esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### The PV Array Simulator  
In order to have consistent formulation of inverter efficiency, the measurements must be conducted in a laboratory environment, where the irradiance and temperature can be controlled reliably. It is difficult to achieve these conditions using the physical PV array (in the field) due to the meteorological uncertainties (particularly temperature variation). Thus, the PV array simulator (PVAS) from Austrian Institute of Technology (AIT) was used to emulate the PV modules of specific types [9; 24]. Unlike the normal simulator (which is switched mode power supply), the PVAS is built using a linear high-voltage Metal Oxide Semiconductor Field Effect Transistor (MOSFET). As a result, the effect of switching frequency ripple on the dc output is minimized. The measured irradiance data (\(G_{i}\) from the weather station) was used as an input to the PVAS. Using these values, a unique \(I\)-\(V\) characteristic curve was  
Figure 5: The photographs of hardware used in data collection: (**a**) the CMP11 pyranometer; (**b**) data acquisition for weather sensors.  
generated. Then using a script generator, a continuous profile of output power (based on the \(I\)-\(V\) curve) was produced at the output of the PVAS. Note that to compute the weighted efficiency, the temperature was set to be constant at 25 \({}^{\circ}\)C.  
Figure 7 depicts the overall block diagram used to measure the efficiency of the inverter, while Figure 8 shows the photograph of the actual laboratory test set-up. The \(G_{i}\) data from the weather station was converted into a specific format that is readable using the PVAS. Since the data is obtained from crystalline modules, the same technology was set in the PVAS parameters setting. Furthermore, the critical inverter specifications, i.e., \(V_{oc}\), \(I_{sc}\), \(V_{mpp}\), and \(I_{mpp}\) were also specified [7]. The output of the PVAS was connected to the tested inverter and the latter was tied to the grid. To avoid measurement uncertainties (due to the voltage and frequency fluctuation), the grid was emulated using an ac power source, connected to a resistive load. The ratio between the module power and the dc side power of the inverter was set to 1:1. This ratio is recommended by references [25; 26]. The electrical properties of the inverter, namely the input dc (\(V_{DC}\), \(I_{DC}\)) and output ac (\(V_{ac}\) and \(I_{ac}\)), were logged using a PM 6000 power analyzer. From these quantities, the \(P_{dc}\) and \(P_{ac}\) were computed, respectively. Four inverters with different technologies and ratings were tested. Their specifications are described in Table 2.  
Figure 6: Example of one second sampled in-plane irradiance daily data in 2014 [4].  
Figure 7: Block diagram of the PV inverter test.
## 3 Background
### Reinforcement Learning  
Reinforcement learning involves an agent making decisions in different scenarios. It comprises the agent, environment, actions, rewards, and observations. Reinforcement learning faces challenges such as excessive reinforcements and high computational costs, especially for complex problems. The dynamics of reinforcement learning are encapsulated in Figure 4, illustrating the interaction between the agent and its environment. Notably, states in this framework are stochastic, meaning the agent remains unaware of the subsequent state, even when repeating the same action.  
Within the realm of reinforcement learning, several crucial quantities are determined:  
* Reward: A scalar value from the environment that evaluates the preceding action. Rewards can be positive or negative, contingent upon the nature of the environment and the agent's action.
* Policy: This guides the agent in deciding the subsequent action based on the current state, helping the agent navigate its actions effectively.
* Value (V): Represents the long-term return, factoring in discount rates, rather than focusing solely on short-term rewards (R).
* Action Value: Like the reward value, but incorporates additional parameters from the current action. This metric guides the agent in optimizing its actions within the given environment.  
Despite the advantages of reinforcement learning over supervised learning models, it does come with certain drawbacks. These challenges include issues related to excessive reinforcements, which can lead to erroneous outcomes. Additionally, reinforcement learning methods are primarily employed for solving intricate problems, requiring substantial volumes of data and significant computational resources. The maintenance costs associated with this approach are also notably high.  
