esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Principles and Methods
### Establishing Equations for Solving Unknown Coefficients  
It can be seen from the above that the coefficients \(e_{1}\)-\(e_{3}\), \(f_{1}\)-\(f_{3}\), \(g_{1}\), \(h_{1}\), and \(f_{3}\) of the analytical function corresponding to the interaction between surrounding rock and lining are still unknown, and corresponding equations need to be established according to the boundary conditions of the supporting structure to obtain the solution. In this paper, it is assumed that the surrounding-lining interface is smooth, that is, the surrounding-lining interface satisfies the conditions of radial stress continuity and radial displacement coordination, and the shear stress is zero.  
Firstly, according to the radial stress continuity condition of the surrounding-lining boundary at the contact surface \(L_{1}\), we can obtain [3]:  
\[2\text{Re}\big{[}\varphi^{\prime}_{3}(z)\big{]}-\text{Re}\Big{\{}e^{2i\theta} \big{[}\mathbb{Z}\varphi^{\prime\prime}_{3}(z)+\psi^{\prime}_{3}(z)\big{]} \Big{\}}=2\text{Re}\big{[}\varphi^{\prime}_{4}(z)\big{]}-\text{Re}\Big{\{}e^ {2i\theta}\big{[}\mathbb{Z}\varphi^{\prime\prime}_{4}(z)+\psi^{\prime}_{4}(z) \big{]}\Big{\}},z=R_{1}e^{i\theta} \tag{10}\]  
Then, based on the displacement compatibility condition at the boundary of the surrounding-lining interface \(L_{1}\), we obtain:  
\[(1-\eta)u_{2}^{r}+u_{4}^{r}=u_{1}^{r}+u_{3}^{r},z=R_{1}e^{i\theta} \tag{11}\]  
where \(\eta\) is the displacement release coefficient determined by the distance between the tunnel face and the lining section, indicating that the tunnel is supported after \(\eta\) times the total displacement [4; 5; 9; 10; 11; 12; 13].  
Then, according to the condition that the shear stress at the boundary of surrounding rock-lining interface \(L_{1}\) is zero, we can obtain:  
\[2 Re\big{[}\varphi^{\prime}_{3}(z)\big{]}-e^{2i\theta}\big{[}\mathbb{Z}\varphi _{3}^{\prime\prime}(z)+\psi^{\prime}_{3}(z)\big{]}=0,z=R_{2}e^{i\theta} \tag{13}\]
## 2 IoT and Opportunistic Networks--State of the Art
### Challenges  
* Intermittent Connectivity: Routing in opportunistic networks with intermittent connectivity requires novel approaches to handle network disruptions and message delivery delays. Recent research emphasizes the design of store-carry-and-forward mechanisms, encounter-based routing, and epidemic protocols to adapt to changing network conditions [7].
* Dynamic Topology: Opportunistic networks exhibit dynamic topology due to node mobility and intermittent contacts. Advanced routing protocols incorporate mobility prediction, context-aware routing, and network coding techniques to improve routing efficiency and adaptability [8].
* Resource Constraints: IoT devices operating in opportunistic networks have limited resources, including battery power and bandwidth. Routing protocols must optimize resource utilization through energy-aware routing, data aggregation, and adaptive transmission strategies [9].  
Content-Centric Networking: Future research directions include the exploration of content-centric networking paradigms in IoT-enabled opportunistic networks. Content-based routing, caching, and dissemination strategies offer opportunities to enhance data delivery efficiency and support diverse application requirements [10].
* Cross-Layer Optimization: Leveraging cross-layer optimization techniques to enhance routing performance and resource utilization in IoT-enabled opportunistic networks. Integrating network, transport, and application layer functionalities enables holistic optimization and adaptation to dynamic network conditions [11].
* Edge Computing Integration: Integrating edge computing capabilities with opportunistic networks willsupport localized data processing and decision-making. Edge-aware routing protocols and distributed computing frameworks enable efficient utilization of edge resources and reduce reliance on centralized infrastructure [12].
