esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Fault Characteristics of Three-Level Inverter  
To explore inverter fault diagnosis, this study considered a three-level T-type inverter, as shown in Figure 2. The faults of such an inverter can be divided into three types: Short-circuit fault, open-circuit fault, and an erroneous trigger signal. A short-circuit fault occurs when switching elements break down, due to excessive voltages across the switch. Moreover, an open-circuit fault occurs when switches cannot be activated, due to the lack of trigger signals for the power transistor. Finally, an erroneous trigger signal occurs when switching elements receive incorrect trigger signals.  
Figure 1: The architecture of fault-tolerant control system for a three-level T-type inverter.  
This study established a simulation environment for the three-level T-type inverter by using PSIM software and monitored the failure of the inverter switches constantly. Consider, for example, an output voltage frequency of 60 Hz. The simulated waveform for a normally operating inverter should exhibit a three-phase balanced structure, and the output voltage waveform of each phase is presented in Figure 3. The voltage waveforms of the three phases have the same shape and size, and the phase difference between them is \(120^{\circ}\), which is an inherent characteristic of a three-phase balanced structure. This balanced structure would be distorted in the event of inverter switch failure. For example, if the inverter switch \(S_{a1}{}^{+}\) fails, the waveform of the \(a\)-phase output voltage (\(v_{uo}\)) is distorted (Figure 4). Similarly, if the inverter switch \(S_{b2}{}^{-}\) or \(S_{c1}{}^{+}\) fails, the waveforms of the phase voltage \(v_{bo}\) and \(v_{co}\) would substantially differ from those observed under normal inverter switch operation. According to these observations, the output voltage waveform would be distorted when a fault occurs in an inverter.  
Figure 4: The output voltage waveform of three phases of inverter operating at 60 Hz and under conditions of faulty \(S_{a1}{}^{+}\) operation: (**a**) Phase voltage \(v_{uo}\); (**b**) phase voltage \(v_{bo}\); (**c**) phase voltage \(v_{co}\).  
Figure 3: The output voltage waveforms of three phases of inverter operating at output voltage frequency of 60 Hz and under conditions of normal switch operation: (**a**) Phase voltage \(v_{uo}\); (**b**) phase voltage \(v_{bo}\); (**c**) phase voltage \(v_{co}\).  
Figure 2: The architecture of three-level T-type inverter.
## 4 Performance Evaluation
### Positioning Accuracy Test
It is obvious that the EMLD applied by ASCA-OLTA brings higher positioning accuracy than the open loop methods which directly extract the measurement from the grid with the sizes of 0.1 chip and 0.05 chip. Since the \(C/N_{0}\) is relatively high, the false detection hardly occurs and the uncertainty in the code search grid can be totally eliminated by the discriminator. As a result, the positioning accuracy of ASCA-OLTA is perfect. While, for the methods that directly extracting the code phase from the grid, the inherent uncertainty will probably distort the measurement accuracy so that the positioning accuracy is disturbed although the \(C/N_{0}\) is high. As shown in the figure, the larger the grid size is, the worse the position accuracy is. Figure 9: Constellation and \(C/N_{0}\) of the satellites. When \(C/N_{0}\) is 20 dB attenuated, the lowest \(C/N_{0}\) of these satellites is 20 dBHz and all of the satellites can still be detected. In this case, according to the signal \(C/N_{0}\), the grid sizes of ACSA-OLTA for some of the satellites are narrowed down to the optimal grid size and the code phase measurement is directly extracted from the grid parameters. The results show that the positioning accuracy of the EMLD based open loop tracking method and the method directly extracting the measurement from the grid with the size of 0.1 chip are probably the same and both of them are poorer than ASCA-OLTA. Since the \(C/N_{0}\) of the satellites ranges from 20 dBHz to 30 dBHz, both of the two methods have satellites with accuracy highly disturbed pseudo range measurements. While ASCA-OLTA can adaptively choose the optimal strategy and properly narrowing down the grid size based on the optimal ones so that the measurement accuracy can be greatly improved and the positioning accuracy is outstanding. When \(C/N_{0}\) is 30 dB attenuated, the signal power of all the satellites is really low and the positioning accuracy is degraded. During the test, all of the satellites except SV.25 can be detected all the time and the GDOP is still less than 2.0. In this case, the grid size of ACSA-OLTA is broadened to keep the correctness of the peak detection and it shows superiority over the open loop tracking method whose measurement is gained from the grid with the fixed size of 0.1 chip. The positioning error of the EMLD based method with the grid size of 0.1 chip is much larger than that of the ACSA-OLTA and it is even worse than the measurement directly gained from the grid. As a result, it is concluded that, when the \(C/N_{0}\) is low, the noise will highly distort the accuracy of the discriminator based method. In addition, by appropriately broadening the detection grid size as the ACSA-OLTA does, the accuracy will be improved in the condition of low \(C/N_{0}\).
