esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
LC-MS-grade solvents (acetonitrile, methanol, water, acetonitrile, isopropanol, and acetone), formic acid, concentrated hydrochloric acid, N,N-dimethylformamide (DMF), L-ascorbic acid, dodecanediolic acid, and 12-aminolauric acid were purchased from Thermo Fisher Scientific (New Waltham, MA, USA). The following internal standards and chemicals were purchased from Millipore Sigma (Burlington, MA, USA): amphetamine-d5, MDMA-d5, ketamine-d4, diazepam-d5, oxazepam-d5, codeine-d3, fentanyl-d5, heroin-d9, buprenorphine-d4, nordiazepam-d5, SPLASH LIPIDOMIX Mass Spec Standard, isobutyryl-L-carnitine, and polyacrylonitrile. The standards 1,11-undecanedicarboxylic acid and 10-hydroxy-2-decenoic acid were purchased from Toronto Research Chemicals (Toronto, ON, Canada). Oasis hydrophilic-lipophilic balanced (HLB) particles (50-65 \(\upmu\)m) and polystyrene divinylbenzene with weak anion exchanger (PS-DVB-WAX) particles (50-65 \(\upmu\)m) were purchased from Waters Corporation (Milford, MA, USA). Thin-film stainless steel combs and the Concept-96 manual kit were purchased from PAS technologies (Magdala, Germany). The dip coater and lab oven were purchased from Ni-Lo Scientific (Ottawa, ON, Canada) and Hogentogler (Columbia, SC, USA), respectively. 1 mL deep-well plates were purchased from Canadian Life Science (Peterborough, ON, Canada). A Vanquish autosampler and pump coupled to a Q Exactive Plus Hybrid Quadrupole-Orbitrap Mass Spectrometer, as well as an Accuocre C30 HPLC column (100 mm \(\times\) 2.1 mm, 2.6 \(\upmu\)m) and an Accuocre C18 HPLC column (50 mm \(\times\) 2.1 mm, 1.5 \(\upmu\)m), were purchased from Thermo Fisher Scientific (New Waltham, MA, USA). High-performance liquid chromatography with high-resolution mass spectrometry detection was performed using a Vanquish autosampler and pump coupled to a Q Exactive Plus Hybrid Quadrupole-Orbitrap Mass Spectrometer. Chromatographic separation was conducted on an Accuocre C30 HPLC column (100 mm \(\times\) 2.1 mm, 2.6 \(\upmu\)m) and an Accuocre C18 HPLC column (50 mm \(\times\) 2.1 mm, 1.5 \(\upmu\)m). Thirteen chromatographic methods in positive mode (Table 1) and twelve chromatographic methods in negative mode (Table 2), based on published metabolomic studies focused on lipid metabolism, were tested on both columns.
## 3 Human Activity Recognition Techniques
### Sensing Techniques
#### 3.1.1 Data Sources and Sensing Modalities  
HAR data can be collected from various sources, including wearable sensors, environmental monitoring stations, mobile devices, smart appliances, and IoT devices. Common wearable sensors include accelerometers, gyroscopes, magnetometers, and physiological sensors. These sensors are compact, affordable, and suitable for real-time HAR, though they may cause discomfort if worn for extended periods. Environmental sensors, or ambient sensors, are placed in the surroundings to capture activity indirectly. These sensors include cameras, infrared sensors, pressure sensors, passive radio frequency sensors, and microphones. While environmental sensors are nonintrusive, they may raise privacy concerns, especially with visual and audio data. Smartphones and other mobile devices are rich sources of data for HAR. Other examples include inertial measurement unit (IMU) sensors, such as accelerometers, gyroscopes, and magnetometers, which are widely integrated into smartphones. GPS data can contextualize activities like walking, running, or commuting. Microphone data can capture audio cues such as footsteps, voices, or environmental sounds. Touchscreen data can reveal interaction patterns, such as swiping or typing behavior, which can infer activities. Barometer data measures changes in altitude for activities like climbing a mountain.  
