esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Bioavailability and Stability of Natural Antioxidant Ingredients in Cosmetics  
One of the key challenges for present and future research is the study of the bioavailability, absorption, metabolism, pharmacokinetics, and biotransformation of antioxidant compounds. The primary objective of these investigations was to determine whether antioxidants, when administered in various formulations, reached their intended target in sufficient concentrations to exert their effects [5].  
Most antioxidants are inherently unstable, posing significant challenges in cosmetic formulation. To ensure their efficacy, the concentration of each antioxidant must be carefully determined, and its activity must be consistently maintained through chemical stabilization. Several structural modifications can enhance the stability of antioxidant compounds, such as the substitution with ester groups (e.g., tocopheryl acetate, ascorbyl palmitate). While these modifications improve stability, they often lead to a reduction in antioxidant activity. Emerging technologies, particularly lipid-based carriers, offer promising solutions for enhancing both the stability and skin penetration of antioxidants. Advanced delivery systems such as nanoparticle emulsions, liposomes, phytosomes, transfersomes, etosomes, niosomes, nanopotes, lipid microparticles, and lipid nanoparticles have been successfully employed in cosme to improve the bioavailability of various antioxidants. These include pycnogenol, quercetin, squalene, p-methoxycinnamic acid, ascorbyl palmitate, vitamin E acetate, vitamins K and A, CoQ10, and extracts from green tea and grape seeds [279].  
Additionally, scientific research and patents describe innovative approaches to improving the skin penetration and stability of peptides. These strategies can be categorized into structural and physical modifications aimed at enhancing bioavailability. Structural permeability enhancers include modifications of peptide sequences, conjugation with fatty acids, triterpenoids, or myristic acid, as well as peptide cyclization. Physical methods involve changes in formulation, such as the use of hydrogels, different types of microneedles, or peptides as carriers for growth factors.  
To further improve stability, modifications to peptide bonds and structures have been explored, including the incorporation of non-canonical amino acids, D-amino acids, and cyclization techniques. Additionally, conjugation with organic moieties such as polyethylene glycol (PEG), ascorbic acid, or biotin has been shown to enhance peptide stability [280].
# A New Bloom Filter Architecture
## 4 Bloom Filter Analysis
### False Face Return Probability  
Since a Bloom filter never produces false negatives, the inputs included in the programming set do not return false faces, even though they may return indeterminable results. Hence, the \(p\)-BF needs to be analyzed for _false face return_ caused by negative inputs. In querying an input included in \(S^{c}\), let \(p_{op}\) be the probability that a specific cell with a face value is selected. Then, \(p_{op}\) can be calculated as  
\[p_{op}=\Big{(}1-(1-\frac{1}{m})^{kr^{\prime}}\Big{)}(1-\frac{1}{m})^{k(N-n^{ \prime})}. \tag{9}\]  
The \(p_{op}\) is the product of the probability that at least one of the \(k\) hash indexes for \(n^{\prime}\) elements included in the specific face set indicates a cell and the probability that none of the \(k\) hash indexes for \((N-n^{\prime})\) elements not included in the set indicate the cell.  
From Labels (7) and (9), the _false face return_ probability  
\[P(F_{t})=L\sum_{i=1}^{k}\binom{k}{i}p_{op}^{i}\cdot p_{oc}^{k-i}. \tag{10}\]\(P(F_{t})\) is the product of the probability that \(i\) of the \(k\) hash indexes return a specific face and the probability that \((k-i)\) of the \(k\) hash indexes return _conflicts_. In other words, when all of the cells, excluding conflict cells, have the same specific value, the value is returned as the output face. \(L\) is multiplied since \(P(F_{t})\) is the summation of the _false face return_ probabilities for each face.
# Future Scenarios for Software-Defined Metro and Access Networks and Software-Defined Photonics
## Reasons for Software Defined Metro and Access Networks
### 5G Radio Access Network  
Another important reason for adoption of the SDN model in the metro and access segments is related to the development of the 5G radio access network (RAN). 5G is the next generation cellular system currently in an embryonic state. Its main requirements are an increased capacity (up to 600 Mb/s peak data rate), a reduced latency (optimized to 1 ms) and a higher connection density (\(\sim\)2000 active users per km\({}^{2}\)) [13]. In addition to the growth of traffic which will be necessary to be carried by metro and access optical network, 5G RAN will have a deep impact on the architecture of the access segment itself, offering the opportunity to embrace SDN and NFV. In fact, the objectives of 5G can be achieved only by exploiting advanced radio access technologies (such as small cells, co-ordinated multi-point, massive multiple-input multiple-output, beamforming, and carrier aggregation), which will need to be supported by novel reconfigurable optical networks interconnecting the RAN nodes among themselves and with the core network [14].  
