esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Geospatial Data for ML/CV Applications:
## 3 Methods  
This section describes the proposed approach for efficient geospatial data acquisition and processing. It relies solely on high-resolution, verified geospatial datasets from Geoportal and LP-Portal. Geoportal provides orthoimagery with detailed and up-to-date spatial information, while LP-Portal offers high-quality road network data.  
The methodology involves several interconnected steps designed to optimize geospatial data for Machine Learning and Computer Vision applications. The process starts with the automated extraction of road data from the LP Portal. Next, parcel data are retrieved from ULDK for each section of the road. Subsequently, the parcel data are enhanced with high-resolution orthoimagery from Geoportal. Then, the Shapely library is used to segment road geometries, isolating areas of interest, particularly road regions, while eliminating irrelevant surroundings. This segmentation not only reduces the dataset size but also speeds up processing efficiency. The segmented images undergo further processing to align with parcel boundaries and are divided into smaller, computationally manageable tiles. These optimized and well-structured datasets retain only essential features, ensuring logical consistency and suitability for downstream ML and CV tasks, such as object detection and spatial analysis. The data processing workflow is visualized in Figure 3.  
During the research, several challenges were identified. Firstly, the variability in data quality from public sources posed a potential risk of introducing inconsistencies in the results, especially when working with datasets of varying resolutions and accuracies. Secondly, the reliance on external APIs, such as those provided by ULDK and Geoportal, created a dependency that could be affected by future updates, structural changes, or alterations to access policies. This could potentially impact the stability and applicability of the data pipeline in future work. Lastly, relying solely on publicly available data sources carries the inherent risk of encountering rate limits or similar access restrictions. However, it is important to note that no such limitations or access issues were encountered during the data retrieval process in this study.
## 2 Materials and Methods  
### System Design  
The classification method uses simple agents (WoC-Bots) without expert knowledge. At the core of each agent is a simple multi-layer perceptron (MLP) classifier with 1 to 4 hidden layers--depending on input size--generally rounded to the nearest integer value of \(input\_size*0.3\). The goal is for the MLP to be as shallow as possible while still extracting features from the input. For the datasets we have tested, using the formula above has proven successful given each agent only receives a small subset of the available input. The rest of the agent is designed as a modular system for efficiently modifying agent behavior. Each agent's MLP classifier is trained with a different, small, subset of features. This initially gives the group of agents a diverse set of knowledge. All agents with relevant knowledge for the classification task then interact with one another, share knowledge, determine the trust they have in other agents and the confidence in their own opinion, and change their opinion given enough evidence.  
As with work described in [8; 14], we use the DeepLearning4j (DL4) [15] implementations of the Adam updater [16], softmax activation function [17], and standard stochastic gradient descent for the MLP implementation and deep neural network. DL4j is \"anopen-source, distributed deep-learning project in Java and Scala spearheaded by the people at Skymind, a San Francisco-based business intelligence and enterprise software firm\". All code was written in Kotlin (versions 1.3.20-1.3.41), running on the Java Virtual Machine (version 11). All features, agent history data, and trained agents were stored in SQLite3 databases. Intellij IDE was used for our development environment.  
Prior to the interaction and opinion aggregation steps, each agent receives 1 to 4 highly correlated features in common between all agents. Feature correlation was determined using principal component analysis, run externally to the system being described here. Other available features were distributed across multiple agents randomly, with duplicate features and multiple agents with the same feature set neither limited nor encouraged. Agents that show poor MLP classification performance during the initial training step are not included in the remainder of the classification steps. Poor performance was defined as less than 50% accuracy on the test set.
## 3 Artificial Intelligence as Enabler for Adoption of Nuclear Maritime Ships
### On-Ship Nuclear Integrated Energy Systems Management  
Efficient onboard energy management is a key factor in extending the utilization of nuclear fuel. Compared to conventional large-scale reactors, small modular reactors (SMRs) offer greater operational flexibility [59]. Since SMRs will serve as the primary energy source for nuclear-powered vessels, they present opportunities for enhanced energy management on board.  
The core idea is to treat the ship as a nuclear integrated energy system (NIES), where the SMR interacts with other energy sources, storage devices, and industrial processes such as desalination [60; 61]. The effective management of such an integrated system is essential to optimizing energy generation and consumption [62], ultimately ensuring prolonged nuclear fuel burnout [63].  
Given the complexity of integrated energy systems arising from the interplay between various energy sources and consumption entities, artificial intelligence (AI) can provide effective solutions for optimizing energy management [63]. In such a system, the primary objective is to maximize the utilization of nuclear fuel and extend its burnout period.  
The use of AI in managing integrated energy systems is not a new concept. Studies have shown that AI can overcome operational challenges arising from the integration of diverse system components [64]. This challenge becomes even more significant when considering that the propulsion system must also be part of the integrated energy system--an aspect not typically accounted for in a conventional NIES (as shown in Figure 2).  
