esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Development of a Management System for Energy Microgrids Using Linear Programming
## 2 Materials and Methods
### Instance Generation  
This research considers energy microgrids in isolated communities of Colombia subsidized by the state. These areas are difficult to access and the energy solution currently implemented is 100% based on the use of diesel generators. This situation presents logistical challenges to ensure the correct and continuous operation of the microgrid.  
Data was collected from three different regions in Colombia: San Andres (SA), Providencia (P), and Puerto Narino (PN). These locations already have diesel-based solutions to generate electricity and their installed capacities are presented in Table 11.  
The parameters considered in the model are classified in technical, demand, and environmental parameters. Technical parameters are defined in Table 12. For batteries and generators, these parameters were drawn from existing bibliographic sources according to the used technology [28]. The default values for each parameter within this category were established by experts in the field. The parameters within the environmental category are solar radiation, wind speed, and temperature. The web tool RenewableNinja [29] was used to collect historical data for these parameters. This web tool provides a global meteorological database from the MERRA-2 system of the National Aeronautics and Space Administration [30] that gives timely (per hour) information. The downloaded data is from January to December 2019.  
Finally, the demand parameter represented by the demand loads of the NIZ in Colombia were obtained and analyzed from the \"Instituto de Planificacion y Promocion de Soluciones Energeticas para Zonas No Interconectadas\" (IPSE) that shared all NIZ's hourly energy supply reports from 2019. These data is supplied to the IPSE by the \"Centro Nacional de Monitoreo\" [24] that measures the actual electrical energy consumption of different isolated communities through telemetry systems. Data for every day of the year is available. However, when data was analyzed, it was observed that there was no significant variation between the same days of the week throughout the year. For this reason, an annual average is used as the actual hourly load for every day of the week. Therefore, 21 instances of the problem were built, seven for each of the three NIZ considered.
# Study on a Landslide Segmentation Algorithm Based on Improved High-Resolution Networks
## 3 Experimental Description  
### Landslide Dataset Production  
The dataset employed in this study consists of images acquired from areas in the Loess Plateau. The dataset consists of 340 remote sensing images, for which the landslide regions were initially unannotated. To annotate the landslide boundaries, this paper utilized the labelme image annotation tool. Figure 6 depicts the annotated boundaries of the landslide areas within the images.  
The final labeling diagram obtained is shown in Figure 7.  
Figure 6: Labelme annotation tool.  
Figure 7: Label diagram.  
To overcome the size limitation for the training data and prevent overfitting, this study employed data augmentation techniques. The objective of employing these techniques was to enhance the diversity of the landslide dataset and optimize the training of the neural network model. The data augmentation methods employed in this study primarily encompassed:  
* Flip transformation: flip images horizontally or vertically to create mirrored versions;
* Rotation transformation: rotate images to a certain angle to simulate variations in the viewpoints;
* Perspective transformation: apply perspective distortion to the images to mimic different camera perspectives;
* Shear transformation: apply shear distortion to the images to introduce geometric deformations;
* Image enhancement based on fractional-order differentiation: The fractional order differential operator offers superior capabilities in preserving the image's edge features [33]. The fractional derivatives in this paper used the G-L definition in the image processing [34], which is defined as follows:  
\[\prescript{R}{a}D_{t}^{T}=\lim_{h\to 0}\frac{1}{h^{p}}\sum_{m=0}^{h}\ (-1)^{i}\frac{\Gamma(v+1)}{j!\Gamma(v-j+1)}f(t- jh), \tag{5}\]  
where \(R\) is the real number field, \(a\) and \(t\) are the independent variables that meet the G-L definition and \(t-a<1\) and \(a<t\), \(T\) is the differential order, \(v\) is the fractional order derivative of the \(v\)-th order, \(i\) and \(j\) are natural numbers from \(1\) to \(n\), and \(h\) is the unit variable. \(\Gamma\left(\cdot\right)\) is the gamma function, which is defined as follows:  
\[\Gamma(n)=\int\limits_{0}^{\infty}e^{-t}t^{n-1}dt=(n-1)!. \tag{6}\]  
Applying fractional-order techniques to enhance gray and poorly textured landslide images can significantly enhance the quality of the dataset. This improvement is visually illustrated in Figure 8.  