Devices designed for gaming or virtual reality provide detailed motion data. Motion controllers can track hand gestures and physical interactions, while head-mounted displays (HMDs) provide head movement and gaze-tracking data. Treadmills and simulators can capture data on walking, running, or driving movements. User-generated data from social media or apps can complement HAR. Check-ins and location tags indicate activities like visiting a gym or restaurant. Activity logs from apps such as fitness trackers record specific actions like jogging or cycling. Data from connected devices in a smart ecosystem also contribute to HAR. Fitness equipment tracks activities on treadmills, cycles, or rowing machines. Smart TVs detect viewing patterns and associated user behaviors. Kitchen appliances' usage patterns can indicate activities like cooking or eating. These diverse data sources can be used individually or in combination, depending on the requirements of the HAR application. Multimodal approaches are particularly powerful in improving accuracy and robustness.
## 3 Results and Discussion
### Thermal Stability of Electro-Sprayed mPCM
Figure 6: TG and DTG curves of \(n\)-eicosane, neat PCL and the microencapsulated \(n\)-eicosane. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Sample**} & \multicolumn{3}{c}{**First Step**} & \multicolumn{3}{c}{**Second Step**} \\ \cline{2-9}  & \begin{tabular}{c} **Initial** \\ **Depadation** \\ **Temperature** (°C) \\ \end{tabular} & \begin{tabular}{c} **Weight Loss** \\ **(100-350 °C) (\%)** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Degadation** \\ **Temperature** (°C) \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Degadation Rate** \\ **(5\%/°C)** \\ \end{tabular} & \begin{tabular}{c} **Weight Loss** \\ **(350-500 °C) (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum** \\ **Degadation Rate** \\ **Temperature** (°C) \\ \end{tabular} &
\begin{tabular}{c} **Maximum** \\ **Degadation Rate** \\ **(\%\%/°C)** \\ \end{tabular} \\ \hline PCL-EA & 358.9 \(\pm\) 3.1 & 4.2 \(\pm\) 0.9 & - & - & 95.0 \(\pm\) 1.1 & 411.4 \(\pm\) 0.4 & 2.2 \(\pm\) 0.1 \\ \hline PCL-Chl & 369.2 \(\pm\) 3.6 & 3.0 \(\pm\) 0.4 & - & - & 96.1 \(\pm\) 0.4 & 413.4 \(\pm\) 1.6 & 2.3 \(\pm\) 0.02 \\ \hline Raw \(n\)-hexadecane & 151.6 \(\pm\) 1.1 & 99.4 \(\pm\) 0.1 & 246.6 \(\pm\) 0.7 & 2.2 \(\pm\) 0.1 & - & - & - \\ \hline \(n\)-hexadecane/PCL & 150.8 \(\pm\) 1.9 & 29.6 \(\pm\) 1.7 & 174.3 \(\pm\) 9.1 & 0.3 \(\pm\) 0.1 & 69.6 \(\pm\) 1.9 & 412.9 \(\pm\) 3.4 & 2.0 \(\pm\) 0.9 \\ \hline \(n\)-hexadecane/PCL & 145.8 \(\pm\) 2.3 & 39.5 \(\pm\) 5.7 & 183.5 \(\pm\) 10.3 & 0.5 \(\pm\) 0.2 & 59.3 \(\pm\) 6.3 & 411.9 \(\pm\) 2.4 & 1.4 \(\pm\) 0.2 \\ \hline \(n\)-hexadecane/PCL & 149.5 \(\pm\) 5.8 & 59.2 \(\pm\) 4.3 & 214.9 \(\pm\) 5.9 & 0.9 \(\pm\) 0.2 & 39.9 \(\pm\) 8.5 & 413.3 \(\pm\) 1.4 & 1.0 \(\pm\) 0.2 \\ \hline \(n\)-hexadecane/PCL & 150.1 \(\pm\) 3.1 & 27.3 \(\pm\) 0.9 & 173.5 \(\pm\) 2.7 & 0.3 \(\pm\) 0.03 & 72.0 \(\pm\) 1.0 & 413.7 \(\pm\) 0.3 & 1.8 \(\pm\) 0.02 \\ \hline \(n\)-hexadecane/PCL & 147.3 \(\pm\) 4.6 & 37.4 \(\pm\) 2.4 & 177.5 \(\pm\) 1.6 & 0.4 \(\pm\) 0.04 & 61.8 \(\pm\) 2.8 & 412.5 \(\pm\) 1.5 & 1.5 \(\pm\) 0.1 \\ \hline \(n\)-hexadecane/PCL & 151.8 \(\pm\) 6.1 & 60.4 \(\pm\) 4.0 & 217.0 \(\pm\) 1.0 & 0.9 \(\pm\) 0.1 & 38.9 \(\pm\) 7.3 & 414.7 \(\pm\) 2.4 & 0.9 \(\pm\) 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Thermogravimetric data for of \(n\)-hexadecane, neat PCL microsphere and \(n\)-hexadecane microcapsules in inert atmosphere at a heating rate of 10 °C-min\({}^{-1}\).
