esa-sceva/satcom-chunk-collection huggingface dataset:
# Communication Network Standards for Smart Grid Infrastructures
## 2 Network Standards That Support Communication in Smart Grid Infrastructures
### Electric Storage  
The electric grid operates under the assumption that the energy is produced and consumed at the same time. This means that the transmission and distribution systems are designed to be able to meet the maximum and not the average power flow, resulting in the underutilization of their components. Energy storage can enhance the reliability of the grid, allowing a more efficient use of baseload generation, while facilitating greater penetration of renewable energy sources. Furthermore, it can be implemented on a large, medium and small scale, while it can be distinguished between real electrical storage, (i.e., storage of electricity that can be introduced directly into the system), and storage in an alternative forms in buffers, (e.g., hydrogen, thermal, etc.) [7, 66, 67].  
In electrical power systems, various forms of energy storage can be utilized, with the most common being the use of batteries (for instance lead-acid, lithium-ion, sodium, etc.). Furthermore, in a broader sense, energy storage can be carried out by storing water in hydroelectric power plants or by storing compressed air, or by producing and storing hydrogen, etc. Distributed energy storage can aid towards the efficient handling of load fluctuations, acting as a manageable means of loading and unloading power from the grid, when necessary. Furthermore, distributed energy resources can be connected directly to the distribution network or integrated into HBES/BACS.  
A key prerequisite for the various forms of storage is safety in relation to the specifications of the equipment and the way it is handled. Moreover, in order to be successfully integrated and operate in a smart grid environment, including HBES/BACS [62], the latter should be informed on their electrical potential as well as their pricing policy. In addition, effective maintenance planning and scheduling is considered necessary. Robustness, cyclical self-discharge consistency, start-up time, lifespan, and power efficiency are also critical factors for their efficient operation [52, 68]. In addition, an essential requirement concerns the communication with the energy resources incorporated in the network, while for the various forms of energy storage, appropriate communication protocols and data modeling for information are required, such as energy storage type, charging status, load history, availability, etc.  
The standards that support these requirements are IEC 61850-7-410 and IEEE P2030.2 [67], while there are not any standards for storage devices other than hydroelectric (IEC 61850-7-410) [49]. Moreover, no appropriate specifications have been yet developed regarding the amount and type of data to be exchanged between smart grid and energy storage systems. Finally, specifications for charging storage devices as well as their periodic inspection should also be developed [6, 7].
# Hop-by-Hop Multipath Overlay Routing for Optimizing Network Resource Allocation in WANs
## 3 System Design
### Implementation  
The overlay hop-by-hop multipath routing is achieved by embedding both the next-hop address and the destination address in the packet header. This approach can be implemented using VXLAN [32], and is also compatible with standard IPv6 protocols [33]. For the IPv6 protocol, we can fill the next-hop address into the destination address field, and place the actual destination address in the IPv6 extension header. At each overlay node, when receiving a packet, if the next-hop address matches itself, it will search for the next hop with the destination address, and change the next-hop address with the result. The workflow of processing packets at the overlay nodes is illustrated in Figure 5. In the underlay network, each router performs shortest-path routing with the next hop locator as the destination. Figure 6 illustrates the relationship of packet fields and the overlay routing information during the overlay hop-by-hop multipath transmission. At each overlay node, the Next Hop Locator is updated according to the lookup of the Destination Locator C on the routing table.  
In the process of next-hop selection, since the flow states are not maintained at intermediate overlay nodes, the same data flow is likely to be distributed across multiple paths. This process can increase the bandwidth of a single flow, but the differences in delay between paths can cause out-of-order of packets. For some reliable transport protocols, such out-of-order delivery can significantly degrade performance. The existing solution is to perform path selection based on flowlets or data units. However, in the proposed scheme, each overlay node has the potential to distribute the same flow across multiple next-hop nodes. Using time or length to segment data units can easily lead to the further fragmen  
Figure 5: The workflow of processing packets at overlay nodes.  
