esa-sceva/satcom-chunk-collection huggingface dataset:
# Reinforcement Learning for Options Trading
## 4 Experiment Result
### Trading Performance with Protective Closing Strategies  
To better apply the models to option trading, we need to add a protective closing strategy to the training stage. In our experiment, the alternative stop-loss thresholds were 1%, 2%, and 3%. If the loss of one step exceeded the threshold, all positions were immediately closed. The mechanism can prevent unbearable losses, especially in options trading.  
In the preceding validation process, we found that the models trained on 60-min trading data performed better on the validation set and surpassed the B and H strategy. Therefore, the training for threshold selection used 60-min trading data as the training set. From the Table 4, we can see that 2% is the better threshold for the PPO and DQN models. The protective closing strategy was unsuitable for the SAC models. That may be because the SAC models have their own stop-loss mechanism in the frequent and even random trading mode. Because the SAC models trade frequently, they can not benefit from long-term holding. Under this condition, the protective closing strategy shortens the holding time further, which tends to reduce the returns, rather than lowering the risk.  
Figures 8 and 9 show the trading performance of the agents with and without the protective closing strategy, compared with the B and H trading strategy. The validation set is the same as the one in the previous section. From Figures 8 and 9, we can see that the protective closing strategy does not lower the returns while reducing the risk of unbearable losses, which is especially important for options trading.  
Figure 6: Trading performance on 50 ETF.  
Figure 7: Trading performance on 300 ETF.  
Since the models with the protective closing strategy performed well on the validation set, they can be applied to trade options in the next stage. Although the SAC model cannot be trained well with protective closing strategy, it is still used to trade options, for comparison.
# ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network
## 6 Evaluation
### Impact of Neighbor Numbers  
In this section, we evaluate ACE performance by varying the number of neighbors. There are only five neighbor relay nodes (5, 6, 7, 8, 9) in use between the source node and the destination node in this scenario. We increase neighbor numbers from 1 to 5 and perform the same experiments that the source node sends on/off messages to the destination node for 1000 times. Figure 5 show the results.  
Figure 5 shows that ACE maintains a stable and low latency regardless of the number of neighbors in contrast to Bluetooth Mesh, which also has a larger variance of latency, verifying its performance improvement in end-to-end latency. In addition, the experiment results show that more neighbors can possibly reduce end-to-end latency. This is because more neighbors can lead to a higher probability of reducing the channel matching time among one of three ADV channels overall. However, the best latency condition in Bluetooth Mesh is just close to ACE because ACE has an inherent low and stable latency characteristic due to the single-channel scanning and broadcasting scheme.
## 2 Analytical Methodology
### Cfd-Dem  
#### 2.1.1 Continuous Fluid Phase  
The thermal-hydraulics analysis in the continuous fluid phase of this research was conducted by the CFD solver provided from the code of STAR-CCM+, which stemmed from the fundamental core set of the governing equations. For the fluid-particle multi-phase system, the fluid was not fully occupied in a CFD cell due to the discrete particles existing in the fluid region. Hence, the CFD governing equations applied in this research consider the fluid volume occupied in a CFD cell in order to introduce the fluid volume fraction (\(\varepsilon_{f}\) [-]) of a CFD cell, defined as below:  
