esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Routing algorithms based on sensor position  
Routing in WSN is a key technology and very challenging problem. Many routing algorithms have been proposed to satisfy the requirement of sensor networks. Based on the position finding technology, routing algorithms attract more attention since position-based routing can decrease the complexity of routing and reduce the consumption. To position-based routing algorithms, it is required that (a) sensors can know its position according to suitable locating technology; (b) the sensing area is totally covered by sensors; (c) the connectivity is assured. In this paper, position information also includes relative position information, such as the hop-number distance.  
In this section, the position-based routing algorithms are surveyed and the different algorithms belonging to different categories are described in detail. Otherwise, the algorithms are also compared based on some metrics.
## 3 Architecture of DT Workshop System
### Transfer Rules for Manufacturing Execution Behaviors under Finite State Mechanism  
In contrast to a traditional workshop, the DT workshop enables the synchronization of the presentation of dynamic executions on virtual objects. The various types of platform assembly and welding operation behaviors require uniform quantitative transfer rules to complete the abstraction of production actions. In accordance with prior research [38; 39] on Finite State Machines (FSM), which can guarantee the integrity of operational behaviors and state transfer in the workshop, the behaviors of workers and machines during assembly and welding operations could be categorized into a finite set of states.  
In the instance of welding operations, there are standby, operational, and shutdown states for a single welding robot. The standby state is usually at the beginning or end of the task, as in Figure 4, \(S_{0}\) and \(S_{1}\), which are transient states, while the continuous processes of operation and shutdown states are expressed as \(\mathbf{S}_{run}\) and \(\mathbf{S}_{ shutdown}\). The transition of the state depends on the trigger event \(a\), which returns the event response information \(r\) after the completion of the state transition. Specifically, when the workstation receives a work order, it first confirms that the welder is in the \(S_{0}\) available state. The DT system issues a welding start command to the instantiated object according to the task trigger \(a\), enters the \(\mathbf{S}_{run}\) welding operation state, and confirms the receipt of \(r\). After the task is completed, the equipment enters the \(S_{1}\) completion state. If there is equipment downtime, insufficient material and other termination conditions, enter \(\mathbf{S}_{ shutdown}\) until the repair is complete to restore the equipment to \(S_{0}\).  
The states and commands of the equipment at each workstation in the workshop are integrated into the database in the form of a time series, enabling simultaneous simulation at the data twin display layer and direct issuance of commands to the machine at the equipment layer. Behavioral information is transmitted between the device layer and the digital twin model in order of task priority, and the \"First-In-First-Out\" principle is adopted when the same level is available. When conflicting data occurs, operation instructions are suspended on the equipment side, and simulation data are backed up and pushed to the system operator for confirmation. Transmission rules are the standard for interaction between the equipment layer and the digital twin model. Simplistic rules decrease the frequency of conflicts and provide robustness for the system's daily operation, but standardized operation training for system participants is required to reduce the risk of interaction errors.
## 4 Experimental Verification
### Force Load Regulation Experiment  
First, experimental testing was conducted on the force loading system to evaluate its PID regulation and load-following capability under constant and time-varying load conditions. Since the follow-up loading system is composed of multiple units, the response  
Figure 33: Multi-stage telescopic wing force–thermal follow-up loading platform.  
Figure 32: Load tracking simulation results.  
of a single unit was tested. To verify the system's response and tracking performance, a constant force of 4000 N and the equivalent calculated time-varying total load were set as the predefined input forces. The loading system was activated to test the response of the follow-up loading unit to the input forces.  
As shown in Figure 34, the blue curve represents the experimental response of the loading force. Throughout the motion process, the experimental response oscillates slightly around 4000 N. An error analysis comparing the experimental and simulation results reveals a maximum load error of 208.4 N. The system's response speed is also within 0.2 s, indicating that the load following is achieved within a short response time, with the experimental error remaining within 5% of the simulation values. The experimental data show fluctuations in the force load, likely caused by precision errors in the servo motor controller. Overall, the experimental response curve aligns with the simulation response curve in terms of trends, and the error is within an acceptable range.  