Figure 6: Changing packet fields during hop-by-hop transmission.  
tation of data flows at each overlay node, which fails to limit the degree of out-of-order delivery at the receiver. To ensure in-order delivery for a certain number of packets, we propose encapsulating a number of packets. Each intermediate node identifies the segment of the encapsulated packet by its header and selects the next hop for each encapsulated unit. The number of packets to be encapsulated is beyond the scope of this study.
# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 7 Further Probabilistic Forecasting and Applications  
As can be seen in the tables (Tables 6-8) that review studies using stochastic, robust, and chance-constrained programming, smart grids need more probabilistic forecasts [124] which are made for other uncertain parameters rather than loads, renewable generation and prices. EV charging-related behaviors, battery state-of-charge (SOC), dynamic line rating (DLR), and network states are some examples. In this regard, a few papers conducted studies to develop probabilistic forecasting of other uncertain parameters that are important for decision-making in smart grids. In this section, we will review these papers that employ probabilistic forecasting models for uncertain parameters, rather than the introduced popular uncertain parameters.
## 2 An Isolated, High-Power, Photovoltaic Pumping System (Monitored System)
### Photovoltaic Generator  
The PVG (56.95 kW) consists of an array of 335 W Trina Solar 44-cell multi-crystalline photovoltaic panels [20], combined in 10 strings of 17 photovoltaic panels in series (5.695 kW), which power the PS. Another 335 W photovoltaic panel [20] powers the control unit. The PV panels have a maximum power voltage of 37.4 V, a maximum power current of 8.96 A, an open circuit voltage of 45.9 V, a short circuit current of 9.45 A and a module efficiency of 16.9%. Table 11 from Appendix A presents the characteristics and parameters of the PV panels [20].
# Millimeter-Wave High-Gain Dual-Polarized Flat Luneburg Lens Antenna with Reflection Cancellation
## 3 Flat Luneburg Design and Implementation  
### Flat Luneburg Lens  
The implementation of the FLL used a multilayer PCB structure similar to the metamaterial method proposed in [20]. Considering that the desired thickness of the FLL is 5 mm and the operating frequency is over 25-35 GHz, the substrate of Rogers 4003C (\(\epsilon_{\mathrm{r}}\) = 3.38, tan\(\delta\) = 0.0027) with a thickness of 0.305 mm was chosen. The used unit cell in each layer was a square patch on a square substrate, and the size of the unit cell is 1.6 mm \(\times\) 1.6 mm, as shown in Figure 5a. The patch is located at the interface between the substrate and air, and the adjacent substrate layers are separated with a fixed air interval. The function of the air interval is discussed later. It is noteworthy that the used unit cell is different from that in [20], where the patch is sandwiched by two substrate layers without any air interval. By tuning the size of the patch, its effective permittivity varies from 3.38 to 13 with good consistency over the bandwidth of 27-31 GHz, as illustrated in Figure 5a.  
The FLL is composed of 10-layer substrates, and each layer comprises 20 \(\times\) 20 unit-cells, as shown in Figure 5b. The total size of the FLL is 32 mm \(\times\) 32 mm \(\times\) 5.75 mm. A circular area with a diameter of 16 mm represents the effective FLL area, filling the unit cells with the proposed unit-cells of patches, and the rest of the peripheral substrates are drilled with a uniform air hole array of 1.6 mm \(\times\) 1.6 mm pixels to suppress the propagation of surface waves in a millimeter-wave band. The detailed patch size of each unit cell was determined according to the curve shown in Figure 5a with a projection subject to its localized permittivity, which is calculated using Equation (1). Hence, a radial variation can be seen in Figure 5b.  
Figure 4: Simulated three-dimensional radiation patterns: (**a**) conventional LL, (**b**) FLL.  
Figure 5: Implementation of FLL: (**a**) unit-cell and its equivalent permittivity, (**b**) implemented FLL. (\(h\) = 0.305 mm, \(a\) = 1.6 mm).
