esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Experimentation and Analysis
### Algorithm Performance Validation
#### 7.1.4 Algorithm Stability Comparison  
The standard deviation of the experimental results shows that the GLSOAG achieved the smallest standard deviation values on the rest of the tested functions with an exponential advantage except for the sub-optimal standard deviation on function F8. The GLSOAG differed from the maximum standard deviation on F1 to F12 by \(1.7564\times 10^{4}\), \(5.3748\times 10^{11}\), \(1.1463\times 10^{6}\), \(6.4512\), \(1.3135\times 10^{8}\), \(1.5900\times 10^{4}\), \(1.3463\times 10\), \(3.1465\times 10^{2}\), \(5.5820\times 10\), \(1.1818\), \(1.0938\times 10^{2}\), and \(3.5318\times 10^{8}\),respectively, with an average dominance of \(5.3797\times 10^{11}\) across the 12 test functions. This reflects that the GLSOAG has strong stability.
## 2 Materials and Methods
### Design of the Detachable Two-Way Propulsion System
Unlike a conventional planetary gear train, it does not include a planet carrier; instead, the planet gears are fixed inside the reverse drive gear, preventing them from orbiting around the sun gear. When the rotary component engages with the reverse drive gear, its ring gear is driven by the pushrims, causing it to rotate in the same direction as the pushrims. The planet gears meshed with the ring gear also rotate in the same direction. However, since the planet gears are fixed, they can only spin on their own axes without orbiting around the sun gear. As a result, the sun gear, which is meshed with the planet gears, rotates in the opposite direction to the planet gears. Figure 6: Engagement of the gear slider and gears: (a) pushing method; (b) pulling method. Figure 7: Structure of the forward and reverse drive gears. The sun gear is connected to the main shaft and transmits the rotational force to the wheels. Consequently, engaging the rotary component with the reverse drive gear activates the new propulsion method (pulling method), where pulling the pushrims backward results in the wheels moving forward. The reverse drive gear was designed with a gear ratio of 1:2, resulting in greater torque output at the wheel during the pulling motion. Although this configuration reduces the distance covered per stroke, it helps compensate for the increased physical demand of pulling by requiring less force from the user. When the two-way propulsion system is attached to the wheels, its main shaft is positioned at the center of each wheel, thereby serving as the wheels' central axis. As a result, when the main shaft rotates due to the gears in the propulsion system, the wheels rotate in the same direction. The mode-switching lever is attached to the front of the propulsion unit and is integrated with a plate (Figure 8). When the lever is lowered, it pivots around its axis, causing a protruding section to press against the plate and apply pressure. This pressure pushes the rotary component inward, engaging it with the reverse drive gear and switching to the pulling condition. Conversely, when the lever is raised, the pressure on the plate is released, allowing the rotary component to move outward and engage with the forward drive gear, switching to the pushing condition.
# Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring
## 7 Test and Results
### Load Classification Results

Table 5: Training, Validation and Test sets performance for additional tests. Figure 7: Training, Validation and Test sets error for different number of input features. The horizontal axis shows the features added to the MLP input for each training session. The superscript identifies the observation window after the event. In [24] random forest algorithms are applied to active and reactive powers for the devices in BLUED dataset. The performance for the load classification step is reported in Table 7.  
In [9] the authors apply a deep neural network, called concatenate convolutional neural network, to high-dimensional spectrograms of high-frequency current data. Data are a subset of BLUED database (537 samples) sampled to 2 kHz and the devices are grouped in six classes: fridge, two groups of lights, two groups of high-power devices and one group with computer and monitor. Table 8 reports the classification performance. Even if all these results are not directly comparable since the datasets, although selected from BLUED data, are different in composition and size, they confirm the goodness of the proposed approach for the appliance classification.
# Estimation of Resonance Frequency for Systems with Contact Using Linear Dynamics Methods
## 6 Results  
### Results--Cantilever Beam  
For all of the pointed cases, the response of the specimen was registered. Based on that, the first resonance frequency was evaluated. An example plot of the resonance frequency for the TW090_1 specimen is shown in Figure 10. The full comparison and differences between all of the cases are collected in Table 4. The biggest difference in all of the cases was registered as 3.94%. The average difference between the estimate and the test result is equal to 1.18%.  
