esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Thermal Analysis in the FEM Domain  
Heat transfer was analyzed to ensure the safety of the ultrasound stimulation system and determine how much heat is generated when ultrasound is applied per second. The following section describes the methods for this analysis. In order to investigate these effects, two different transient analyses were conducted. The temperature distribution within the FEM domain includes the two main heat transfer media: temporal bone and endolymph. To model heat transfer in the bone domain containing large blood vessels, we used Pennes' bioheat equation [44], which accounts for the perfusion effect [45]. Unlike the bone domain, which primarily relies on conduction and perfusion effects, the endolymph domain considers convective effects due to fluid flow. Therefore, in a different approach,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Endolymph** & **Temporal Bone** & **Blood** \\ \hline Sound velocity \(c\) (m/s) & 1497 [40] & 2653 [41] & - \\ Attenuation coefficient \(\alpha\) (Np/m) & 0 [41] & 50 [41] & - \\ Density \(\rho\) (kg/m\({}^{3}\)) & 997 [40] & 1797 [41] & 1060 [42] \\ Viscosity \(\mu\) (Pa-s) & 0.0089 [40] & - & - \\ Specific Heat C (J/(kg-K)) & 4181 [40] & 1300 [42] & 3840 [42] \\ Thermal conductivity \(K\) (W/(m\(\cdot\)K)) & 0.61 [40] & 0.650 [42] & 0.53 [42] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties for endolymph and temporal bone.  
the endolymph was modeled using a convective-diffusion equation, which accounts for endolymph flow induced by acoustic dissipation [32]:  
\[\rho_{t}C_{t}\frac{\partial T}{\partial t}=K_{t}\
abla^{2}T-\rho_{b}C_{b}\omega_{ b}(T-T_{i})+Q_{ext}+Q_{meta} \tag{9}\]  
\[\rho_{e}C_{e}\frac{\partial T}{\partial t}=K_{e}\
abla^{2}T-\rho_{e}C_{e} \overrightarrow{v}\cdot\
abla T+Q_{ext} \tag{10}\]  
where \(\rho\) and \(C\) are the mass density and specific heat capacity, respectively, and \(T\) represents the temperature at a position and time. The subscripts \(t\), \(e\), and \(b\) denote temporal bone, endolymph, and blood, respectively. On the right-hand side, \(K\) denotes thermal conductivity, and the second term accounts for the perfusion effect. The remaining parameters, \(Q_{ext}\) and \(Q_{meta}\), correspond to the heat generated by external sources and the metabolic heat produced by the tissue, respectively. In this simulation, the external thermal source results from acoustic dissipation; thus, the external heat source can be defined using the acoustic field [32; 43] as follows:  
\[Q_{ext}=2\alpha I \tag{11}\]  
For the heat transfer simulation, an initial constant temperature \(T_{i}\) = 37 \({}^{\circ}\)C was set across the entire domain, and the FEM boundary was maintained at a consistent temperature (\(T\) = \(T_{i}\)). Furthermore, since the model assumes no significant metabolic heat production, it was considered that \(Q_{meta}\) = 0.
## 6 Solutions to Optimization Problems
### Game Theory  
The joint communication performance and energy-efficient (RISeUAV or UAV-BS) trajectory optimization can be regarded as the (bandit or Stackelberg) game theory approaches [91; 174]. Because the UAV may navigate more spaces to improve communication performance, it consumes more energy for propulsion. Other game players can be bandwidth split, energy trading for communication, RIS allocation, etc.  
The RIseUAV navigation for WiGig communication is modeled as a contextual multi-armed bandit (MAB) game [91], where the UAV acts as the learner. It aims to maximize its rate (reward) by serving various trajectory hotspots, considered as the bandit's arms. The joint optimization of received signal strength at the UAV and users' energy efficiency is modeled as a single-leader/multiple-followers Stackelberg game [175]. The UAV, as the leader, optimizes phase shifts for improved signal quality, while users, as followers, engage in a non-cooperative game to maximize their energy efficiency by adjusting uplink transmission power.  
