esa-sceva/satcom-chunk-collection huggingface dataset:
# Decellularized Scaffolds for Skin Repair and Regeneration
## 2 Tissue Sources for Preparation of ECM-Based Biomaterials for Skin Repair  
In the last decades, decellularized scaffolds for use as skin substitutes have been intensively developed from various tissue sources such as dermis, skin flaps, peritoneum, intestinal tissues, and amnion/chorion tissues (Figure 1).  
Allogeneicic sources, such as tissues from cadaveric donors, and xenogeneic materials, such as porcine, bovine, and goat tissues, have been used, with different long-term results [26]. The decellularization protocols typically combine physical and chemical treatments to remove cellular antigens, involving freeze-thawing or detergents and/or enzymes that can alter the 3D structure of the ECM [27]. Additionally, the US Food and Drug Administration (FDA) requirements impose that acellular mammalian-derived products undergo \"viral inactivation,\" which implies the use of detergents resulting in the removal of many important biologic components from the tissue, such as lipids, glycans, and elastins [28]. As an alternative, skin grafts from fish such as Atlantic cod have been proposed because viral and prion transmission risk is nonexistent. These tissues can be subjected to gentle processing that does not disrupt the structure or bioactive composition of the original ECM [29].  
Thus, decellularized tissues can be obtained from a variety of sources and implanted either orthotopically, i.e., the reconstructed tissue exhibits similar properties to those of the source, or ectopically, i.e., to a site that is completely different from origin site of the decellularized tissue [30]. In this review, we focus on decellularized biomaterials with the potential to be used as dermal or skin substitutes for clinical applications.
# Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques
## 4 Particle Swarm Optimization
### Position and Velocity Updates  
To achieve the optimal solution, the swarm particles need to update their velocity and position. The velocity of the \(j\)th particle can be represented by the \(D\)-dimensional vector in which each element is a random real number, and it represents the change in channel allocation for a given iteration. For example, consider a particle for low load traffic; the velocity vector (\(-13\ 5\ 2\ -8\ 33\ 24\ -22\ \ldots\ -19\ 41\ 6\ 55\)) is added to the position vector (\(5\ 42\ 58\ 69\ 21\ 6\ 18\), \(\ldots\,6\ 11\ 22\ 30\)) to form the new position of the particle (\(8\ 47\ 80\ 61\ 54\ 30\ 4\,\ldots\,13\ 52\ 28\ 85\)). In order to bring the updated position of the particles within \(D\)-dimensional space, the velocities can be clamped using the given range \([-V_{max},V_{max}]\).
## 4 Trusted Network Secure Routing Algorithm
### Reputation and Ant-Colony Optimization
#### 4.4.2 Path Construction  
The proposed routing protocol for wireless sensor networks is based on the comprehensive pheromone values of the nodes. The potential and optimized next-hop nodes are determined in accordance with the state transition rule. Under normal circumstances, the probability that the node with the largest comprehensive pheromone value will be selected is relatively large. In the algorithm, we use a probability roulette wheel mechanism to select the following node at a point. The specific formula for the synthetic pheromone is given as Equation (18) in Section 4.4.1. Based on this expression, the formula for calculating the state transition rule [29; 30; 31] is shown in Equation (1).  
* \(i\) and \(j\) are nodes in the network.
* \(P_{ij}(t)\) is the integrated pheromone concentration on the path from \(i\) to \(j\) at time \(t\).
* _allowed\({}_{k}\)_ represents the set of valid next-hop nodes that have not yet been visited.
* \(\alpha\) and \(\beta\) are two constants. They are the weight values for the synthetic pheromone and the heuristic information, respectively, and they satisfy \(\alpha\geq 0\) and \(\beta\geq 0\). \(\alpha\) reflects the degree of influence of the comprehensive pheromone on the path selection, and \(\beta\) represents the effect of the heuristic information on the path selection. Increasing the value of \(\alpha\) will increase the influence of the pheromone in the path selection process. The pheromone used in the improved algorithm presented in this paper is based on many factors, and its role should be emphasized; thus, in this paper, \(\alpha=2\) and \(\beta=1\).
