esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Camera Placement Optimization Based on Reinforcement Learning
#### 3.3.3 Definition of the DQN  
The Q-network is primarily used to calculate the system reward after performing action \(a\) in state \(s\), which includes two main components: the coverage value improvement (r) of the monitoring system after executing action a and the potential maximum reward from the subsequent state (s\({}^{\prime}\)). Compared with DNN and other reinforcement learning methods such as SARSA, DQN enjoys the following advantages: it is capable of handling continuous action spaces and addressing complex action selection problems; it is able to efficiently manage large-scale state and action spaces; it is capable of learning from experience and automatically adjusting strategies; and it is capable of dealing with partially observable environments. These advantages make it suitable for spatial syntax analysis. Existing Q-networks are commonly structured as multi-layer perceptrons, with the input being a  
Figure 5: Reward computation.  
state space vector and the output a vector of rewards for each action. In this study, we improved the original DQN for better application in video camera placement optimization. An attention mechanism is applied to enhance the different importance of the raster map. A dense layer is employed as the attention module. Assuming there are k cameras in the scene, the input state vector has a dimension of 3k + 1, while the output vector of action rewards has a dimension of 6. Given typical scenarios with around 10-20 cameras, the hidden-layer dimensions are set as [256, 256, 128] to ensure both computational accuracy and efficiency. Figure 6 describes the framework of the proposed DQN training process. After training, the DQN will be used to optimize the camera deployment and generate the suggested configuration to maximize the space syntax reward of the visual coverage fields. In this paper, the original DQN is used and adjusted with the reward function, which can be further improved to take more constraints into consideration, such as installation cost and different types of cameras. Therefore, the proposed framework can be easily extended into different applications.
## 2 Selection of Vertiports
### Vertiport Repositioning
#### 2.4.4 Final Vertiport Locations  
Examples of the adjusted final vertiports are shown in Figure 8. Most vertiports were selected near Han River Park or in cloverleaf highway interchanges (IC) in Gangnampau and Seocho-gu. For fully populated residential areas, rooftop parking lots, such as Cheongnyangni Station in Dongdaemun-gu, or heliports in high-rise buildings were selected as vertiports. In addition, a vertiport was located at Gimpo Airport in Gangseo-gu to be used as an airport shuttle, the largest market for early UAM.  
One of the input parameters required by the K-means algorithm is the number of clusters (\(k\)), which is the number of vertiports. Due to the nature of the K-means algorithm, the location of the center of the cluster varies depending on the number of clusters. Therefore, a sensitivity analysis is needed to investigate how locations of the vertiports change for the changes of the parameter \(k\). The locations of the vertiport when the number of vertiports is 10 and 40 are shown in Figure 9. Increasing the number of vertiports leads to an increase in passenger convenience by reducing distance and travel time from the departure point to vertiports and from vertiports to the final destination. However, if the \(k\) value increases, the  
Figure 8: Examples of final 100 vertiports.  
Figure 7: Location of repositioned vertiports.  
costs of building vertiports and operation will also be increased. In addition, the locations of the 10 vertiports are different from the 40 vertiport locations. Therefore, the optimal number of the vertiports should be determined before clustering is conducted.  
According to the reference [19], 100 vertiports are enough for a metropolitan area having 10 million people, such as Seoul. Hence, in this study, we have chosen 100 vertiport locations for optimal vertiports.
## 2 Optimization Model of Bench Drilling and Blasting Parameters in Open-Pit Mine  
In recent years, among a large number of parameters that affect the blasting effect and blasting quality, the block theory is widely accepted by scholars [17]. The main parameters for blasting block are hole spacing, explosive consumption, and resistance line; thus, the design of these parameters can change the overall block and improve the blasting quality and effect [18; 19].  
According to the actual situation of mine production, the blasting mining cost is divided into drilling cost \(C_{1}\), blasting cost \(C_{2}\), and shipping cost \(C_{3}\).  
The cost of drilling in a significant number of drilling and blasting operations exhibits a strong positive correlation as follows:  
\[C_{1}=\frac{m_{1}L}{HS\gamma} \tag{1}\]  
where \(C_{1}\) represents the overall expenditure for drilling all holes; \(m_{1}\) denotes the cost per meter drilled; \(L\) is the hole depth; \(H\) is the height of the step; \(S\) is the bore area of the pore network; \(\gamma\) is the bulk density of the ore.  