When the pre-applied load is changed to a time-varying load, Figure 35 shows the load-following behavior. The loading system rapidly achieves load following, similar to the constant load case, within 0.4 s, with the maximum loading error remaining under 5%. This further verifies the accuracy of the theoretical and simulation analyses, as well as the responsiveness of the force-thermal follow-up loading platform.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 5 Proposed Scheme-II (Key Recovery System Using Proxy Re-Encryption)
### Session Key Exchange and KRF Generation Phase  
In this phase, a session key is exchanged between \(Dev_{A}\) and \(Dev_{B}\), and a \(KRF\) is generated. Furthermore, in the \(KRF\) generation phase, after generating \(KRF\), the ciphertext \(C\) is communicated with \(KRF\) as shown in Figure 10.  
* **Step 1:**\(Dev_{A}\) selects \(a\in\mathbb{Z}_{q}^{*}\) and calculate partial session key \(PSK_{A}\). \[PSK_{A}=g^{a}\] (36) \[Dev_{B}\text{ also selects }b\in\mathbb{Z}_{q}^{*}\text{ and calculates partial session key }PSK_{B}.\] \[PSK_{B}=g^{b}\] (37) After that, \(Dev_{A}\) and \(Dev_{B}\) exchange \(PSK_{A}\) and \(PSK_{B}\) with each other.
* **Step 2:**\(Dev_{A}\) and \(Dev_{B}\) calculate the session key \(SK=(PSK_{B})^{a}=(PSK_{A})^{b}\) using the exchanged values \(PSK_{A}\) and \(PSK_{B}\).
* **Step 3:**\(Dev_{A}\) generates the ciphertext message \(C=E_{SK}(M)\) using the generated session key \(SK\).  
* **Step 4:** After that, \(Dev_{A}\) selects a random value \(t,c\in\mathbb{Z}_{q}^{*}\) and \(\sigma\in\{0,1\}^{l_{2}}\), and generates \(KRF\) using \(SK\), \(pk_{Med}\), \(pk_{KRC}\) and \(pk_{KRA}\) as follows: \[\pi_{i}=X_{i}\cdot S^{H_{1}(ID_{i},X_{i})}\] (38) \[V_{i}=\pi_{i}^{H_{6}(Y_{i})}\cdot Y_{i}\] (39) \[\tau=H_{5}\ (SK,\sigma,ID_{Med},pk_{Med})\] (40) \[\alpha=Y_{Med}^{\varsigma}\] (41) \[\beta=Z_{Med}^{\varsigma}\] (42) \[KRF_{1}=g^{t}\] (43) \[KRF_{2}=Y_{KRC}^{\tau}=g^{\tau\cdot y_{KRC}}\] (44) \[KRF_{3}=(SK\parallel\sigma)\oplus H_{2}(g^{\varsigma})\] (45) \[KRF_{4}=(\alpha\cdot\beta)^{H_{4}(V_{KRA}^{\tau})\cdot H_{4}(V_{ Med}^{\tau})}\] (46) \[KRF=(KRF_{1},KRF_{2},KRF_{3},KRF_{4})\] (47)  
After that, \(Dev_{A}\) and \(Dev_{B}\) communicate with each other using \((C\parallel KRF)\).
