esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Access Selection Algorithm for Large-Scale Satellite Networks Based on Dynamic Domain
## 3 DAA Algorithm Design
### Access Switching Mechanism
#### 3.2.1 Insufficient Satellite Resources
ES cannot obtain the status of satellite resources other than \(\tilde{\mathbf{v}}\) through the ACKs and cannot select backup access satellite \(\tilde{\mathbf{v}}^{\prime}\) in set \(\mathbf{V}^{\prime}\). To solve this problem, we perform the following optimization of the DAA algorithm. If \(S_{n,m}^{L}\) is the next hop of \(\tilde{\mathbf{v}}\) but \(S_{n,m}^{L}\
otin\mathbf{V}^{\prime\prime}\), \(S_{n,m}^{L}\) adds IP to the ACKs before forwarding the ACKs. After receiving the ACKs, the ES decodes the IP address of the next hop of \(\tilde{\mathbf{v}}\) and uses this IP address as the virtual destination address. When \(\tilde{\mathbf{v}}\) resources are insufficient, the ES selects \(\tilde{\mathbf{v}}^{\prime}\) according to Figure 5, then adds the virtual destination address to the data packet to send to \(\tilde{\mathbf{v}}^{\prime}\). \(\tilde{\mathbf{v}}^{\prime}\) computes the route to the virtual destination address. The satellite corresponding to the virtual destination address receives the packet forwarded by \(\tilde{\mathbf{v}}^{\prime}\) and forwards the packet according to the previous path. As the distance from \(\tilde{\mathbf{v}}^{\prime}\) to the virtual destination address is smaller than that to the actual destination address, the selection and use of virtual destination addresses can reduce the resource cost of routing after switching to a backup access satellite. Figure 7: The relationship between packet transmission path and communication domain Equation (2).
# Efficient Data Delivery Scheme for Large-Scale Microservices in Distributed Cloud Environment
###### Abstract  
The edge computing paradigm has emerged as a new scope within the domain of the Internet of Things (IoT) by bringing cloud services to the network edge in order to construct distributed architectures. To efficiently deploy latency-sensitive and bandwidth-hungry IoT application services, edge computing paradigms make use of devices on the network periphery that are distributed and resource-constrained. On the other hand, microservice architectures are becoming increasingly popular for developing IoT applications owing to their maintainability and scalability advantages. Providing an efficient communication medium for large-scale microservice-based IoT applications constructed from small and independent services to cooperate to deliver value-added services remains a challenge. This paper introduces an event-driven communication medium that takes advantage of Edge-Cloud publish/subscribe brokers for microservice-based IoT applications at scale. Using the interaction model, the involved microservices can collaborate and exchange data through triggered events flexibly and efficiently without changing their underlying business logic. In the proposed model, edge brokers are grouped according to their similarities in event channels and the proximity of their geolocations, reducing the data delivery latency. Moreover, in the proposed system a technique is designed to construct a broker-based utility matrix with constraints in order to strike a balance between delay, relay traffic, and scalability while arranging brokers into proper clusters for efficient data delivery. Rigorous simulation results prove that the proposed publish/subscribe model can provide an efficient interaction medium for microservice-based IoT applications to collaborate and exchange data with low latency, modest relay traffic, and high scalability at scale.  
microservices; distributed publish/subscribe broker; implicit collaborative filtering; geolocation awareness; microservices-based IoT applications
## 3 Therapeutic Potential of Exosomes in Different Diseases
### Therapeutic Potential of Exosomes in Soft Tissue  
Adipose-derived stem cell released exosomes (ADSC-Exos) have a wide range of potential therapeutic potentials and have applications in regenerative engineering, tumor disorders, and skin repairs. Previous studies have reported the applications of naturally derived exosomes in diverse disease models with modifications [54; 99]. However, recent researches have brought to light the contents and characteristics of exosomes with adjustments achieved with the use of other substances. The ADSC-Exos have important functions in biological and pathological pathways [100]. However, their applications in clinics are yet to be identified and research is currently ongoing, including the therapeutic doses used. Nevertheless, it was found that the ideal dose was 50 \(\mu\)g/mL when using exosomes for wound healing, but in a separate experiment, ADSC-Exos were used at a dose of 40 \(\mu\)g/mL and effectively enabled adipogenic differentiation [101]. It was observed that several intra-articular injections of human MSC-derived exosome have been shown to promote cartilage repair and regeneration [32]. After 12 weeks of treatment, accelerated tissue filling and increased collagen synthesis were observed in osteoarthritis rat lesions leading to the restoration of cartilage and bone [32].  