## 3 Results and Discussion
### Optimisation of Volatiles Extraction from Pineapple By-Products by HS-SPME
#### 3.2.3 Determination of Optimal Conditions  
The studied response variable was optimized by the combination of the factor levels, which maximizes the response on the indicated region. As a result, the optimal extracting conditions were: 30 \({}^{\circ}\)C of temperature, 60 min of extraction time, and NaCl 0.012 M. Under these conditions, the response value predicted by the model was 8.54 \(\times\) 10\({}^{8}\). To verify the models, three experiments were performed under optimal conditions with experimental values of 8.09 \(\times\) 10\({}^{8}\)\(\pm\) 1.39 \(\times\) 10\({}^{8}\), and did not differ significantly from the predicted value by the model. Figure 2: Response surface and contour plots showing interactions on the sum of areas values: (**a**): temperature vs. NaCl with fixed time at 60 min; (**b**) time vs. NaCl with the fixed temperature at 30 \({}^{\circ}\)C; (**c**) temperature vs. time with fixed NaCl at 0 M.  
Identification of the Main Volatile Compounds Present in Pineapple By-Products by Using HS-SPME-GC-MS  
Based on the results obtained, it can be confirmed that the volatile profile differs significantly when comparing pineapple peel and core samples. The main volatile compounds identified for each type of by-product are shown in Table 5.  
In peel samples the main volatile compound found is ethyl acetate, contributing more than 40% to the total area of the main volatile compounds identified, followed by dimethyl sinalediol (6.8-14.7% area); D-limonene (9.6-10.3% area) and isopentyl acetate (3.1-7.8% area). On the other hand, in core samples, octanoic acid ethyl ester is a volatile compound that represents the higher contribution to the total fraction of the main identified compounds accounting for 11.3-16.1% of the total area and followed by 3-methyl-1-butanol (9.0-12.8% area); isopentyl acetate (7.3-12.8% area), ethyl acetate (6.7-12.3% area), and dimethyl sinalediol (6.3-9.7% area). These results are following the main volatile compounds found in pineapple samples in previous works [6; 8]. However, it should be noted that ethyl acetate is considered one of the most characteristic volatile compounds of pineapple because it is responsible for its fruity aroma, as previously described in other studies [7; 8; 39; 40]. This compound is the result of the reaction of acetyl CoA with higher alcohols that are formed from the degradation of amino acids or carbohydrates in pineapple [41]. In addition, the compound isopentyl acetate and other esters such as methyl hexanoate; methyl 3-(methylthio) propanoate, and ethyl 3-(methylthio) propanoate, among others, have also been reported among the prevailing esters in juices made from fresh-cut pineapples. The methyl-branched esters such as isopentyl
# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 3 Analysis of the State of the Art
### Technical Evaluation and Thoughtful Analysis
Particular attention should be given to algorithms such as SVM, kNN, RF, and modern deep learning techniques (CNN, LSTM, autoencoders), which are increasingly used in the analysis of complex signals and structural systems. The quantitative evaluation of machine learning applications in vibroacoustic diagnostics reveals not merely incremental improvements but substantial performance gains that fundamentally reshape the capabilities of diagnostic engineering. Deep learning methods, for instance, have demonstrated an average of 20% higher effectiveness compared to traditional feature extraction approaches in complex tasks like rotating machinery diagnostics. Furthermore, the strategic integration of data from multiple sensors consistently yields accuracy improvements exceeding 15%, particularly in the detection of complex, coexisting faults, with some studies achieving near 100% accuracy through such comprehensive data fusion. The efficiency of the diagnostic process itself is significantly enhanced by automatic feature extraction, leading to improvements of up to 30%. These compelling numerical confirmations underscore that machine learning is not just an alternative but a superior paradigm for handling the increasing complexity, noise, and nonlinearity inherent in modern industrial and structural data, thereby setting new benchmarks for diagnostic precision and reliability. Despite the remarkable accuracy achievements, the practical