In RIS-assisted UAV communication for IoT sensor networks, IoT devices use energy from various providers, prompting an energy trading mechanism via a hierarchical Stackelberg game [92]. IoT devices aim to maximize their rate benefit minus payment for wireless charging, while energy providers balance revenue and energy costs. To assess fairness, objective functions based on sum-rate and minimum-rate maximization are defined, optimizing UAV trajectory, BS transmit power, and RIS phase shift.
###### Abstract  
Medical ultrasound imaging is extensively employed for diagnostic purposes. However, image quality remains a major obstacle to achieving greater accuracy. Conventional supervised deep learning denoising methods often rely on matched noise-free and noisy image pairs, which can be highly challenging in practical ultrasound applications. Moreover, due to the limitations associated with independent noise, existing unsupervised denoising methods such as Neighbor2 Neighbor are unable to efficiently address correlated noise in ultrasound images. Meanwhile, these methods utilize a random neighborhood downsampling technique, frequently resulting in pixel loss. Hence, this study proposes a novel Neighbor2 Neighbor algorithm, which reconstructs ultrasound images by improving the downsampling approach. Moreover, it incorporates a structural similarity index and a regularization term, thereby enhancing its ability to suppress both independent and correlated noise. Extensive experiments on an ultrasound image dataset demonstrate that the proposed Neighbor2 Neighbor algorithm outperforms the state-of-the-art baseline algorithms in peak signal-to-noise ratio (PSNR), mean structural similarity index measure (MSSIM), feature similarity index measure (FSIM), and edge preservation index (EPI).  
unsupervised learning; inhibition of spot; ultrasound images; denoising Article
# IPN HandS: Efficient Annotation Tool and Dataset for Skeleton-Based Hand Gesture Recognition
## 2 Related Work
### Skeleton-Based HGR Datasets  
Skeleton-based HGR datasets are essential for training models that efficiently capture hand dynamics while remaining robust to background and lighting variations. For instance, the SHREC 2017 dataset [21], introduced through the SHREC'17 competition, contains 14 dynamic hand gesture classes (28 classes if considering two finger configurations per gesture) performed by 28 participants, captured with an Intel RealSense depth camera. This dataset is based on the Dynamic Hand Gesture (DHG-14/28) dataset [22], which provides 2800 isolated gesture sequences with 3D hand landmark annotations (22 landmarks per hand) from 20 subjects. Similarly, the Briareo dataset [23] was captured using depth sensors and infrared stereo cameras for dynamic hand gesture recognition in an in-car environment. The dataset contains 12 classes performed 3 times by 40 subjects with a total of 120 sequences per gesture (each sequence lasts at least 40 frames). These datasets consist of short, isolated gesture clips recorded in controlled settings, providing high-quality skeleton data but lacking real-world complexity.  
In contrast to existing skeleton-based HGR datasets, our IPN HandS focuses on continuous, real-world gesture recognition scenarios captured with standard RGB cameras. The dataset includes 50 subjects performing 14 gesture classes, which encompass both static postures and dynamic movements, all recorded in long, uninterrupted video sequences. As shown in Table 1, it comprises over 5000 annotated gesture instances and around 800,000 frames that feature dense, frame-level hand skeleton annotations. Unlike previous datasets relying on depth or infrared sensors and consisting of isolated gestures, IPN HandS captures continuous gesture sequences in natural environments with varying lighting conditions and complex backgrounds. This design closely mimics real-world HGR applications, particularly those involving touchless screen interactions.  
Table 1 provides a comparison of the aforementioned skeleton-based HGR benchmarks. IPN HandS stands out as the only large-scale dataset fully captured in RGB, making it the most accessible modality for real-world implementations. Its continuous nature, diverse range of gestures, and extensive scale establish it as a more representative and challenging benchmark for the development and evaluation of HGR systems in unconstrained settings.
