esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid JADE-Sine Cosine Approach for Advanced Metaheuristic Optimization
## 3 Proposed Model
### Detailed Mechanism of JADESCA  
The JADE algorithm is well-regarded for its adaptivity, which adjusts mutation and crossover rates based on feedback from previous generations, thereby tailoring its strategy to different optimization landscapes. However, despite these strengths, JADE often encounters challenges related to convergence speed and a propensity to get trapped in local optima. This limitation is particularly pronounced in complex multi-modal landscapes where global optima are surrounded by numerous local optima.  
In the JADE phase, the algorithm adaptively adjusts mutation factors and crossover probabilities to explore the search space efficiently. The mutation operation is described by Equation (2), where each individual is modified using the difference between randomly selected individuals and the best-found solution, scaled by dynamically adjusted factors:  
\[\mathbf{V}(i,:)=\mathbf{P}(i,:)+F(i)\times(\mathbf{X}_{\text{pbest}}-\mathbf{P }(i,:))+F(i)\times(\mathbf{P}_{1}-\mathbf{P}_{2}). \tag{2}\]  
In this equation, \(F(i)\) is a Cauchy-distributed random variable representing the scaling factor, and \(\mathbf{X}_{\text{pbest}}\), \(\mathbf{P}_{1}\), and \(\mathbf{P}_{2}\) are individuals from the current population or the external archive.  
The crossover operation, as detailed in Equation (3), generates a trial vector by mixing components from the mutant vector and the target vector based on a binomial distribution controlled by a normally distributed crossover rate \(CR(i)\):  
\[\mathbf{U}(i,j)=\begin{cases}\mathbf{V}(i,j)&\text{if }\text{rand}()\leq CR(i) \text{ or }j=\text{jrand}\\ \mathbf{P}(i,j)&\text{otherwise}\end{cases}. \tag{3}\]  
Here, jrand is a randomly chosen index ensuring that \(\mathbf{U}(i,\cdot)\) gets at least one component from \(\mathbf{V}(i,\cdot)\).  
Following the JADE phase, the algorithm transitions to the sine cosine algorithm (SCA) phase. This transition uses the best solutions obtained from JADE as inputs, aiming to refine these solutions by employing the update rule given in Equation (4).  
In the SCA phase, the position of each solution is adjusted using trigonometric functions to ensure diverse exploratory movements towards the best-found position. The update mechanism is governed by Equation (4), which utilizes sine or cosine functions based on a random decision:  
\[\begin{split}\mathbf{X}(i,j)=&\mathbf{X}(i,j)+r_{1} \times\begin{cases}\sin(r_{2}),&\text{if }\text{rand}()<0.5,\\ \cos(r_{2}),&\text{otherwise}\end{cases}\\ &\times|r_{3}\times\text{Destination\_position}(j)-\mathbf{X}(i,j)| \end{split}. \tag{4}\]  
This equation uses parameters \(r_{1}\), \(r_{2}\), and \(r_{3}\) to control the amplitude, phase, and influence of the distance to the destination position, respectively. These parameters are crucial for balancing exploration and exploitation within the search space.
## 5 Conclusions  
In this study, a novel hierarchical classifier has been proposed to facilitate the RFI classification and characterization. The proposed classifier not only classifies the type of the received signal but also determines its modulation precisely. Moreover, three robust approaches have been developed, namely MLP + 10-fold cross-validation, MLP + GA, and MLP + PCA. The results confirm that for the RFI classification phase, the classification accuracy reaches 99.81% using the MLP + GA technique, depending on the chosen batch size. In general, MLP + GA performs more precisely in determining the modulation type and RFI classification. Moreover, the results approve that PCA-based feature selection is more efficient in terms of computation time and computational complexity versus GA. In future studies, we intend to look at deep learning-based classification techniques using raw received data to avoid complicated feature extraction and selection steps.
