esa-sceva/satcom-chunk-collection huggingface dataset:
# Biodegradable Polymeric Architectures via Reversible Deactivation Radical Polymerizations
## 2 Biodegradable Polymeric Architectures
### Micelles, Vesicles and Capsules
#### 2.4.3 Capsules  
Multilayered polymer capsules assembled via layer-by-layer (LbL) technology have generated significant scientific and technological interest over the past decade because of their potential as advanced delivery and microreactor systems [109; 110]. [PERSON] and coworkers are pioneering the preparation of versatile capsules via self-assembly for drug and gene delivery and controlled release, among which some of them are biodegradable [111; 112; 113; 114]. For example, they fabricated low-fouling poly(N-vinyl pyrrolidone) (PVPON) capsules with engineered biodegradable properties via LbL process mediated by hydrogen bonding interaction. Due to the introduction of intra-disulfide linkages among the capsules they underwent destruction within 4 h in the presence of 5 mM glutathione. The cross-linked multilayers endowed the capsule with low-fouling properties to a range of proteins, including fibrinogen, lysozyme, immunoglobulin G, and bovine serum albumin [115]. Disulfide-stabilized poly(methacrylic acid) capsules that undergo reversible swelling in response to changes of external pH and degrade in the presence of a physiological concentration of glutathione were also prepared and investigated.  
In [PERSON]'s study, the preparation of pH responsive, biodegradable, biocompatible and cross-linked polymer capsules for controlled drug release was presented. The capsules were prepared using silica particles as templates for surface grafting of poly (acrylic acid) (PAA) and PAA-_co_-poly(polyethylene glycol) acrylate) (PAA-_co_-PPEGA) block copolymer via RAFT polymerization directly from silica particles, followed by cross-linking with cystamine dihydrochloride and removal of the silica template in the presence of hydrofluoric acid, respectively. The resultant polymer capsules were water soluble and biocompatible with a mean diameter of approximately 260 \(\pm\) 10 nm. These polymer capsules were non-toxic to human cells at a low concentration, which are favorable to be utilized as drug carriers for pH responsive and biodegradation controlled drug release. Doxorubicin hydrochloride (DOX) was used as a model drug to test the drug loading and releasing properties of the polymer capsules. It was found that the DOX could be effectively loaded into the PAA and PAA-_co_-PPEGA capsules with a loading capacity up to 52.24% and 36.74%, respectively. The pH and biodegradation controlled release behaviors of DOX loaded PAA-PPEGA capsules were also explored. The results implied that both PAA and PAA-_co_-PPEGA capsules are promising platforms for pH and biodegradation controlled drug delivery systems, while the PAA-_co_-PPEGA capsules exhibit less cytotoxicity (Figure 11) [116].  
Figure 11: The preparation of pH responsive, biodegradable, biocompatible and cross-linked polymer capsules for controlled drug DOX (the red dots) release. Copyright 2014, Elsevier.
# A Low-Cost Remotely Configurable Electronic Trap for Insect Pest Dataset Generation
## 2 Materials and Methods
### Components
#### 2.1.4 Central Server  
The central server enables remote management and configuration of the devices through bidirectional communications, in addition to allowing real-time tracking of the pest's evolution. The server, running Windows 11, is a personal computer with an 11 th generation Intel(R) Core(TM) i5-1135G7 processor (Intel, Santa Clara, CA, USA) (2.40 GHz base, 2.42 GHz boost) and 16 GB of RAM. It hosts crucial services for remote configuration, including a robust Web API for image configuration, a dedicated database for configuration details and image metadata storage, and an image repository for secure trap image storage. Figure 4 presents an overview of the constituent modules of the server.  
* Hosting Layer: This layer is built on top of Internet Information Services (IIS) 10.0, serving as the foundational component within the WebServer infrastructure. This layer is responsible for hosting and managing the Web API, ensuring its accessibility and performance. It acts as an intermediary between client requests and the Web API, efficiently managing the processing and delivery of web content. The hosting layer is essential for the smooth operation of web services and plays a key role in maintaining the stability and responsiveness of hosted applications.