# Land Suitability Mapping Using Geochemical and Spatial Analysis Methods
## 2 Study Area  
The area studied is located in the administrative region of Epirus in western Greece and is a part of the Ioannina basin (Figure 1A). A part of the Ioannina city is situated within the limits of the study area. The city is the largest city of Epirus and the eighth-largest city in Greece. The study area hosts several human activities such as agriculture and tourism. According to [PERSON] et al. [48], the Ioannina basin is a karst landscape that is characterized as pole. This type of surface karst feature is an elongated depression, including a flat floor. Thus, the study area has a smooth relief with gentle slopes. Its morphology comprises a lowland area with an altitude of 460 m a.s.l. and a hilly area reaching an elevation of 760 m a.s.l. (Figure 1B). It covers an area of 101 km\({}^{2}\) and is drained by an artificial network.  
The mean annual precipitation reaches about 1090 mm, and the rainiest month is December (171.3 mm mean annual rainfall). The mean annual temperature is 14.3 \({}^{\circ}\)C. The  
Figure 1: (**A**) Location map of the study area; (**B**) road network, drainage network and elevation of the study area.  
warmest month is July, with a mean monthly temperature of 25 \({}^{\circ}\)C and the coldest the January (4.7 \({}^{\circ}\)C mean monthly temperature). Regarding the wind, the dominant wind direction is the west (W). The annual frequency of westerly winds reaches up to 7% [49].  
Figure 2 illustrates the geological formation of the study area. It is comprised of the Ionian geotectonic zone and post-alpine sediments formation [50]. The alpine formations consist of Jurassic schists; (b) Upper-Jurassic Vigla limestones; (c) Senonian micro-breccia limestones; (d) Eocene limestones [51; 52]; and (e) Eocene to Oligocene lignites [53]. The post-alpine sediments include Pliocene limnic sediments and Quaternary deposits [54]. The Quaternary deposits cover a significant part of the area studied (Figure 2). Their composition includes fragments of flint, terra-rossa and Fe-Mn oxides [48; 55]. The following soil types are observed in the land uses of the study area [56]: (a) Calcari-lithic Leptosol (Ic), which are found in areas where the soil has been eroded to the extent that hard rock comes near to the surface; (b) Chronic Luvisol (Lc), which generally occur on well-drained landscapes and are fertile soils suitable for a wide range of cultivated crop plants; and (c) Calcaric Fluvisol (Jc), which are found in periodically flooded areas.