Advanced AI solutions can operate autonomously, efficiently optimizing energy distribution to meet the ship's objectives. Notably, the integration of renewable energy sources with flexible SMRs has proven to enhance efficiency [65; 66], particularly for large commercial maritime vessels that travel long distances across oceans and are exposed to varying environmental conditions, such as extended periods of sunlight or strong winds [67]. Additionally, long-haul voyages subject ships to diverse travel conditions, leading to fluctuating energy demands and shifting onboard priorities. In such cases, AI can dynamically adjust the energy system's operation in real time to align with the vessel's needs while maintaining overall efficiency. Notably, improving onboard energy efficiency can significantly reduce operational costs. These savings can be further enhanced by implementing industrial processes such as water desalination [68], which minimizes the need for port stops to replenish water supplies, ultimately making voyages more cost-effective.
## 2 Result and Discussion
### Multivariate Analysis of Flower Volatiles during Development from Nine Cultivars
For example, the profiles of flower volatiles of Huyou at unopened, half opened, and fully opened stages were similar to those of a citrus in subgroup 2C, 2D and group 3, respectively. Similar clustering was also observed in Satsuma. Altogether, all these results confirmed that citron, pomelo and mandarins were the three basic species of cultivated citrus, and indicated that the genetic influences on HCA analysis could be stronger than geographical and temporal factors, which was in accordance with the previous studies [16,19,20,21,24]. In our other recent study, leaf volatiles of nine citrus were analyzed and similar conclusions and implications were produced, although to a lesser extent [21]. However, the influence of blooming stages on cluster analysis varied greatly among different citrus. For true citrus types, citrons, true mandarin (Ponkan), and to a lesser extent, pomelos, volatiles from all blooming stages clustered together (Figure 5), while for Satsuma, flower volatiles at unopened and half opened stages were clustered together in a same subgroup (2C), but at full opened stage clustered with another subgroup (2B) (Figure 5). This might be due to the almost doubled amount of linalool, 15-fold increment in methyl anthranilate, the second most abundant volatile in full opened flower, as well as a sharp decrease in \(\gamma\)-terpinene, dropping from the second rank down to the 34 th, and \(p\)-cymene, falling from the third rank to undetectable during flower blooming (Tables 3-5). Similarly, unopened Huyou flowers emitted volatiles close to mandarins/sweet oranges, while half or fully opened flowers had volatiles closer to pomelos (Figure 5), which could have resulted from the substantial increase in linalool and methyl anthranilate as well as sharp decrease in \(\gamma\)-terpinene and \(p\)-cymene (Tables 3-5). All these data indicated that for some citrus, especially those hybrids, the flower volatiles profiles could vary greatly during blooming, and equivalent blooming stages need to be strictly compared when using flower volatiles as a marker for revealing the genetic relationships between citrus cultivars. Genetic diversity has been recently estimated based on volatile compounds from both fruit peel and leaf of citrus and its relatives suitable for interspecies phylogenetic studies [16], based on EST-SSR markers [49], and also chemical polymorphism of citrus leaf volatiles of different citrus types [21]. Our results, in relation to previous studies, present new insights into flower volatiles variability among citrus types, and could be helpful for characterization of citrus cultivars based on flower volatiles during blooming.
## 2 Material and Methods
### Calibration and Rescaling of the HyPlant Fluorescence Maps  
The accurate modelling of the atmospheric RT component inside the spectral fitting retrieval scheme is fundamental to achieve accurate and realistic fluorescence maps. In fact, uncertainties in atmospheric modelling rapidly propagate to the final fluorescence products, introducing significant discrepancies between airborne estimates in comparison with ground-based measurements. For this reason, we tested the possibility of re-calibrating the HyPlant fluorescence maps based on ground-based SIF measurements collected simultaneously in nine locations distributed from the edge to the middle of the peatland and representing different vegetation types, from vascular (at the edge of the peatland) to moss (in the middle) dominated groups (see Section 2.3).  
Ground-based measured SIF at the plot-scale (Table 2, Figure 2) was compared with HyPlant measured SIF at both 687 nm and 760 nm. Hyplant-derived SIF and VIs values were calculated as an average of nine pixels (\(3\times 3\)) centered around each ground plot. Then, we tested a 1:1 linear regression model between SIF values computed from HyPlant and ground-based data and evaluated the strength of the correlation between SIF values for both chlorophyll fluorescence peaks at 687 nm (R\({}^{2}\) = 0.92) and 760 nm (R\({}^{2}\) = 0.87). The high correlation provided evidence that HyPlant-derived SIF can be rescaled to obtain more accurate SIF maps in absolute terms. It is important to note that the applied procedure did not change relative SIF spatial patterns across the whole image. This approach also facilitated a needed validation of obtained HyPlant SIF maps.  
