esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Improved A-Star Algorithm
### Traditional A-Star Algorithm  
A-Star algorithm was first proposed and described in detail in [15]. The A-Star algorithm aims to find a path from the start node (\(N\left[start\right]\)) to the destination node (\(N\left[dest\right]\)) with the smallest cost, e.g., least distance. The flowchart of the traditional A-Star algorithm is denoted in Algorithm 1.  
```
1:Mark \(N\left[start\right]\) as \(openlist\).
2:while\(openlist\
eq\varnothing\)do
3: Select the open node \(N\left[i\right]\) from the \(openlist\) whose value of evaluation function \(f\left(N\left[i\right]\right)\) is smallest.
4:if\(N\left[i\right]=N\left[dest\right]\)then
5:return \"path is found\".
6:else
7: Mark \(N\left[i\right]\) as \(closelist\).
8:if Successor \(N_{i}\left[j\right]\) of node \(N\left[i\right]\) does not belong to \(closelist\) or \(obstacle\) node then
9: Mark \(N_{i}\left[j\right]\) as \(openlist\) and calculate \(f\left(N_{i}\left[j\right]\right)\).
10:if\(N_{i}\left[j\right]\) belongs to \(openlist\) and \(f\left(N_{i}\left[j\right]\right)\) is smaller than \(f\left(N_{m}\left[j\right]\right)\) when \(N\left[m\right]\) was marked as \(closelist\)then
11:\(f\left(N\left[j\right]\right)=f\left(N_{i}\left[j\right]\right)\) and set parent node of \(N\left[j\right]\) as \(N\left[i\right]\).
12:endif
13:endif
14:endif
15:endwhile
16:return \"path is not found\".
```  
**Algorithm 1** Traditional A-Star algorithm (\(N\left[start\right]\to N\left[dest\right]\))  
The evaluation function \(f\left(N\left[i\right]\right)\) of \(N\left[i\right]\) is defined as:\[f\left(N\left[i\right]\right)=g\left(N\left[i\right]\right)+h\left(N\left[i\right] \right), \tag{8}\]  
where \(g\left(N\left[i\right]\right)\) is the cost of the path from \(N\left[start\right]\) to \(N\left[i\right]\), and \(h\left(N\left[i\right]\right)\) is a heuristic function that estimates the cost of the cheapest path from \(N\left[i\right]\) to \(N\left[dest\right]\). \(h\left(N\left[i\right]\right)\) can be Euclidean distance (Manhattan distance or Chebyshev distance) from \(N\left[i\right]\) to \(N\left[dest\right]\), or other functions denoting the cost from \(N\left[i\right]\) to \(N\left[dest\right]\) as in [9]. Please note that in contrast to the A-Star algorithm, the Dijkstra algorithm has no heuristic function that can accelerate the rate of finding an optimal path. The Euclidean distance is selected as \(h\left(N\left[i\right]\right)\) in this article.
## 3 Materials and Methods
### Fiber Selection  
In this study, the manual sampling SPME holder was used and four different fused silica fibers (Supelco-Aldrich, Bellefonte, PA, USA) were tested: polydimethylsiloxane (PDMS) with 100 \(\upmu\)m thickness, carboxen/polydimethylsiloxane (CAR/PDMS) with 75 \(\upmu\)m thickness, polydimethylsiloxane/divinylbenzene (PDMS/DVB) with 65 \(\upmu\)m thickness, and divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) with 50/30 \(\upmu\)m thickness. Prior to the sampling, all the above fibers were conditioned using their recommended temperature and duration: PDMS, 250 \({}^{\circ}\)C for 30 min; CAR/PDMS, 300 \({}^{\circ}\)C for 30 min; PDMS/DVB, 230 \({}^{\circ}\)C for 30 min; and DVB/CAR/PDMS, 270 \({}^{\circ}\)C for 60 min.  
Different fibers were first manually inserted into the sample vial that had been incubated in a dry heat block for 30 min at 80 \({}^{\circ}\)C, and exposed to the headspace of aqueous saline for the extraction at the same temperature for 30 min. After extraction, the fibers were immediately injected into the GC injection port for desorption at 250 \({}^{\circ}\)C for 3 min in splitless mode. The samples of sweet potato cultivar Anna were used to test the specific extraction capacity of fibers based on the total chromatographic area and number of chromatographic peaks.
