esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Management in Microgrids with Renewable Energy Sources: A Literature Review
## 3 Microgrid Energy Management with Renewable Energy Generation
### Optimization Techniques
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Optimization Technique** & **Contributions** & **Constraints** & **Drawbacks** & **Single/Multi-objective** \\ \hline \multirow{3}{*}{[31]} & \multirow{3}{*}{No linear and mixed integer} & Robust optimal EMS MPC-based to obtain & Power balance & & \\  & & the optimal power scheduling for the & Battery & & \\  & & programing & different generators, including deferable & & & \\  & & and dump loads. & & Renewable Sources & & \\ \hline \multirow{3}{*}{[32]} & Linear and mixed integer & Energy management strategy based on the & & & & \\  & linear & combination of three operating strategies & Battery & & \\  & programming & (continuous run mode, power sharing & Generation dispatch & & \\  & & mode, and ON/OFF mode). & & & \\ \hline \multirow{3}{*}{[35]} & \multirow{3}{*}{Mixed integer no linear} & Reduced the overall operational costs & DC power & Battery & \\  & programming & while maintaining a secured operation of & Converter power & & \\  & & the stand-alone MG. & Load & & \\  & & Distributed generators power & & & \\ \hline \multirow{3}{*}{[45]} & \multirow{3}{*}{Linear programming} & Integration of linear programming-based & & Power Balance & & \\  & with artificial intelligence techniques to & & Generation limits of distributed generation & & \\  & & sole multi-objective optimization. & & & \\ \hline \multirow{3}{*}{[46]} & \multirow{3}{*}{Particle swarm algorithm (PSO)} & Combination of two optimal storage & Power of the generators & & \\  & energy units. & & Power exchange with the grid & & \\  & Less computation time than GA. & & Charge/D discharge of the storage units & & \\  & & & Supply and demand balance & & \\ \hline \multirow{3}{*}{[48]} & Particle swarm algorithm (PSO) with Gaussian & & & Active power & Power losses are not considered. & \\  & & & & Voltage & Emissions of distributed \\  & mutation & & & Current & generation are not considered. & \\ \hline \multirow{3}{*}{[50]} & \multirow{3}{*}{Artificial bee colony} & \multirow{3}{*}{Two layer control model used to minimize} & Power balance & Complex formulation. Emission & \\  & & operational cost of a microgrid. & & Non-dispatchable resources & & \\  & & & Storage elements & & \\ \hline \multirow{3}{*}{[51]} & \multirow{3}{*}{Fuzzy logic (Grey Wolf Optimization)} & Optimization of the size of the battery & Power balance & Battery degradation cost is not & \\  & & energy storage and of the generation plan. & Power of the generators & & \\  & & Battery load & & \\ \hline \multirow{3}{*}{[53]} & Evolutionary algorithm (EA) and the particle swarm optimization (PSO) & Application of an energy hub model for optimization of a multicarrier MG. & Power balance & Deterministic conditions & \\  & & optimization of a multicarrier MG. & Voltage in the transformers & assumed are a limitation. & Mono-objective \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of microgrid optimization techniques.
## 2 Methodology
### Sun Detection
#### 2.2.1 Framework  
The schematic overview of the proposed methodology is shown in Figure 2.  
The system begins by acquiring a color image, where the algorithm executes the first process that consists of the dome's detection, which is the region of interest (ROI), that is where the Sun should be in the image. Once this is done, the algorithm attempts to detect the Sun within the ROI, separating it from the rest of the elements in the image (also known as the segmentation process). Then, if the executed process finds the Sun, the system will store the position of the Sun in the image, the position that will be used as input to the tracking process through the particle filter. However, if the algorithm is not able to segment the Sun in the image, it is estimated through predefined and well-known equations, and this estimation is used as input to the particle filter. With this methodology, the proposed system  
Figure 1: System prototype.  
is always able to return a position of the Sun in the image, no matter what conditions are present in the sky. This process is repeated automatically with each new image acquired by the sensor, except for the detection of the dome, which has already been performed on the first image, since it will not change its position while the system is running.  
In the following sections, each of the processes followed by the proposed system are described in detail.
