esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Bacterial Strains and Culture Conditions  
_Escherichia (E.) coli_ (DSM 682), _Staphylococcus (S.) aureus_ (DSM 799, MSSA reference strain), _Fusobacterium (F.) necprohorum_ (DSM 21784), _Porphyromonas (P.) levii_ (DSM 23370), and _Clostridium (C.) sporogenes_ were selected for the experiments, whereas Treponema spp. was left out because it is more difficult to cultivate. The bacterial strains _E. coli_ (origin unknown), _S. aureus_ (isolated from a human lesion) and the two anaerobes _F. necprohorum_ (isolated from a bovine hepatic abscess) and _P. levii_ (isolated from bovine rumen) were obtained from the German Collection of microorganisms and Cell Cultures GmbH (DSMZ, Braunschweig, Germany). _C. sporogenes_ was taken from an own stock (originated from a milk sample from an older test series) of the LMQS. _E. coli_, _S. aureus_ and _C. sporogenes_ were plated on Columbia blood agar with sheep blood (Oxoid GmbH, Wesel, Germany). _F. necprohorum_ and _P. levii_ were plated on Schaedler Anaerobic Agar with Sheep Blood, Haemin and Vitamin K 1 (Oxoid GmbH, Wesel, Germany). The incubation period for _E. coli_ and _S. aureus_ was 24 h, for _C. sporogenes_ and _P. levii_ 48 h and for _F. necprohorum_ 72 h. All microorganisms were incubated at 37 \({}^{\circ}\)C. During incubation F. necprohorum, _P. levii_ and _C. sporogenes_ were kept in an anaerobic jar (Merck KGaA, Darmstadt, Germany) with resazurin anaerobic indicator paper (Oxoid GmbH, Wesel, Germany) and AnaeroGen 3.5 L bags (Oxoid GmbH, Wesel, Germany). At each experiment, to prevent negative effects of the oxygen, the latter bacteria strains were exposed to air for a maximum of 30 min before the transfer to the anaerobic environment. For safekeeping of the bacterial strains, colonies of each bacterial strain were transferred to a separate cryotube (Carl Roth, Karlsruhe, Germany) and frozen at \(-80\)\({}^{\circ}\)C. Before starting the experiments, one bead from each cryotube was spread onto a blood agar plate and grown as described above.
## 2 Seismic Analysis of Liquid Storage Tanks
### Failure Modes
#### 2.3.3 Sloshing  
Sloshing refers to the oscillatory motion of the liquid's free surface in response to seismic excitation. This phenomenon can significantly impact the structural performance of liquid storage tanks, particularly in tanks with insufficient freeboard (the distance between the liquid surface and the roof). Sloshing-induced forces are a critical design consideration, as they can cause roof damage, rupture of roof connections, and liquid spillage, compromising the tank's functionality after an earthquake.  
The freeboard height (\(H_{f}\)) is defined as follows:  
\[H_{f}=L-H \tag{16}\]  
where \(L\) is the total height of the tank and \(H\) is the height of the liquid within the tank. According to API 650 [20], during seismic events, the maximum sloshing wave height (\(\delta_{s}\)) can be calculated as follows:  
\[\delta_{s}=R\;\frac{S_{a}(T_{c})}{g} \tag{17}\]  
Figure 4: “Elephant’s foot” buckling of storage tank during Northridge earthquake in California [16].  
where \(R\) is the radius of the tank, \(g=9.82\) m/s\({}^{2}\) is the gravitational acceleration, and \(S_{a}(T_{c})\) is the spectral acceleration at the convective period, \(T_{c}\). When \(\delta_{s}>H_{f}\), the liquid may spill over the tank's edge, posing environmental and operational risks. In tanks with pre-existing deformations, the sloshing response can deviate significantly from the predictions of idealized models. Deformations such as dents or uneven wall thicknesses alter the tank's natural frequencies, particularly the convective period \(T_{c}\), which governs sloshing behavior. This results in changes to the spectral acceleration \(S_{a}(T_{c})\) and, consequently, the sloshing wave height \(\delta_{s}\).
