esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
Our novel design for a rotary burner needs more optimization because similar wood pellet heat sources achieve production of CO emissions at the level of tens to hundreds mg.m\({}^{-3}\)\({}_{10\%\;\Omega}\)[27] and lower, being classified as Emission Class 5 according to the STN EN 303-5 standard and Commission Regulation (EU) 2015/1189--500 mg.m\({}^{-3}\)\({}_{10\%\;\Omega}\) for automatic heat sources for wood biomass. Typical time courses of NOx emissions from experiments on the retort and rotary burners are shown in Figure 8. Similar to the previous cases, the course of NOx production is more variable when using a rotary burner. The average measured NOx production values for the rotary burner were approximately 12.5% higher than the measured values for the retort burner. These slightly higher values could be due to the higher temperature in the combustion chamber due to the higher amount of supplied air, which was partially preheated. These values do not deviate from the usual values for similar heat sources [26; 27]. The measured average values of NOx are bordering on or just above the required limit set by Commission Regulation (EU) 2015/1189--200 mg.m\({}^{-3}\)\({}_{10\%\;\Omega}\) for automatic heat sources for wood biomass. This higher NOx concentration needs to be solved in our novel design for a rotary burner by construction modification to achieve a lower combustion temperature in the combustion area of the burner. Figure 7: Typical time courses of CO emissions depending on the type of burner from experiments. The rotary burner produced 23% lower average OGC production in comparison with the retort burner, which could be due to the higher amount and better redistribution of combustion air, similar to the reasons suggested above for the lower CO emissions production. Average PM production during the experiments was almost 45% higher for the rotary burner in comparison with the average PM production for the retort burner. All these measured values for OGC and PM correspond with those for similar heat sources [26; 27], but the PM production needs to be lower to fit the limits for Emission Class 5 of the STN EN 303-5 standard and Commission Regulation (EU) 2015/1189--40 mg.m\({}^{-3}\)\({}_{10\%\;\mathrm{O}2}\) for automatic heat sources for wood biomass. This could be achieved by further optimization of the burner operation process, especially by construction modification of the combustion air supply.
# Self-Service Data Science in Healthcare with Automated Machine Learning
## 2 Overview of AutoML Methods
### Fixed Pipelines  
Auto-WEKA [8, 12], Hyperopt-Sklearn [13], Auto-Sklearn [14], PoSH Auto-Sklearn [15], and ML-Plan [16] are all methods that have a fixed pipeline length. Auto-WEKA searches the search  
Figure 1: Self-service data science research in context of related data science disciplines.  
space by a tree-based BO method: SMAC [8; 17]. Hyperopt-Sklearn searches the search space is using Hyperopt, which makes use of either random search or Tree Parzen Estimators (TPE) with BO [18]. To improve efficiency in search, Hyperopt-Sklearn makes a distinction between conditional and non-conditional hyperparameters. Conditional parameters always need to be assigned, and non-conditional parameters depend on the chosen algorithm in the pipeline [13]. PoSH-Auto-Sklearn is an extension of Auto-Sklearn method described above and builds on the scikit-learn library. Successive halving was introduced in the search process to improve the efficiency of the method. In addition to this, they build a portfolio based on OpenML datasets [19], which contained meta-information to warm-start the search process. Finally, the ensemble technique of Auto-Sklearn was altered to exclude poor performing models. In PoSH Auto-Sklearn a model is not added to the ensemble if it performs over 3% worse than the best model. ML-plan uses a hybrid approach to construct pipelines. It combines the ideas and concepts from two different approaches: the idea of evaluating candidates at runtime [8] and the idea of using hierarchical task networks [20] for pipeline planning [16]. ML-Plan contributes to the existing knowledge base by proposing a two-phase search with HTN and a dedicated system to prevent overfitting. The authors of ML-Plan claim to have invented the first AutoML technique that prevents overfitting [16].  
PoSH Auto-Sklearn outperforms all other methods as it is the winner of the latest AutoML competition at the time of writing [9]. Auto-WEKA, Hyperopt-Sklearn, and Auto-Sklearn were the first three methods that were developed to tackle the CASH problem. What is noteworthy to observe is that Auto-Sklearn has served as a basis for multiple other AutoML systems, whereas the other two methods have not. We assume that this is due to the warm-start procedure that is built into Auto-Sklearn.