This study focuses on predicting gold prices based on next-day tweets sourced from news and media datasets. Gold prices exhibit rapid fluctuations daily, necessitating a robust prediction strategy. To achieve accurate predictions, this research employs a comprehensive approach integrating deep reinforcement learning (DRL), long short-term memory (LSTM), variational mode decomposition (VMD), and natural language processing (NLP). The prediction time spans from 2012 to 2019, utilizing tweets related to gold prices. DRL is enhanced by incorporating sentiment analysis of media news feeds and Twitter data, elevating prediction accuracy. The dataset used for this analysis was retrieved from the link [[https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-in-commodity-market-gold](https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-in-commodity-market-gold)]([https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-in-commodity-market-gold](https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-in-commodity-market-gold)) accessed on 1 February 2023. This dataset, spanning from 2000 to 2021,  
Figure 4: The reinforcement learning process.  
encompasses diverse news sources and is meticulously classified as positive or negative by financial experts, ensuring the robustness and reliability of the data.
## 5 Simulation Result Analysis and Discussion
### Quantification of Results  
In addition to graphical representations to provide a clearer view, the LVRT has been evaluated using several performance indices, such as the PV active power deviation and DCL voltage deviations. A lower value of the indices indicates an enhanced system performance [32]. The performance indices are calculated using Equations (20)-(21):  
\[\mathrm{DCL_{vol}}=\ \frac{\int_{0}^{\mathrm{T}}\lvert\Delta\mathrm{V_{dc}} \rvert\ \mathrm{dt}}{\mathrm{V_{dc\_rated}}} \tag{20}\]  
\[\mathrm{PV_{power}}=\ \frac{\int_{0}^{\mathrm{T}}\lvert\Delta\mathrm{Ppv} \rvert\ \mathrm{dt}}{\mathrm{P_{pv\_rated}}} \tag{21}\]  
where \(\Delta\mathrm{V_{dc}}\) and \(\Delta\mathrm{Ppv}\) stand for the deviation of the DC-link voltage and PV array power. The period of interest is from 0 s to T = 5 s. The performance comparison of various control techniques is given in Table 1. It is clear from Table 1 that the improvement is significant when using the proposed control scheme, since it provides the lowest index values. Hence, the proposed control scheme is better than the no control (MPPT) scheme to enhance the LVRT of the system, under both symmetrical and asymmetrical faults.
## 2 Background  
Medical images provide extensive information that can be used to diagnose diseases and track the progress of patients. Chest X-ray images necessitate a thorough examination by radiologists, who then document the results of their analyses in full-text reports. To generate accurate reports, radiologists must have expertise in diagnosing medical images. Nonetheless, many reports do not provide a conclusive diagnostic due to the large number of potential diagnoses. Furthermore, the amount of time it takes radiologists to prepare full-text reports is an issue of concern. In modern-day hospitals, automated medical imaging techniques are commonly employed to help alleviate these problems.  
Convolutional-Neural-Network-based systems are commonly used for medical image analysis. ConvNets are powerful feature extractors able to discover relevant information in images without the need for human intervention.
# An Indoor Smart Parking Algorithm Based on Fingerprinting
## 2 Related Works  
The parking problem is still an open issue for many researchers from diverse research fields. In outdoor contexts, several proposals have been made to provide algorithmic solutions to the problem, such as authors of [10], which produced an algorithm to complete the parking process by optimizing the waiting time and considering the destination of the user when assigning a parking slot to him. Additionally, the authors in [11] provided an algorithm based on a known optimization problem, which is the ant colony optimization, by getting inspired by the behavior of real ants looking for food. The authors of [12], instead, proposed a game-theoretic solution, by modeling the parking problem as a multi-agent game in which drivers compete towards the same resource. In this case, the solution is provided by means of a social equilibrium, the Nash equilibrium.  
When moving to an indoor setting, one can observe that the literature on smart parking is much poorer. Nevertheless, some interesting solutions to the problem has been proposed, such as the authors in [13] which provided a vision-based parking lot system, where wide-angle fisheye-lens are used and in which vacant parking spaces can be detected by a background subtraction scheme. However, in [14], the authors provided a system based on smartphones that tracks the vehicle's location in real time and uses landmark recognition methods against noises. Additionally, in [15], they proposed a new map representation for indoor localization: precisely, they considered the permanent elements as static objects and the changing elements as semi-static objects. Recently, the authors of [16] developed a car-searching mobile app based on a turn-by-turn navigation strategy able to correct the user's heading orientation.  