* Web Application Layer: This layer is responsible for processing user requests, executing business logic, and generating dynamic content. Situated within this layer is the Web API, built using ASP.NET Core 7, within the confines of the.NET Core 7.0 runtime environment. The endpoints, which facilitate remote configuration, are described in Table 3. The API documentation is administered through the utilization of the Swagger user interface, enabling a graphical interaction with the API, all without necessitating a detailed understanding of the underlying implementation logic.
* Data Layer: The Data Layer is responsible for the management and persistence of data within the WebServer architecture. This layer encompasses database systems, repositories, and data storage mechanisms, ensuring the integrity, security, and availability of critical information. At the core of the Data Layer lies a relational database, namely SQL Server, alongside the image repository. The development of the database  
Figure 4: Central server structure.  
was undertaken utilizing SQL Server Management Studio (SSMS) and Microsoft SQL Server 2019 Express (SQLEXPRESS). Within the realm of database connectivity and management, ADO.NET Entity Framework is employed to establish the database context, with a specific focus on Entity Framework 7 (EF7). Figure 5 shows the main entities of the database. With regard to the image repository, a two-track approach has been defined, where one track is designated for the storage of test status images (Camera Testing State) and the other for monitoring status images (Monitoring State). Within the database, metadata are recorded for each image, which includes the relevant trap configuration parameters.
# First Request First Service Entanglement Routing Scheme for Quantum Networks
## 4 First Request First Service Entanglement Routing Scheme
### Complexity Analysis  
Assume the number of nodes in the network is \(n\) and the number of ports within each node is \(m\). Our FRPS routing scheme has the time complexity of \(O(m^{2}n^{2})\) and space complexity of \(O(m^{2}n^{2})\).  
In our FRFS routing scheme, the modified Dijkstra algorithm runs twice (finding paths from S to A and B, respectively). Thus, the FRFS routing scheme and the modified Dijkstra algorithm have the consistent complexity. The complexity analysis of the modified Dijkstra algorithm is as follows.  
Time complexity: The algorithm visits the \(O(mn)\) ports with the lowest loss until the target port is found. When a port is selected, the loss and path of the other \(O(mn)\) ports are updated. Therefore, the time complexity is \(O(m^{2}n^{2})\).  
Space complexity: The storage is mainly consumed by variables \(V,C,L\), \(P\) and \(U\). \(V\) contains \(n\) pass-through loss matrices and each matrix has the space complexity of \(O(m^{2})\). \(C\) contains \(n(n-1)\) link loss pass-through matrices, and the space complexity of each matrix is \(O(m^{2})\). \(L\) is composed of \(mn\) link loss from the entangled source to all ports. \(P\) is composed of the \(mn\) paths from the entangled source to all ports, and each path has the complexity of \(O(n)\). \(U\) contains \(mn\) visited flags of all ports. Therefore, the total space complexity is \(O(m^{2}n^{2})\).
## 4 Feature Visualization Analysis Based on RDCA
### Visual Analysis of Feature Classification Ability  
To verify the separability of the features \(\mathbf{x}_{pi}\), the stochastic neighbor embedding based on Student-t distribution (t-SNE) is utilized to analyze the high dimensional feature data [31]. Particularly, the t-SNE converts affinities of data points into the Student-t distribution probabilities. Thus, it is suitable for the visualization of high-dimensional PQDs feature images.  
For a clearer view of the distribution of features, six types of PQDs are selected and fed into the t-SNE including L1, L5, L10, L13, L14, and L15. Three hundred feature samples are selected per type. Two parameters of the t-SNE, i.e., perplexity and iterations, are set to 40 and 3000, respectively. Figure 8 shows the results of t-SNE on feature data \(\mathbf{z}_{pi}^{3}\), generating by the third convolutional layer in the RDCA-3.  
Figure 8: Visualization of PQD features \(\mathbf{z}_{pi}^{3}\) based on t-SNE and RDCA-3.  
Figure 7: Deconvnet analysis on the features of RDCA-3.  
