esa-sceva/satcom-chunk-collection huggingface dataset:
_Article_  
**Stability Augmentation of a Grid-Connected Wind Farm by Fuzzy-Logic-Controlled DFIG-Based Wind Turbines**  
**[PERSON] 1,* \(\copyright\)**, [PERSON] 2, [PERSON] 3, [PERSON] 1, [PERSON] 1 and [PERSON] 1**  
1 Department of Electrical and Electronic Engineering, Kitami Institute of Technology (KIT), 165 Koen-cho, Kitami, Hokkaido 090-8507, Japan; [PERSON]ADDRESS] (A.U.); [EMAIL_ADDRESS] (R.T.); [PERSON]_ADDRESS] (J.T.)  
2 Department of Electrical and Electronic Engineering, American International University-Banglades (AIUB), Ka-66/1, Kuratoli Road, Kuril, Khilkhet, Dhaka 1229, Bangladesh; [EMAIL_ADDRESS]  
3 Department of Electrical and Computer Engineering, Curtin University, Perth, WA 6845, Australia; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +81-157-26-9266  
Received: 30 November 2017; Accepted: 19 December 2017; Published: 24 December 2017  
**Abstract:** Wind farm (WF) grid codes require wind generators to have low voltage ride through (LVRT) capability, which means that normal power production should be resumed quickly once the nominal grid voltage has been recovered. However, WFs with fixed-speed wind turbines with squirrel cage induction generators (FSWT-SCIGs) have failed to fulfill the LVRT requirement, which has a significant impact on power system stability. On the other hand, variable-speed wind turbines with doubly fed induction generators (VSWT-DFIGs) have sufficient LVRT augmentation capability and can control the active and reactive power delivered to the grid. However, the DFIG is more expensive than the SCIG due to its AC/DC/AC converter. Therefore, the combined use of SCIGs and DFIGs in a WF could be an effective solution. The design of the rotor-side converter (RSC) controller is crucial because the RSC controller contributes to the system stability. The cascaded control strategy based on four conventional PI controllers is widely used to control the RSC of the DFIG, which can inject only a small amount of reactive power during fault conditions. Therefore, the conventional strategy can stabilize the lower rating of the SCIG. In the present paper, a new control strategy based on fuzzy logic is proposed in the RSC controller of the DFIG in order to enhance the LVRT capability of the SCIG in a WF. The proposed fuzzy logic controller (FLC) is used to control the reactive power delivered to the grid during fault conditions. Moreover, reactive power injection can be increased in the proposed control strategy. Extensive simulations executed in the PSCAD/EMTDC environment for both the proposed and conventional PI controllers of the RSC of the DFIG reveal that the proposed control strategy can stabilize the higher rating of the SCIG.  
**Keywords:** squirrel cage induction generator (SCIG); doubly fed induction generator (DFIG); fuzzy logic controller (FLC); PI controller; low voltage ride through (LVRT); power system
# Enhancing Flow Field Performance of a Small Circulating Water Channel Based on Porous Grid Plate
## 3 Resistance Coefficient of a Porous Grid Plate
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Porosity** & **Velocity (m/s)** & **Pressure Drop (Pa)** & **Fitting Equation** & **Viscous Resistance Coefficient** & **Inertial Resistance Coefficient** \\ \hline  & 0.5 & 407.78 & & & \\  & 1 & 1638.6 & & & \\
0.5026 & 1.5 & 3468.1 & \(\Delta\)P/\(\Delta\delta\) = 1511.1r\({}^{2}\) + 85.999\(\circ\) & \(4.3\times 10^{7}\) & 1511.1 \\  & 2 & 6258.1 & & & \\  & 2.5 & 9461.8 & & & \\ \hline  & 0.5 & 265.66 & & & \\  & 1 & 1008.6 & & & \\
0.6013 & 1.5 & 2158.3 & \(\Delta\)P/\(\Delta\delta\) = 726e\({}^{2}\) + 303.1r & \(1.51\times 10^{8}\) & 726 \\  & 2 & 3503.1 & & & \\  & 2.5 & 5280.5 & & & \\ \hline  & 0.5 & 245.59 & & & \\  & 1 & 884.3 & & & \\
