esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Total-Forms of Cyanotoxins Microcystins in Biological Matrices: A Methodological Review
## 2 Quantification of Total MC by the MMPB Method
### Limitations and Difficulties Faced during the MMPB Method  
#### 2.5.1 Sample Turnaround Time  
One of the first obstacles to the MMPB method concerns the processing time of a sample. Steps such as an enzymatic pre-treatment with trypsin can range from 2 to 18 h. Moreover, the work by [PERSON] et al. [29] showed that the most efficient trypsin pre-treatment lasted 18 h. This step is followed by the oxidation step, which can last several hours (1 to 3 h). Then, depending on the strategy chosen, other time-consuming steps are to be undertaken: filtration (sometimes with important volumes), LLE or SPE purification, and the concentration of the samples. The realization of the oxidation proves to be both time-consuming and fastidious due to the number of steps to realize it, which increase the sample turnaround time and can limit the simultaneous processing of a large number of samples.
## 2 Model Description  
Induction motor stalling is the leading cause of voltage collapse in a radiation system [24]. Therefore, a single-load system was chosen in this paper, as Figure 1a shows. In Figure 1a, the induction motor is connected to a low-voltage bus 4, while the practical criterion is used for the voltage curve of the high-voltage bus 1.  
The equivalent circuit of a single-load system in which the equivalent capacitances of the branches and the transformers are ignored is shown in Figure 1b. In Figure 1b, \(R_{l}\) is the branch resistance, \(X_{l}\) is the branch reactance, \(R_{s}\) is the stator resistance, \(X_{s}\) is the stator reactance, \(X_{m}\) is the excitation reactance of the motor, \(X_{r}\) is the rotor reactance, \(R_{r}\) is the rotor resistance, \(s\) is the slip.  
In this paper, the first-order model is adopted for the induction motor, and the rotor motion equation of the induction motor is shown as Equation (1)  
\[2H\frac{ds}{dt}=T_{l}(s)-T_{e}(U_{1},s) \tag{1}\]  
Here, \(H\) is the rotor inertia constant, \(T_{l}\) is the load torque, \(T_{e}\) is the electromagnetic torque, \(U_{1}\) is the bus 1 voltage.  
The electromagnetic torque of the induction motor is Equation (2).  
\[T_{e}(U_{1},s)=\frac{Z_{eq}^{2}U_{1}^{2}\frac{R_{e}}{s}}{\left(R_{eq}+\frac{R_{e} }{s}\right)^{2}+\left(X_{eq}+X_{r}\right)^{2}} \tag{2}\]  
Here,  
\[R_{eq}+jX_{eq}=\frac{jX_{m}(R_{s}+R_{l}+jX_{s}+jX_{l})}{R_{s}+R_{l}+j(X_{s}+X_{ l}+X_{m})},\ Z_{eq}=\frac{X_{m}}{\sqrt{\left(R_{s}+R_{l}\right)^{2}+\left(X_{s}+X_{ m}+X_{l}\right)^{2}}}\]  
There are mainly three kinds of load: the constant torque load, the constant power load, and the fan-pump load. The load torques are as follows.  
Constant torque load shown as Equation (3).  
\[T_{l,ConT}(s)=T_{0} \tag{3}\]  
Constant power load shown as Equation (4).  
\[T_{l,ConP}(s)=T_{0}+P_{0}/(1-s) \tag{4}\]  
Fan-pump load shown as Equation (5).  
\[T_{l,Fan}(s)=T_{0}+T_{1}(1-s)^{2} \tag{5}\]  
Here, \(T_{0}\), \(T_{1}\), and \(P_{0}\) are constant parameters.
# Benchmarking of Load Forecasting Methods Using Residential Smart Meter Data
## 2 Materials and Methods
### Forecasting Approaches
#### 2.2.2 Random Forest Regressor  
Random Forest is characterized as an ensemble machine learning algorithm, as each tested decision tree is fit on a slightly different training dataset and uses different features, and in turn, results in a slightly different performance. An example of an individual decision tree applied in a load forecasting application is available in Figure 2.  
The first step of the CART (Classification and Regression Tree) algorithm consists in splitting at best the root into two different child nodes if it implies a cost function (in general, the variance of child nodes) reduction [12]. Then, the child nodes are also divided according to the same procedure. The expansion of the tree is stopped by a termination criterion. It is common to stop the tree when a maximum number of levels has been reached, or when a node contains less than a defined number of observations [12,18].  
