esa-sceva/satcom-chunk-collection huggingface dataset:
# Microencapsulation of Polyphenols and Their Application in Food Technology
## 3 Polyphenols: Properties and Significance in Food
### Application of Polyphenols in Food Technology
#### 3.3.3 Antimicrobial Properties  
Polyphenols exhibit significant antimicrobial activity, making them a promising tool for enhancing food safety. Their mechanisms of action include disrupting microbial cellmembranes, inhibiting metabolic enzymes, and damaging the genetic material of microorganisms [104; 105]. Polyphenols, such as flavonoids, tannins, and phenolic acids, interact with the lipids and proteins of microbial cell membranes, altering their permeability. This process compromises membrane integrity, leading to the leakage of ions and nutrients, ultimately causing cell death. Moreover, polyphenols inhibit the enzymes crucial for microbial metabolism, such as ATPases, thereby limiting the microbes' ability to replicate and survive. Thanks to their antimicrobial properties, polyphenols are widely utilised in various food products to mitigate spoilage and contamination risks. For instance, rosmary and green tea extracts effectively suppress the growth of bacteria such as _Listeria monocytogenes_ and _Escherichia coli_ in processed meats. Similarly, polyphenols from pomegranate and grape seeds are employed as natural antimicrobial agents in beverages and dairy products [106]. The increasing consumer demand for clean-label products has driven the adoption of polyphenols as natural preservatives. Their application allows manufacturers to eliminate synthetic preservatives, such as nitrates and benzoates, while enhancing the health benefits of food products. Despite their advantages, the use of polyphenols in the food industry faces challenges such as chemical instability and limited water solubility. Encapsulation techniques, including those associated with liposomes and nanoparticles, have been developed to overcome these limitations, improving the bioavailability and efficacy of polyphenols. As extraction technologies and food formulations evolve, the application of polyphenols in the food industry is expected to grow. Research is increasingly focusing on the synergistic effects of polyphenols with other natural bioactive compounds, which may lead to the development of more effective and versatile solutions [107].
# Extending UWOC System Applications through Photon Transmission Dynamics Study in Harbor Waters
## 3 The Simulation Results and Discussion
### Analytical Evaluation of the Received Aperture and FOV
However, beyond a certain point, the rate of increase in time delay spread significantly slows down. This slowdown occurs because a larger FOV eventually encompasses most of the multiple-scattered photons within the Reception Plane. Therefore, further increases in the FOV do not substantially add to the number of photons captured, leading to only marginal changes in the time delay spread. This finding illustrates the intricate balance between FOV and the efficiency of photon capture in determining the time delay characteristics of UWOC systems. Lastly, the simulation results and theoretical analysis collectively suggest that increasing the received aperture and FOV, particularly over longer communication distance, make little contribution to reduce the normalized power loss and mitigate the time delay spread, thus not significantly enhancing communication quality. The primary reason is that, although a larger received aperture and FOV can capture more scattered photons, beyond a certain threshold, these parameters expand to cover nearly the entire peak intensity region of the Gaussian beam or most of the multiple-scattered photons on the Reception Plane. Based on these findings and the fixed transmitted full divergence angle at 0.5 mrad, the most suitable received FOV range is about 1-3.2 rad, and the most ideal received aperture is about 0.15-0.4 m. The variations in normalized power loss, under threshold conditions,  
Figure 12: Assessment of time delay spread relative to attenuation length and received aperture across four types of harbor water. (**a**) Harbor I; (**b**) Harbor II; (**c**) Harbor III; (**d**) Harbor IV. predominantly register at less than 2 dB. Furthermore, statistical analyses concerning the time delay spread, also under these threshold conditions, reveal that it generally maintains the same order of magnitude, given the same AL. To gain a thorough understanding of the UWOC system's performance in harbor water condition, we meticulously analyze the computational cost, a critical factor in our numerical simulations. The simulations are conducted on a computer equipped with an AMD Ryzen 9 5900X 12-core processor, operating at a main frequency of 3.7 GHz, and 64.0 GB of RAM, selected for its capability to efficiently process computationally intensive tasks. On average, the simulation process for each data point presented in Figures 8-13 requires approximately 4.5 h.
