esa-sceva/satcom-chunk-collection huggingface dataset:
# Overload Mitigation of Inertial Grid-Forming Inverters Under Frequency Excursions
## 2 Grid-Forming Inverter Modelling and Overload Mechanisms
### System Description  
The simplified diagram of a GFM inverter exchanging power with a grid is shown in Figure 1a. The grid is modelled using its Thevenin equivalent, a voltage source with a series RL element, denoted by the subscript \"g\". The inverter is connected to the point of common coupling (PCC) through a LC filter to comply with power quality standards. For the sake of simplicity, the energy source of the inverter is assumed to be an ideal DC source.  
Figure 1: Simplified scheme of a grid-connected GFM inverter. (**a**) Hardware structure. (**b**) Control structure.  
The GFM controller consists of two control layers (Figure 1b). The outer layer consists of the power synchronization loop (PSL) and the reactive power control (RPC). The PSL outputs the angular frequency \(\omega_{r}\) based on active power measurement \(P\) and active power and angular frequency setpoints, \(P^{*}\) and \(\omega_{r}^{*}\). Several PSL structures have been proposed in the literature. We propose a modified virtual synchronous generator that decouples damping and frequency regulation [29]. This controller includes an inertia constant \(H\), a frequency regulation coefficient \(D\), and a damping term \(k_{d}\).  
The RPC outputs the voltage amplitude \(E\) based on reactive power measurement \(Q\) and reactive power and voltage amplitude setpoints, \(Q^{*}\) and \(E^{*}\). A typical reactive power droop is shown in the figure. The dynamics of the RPC are excluded from this analysis because its dynamics are much faster and the impact of the frequency excursion on reactive power is minimum in inductive lines [30].  
The inner controller of the GFM inverter uses \(\omega_{r}\) and \(E\) to generate the voltage at inverter terminal \(v_{c}\). From the perspective of the PSL, the inner controller modifies the impedance between the inverter and the PCC. A PCC voltage controller cancels out the influence of \(L_{c}\), whereas a virtual admittance could modify its value [31]. This paper assumes no internal controller, so the inverter sees both filter and grid impedance. This assumption simplifies the analysis without impacting the conclusions.
# An Enhanced Deep Q Network Algorithm for Localized Obstacle Avoidance in Indoor Robot Path Planning
## 3 Implementation Based on PER-D2 MQN Algorithm
### Prioritized Experience Replay Buffer  
As one of the two major features of the DQN algorithm, the Experience Replay Buffer (ERB) is a storage device that stores experience data during the agent's interaction with the environment. By randomly sampling small batches of experience from it, the time correlation is broken and the stability and efficiency of training are improved. The experience data set is continuously updated. When the buffer is full, the old experience is replaced so that the new and old data can be used together to train the model. However, the uniform random sampling of ERB may ignore some experiences that are more important to the learning process and fail to make full use of these key experiences to accelerate learning.  
To solve the above shortcomings, the PER [16] was proposed. It considers the importance of empirical data through the TD error \(\delta_{i}\), as shown in Equation (4):  
\[\psi=|\delta_{i}|+e \tag{4}\]  
\(e\) is an extremely small positive constant used to prevent the priority from being 0. In order to perform weighted sampling according to priority, PER uses the proportional priority method. The probability \(P(i)\) of experience i being sampled is defined as shown in Equation (5):  
\[P(i)=\frac{p_{i}^{\alpha}}{\sum_{k}p_{k}^{\alpha}} \tag{5}\]  
where \(\alpha\) is a hyperparameter that controls the degree of prioritization, reverting to uniform random sampling when \(\alpha=0\), and sampling exactly according to priority when \(\alpha=0\). In order to correct the bias caused by weighted sampling, importance sampling weights \(\omega_{i}\) are introduced to adjust the gradient update of the loss function as in Equation (6):  
\[\omega_{i}=\left(\frac{1}{N}.\frac{1}{P(i)}\right)^{\beta} \tag{6}\]  
\(N\) is the total size of the experience pool, and \(\beta\) is a hyperparameter used to balance the initial and later importance corrections, usually starting from a smaller value and increasing gradually, eventually approaching 1. The PERB used in this paper updates a tuple of experiences at moment t as shown in Figure 4.  
Figure 4: The updating process of the experience vector at moment t.