## 4 Experiments
### Main Results
This is because UniFormer-S utilizes a shallow network with convolutional layers that do not implement token selection, preserving the complete features in the shallow layers. In contrast, the redundancy in deep feature maps is more pronounced since crucial features are concentrated in a small portion of the regions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **GFLOPs** & **Top-1 (\%)** & **Top-5 (\%)** \\ \hline Baseline & 167 & 80.8 & 94.7 \\ Random/0.8 & 144.4 (\(-\)13.4\%) & 79.3 & 93.7 \\ Random/0.5 & 99.2 (\(-\)40\%) & 29.7 & 44.8 \\ Random/0.3 & 74 (\(-\)55.5\%) & 1.1 & 3.5 \\ TSNet/0.8 & 144.4 (\(-\)13.4\%) & 80.5 & 94.3 \\ TSNet/0.5 & 99.2 (\(-\)40\%) & 79.5 & 93.8 \\ TSNet/0.3 & 74 (\(-\)55.5\%) & 79.1 & 93.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons with baseline and random selection method on Kinetics-400. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **\#Frames** & **GFLOPs** & **Top-1 (\%)** & **Top-5 (\%)** \\ \hline X3D-M [28] & 16 \(\times\) 3 \(\times\) 10 & 186 & 76.0 & 92.3 \\ X3D-XL [28] & 16 \(\times\) 3 \(\times\) 10 & 1452 & 79.1 & 93.9 \\ VTN [10] & 250 \(\times\) 1 \(\times\) 1 & 3992 & 78.6 & 93.7 \\ TimeSformer [15] & 96 \(\times\) 3 \(\times\) 1 & 5110 & 79.7 & 94.7 \\ MViT-B16 [13] & 16 \(\times\) 1 \(\times\) 5 & 353 & 78.4 & 93.5 \\ ViViT-L [14] & 16 \(\times\) 3 \(\times\) 4 & 17352 & 80.6 & 94.7 \\ \hline VideoSwin-T [12] & 32 \(\times\) 3 \(\times\) 4 & 1056 & 78.8 & 93.6 \\ TSNet(VideoSwin-T)/0.8 & 32 \(\times\) 3 \(\times\) 4 & 871.9 (\(-\)17.4\%) & 78.3 & 92.9 \\ TSNet(VideoSwin-T)/0.5 & 32 \(\times\) 3 \(\times\) 4 & 628.1 (\(-\)40.5\%) & 77.0 & 91.4 \\ TSNet(VideoSwin-T)/0.3 & 32 \(\times\) 3 \(\times\) 4 & 484.8 (\(-\)54\%) & 76.2 & 90.7 \\ \hline UniFormer-S [17] & 16 \(\times\) 1 \(\times\) 4 & 167 & 80.8 & 94.7 \\ TSNet(UniFormer-S)/0.8 & 16 \(\times\) 1 \(\times\) 4 & 144.4 (\(-\)13.4\%) & 80.5 & 94.3 \\ TSNet(UniFormer-S)/0.5 & 16 \(\times\) 1 \(\times\) 4 & 99.2 (\(-\)40\%) & 79.5 & 93.8 \\ TSNet(UniFormer-S)/0.3 & 16 \(\times\) 1 \(\times\) 4 & 74 (\(-\)55.5\%) & 79.1 & 93.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with the state-of-the-art models on Kinetics-400.
## 1 Introduction
Most have focused on the joint radar jamming system and JRCS, while some have examined the design of integrated shared waveforms for additional interference. Reference [22] proposed a jamming method based on intermittent sampling inverse order filling for generating leading false targets. Reference [23] analyzed the effects of noise convolution and radio frequency (RF) noise jamming on the shared signal target detection and communication functions and proposed a method to interfere with shared signals using noise convolution and RF noise. However, the paper only studied the anti-jamming capability of the shared signal and did not investigate the jamming capability of the shared signal. An integration signal waveform for joint communication jamming was proposed in [24], utilizing false target jamming to increase the detection threshold for a constant false alarm rate. It uses the delay superposition of multiple sets of frequency modulation (FM) slope mismatch jamming signals. By the modulation mapping of the communication data, linear frequency modulation (LFM) signals with different Doppler and FM slopes can carry bits of data, ensuring that the communication instruction is completed while carrying out effective jamming. We have summarized the major contributions related to the type of paper proposal in Table 1.  