* \(D_{ij}\) is the heuristic information. The traditional heuristic information is the reciprocal of the distance between two nodes. However, in the MPASR algorithm, the main emphasis is placed on the influences of the node reputation, residual energy and transmission delay on data packet transmission. Thus, the weight value for the heuristic information is set to 1 in this paper.
## 1 Introduction
(2023) compared the effects of CNNs and Transformer architectures in deepfake detection. Using ResNet and Swin Transformer models, they achieved 92.02% accuracy and a 97.61% AUC on the DFDC dataset. The study analyzed the advantages of both architectures and their performances on different datasets [10]. [PERSON] et al. (2021) proposed a hybrid CNN-LSTM model for video-based deepfake detection leveraging optical flow features. By combining the spatial awareness of CNNs with the temporal analysis capabilities of LSTMs, this model achieved 66.26% accuracy on the DFDC dataset, demonstrating its effectiveness [11]. [PERSON] et al. (2020) focused on detecting deepfake videos using Convolutional Vision Transformer (CViT). This model achieved 91.5% accuracy and a 0.91 AUROC on the DFDC dataset by analyzing facial regions in detail. Despite its high accuracy, the model's intensive resource usage limits its applicability to constrained systems [12]. While existing deepfake detection methods have achieved significant success in the literature, they also possess certain limitations. Studies have revealed that these methods often rely heavily on specific datasets and focus on superficial features of fake videos [13]. Despite the effectiveness of deep learning-based models in forgery detection, their generalization capabilities have been observed to falter when faced with more complex and advanced deepfake technologies [14]. For example, while some models achieve high accuracy in specific scenarios, their performance tends to decline on broader and more diversified datasets [15]. Additionally, advanced detection techniques that address the dynamic and temporal structures of deepfake videos are still limited in number [16]. These studies in the literature emphasize a critical need for the development of more comprehensive and robust algorithms. This thesis proposes the integration of QTL and Vision Transformer (ViT) technologies. By combining these two powerful technologies, the aim is to achieve higher accuracy and performance in deepfake detection. By merging the computational advantages of QTL with ViT's deep image processing capabilities, the model seeks to more precisely identify spatial and temporal inconsistencies. Specifically, ViT's ability to learn global features of images facilitates the detection of fine details in manipulated videos, bringing a new dimension to deepfake detection. This approach aims to provide a unique contribution to the existing literature by addressing its gaps and shedding light on the more complex dimensions of deepfake technologies.
# Induced Emotion-Based Music Recommendation through Reinforcement Learning
## 2 Related Work
### Reinforcement Learning Notes
#### 3.3.2 Markov Decision Processes  
_Markov Decision Processes_ (MDPs) can be used to provide a mathematical representation of the model described above since they are generally used for describing \"decision making\" contexts in which the decision maker affects the result of the decisions. We focus on environments in which the number of actions and states is- finite (\"finite\" MDPs).  
Formally, an MDP is a tuple \((S,A,P,R)\), where (i) \(S\) is the _set of states_, (ii) \(A\) is the _set of actions_ that the agent can undertake, (iii) \(P\) is the probability that action in some state \(s\) will result in the state \(s^{\prime}\), i.e., \(P_{a}(s,s^{\prime})=P(s^{\prime}=S_{t+1}|S_{t}=s,A_{t}=a)\), (iv) \(R\) is the expected reward as a result of action \(a\) which led the environment to go from state \(s\) to state \(s^{\prime}\).  