###### Abstract
A problem, however, may be sensory characteristics such as taste or smell, which are not favorable in Spirulina [24]. Potato protein is an ingredient that is extracted from potato juice. It, in turn, is a waste product in potato starch production, which is the primary purpose of worldwide potato production [25]. According to the literature, potato protein preparations contain up to 80% of protein [26]. This process allows the potato juice to be put to good use and not be a waste product. It is a very economical and environmentally friendly solution that can be much more sustainable than animal production [27]. Potato proteins could be used as an enrichment ingredient in the production of bread, pasta, yogurt, and their analogs. Studies have proved the existence of good technological properties such as water solubility and heat-induced gelation [26; 28]. Rice proteins can also be a potential unconventional protein source. Rice is the second most cultivated cereal in the world and is grown in more than 100 countries. The largest share is in China, where 50% of the world's rice is grown [29; 30]. Although rice alone contains only 7-9% protein, it is one of the primary dietary protein sources in South and Southeast Asian countries. The best raw material for the extraction of protein from the rice plant is rice bran, which is often a waste product. It is another potential source of protein that can use up waste in the food industry [31]. In protein preparations derived from rice bran, after appropriate technological processing, about 80% of protein can be obtained [32]. Many scientific papers contain descriptions of the individual proteins listed above. However, there is a lack of publications that directly compare these proteins with each other. Therefore, this study aimed to examine all the above mentioned protein preparations (SCP, edible insects, algae, potato protein, rice protein) under the same conditions, using the same methods, and to compare them with a standard in the form of whey protein concentrate, which is currently most commonly used to increase the protein content in food products, especially fermented foods and foods for athletes. The study was based on determining the nutritional value, the physicochemical properties, the content of significant elements, and the toxins. The results can be used to evaluate the suitability of individual protein sources for food production as a protein-enrichment ingredient.
## 2 Materials and Methods
### Data Analysis  
The collected data were tabulated in a Microsoft Excel spreadsheet (Microsoft Inc., Redmond, WA, USA), and statistical analysis was performed using SPSS version 24.0 (IBM Corp. Released 2016; IBM SPSS Statistics for Windows, Version 24.0. Armonk, NY, USA: IBM Corp.). The changes in the dimensions for all six parameters were calculated by deducting the after values from the before values. These data were mostly normally distributed, as checked by the Shapiro-Wilk Test (Supplementary Table S1). Since the discrepancies were both positive and negative in magnitude, the absolute value of the difference was taken for further analysis. This value represents the dimensional change. These data showed some deviation from a normal distribution, as shown in Supplementary Table S2. \(T\)-tests were used to compare the mean dimensional changes. Furthermore, the mean dimensional changes for all the six parameters were compared using a two-way analysis of variance. The alpha error was set at 5%, and a \(p\)-value of less than 0.05 was considered statistically significant.  
Figure 3: Dentures after processing: G1: conventional compression molding technique with non-anatomical tooth form; G2: injection molding technique with non-anatomical tooth form; G3: conventional compression molding technique with semi-anatomical tooth form, G4: injection molding technique with semi-anatomical tooth form.
## 2 Materials and Methods
### Deep Fuzzy Cognitive Map Model
#### 2.2.1 Fuzzy Cognitive Maps  
FCMs are soft computing tools that are a combination of fuzzy logic and neural networks [13]. FCMs were introduced by [PERSON] [10] as an advanced version of cognitive maps with the application of fuzzy casual functions with real numbers to the connections. The FCM is a fuzzy diagram that transforms a system into concepts, where each concept represents a variable, a state, or a characteristic of a system. Between concepts, there are weight values/interconnections that demonstrate how the concepts interact with each other. There are three types of weight values. More specifically, \(w_{\bar{y}}=0\) means that there is no causality between concepts, \(w_{\bar{y}}>0\) indicates a positive relationship, and \(w_{\bar{y}}<0\) determines a negative causality. Expert knowledge defines the number of nodes and the initial values of the interconnections among concepts. An FCM can be described by a weight matrix that includes all the interconnections among concepts and the state vector, which has the values of concepts [10]. The value of each concept is influenced by the values of the connected concepts with the corresponding causal weights and by its previous state. The sum of the concept values of nodes together demonstrates the state vector of the system.  