By incorporating data augmentation techniques, the variability of the dataset is increased, allowing the deep neural network to generalize more effectively and enhance its performance in landslide segmentation.  
Figure 8: Sample image enhancement based on fractional order: (**a**) unprocessed original image; (**b**) enhanced by fractional-order differentiation.  
After applying data augmentation techniques, the final dataset of landslide images consisted of a total of 640 images, and the dataset was divided into training and validation sets using a ratio of 9:1.
# Link and System-Level NOMA Simulator: The Reproducibility of Research
###### Abstract  
This study focuses on the design of a MATLAB platform for non-orthogonal multiple access (NOMA) based systems with link-level and system-level analyses. Among the different potential candidates for 5G, NOMA is gaining considerable attention owing to the many-fold increase in spectral efficiency as compared to orthogonal multiple access (OMA). In this study, a NOMA simulator is presented for two and more than two users in a single cell for link-level analysis; whereas, for system-level analysis, seven cells and 19 cells scenarios were considered. Long-term evolution (LTE) was used as the baseline for the NOMA simulator, while bit error rate (BER), throughput and spectral efficiency are used as performance metrics to analyze the simulator performance. Moreover, we demonstrated the application of the NOMA simulator for different simulation scenarios through examples. In addition, the performance of multi-carrier NOMA (MC-NOMA) was evaluated in the presence of AWGN, impulse noise, and intercell interference. To circumvent channel impairments, channel coding with linear precoding is suggested to improve the BER performance of the system.  
Keywords:NOMA; link-level simulator; system-level simulator; reproducible research; linear precoding; impulse noise +
Footnote †: journal: _Electronics_
# Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies
## 4 The Proposed Combination Algorithms
### Simple Combined Algorithm  
In this case, a fixed threshold is employed to determine whether the CHs uses one-bit fusion rule or two-bit fusion rule. The threshold, \(\gamma_{c}\), is the mean value of all participating clusters' SNRs. It is assumed that there are \(C\) participating cluster in the CRN. At cluster level, as a first step, each CH estimates its SNR and forwards it to the FC to compute the threshold, \(\gamma_{c}\). Second step, each CH compares its SNR, \(\gamma_{c}(i)\), with the threshold; if its greater than it, then the CH will employ one-bit fusion rule and its probability of detection is computed using Equation (8). Otherwise, the CH will employ two-bit fusion rule and its probability of detection is computed using Equation (11). Finally, at CRN level, the global probability of detection is computed using the generalized formula as in Equation (6), since probabilities of detection of the participating clusters are not equal. Algorithm 1 shows the mechanism of the simple combined fusion algorithm.  
```
1: The FC collects the location information of each SU and determines the CHs based on the maximum reporting channel gain.
2: Each SU forwards its local observations and its estimated SNR to its CH.
3: Each CH sends its average SNR, \(\gamma_{c}(i)\), to the FC to find the average SNR, \(\gamma_{c}\), of all the clusters.
4:for\(i=1:C\),
5:\(Q_{d}\) is computed using Equation (8), if \(\gamma_{c}(i)>\gamma_{c}\). (i.e., one-bit hard scheme) Otherwise \(Q_{d}\) is calculated using Equation (11). (i.e., two-bit softened hard scheme)
6:end
7: The global probability of detection is made at the FC using Equation (6).
```  
**Algorithm 1** Simple Combined Algorithm
# Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology
## 2 System Overview
### Server Back-End  
The data collected at the sensor node are transmitted through the NB-IoT network and the Internet to the WiNet application back-end server, as illustrated in Figure 7.  