The main principle is called bagging, where a sample of size \(n\) from the training set is selected randomly and fitted to a regression tree. This sample is called a bootstrap, and is chosen with replacement, i.e., the same observations may appear many times. A bootstrap sample is obtained by selecting randomly \(n\) observations with replacement; each observation has the probability of \(1/n\) to be selected. The independent identically distributed random variables represent this random selection. The bagging algorithm selects several bootstrap samples, applies the CART algorithm to them to obtain a collection of predicting trees, and then aggregates the output of all these predictors.  
Figure 2: Example of a Random Forest Regressor model (just an example, not corresponding to any decision tree applied during the research).  
In addition to bagging, to split a node, only a predefined number of the features are selected, and the RF algorithm tries to find the best cutting among only the selected features. The intention is to find a good combination to cut by minimizing a cost function, and the procedure continues until all the trees are fully developed. RF is based on predictions derived from different trees, which are averaged resulting in better performance than any single tree in the model.  
The main advantage of bootstrap aggregation is immunity to noise, since it generates uncorrelated trees through different training sets. A weak predictor (regression tree) may be sensitive to noise, while the average of many uncorrelated trees is not. The selection of a random subset of features at each split has the same purpose of avoiding overfit.  
RF models in the scope of this research were created and tested using Ensemble package [25] available in the Scikit library of Python. By default, 100 trees were considered in the forest, the mean squared error was chosen as the function to measure the quality of a split, and nodes were expanded until all leaves were pure or contained less than 2 samples.
## 4 Methods
### Fully-Self-Adaptive Harmony Search (FSHS) Algorithm
#### 4.1.3 Bandwidth (BW)  
The studies show that determining the appropriate value for the bandwidth, BW, greatly affects the performance of the HS algorithm [19]. To be more accurate, a large BW in the initial iterations can significantly help to search the problem space and find better areas. In addition, small BW in the final iterations contributes to a local search in the neighborhood of the solutions found by the searching algorithm. So far, many methods have been proposed to adjust this parameter; however, the proper adjustment of this parameter to solve various problems is much more difficult than other parameters. In this research, a new mechanism for adjusting the BW parameter is presented, based on the proposed method in the SCA [26] algorithm to overcome such challenges. Instead of using a constant value for the BW, a variable domain is used in the proposed approach. The idea behind this method is to increase the probability of using better values by making alternating changes in the base BW range. For this purpose, the difference between the minimum and maximum values of the memory harmony decision variables is used as the value of the base BW. Then, the BW value is changed periodically, using the trigonometric function cosine and Equations (15)-(18). Furthermore, each decision variable has a specific BW in this method:  
\[\overset{\rightarrow}{\beta}=\left(-\left(\frac{\text{NI}}{10}\pi\right), \left(\frac{\text{NI}}{10}\pi\right)\right) \tag{15}\]  
\[\text{D}_{\text{j}}=\text{Max}(\text{X}_{\text{j}})-\text{Min}(\text{X}_{ \text{j}}) \tag{16}\]  
\[\text{BW}_{\text{j}}^{\text{g}}=\text{D}_{\text{j}}+(\text{D}_{\text{j}}\times \cos(\beta_{\text{g}})) \tag{17}\]  
\[\text{X}_{\text{j}}^{\text{new}}=\text{X}_{\text{j}}^{\text{new}}+\text{U}(- 1,+1)\times\text{BW}_{\text{j}}^{\text{g}} \tag{18}\]  
where \(\beta\) is a vector with length NI between \(-(\frac{\text{NI}}{10}\pi)\) and \(+(\frac{\text{NI}}{10}\pi)\) with intervals \((\frac{2\times\text{NI}}{10\times\text{NI}}\pi)\); \(\text{D}_{\text{j}}\) is the difference between the maximum and minimum values of \(\text{X}_{\text{j}}\) in the harmony memory; and \(\text{BW}_{\text{j}}^{\text{g}}\) is the bandwidth of the decision variable \(\text{X}_{\text{j}}\) in the generation of g. In addition, \(\text{X}_{\text{j}}^{\text{new}}\) is the jth decision variable of the new harmony \(\text{X}^{\text{new}}\) and \(\text{U}(-1,+1)\) is a random value with uniform distribution between \(-1\) and \(+1\).