## Extraction methods using organic solvents
### Liquid sample preparation
\begin{table}
\begin{tabular}{l l l c c c c} \hline \hline
**Matrix** & **Extraction** & **Surfactants** & **Recoveries** & **LOD** & **Instrumental** & **Ref.** \\  & **technique** & & **(\%)** & & **Analysis** & \\ \hline Sea water & CPE & Oligoethylene glycol monoalkyl & 66-119 & 1.0-5.0 & LC-UV & [83] \\ waste water & & ether (Genapol X-080) & & (\(\mathrm{\SIUnitSymbolMicro}\mathrm{L}^{-1}\)) & & \\ Sea water & CPE & Polyoxyethylene 10 lauryl ether & 44-115 & 0.6–3.5 & LC-UV & [84] \\ waste water & & (POLE) Polyoxyethylene 9 lauryl & & (\(\mathrm{\SIUnitSymbolMicro}\mathrm{L}^{-1}\)) & & \\  & & ether (Pollocanol) Polyoxyethylene & & & & \\  & & 6 lauryl ether (\(\mathrm{C}_{12}\mathrm{E}_{6}\)) & & & & \\ Water & CPE & Poly(oxyethylene)-7,5-(_p-tert_- & 62-101 & 2.0-5.0 & LC-UV-EC & [85] \\  & & octylphenyl) ether (Triton X-114) & & (\(\mathrm{\SIUnitSymbolMicro}\mathrm{L}^{-1}\)) & & \\ River water & CPE & Triton X-114 & 93-103 & 2.0-2.5 & CE-UV & [95] \\ lake water & & & & (mol.\(\mathrm{L}^{-1}\)) & & \\ Sediments & MAME & Polyoxyethylene-6-lauryl ether & 81-120 & 1.2–12.7 & LC-UV & [87] \\  & & & (\(\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{g}^{-1}\)) & & \\ Soil & MAME & POLE & 70-118 & – & LC-UV & [88] \\ Sediments & MAME & (Polidocanol) Oligoethylene glycol & 79-117 & 2-20 & LC-UV & [94] \\  & & monoalkyl ether (Genapol X-080) & & (\(\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{g}^{-1}\)) & & \\ Sea water, & SPME-MD & POLE & 89-107 & 1.1-5.9 & LC-UV & [86] \\ sewage & CW-TPR & & & (\(\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{L}^{-1}\)) & & \\ water & & & & & \\ ground & & & & & & \\ water & & & & & \\ Sea water, & SPME-MD & POLE, Polidocanol, Polyoxyethylene & 80-109 & 0.3–3.5 & LC-UV & [50] \\ sewage & CW-TPR & 6 lauryl ether (\(\mathrm{C}_{12}\mathrm{E}_{6}\)) & & (\(\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{L}^{-1}\)) & & \\ water & PDMS & Hexadecyltrimethylammonium & & & \\ ground & PDMS- & bromide (HTAB) & & & \\ water & DVB PA & & & & \\  & Carboxen- & PDMS & & & \\ Wood & MAME- & POLE & 71-125 & 0.002-0.12 & GC-MS & [90] \\  & MSPME & & & (\(\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{g}^{-1}\)) & & \\  & PA & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Methods for the determination of phenolic compounds using surfactant solutions.
## 2 Materials and Methods
### Decomposition of Spectra  
The decomposition was performed by using a fitting algorithm implemented in Matlab R2012b. Initially, the number of Gaussian sub-peaks was varied from one to ten. The results showed that four sub-peaks are sufficient to reproduce the experimental band without systematic variations. Therefore, the detailed fitting was carried out with a fixed number of four sub-peaks. In this procedure, the peak position, the intensity, and the width were the fitting parameters.
# Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples
## 2 Materials and Methods
### Evaluation Metrics  
The evaluation metrics employed in this work to assess the performance of the data classification are accuracy (ACC), sensitivity, specificity, precision (PPV), F\({}_{1}\) score (F1) and balanced accuracy (BA). ACC is a metric capable of evaluating overall performance of the classifier by dividing the sum of total number true positives (TP) and true negatives (TN) by the total population of samples (Equation (5)). Sensitivity measures the ability of the classifier to correctly classify the positive samples as presented in Equation (6), where FN is the number of false negatives. On the other hand, specificity measures the ability of the classifier to correctly classify the negative samples as presented in Equation (7), where FP is the number of false positives. BA allows a measurement of the performance of the classifier by balancing the weights of sensitivity and specificity results (Equation (8)). As will be shown later in this work, this metric was necessary to improve the optimization of the hyperparameters of the supervised classifiers. Finally, PPV is the proportion of positive results correctly classified, as expressed in Equation (9), while F1 is the harmonic mean of precision and sensitivity (Equation (10)).  
