esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Accumulated Data Analysis within the M-PVMS System  
### The Data Identification for Training Data Set  
To identify data for training sets, we identified the right date through the correlation analysis of all PV power-related data from the integrated server and the public data center. For extracting the right data, it is necessary to find the correlative relationship between solar power generation data and environmental information in order to predict future power generation. That is, we predicted the power generation the day after today with the identified data. Table 1 shows the significant correlation after measuring the relationship between the current output and the environmental data on the Pearson correlation coefficient. The environmental sensor consists of an environmental sensor in a Korea public data center and an environmental information sensor that are installed in a solar power station. Significant data types in Korea public data centers are total cloud amount, lower-middle cloud amount, and insolation. Significant data types in the solar power plant environmental information sensor are vertical solar radiation and horizontal solar radiation. Pearson's correlation coefficient expresses the correlation of each item with a value between \(-1\) and \(1\). If the value is close to \(-1\), there is a correlation that the variable y decreases as the variable x increases. If the value is close to \(0\), there is no correlation. If the value is close to \(1\), there is a correlation that the variable y increases when the variable x increases.  
Figure 10: Example code of IntegratedSolarEnergyModel.xml.  
According to these data, the amount of insolation is inversely correlated when the clouds are high. Hence, the current output of power generation has a correlation coefficient that is proportional to the insolation. Therefore, we used these data, such as horizontal solar radiation, vertical solar radiation, total cloud cover, lower-middle cloud cover, and output, in learning for prediction.
## 2 Overview of the Relevant Standards
### IEC 61727:2004 Standard  
IEC 61727:2004 is an international standard specifying requirements for connecting PV inverters to the distribution grid [27]. It applies to PV systems using non-island inverters. The standard addresses several key aspects of the inverter-utility interface, including power quality. Like IEEE 1547-2018, the standard IEC 61727:2004 also aims to ensure high power quality by limiting power distortion and DC injection from DERs. This section describes parts of the standard relevant to the research, specifically focusing on the limitations of DC injection and the harmonics and waveform distortion [27].  
In addition to the fact that IEEE 1547-2018 offers more detailed and, for some limitations, stricter requirements, one of the main differences is that the THD factor is used as a quantitative parameter for power distortion, which does not consider inter-harmonics. The limitations related to the maximum values of individual current harmonics and the DC injection are given in Table 2. The THD factor is limited to 5%.
## 4 Case Study
### Results and Discussion
#### 4.2.3 The Impact of the Size of the Training Dataset on Prediction Accuracy  
This study tested the prediction accuracies of 14 BPNNs trained using 14 training datasets with their sizes increasing from 1000 to 14,000 in intervals of 1000. The validation  
Figure 8: Cumulative probability distribution of prediction errors.  
Figure 7: Comparison of actual values and prediction values of ground settlements at five settlement observation points: (**a**) 350–1, (**b**) 350–2, (**c**) 350–3, (**d**) 350–4, (**e**) 350–5.  
dataset included 2000 sets of data. As illustrated in Figure 9, the results show that the value of \(RMSE\) decreased significantly as the size of the dataset increased: from 2.245 mm with 1000 training data to 1.440 mm with 14,000 training data. Moreover, the computational time for training an optimum BPNN increased approximately linearly, from 0.376 s to 2.679 s when the number of training data increased from 1000 to 14,000. Such results indicate that the prediction accuracy of the optimum BPNN can be further improved when more training data are collected in the future, while the increase in computational time for training an optimum BPNN is still acceptable.