Figure 10: Example response measured with vibrometer at the end of the TW090_1 specimen (contact 40 mm + mass 7.6 g).  
Figure 9: Satellite antenna panel mounted on the vibration machine with laser vibrometer measurement point.  
The difference between the resonance frequency and always closed modal analysis was that the higher the contact, the distance was longer. The biggest difference between the modal analysis and the tested specimens was observed for the 80 mm contact distance, and it was equal to 72%. For the neglected contact modal analysis, for the same contact distance difference between the simulation and experimental test, it was equal to 58%. These values vary slightly for each specimen or included mass, in the range of \(\pm\)1%.
# The Health-Promoting Potential of Fruit Pomace and Its Application in the Confectionery Industry
## 3 Fruit Pomace: A Nutrient-Rich By-Product with Potential in Food Technology
### Characteristics of Selected Fruit Pomace--Ingredients and Properties
\begin{table}
\begin{tabular}{c c c c} \hline
**Sources of Pomace** & **Biactive Compounds** & **Health Benefits** & **Reference** \\ \hline \multirow{6}{*}{Apple} & Phenolic acids (52.3-2453.76 mg/100 gj: chlorogenic acid, hydroxycinamic acids, ferulic acid, p-coumaric acid, spinagic acid, p-coumaridyl-quinic acid, caffeine acid, 2-hydroxybenzoic acid, gallic acid, gallic acid-glucose, gentistic acid, vanmiliic acid, cinnamic acid, protocatechic acid. & digestive health; blood sugar regulation; prebiotic, antimicrobial, anti-inflammatory, anticancer, antioxidant, and \\  & Flavonoids (2.153-120.96 mg/gj: quercetin, catechelins, epicatechin, phploridzin, & anti-inflammatory, anticancer, \\  & isommathema, kenormel, myriction, rutin, & antioxidant, and \\  & Anthocyanians (5-0.13 mg/100 gj: cyanidin-3-O-galactoside; & cardio-protective effects \\  & Trierprees: oleanolic acid, msalinic acid, unsolic acid; & \\  & Dietary Fiber (36.5-57-9%). & \\ \hline \multirow{6}{*}{Grape} & Stillbness: resveratrol (0.052-0.160 mg/gj: Phenolic Acids: gallic acid (0.116-0.729 mg/gj, \\  & protocatechic acid, p-hydrooxyphenylacetic acid, vanmiliic acid, homovanillic acid, \\  & homoprotectable acid, protocatechic acid, spintastic acid, syring acid, 4-O-methylgallic acid, \\  & abert, 3-O-methylgallic acid, dihydro-3-Oumaric acid, hydrofenickel acid, \\  & siderentralic acid; Flavonoids: epigolaolaolaola, catechins, epicatechin, propyonidin B2; \\  & Flavonoid (9.87-50.32 mg CE/gj); Flavonoid Glycos: hyperoside, isomiquerciArtin, rutin, \\  & quarcrin: Flavonoid Agyvers: quercetin (0.047-0.557 mg/gj), lutetolin: Flavonoks: \\  & mycrinet-3-O-Mannoside: Antovoyamins (375-4527): 3-O-glucosides of deblphinidzin, \\  & cyanidin, petenturbarion, pelargonitis, penditin, malvidzin. & \\ \hline \multirow{6}{*}{Mandarin orange} & Phenolic Acids: gallic acid (0.232-0.307 mg/gj), p-coumaric acid, chlorogenic acid, ferulic & \\  & railroad: flavonoid: besertigent, quercetin TPC (41.26-46.17 mg GAE/gj: TPC (28.17-33.21 mg \\  & QE/gj: TCC (1.91-17 mg \(\beta\)-carotene/gj). & \\ \hline \multirow{6}{*}{Orange} & TPC (2.41 g GAE/100 gj: \\  & TPC (1.36 g RE/100 gj: \\  & Pronathocyanians (0.003 mg CyE/100 gj. \\ \hline \multirow{6}{*}{Lemon} & TPC (2.46 g GAE/100 gj: \\  & TPC (1.53 g RE/100 gj: \\  & Pronathocyaninds (0.004 mg CyE/100 gj. \\ \hline \end{tabular}
\end{table} 
Table 2: The content of bioactive compounds and their health properties in selected fruit ponace.