The server application was developed using the CakePHP software framework. The data were sent from the sensor node to the server by using the UDP protocol. The Python service translates the UDP packets into an HTTP request. Each time a new message conveying the sensor measurements is received, a corresponding HTTP request is created, which is then received by the CakePHP back-end. The specific API endpoint developed within the back-end checks the data and if the format is correct, the measurements are stored in a MySQL database, as illustrated in the diagram in Figure 8. It can be seen in the diagram that every user in the system has a connected gateway device.  
Figure 8: The Winet MySQL database schema.  
Figure 7: The Winet server.  
The server collects data from the sensor node and also takes over the data from the external weather forecast API. The weather data are collected from the Weatherbit.io API that provides current weather observations as well as weather forecasts on an hourly basis. The algorithms discussed in the previous sections are periodically run for the collected data to enable predictions. The user has the following options to access the data: the Web application and the Android mobile application, which are described in the following subsection.
# Fault Studies and Distance Protection of Transmission Lines Connected to DFIG-Based Wind Farms
## 2 Fault Analysis of DFIG-Based Wind Farm and Its Verification
### DFIG Short Circuit Current
Due to the disconnection of the wind farm, the wind farm side positive and negative sequence circuits are equal to open roads, as shown in Figure 4a. There is no positive or negative sequence current on the wind farm side. However, the ground point of transformer T\({}_{2}\) provides a zero-sequence circuit. The wind farm side three-phase currents on the transmission line are all zero sequence components, as shown in Figure 4b. The fault occurs at 30 ms and the fault duration is about 40 ms; then, the differential protection initiates and the circuit breaker trips. Except for the first few milliseconds of the transient process, \(\dot{I}_{WA}\), \(\dot{I}_{WB}\), \(\dot{I}_{WC}\), and \(\dot{I}_{W0}\) are basically the same after the fault. That is to say, the short circuit current on the transmission line connected to the wind farm only contains zero-sequence components.
## 1 Introduction  
Recently, the importance of researching effective defect detection systems has been increasing. The defect detection systems using deep learning show better performance than the conventional feature-based defect detection system in complex patterns.  
The methods of detecting defects based on deep learning are largely divided into the methods using supervised learning and unsupervised learning. In supervised learning, methods such as image segmentation [1], classification [2], and image detection [3] are used. In unsupervised learning, methods such as autoencoders are used.  
Studies of defect detection using supervised learning are actively progressing. The methods have high defect detection rate, but they need sufficient defect data for training. Due to the industrial area's characteristics, the process of accumulating data over a long period of time is necessary to actually apply these studies.  
There are studies on defect detection based on unsupervised learning using autoencoders. The studies include autoencoders for pre-training method [4] and detecting abnormal regions using the output from the autoencoders [5,6]. Studies on detecting abnormal regions using the output of the autoencoders have the advantage of not requiring labeled defect data but have lower detection rate than methods using supervised learning.  
It is difficult to get a large amount of actual defect data. However, experienced workers can express the shapes and the types of defects. In this paper, we propose an efficient approach of identifying defects without actual defect data by generating artificial defect data using types and characteristics of the defects known by experienced workers to train stacked convolutional autoencoders.  
Our contributions are:  
1. We define a defect detection system for environments where it is hard to obtain a large amount of data.
2. We define a method of generating synthetic defects by using characteristics of the defects based on the knowledge of the experienced workers. We can train an autoencoder where the input is a defect image generated artificially and the output is the corresponding clean image.  
The paper is organized as follows: Section 2 describes the conventional ways of detecting defects using unsupervised learning. Section 3 describes the proposed system components and implementations. Section 4 describes our practical implementation and test results. Finally, our summary and conclusions are provided in Section 5.
## 2 The Proposed Timing Mismatch Calibration Technique  
In this section, a novel, fully digital calibration technique is proposed to calibrate timing mismatches in an M-channel TIADC system. To streamline the matter, it is assumed that the offset and gain discrepancies have been calibrated. Firstly, we analyzed the TIADC model in the frequency domain to extract the spurious signals. Subsequently, the timing mismatch calibration structure is presented according to the aforementioned model.