## 3 First Generation: Virtual Inertia Topologies
### Ise Lab's Topology: A Swing Equation Based Topology  
The topology developed by Ise lab for virtual inertia implementation is similar to the synchronverter approach described previously, but instead of using a full detailed model of the SG, the topology solves the power-frequency swing equation every control cycle to emulate inertia [40]. The schematic diagram of the topology illustrating the operation principle is shown in Figure 8a. The controller senses the inverter output current \(i\) and the voltage of the point of connection \(v\), and computes the grid frequency \(\omega_{g}\) and active power output of the inverter \(P_{out}\). These two parameters are inputs to the main control algorithm block along with \(P_{in}\) which is the prime mover input power [41]. Within the control algorithm, the swing equation given by Equation (10) is solved every control cycle thus generating the phase command \(\theta\) for the PWM generator. The typical swing equation of a SG is:  
\[P_{in}-P_{out}=J\omega_{m}(\frac{d\omega_{m}}{dt})+D_{p}\Delta\omega \tag{10}\]  
\[\Delta\omega=\omega_{m}-\omega_{g} \tag{11}\]  
where, \(P_{in}\), \(P_{out}\), \(\omega_{m}\), \(\omega_{g}\), \(J\), and \(D_{p}\) are the input power (similar to the prime mover input power in a SG), the output power of the inverter, virtual angular frequency, grid/reference angular frequency, moment of inertia, and the damping factor, respectively. A model of the governor, as shown in Figure 8b, is utilized to compute the input power \(P_{in}\) based on the frequency deviation from a reference frequency \(\omega^{*}\).The governor is modeled as a first-order lag element with gain \(K\) and time-constant \(T_{d}\). \(P_{0}\) represents continuous power reference for the DG unit. The delay in the governor model leads to higher ROCOF and thus higher frequency nadirs as a consequence. The voltage reference \(e\) can be generated through \(Q-v\) droop approach as described in [42; 43].  
Similar to the synchronverter, derivative of frequency is not needed to implement the control algorithm. This is highly beneficial as frequency derivatives are know to introduce noise in the system which makes the system difficult to control. Additionally, this topology can be used to operate DG units as grid forming units. However, problems related to numerical instability still remain, which along with improper tuning of parameters \(J\) and \(D_{p}\), can lead to oscillatory system behavior [41].  
Figure 7: Synchronverter topology: (**a**) overall schematic showing operating principle; (**b**) detailed control diagram showing the modeling equations.
# A Multi-Task Dynamic Weight Optimization Framework Based on Deep Reinforcement Learning
## 4 Experiments
### Comparison Experiments  
Table 1 presents experimental results averaged over 20 independent trials. Our framework demonstrates consistent advantages across all evaluation metrics:  
**Key Observations:**  
**The superiority of DRL-Based Adaptation**: The proposed framework achieves 0.5-3.2% higher mean accuracy than STL and MTAN across datasets, demonstrating effective knowledge transfer without negative interference. The dynamic weight adjustment through the DRL agent prevents dominant tasks from overwhelming others, unlike fixed-weight HPS.  
**Efficiency Advantage**: Our method reduces training time by 35-62% compared to evolutionary-based HPS_DE and gradient-based MGDA. The Actor-Critic architecture enables real-time weight optimization without population evaluations or second-order gradient computations.  
**Heterogeneous Task Handling**: On CIFAR10 MNIST, DRL-based balancing improves Task 1 accuracy by 2.8% over HPS_DE while maintaining 95.67% on Task 2. The geometric mean reward design explicitly prioritizes struggling tasks without the catastrophic forgetting of high-performance tasks.  