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 4 Simulation Experiment
### Functional Comparison  
In Table 2, we evaluate four key criteria: fidelity, lightweight design, user scale, and openness.  
\begin{table}
\begin{tabular}{c|c c c c} \hline \hline
**Simulation Software** & **Fidelity** & **Lightweight** & **User Scale** & **Openness** \\ \hline STK & **high** & high & city-scale & medium \\ Hypatia [22] & low & high & city-scale & medium \\ StarPerf [4] & low & low & city-scale & high \\ StarryNet [5] & high & high & city-scale & high \\ SatScope (ours) & medium & low & large-scale & high \\ \hline \hline \end{tabular} Dark gray indicates the optimal-performing item within the same indicator, and light gray represents the second-best item. Our functional comparison table demonstrates how to achieve a lightweight, large-scale, and highly open satellite simulation simulator under acceptable fidelity conditions.  
\end{table}
Table 2: Comparison of functionalities among different simulation emulators.  
Figure 11: Latency CDF. Users with high latency are primarily located in Asia.  
The modular design of STK enables users to select functionality modules according to specific requirements, facilitating its flexible application in projects of varying scales. In contrast, Hypatia and StarPerf focus on the quality of service (QoS) in satellite networks between major cities, with traffic data determined based on regional economic development. StarryNet employs a digital twin approach to simulate satellites; however, the substantial computational and storage costs associated with maintaining protocol stacks for each node limit the scalability of user numbers. SatScope, on the other hand, incorporates network measurements to analyze connectivity between user and satellite nodes, marking the first large-scale inclusion of user nodes within simulation scopes.  
Emerging large-scale constellations are rapidly evolving, allowing for the analysis of network characteristics of any commercial satellite network based on publicly available data. Tools such as StarPerf, StaryNet, and SatScope have achieved open-source code availability, thereby supporting the optimization of various satellite communication systems. SatScope is capable of accommodating different orbital shells and simulation intervals. Researchers can use SatScope to generate large-scale real-time network topologies and leverage the Scapy library to create protocol stacks, facilitating the analysis of performance across different communication protocols in extensive satellite networks.
## 2 Basic Concepts and Work Scope
### Acronyms Used in the Article  
Table 1 lists the acronyms of general use used in this article. Acronyms specific to the datasets used in this study are described in Section 5.2.
# Fatigue Life Analysis of Cylindrical Roller Bearings Considering Elastohydrodynamic Lubrications
## 2 Quasi-Static Modeling of Cylindrical Roller Bearings Considering Elastohydrodynamic Lubrication  
The structure of a cylindrical roller bearing is illustrated in Figure 1. The key parameters include the roller diameter \(\mathrm{D_{b}}\), bearing pitch diameter \(\mathrm{D_{m}}\), outer ring diameter \(\mathrm{D_{o}}\), inner race diameter \(\mathrm{D_{i}}\), and the positional angle \(\mathrm{\varphi_{i}}\) of the rollers. Additionally, the angular velocity of the inner race is \(\mathrm{\omega_{i}}\), the outer ring remains stationary, the spin angular velocity of each roller is \(\mathrm{\omega_{bj}}\), and the orbital angular velocity relative to the cage is \(\mathrm{\omega_{m}}\).
###### Abstract  
Bearing faults account for over 40% of induction motor faults, and for this reason, for several decades, much attention has been paid to their condition monitoring, through vibration measurements and, more recently, through electromagnetic signal analysis. Furthermore, in the last few years, research has been focused on evaluating deep learning algorithms for the automatic diagnosis of these faults. Therefore, the purpose of this study is to propose a novel procedure to automatically diagnose different types of bearing faults and load anomalies by means of the stator current and the external stray flux measured on the induction motor in which the bearings are installed. All the data were collected by performing experimental tests in the laboratory. Then, these data were processed to obtain images (scalograms and spectrograms), which were elaborated by a pre-trained Deep Convolutional Neural Network, modified through the transfer learning technique. The results demonstrated the ability of the electromagnetic signals, and in particular of the stray flux, to detect bearing faults and mechanical anomalies, in agreement with the recent literature. Moreover, the Convolutional Neural Network has been proven to be able to automatically discriminate bearing defects and with respect to the healthy condition.  