# Recent Advances on Surface Modification of Halloysite Nanotubes for Multifunctional Applications
## 2 Surface Modification of HNTs and the Relevant Properties
### Surface Coating Modification  
Surface coating modification refers to that the surface of HNTs is coated with a layer of polymer or inorganic material by means of the electrostatic adsorption to achieve the purpose of changing HNTs performance. [PERSON] et al. (2017) prepared drug-loaded porous microspheres (Hal-CTS/Asp) by thorough emulsification in the water/oil microemulsion. The HNTs were coated with chitosan (CTS) and aspirin (Asp) molecules that were adsorbed to the inside of the microspheres as a model drug. The results indicated that the microspheres had the characteristics of a high surface area and large-interconnected pores, which was conducive to the adsorption of aspirin. The modified HNTs had an excellent loading capacity (42.4 wt %), which was twenty times higher than the unmodified ones (2.1 wt %). Meanwhile, the special microspheres showed low drug release rate and pH sensitivity when compared with the pristine HNTs. [PERSON] et al. (2017) successfully prepared alginate/HNTs composite tissue engineering scaffolds by electrostatic adsorption. The scaffolds showed a significant enhancement in thermal stability and cell-attachment properties.
# A Study on the Amplitude Comparison Monopulse Algorithm
## 6 Simulations
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\sigma\) & **Target Position** & **Conventional** & **Full (Analysis)** & **Sum-Difference (Analysis)** & **Prediction** \\ \hline \multirow{2}{*}{0.01} & Azimuth & 0.0269 & 0.0269 (0.0268) & 0.0260 (0.0268) & 0.0268 \\  & Elevation & 0.0268 & 0.0268 (0.0268) & 0.0268 (0.0268) & 0.0268 \\ \hline \multirow{2}{*}{0.05} & Azimuth & 0.1338 & 0.1331 (0.1335) & 0.1330 (0.1336) & 0.1339 \\  & Elevation & 0.1346 & 0.1339 (0.1335) & 0.1339 (0.1336) & 0.1339 \\ \hline \multirow{2}{*}{0.1} & Azimuth & 0.2699 & 0.2595 (0.2656) & 0.2592 (0.2660) & 0.2678 \\  & Elevation & 0.2689 & 0.2593 (0.2656) & 0.2591 (0.2660) & 0.2678 \\ \hline \multirow{2}{*}{0.3} & Azimuth & 0.8450 & 0.7235 (0.8581) & 0.6066 (0.8471) & 0.8033 \\  & Elevation & 0.8468 & 0.6959 (0.8528) & 0.6066 (0.8447) & 0.8033 \\ \hline \multirow{2}{*}{0.5} & Azimuth & 6.2642 & 3.4008 (2.8086) & 2.7784 (2.9192) & 1.3388 \\  & Elevation & 7.1466 & 3.4119 (2.8092) & 2.7817 (2.9239) & 1.3388 \\ \hline \multirow{2}{*}{1} & Azimuth & 610.3174 & 8.3577 (11.4122) & 8.8205 (21.0155) & 2.6776 \\  & Elevation & 251.8055 & 8.3743 (11.4167) & 8.9047 (20.9119) & 2.6776 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: RMS errors for noise levels: \(\theta_{3 dB}=3^{\circ},~{}\theta_{s}=1^{\circ},~{}\theta_{az}=0^{\circ},~{} \theta_{el}=0^{\circ}.\)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Deviations** & **Conventional** & **Full (Analysis)** & **Sum-Difference (Analysis)** & **Prediction** \\ \hline \(\theta_{az}=0^{\circ},~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} \) & 0.2699 & 0.2595 (0.2656) & 0.2592 (0.2660) & 0.2678 \\ \(\theta_{d}=
###### Abstract  
In this paper, conventional and advanced exergy analyses are comprehensively introduced on an innovative transcritical CO2 energy storage based trigeneration system. Conventional exergy analysis can quantify in an independent way the component exergy destruction. However, the advanced technology is able to evaluate the interactions among components and identify the tangible promotion potential by allowing for the technical and economic limitations. In this method, the component exergy destruction is separated into avoidable and unavoidable parts, as well as the endogenous/exogenous parts. Calculation of the split parts is carried out by utilizing the thermodynamic cycle-based approach. Results coming from conventional exergy analysis indicate that the first three largest exergy destructions are given by cold storage, compressor 1, and heat exchanger 3. However, advanced analysis results demonstrate that the cold storage, compressor 1, and compressor 2 should be given the first improvement priority in sequence by depending on the avoidable exergy destruction. The turbine efficiency produces a higher impact on overall exergy destruction than compressor efficiency. The pinch temperature in cold storage causes the highest effect on exergy destruction amongst all the heat exchangers. There exists an optimum value in the compressor inlet pressure and ambient temperature.  