# Smart Waste Collection System with Low Consumption LoRaWAN Nodes and Route Optimization
## 2 State of the Art  
The use of WSN in urban and suburban areas includes very different applications as [PERSON] et al. described in their literature survey work [11]. Among those applications, there are several works related to the efficient management of waste in cities [12; 13; 14]. [PERSON] et al. [12] proposed a system based on WSN employing the IEEE 802.15.4 technology in their nodes. This system also measures the filling level of the containers with an ultrasonic sensor, but they only focused on the collection of the data and they did not describe the route optimization problem in depth. [PERSON] et al. [13] presented a similar work. However, they employed WiFi as the access network interface. [PERSON] et al. [15]presented another approach where they included weight sensors, although they only used them for an interaction with the user, not for route optimization. [PERSON] et al. (2014) presented an optimization approach for waste collection, but they did not measure the weight of the containers or use it in their optimization system.  
Apart from these works, several solutions are available in the market, such as the ones provided by companies like Enevo (2016), SmartBin (2017) and SenseDumpster (2018) among others. These companies offer devices that are able to measure the filling level of containers, mainly with ultrasonic sensors. However, as they do not measure weight with their devices, they do not consider it for the route optimization systems. There are other systems, like BigBelly (2019), which provides a container with built-in sensors as a product.  
The following sections present (1) several approaches used in the literature for obtaining different types of data, such as volume and weight, (2) the WSN that can be employed for these kinds of smart waste systems and (3) a review of the Capacitated Vehicle Routing Problem (CVRP) used in this work in order to optimize the collection routes.
## 2 Correlation Analysis among FE of Marine Structure  
### Correlation between the Finite Elements  
Based on the finite element theory, marine structures can be discretized into a certain number of finite elements. In the case of one- and two-dimensional structures, the stress changes of finite elements exhibit a degree of similarity or regularity on a global scale when the external loads change. However, for spatial structures, the stress variation of the finite elements presents a complex distribution. Furthermore, the global regularity of the finite elements decreases with the increasing complexity of the structure.  
To describe the stress distribution of complex spatial marine structures, a structure can be divided into several subsets based on correlation analysis, wherein finite elements with similar or regular responses to external loads are sorted into the same subset. Representative finite elements are then selected from these subsets, which are called \"feature elements\".  
Based on ANN, a relationship between the feature elements and the stress values of finite elements that show a significant correlation with them can be established. Consequently, the global stress data of the structure can be obtained by placing stress sensors on the feature elements.
## 3 Proposed Scheme
### System Model  
The spectrum detection system for ISAC services in this paper consists of \(n\) nodes, each of which holds a local dataset of observed signals. The local node is represented as \(C=\{c_{1},c_{2},\dots,c_{n}\}\). Initially, an auto-encoder within each node generates model parameters, which are then uploaded to a central server. This server oversees the FL process, decoding and aggregating these parameters. Let's Let us denote \(w\) as the global model parameter and \(w_{n}\) as the local model weight of client \(n\). The global model of the CNN network is initialized with random weights and distributed to all nodes.  
Each node updates its local model parameters using gradient descent. Specifically, at time step \(t\), the local model weight \(w_{n}^{t}\) of client \(n\) is updated according to:  
\[w_{n}^{t}=w^{t-1}-\eta\
abla F_{n}\left(w^{t-1}\right), \tag{1}\]  
where \(\eta\) represents the learning rate of the client-side model. \(F_{n}(w)\) stands for the local loss function at client \(n\), and \(\
abla F_{n}(w)\) denotes the local gradient at client \(n\). Essentially, each node optimizes its local model parameters based on its own dataset to drive convergence.  
After each round of training, the server receives updated model parameters from each node and computes the difference between the local and global models. Parameter  
Figure 1: FL Framework with auto-encoder.  
aggregation occurs next, with the server aggregating local updates from nodes using a weighted average. Suppose \(C_{t}\) represents the collection of clients selected at round \(t\), and \(|C_{t}|\) is the number of clients selected. The global model \(w^{t}\) at time step \(t\) is updated as follows:  
\[w^{t}=w^{t-1}+\frac{1}{|C_{t}|}\sum_{n\in C_{t}}\alpha_{n}w_{n}^{t}-w^{t-1}, \tag{2}\]  
where \(\alpha_{n}\) signifies the weight of client \(n\). Subsequently, the latest global model parameters are disseminated to each node to initiate the next round of model training. This iterative process continues until the spectrum-aware model converges or achieves a predefined accuracy threshold.