## 2 Materials and Methods  
### Open-Loop Control Problem  
Let us consider a nonlinear dynamical system described by a difference equation  
\[x(t+1)=f(t,\ x(t),\ u(t)), \tag{1}\]  
where \(t\) represents discrete time, \(t\in T=[0,1,\ldots,N-1]\); the number of stages \(N\) is given; \(x\) is the state vector, \(x\in\mathbf{R}^{n}\); \(u\) is the control vector, \(u\in U(t)\subseteq R^{q}\); \(U(t)\) is the control values feasible set which, for each \(t\), comprises the direct product of segments \([a_{i}(t),\ b_{i}(t)]\),\(i=1,2,\ldots,q\); and \(f(t,\ x,\ u)=(f_{1}(t,\ x,\ u),\ldots,f_{n}(t,\ x,\ u))^{T}\) is a continuous vector function.  
The initial state vector is given:  
\[x(0)=x_{0}. \tag{2}\]  
It is assumed that only the discrete time information \(t\) is used in the control law, so open-loop control is considered.  
The set of allowable processes \(D(0,\ x_{0})\) is a set of pairs \(d=(x(\cdot),\ u(\cdot))\), where \(x(\cdot)=\{x_{0},\ x(1),\ldots,x(N)\}\) is a trajectory, \(u(\cdot)=\{u(0),\ u(1),\ \ldots,\ u(N-1)\}\) is an admissible control, and \(u(t)\in U(t)\), satisfying the state Equation (1) and initial condition (2).  
The cost functional on the set \(D(0,x_{0})\) is defined as  
\[I(d)=\sum_{t=0}^{N-1}f^{0}(t,\ x(t),\ u(t))+F(x(N)), \tag{3}\]  
or  
\[I(d)=F(x(0),\ldots,x(N);\ u(0),\ldots,u(N-1)), \tag{4}\]  
where \(f^{0}(t,\ x,\ u),\ F(x),\ F(x(\cdot),\ u(\cdot))\) are given continuous functions.  
It is required to find a pair \(d^{*}=(x^{*}(\cdot),\ u^{*}(\cdot))\in D(0,\ x_{0})\) that minimizes the cost functional, i.e.,  
\[I(d^{*})=\min_{d\in D(0,x_{0})}I(d). \tag{5}\]  
The required trajectory \(x^{*}(\cdot)=\{x_{0},\ x^{*}(1),\ldots,x^{*}(N)\}\) and control \(u^{*}(\cdot)=\{u^{*}(0),\ u^{*}(1),\ \ldots,\ u^{*}(N-1)\}\) (elements of a pair \(d^{*}\)) are the optimal trajectory and optimal control, correspondingly.  
To solve the problem (5), an algorithm that imitates the behavior of a perch school is proposed. This algorithm belongs to the class of nature-inspired metaheuristic swarm intelligence algorithms [9; 11; 16]. This problem was also solved using the Sparrow Colony Optimization Method, with results close to those received in this paper [27].