bearing fault; diagnostics; induction motor; electromagnetic signal; deep learning; convolutional neural network; transfer learning +
Footnote †: journal: Applied Sciences  
18 July 2021  
Accepted: 20 August 2021  
Published: 26 August 2021  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Copyright:** 2021 by the authors. Licence MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Mathematical Model
### Structural Model  
#### 2.2.1 Plasticity  
The equilibrium equation was combined with compatibility equations, constitutive laws, and the relationship between temperature variation and strain tensor. The temperature variation was evaluated with respect to a reference temperature at which no thermal strain was present. The total strain tensor \(\varepsilon\) can be decomposed into:  
\[\varepsilon_{ij}=\varepsilon_{ij}^{el}+\varepsilon_{ij}^{pl} \tag{5}\]  
where \(\varepsilon_{ij}^{el}\) and \(\varepsilon_{ij}^{pl}\) represent the elastic and plastic components of \(\varepsilon\), respectively. Plasticity was modeled through the von [PERSON] yield surface criterion, Chaboche kinematic hardening rule, a multilinear isotropic hardening model, and a Prandtl-Reuss flow rule, while creep phenomena were modeled by means of a combined time hardening law. A detailed description of the characteristics of the present viscoplastic model in comparison to other ones can be found in [11]. Materials subjected to cyclic loading harden both kinematically and isotropically. The multilinear isotropic model parameters were obtained by extrapolating data from the experimental stabilized stress vs. plastic strain curves at different temperatures for CuCrZr materials [15]. The Chaboche kinematic hardening model with three back stresses (each one corresponding to the Armstrong-Frederick nonlinear model) was chosen in order to consider the effects of kinematic hardening (see Figure 3) [26].  
The total backstress tensor \(\alpha\) can be expressed as follows:  
\[\alpha=\frac{\Delta\sigma}{2}-\sigma_{0}=\sum_{i=1}^{3}\frac{C_{i}}{\gamma_{i} }\left[\left(1-\exp\left(-\gamma_{i}*\varepsilon_{pl}\right)\right)\right] \tag{6}\]  
where \(C_{i}\) and \(\gamma_{i}\) are material parameters and \(\sigma_{0}\) is the yield surface size. The calibration of the six coefficients (\(C_{i}\), \(\gamma_{i}\)) was performed using a single stabilized stress-strain hysteresis loop for each temperature value [4]. This data elaboration was performed by means of the MATLAB \"Curve Fitting\" Toolbox [27] that allowed us to write custom equations adoptedin combination with the nonlinear least square method. The corresponding coefficient of determination \(R^{2}\) is equal to 0.98, where \(R^{2}\) is evaluated as follows:  
\[R^{2}=1-\frac{\sum_{i}\left(y_{i}-f_{i}\right)^{2}}{\sum_{i}\left(y_{i}-\overline {y}\right)^{2}} \tag{7}\]  
where \(y_{i}\) are the original experimental data, \(f_{i}\) are the predicted values, and \(\overline{y}\) is the mean of the experimental data set [28].
## 3 The KLSVM Model
### Computing the Margin Width  
To define the margin, let us assume that all parameters get all the link data correctly. For the setting of the parameter, the decision boundary is invariance to its scaling. Thus, we remove the scale invariance by defining class \(+1\) in some region, class \(-1\) in another and making those regions as far apart as possible (see Figure 3). Then, we can define this margin explicitly in terms of the hyperplane. Since no data are inside the hyperplane, we define the margin as the distance between the two regions:  
\[f(x)=wx^{\prime}+b\] (2a) \[f(x)>+1\] in class \[+1\] region (2b) \[f(x)<-1\] in class \[-1\] region (2c)  
all passing through zero centre  
\[f(x)=0 \tag{2d}\]  
Figure 3: KLSVM: A modeled decision boundary obtained from the real dataset. The hyperplane separates two regions of the Very Good Link and the Good Link data points. The plot shows that the proposed method proves the accurate performance of the classifier.  
We define margin \(M=||x^{+}-x^{-}||=||rw||\) and vectors \(w\in\Re^{n+1}\) to be perpendicular to the boundaries \(f(x)=-1\), \(f(x)=+1\) and \(f(x)=0\) such that \(wx+b=0\) and \(wx^{\prime}+b=0\)\(\Rightarrow w(x^{\prime}-x)=0\) is Orthogonal.  