# Casting Process Improvement by the Application of Artificial Intelligence
## 3 Literature Review
### Improvement of the Investment Casting Process  
Investment casting is a very popular and applicable casting technique in present-day industry. Its development is in direct correlation with the aircraft industry, as the production of individual components for the aircraft industry gave investment casting the foremost status in the casting industry. This casting technology is also characterized by nonlinear and complex relationships between the process parameters and characteristics that reflect the quality of the casting part, such that the application of artificial intelligence techniques has a great benefit. [PERSON] et al. presented a concept that includes a series of neural networks (ANNs) in order to precisely plan the investment casting process. Their starting point was that numerical simulations were not helpful for engineers for workpieces other than the ones simulated. In accordance with that was their attempt to perform a generalization using artificial neural networks (ANN), by taking into account a large number of process parameters (the mold temperature, melting temperature, casting part material, number and location of feeding points, diameter and length of inflow channels, and the angle of channel with respect to the main sprue axis, etc.). The development of the artificial neural networks architectures was based on many precisely defined simulations [54]. [PERSON] et al. presented the optimization of the injection process parameters with multiple performance features in the investment casting process using the orthogonal array with grey-fuzzy logic. The parameters that were subject to optimization were the injection temperature, injection time, and injection pressure, and the accompanied performances were the linear shrinkage and surface finish. The authors presented grey-fuzzy logic as an effective tool to obtain an optimal combination of the process parameters, and their general conclusion was that the quality of wax patterns in the investment casting process can be significantly improved through this approach [55]. In order to establish the correlation between the mechanical features of investment castings on the one side and process parameters and chemical composition on the other side, [PERSON] and [PERSON] used ANN. The data of related process parameters (shell making, dewaxing, wax making, melting, etc. ), the chemical
# Monitoring of Industrial Machine Using a Novel Blind Feature Extraction Approach
###### Abstract  
Machinery with several rotating and stationary components tends to produce non-stationary and random vibration signatures due to the fluctuations in the input loads and process defects due to long hours of operation. Traditional heuristics methods are suitable for the detection of fault signatures, however, they become more complicated when the level of uncertainty or randomness exceeds beyond control. A novel methodology to identify these fault signatures using optimal filtering of vibration data is proposed to eliminate any false alarms and is expected to provide a higher probability of correct diagnosis. In this paper, a detailed pipeline of the algorithms are presented along with the results of the investigation that was carried out. These investigations are performed using open-source vibration data published by the NASA prognostics centre. The performance of these algorithms are evaluated based on the ground truth results published by NASA researchers. Based on the performance of these algorithms several parameters are fine-tuned to ensure generalisation and reliable performance.  
blind feature extraction; Blind Source Separation (BSS); Spectral Kurtosis; vibration monitoring; early fault detection
# GPS/BDS RTK Positioning Based on Equivalence Principle Using Multiple Reference Stations
## 2 Methodology
### The Modified Kalman Filter
With more epoch-data accumulation, the Kalman filter solution will get better and better, and the float ambiguities are more precise in favor to improve the performance of AR.
# Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
###### Abstract
In the second experiment, the model for the gearbox is detailed for the entire group of turbines of the plant (therefore, a global model approach), and then the model is used to evaluate the behaviour of all the turbines of the plant. The gearbox is the device used to multiply the low speed but high torque rotation of the rotor to an optimal speed in which the generator converts the maximum amount of mechanical energy produced by the wind into electric energy. That energy transformation stresses the gearbox's gears due to the difference of input torque to the opposite generator torque at the output. Therefore, parts of the gearbox suffer from fatigue and experience an increase of temperature that reduces the lubrication effectiveness. The malfunctioning of the gears, especially in the early stages, is often difficult to detect. It should be noted that the gearbox is the component with the highest failure downtime. Even if the gearbox manufacturing technology is mature and reliable, this is a subsystem prone to breakdowns and failures within a 5 year operational period due to the harsh working conditions it is subjected to, and it must be replaced [7]. In addition to the cost of replacement, there is a system shutdown involved with the failure of the gearbox that can last for a long period of time because this is one of the slowest systems to repair. A replacement of the gearbox can cost up to 14.5% of the maintenance cost of the wind turbine [3]. Unsurprisingly, predicting gearbox failures therefore becomes a priority. Different studies, such as Reference [8], conclude that ANN outperforms traditional regression models. In Reference [9], ANN models are combined and derived from SCADA data, and in Reference [10] the authors implement a non-linear auto-regressive neural network to model the normal temperature of five different gearbox bearings. Other approximations can be found in References [11; 12]. The interesting advantage of using ELM is that the training of SLFNs is done very quickly through the calculation of a Moore-Penrose inverse matrix and through simple matrix algebra manipulations. Because the models can be trained and tested very efficiently, modifying their size and obtaining an objective measure of their performance on a test data set becomes a feasible and simple task. In this way, a grid-search can be carried out for the parameters of the model, thereby optimising inputs, targets and the size of the model itself. As will be shown later, sizing the model correctly is often a key step to maximise its predictive ability while avoiding over-fitting. The rest of the paper is organised as follows--in Section 2, the approach is briefly presented, describing the characteristics of the SCADA database, the normalisation method and the details of the ELM technique used for building the models. In Section 3, the experimental results are presented, comparing models of individual turbines and models of the ensemble of turbines of the wind plant. Finally, Section 4 is dedicated to discussions and conclusions.
# Development and Testing of an Active Noise Control System for Urban Road Traffic Noise
## 2 Characterization of Road Traffic Noise
### Noise Characteristics Analysis
#### 2.2.2 Noise Characterization of Different Types of Vehicles
Therefore, when formulating noise reduction strategies, it is essential to refer to the actual noise characteristics of the target area and determine the frequency range where energy is primarily concentrated.
## Materials and Methods
### Data Sources
#### 2.2.3 Climate Data  
To explore the impact of the climate factors on the vegetation WUE, the precipitation, temperature and solar radiation data from the Climatic Research Unit (CRU), version TS3.22 ([[http://www.cru.uea.ac.uk/](http://www.cru.uea.ac.uk/)]([http://www.cru.uea.ac.uk/](http://www.cru.uea.ac.uk/))) [46] were used in this study. These gridded data have a spatial resolution of 0.5\({}^{\circ}\) (~56 km) and a monthly temporal resolution. They were obtained from more than 4000 meteorological stations and interpolated based on spatial autocorrelation functions [47; 48]. The CRU datasets are characterized by high quality and a wide application range and have become a basic dataset for use in research on climate change [49; 50; 51].  
A set of seven variables was selected as the drivers of the changes in WUE from 1982 to 2015 (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable Class** & **Variable Name** & **Definition and Units** & **Data Source** & **Spatial Resolution** \\ \hline \multirow{8}{*}{Climate factors} & \multirow{2}{*}{Mr} & \multirow{2}{*}{mean precipitation during the growing season} & \multirow{2}{*}{CRU} & \multirow{2}{*}{0.5\({}^{\circ}\)} \\  & & & from 1982 to 2013 (mm) & \\ \cline{1-1} \cline{4-5}  & & & mean temperature during the growing season from & \\ \cline{1-1} \cline{4-5}  & & 1982 to 2013 (C) & & \\ \cline{1-1} \cline{4-5}  & & & mean solar radiation during the growing season & \\ \cline{1-1} \cline{4-5}  & & & from 1982 to 2013 (W m\({}^{-2}\)) & \\ \cline{1-1} \cline{4-5}  & & & the trends of mean precipitation during the & \\ \cline{1-1} \cline{4-5}  & & & growing season from 1982 to 2013 (m\({}^{-}\) yr\({}^{-1}\)) & \\ \cline{1-1} \cline{4-5}  & & & the trends of mean temperature during the & \\ \cline{1-1} \cline{4-5}  & & & growing season from 1982 to 2013 (C yr\({}^{-1}\)) & \\ \cline{1-1} \cline{4-5}  & & & the trends of solar radiation during the growing & \\ \cline{1-1} \cline{4-5}  & & & season from 1982 to 2013 (W m\({}^{-2}\)) & \\ \cline{1-1} \cline{4-5}  & & & land cover changes from 1990 to 2015 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variables used as the drivers of the changes in WUE throughout the study area.