Figure 4: Sample schematic of the Cifar10 MultiMnist dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Dataset** & **Method** & **Task 1 Accuracy** & **Task 2 Accuracy** & **Task 3 Accuracy** & **Mean Accuracy** & **Training Time (Minutes)** \\ \hline \multirow{8}{*}{MultiMNIST} & STL & 98.62\% & 95.08\% & - & 96.85\% & 34 \\  & MTAN & 97.85\% & 96.73\% & - & 97.29\% & 37 \\  & HPS & 97.14\% & 95.35\% & - & 96.25\% & 17 \\  & HPS & 99.14\% & [99.04\%] & - & [99.09\%] & 27 \\  & MGDA & 97.26\% & 95.90\% & - & 96.58\% & 52 \\  & MDMTN & 99.08\% & 98.67\% & - & 98.88\% & 25 \\  & MTL with DRL & [99.18\%] & 98.94\% & - & 99.06\% & [13] \\ \hline \multirow{8}{*}{CIFAR10 MNIST} & STL & 54.32\% & [98.56\%] & - & 76.62\% & 18 \\  & MTAN & 59.14\% & 97.25\% & - & 78.20\% & 29 \\  & HPS & 56.86\% & 96.15\% & - & 76.50\% & [16] \\  & HPS & 60.90\% & 97.02\% & - & 78.96\% & 47 \\  & MGDA & 46.28\% & 96.80\% & - & 71.54\% & 67 \\  & MDMTN & 59.28\% & 94.45\% & - & 76.86\% & 41 \\  & MTL with DRL & [62.90\%] & 95.67\% & - & [97.28\%] & 17 \\ \hline \multirow{8}{*}{CIFAR10 MultiMNIST} & STL & 48.75\% & 96.86\% & 96.92\% & 81.34\% & 51 \\  & MTAN & 53.16\% & 96.95\% & 96.87\% & 82.33\% & 57 \\  & HPS & 51.31\% & [97.49\%] & [97.38\%] & 82.15\% & [33] \\ \cline{1-1}  & HPS\_DE & 53.20\% & 97.00\% & 97.07\% & 82.42\% & 60 \\ \cline{1-1}  & MEDA & 43.83\% & 96.18\% & 95.12\% & 78.38\% & 76 \\ \cline{1-1}  & MDMTN & 57.18\% & 96.15\% & 96.25\% & 83.19\% & 49 \\ \cline{1-1}  & MTL with DRL & [60.41\%] & 96.80\% & 96.99\% & [84.73\%] & [33] \\ \hline \end{tabular}
\end{table}
Table 1: Performance comparison on different datasets.
# A Dependable Localization Algorithm for Survivable Belt-Type Sensor Networks
## 3 Hop-Distance Estimation Correction  
In this section, we examine the connectivity among nodes and correct the hops-distance estimates based on the differences in connectivity. The non-ranging positioning algorithm can be described as follows: Firstly, adopting the network to perceive information such as (ID, location, hops count, etc.) between the unknown node and the anchor node. Secondly, using information fusion and mathematical calculation methods to estimate the distance between nodes. Finally, the location of the unknown node can be estimated. The basic steps are:  
1. Using the protocol of distance vector exchange in sensor networks, the hop count \(h\) and distanced information between unknown nodes and anchor nodes are collected. In the network, packets containing location information are forwarded until all nodes are aware of the location of each anchor node. With data fusion technology, data in all packages is associated.
2. According to the location information of other anchor nodes received by the known node, the hop-distance conversion model is established. Using the distance formula, we can estimate the actual distance about per hop, and then broadcast it over the entire network.
3. We can obtain the estimated distance between the unknown target node and each anchor node. By applying the mathematical method (triangular method and maximum likelihood estimation method), we can further estimate the position coordinates of the target node and correct the calibration.
## 4 Numerical Simulation of Excavation and Support
### Determination of The Optimal Longitudinal Bench Length  
During the excavation process with the bench method, the most critical factor affecting deformation of the tunnel is the length of the bench. In this section, the deformation of the tunnel under different bench lengths was simulated to determine the optimal bench length. A representation of the bench method is shown in Figure 5.  
Four models with 5, 10, 15 and 20 m bench lengths, respectively, were selected for simulations. Calculation results of these models are shown in Table 3. The distributions of peripheral displacement and the plastic zone with  
With the increase of the bench length, the vault settlement, sidewall convergence, bottom uplift, and longitudinal displacement of the tunnel face all clearly increase, while the axial force and bending moment of the steel arch frame tend to decrease. The total range of the plastic zone increases with the increase in bench length, and is mainly distributed at the bottom, the sidewalls and the tunnel face. The plastic zone near the tunnel face tends to decrease with the increase of bench length. This is because a bench with sufficient length can stabilize the tunnel face. Therefore, increasing the bench length can effectively control the development of the plastic zone of the tunnel face.  
From the above conclusions, it can be further concluded that the short bench method can effectively control the deformation of the surrounding rock and the plastic zone. As shown in Figures 6 and 7, the tunnel plastic zone and displacement decrease with the reduction of bench length, but a bench length that is too short makes it difficult for construction machinery to work (for example, the length of a rock drilling trolley is about 9-13 m). Considering that enough working intervals should be reserved on the bench in actual construction, the optimal bench length can be determined to be 10-14 m (about 1.5-2 D, where D represents the diameter of the tunnel).  
Figure 6: Distribution of plastic zone under different bench lengths: (**a**) 20 m bench length; (**b**) 15 m bench length; (**c**) 10 m bench length; (**d**) 5 m bench length.