Choosing \(x^{-}\) such that \(f(x^{-})=-1\); let \(x^{+}\) be the closest points with \(f(x^{+})=+1\) defined as \(x^{+}=x^{-}+rw\) since \(w\) is orthogonal to the planes. These closest points on the margin also satisfy \(w\cdot x^{-}+b=-1\) and \(w\cdot x^{+}+b=+1\). Since \(x^{-}\) is on the negative hyperplane and \(x^{+}\) is on the positive hyperplane, their linear responses are \(-1\) and \(+1\), respectively, such that  
\[w\cdot(x^{-}+rw)+b=+1 \tag{3a}\] \[\Rightarrow r||w||^{2}+w\cdot x^{-}+b=+1\] (3b) \[\Rightarrow r||w||^{2}=1=+1\] (3c) \[r=\frac{2}{||w||^{2}} \tag{3d}\]  
Since  
\[M=||rw||=\frac{2}{||w||^{2}}||w||=\frac{2}{\sqrt{w^{T}w}} \tag{4}\]
# A Study on a Radio Source Location Estimation System Using High Altitude Platform Stations (HAPS)
###### Abstract  
Currently, there is a system in Japan to detect illegal radio transmitting sources, known as the DEURAS system. Even though crackdowns on illegal radio stations are conducted on a regular basis every year, the number of illegal emission cases still tends to increase, as ordinary citizens are now able to handle advanced wireless communication technologies, e.g., via software-defined radio. However, the current surveillance system may not be able to accurately detect the source in areas where large buildings are densely packed, such as urban areas, due to the effects of reflected waves. Therefore, in this study, we proposed a system for estimating the location of the source of transmission using a high-flying unmanned aerial vehicle called HAPS. The simulation results using numerical analysis software show that the proposed system can estimate the location of the source over a wider area and with higher accuracy than conventional monitoring systems.  
radio emitter; localization; HAPS; sensors; estimation accuracy
# Tax or Subsidy? Optimal Carbon Emission Policy: A Supply Chain Perspective
## Modeling  
### Basic Model Settings  
In this model, the consumer demand is positively affected by the greenness of products produced by the manufacturer which we define as a quantifiable, measurable attribute. [PERSON] and [PERSON] (2012) refer to this phenomenon as the demand expansion effect of greening efforts. Consumers are more inclined to buy appliances with lower energy consumption levels and vehicles with higher fuel efficiency, which are examples of product greening improvements that have contributed to increased demand.  
This paper considers the single choice decision-making process of product greenness, assuming that \(a\) is the total market demand faced by enterprises, and \(p\) is the price of green products. Further, \(\theta\) is defined as the greening level of the product and is assumed to be continuous; both positive and negative values of \(\theta\) are acceptable. In addition, \(b\) and \(\gamma\) are consumers' green-sensitivity and price sensitivity coefficients, respectively. We assume that market demand is linearly decreasing in price and increasing in the 'greening level of products'. Linear demand models are often used in research because of their processability, and can draw interesting conclusions about model parameters. Therefore, market demand for products is as follows:  
\[d=a+b\theta-\gamma p \tag{1}\]  
where \(a+b\theta>\gamma p\) and \(b,\gamma>0\).  
The above expression reflects the phenomenon of increasing consumer demand caused by product greenness. The cost per unit of products produced by an enterprise is \(c+\lambda\theta\), in which constant \(c\) is the production cost per unit of a product when the level of greening is 0 and \(\lambda\) is the greening cost per unit, showing that the unit cost to the enterprise increases with the greenness of products. Product costs decrease if \(\theta\) is less than 0, and product costs increase otherwise.  
Enterprises must decide the level of greening for their own products. We next analyze decision-making under the influence of consumer demand expansion with and without government carbon emission policies in existence. We also explore the amount of tax or subsidies that should be set by the government alongside carbon emission policies. In this way, we are able to measure the effect of demand expansion and the influence of government policies on business' decision-making. Based on this, we explore the optimal level of government decision-making.
# A Novel Approach of Modelling and Predicting Track Cycling
## 2 Materials and Methods
### Determination of Model Parameters
#### 2.4.2 Exercise Protocol  
Before the first run, the athletes with their clothes and bikes were weighed to determine the mass of the rider-bike system. The warm-up prior to the tests consisted of 15 min of low-intensity cycling (1-1.5 W per kg bodyweight), followed by three short maximal sprints on the cycling track. The athletes rested passively for 10 min between warm-up and testing.  
All athletes first performed two 6 s sets of maximal-cadence low-resistance cycling on a free-standing roller (motoric sprint) and two 6 s maximal sprints in a seated position on the track to generate additional data points in the high-frequency cadence range for a valid determination of the \(F/v\) profiles. These points were added to the fatigue-free pedal revolutions from the acceleration phase of the time trials [32].  