## 2 Materials and Methods  
Studies regarding the surgical methods for graft maturation enhancement in the setting of ACL reconstruction were included in this study. The use of orthobiologicals, due to the numerous reviews regarding their use, was considered to be beyond the scope of this review. Studies regarding ACL reconstruction with artificial grafts were excluded from the study.  
A primary search was performed in the PubMed database. To screen for surgical methods of ACL maturation enhancement, the searched item was \"(anterior cruciate ligament reconstruction) AND ((enhancement) OR (augmentation))\". The search was performed without limitation regarding the year of publication, language or study design. After the screening of abstracts, followed by consultation between the authors, six methods of surgical enhancement of ACL maturation enhancement were identified, namely:  
* Preservation of remnants of a native ACL;
* Preservation of hamstring tibial attachment during an ACL reconstruction with a hamstring autograft;
* Enveloping of a graft with a periosteum;
* Preservation of muscle tissue on a harvested graft;
* Augmentation of a graft with an amniotic tissue;
* The usage of an intra-articular synovial graft.  
The secondary search aimed to collect the data regarding the scientific basis of the abovementioned methods and to summarize their outcomes. The secondary search was performed in the PubMed database without restriction regarding the year of publication or study design; only the studies in English were included. The search items were as follows:  
* (anterior cruciate ligament reconstruction) AND (remnant preservation);
* (anterior cruciate ligament reconstruction) AND (attached hamstring graft);
* (anterior cruciate ligament reconstruction) AND (periosteum);
* (anterior cruciate ligament reconstruction) AND (muscle remnant);
* (anterior cruciate ligament reconstruction) AND ((amnion) OR (amniotic tissue));
* (anterior cruciate ligament reconstruction) AND (synovialization).  
The references of included studies were screened to search for additional data sources. The data extracted from each article included the authors of the manuscript, year of publication, study design, type of the intervention, length of the follow-up and the key findings.
## 4 Research Directions for System Designs and Optimization Concepts
### Optimization in PHY Security: Current Status and Main Issues
#### 4.2.2 Secure Beamforming Techniques  
The basic idea of beamforming is to transmit signals effectively in the direction of Bob and Eve, which aims to enhance the signal quality of the main transmission channel and degrade the quality of wiretap channels. Beamforming is a typical signal processing method that has been shown to be a promising means to achieve PLS [95; 111; 112; 113]. The optimization concern of secure beamforming-based designs is to reduce the interference signal at the intended receiver so that the desired recipient can receive the desired QoS via the transmitted signal. For secure beamforming, the optimization problems are neither concave nor convex due to the characteristics of the logarithmic functions in the performance metrics. Due to this, optimization problems are solved using basic numerical calculations in many situations, for example, as in [63; 90; 114; 115; 116]. However, the complexity of the numerical methods is computationally prohibitive, which affects the design of secure beamforming within practical applications. In order to reduce the complexity, some low-computational algorithms have been explored in the literature, which facilitate practical application scenarios, such as in [117; 118; 119; 120; 121; 122]. Therefore, due to their simple design schemes, the developedalgorithms are attractive alternatives that attempt to maximize secrecy performance and, at the same time, minimize interference to the secure transmission.
## 4 Effects of Weather Conditions in the Gulf of Guinea on the IoT Quality of Service  
This section analyzes the IoT OTH maritime surveillance quality of service in meteorological conditions specific to the Gulf of Guinea. These weather conditions are:  
* Clear weather, with or without light wind, both on HFSWR sites and at open sea, so called \"calm sea\",
* Clear weather at HFSWR sites but with strong wind at open sea,
* Storms (heavy rain and wind) at HFSWR sites, which partially cover open sea area.  
Please note: Since the IoT OTH maritime surveillance service covers an area of over 100,000 square kilometers, and with HFSWR sites nearly 100 km apart (over the airline), it is almost impossible to have identical meteorological conditions throughout that space. In practice, it often happens that a combination of the aforementioned meteorological conditions occurs.  
In the zone of interest there is a clear distinction between dry and rainy periods in the year. Dry season, which has its peak from December to February, is considered as a good weather period from the satellite link point of view, since rainfall is minimal and storms are quite rare. On the other hand, the period from May to July (peak of rain season) is considered as a bad weather period, since during that period rain occurs daily and violent storms are quite frequent. Figure 7 gives an overview of rain precipitation during a year in the zone of interest based on the data from the World Meteorological Organization [28]. Figure 7 shows that the period May-July is the rainiest while the period December-February is the driest.  
