esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Sustainable Regional Planning: Potential of Commuter Rail in the Madrid Urban Region
## 3 Results and Discussion
### Characterization of the Municipalities with and without Commuter Stations
It is a dynamic promoted by public and private agents who have developed the urban region and the commuter railway network with little or no coordination. Second, urban expansion has been carried out under dispersed forms of occupation, independent and not oriented to commuter stations. This has been supported by flows that progressively become peripheral, further away from the stations, and acquire a more multidirectional character, so that the radial commuter network has lost competitiveness to the road network. The development and configuration of the latter is the third factor that should be highlighted. In the last 3 decades, the road network in the Madrid urban area has been greatly developed. The historic radial configuration based on six roads that was conceived in the 18 th century to connect the capital with the rest of the country (N-I to N-VI) was gradually adapted for the new means of transport, but in the last three decades, most have been not only widened and transformed into highways (A1 to A6), but also duplicated with new radial highways that run approximately parallel to the pre-existing ones (e.g., R2, R3, R4). On top of that, new concentric ring highways around Madrid have been built interconnecting the radial highways (M30, M40, M45, and M50). Figure 3: Types of municipalities by density and passengers (in and out) per population in 2018. One way to evolve towards a more sustainable urban development and mobility system could be to encourage the use of the existing commuter rail network and develop its full potential from a planning perspective. The following section explores urban development potential around the commuter stations and puts forward a classification based on their urban development capacity in the short, medium and long terms.
## 4 Discussion and Conclusions  
Based on the scenario analysis, three typical traffic flow scenarios were proposed: manual driving scenario, intelligent vehicle mixing scenario, and vehicle convoy scenario, considering the future conditions of vehicle networking, intelligence, and mixed traffic with different levels of driving automation. Data collection was conducted and microscopic traffic flow models were calibrated for three interchanges on the Xi'an Ring Expressway. Through simulation experiments, the carbon emission characteristics of traffic flow under each scenario were revealed. The results indicated that carbon emissions decreased with an increase in the intelligent vehicle mixing rate. The implementation of a dedicated lane for intelligent vehicles helps reduce carbon emissions. Carbon emissions showed an initial decreasing trend and then increased with an increase in vehicles exiting the off-ramp. Adjusting the headway between ACC and CACC vehicles can reduce carbon emissions under different intelligent vehicle mixing rates. In the case of vehicle convoy driving, both electric vehicle convoys and mixed convoys of gasoline and electric vehicles had minimum carbon emissions when they were composed of two vehicles. This research provides methodological support for exploring the carbon emission characteristics of heterogeneous traffic flow in off-ramp impact areas after the widespread adoption of intelligent vehicles and serves as a basis for formulating low-carbon development strategies for future highway systems.  
Conceptualization, X.S. and Y.X.; methodology, X.S. and F.C.; formal analysis, B.L. and L.L.; resources, Y.X.; data curation, L.L.; writing--original draft preparation, X.S., F.C. and B.L.; writing--review and editing, Y.X. and X.S.; supervision, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the General Program of the National Natural Science Foundation of China (No. 52172338), Natural Science Foundation of Shaanxi Province (No. 2022Q-527), Fundamental Research Funds for the Central Universities, CHD (No. 300102341502 and No.300102341201).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the first author.  
The authors declare no conflict of interest.  
Figure 10: Impact of the size of the mixed fleet.
# Hospital Readmission and Length-of-Stay Prediction Using an Optimized Hybrid Deep Model
## 2 Related Works
[30] used a decision tree to predict the COVID-19 patients' LOS. They analysed 2017 patients from January to July 2020. Their work results indicate an R2-score of 49.8% and a median absolute deviation of 2.85 days. For predicting of discharge time in COVID-19 patients, [PERSON] et al. [31] used the health records of 1182 patients. They used only age and gender as input features for the discharge time prediction of COVID-19 patients. They tested the gradient boost algorithm, Cox regression, and fast SVM for the discharge time prediction. They reported that the gradient boost algorithm achieved the best result, with an accuracy of 71.7%. A summary of the reviewed research is shown in Table 1.  