Regeneration and cutaneous healing are complex mechanisms and need a well-orchestrated integration of many molecular and biological processes, including differentiation, proliferation, cell migration, extracellular matrix deposition, and apoptosis [102]. ADSC-Exos have been found to solve problems mainly resulting from excessive scar formation and delayed cutaneous healing [103]. The internalization of ADSC-Exos by fibroblasts leads to proliferation and migration of collagen type I and III increasing via the PI3K/Akt signaling process. Furthermore, ADSC-Exos could induce migration, proliferation as well as suppressing HaCaT cell apoptosis through the signaling of Wnt/\(\beta\)-catenin [104]. All findings explained the promising potentials of ADSC-Exos in promoting cutaneous wound healing [105]. In ADSC-Exo treated mice, scar formation and clear zone of re-epithelialization were promoted, and the scar areas became narrower in the wounds. Furthermore, ADSC-Exosome could enhance the reconstruction of the extracellular matrix in the regeneration of cutaneous wound by modulating collagen type I: type III proportions, and also transforming growth factor-beta 3 (TGF-\(\beta\)3): TGF-\(\beta\)1 and MMP3: metalloproteinases 1 (TIMP1) tissue inhibitor, and inhibiting the fibroblasts differentiation into myofiblasts decreasing scar formation [106]. Furthermore, it was also observed that the use of ADSC-Exos leads to the attenuation of the necrotic flap, neovascularization activation, and alleviation of inflammatory reaction after I/R disorder skin flap apoptosis [103]. In summary, it highlights the potentials of ADSC-Exo use in promoting cutaneous regeneration and complete healing. However, the molecular mechanism will need to be evaluated for clinical applications [36].
## 2 Materials and Methods
### Evaluation Metrics and Loss  
As it is customary in semantic segmentation problems, \(DSC\) was employed as the main evaluation metric, which guided most design decisions. \(DSC\) is defined in Equation (2), where N denotes the total number of voxels in an image, \(\hat{y}_{i}\in[0,1]\) represents the prediction of the CNN at voxel \(i\), \(y_{i}\in\{0,1\}\) is the GT label at voxel \(i\), and \(\epsilon=1\) is a small arbitrary value that prevents division by zero.  
\[DSC(y,\hat{y})=\frac{2\cdot\sum_{i}^{N}\hat{y}_{i}\cdot y_{i}+\epsilon}{\sum_{ i}^{N}\hat{y}_{i}+\sum_{i}^{N}y_{i}+\epsilon} \tag{2}\]  
As a loss function, \(DSC\) is much better able to deal with unbalanced segmentation masks in comparison with binary cross-entropy. However, several studies acknowledge its deficiencies along the boundaries of the mask [23], or when the target is very small [32] (as in lesion segmentation). Ref. [23], for instance, utilizes a composite loss which penalizes wrong segmentations proportionally to the distance to the boundary of the GT. Despite multiple attempts at incorporating a similar loss to our model, we finally decided against it, since it did not provide any performance advantages during validation. Therefore, the finally used loss function \(\mathcal{L}\) is directly derived from \(DSC\), as illustrated in Equation (3).  
\[\mathcal{L}=1-DSC \tag{3}\]  
In addition to \(DSC\), two distance-based metrics were also employed: Average Boundary Distance (ABD) and 95 th percentile Hausdorff Distance (HD95). These metrics were computed as described in the Promise12 challenge [21] and represent the average and the 95 th percentile largest distance (in mm) between the surface of the predicted mask and the GT mask, respectively.  
When comparing these metrics among groups, the Wilcoxon signed-rank test was employed, which is the non-parametric equivalent of the paired t-test. The Wilcoxon test was needed due to the distribution of the metrics in the test set not being normal (\(p\)-value \(\leq\) 0.001 using [PERSON]'s normality test for \(DSC\), ABD and HD95 results).