## 5 Conclusions  
The importance of services related to safety and security, environmental protection, and research at sea is well documented and, consequently, numerous efforts have been made by researchers in the past years to deploy reliable MIoT networks at sea. Many sensor nodes are required for high-spatial-resolution monitoring, their lifetime should be extended for as long as possible, and the cost of operation should be minimized. As such, the predominant use of shorter-range and low-power communication protocols is desired. All data can be aggregated in a central node, called a coordinator, which is composed of three modules--power, processing, and wireless communications--where the latter has a short-range module to receive data from the sensor nodes and a long-range communication method to relay the data to a base station, which can be through satellite communication or GPRS, for example. This way, it is possible to create several independent clusters of sensors, or subnetworks, to ensure a low-cost and high-resolution monitoring.  
From the review, it is possible to conclude that short-range communications can be established using ZigBee, as it is well suited for applications in the ocean due to its mesh-type topology and the robustness that it adds to the network in an environment where the loss of line of sight is expected. Additionally, it requires no subscription, network integration is easy, and the cost is low. In terms of long-range communication, it is possible to conclude that LTE or long-range Wi-Fi are adequate for use when the coordinator that needs to transfer data is near the shore. For truly remote devices, the Iridium network and eventually Starlink appear to be the best available solutions, despite the high costs involved.  
This work is cofunded by the project K2D: Knowledge and Data from the Deep to Space with reference POCI-01-0247-FEDER-045941, cofinanced by the European Regional Development Fund (ERDF), through the Operational Program for Competitiveness and Internationalization (COMPETE2020), and by the Portuguese Foundation for Science and Technology (FCT) under the MIT Portugal Program. This work is also cofinanced by national funds through FCT-Fundacao para a Ciencia e Tecnologia, I.P., under project SONDA (PTDC/EME-SIS/1960/2020). T.M. thanks FCT for grant SFRH/BD/145070/2019.  
Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 3: Representation of a hypothetical wireless communication network for ocean water quality monitoring using both short- and long-range communication protocols.
## 4 The Scaled Boundary Finite Element Method (SBFEM)
### Balanced Hybrid-Polygon Quadtrees  
Early efforts in SBFEM were limited due to the lack of specialized meshes. With the advent of polygon and virtual finite element methods [203; 222; 223], this was partially remedied, allowing for treatment of more involved and practical numerical examples. Specifically the use of the quadtree decomposition [51] has established itself as the predominant mesh choice [205; 224; 225], since it elegantly complements SBFEM's polygon underpinnings. By restricting the differences in cell sizes between neighbors to a ratio of 2:1, i.e., by enforcing balanced quadtrees (Figure 4b), it suffices to precompute only 16 realizations of SBFEM subdomains, while issues commonly associated with hanging nodes are alleviated.  
Features which do not align with the square grid of the quadtree decomposition require special treatment. In the standard FEM, this is achieved by mans of refinement near boundaries, until the lower threshold to a user-specified block size is reached. Generally, this results in step-like boundaries (Figure 4a) and excessively fine meshes. This is mitigated in SBFEM by employing polygon clipping. Consequently, the mesh consists of (a) standard square cells and (b) clipped polygon cells (Figure 4c). So-called hybrid quadtree meshes combine both types of cells, with the benefit of improved approximation of the geometry, at coarser discretisation levels. Standard FEM quadtree decompositions are nonetheless also adopted in SBFEM analyses, mostly in the context of automated image-based stress analysis [226], where the input data (pixel information) is inherently jagged by nature.  
Figure 4: Polygon clipping operating on a balanced quadtree decomposition enables accurate geometry representation with coarser meshes. (**a**) Conventional FEM-based quadtree decomposition; (**b**) Balanced quadtree decomposition; (**c**) Balanced hybrid-polygon quadtree decomposition.  
In conclusion, balanced quadtree meshes are economical to construct, automatically provide a certain degree of adaptivity around changing domain features and permit efficient analysis using the SBFEM by exploiting precomputation.