###### Abstract  
Multi-epoch double-differenced pseudorange observation (MDPO) is a dual-satellite lunar navigation algorithm specially designed for a precursor mission, using a minimum number of lunar orbiting small satellites to realize a GNSS-like radio navigation system for the Moon. In this study, we evaluated the performance of the MDPO algorithm by using real pseudorange measurements obtained from a pair of GNSS ground stations, one of which represented a lander, and the other a rover on the Moon. It was natural that the resulting positioning accuracy varied largely by satellite geometry, but the estimated error distributions of the double-differenced pseudorange observations were consistent and agreed with the predicted value. The results showed that the MDPO algorithm worked properly with the real GNSS observables and was capable of providing the expected navigation performance for future lunar exploration missions.  
GNSS; navigation; lunar exploration; lunar rover; small satellites 2022
# Effect of Thermal Treatment on Nano- and Micro-Copper Particles for Jewelry Making
## 2 Materials and Methods  
### Materials  
The copper particles were synthesized by the chemical reduction method in an aqueous solution. Copper sulfate pentahydrate (CuSO4 5H2O, Merck), ascorbic acid (C6H8O6), and polyvinylpyrrolidone (PVP, laboratory-grade, of the average molecular weight of 40,000) were used as a precursor, reducing agent, and capping agent, respectively. All chemicals were analytical grade and used as purchased without further purifications. The synthesis of copper nanoparticles began by adding 250 mL of 0.1 M copper sulfate pentahydrate solution to 250 mL of PVP (0.1%) solution with vigorous stirring for 30 min. Then, 200 mL of 0.2 M ascorbic acid solution was added to the mixture under continuous stirring, followed by slowly adding 1.0 M sodium hydroxide to adjust the solution to pH 7. The prepared solution was continuously stirred at a constant rate of 400 rpm with heating at 80 \({}^{\circ}\)C for 2 h. The color of the solution turned from blue to brick red and then reddish brown. After completion of the reaction, the solution was removed from the heat source, and the precipitate was separated by filtration and washed with deionized water three times. Under these conditions, average particle size of the copper nanoparticles was controlled in the range 120-200 nm. To synthesize micro-copper particles, copper sulfate pentahydrate solution was distilled in deionized water without the PVP capping agent, with concentrations of the other reagents and the reaction process the same as for the nano-copper synthesis. Under this condition, average size of the copper micro-particles ranged 3-5 microns. After drying, the micro- and nano-particles were stored in glass vials for further copper clay production. The particle sizes were confirmed by a scanning electron microscope (SEM).
## 4 Results and Discussion
### Sensor Robot Design as Access Point
#### 4.1.5 Location Information Unit Determined by Global Positioning System (GPS)  
The image manager includes an input unit (interface) capable of receiving image setting information. When the operator inputs image setting information through the input unit, the image manager can include one or more images. The image manager can also set an image to be projected among at least one image based on the image setting information stored in the external storage device. The operation is based on the location information of the robot device obtained by the location information unit (250 in Figure 7a). For example, when it is determined that the robot device is within a preset range at the traffic accident point, based on the acquired location information, the image manager views the warning image to be projected by the image projector in an 'accident occurrence'. The image manager can also determine which robot device is located closer to the approaching vehicle based on the acquired location information.  
Additionally, the location information unit can acquire longitudinal and latitudinal information of the robot device using a GPS and an indoor-outdoor positioning system. The location information of the robot device can also be obtained through a media access control address of a connected WIFI access point.
# Predicting the Quality of Tangerines Using the GCNN-LSTM-AT Network Based on Vis-NIR Spectroscopy
## 3 Neural Networks
### Cnn-At  
CNN-AT is the combination of three blocks of hierarchical CNNs and an attention mechanism followed by a multi-layer perceptron (MLP). The flowchart of CNN-AT is shown in Figure 7.  
CNN-AT consists of three CNN blocks (CONV1, CONV2, and CONV3) with diverse kernel sizes and different numbers of filters. Specifically, Conv2d in CONV1 possesses 8 filters with kernel size \(1\times 25\), Conv2d in CONV2 has 16 filters with kernel size \(1\times 15\) and Conv2d in CONV3 contains 32 filters with kernel size \(1\times 11\). Other compositions are the same in the three blocks: each Conv2d layer is followed by instance normalization (IN), parametric rectified linear unit activation (PReLU) [30], dropout, and max-pooling operations. The CNN-AT network incorporates an attention mechanism after the CNN blocks to selectively attend to specific parts of the input features. The output of the attention mechanism is then flattened and fed into MLP for further feature extraction and regression prediction. The MLP includes two fully connected layers (hidden size = 128), IN, PReLU, and a dropout rate of 0.2.