# Plant-Growth-Promoting Bacteria Mitigating Soil Salinity Stress in Plants
## 5 Role of PGPB in Alleviating Salinity Stress to Plants
### Contribution to Osmolyte Accumulation in Plants  
Plants accumulate osmolytes to maintain osmotic balance in the cells. The most widely used osmolytes by the plants are oligo- and disaccharides, alcohols, proline, and glycine [132]. In addition, some of these compounds are used by the PGP endophytes [133]. Some sugar alcohols are, in fact, signaling molecules, or they participate in osmoregulation under salinity stress. Beneficial bacteria can regulate water potential and stomatal opening in plants by the accumulation of diverse organic compounds, such as sugars, amino acids, alcohols, and glycerol, via hydraulic conductivity [14; 134]. There is evidence that betaine and proline, such as mannitol and sorbitol, function as osmoregulators of plants in such deleterious environments [132]. In maize rhizosphere, _B. megaterium_, a capsule-forming microbe, support plants by improving root hydraulic conductivity under salinity [135]. Similarly, the _B. amyloliquefaciens_ strain, in the presence of 200 mM of NaCl, stimulated salt tolerance in rice plants by the upregulation of expression of some genes in the soil environment, while in hydroponics, other genes were downregulated [84].
## 4 Experiments and Discussion
### Methods in Comparison Study
#### 4.3.3 Kalman Filter  
The Kalman filter [1] is an iterative algorithm. Compared to RNN, the Kalman Filter only inputs observation (or prediction if in offline mode) from the previous road segment without storing more data.
# The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges
###### Abstract  
Present-day technologies used in SHM (Structural Health Monitoring) systems in many implementations are based on wireless sensor networks (WSN). In the context of the continuous development of these systems, the costs of the elements that form the monitoring system are decreasing. In this situation, the challenge is to select the optimal number of sensors and the network architecture, depending on the wireless system's other parameters and requirements. It is a challenging task for WSN to provide scalability to cover a large area, fault tolerance, transmission reliability, and energy efficiency when no events are detected. In this article, fundamental issues concerning wireless communication in structural health monitoring systems (SHM) in the context of non-destructive testing sensors (NDT) were presented. Wireless technology developments in several crucial areas were also presented, and these include engineering facilities such as aviation and wind turbine systems as well as bridges and associated engineering facilities.  
wireless sensor network; aircraft systems; wind turbines; bridges; SHM systems mobile SCADA; WAN/cellular technology +
Footnote †: journal: _inventions_  
_Review_  
The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges **222**, \(7\), 18_. [[https://doi.org/10.3390/inventions/2010018](https://doi.org/10.3390/inventions/2010018)]([https://doi.org/10.3390/inventions/2010018](https://doi.org/10.3390/inventions/2010018))
# Coupling Efficiency of a Partially Coherent Radially Polarized Vortex Beam into a Single-Mode Fiber
## 3 Results and Discussion
Next, we simulated how the density plot of the coupling efficiency varied with NA and initial spatial coherence under different topological charges in Figure 4. In Figure 4, the numbers in brackets are the peak value of the coupling efficiency and corresponding value of the NA and spatial coherence width. One can clearly see the distribution of the coupling efficiency in the whole region under different topological charges. One can also find out clearly the peak of the coupling efficiency under different topological charges and the corresponding value of the coherence length and NA. Therefore, for the suitable value of \(\delta_{0}\) and \(m\), we could obtain the maximum coupling efficiency. Figure 3: The coupling efficiency of the symmetric beam with \(\omega_{0}=10\) mm and \(\lambda=1550\) nm varied with NA for different values of \(\delta_{0}\) under different topological charges. Figure 5 shows the coupling efficiency of the beam versus NA for different wavelengths \(\lambda\) under different topological charges. From Figure 5, we can find that all the coupling efficiencies showed the similar change rule for smaller topological charges (see Figure 5a-c); the coupling efficiency increased at first and then decreased with increasing NA, and there was a peak value of the coupling efficiency. However, the coupling efficiency increased with decreasing wavelength for smaller NA (for example, NA < 0.08), and for larger NA (for example, NA > 0.16), the coupling efficiency increased with the increasing wavelength. However, for larger topological charges (for example \(m>3\), we did not plot it in here), we found that the coupling efficiency shows the same change rule as Figure 5d; it increases with the increasing of NA and wavelength. Moreover, from Figure 5b, we could also find that the coupling efficiency showed some different change rule due to the different properties of PCRP vortex beams with \(m=1\). Figure 4: The density plot distribution of how the coupling efficiency of the symmetric beam with \(\omega_{0}=10\) mm and \(\lambda=1550\) nm varies with numerical aperture and initial spatial coherence under different topological charges. Figure 5: The coupling efficiency of the beam with \(\delta_{0}=5\) mm, \(\omega_{0}=10\) mm versus numerical aperture for different wavelengths \(\lambda\) under different topological charges.