An MDP searches for the \"best policy\" for the agent. A policy function \(\pi\) maps a pair \(\langle s,a\rangle\), where \(s\in S\) is a state and \(a\in A(s)\) is an action, to the probability \(\pi(a|s)\) of undertaking \(a\) when in \(s\). \(\pi\) is used for estimating a state the _expected reward_\(V_{\pi}(s)\) when starting in \(s\):  
\[V_{\pi}(s)=E[\sum_{k=0}^{\infty}\gamma^{k}R_{t+k+1}|S_{t}=s]\]  
We say that \(V_{\pi}(s)\) is the _state-value function_ for \(\pi\). However, for a similar function based on the agent's actions, it is necessary to relate the choice of action \(s\) under the policy \(\pi\). So, we define the _expected reward_ starting from \(s\) and taking the action \(a\) following \(\pi\):  
\[q_{\pi}(s,a)=E[\sum_{k=0}^{\infty}\gamma^{k}R_{t+k+1}|S_{t}=s,A_{t}=a]\]The optimal Q-value function (\(q^{*}\)) gives us the maximum reward obtainable from a given state-action pair with any policy _pi_. The best policy \(\pi^{*}\), therefore, is to take the best action, as defined by \(q^{*}\), in each step.
# Steering the Clinical Translation of Delivery Systems for Drugs and Health Products
## 2 Lectures
### Session B: Can DDS Meet Clinical Needs in Ophthalmology?
#### 2.2.3 Topical Drug Delivery Systems to Tackle Ocular Diseases  
[PERSON]  
Departamento de Farmacologia, Farmacia y Tecnologia Farmaceutica, I+D Farma (GI-1645), Facultad de Farmacia and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain; [EMAIL_ADDRESS]  
Delivery of drugs to the ocular structures must overcome numerous anatomical barriers and eye defense mechanisms. The efficacy of systemic administration is limited by the blood-ocular barriers. Periocular and intra-ocular injections allow treating diverse ocular pathologies, but risks associated with their administration prompt the development of more efficient topical formulations. Indeed, topical formulations are comfortable and safe, but the drug ocular bioavailability is limited to 1-10% of the dose. Low cornea permeability, short residence time, rapid tear fluid turnover, and effluxpumps negatively affect drug absorption. To overcome these hurdles, two different strategies are under investigation: nanocarriers and contact lenses.  
Drug nanocarriers have advantageous properties for both anterior and posterior segments treatments. In this regard, polymeric nanomicelles are particularly appealing [10]. They are spontaneously formed, encapsulate hydrophobic drugs, withstand the dilution in the lachrymal fluid and favor drug partition toward the corneal epithelium. Moreover, unimers may inhibit efflux pumps. The nanomicelles can penetrate through different pathways or undergo in situ gelling transitions favoring ocular permanence. Moreover, drug-loaded polymeric micelles may reach the posterior segment of the eye through the conjunctival-scleral route. Relevant examples of nanomicelles for lipoic acid, acrylovir [11], or cyclosporine ocular delivery are discussed, paying attention to the pre-clinical tests suitable for predicting in vivo performance.  
Contact lenses (CLs) are the devices that can stay on the eye for more time with good patient compliance. The therapeutic benefits that CLs can offer as drug release platforms are enormous since controlled concentrations of drug on the cornea surface may greatly enhance ocular bioavailability while minimizing loss of drug by premature clearance or by unwanted systemic absorption, resulting in improved efficacy with minimized collateral effects [12]. Approaches to endow CLs with capability to host drugs and control their release are discussed paying special attention to bioinspired strategies [13] (Figure 3).  
**Acknowledgments:** This work was supported by MINECO [SAF2017-83118-R], Agencia Estatal de Investigacion (AEI) Spain, Xunta de Galicia [ED431C 2016/008] and FEDER.
## 1 Introduction  
In the context of Industry 4.0, user requirements for high-technology products such as industrial robots are increasingly diverse and experience-driven. Consequently, satisfying users' emotional and aesthetic expectations through product styling has become critical for adoption, user satisfaction, and market competitiveness. However, users' affective imagery of product form is complex, and traditional design methods rely heavily on designers' subjective intuition, lacking systematic, objective support.  
To address these limitations, we propose a closed-loop methodology for affect-oriented styling of industrial cleaning robots that integrates four key components:Semantic Quantification: user-generated descriptive terms are harvested via web crawling and analyzed through an Extenics-based primitive model to quantify the relevance and priority of key affective descriptors; Eye-Tracking: Controlled eye-tracking experiments capture users' visual attention distributions, identifying critical exterior regions and elemental features for a styling-feature library; Imagery-Element Mapping: semantic-differential evaluations and statistical analyses establish significant mappings between affective keywords and specific styling elements; Shape Grammar Generation and Optimization: we define a geometry-rule matrix that translates quantified affective semantics into executable shape grammar operations, generating candidate variants. An orthogonal experimental design systematically combines these variants, and grey relational analysis ranks them against weighted affective priorities to select the optimal design solution.  