Highly innovative detection setups are ARDESIA, the MAXRS detection setup at the Riken-RAL muon beamline and the imaging facilities at the LENA Laboratory. Paths for next-generation instruments have been also suggested. [PERSON] et al. [8] discussed and analyzed the main features of a CubeSat mission targeting intense and short bursts of gamma rays. The CubeSats represent a unique opportunity to access space quickly and in a cost-effective way. CubeSats are standard and miniaturized satellites consisting of multiple identical units, with dimensions of about 10 \(\times\) 10 \(\times\) 10 cm\({}^{3}\), and very limited power consumption. To date, several hundreds of CubeSats have been launched targeting scientific, educational, technological, and commercial needs. [PERSON] and [PERSON] [9] review and summarize a number of Liquid Argon Time Projection Chamber (LArTPC) experiments, one of the most widely used scintillators in particle detection, due to their low cost and high-resolution three-dimensional particle reconstruction. They briefly describe the specific technologies that are currently employed by single-phase LAr experiments (ICARUS T600, MicroBooNE, SBND, LArIAT, DUNE-SP, ProtoDUNE-SP, ArgonCube and Vertical Drift) and dual-phase LAr experiments (DUNE-DP, WA105, ProtoDUNE-DP and ARIADNE). [PERSON] et al. [10] reported on the design of high-precision neutrino beams, their current limitations, and the latest techniques envisaged to overcome such limits. They put emphasis on monitored neutrino beams and advanced diagnostics to determine the flux and flavor of the neutrinos produced at the source. They also discussed ab initio measurements of the neutrino energy to remove any flux-induced bias in the determination of the cross sections. [PERSON] and [PERSON] [11] gave a description of the structure and the operating principles of the multigap resistive plate chamber (MRPC) that has been arousing broad interest over the last few decades and has become a new standard technology for time-of-flight systems in high-energy physics experiments. They review applications of the time-of-flight system in several famous experiments. The performances, including time resolution and particle identification, are discussed in detail and some recent advances in next-generation MRPC are also outlined. Preparation, writing, review and editing, A.G. and L.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Thanks to all the authors and peer reviewers for their valuable contributions to this Special Issue. We would also like to express our gratitude to all the staff and people involved in this Special Issue. The authors declare no conflict of interest.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 1 Background and Motivation
### Paper Organization  
The remainder of this paper is organized as follows: Section 2 discusses the theoretical framework and methodologies employed, including our multi-criteria evaluation system and PCA-based fusion algorithm. Section 3 details the implementation steps of our forecasting framework, covering data preprocessing, feature engineering, model integration, and forecast fusion. Section 4 presents experimental results and comparative analyses across multiple regions and weighting scenarios. Finally, Section 5 concludes this paper with key findings and directions for future research.  
Our research provides grid dispatch centers with a practical solution for enhancing forecast accuracy while effectively managing the challenges of multi-source data heterogeneity, ultimately supporting safer and more economical power system operations.
# Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model
## 3 Methods
### Model Optimisation  
The objective of any neural network is to minimize the cost functions for the most accurate prediction performance by optimising the weights and biases with appropriate activation functions [41]. The Time2 Vec activation function, \(\mathcal{F}\), in Equation (19), as well as the activations functions of the LSTM layer, \(\sigma_{c}\) and \(\sigma_{h}\), in Figure 7, are pivotal to the design of LSTM or T2V-LSTM classifiers by affecting their predictive performance.  
GridSearchCV [42] is a hyper-parameter optimisation method based on a given neural network model to optimise the individual model for each combination of hyper parameters, such as the number of epochs, batch sizes, and activation functions. The optimization of hyper parameters intends to maximise prediction accuracy by minimising the cost functions and training times of neural networks. The tunes of hyper parameters are achieved by GridSearchCV, which adopts the \(k\)-fold cross-validation (CV) to train and test the neural network by grid-searching the combination of hyper parameters to generate the highest average score across \(k\) repeated times. The hyper parameters tuned for T2V-LSTM can be seen in Table 4.  
The optimum activation functions for both Time2 Vec, \(\mathcal{F}\), and the hidden layer, \(\sigma_{c}\), are given by _Tanh_, seen in Equation (28), while the optimal activation function for the final classification output layer, \(\sigma_{h}\), is yielded by _Softmax_ in Equation (29).  
\[Tanh(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{28}\]  
\[Softmax(x)_{i}=\frac{e^{x_{i}}}{\sum\limits_{j=1}^{N}e^{x_{j}}} \tag{29}\]  
The softmax activation function [43] is a combination of sigmoid functions applied for multivariate classification tasks by normalising the outputs with probabilities of each class ranging from 0 to 1, so the target class is expressed by the highest probability.