### Literature Review
#### 1.1.1 The Physiological Parameters for Health Status Determination
Stage I hypertension: Systolic blood pressure (BP) range 130-139 or diastolic BP range 80-89 mm Hg; Stage II hypertension: Systolic BP \(\geq\) 140 or diastolic BP \(\geq\) 90 mm Hg. Pulse oximmetry, though not usually considered as a vital sign, can be a very important measure to ascertain an individual's health status, and hence it is called the fifth vital sign [20]. Experiments to determine the use of pulse oximmetry as a vital sign have been conducted in the past, for example, in an emergency in geriatric assessments using pulse oximmetry to measure the oxygen saturation in geriatric patients, which has led to improved diagnosis and treatment [20]. Gas measurements in blood provide critical information regarding the oxygenation, ventilation, and acid-base concentration in blood; however, these measurements are not frequent. It is wellknown that oxygenation can change very quickly, and in the absence of continuous oxygenation measurements, these changes may go undetected until it is too late. Pulse oximeters measure the blood oxygen saturation continuously and noninvasively using SpO2 sensors. The blood-oxygen saturation indicates the hemoglobin concentration, due to the hemoglobin affinity to oxygen in the arterial blood, which becomes saturated with oxygen. In healthy adults, the saturation range can vary from 94% to 100%. The SpO2 sensor has a pair of light-emitting diodes (LEDs) and a photodiode on a probe element that is clipped to the patient's body (usually a fingertip or an earlobe). The red LED has wavelength of 660 nm, the other is an infrared element with wavelength of 910 nm. Absorptions on each wavelength differs significantly with changes in oxygenated and deoxygenated concentrations of blood; therefore, from the differences in absorption due to red and infrared light, the oxy/deoxyhemoglobin ratio can be calculated. As the amount of blood in the capillaries depends on the actual blood pressure on the capillary wall (due to heartbeats), the heartbeat rate can be measured as well with the pulse oximeter.
# Performance of SIMO FSO Links over Mixture Composite Irradiance Channels
## 2 Channel Model  
The statistical channel model is given as [4]:  
\[y_{m}=\eta_{m}xI_{m}+n,\ \ with\ \ m=1,2,\ldots,M, \tag{1}\]  
where \(y_{m}\) represents the \(m\)-th of the \(M\) signal copies at the receiver, \(\eta_{m}\) is the effective photocurrent conversion ratio of each receiver, \(I_{m}\) stands for the normalized received irradiance of the optical signal, \(x\) is the modulated signal, which takes the binary values \"1\" or \"0\", and \(n\) represents the AWGN with zero mean and variance equal to \(N_{0}/2\). The irradiance can be expressed as:  
\[I_{m}=I_{a,m}I_{p,m}I_{l,m}, \tag{2}\]  
where \(I_{a,m}\) and \(I_{p,m}\) represent the irradiance values due to the atmospheric turbulence and the pointing error effects for the \(m\)-th of the \(M\) signal copies. Additionally, \(I_{l,m}\) stands for the deterministic path loss parameter which without loss of generality is assumed to be equal to unity.
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 4 Simulation Results and Analysis
### Path Planning with Constrained Steering Angle  
In practice, USVs can only turn a limited angle each time. We assume that a USV's maximum steering angle each time is 30 degrees, and the safe distance is 10 pixels. We define two experimental maps with a size of 200 \(\times\) 200 and 400 \(\times\) 400 pixels, respectively. We perform the simulations with 8 neighbor nodes, 48 neighbor nodes and 48 neighbor nodes with a constrained steering angle, respectively. Figures 9 and 10 show the experimental results on the two maps, respectively.  
From Figures 9a and 10a, we find that because the steering angle of eight-neighbor search is always an integral multiple of 45 degrees, many turns (marked with red circle) have a steering angle larger than the maximum steering angle of the USV. Therefore, we argue that the search approach with eight neighbor nodes is unsuitable for a real USV.  
We increase the number of neighbor nodes to 48 and obtain the planned path shown in Figures 9b and 10b. We find that the number of turns exceeding the maximum steering angle is reduced. This is because the steering angle of the 48-neighbor search is always an integral multiple of 11.25 degrees. In most cases, the turning degree of the planned path is no more than 30 degrees. However, the planned path still has three turns exceeding the maximum steering angle near obstacles.  
We add a constrained steering angle to the algorithm with 48-neighbor search and obtain new paths, as shown in Figures 9c and 10c. Compared to the planned paths without the constrained steering angle, the new paths are much smoother, and all the steering angles  
Figure 10: Path planning on 400 \(\times\) 400 map.  
Figure 9: Path planning on 200 \(\times\) 200 map.  
are smaller than the maximum steering angle of the USV. Table 3 shows the experimental results of the number of searched nodes, path planning time and the number of sharp turns exceeding the maximum steering angle based on different neighbor search approaches and the constrained steering angle.  
We find that when the number of neighbor nodes is increased from 8 to 48 on the \(200\times 200\) map, the number of searched nodes is also increased from 745 to 1813. This is because when we increase the number of neighbor nodes, there are more nodes to be expanded during the path planning process. Thus, the planning time is also increased from 1.16 s to 4.07 s. However, the number of sharp turns exceeding the maximum steering angle is significantly decreased from 25 to 5 when the number of neighbor nodes is increased from 8 to 48. When we add the constrained steering angle, the number of searched nodes and the planning time are slightly increased, but the number of sharp turns is significantly decreased to 0. We find similar results on the 400 \(\times\) 400 map. Thus, we can conclude that the constrained steering angle can ensure that the planned path is more realistic and can be applied to USVs in practice.
