esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Optimization of Functionally Redundant Robots through Motion Design
## 4 Trajectory Optimization and Results
### Test-Case 1
The time history of the optimal tool orientation, i.e., the one that minimizes the overall energy cost, is shown in \[\theta(t)\in[-\pi/9,\pi/9]\)\(rad\); \[\psi(t)\in[25/18\pi,29/19\pi]\)\(rad\]1  
To better highlight the improvement in the energy consumption brought by the proposed method, the time history of the energy required in the non-optimized task and in the energy-optimal task are compared in \[\theta(t)\in[-\pi/9,\pi/9]\)\(rad\); \[\psi(t)\in[25/18\pi,29/19\pi]\)\(rad\]2 The significant energy saving resulting from the optimized tool orientation is evident. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimization Variables** & **Manipulability Index** & \(\mathbf{\Delta}\) & **Energy [J]** & **Energy Saving** \\ \hline No optimization & 0.0476 & - & 220.4 & - \\ Constant pitch and yaw & 0.0508 & 6.72 \% & 238.4 & \(-8.17\%\) \\ Constant speed pitch and yaw & 0.0563 & 18.27 \% & 227.9 & \(-3.4\%\) \\ Trapezoidal pitch and yaw speed profile & 0.0570 & 19.75 \% & 226.5 & \(-2.77\%\) \\ Poly3 pitch and yaw & 0.0572 & 20.12 \% & 226.9 & \(-2.95\%\) \\ Poly5 pitch and yaw & 0.0575 & 20.81 \% & 227.7 & \(-3.31\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Manipulability maximization: test-case I, motion along a horizontal line. Figure 5: Energy-optimal tool orientation: trapezoidal pitch and yaw angles. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Optimization Variables** & **Energy [J]** & **Energy Saving** & **Manipulability Index** \\ \hline No optimization & 220.4 & - & 0.0476 \\ Constant pitch and yaw & 214.5 & 2.68 \% & 0.0507 \\ Constant speed pitch and yaw & 185.2 & 15.97 \% & 0.0499 \\ Trapezoidal pitch and yaw speed profile & 179.4 & 18.60 \% & 0.0499 \\ Poly3 pitch and yaw & 181.1 & 17.83 \% & 0.0488 \\ Poly5 pitch and yaw & 179.9 & 18.37 \% & 0.0502 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Energy minimization: test-case I, motion along a horizontal line.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 5 Integrating Cognitive Radio in LPWAN for the Internet of Things
### Network Architecture to Support CR-LPWAN  
A simple network architecture to support CR-LPWAN systems is depicted in Figure 5. Although different architectures may be required for different IoT use-cases, nevertheless, the architecture of Figure 5 is only presented to demonstrate the possibility for CR-LPWAN. Typically, CR-LPWAN devices for different IoT applications can be situated or located within a larger PU network (often  
Figure 4: Essential PHY layer functions to be executed in the LPWAN module of the front-end model.  
Figure 3: A simple CR-LPWAN PHY layer architecture (adapted from [84]).  
cellular networks). The CR-LPWAN GW senses for when such PU signals are absent over a period of time in order to initiate opportunistic communication. They will continue to sense for re-initiated PU signals in order to quickly vacate such occupied bands.  
In this regard, there are three possible schemes for CR-LPWAN-based network architectures, briefly highlighted as follows:
## 2 Context and Research Objectives
### FoF Modeling and Simulation Challenge
The co-simulation capacity can support the agreement of networked manufacturing plant owners on a coherent production plan that reflects best compromise according to the agreed parameters of the decision. It potentially would serve as input to smart contract mechanisms, which distributed manufacturing ecosystems will need to implement [46]. The promises for optimization are quite straightforward and very much promoted already by automation vendors in the pleading for DTs. What seems to have been much neglected, however, in both research and industry, is the potential for DTs to support enhanced resilience towards industrial hazards or deliberate threats [47]. It is important to note that the whole concept of FoF brings with it a handful of new threats, which we should not leave unaddressed. Almost any connected device, whether on the shop floor, in an automated system or remotely located at a third-party contract manufacturer, can be a source of new risks (even those that only peripherally or indirectly touch the production process). The introduction of evermore connected manufacturing assets, the increased reliance on Artificial Intelligence (AI) for decision making, the development of practices such as collaborative manufacturing and the increasing use of Cloud Manufacturing (CMfg) services altogether widen the attack surface up to what is sometimes called an attack fractal. DTs themselves, if not properly secured, offer a new entry point for cyber-attackers to perform industrial sabotage and industrial data theft. As any other asset, the DT must be subject to security measures [48]. More interestingly, we propose in this paper implementations of FoF Digital Twin technology as a mean for enhanced resilience,notably towards cyber-threats and other industrial hazards. The association of DTs with cybersecurity simulation and training tools, commonly called Cyber-Ranges (CR) [49], enables to comprehending the complexity of event propagation from physical asset to digital networks and vice versa, unleashing an unprecedented potential for SoS security architecture decision support, continuous process security and safety monitoring and simulation-based decision making. Considering that 60% of all cyber-attacks involve a willing or unwilling human, commonly named insider [50], the inclusion of human behavior modeling capacity substantially enhances this resilience capacity. Ultimately, the FoF DT is the mean to support optimal decision making with considerations for both process optimization and resilience objectives.