## 2 High-Field-Gradient Magnet Structures
### Structural Design of the Gradient Magnet  
The gradient magnet comprises four coaxial but non-concentric coils, as illustrated in Figure 3. Coils 1-4 are wound around their respective mandrels, with each mandrel assembled as a unit through the articulating mandrel and articulating flange. Coils 1 and 2 are fabricated from HF Nb\({}_{3}\)Sn/ITER Nb\({}_{3}\)Sn, while coils 3 and 4 are composed of ITER PF NbTi. The sub-coils were wound on a 2 mm thick stainless steel (SS-316L) mandrel using a winding tension of 20 N. At 4.2K, the critical current of HF Nb\({}_{3}\)Sn short wire samples exceeds 622 A at 12 T. For ITER Nb\({}_{3}\)Sn, the critical current is 240 A at 12 T, and for ITER PF NbTi, the critical current is 190 A at 7 T. The wire parameters are detailed in Table 5.  
Zero liquid helium evaporation low-temperature system  
Superconducting magnet electromagnetic device  
Vacuum vesselNS2 coilNS1 coilMagnet flanges  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Coil Number** & **Coil 1** & **Coil 2** & **Coil 3** & **Coil 4** \\ \hline Superconducting wire categories & HF Nb\({}_{3}\)Sn & ITER Nb\({}_{3}\)Sn & ITER PF NbTi & ITER PF NbTi \\ Wire diameter/mm & 0.98 & 0.97 & 0.777 & 0.777 \\ Inside diameter/mm & 125.6 & 152.46 & 218.24 & 125.6 \\ Outer diameter/mm & 142.86 & 186.64 & 234.70 & 138.73 \\ Bottom coordinate/mm & \(-\)99.64 & \(-\)143.02 & \(-\)142.70 & 189.64 \\ Top coordinate/mm & 99.64 & 99.64 & 99.64 & 316.35 \\ Layers & 20 & 40 & 24 & 19 \\ Number of turns in odd-numbered layers & 200 & 246 & 306 & 160 \\ Peak field/T & 13.04 & 9.972 & 6.139 & 5.680 \\ \hline \end{tabular}
\end{table}
Table 4: Magnetic coil parameters.  
Figure 3: Structural schematic of gradient magnet.  
Figure 2: The optimization iteration of electromagnetic field design.  
Additionally, the quench-protection system is mounted on the top surface of the upper flange. This is because the diodes in the quench-protection system cannot withstand excessively strong magnetic fields. At the same time, in order to facilitate line connection, the quench-protection system should be as close as possible to the coil. In coils 1-4, the pre-tensioned layer is located outside the winding. The pre-tensioned layer material for Nb\({}_{3}\)Sn coil is \(\upless\)0.78 mm SS-316L stainless steel wire (Nanjing Futeng Electromechanical Equipment Co., Ltd., Nanjing, China), while for NbTi coil, it is a cross-section of 1.6 mm \(\times\) 0.8 mm 6061 aluminum alloy tape (Wuxi Tongli Electrician Co., Ltd., Wuxi, China). Insulating layers are placed between the coil and the mandrel, as well as between the coil and the pre-tensioned layer, to preserve coil insulation.
# Water and Wastewater Management in Agriculture
###### Abstract  
The growing world population requires highly efficient food production methods. Modern agricultural pracises such as soilless cultivation, livestock farming, and aquaculture rely heavily on environmental resources such as water and nutrients and therefore have an increasing impact on the natural environment, resulting in problems such as eutrophication, soil fertility decline, and soil contamination [1; 2; 3]. However, the negative impacts of these pracises can be mitigated through the use of modern crop production technologies; sustainable water and wastewater management; and the implementation of innovative approaches relative to wastewater treatment, reuse, and nutrient recovery [4; 5; 6]. The solutions most useful in rural conditions include nature-based solutions (NBSs), which are described as natural processes that can help improve wastewater and water management. There is a growing interest in the urban applications of NBS [7; 8; 9], especially constructed wetlands (CWs) for the treatment of domestic and agricultural wastewater [10; 11; 12]. This alternative water source is increasingly recognised as a vital source of irrigation as agriculture is the sector most affected by water scarcity, especially in semi-arid regions [13]. However, its utilisation must be accompanied by the strict monitoring of the natural environment in order to protect it and promote sustainable development. The overarching goal of these endeavours is sustainable agriculture [14].  