Schematically, it indicates from Figure 8 that the distance and location of features for different types of PQDs are distinct. The purpose is to explain the change of disturbance features through visual analysis. Three hundred feature samples are selected per type. For instance, the classes L5 and L14 are distributed on both sides of the graph, respectively. Conversely, some features overlap each other, such as the L14 and the L15, which indicates that they have similar characteristics. The L1 and L5 are gathered together indicating that the corresponding original signals can be reconstructed with fewer features. It can be revealed from the comparison that the features extracted from the RDCA-3 are separable, which provides a guarantee for PQDs classification.
## 9 Efficiency Analysis  
### Experimental Environment  
The experiment uses C language to realize the MHC method of the network layer, and the experimental code runs on multiple PCs. The experiment uses the MHC method to set up the sender and receiver of network-layer data transmission. The PC configuration is Intel(r) Core(tm) i7-10875H CPU @ 2.30 GHz and 16 GB RAM. The TCP protocol of the transport layer does not contain additional settings, and its protocol header length is 20 B. The experiment compared the network layer's MHC method with the network layer's communication method using the traditional IP protocol, the AH protocol, and the ESP protocol of IPSec. The MHC method uses the SHA256 algorithm as the primary hash function, while the IPSec uses the HMAC-SHA1-96 algorithm as the hash function used to calculate the HMAC. The asymmetric encryption algorithm in the \((MHC,Gen,Sig,Ver)\) scheme is the RSA-2048 algorithm.
## 3 System Model and Optimization Formulation
### Diverse QoS Constraints of Multiple Requests
#### 3.2.2 Reliability Requirement  
For reliability demanding services, we employ the outage probability as the transmission reliability metric to be guaranteed. With outage threshold \(\gamma_{0}\) and tolerable outage probability \(p_{k}^{O}\) of service \(s_{k}\), the reliability requirement can be expressed as,  
\[P\{SINR_{k}\leqslant\gamma_{0}\}\leqslant p_{k}^{O} \tag{5}\]  
According to [22], reliability constraint in Equation (5) can be transformed into  
\[SINR_{k}\geqslant\gamma_{\text{eff}}=\frac{\gamma_{o}}{\ln\left(\frac{1}{1-p_ {k}^{O}}\right)} \tag{6}\]
## 4 Classification of Routing Protocols
### Cross-Layered Routing Protocols
#### 4.5.3 Timezone Coordinated Sleeping Scheduling (TICOSS)  
In [73] [PERSON] _et al._, proposed a cross layer protocol, named as TICOSS, designed to improve the IEEE 802.15.4 MAC layer standard. TICOSS is based on MERLIN discussed in [74], which supports multi-hop communication over IEEE 802.15.4 by dividing the network into time zones. This protocol optimizes the IEEE 802.15.4 in the following three perspectives:  
* It provides the nodes to use the alternate periods of activity and inactivity to reduce the energy consumption,
* It reduces the packet collisions occurred due to the hidden terminals, and
* It facilitates the nodes to forward the data packets towards the coordinator (PAN in case of IEEE 802.15.4) using shortest path routing.  
In the association phase, TICOSS replaces the 64-bits node ID used in IEEE 802.15.4 by a 16-bits node ID. All nodes associate with the network, after exchanging the required information with the coordinator. After association phase, TICOSS divides and synchronize the network into time zones. First of all the coordinator broadcasts its initial zone message containing its time zone (TxZone, which is zero) and clock information to the neighbor nodes. After adjusting their time zones to 'TxZone+1' and internal clock, the neighbor nodes of the coordinator forward a new zone message to their neighbors. This process continues until all nodes set their time zone and update their internal clocks. To support node's mobility, addition or removal, TICOSS uses a predefined expiration time for each time zone, and the time zone of any node expires if it does not receive time zone update message, which all nodes broadcast after a specific interval of time. Each node maintains a table to store the updates containing the timestamps and source node ID, which will help the receiving nodes to figure out the updates of the exiting nodes and discard them. To allocate the time slots to all nodes TICOSS uses V-scheduling table, so that all nodes know their periods of activity and inactivity. Nodes have same time zone communicate in the same time slot. V-table supports three sort of transmission: upstream transmission, downstream transmission and local broadcasts.
