esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparative Analyses of Soil Moisture Anomalies Versus Land Surface Temperature Anomalies
#### 3.3.2 Spatial Distribution of Anomalies  
As the next step of the evaluation, the consistency in SMAI and LST behavior and the spatial distribution and variability of their anomalies was analyzed. Mean monthly anomalies were calculated with respect to the entire period of 2007-2018 and visualized in color maps. An example of spatial distribution over Bulgaria of the SMAI and LST anomalies for a selected 'dry' period, July-September 2012 is shown on Figure 7. The dry SMAI anomalies and the warm LST anomalies are indicated by reddish colors.  
SMAI anomalies at 50 cm soil depth (Figure 7a-c) show that dry soil moisture anomalies are progressively decreasing from July to September. For most parts of Bulgaria, the LST anomalies exhibit a difference from the SMAI trend in 2012: in August (Figure 7e), the LST anomalies slightly decrease compared with July (Figure 7d) and then increase again up to 4-6 \({}^{\circ}\)C in September (Figure 7f). The fraction of vegetation cover in September 2012 is relatively lower, leading to a relative decrease in evapotranspiration and, consequently, to an increase in LST and its anomalies.  
This behavior of LST anomalies is not observed for the southwestern parts of the country where the LST anomalies decrease progressively from July to September (Figure 7d-f) becoming 'green' in September. The southern flow of Mediterranean Sea air masses and the cooling effect of the mountain region mitigate the decrease of the fraction of vegetation cover in the region and the anomalies of the two biogeophysical indices.  
However, a very good coincidence in the spatial distributions of 'dry (in red)' soil conditions and 'hot (in red)' land surface regions is shown, comparing the maps of the spatial distribution anomalies of the biogeophysical indices month by month: locations with the indicated dry negative SMAI anomalies (Figure 7a-c) correspond to regions with positive LST anomalies (Figure 7d-f) for the same period. Some existing mismatches in the degree of SMAI-LST anomalies are due to the highly inhomogeneous land cover as well as the complicated nature of their biogeophysical relations: LST is directly linked to precipitation, cloudiness, solar irradiance, while SMAI is influenced by the cumulative effect of these meteorological parameters and with functional links with vegetation.  
Figure 6: Negative linear regression models of the relationship between LSASAF LST anomalies and SMAI (for 50 cm, black; 100 cm, red; mean, green) for (**a**) June, (**b**) July, and (**c**) August over Bulgaria. The monthly mean (2007–2018) is used to calculate the anomalies.
Sensors_**2011**, _11_, 2385-2407; doi:10.3390/s110302385  
_Series of_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Sparsity-Based Spatial Interpolation in Wireless Sensor Networks**  
**[PERSON], [PERSON], [PERSON] * and [PERSON]**  
Department of Communication Engineering, Xiamen University, Xiamen 361005, China;  
E-Mails: [EMAIL_ADDRESS] (D.G.); [EMAIL_ADDRESS] (X.Q.); [EMAIL_ADDRESS] (Y.Y.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-592-258-0142; Fax: +86-592-258-0142.  
_Received: 8 November 2010; in revised form: 26 December 2010 / Accepted: 9 February 2011 / Published: 25 February 2011_  
**Abstract:** In wireless sensor networks, due to environmental limitations or bad wireless channel conditions, not all sensor samples can be successfully gathered at the sink. In this paper, we try to recover these missing samples without retransmission. The missing samples estimation problem is mathematically formulated as a 2-D spatial interpolation. Assuming the 2-D sensor data can be sparsely represented by a dictionary, a sparsity-based recovery approach by solving for \(l_{1}\) norm minimization is proposed. It is shown that these missing samples can be reasonably recovered based on the null space property of the dictionary. This property also points out the way to choose an appropriate sparsifying dictionary to further reduce the recovery errors. The simulation results on synthetic and real data demonstrate that the proposed approach can recover the missing data reasonably well and that it outperforms the weighted average interpolation methods when the data change relatively fast or blocks of samples are lost. Besides, there exists a range of missing rates where the proposed approach is robust to missing block sizes.  
**Keywords:** data interpolation; sparsity; wireless sensor network
# Aerial Tele-Manipulation with Passive Tool via Parallel Position/Force Control +
## 5 Experimental Results
### Experimental Setup  
Our aerial manipulator was equipped with a lightweight tool (Figure 2) with a total weight of 0.05 kg. It was rigidly connected to the top of a quadrotor UAV with 1.0 kg  
Figure 6: Block diagram of teleoperation system using the aerial manipulator in free flight (**left**) and in contact interaction (**right**).  
weight. The quadrotor platform used for the experiments was a Mikrokopter\({}^{\copyright}\) x4 platform (HiSystems GmbH). The distance vector from the quadrotor COM to the end-effector surface was \(\mathbf{d}=\) [0 m, 0.5 m and 0.2 m]. The link lengths were \(d_{1}=0.51\) m, and \(d_{2}=0.02\) m long. A compliant spherical joint, that connects the lightweight rigid link to the end-effector, and an elastic shock absorber damper on the end-effector helped to establish smooth contacts. The end-effector surface was covered by a high friction material to expand the feasible force vector range.  