###### Abstract  
This paper presents a hybrid approach combining phase space reconstruction (PSR) with a convolutional neural network (CNN) for power quality disturbance (PQD) classification. Firstly, a PSR technique is developed to transform a 1D voltage disturbance signal into a 2D image file. Then, a CNN model is developed for the image classification. The feature maps are extracted automatically from the image file and different patterns are derived from variables in CNN. A set of synthetic signals, as well as operational measurements, are used to validate the proposed method. Moreover, the test results are also compared with existing methods, including empirical mode decomposition (EMD) with balanced neural tree (BNT), S-transform (ST) with neural network (NN) and decision tree (DT), hybrid ST with DT, adaptive linear neuron (ADALINE) with feedforward neural network (FFNN), and variational mode decomposition (VMD) with deep stochastic configuration network (DSCN). Based on deep learning algorithms, the proposed method is capable of providing more accurate results without any human intervention for PQDs. It also enables the planning of PQ remedy actions.  
Keywords:convolution neural network (CNN); deep learning; power quality disturbance (PQD); phase space reconstruction (PSR); smart grid +
Footnote †: journal: Applied
# Sparse Weighting for Pyramid Pooling-Based SAR Image Target Recognition
## 2 SAR Image Recognition with Sparse Weighting Spatial Pyramid Pooling
### Weighted SPM Sparse Representation  
After weighting the sub-regions, the weighted sub-regions were integrated to reconstruct a global feature vector according to the SPM model. Each sub-region vector \(y_{k}\) was weighted by a corresponding weight to be represented as a sub-region weighted query vector:  
\[y_{k}^{\omega}=\omega_{k}y_{k}. \tag{8}\]  
We connected all sub-regions' weighted query vectors in series to produce a weighted feature vector as  
\[y^{\omega}=\Big{[}y_{1}^{\omega T},y_{2}^{\omega T},\cdots,y_{K}^{\omega T} \Big{]}^{T}. \tag{9}\]  
Consider that \(y^{\omega}\) can be represented by \(F\) in a linear manner as follows:  
\[y^{\omega}=Fu^{\omega}. \tag{10}\]  
To obtain the primary label information from the training dataset matrix and weighted query feature vector, the SR classification method [13] was applied to complete the target image classification:  
\[\hat{u}^{\omega}=\text{argmin}_{u^{\omega}}\big{\{}||Fu^{\omega}-y^{\omega}|| _{2}^{2}+\lambda||u^{\omega}||_{1}\big{\}}. \tag{11}\]  
Linear approximation [15] is capable of selecting significant training vectors to provide a good discrimination among all the classes.