The cost of step blasting is mainly related to the consumption of explosives, hole load area, and step parameters and mainly consists of the consumption of explosives and blasting materials.  
\[C_{2}=m_{2}q+\frac{m_{3}L}{HS\gamma} \tag{2}\]  
where \(q\) is the single explosive consumption; \(m_{2}\) is the unit price of the explosive; \(m_{3}\) is the cost of blasting equipment consumed per meter.  
Figure 2: A flow chart for parameter optimization analysis.  
The loading process mainly includes two parts: shoveling cost and transportation cost. The shoveling process is affected by the degree of rock blocks, and the average block size is taken as the independent variable of shoveling cost, which is obtained by statistics on the consumption of shoveling equipment and the amount of shoveling per unit time. The transportation cost is related to the consumption of vehicles and the average block size, among which the average block size mainly affects the load rate of vehicles. Shipment costs are grouped into two parts as follows:  
\[C_{3}=A_{1}X+A_{2}+m_{4}(A_{3}X^{2}+A_{4}X+A_{5}) \tag{3}\]  
where \(X\) is the average lumpiness of the shipped ore; \(m_{4}\) is the transportation cost per ton of ore; \(A_{1}\)-\(A_{5}\) is the correlation coefficient of the statistical model.  
In the mine's production process, the results of open-pit drilling and blasting are affected by many factors, and the average lumpiness after blasting directly affects the subsequent production cost. Therefore, the model of blasting production cost is established as follows:  
\[C=C_{1}+C_{2}+C_{3} \tag{4}\]  
The cost of rock drainage is in line with the transportation cost, as it encompasses the expenses related to personnel operation and management included in the shipping cost.
# Transformative Technology for FLASH Radiation Therapy
## 4 Fixed-Field Gradient Accelerators for FLASH-RT
### Non-scaling FFGA for FLASH
#### 4.2.1 Overview of Principle Design  
A compact 250 MeV/nucleon, fixed-magnetic-field turnkey machine has been designed in a racetrack format with a variable-energy continuous-output beam without a degrader, and with low-loss operation. The design is isochronous and produces a continuous beam for the ion species with a charge-to-mass ratio of \(\frac{1}{2}\) (H\({}_{2}\)\({}^{+}\), D\({}^{+}\), He\({}^{2+}\), Li\({}^{3+}\), B\({}^{5+}\), C\({}^{6+}\), N\({}^{7+}\), O\({}^{8+}\), and Ne\({}^{10+}\)) and is therefore capable of accelerating all ion species to therapeutic energies. An outer ring can be added to further the energy reach of the ions to the full 430 MeV/nucleon.  
Accelerating ions with an approximately constant charge to mass ratio has the advantage of equal beam transmission independent of ion species. Identical operation and extraction are maintained for all the therapeutic ions, including protons (H\({}_{2}\)\({}^{+}\)), implying a turnkey operation even when switching between ions. Further, the possibility of accelerating and extracting multi-ion composite beams from a mix, or cocktail, of injected ion species (with an effectively equivalent charge-to-mass ratio) is ground-breaking technology. This approach also provides the rapid switching between ion species, a capability based on the <1% rapid adjustments in the RF frequency.  
The complete system involves the injector, a pre-accelerator, and the therapy ring--similar to cascade synchrotron systems. To allow variable-energy extraction in a long straight section, the energy range must be restricted in order to extract inner, low-energy orbits using extraction magnets with feasible strengths and in particular apertures. The pre-accelerator extraction energy not only facilitates variable-energy extraction in the higher energy ring, but it can also support an independent beamline for eye treatments and R&D.