## 4 Discussion
### Uncertainty of Different Precipitation Types on Landslide Disasters  
The rainfall process is an essential factor in determining regional susceptibility to landslides because it affects the moisture content of rocks and soil, reducing their shear strength and thus promoting landslides. However, the diverse and complex nature of rainfall and soil moisture has introduced significant uncertainties into the model's outputs when simulating the various factors influencing landslide risk.  
The geographical location of Zhejiang Province is characterized by frequent typhoons and monsoon precipitation, which are characterized by extremely heavy, short-duration precipitation events (typhoons) and continuously long events, such as plum rains [90]. Typhoon precipitation generally saturates soil, destabilizing the geological formations of slope surfaces, and induces landslides. Deformations due to landslides are linked closely to the intensity and duration of typhoon-induced precipitation events [19]. As well as being characterized by very high precipitation intensities over short durations, typhoons have an uneven spatial distribution and unpredictable landfall locations and paths. These factors also contribute to the suddenness and unpredictability of landslide occurrence. Moreover, plum rain season precipitation is mediated by the East Asian summer monsoon, and its year-to-year variability adds even more uncertainty to landslide susceptibility assessments [91].  
Furthermore, soil moisture exists in a variety of forms. However, in Central Asian countries, including Tajikistan and Kazakhstan, the water content is influenced not only by precipitation but also by snowmelt from local mountains and thawing permafrost [92]. The melting of snow in the mountains releases large volumes of water, which rapidly infiltrate the ground, increasing saturation and, consequently, reducing shear strength. This, in turn, heightens the risk of landslides, particularly in mountainous regions [93]. The rate of snowmelt is by far influenced by many aspects relative to temperature change, the incidence of sunlight, micro-topography, and the physical properties of the snow itself, complicating landslide prediction and assessments.  
In conclusion, many uncertainties exist in the prediction of landslide disaster events due to the interaction among diverse precipitation types and forms of soil moisture. Several precipitation types and soil-moisture forms may co-occur in space and time, which will enhance the highly complex, nonlinear mechanisms of landslide initiation. Therefore, future landslide risk assessments should ideally be multidimensional in their approach in order to outline the combined effects of various precipitation types and soil environments with a view of formulating detailed, targeted prevention and evaluation strategies.
## 3 Results
### Bibliometric Analysis
#### 3.1.2 Bibliometric Analysis of Laboratory-Scale Experimentation  
The bibliometric study based on the collection and analysis of keywords related to laboratory-scale experimentation (Strategy II) revealed 50 terms correlated with each other (Figure 4). Several distinct clusters emerge from the network map. These include:  
Figure 3: Research trends using authors’ keywords (frequency = 5.5 words per year).  
Salt Cavern Storage: This cluster revolves around terms like salt rock, permeability, and seal integrity, which are critical factors in the feasibility of storing hydrogen in salt caverns.
* Saline Aquifers and Depleted Reservoirs: Closely linked to CCS (carbon capture and storage), this cluster encompasses key properties necessary for characterizing and modeling these storage structures. These include capillarity, two-phase flow, equation of state, solubility, microbial community, bacteria, hydrogen sulphide, porosity, wettability, H\({}_{2}\)-brine interaction, adsorption, contact angle, shale, and organic acids.
* This broader cluster is related to the concept of energy storage, with key terms such as renewable energy, injection strategy, monitoring, and methanation, reflecting the focus on integrating hydrogen storage with renewable energy systems.  
Additionally, artificial intelligence, machine learning, and molecular simulation appear in more peripheral clusters, indicating that computational techniques are being explored but are still emerging in this field due to the scarcity of experimental data available.  