deployment of machine learning models in real industrial environments continues to face significant challenges, primarily concerning robustness to environmental noise and the pervasive scarcity of labeled data. Studies indicate that environmental noise can still degrade diagnostic accuracy by 5-10%, highlighting the ongoing need for more resilient algorithms. In response to the high cost and time associated with manual data labeling, unsupervised and semi-supervised learning methods are gaining considerable traction, demonstrating their capacity to reduce labeled data requirements by up to half while maintaining high accuracy. Concurrently, the optimization of ML models for computational efficiency remains a priority, especially for real-time, embedded applications where classical algorithms like kNN and decision trees can still achieve over 95% accuracy with lower resource demands. This strategic focus on robustness, data efficiency, and computational practicality is crucial for transitioning advanced ML concepts from research to widespread industrial adoption and scalability.
###### 1 Introduction
CA simulates the development of society and culture, which can be divided into two parts, the population space and the belief space, which are independent from each other but interconnected through communication protocols. CA extracts the implicit information carried by the population evolution process, such as the location of the optimal individuals or the range of the best individuals, into the belief space and stores it in knowledge sources. CA provides a new framework and mechanism for evolutionary models or swarm intelligence systems [18], such as genetic algorithms [19], ant colony algorithms [20], particle swarm algorithms [21] and differential evolution [22], etc. The two-layer evolutionary mechanism of CA improves the efficiency of the algorithm. Compared with other evolutionary algorithms, CA has stronger global optimization capability and higher optimization precision, and it has been successfully applied to optimization problems such as clustering analysis [23], sensor localization [24], multi-objective optimization [25] and vehicle routing [26]. Although the cultural algorithm can use knowledge sources to improve evolutionary efficiency, its global convergence and evolutionary efficiency are deficient due to its single mutation operator [27]. Therefore, the cultural algorithm needs to be improved for better performance of the optimization. In this paper, an efficient hybrid evolutionary optimization method coupling CA with GAs (HCGA) is introduced with a validation background of the application of evolutionary algorithms to aerodynamic optimization design. Considering the features of CA and GAs, the proposed algorithm reconstructs the framework of cultural algorithms, which usesGAs as a population space evolutionary model of the cultural framework, with the three types of knowledge, namely situational knowledge, normative knowledge and historical knowledge; these kinds of knowledge construct the knowledge sources of the belief space. In addition, HCGA introduces population variance and population entropy to determine population diversity, and it develops a new knowledge-guided \(t\)-mutation operator to dynamically adjust the mutation step based on the change of population diversity during the evolutionary process. It further introduces the \(t\)-mutation operator into the influence function to balance the exploration and exploitation ability of the algorithm and improve its optimization efficiency. The rest of the paper is organized as follows. A brief introduction to the basic principles and framework of the cultural and genetic algorithms is given in Section 2. The proposed algorithm HCGA is introduced in Section 3. Numerical results and comparisons are presented and discussed in Section 4.2. The HCGA is applied in Section 5 to the aerodynamic optimization design of the wing cruise factor. Conclusions and perspectives are discussed in Section 6.