# Automatic Control Optimization for Large-Load Plant-Protection Quadrotor
## 2 Materials and Methods
### Attitude Controller Design for Large-Load Plant-Protection Quadrotors
#### 2.2.1 Quadrotor Attitude Controller with Reinforcement Learning  
The RL frame includes the following factors--the agent, environment, state \(\mathbf{s}_{t}\), action \(\mathbf{a}_{t}\), policy \(\pi(\mathbf{s}_{t})\) and reward \(r_{t}(\mathbf{s}_{t+1},\mathbf{a}_{t})\). During the training process, the environment outputs the state \(\mathbf{s}_{t}\), and the agent takes action \(\mathbf{a}_{t}\) with the policy \(\pi(\mathbf{s}_{t})\) and then obtains a reward \(r_{t}(\mathbf{s}_{t+1},\mathbf{a}_{t})\). To describe the performance of the agent in the long term, the value of the current state and action \(V_{t}(\mathbf{s}_{t+1},\mathbf{a}_{t})\) is introduced:  
\[V_{t}=r_{t}+\lambda r_{t+1}+\lambda^{2}r_{t+1}+...=r_{t}+\lambda V_{t+1}, \tag{10}\]  
where \(\lambda\in[0,1)\) is the discount factor. Equation (10) shows that the stat-action value \(V_{t}\) is the sum of expected future rewards. Although these rewards are unknown, we can estimate them from previous outcomes. We can assume that an optimal policy \(\pi_{opt}\) can maximize \(V_{t+1}\):  
\[\begin{split} V_{t}(\mathbf{s}_{t+1},\mathbf{a}_{t})&=r_{t} (\mathbf{s}_{t+1},\mathbf{a}_{t})+\lambda max_{\mathbf{a}_{t}+1\in A}(V_{t+1}(\mathbf{s}_{t+1},\mathbf{a}_{t+1}))\\ &=r_{t}(\mathbf{s}_{t+1},\mathbf{a}_{t})+\lambda V_{t+1}(\mathbf{s}_{t+1},\pi_ {opt}(\mathbf{s}_{t+1})),\end{split} \tag{11}\]  
where \(A\) is the action space of the agent. During the training process, we apply the current policy \(\pi\) instead of \(\pi_{opt}\).  
In this article, the agent is a quadrotor with four rotors, and the environment receives the angular velocities of the rotors and outputs the attitude and angular velocities in three axes.
## 3 Results and Discussion  
### Hydrogen Bond Analysis  
The specific outcomes are meticulously documented and presented in Table 2. The differing molecular structures of various epoxy resin types resulted in variations in their hydrogen bonding capabilities. The hydroxyl groups present on the sand surface also influenced hydrogen bond formation. These discrepancies led to distinct migration, diffusion, and degradation behaviors of different epoxy resin types within the sand, ultimately affecting their performance in practical applications. DGEBA did not form hydrogen bonds with sand, indicating that the adsorption between DGEBA and sand was primarily driven by non-polar interactions such as van der Waals forces and electrostatic interactions. This suggests that the adsorption strength between DGEBA and sand was relatively weak, potentially leading to easier detachment from the sand surface. DGEDDS formed eight hydrogen bonds with sand, signifying strong polar interactions between the resin and sand. The formation of hydrogen bonds significantly enhanced the adsorption strength between DGEDDS and sand, resulting in more stable adsorption on the sand surface. AEOR only formed one hydrogen bond with sand, suggesting weaker polar interactions compared to DGEDDS. However, the interaction still existed, making the adsorption strength of AEOR slightly stronger than that of DGEBA, but significantly weaker than that of DGEDDS.  
The interface hydrogen bond count per unit molecular weight at the interface was found to be 0 for DGEBA, 9.188 \(\times\) 10\({}^{-3}\) for DGEDDS, and 1.017 \(\times\) 10\({}^{-3}\) for AEOR. By combining an analysis of the number of hydrogen bonds and the number of hydrogen bonds per unit molecular weight, the following conclusions were drawn: The molecular structure of DGEDDS enabled it to form a large number of hydrogen bonds with sand. Moreover, these hydrogen bonds maintained a high density even on a per-unit-molecular-weight basis. This explained the strong adsorption capacity of DGEDDS. While AEOR exhibited a certain degree of hydrogen bonding with sand, its strength and quantity were lower than those of DGEDDS. The adsorption between DGEBA and sand was primarily driven by non-polar forces. These results highlighted the significant contribution of hydrogen bonding to the adsorption of epoxy resins on sand, particularly in the case of DGEDDS.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**adsorption System Types** & \(N_{total}\) & \(N_{resin}\) & \(N_{sand}\) & \(N_{surface}\) & \(N_{unit}\) \\ \hline DGEBA-Sand & 174 & 0 & 174 & 0 & 0 \\ DGEDDS-Sand & 192 & 2 & 182 & 8 & \(9.188\times 10^{-3}\) \\ AEOR-Sand & 151 & 0 & 150 & 1 & \(1.017\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of interface hydrogen bonds and number of hydrogen bonds per unit molecular weight for the three models.
# Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
## 4 Results
### Complex Trajectory Gesture Recognition Results
#### 4.2.1 Network Training  
The gesture dataset consists of 3000 samples, with an average of 750 samples collected for each type of gesture. Due to the insufficient sample size, the dataset is divided into training, validation, and test sets in a ratio of 6:2:2. The purpose of the training set is to train the neural network and adjust the model parameters. The validation set aids in the network training process by assisting in hyperparameters tuning and addressing underfitting or overfitting issues. Finally, the test set evaluates the performance of the trained neural network model.  
To compare the three-channel CNN with the bidirectional LSTM fusion neural network, the two neural networks were trained under the same conditions. The batch size for training is set to 32, so the number of iterations for a single round is 50, and the maximum number of training iterations is set to 40. The optimization algorithm selects the Adam method, with a loss function of cross-entropy loss.  
Firstly, compare the impact of initial learning rates on neural network training. Set the initial learning rates to \(1\times 10^{-5}\), \(5\times 10^{-5}\), \(1\times 10^{-4}\), \(5\times 10^{-4}\), and \(1\times 10^{-3}\) to train the neural network. The training accuracy and loss curves for the three-channel CNN are shown in Figure 18a, and the results for the bidirectional LSTM fusion neural network are shown in Figure 18b.  
According to the experimental results, the optimal initial learning rate for a three-channel CNN is \(1\times 10^{-4}\). For bidirectional LSTM fusion neural networks, the optimal initial learning rate is \(1\times 10^{-4}\).  
Afterwards, the initial learning rates of the two neural networks are set to the optimal parameters obtained previously for training, and an early cut-off is set when the training accuracy no longer improves. The recognition accuracy and convergence speed of the two neural networks are compared. By training two types of neural networks using datasets, the training accuracy and loss curves are obtained as shown in Figure 19.  
Figure 18: Training accuracy and loss curve of three-channel CNN and bidirectional LSTM fusion neural network. (**a**) The validation accuracy of three-channel CNN. (**b**) The validation loss of three-channel CNN. (**c**) The validation accuracy of bidirectional LSTM. (**d**) The validation loss of bidirectional LSTM.  
According to the training results, the recognition accuracy of the three-channel CNN on the validation set after training is 97.35%, while the recognition accuracy of the bidirectional LSTM fusion neural network is 98.94%. The experimental results show that the bidirectional LSTM fusion neural network has better recognition performance in the validation set, and under the same initial learning rate conditions, the bidirectional LSTM fusion neural network converges faster and has better convergence characteristics.
# The Future of the Human-Machine Interface (HMI) in Society 5.0
## 1 Introduction
### Building Society 5.0: Challenges and Opportunities  
Society 5.0 is based on the idea of a human-centered society where technology is used to solve social problems and promote the well-being of individuals and communities.  
Figure 1: Vision of Industry 5.0 and Society 5.0 (Adapted from [2]).  
While there are numerous opportunities presented by Society 5.0, there are also several challenges that must be addressed to achieve its goals.  
**Challenges:**  
Inequality: The integration of technology into society has the potential to exacerbate existing social inequalities, such as access to healthcare, education, and employment opportunities. It is important to ensure that these technologies are accessible to all, regardless of socioeconomic status [16].  
Privacy and Security: The increasing use of advanced technologies has raised concerns about data privacy and security. It is essential to establish regulations and protocols to protect sensitive data and prevent misuse of technology [17].  
Digital Divide: The digital divide refers to the gap between those who have access to technology and those who do not. It is important to ensure that everyone has access to technology to prevent exclusion and promote equality [18].  
Ethical considerations: The use of advanced technologies raises ethical questions about their impact on society and the potential for unintended consequences. It is important to establish ethical guidelines and principles to ensure that these technologies are used in a responsible and ethical manner [19].  