Figure 5 shows how dominant keywords in experimental studies have evolved over time. Early focus areas include basic chemical properties and environmental factors, while later years show a shift towards more practical aspects, such as storage in specific geological formations and energy strategies. Some keywords, like those related to modeling and specific properties, have remained prominent for extended periods and continue to be relevant today, indicating that research in these areas is still active and ongoing.  
Based on a conceptual analysis, the above terms can be grouped into 3 main topics: (i) the materials involved (fluids and solids) and their properties, (ii) the interactions between these materials (chemical, mechanical), and (iii) the reproducibility of the processes in the laboratory. Therefore, in the following sections of the paper we are going to cover these topics: the materials and their properties are described in Section 3.2.1 (main rock types) and Section 3.2.2; the interactions are discussed in Section 3.2.3; and the reproducibility of these processes in the laboratory is reflected on in Section 4.  
Figure 4: Network of keywords related to laboratory-scale experimentation in the field of UHS.
## 3 Methodology
### Deep Learning Approach
#### 3.6.1 The Multilayer Convolutional Neural Network  
The proposed MCNN model, illustrated in Figure 2, is designed to enhance performance in ASA by leveraging advanced techniques within the CNN framework. CNNs, initially developed for image recognition tasks inspired by biological research on visual perception, have shown significant potential in NLP, particularly for text classification. This model integrates an embedding layer, convolutional layers, normalization, max-pooling, dropout layers, and fully connected output layers, as detailed below:  
1. Input Layer: The embedding layer is the initial component of the model, responsible for converting words in the input array into vector representations. Our model's input layer uses advanced word embeddings to capture the semantics and context of Arabic text. We employ AraBERT, Word2 Vec, and fastText. AraBERT, pre-trained on extensive Arabic datasets, captures deep semantic relationships. Word2 Vec, using the Skip-gram model with a window size of 5 and vector dimension of 300, reveals syntactic and semantic patterns. FastText, addressing Arabic's complex morphology with a window size of 40 and vector dimension of 300, enhances sentiment analysis by treating words as character n-grams. These methods provide robust inputs for our sentiment analysis model.
2. Convolutional Layer Blocks: The model includes three one-dimensional (1D) convolutional layers with batch normalization to enhance feature extraction. Using \"same\" padding ensures consistent input dimensions. Rectified Linear Unit (ReLU) activation functions are applied uniformly across all layers to capture non-linearities.
3. Normalization and Pooling: To ensure model stability and prevent overfitting, batch normalization layers are added after each convolutional layer. Then, max pooling 1D layers with appropriate pool sizes are added, effectively reducing the dimensionality of feature maps and increasing computational efficiency.
4. Dropout Regularization: To combat overfitting further, dropout layers are strategically placed after the convolutional layers. These layers randomly deactivate 2.5%, 10%, and 15% of neurons in the first, second, and third convolutional layers, respectively.
5. Fully Connected Layers: To facilitate comprehensive learning and classification, the model incorporates fully connected layers. This architecture includes a hidden layer with 1024 neurons and an output layer customized based on the dataset's class count. Activation functions, such as sigmoid for binary classification and softmax for multi-class classification, ensure accurate predictions across different scenarios.  
The proposed CNN model for Arabic text classification demonstrates a carefully designed architecture integrating embedding layers, convolutional blocks, normalization, pooling, dropout layers, and fully connected layers. This model configuration is designed to effectively capture local features in Arabic text data and achieve robust classification performance.
## 6 Future Directions
### High Mobile Experience  
It would not be wrong to say that along with 6G and space networks, a digital universe circle will be entered. With technologies such as augmented reality, virtual reality, and extended reality combined with 6G networks, the reality of holograms will grow significantly. In addition, the tactile internet [130] and the haptic internet will ensure that all of the five human senses have a digital counterpart. In daily life, it is expected to experience a mobile experience surrounded by driverless vehicles and IoE technologies. In order for these applications to be provided efficiently, the requirements of low delays below milliseconds, high-speed data transmission, and high reliability must be met. Developments in technologies such as THz communication and beamforming are very important for the interests of the coalition of XR, augmented reality (AR), virtual reality (VR), haptics, tactile internet technologies, and 6G and satellite networks. Privacy is one of the priority issues for a person who is so surrounded by the mobile world. In order to achieve this, solution techniques, such as transfer learning, distributed learning, and federating learning, that keep data locally have great potential.