# Smart Energy Meters for Smart Grids, an Internet of Things Perspective
## 3 Computational Platforms
Theft detection uses smart meter data to estimate the total non-technical losses, whereas anomaly detection estimates unwarranted fluctuations in the power grid or generation. Consumer profiling gauges the type of consumer, e.g., commercial or small industrial, from the smart meter data. Activity detection uses energy data to predict the occupants' daily life activities, e.g., sleeping or \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Hardware** & **Communication/Data** & **Location** & **Comments** \\ \hline
[177] & 8051 & Cellular (GSM) & Home &
\begin{tabular}{c} Enables energy meter to transmit \\ data via GSM using messaging \\ \end{tabular} \\ \hline
[218] & RISC (MSP430) & & Home & Privacy preserving \\ \hline
[219; 220] & ARM (Cortex-M) & & Home–neighbor hood & Secure communications \\ \hline
[221] & SBC (Arduino) & Cellular (GSM) & Home & \\ \hline
[222] & MIPS (PIC32) & & Home &
\begin{tabular}{c} DSP processor interface for \\ advanced signal processing \\ \end{tabular} \\ \hline
[223] & STM32 & \
\begin{tabular}{c} Power line \\ communication \\ \end{tabular} & Network &
\begin{tabular}{c} A network design for the \\ smart grid \\ \end{tabular} \\ \hline
[224] & ATmega32 & & PV modules & Photovoltaic module monitoring \\ \hline
[225] & Arm (Cortex M3) & Bluetooth & Home &
\begin{tabular}{c} Modular smart meter with \\ programmable functionality \\ \end{tabular} \\ \hline
[226; 227] & STM32 & Modular/HTTP & Edge &
\begin{tabular}{c} Implements PQ measurements. \\ Provides web interface \\ \end{tabular} \\ \hline
[228] & Raspberry Pi & LoRa and Blockchain & Edge-Fog &
\begin{tabular}{c} A secure gateway to collect smart \\ meter data \\ \end{tabular} \\ \hline
[229] & STM 32 & NB-IoT & Home &
\begin{tabular}{c} Multiuser meter with sub-meter \\ design for independent metering, \\ storage, and communication \\ \end{tabular} \\ \hline
[230] & - & \
\begin{tabular}{c} LPWAN/PLC \\ REST, MQTT \\ \end{tabular} & Edge/fog/cloud &
\begin{tabular}{c} API design for smart meter \\ data collection \\ \end{tabular} \\ \hline
[231] & & NB-IoT, MQTT & Edge/fog/cloud &
\begin{tabular}{c} NB-IoT-based smart \\ metering infrastructure \\ \end{tabular} \\ \hline
[232] & - & Wi-Fi, Cellular, MQTT & Edge/cloud &
\begin{tabular}{c} Smart meter data collection, AMI, \\ non-intrusive load monitoring \\ \end{tabular} \\ \hline
[233] & SoC (ESP32) & Wi-Fi, Blynk TCL & Edge & Theft detection \\ \hline
[234] & ATmega32 & Cellular & Home/edge & Security, energy management \\ \hline
[235] & SoC (ESP8266) & Cellular & Home & Uses Hall effect sensor \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Review of different implementations from the perspective of hardware, software, and communications. watching T.V., etc., and finally, fault detection uses measurements to detect the type and location of fault for the protection of the grid.
