esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Architecture  
### Overall System Architecture  
The whole system is composed of five parts: energy control, remote interaction, data storage and access, environmental control, and observation system. The atmospheric parameters, geomagnetism, aurora, and ionosphere in various regions are noted through the observation system. The relevant scientific analysis team will combine this data to analyze the sun's activity to predict the impact on the earth. The energy control system powers the measurement of the entire observation system, and the remote interactive system provides instructions. The operation status of each observation subsystem and the query and storage of observation data require an environmental control assurance system and a data storage access system to ensure its regular operation. The block diagram of the complete system is shown in Figure 1.  
In Figure 1, five parts are marked. Connect any two parts with double arrows or single arrows to indicate the energy and data connection between them. The orange arrow represents the data flow, and the red arrow represents the energy flow. The signal transmission method includes a wired network and wireless network. The transmission method of energy is wired transmission. Currently, it is not necessary to display renewable energy such as wind energy and light energy in the energy transmission system. The direction of the signal flow can be seen. The remote communication and on-site interactive system are interconnected in real-time with the domestic through the satellite network. In addition to the signal connection between the remote interactive system and other systems, there are also interactive signals between the data storage access system and the observation system.  
For the energy flow, the energy input of the external environment and the energy loss of the system are omitted in Figure 1. The red energy flow only expresses the transmission of electrical energy between systems. The energy storage part of each system itself is included in the system. The total power generation part of the entire system is included in the energy system, and the distributed power part is included in the environmental protection control system. As the on-site signal gathering place, the environmental protection control system and the other four systems have separate two-way signal transmission, ensuring that the work of each system can be directly controlled through the remote interactive system in China, and the status parameters and database document information of the system can be directly obtained. The power generated by the energy system is first transmitted to the environmental control system, and the power distribution module of the environmental system distributes the power to the remaining three systems again.
# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Categories** & **Parameters** & **Units** & **Description** \\ \hline \multirow{8}{*}{Monitoring Parameters} & IAS & KTS & Corrected Airspeed \\  & VAPP & KTS & Approach Reference Speed \\  & GLIDE DEVC & DDM & ILS Glideslope Deviation \\  & LOC\_DEVC & DDM & ILS Heading Deviation \\  & ROLL & DEG & Roll Angle \\  & IVV & FT/MIN & Decent Rate \\ \hline \multirow{4}{*}{Pilot Operating Parameters} & TLA & DEG & Throttle Resolver Angle \\  & SEL\_COURSE & — & Selected Course \\  & FF1 & PPH & Fuel Mass Flow Rate ENG.1 \\  & VB11 & \% & N1 VIB ENG.1 BRG1 or TRF \\ \hline \multirow{8}{*}{Flight State Parameters} & HEIGHT & FT & Corrected Height \\  & RALTC & FT & Radio Height \\  & ALT\_STDC & FT & Altitude Standard Recorded \\  & VRTG & G & Normal Acceleration \\  & LATG & G & Lateral Acceleration \\  & LONG & G & Longitudinal Acceleration \\  & SPOL\_POS & — & Spoiler Position \\  & VOR\_FRQ & HZ & Vor Frequency \\  & N11 & \% & Low Rotor Speed ENG.1 \\  & FLAPC & DEG & Flap Actual Position \\  & HEAD & DEG & Magnetic Heading \\  & PITCH & DEG & Pitch Angle \\  & RUDD & DEG & Rudder Position \\  & FPA & DEG & Flight Path Angle \\  & VREF & KTS & Landing Reference Speed \\ \hline \multirow{4}{*}{Environmental Parameters} & WIN\_DIR & DEG & Wind Direction \\  & WIN\_SPD & KN & Wind Speed \\  & LONPC & DEG & Present Position Longitude \\  & LATPC & DEG & Present Position Latitude \\ \hline \multirow{2}{*}{Indicating Parameter} & TIME R & — & Time \\  & FLIGHT\_PHASE & — & Flight Phase \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification and description of QAR parameters. In response to data quality issues, this paper first supplements missing values using the nearest neighbor principle. Then, we perform dimensionality reduction on the data and standardize the sampling rate. By combining the industry characteristics of QAR data with feature engineering techniques, new features can be generated to improve the accuracy of the results. In response to the shortcomings of traditional detection methods, we use machine learning and automated models to classify data and calculate contribution rates to achieve automated anomaly detection and intelligent factor analysis of civil aviation flight data. In summary, the main contributions of this work are listed as follows:  
1. An automated anomaly detection method is proposed, tailored to the unique characteristics of QAR data and the requirements of FOQA. This model can detect various abnormal events in one operation routine. 2. In order to achieve efficient convergence rates and lower over-fitting risks, the advanced feature engineering techniques and hyper-parameter auto-tuning techniques are utilized. 3. An automated and intelligent causal analysis technology for abnormal events is proposed to address the limitations of the traditional FOQA working method. 4. Method evaluation experiments are conducted on real QAR datasets. The evaluation and comparison results are presented with in-depth analysis.
# Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm
### Objectives and Research Framework
#### 1.2.2 Research Framework  
In this study, we investigated the feasibility of integrating the SSS algorithm with the IMS to guide the development of an intelligent maintenance platform for smart factories. The Maintenance Methods Section primarily focuses on optimizing predictive maintenance systems, where the SSS algorithm provides the best alternatives for automated machine maintenance while minimizing routing costs. This study utilizes case study data from electric wire factories, an automated industry where machines perform nearly all tasks traditionally carried out by humans along the production lines. The researcher obtained approval for access to certain information to conduct this research while adhering to ethical standards. The research procedures were established in accordance with the predefined research framework, outlined in the following priorities (Figure 12).
# Research on Map-SLAM Fusion Localization Algorithm for Unmanned Vehicle
## 4 Experimental Verification  
The platform was modified from the Junipai A70E electric vehicle, and the body was loaded with sensors such as IMU, GNSS and a monocular camera. The IMU sensor adoptsMEMS products from Xsens, and the GNSS receiver adopts Huace P3 series products, and adopts the centimeter-level positioning service of Thousand Seek. Further, unmanned experiments were carried out on the Qingshuihe campus of the University of Electronic Science and Technology of China and the Longchi Tunnel. As shown in Table 1.  
In the experiment, the monocular camera + IMU sensor installed on the roof was used to complete the SLAM system. The body has sensors such as GNSS [24], IMU, and a camera. The GNSS dual-antenna device applied in the paper is used for true value positioning and orientation in unmanned driving experiments. Due to the dual antennas used, only the yaw angle and pitch angle of the vehicle can be calculated during orientation, but the roll angle cannot be calculated. Considering that the change of the roll angle of the vehicle body is small, it can be ignored in the actual application process. Most of the poses collected by GNSS represent the current position of the vehicle, so the GNSS antenna is required to be installed as parallel to the vehicle as possible. Figures 6 and 7 below shows the relative positional relationship between the sensors fixed on the roof.  
To evaluate the algorithm performance, an experimental analysis was performed. The work of map structuring can be stored in the map by the data collection vehicle and processed and obtained. It can also be completed by querying and interpolating the latitude and longitude on the Baidu map. Due to the basic deviation of the network map, the map data can be collected at the location with GNSS alignment.  
The experimental data are that the data acquisition vehicle runs in the campus environment and the lane line area in the Longchi tunnel environment. This collects images and GNSS data. In order to judge the accuracy and performance of the algorithm, the true value data of the lane line position are needed. In this paper, the true value data of the lane line position were collected by a special trajectory acquisition vehicle. After the data were collected, the image data were used as the input of the algorithm, and the data collected by GNSS were used as the real value to evaluate the performance index of the algorithm. In the tunnel of the Longchi scene, there is a lack of real positioning value. This paper used the processed latitude and longitude interpolation results in the map as the real value, and fused the output positioning results and map data for trajectory error calculation.
# Remote Sensing in Urban Forestry:
## 2 Remote Sensing Applications in Urban Forests  
To summarise recent applications of remote sensing in the field of urban forestry, we focused our search on research articles published in peer-reviewed journals, in English, within the time span between January 2013 and March 2019. Multiple databases, including ISI Web of Science, Elsevier's Scopus, Google Scholar and ScienceDirect, were browsed using the terms \"remote sensing OR LiDAR\" AND \"urban forest OR tree OR plant OR vegetation OR greenspace\". More than 200 studies were found to satisfy the aforementioned conditions. These studies were further filtered based on the following criteria:  
1. The research must focus exclusively on urban forests or greenspaces. Studies involving natural forests are excluded.  
2 The research must use remotely sensed data obtained from airborne/spaceborne imagery or LiDAR. Studies involving data based on other techniques, such as pure photogrammetry or terrestrial-based methods, are excluded.
3 The research must involve at least one of the three themes: multi-source, multi-temporal, and multi-scale inputs. Studies unrelated to any of these themes are excluded.  
The final number of studies found totalled 63; these were classified in terms of 10 different applications and three distinctive themes (Table 1). A detailed description of each study is reported in Supplementary Materials. The articles cover remote sensing applications in diverse areas, including measurement of urban forest attributes (e.g., biomass, species composition, canopy cover and spatial configuration) and quantification of ecosystem services (e.g., carbon storage, water retention, air quality improvement, and urban heat island mitigation). In the following sections, the articles are reviewed and discussed in terms of the themes multi-source, multi-temporal and multi-scale input; their strengths and limitations are summarised in Table 2.