0.6361 & 1.5 & 1850.1 & \(\Delta\)P/\(\Delta\delta\) = 575.27r\({}^{2}\) + 322.04r & \(1.61\times 10^{8}\) & 575.27 \\  & 2 & 2914.9 & & & \\  & 2.5 & 4398.2 & & & \\ \hline  & 0.5 & 247.56 & & & \\  & 1 & 859.63 & & & \\
0.6599 & 1.5 & 1735.6 & \(\Delta\)P/\(\Delta\delta\) = 521.02r\({}^{2}\) + 344.31r & \(1.72\times 10^{8}\) & 521.02 \\  & 2 & 2738.9 & & & \\  & 2.5 & 4118.6 & & & \\ \hline  & 0.5 & 344.95 & & & \\  & 1 & 1055.1 & & & \\
0.7088 & 1.5 & 2050 & \(\Delta\)P/\(\Delta\delta\) = 502.29r\({}^{2}\) + 564.25r & \(2.82\times 10^{8}\) & 502.29 \\  & 2 & 3107.3 & & & \\  & 2.5 & 4547.2 & & & \\ \hline \end{tabular}
\end{table} 
Table 1: The obtained resistance coefficients. \(\Delta\)P: pressure drop generated when the fluid flows through a porous grid plate and \(\Delta\delta\): thickness of the porous grid plate.
## 2 Materials and Methods
### Flood Inventory Map  
As documented by [PERSON] et al. [23], high-resolution remote sensing data were crucial in mapping the spatial evolution of the flood event, demonstrating how extreme precipitation combined with the region's geomorphology have led to widespread flooding. To develop a comprehensive geodatabase of historical flood events along the Basento River, information on historically flooded areas was collected and harmonized from various institutional datasets, including those from the Basilicata Region, the Southern Apennines District Basin Authority, and the University of Basilicata. The acquisitions of the flood extent derive from data available on the Copernicus Emergency (CMS) platform and COSMOS-SkyMed (CSK) images supplied by the Italian Space Agency (ASI) upon request from the DPC. The latter were obtained in Stripmap mode with a 2.5 m resolution and characterized by horizontal or vertical polarization. Additional details regarding the investigated events and satellite imagery are documented in recent scientific publications [23; 24; 25]. Specifically, the datasets used to support this study's objectives include the following:  
* March 2011 flood event: For this event, RGB images of the flooded areas acquired by Cosmo-SkyMed and provided by ASI to the DPC were georeferenced. Subsequently, a semi-automated supervised classification procedure was applied to identify the pixels affected by the flooding.
* October 2013 flood event: Flood extent maps were made available by the Copernicus EMS service, with acquisitions from the SPOT-6 satellite on October 17 th, 2013, at 11:40 AM.  
Figure 1: Geographic location of the Basento River basin.  
* December 2013 flood event: Flood extent maps were provided for the Ionian coast of Basilicata, referring to the acquisitions from December 2 nd and 3 rd at 4:31 AM. Furthermore, the Copernicus EMS portal enabled the integration of this information with satellite acquisitions from December 4 th and 5 th at 10:55 AM and 12:55 PM, respectively. This allowed the creation of a map highlighting the evolution of the flood events in the affected area.  
The validation map of historical flood events from multiple data sources is illustrated in Figure 2.
# Intelligibility and Listening Effort of Spanish Oesophageal Speech +
## 4 Experiment 2: Listening Effort for High-Intelligibility Oesophageal Speech
### Analysis and Results
#### 4.2.1 Intelligibility  
The audio responses of the sentence recognition task were transcribed by a native Spanish speaker, who was also a speech expert. WER was calculated using the same methods as stated in Section 3.2. As the WER was found to be highly correlated with all inclusive WERs in Experiment 1, we decided to proceed with all inclusive WERs only.  
Percentage WER scores for OS was 18.88 \(\pm\) 21.30 and for the healthy speaker it was 11.69 \(\pm\) 16.13 (Figure 4a). ANOVA showed that speaker type had an effect on WER (F(1,15) = 27.20, \(p<0.001\), \(\eta^{2}=0.645\)).
