esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Particle Swarm Optimization Algorithm (PSO)  
PSO is a popular meta-heuristic optimization algorithm, and the successful application of PSO can be found in many cases, including flyrock [32; 33; 34; 35; 36], air-overpressure [37] and rock strength [38; 39]. In PSO, various particles will be created in a search space with randomly set location and velocity, then, the fitness value of each particle will be calculated by using the specified fitness function. In practice, particles in search space represent various solutions, and the main task of PSO is to find the optimal solution to the selected problem. For this aim, these particles move according to the velocity and location update equations with the guidance of the fitness value, and that procedure will be ended when the optimal solution was found [40].  
To find the best position (\(g_{best}\)) of the particle, the velocity and position of particles were updated from the current position (\(X_{c}\)) and velocity value (\(V_{c}\)) to a new position (\(X_{n}\)) and velocity value (\(V_{n}\)), using the following equations [41; 42].  
\[\left\{\begin{array}{c}V_{n}=\omega V_{c}+c_{1}p_{1}(P_{best}-X_{c})+c_{2}p_ {2}(g_{best}-X_{c})\\ X_{n}=X_{c}+V_{n}\end{array}\right. \tag{2}\]  
where the optimal position of a particle is expressed by using \(P_{best}\). \(p_{1}\), \(p_{2}\), \(c_{1}\) and \(c_{2}\) are two random values and two positive acceleration constants.
## 4 Simulation Study  
### Simulation Configurations  
In the simulation configuration, as shown in Table 1, parameters can be assigned within four categories: the surveillance camera, the pedestrians, the road, and the simulator.  
Unless mentioned otherwise, the default values for each setting in the simulation are those listed in Table 1.  
In addition, pedestrians commonly move along the road at an average walking speed of 1.7 m/s, targeting adjacent intersections as their destination, and when they reach their destination, they randomly select one of the adjacent intersections as their new destination. This process was repeated until the simulation ends. Cameras were placed randomly but constrained to be adjacent to the road. Similarly, the horizontal viewing angle of the cameras was randomized, but we constrained the camera's viewing area to include the road.  
The simulation evaluates the impact of the number of cameras (density); the number of pedestrians as target objects; the size, focal length, and resolution of the surveillance camera sensors; and the installation height of the cameras in terms of several performance metrics. For the performance evaluation, the key performance indicators are (i) detection success time \(T_{suc}\), which is the average time taken for a pedestrian to be detected by at least one of the surveillance cameras, and detection failed time \(T_{fail}\), which is the average time taken for a pedestrian not to be detected. This is further subdivided into two situations: (ii) when the pedestrian is out of the effective coverage area \(T_{fail}^{out}\), and (iii) when the pedestrian is within the effective surveillance distance but the viewing angle of the surveillance camera is angled in the wrong direction \(T_{fail}^{flip}\). Furthermore, to evaluate the surveillance quality of the monitored pedestrians, the surveillance resolution value described in Section 3.4 was measured every 100 ms. In each simulation, the values in the sample were obtained by taking the average of 10 iterations.
## 3 Results  
### Distribution Characteristics of HMs in Sediments  
The statistical characteristics of HM concentrations in YRB sediments are presented in Table S3. The mean concentrations were observed in the following descending order: Zn (\(109.73\pm 71.89\) mg/kg) \(>\) As (\(68.32\pm 16.06\) mg/kg) \(>\) Cr (\(28.92\pm 7.3\) mg/kg) \(>\) Pb (\(21.73\pm 9.49\) mg/kg) \(>\) Ni (\(12.18\pm 3.77\) mg/kg) \(>\) Cu (\(7.66\pm 2.68\) mg/kg) \(>\) Cd (\(0.14\pm 0.08\) mg/kg). Notably, the CV values exhibited significant spatial heterogeneity across river reaches. The maximum CV for Zn (\(66.02\%\)) was recorded in middle reaches, while the peak CV for Cd (\(87.58\%\)) was identified in lower reaches. The variability classification revealed moderate variation for Cr (\(25.25\%\)), Ni (\(30.94\%\)), Cu (\(35.06\%\)), and As (\(23.51\%\)), contrasted with high variation patterns for Zn (\(65.52\%\)), Cd (\(58.47\%\)), and Pb (\(43.65\%\)).  