None of the above-mentioned methods has taken any action to predict patients' long-term hospitalisation LOS. All existing LOS classifications are restricted to three or fewer classes. Furthermore, the existing models have a low performance for classifying readmitted patients into more than two categories. Most of the reviewed works have focused on using standard ML models for LOS prediction and their performance has been reported on a single disease only. To overcome the limitations of previous works, we propose a method to predict both the readmission and the LOS in patients with various conditions using a novel hybrid deep model (GAOCNN). In the GAOCNN, the CNN predicts the hospital readmission and the LOS, while the genetic algorithm (GA) optimizes the parameters of the layers to improve the performance. The proposed model is evaluated using three datasets of diabetic, COVID-19, and ICU patients. To compare GAOCNN performance with other artificial intelligence techniques, we used a traditional ML model such as SVM or a traditional DL model such as Visual Geometry Group (VGG16) [32]. Furthermore, we combine traditional ML with DL models such as CNN+SVM to ensure the capability of GAOCNN compared to the hybrid model. The experimental results indicate superior performance to ML, DL, and hybrid models. Compared to similar research, proposed algorithms can also help predict LOS with a lower time frame. Lower time frame length leads to a better knowledge of the number of patients each day, and this knowledge can help the hospital manage nurse scheduling programs better, especially during a pandemic.
# How to Correctly Detect Face-Masks for COVID-19 from Visual Information?
###### Abstract  
The new Coronavirus disease (COVID-19) has seriously affected the world. By the end of November 2020, the global number of new coronavirus cases had already exceeded 60 million and the number of deaths 1,410,378 according to information from the World Health Organization (WHO). To limit the spread of the disease, mandatory face-mask rules are now becoming common in public settings around the world. Additionally, many public service providers require customers to wear face-masks in accordance with predefined rules (e.g., covering both mouth and nose) when using public services. These developments inspired research into automatic (computer-vision-based) techniques for face-mask detection that can help monitor public behavior and contribute towards constraining the COVID-19 pandemic. Although existing research in this area resulted in efficient techniques for face-mask detection, these usually operate under the assumption that modern face detectors provide perfect detection performance (even for masked faces) and that the main goal of the techniques is to detect _the presence_ of face-masks only. In this study, we revisit these common assumptions and explore the following research questions: (i) How well do existing face detectors perform with masked-face images? (ii) Is it possible to detect a proper (regulation-compliant) placement of facial masks? and iii) How useful are existing face-mask detection techniques for monitoring applications during the COVID-19 pandemic? To answer these and related questions we conduct a comprehensive experimental evaluation of several recent face detectors for their performance with masked-face images. Furthermore, we investigate the usefulness of multiple off-the-shelf deep-learning models for recognizing correct face-mask placement. Finally, we design a complete pipeline for recognizing whether face-masks are worn correctly or not and compare the performance of the pipeline with standard face-mask detection models from the literature. To facilitate the study, we compile a large dataset of facial images from the publicly available MAFA and Wider Face datasets and annotate it with _compliant_ and _non-compliant_ labels. The _annotation dataset_, called Face-Mask-Label Dataset (FMLD), is made publicly available to the research community.  
COVID-19; masked-face detection; face-mask classification; face-mask recognition; COVID-19 compliant mask detection +
Footnote †: journal: Applied Sciences
## 3 Adaptive Fractional-Order Anti-Saturation Synchronous Control Method
### FDC Unification Module (FDC-Ulm)
Since the proposed FDC-UM is used for the LTI system, when the control voltage overpasses the voltage limitation, the control voltage will induce a nonlinear behavior and affect the dynamic response characteristics of the dual-motor system, as shown in Equation (15). When the total control voltage of the feedback control system \(u_{FBC}\) and the friction compensator \(u_{FC}\) are beyond the voltage limitation, the control voltage \(u\) is limited to the highest (lowest) voltage \(u_{max}\) (\(-u_{max}\)). \[u=\left\{ \begin{array}{l}-u_{\max},\ for\ (u_{FBC}+u_{FC})\leq-u_{\max},\\ u_{FBC}+u_{FC},\\ u_{\max},\ for\ (u_{FBC}+u_{FC})\geq u_{\max}.\end{array} \right., \tag{15}\]  
Figure 4 shows the influence of the saturation effect on the synchronization error of the dual-motor system. In Figure 4a, the synchronization errors of the dual-motor system are compared with the PID feedback control method and the FDC-UM-based synchronous control method. Figure 4b shows the control voltage of the motor system in the range of [\(-u_{max}\), \(u_{max}\)]. We can see that the saturation effect influences the synchronization errors of the two control methods and deteriorates the performance of the FDC-UM. Therefore, to ensure the synchronous motion performance of the dual-motor system, the saturation effect needs to be eliminated.