This paper uses the joint communication jamming system for a joint signal based on the FBMC chirp. This waveform can improve the transmission rate of communication information and interfere with the opponent's radar detection to protect our intended target. First, we propose modulating communication information to different symbols after grouping and coding with the intercepted radar signal. An offset quadrature amplitude modulation (OQAM) scheme is used to map a series of bits of data to complex symbols. Then, the Doppler estimation is performed using the modulated chirp signal, and the frequency bias is compensated. The jamming and communication signals are demodulated and separated orthogonally, which can then be used for subsequent radar jamming and to assist the communication signal in achieving channel estimation. The major contributions of this paper are as follows:
## 1 Summary  
Urban agglomerations in emerging countries in the Global South are experiencing rapid changes in their urban extent and morphology, due to the growth of population, rural-urban migration, and rising incomes. Access to updated information on the qualitative and quantitative status of settlements is relevant, in order to monitor and inform the housing sector, spatial and infrastructure planning, municipal revenue collection and budgeting, and the supply of social services. Very high-resolution (VHR) multispectral satellite images are one important, cost effective source of regular, updated, data on urban land use and built-up areas [1]. The authors acquired a Pleiades satellite image from August 2015 for the central part of the capital city of Rwanda, Kigali. Object-based image analysis (OBIA) and expert-based post-classification were then applied to derive building footprints and building heights and to assign every building to one of nine building archetypes. In a second step, building footprint data from aerial images of the same area in 2008-2009 were analyzed, to identify the change of the building stock in the respective period. In total, 165,625 built entities were detected for 2008-2009 and 211,458 for 2015; this entails a 27.7% increase in the number of buildings. The Government of Rwandahas expressed an interest in cost-effectively monitoring and managing the building supply in Kigali. Information from the geospatial dataset can be of use for various research and policy objectives in spatial planning, infrastructure planning, property valuation--which can help in municipal revenue collection and administration--and economic geography more broadly. Further analysis of the proposed dataset might also provide insights that can be transferred to cities with similar geographic and demographic settings. We anticipate that these data will have many other uses to improve our ability to understand dynamics of urban growth. In addition, the datasets can be valuable for the training and testing of image analysis and computer vision methods. The dataset presented is a revised version of a dataset that was used for a published report on the housing supply in Kigali [2].
## 4 Detailed Design and Implementation
### Create the 3D Virtual Model of the Machine  
Digital Twin technology enhances manufacturing by providing a virtual representation of physical assets for real-time monitoring and analysis. Key steps in creating a 3D model of a machine using AutoCAD include the following:  
* Data Collection: gather real-time operational data from CNC machines to inform the Digital Twin.
* Model Development: create an accurate 3D model in AutoCAD, reflecting the machine's design and specifications.
* Integration: embed monitoring features into the model using data from the Digital Twin to visualize performance and detect anomalies.
* Simulation: use the Digital Twin for scenario simulations to predict machine behavior and optimize operations.
* Continuous Improvement: update the Digital Twin with new data for ongoing refinement of the 3D model.  
Figure 17: Pseudocode for operator information flow model.  
Figure 18: Block diagram for retrieving the data required by the operator.  
The integration process begins with data collection from CNC and printing machines, using the Notification Hub Server to capture real-time operational data (e.g., spindle speed, temperature, print pressure). These data are aggregated and synchronized across machines at an average performance of 1 s for each 1 of 150 resources. Data synchronization relies on SQL databases, and real-time data are linked to a 3D model using platform NET for visualization.  
The simulation process involves running scenario-based simulations to monitor machine performance under different conditions.  
The continuous improvement process is driven by ongoing data collection, with real-time data feedback used to update the Digital Twin. Models are refined as machines wear down or as production conditions change.  
Below, the 3D CNC Model design can be found in Figure 19:
# Study of the Diffusion of Rejuvenators and Its Effect on Aged Bitumen Binder
## 2 Materials and Methods  
### Materials  
The 80/100 penetration grade bitumen provided by the Panjin asphalt company (Sichuan, China) was used in this research. The short-term aged bitumen used in this research was aged using the standard rolling thin-film oven test (RTFOT) at a temperature of 163 \({}^{\circ}\)C for 85 min. Long-term aged bitumen was obtained by means of a pressure aging vessel (PAV) and an ultraviolet aging oven (UV). The PAV was conducted at 95 \({}^{\circ}\)C with pressure of 2.1 MPa for 20 h. The ultraviolet aging oven was set with an ultraviolet intensity of 79.5 W/m\({}^{2}\) and aged for 168 h. Both short-term aged and long-term aged binders were then treated with rejuvenators. The rejuvenating contribution to short-term aged binder was studied to understand the recovering ability during hot mixing.  