The robot positioning was performed by a Vicon tracking system (Vicon Capture Systems, London, UK). The quadrotor thrust and rotational controller was implemented on its onboard microcontroller (Atmel AVR 8-bit, ATmega-1284, running at 20 MHz), based on the inertial sensors of the robot, the rest of the control law was implemented on an external PC (Core i7, 16 GB RAM, running Ubuntu 14), communicating with the robot using a pair of Zig-Bee transceiver chips. An Omega.3 haptic device (Force Dimension, Nyon, Switzerland) was used as the master device. We used the force/torque ATI sensor (ATI Industrial Automation, Apex, NC, USA) embedded inside the object to measure the applied force by the aerial manipulator. The software was implemented in the ROS, and all control loops ran at a frequency of 100 Hz.
# Surface Plasmon Resonance-Based Fiber Optic Sensors Utilizing Molecular Imprinting
## 5 Molecular Imprinting
### MITs for MIP Synthesis  
#### Conventional Methods  
A variety of synthesis procedures for MIP preparation have been reported by a number of research groups [77; 78; 79; 80]. Usually, MIP synthesis is done by two types of processes: free radical polymerization and sol-gel processes. In the first type of polymerization method, \"bulk polymerization\" is the most commonly used procedure for MIP preparation. Small particles of MIPs (up to millimeter size) are prepared by simply crushing or grinding the bulk MIP mechanically. However, in this method, a large amount of template is required and it also suffers from non-uniform distribution of the particle size [81]. Thus, to control the diameter of MIP particles, a few alternative methods are developed such as in-situ (seed) polymerization, suspension polymerization, emulsion polymerization, precipitation polymerization and graft polymerization [79; 82]. Among these, suspension polymerization is the simplest method to adopt. The MIPs particles fabricated by this method have sizes in the broad range of 5-50 \(\upmu\)m due to the surfactant amount and the stirring speed. However, because of the disturbance of the dispersive medium, the method suffers from the limitation of a wide range of particles size and poor recognition [83]. MIP particles with high yield and mono-dispersity could be synthesized effectively by using the emulsion polymerization method, although, the method cannot be used for biological applications due the presence of the surfactants used [84]. MIP particles with controlled diameter can be achieved by the in-situ polymerization method which is also named seed polymerization. The particles prepared in this method are approximately the same in shape and size which is required for chromatography applications, but the method has its own restrictions of time consumption and use of aqueous suspensions, and it shows poor selectivity [85; 86]. More uniform size distribution of particle size could be obtained from the precipitation polymerization method but it requires large amounts of solvent and strictly controlled ambient conditions such as temperature, stirring speed, etc. [87; 88]. The sol-gel method is the more advantageous method as compared to the aforementioned methods in terms of its easy synthesis procedure at room temperature, immunity to chemical and thermal influences and the use of eco-friendly porogens such as ethanol, ultrapure water, etc. [12; 16].
# Analysis of Fire Resistance of Prestressed Concrete T-Beam Based on ABAQUS Numerical Simulation
## 3 Calculation Results and Parameter Analysis of Fire Resistance of Prestressed Concrete T-Beam
### Finite Element Model Calculation Results  
#### 3.2.1 Definition of Fire Resistance  
The fire resistance of building construction is the time experienced by the building construct from the onset of fire to the loss of stability, load-bearing capacity, or inability to continue functioning under the specified test conditions and the standard heating curve. By the \"Fire-Resistance Tests-Elements of Building Construct\" (GB/T9978-2008) [27], the fire resistance of building construction is determined by the spanwise deflection of the building construct, the rate of change of spanwise deflection, the temperature of the backfire surface, and the inability to perform its role in the event of combined damage. According to the code, the conditions were followed for the prestressed concrete T-beam model to reach fire resistance. The ultimate mid-span deflection of the prestressed concrete T-beam is 54 mm, or the rate of change of the ultimate mid-span deflection is 2.4 mm/min when the bending deformation of the prestressed concrete T-beam reaches 120 mm.
