esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Based on the thermal simulations and dynamic analysis of the Halmahera arc and its periphery presented in this paper, the following conclusions are drawn. The thermal simulation results reveal that the depth of oceanic crust dehydration in the subduction zone and the mantle wedge temperature structure are strongly affected by both the plate subduction rate and the overriding plate movement rate. When the subduction rate is constant, a higher overriding plate movement rate promotes arc magmatism. Conversely, for a fixed overriding plate movement rate, a lower subduction rate enhances the potential for arc magmatism. The disparity in volcanic age within the Halmahera arc is attributed to the varying movement rates of the overriding microplates in the subduction zone. During the Middle Miocene, the Australian plate drifted northward and persistently pushed the Obi Island microplate in the south via the left-lateral strike-slip faults, augmenting its movement rate. In contrast, the movement rate of Halmahera Island microplate in the north was slow, as it was hardly affected by the Australian plate. The Obi Island microplate in the south moved at a higher rate and formed a high-temperature area conducive to arc magmatic activity more rapidly above the slope of the subduction zone, thereby facilitating the earlier formation of the Obi Island arc's volcanic activity in the late Miocene (10 Ma). Conversely, the Halmahera Island microplate in the north moved at a relatively lower rate, and it took a longer time to form a high-temperature zone favorable for magmatic activity beneath the arc, giving rise to the later formation of the Halmahera arc volcanic activity in the late Pliocene (5 Ma). Our results enhance the understanding of magmatism and dynamics in the Halmahera arc and its surroundings. Additionally, they offer a valuable thermal simulation method for future studies of similar retreating subduction zone island arc volcanoes.  
Conceptualization, formal analysis, methodology, software, writing--original draft, [PERSON] ([PERSON]); conceptualization, writing--review and editing, [PERSON]; supervision, [PERSON] ([PERSON]); validation, H.W.; methodology, G.Z.; investigation, Z.Y.; supervision, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Petrochina Basic and Frontier Science and Technology Project (No. 2024 ZZ5601), the National Natural Science Foundation of China (Grant Nos. 42176052, 42276072).  
Figure 8: The explanation of the cause of volcanic age differences in Halmahera arc. (**a**) Tectonic setting of the Halmahera arc and its vicinity. (**b**) The cause of the young volcanic age in Halmahera Island in the north. (**c**) The cause of the old volcanic age in Obi Island in the south.
# BPMNE4 IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes +
## 1 Introduction  
The Internet of Things (IoT) has attracted a lot of attention in recent years, making it one of the core technologies for realizing digital twins [1, 2]. This trend is fueled by the fact that the electronic components of IoT devices have become cheaper, smaller and more powerful [3]. IoT devices can be found in smart cities, cyber-physical systems, and many other smart applications. As IoT devices are equipped with sensors, actuators, software, protocols, and network interfaces, data can be captured from the physical environment and be exchanged over the IoT network [4]. In this context, sensors are used to capture data from the physical world (e.g., brightness, air quality, temperature or humidity), whereas actuators transform electrical signals into mechanical motion (e.g., translate or rotate) or change physical quantities such as light or pressure [5, 6].  
Business Process Management (BPM), in turn, deals with the modeling, implementation, execution, monitoring, and analysis of business processes [4, 7]. Incorporating IoT capabilities into BPM systems, therefore, offers promising perspectives for bridging the gap between the digital and the physical world, for automating processes and decision making, and for optimizing business processes based on IoT data. By integrating business process with the Internet of Things, it further becomes possible to automate various types of physical tasks, e.g., to start an air conditioner, to turn on/off the light, or to open/close the window [7].  
To enable a holistic support for IoT-driven processes, IoT-specific elements are needed that allow modeling, executing and monitoring the IoT-specific aspects of business processes. Modeling IoT-enabled processes shall foster the understanding of how these processes operate. During process enactment, it should be also possible to detect and handleIoT device errors such using faulty sensors or the non-reachability of IoT devices. Though there exist many process modeling languages such as Event Process Chains (EPCs), Petri Nets, Business Process Modeling and Notation (BPMN) 2.0, Flowcharts, and Role Activity Diagrams, these languages are not sufficiently expressive to model IoT-driven BPs [4].
## 2 Related Works  
### Charging Station Location Problem  
In recent years, the charging station location problem (CSLP) has garnered considerable attention from researchers, leading to a multitude of studies exploring different aspects and perspectives [4; 6; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. Notable contributions include a deployment framework proposed by [PERSON] et al. [16], which considers existing competitors in the planning of PEV fast-charging stations. [PERSON] et al. [18] introduced a two-stage data-driven method for determining CS station locations on highways.  