Densifying macro cells and adding small cells will make topology and traffic distribution of the mobile backhaul (MBH) networks more complex. Dense wavelength-division multiplexing (DWDM) could be the candidate technology for future passive optical networks (PONs), since high-capacity point-to-point logical connections can be provided for backhaul links. Moreover, 5G is also expected to dramatically change the fronthaul network segment, i.e., a RAN where the remote radio unit (RRU), co-located with the antenna, is split from the base band unit (BBU) communicating with the RRU through the common packet radio interface (CPRI). Mobile fronthaul (MFH) networks could also take advantage from DWDM-PONs. A convergence of fronthaul and backhaul in a unified network segment will also be possible, referred to as Xhaul [14].  
The centralization of the BBUs, called centralized-RAN (C-RAN), is considered one of the most relevant evolutional trends to support 5G solutions. The main advantage of C-RAN is the possibility of implementing coordination features among BBUs to mitigate radio interference, especially in small-cell deployments where user equipment (UE) is often within reach of a number of BBUs [15]. The control plane centralization promoted by the SDN paradigm will facilitate not only the required architectural agility needed in C-RAN solutions, but also the provision of fast re-configurability and high capacity on-demand for certain locations [16]. In an extreme implementation, C-RANs could evolve toward cloud based infrastructures (Cloud RANs) and Virtualized RANs (V-RANs), where BBUs can be activated in general purpose servers following the concept of NFV and network slicing [14]. Several possible solutions to implement software-defined metro and access network fulfilling 5G RAN requirements are described in the Section 5.3.
## 3 Materials and Methods
### Multi-Next-Hops Approach Proposed for FzGR
#### 3.1.1 Main Factors on Which MNH-FGR Is Based  
Alternative routes: The multimedia traffic should be delivered satisfying the transmission delay and delay requirements. In typical routing protocols, the nearest neighbor leading to the shortest path is heavily used, while other effective neighbors leading to alternate paths are unworkable. This is the case for GPSR and FzGR protocols, where neighbors that are able to meet the requirements of VANET networks are ignored. Sending capacity: A media stream is usually composed of large packets. Therefore, if only one channel is used to route these packets, the transmission requirements may be several times greater than the maximum transmission capacity of the nodes.  
Real-time decision: Since the topology can change at any time in an unpredictable way, it is safer to make routing decisions in a distributed and real-time manner. This will make it possible to react in advance to changes in topology and to avoid the transmission of packets to unavailable or unreliable nodes.  
Node selection: In highly deployed networks, different neighbors can be selected as candidates to transfer packets. To deduce an appropriate selection, the node selection process must take into account the distance of each neighbor from the final destination, the weight of the different links, and the predefined threshold of the weight that should not be exceeded.  
Load balancing: To maximize the uptime of VANETs and, therefore, minimize link failures, load balancing must be considered when designing a multi-hop routing protocol.  
We describe, in the following, the load-balancing process implemented in our algorithm.
## 3 Structure Design for FH Signal Tracking and Sorting  
In this section, first, an improved MWC structure is applied to track sub-channels with signals in the time domain, and second, a dynamic programming algorithm is proposed for the sorting.
# GIS-Based Multi-Objective Routing Approach for Street-Based Sporting-Event Routing
## 2 Methodology  
In this section, we propose a street-based sporting-event-routing process using GIS data and algorithms to prepare for potential street-based events in cities. Based on open GIS, we generated information on the host city, searched for a route using a modified version of the Floyd-Warshall algorithm, and derived an optimal route through the NSGA-II approach. In addition, we evaluated the impact on the city through an automated process that incorporated the derived dataset into the BIM environment.  
Figure 2 illustrates the process of the proposed routing model; it consisted of the following: (1) data collection and processing; (2) route exploration and optimization; and (3) city-impact assessment through BIM-model creation.  