## 3 Operational Requirements
### Fruit Identification and Selection
#### 3.1.1 Advanced Computer Vision Systems  
The precise identification and selection of ripe fruits is one of the most critical and complex functions a harvesting robot must perform [9]. This capability not only determines the robot's harvesting effectiveness but also influences the quality of the final product and the overall efficiency of the harvesting process. To achieve this, the robot must be equipped with a combination of advanced technologies that work together to ensure optimal performance.  
The first step in machine vision recognition is the capture of images of the fruits in the field. This is done using RGB cameras and hyperspectral cameras:  
* **RGB cameras:** These cameras capture color images, which are useful for detecting fruit color, a visual indicator of ripeness. The captured colors can be processed to identify whether the fruit has reached the characteristic color of ripeness [10].
* **Hyperspectral cameras:** These cameras capture images at a wide spectrum of wavelengths, beyond what the human eye can see. This makes it possible to analyze internal characteristics of the fruit, such as sugar content and firmness, which are important in determining ripeness [11]. Figure 2 shows a fruit recognition system using artificial vision.  
Figure 1: Robot operation flow chart.  
Once the images are captured, advanced image processing algorithms are used to analyze and extract relevant features. This processing includes several steps:  
1. **Pre-processing:** It consists of improving the quality of the image and eliminating noise. Techniques such as lighting standardization and noise filtering are common at this stage [12].
2. **Image segmentation:** Separates the fruits from the background of the image. This can be achieved by segmentation algorithms such as color threshold, edge-based segmentation, or the use of neural networks for semantic segmentation.
3. **Feature extraction:** This involves identifying specific characteristics of fruits, such as color, texture, shape, and size. These characteristics are used to determine the state of ripeness of the fruit [13].
# Network Coding for Efficient Video Multicast in Device-to-Device Communications
## 1 Introduction
The source node sends the encoded video file to the destination nodes through multicast connections. After receiving the data, the destination nodes perform re-encoding operations, and then the re-encoded packets are sent back to the network. Finally, the nearby nodes can obtain and decode the original data through the re-encoded packets. Both the simulation results and actual experimental results showed that the proposed scheme could reduce the computation overhead of the source node, balance the computation load, and reduce the delay caused by the encoding and decoding operation. The remaining of the paper is organized as follows. In Section 2, we introduce some previous studies related to our research; in Section 3, the system model of the proposed scheme is introduced; in Section 4, the performance of the proposed scheme is analyzed through experiments; finally, in Section 5, we conclude our work.
## 3 Results
### Analysis of Energy Evolution Law
The relationship between the elastic modulus \(E_{0}\) of the specimen and the stress \(\sigma\) and strain \(\varepsilon\) is:  
\[E_{0}=\frac{d\sigma}{d\varepsilon} \tag{6}\]  
Based on this, a study was conducted on the energy storage and dissipation during uniaxial compression of different coal-rock ratio specimens, and the changes in total energy, elastic strain energy, and dissipated energy were calculated, as shown in Figure 10.  
When the total accumulated energy of the specimen reaches the energy storage limit, the specimen exhibits failure. Therefore, the R specimen has the highest strength, corresponding to the highest total accumulated energy of 195.6 KJ/m\({}^{3}\); the C specimen has the lowest strength, corresponding to the lowest total accumulated energy of 28.44 KJ/m\({}^{3}\), which verifies the accuracy of the experimental results. The decrease in total accumulated energy with increasing coal content indicates that the critical failure energy of the specimen  
Figure 10: Energy evolution diagram of the sample. decreases linearly, and the increase in coal content reduces the energy absorption capacity of the specimen. The dissipated energy also exhibits a similar evolution pattern to the total accumulated energy. During the early stage of compression, the original pores and fractures inside the coal-rock body are gradually compacted, with only a small amount of fracture development. Plastic deformation during this stage is minimal, resulting in less dissipated energy. However, when instability failure occurs, macroscopic cracks propagate completely, and the coal-rock body loses its bearing capacity completely, leading to a significant increase in dissipated energy. The greater the strength of the specimen, the more dissipated energy is produced when instability failure occurs. Comparing the energy storage and dissipation patterns of the primary coal-rock combination CSR (5:5) and the artificial coal-rock combination CR (5:5), the total accumulated energy of the primary coal-rock combination is less than that of the artificial one. As mentioned above, the greater the strength of the specimen, the more total accumulated energy it has. However, in terms of dissipated energy, the artificial coal-rock combination exhibits the opposite pattern to the primary coal-rock combination. This may be because the interface of the primary coal-rock combination is uneven, with mutual embedding between coal and rock. When the coal body fails, it induces the rock to fail, releasing a large amount of energy transferred to the coal body, exacerbating the damage and fracture of the coal body, resulting in a large amount of dissipated energy. In contrast, the interface between coal and rock in the artificial coal-rock combination is relatively smooth, and the feedback mechanism of coal and rock failure is weaker. From the perspective of failure characteristics, the rock part is relatively intact, resulting in less dissipated energy.