In order to rescale the SIF data retrieved from HyPlant, we processed each single pixel value of SIF within the whole SIF map based on the equation below:  
\[SIF_{HyPlant\_corrected}=SIF_{HyPlant}a+b \tag{1}\]  
Due to specific features of the studied area and occurrence of the shallow lake in the middle of the peatland, around 10% of rescaled pixels were still negative. These negative pixels were set to zero for the purpose of getting the final \(SIF_{HyPlant\_corrected}\) maps expressed in positive scales. The same procedure was applied to SIF maps derived at 687 and 760 nm.
## 2 Theoretical Aspects of Lamb Waves
### Physical Module and Control Equations of Finite Element Simulation  
The simulation model of Lamb waves in this paper is developed in the transient acoustic pressure module of the COMSOL Multiphysics(r) version 6.2 software, which  
Figure 3: Dispersion Curve Diagram of aluminum Plate. (**a**) Group Velocity Dispersion Diagram; (**b**) Phase Velocity Dispersion Diagram.  
is based on the finite element method. This module is used to solve the control partial differential equations (PDE) for the propagation of sound waves as follows:  
\[\frac{1}{\rho c^{2}}\frac{\partial^{2}\rho}{\partial t^{2}}+\
abla(-\frac{1}{ \rho}(\
abla^{2}p-q_{d}))=\frac{\partial Q}{\partial t} \tag{4}\]  
In the equation, \(p\) represents the pressure of the sound wave (\(Pa\)), \(\rho\) represents the density of the material (kg/m\({}^{3}\)), \(c\) represents the speed of the sound wave in the given medium (m/s), \(q_{d}\) represents the energy dipole domain source (N/m\({}^{3}\)), and \(Q\) represents the energy monopole point source (m\({}^{3}\)/s).  
The fundamental concept behind implementing damage identification using Lamb waves involves leveraging the propagation characteristics of Lamb waves within a structure. By employing sensors to receive response signals, distinctive features related to damages are extracted from the received signals. These features are then employed to determine the structural damage state, identifying structural defects. When Lamb waves propagate to regions with defects or damages within the structure, alterations occur in their propagation characteristics, such as changes in wave velocity and amplitude. Consequently, by analyzing the received wave signals, it becomes possible to identify defects or damages.  
However, due to the coexistence of multimodal behavior and dispersion characteristics in Lamb waves, Lamb wave signals are inherently intricate. This complexity presents challenges in signal analysis and processing, making it necessary to apply signal processing techniques and algorithmic refinement to extract damage-related features from the signals.
## 6 Ethical and Privacy Issues
### Consent and Transparency in User Interactions  
Transparency in how chatbots gather, use, and store user data is crucial for building trust. Users should be informed about the extent of the chatbot's capabilities, including its use of personal data and the limitations of its advice, particularly in sensitive fields such as medical and healthcare applications. Concerns similar to these have been raised in the context of higher education in India, where the adoption of AI chatbots has been scrutinised [52].  
Recommendations  
The following recommendations are proposed to address consent and transparency issues in User Interactions in chatbots used in sensitive applications:  
* Clearly communicate to users how their data will be used, what data will be stored, and for how long through easily understandable privacy policies, as emphasised in [53], which stresses on the importance of exploring encryption secure protocols and authentication mechanisms to ensure the confidentiality and integrity of patient data.
* Implement user consent protocols, thus ensuring users that are fully aware of and agree to how their information is handled and whether it is used for training the AI model.
* Provide users with options to control their data, such as the ability to delete their conversation history or opt-out of data collection and data training.
## 3 Methodology
### Architecture Patterns
#### 3.2.2 Message-Oriented Broker Pattern  
This asynchronous communication pattern permits concurrency and high scalability and can be used as a data distribution intermediary for other components called publishers and subscribers. The data that come mainly from the sensors are sent to the distribution intermediary (the broker), thus achieving a decoupling of the readings and writes of the data [31; 32]. In general, system components such as mobile devices could take advantage of publishing data in these messaging brokers that can be consumed by other parts of the system.
# Voting-Based Document Image Skew Detection
## 2 Skew Detection Algorithms
### Frequency Domain  
#### 2.1.1 Mechanism  
For tilted pages, near-vertical and near-horizontal lines occur in the frequency domain, due to the definition of the Fourier Transform and because text documents are composed of mostly vertical and horizontal sinusoids.  
For an image which contains stripes, the Fast Fourier Transform (FFT) [21] yields points which align in a straight line, passing through the center of the FFT image and perpendicular to the stripes, as shown in Figure 1.  
Figure 1: (**a**) Stripes image sample \(I\); (**b**) Fast Fourier Transform (FFT)(I); (**c**) OTSU(FFT(I)).  