We collected and processed the data in a GIS-software (Q GIS version 3.26.0) environment. Based on the selected venues, we generated a GIS dataset for transportation networks, buildings, and facilities within a specific range. We selectively extracted the generated data based on information system. We then pre-processed the extracted data so we could reduce the amount of computation by lowering the amount of data through pre-processing. Next, we explored a route based on the route variables and derived an optimal route grounded in the weights of the environmental variables. We integrated the derived routes and elements constituting the city into the BIM environment through the GIS-to-BIM process. We evaluated the impact on the city based on the relationship between the urban infrastructure and potential event routes. The impact assessment determines whether there is interference between the urban infrastructure and routes, and collects and extracts the quantity of interfering elements; it also supports stakeholder decision-making by visualizing data.
## 3 Results
### Ammoniacal Nitrogen Reduction
striatiotes_ reduced the ammoniacal nitrogen values from \(19.9\pm 0.17\) mg/L, \(23.2\pm 0.2\) mg/L, \(18.63\pm 0.15\) mg/L, \(22.1\pm 0.1\) mg/L, \(21.86\pm 0.05\) mg/L, \(21.13\pm 0.08\) mg/L, and \(22.05\pm 0.01\) mg/L of the influent wastewater samples to \(11.04\pm 0.25\) mg/L, \(14.65\pm 0.14\) mg/L, \(13.43\pm 0.05\) mg/L, \(10.34\pm 0.02\) mg/L, \(6.26\pm 0.057\) mg/L, \(5.4\pm 0.17\) mg/L, and \(2.5\pm 0.08\) mg/L, respectively, from the 2 nd day to the 14 th day of the sampling period at 2-days intervals. Meanwhile, _S. molesta_ decreased the ammoniacal nitrogen values from \(19.9\pm 0.17\) mg/L, \(23.2\pm 0.2\) mg/L, \(18.63\pm 0.15\) mg/L, \(22.1\pm 0.1\) mg/L, \(21.86\pm 0.05\) mg/L, \(21.13\pm 0.08\) mg/L, and \(22.05\pm 0.01\) mg/L of the influent samples to \(14.8\pm 0.1\) mg/L, \(17.49\pm 0\) mg/L, \(10.60\pm 0.1\) mg/L, \(8.02\pm 0.01\) mg/L, \(6.56\pm 0.06\) mg/L, \(3.80\pm 0.1\) mg/L, and \(2.1\pm 0\) mg/L, respectively. _E. crassipes_ lowered the ammoniacal nitrogen values of the influent samples from \(19.9\pm 0.17\) mg/L, \(23.2\pm 0.2\) mg/L, \(18.63\pm 0.15\) mg/L, \(22.1\pm 0.1\) mg/L, \(21.86\pm 0.05\) mg/L, \(21.13\pm 0.08\) mg/L, and \(22.05\pm 0.01\) mg/L to \(8.2\pm 0.1\) mg/L, \(10.89\pm 0.16\) mg/L, \(13.37\pm 0.20\) mg/L, \(10.01\pm 0.01\) mg/L, \(7.92\pm 0.06\) mg/L, \(5.02\pm 0.01\) mg/L, and \(2.4\pm 0.17\) mg/L, respectively, during the 14-day phytoremediation process. The maximum ammoniacal nitrogen reduction percentage for the _P. striatiotes_, _S. molesta_, and _E. crassipes_ effluent samples was found to be 88.66%, 90.47%, and 89.11%, respectively, on the 14 th day of the experiment. However, the performances of the three plants showed significant differences (\(p<0.05\)) when compared to the values of the influent samples.
## 5 Discussion  
### Comparative Analysis on Computational Complexity  
For the complexity analysis, neural networks consume a lot of computational power depending on the depth of the network. The proposed RPLLC algorithm is based on just the matrix multiplication of image with HSL color conversion formula. Putting all that information in simpler words, if image '\(I\)' comprises of three color channels for the elementary operation of matrix multiplication provided the matrices are square matrices, the time complexity is almost \(O(n*3)\) where \(n\) is size of image (digits present in the matrix). Whereas, for the CNN (feed forward in nature), assuming all the different layers of convolution, pooling, dropout along with the added complexity of activation function in each layer; the computational complexity is proportional to \(O\Big{(}n^{N_{0}.\text{ }of\text{ }layers}\Big{)}\). The implementation of CNN used for the experiments include up to 9 layers so we can expect \(O(n^{9})\). Finally, the computational complexity associated with the Support Vector Machines for image classification it is approximated to \(O(2(k+1)n)\), where '\(k\)' represents piecewise linear approximations of degree 1 and '\(n\)' represents number of input dimensions [58].