The \"Farm to Fork\" of the European Green Deal is a new strategy based on the assumption that agriculture is the most influential economic sector for the environment and climate [15]. At the same time, agriculture must remain a sector that produces healthy food with high nutritional quality and the lowest possible impact on the natural environment. We will not be able to implement the \"Farm to Fork\" strategy of the European Green Deal without finding new sources of water and fertilizers. Examples of such activities are solutions for the treatment of wastewater from soilless plant cultivation and the recovery of water and fertilizer components from treated wastewater [16].  
Many countries have already introduced solutions to reduce water consumption [17]. At the same time, system solutions are being sought for the reuse of treated wastewater. The EU has focused on the reuse of wastewater in agriculture [18]. Currently, over 1.2 billion m\({}^{3}\) of wastewater-derived water is used in European agriculture, which corresponds to around 2.5% of treated municipal wastewater. By 2025, this figure will be over 6.5 billion m\({}^{3}\)[19].  
This Special Issue presents the latest developments in water and wastewater management in agriculture and the environmental technology of aquaculture. Topics examined include innovative technological approaches to water and wastewater treatment in agriculture and aquaculture, strategies for treating nutrient-laden runoff from agricultural land, examples of effective water and wastewater management pracises, and the impact of agriculture on surface water quality.
# Stability Analysis in Multi-VSC (Voltage Source Converter) Systems of Wind Turbines
###### Abstract  
In this paper, a holistic nonlinear state-space model of a system with multiple converters is developed, where the converters correspond to the wind turbines in a wind farm and are equipped with grid-following control. A novel generalized methodology is developed, based on the number of the system's converters, to compute the equilibrium points around which the model is linearized. This is a more solid approach compared with selecting operating points for linearizing the model or utilizing EMT simulation tools to estimate the system's steady state. The dynamics of both the inner and outer control loops of the power converters are included, as well as the dynamics of the electrical elements of the system and the digital time delay, in order to study the dynamic issues in both high- and low-frequency ranges. The system's stability is assessed through an eigenvalue-based stability analysis. A participation factor analysis is also used to give an insight into the interactions caused by the control topology of the converters. Time domain simulations and the corresponding frequency analysis are performed in order to validate the model for all the control interactions under study.  
wind farm; wind turbine; voltage source converter; nonlinear system; equilibrium points; state-space model; eigenvalue analysis; participation factors +
Footnote †: journal: Applied  
1
Footnote 1: email: {[[https://doi.org/10.3390/app14083519](https://doi.org/10.3390/app14083519)]([https://doi.org/10.3390/app14083519](https://doi.org/10.3390/app14083519))}
# Interference-Aware Radio Resource Management for Cognitive High-Throughput Satellite Systems
## 2 Architecture for CR-Based Multi-Gateway Multi-Beam HTS Systems
### Multi-Gateway  
As a matter of fact, the feeder link is one of the system's bottlenecks in the case of high-throughput multi-beam satellite systems, since it carries the entire aggregated traffic of all users between the satellite and the ground core network. Recently, some techniques have been proposed in order to optimize the feeder link spectrum resource. One solution is to move feeder links into the Q/V bands (40/50 GHz), but this makes the system more vulnerable to rain attenuation. In this regard, multiple gateways that exploit the spatial diversity and reuse the feeder link resource to distribute the communication capacity can be the preferred solution [31; 32]. The advantages of employing multi-gateways can be summarized as follows: (1) Having multiple gateways means that each gateway only handles a smaller number of beams, which implies a lower signal processing complexity, and (2) in case one of the gateways fails, the traffic can be rerouted through other gateways to avoid service outages.