## 2 Materials and Methods
### The Communication Simulator
#### 2.2.2 Key Parameters for the Simulations  
The parameters that OFDM and UFMC modulations have in common are the number of bits per subcarrier \(m\), the number of subcarriers \(N\), the total number of symbols generated for the simulations \(N_{symb}\), and the number of bits coded per subcarrier \(bitsPerSubCarrier\). In this paper, several sizes of QAM modulation were used to encode bits. They are the 2-QAM (also called binary phase shift keying (BPSK)), 4-QAM (also called quadrature phase shift keying (QPSK)) and 16-QAM. For OFDM, the size of the guard interval (GI) is often proportional to \(N\). Logically, the important parameters of the UFMC scheme are the number of sub-bands \(N_{subband}\) (\(\tilde{B}^{th}\) filter in Figure 8), the number of subcarriers per subband \(SubbandSize\), the side-lobe attenuation \(sobeAtten\), the type of filter used \(FilterType\) and the filter's length \(filterLen\).  
The strategy followed to generate the UFMC waveform was to adopt the OFDM parameters of the ITU G.9991 standard [30]. It proposes a set of 256, 512, or 1024 subcarriers for bandwidths of 50, 100, or 200 MHz, respectively. As we target as a first step the lowest bit rates and simplest settings, it was decided to work with 256 subcarriers for both modulation schemes. The GI suggested in the standard for 256 subcarriers (\(N\)) is \(\frac{N}{32}\).  
The parameters for UFMC were subsequently adapted to have a similar comparison base. This involved setting the number of effective subcarriers to \(N\) 256, employing 21 sub-bands, each containing 12 subcarriers, and using a Chebyshev filter length (\(L\)) of 19, approximately twice the size of the guard interval (GI). To calculate the bit error rate (BER),  
Figure 8: Schematic of a UFMC communication system applied to VLC communication.  
Figure 9: Representation of the juxtaposition mechanism on complex data.  
10,000 symbols were generated, leading to the results.
# Swarm Robotics: A Perspective on the Latest Reviewed Concepts and Applications
## 6 Applications Using Swarm Robotics
### Autonomous Aerial Tasks  
One of the most popular swarm robotics applications is the Unmanned Aerial Vehicles (UAVs), popularly known as \"drones\". With swarm robotics, UAVs can flock and fly in formation without the need for a centralized control unit, even with a larger number of individuals. These techniques also apply to urban traffic management [140], farm inspection and mapping [141], and many others.  
[PERSON] et al. [140] introduce an aerial swarm that constantly supervise the traffic in SwarmCity, a simulated city built up in Unity game engine wherever drones and vehicles are designed in a realist manner. The airy swarm's control algorithm is supported on six behaviors with 23 constants required to be tuned. The optimization of these constants is executed with a genetic algorithm in a simple and speedy simulator. The resulting principal configurations are assayed in SwarmCity, proving significant efficiencies in supervising vehicles on total vehicles throughout time windows. The algorithm achieves an excellent performance using a reasonable computational time for the optimization.  