\[ACC=\frac{TN+TP}{TN+TP+FP+FN} \tag{5}\]  
\[Sensitivity=\frac{TP}{TP+FN} \tag{6}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{7}\]  
\[BA=\frac{Sensitivity+Specificity}{2} \tag{8}\]  
\[PPV=\frac{TP}{TP+FP} \tag{9}\]  
\[F1=\frac{2\cdot TP}{2\cdot TP+FP+FN}=2\cdot\frac{PPV\cdot Sensitivity}{PPV+ Sensitivity} \tag{10}\]
## 1 Introduction
Based on Hopfield neural network, [PERSON] et al. [9] proposed a new approach using for harmonic detection, and the convergence and real-time performance was proved to be satisfied. A parallel neural networks-based method was adopted by [PERSON] to identify harmonics in single phase systems [10]. Subsequently, the adaptive linear neural network (Adaline) is extensively applied for harmonic parameters estimation, and the Least mean square (LMS) algorithm is used to adjust the weight of the Adaline. But if the tested signal is distorted by impulse noise, the performance of linear adaptive filter with LMS-based algorithms will significantly degrade. For impulse noise suppression, a new adaptive filtering algorithm using M-estimate was carried out by [PERSON] to solve this problem [11]. M-estimate is a piecewise estimator which can detect impulse noise and ignore the large signal error when the measured signal is contaminated by the impulse signal. For active impulse-like noise control, [PERSON] [12] proposed a new M-estimator based algorithm called fair algorithm and its effectiveness and convergence performance were validated by the simulation results. In 2000, [PERSON] et al. [13] proposed two new gradient-based adaptive algorithms named the transform domain least mean M-estimate (TLMM) and the least Mean M-estimate (LMM) algorithms which possess better convergence and anti-impulse noise performance than RLS and LMS according to the simulation result. Based on the robust statistics theory, [PERSON] et al. [14] proposed the filtered-x least mean M-estimate (FxLMM) algorithm to get a better noise control effect on active noise control (ANC). It was proved to be a very promising way for ANC with numerous simulation results. The most obvious distinction between harmonic identification for electro-hydraulic servo shaking table system and for power system is that the former requires better real-time and precision performance [15; 16]. Furthermore, harmonic distortion existing in an electro-hydraulic shaking table is dynamic and has notable nonlinear characteristics. However, the aforementioned harmonic identification methods can only be used to identify harmonics offline and their convergence and dynamic tracing performance have a significant decline when the system response is contaminated by the impulse noise. Unlike other abovementioned algorithms, the LMM algorithm, a LMS-like algorithm, uses a more robust \"M-estimator\" to replace LMS for the purpose of harmonic identification. Instead of minimizing the variance of the error signal directly, it chooses to minimize the M-estimate of the error signal and can be effectively used to detect and suppress the impulse noise, which is verified by simulation results. Furthermore, good precision identification results, both the amplitude and the phase of harmonics, are shown by using the [PERSON]'s three-part M-estimator. In this article, The LMM algorithm is utilized to adjust the weight of the Adaline for harmonic identification, and, subsequently, its numerous merits are verified through simulation and experimentation.
## 2 Operation and Control  
In this scenario (SG), operators need to react with shorter reaction times, since this is the way to guarantee QoS. Thus, the operation and control will be improved. The question is _\"How will the grid of the future be operated?\"_, therefore, the follwing are necessary:  
* Hierarchical operation. Although the medium and high voltage will undergo some changes, the low voltage will be the most affected, since it will operate automatically.
* A greater degree of penetration of renewable energy will help to achieve a sustainable and respectful behavior.
* In a transparent and accessible way with the new agents. The new agents mentioned will be renewable, advanced sensors, software and hardware improved and new paradigms of electrical grids (microgrids).
* Therefore, the lines of investigation placed in the area of Operation and Control are: \[\begin{array}{ll}\odot&\text{Advanced Electrical Equipment,}\\ \odot&\text{Communications and Automation,}\\ \odot&\text{Fault Detection, Replacement and }QoS,\\ \odot&\text{DERs,}\\ \odot&\text{Operation Support Tools, and}\\ \odot&\text{Connection to the Control System, Integration of Applications.}\end{array}\]  
Next, the main contributions in each of the mentioned research lines are presented.
## 3 Methodology
### Time Series Forecasting Models
#### 3.1.1 Long Short-Term Memory Network  
Long Short-Term Memory (LSTM) networks were introduced in 1997 [26]. They can model temporal dependencies in larger horizons without forgetting the short-term patterns.  
LSTM networks are composed of units that are called LSTM memory cells [26], these cells contain some gates that process the inputs. These gates are:  
1. Input gate, which takes care of preventing the memory cell from being modified by irrelevant perturbations.
2. Output gate, which takes care of protect other cells from perturbations stored in the current memory cell.
3. Forget gate, which allows the memory cell to reset the information if it becomes irrelevant.
## 4 Results and Discussion
### Discussion of Results by Means of Comparative Charts
#### 4.3.3 Effect of SD and Curing Time on Sulphate Resistance  
At all percentages of cement and in each curing time, the results show improvement in the sample response to sulphate attack. In all cases, _IMP_ showed an increase due to increase of SD and curing time. This behaviour was observed in all curing times. As an example, the samples for curing time of 90 days and 7% cement is evaluated. (Figure 12a-c).  