In the observed case, a satellite connection that operates in X band has been chosen as a backbone of the overall system, since it shows better resilience to weather conditions in general [29] than Ku or Ka bands [30]. However, since the impact of climate in the zone on the satellite link communication can be significant, this paper will examine its influence on the overall IoT OTH maritime surveillance service as well.  
In the further text, three different, most common weather scenarios (atmospheric conditions) in the Gulf of Guinea that influence performances of the developed IoT OTH maritime surveillance sensor network are analyzed in detail.  
Figure 7: Average precipitation per months.
# Pentaband Dual-Polarized Antenna for Multiservice Wireless Applications
## 4 Conclusions  
This study introduced a novel MIMO antenna design that exhibits dual-band CP and operates in five distinct frequency bands, making it ideal for a variety of applications, including navigation, WLAN/Bluetooth, and fixed/mobile satellites. The approach of achieving multiband operation by manipulating the current distribution on the patch antenna using different configurations of slots and apertures is also discussed. A ground-plane slit is also included to enhance isolation between antenna elements at the center resonance band. The measurement process involves calibration of the vector network analyzer using a standard calibration kit and performing a frequency sweep from 2 GHz to 9 GHz. The experimental results show that the antenna has impedance bandwidths of 9.5%, 15%, 15.6%, 4%, and 6% in the pentaband, confirming the simulation results. The proposed antenna also demonstrates a maximum gain of 3.9 dBi and good diversity performance, with an ECC value below 0.004 and a DG value of 9.8 dB, which ensures that the signal-to-noise ratio is not compromised in a diversity scenario. This design has significant improvements over previous designs that have only demonstrated LP or CP in single- or dual-band MIMO antennas. These results suggest that the proposed MIMO antenna is a promising candidate for use in various wireless communication systems.  
Conceptualization, A.U.; methodology, A.U.; software, A.U.; validation, A.U; investigation, V.A.; resources, V.A.; data curation, V.A.; writing--original draft preparation, A.U.; writing--review and editing, A.U.; visualization, A.U.; supervision, V.A.; project administration, V.A.; funding acquisition, A.U. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement**: No data availability statement.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 4 Clinical Applications of AI to CHF Management
### Telemedicine and Mobile Health  
One of the possible applications of AI [30] is in telemedicine and smart-Home technologies. Thanks to remote patient monitoring and management platforms, it is possible to tailor personalised treatment planning, correct drug dosing, and identify patients at risk for adverse events and re-admissions. Among the others, Veta Health [31] is an AI-powered telemedicine platform allowing remote monitoring and management of heart failure patients. As another example, Welby remote monitoring platform uses AI and machine learning algorithms to remotely monitor and manage heart failure patients monitoring their blood pressure levels and achieving weight loss by connecting patients with a clinical care team that can work on nutritional counselling, tracking real-time view of patient blood sugar [[https://www.welby.care/about](https://www.welby.care/about)]([https://www.welby.care/about](https://www.welby.care/about)) accessed on 5 April 2023. According to the World Health Organization (WHO)'s Global Observatory for eHealth, Mobile Health (mHealth) consists of \"medical and public health practice supported by mobile devices, such as [cell] phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices\" [[http://www.who.int/tb/areas-of-work/digital-health/faq/en/webcite](http://www.who.int/tb/areas-of-work/digital-health/faq/en/webcite)]([http://www.who.int/tb/areas-of-work/digital-health/faq/en/webcite](http://www.who.int/tb/areas-of-work/digital-health/faq/en/webcite)) accessed on 5 April 2023. These will offer tremendous potential for monitoring health through phone calls, text messages, data recording, highly portable peripheral devices, and activity monitoring, which may find utility for novel models of health care delivery that are cost-effective, accessible, and patient-centric [32]. Their potential impact is relevant and will be a key tool to complement telemedicine, but will also be a key technology when used together with smart environments and the Internet of Things. MHealth has multiple potential applications. Among the most interesting in CHF is the possibility to track their health by checking their heart rate, blood glucose levels, medication dosages, and sleep cycles. It also allows for remote consultations and maintaining electronic health records [33].
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 5 Results
### \"Evade from Above\" Trajectory  
Figure 34 shows the 3D graph of the \"evade from above\" trajectory.  
