esa-sceva/satcom-chunk-collection huggingface dataset:
However, this method is not suitable for cargo transport, because wagons are traveling through different countries and it is not possible to regulate the data transmission [7]. In 2010, Greek scientists ([PERSON] et al. [8]) performed research with the objective to determine the defects and cracks of the wheels in use based on the acoustic noise they generate. The testing data and evaluations are published in this article. During the testing, acoustic noise produced by wheels of the rolling stock (with and without defects) was measured. Even wheels without defects produce noise as they roll along the rail, so this sound is used as a reference when comparing the noise produced by the wheels that have defects. Rolling stock with wagons was moving at different speeds. Sensors attached to the rail neck at the sides of the rail were used to perform measurements. High frequency (30 to 1000 kHz) piezoelectric sensors were used during testing. The R3i, R15, R30 and R50 sensors manufactured by the USA-based company Physical Acoustics Corporation were used. They can record and send to the hard disk signals that are longer than 10 s. The data were collected, recorded and processed using AE-Win and Noesis software, manufactured by the same manufacturer (which manufactures the sensors). Data for this research were collected from three sources in three different locations: two trains and one tram. The research showed that using these sensors installed on the rails can potentially detect the defects of the wheel geometry, e.g., flat spots. In 2015, Ukrainian scientists ([PERSON] et al. [9]) announced that they had developed a moving wagon wheel defect diagnostics system based on the acoustic sound. They installed microphones under the wagon frame above the wheelset and recorded sound, which was analyzed by the sound analyzer and transmitted through GPS to the control posts. However, this method is only suitable for passenger trains, metro and tram, because these wagons have a permanent location of deployment. A Russian company claims to have equipment that can help register the data needed to detect wheels with defects. Their offered scheme is provided ([PERSON] [10]). It uses six BC 501 microphones and two RF603 optical sensors to detect the movement speed and position of the rolling stock; acoustic noise spectrum analyzer Zet017-u8 and Zetlab software are used to process the data. All of these methods show that such faults can be detected; however, the parameters that allow for clearly determining if the wheelset is defective or not, have not yet been established. Therefore, further research is needed to determine these parameters.
## 1 Introduction
Secondly, the work explicitly integrates economic analysis, environmental sustainability considerations--including critical management of methane emissions--and reliability improvements into a comprehensive optimization framework, systematically evaluating their trade-offs rather than treating them separately. Thirdly, by critically examining the environmental sustainability of biomass integration, the study explicitly emphasizes the importance of effective methane emission management practices, a factor frequently overlooked in the existing literature. Finally, the structured and replicable methodology developed here can guide policymakers and rural communities globally,facilitating sustainable, site-specific energy planning and offering practical, scalable insights for rural energy transition. This paper is organized as follows: Section 2 introduces the study site, highlighting its energy challenges and opportunities while proposing solutions that shape the methodology. It then defines the key input variables, such as meteorological data and load demand, reviews the HOMER components used in the analysis, and assesses the economic parameters relevant to addressing the problem. Section 3 offers a detailed evaluation of the simulation results, including comparative cost, reliability, and environmental performance studies. Section 4 then discusses the impact of changes, variable sensitivity, and method limitations. Finally, Section 5 concludes the paper with key findings and implications.
## 8 Conclusions  
The use of spatio-temporal information aims to understand the movement patterns of voluntary users. This information can be used to make an appropriate recommendations such as to know, for example, in which time slot you mostly visit a certain urban area through certain roads.  
More specifically, the problem of path detection is important for transport services, which are interested in finding corridors in public transport networks that share multiple routes (e.g., by bicycle, subway, car sharing).  
The complexity of naive approaches to detect corridors in GPS datasets in too high in real world cases, and more efficient strategies are necessary. In this paper we have presented a new approach that, based on the application of the Apriori algorithm, allows the filtering of a dataset to obtain a small set of candidate corridors. At the end, the processing of real datasets can be achieved in minutes.  
The results of this analysis could have many interesting applications. For instance it would be possible to create a social network to connect people to the same travel location, give travel recommendations or point out the lacking of public transport lines in certain areas to the city council.  