# Combined kNN Classification and Hierarchical Similarity Hash for Fast Malware Detection
## 1 Introduction
This classifies the training data into a normal group, a malicious group, and an undecided group. In addition, we extract features and train the deep-learning model for malware detection. Thus, we reduce the detection time, compared to using only the deep-learning model, by using kNN classification. When a test file is provided, we compute a similarity hash and search the kNN using a VP tree. Then, if the kNN is in the normal group or malicious group, the file is determined to be normal or malicious, respectively. Otherwise, we extract the features from the test file and determine whether it is malicious or not using the deep-learning model. Thus, we increase the detection rate, compared to using only the similarity hash, by using the deep-learning model. The contributions of this paper are as follows. First, by proposing the kNN classification for malware detection, we increase the malware detection rate compared with the similarity hash-based  
Figure 1: Proposed system for fast malware detection. TLSH: Trend Micro locality-sensitive hashing. malware detection and reduce the detection time compared with deep learning-based malware detection. Second, by providing a VP tree, we reduce the search time of the similarity hash. Third, on conducting experiments about kNN classification and VP tree, we report that the malware detection rate is increased by 25%; the detection time is reduced by 67%; and the search time of the similarity hash is decreased by 20%. This paper is organized as follows. In Section 2, we list related work. In Section 3, we introduce TLSH and the VP tree. In Section 4, we present kNN classification for malware detection and a VP tree using a similarity hash. In Section 5, we provide the experimental results. Finally, in Section 6, we conclude the paper.
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
## 2 Materials and Methods
### Datasets
#### 2.1.1 Erkomaishvili Dataset  
This dataset includes 100 monophonic audio files of traditional Georgian vocal music performed by a professional singer, [PERSON]. Each audio file contains the fundamental frequencies, segment annotation, onset annotations, and sheet music in XML. Moreover, it contains more than seven hours of music with 40,135 onset annotations. The annotations were estimated manually except for the fundamental frequencies, whose calculation was semi-automated. Moreover, in this dataset, the points for onset and offset in successive notes were deemed to coincide, i.e., the offset of the previous note is the onset of the new note. Since the files were recorded in 1966, the audio files have poor quality.  
In addition, the recordings are of natural melodic singing rather than only some scales or arpeggios. Therefore, it is a challenging dataset for automatic annotation algorithms.
# Determination of Cycle to Cycle Battery Cell Degradation with High-Precision Measurements
## 3 Experiments
### Measurement Procedure  
The cells are cycled differently in order to determine their charge losses, which allows statements to be made about the calendrical and cycle aging as well as cell-internal leakage currents. Therefore, state-of-the-art LG INR18650 MJ1 cells (NMC chemistry) with a nominal capacity of 3500 mAh and a maximum cell voltage of 4.2 V are used. All cells come from the same production batch.  
Three cells are cycled with different C-rates (which is referred to as C/x-scheme) between 2.5 V and 4.2 V without any rest period as shown in Figure 8. The first cell was periodically cycled in \(t_{\text{cycle}}=8\) h (C/4), the second in \(t_{\text{cycle}}=24\) h (C/12) and the third in \(t_{\text{cycle}}=72\) h (C/36). This procedure was chosen to determine differences in aging behavior with regard to the magnitude of the cycling current and duration.  
A fourth cell is also cycled between 2.5 V and 4.2 V with the cycling scheme from Figure 9, which takes a total of 72 h per cycle like the C/36 measurement. This cycling scheme is characterized by a holding phase at 100% SoC and is therefore briefly referred to as \"high-SoC-scheme\". Due to the same cycling times, the results of the standard cycling method and the alternative cycling scheme are now comparable with each other.  
It is expected that the calendrical aging of the high-SoC-scheme is greater than the aging of the C/x-schemes in the same time period, since a high state of charge leads to increased parasitic reactions [31]. To cycle the cell with the high-SoC scheme, it is firstly fully charged to its initial charge capacity Q\({}_{\text{C1}}\). Then, the cell is left floating (open circuit) for \(t_{\text{store}}=64\) h. After finishing the open circuit holding period, the cell is cycled, and the cells discharge capacity Q\({}_{\text{C2}}\) such as the charge capacity Q\({}_{\text{C3}}\) are measured. This cycling process is repeated periodically. Q\({}_{\text{C2}}\) differs from Q\({}_{\text{C1}}\) because charge is lost due to leakage currents and calendrical aging during the holding period. Q\({}_{\text{C3}}\) differs from Q\({}_{\text{C1}}\) because the cell suffered calendrical aging from the holding phase and cyclic aging from one cycle. Having Q\({}_{\text{C1}}\), Q\({}_{\text{C2}}\) and Q\({}_{\text{C3}}\) makes it possible to distinguish between cycle aging, calendrical aging and lost leakage charge due to leakage currents.  