In [17], the authors also used fingerprinting applied to smart parking, with the aim of recording parking positions, through the K-nearest neighbor and fingerprinting techniques based on the RSSI Bluetooth with distance calculation parameters using Manhattan distance. However, they did not formalize and face the problem of finding a suitable parking slot in an indoor context. To the best of my knowledge, this is the first work addressing the indoor parking problem, by providing a solution to associate vehicles to available parking slots by getting drivers as close as possible to their final destination, by means of a fingerprinting-based localization technique.
# Recent Progress of Metal-Air Batteries--A Mini Review
## 5 Advanced Manufacturing of MABs
### 3D Printing  
Traditional thin film 2D batteries have suffered from limited energy capacity. 3D printing of flexible micro-batteries with nanostructures can overcome this weakness. Currently, the printed parts of batteries can be electrodes, current collector, solid-state separator, and catalyst in metal-air batteries [104]. [PERSON] [105] reviewed 3D printing energy storage devices with a sandwich-type and in-plane architecture and demonstrated that the electrochemical energy storage systems can be greatly promoted with 3D printing. [PERSON] et al. [106; 107] reported 3D fully printed electrodes for Li-ion batteries. Li\({}_{4}\)Ti\({}_{5}\)O\({}_{12}\) (LTO) and LiFePO\({}_{4}\) (LFP) were separately employed as anode and cathode materials. Electrode material inks were printed onto the substrate, forming multilayer electrodes and an anode and cathode in an interdigitated structure. The results showed that the charge and discharge of 8-layer full cell delivered  
Figure 17: (**a**) Schematic of aluminum–air batteries (AAB) working principle, (**b**) Photo of a packaged battery cell, (**c**) The first cycle discharge capacity with different laser sintering powers. (**d**) Discharge capacity for 3D printed anode and relationship between anode thickness and capacity [3] (Copyright © 2018, Electrochemical Society).  
1.2 mAh cm\({}^{-2}\) at a rate of 0.5 C. EIS test revealed the thicker electrode had higher resistance. Meanwhile, CV testing showed the thin wall displayed broader redox peaks. Furthermore, both thin and thick electrodes exhibit excellent Coulombic efficiencies. 3D printed LIBs had 4.45 mAh cm\({}^{-2}\) at 0.14 mA cm\({}^{-2}\), corresponding a full cell delivering 14.5 mAh cm\({}^{-2}\) at 0.2 mA cm\({}^{-2}\). The same printed technology can also be employed for MABs. For MABs, screen printing has been used in catalysts [108]. A remarkable shortcoming is the narrow choice of suitable materials for printing. In addition, expensive equipment also limits application. However, 3D printing is the destructive technology in MAB manufacturing due to unprecedented designing freedom, high precision, and cost-effective processing.
# Dynamic Stability of an Electric Monowheel System Using LQG-Based Adaptive Control
## 3 Kinematics and Dynamics
### Linearization  
Linearization is a method used to calculate the linear approximations of a nonlinear system function that are valid within a small range of values around an operating point. The linear approximation is assessed using the first-order Taylor series expansion, thereby ensuring the system's local stability and steady-state condition at an equilibrium point. This helps in analyzing a system's stability and disturbance rejection property. For a system or a function defined as \(\frac{dx}{dt}=F(x,t)\), the linearized system is expressed as \(\frac{dx}{dt}\approx F(a,t)+DF(a,t)(x-a)\), where \(a\) is the operating point and \(DF(a,t)\) is the first-order differentiation at point \(a\).  