# OTFS-Based Handover Triggering in UAV Networks
## 5 Proposed UAV-Ground Handover Triggering Based on DD Channel Estimation
### RSS-Based Handover Triggering  
The ratified 3 GPP handover routine, which is still used for 5G-NR [14], is based on always monitoring the RSS between the serving/target cells and the UE [14]. If the measured RSS of the serving (_Ser_) cell falls below that of the target (_Tar_) cell by a certain value for a time-to-trigger (TTT) interval, a handover decision is taken by the control unit. Hence, the communication session is handed over to the target cell from the serving one using a specified signaling procedure. This can be expressed mathematically as follows:  
\[HO(Ser\to Tar)=\Big{\{}1,\Big{(}RSS^{Tar}[z]-RSS^{Ser}[z]\Big{)}>P_{th}\Big{\}},\;For\;|z|\geq TTT, \tag{24}\]  
where \(RSS^{Tar}[z]\), \(RSS^{Ser}[z]\), and \(P_{th}\) are the RSS of the target cell in dBW, the RSS of the serving cell in dBW, and the threshold power in dB, respectively. Also, \(|z|\geq TTT\) indicates that the condition in (24) is satisfied for a period of TTT. The \"1\" in (24) indicates that a handover should be executed if the condition \(\Big{(}\Big{(}RSS^{Tar}[z]-RSS^{Ser}[z]\Big{)}>P_{th}\Big{)}\) for \(|z|\geq TTT\) is satisfied. Based on the DD channel representation given in (1), the time-sampled DT channel at each delay tap \(\alpha_{i_{q}}\) can be deduced as given in the following [13, 14]:  
\[h_{DT}\Big{[}\alpha_{i_{q}},z\Big{]}=h_{i_{q}}e^{j2\pi\hat{p}_{iq}(z-\alpha_{i _{q}})},\;0\leq z\leq NM-1 \tag{25}\]  
Thus, the sampled RSS value at the Rx side can be written as follows:  
\[RSS[z]=P_{Tx}\Big{|}\sum_{q=1}^{Q}\sum_{i_{q}=1}^{P_{t}}h_{DT}\Big{[}\alpha_{i _{q}},z\Big{]}\Big{|}^{2}, \tag{26}\]Then, by substituting (25) into (26), \(RSS[z]\) can be written as follows:  
\[RSS[z]=P_{\rm Tx}\left|\sum_{q=1}^{Q}\sum_{i_{q}=1}^{P_{q}}h_{i_{q}}e^{2\pi\beta_{ i_{q}}(z-a_{i_{q}})}\right|^{2}, \tag{27}\]  
which can be expanded to the following:  
\[\begin{array}{rl}RSS[z]&=P_{\rm Tx}\left(\sum_{q=1}^{Q}\sum_{i_{q}=1}^{P_{q}} h_{i_{q}}e^{2\pi\beta_{i_{q}}(z-a_{i_{q}})}\right)\left(\sum_{q=1}^{Q}\sum_{i_{q}=1}^{P_{q}} h_{i_{q}}^{*}e^{-2\pi\beta_{i_{q}}(z-a_{i_{q}})}\right),\\ &=P_{\rm Tx}\sum_{q=1}^{Q}\sum_{i_{q}=1}^{P_{q}}\sum_{q^{\prime}=1}^{Q}\sum_{i_ {q}=1}^{P_{q}}h_{i_{q}}h_{i_{q}}^{*}e^{2\pi(z(\beta_{i_{q}}-\beta_{i_{q}})+ \beta_{i_{q}}\alpha_{q^{\prime}}-\beta_{i_{q}}\alpha_{i_{q}})},\end{array} \tag{28}\]  
where \(P_{\rm Tx}\) is the Tx power in Watts. As given by (28), RSS is highly time-varying and depends on the time instance \(z\). Also, it results from multiplexing all channel paths, including their channel coefficients, delay spreads, and Doppler shifts. Thus, it will be inefficient in high-speed scenarios like the UAV-to-ground networks under consideration. This is because the signals received from the different GBSs will be highly fluctuated and overlapped. Hence, the UAV will be handed over multiple GBSs within a short time period, resulting in an incredible handover overhead and low throughput in consequence. Nevertheless, frequent communication failures may occur due to this incredible number of handovers.
# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 3 Results
### Comparative Analysis of Regression Models for Almond Yield Prediction
Combining irrigation with climate data generally improved the model's performance, compared to using either feature individually, resulting in lower _MAE_ and _RMSE_ values, ranging from 116 to 137 kg/ha and 158 to 177 kg/ha, respectively. Similarly, incorporating all feature types tended to improve the model's predictive performance, especially when using irrigation, climate data, and VIs from PlanetScope, which achieved the best performance with the XGB model (_MAE_: 95 kg/ha; _RMSE_: 119 kg/ha).