## 2 Materials and Methods
### Extraction Configurations
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**IP** & **Iter.** & **Mask** & **Label** & **Description** \\ \hline \multirow{3}{*}{N3} & 1 & - & N3 iter = 1 & 1 iteration of N3 inhomogeneity correction without a mask. \\ \cline{2-5}  & 4 & - & N3 iter = 4 & 4 iterations of N3 without a mask (FreeSurfer default). \\ \hline \multirow{6}{*}{ZS} & 1 & - & ZS iter = 1 & 1 iteration of Z-score intensity normalization without a mask. \\ \cline{2-5}  & 4 & - & ZS iter = 4 & 4 iterations of Z-score int. norm. without a mask. \\ \cline{2-5}  & 1 & BET0.1 & ZS m = BETf0.1 & 1 iteration of Z-score with a BETf0.1-extracted mask. \\ \cline{2-5}  & 1 & BET0.5 & ZS m = BETf0.5 & 1 iteration of Z-score with a BETf0.5-extracted mask. \\ \cline{2-5}  & 1 & BET0.8 & ZS m = BETf0.8 & 1 iteration of Z-score with a BETf0.8-extracted mask. \\ \hline \multirow{6}{*}{KDE} & 1 & BET0.1 & KDE = BETf0.1 & 1 iteration of KDE-based intensity normalization with a BETf0.1-extracted mask. \\ \cline{2-5}  & 1 & BET0.5 & KDE m = BETf0.5 & 1 iteration of KDE with a BETf0.5-extracted mask. \\ \cline{2-5}  & 1 & BET0.8 & KDE m = BETf0.8 & 1 iteration of KDE with a BETf0.8-extracted mask. \\ \hline \multirow{6}{*}{WS} & 1 & BET0.1 & WS m = BETf0.1 & 1 iteration of WhiteStripe intensity normalization with a BETf0.1-extracted mask. \\ \cline{2-5}  & 1 & BET0.5 & WS m = BETf0.5 & 1 iteration of WhiteStripe with a BETf0.5-extracted mask. \\ \cline{1-1} \cline{2-5}  & 1 & BET0.8 & WS m = BETf0.8 & 1 iteration of WhiteStripe with a BETf0.8-extracted mask. \\ \hline - & - & - & - & No intensity processing procedure (control). \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A description of the intensity processing procedures. IP procedures were executed prior to brain extraction with one of the configurations detailed in Table 1. The IP (intensity processing) column contains the intensity processing technique used. The Iter. (iterations) and Mask columns contain the number of iterations that a technique was run for and the brain extraction configuration used to generate the brain mask that was applied during intensity processing, if applicable. The Label column contains the short-hand notation used when referencing the IP procedure. The Description column provides a short description of each IP procedure. \(1\) mm brain-only mask for masking and performs the initial extraction with FSL using the described BET configuration [20].
# Compact Spatial Pyramid Pooling Deep Convolutional Neural Network Based Hand Gestures Decoder
## 3 Basic Theory for Optimizing DCNN  
### Deep Convolutional Neural Network and Node Pruning  
The basic concept of a DCNN originated from the theory that an input matrix that controls edge features can be used to understand and define the patterns that appear. Any affine transformation cannot be used to obtain valuable visual information. Many previous work and iterates have described the basic DCNN theory thoroughly [29; 30]. Convolution is a sparse operation, and the parameters are reused by sharing. This will extract the edge or essential pattern information from visual feedback and label it as the supplied data. Figure 1 shows a simplified DCNN with 2 (two) convolutional layers, followed by a flattenws layer with the extracted feature vector that are used to classify the given image input. Convolution is a mathematical operation that can be applied to a matrix. Basically, all of the images that serve as the input for a convolutional neural network can be represented as an \(N\)-dimensional matrix. The filter sub-matrix is the underlying weight of the neural network and the inputs are integrated across the channels, which are the image dimensions.  