## 2 Experimental Schemes  
### Experimental Materials  
To isolate the independent influence of particle size on the strength of cohesionless soil, this investigation employs a controlled-variable experimental methodology. Smooth spherical glass beads of different diameter (Figure 1) were selected as the idealized particles material for comprehensive triaxial testing. This systematic approach effectively eliminates confounding factors such as surface roughness, particle shape, and interparticle cementation effects, thereby enabling a focused investigation of particle size as an independent variable influencing the macroscopic strength behavior of cohesionless soil.  
The diameter (\(D\)) of the specimen used in the conventional triaxial test is 61.8 mm. According to relevant standards [38], the maximum allowable particle size \(d_{\text{max}}\) within the specimen is \(D/10\). This means that the maximum particle size in conventional triaxial test should not exceed 6.18 mm. Therefore, in this study, glass beads with diameters (\(d\)) of 2 mm, 3 mm, 4 mm, 5 mm, and 6 mm were selected for the conventional test. To extend the range of particle sizes investigated, large-scale triaxial tests were also conducted using larger-sized glass beads. The diameter (\(D\)) of the large-scale specimen was 300 mm. The diameters of the large glass beads were scaled and rounded according to the specimen diameter ratio (300 mm/61.8 mm). The diameter of the large glass beads were set to 10 mm, 15 mm, 20 mm, 25 mm, and 30 mm.  
The actual density values of the customed glass beads, as well as the maximum dry density (\(\rho_{\text{dmax}}\)) and minimum dry density (\(\rho_{\text{dmin}}\)), were measured for each bead size. The basic physical properties and specimen preparation parameters of glass beads of various sizes are summarized in Table 1. Specimens for conventional triaxial tests and large-scale triaxial tests were prepared using small and large glass beads, respectively, to create a single-particle-size particle specimen.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Glass Bead Diameter** & **2** & **3** & **4** & **5** & **6** & **10** & **15** & **20** & **25** & **30** \\
**(mm)** & & & & & & & & & \\ \hline \(\rho\) (g/cm\({}^{3}\)) & & & & & & 2.50 (\(\pm\)0.003) & & & & \\ \hline \(\rho_{\text{dmax}}\) (g/cm\({}^{3}\)) & 1.63 & 1.62 & 1.60 & 1.59 & 1.58 & 1.55 & 1.50 & 1.49 & 1.48 & 1.47 \\ \(\rho_{\text{dmin}}\) (g/cm\({}^{3}\)) & 1.43 & 1.43 & 1.41 & 1.40 & 1.40 & 1.53 & 1.47 & 1.45 & 1.43 & 1.41 \\ Specim size (\(D\times h\)) & & & 61.8 mm \(\times\) 120 mm & & & & 300 mm \(\times\) 600 mm & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The maximum and minimum dry density of glass beads used in the test.  
Figure 1: Smooth spherical glass beads.
## 2 Related Work  
Multilingual natural language understanding (NLU) in healthcare has seen significant advancements through the adoption of transformer-based models that enable cross-lingual generalization and multitask learning. However, most of this progress has been centered around retrospective clinical narratives, such as discharge summaries or EHR notes, rather than dialogue or interactive decision support. Furthermore, few of the existing systems can perform joint semantic parsing and intervention recommendation, especially under multilingual and low-resource constraints.  
The mT5-Base model [19] has been widely adopted for multilingual sequence-to-sequence tasks. As a fully text-to-text architecture pre-trained on the mC4 corpus across more than 100 languages, mT5 is suitable for semantic parsing tasks such as converting utterances into structured clinical frames.  