###### Abstract
Based on this idea, [PERSON] [28] designed a one-dimensional QZS LR structure with ultralow frequency bandgaps by introducing the QZS system into the mass-in-mass (MIM) resonator proposed by [PERSON] [29]. [PERSON] [30] designed semi-active electromagnetic QZS resonators to suppress the transverse wave of the beam. At present, there is no structure or theory to control the vibration of the plate by using QZS local resonators, which needs further research. In order to suppress the ultralow frequency vibration of the plate, the method we use in this paper is to first design a QZS resonator with ultralow frequency bandgaps and then expand the widths of the bandgaps by damping and multi-bandgap design to realize the control of the broadband ultralow frequency vibration of the plate. The work is organized as follows. The theoretical derivation is presented in detail in order to investigate the dispersion relation of the LR plate with QZS resonators in Section 2. The effects of QZS resonator parameters on the bandgaps and the simulation calculation of structural vibration response are presented in Section 3. Meanwhile, in order to enhance the engineering applicability of the structure, a QZS LR structure with adjustable bandgaps is designed in Section 3. Finally, conclusions are drawn in Section 4.
## 2 Designed Methodology
### Stage 1: Charging Schedule Optimization Without BESSs
#### 2.1.2 Optimization of the Charging Schedule Using GA  
The analyzed system has a wide range of search space; thus, a stochastic population-based evolutionary algorithm, such as a GA, fits much better than a stochastic individual-based algorithm. Among the group of evolutionary algorithms, GAs have some advantages: they are flexible, and the best individual of each iteration survives for the next population.  
Each gene of the chromosomes of the GA represents a whole-day schedule of the bus fleet, where the time each bus is on the move and the time it is at the depot are defined. Therefore, the whole gene of a chromosome is a 96 xN matrix that represents a possible charging scenario of the whole bus fleet.  
Optimization Function  
The objective of this first EMS, EMS1, is to minimize the charging cost of the EV fleet without considering BESSs. Thus, the function to be minimized, i.e., the fitness function of the GA, is as follows:  
\[f=\sum_{n=1}^{N}\left(\sum_{t=1}^{T}P_{EVCS}(n,t)\cdot\epsilon_{G}(t)\cdot S_{D }(n,t)\cdot\eta_{C}^{-1}\right) \tag{4}\]  
\(\epsilon_{G}(t)\) is the grid electricity price at interval \(t\), \(\eta_{C}\) represents the efficiency of the EVCS converters, and \(S_{D}(n,t)\) is a binary variable that defines if the \(n\)th bus is on the move (\(S_{D}(n,t)=0\)) or at the depot (\(S_{D}(n,t)=1\)).  
GA Technique Selection  
The different techniques used in each step are described in this section. A penalty function is applied to the fitness function calculation so that the chromosomes related to all cases where a constraint is not respected do not survive the next generation.
# Numerical Modeling of Plasticity in Metal Matrix Fiber Composites
## Appendix A  
All steps of the numerical homogenization of the elastic properties of a composite are detailed in [37]. In the tensile experiment along the OX axis, the average strain \(\varepsilon_{x}\) is set, and other components of the strain tensor are equal to zero. According to the results of the problem solution in the postprocessor of the program complex ANSYS, the average stresses are found as follows: \(\sigma_{x}\), \(\sigma_{y}\), and \(\sigma_{z}\). This makes it possible to determine the components of matrix (10).  