Conceptualization, C.C. and J.V.; methodology, C.C. and J.V.; software, C.C.; validation, C.C.; writing-review and editing, C.C. and J.V.; visualization, C.C.; supervision, J.V. All authors have read and agreed to the published version of the manuscript.  
Figure 8: A final corridor obtained by the filtering process, shared by \(S=50\) trajectories and \(3.7\) km long.  
**Acknowledgments:** This work was partially developed during a visit of the first author to the University of Barcelona. She would like to express her gratitude for providing a fruitful work environment.  
**Funding:** The first author was supported by _PO FSE Sicilia 2020_, Ph.D. grant. Part of the research was supported by RTI2018-095232-B-C21 and 2017 SGR 1742 grants.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Organoleptic and Nutraceuticaltical Quality of Soilless-Grown Pisanello Tomatoes (2021)  
Soilless cultivation is an efficient tool to manage plant-growing parameters such as the EC of the nutrient solution, mineral uptake, and irrigation via modern fertigation equipment and automation technologies for improvements in fruit quality in terms of chemical composition and bioactive compounds [21; 22]. For these reasons, the nutraceuticalial quality was also evaluated for Pisanello tomatoes grown at the Department of Agriculture, Food and Environment (DAFE), University of Pisa, Italy, in two different soilless systems (rockwood soilless system and aeroponic cultivation; Table 4). The organoleptic analysis of soilless-cultivated Pisanello tomatoes showed higher pH values in rockwood-grown tomatoes compared to those cultivated using aeroponics. Conversely, EC, TA, TSS content, and DMC were higher in aeroponic-grown tomatoes than in those cultivated in rockwood (Table 4). Similar results were reported by other authors [53; 54]. [PERSON] et al. [54] observed higher DMC in fruit tomatoes cultivated using \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multirow{2}{*}{**pH**} & \multirow{2}{*}{**EC**} & \multicolumn{2}{c}{**TA**} & \multirow{2}{*}{**TSS**} & \multirow{2}{*}{**DMC**} & \multirow{2}{*}{**Firmness**} \\  & & **(dS m\({}^{-1}\))** & & **(\({}^{\circ}\)Brix)** & & **(\%)** & **(kg cm\({}^{-2}\))** \\ \hline Pisanello Rockwood & 4.23 \(\pm\) 0.03 a & 9.00 \(\pm\) 0.04 b & 0.77 \(\pm\) 0.01 b & 5.72 \(\pm\) 0.10 b & 6.58 \(\pm\) 0.13 b & 1.23 \(\pm\) 0.05 a \\ Pisanello Aeroponics & 4.10 \(\pm\) 0.03 b & 10.74 \(\pm\) 0.15 a & 1.03 \(\pm\) 0.02 a & 6.77 \(\pm\) 0.19 a & 7.49 \(\pm\) 0.11 a & 1.41 \(\pm\) 0.13 a \\ \hline Significance & ** & *** & *** & ** & *** & ns \\ \hline \hline \end{tabular}  
* Data were analysed by Student’s \(t\)-test. ns: not significant, **: \(p\) < 0.01, ***: \(p\) < 0.001. The mean values \(\pm\) standard deviation (SD) followed by different letters are statistically different (\(p\) < 0.05) according to the Student’s \(t\)-test.  
\end{table} 
Table 4: Quality parameters evaluated on Pisanello tomatoes grown at the Department of Agriculture, Food and Environment (DAFE), University of Pisa, Italy, in two different soilless systems (rockwood and aeroponics), harvested in July 2021.
# PM2.5 Concentration Prediction Based on CNN-BiLSTM and Attention Mechanism
## 2 Methodologies  
The framework of the proposed CNN-BiLSTM-Attention model for PM2.5 concentration prediction is given in Figure 1. In general, the original data is divided into a series of samples. Additionally, the feature set, related to PM2.5 concentration, meteorological data, and air quality data, are split from the samples. Their values are then normalized into the range of 0 to 1. The processed dataset is input into the model for training, after that,the trained model is used to predict the PM2.5 concentration. To sum up, the framework contains two phases, a data modeling phase and a prediction modeling phase. The specific contents of these two phases are described as follows.