## 1 Introduction
For example, a fast prediction model for the electromagnetic sensitivity of the BTM (Balise Transmission Module) system based on a BP neural network algorithm is established in [13]. BP neural networks have the advantage of being computationally simple and easy to use, but they also have the obvious disadvantage that network training is prone to get stuck in local minima and the learning process converges slowly. To solve the local minimization problem, a combination of a global optimization algorithm and a BP algorithm can be used, such as the PSO-BP algorithm. It can overcome the limitation that traditional BP neural networks have of becoming stuck in local extremes. The PSO-BP algorithm searches for the optimal weights of the neuron nodes in each layer within a defined number of iterations, while improving the generalization ability of the network and reducing the fluctuation and contingency of the results [14]. The PSO-BP neural network algorithm can be applied to predict electromagnetic compatibility problems. A crosstalk prediction method based on the PSO-BP neural network algorithm is proposed to predict the crosstalk problem of three core twisted wires in [15]. In [16], a weight adaptive model prediction control system based on the PSO-BP neural network is studied. The effectiveness and accuracy of the PSO-BP algorithm to achieve the prediction are verified by numerical calculations or simulation. In this paper, a fast prediction method is proposed to predict the electromagnetic response of the LTE-R antenna to pantograph arcing. First, the pantograph arcing data and their characteristics are obtained with a measurement antenna at the window of the train. Then a cascade neural network model based on the PSO-BP algorithm is constructed to obtain the coupling coefficient between the LTE-R antenna at the roof and the measurement antenna at the window. Consequently, the electric field strength received at the LTE-R antenna port is further calculated. Finally, the LTE wireless communication system model is constructed to analyze the impact of pantograph arcing on the transmission quality of the LTE-R wireless communication system. The rest of the paper consists of four sections. In Section 2, the pantograph arcing data is collected and analyzed. The construction process of the PSO-BP cascade neural network is presented in Section 3. The modeling of the LTE-R wireless communication system is studied, and the impact of the pantograph arcing on the LTE-R wireless communication system is analyzed and validated in Section 4. Finally, the conclusions are given in Section 5.
## 2 Materials and Methods
### Application to Clinical Samples  
Plasmatic levels of Fingolimod and Fingolimod-P were measured in 15 patients with RRMS, enrolled at the Foundation IRCCS Neurological Institute Carlo Besta (Milan, Italy), within the PRAG-MS multicenter randomized pragmatic trial.  
The trial complied with the 1975 Declaration of Helsinki as revised in 2008 and was approved by the Institutional Ethics Committee and from the National Competent Authorities in March 2017. Written informed consent was obtained from each participant prior to entering the study. Fingolimod was administered orally at a dose of 0.5 mg once a day for 24 months and drug measurement was performed at 6, 12 and 24 months during the treatment period, i.e., the time-points pre-selected for clinical assessments. The included RRMS patients were both males (M) and females (F), with a M:F = 4:11 (M 27%) and a mean age of 36.3 years (\(\pm\)10.3) at the beginning of the trial. Mean disease duration at inclusion was 6.4 years (\(\pm\)10.0) with a median EDSS (Expanded Disability Status Scale) of 1.5 (0-4.5).  
Blood samples were collected and analyzed from all 15 patients at month 6, from 11/15 at month 12 and from 6/15 at month 24. Blood was collected shortly after the treatment intake (2-5 h) in K3-EDTA tubes, and plasma was separated by centrifugation at 3000 r.c.f (4 \({}^{\circ}\)C) for 15 min and stored at \(-20\)\({}^{\circ}\)C or \(-80\)\({}^{\circ}\)C, until used for the analysis. The researchers involved in the drug measurements had no access to the patient data.