The spatial distribution patterns of HMs in YR sediments are presented in Figure 2. HMs in the sediments exhibited significant spatial differences (\(p<0.05\)). The spatial distribution patterns of Cr, Cu, and Ni were similar, with larger fluctuations in the upstream and smaller changes in the midstream and downstream. In contrast, Pb exhibited larger fluctuations in the upstream and downstream. The spatial distribution patterns of As and Zn were completely different. The concentration of As gradually increased from the upstream to the downstream, while that of Zn gradually decreased. The concentration of Cd was relatively low compared to other HMs, with smaller changes in the upstream to the downstream.
# Network Diffusion Algorithms and Simulators in IoT and Space IoT: A Systematic Review
## 4 Analysis
#### 4.1.1 NS-3
Similarly, a 5G network simulation with 5700 nodes and 19 towers required 10.4 h to simulate 1.5 s of real-time activity [110]. Memory usage is also significant, with simulations demanding at least 8 GB RAM for moderate scenarios and exceeding 32 GB RAM for larger-scale simulations [111]. High-performance computing clusters with 128 GB to 1 TB RAM per node are often used to handle such large-scale models efficiently. * Limited Built-in Support for Some IoT Scenarios: May require additional customization and module development to accurately simulate certain IoT-specific scenarios. * Complex Configuration: Setting up simulations can be complex due to detailed configuration requirements. Notable Studies Using NS-3
* Ref. [30]: Simulated and compared different architectures' performance in terms of delay, throughput, and control overhead. * Ref. [4]: Extended NS-3 with modules to simulate LoRa systems, enabling performance analysis in various scenarios. * Ref. [68]: Developed a reliability simulation framework using NS-3 to evaluate power, performance, temperature, and reliability aspects. * Ref. [95]: Integrated Docker with NS-3 to simulate realistic IoT botnet scenarios. Performance Metrics
* Scalability: NS-3 is widely recognized for its scalability, making it suitable for large-scale IoT applications like LoRa networks and UAV simulations. * Latency and Propagation Delay: It is often used to evaluate latency in large-scale networks, including fog and edge computing environments. * Packet Delivery Ratio (PDR): NS-3 is frequently used to evaluate the PDR in various IoT environments, especially when testing different communication protocols.
# PGN: Progressively Guided Network with Pixel-Wise Attention for Underwater Image Enhancement
## 1 Introduction
Our model is based on progressively guided pixel-wise attention, which decomposes the entire underwater image enhancement process into supervised stages. Specifically, we use clear underwater images at each stage to generate a guidance map through a lateral Pixel-Wise Attention Module to guide network learning. This strategy can progressively learn more accurate information and deepen the network's image understanding and thus improve the network's enhancement performance. The main contributions of this paper are summarized as follows:  
1. A Progressively Guided Network (PGN) for underwater image enhancement is proposed, which can progressively deepen the network's understanding of image structures and details through a multiple-stage supervision strategy. 2. A Pixel-Wise Attention Module (PAM) is proposed. The PAM extracts features from clear images and serves as a guide to further enhance feature representation at each stage. 3. Experiments on publicly available datasets and real-world underwater images show that our method outperforms state-of-the-art methods in terms of both visual quality and quantitative metrics.