# Predictive Speed-Control Algorithm Based on a Novel Extended-State Observer for PMSM Drives
## 1 Introduction
In this paper, according to the continue time predictive control theory, a novel predictive speed controller based on the extended-state observer is proposed for PMSM, which combines the speed loop and the \(q-\)axis current loop together. The predictive controller has only one adjusted parameter, and it simplifies the design of the controller. Meanwhile, a novel extended-state observer is designed to observe the load disturbance, which is used for the predictive controller. Finally, the effectiveness of the proposed approach is verified by simulation and experiment in LINKS-RT platform, and the results prove that the novel controller can achieve good speed tracking and strong robustness for the disturbance in different conditions. This paper is organized as follows. Section 2 provides a mathematical model of PMSM. Section 3 introduces the design of generalized predictive controller. A novel extended-state observer is proposed to estimate the load disturbance in Section 4. Section 5 shows the simulation and experimental results. The conclusion is presented in Section 6.
# Energy Efficiency Improvement in Reconfigurable Photovoltaic Systems: An Evaluation of Team Systems
## 6 Obtained Results  
To evaluate and contrast the performance of reconfigurable and non-reconfigurable photovoltaic systems, several emulations have been performed simulating different meteorological conditions. Conditions similar to sunny, cloudy, and partly sunny days have been considered.  
There are unlimited possibilities for irradiance patterns in different weather conditions, so it is impossible to emulate every situation. The choice of the irradiance profile used has been based only on energy criteria since this research work tries to evaluate the energy gains in reconfigurable PV systems when the PV inverters operate in low-load conditions.  
The power output of the inverters on a \"sunny day\" is higher than the reconfiguration threshold most of the time, being lower than the reset threshold only during the first and last part of the day. This means that most of the time PV inverters do not operate in low-load conditions. On the other hand, the power output of the PV inverters on a \"cloudy day\" is lower than the reconfiguration threshold throughout the day and, consequently, the inverters always operate in low-load conditions.  
In the case of irradiance and ambient temperature data, websites such as SoDa [50] offer valuable information based on the desired location and minute-by-minute data series. To obtain a per-second dataset to perform a real time emulation, data interpolation is performed as part of the emulation process. Additionally, the zero values of irradiance at the start and end of the day are removed in an effort to reduce the emulation time. Figure 12 depicts a plot of the used irradiance data.  
The value of the irradiation data, expressed in W/m\({}^{2}\), used for the three types of days considered is obtained by scaling the series shown in Figure 12.  
Figure 12: Irradiance values, expressed in arbitrary units, used for the systems emulation.  
This alternative has been chosen because the expected behavior of reconfigurable PV systems, when compared to non-reconfigurable systems, does not depend greatly on the irradiation profile used.
# Engine Fault Detection by Sound Analysis and Machine Learning
## 2 Dataset  
Audio signal recordings were collected from vehicles, which were serviced at official Ford or Toyota service centers and ensured a diverse range of cars from these reputable brands. A cellphone served as the recording device, which captured sounds, while the cars were stationary, and their engines were idling at ideal operating temperatures. As seen in Figure 1, the cellphone was positioned 15 cm above the hood and centered, with the hood closed to mimic real-world conditions. Engine sounds were recorded for 30 s each, sampling at a frequency of 48 kHz to capture detailed acoustic information.  
Professional mechanics diagnosed the cars as either healthy or with one of the following faults: spark plug issues, airflow irregularities, electrical malfunctions, engine/turbo problems, or front-end problems. The distribution of each diagnostic class is outlined in Table 3. Distribution of vehicle diagnostic classes.  
Spark Plug Issue: Typically related to ignition problems, which result in misfires, rough idling, and decreased engine performance.  
Airflow Irregularities: Pertaining to issues with the air intake system that affect engine combustion and efficiency.  
Electrical Malfunctions: Encompassing faults within the vehicle's electrical system that directly impact engine performance. This may include issues with sensors, wiring, or other electrical components that affect engine operation and efficiency.  
Engine/Turbo Problems: Referring to issues within the engine or turbocharger system that impact power delivery and overall engine performance.  
Figure 1: Setup for audio signal recording.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Diagnostic Class** & **Number of Cases** \\ \hline Healthy & 50 \\ Spark Plug Issues & 40 \\ Airflow Irregularities & 52 \\ Engine/Turbo Problems & 44 \\ Front-End Problems & 48 \\ Electrical Malfunctions & 46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distribution of vehicle diagnostic classes.  