## 4 Development of Optical Sensing Techniques for Red Meat Quality Prediction
### Spectroscopic Techniques
#### 4.1.2 Raman Spectroscopy
In addition, in Raman spectral measurements, the entire spectral range of fundamental vibrations (usually from 50 to 3500 cm\({}^{-1}\)) can be captured in a single exposure. On the other hand, FT-IR instruments usually need reconfiguration to cover the spectral region below 400 cm\({}^{-1}\). Raman spectroscopy is commonly used to analyse the secondary and tertiary structures of proteins, and it is a useful method for studying the structures of water and lipids [167]. Despite the advantages, several factors, like spectra pre-processing methods, calibration models and data analysis techniques, play a crucial role in determining the predictive performance of Raman spectroscopy for meat sensory trait analysis. Furthermore, the limited size of the laser spot (roughly 50 \(\upmu\)m according to [PERSON] et al. [168]) means only a tiny portion of the sample is exposed to the laser. This poses a challenge in obtaining accurate and representative Raman spectra due to the heterogeneous nature of the meat matrix. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Source** & **Animal** & **Muscle** & **Number of Samples** & **Wavelength Range (nm)** & **Statistical Method** & **Measured Attribute** & **Performance** \\ \hline
[165] & Beef & _M. longissimus dorsi_ & 112 & 100–3300 & UVE-PLS & Colour (\(\alpha^{*}\)) & R\({}^{2}_{\text{P}}=0.87\) \\  & & & & & Colour (b\({}^{*}\)) & R\({}^{2}_{\text{P}}=0.91\) \\  & & & & & Chewiness & & \\
[166] & Pork & _M. longissimus_ & 169 & 400–2000 & PLSR & (Sensory) & R\({}^{2}_{\text{P}}=0.438\) \\  & & & & & Tenderness & & \\  & & & & & (Sensory) & & \\  & & & & & Juciness (Sensory) & R\({}^{2}_{\text{P}}=0.827\) \\
[159] & Lamb & _M. seminmembransous_ & 80 & 500–1800 & PLSR & (Texture Analyser) & R\({}^{2}_{\text{CV}}=0.27\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
## 2 Materials and Methods
### Meteorological and Chemistry Simulation
In this study, the meteorological parameters simulated by the WRF-Chem model served as inputs for the MODIS-PSN model to estimate GPP. The daily terrestrial GPP in each simulation grid was calculated using the MODIS-PSN algorithm, which integrates absorbed photosynthetically active radiation (PAR) and the light energy utilization rate [37; 40]. The absorbed PAR was estimated by multiplying the incident PAR from the model output with the absorbing fraction determined by the underlying plant canopy. The light energy utilization rate was obtained by querying the biological property lookup table based on vegetation cover type and adjusted for daily minimum temperature (Tmin) and the average difference between water vapor pressure at saturation and actual vapor pressure (VPD). The widely used IGBP classification system for land cover categorization was employed to estimate GPP in this study [41]. Comparisons with MYD17A2H product [42] revealed that this method successfully reproduced spatial patterns of GPP, exhibiting high correlation coefficients (R = 0.59-0.78, \(p\) < 0.001) and relatively low bias (nationwide RMSE < 78.56 gC/m\({}^{2}\)) (see Table S6).
