where the reinforcement of the integration of embedded components and cyberphysical systems is crucial to achieve interconnected and collaborating spaces. As mentioned before, these kinds of systems need to comply with several requirements like security and safety, low power consumption and size, usability and adaptability [64].  
Body area networks are still a relatively new technology with problems and limitations that must be solved in order to reach the standards required for extensive use. Many research teams around the world are working to look for solutions to turn BAN products in effective deployments. The principal challenges that BANs face are: signal propagation, interference and human-computer interaction [47].  
BAN nodes, on or within the human body, are affected by several communication issues. The strength of the signal varies depending on the location and orientation of the nodes. Some researchers are studying ways to transmit information among devices using the human body as a means of transmission [12].  
Interference becomes a serious problem when there are several devices or BANs emitting simultaneously. This causes increased packet loss due to transmission channel saturation. Researchers are developing ways to deal with interference. One is the use of biometrics (glucose level, blood pressure, heart rate) to identify a BAN user. Another is the use of multiple channels, where each user could have its own communication channel. The use of multiple channels enhances packet delivery, thus greatly preventing packet loss.  
Any technological solution should have a user-friendly interface to show the information status or to receive commands. In the case of BAN systems, usability requirements are high. Users of this type of devices are usually unfamiliar with the use of this technology. To avoid this difficulty, some researchers are manufacturing T-shirts with sensor tissue that can monitor the wearer by themselves.
# Scene Text Recognition That Eliminates Background and Character Noise Interference
###### Abstract  
In natural photographs, complex background noise and character noise frequently interfere with scene text identification. To solve the aforementioned concerns, this paper proposes a novel scene character identification model that eliminates noise from both the backdrop and the character (ENBC). The model is divided into three pieces. To begin, the high-level character feature extraction module uses ASPP dilated convolution with varying expansion rates to obtain features at various scales, thereby expanding the receptive field to capture the character feature area more effectively, eliminating noise interference from the character itself, and improving the character shape features. Second, the multi-level character feature fusion module merged the high-level character feature information after upsampling with the low-level character feature information in the backbone network, separated the foreground characters from background interference, removed background noise, and output the resulting image. Third, the recognition enhancement module enhances character context modeling by considering both forward (left-to-right) and backward (right-to-left) information from the text sequence. The experimental results show that the model can effectively minimize background and character noise interference, boosting recognition accuracy by at least 4.2% on the synthetic scene dataset. When compared to other popular techniques on the IIIT5K, ICDAR-2015, ICDAR-2003, and CUTE80 public datasets, recognition accuracy improves by an average of 6.97%.  
Keywords:scene text recognition; deep learning; background noise; character's own noise; enhanced character features +
Footnote †: journal: applied sciences  
Article
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 3 Micro-Supercapacitor Carbon Electrodes
### Graphene  
Graphene is a one-atom thick planar sheet of sp\({}^{2}\) bonded carbon atoms that are arranged in a closely packed honeycomb crystal lattice. It has excellent electrical, mechanical, and morphological properties, as well as good thermal and chemical stability. Graphene can be made via a hydrothermal method.  
Quasi-solid-state micro-supercapacitors were fabricated with cellular graphene film and polyvinyl alcohol/H\({}_{3}\)PO\({}_{4}\) (Figure 5) and show excellent electrochemical performance [83]. Similarly, such flexible electrolyte was used with graphene fibre electrodes, produced by electrochemical exfoliation of graphite foil thin strips, which exhibited a capacitance of about 248 mF/cm\({}^{2}\) at about 2 mA/cm\({}^{2}\)[84]. Graphene structures were also produced using chemical vapour deposition to construct flexible micro-supercapacitors with excellent cycling stability and a high areal capacitance of 1.5 mF/cm\({}^{2}\) at a scan rate of 10 V/s [85]. Ultralong cycle life was achieved for micro-supercapacitors fabricated with laser irradiated graphene, with 100% retention of the initial capacitance even after 100,000 cycles [86]. Electrodes of graphene-coated copper foil with ruthenium oxide, deposited by cathodic electroplating, were fabricated for flexible supercapacitors, and they have a specific capacitance of 1561 F/g at a scan rate of 5 mV/s and a high energy density of about 13 Wh/kg at a power density of about 21 kW/kg [87]. Graphene was doped with fluorine to increase the specific capacitance, and a composite was made by combination with nanoscale carbide-derived carbon, which has a high specific surface area and hierarchical pore structure. The specific capacitance of this composite in aqueous electrolyte was 321 F/g at a scan rate of 5 mV/s [88]. Planar integrated systems of micro-supercapacitors were made with 2D hierarchical ordered dual-mesoporous polypyrrole/graphene nanosheets, with a large surface area of 112 m\({}^{2}\)/g, and capacitance of 376 F/g at 1 mV/s [89].  
method [40]. Onion-like carbon was decorated with platinum nanoparticles to increase the specific capacitance by four times in comparison to undecorated onion-like carbon [95]. In another study, highly graphitized carbon onion was physically grafted with 1-nitropyrene to achieve an increase in the capacity of the electrode by almost two times to 38 mAh/g, and the initial capacity remained very high even after 4000 cycles [96].