Figure 11: Effect of sawdust addition on _IMP_ values after exposure to sulphate: (**a**) 90 days curing; (**b**) 28 days curing; (**c**) 7 days curing.
## 4 The Proposed Model
### Network Design (Architecture)
#### 4.3.2 The Discriminator  
The discriminator network consists of a tuple of five, each comprising \"convolutional nonlinearity and max-pooling followed by three fully connected layers\". Recall that the main goal of the discriminator network is to determine whether each input is genuine or generated by the generative network. To achieve this goal, five tuples are used to extract the salient features of the inputs, whereas the fully connected layers are used to embed these features in the learning space before passing them on to the last layer, which consists of two neurons representing the two possible cases: genuine, or generated by the generative network.  
The number of stacks refers to the number of generator and discriminator networks used in this study, whereas the distribution of \(p_{y}\) refers to the probability distribution with which a new sample is drawn from the dataset and fed into the model. The greatest advantage of using a uniform distribution is that all the samples have the same chance of being used. Furthermore, a Gaussian distribution with a mean of zero and a small standard deviation is used to create random noise for the input. The choice of this distribution is justified by the fact that almost all noise occurring in the real field follows this distribution.  
The proposed model was implemented on a single machine running Ubuntu 22.04.1 Long-Term Support with an Intel(r) Core(tm) i9-10900X X-series processor (19.25M cache, 3.70 GHz), 64 GB, and a GeForce RTX 2060 graphics card. Python 3.10.7, with the TensorFlow and Keras libraries, was used as the programming environment, over which the model was coded.
# Smart Low-Cost Control System for Fish Farm Facilities
## 2 Identification of Critical Factors for Control Systems for Aquaculture
### Factors Affecting Fish Wellbeing and Performance
#### 2.1.5 Factors of the Control Domain  
Finally, for the control domain, two groups of factors can be identified, those linked to the monitoring and the ones for the control, which can be subdivided into control of water quality and control of other aspects. Focusing on monitoring, the monitoring can be for water, fish, or others. In terms of water quality, the most important factors are water temperature, DO, and pH [96; 97]. Fish detection [98], location [99], and counting [100] are essential for fish monitoring. Other elements to be monitored in fish farms include the feed falling [97] and the phytoplankton and zooplankton production in the hatchery [101].  
With regard to water quality control in open circuits on the land, filters are, in most cases, the only viable option. Generally, in offshore facilities, no control of water quality can be conducted. In RAS, several aspects of water quality can be modified and controlled,  
Figure 4: Factors of the Species Biotic Domain. The species in the aquaculture facilities defined these factors, which can be divided into the growing stage, physiology of the species, and social behavior. Some of the fish’s behavior depends on other aspects. For example, cannibalism depends on the growing stage and the growing uniformity.  
and the actuators are carbon dioxide removal, biofiltation, heating or cooling systems, salinity correction, skimmer use, and disinfection systems [102]. Concerning the other aspects, light [103] is the most important one, followed by current and feeding. For the current, the option to modify the flow in different areas of the facilities and the possibility to open/close the water flow isolating the full facility of some tanks, are the most important. Finally, the most common factors for feeding control are the adjustable feeding velocity and schedule and the option of including auto-demand feeders [104]. Figure 6 summarizes all the control factors identified in this paper for the Smart Low-Cost Control Systems for Aquaculture Facilities.  
Figure 5: Factors of the External Biotic Domain. These are the factors that depend on the other living beings in the fish farm facilities and their surroundings. It includes the effects of animals, plants, and other living beings on the fish, the facilities, and control mechanisms.  
Figure 6: Factors of the Control Domain. There are two main groups of factors in the Control Domain, the monitoring and the control factors, which are in turn divided into water quality, light, current, and feeding control. In sea cages, only monitoring and control of feeding can be applied. In RAS facilities, most of the parameters can be monitored and controlled.