# Rapid Synthesis of Silver Nanowires in the Polyol Process with Conventional and Microwave Heating
## 3 Results and Discussion
It should also be emphasised that the available literature lacks detailed data characterising the reactivity of multi-hydroxyl alcohols concerning synthesis in the polyol process. In the case of syntheses carried out with 1,2-ethanediol, 1,3-propanediol, 1,3-butanediol and 1,4-butanediol used as reductants, one can observe a moderate effect exerted by the heating procedure applied upon the average product diameter. A comparison of the corresponding results obtained for conventional and dielectric heating shows that, in the latter case, the average diameters of silver nanowires produced are higher or almost equal, and the diameter distribution is shifted towards the fractions with higher diameter values. Except for 1,2-ethanediol, for the set of polyols under analysis, one can notice a slight effect of the heating procedure on the diameter standard deviation. These effects may be attributed to the diversified structures of test systems used for syntheses and the method of energy transfer. Dielectric heating provides uniform heating throughout the entire volume of the reaction medium, which promotes faster growth and a larger diameter of the nanowires produced. In turn, despite stirring, the temperature distribution inside the conventionally heated reactor may not be uniform. Consequently, this diminishes the average growth rate of nanowires and influences their smaller size. In Figure 8, one may see more V-shaped nanostructures sporadically occurring in the final product, formed during the reduction with 1,3-propanediol in the microwave-assisted process. The V-shaped structures consist of at least two nanowires or nanorods of similar diameters. The angle between the respective units is obtuse or almost a straight angle, as seen in Figure 8a-d. It suggests that the growth of observed nanowires occurs by joining the ends of one-dimensional objects through the active planes [111], which are then expanded using silver atoms that are the product of the reduction reaction or the dissolution of fine nanoparticles as a consequence of Ostwald ripening. A relatively small number of so-formed objects indicates that this mechanism is not predominant.
###### Abstract  
The global shipping industry is evolving towards deep integration of digital transformation, intelligent upgrading, and green development. Meanwhile, recent geopolitical shifts have introduced heightened uncertainties into international shipping, compounding the challenges and escalating the demands for weather routing services for ocean-going ships. This paper provides a systematic review and expert perspective on China's current status and key challenges in ocean-going weather routing services. Based on operational insights from China's national meteorological service synthesized with a review of current trends and the literature, it further explores the future development of China's ocean-going weather routing services and technologies from multiple dimensions: enhancing maritime weather observation capabilities, developing advanced weather routing service models, upgrading autonomous and controllable global satellite communication systems, promoting intelligent navigation technologies to facilitate shipping's low-carbon transition, and expanding meteorological support capabilities for Arctic shipping routes. The analysis identifies critical gaps and proposes strategic directions, offering a unique contribution to understanding the trajectory of weather routing services within China's specific national context from the perspective of its primary national service provider.  
ocean-going business vessels; weather routing services; ship speed loss; arctic shipping routes; future prospect
# Online Static Security Assessment of Power Systems Based on Lasso Algorithm
###### Abstract  
As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is proposed in this paper to apply to online static security assessment (OSSA). The assessment is based on a security index, which is applied to select and screen contingencies. Firstly, the multi-step adaptive Lasso (MSA-Lasso) regression algorithm is introduced based on the regression algorithm, whose predictive performance has an advantage. Then, an OSSA module is proposed to evaluate and select contingencies in different load conditions. In addition, the Lasso algorithm is employed to predict the security index of each power system operation state with the consideration of bus voltages and power flows, according to Newton-Raphson load flow (NRLF) analysis in post-contingency states. Finally, the numerical results of applying the proposed approach to the IEEE 14-bus, 118-bus, and 300-bus test systems demonstrate the accuracy and rapidity of OSSA.  
power system security; static security assessment; contingency ranking; contingency screening; machine learning; least absolute shrinkage and selection operator (Lasso); smart grid Article Online Static Security Assessment of Power Systems Based on Lasso Algorithm  
[PERSON], [PERSON], [PERSON], [PERSON], Y.  
problems, especially for online applications. The power system static security assessment can be divided twofold, into system monitoring and contingency analysis [9]. The system monitoring can provide the operating conditions of the power system to the operators, and the updated information contains bus voltages, currents, power flows, and so on. The contingency analysis is employed to evaluate an outage event in the power system. Once the system is in an insecure operation condition, the corresponding security controls such as preventive or corrective control actions will be started up immediately to ensure the insecure outage event back to the security condition [10; 11]. Although two major factors are involved in static security assessment, contingency analysis, which includes contingency screening and ranking, plays a more critical role [9].