# Mobile Personal Multi-Access Edge Computing Architecture Composed of Individual User Devices
## 2 Proposed Mobile Personal Multi-Access Edge Computing Architecture
### Working Distribution and Merging Process  
To maximize the distributed computing task processing speed, all the resources of mMECS, which plays the role of worker from the beginning to the end of the entire task execution process, should be utilized to ensure continuous operation. However, the proposed scheme utilizes mobile devices with diverse and limited computing power as MECS, so the existing computing method that distributes computing task considering all situations properly is not suitable. Since the proposed scheme utilizes the mobile node as MECS so there are a number of considerations for rearranging computing task when mMECS is moved. For this, the proposed scheme uses the method which sets the length of the segment to the smaller size so that mMECS, which is relatively poor in computing power, can be processed quickly. Figure 3 shows the task processing time of the distributed model based on task segment size.  
In the case of a task segment-based model, when a large segment size is set, bottlenecks and additional processing time may be caused since each of the mMECS with different computing capacities  
Figure 3: Task processing time in the task distribution model based on task segment size.  
has different task processing speeds. On the other hand, the distributed model used in the proposed scheme maximizes the task processing speed by allocating only a small number of task segments to mMECS at a time and assigning additional task segments right after the task processing is complete.  
In other words, the proposed distributed model has a shorter segment length, so that the task distribution for the mMECS can be more balanced, and it is possible to use the worker's resources to the maximum.
## 3 Results and Discussion
### Thermodynamic Analysis of Hydrogen Dissociative Adsorption on Pipeline Steel Surfaces
#### 3.3.1 Calculation of Adsorption Energy  
The change in adsorption energy, \(\Delta\)E adsorption, essentially corresponds to the enthalpy change, \(\Delta\)H, reflecting the thermal effect during the adsorption process. The adsorption energy is calculated by  
Figure 8: Electron differential density profile.  
Figure 9: Charge difference density profile.  
tion action on solid surfaces mainly involves the formation or breaking of chemical bonds; thus, \(\Delta\)E adsorption directly indicates the change in system energy caused by the change in chemical bonds [56]. Specifically, if \(\Delta\)E adsorption \(<0\), it implies that the adsorption process is exothermic, forming stable chemical bonds, which typically means that the process is spontaneous. The relevant information about the model and the computational results are presented in Table 2.  
The change in adsorption energy, \(\Delta\)E adsorption, can be expressed by Equation (6):  
\[\Delta E_{\text{adsorption}}=E_{\text{Sys}}-(E_{\text{H2}}+E_{Fe}) \tag{6}\]  
This equation indicates that the change in Gibbs free energy \(\Delta G\) during the adsorption process can be calculated as the sum of the change in adsorption energy \(\Delta\)E adsorption and the change in the number of gas molecules \(\Delta\)n gas multiplied by the chemical potential \(\mu(T,P)\) at a specific temperature T and pressure P. This equation reveals the thermodynamic characteristics of the adsorption process, highlighting that the spontaneity of adsorption is influenced not only by the change in adsorption energy but also by the variation in the number of gas molecules during the adsorption and their change in chemical potential. This provides an important theoretical foundation for determining whether the adsorption process is spontaneous and for further understanding the adsorption mechanism.
# A Web of Things-Based Emerging Sensor Network Architecture for Smart Control Systems
## 1 Introduction
In the proposed WoT-ESN scheme, smart home services are integrated with the Web using a smart gateway. The scheme consists of two components: (1) smart home services at the smart home end and (2) RESTful smart home services at the WoT end. The smart home services aim to enhance the energy consumption efficiency and to reduce the adverse effects of coexistence interference. The smart home's operations are semi-automated to optimize the energy consumption. This provides a real-time decision-making mechanism to generate necessary events to save energy. However, the user has the sole authority over approving or rejecting the generated events. Meanwhile, a human machine interface (HMI) is embedded into the decision-making system to approve/reject generated events at the absence of the user. The events are notified to the user and the system waits for a time \(\alpha_{T}\) for a user response. If the user response is not received within the predefined time, the HMI approves/rejects the generated event in accordance with the previous records. The smart home services are exposed to the Web via RESTful APIs. Remote users can control the smart home operations via the proposed smart gateway. It acts as the bridge between smart things and the Internet. Furthermore, it performs the service translation of the proprietary protocols of smart home devices and sensors, since the services cannot be exposed over the Web using proprietary protocols such as ZigBee The RESTful architecture binds URI to each functionality of smart things. Thus, the remote smart home user can request smart home services via the Web. The rest of the paper is organized as follows: Section 2 presents a brief overview of the similar architectures and methods used in the field of WoT. Section 3 illustrates the proposed scheme in detail. The simulation and results are discussed in Section 4. Finally, the conclusion is given in Section 5.