Front-End Problems: Including issues with steering, suspension, or other components affecting the vehicle's front-end operation.  
The collected dataset comprises audio signals, which were collected from a wide range of gasoline-engine vehicles such as Ford Focus (2014-2021), Ford Kuga (2020-2021), Ford Ecosport (2021), Ford Mondeo (2016), Toyota Corolla (2015), and Toyota Auris (2010). It was aimed to ensure diversity in vehicle models to capture a comprehensive range of engine sounds and fault types.  
In addition, an example signal for each vehicle diagnostic class is provided in Figure 2 in order to demonstrate the characteristics of the recorded audio signals for each class.
## 2 Materials and Methods
### In Silico Methods  
#### 2.3.1 Sequence Resources and Multiple Sequence Alignment  
The _E. faecalis_ ATCC 29212 strain protein primary structures (197 sequences) were downloaded from the NCBI genome database (NCBI RefSeq assembly: GCF_000742975.1) in FASTA format, while the _P. aeruginosa_ (110.903 sequences), _S. aureus_ (108.451 sequences), and _E. coli_ (543.617 sequences) protein primary structures were retrieved and extracted from the entire \"Non-redundant protein sequences (nr)\" database, which contains nearly all protein sequences available at NCBI [23]. The Blastp tool performed a multiple sequence alignment (MSA) between the _E. faecalis_ sequences and the _P. aeruginosa_, _S. aureus_, and _E. coli_ sequences (collected in a unique FASTA file containing 762.971 sequences) using BLOSUM 62 matrix, a word size of 6, E-value of 0.01, adding the flag -sorthits 3 and -sorthsps 0 to sort the MSA output for identity and E-value, respectively; all other parameters were set by default [23]. MSA results provided an output of 150.305.287 sequence alignments; thus, a python script written in-house extracted all _E. faecalis_ sequences (38 sequences), which did not show any identity/similarity with the sequences of the other bacteria strains (with a similarity/identity value less than 20%). The 38 sequences of _E. faecalis_ were analyzed in the UniProt database [24] and all sequences annotated as \"obsolete\" were discarded. From the UniProt searches, only one sequence was obtained: \"endocarditis and biofilm-associated pilus tip protein EbpA\" (NCBI Reference Sequence: WP_148304834.1).
## 1 Introduction
Therefore, the goal of this study was not to reinforce these past findings but to try to open up another approach to improve flood hazard assessment in floodplains, that is, improving flood hazard mapping and main flow variables (flow depth) outside the main river channel (that is, into the flood plains) through the use of calibrated but not \"natural\" values of Manning's roughness coefficient. In other words, we looked for a Manning value or range of values that compensated for the effect that the absence of bathymetric data would have on the hydraulic modelling results. This new approach was supported by the assumption that most flood-exposed elements (people and goods) are located out of the river channel and not inside it; therefore, the flow parameters (depth and velocity) in the river channel are not really key points for flood hazard assessment in most cases. Under these assumptions, the use of two LiDAR DEMs (with and without bathymetric data) for the Douro River reach in Zamora city (Spain) allowed us to calibrate a Manning's roughness coefficient for the main river channel (which is consistent with the value obtained previously from three cross-sections of the river) so that the flooded area and the flow depth in the floodplain converged with the results obtained in the control (or benchmark) model. The hydrodynamic model using the calibrated value show very similar flooded areas, as well as flow parameters (both for the 500-year and 100-year return period peak flows); thus, it can be used for flood hazard and risk assessment. Furthermore, the results of this new approach were compared with results from previous methodological approaches, such as the modified inflow hydrographs. Moreover, the approach proposed here can be replicated for any kind of river and it is cost-saving, as it does not require large ground surveys.
# Four-Port MIMO Antenna System for 5G n79 Band RF Devices
## 3 Results and Discussion
### Surface Currents  
The surface current patterns of the proposed MIMO antenna are shown in Figure 7. Studying surface currents helps for knowing the electric current flowing at a particular point in a conductor. Generally, to understand the influence of the coupling between the antenna elements while using this approach, one of the four ports should be excited while the other three ports remain terminated with a 50 \(\Omega\) load, as suggested by [33].  