# A Tunable Metamaterial Joint for Mechanical Shock Applications Inspired by Carbon Nanotubes
## 5 Evaluation and Characterization of the Compressive Behavior of the Joint
### Dynamic Impact Analysis
As it can be seen, the total strain energy of both the upper and lower wireframes was maximized when the impact axial loading reached its peak and then evenly vanished. On the other hand, the total plastic work stored in the beams was stabilized after the load peak. The effect of the mass increase around the flywheel became distinct due to the higher remaining plastic work observed for the case of \(\mu_{\rm add}=4\,\)Kg/m. Figure 11: Cont. Figure 11: Time variation of the upper face vertical deflection under a half\(-\)sine impact shock characterized by a frequency of (**a**) 30 Hz, (**b**) 40 Hz, and (**c**) 50 Hz, for a variety of mass additions around the flywheel perimeter. Figure 12: Time variation of the middle flywheel axial rotation under a half\(-\)sine impact shock characterized by a frequency of (**a**) 30 Hz, (**b**) 40 Hz, and (**c**) 50 Hz, for a variety of mass additions around the flywheel perimeter. To summarize the whole numerical outcome, Figure 14 is given, which demonstrates the fact that the impact resistance of the joint may be efficiently tuned and controlled by the flywheel mass magnitude. This figure presents the total plastic deformation work \(w_{\text{pl}}\) in both the upper and bottom wireframes with respect to the added mass around the flywheel. As it may be seen from the specific figure, less plastic work was produced by the wireframes as the flywheel mass increased, implying that these load-carrying components received a less significant permanent deformation and damage. However, special care and extensive parametric studies are required for each frequency in order to select the proper mass adjustment. For example, the addition of a perimetric mass \(\mu_{\text{add}}=1\,\text{kg}/\text{m}\) under an impact frequency of 30 Hz seemed not to have any positive effect on the wireframe's structural health. It should be mentioned that due to the complexity of performing impact test measurements, the experimental dynamic investigation of the proposed joint is to be performed in  
Figure 14: Total plastic deformation work in the wireframes versus the mass addition around the flywheel perimeter for several impact frequencies. Figure 13: Time variation of both the total strain energy and plastic deformation work in the wireframes under a 50 Hz half-sine impact shock, for zero and 4 kg/m mass addition around the flywheel perimeter. a future study with the use of an even more specialized--in terms of inherent damping--raw material.
## 1 Introduction
To overcome this shortcoming [PERSON] and [PERSON] [28] tried DPSM with equivalent sources densities instead of the unique point source. They proposed two modifications--(1) G-DPSM or Gaussian DPSM replacing the single point source by a family of sources whose strength variations follow a Gaussian profile, and (2) ESM (Element Source Method): replacing discrete point sources by continuously varying element sources. Although these two modifications avoided the near field inaccuracy problem both approaches had their limitations. Results generated by G-DPSM were strongly affected by the Gaussian distribution function parameters and the ESM formulation was relatively complex. Thus, one major advantage of the DPSM formulation, its simplicity, was compromised by ESM. The concept of equivalent source density, including elemental point sources, linear, surface, or volume repartition, was presented and advocated by [PERSON] et al. [29]. The objective of this paper is to propose a new ESD (equivalent source density) approach which is much simpler and can improve the near field computation accuracy without increasing the matrix size that is to be inverted for obtaining the solution. In this solution, the single point source is replaced by a source family. Point sources in a family are denoted as quantum sources to distinguish them from the regular point sources used in the classical DPSM formulation. A transducer can be modeled by \(M\) number of regular point sources in classical DPSM formulation or \(M\times F\) quantum sources in the modified formulation. If every one of the \(M\) point sources are replaced by \(F\) number of quantum sources then the total number of quantum sources is equal to \(M\times F\). The quantum source strengths are set such that a given radiation pattern from the quantum source family is obtained. G-DPSM, proposed by [PERSON] and [PERSON] [28], assumed a Gaussian distribution of source strength variations in a source family and their final results were strongly affected by the Gaussian distribution function parameters. However, in the technique proposed here, the strength variation of the quantum sources in a family is assumed to be unknown, but the radiation pattern generated by a source family is considered to follow a specific pattern--Gaussian distribution is one possibility,but not necessarily the only possibility. However, for not assuming a Gaussian distribution of the source strengths in a family significantly increases the number of unknown source strengths. It is illustrated in the Formulations section how this improvement can be achieved without increasing the final matrix size that is to be inverted. Thus, this modification can improve the computational accuracy without significantly increasing the computational time.