\[\varepsilon_{f}=\frac{V_{f}}{V_{cell}}, \tag{1}\]  
where \(V_{cell}\) [m\({}^{3}\)] and \(V_{f}\) [m\({}^{3}\)] are the CFD cell and fluid volumes in a CFD cell, respectively. The governing equations of the CFD solver, considering \(\varepsilon_{f}\), are listed below, including the conservation equations for mass (continuity equation, Equation (2)), momentum (Navier-Stokes equation, Equation (3)), and energy (Equation (4)), respectively [32]:  
\[\frac{\partial\varepsilon_{f}\rho_{f}}{\partial t}+\
abla\cdot( \varepsilon_{f}\rho_{f}\overset{\rightarrow}{V}_{f})=0, \tag{2}\] \[\frac{\partial(\varepsilon_{f}\rho_{f}\overset{\rightarrow}{V}_ {f})}{\partial t}+\
abla\cdot(\varepsilon_{f}\rho_{f}\overset{\rightarrow}{V }_{f}\overset{\rightarrow}{V}_{f})=-\varepsilon_{f}(\
abla p+\rho_{f} \overset{\rightarrow}{S})+\
abla\cdot(\mu_{f}\varepsilon_{f}\
abla\overset{ \rightarrow}{V}_{f})+\overset{\rightarrow}{S}_{m}, \tag{3}\]\[\frac{\partial\epsilon_{f}\rho_{f}C_{p,f}T_{f}}{\partial t}+\
abla\cdot(\epsilon_{f }\rho_{f}C_{p,f}\overset{\rightarrow}{V}_{f}T_{f})=\
abla\cdot(k_{f}\epsilon_{ f}\
abla T_{f})+S_{E}, \tag{4}\]  
where \(\rho_{f}\) [kg/m\({}^{3}\)], \(\overset{\rightarrow}{V}_{f}\) [m/s], and \(\mu_{f}\) [kg/m\(\cdot\)s] are the fluid density, velocity, and dynamic viscosity, respectively. \(p\) [kg/m\(\cdot\)s\({}^{2}\)] and \(\overset{\rightarrow}{S}\) [m/s\({}^{2}\)] are the pressure and gravity. \(C_{p,f}\) [J/kg\(\cdot\)K], \(k_{f}\) [W/m\(\cdot\)K], and \(T_{f}\) [K] stand for the heat capacity, thermal conductivity, and the temperature of the fluid. \(\overset{\rightarrow}{S}_{m}\) [N/m\({}^{3}\)] and \(S_{E}\) [W/m\({}^{3}\)] are presented as the momentum and energy source terms contributed by the feedback from the discrete particle phase. The details of \(\overset{\rightarrow}{S}_{m}\) and \(S_{E}\) will be further explained in the latter Section 2.1.3.
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 5 Results
### Proportional-Derivative-Integral Fuzzy Control  
Figure 23 illustrates the comparison between desired (\(q_{d}\)) and actual (\(q\)) trajectories for sequence A.  
Figure 24 illustrates the graph of the actual trajectory in 3D.  
Figure 21: Sequence of trajectory B, as a function of time, using Computed Torque PID control.  
Figure 22: 3D graph of trajectory B.  
Figures 23 and 24 demonstrate that the controller successfully tracked the desired trajectory with negligible deviations along the YZ coordinate axes.
## 3 Method
### Model Based on BPMN-Integrated SC Interaction
The third model based on BPMN, as shown in Figure 4, consists of two SCs: the current SC, which is responsible for automated compensation execution, and the external SC, which maintains the insurance. The process begins with the user purchasing insurance for their flight; the CAA provides the relevant flight information, and the current SC verifies the validity of the insurance by calling the external SC. If the check passes, the current SC records the purchased insurance for the user. Within a specified timeframe, the user has the option to cancel the insurance. In such cases, the current SC checks if the user has purchased insurance and calls the external SC to verify whether the cancellation falls within the allowed time limit. If the requirements are met, the current SC cancels the insurance. The CAA is responsible for recording and maintaining the takeoff and landing times of  
Figure 4: The modeling results based on BPMN in terms of purchasing flight delay insurance and obtaining compensation. Figure 5: The modeling results based on BPMN for insurance company registration. flights. After a flight lands, the CAA inputs the landing time, and the current SC uses the actual landing time to determine whether compensation is required. If compensation is necessary, the current SC calls the external SC to query the percentage of the compensation. Subsequently, the SC automatically transfers the compensation amount to the user.