[PERSON] et al. [141] propound autonomous capacity supervision over rice farms by using quadcopters. Real-time control of vehicles is even defiance as they show a high nonlinear behavior, particularly for nimble handling. Moreover, these vehicles must act under unsure circumstances such as gale and storm perturbations as even as positioning mistakes reasoned by inertial measures units and global positioning system. To control these obstacles, type-2 fuzzy neural networks (T2-FNNs) are projected to control a quadcopter. The new particle swarm optimization-sliding mode control (PSO-SMC) theory-based hybrid algorithm is suggested to teach the T2-FNNs. In particular, the continuous version of PSO is chosen to recognize the previous part of the T2-FNNs when the SMC-based update rules are used for the learning of the subsequent part throughout the control. The simulated results for the T2-FNNs are contrasted with the conventional proportionate-derivative (PD) controllers for distinct case studies. According to the authors, the results demonstrate that their procedure reduces the trajectory path integral squared error by 26% on PD controllers' standard case, when this rate increase to 95% at unsure circumstances.
## 4 Experiments and Results  
To evaluate the effectiveness of our algorithm, we selected three models of RVT format, which is the mainstream BIM format, including two building models and a structural model, as shown in Figure 6. The basic information of the BIM experimental data is shown in Table 1.  
Our experiments and analysis focused on imperceptibility, the original data file size change, and robustness.
# Prediction of Key Parameters of Wheelset Based on LSTM Neural Network
## 4 LSTM Prediction Model
### Model Construction and Parameter Optimization
#### 4.3.3 Model Evaluation Metrics  
For evaluating the model, the following metrics are adopted: root mean square error (_RMSE_), mean absolute error (_MAE_), and mean absolute percentage error (_MAPE_). These three metrics can assess the accuracy between the prediction values and the actual values.  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}{(\hat{y}_{i}-y_{i})}^{2}}} \tag{13}\]  
\[MAE=\frac{1}{n}{\sum_{i=1}^{n}{\left|{{\hat{y}_{i}}-{y_{i}}}\right|}} \tag{14}\]  
\[MAPE=\frac{{100\%}}{n}{\sum_{i=1}^{n}{\left|{\frac{{{\hat{y}_{i}}-{y_{i}}}}{{ y_{i}}}}\right|}} \tag{15}\]  
After calculation, the wheel diameter values of the validation set results in _RMSE_ is 0.093 mm, _MAE_ is 0.076 mm, and _MAPE_ is 0.13%. The flange thickness values of the  
Figure 12: Flowchart of parameter tuning and model establishing.  
validation set results in \(RMSE\) is 0.061 mm, \(MAE\) is 0.046 mm, and \(MAPE\) is 0.37%. The flange height values of the validation set results in \(RMSE\) is 0.104 mm, \(MAE\) is 0.085 mm, and \(MAPE\) is 0.48%. The predicted results of the wheel diameter, flange thickness, and flange height prediction models respectively established in this study basically meet the expected requirements.
# The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products
## 1 Introduction
HY-2A was launched in 2011. It is the first Chinese ocean dynamic environment satellite. The onboard instruments consist of a radar altimeter, a microwave scatterometer, a scanning microwave radiometer, and a calibration microwave radiometer. Ever since the ground application system was built in 2012, the data processed by the system has been able to derive various ocean properties, such as sea surface wind field, swell field, marine gravity field, ocean circulation and sea surface temperature [9]. However, during the operational stage, we found that a gap still remained in closing the \"last mile\" of data delivery to the users. The derived data do not always satisfy users' expectations, and the system is not flexible in responding to the changing demands of processing customized products. The HY-2A Customized Automatic Processing Framework (CAPF) addresses these challenges. It is applied for providing a solution for meeting the diversity of users' personalized needs for advanced products and expanding the breadth of data applications. The framework is the complement to the existing ground application system. The remainder of the article is organized as follows. Section 2 reviews the HY-2A onboard sensors and their corresponding products, the existing ground application system, the relevant research on remote sensing data services and the challenges we are facing. Section 3 presents the technical solution to address the challenges of adapting users' changing demands for a diversity of data applications. Section 4 demonstrates the feasibility of the framework using some of case studies. The article ends with a discussion on future activities and conclusions.