## 4 Experiments  
### Datasets and Evaluation Metrics  
We used CARLA32-128 and Ruby32-128, which are described in Section 3.1, to verify the effectiveness of our methods. We randomly split both datasets into training sets and testing sets, with the specific sample numbers shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Names** & **Number of Training Frames** & **Number of Testing Frames** \\ \hline CARLA32-128 & 5162 & 1863 \\ Ruby32-128 & 1456 & 946 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of frames in the training and testing sets for CARLA32-128 and Ruby32-128.  
We chose the PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index measure) loss as the evaluation metrics. The PSNR is extended from MSE (mean square error) and describes the ratio between the maximum possible power of a signal and the power of corrupting noise, which affects the fidelity of its representation. The higher the PSNR, the better the consistency between the generated data and ground truth. It is defined as follows:  
\[\begin{split} PSNR=10\cdot\log_{10}(\frac{MAX_{I}^{2}}{MSE}),\\ MSE=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}[I_{1}(i,j)-I_{2} (i,j)]^{2},\end{split} \tag{8}\]  
where \(MAX_{I}\) represents the maximum pixel value of the whole image and \(I_{1}(i,j)\) and \(I_{2}(i,j)\) are two images of the same shape. \(m\) and \(n\) are the width and height of the projected image. The SSIM loss is the value of the average loss for the testing images. A lower SSIM loss implies a closer resemblance between the model's predictions and the ground truth.
# Virtually Connected in a Multiverse of Madness?--Perceptions of Gaming, Animation, and Metaverse
## 2 Background
### Virtual Reality  
Virtual reality has been one of the concepts and development technology that was launched through the gaming area. The term engineers use is virtual, which means substitute computers and peripherical devices instead of human senses [17]. So, virtual reality can be seen as a technology that can replace a user's primary senses for computer data [18]. It is also considered an electronic simulation of experienced environments [19], allowing users to get different sensory experiences of real things through simulation, but it does not mean a new experience can occur [17]. It can be seen as an artificial reality from the actual world [17].  
Virtual reality relies on computer graphic systems combined with different displays and interface devices that allow immersion through a 3D computer-generated environment [20]. It is considered a new medium which is only possible through the technological advances creating practical applications and new ways of communication [21]. Virtual reality profoundly impacts daily human lives because humans will constantly challenge the limits of existing technology and optimize the combination of resources to push the progress of science and technology forward [22]. Virtual tools provide various means of accessing, viewing, and analyzing data within a focal point to offer spatiality, immersion, and interaction [23].  
To understand virtual reality best, we must understand its key important elements. One key element is the participants because all the virtual reality magic happens in their minds. This experience is not the same for each participant because of their experiences, culture, and history [21]. Then we have the creators, as the second key element, who are the person or team that designs and implements the created work to be experienced [21]. The third key element is the virtual world. It is considered the content of a given medium and can exist without being displayed in a virtual reality system. When we observe that world through the possibility of bringing objects and interactions in a physically immersive, interactive way, we experience it via virtual reality [21]. The fourth key element is immersion, the sensation of being in an environment that can be a mental state or accomplished physically. Physical immersion is considered a characteristic that defines virtual reality [21]. The fifth, and last key element, is interactivity because it allows alternate realities through computers, games, and other systems or devices [21].  
Virtual reality is seen as an advanced human-computer interaction interface that allows the simulation of realistic environments [24]. This interactivity can also be defined as communication media because users can modify a form or content mediated by the environment in real time [19]. This concept can have different forms, such as cab simulation, projected reality, augmented reality, telepresence (the feeling of being physically somewhere other than where the user is [25]), and desktop virtual reality (keyboard, mouse, monitor, headphones) [24].