# SARFNet: Selective Layer and Axial Receptive Field Network for Multimodal Brain Tumor Segmentation
## 4 Experiments and Results  
### Datasets and Preprocessing  
The BraTS dataset refers to the brain tumor segmentation challenge dataset, which is a public medical image dataset used to research and develop brain tumor segmentation algorithms. The dataset is provided by multiple medical centers and contains image data from multiple patients who underwent brain tumor MRI. The BraTS 2018 [62; 63; 64; 65; 66], which consists of 285 patients used for training and 66 patients used for online validation, has  
Figure 4: The structure of the proposed AAM module. In this module, rows are constructed first, followed by columns.  
become popular. Generally, the cases all contain ground truths labeled by board-certified neuroradiologists. In comparison, the ground truths of 66 cases are hidden from the public, and the results of the model can be obtained only by online validation. Our training strategy was to use all 285 cases for training. Our results were evaluated on the official BraTS platform ([[https://www.med.upenn.edu/sbia/brats2018.html](https://www.med.upenn.edu/sbia/brats2018.html)]([https://www.med.upenn.edu/sbia/brats2018.html](https://www.med.upenn.edu/sbia/brats2018.html)), accessed on 13 May 2024).  
The brain tumor competition team conducted data preprocessing, including registration, resampling, and skull removal. However, further data preprocessing was also necessary for segmentation tasks. First, we reduced as much of the background as possible while ensuring that all the brain images were included and randomly re-cropped the fixed patch size of the image to 128 \(\times\) 128 \(\times\) 128. Second, the Z-score method was used to standardize each image to reduce the contrast differences between the four sequences. All intensity values were clipped to the 1 st and 99 th percentiles of the nonzero voxel distribution of the volume. Similarly, we applied data augmentation techniques to prevent overfitting. During image processing, we performed channel rescaling between 0.9 and 1.1, a channel intensity shift between \(-0.1\) and 0.1, additive Gaussian noise of a centered normal distribution with a standard deviation of 0.1, channel dropping, and 80% random flipping along each spatial axis.
## 3 Results
\begin{tabular}{c c c c c c} \hline
**EEG Indices** & **Site** & **Before Inhalation (\(\mu\)V\({}^{2}\))** & **During Inhalation (\(\mu\)V\({}^{2}\))** & **\(t\)-Test** & **\(p\) Value * \\ \hline \multicolumn{6}{c}{**Both Genders**} \\ \hline \multirow{2}{*}{RST} & Af4 & 0.2598 \(\pm\) 0.0519 & 0.1882 \(\pm\) 0.0395 & 2.363 & 0.029 \\  & Cp5 & 0.2838 \(\pm\) 0.0674 & 0.2084 \(\pm\) 0.0461 & 2.231 & 0.038 \\ \hline RMT & Cp5 & 0.3897 \(\pm\) 0.0969 & 0.2926 \(\pm\) 0.0750 & 2.143 & 0.045 \\ \hline \multirow{2}{*}{RSMT} & Af4 & 0.5810 \(\pm\) 0.1191 & 0.3760 \(\pm\) 0.1004 & 2.388 & 0.027 \\  & Cp5 & 0.6735 \(\pm\) 0.1557 & 0.5010 \(\pm\) 0.1165 & 2.448 & 0.024 \\ \hline \multicolumn{6}{c}{**Women**} \\ \hline \multirow{2}{*}{RST} & Af4 & 0.3588 \(\pm\) 0.0891 & 0.2453 \(\pm\) 0.0729 & 2.383 & 0.041 \\  & Cp5 & 0.4377 \(\pm\) 0.1140 & 0.2955 \(\pm\) 0.0838 & 2.627 & 0.027 \\  & Cp6 & 0.4045 \(\pm\) 0.0959 & 0.3210 \(\pm\) 0.0923 & 2.299 & 0.047 \\ \hline \multirow{6}{*}{RMT} & O2 & 0.6098 \(\pm\) 0.1883 & 0.4806 \(\pm\) 0.1747 & 2.569 & 0.030 \\  & F8 & 0.4973 \(\pm\) 0.1614 & 0.3731 \(\pm\) 0.1426 & 2.569 & 0.030 \\  & Cz & 0.4589 \(\pm\) 0.1431 & 0.3278 \(\pm\) 0.1397 & 2.424 & 0.038 \\  & Pz & 0.4835 \(\pm\) 0.1337 & 0.3313 \(\pm\) 0.1046 & 3.276 & 0.010 \\  & Af4 & 0.4475 \(\pm\) 0.1200 & 0.3385 \(\pm\) 0.1129 & 2.399 & 0.040 \\  & Fc2 & 0.4240 \(\pm\) 0.1502 & 0.3252 \(\pm\) 0.1340 & 2.690 & 0.025 \\  & Cp5 & 0.6034 \(\pm\) 0.1633 & 0.4183 \(\pm\) 0.1376 & 2.870 & 0.018 \\  & Cp6 & 0.5189 \(\pm\) 0.1448 & 0.3897 \(\pm\) 0.1414 & 2.463 & 0.036 \\ \hline \multirow{6}{*}{RSMT} & Cz & 0.7992 \(\pm\) 0.2305 & 0.5826 \(\pm\) 0.2232 & 2.360 & 0.043 \\  & Pz & 0.8989 \(\pm\) 0.2293 & 0.6713 \(\pm\) 0.2076 & 3.173 & 0.011 \\  & Af4 & 0.8063 \(\pm\) 0.2039 & 0.5838 \(\pm\) 0.1850 & 2.945 & 0.016 \\  & Cp5 & 1.0411 \(\pm\) 0.2567 & 0.7138 \(\pm\) 0.2113 & 3.409 & 0.008 \\  & Cp6 & 0.9235 \(\pm\) 0.2356 & 0.7107 \(\pm\) 0.2265 & 2.829 & 0.020 \\ \hline SEF90 & P8 & 28.6560 \(\pm\) 3.0302 & 24.8779 \(\pm\) 3.6371 & 2.595 & 0.029 \\ \hline \multicolumn{6}{c}{**Men**} \\ \hline AA & F8 & 3.5391 \(\pm\) 2.2358 & 4.6670 \(\pm\) 2.4059 & \(-\)2.452 & 0.037 \\ \hline \multirow{2}{*}{AFA} & F8 & 0.4061 \(\pm\) 0.2292 & 0.7929 \(\pm\) 0.3517 & \(-\)2.411 & 0.039 \\  & T8 & 0.3116 \(\pm\) 0.1430 & 0.6550 \(\pm\) 0.2507 & \(-\)2.278 & 0.049 \\ \hline \multicolumn{6}{c}{**RahB**} \\ \hline \end{tabular}
## 2 Materials and Methods
### Functional Connectivity Analysis  
Functional connectivity analysis was carried out using the PLV index [28]. First, a continuous wavelet transform was applied (i.e., the complex Morlet wavelet), with this wavelet being used as a kernel to compute the PLV, which is defined by Equation (1):  
\[\mathrm{PLV}(t)=\frac{1}{N}\left|\sum_{n=1}^{N}e^{i\theta(t,\,n)}\right| \tag{1}\]  
where \(n\) is an index for the trial number and \(\theta\) indicates the phase difference. The phase-locking value yielded by PLV ranges from 0 to 1, with 1 indicating that two signals have an identical relative phase across \(N\) trials. Conversely, values that approach 0 indicate little to no phase synchrony between the signals. For every subject, the PLV was calculated for all possible 253 bipolar channel combinations in five frequency bands as defined above. Next, inspired by [29], 'Global Connectivity' and 'Homotopic Pair Connectivity' were computed using the extracted PLV values and were compared between the groups.  
Figure 1: EEG signals were collected from 23 bipolar channels based on the 10–20 international system.
# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 4 Performance Evaluation
### Performance Measures
#### 4.2.2 Packet Loss  
The signal quality can be degraded owing to the SINR, causing packet loss, which means that when the signal strength is low, the probability of packet loss is high [32]. Packet loss refers to the number of packets lost during transmission. The SINR, measured in dB, is the ratio of the signal level to the noise level. A high SINR value represents good signal quality, whereas a value below 0 indicates more noise, which implies a low connection speed and a high probability of losing connection [33]. The SINR of the UE must be measured and evaluated during the HO process. The UE at the cell boundary has a low SINR and significant interference, which causes HO to the neighboring cell [34]. The Packet Error Rate (PER) is used to estimate this degradation and evaluate network reliability [35]. PER denotes the ratio of the errors in the packet that reaches the destination, resulting in packet retransmission and, therefore, increasing the transmission latency.  
The SINR calculation for 5G NR is presented in Formula (4):  
\[\gamma_{db}=10\log_{10}\left(\frac{p_{s}}{p_{i}+p_{n}}\right) \tag{4}\]  
where  
* \(p_{s}\)_represents_ the signal power.
* \(p_{i}\)_represents_ the signal inference.
* \(p_{n}\)_represents_ the signal noise.  