If a single node of a neural network can be denoted as the \(y\), which is the mathematical summation of \(i\) inputs \(x\) and weights, the node can be expressed as the following matrix form,  
\[y\ =\ \sum_{i=1}^{n}w_{i}x_{i}+b \tag{1}\]  
Here, \(b\) is the bias, and to reduce the result to a threshold based non-linearity, an activation function \(\sigma(x)\) can be used, as follows,  
\[\hat{y}\ =\ \sigma(y) \tag{2}\]  
Accordingly, the convolution accepts a volume size of \(W,H,D\) as the weight, height, and dept the number of filters \(K\) their stride \(S\), and followed by the spatial extent \(F\) and calculates the following,  
Figure 1: Simplified deep convolutional network with two (2) convolutional layers and one (1) single fully connected layer.  
\[\hat{W} = \frac{W-F+2}{S+1}\] \[\hat{H} = \frac{H-F+2}{S+1} \tag{3}\] \[\hat{D} = K\]  
Here, \(\hat{W},\hat{H},\hat{D}\) are the resulting output matrix dimensions. It is important to mention that the weight and height are computed equally by symmetry and the output convoluted matrix obtains the exact filter-sized volume for the next convolutional layer to process. Thus, based on the equation that is shown above, a simple ranking on the nodes based on the ascending values of \(\hat{W}\) can be used in order to rank the filters of each layer in both the convolutional and fully connected layers. \(l1\) normalization of the weight vectors can be calculated as the element-wise vector absolute value, as shown below,  
\[l1(\hat{W})\ =\ |\hat{W}| \tag{4}\]  
Subsequently, the nodes can be sorted from the calculated \(l1\) normalization and pruned based on the least scored nodes, as needed. Figure 2 displays the block representation of node pruning in one particular layer of the DCNNs.
## 3 Data and Methods
### Analysis of Cementing Quality Prediction Results  
In this study, a cementing quality prediction algorithm was established based on SVR, and the penalty parameter and kernel function parameter were optimized. The parameter optimization process was carried out via Python programming, and the GA, GS, and BOA optimization methods were adopted. The prediction results of the four algorithms are shown in Table 5.  
From Table 5 and Figures 6 and 7, it is evident that the _MRE_ of GA-SVR is 7.30%, and the _RMSE_ is 2.318, both of which are far smaller compared with SVR, GS-SVR, and BOA-SVR, indicating that this algorithm is the most suitable for cementing quality prediction. Table 5 indicates that GA-SVR is the least time-effective method. This suggests that, compared with SVR and BOA-SVR, it is more complex and takes more comprehensive factors into consideration. Meanwhile, there are nonlinearities and uncertainties in the influencing factors of cementing quality; while GA-SVR performs better in dealing with high-dimensional, multimodal, and nonlinear problems, GS-SVR is more suitable for dealing with large-scale datasets, and BOA-SVR is suitable for the problems that need to find the global optimal solution. Based on this property, it is more appropriate to use the GA-SVR algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Penalty**} & **Kernel** & \multirow{3}{*}{**Number**} & \multirow{3}{*}{**Actual**} & **GA-SVR** & \multirow{3}{*}{**Relevant**} & \multirow{3}{*}{_MRE_} & \multirow{3}{*}{**Time/s**} \\  & **Function** & & & **Cementing** & & & & \\ \cline{5-10}
**Parameter** \(C\) & **Parameter** & & & **Quality** & & & & \\  & \(g\) & & & **/\%** & & & & \\ \hline \multirow{8}{*}{85.08} & & 14 & 13.37 & 13.56 & 0.0142 & & & \\  & & 16 & 19.34 & 20.09 & 0.0388 & & & \\  & & 17 & 17.29 & 20.63 & 0.1932 & & & \\  & & 20 & 22.31 & 22.06 & 0.0112 & & & \\  & & 26 & 20.19 & 20.30 & 0.0054 & & & \\  & & 42 & 28.34 & 25.09 & 0.1148 & & & \\  & & 43 & 29.76 & 25.41 & 0.1462 & & & \\  & & 44 & 21.39 & 22.67 & 0.0598 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: GA optimization results and model evaluation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & _MRE_ & _RMSE_ & _Time_ \\ \hline SVR & 0.0980 & 2.851 & \textless{}1 \\ GS–SVR & 0.0846 & 2.653 & 44.8 \\ BOA–SVR & 0.0800 & 2.533 & 19.8 \\ GA–SVR & 0.0730 & 2.318 & 36.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation before and after model optimization.