By the substitution of variables such as \(x_{1}=x,x_{2}=\theta,x_{3}=\dot{x},x_{4}=\dot{\theta}\), from (8) and (9), we get  
\[\ddot{x}=\frac{bml\dot{\theta}cos\ \theta+m^{2}l^{2}gsin\ \theta cos\ \theta+(l+ml^{2})(F-c\dot{x}+ml\dot{\theta}^{2}sin\ \theta)}{m^{2}l^{2}sin^{2}\theta+Mml^{2}+(M+m)I}. \tag{10}\]  
\[\ddot{\theta}=\frac{-(Fmlcos\ \theta-cml\dot{x}cos\ \theta)+m^{2}l^{2}\dot{\theta}^{2} sin\ \theta cos\ \theta+(M+m)(b\dot{\theta}+mglsin\ \theta))}{m^{2}l^{2}sin^{2}\ \theta+Mml^{2}+(M+m)I}, \tag{11}\]Writing Equations (10) and (11) in matrix form,  
\[f(X,U)=\left[\begin{array}{c}x_{3}\\ x_{4}\\ \frac{bm1x_{4}cosx_{2}+m^{2}l^{2}gsinx_{3}cosx_{2}+(l+m^{2}l)(F-cx_{3}+mlx_{2}^{ 2}sinx_{2})}{m^{2}l^{2}sin^{2}x_{3}+Mml^{2}+(M+m)l}\\ \frac{-(Fmlosx_{2}-cmlx_{3}cosx_{2})+m^{2}l^{2}gsinx_{3}cosx_{4}+(M+m)((bx_{4} +mglsinx_{2}))}{m^{2}l^{2}sin^{2}x_{2}+Mml^{2}+(M+m)I}\end{array}\right].\]  
Now, considering the operating point as \((X_{0},U_{0})=([\begin{array}{cccc}0&\pi&0&0&\end{array}],0)\) for linearization, we obtain our state space matrix as  
\[\dot{X}=\begin{bmatrix}0&0&1&0\\ 0&0&0&1\\ 0&\frac{m^{2}l^{2}g}{\alpha}&\frac{-(l+m^{2})c}{\alpha}&\frac{-bm1}{\alpha} \\ 0&\frac{mg(M+m)}{\alpha}&-\frac{-mlc}{\alpha}&\frac{-b(M+m)}{\alpha}\\ \end{bmatrix}X+\begin{bmatrix}0\\ 0\\ \frac{l+m^{2}}{\alpha}\\ \frac{\frac{ml}{\alpha}}{\alpha}\end{bmatrix}F,\]  
where \(\alpha=I(M+m)+Mml^{2}\). The force applied \(F\) is generated by the PM DC motor, and the relation between \(F\) and the applied voltage \(V_{m}\) is \(F=\frac{k_{y}V_{m}r-k_{y}k_{y}}{R_{m}r^{2}}=\frac{k_{y}V_{m}r-k_{y}k_{y}x_{3}} {R_{m}r^{2}}\).  
By substituting the values of constants, we get  
\[\dot{X}=\begin{bmatrix}0&0&1&0\\ 0&0&0&1\\ 0&11.772&-0.0153&-1.067\times 10^{-4}\\ 0&28.776&-0.0204&-2.6074\times 10^{-4}\end{bmatrix}\begin{bmatrix}x\\ \theta\\ \dot{x}\\ \dot{\theta}\end{bmatrix}+\begin{bmatrix}0\\ 0\\ 0.0446\\ 0.0595\end{bmatrix}V_{m},\]  
\[Y=\begin{bmatrix}1&0&0&0\end{bmatrix}\begin{bmatrix}x&\theta&\dot{x}&\dot{ \theta}\end{bmatrix}^{T}.\]  
Upon analysis, it has been found that rank of state matrix A is 3, and the ranks of both the controllability as well as observability matrices are also 3. Hence, it can concluded be that the system is both controllable as well as observable, as the ranks are the same as that of matrix A.
# Lithium-Ion Battery Prognostics through Reinforcement Learning Based on Entropy Measures
### ML and DL Techniques  
Recently, Machine Learning (ML) and Deep Learning (DL) algorithms have found very significant and useful applications in research and practice. These concepts have been used to develop various models for predicting different characteristics in diverse fields. In general, ML and DL algorithms aim to capture information from past data, learn from that data, and apply what they have learned to make informed decisions. Therefore, the associated systems are not required to be broadly programmed in all aspects.  