The large possibility of varying the inorganic and organic constituents enable scientists to tune the materials' structures at the molecular scale [48; 49]. In addition, the porosity and functionalization of MOF-derived adsorbents can be easily developed. Furthermore, the co-ordination bonds produced during the formation of MOFs via linking metals with heteroatom-derived ligands produce high variations in electron-deficient metal sites on the adsorbent surfaces which act as attractive centers to enhance adsorption, photocatalysis, extraction and preconcentration applications [50; 51; 52]. The organic linkers can be obtained via chemical synthesis, extracted from natural products or collected from biomass or waste-derived materials. For example, [PERSON] et al. applied a bioligand originating from eggshells to produce bio-nanocomposite-derived MOFs via ultrasonic assessed synthesis. The fabricated eggshell-derived framework exhibited a surface area of 1263.9 \(\mathrm{m}^{2}\):g\({}^{-1}\) and showed a high level of efficiency for the adsorption of heavy metals and dyes [53]. Copper-derived metal-organic frameworks have been intensely applied in separation applications with desired levels of efficiency [54; 55; 56]. This work aimed to prepare a copper-based metal-organic framework as an adsorbent for the separation and enrichment of arsenic(III) via microextraction. Imidazole was used as an organic linker to co-ordinate the copper ions and to provide the nitrogen centers in the final structure of the MOFs. The fabricated metal-organic frameworks were examined via SEM, TEM, EDS and XRD. The microextraction process was investigated to enhance the recovery% of the arsenic(III) by optimizing the pH, eluent type, sample volume and co-existing ions. In addition, the developed microextraction process was validated via spiking-based addition/recovery and inter- and intra-day investigations. Furthermore, arsenic(III) was extracted and preconcentrated from real field samples such as water and food collected from Riyadh City, Saudi Arabia.
## Materials and Methods
### Variational Mode Decomposition (VMD)
#### 2.1.4 Determine VMD Parameters  
(1) Determine the number of modes  
VMD needs to determine the number of K modalities before decomposing the signal. Study [29] found that if the K value is too small, multiple components of the signal in one modality may appear at the same time, or a component cannot be estimated. Conversely, the same component appears in multiple mode, and the modal center frequency obtained iteratively will overlap.  
Considering these problems, this paper uses a simple and effective modal fluctuation method to determine the number of K modes. The algorithm flow is as follows [30]:  
* **Step 1**: Estimate the initial value of the modal number k through the spectrum diagram of the signal;
* **Step 2**: The modal number is k, whether the modal center frequencies overlap;
* **Step 3**: If the center frequency overlaps, reduce the number of modalities for VMD decomposition until no center frequency overlap occurs, and output is K;
* **Step 4**: If there is no overlap in the center frequency, increase the number of modalities for VMD decomposition, until the center frequency overlaps and output is K \(-1\).  
(2) Penalty factor  
The introduction of the penalty factor changes the constraint variational problem into a non-binding variational problem. In the operation of the VMD program, only the modal bandwidth and convergence speed (after decomposition) are affected. To avoid modal aliasing and guaranteeing a certain convergence speed, the standard VMD has a strong adaptability with a penalty factor of 2000. In this work, the penalty factor adopts a default value of 2000 during the VMD decomposition [31].
# Study on Actuator Line Modeling of Two NREL 5-MW Wind Turbine Wakes
## 6 Conclusions and Discussions  
The purpose of the present research is to investigate the harmful effect caused by the wake flow, so a single wind turbine and double wind turbines aligned in a line are modeled using ALM. The major results are summarized as follows:  
1. It is reasonable to set the wind speed as 8 m/s. The NREL 5-MW wind turbine could obtain a better working status at this speed, the damage to the blades is minimal, and its wake recovery is fast.
2. It is proved through the velocity deficit that the separation distance of two turbines should not be less than 5\(D\), because of the great influence of the wake. This conclusion is the same as that found by [PERSON] et al. [33]. A more in-depth investigation found that the best separation is nearly 7\(D\).
3. The upstream wind turbine has a great effect on the downstream one. Due to the wake generated by the upstream wind turbine, the output power and the loading of the downstream one are heavily influenced.  