###### Abstract  
This investigation concentrates on refining the accuracy of earthquake parameters as reported by various Saudi seismic networks, addressing the significant challenges arising from data discrepancies in earthquake location, depth, and magnitude estimations. The application of sophisticated machine learning techniques, particularly the Isolation Forest algorithm, has markedly enhanced the precision in the estimation of seismicity parameters by effectively identifying and eliminating outliers and discrepancies. A newly developed and refined seismicity catalog was employed to accurately determine key seismic parameters such as the magnitude of completeness (\(M_{c}\)), \(a\)-value, and \(b\)-value, thereby underlining their indispensable role in regional seismic hazard assessment. The research underscores the substantial impact of data inconsistencies on the evaluation of seismic hazards, thereby advocating for the advancement of research methodologies within the field of seismotechous. The insights derived from this study significantly contribute to a more profound understanding of the seismotechous process in the region. These insights are crucial for the development of comprehensive seismic hazard assessments and the formulation of targeted earthquake preparedness strategies, thereby enhancing resilience against seismic risks in the region.  
seismic hazard; Red Sea; seismicity discrepancies; Saudi seismic networks +
Footnote †: journal: Applied Sciences  
1  
A Unified Seismicity Catalog Development for Saudi Arabia: Multi-Network Fusion and Machine Learning-Based Anomaly Detection  
1
# Forgetful Forests: Data Structures for Machine Learning on Streaming Data under Concept Drift
## 1 Introduction  
Supervised machine learning [1] tasks start with a set of labeled data. Researchers partition that data into training data and test data. They train their favorite models on the training data and then derive accuracy results on the test data. The hope is that these results will hold on to yet-to-be-seen data because the mapping between input data and output label (for classification tasks) does not change, i.e., is independent and identically distributed (i.i.d.).  
This i.i.d. paradigm works well for applications such as medical research. In such settings, if a given set of lab results L indicates a certain diagnosis d at time t, then that same set of input measurements L will suggest diagnosis d at a new time t'.  
However, there are many applications where the mapping between the input and output label changes: movie recommendations, variants of epidemics, market forecasting, or many real-time applications [2]. Predicting well in these non-i.i.d. settings is a challenge, but it also presents an opportunity to increase speed because a learning system can judiciously \"forget\" (i.e., discard) old data and learn a new input-output mapping on only the relevant data and thus do so quickly. In addition to discarding data cleverly, such a system can take advantage of the properties of the data structures to speed up their maintenance.  
These intuitions underlie the basic strategy of the forgetful data structures we describe in this paper. As an overview, our methodology is to apply the intuitions of the state-of-the-art algorithms for streaming data with concept drift in a way that achieves high quality and high speed. This entails changing incremental decision tree building techniques as well as random forest maintenance techniques.  
We will first introduce the design of our proposed algorithms in Section 3, then we will describe how we tuned the hyperparameters in Section 4, and finally, we will compare our algorithms with four state-of-the-art algorithms in Section 5. The comparison will be based on accuracy, F1 score (where appropriate), and time.
# Wavefront Aberration Measurement Deflectometry for Imaging Lens Tests
## 5 Discussion  
In the experiment section, the differences between the results measured by WAMD and Zygo interferometer can be partly attributed to the accuracy of the alignment tools. This speculation was verified by the simulation, as shown in Figure 10. The plano-convex lens measured in the experiment was simulated, and alignment errors were introduced intentionally. The results are shown in Figure 10. Figure 10a shows the actual wavefront aberration when the LUT was accurately aligned on the optical axis of the measurement system. When the LUT was 5 mm off axis in the x-direction and rotated \(1^{\circ}\) about the x-axis, the wavefront aberration measured by WAMD is shown in Figure 10b. Figure 10c shows the error in this condition, and the corresponding Zernike coefficients are compared in Figure 10d. From the simulation results shown in Figure 10, it can be observed that when the lens was not aligned accurately on the optical axis of the measurement system, additional errors (mainly coma and astigmatism) were introduced into the measurement result, which were similar to some of the errors existing in the experiment. Therefore, it can be predicted that the measurement accuracy can be further improved with more accurate alignment tools.  
Figure 10: Wavefront aberration of the plano-convex lens in the simulation when alignment errors exist: (**a**) actual wavefront aberration; (**b**) wavefront aberration measured by WAMD when the LUT was 5 mm off-axis in the x-direction and rotated \(1^{\circ}\) about the x axis; (**c**) difference between (**a,b**); (**d**) comparison of Zernike coefficients.  