Activated carbon is a predominantly amorphous solid characterized by an extraordinarily large pore volume and specific surface area [16]. The unique properties that activated carbon exhibits have made it suitable for use as an adsorbent against a significantly wide range of both gaseous and water-soluble pollutants [17]. Moreover, importantly for this research, activated carbon has the ability to effectively adsorb formaldehyde from both wastewater and the gas phase [18]. Besides being a great absorber, activated carbon has also been used before as a filler for polymer composites such as, for example, both low- and high-density polyethylene [19; 20], polypropylene [21; 22], epoxy resin [23; 24], and poly(lactic acid) [25]. An interesting example of activated carbon is the one obtained from coconut shells (ACCS). Coconut shells are a well-known precursor among various carbon-rich substances. The product of their conversion to activated carbon is usually characterized by a surface area of about 800-1500 m\({}^{2}\)/g [26]. Moreover, the advantages of ACCS include environmental factors related to the use of waste biomass, as well as its low cost, non-abrasive nature, safety in handling, low density, superior specific properties, etc. [27; 28]. [PERSON] et al. [29] recently investigated the possibility of modifying UF resin with ACCS in particleboard production. The authors showed an improvement in the particleboard's mechanical properties, but the investigated loading range was between 1.5 and 7.5%. The aim of the present study was to investigate the effect of incorporating small amounts of ACCS into UF resin used for bonding particleboard. Low quantities up to 1% were previously used by [PERSON] et al. [30], and the authors obtained promising results, showing an improvement in the properties of medium-density fiberboards (MDFs). However, the authors used activated carbon from sanding dust, and, as numerous studies indicate, the source has a significant impact on crucial features of the obtained carbons [31; 32], which in turn may considerably affect the outcomes. Moreover, the effects may vary due to the differences between the gluing of wood fibers and the gluing of wood particles. In summary, the aim of the presented research was to investigate the effects of urea-formaldehyde adhesive modification with small amounts of activated carbon from coconut shells, ranging from 0.25 to 1.5%, on the properties of manufactured interior-grade particleboard.
# Chaotic Communication System with Symmetry-Based Modulation
## 2 Materials and Methods
### Communication System Architecture  
The proposed symmetry coefficient modulation approach is similar to the chaotic parameter modulation method [22]. The transmission of data is performed as follows. First, the message is encoded into a set of symbols corresponding to a defined non-binary \(n\)-symbols alphabet. Each symbol of the alphabet corresponds to a certain value of symmetry coefficient. The set of symbols forms an information message \(m(t)\) which affects the symmetry coefficient \(s\) on the transmitter side. The receiver consists of several slave systems, each with a specific symmetry coefficient corresponding to a particular symbol. The signal \(x(t)\) in the channel is applied to all receiver systems, and a synchronization error \(\Delta x\) between \(x(t)\) and each receiver response \(x_{i}^{*}(t),i\in[1,N]\) is calculated. When the value of synchronization error \(\Delta x\) on one of the receivers goes below a certain threshold, it is treated like a receiving of the symbol conformed to the symmetry coefficient on this receiver system. Thus, the accepted message \(m^{*}(t)\) is decoded symbol-by-symbol.  
The physical receiver may be represented by a single digital device, where systems with different symmetry coefficients are executed (Figure 2), or a set of devices, where a certain symmetry coefficient corresponds to the certain device (Figure 3). In the second case, to transmit a message, an asynchronous protocol can be used, in which various duration of the synchronized and non-synchronized states will denote different symbols, in the simplest case, binary 0 and 1.  
Figure 3: Block-diagram of one-to-many SCM communication system suitable for asynchronous binary coding with \(n\) receivers.  
Figure 2: Block-diagram of the peer-to-peer SCM communication system with alphabet of \(n\) symbols.
## 3 Energy Production Decarbonization and the Pego Power Plant  
Various types of biomass can be used as an energy source for power generation in Europe, including agricultural surplus and byproducts, fast-growing energy crops grown in areas available due to reduced agricultural overproduction, and wood waste from forestry or wood processing [92]. However, concerns exist regarding the sustainability of transporting biomass over long distances. [PERSON] et al. [93] report that carbon emissions related to transportation, measured in grams of carbon per ton of biomass per kilometer, would be 1.45 for sea transport and 31.7 for road transport.  
Using biomass exclusively for energy production would require building many decentralized plants, which would be time-consuming and expensive and need substantial financial investments and storage capacities due to the fuel's seasonal availability [94]. However, using local biomass as fuel can boost the local economy by creating jobs and supporting rural development. Replacing coal power plants with biomass energy could also have a positive economic impact by offsetting job losses from coal plant closures [95]. To use biomass as an alternative fuel in coal-fired power plants, the biomass waste must have characteristics similar to coal, enabling the use of existing coal combustion systems [96].  