# A Dual-Branch Speech Enhancement Model with Harmonic Repair
## 2 Proposed Methods
### The Harmonic Repair Module  
Although speech signals can be extensively damaged due to noise, the harmonic parts usually reside in higher energy regions and are not completely masked. Since deep learning models prioritize fitting high-energy and more robust (prominent) harmonic structures due to gradient descent and convergence [17], harmonic waveforms exhibit significant comb-like features, meaning that even if part of them are damaged by noise, the remaining parts contain information that can infer the original harmonic distribution. To model harmonic data in the spectrum, the model uses a harmonic-to-fundamental frequency transformation matrix \(Q\)[18], which calculates the corresponding harmonic distribution using the predicted fundamental frequency. The input \(X_{P}\in\mathbb{R}^{T\times F}\), after convolution energy normalization, produces a query-key matrix \(K\). Matrix multiplication between \(K\) and \(Q\) and the application of the sigmoid method obtains a confidence vector for the pitch of the fundamental frequency, indicating the likelihood of each candidate value corresponding to the pitch.  
The harmonic repair module uses high-resolution comb tooth spacing to infer the damaged harmonic distribution and fine-tunes the result using convolution. Figure 2 shows the structure of the harmonic repair module. Unlike the traditional attention mechanism, which calculates attention weights using query vectors and key-value pairs, the harmonic repair mechanism calculates and repairs harmonic information based on the spectral and harmonic-pitch converter, using a residual connection [19] to mitigate the vanishing gradient problem. The locality of convolution is the fundamental guarantee for harmonic modeling, so the module retains the spectral structure even after processing [20].  
The harmonic distribution \(H\) can be represented as \(softmax\big{[}sig\big{(}K\cdot Q^{T}\big{)}\big{]}\). \(X_{P}\) and \(H\) are rectified using one-dimensional convolution, and then \(H\) is applied to \(X_{P}\) for element-wise multiplication, and the result is convolved to output \(X_{out}\). Using the harmonic repair module can effectively repair and restore voice-focused frequency bands in audio and suppress the impact of harmonic-like noise in noisy speech on the enhancement results.
## 2 MBR Types and Configuration
### Anaerobic MBR (AnMBR) for OMPs Removal
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{2}{c}{**Micropollutants**} & \multicolumn{5}{c}{**\% Removal**} \\ \hline  & **A-[87]** & **B-[88]** & **C-[89]** & **D-[90]** & **E-[74]** & **F-[86]** & **G-[80]** \\ \hline
17\(\alpha\)-Estradiol & - & - & 27 & - & - & - & - \\ \hline
17\(\alpha\)-Ethynylestradiol & - & - & 15 & - & 100 & - & - \\ \hline
17\(\beta\)-Estradiol & - & - & 60 & - & 100 & - & - \\ \hline
4-(tert-octyl)) phenol & - & - & - & - & 0 & - & - \\ \hline
4-p-nonylphenol & - & - & - & - & 0 & - & - \\ \hline
4-n-nonylphenol & 94 & 96 & - & - & - & - & - \\ \hline Amitriptyline & 99 & 90 & 47 & 77 & - & - & - \\ \hline Androstenedione & - & - & - & - & - & - & - \\ \hline Androsterone & - & - & 16 & - & - & - & - \\ \hline Aspartame & - & 91 & - & - & - & - & - \\ \hline Atenol & 77 & - & 98 & - & - & - & - \\ \hline Atrazine & 32 & 32 & 6.8 & 4 & - & - & - \\ \hline Bisoprolol & - & - & - & 30 & - & - & - \\ \hline Benzophenone & - & 62 & - & - & - & - & - \\ \hline Bisphenol A & 99 & 4 & 32 & 15 & - & - & 81 \\ \hline Butylparaben & - & - & - & 81 & - & - & - \\ \hline Caffeine & 90 & 60 & 77 & 20 & - & - & - \\ \hline Carazolol & - & 50 & - & - & - & - & - \\ \hline Carbamazepine & 50 & 10 & 4.8 & 4 & - & 38 & 10 \\ \hline Celestolide & - & - & - & - & - & 48 & \\ \hline Clozapine & 99 & 81 & 28 & 75 & - & - & - \\ \hline DEET & 99 & 10 & 1.4 & 5 & - & - & - \\ \hline Diazepam & 54 & 20 & - & 6 & - & 38 & 2 \\ \hline Diazinon & 93 & 91 & - & 79 & - & - & - \\ \hline Diclofenac & 3 & 5 & 1 & - & - & 40 & 23 \\ \hline Dilantin & - & 6 & 21 & - & - & - & - \\ \hline Diuron & 62 & 16 & - & 7 & - & - & - \\ \hline EE2 & - & - & - & - & - & 12 & - \\ \hline Erythromycinin & - & - & - & - & - & 53 & 98 \\ \hline Enalapril & - & - & 37 & 23 & - & - & - \\ \hline Estradiol E2 & - & - & - & - & - & 59 & - \\ \hline Estriol & - & - & 1 & - & - & - & - \\ \hline Estrone & - & - & 1 & & 100 & 82 & 100 \\ \hline Ethinylestradiol & - & - & - & - & - & 82 & - \\ \hline Etiochanolone & - & - & 52 & - & - & - & - \\ \hline Fluoxetineine & - & - & - & - & - & 22 & 92 \\ \hline Galaxolide & - & - & - & - & - & 84 & - \\ \hline Gemfibrozil & 18 & 11 & 13 & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: OMP removal in AnMBR and major operating conditions.