# Methods for Aggregation and Remuneration of Distributed Energy Resources
## 3 Scheduling Formulation
\begin{array}{l}P_{(i,t)}^{inj}=\sum\limits_{p=1\in i}^{P}P_{(p,t)}^{DG}+\sum \limits_{s=1\in i}^{S}P_{(s,t)}^{Sup}\\ +\sum\limits_{c=1\in i}^{C}\left[\begin{array}{l}P_{(c,t)}^{rad}+P_{(c,t)}^{ cut}+\sum\limits_{d=1}^{T}\left(P_{(c,t,d)}^{shift}+P_{(c,d,t)}^{shift} \right)+\\ P_{(c,t)}^{ens}-P_{(c,t)}^{load}\\ \end{array}\right]\\ Q_{(i,t)}^{inj}=\sum\limits_{p=1\in i}^{P}Q_{(p,t)}^{DG}+\sum\limits_{s=1 \in i}^{S}Q_{(s,t)}^{Sup}-\sum\limits_{c=1\in i}^{C}\left[Q_{(c,t)}^{load}\right] \\ \forall t\in\{1,\ldots,T\}\\ \\ V_{(i,t)}^{min}\leq V_{(i,t)}\leq V_{(i,t)}^{max}\\ \theta_{(i,t)}^{min}\leq\theta_{(i,t)}\leq\theta_{(i,t)}^{max}\\ \forall i\in\{1,\ldots,B\},\quad\forall t\in\{1,\ldots,T\}\\ \\ P_{(p,t)}^{minDG}\leq P_{(p,t)}^{DC}\leq P_{(p,t)}^{maxDG}\\ Q_{(p,t)}^{minDG}\leq Q_{(p,t)}^{DG}\leq Q_{(p,t)}^{maxDG}\\ \forall p\in\{1,\ldots,P\},\quad\quad\forall t\in\{1,\ldots,T\}\\ \\ P_{(s,t)}^{minSup}\leq P_{(s,t)}^{Sup}\leq P_{(s,t)}^{maxSup}\\ Q_{(s,t)}^{minSup}\leq Q_{(s,t)}^{sup}\leq Q_{(s,t)}^{maxSup}\\ \forall s\in\{1,\ldots,S\},\quad\quad\forall t\in\{1,\ldots,T\}\\ \end{array}  \tag{7}\]  
Now concerning the demand response programs, in Equation (8) it is shown the conditions for a load reduction program, where energy curtailment is continuous, instead of discrete as in the load curtailment program--Equation (9), subject to a binary variable for decision making regarding the scheduling or not of a given consumer. \[P_{(c,t)}^{rad}\leq P_{(c,t)}^{max\_rad},\quad\quad\forall c\in\{1,\ldots,C\},\forall t\in\{1,\ldots,T\}\\ \tag{8}\]  
\[ \begin{array}{l}0\leq P_{(c,t)}^{cut}\leq P_{(c,t)}^{max\_cut}\\ P_{(c,t)}^{cut}=P_{(c,t)}^{max\_cut}\cdot\lambda_{(c,t)}^{cut},\\ \forall c\in\{1,\ldots,C\},\forall t\in\{1,\ldots,T\},\quad\quad\lambda_{(c,t )}^{cut}\in\{0,1\}\\ \end{array}  \tag{9}\]  
The load shifting program conditions are defined by Equations (10) to (12), based on [42]. This program intends to allow the consumer to transfer loads between the periods considered, thus improving the aggregator operation flexibility. \[ \begin{array}{l}P_{(c,t,d)}^{min\_shift}\leq P_{(c,t,d)}^{shift}\leq P_{(c, t,d)}^{max\_shift}\\ \forall c\in\{1,\ldots,C\},\forall t\in\{1,\ldots,T\}\\ \\ \sum\limits_{d=1}^{T}P_{(c,t,d)}^{shift}\leq P_{(c,t)}^{max\_shift-out}\\ \forall c\in\{1,\ldots,C\},\forall t\in\{1,\ldots,T\}\\ \\ \sum\limits_{d=1}^{T}P_{(c,d,t)}^{shift}\leq P_{(c,t)}^{max\_shift-in}\\ \forall c\in\{1,\ldots,C\},\forall t\in\{1,\ldots,T\}\\ \end{array}  \tag{10}\]
# Real-Time Object Tracking Algorithm Based on Siamese Network
## 3 Methodology
### Fully Convolutional Siamese Network  
Recently, the fully convolutional Siamese network SiamFC algorithm has been extensively applied in various fields of target tracking. The algorithm consists of two inputs, one is the template branch, that is, the object to be tracked, and the first frame of the video sequence is usually selected as the template input to obtain the template feature map; the other is the search branch, that is, the image search area of each subsequent frame is used as the search image input to obtain the search feature map. SiamFC regards tracking as a process of template matching. That is, feature extraction, followed by cross-correlation operation on the generated feature maps, that is, the convolution computation, so as to generate a heat-map. The cross-correlation operation is as Equation (1) follows.  
\[f(Z,X)=\varphi(Z)*\varphi(X)+b\cdot I \tag{1}\]  
where, \(Z\) and \(X\) indicate the template image and the search image, respectively. \(\varphi\) is the network for feature representation of templates and search areas, referring to AlexNet [26]. \(\varphi\) is a transformation operation, and the highest corresponds of the response value represents the predicted position of \(Z\). \(I\) is the identity matrix, and \(b\) is the bias term.  