When evaluating the transition of a coal power plant to biomass use, it is essential to consider both technical challenges and environmental impact. Using imported or deforestation-derived biomass may have external costs that should be factored in, making local biomass waste a preferable option. Transitioning from coal to biomass is not a one-size-fits-all solution due to limited biomass resources in some areas. However, for the Pego Coal Power Plant in Portugal, where biomass resources are abundant, switching from coal to biomass energy could be a feasible alternative.  
To achieve goals, such as decarbonizing economies, it is important to implement public policies and engage society as a whole. Positive changes in human behavior can contribute to reducing greenhouse gas emissions by over 20%, making public awareness crucial [97]. In the European Union, 90% of the population sees climate change as a serious threat [98].  
Portugal has taken a strong international stance on decarbonizing its economy and aims to achieve carbon neutrality by 2050 [99]. This goal aligns with the Paris Agreement, reflecting Portugal's commitment to the global effort of limiting temperature increases [100]. The country has set a target to phase out coal in energy production by 2023 [101]. The closure of its two coal-fired power plants, Sines and Pego, is critical to decarbonizing Portugal's economy as they accounted for 20% of the nation's greenhouse gas emissions [102]. The Pego Power Plant underwent a retrofitting process in 2009 to reduce emissions and was overseen by a holding group consisting of three companies [103].
# RFID Authentication System Based on User Biometric Information +
## 3 Design Background and Overview
### Tag Coupling
#### 3.2.1 Backscatter Coupling  
After the user touches the target tag, the electromagnetic wave emitted by the antenna activates it and brings back the tag information. This process is based on the spatial propagation law of electromagnetic waves. The recognition range is greater than 1 m.  
Passive tags require sustained energy from electromagnetic waves emitted by the reader to keep working. We know that a changing magnetic field creates an electric field, and a changing electric field creates a magnetic field. When magnetic flux changes, an induced current will be generated in the closed coil. The current in the signal transmitter radiates radio electromagnetic waves through the antenna, forming a changing electromagnetic field. The changing electromagnetic field is induced by the antenna coil at the receiving end, and the voltage is generated inside the coil. The tag can be thought of as a closed coil that generates an induced current inside when it senses the signal emitted by the reader. It should be noted that the induced current generated in the tag will also radiate electromagnetic waves back to the reader antenna after modulation, and generate signals that can be recognized by the reader antenna, also known as backscattered signals.
# A Cyber-Physical System for Wildfire Detection and Firefighting
## 3 Materials and Methods
### Wildfire Detection Node  
The wildfire detection node is designed to be a wireless edge device that is installed in a forest to detect flames in real time using object detection techniques. In addition to its flame detection capability, it also includes several other sensors that can provide additional environmental data. These sensors include a temperature and humidity sensor, geolocation devices, a CO\({}_{2}\) sensor, and an Infrared (IR) sensor. The temperature and humidity sensor measures the weather in the forest, which can be used to identify potential fire hazards. Additionally, the geolocation devices can provide the location of the node within the forest, which can be used to track the spread of a fire. Moreover, the CO\({}_{2}\) sensor detects the presence of carbon dioxide, which can be an indicator of combustion. The IR sensors can detect heat signatures, which can be used to detect flames even in low-light conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Robot Name**} & **Microcontroller/** & \multirow{2}{*}{**Sensors/Actuators**} & \multirow{2}{*}{**Mobility**} & \multirow{2}{*}{**Communication**} \\  & **Processors/OS** & & & \\ \hline \multirow{4}{*}{_3 DR Iris_} & Cortex M4 168 & \multirow{4}{*}{IMU, GPS, Intel} & \multirow{4}{*}{quadcopter} & \multirow{4}{*}{Bluetooth, WI-FI} \\  & Mhz/256 KB RAM/2 & & & \\ \cline{1-1}  & MB Flash, ROS Noetic, & & & RealSense D435 camera \\ \cline{1-1}  & PX4-Autopilot & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: UAV technical details.  
Together, these sensors can provide valuable data that can be used to detect wildfires early, respond quickly, and potentially prevent the fire from spreading. The wireless nature of the node also means that it can be deployed in remote areas and communicate its data back to a central hub or control room in real time, making it a powerful tool for wildfire detection and prevention.  