# A Simple Model to Predict Machined Depth and Surface Profile for Picosecond Laser Surface Texturing
###### Abstract  
A simple mathematical model was developed to predict the machined depth and surface profile in laser surface texturing of micro-channels using a picosecond laser. Fabrication of micro-craters with pulse trains of different numbers was initially performed. Two baseline values from the created micro-craters were used to calculate the estimated simulation parameters. Thereafter, the depths and profiles with various scanning speeds or adjacent intervals were simulated using the developed model and calculated parameters. Corresponding experiments were conducted to validate the developed mathematical model. An excellent agreement was obtained for the predicted and experimental depths and surface profiles. The machined depth decreased with the increase of scanning speed or adjacent interval.  
laser surface texturing; mathematical model; machined depth; surface profile Article
## 5 Machine Learning Models
### Development of the ANN-Based Prediction Model  
This section outlines the methodology to be adhered to for the construction of the predictive model. To train the hidden neurons, an appropriate learning technique must be created. The network is trained using the Levenberg-Marquardt (LM) variant of the backpropagation (BP) method. The neural network model for this study utilized the tan-sigmoid transfer function in the output and hidden layers. MATLAB R2019b was employed as the environment for developing and training the network, offering the essential tools and capabilities. In total, a dataset of 11,480 CDAF values were simulated for the five-story building models (M\({}_{5\text{ref}}\), M\({}_{5\text{bs5}}\), M\({}_{5\text{ms5}}\), and M\({}_{5\text{bs5}}\)) in this study, corresponding to 41 non-structural component vibration periods, seven damping ratios, two excitation directions (where 0 and 1 are assigned to two orthogonal directions (X and Y), respectively), four soft-story conditions (where 0, 1, 2, and 3 are assigned to the reference, bottom, middle, and top soft-story models, respectively), and five-floor levels. Similarly, a dataset of 22,960 CDAF values were simulated for the ten-story building models (M\({}_{10\text{ref}}\), M\({}_{10\text{bs5}}\), M\({}_{10\text{ms5}}\), and M\({}_{10\text{bs5}}\)) corresponding to 41 non-structural component vibration periods, seven damping ratios, two excitation directions (where 0 and 1 are assigned to two orthogonal directions, respectively), four soft-story conditions (where 0, 1, 2, and 3 are assigned to the reference, bottom, middle, and top soft-story building models, respectively), and ten-floor levels. The soft-story height is limited to only 5 m for the development of prediction models. To ensure the proper training and evaluation of the neural network model, each dataset is divided into three subsets: a training set comprising 70% of the entire dataset, a validation set comprising 15%, and a testing set comprising the remaining 15%. Prior to training, the entire dataset undergoes preprocessing. This involves normalizing the dataset to ensure that all variables are scaled between \(-1.0\) and \(1.0\), thus providing equal weighting to each variable in the analysis. Such normalization can be carried out using Equation (3).  
\[X_{n}=\frac{2(X-X_{min})}{(X_{max}-X_{min})}-1 \tag{3}\]  
where \(X_{n}\) is the normalized value, and \(X_{max}\) and \(X_{min}\) are the maximum and minimum values of the variable \(X\).