\[a_{11}=\sigma_{x}/\varepsilon_{x};\ a_{12}=\sigma_{y}/\varepsilon_{x};\ a_{13}= \sigma_{z}/\varepsilon_{x}. \tag{11}\]  
The tensile experiment in the OZ axis is carried out with a single given average strain \(\varepsilon_{x}\). As a result of the calculation, the average stresses \(\sigma_{z}\) and \(\sigma_{y}\) are determined, and two more components of the matrix (10) are calculated:  
\[a_{33}=\sigma_{z}/\varepsilon_{z};\ a_{23}=\sigma_{y}/\varepsilon_{z} \tag{12}\]  
The last two experiments were performed to model shear in the XOY and XOZ planes. In each of these experiments, a single non-zero strain, \(\gamma_{xy}\) or \(\gamma_{xz}\), was set, and the average tangential stresses, \(\tau_{xy}\) or \(\tau_{xz}\), were calculated. Then, the last diagonal components of matrix (10) were determined:  
\[a_{44}=\tau_{xy}/\gamma_{xy};\ a_{66}=\tau_{xz}/\gamma_{xz} \tag{13}\]  
In practical applications, the technical characteristics of the elasticity of orthotropic materials are widely used. For such a representation, it is necessary to invert the matrix in relation (10) and write the relationship between strains and stresses in the inverse form:  
\[\left[\begin{array}{c}\varepsilon_{x}\\ \varepsilon_{y}\\ \varepsilon_{z}\\ \gamma_{xy}\\ \gamma_{yz}\\ \gamma_{xz}\end{array}\right]=\left(\begin{array}{cccccc}b_{11}&b_{12}&b_{ 13}&0&0&0\\ b_{21}&b_{22}&b_{23}&0&0&0\\ b_{31}&b_{32}&b_{33}&0&0&0\\ 0&0&0&b_{44}&0&0\\ 0&0&0&b_{55}&0\\ 0&0&0&0&0&b_{66}\end{array}\right)\times\left[\begin{array}{c}\sigma_{x}\\ \sigma_{y}\\ \sigma_{z}\\ \tau_{xy}\\ \tau_{yz}\\ \end{array}\right] \tag{14}\]The modulus of elasticity, Poisson's ratio, and shear modulus are related to the components of the inverse matrix as follows:  
\[\begin{split} b_{11}=\frac{1}{E_{x}};\;b_{22}=\frac{1}{E_{y}};\;b_{33} =\frac{1}{E_{z}};\;b_{12}=-\frac{v_{yx}}{E_{y}};\;b_{13}=-\frac{v_{yx}}{E_{z}}; \;b_{23}=-\frac{v_{xy}}{E_{z}};\\ b_{44}=\frac{1}{C_{xy}};\;b_{55}=\frac{1}{C_{yz}};\;b_{66}=\frac{1}{G_{ xx}}\end{split} \tag{10}\]
# Change-Point Detection in Autoregressive Processes via the Cross-Entropy Method
## 4 Numerical Experiments
### Example 2: Simulated AR(1) Processes with a Single Change-Point
#### 4.2.2 Example 2.2
Figure 4: Under scenario 2, frequency of the estimated locations was filtered by the correct number of change-points; the true change-point number was 100. (**a**) The results under \(\rho_{1}=0.1\), \(\rho_{2}=0.5\). (**b**) The results under \(\rho_{1}=0.1\), \(\rho_{2}=0.9\). (**c**) The results under \(\rho_{1}=0.5\), \(\rho_{2}=0.9\). three methods in terms of accuracy of estimating the location of the change-point. We found that the CE.AR method tends to concentrate on the accuracy of the change-point locations more than the other two methods, as long as the number of change-points is accurately estimated. The detection accuracies of three methods for estimating the number of change-points is summarized in Tables 2-4, which illustrate that the detection rates of all methods increase as the shift in the mean increases. From Table 4, it is evident that the CE.AR method performs better when \(\rho\) changes from \(-0.5\) to \(0.5\), from \(-0.5\) to \(0.9\), and from \(0.1\) to \(0.9\); strucchange generally performs better than CE.AR and ARiseg. Therefore, we can conclude that the CE.AR method is not sensitive to mild coefficient changes, and it is suitable for the situations in which either there is a dramatic change in the mean or the correlation coefficient.
# QRLIT: Quantum Reinforcement Learning for Database Index Tuning
## 2 Background
### Reinforcement Learning  
In artificial intelligence, reinforcement learning is a branch inspired by the natural process of learning through reinforcement. Entities known as agents learn a policy \(\pi\) that maps states of the environment to actions with the purpose of maximizing the value of accumulated rewards over time in a stochastic environment modeled by a Markov decision process (MDP) [21]. An MDP is defined by a tuple with five elements \((\mathrm{S},\mathrm{A},\mathrm{P},\mathrm{R},\gamma)\), where \(\mathrm{S}\) represents the state space, \(\mathrm{A}\) the action space, \(\mathrm{P}\) the state transition function defining the dynamics of the MDP, \(\mathrm{R}\) the reward function, and \(\gamma\) a discount factor with \(0\leq\gamma\leq 1\)[21].  