## 4 An Integrated Suite of Geo-Visualisation Tools
### Visualisation Using SIEVE  
Visualisation has become an integral part in many GIS and their applications. General purpose GIS were originally designed for the classical 2D cartographic representation of points, lines and polygons and raster data. Hence GISs often do not reflect the current state of the art visualisation capabilities. Moreover, within existing GIS it is difficult or even impossible to integrate cutting-edge visualisation technology [12]. In the last few years scientists have started using game engines in their research and development [13]. There are many games which contain highly integrated packages called engines, each with a specific functionality. A terrain engine creates and renders the ground, while a physics engine handles the motion behaviour of characters and objects, such as realistic simulation of gravitational forces and kinetics [14].  
The research presented in this paper includes a modified version of a game engine called SIEVE and a real time bidirectional coupling solution with GIS called \"live link\" [15] by using the Landscape Constructor interface. SIEVE is efficient in rendering micro scale 3D virtual environments; however, it has limitations in rendering macro scale 3D virtual environments. Moreover, SIEVE currently cannot show temporal changes, and also, in terms of total number of objects to be rendered in a 3D virtual environment-it can render up to twenty thousand 3D objects or a much larger number of billboards, in one scenario.
# A Regionalization Approach Based on the Comparison of Different Clustering Techniques
## 4 Discussion
### Limitations of the Algorithms Used  
Throughout this work, we observed that the regions obtained with K-means++ exhibited spatial coherence regardless of whether spatial contiguity was included. Applying PDC, we noted limitations in assigning probabilities to each cell. The probability matrix assigns, in most cases, different regions to neighboring cells, especially in the northern part of the Iberian Peninsula and the entire French region. It was necessary to reduce the number of regions by merging those with similar probabilities to improve the practical application of the results [28]. Using Infomap to build communities from a distance matrix with edges, we often encounter a situation where nodes with a high degree of connectivity (hubs) dominate the information flow. This concentration can lead to bias in clustering when we set the number of regions to five. However, by reducing the number of regions to three, the algorithm is compelled to form larger communities that consider both the highly connected nodes and the more isolated ones. The DBSCAN algorithm has serious limitations when dealing with multidimensional data, such as climatic and/or edapidata. Despite the unsatisfactory results, we found that incorporating spatial contiguity as principal coordinates provides some context to the results. We achieved coherent region ordination when including principal coordinates as spatial contiguity; this demonstrates how incorporating spatial autocorrelation through orthogonal vectors could improve the results.
# Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids  
[PERSON]  
1 School of Computer Science and Technology, Anhui University, Hefei 230601, China; [EMAIL_ADDRESS]  
2 Engineering Research Center of Power Quality, Ministry of Education, Anhui University, Hefei 230601, China; [EMAIL_ADDRESS] (G.L.); [EMAIL_ADDRESS] (Q.W.)  
3 Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne 3122, Australia; [EMAIL_ADDRESS]  
4 State Grid Anhui Electric Power Co. Ltd. Research Institute, Hefei 230601, China; [EMAIL_ADDRESS]  
5 Correspondence: [EMAIL_ADDRESS]; Tel: +86-158-5511-5115  
companies to minimize the total operation cost of the system including distribution networks and MGs through coordinated operation [12]. However, the above optimization methods are all aimed at a situation in which there is no direct conflict of interest between MGs and upper manager. And these methods are not very suitable for competitive hierarchical power market structures [13].  