# VPP Participation in the FCR Cooperation Considering Opportunity Costs
## 1 Introduction
### FCR Cooperation  
The FCR Cooperation is an international market that is presently open to nine European countries [3]. In general, it is expected that such a market integration is a benefit for the participants, which can share resources in a wider market, leading to market efficiency. Since Spain's bordering country (France) already participates in this market, Portugal and Spain may aspire to integrate it in the near future. Spain and Portugal's transmission grids are operated by different TSOs; therefore, each of them would join the FCR Cooperation as a separate entity. BSPs from the participating countries provide supply bids to the FCR Cooperation, whereas TSOs demand them to ensure that the system frequency stability is kept close to 50 Hz. The product traded in this day-ahead market is valid for 4 hours; therefore, there are six daily market sessions.  
According to the market rules [3], in this market, there is an international price, called the \"cross-border marginal price\" (CBMP), and in each country, there is a \"national\" local marginal price (LMP), which may or may not differ from the CBMP, as follows. For each country participating, there is a minimum and maximum aggregated volume [MW] of bids from BSPs that can be accepted in the process of market clearing. These are known as the \"core\" and \"export limit\" constraints, respectively: the maximum is due to the interconnection capacity and the minimum ensures that all countries participate to solve the frequency deviations (which affect all of the interconnected area).  
If the aggregated volume of accepted bids from BSPs from a country is not hitting any constraint (\"hitting a constraint\" means that the aggregated volume of BSPs from a country is exactly equal to the \"core\" or \"export limit\" of that country [3]), the LMP in this country equals the CBMP. In case the sum of the accepted bids from the BSPs of a given country hits the \"core\" constraint, the local marginal price (LMP) in that country will be the price of the last bid, which will be higher than the CBMP; conversely, if the \"export limit\" constraint is hit, the LMP in that country is lower than the CBMP. The CBMP is the most expensive bid accepted in the session in a country where no constraints are hit.
## 3 Modeling Approaches
### Adaptive Boosting (AdaBoost)  
AdaBoost is widely recognized as one of the most renowned boosting algorithms [48]. It is an ensemble method based on a linear superposition model. The input data, \(X\), are first fed into a base learner for classification, where misclassified data are given a larger weight and correctly classified data are given a smaller weight. The input data \(X\) are then rowed according to the weights obtained and fed into the next base learner. The reason for this is to give more attention to the misclassified data in the next prediction. This process is repeated until a certain number of base learners have been obtained. Finally, the predictions of all base learners are combined as a linear superposition of model instances, weighted according to their effect on the training set. All base learners involved in the training and prediction process should predict better than random guesses based on the training set. Figure 6 provides an illustration of the aforementioned process.  
In this study, three integrated models were constructed for evaluating landslide susceptibility using CART, SVM and BLS as the base learners of the AdaBoost algorithm. The predictions of the base learners for landslide events were combined linearly to generate the outputs of the integrated models.