## 4 Experimental Validation
### Comparison of Pressure Drop in Conformity with Impact Energy under Off-State
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Dissipated Energy**} & \multicolumn{6}{c}{**Pressure Drop**} \\ \cline{2-10}
**Input Current** & \multirow{2}{*}{**Experiment**} & \multicolumn{2}{c}{\(\Delta P-\hat{s}\) **Model**} & \multicolumn{2}{c}{**Relative Error**} & \multicolumn{2}{c}{**RMS Error**} & \multicolumn{2}{c}{**RMS Relative Error**} \\ \cline{3-10}
**(A)** & & **(J)** & **(J)** & **(\%)** & **(kPa)** & **(\%)** & **(\%)** \\ \cline{3-10}  & & **\(\Delta P_{total}\)** & \(\Delta P_{maj}\) & \(\Delta P_{total}\) & \(\Delta P_{maj}\) & \(\Delta P_{total}\) & \(\Delta P_{maj}\) & \(\Delta P_{total}\) & \(\Delta P_{maj}\) \\ \hline
0 & 1339 & 1149 & 621.2 & 14.18 & 53.61 & 189.5 & 735.8 & 11.66 & 31.72 \\
0.5 & 1377 & 1253 & 757.2 & 8.984 & 45.01 & 172.4 & 749.5 & 10.80 & 28.70 \\
1.0 & 1385 & 1253 & 815.7 & 9.564 & 41.10 & 245.6 & 920.5 & 16.36 & 33.66 \\
1.5 & 1388 & 1208 & 822.3 & 12.99 & 40.75 & 381.5 & 1087 & 19.08 & 37.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dissipated energy in MR shock absorber for input current; RMS values of error for model-based and measured pressure data over a stroke velocity range of 0.1 m/s or more.
# A Mixed Reality Interface for a Digital Twin Based Crane
### Crane HMI Solutions  
This work selected an industrial overhead crane platform with its digital twin as a case study. Several crane HMI solutions have been presented in previous research works. A conceptual architecture of a speech-based HMI equipped with AR technology and interactive systems was proposed in [16] for controlling mobile cranes. The use of AR and wearable technologies was also tested for industrial maintenance work in the crane industry to facilitate knowledge sharing among maintenance technicians in their everyday work [17]. An approach integrating 4D building information modeling and AR, proposed in [18], provided users with real-time navigation for tower crane lifting operation with enhanced safety and efficiency. An assistance system for mobile cranes leveraging AR was developed in [19] to provide safety-related information for the crane operator. However, as addressed in the section above, those solutions were limited to one-directional data flow between the crane and the AR interface. In other words, AR was only used to improve data visualization, without involving any interactive component, through which users could directly control the crane.  
The industrial crane platform in our research environment contained various practical components of a digital twin [20], including two HMI solutions, a web-based interface [21] and a HoloLens-based MR app [22]. Both of them enabled users to monitor and control the crane movement. The web app was empowered by the advanced connectivity solution leveraging a GraphQL wrapper, which served as an additional interface between the crane OPC UA server and the client application, thus easing the cross-platform project development and ensuring scalability. However, the web app only emulated a basic control and monitor panel without additional features to enable an immersive or intuitive user experience. On the other hand, the HoloLens app brought the user experience to another immersive level, while its communication solution that relied on the WebSocket to access the crane OPC UA server was obsolete. Furthermore, only if users started the application every time at a specific location could the holograms be placed in a correct manner.  
The limited mobility issue indicates a major challenge in MR, spatial registration and tracking of the MR environment, which ensure the alignment between the physical and virtual world. Registration determines the static offsets between the coordinate systems of markers, sensors and other objects, while tracking enables continuous detection of the object position and orientation in relation to a certain coordinate system in a real-time manner [23]. Spatial Relationship Graphs (SRGs), representing directed cyclic graphs, can be adopted to specify the static and dynamic spatial properties of real or virtual objects and thus depict a high-level infrastructure of the registration and tracking environment [24].