Figure 1: Complete Flow Chart of SiamFP Algorithm. (**a**) denotes feature pyramid extractor. (**b**) denotes optical flow network. (**c**) denotes siamese network. (**d**) denotes attention network.
# Machine Learning-Based Classification of Mushrooms Using a Smartphone Application
## 2 Materials and Methods
### Classifier Training and Testing  
RGB channels were normalized with means of 0.485, 0.456, 0.406, and standard deviations of 0.229, 0.224, and 0.225, respectively, as suggested in the literature [27]. Optimal hyperparameters such as the number of layers, the number of neurons, and the learning rate were calculated using this model. In addition, batch size was set to 32, and a cross-entropy loss [28] was used as a cost function for all models with an optimizer set to Adam [29] with a learning rate of 0.0001, and L2 penalty to 0.0001. Moreover, the AMSGrad variant was utilized as suggested by [PERSON] et al. [30] while the first beta parameters were modified from  
Figure 1: Conceptual diagram explaining the machine-learning-based mushroom image classification. (**A**) Shows the input images to output layers which are generated from a server. Once the training model is verified, it is transferred to a smartphone device. (**B**) Series of layers starting from 224 (H) \(\times\) 224 (W) \(\times\) 3 (channels) and subsequent convolution of 2 \(\times\) 2. The image is flattened as a one-dimensional array of 4500 elements and fed to a feed-forward neural network. This network is subsequently reduced to 300, 300, and 3 neurons of fully connected layers corresponding to the number of classes.  
0.9 to 0.7 with 20 epochs. Using our training data, stratified \(k\)-fold cross-validation (\(k=10\)) was performed to determine how well our systems were prepared to classify designated mushroom types.  
To test the performance of our models, 96, 384, and 220 images were used for each model. Based on correct and incorrect predictions, a cross-validation table was generated and expressed as true positive (\(TP\)), true negative (\(TN\)), false positive (\(FP\)), and false negative (\(FN\)) results; these values were used to report the statistical parameters and receiver operating curves (ROC) as follows.  
\[\text{Sensitivity}=\frac{TP}{TP+FN} \tag{1}\]  
\[\text{Specificity}=\frac{TN}{TN+FP} \tag{2}\]  
\[\text{Accuracy}=\frac{TP+TN}{TP+FP+TN+FN} \tag{3}\]
# Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices
## 2 Materials and Methods
### Methods
\begin{table}
\begin{tabular}{c c} \hline \hline
**Vegetation Index** & **Calculation Formula** \\ \hline Normalized Difference Green Index (NDVIg) & \(R_{750}-R_{550}/R_{750}+R_{550}\) \\ Normalized Difference Red-Edge Simple Ratio (NDRSR) & \(R_{672}-R_{712}/R_{872}+R_{712}\) \\ Normalized Difference Water Index (NDWI) & \(R_{872}-R_{1245}/R_{872}+R_{1245}\) \\ Normalized Difference Red Edge (NDRE) & \(R_{750}-R_{720}/R_{750}+R_{720}\) \\ Modified Normalized Difference Vegetation Index (MNDVI) & \(R_{750}-R_{750}/R_{750}+R_{705}-2*R_{445}\) \\ Vegetation Dryness Index (VDI) & \(R_{970}-R_{900}/R_{970}+R_{700}\) \\ Normalized Difference Moisture Index (NDMI) & \(R_{1649}-R_{1792}/R_{1649}+R_{1792}\) \\ Modified Red-Edge Ratio (MSR) & \((R_{750}-R_{705}-1)/(R_{750}/R_{705}+1)\) \\ Simple Ratio (SR) & \(R_{872}/R_{861}\) \\ Normalized Difference Green Vegetation Index (GNDVI) & \(R_{780}-R_{850}/R_{870}+R_{850}\) \\ Vogelmann Red Edge Index (VREI) & \(R_{742}/R_{722}\) \\ Modified Triangular Vegetation Index (MTCI) & \(R_{754}-R_{709}/R_{709}+R_{681}\) \\ Normalized Difference Red Edge Normalized Index (RENDVI) & \(R_{750}-R_{702}/R_{750}+R_{702}\) \\ Three-Band Water Index (TBWI) & \(R_{973}-R_{1720}/R_{1447}\) \\ Photochemical Reflectance Index (PRI) & \(R_{529}-R_{569}/R_{529}+R_{5699}\) \\ \hline \hline \end{tabular} Note: “\(i\)” in \(R_{i}\) represents different wavelength bands.  