**Opportunities:**  
Improved healthcare: Society 5.0 can facilitate the development of personalized healthcare solutions, leveraging AI and the IoT to monitor and diagnose health issues in real time [20].  
Sustainable living: Society 5.0 can help reduce the environmental impact of human activities through the development of smart cities, green energy solutions, and sustainable transportation systems [21].  
Digital transformation: Society 5.0 can help businesses and organizations become more efficient and productive using advanced technologies such as robotics, automation, and AI [22].  
Economic growth: Society 5.0 can create new industries and job opportunities, promoting economic growth and development [23].  
The green and digital transitions of Industry 5.0 and Society 5.0 aim to promote intentional and creative living. Universities and businesses have a crucial role to play in achieving this goal. Along with the development of Information Technology (IT), efforts must also be made to enhance industrial innovation and improve the information literacy of every citizen to create a people-centric way of life [24]. As we move towards Society 5.0, universities must not only advance technology but also foster information literacy through personalized education. Industry 5.0 and Society 5.0 are interconnected, and the fifth industrial revolution will accelerate social progress, aided by society's transformation. In Table 1, the similarities and opportunities presented by these two revolutions are summarized.
## 5 Experimental Results
### Experiment 1: Results without the Feature Selection Optimization  
We added an additional feature selection optimization step after the deep feature learning process. The n features extracted from the CNN model were used by an FS optimizer that selected the best \(m<n\) features. This step was tested by using many well-known optimizers, plus the proposed one. The resulting feature set was expected to improve the performance of the classifiers, even though it added some extra time for the machine learning pipeline to finish the whole processing. To measure how much the additional FS optimization step improved the performance, we evaluated the deep learning model without using a feature selection optimizer and reported the results. Table 3 shows the performance of directly training regular machine learning classifiers based on the deep features extracted from the CNN model. In other words, Table 3 illustrates the results of the three classifiers (DT, LDA, SVM) for each dataset without using any FS algorithms. The top row shows the results for the ISIC-2016 dataset, the second row shows the results for the PH2 dataset, the third row shows the results for the Chest-XRay dataset, and the last row shows the results for the Blood-Cell dataset. For the ISIC-2016 dataset, the SVM achieved the best results compared to DT and LDA (accuracy = 0.8602, recall = 0.8601, precision = 0.8546, and F1-score = 0.8567). For the PH2 dataset, the SVM still achieved the best results (accuracy = 0.9571, recall = 0.9571, precision = 0.9574, and F1-score = 0.957). The SVM achieved the best results for the Chest-XRay dataset as well (accuracy = 0.8718, recall = 0.8717, precision = 0.8906, and F1-score = 0.865); it also did for the Blood-Cell dataset (accuracy = 0.8846, recall = 0.8845, precision = 0.905, and F1-score = 0.8865). In the next subsections, we describe the use of an extra step after the deep learning process to select the best list of features from the deep features extracted by the CNN module.
# Demand-Response Control in Smart Grids
## 5 Fair Energy-Scheduling Method
### Phase II: Auctioning  
The energy scheduling continues to Phase II only if \(E_{rem\_req(i)}>0\); that is, some consumer agents have a higher energy requirement than the energy \(E_{eq}\) that was scheduled during Phase I, and also if \(E_{total}>0\). If \(E_{total}=0\), the consumer agents will have to buy the rest of their energy from the utility grid.  
If the following conditions are met, the consumer agent will engage in energy scheduling during Phase II:  
(i) \(E_{req(i)}>E_{eq}\)  
(ii) \(E_{total}>0\)  
During Phase II, the auctioneer agent conducts an auction-based energy scheduling procedure in which all purchasers whose energy requirements were not met (\(E_{rem\_req(i)}>0\)) during Phase I participate in the auction. The authors in [32] proposed a Vickrey auction approach; in our work, we propose a Dutch auction approach. The following section demonstrates a utility of our choice.  