Regarding the FCM inference and the evolution of the system, FCM calculates iteratively the state of concepts until it reaches the equilibrium point by multiplying the vector of concepts' values with the weight matrix [14; 28; 29]. To normalize the FCM-predicted values of concepts into a specific range after multiplication, a transfer function is used, where a sigmoid, bivalent, or trivalent function is applied. The sigmoid function is demonstrated in type (2). The equation is presented below.  
\[A_{i}^{(K+1)}=f\Bigg{(}A_{i}^{(K)}+\sum_{ij}^{N}w_{ij}A_{j}^{(K)}\Bigg{)} \tag{1}\]  
Figure 1: Figure of proposed methodology for DeepFCM: (**a**) high–level flowchart and (**b**) detailed flowchart.  
where \(A_{i}^{(K+1)}\) is the value of the concept iteration \((K+1)\), \(A_{j}^{(K)}\) is the concept at the iteration \((K)\), and \(f\) is the transfer function.  
\[f(x)=\frac{1}{1+e^{-x}} \tag{2}\]  
In the generated weight matrix, the diagonal has zero values since every concept influences other concepts but not itself [14].  
After multiple iterations, the FCM could lead to one of the following scenarios:  
1. The equilibrium point is where the current states of the FCM have converged to steady values.
2. Limit cycle behavior, where the final state of the FCM, which indicates the outputs, in each iteration takes specific values.
3. Chaotic behavior, where each concept takes random and unstable values.
## 3 Results
### Emulsion Properties
#### 3.1.2 Particle Size Distribution  
The particle size distribution in the emulsion affects the physical stability of the emulsion. It is a well-known fact that the smaller the dispersed particles, the more stable the emulsion is created [30].  
Samples of emulsions containing WPC (EMW and LMW) exhibited a bimodal particle size distribution (Figure 2). In both emulsions, the first fraction ranged from 0.04 to 0.95 \(\upmu\)m and was mainly composed of protein-coated particles. The particle size of the second fraction ranged from 1.10 to 15.14 \(\upmu\)m, representing dispersed oil phase droplets and their agglomerates. Larger agglomerates (in the range up to 100 \(\upmu\)m) that were observed in the microscopic images of LMG likely underwent disintegration, indicating relatively weak interaction forces between dispersed particles. Similar observations of the bimodal size distribution of emulsions based on regenerated whey proteins and maltodextrin were reported by [PERSON] et al. [31] and [PERSON] [32]. In general, bimodal emulsions have special rheological properties, e.g., relatively low bulk viscosity for semi-solid products, high flow adaptability, and a tendency to reduce the viscosity of a particulate suspension [33].  
The size distribution of emulsions containing arabic gum (EMG and LMG) was close to monomodal. In the case of monomodal emulsions formulation, Stokes' law is fulfilled. This indicates that the higher the emulsion viscosity and the smaller the droplet diameter, the smaller the sedimentation is observed [34]. Monomodal distribution was particularly evident in the sample containing ethyl esters (EMG), where there was a single main particle fraction ranging from 1.44 to 17.38 \(\upmu\)m, representing dispersed oil phase droplets. The particle size distribution in the emulsion containing oil (LMG) resembled a monomodal distribution in the particle size range of 0.08 to 10.00 \(\upmu\)m. A clear break in the curve was visible in the diameter range of 2.19 to 5.02 \(\upmu\)m, indicating the presence of a distinct particle fraction. Comparing the particle sizes in LMG and EMG samples, it can be observed that emulsions based on ethyl esters had larger oil droplets. Furthermore, this phenomenon did not occur in emulsions, in which WPC was used as an emulsifier.  
Figure 1: Optical microscope images of emulsions (red loop—agglomerates). LMW, LMG, EMW, EMG—emulsion variants, which compositions are presented in Table 1.