By uniting qualitative semantic analysis, quantitative physiological measurement, generative design rules, and multi-objective optimization, our approach delivers a scientifically grounded, verifiable workflow from user insight to finalized styling solution. It advances the field by embedding affective semantics directly into geometric generation and provides practitioners with both theoretical foundations and practical tools for emotionally intelligent industrial robot design.  
The remainder of this paper is organized as follows: Section 2 reviews prior work on emotional design for industrial robots; Section 3 details the materials, methods, and technical workflow; Section 4 presents case-study results, including mapping outcomes, shape grammar derivations, and orthogonal-experiment findings; Section 5 discusses the implications and concludes with key findings and future research directions.
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## 2 Problem Modeling
### Performance Evaluation  
The system capacity is a common measure of a communication system. The average capacity of the LEO system in the orbital plane is proposed to simplify the analysis of the average communication capacity. For an active beam \(i\), (\(i>K(\psi)\)), the elevation angle expressions for the upper and lower boundaries of the beam coverage are formulated as follows  
\[\alpha^{i}_{up}(\psi)=\chi(\psi)+(2i-2-N)\mu_{b}, \tag{13}\]  
\[\alpha^{i}_{down}(\psi)=\chi(\psi)+(2i-N)\mu_{b}. \tag{14}\]  
According to the expression (11) and Equations (13) and (14), the latitude corresponding to the upper and lower boundaries of \(b_{i}\) can also be obtained, and the boundaries are denoted as \(\psi^{i}_{up}\) and \(\psi^{i}_{down}\) here.  
\[c_{i}(\psi)=B_{w}\int_{\psi^{i}_{down}}^{\psi^{i}_{up}}\log_{2}(1+\frac{p_{i} G_{n}(\alpha(x))G_{r,max}(\frac{\varsigma}{4\pi d(x)f})^{2}}{kTB+\sum_{b_{j}<b_{j} \
eq b_{i}}p_{i}G_{n}(|a_{j}-\alpha(x)|)G_{r,max}(\frac{\varsigma}{4\pi d(x)f })^{2}})dx \tag{15}\]  
The average communication capacity of \(b_{i}\) can be expressed as Equation (15), where \(G_{r,max}\) is the maximum gain of receiving antennas. The noise of the communication system is \(kTB\), where \(k\) is the Boltzmann constant, \(T\) is the system noise, and \(B\) beam bandwidth; \(d(x)\) is the distance from the point with latitude \(x\) in the coverage to the satellite, and \(\alpha(x)\) is the elevation angle of the LEO satellite with respect to latitude \(x\). \(S^{b_{i}}\) is the set composed of beams of the same frequency as \(b_{i}\), and \(c\) is the speed of radio wave in vacuum.  
The total capacity of the LEO satellite is the sum of the average capacity of all activated beams, which is expressed as in Equation (16)  
\[c(\psi)=\sum_{i=K(\psi)}^{N}c_{i}(\psi). \tag{16}\]  
To evaluate the performance of interference avoidance schemes, the integral of the total communication capacity of LEO satellite over latitude is used as an evaluation metric with the expression  
\[c(\psi)=\int_{0}^{90}\sum_{i=K(\psi)}^{N}c_{i}(\psi)d\psi. \tag{17}\]
### Virtual Reality and ASD  
Virtual Reality (VR) has been an active area of research in the development of interventions for this population for over two decades. Empirical studies have recognized VR as an important therapy tool for addressing the core symptoms associated with ASD such as difficulties in social interaction [5] and communication [6]. In addition, virtual reality presents dynamic and controllable environments in which educational and training practices such as disaster awareness [7], driving [8] and independent functioning [9] training can take place.  