# Modular Dynamic Neural Network: A Continual Learning Architecture
## 1 Introduction
Another important advantage of the implemented structure is that classifications can be made using only a percentage of the modules, meaning that the addition of new modules will not have the same negative impact on scalability as it would if every new module had to be processed for every classification. While the scalability of the presented framework has not yet been proven on large datasets, the use of a percentage of the network is undoubtedly lighter in terms of computational requirements compared to the use of the entire network. The main contribution of this paper is a framework that can learn new classes without having to retrain its entire network of classifiers, with features that help it to cope with the scalability problems that generally come with this type of expanding architecture. The presented framework is in itself original, as the methodology behind its architecture does not directly fit into any of the categories normally used to define CL methods, such as regularization, memory/replay, architecture or parameter-isolation-based methods [3; 8], where it is, in fact, a kind of mixture of the mentioned categories. It is partially regularization-based because parts of the network are selectively not interfered with while training; partially memory/replay based because some data are stored and re-used; partially architecture-based because the network of modules does expand over time; and partially parameter isolation-based because specific parts of the network are assigned specific tasks. On top of being original in itself, the presented framework possesses a feature that is of particular interest in terms of innovation, which is the modular dynamic training component that is the main reason it is able to learn continuously, as modules can be added over time which are responsible for recognizing classes or groups of classes, and the assignment of which classes are attributed to which modules is done automatically as the network grows progressively. The test results on the CORe50 dataset [9] show state-of-the-art results when compared with other CL architectures and, in some cases, the MDNN was even able to almost match results obtained on the same data without CL restrictions (i.e., the final accuracy after learning classes incrementally was similar to that of another network presented with all the data at once). The tests made primarily evaluate the framework's ability to learn classes incrementally and to re-evaluate its accuracy on old classes as new ones are added to make sure they were not forgotten. The present section introduced the context and goals. Section 2 includes the related work on continual learning along with some background concepts. Section 3 addresses the presented architecture, Section 4 shows the tests and results of the implemented CL architecture and, finally, the conclusions and future work are presented in Section 5.
## 2 Deterministic Model of a Dual-Rotor System
### The Riccati Whole Mean Transfer Model of the Coupling Element  
The transfer relationship between state vectors \(\textbf{Z}_{i+1}\) and \(\textbf{Z}_{i}\) on the left and right nodes at the coupling elements is provided in Equation (2), which can be expressed as  
\[\textbf{Z}_{i+1}=\underline{\textbf{U}}_{C,i}\textbf{Z}_{i} \tag{6}\]  
\[\underline{\textbf{U}}_{C,i}=\textbf{C}_{i}\underline{\textbf{U}}_{i} \tag{7}\]  
where \(\underline{\textbf{U}}_{C,i}\) represents the whole mean transfer matrix at the coupling elements, and the coupling matrix \(C_{i}\) can be expressed as [21]  
\[\textbf{C}_{i}=\begin{bmatrix}1&0&0&0&0&0&0&0\\ 0&1&-k_{c}&0&0&0&k_{c}&0\\ 0&0&1&0&0&0&0&0\\ 0&0&0&1&0&0&0&0\\ 0&0&0&0&1&0&0&0\\ 0&0&k_{c}&0&0&1&-k_{c}&0\\ 0&0&0&0&0&0&1&0\\ 0&0&0&0&0&0&0&1\end{bmatrix}_{i} \tag{8}\]  
where \(k_{c}\) represents the linear stiffness of the intermediate supports.  