As shown in Figure 7, there is an intense amount of current at the bending section of the hook. This constructive interface can be seen at the edges of the rectangular slot at the ground plane. This makes clear that the basic resonance is being generated because of the hook length or because of parameter f. As previously detected in parametric analysis, this parameter dominates for acquiring resonance at the desired band, and any change in length causes the proposed design to change the resonance dominantly.  
Figure 6: Radiation patterns at 4.8 GHz: (**a**) Theta 90 Ant 1, Ant 3, (**b**) Theta 90 Ant 2, Ant 4, (**c**) Phi 90 Ant 1, Ant 2, and (**d**) Phi 90 Ant 3, Ant 4.  
Figure 7: _Cont_.
## 3 System Description
### Methodology
#### 3.1.3 Iterative vs. Conventional Placement  
At this stage, CLQ and the best case with minimum \(SNR\) fluctuations that were obtained from Section 3.1.1 were used to perform posture analysis under low-lighting conditions and to obtain the percentage of outage areas for each case. The optimal arrangement that was retrieved with better BER support and availability was then compared with placement obtained in Section 3.1.4.
## 3 Results and Discussion
### Prediction of Ipomeamarone Content and Total Colony Count Using E-Nose Combined with Chemometrics
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Data Set** & \multicolumn{2}{c}{**Calibration**} & \multicolumn{2}{c}{**Cross-Validation**} & \multicolumn{2}{c}{**Prediction**} & \\ \hline
**Model** & **R\({}_{\text{c}}\)\({}^{2}\)** & \begin{tabular}{c} **RMSEC** \\ **(\(\upmu\)g g\({}^{-1}\) FW)** \\ \end{tabular} & **R\({}_{\text{cv}}\)\({}^{2}\)** & \begin{tabular}{c} **RMSECV** \\ **(\(\upmu\)g g\({}^{-1}\) FW)** \\ \end{tabular} & **R\({}_{\text{P}}\)\({}^{2}\)** & \
\begin{tabular}{c} **RMSEP** \\ **(\(\upmu\)g g\({}^{-1}\) FW)** \\ \end{tabular} & **R\({}_{\text{P}}\)\({}^{2}\)** &
\begin{tabular}{c} **RMSEP** \\ **(\(\upmu\)g g\({}^{-1}\) FW)** \\ \end{tabular} & **RPD** \\ \hline KNN & 0.836 & 2.183 & 0.834 & 2.200 & 0.932 & 1.410 & 3.852 \\ PLS & 0.771 & 2.581 & 0.723 & 2.840 & 0.875 & 1.909 & 2.845 \\ PCR & 0.776 & 2.556 & 0.724 & 2.835 & 0.877 & 1.891 & 2.872 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Predicted results of ipomeamarone for _C. fimbriata_-infected sweetpotatoes by KNN, PLS, and PCR.
## 3 Simulation Results and Discussion
### Crosstalk between Strips  
This section shows the simulation of crosstalk between different strips of the XS anode, as shown in Figure 6. The capacitance between the horizontal and vertical strips of XS anodes of different sizes was calculated. It can be seen from the figure that the inter-strip capacitance is between 2 and 8 pF. Under the same substrate thickness, the inter-strip capacitance increases as the strip width increases. In addition, for the same strip width, the capacitance between strips decreases as the substrate thickness increases. Reducing the capacitance between electrodes can reduce crosstalk. Low strip capacitance is achieved by using extremely thick substrates. Under the same conditions with other anode parameters, increasing the anode period can reduce the strip capacitance of the anode. However, the period size of the anode is also limited by the size of the electron cloud reaching the anode.  
Figure 4: The surface charge density of the XS anode.  
Figure 5: The change in charge ratio with distance L under different area ratios. The area ratios of four XS anodes with different sizes are 0.8, 0.9, 1.0, 1.1.  
Therefore, the design requirements and detector structural parameters must be considered comprehensively during actual design. Through simulation, we found that when the substrate thickness is 120 \(\upmu\)m, w1 is 110 \(\upmu\)m, and w2 is 530 \(\upmu\)m, the crosstalk is small.