# Performance Evaluation of a Mesh-Topology LoRa Network
###### Abstract  
Research into, and the usage of, Low-Power Wide-Area Networks (LPWANs) has increased significantly to support the ever-expanding requirements set by IoT applications. Specifically, the usage of Long-Range Wide-Area Networks (LoRaWANs) has increased, due to the LPWAN's robust physical layer, Long-Range (LoRa), modulation scheme, which enables scalable, low-power consumption, long-range communication to IoT devices. The LoRaWAN Medium Access Control (MAC) protocol is currently limited to only support single-hop communication. This limits the coverage of a single gateway and increases the power consumption of devices which are located at the edge of a gateway's coverage range. There is currently no standardised and commercialised multi-hop LoRa-based network, and the field is experiencing ongoing research. In this work, we propose a complementary network to LoRaWAN, which integrates mesh networking. An ns-3 simulation model has been developed, and the proposed LoRaMesh network is simulated for a varying number of scenarios. This research focuses on the design decisions needed to design a LoRa-based mesh network which maintains the low-power consumption advantages that LoRaWAN offers while ensuring that data packets are routed successfully to the gateway. The results highlighted a significant increase in the packet delivery ratio in nodes located far from a centralised gateway in a dense network. Nodes located further than 5.8 km from a gateway's packet delivery ratio were increased from an average of 40.2% to 73.78%. The findings in this article validate the concept of a mesh-type LPWAN network based on the LoRa physical layer and highlight the potential for future optimisation.  
LoRa; LoRaWAN; IoT; network; mesh; multi-hop; sensor networks; LPWAN; ns-3
## 5 Evaluation and Results
### Experimental Setup and Evaluation
#### 5.3.2 Performance Metric  
The performance metric used to assess our method is defined below:  
* **Reactivity**: This is the measure of how much the protocol is able to react to changes in the monitored phenomenon. This implies the adaptability of the sample interval to the changes in the signal.
* **Relative Update Rate** (\(R_{update}\)): This is the ratio of the number of transmissions using SWIFTNET to the number of transmitted packets, when using a default adaptive prediction algorithm for the entire monitoring period. The relative update rate for the default adaptive prediction model is \(1\), since all updates will be sent when required. It is expected that the relative update rate, \(R_{update}\), is bounded as \(0\leq R_{update}\leq 1\); the lower this value is, the better the gain in terms of energy savings.
* **Accuracy**: The accuracies of the methods are measured using both the Relative Error (RE) and the Mean Absolute Error (MAE) metrics. This reflects the accuracy of the reconstructed samples with respect to the corresponding observation.
## 3 Results and Discussion
### Fracture Failure Process Analysis Subjected to F-T Conditions
#### 3.2.1 Fracture Failure Evaluation by AE Index
(2) Tensile fracture failure analysis under F-T conditions  
The AE signal's direct parameters (including amplitude and count) versus tensile load level are plotted in Figure 6 for bitumen subjected to F-T conditions. As illustrated, the results of signal amplitude and the count would have more significant and detailed fracture stages accompanied by the load level with loading time. The variation trends of the AE signal's direct parameters (including amplitude and count) for the asphalt mixture show a similar change tendency under F-T conditions. Combined with the intensity change in the direct parameters and the load level change, the fracture damage procedure can also be separated into four stages, i.e., (1) I--micro-crack initiation, (2) II--micro-crack accumulation, (3) III--damage development, and (4) IV--macro-crack generation. The development trend of these four stages under the tensile mode is similar to that under the compression mode.
## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Trustworthiness
#### 4.2.3 Trusted Machine Learning  
Byzantine attacks frequently target dispersed education platforms while changing model variables and combining gradients between several learners. Thus, in order to overcome the byzantine attack, a framework known as PiRATE using the sharding mechanism of blockchain has been proposed and is effective in distributed machine learning in next-generation knowledge-based networks [261].  
Some have attempted Distributed Federated Learning (DFL) by making use of a blockchain-driven agreement process to train artificial intelligence models within end devices for trustworthy shared training in knowledge-defined vehicular networks [262]. Moreover, secure collaborative deep learning at the device level in IoT KDN to avoid having only one spot of malfunction, data poisoning, privacy leaks, etc., where blockchain is deployed to guarantee immutability and secrecy in deep learning, called BlockDeepNet, has been studied [263]. Additionally, in [264], blockchain has been utilized by using a voting-based consensus approach for validation and having forwarding elements that are registered and verified using zero knowledge proof to provide secure data for a deep Boltzmann machine learning model for flow analysis to detect switch anomalies. Furthermore, taking into account the tendency of ML methods learned on private data to reveal information for hostile assaults, a framework known as PriModChain has been proposed that leverages the federated machine learning secured by the Ethereum blockchain and smart contracts in intelligent knowledge-based industrial Internet of Things networks [265].  