Early work conducted by [PERSON] [10] in 1996 suggested key aspects of this technology which can be applied to effective and engaging therapeutic solutions for both children and adults living with autism, and despite extensive advancements in how VR is delivered these can still be applicative to contemporary research practices. Firstly, many individuals with autism respond to and enjoy using virtual reality technology [11]. This translates to users actively engaging with interventions, with the enjoyment providing motivation to progress with the therapy program [12; 13]. Secondly, virtual environments provide an experience with consistent and predictable interactions and responses [14]. This can be in the form of limiting social and communication pressures that can often cause anxiety in real world situations [15]. Furthermore, those with autism can frequently experience complications in processing sensory information, with particular prevalence in the auditory and visual domains. These irregularities in sensory receptiveness can often be a catalyst for a deterioration in behavior observed as aggressive or autonomic fear responses [16]. Virtual reality can control all audio and visual stimuli that are encountered during an intervention session, simplifying complex sensory arrays and thus reducing the elicitation of anxiety [10; 17]. The control of VE's and interactions within them can also construct important opportunities for individualized treatment [10]. Re-adjusting the sensory and learning parameters of the intervention to compensate for the complex needs of the individual [11]. Finally, virtual reality technology can replicate realistic representations of real-world situations in which participants can learn and practice new behaviors and real-life skills. The increased feeling of presence and ecological validity as a result of this realism can have a positive impact on the generalization of newly acquired skills from the virtual intervention to their real-world applications [11]. However, the visual experience of the user is often the focus of research when investigating virtual reality interventions for ASD. Although what the user sees can increase generalization via realistic visual similarities with the real world, the sense of realism is reliant on the extent and fidelity of the VR technology delivering the multi-sensory stimuli to the user, including audio.
# A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms
## 2 Architecture
### Relative Navigation Based on Vision and UWB  
In this work, we propose the use of local visual information and UWB to perform relative localization. Here, we aim to utilize the onboard sensors including cameras and UWB for relative navigation. In this way, there is no need for inter-communication and localization infrastructure.
# A Review of Posture Detection Methods for Pigs Using Deep Learning
## 2 Data Acquisition and Sub-Tasks for Pig Posture Detection
### Sub-Tasks for Image-Based Pig Posture Detection
#### 2.2.4 Pig Identification and Tracking  
Pig identification and tracking refer to the recognition and preservation of the identity of each pig, such as a virtual unique identity or a given unique name, and simultaneous tracing of the trajectory of individual pigs throughout the whole monitoring period [65], as Figure 5b shows. Identification and tracking of individual pigs makes it possible to analyze the posture at the individual level rather than at the group level [6]. Through the integration of pig identification and tracking into posture detection, the abnormal behavior of a specific pig in a certain period can be estimated, which is significant for early risk alerts in pig farming [66].  
However, for grouped pigs with highly similar appearances in intensive farming, it is challenging to apply machine vision technology. One suboptimal solution is to make color marks on the back of pigs, but it has little effect and cannot be sustained. With the assistance of non-visual methods, RFID tags with unique identifiers were permanently fixed to pig ears for the identification of pigs in pens [22]. However, this method is intrusive, vulnerable to disturbances, and requires significant labor to install. For higher feasibility, some researchers attempted to imitate human face recognition and adopted it to pig face recognition for discrimination of individual pigs [67; 68]. However, the surveillance cameras used in practice are usually in a top view, which have difficulty capturing the entire face of pigs, and therefore pig face recognition is nearly impossible to apply in practice.  
To solve this problem, the multiple object tracking (MOT) [69] technique was introduced. By integrating MOT algorithms, such as the Munkres variant of the Hungarian assignment algorithm and Kalman filters to the CNN-based pipeline (such as YOLOv2 [70] and Faster R-CNN), the pig detection model can preserve and recover the identity of pigs determined in the initial frame throughout the whole consecutive video sequence [28; 71]. By integrating pig identification and tracking into posture detection, a pig profile comprising the posture, behavior, and position in a certain period can be generated for each individual in the study by [PERSON] et al. [28], which can provide valuable information for pig management.