To stimulate the energy transaction potential of MGs, a multi-market participation framework is proposed for distribution network operators (DNOs) [14]. Since market participants belong to different stakeholders, DNOs and MGs can trade energy not only through cooperation, but also through competition. The relationship among participants is suitable to be solved by game theory. In [15], an incentive mechanism based on cooperative game theory is proposed to reduce the peak ramp of distribution networks and improve the benefits of MGs. In [16], a two-level day-ahead scheduling structure based on Stackelberg game is proposed to stimulate MGs participating in power sale bidding, which could reduce the total cost of the DNO. A bi-level programming based on Stackelberg is adopted by the DNO to improve the benefits of MGs while the cost of the DNO reaches the minimum [17]. However, these methods are based on day-head energy market, and there are still some shortcomings in dealing with the uncertainties of renewable energy generation and load demand.  
In this paper, a two-level optimization method is proposed for hour-ahead MMG energy scheduling in distribution network energy markets, where the EMO is the upper manager of the whole MMG system, and the MGO is lower manager of the local MG. This method can be implemented in two stages. According to the short-term forecast information, the local MGO optimizes energy scheduling of storage units by adopting rolling optimization in the first stage. The energy transactions between the EMO and MGs are optimized in the second stage, which is modeled as a Stackelberg game. The EMO is the leader of the game who determines the prices of the next hour to maximize its own utility, while the MGs are followers who respond to prices by adjusting local load demands. Through two stages of optimization, the economic utilities of the EMO and MGOs are both improved. The other significant feature of this proposed method is that energy storage and load demands are dispatched hourly, which makes it more reasonable to manage energy trading in MMGs under the uncertainties of PV output and load demands.
###### Abstract  
Eugenol (EU) and isoeugenol (IZO-EU) are naturally occurring compounds known for their strong antibacterial properties, which makes them ideal candidates for application as preservatives in cosmetic products. Primarily derived from clove oil, EU demonstrates potent antibacterial effects against a wide range of microorganisms, including Gram-positive and Gram-negative bacteria, as well as yeast-like fungi such as _Candidia albicans_. Their antibacterial action is mainly connected with their ability to disrupt microbial cell membranes and inhibit key enzymatic processes. IZO-EU, a structural isomer of EU, also shows significant activity against various pathogens. In these studies, the effectiveness of EU and IZO-EU as preservatives in cosmetic formulations was tested. The antibacterial activity tests, using the disk diffusion method, assessed their effectiveness against _Enterococcus faecalis_, _Staphylococcus aureus_ (Gram-positive), _Escherichia coli_, _Pseudomonas aeruginosa_ (Gram-negative), and _Candidia albicans_. Creams containing the different concentrations of EU and IZO-EU (0.5%, 1.5%, 2.5%) were tested for microbiological purity, texture, consistency, and stability over a defined storage period and under controlled conditions (temperature, humidity, and light exposure). Microbiological purity was assessed through the standard culture methods, while the texture and consistency were evaluated using rheological measurements and sensory analysis. The obtained results demonstrated that both compounds effectively preserved the creams, maintaining the microbiological purity without significantly altering the texture, consistency, or stability throughout the storage period. Additionally, EU and IZO-EU not only enhanced the antibacterial protection of the formulations but also contributed to the pleasant fragrance. Furthermore, preliminary studies were conducted on their anticancer properties using the LoVo (colon cancer) and U87 MG (g glioma) cell lines. These studies revealed the hormetic effects at the low concentrations and cytotoxicity at the higher doses, suggesting that EU and IZO-EU may have therapeutic potential beyond cosmetic applications. These studies support the application of EU and IZO-EU as natural, multifunctional ingredients in cosmetics, offering both preservative and sensory benefits while meeting the growing consumer demand for natural and sustainable solutions in the beauty industry.  
**Keywords:**_Staphylococcus epidermidis; Candida albicans; terpenenes; Gram-negative bacteria_
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Optimal Decision
#### 3.3.5 Ann  
The artificial neural network (ANN) is an alternative ML algorithm for classification. The input of the ANN-based classifier is not limited to the in-phase (I) and quadrature (Q) components of the present point, whereas the I/Q components of the adjacent points in the time domain can also serve as features. The output of the ANN-based classifier is the estimated label. In this context, an ANN-based classifier serves as the nonlinear mapping process. In [70], ANN is used for the 8-color-shift keying (8-CSK) decision in an RGB-LED VLC system, and other ML algorithms are investigated for comparison.