## 2 Materials and Methods  
### Chemicals, Membranes and Electrodes  
The experiments were conducted for the technical evaluation of the NF and EC processes employing a range of concentrations of metals in aqueous solution based on the available secondary data on electroplating effluent quality in the literature and also in the study area [27; 28]. All chemicals used in this research were of analytical grade, and synthetic composite metal solutions were prepared by dissolving the appropriate mass of each metal in high purity Milli-Q water (18.2 M\(\Omega\) cm). Copper (III) sulphate pentahydrate CuSO\({}_{4}\cdot\)5H\({}_{2}\)O, nickel (II) sulphate hexahydrate NiSO\({}_{4}\cdot\)6H\({}_{2}\)O, zinc sulphate ZnSO\({}_{4}\cdot\)7H\({}_{2}\)O, sodium hydroxide (NaOH), nitric acid (69-72%), sodium chloride (NaCl) and calcium carbonate extra pure (CaCO\({}_{3}\)) were all obtained from Merck Specialties Private Ltd. Quickklime (CaO), nanofiltration membrane (Permionics, Flat Sheet Membrane HFN-300 AR) and stainless steel (SS-304) electrodes were procured from the market as commercially available. The stated membrane was specifically employed so as to effectively work in both acid and basic medium. Stainless steel electrodes were used, as they are less susceptible to corrosion, and have reportedly shown a better performance in earlier studies. The grade of steel used is reported to not contain Zn, and have very low amount of carbon.
# Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network
## 2 Materials and Methods
### Proposed Architecture
#### 2.1.5 Attention Layer  
The attention mechanism employed in the proposed multi-scale convolutional neural network (MSCNN) is to enhance the model's ability to focus on the most relevant features of the input data while effectively ignoring less important information. This capability is crucial for tasks such as vegetation detection, where distinguishing between subtle variations in plant types and backgrounds can significantly impact segmentation accuracy. The attention mechanism is initiated by computing the attention scores, which determine how much focus should be placed on different parts of the input feature map. For a given input feature map \(F\), the channel attention score \(A_{c}\) can be computed as follows [22; 23]:  
\[A_{c}=\sigma(MLP(AvgPool(F))+MLP(MaxPool(F))) \tag{2}\]  
where \(\sigma\) denotes the sigmoid function, and \(MLP\) is multi-layer perception operation. Subsequently, the determination of the attention scores is outlined as  
\[F^{\prime}=A_{c}\odot F \tag{3}\]  
where \(\odot\) denotes element-wise multiplication. Furthermore, the spatial attention score \(A_{s}\) is computed as follows:  
\[A_{s}=\sigma(f^{\gamma\times 7}[AvgPool(F^{\prime});MaxPool(F^{\prime})]) \tag{4}\]where \(f^{7\times 7}\) represents a convolution operation with a filter size of \(7\times 7\). Thus, the final refined feature map is computed as  
\[F^{\prime\prime}=A_{s}\odot F^{\prime} \tag{5}\]  
The attention module is designed to prioritize regions with high vegetation density, allowing the model to allocate more computational resources to these areas while suppressing noise from non-vegetation regions. Additionally, it helps to assign weight to the importance of different feature maps, ensuring that the model emphasizes features indicative of vegetation characteristics such as color, texture, and shape.