The rejuvenator-treated aged bitumen was prepared in two steps. Firstly, the aged bitumen was blended with 8% of R-1 or R-2, separately, using the propeller mixer (GS-1, Tianjin, China) for 30 min at 120 \({}^{\circ}\)C with a constant speed of 200 rpm. Secondly, the rejuvenator-treated bitumen was then allowed to sit at room temperature for 3 days before being used for further rheological analysis to ensure completed diffusion.  
The rejuvenators, named R-1 and R-2, used throughout this research were provided by CRAFCO Company (Wuxi, P.R. China). Table 1 displays the basic technical data of R-1 and R-2. They are liquid and made from emulsified bitumen, containing about 61.8% to 76.5% of residue after fully cured.
## 4 Evolutionary Algorithms for Task Scheduling Problem
### Proposed Evolutionary Algorithm
#### 4.2.1 Encoding of Chromosomes  
An individual specified by a chromosome in genetic algorithm represents a solution of task scheduling. For encoding of chromosomes, an \(n\)-dimensional array corresponding to \(n\) genes is used. The sequence number of gene is the sequence number of task; each gene has a value of an integer \(k\) in range \([1;m]\) with \(m\) the number of nodes, which indicates that the corresponding task is assigned to the node numbered \(k\).  
For the example shown in Section 4.1.1, the _chromosome_ expresses that solution would be:  
\[chromosome=[3,1,3,2,1,2,2,3,1,3]\]  
Node 1 executes the set of tasks \(\{2,5,9\}\), the set of tasks \(\{4,6,7\}\) is assigned to be processed in node 2, while node 3 is responsible for the set of tasks \(\{1,3,8,10\}\)  
This chromosome encoding method was chosen because of the flexibility of performing genetic operations, such as crossover and mutation, to create new individuals exploring the solution search space, simultaneously inheriting quality gene segments from parents. The order of tasks processed is not considered because performing tasks in a different order on one machine does not affect the objectives, namely makespan and total cost.
## 4 Experiments
### Quantitative Results
#### 4.2.2 Ablation Study  
The roles of motion feature extraction and the social attention module were evaluated through ablation experiments. To validate the effectiveness of pedestrian motion features, we conducted two experiments. In the first experiment, we replaced the comprehensive motion features with only the relative positions of pedestrians as inputs into the social attention module. In the second experiment, we tested the effectiveness of the social attention module by replacing it with a social pooling module. The experimental results are listed in Table 2. After introducing the pedestrian motion features, the average ADE and FDE across all five scenarios decreased by 3.7% and 2.6%, respectively. This demonstrates that comprehensively considering the relative distance, speed, and velocity angle between pedestrians can more accurately capture the social interaction factors among them. However, in the Hotel scenario, where pedestrian movement is mostly linear and social interaction is weak, the introduction of motion feature extraction led to an increase in the ADE. After introducing social attention, compared to social pooling, the average ADE and FDE decreased by 5.4% and 2.6%, respectively, indicating that the attention mechanism is better suited to modeling complex pedestrian social interaction behavior.
# Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
## 3 Signal Acquisition and Feature Extraction
### Feature Extraction  
#### 3.2.1 Time-Domain Feature Parameters  
Time-domain analysis is used to directly obtain the time-domain statistical parameters of fault signals in the time domain. It can simply and intuitively represent the changes of the fault signals. The time-domain feature parameters include dimensional parameters and dimensionless parameters. Assume that the signal obtained is a discrete sequence \(x_{i}\), \((i=1,2,3,\ldots,N)\) with finite length, where \(N\) represents the number of data points of each sample. Three commonly used time-domain feature parameters are used in the paper. The equations are as follows.  
Root Mean Square (RMS):  
\[X_{\text{RMS}}=\sqrt{\frac{1}{N}{\sum\limits_{i=1}^{N}{{{x_{i}^{2}}}}}} \tag{19}\]  
The RMS represents the average energy measure of the signal and is very stable [42].  