For all \(0<\mathrm{h}\leq\mathrm{NbHours\_Day}\) do  
\(\mathrm{E_{total}=0}\)  
\(\mathrm{E_{req}=0}\)  
For all \(0<\mathrm{i}\leq\mathrm{NbTotal}\) do  
\(\mathrm{Read}\) (\(\mathrm{L_{i}}\), \(\mathrm{G_{i}}\))  
If (\(\mathrm{G_{i}>L_{i}}\))  
\(\mathrm{ListProducer.Add(i)}\)  
\(\mathrm{E_{total}=E_{total}+(G_{i}-L_{i})}\)  
\(\mathrm{Else}\)  
\(\mathrm{ListConsumer.Add(i)}\)  
\(\mathrm{E_{req}=E_{req}+(L_{i}-G_{i})}\)  
End if  
End for  
NbConsumers = ListConsumer.length()  
NbProducers = ListProducer.length()  
\(\mathrm{E_{eq}=E_{total}/NbConsumers}\)  
Total-Cost(i) = 0, \(\forall i\leq NbConsumers\)  
For all \(0<\mathrm{i}\leq\mathrm{NbConsumers}\) do  
\(\mathrm{If}\) \(\mathrm{E_{req(i)}<E_{eq}}\) then  
\(\mathrm{E_{b(i)}=E_{req(i)}}\)  
\(\mathrm{E_{rem(i)}=0}\)  
Else  
\(\mathrm{E_{b(i)}=E_{eq}}\)  
\(\mathrm{E_{rem(i)}=E_{req(i)}-E_{eq}}\)  
End if  
\(\mathrm{Total\_Cost(i)=E_{b(i)}\ ^{*}t}\)  
\(\mathrm{E_{total}=E_{total}-E_{b(i)}}\)  
End for  
While \(\mathrm{E_{total}>0}\) do  
Switch auctionKind do  
Case 1: VickreyAuction VA = new VickreyAuction  
Case 2: DutchAuction DA = new DutchAuction  
End switch  
End while  
End for
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
## 2 Ground RFI Impacts on GEO SAR Signal
### Distributed RFI Source  
Practically, many RFI sources on the ground are similar and independent distributed within a fixed area (e.g., personal electronic devices). Therefore, in this case, the emission of the RFI is noise-like, which is very similar to a black-body.  
According to the basic theory of the black-body in microwave spectrum, under the assumption that the antenna receives the full EM field illuminated by the black-body, the antenna directivity within the solid angle is uniform and the bandwidth is small compared with the carrier frequency, we have [32]  
\[P_{RFI}=k_{B}B_{g}T_{RFI}, \tag{6}\]  
where \(T_{RFI}\) is the brightness temperature contributed by the distributed RFI sources. As the antenna area and the area of the beam foot-print are in the inverse proportion, \(P_{I}\) will be independent on the antenna area. It only depends on the brightness temperature contributed by the RFI and the bandwidth of the receiver.
## 5 Data Processing and Experimental Results: SAR Mode  
SAR raw data acquired during the campaign have been delivered by CO.RI.S.T.A. and processed by IREA-CNR and Politecnico di Milano.
## 4 Experimental Results
### Time Computation  
The proposed method was implemented in a VS2019 environment using a hybrid program code based on C++ and OpenCV. All the experiments were conducted on a laptop with an Intel Core i7 CPU, 2.60 GHz, and 32 GB RAM. The computation times for all the compared methods are shown in Table 2. The time of GMC and HMT was similar, and they were the shortest because they adopt the same parameter value to the whole shadow areas in an image. OWC and LCC use the mean and variance of each shadow area, which resulted in longer computation times. CRM requires segmentation results in order to obtain the non-shadow region information; therefore, it required the longest time to accomplish shadow compensation. Both our method and OOPR solved the parameter values for each shadow; but because OOPR adopts an IPOOL strategy, it is more complicated than our approach and therefore required a little more time. Although our method's computation time was moderate but not the fastest, it accomplished a better compensation result for each shadow area.  
Figure 11: Comparison for the average of quality indices of 25 test images.