Researchers have also focused on optimizing the objectives in CSLP. [PERSON] et al. [14] adopted a model to minimize drivers' discomfort, while [PERSON] et al. [15] designed a model to minimize CO\({}_{2}\) emissions. Other considerations, such as brand preferences [20] and the total number of charging stations [23], have also been taken into account. However, the existing studies primarily focus on optimizing the location of charging stations, while overlooking the number of chargers at each station and the fairness of their distribution among users.  
Various algorithms have been applied to address CSLP. [PERSON] et al. [21] proposed a large-scale charging station concept solved using the K-means algorithm. Genetic algorithms [13; 22], Bayesian optimization [20], and greedy algorithms [5] have also been utilized for determining optimal charging station locations. Given that CSLP is an NP-hard problem, many approximation algorithms have been employed. However, as problem size and constraints increase, computational efficiency becomes a significant limitation for these methods. [PERSON] et al. [6] presented a reinforcement learning approach to address CSLP, demonstrating its potential advantages in scalability and computational efficiency.  
However, in the implemented studies, the fairness issue in charging station location planning has been scarcely addressed. Furthermore, reinforcement learning, as a promising approach for solving optimization problems, still has ample room for exploration in the context of CSLP. This paper aims to consider elements overlooked in previous research and explore the potential for problem solving using advanced reinforcement learning algorithms.
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 4 Results and Discussion
### Energy Consumption Comparison between Proposed and Base Study  
The energy usage comparison between the suggested paradigm (ESFRM) and the (SEFR) is shown in Figure 13. As a result, it is demonstrated that the proposed study used an average of 13.67% less energy than that of the based technique when applying intended parameter values.
# Multi-Class Intrusion Detection Based on Transformer for IoT Networks Using CIC-IoT-2023 Dataset
## 2 Methodology
### Data Normalization  
In order to improve the performance of deep learning models, feature normalization techniques are usually used to achieve the above purposes. We transform the numerical values of the features so that they are relatively consistent. The method we use is Standard-Scaler technology, which is used to convert the value to a standard normal distribution with a mean of 0 and a standard deviation of 1. This specific method is to calculate the ratio of the difference between the original value and the mean and the standard deviation.
# The Design and Implementation of a Phased Antenna Array System for LEO Satellite Communications
## 3 Theoretical Considerations on Phased Antenna Arrays for Leo Satellite Communications
### The Phase Shifting Algorithm
Azimuth positions of LEO satellites can be provided by one of the free satellite  
Figure 3: Simulated beam steering for the array topology in Figure 2b, obtained with negative phase taper (plotted in black) according to Equation (7), and positive phase taper (plotted in red) according to Equation (9). The values of \(\varphi_{0}\) were 0\({}^{\circ}\) in (**a**) and 45\({}^{\circ}\) in (**b**), respectively. tracking software (e.g., Orbitron, version 3.71 [31]). The developed numerical algorithm can achieve a high level of performance, needing reduced computational resources.
## 3 IoT Applications
### Smart Grid  
A smart grid is an electrical system that contains several efficient and energy efficiency features such as infrastructure for intelligent metering, intelligent power panels, smart equipment, control system, alternative/renewable energy, etc. The term \"smart grid\" refers to a concept that encapsulates the entire power generation and distribution system in a single frame. It is an electricity system built on digital technology that uses two-way digital communication to supply electricity to consumers. To put it another way, a Smart Grid is a grid that makes the entire system smarter or cleaner. Clean energy is presently in high demand all around the world. In 2003, the first time the term \"Smart Grid\" was stated by [PERSON] in an article [52]. Smart grid technology allows real-time monitoring, coordination, and control of the electric energy grid via communication networks between physical components, resulting in more effective and economical grid management. The widespread availability of Internet connectivity in most houses has made the smart grid more viable to adopt. The smart grid consists of Supervisory Control And Data Acquisition (SCADA), Energy Management System, Grid Communication Systems, and Distributed Energy Resources (DERs). In a smart grid system, users' data privacy and security are crucial and challenging issues. A cyber-physical exploit is a safety failure in cyberspace that negatively affects a CPS's physical environment [53]. There have been a number of significant cyber-physical incidents have been reported in the sector in recent years. A computer worm called \"Stuxnet\" leveraged four zero-day flaws and cryptographically signed certificates to evade intrusion detection. It hit the Iranian nuclear fuel enrichment complex in June 2010 where the programmable logic controllers (PLC) of the SCADA system were the targets [54]. Three Ukrainian electric power distribution firms were hacked in a coordinated operation in December 2015. Thirty substations were blacked out for almost three hours, resulting in 225,000 consumers experiencing wide-area power outages. To frustrate claims of disruptions, a telephonic DoS attempt was conducted while authorized members' virtual private network accounts were stolen using the Black-Energy3 virus [55]. At present different AI based techniques are used to detect and defend the mechanism of the smart grid security system.