With regard to silica and silica-polymer hybrid aerogels, polyimide-silica aerogel composites by [PERSON] et al. (2014) [14], sodium dodecyl sulfate-doped gelatin-silica hybrid aerogels by [PERSON] et al. (2016) [15], monolithic aerogels and granular aerogels by [PERSON] et al. (2018) [16], and other materials have been studied. With regard to silica aerogel-textless composites, non-woven polyethylene terephthalate (PET)-silica aerogel blankets by [PERSON] et al. (2009) [17], non-woven PET-aerogel composites by [PERSON] et al. (2012) [18], cotton non-woven fabric-silica aerogel blanket by [PERSON] et al. (2015) [19], silica aerogel blanket prepared from sols with varying silica content by [PERSON] et al. (2017) [20], aerogel-polyethylene (PE)/PET non-woven bonded blankets by [PERSON] et al. (2019) [21], in situ synthetic silica aerogel/PET blankets by [PERSON] et al. (2019) [22], glass fiber blankets impregnated with aerogel powder (2-40 \(\upmu\)m particle size) by [PERSON] et al. (2021) [23], and other materials have been studied. For example, a study by [PERSON] et al. on glass fiber blankets impregnated with aerogel powder [23] showed that the sound absorption coefficient above 700 Hz can only reach about 0.4 at a thickness of 10 mm. Despite these developments, there remains a significant gap in mathematical modeling to comprehend the key physical mechanisms behind the acoustic behavior of aerogels. Therefore, the authors employed a computational microstructural modeling approach based on materials model-based research (MBR) [24] to predict the thermal insulation and sound absorption properties of porous materials. This method facilitated the development of a porous material that exhibits exceptional thermal insulation and sound absorption properties. The material was engineered by integrating aerogel particles, which are characterized by multiple independent spaces with diameters of several tens of nanometers, into a polyethylene terephthalate (PET) fiber non-woven fabric. The appropriate particle size and particle shape and blending ratio were crucial for achieving the desired properties. The result is a non-woven fabric with heat-insulating and sound-absorbing properties that has improved performance with aerogel particles. In previous research [25], we discussed a material that insulates and absorbs sound by damping sound waves through vibration in a structure containing fine porous and soft sites. In this study, by incorporating nanoporous ellipsoidal particles with long sides ranging from tens to hundreds of microns into non-woven fabrics with voids of similar dimensions, we enhanced the labyrinthine nature of the sound waves' traversal through the material. This structure allows the nanoporous particles to provide thermal insulation, while the microporous voids facilitate sound absorption, achieving superior performance in both aspects.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 2 Security Overview
### Review of Cloud Security  
Cloud security is an extremely significant deployment, maintenance, and continuity phase for the business running the infrastructure. According to [14], the most important practices for ensuring cloud security are due diligence, access management, data protection, as well as surveillance and defense. Furthermore, they identified key risk examples, including unsecured storage services, email compromise, data loss, systems and data destruction, data breaches, ransomware attacks, and supply chain attacks. Despite the cloud security measures we may deploy and the mitigations we take, there are prominent issues in cloud security. The authors investigate cloud-based web applications countermeasures for various common attacks, which include Cross-Site Scripting (XSS) and SQL injection for different cloud service models such as IaaS, PaaS, and SaaS by using the form of penetration testing to provide successful mitigation. The paper makes use of a table to provide a concise comparison between vulnerability scanning tools (used in the formative stages of a penetration test framework). With comparisons between WebScarab, OWASP ZAP, Uniscan, Nikto, and W3 af, ZAP is the most effective in testing cloud environments for finding the mainstream attack vectors.  
In the work of [15], the authors discuss advances in cloud security by developing SaaS by deploying a reliable cloud service architecture that can perform policy customization. They produce an overview of the proposed architecture throughout the work, ensuring its trustworthiness, availability, and consistency with the customer's security policies. The solution offers cloud subscribers the ability to do three things, including customizing and executing a security policy for the cloud environment, verifying that the ongoing migration operations of the computing environment comply with their customized security policy, and finally, auditing the security of present and historical requests throughout the VM's lifecycle through a non-reputation logger in the blockchain.  
Six entities are involved in the process: customers, cloud managers, registry, attribute authorities, blockchain auditor, and server cluster. There are three stages of the deployment: Stage 1 (Initial Stage), Stage 2 (Attribute Authorization), and Stage 3 (VM Deployment).  
Finally, we look at the study to implement machine learning for cloud security. In the research undertaken by [16], they review the ability to secure cloud environments by implementing machine learning--the study focuses on the machine learning techniques used to solve, detect, or prevent issues and vulnerabilities. The first finding is the vulnerabilities typically found, which are not limited to phishing attacks, zombie attacks, denial-of-service, and man-in-the-middle attacks. Thirty machine learning techniques were used either as hybrid or standalone across all papers reviewed. The study concluded that there had been very few surveys on machine learning techniques in a cloud security form.