Equation (5) represents the model of packet losses based on SINR \(\gamma_{db}\) and PER \(\bar{\tau}\).  
\[\bar{\tau}_{\gamma_{db}}=\left\{\begin{array}{ll}1,&\quad if\ \gamma_{db}<0\\ 0.8,&\quad if\ 0\leq\gamma_{db}<5\\ 0.3,&\quad if\ 5\leq\gamma_{db}<10\,\ if\ \gamma_{db}<0=1;\ Complete\ packet\ loss\\ 0.1,&\quad if\ 10\leq\gamma_{db}<20\\ 0.01,&\quad if\ \gamma_{db}\geq 20\end{array}\right. \tag{5}\]  
Each packet is received with a probability of \(1-\bar{\epsilon}\), and the expected number of received packets (\(N_{r}\)) from the original number of packets (\(N_{t}\)) is  
\[N_{r}=N_{t}\times(1-\bar{\epsilon}_{2t_{db}})^{n} \tag{6}\]  
where \(n\) is the packet length.
## 3 Results and Discussion
### Parameter Screening with Plackett-Burman (PBD) and RSM Modeling
#### 3.1.3 Actual vs. Predicted Investigating Plots for the Responses  
The model plots the FWL safety coefficient's observed vs. expected responses in Figure 3c. The graphic shows the accurate prediction and use of the FWL safety coefficient in the railway embankment slope response model. The datapoints align well with the line of best fit, indicating good agreement between actual and predicted outcomes [41]. Figure 3d depicts a randomly and sparsely scattered residual plot for the FWL safety coefficient response model, showing that errors are not correlated and have equal discrepancies. The normal residual plot is appropriate because most responses cluster around the line of fit, validating the accuracy and reliability of the generated model (Figure 3). Strength lines represent lines of best fit, while blue, green, and red dots denote the level of agreement of predicted values corresponding to low, medium, and high values of the factor of safety, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Output** & **SD** & **PRESS** & **R2** & **Adj.R2** & **Pred.R2** & **Adj.P** & **_p-Value_ & **COV** & **Remarks** \\ \hline RWL & 0.00(05) & 0.00(00) & 0.99(99) & 0.99(99) & 0.99(98) & 898.00 & \textless{}0.0001 & 0.02(33) & significant \\ FWL & 0.00(61) & 0.00(32) & 0.99(51) & 0.99(25) & 0.97(80) & 76.62 & \textless{}0.0001 & 0.31(13) & significant \\ \hline \hline \end{tabular}  
* SD: standard deviation, COV: coefficient of variance, \(R^{2}\): correlation coefficient, Adj.R2: adjusted correlation coefficient, Pred.R2: predicted correlation coefficient, Adq.P: adequate precision.  
\end{table}
Table 3: Statistical analysis details for the responses.  
Figure 3: Investigative plots for (**a,b**) RWL and (**c,d**) FWL model suitability.
## 5 Experimental Evaluation
### Experimental Setup  
#### 5.1.1 Parameter Settings  
The MTD3 CO algorithm was implemented by TensorFlow, where the encoder-decoder network consisted of two layers of long short-term memory (LSTM) networks with 256 hidden units each, and a fully connected layer as the strategy network \(\pi_{\phi}\) in the algorithm, including two value networks \(Q_{\theta_{1}}\) and \(Q_{\theta_{2}}\). Both the inner-loop training and the outer-loop training learning rates were set to \(5\times 10^{-4}\) The algorithm's noise parameter \(\widetilde{\sigma}\) was set to 0.2, and the clipping parameter \(c\) was set to 0.5; thus, the noise was normally distributed in the range of \((-0.5,0.5)\). In inner-loop training, the gradient update number \(k\) was set to 6, the parameter d for delayed update of the target network was set to 2, and the target network's learning rate \(\tau\) was set to 0.005. See Table 3 for related parameter settings.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Network architecture & Encoder–decoder network with 2 layers of LSTM networks (each with 256 hidden units) and a fully connected layer \\ \hline Strategy network & \(\pi_{\phi}\) \\ \hline Value networks & \(Q_{\theta_{1}}\) and \(Q_{\theta_{2}}\) \\ \hline Inner-loop and outer-loop training learning rates & \(5\times 10^{-4}\) \\ \hline Noise parameter & 0.2 \\ \hline Clipping parameter & 0.5 \\ \hline Range of noise & \((-0.5,0.5)\) \\ \hline Gradient update number in inner-loop training & 6 \\ \hline Parameter d for delayed update of target network & 2 \\ \hline Target network learning rate & 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used in the MTD3 CO algorithm.