# DR-Transformer: A Multi-Features Fusion Framework for Tropical Cyclones Intensity Estimation
## 3 Methods
### Image-to-Intensity Feature Learning
#### 3.1.2 Rotation-Invariance Feature Learning  
TC is a complex and dynamic system with continuous rotation. Although its shape also changes slightly during the process of rotation, its characteristics and intensity are often constant. Therefore, it is more meaningful to the extract rotation-invariance feature than the general image recognition tasks. The idea of rotation invariance is that the feature embedding extracted from \(x\) and its randomly rotated counterpart \(x^{r}\) should be close. Hence, we follow the common practice in semi-supervised learning to achieve this.  
Formally, an image \(x\) rotated by \(r\) degrees is denoted as \(x^{r}\) and \(r\in R=\{0^{\circ},90^{\circ},180^{\circ},270^{\circ}\}\) is an angle selected from a set of possible angles. Then, the feature prototype is defined as  
\[\overline{f}=\frac{1}{|R|}\sum_{r\in R}f(x^{r}) \tag{7}\]  
We further use the mean squared error as RI loss, which is given by  
\[\ell_{ri}=\sum_{r\in R}MSE(f(x^{r}),\overline{f}) \tag{8}\]  
The goal of the rotation-invariance loss is that the feature of an image and its rotated copies are expected to be similar as much as possible. We solved this by minimizing the distance between each feature \(f(x^{r})\) and feature prototype \(\overline{f}\). The feature prototype is a fusion of the essential feature of the rotating TC and we further fed it to the transformer.  
That means our DC embedding leads to fewer errors and is more adaptable to TC intensity estimations.
## 4 Experimentation and Results
### Computation Time and Running Time
#### 4.6.3 Hardware Considerations  
A computer with an Intel Core i7 CPU, 32 GB of RAM, and an NVIDIA GTX 1080 Ti GPU was used for the research. The CPU/GPU load was well under control throughout the deployment, and the model used about 200 MB of memory for each inference. This ensures that the system can function effectively on standard hardware setups commonly used in retail settings, as indicated in Table 13.
# Generation of Complete SAR Geometric Distortion Maps Based on DEM and Neighbor Gradient Algorithm
###### Abstract  
Radar-specific imaging geometric distortions (including foreshortening, layover, and shadow) that occur in synthetic aperture radar (SAR) images acquired over mountainous areas have a negative impact on the suitability of the interferometric SAR (InSAR) technique to monitor landslides. To address this issue, many distortion simulation methods have been presented to predict the areas in which distortions will occur before processing the SAR image. However, the layover and shadow regions are constituted by active as well as passive subregions. Since passive distortions are caused by active distortions and can occur in the flat area, it is difficult to distinguish the transition zone between passive distortion and non-distortion areas. In addition, passive distortion could cover part of the foreshortening or active layover/shadow areas but has generally been ignored. Therefore, failure to simulate passive distortion leads to incomplete simulated distortions. In this paper, an algorithm to define complete SAR geometric distortions and correct the boundaries among different distortions is presented based on the neighbor gradient between the passive and active distortions. It is an image-processing routine applied to a digital elevation model (DEM) of the terrain to be imaged by the available SAR data. The performance of the proposed method has been validated by the ascending and descending Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) images acquired over the Chongqing mountainous area of China. Through the investigation of passive distortion, we can have a deeper understanding of the formation and characteristics of these distortions. Moreover, it provides very meaningful information for research on areas such as landslide monitoring.  
InSAR; DEM; passive distortion; active distortion; neighbor gradient; landslides +
Footnote †: journal: Applied Sciences