ALM is used in the model, and the result is shown to be accurate enough, but some limitations exist. For example, the elasticity of the blades is not considered adequately and added into the fully coupled system of the floating offshore wind turbine. Further study is required to refine the model and make it more widely acceptable.  
This work is supported by the National Natural Science Foundation of China (Nos. 51739001, 51579056 and 51279041), Foundational Research Funds for the Central Universities (Nos. HEUCDZ1202, HEUCF170104), Defense Pre Research Funds Program (No. 9140A14020712 CB01158), and Open fund of the State Key Laboratory of coastal and offshore engineering of Dalian University of Technology (LP1707) to which the authors are most grateful. [PERSON] also thanks the [PERSON] Visiting Chair Professorship scheme of the Chinese Ministry of Education, hosted by HEU.  
[PERSON] made the computations, conducted data analysis, and wrote this paper; [PERSON] did the proof reading; [PERSON] guided the whole project.  
The authors declare no conflict of interest.
# Pseudo-3D Receiver Deghosting of Seismic Streamer Data Based on \(l_{1}\) Norm Sparse Inversion
## 1 Introduction
It is worth noting that this method of using an \(l_{1}\) norm to regulate objective functions is not limited to the field of seismic exploration, but it can be used in various fields as a potential methodology, such as sensor development and image recognition. The synthetic and field examples indicate that the performance of our framework appears to be successful on a conventional 2D streamer dataset.
## 4 NFV and SFC Frameworks Taxonomy  
In this section, we present the implementation frameworks proposed for NFV/SFC deployments.  
Some of these implementations are set out to solve specific problems in the SFC domain, such as resource allocation and service orchestration, performance tuning, resilience, and fault recovery. In Section 5, we present and discuss the open challenges that are related to the implementation frameworks discussed.  
Figure 8 depicts a summary of the taxonomy of the frameworks presented in our work. Resource allocation and service orchestration frameworks deal with the efficient utilization of available resources, by employing techniques such as synthesizing packet processing graphs and offloading packet processing tasks onto smart NICs, while ensuring that traffic is steered to the right network functions in a service chain, and efficiently managing the life-cycle of network functions.  
The frameworks presented under the performance-tuning category are concerned with improving the overall performance of SFCs by employing techniques such as modular SFC deployments, the use of lightweight packet processing elements, the use of acceleration frameworks for packet processing, and deep learning techniques to improve the overall chain-wide performance.  
The third category in our taxonomy, resilience, and fault recovery, consists of frameworks that handle the problem of fault tolerance in SFC. These frameworks employ techniques such as network function replication and piggybacking of NF state changes across service chains to achieve resilience in SFCs.  
Each of the presented frameworks strive to achieve diverse objectives and are hence very often evaluated against different and incompatible baselines. We have therefore chosen to create a taxonomy of the surveyed frameworks to be able to compare, quantitatively and qualitatively, the different works in their own contexts. Rather than forcing a comparison of potentially disjoint performance characteristics between frameworks that could have led to superficial superiority claims, we have carefully described what each framework has been designed to achieve, what technology and approach has been used, and what performance (or other quality) benchmarks have been performed. Frameworks under each category are discussed next.
# Research on Improving Satellite Positioning Precision Based on Multi-Frequency Navigation Signals
## 4 Results
When the number of downlink frequencies is 12, in the CAPS system, the positioning precision in the east and north directions are 0.57 and 2.18, respectively, which are 52% and 68% improved compared with the single frequency; the positioning precision of the two-dimensional space is improved by 67% compared with the single frequency. In the GNSS system, the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{ \begin{tabular}{c} **The Number of Downlink** \\ **Frequencies** \\ \end{tabular}  } & \multicolumn{3}{c}{**RMS (m)**} & \multicolumn{3}{c}{**IP (\%)**} & \multicolumn{3}{c}{**STD (m)**} \\ \cline{2-11}  & **E** & **N** & **U**  
positioning precision in the east, north, and zenith directions are 0.17, 0.23, 0.43, respectively, which are improved by 72%, 71%, and 71% compared with the single frequency; the positioning precision of the three-dimensional space is improved by 71% compared with the single frequency. The system standard deviation decreases as the number of frequencies increases, and it is seen that the accuracy and stability of the calculation results are improved.