Circuit Diagrams  
Figures 3 and 4 show the circuit diagram and the schematic for the wildfire detection system. In the following paragraph, a brief overview of the components will be provided to enhance comprehension of the pinout, sensor function, the application context, and how these sensors can give help in early fire detection.  
Figure 3: The circuit diagram designed using Fritzing to display the pin configuration.  
Figure 2: Cyber-physical system for wildfire detection and firefighting.
## 9 Conclusions  
UAV-enabled, RIS-assisted wireless communication offers a transformative solution for enhancing the efficiency and reliability of future B5G and 6G networks, particularly for the Internet of Vehicles (IoVs). By harnessing the unique capabilities of UAVs and RIS, these systems effectively address mmWave signal propagation challenges, such as attenuation and blockage, while significantly improving spectral and energy efficiency. Insightful channel modeling, real-time energy-efficient UAV trajectory optimization, and innovative optimization techniques contribute to robust and sustainable communication networks. The diverse applications of UAV base stations and RIS-enabled UAVs in IoT sensor data collection, V2X communication, mobile edge computing, and smart city infrastructure demonstrate their versatility and essential role in modern wireless networks. Additionally, integrated localization and communication are vital for future dynamic vehicular networks, with UAVs and RISs overcoming practical barriers. Through this comprehensive review, this paper provided valuable insights into current directions, existing challenges, and emerging research topics, highlighting the potential of UAV-enabled, RIS-assisted communication systems to pave the way for more resilient and high-performance wireless networks.  
Conceptualization, M.E. and A.V.S.; methodology, M.E.; software, M.E.; validation, M.E. and A.V.S.; formal analysis, M.E.; investigation, M.E.; writing--original draft preparation, M.E.; writing--review and editing, M.E. and A.V.S.; visualization, M.E.; supervision, A.V.S.; project administration, A.V.S.; funding acquisition, A.V.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Australian Research Council. Also, this work received funding from the Australian Government, via grant AUSMURIB000001 associated with ONR MURI grant N00014-19-1-2571.  
No new data were created or analyzed in this study.  
The authors declare no conflicts of interest.
# Deep Reinforcement Learning-Based Dynamic Pricing for Parking Solutions
## 3 Proposed Method
### Deep Reinforcement Learning-Based Dynamic Pricing (DRP-DP)
#### 3.2.5 Markov Decision Process (MDP)  
Markov decision process (MDP) is a discrete-time stochastic control mechanism for modelling sequential decision making with state transition in a framework. At each time, the system might be in a different state. The decision maker makes an action in that state and the state transition will transfer from one state to another. The decision maker aims to seek a future state with the maximum reward. The proposed method is a partially observable Markov decision problem where the player only knows the opponent's parking capacity, which is open to the public. The parking vendor does not have access to traffic information and the vehicle arrival rate throughout the simulation. Thus, the dynamic pricing problem is defined by the following components:  
* **Time step:** Each time step represents a time over a finite time horizon from Monday to Sunday. It can be a different period section, such as morning, afternoon and night.
* **State:** To derive an intuitive state representation, it only contains the time step, the player's parking occupancy and the opponent's observable parking occupancy. The other predefined environment parameters such as parking capacity and parking pricing policy act as a constant value over the simulation.
* **Observation:** At every time step, the observation is done after the player performs an action. The vehicle arrival rate will be forecasted using SARIMAX. With the forecasted vehicle arrival rate, the driver simulates and performs decision making by observing the environmental variables such as parking fee and distance of parking vendor. The driver makes their decision on price and distance preferences. If there are rooms between the forecasted arrival rate and smoothed arrival rate, it will be declared as a controllable vehicle arrival. It will perform a decision on whether to continue parking or pay the next visit based on parking price and parking occupancy.
* **Action:** An action in the state is the discount performs at the current time step. The action is modelled as a fixed discount. The discount will be applied to the parking fee in the current time step.
* **Transition:** It is nondeterministic using the model-free RL methods for solving the parking problems. Thus, the transition moves from the current state to another state with the action taken.
* **Reward:** In this model, we consider multiple objectives: maximising parking occupancy rate, increasing parking revenue and traffic regulation result. Thus, the reward is obtained based on these objectives after taking an action in the state.