The mBART model, designed as a multilingual denoising autoencoder, supports language generation across over 25 languages. When combined with a multilayer perceptron (MLP) classifier [24], mBART can encode multilingual dialogue and output intervention labels. This hybrid setup is effective for classification tasks in resource-constrained languages and was used in our benchmarks for intervention prediction. However, the system treats each utterance independently, with no explicit mapping to underlying symptom structures or temporal markers. It lacks interpretability and does not utilize a structured representation that reflects patient risk or clinical decision logic.  
XLM-RoBERTa [21], paired with a BiLSTM classifier, has been employed for sequence labeling tasks such as slot-filling and medical named-entity recognition (NER) [25]. Its strong multilingual representation capabilities make it suitable for token-level symptom detection. The model does not include contextual metadata like patient demographics or time references and is limited to basic, surface-level extraction.  
In contrast to these approaches, TriagE-NLU introduces a unified architecture that integrates semantic parsing with clinical intervention classification, grounded in a structured, ontology-aligned representation of emergency dialogue. It processes not only text transcriptions but also multimodal inputs such as patient metadata and temporal context. The model incorporates a hybrid rules-and-policy decision engine informed by clinical guidelines and supports privacy-preserving training through federated learning. These capabilities allow it to generalize across languages, adapt to local protocols, and provide interpretable and medically actionable outputs in telemedicine deployments.
# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 4 Two-Step Approach  
In the optimization problem of the mobile photovoltaic-energy storage-diesel microgrid system in this paper, two steps are required to solve the problem [26]. The first step is to use the CPS-MOEA (multi-objective evolutionary algorithm with classification-based preselection) to obtain the optimal Pareto front. The second step is to use the FCM (fuzzy C-means clustering) and GRP (grey relational projection) algorithms for comprehensive decision-making to facilitate commanders to make more scientific and reasonable choices based on their personal preferences. This is shown in Figure 2.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 5 Use Cases
### Avionics-Associated System  
Telemedicine has been employed in aviation and domestic environments to ensure the safety and security of passengers and crew members. In [121], a sensor based telemedicine system for aviation was developed. The system relied on an optical cellular network using infrared wireless links. An infrared link was employed over cable or RF as the communications technology in order to limit interference. The design architecture of the avionics system was presented as well.
## 3 Results and Discussion
### Texture Analysis  
The results from TPA (hardness, cohesiveness, springiness, and chewiness) are presented in Table 2. The textural analysis of the treated and non-treated deer meat samples showed a significantly lower hardness on day 21 than on day 1, and the same tendency was observed in cohesiveness and chewiness. Hardness, cohesiveness, and chewiness decreased in the course of the storage period, which is in accordance with several studies [50; 51; 52]. These results could also be related to the changes in the patterns of myofibrillar degradation between 1 and 21 days, as mentioned by the authors: [PERSON] et al. [53] determined a positive correlation between the concentrations of troponin T and the hardness value.  
Additionally, there is no significant difference in all texture analyses between treated and non-treated samples except on day 14. However, all treated sample values were slightly lower than non-treated ones. Multiple researchers have documented enhanced tenderness through a reduction in shear force values in meat following marination with organic acids [54; 37; 55]. This impact could be due to some solubilization of the collagenous tissue. [PERSON] and [PERSON] [56] and [PERSON] et al. [57] found that lactic acid injection enhanced connective tissue degeneration.  
Figure 1: Means \(\pm\) standard error for aerobic plate count (log CFU/g) of treated (2% lactic acid and 2% ascorbic acid mixture) and non-treated (control) vacuum-packed venison samples during 21 days of retail display at \(4\pm 1\) °C. Means with no letters (a and b) in common indicate significant differences in treatment effects (\(p\) < 0.05).
## 5 Verifying Accuracy via Physical Structure Test  
### Physical Test Object  
The stress values in the sensor-free areas are generated by AIGC. To verify the accuracy of the ANN in obtaining the global stress of the connector structure, physical structural tests were conducted using a 1:1 model. The main parameters of the single-ear connector model are presented in Table 2.