CO2 energy storage; trigeneration system; advanced exergy analysis; parametric evaluation +
Footnote †: journal: Applied Sciences
# Spatial Data Reconstruction via ADMM and Spatial Spline Regression +
## 6 Related Work  
The Telecom Italia Big Data Challenge dataset is widely used to study different problems [2; 3; 4; 5; 20; 21; 22; 23; 24]. However, little research work has been done to estimate the spatial distribution of cellphone activity itself, despite the great value of this problem.  
There are various tasks where the key problem is estimating a spatial field over a region based on observations of sampled points, such as house price estimation and population density estimation. [PERSON] et al. [26] model the underlying surface of land desirability using kernel-based interpolation. However, it is hard to choose the form of kernel functions and tune a large number of hyper-parameters. Spatial Spline Regression technique is applied to the problem of population density estimation in [PERSON] et al. [16]. However, in our problem, we only get the accumulated activity density in base stations, rather than real densities in each base station location. In addition, BS locations distribution is highly sparse in our case.  
Although a range of kernel-based methods [26; 31; 32] can be applied to fit a spatial field, the common drawback of these approaches is that, by using uniformly damping weights in distance-based kernels, they tend to link weakly related data points across areas in a non-convex domain. Spatial spline regression [16] on the other hand uses finite-element analysis approach to jointly solve for \(f\) and \(\beta\) from the model described by Equation (8) over any irregularly shaped domain \(\Omega\).  
As it was discussed earlier, the fine-grained data for the distribution of the volume of calls and SMS are not usually available. A common type of data is the data collected by cell phone base stations. Sometimes, cell phone providers interpolate the data collected by the base stations as is discussed in [PERSON] et al. [33]. Some researchers interpolate the data to obtain fine grained distributions as in [PERSON] et al. [29]. However, in [PERSON] et al. [29], authors do not evaluate the performance of the interpolated distribution. To the best of our knowledge, there is no extensive work done in trying to obtain optimal reconstructions of fine grained cell phone data distribution. We are the first to apply the latest spatial functional analysis techniques to cellphone activity distribution modeling, assuming the activity densities consist of a regression part based on social or demographical statistic features and a spatial field that captures the underlying smoothness property of cellphone activities. In particular, we leverage the idea of spatial spline regression to handle any irregularly shaped geographic regions. We have developed a novel Constrained Spatial Smoothing approach and corresponding training algorithm to recover spatial distribution of cellphone activities from highly sparse observations.  
Figure 7: Influence of \(\lambda\) to estimation’s Mean Relative Error when \(n_{\text{BS}}=200\) and \(n_{\text{BS}}=100\) for stress-testing. The figure is based on the November dataset. Results on the December dataset are similar.
# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
## 3 Experimental Setup and Data Collection  
This section outlines the methodologies and procedures implemented in our study to ensure robust data collection and analysis. The experimental setup is designed to systematically investigate the relationship between LTE signal performance and environmental conditions. We will detail the equipment used, the data collection methods, and the selected measurement locations for our experiments.