The whole mean transfer matrix at the coupling element is transformed into the Riccati whole mean transfer matrix, \(\underline{\textbf{S}}_{C,i}\), and then, the process can be calculated as follows:  
\[\underline{\textbf{S}}_{C,i}^{R}=\underline{\textbf{S}}_{C,i}^{L}+\underline{ \textbf{R}}_{i} \tag{9}\]  
where the superscripts \(R\) and \(L\) represent the right and left end sections at the coupling element, and \(\underline{\textbf{R}}_{i}\) is the stiffness matrix at the intermediary support element, which can be expressed as  
\[\underline{\textbf{R}}_{i}=\begin{bmatrix}0&0&0&0\\ -k_{c}&0&k_{c}&0\\ 0&0&0&0\\ k_{c}&0&-k_{c}&0\end{bmatrix}_{i} \tag{10}\]
# A New Internet of Things Hybrid VLC/RF System for m-Health in an Underground Mining Industry
## 2 Wireless Communications Technologies for m-Health
### RF Based Technologies  
* **Bluetooth:** ref. [6] The Bluetooth Low Energy (LE) radio is designed to operate at very low power levels, from 1 mW to 100 mW, operates in the 2.4 GHz frequency band, and supports data rates of 125 Kbps up to 2 Mbps. The coverage distance can be 100 m [7].
* **Wi-Fi:** ref. [8] Wi-Fi is a family of wireless network protocols based on the IEEE 802.11 [8] family of standards, which are commonly used for local area networks of devices and Internet access. Wi-Fi works in frequency bands of 2.4/5 GHz and supports data rates from 1 Mbps to 1 Gbps. The maximum coverage distance can be 100 m [9].
* **Zigbee:** ref. [10] Zigbee is a specification based on IEEE 802.15.4 [10] for a set of high-level communication protocols used to create personal area networks. It operates in the 2.4 GHz frequency band, supports data rates of 250 kbps, and reaches coverage ranges of up to 300 m [11].
# Compact UHF Circularly Polarized Multi-Band Quadrifilar Antenna for CubeSat
## 4 In-Flight Antenna Performance  
A picture of the antenna integrated within the 3U structure is shown in Figure 14. Solar panels are deployed, and the proposed UHF antenna is visible on the front of the satellite. The 3U CubeSat, named LS3, was launched on 28 September 2020 by the Soyuz launch vehicle and commissioned in February 2021. The satellite (Norad ID 46492) has a polar orbit with a 526.6 km perigee, a 550 km apogee, an inclination of 97.8\({}^{\circ}\), and a period of 95.3 min. The communication payload has 30 dBm output power capabilities at 868 MHz and 915 MHz. Lacuna Space is authorized to transmit over Europe, corresponding to a latitude ranging from 41\({}^{\circ}\) to 58\({}^{\circ}\). The experiments were carried out in the city of Antibes, France (43.55, 7.13), from February to May 2021. Lacuna Space uses a Chirp Spread Spectrum modulation (LoRa) with a 250 kHz frequency bandwidth for the downlink. This waveform is robust to the Doppler frequency offset effect. A terminal using a trifilar antenna [32] and an electronic module based on the SX1262 transceiver was used as the terrestrial receiver. The receiving antenna provides a 2.5 dBic realized gain at zenith and was placed 10 cm above the ground using a tripod. It should be noted that no additional radio frequency components (low-noise amplifier, filter, etc.) were used during the experiment, which confirms the capability to receive a broadcast signal from LEO orbit with a low-cost and low-power consumption terminal. The Received Signal Strength Indicator (RSSI) was extracted for every received packet. The left part of Figure 15 shows the RSSI versus elevation for 28 different passes. The same results are shown over a Europe map on the right of Figure 15, with every point being the satellite's position when it transmitted the packet. The position of the terrestrial received is indicated with the gray marker. During the experiment between the satellite and the terrestrial receiver, a maximum communication distance of 1232 km was reached.  
Figure 13: Axial ratio versus theta measurement of the antenna without and with structure.