Kurtosis:  
\[K=\frac{1}{N}{\sum\limits_{i=1}^{N}{x_{i}^{4}}} \tag{20}\]  
The kurtosis is highly sensitive to early faults and impact signals, but its stability is poor [43; 44]. Kurtosis index:  
\[K_{f}=\frac{K}{X_{\text{RMS}}^{4}} \tag{21}\]  
The kurtosis index is very sensitive to the impact of vibration signals [45].
## 4 Results and Discussion
### Tests in Close-to-Real Conditions  
#### 4.3.1 Designing Anchors to Fasten Building Facades  
In the course of the research on the optimal resin composition for the anchorage production, a new original anchor shape intended for the production of composite materials has been designed [26; 27]. The anchor shape was designed aiming at topology optimisation using the _Optistruct_ software-based algorithms as an openwork profile with an X-type grate (Figure 19). Given the environmental interactions (UV, precipitation, temperature changes, etc.), the outer anchor part was designed as a stainless-steel insert, whereas the composite part was designed in the inner wall zone coated with thermal insulation layers made of mineral wool. The rear part of the anchor has a strongly reduced cross section in the fixing zone in the wall construction base and was adapted for embedment in glue [28; 29] in openings in the construction base made of concrete or full brick. The anchor shapes were originally supposed to replace stone masonry anchors from a flat bar with 20 \(\times\) 4 mm cross-section dimensions and were designed to safely propagate load by a 25 kg panel fixed on an anchor of a 200 mm projection. According to [30], the design criterion for the acceptable vertical anchorage bend was assumed to be 1 mm.  
Figure 19: Design of anchor for fastening building facades.  
Figure 18: Abrasion of EP and VE composites reinforced with basalt powder (BP), basalt fibre (BF) and basalt textile (BT).
## 3 Experiment and Parameter Setup  
### Sampling Scheme  
The membership-fusing model was applied to the three-stage sludge-treatment process in Beijing Gaobeidian Wastewater Treatment Plant, a municipal wastewater treatment plant in Beijing, China. The sludge-treatment technical route was abstracted into the three-stage process in consistent with that illustrated in Figure 2, as is shown in Figure 3. As centrifugal thickening and pre-dewatering are the two physical operations with the same pressure on the environmental condition, which makes little difference on the methanogen community structure, these two processing stages are both included in Stage I. Sampling points were set at the exit of each processing stage. Six paralleling samples were collected for one sampling point at each sampling time. Sludge samples were collected once a day in the morning in every September with 30 days from the past six years, 2014 to 2019. Eighteen samples obtained each day were kept away from light and stored at \(-20\)\({}^{\circ}\)C within three days for the gene sequencing.  
Figure 3: Sludge-treatment process and distribution of sampling points of Beijing Gaobeidian Wastewater Treatment Plant.
# A Study on Sensitive Bands of EEG Data under Different Mental Workloads
## 2 EEG Dataset  
### Experimental Subject  
In this experiment, 23 subjects are selected. All 23 subjects are graduate students of Beihang University, which limited the individual differences of the subjects to some extent. In addition, in order to minimize the influence of factors other than individual differences, each subject is required to ensure adequate sleep and good mental state before the experiment. All experiments of every subject are carried out and completed between 9 a.m. and 12 a.m.
## 3 Results and Discussion
### Prediction of IENU
#### 3.3.1 Linear Regression  
Data 2 exhibited the lowest testing RMSE (8.29), suggesting accurate predictions, and a comparatively low training RSE (6.864), indicating a robust model fit. Data 4, with a testing RMSE of 7.82, outperformed Data 2 in testing accuracy but has a slightly higher training RSE of 8.073, though still below Data 3's. Conversely, Data 1 showed the poorest performance, with the highest testing RMSE (66.86) and training RSE (90.78), indicating the least accurate predictions and model fit. Data 3, despite the highest R2 value during testing, also had high testing RMSE (45.96) and training RSE (98.55), reflecting lower accuracy and fit (Table 6).  
Overall, Data 2 was the top performer, effectively balancing prediction accuracy and model fit, while Data 4 served as a viable alternative, although its low R2 value indicates sensitivity in this metric. Despite the moderate explanatory power demonstrated by all models, the model's explanatory power is limited, as the three variables collectively explain only 27% of the variation in the data. In contrast, a multiple LR study by [70] accounted for 74% of the variation in nitrogen use efficiency. Analysis of the coefficients reveals that T had the most substantial impact on IENU (2.96), followed by NC (0.12), and then D (0.03).  
Figure 2: Comparison results of train/test data using ML models for PY prediction (data 2).