Lines in the frequency domain emerge from the patterns of parallel lines of text and horizontal/vertical separators, including whitespace. The angles of these lines lead to the document skew angle.  
The FFT image is binarized with Otsu [22] and then the Hough transform [23] is applied to detect the dominant lines. Only lines that pass through the center of the FFT image are of interest. The skew angle is calculated as the angle between each dominant line and the closest axis. A weighted sum of these lines is then returned. The vertical line represents horizontal stripes in the input image, which is the information of the text rows, and the horizontal line represents vertical separators, including whitespace.
# Minimization of Average Peak Age of Information for Timely Status Updates in Two-Hop IoT Networks
## 2 Related Work
### Blocklength Optimization for AoI Reduction  
Among various considerations, short-packet communications are particularly important in IoT networks, as they inevitably lead to decoding errors due to operation in the finite blocklength regime [9]. The seminal work by [PERSON] et al. [10] established a theoretical framework for characterizing the PER as a function of blocklength. A longer blocklength can reduce AoI as it improves the PER, but it can also increase AoI due to a longer transmission time. Accordingly, finding an optimal blocklength that balances this tradeoff has been considered an important problem, and many studies (e.g., [13; 14; 15; 16; 17; 18]) have investigated it in the context of AoI minimization.  
Specifically, the study in [13] analyzes the average AoI under various packet management schemes, namely non-preemption, preemption, and retransmission, and optimizes the blocklength to minimize the average AoI under an AWGN channel. The work in [14]further examines the correlation and tradeoff among the average AoI, PAoI, and delay in an LCFS system with retransmission and non-preemption, and it proposes a joint optimization of blocklength and update rate to minimize the weighted sum of delay and the average PAoI under an AWGN channel. More recently, the study in [15] considers a slotted ALOHA network in which multiple devices transmit short status packets to a common access point, and it derives the optimal blocklengths that minimize the average AoI and PAoI, respectively, under AWGN channels.  
Blocklength optimization for AoI reduction has also been studied in fading environments. The study in [16] evaluates two classic transmission protocols for machine-type communications under Rayleigh fading and derives suboptimal blocklengths to minimize the average AoI for each protocol. The work in [17] analyzes decode-and-forward relaying in both half-duplex and full-duplex modes, and it jointly optimizes the transmit power and blocklength to minimize the average AoI under Rayleigh fading. The study in [18] focuses on a two-hop status update system with energy harvesting (EH) and optimizes the blocklengths for both hops to minimize the average AoI under Rayleigh fading.
# Simulation Analysis of Cyclone Separator for Separation of Cenospheres
## 5 Conclusions  
In this study, we proposed and simulated a dry separation technique that employed a cyclone separator to validate its effective separation of cenospheres. In order to achieve this goal, we carefully designed and constructed a model of the cyclone separator and performed an exhaustive performance analysis of the model through computational fluid dynamics simulations. In particular, we focused on three key design parameters, namely, the radius of the upper discharge opening, the height of the sloping wall, and the material incidence velocity, and evaluated their effects on the separation efficiency. The simulation results reveal that the upper discharge opening radius has a relatively small effect on the separation performance, whereas the variation of the slant wall height and the incidence velocity has a significant effect on the separation efficiency. During the parameter optimization process, we found that the Newton's efficiency was significantly improved under the conditions of lower slant wall height and incident velocity. Specifically, when the radius of the upper discharge opening is set to 90 mm, the height of the sloping wall is 400 mm, and the incidence velocity is 2.5 m/s, the Newton's efficiency can reach the optimal value of 0.55. The optimal Newtonian efficiency obtained from this simulation is not much different from the Newtonian efficiency of wet separation. This result not only shows the potential of dry separation technology but also provides a strong impetus for further research on the dry separation of cenospheres.  
The Newton's efficiency obtained through specific experiments is 0.543, which is not much different from the simulation results. However, the blower speed set in the experimental process is 16 m/s, which is different from the simulation. Because in the experimental process this study has directly sent the fly ash cenospheres mixture through the material column, the speed is too small, and the mixture is deposited in the pipe and cannot be blown into the cyclone by the wind, and the simulation gives the initial speed of the mixture, so the experiment will have a partial difference with the simulation. However, both the simulations and experiments show that the cyclone can be used as a separator for cenospheres.  
Conceptualization, P.H.; methodology, P.H.; validation, R.S.; formal analysis, R.S.; data curation, R.S.; writing--original draft, R.S.; writing--review and editing, R.S., T.H., K.H. and Q.C.; visualization, P.H.; supervision, P.H., S.Y., N.C. and J.W.; project administration, N.C. and J.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially sponsored by the \"National Key R&D Program of China\" (2021 YFE0109800).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.