## 1 Introduction
### Expressions for Estimating Efficiency  
In general, the efficiency \(\eta\) can be defined as the ratio between the harvested power and the input mechanical power. [PERSON] et al. [12] presented an expression for the efficiency as the ratio between the power dissipated across the applied load resistor and the power dissipated by the input voltage across the total impedance of the entire circuit. They considered the Butterworth-van Dyke model for the circuit. The final presented expression for the efficiency was found to be dependent only upon the quality factor and the electromechanical coupling coefficient of the system at the resonance operation condition and an applied load resistance of \(1/C_{p}\omega_{n}\). [PERSON] and [PERSON] [13], [PERSON] et al. [14], and [PERSON] et al. [15] worked on developing an expression for estimating the efficiency based on the same concept of the ratio between the output and the input. When unifying the symbols of the variables in the introduced equations, the derived formula from [PERSON] et al. [14] was found to be very close to what introduced by [PERSON] and [PERSON] [13], see Table 1. The formula derived by [PERSON] et al. [15] was found to be exactly the same as the one introduced by [PERSON] et al. [14]. [PERSON] and [PERSON] [16] derived an expression for the average efficiency at the steady state by dividing the average harvested energy by the average change in the energy state of the system (heat losses + energy lost in damping + harvested energy). They reported that for the conservative case (heat losses = 0), their formula would be the same as the one proposed by [PERSON] and [PERSON] [13].  
Table 1 displays the reviewed equations introduced for the efficiency. The symbols of the variables in the displayed equations in the table are edited from than their original sources in order to use the same symbol for the same variable and hence, to avoid repeatedly defining the variables. This will help differentiate between the proposed equations for the efficiency. The efficiency in general was found to be dependent on the mechanical quality factor, the coupling coefficient, the ratio of the excitation frequency to the resonance frequency, and the ratio of the applied load resistance to the magnitude of \(1/C_{p}\omega_{n}\).  
The efficiency is one factor to appraise between proposed vibration-based generators, but it is not enough to decide the effectiveness, because other criteria, such as volume, were not included. The efficiency will not give an accurate evaluation, especially between the broadband energy harvesters, because the bandwidth was not included. [PERSON] et al. [15] reported that the efficiency, as the ratio between the output and the input, is not sufficient to decide the effectiveness of piezoelectric energy harvesters. The input power differs depending on the phase and in general has no uniform definition. The output power of the vibration energy harvester is dependent on many factors and varies if the operation is on resonance or off resonance.
## 3 Security Attacks in Smart Healthcare Systems
### Denial-of-Service Attacks  
Healthcare systems are most vulnerable to voluminous attacks (e.g., DoS and flooding), where hackers or cybercriminals make the network resource inoperable by flooding the network with a massive number of packets. As a result, several critical services in healthcare systems, such as data sharing, appointment scheduling, and patient reports, cannot be accessed by medical personnel. The attack could be more severe when multiple machines are connected with each other to target a specific machine or resource in the healthcare systems, commonly known as distributed denial-of-service (DDoS) attack. The traditional DoS attack sends multiple packets from a single machine; conversely, in a DDoS attack, the attackers use previously-hijacked machines and interconnect them (botnets) so that each machine sends multiple packets to the target machine simultaneously, thereby harming the healthcare ecosystems. Several cases have been reported in the past where DDoS has been considered one of the most significant attack vectors in the healthcare environment. For example, in 2014, attackers unleashed a DDoS attack on the Boston Children's Hospital, where \(\approx\) 40,000 network resources were manipulated and controlled by the attacker's home computer [57]. This attack was so massive that the Boston hospital not the only facility to be affected; the attack also had severe effects on other hospitals connected to the same network interface. Furthermore, the impact of DDoS attacks become more severe when integrated with other passive or active attacks, such as malware and injection attacks, leading to huge losses to healthcare infrastructure. Cybersecurity and Infrastructure Security Agency (CISA) recently disclosed a new vulnerability in patient monitoring systems (CME8000 devices) that could cause a massive DDoS attack [58]. The attacker can craft multiple user datagram protocol (UDP) packets to crash the CME8000 devices and to gain momentary access to the system setting, where they can install malicious firmware. Consequently, CME8000 devices' functionality is permanently changed, which places the patient's life at risk. The authors of [59] designed a device authentication mechanism for IoT-enabled healthcare systems. They have adopted the authentication mechanism to secure the IoT-based healthcare system against DoS attack along with the other attacks such as MITM and eavesdropping attacks.