Figure 9: Wavefront aberration of the telephoto lens in the experiment: (**a**) measured by WAMD; (**b**) measured by a Zygo interferometer; (**c**) difference between (**a,b**); (**d**) comparison of Zernike coefficients.
## 2 Literature Review
### Fuel Management Strategies of UAVs  
UAVs can use an electric motor, internal combustion engine, or hybrid engine. The advantage of electric UAVs is the flexibility in terms of size and weight: they are small and light aerial vehicles. Electric UAVs can operate with a replaceable or rechargeable battery. However, their flying range does not exceed 50 km [12], and the recharge time of the batteries is very long. UAVs with an internal combustion engine are much heavier, but the ratio between weight and flying range presents a good performance [13]. Another advantage of internal combustion UAVs is the fast speed of the refueling operation [12; 13].  
One of the strategies to deal with the flying range limitation is to force the inclusion of intermediate stops within the UAV route to perform refueling operations [14]. Another strategy is to perform battery assignment and scheduling for the UAV based on the time between charges and discharge time [15]. In the case of electric UAVs, battery management involves battery charging and storage management, whereby recharging stations demand a specialized type of infrastructure [16]. [PERSON] et al. [12] present a literature review focusing on strategies to deal with the technical limitations of UAVs.  
The refueling strategies and routing decisions reported in the literature involve refueling station design. Planning routes under collaborative environments is another strategy to deal with flying range limitations. [PERSON] et al. [17] present an approach in which a fleet of UAVs operates in collaboration with a fleet of trucks. The trucks operate as refueling stations. The truck and drone routes are defined in advance, where intermediate stops to recharge the drone battery are included in the routing problem. [PERSON] et al. [18] tackle the problem of determining the location of drone facilities. The problem is approached as a p-median, where one wants to maximize the coverage of the stations by locating stations that can quickly respond to the needs of UAVs. The authors recognize that the number of stations and their location depend on the points that demand travel routes and the network structure. The decision regarding the number of stations and where to locate them is oriented toward minimizing the travel distance. The authors propose a heuristic approach to solve a deterministic problem dealing with unmanned aerial vehicle station location.  
[PERSON] et al. [19] propose a Bayesian network to predict the environment in which the drone will perform, based on a set of possible situations, which allows a failure mode and effects analysis. This information is useful for the estimation of speed and fuel consumption. Most studies focus on predicting fuel consumption and are based on stochastic travel times to determine route reliability [17]. The contribution of our research is to develop a systematic method that humanitarian relief agencies and decision-makers can use to determine which type of refueling strategy is more convenient for their specific region.
# Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement
## Architecture of the Real-Time Wide-Area Rain-Rate Measurement System
### Tipping Bucket Rain Gauges Sensors  
After a pre-set amount of precipitation fall, the rain gauge lever tips and sends an electrical signal to the data logger. Periodically, the data logger sends the log of the received data to the NEFOCAST service center using the satellite network. Figure 4 shows the architecture of the rain gauge sensor system in the Earth station.  
For each tip, the rain gauge collects an amount of water equivalent to 0.2 mm rainfall. Tips are generated by a reed switch within the rain gauge. The data logger keeps track of each tip: These data are then integrated over a configurable time interval in the NEFOCAST Service Center. Debouncing is applied to the rain gauge signal in order to avoid possible false detections. The data logger is divided in two parts: A probe and a core module. The probe is installed inside the rain gauge and communicates with the core module using a dedicated Wi-Fi link. This probe is implemented using a NodeMCU ESP8266 microcontroller. Signals from the rain gauge are read on one of the probe's general purpose input output (GPIO) pins and a multicast user datagram protocol (UDP) message is generated for each of them. Each data logger has an associated unique ID that is transmitted within the messages.  
This choice allows the use of the same data logger to collect statistics from multiple probes at the same time. The core module listens for multicast UDP packets coming from the probes, aggregates and forwards them to the NEFOCAST service center using the satellite link provided by the SmartLNB. Messages are also stored within the data logger: In the case of communication issues with the NEFOCAST service center (e.g., no satellite link available due to heavy rain), messages that have not been yet delivered will be sent as soon as the connection will be restored.