# A Multi-Branch DQN-Based Transponder Resource Allocation Approach for Satellite Communications
## 2 Related Work  
Among traditional operations research methods, combinatorial optimization, linear programming, and non-convex optimization are widely adopted for resource allocation optimization problems [3; 4; 5]. With the growing complexity of actual resource allocation problems, the much-studied heuristic algorithms in combinatorial optimization problems have seen increasing use to address resource allocation problems. In [8], a downlink resource allocation model adapting to observation task increments is established, and a novel algorithm based on evolutionary computation is proposed. The simple ant colony optimization algorithm (SACO) [9] and Tabu search (TS) [10] have also been implemented to solve the satellite resource allocation problem. [11] proposes an implementation of the heuristic algorithm based on the genetic algorithm and particle swarm optimization (GA-PSO) to solve the joint power and frequency allocation problem.  
Figure 2: MBDQN is used to conduct efficient satellite resource allocation and improve resource occupancy.  
At present, with the advancement of deep learning and reinforcement learning, deep reinforcement learning has seen growing applications in the field of combinatorial optimization, especially for resource allocation. According to the learning content of the agent in the process of interacting with the environment, model-free reinforcement learning algorithms can be divided into two categories: policy optimization algorithms (e.g., REINFORCE) that directly learn the action execution strategy, and value optimization algorithms (e.g., Q-learning) that learn a value function for action execution decisions. In addition to REINFORCE, policy optimization algorithms also include trust region policy optimization (TRPO) [13], proximal policy optimization (PPO) [14], advanced actor-critic (A2C) [15], asynchronous advantage actor-critic (A3C) [15], etc. Moreover, Deep Q-Network (DQN) [16] is a typical value optimization algorithm.  
Furthermore, reinforcement learning algorithms designed for the allocation problem can search for a locally optimal solution in a shorter time than heuristic algorithms. Ref. [17] proposes a reinforcement learning heuristic optimization (RLHO) framework. It guides the PPO algorithm to obtain a better initial state with the reinforcement learning algorithm for solving the bin packing problem. In [18], an innovative resource management framework that applies DRL is proposed for the next-generation heterogeneous satellite networks. In [19], an improved graph-based minimum clique partition algorithm is proposed to revise the observation satellite scheduling problem for preprocessing in the task clustering phase by considering the maximum task priority and the minimum observation slewing angle under constraint conditions. Ref. [12] proposes a deep reinforcement learning-based framework (DRLF) to solve the problem of unknown dynamics and prohibitive computation for dynamic resource allocation.
# Corrosion and Scaling in Geothermal Heat Exchangers
###### Abstract  
Geothermal power is an attractive and environmentally friendly energy source known for its reliability and efficiency. Unlike some renewables like solar and wind, geothermal energy is available consistently, making it valuable for mitigating climate change. Heat exchangers play a crucial role in geothermal power plants, particularly in binary cycle plants, where they represent a significant portion of capital costs. Protecting these components from deterioration is essential for improving plant profitability. Corrosion is a common issue due to direct contact with geothermal fluid, which can lead to heat exchanger failure. Additionally, temperature changes within the heat exchanger can cause scaling, reduce heat transfer efficiency, or even block the tubes. This review critically examines the challenges posed by corrosion and scaling in geothermal heat exchangers, with a primary focus on three key mitigation strategies: the application of corrosion-resistant alloys, the utilization of protective coating systems, and the introduction of anti-scaling agents and corrosion inhibitors into the geothermal fluid. The paper discusses recent strides in these approaches, identifying promising advancements and highlighting impending obstacles. By bridging existing knowledge gaps, this review aims to offer valuable insights into material selection, heat exchanger design, and the progression of geothermal energy production. Ultimately, it contributes to the ongoing endeavor to harness geothermal energy as a sustainable and enduring solution to our energy needs.  