In next-generation 6G KDNs, even though they are controlled and managed centrally, scattered education is a possibility for machine learning algorithms. Therefore, in order to ensure trustworthy model training, a Blockchain-Based Distributed Deep Learning (BBDDL) design is proposed, wherein, to validate the machine learning algorithms, bipartisan blockchain agreement is required [266]. In addition to improving trustworthiness in AI systems with the integration of blockchain, some have attempted to improve the explainability of AI outputs. Additionally, an arrangement made up of AI was recently investigated in [267], where the decision-making output relies on a decentralized agreement of various AI and comprehensible AI predictions that are carried out using blockchain.
# Experimental Investigation of TR-UHPC Composites and Flexural Behavior of TR-UHPC Composite Slab
## 2 Axial Tensile Performance of TR-UHPC
### Results and Analyses of Axial Tension Tests
#### 2.3.1 Failure Mode  
Figure 4 illustrates the failure modes of all tensile specimens. Compared with the plain UHPC axial tensile specimen, the tensile strength of the TR-UHPC composites were substantially increased. As shown in Figure 4, the plain UHPC specimen failed with UHPC matrix fracture and steel fiber pull-out, while the TR-UHPC specimen failed with fiber textile rupture after matrix cracking.  
The failure process of the TR-UHPC tensile specimens can mainly divided into three stages: the elastic stage, post-cracking stage, and rupture failure stage. In the elastic stage, the UHPC matrix and the textile are deformed consonantly and subjected to tension. Meanwhile, the modulus of elasticity of the TR-UHPC composite is related to the properties and proportions of the UHPC matrix and the fiber textile. After the UHPC matrix cracked,  
Figure 3: Experiment program and setup of the axial tension test.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen Group** & **Initial Cracking Stress (MPa)** & **Initial Cracking Strain (\(\mu\)x)** & **Peak Stress (MPa)** & **Peak Strain (\(\mu\)x)** \\ \hline T-U & 7.48 (1.4\%) & 144.5 (7.3\%) & 7.96 (5.6\%) & 164.3 (17.7\%) \\ T-G2 & 8.16 (3.3\%) & 182.4 (11.0\%) & 8.60 (3.9\%) & 222.3 (24.5\%) \\ T-C2  
the TR-UHPC composites entered the post-cracking stage with an increase in the tension value. Note that only the T-B4 specimens experienced a multi-cracking progress because its textile reinforcement ratio exceeded the critical textile reinforcement ratio reported in reference [31], and the bonding performance between the BFT and UHPC matrix was excellent, which is like suitably reinforced concrete beams. In contrast, the T-B2 specimens with a lower textile reinforcement ratio only had one principal crack during tensioning. T-C2 and T-G2 specimens experienced the same failure mode, that is, fiber textile rupture. Due to the weak bonding performance between the CFT/GFT and UHPC matrix, their crack pattern was single cracking.
## 3 Problem Statement
### Model
#### 3.2.4 Constellation  
As the constellation tasks can be performed by any satellite, constraint (12) restricts the minimum and maximum number of subsystems to be conducting a job \(j\) at an instant \(t\). Such constraints establish a minimum and maximum simultaneity factor \(\psi\).  
\[\sum_{s=1}^{S}x_{s,j,t} \geq\psi_{j}^{\text{min}},\ \forall t,\forall j \tag{12a}\] \[\sum_{s=1}^{S}x_{s,j,t} \leq\psi_{j}^{\text{max}},\ \forall t,\forall j \tag{12b}\]  
Minimum (\(g_{j}^{\text{min}}\)) and maximum (\(g_{j}^{\text{max}}\)) general startups are defined for constellation tasks, as stated by constraints (13a) and (13b). Notice that these constraints work the same way as the previously presented (8a) and (8b), with the included consideration of all the constellation satellites.  