# Nonlinear Tank-Level Control Using Dahlin Algorithm Design and PID Control
## 2 Materials and Methods
### Controller Design
#### 2.3.3 The Dahlin Controller for the Coupled Tank Model  
When a process has the second-order transfer function,  
\[G(s)=\frac{K}{(T_{I}s+1)(T_{2}s+1)}e^{-N\tau_{\rm S}}, \tag{22}\]  
The Dahlin controller has the Z\(-\)transform [31], as follows:  
\[D(z)=\frac{(1-e^{-\lambda T})\Big{[}1-(e^{-T/T_{1}})z^{-1}\Big{]}\Big{[}1-(e^{ -T/T_{2}})z^{-1}\Big{]}}{K[1-e^{-\lambda T}z^{-1}-(1-e^{-\lambda T})z^{-N-1}]( C_{3}+C_{4}z^{-1})}, \tag{23}\]where  
\[C_{3}=1+\frac{T_{1}e^{-T/T_{1}}-T_{2}e^{-T/T_{2}}}{T_{2}-T_{1}},\ C_{4}=e^{-T/T_{1}}e^ {-T/T_{2}}+\frac{T_{1}e^{-T/T_{2}}-T_{2}e^{-T/T_{1}}}{T_{2}-T_{1}}. \tag{24}\]  
A possible way to avoid ringing of the control signal of the Dahlin controller (23), which is designed for the system described with the transfer function (22), is to include \(z=1\) in the factors \([1+(1-e^{-\lambda T})z^{-1}](C_{3}+C_{4}z^{-1})\) of the denominator in the Dahlin controller transfer function [27; 30; 31]. For \(N=1\), this modified Dahlin controller, which can eliminate the effect of sudden changes in the control signal, has the following form:  
\[D(z)=\frac{(1-e^{-\lambda T})\Big{[}1-(e^{-T/T_{1}})z^{-1}\Big{]}\Big{[}1-(e^{- T/T_{2}})z^{-1}\Big{]}}{K(2-e^{-\lambda T})(C_{3}+C_{4})(1-z^{-1})}. \tag{25}\]  
The control law (25) for arbitrary \(N\) can be written as the control law of a digital PID regulator. PID controller expressed via Dahlin algorithm is as follows:  
\[D(z)=K_{p}\bigg{[}1+\frac{T/T_{I}}{1-z^{-1}}+\frac{T_{D}}{T}(1-z^{-1})\bigg{]}, \tag{26}\]  
where  
\[\begin{array}{c}K_{P}=\frac{(1-e^{-\lambda T})\big{[}e^{T/T_{1}}+e^{T/T_{2} }-2\big{]}}{K(e^{T/T_{1}}-1)(e^{T/T_{2}}-1)[1+N(1-e^{-\lambda T})]},\\ \\ \frac{T}{T_{I}}=\frac{(e^{T/T_{1}}-1)(e^{T/T_{2}}-1)}{e^{T/T_{1}}+e^{T/T_{2}}- 2},\ \frac{T_{D}}{T}=\frac{1}{e^{T/T_{1}}+e^{T/T_{2}}-2}.\end{array} \tag{27}\]
## 4 Methodology
### Extending to a Biased-Randomized Algorithm  
To modify the original greedy and deterministic behavior of our heuristic, the selection of candidates from the \(SL\) is randomized by introducing a skewed probability distribution into the selection process. For the biased-randomized component, we employ a geometric distribution, which is controlled by a single parameter, \(\beta\in(0,1)\). \(\beta\) values closer to 1 increase the probability that the highest saving merges are selected. On the contrary, as \(\beta\) approaches 0, a near-uniform randomization is obtained. In this way, \(\beta\) controls the level at which the selection probabilities decrease along the sorted \(SL\). Hence, unlike deterministic heuristics, which always generate the same solution when starting from the same initial solution, different decisions are taken at each selection iteration, consequently generating different solutions. Algorithm 1 represents the selection process of LH with biased-randomization, which replaces the greedy selection of edges from the SL.  
```
Data: savings list \(SL\), parameter \(\beta\in[0,1]\)
1\(l\leftarrow\) getNumberOfEligibleEdgesFromList (\(SL\));
2 Randomly select position \(x\in\{1,...,l\}\) according to distribution Geom (\(\beta\));
3\(e\leftarrow\) selectTheXthEligibleEdgeFromList (\(x,SL\));
4 return\(e\);
```  
**Algorithm 1**brSelection.  
Considering all of the stages which have been introduced, Algorithm 2 represents the overall structure of our biased-randomized algorithm with local search (BRLH). This algorithm is then embedded into a multi-start procedure in order to obtain a variety of solutions (MS\({}_{\text{BRLH}}\)).  
```
Data: set of nodes \(V\), geometric distribution parameter \(\beta\)
1\(sol\leftarrow\) createDummySolution (\(V\));
2\(sol\leftarrow\)\(\text{CWS}_{1}\) (\(sol\), \(\beta\));
3\(sol\leftarrow\)\(\text{CWS}_{2}\) (\(sol\), \(\beta\));
4\(sol\leftarrow\) localSearch (\(sol\));
5 return\(sol\)