# Data Fusion-Based Structural Damage Identification Approach Integrating Fractal and RCPN
## 2 Counter-Propagation Network (CPN)
According to step (5), Equations (6)-(14) can be simplified as: \[v_{\lg}(t+1)=v_{\lg}(t)+\beta b_{j}(c_{j}-c_{l}^{\prime})\quad l=1,2,\ldots,M\] (8)  
Namely, only the connection weight vector \(V_{g}\) of the winning neuron in the competition layer and the neuron in the output layer can be adjusted. The other connection weight vectors have no need to be adjusted, and the original value can remain unchanged. (8) The weighted input of each neuron in the output layer is solved and taken as the actual output value of the output neuron; \(c_{l}^{\prime}=\sum\limits_{j=1}^{Q}b_{j}\eta_{\lg}\), \(l=1\), \(2\),..., \(M\), can then be reduced to \(c_{l}^{\prime}=v_{\lg}\). (9) Return to step (2) until the \(p\) input modes training is complete. (10) Then \(t=t+1\), the input mode \(A_{k}\) is re-provided to the network learning and ends when \(t=T\). It can be seen that the CPN network model has the advantages of simple structure, with no need for multiple rounds of data set and no need for error standards to limit network convergence. However, in practical application, the CPN network model often requires many data points to obtain the ideal function, which usually leads to the increase of computing memory and time [36]. In addition, in the learning process of CPN, if the values of multiple samples are similar to each other, the same weight value will be adjusted, so that the connection weight vector cannot converge, ultimately leading to the failure of network training.
## 3 Results
### Feature Inference and Extraction from Non-Etched Metallographic Microstructures  
#### 3.2.1 Nodule Extraction  
The extraction of nodules from metallographic images is a fundamental step in the comprehensive characterization of nodular cast iron. Accurate identification and segmentation of individual nodules are crucial for further morphological feature extraction and overall quality assessment. In this research work, a CV pipeline was implemented for nodule extraction. The process incorporates several pre-processing steps, including noise reduction, contrast enhancement, and adaptive thresholding, followed by segmentation algorithms specifically designed to delineate nodules within complex microstructural backgrounds. This approach was validated on a representative dataset of metallographic images, demonstrating high accuracy in nodule detection.  
Among the challenges encountered, the merging of closely spaced nodules proved to be the primary source of segmentation errors, alongside variability in image quality and the presence of artifacts. In order to avoid these issues, the segmentation parameters were carefully tuned to reduce both false positives and false negatives, achieving an overall detection accuracy exceeding 98%. Nodules below a certain size threshold were excluded from the analysis, based on expert criteria, to eliminate noise and irrelevant particles.  
An illustrative example of the nodule extraction results is provided in Figure 12, showing the effectiveness of the segmentation pipeline in isolating individual nodules despite the presence of adjacent particles and other inconsistencies.
# Study of the Critical Speed of Ultra-High-Speed Diamond Drilling Technology in Marble
## 3 Simulation and Discussion  
### Effect of Varied Rotational Speed on MSE  
The MSE calculation during stable cutting involves drilling pressure and torque, as shown in Figure 6a depicting MSE variation with drilling speed at a constant penetration rate. A preliminary analysis identifies 7 m/s as the critical speed for ultra-high-speed conditions. In the initial phase, MSE increases with speed, but a decline is observed after reaching 4 m/s, suggesting that a moderate speed increase within the conventional range can reduce MSE. However, at 5 m/s, MSE sharply rises, leading to significant energy loss, prompting many drillers to prefer lower speeds. At 7 m/s, MSE exhibits a second decline with a broader range and steeper slope than at 4 m/s. The lowest MSE occurs around 2 m/s. Beyond 9 m/s, MSE gradually stabilizes. Between 7 m/s and 9 m/s, MSE decreases rapidly with speed, aligning with findings from [PERSON], [PERSON], and others, affirming the model's applicability. Equation (9) highlights the significant impact of drilling pressure, torque, drilling speed, drill bit radius, and speed on MSE in diamond drilling. As the simulation assumes constant drilling speed without considering bit wear, drilling pressure and torque are vital for MSE assessment. Figure 6b depicts drilling pressure and torque curves, generally increasing with speed in the conventional range, with a turning point at 6 m/s. At 7 m/s, drilling pressure reaches 22 N. In the second phase, akin to the MSE trend, drilling pressure drops to 549 N at 9 m/s, a 7.6% reduction from the average of 507 N in the first phase. The 7-9 m/s range is crucial compared to conventional speeds. Drill bit torque consistently decreases with speed, reaching a limit at 9 m/s for ultra-high-speed conditions.  