Figures 33 and 34 show that the Computed Torque PD controller successfully followed the desired trajectory with minor vibration in the Z coordinate axis in the \"Evade from Above\" trajectory. All controllers executed the trajectories acceptably, considering the operation time and spatial movements of the robot's joints.  
In general, the controllers performed the desired trajectory adequately; deviations were observed in the \(X\)-axis with the PD-PI fuzzy controller and in the \(Z\)-axis for the MPC controller. The PID controller showed the worst response, while the others displayed similar behavior, with the Computed Torque PD, Computed Torque PID, and PID with Gain Scheduling controllers standing out as the most accurate. Visually, the PID controller with Gain Scheduling was the second best, with the Computed Torque PD controller showing the best performance.
# Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold
## 4 Experimental and Simulation Results
### Calculation of Detection Points in the Frequency Domain  
The calculation of detection points in the frequency domain is similar to the proposed method in time domain, but, as shown in Figure 6, the input signal is passed through a 2 kHz low-pass filter. Then Fourier coefficient is calculated with two-thousand samples. As explained, the width of averaging window is selected 100 (\(w\) = 100) so the length of the vector \(\overset{\rightarrow}{A}\) will be the average with 20 samples. As shown in Figure 15 because of present target in low frequency, first term coefficients is greater than other. After the calculation of vector, \(\overset{\rightarrow}{A}\), as is shown in Figure 6 the detection points are calculated in frequency domain. Table 2 shows an example of the detection points for 19 test input signals.  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Type** & \(\boldsymbol{a_{ttp}}\) \\ \hline
1 & Ship Engine 1 & 0.83 \\
2 & Ship Propeller 1 & 0.62 \\
3 & Ship 1 & 0.39 \\
4 & Ship Engine 2 & 0.61 \\
5 & Ship 2 & 0.59 \\
6 & Ship Engine 3 & 0.6 \\
7 & Ship Engine 4 & 0.5 \\
8 & Ship Engine 5 & 0.68 \\
9 & Ship Engine 6 & 0.82 \\
10 & Ship Propeller 2 & 0.21 \\
11 & Ship Engine 7 & 0.46 \\
12 & Ship Engine 8 & 0.53 \\
13 & Ship Engine 9 & 0.7 \\
14 & Ship Engine 10 & 0.58 \\
15 & Ambient Noise 1 & 0.43 \\
16 & Ambient Noise 2 & 0.39 \\
17 & Ambient Noise 3 & 0.37 \\
18 & Ambient Noise 4 & 0.38 \\
19 & Ambient Noise 5 & 0.26 \\ \hline \end{tabular}
\end{table}
Table 1: Detection point of 19 test signals in a specific sequence in the time domain.  
Figure 14: Vector \(\overset{\rightarrow}{a}_{tp}\) for two target signals (13 and 14) and two ambient noise signals (15 and 16) in the time domain.  
Figure 16 shows the vector \(\overset{\rightarrow}{A_{ftp}}\) for four test input signals (No. 13 to 16).  
Figure 16 shows the vector \(\overset{\rightarrow}{A_{ftp}}\) for two target signals (13 and 14) and two ambient noise (15 and 16).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Type** & \(A_{ftp}\) \\ \hline
1 & Ship Engine 1 & 0.46 \\
2 & Ship Propeller 1 & 0.63 \\
3 & Ship 1 & 0.65 \\
4 & Ship Engine 2 & 0.47 \\
5 & Ship 2 & 0.63 \\
6 & Ship Engine 3 & 0.83 \\
7 & Ship Engine 4 & 0.43 \\
8 & Ship Engine 5 & 0.48 \\
9 & Ship Engine 6 & 0.49 \\
10 & Ship Propeller 2 & 0.57 \\
11 & Ship Engine 7 & 0.53 \\
12 & Ship Engine 8 & 0.61 \\
13 & Ship Engine 9 & 0.43 \\
14 & Ship Engine 10 & 0.38 \\
15 & Ambient Noise 1 & 0.37 \\
16 & Ambient Noise 2 & 0.36 \\
17 & Ambient Noise 3 & 0.21 \\
18 & Ambient Noise 4 & 0.32 \\
19 & Ambient Noise 5 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detection points for 19 test input signals in a specific sequence in the frequency domain.  
Figure 16: Vector \(\overset{\rightarrow}{A_{ftp}}\) for two target signals (13 and 14) and two ambient noise (15 and 16).  
Figure 15: Mean vector \(\overset{\rightarrow}{A}\).