ML is used to synthesize the fundamental relationships between large amount of data to solve real-time problems such as big-data analytics and evolution of information [13]. DL, in turn, is able to process a large number of features and, hence, is preferred when computing huge datasets and unstructured data. DL facilitates analysis and extraction of important information from raw data by using computer systems. [14]. Different types of parameters with various quantities can be applied to the developed models as the input to obtain expected predictive variables as the output.  
Deep Learning techniques, including Long Short-Term Memory (LSTM) [15] and Reinforcement Learning (RL) [16], can fit numerical dependent variables and have great generalization ability, and therefore, are applicable to battery data. The LSTM algorithm, a Deep Learning algorithm with multiple gates, performs on the basis of updating and storing key information in the time series data [15], and is applicable to battery prognostics. The RL algorithm, on the other hand--as one of the latest Deep Learning methods and tools--has the capability of creating a simulation of the whole system and making intelligent decisions (i.e., charge, replace, repair, etc.) after it is utilized to predict the battery RUL and SOH for the purpose of battery PHM and BMS [16].
###### Abstract
(2012) investigated a variable preload technology for machine tool spindles working at different ranges of rotation speed, and the experimental results showed that the variable preload technology can reduce the temperature rise of the system in the high speed condition compared with the application of constant preload and improved the bearing stiffness at the low speed range. [PERSON] et al. (2013) developed an analytical method for determining the optimum preload based on the critical state between the skidding and rolling of ball bearings for different speed ranges, and their results were verified with the help of a spindle bearing experimental setup. [PERSON] and [PERSON] (2014) investigated the thermal effect of the spindle bearing system during high speed rotations when the preload was applied, and the time-varying thermal effects on the preload and stiffness of bearings was obtained. [PERSON] et al. (2015) investigated the effect of external load and preload on the number of rolling elements in the contact zone based on the a quasi-dynamic model and determined an optimum preload for a simplified bearing-rotor system by taking the bearing fatigue life as the optimization target. For the shaft-bearing system with high speed and high precision requirements, bearing preload is necessary to increase bearing stiffness and suppress vibration. In addition, the angular misalignment of the rolling bearing due to mounting error is common and unavoidable and would cause considerable variations in ball-raceways' contact force distribution, which affects the bearing fatigue life. Currently, very few studies have investigated the effects of the combined preload and angular misalignment on the fatigue life of rolling element bearings and the shaft-bearing system at high speed. The fatigue life variation of rolling element bearings and the shaft-bearing system with the combined preload and angular misalignment has not been well understood. In this sense, understanding the fatigue life variation considering the combined preload and angular misalignment is important for proper selection and assembly of rolling bearings in the shaft-bearing system, which can be achieved by analyzing the effects of the combined preload and angular misalignment on the fatigue life of rolling element bearings and the shaft-bearing system. In this paper, a generic shaft-bearing system model combing the shaft model and the bearing model is introduced. A numerical method is presented to improve the computation efficiency of the system model. The ball-raceway contact forces of angular contact ball bearings (ACBBs) in shaft-bearing systems are evaluated under the complex operation conditions considering both the preload and angular misalignment. Based on the fatigue life theory, the fatigue life of each ACBB is calculated. The effects of the combined preload and angular misalignment on the fatigue life of ACBBs and the shaft-bearing system are discussed. Finally, some useful conclusions are given.
## 3 Experiments and Results
### Synthetic Data  
We synthesize a virtual perspective camera with zero-distortion, zero-skew and unit aspect ratio. The principal point lies at the image center. The image resolution is \(1280\times 800\) pixels and the focal length is \(50\) mm. The position of the camera is fixed at \(O_{c}=(0,0,50)\), and the orientation is kept at \(R=\left[\begin{array}{ccc}1&0&0\\ 0&1&0\\ 0&0&1\end{array}\right]\), which means the camera frame \(S_{C}\) and world frame \(S_{W}\) are parallel.  
The synthetic data consist of three thousand 3D points that are randomly generated in the world frame. These points are randomly distributed in the box of \([-20,\,20]\times[-2,\,2]\times[190,\,210]\). For each test, three synthetic 3D points are randomly selected from the synthetic data, and project them into image points using the virtual perspective camera. Now the synthetic data have three thousand 2D-3D point correspondences.