Q-learning is a modal-free algorithm used to solve reinforcement learning problems based on temporal difference (TD) learning methods [21]. These methods involve learning to make optimal decisions directly from experiences without a model of the environment's dynamics [21]. The core idea behind this algorithm is to learn a tabular policy, known as a Q-table, which stores the values of actions for each state. These values, called Q-values, represent the quality of each action in a specific state. In other words, it refers to how effective that action is in obtaining a good reward. So, the greater the value, the higher the potential reward and the better the action is considered.  
As a fundamental step in this algorithm, after the agent executes an action and receives feedback from the environment (reward and new state), it is crucial to update its policy. This process uses Equation (1), where \(Q(s,a)\) represents the Q-value of action a executed in state \(s\), where \(\alpha\) is the learning rate, \(r\) is the reward obtained, \(\gamma\) is the discount rate that influences the impact of future rewards, and \(\max_{a}Q(s^{\prime},a^{\prime})\) represents the Q-value of the action with the highest value in the new state of the environment \(s^{\prime}\).  
\[\begin{split}\text{TDError}=r+\gamma\max_{a}Q(s^{\prime},a^{ \prime})-Q(s,a)\\ \text{Q}(s,a)\leftarrow Q(s,a)+\alpha(\text{TDError})\end{split} \tag{1}\]  
In Q-learning, there is a limitation in balancing exploration and exploitation, as it is important to explore the environment's states to prevent the agent from getting stuck in a local maximum. To find this balance, the algorithm can use a strategy known as Epsilon-greedy [21]. This strategy uses an exploration rate epsilon \(\varepsilon\), which decreases at the end of each episode. Therefore, with this algorithm (Equation (2)), the agent explores with a probability of \(\varepsilon\) or follows the learned policy (exploits) with a probability of \(1-\varepsilon\).  
\[a=\left\{\begin{array}{ll}\text{argmax}_{a\in A},&\text{with probability }1-\varepsilon\\ \text{random}_{a\in A},&\text{otherwise}\end{array}\right. \tag{2}\]
## 4 Methodology
### Data Preprocessing  
Two major techniques are used in the data preprocessing phase, i.e., (i) data differencing and (ii) time labeling. Additionally, there are two other techniques, i.e., (iii) concatenation and (iv) window slicing.  
In regard to data differencing, due to a large amount of energy consumption and the fact that the value is not commonly stationary, the difference between the data value and its previous data value is taken into account. By removing these patterns through data differencing, the resulting stationary TS can be more easily modeled and forecasted. This technique is often required for many forecasting models [37].  
In time labelling, the amount of energy consumed during peak time points is typically greater than that during non-peak times. Consequently, the labeling convention assigns a value of 1 to peak time and 0 to non-peak time [38]. These labeled time periods serve as valuable features for training the forecasting model. In the concatenation phase, the data from data differencing and labeled time are concatenated and then sliced into vectors with a length of \(k\) during the window-slicing phase.
## 2 Materials and Methods
### Materials
#### 2.1.1 A36 Steel  
A36 steel, a commonly used structural steel, exhibits favorable weldability, processability, and a relatively low cost. Nevertheless, under cold climatic conditions, its mechanical properties fall short of those of Q345. Specifically:  
1. Strength Differential: While the yield and tensile strengths of A36 steel suffice for general structural requirements, they are comparatively lower than those of Q345. Under extreme cold conditions, the material's properties may further deteriorate, leading to inadequate stability of the sawing mechanism under heavy loads and posing potential safety hazards.
2. Toughness Issue: A36 steel exhibits poor impact toughness at low temperatures, rendering it more susceptible to brittle fracture. This is a non-negligible drawback for sawing mechanisms that frequently operate in cold environments. In contrast, Q345 maintains good toughness even at low temperatures, effectively preventing the occurrence of brittle fracture.
3. Corrosion Resistance: A36 steel possesses relatively weak corrosion resistance, making it vulnerable to corrosion in wet or salty cold environments, which can subsequently shorten its service life. In contrast, Q345, with its superior corrosion resistance, is better suited to withstand harsh working conditions.