# Research on Non-Contact and Non-Fixed Cable Force Measurement Based on Smartphone
## 2 The Proposed Method for Cable Force Determination
### Image Data Processing and Analysis
#### 2.2.1 Initial Target Positioning  
Due to the complex background environment and the poor detection stability of the Hough variation method [28; 29; 30], binarization and blob detection were applied to extract the position information of the pasted target. Since the test can be conducted during daytime or night, there would be considerable color differences for the detected target. Thus, image binarization based on red pixels is performed and can be expressed as Equation (2).  
\[P_{i,j}=\left\{\begin{array}{ll}255&IsRED\\ 0&other\end{array},IsRED(P_{i,j})=P_{i,j}^{R}>80\text{ and }P_{i,j}^{R}/(P_{i,j}^{G}+1)>T\text{ and }P_{i,j}^{R}/(P_{i,j}^{B}+1)>T \tag{2}\]  
In the equation, \(P_{i,j}\) is the pixel points with the coordinates (\(i\), \(j\)). \(P_{i,j}^{R}\), \(P_{i,j}^{G}\) and \(P_{i,j}^{B}\) are values of the red, green and blue channel of the point \(P_{i,j}\). T is the threshold for red color for which its empirical range is 1.5-2.2. This means that the red channel value of the pixel point considered as red is larger than 1.5 times of the green and blue channels and less than 2.2 times. In the experiment, T was set to be 1.8 for daytime scenario and 2.0 for the night-time scenario. To cope with complex situations where environmental noises are considerable, the T value in the developed software can be further adjusted to 2.2. It is noted that the requirement \(P_{i,j}^{R}>80\) aims to remove the target pixels that blend into the background in a dark environment in order to improve the extraction accuracy of target edges. After color-based filtering, blob detection algorithms can be applied to generate the coordinates and radius of the target.
# Automated Bone Age Assessment with Image Registration Using Hand X-ray Images
## 2 Related Work
Mask R-CNN is used in the work by [PERSON] et al. [36] in order to segment the hand from an X-ray image, where a residual attention network is then used to predict the bone age. It is a unique end-to-end approach, where the segmentation and regression networks are trained together, not separately. Instead of looking at the whole hand region, the work in [37] locates each of the 13 regions in the TW protocol and classifies them into one of the age categories using VGG-16 architecture. The final bone age prediction is obtained by adding up the scores from all 13 regions.
## 3 Results
### Metabolite Characterization Based on HS-SPME-GC-MS  
PCA of the total volatile organic compounds (VOCs) showed a significant difference between QC and the fermented grains samples (Figure 4A). However, the VOCs from the samples of 6 days and 8 days, 16 days and 27 days fermentation were partially overlapped. All VOCs from the different groups were separated well when the supervised OPLS-DA model was used (Figure 4B). The OPLS-DA model was cross-validated and underwent 200 permutation tests. An R2X value of 0.606 and an R2Y value of 0.989 were obtained. The value of Q2 was 0.88, which indicated the model had an excellent predictive ability. The \(p\) values of R2Y and Q2 were all lower than 0.005, implying that the model was statistically valid without overfitting (Figure 4C).  
A total of 181 volatile compounds were identified in the fermented grains by HS-SPME-GC-MS, including esters (75, 41.44%), alcohols (16, 8.84%), aldehydes (10, 5.52%), ketones (6, 3.31%), organic acids (9, 4.97%), alkanes (22, 12.15%), alkenes (20, 11.05%), and unclassified metabolites (23, 12.71%) (Table S4). All compounds were classified into two clusters by HCA (Figure S5). A total of 119 compounds formed Cluster I, and their concentrations were obviously decreased from 6 days of fermentation. Another 62 compounds, including ethyl butyrate, acetic ester, benzyl alcohol, ethanol, and ethyl isovalerate, formed Cluster II, and the dynamic changes in their concentrations showed the opposite trends to that of Cluster I. Based on the analysis, some ethyl esters, alcohols, and aldehydes were strongly correlated with the unique flavor of NLS, indicating these compounds possibly contributed to the flavor generation.  
Figure 4: Principle component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) of the compounds based on the HS-SPME-GC-MS detection. (**A**) The PCA model. (**B**) The OPLS-DA score plot of the different fermentation samples. (**C**) The 200\(\times\) permutation tests of the different fermentation samples. D0, D6, D8, D16, and D27 represent the fermentation samples collected on 0, 6, 8, 16, and 27 days, respectively.  
A previous study reported that 12 compounds mainly contributed to the flavor of NLS [9]. In this fermentation batch, 7 of these 12 compounds, including ethyl acetate, ethyl 2-methylbutyrate, ethyl isovalerate, ethyl butyrate, isoamyl acetate, ethyl caprylate and 3-methylbutanol were identified. However, the other five compounds, including ethyl acrylate, ethyl valerate, \(\gamma\)-nonalactone, \(\beta\)-damascenone, and geosmin, were not detected. We postulated that their abundances were below the detectable limits under this condition.