geothermal plant; heat exchanger; corrosion; scaling; coating +
Footnote †: journal: Applied  
Heat exchangers are among the most critical components in geothermal power plants, particularly in organic Rankine cycle (ORC) power plants [9], where heat exchangers account for a substantial portion of capital costs. Consequently, protecting these components from in-service deterioration is essential to enhance plant profitability. The protection of heat exchangers presents formidable challenges due to the array of potential damage mechanisms. Corrosion is a prevalent issue, occurring due to direct contact with geothermal fluid that can result in complete heat exchanger failure [10]. Additionally, the temperature drop within the heat exchanger invariably leads to scaling, which can impede heat transfer efficiency [11] or completely obstruct the heat exchanger tubes [12].  
These challenges first emerged in the 1980s [13], prompting extensive investigations into mitigation strategies. However, accessing this wealth of information has proven challenging, as no comprehensive review consolidates the advances in degradation mitigation for geothermal heat exchangers. Accessible information is pivotal for material selection, heat exchanger design, and, by extension, the advancement of geothermal energy production. Therefore, to bridge this knowledge gap, this review aims to provide a comprehensive overview of published research on the corrosion and scaling of geothermal heat exchangers.
# Multiple Instances QoS Routing in RPL: Application to Smart Grids +
## 3 Proposed Solution
### mOFQS Metric
Consequently forcing paths where nodes consumed less their batteries (path 3 where node 3 and 4 have more than 80% energy left in their batteries unlike path 1 where nodes 2 and 5 have less than 30% energy left). We achieve then a traffic distribution along the nodes by passing by path 3 and extending the network's lifetime. Figure 4: Network with different \(ETX\), delay \(d\) (in ms) and \(PS\) values. Figure 3: \(mOFQS\) variation with \(\alpha\).
## 3 Results and Discussion
### Bread Analysis
#### 3.3.2 Specific Volume Measurements  
The specific volume of the wheat flour (control) bread and the hemp protein breads are depicted in Figure 7. The specific volume of the sampled items showed significant differences. As shown in Figure 7, the addition of hemp protein resulted in a decrease of the specific volume of the fortified breads. [PERSON] et al. [63] reported that the incorporation of protein in bread results in a decrease in the loaf volume and this decrease is related to the amount of protein added. Thus, the lack of gluten protein and the disruption of gluten disulfide links prevents water retention in the protein network, resulting in a decrease in the volume of the bread [63]. Similar results were also reported by [PERSON] et al. [16]. Many researchers [64; 65] reported that the addition of protein resulted in a decrease in volume, probably due to the interference of protein in gluten formation, which in turn prevented the dough from retaining CO\({}_{2}\)[66]. Contrary to our results, [PERSON] et al. [27] found an increase in volume of gluten-free breads with the addition of hemp protein. The researchers attributed this to the surface-active and foaming properties of hemp protein, which contributed to the stabilization of CO\({}_{2}\) in the bread. These contradictions in the results of specific volume could be attributed to differences in the source of protein, the form of added protein, the processing protein methods, the concentration of protein [49], and to the absence of a gluten matrix.  
Figure 5: Color parameters (L*, a*, b*) of wheat bread (control, 0% of hemp protein) and of breads containing 5, 10, and 15% hemp protein powder. Vertical bars represent the 95% confidence intervals calculated based on the error mean square from the ANOVA. Different letters within the same day group or numbers for the same recipe on different days shown as superscripts, denote significant differences (\(p\) < 0.05) between the means, based on [PERSON]’s multiple comparison test.