```  
**Algorithm 2**BRLH.
# Greenhouse Temperature Prediction Based on Time-Series Features and LightGBM
## 5 Results and Analysis
### Hyperparameter Selection  
The hyperparameters of a model are generally determined by cross-validation so that the model performs better on new data [34]. Since the experimental data in this paper are time-series data and there is a strict time-series relationship between the training set and the test set, the premise of the traditional cross-validation method is that the samples are independent and identically distributed [35]. Time-series data contain information such as that of the periodicity and the relationship between past and future data in the change process. This situation does not meet the basic assumption of independent and identically distributed data in cross-validation, and the cross-validation method based on time series should thus be adopted. Commonly used timing-based cross-verification methods [36; 37] are the blocked cross-validation [38] procedure, which is similar to standard cross-validation, hv-blocked cross-validation proposed by [PERSON] [39], which extends blocked cross-validation to further increase the independence among observations, and the modified cross-validation procedure [40], which removes observations from the training set that are correlated with the test. [PERSON] et al. compared and analyzed all the cross-validation methods for time series [41]. Based on their results, blocked cross-validation was used in the present study to determine hyperparameters.
## 2 Related Theory
### LSTM Neural Network  
Data-driven methods for predicting the bearing RUL are usually based on monitoring data collected by sensors, such as temperature and vibration. There is a strong time dependence on these data. The recurrent neural network (RNN) model is characterized by taking sequence data as input and performing operations in the evolutionary direction of the sequence. Therefore, RNN can recognize time series and suit to solve RUL prediction problems. However, traditional RNN suffers from long-term dependency problems, and when training on long sequence data, gradient disappearance or gradient explosion may occur, resulting in ineffective training of the model. To solve this problem, [PERSON] and [PERSON] proposed the LSTM model [30]. The structure of LSTM is shown in Figure 2. There are two information pathways in LSTM: one for storing long-term memory and one for short-term processing information and adding valid information to the other information pathway. In addition, three gating units, the input gate, forget gate and output gate, are added to the LSTM to control the information flow of input data in the LSTM unit. The formula of LSTM can be expressed as:  
\[f_{t}=\sigma\Big{(}W_{f}x_{t}+V_{f}h_{t-1}+b_{f}\Big{)} \tag{4}\]  
\[i_{t}=\sigma(W_{i}x_{t}+V_{i}h_{t-1}+b_{i}) \tag{5}\]  
\[\tilde{c}_{t}=tanh(W_{c}x_{t}+V_{c}h_{t-1}+b_{c}) \tag{6}\]  
\[o_{t}=\sigma(W_{o}x_{t}+V_{o}h_{t-1}+b_{o}) \tag{7}\]  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\tilde{c}_{t} \tag{8}\]  
\[h_{t}=o_{t}\odot tanh(c_{t}) \tag{9}\]  
where \(x_{t}\) represents the input at moment \(t\). \(h_{t}\) and \(c_{t}\) represent the hidden state and the cell state at the moment \(t\), respectively. \(f_{t}\) is the output of the forget gate. \(i_{t}\) is the output of the input gate. \(o_{t}\) is the output of the output gate. \(W_{f},V_{f},W_{i},V_{i},W_{c},V_{c},W_{o}\), and \(V_{o}\) represent the weight matrixes. \(b_{f},b_{i},b_{c}\), and \(b_{o}\) represent the bias matrixes. \(\sigma\) and \(tanh\) denote the sigmoid and hyperbolic tangent activation functions, respectively. \(\odot\) denotes point-by-point multiplication.  
Figure 1: The structure of AE.
# A Channel Compensation Technique Based on Frequency-Hopping Binary Offset Carrier Modulated Signal
## 3 Performance Analysis  
This section will mainly focus on the performance analysis of key aspects of the proposed system, including its acquisition scheme, tracking loops design, and matched filters' improvement to signals.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 3 Selected Case Studies and Numerical Results
### Decentralized State Estimation  
Network state estimation (SE) processes noisy or inaccurate data and evaluates the true operating state, and hence, the stability, of a grid. SE is essential, for example, in terms of optimal power flow, contingency analysis, and corrective control. Many studies have sought to decentralize SE and thus, develop hierarchical SE (HSE) and distributed SE (DSE). During HSE, each local estimator acquires SE data only for a sub-area. A central estimator calculates global estimates by adjusting all local SE results. In contrast, DSE avoids the need for central coordination. In the literature, DSE was explored using the alternative direction method of multipliers (ADMM), by which an agent acquires and shares local state estimates only with neighboring agents. As grid communications continue to improve, local estimates can readily be shared within and between sub-areas. The ADMM can be achieved using only global observability [68] (rather than local observability), thus reducing the number of measurements required.