The athletes then completed two 500 to 1000 m all-out sprints from a standing start with two different gear ratios. In the first run, athletes were asked to choose their preferred gear. In the second run, a much larger gear was selected. The sprints were separated by at least 20 min of low-intensity cycling (1-1.5 W per kg bodyweight) for active recovery and 20 min passive recovery. The next test began when comparable levels of blood lactate were observed.  
Throughout the testing, pedal force and crank velocity were monitored continuously with a special power meter. The system supplied by the Institute for Research and Development of Sports Equipment (FES) in Berlin captures the tangential force on the crank arm with a sampling frequency of 200 Hz. This system allows for the observation of sport-specific movements and the creation of sport-specific force-velocity and power-velocity profiles [33]. From this high-resolution power-meter data, the mean tangential force \(F_{T}\) at both pedals, averaged per one revolution, as well as the corresponding mean pedal rate \(PR\), was derived.  
The air density was measured prior to each run with a commercial weather meter (KESTREL 5200, Kestrel AU) located close to the cycling track. Before and every tenth minute after each sprint, 20 mL capillary blood was collected from the hyperaemic ear lobe for haemolysis and enzymatic-amperometric determination of lactate (Biosen, Eppendorf/Germany).
# Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods
## 2 Materials and Methods
### Long Short-Term Memory (LSTM)  
Long short-term memory (LSTM) networks are an extension of recurrent neural networks (RNNs), originally introduced by [44]. RNNs have a recurrent mechanism that flows through their layers recursively, enabling them to retain information across time steps. This recurrence allows RNNs to store and utilize past information. However, standard RNNs are known to be difficult to train on tasks involving long-term temporal dependencies [38]. This is where LSTMs come into play, as they are specifically designed to maintain long-term dependencies and efficiently store information in memory [44; 45; 46]. LSTMs address vanishing and exploding gradient problems, making learning through RNNs more effective when dealing with long sequences [47; 48]. LSTMs are equipped with a memory cell, which contains three key elements: the forget gate, the input gate, and the output gate, as illustrated in [47].  
1. Forget gate. The forget gate equation governs how much of the previous cell state should be discarded: \[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},\widetilde{x}_{t}]+b_{f}\Big{)}\] (1) where, \(\sigma\): Sigmoid activation function. \(f_{t}\): Forget gate that determines the extent to which previous cell state information should be forgotten. \(W_{s}\): Weight matrix for the forget gate. \(b_{s}\): Bias for the forget gate. \(h_{t-1}\): Hidden state from the previous time step. \(\widetilde{x}_{t}\): Input at time (normalized rainfall data).
2. Input gate. The input gate equation determines how much new information should be added to the cell state: \[i_{t}=\sigma(W_{i}\cdot[h_{t-1},\widetilde{x}_{t}]+b_{i})\] (2) where, \(i_{t}\): Input gate controlling the amount of new information to be stored in the cell state. \(W_{s}\): Weight matrix for the input gate. \(b_{s}\): Bias for the input gate. The new candidate information to be added to the cell state is computed as follows: \[\widetilde{c}_{t}=\tanh(W_{c}\cdot[h_{t-1},\widetilde{x}_{t}]+b_{c})\] (3) where, \(\widetilde{c}_{t}\): Candidate cell state, representing new information to be added to the current cell state. \(W_{c}\): Weight matrix for updating the cell state. \(b_{c}\): Bias for updating the cell state. \(\tanh\): Hyperbolic tangent activation function.
3. Cell state. The cell state is updated by combining the information retained by the forget gate and the new information provided via the input gate: \[c_{t}=f_{t}\cdot c_{t-1}+i_{t}\cdot\widetilde{c}_{t}\] (4) where, \(c_{t-1}\): Previous cell state. \(c_{t}\): Updated cell state at a time.
4. Output gate. The output gate equation determines how much of the updated cell state should contribute to the new hidden state: \[o_{t}=\sigma(W_{o}\cdot[h_{t-1},\widetilde{x}_{t}]+b_{o})\] (5) where,\(W_{o}\): Weight matrix for the output gate. \(b_{o}\): Bias for the output gate.
5. Hidden state. The hidden state is computed using the output gate and the updated cell state: \[h_{t}=o_{t}\cdot\tanh(c_{t})\] (6)