Figure 7 depicts the relationship between the diamond-specific depth of cut (dd) per revolution and speed. As the speed rises, the diamond depth of cut per revolution sharply declines. In the first phase, continuous speed growth leads to a notable drop in diamond-specific depth of cut, reducing rock volume per revolution. When correlated with Figure 6a, the decrease in diamond-specific depth of cut corresponds to an increasing MSE trend, despite a descent from 4 m/s to 5 m/s, indicating an overall MSE increase.  
Conventional drilling procedures show that merely increasing speed does not boost drilling efficiency; it accelerates diamond wear, reducing the entire diamond-embedded drill bit's lifespan. Once 7 m/s is reached, the diamond-specific depth of cut per revolution stabilizes. However, explaining the substantial MSE reduction during the second phase of diamond rock breaking based solely on the angle of diamond-specific depth of cut per revolution is inadequate.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 5 Rainfall Observation of PR Data
### Climate Characteristics of Rainfall
#### 5.3.2 Rainfall in Tropical Cyclones  
PR could observe the large scale precipitation systems in a complete spatial view, and there were studies on rainfall properties in tropical cyclones, especially the contribution of stratiform and convective precipitation. They noticed that the stratiform precipitation dominated in the total areal coverage, and the largest proportion of stratiform precipitation area appeared in the inner rainband region while the largest proportion of convective precipitation area appeared in the outer rainband, but the convective precipitation made a comparable contribution to the total precipitation with the stratiform precipitation due to its large rainfall rate [138; 139; 140; 141]. In addition, they noticed that the increase in occurrence and coverage of stratiform precipitation was accompanied by the intensification of tropical cyclones [140; 141]. These results showed the importance of stratiform precipitation to tropical cyclones and may provide useful references for monitoring tropical cyclones.
## 2 Problem Background
### Limitations of Existing Fair Routing Solutions for Sensed Data Collection
#### 2.4.2. Unsuitable Forwarding Utility for Less Popular Nodes  
It is important to note that, without providing less popular nodes with an alternative means to contribute in routing, directing messages towards them hardly improves fairness (although less popular nodes can contribute to an extent through a transitive property [37, 47] or by aggregating nodes' context parameters [69], this is unable to fully utilize their potential and also fails to completely evade the popular nodes since their popularity can still be perceived over multiple hops in the point-to-point communication model, in which destinations are identified by their ID, especially under high degrees of spatial locality). Without such means, less popular nodes will receive more than they can send. Besides frequent buffer overflows and throughput degradation, routing may still be unfair based on the following rationale: receiving rates kept equal, less popular nodes will do less sending and thereby consume less resources, while popular nodes from which the messages are being received continue to do more sending. Therefore, increasing the flow of messages towards less popular nodes without providing them with a suitable forwarding utility (i.e., one that requires minimal involvement from popular nodes to deliver messages) leaves no significant impact on fairness and compromises achievable throughput instead. Such approaches rather reduce fairness in scenarios characterised by high degrees of spatial locality.
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 5 Protocol Stack
### Link Layer  
Link layer is split into the MAC sublayer and the radio duty cycling (RDC) sublayer (see Table 4). The MAC sublayer consists of two levels: (1) a TDMA scheme to divide the time in slots; and (2) a carrier sense multiple access with collision avoidance (CSMA/CA) technique performed by the group of STAs allocated in each slot (see Figure 3). Slots' duration and STA allocation are managed by the GW. The RDC sublayer tries to keep the radio module turned off while providing enough rendezvous points for two nodes to be able to communicate with each other.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**MAC Sublayer**} & \multicolumn{1}{c}{**RDC Sublayer**} \\ \hline
**Scheduling-Based Protocol** & **Contention-Based Protocol** & \\ \hline TDMA & CSMA/CA, _or other_ & NullRDC, X-MAC, _or other_ \\ \hline \hline \end{tabular}
\end{table}
Table 4: HARE link layer.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Layer**} & \multicolumn{1}{c}{**Features**} \\ \hline \multirow{3}{*}{**Transport**} & End-to-end acknowledgement (e2e ACK) \\  & Poisoning mechanism (packet loss detection) \\  & Transmission windows \\  & Distributed caching \\ \hline \multirow{3}{*}{**Network**} & Addressing system \\  & Association \\  & Routing \\ \hline \multirow{3}{*}{**Link**} & Beaconing system \\  & Wakeup patterns \\ \cline{1-1}  & Data transmission, aggregation and segmentation \\ \cline{1-1}  & Power regulation mechanism \\ \hline \hline \multicolumn{2}{c}{**Physical**} & _Hardware dependant_ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main features of HARE protocol stack.  
The use of a scheduling-based TDMA scheme in the MAC sublayer of HARE is mandatory to structure transmissions in ring-based slots. Conversely, both the contention-based protocol of the MAC sublayer and the RDC sublayer used in these slots can be freely chosen from state-of-the-art available options [36].