\end{table} 
Table 3: Vegetation indices selected in this study. to iteratively adjust the weights in the network until it achieves the weights that minimize the cost function, thereby enhancing the precision of the wheat yield estimation model. Random Forest (RF) is a bagging ensemble method based on a collection of decision trees. In this approach, if you have Nc classifiers, the original dataset is split into Nc subsets (with replacement), known as bootstrap samples. In contrast to a single decision tree, the splits in a Random Forest occur randomly. Rather than searching for the optimal choice at each split, the Random Forest employs random subsets of features for each tree (typically, the number of features is calculated using sqrt(\(\cdot\)) or log(\(\cdot\))). The goal is to find thresholds that best separate the data. Ultimately, many trees are trained in a somewhat weak manner, and each tree produces different predictions. Each tree focuses on a part of the sample space, allowing for inaccurate predictions in some regions. However, scikit-learn employs algorithms that average the results, resulting in highly accurate predictions. Even though theoretically trained Random Forests differ in probability averaging, the differences in average predictions are generally minimal. Therefore, this method often yields reliable results.
## 4 The Application of Edge Computing System
### Anomaly Detection  
Various anomaly detection approaches have been discussed in the literature. For instance, the method proposed in [193] for smart meter fault detection is a decision tree (DT) approach that filters abnormal data using meter data analytics and classifies them according to energy loss levels. This method provides online detection and verification for large-volume meters, thereby reducing the need for on-site inspections. To address the latency issues in data transmission, ref. [194] proposes anomaly detection using prediction algorithms such as Deep Neural Network (DNN), Support Vector Regression (SVR), and K-Nearest Neighbor (KNN). The study shows that DNN is the most effective solution with low latency (1.25 ms). The system ensures seamless data flow and collaboration between Smart Meters, Concentrators, Edge-MDMS, and Cloud-MDMS, enabling early detection and alerting of anomalies.  
In another approach, ref. [195] developed a hybrid model to identify anomalies in building energy data. This model combines polynomial regression with Gaussian distribu  
Figure 14: System design and interaction between EC and Smart Meters.  
tion to detect anomalies in school electricity consumption data. Focusing on cybersecurity and data security, ref. [196] proposes an EC-based anomaly detection method for AMI. This method is implemented at the endpoint of the data concentrator, effectively mitigating the risk of privacy leaks and reducing both communication latency and computation time. Successful results were achieved with 99.05% detection accuracy and a 0.74% false detection rate. Similarly, ref. [197] introduces privacy-preserving anomaly detection methods through FL, an innovative approach that leverages distributed edge devices to detect anomalies while maintaining data privacy. By enabling local data processing, FL eliminates the need to transfer sensitive information to centralized servers, ensuring user privacy. This is particularly beneficial in scenarios like transformer-based models for sequential anomaly detection, where decentralized training enhances model accuracy and reduces communication overhead.  
In the context of Edge-AI, ref. [198] highlights the convergence of EC and AI, which enables the deployment of advanced AI models to be deployed closer to users, significantly enhancing performance. A key framework in this domain is Federated Continual Learning (FCL), which supports distributed learning across clients while preserving data privacy. FCL ensures stable model performance in dynamic environments by retaining knowledge from previous tasks, making it ideal for anomaly detection applications that require real-time adaptability across distributed edge devices.  
Finally, ref. [199] developed a Granger causality test-based approach to detect electricity theft among low-voltage (LV) users. This method detects electricity theft with data measured at the distribution transformer unit (DTU) and avoids false alarms caused by corrupted data.