## 3 Results  
### Network Structure  
We analyzed the information provided by each of the three weighted networks, by inspecting both their degree and strength probability distributions. Figures 1 and 2 show the degree probability distribution \(p(k)\) and the strength probability distribution \(p(s)\) for the networks, respectively.  
and strength distributions of all the networks are heavy-tailed distributions varying over several orders of magnitude. Other works report similar findings for transportation networks. For instance, the airline traffic among different urban areas in the world shows \(p(k)=k^{-\alpha}f(k/k_{x})\), where \(\alpha=2\) and \(f(k/k_{x})\) is an exponential cutoff function which takes into account the physical constraints on the maximum number of connections that a single airport can handle [18; 25]. The authors in [15] used the complex network theory to to assess the fitness of the current physical transportation network against the conceptual road network by using the origin-destination movement matrix showing a power law relation between the frequency of a degree and the degree of a node in the OD network. In general, the scale-free properties of a wide range of degree and strength values clearly indicate the heterogeneous behavior of both topology and dynamic flows on the infrastructure transportation networks. Indeed, in such networks a small number of hubs with many strong connections coexist with a large number of nodes with few weaker connections [26]. As in [18] we investigated the dependence of \(s_{i}\) on \(k_{i}\), finding for all the networks a correlation law:  
\[s(k)=\delta k^{\alpha} \tag{8}\]  
In Figure 3 the strength \(s(k)\) is reported as a function of the degree \(k\) of the nodes for the three networks \"study\", \"work\" and \"occasional\". For the \"study\" network, we found \(\alpha=1\) meaning a direct correlation between the strength and the degree of the nodes. The network \"occasional\" shows \(\alpha=1.24\) and for the \"work\" network, we found \(\alpha=1.57\). For the latter two cases an exponent \(\alpha>1\) implies that the strength of vertices grows faster than their degree.
## 2 ANM Architecture
### Evolutionary Learning at the IP Neuron Level  
In the present implementation, there are 256 IP neurons, which are divided into eight subnets, each comprising 32 IP neurons. The input/output interface of these subnets (to be described in Section 3) is the same so that each subnet will receive the same input signals and generate the same output behavior if the firing pattern of the IP neurons is also the same. The algorithm of the evolutionary learning at the IP neuron level is shown in Figure 3. Evolutionary changes at each level (parameter) are summarized as follows:  
1. At the _readout_ enzyme pattern, a change at this level may alter the pattern of inputs that determines the initiation of cytoskeletal signals inside an IP neuron.
2. At the Ci-type pattern (increasing or decreasing the number of its constituent molecules), a change at this level may alter the signal configuration of an IP neuron.
3. At the _MAP_ pattern, a change at this level may alter the influence of different types of cytoskeletal signals, which in turn may modify the signal configuration of an IP neuron.
4. At the _readout_ enzyme pattern: a change at this level may alter the output of an IP neuron.  
At present, we have configured the operation mode of the system to allow only one level of evolutionary learning at a certain time, and all other levels of learning are turned off. After learning for a fixed period of time, the system will turn off this level of learning and turn on other levels of learning. In this way, the system takes turns opening each level of learning.  
Figure 2: Conceptual architecture of an information processing (IP) neuron. Each grid location, referred to as a site, has at most one of three types of components: \(C_{1}\), \(C_{2}\), or \(C_{3}\). Some sites may not have any component at all. A _readin_ enzyme converts an external signal into a cytoskeletal signal. Specific combinations of cytoskeletal signals will activate a _readout_ enzyme, which in turn causes the neuron to fire. The neighbors of an edge site are determined in a wrap-around fashion. An _MAP_ links two neighboring components of different types together.  
1. **Generate** at random the initial _MAP_, _component-type_, _readin enzyme_, and _readout enzyme_ patterns of each neuron of each subnet.
2. **Evaluate** the performance of each competing subnet and **select** the three best-performing subnets.
3. **Copy** the _MAP_, _component-type_, _readin enzyme_, and _readout enzyme_ patterns of each neuron from any one of the three best-performing subnets to a lesser-performing subnet.
4. **Vary** the _MAP_, _component-type_, _readin enzyme_, and _readout enzyme_ patterns of each neuron in the lesser-performing subnets.
5. **Go to Step 2** unless the stopping criteria are satisfied.