\[\sum_{s=1}^{S}\sum_{t=1}^{T}\phi_{s,j,t}\geq g_{j}^{\min},\ \forall j \tag{13a}\] \[\sum_{s=1}^{S}\sum_{t=1}^{T}\phi_{s,j,t}\leq g_{j}^{\max},\ \forall j \tag{13b}\]  
A given subset of tasks, i.e., \(\Xi\subset\mathcal{C}\), can leverage from network behavior, i.e., run synchronously within the entire constellation (all satellites, \(|S|\)) at the same time. Such behavior can be enforced by a simultaneous start and ending, as in constraints (14a) and (14b).  
\[\sum_{s^{\prime}\in\mathcal{S}}\phi_{s^{\prime},j,t}\geq|S|\phi_{s,j,t},\forall t,\forall j,\forall s\in\Xi \tag{14a}\] \[\sum_{s^{\prime}\in\mathcal{S}}\alpha_{s^{\prime},j,t}\geq|S| \alpha_{s,j,t},\forall t,\forall j,\forall s\in\Xi \tag{14b}\]  
In order to achieve a system of redundancy in the execution of tasks, we introduce the binary variable \(\beta\), which is equal to 1 when two tasks are running simultaneously in two different satellites, and equal to 0 when they are not (which is given by means of a big-M formulation). This identification, which is used to enforced mission reliability, is given by:  
\[\phi_{s,j,t}+\phi_{s^{\prime},j,t}\geq 2-M(1-\beta_{s,s^{\prime},j,t}), \forall t,\forall j,\forall s\in\mathcal{R},\forall s^{\prime}\in\mathcal{R} \tag{15a}\]  
notice that the redundancy is enforced only for a specified subset \(\mathcal{R}\subset\mathcal{C}\).
# Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems
## 5 Design of Executor Model
### Model Training  
For multilayer networks without recursion, the steepest gradient descent methods (most often the backpropagation method) are used for training. However, these methods often have a problem with falling into local minima and learning for a specific dataset, which reduces their usefulness when data from outside the set appear.  
Model training consists in adjusting the parameters \(q1_{i}^{k},\ldots,q4_{i}^{k}\) of all neural networks so that when the request \(r_{i}\) arrives, the estimated time \(tp_{i}\) will be close to the real time \(tr_{i}\) needed for the server to service the request.  
The method in which weights are randomly changed was adopted as the learning method. This method has the ability to break out of any local minimum and reach the global optimization minimum. In addition, this method does not have to be based only on one-way networks (recursions or any other connections are possible).  
The method is as follows: each weight of the neural network is treated as a separate, independent variable, regardless of its place in the network. The weights are randomly modified with random values, but smaller changes are more likely (Gaussian distribution has been proposed). If the approximation error after the weight modification has decreased, then the new weights are adopted; otherwise, they are rejected. With some probability, the algorithm can generate any combination of changed weights, making it possible to leave the local minimum and search all available space.  
The Box-Muller [48] transformation was used to generate values in the Gaussian distribution (4):  
\[\begin{array}{l}R^{2}=-2\ln U_{1},\ \ \ \ \theta=2\pi U_{2}\\ Z_{1}=R\cos\theta,\ \ \ \ Z_{2}=R\sin\theta\end{array} \tag{4}\]  
where:  
Figure 6: Initial diagram of the executor model (\(NM^{k}\)) element.  
\(U_{1}\)\(i\)\(U_{2}\)--independent samples chosen from the uniform distribution on the unit interval \((0,\;1)\);  
\(Z_{1}\)\(i\)\(Z_{2}\)--independent random variables with a standard normal distribution and with standard deviation \(sd=1\)
# A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities
## 3 The Proposed Strategy
### STUKF- and MB-based Centralized Fault Diagnosis  
In this section, we introduce the faulty cluster-heads detection algorithm, which is the second phase of the \(SFBT\). Specifically, we apply an \(STUKF\)-based joint estimation method to predict the state and parameter of each cluster-head \(CH_{i}\) first; then, the \(MB\) is adopted to find the faulty ones.
# Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks
## 4 Results and Discussion
### Simulation Results
#### 4.4.5 Detection Accuracy (Type-I Attack)  
The detection accuracy of our scheme and OCEAN for different simulation times is shown in Figure 8. As compared to OCEAN, our scheme detects all malicious nodes within less time because OCEAN cannot detect such nodes that partially drop some packets to sustain its reputation threshold. In detection accuracy, our scheme outperforms OCEAN and isolates malicious nodes from the network within the shortest time.