Figure 8: Operating the cell with the C/x-scheme means that it is charged with a constant current between the lower and upper terminal voltage. \(t_{cycle}\) stands for the approximate duration of a charging or discharging process (the exact time varies with decreasing cell capacity because the cycling current stays constant).
## 4 Hazard
### The Role of Hazard Assessment  
A hazard is a potential source of harm or adverse health effect on a person or persons. From an environmental and ecosystem point of view one would include a range of significant organisms since persons may be indirectly harmed by harm to such organisms, e.g., through the food chain, such as bacteria, phytoplankton, fish and earthworms. Human related hazard assessment relies on _in vivo_ (animal) as well as _in vitro_ experiments, because human data (either from human bio-monitoring or from accident studies) will most likely not be available. Both _in vitro_ as well as _in vivo_ studies are prone to give false positive/negative results as an _in vitro_ system cannot cover all aspects of ADMET (adsorption, biodistribution, metabolism, excretion and toxicity) of NMs even though cells of human origin are often used.  
Neither _in vitro_ nor _in vivo_ systems can follow the same internal life cycle of NMs in tissue or cell environment (different proteins and metabolisms) as could be expected in human. Thus even more with engineered NMs than with conventional chemicals relevant hazard assessment has to be established for the purposes of a specific risk management activity, since risk (particularly in relation to occupational and consumer safety and health) is the likelihood that a person may be harmed or suffer adverse health effects if _exposed_ to a hazard. Additionally, sensitive group such as children or people with certain diseases such with asthma, cardiovascular diseases or individuals with higher genetically predisposed susceptibility to NMs should be also taken into consideration.  
Hazard plus exposure puts us in the arena of risk and risk management. Nanosafety is concerned with the identification of new hazards (with occupational, health and environmental implications) arising from the specific character of manufactured (or \"engineered\") nanoscale entities. The central question of the emerging discipline of nanosafety is therefore: What are the _potential sources_ of harm or adverse health effects on a person or persons--and more broadly on organisms ecologically intertwined with the health of humans--that are specific to manufactured nanoscale entities and the materials in which they may be \"embedded\"?  
Although hazard assessment is logically prior to risk management, in creating a science and technology of nanosafety the researchers have to integrate across characterisation, hazard, release, exposure and risk management (which will always entail \"risk perception\") [15]. Attempts must be made to bridge the traditional gap between exposure studies and hazard assessment studies ('impact studies'), by designing an experimental strategy that would cover all these aspects. Ideally such a bridge would identify strategies for the simplification and harmonisation of experimental design whilst still capturing the major transformations undergone by NMs across their life cycle. In an integrated approach experiments would mimic more realistically the fate of NMs as they may undergo transformations from release, through exposure, to uptake and impact in biological systems.
# Detecting Arcing Faults in Switchgear by Using Deep Learning Techniques
## 2 Materials and Methods
### Proposed Methods
#### 2.1.3 1D CNN  
In this study, 1D convolution operations were used to extract representative features of arcing and non-arcing in both the time and frequency domains. The architecture used was a basic 1D-CNN with convolutional layers, filters, MaxPooling, a fully connected layer, and a classification layer. Here, the 1D-CNN can match the one-dimensional characteristics of datasets in the time and frequency domains. To introduce nonlinearity, the rectified linear unit (RELU) was utilized as the activation function. Additionally, batch normalization and dropout were used to normalize inputs and prevent overfitting. Although deep CNNs have gained prominence in image classification competitions like ImageNet [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], they are primarily used for large-scale 2D visual databases such as video analysis and image identification. However, there are not many uses for 1D sequence data in forecasting and classification issues. Arcing and non-arcing categorization can be seen as a serial modeling process, making short and concise 1D-CNNs ideal for real-time and limited applications [50]. Equation (1) through (3) and Figure 2 illustrate the convolution layers, filters, and fully connected layer utilized in the 1D-CNN architecture. In this study, one-dimensional feature maps were used to match the one-dimensional nature of the datasets in the time and frequency domains. The 1D-CNN architecture, combined with the Mel-Spectrogram transformation, was able to identify arcing faults in switchgear with high accuracy.  
\[x_{o,fl}^{l}=f\left(\sum_{in}x_{i}^{l-1}*k_{io,fl}^{l}+b^{l}\right) \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Fault** & **Number of Samples in TDA** & **Number of Samples in FDA** \\ \hline Arcing Faults & 54 \(\times\) 20,001 & 53 \(\times\) 10,001 \\ \hline Corona Faults & 41 \(\times\) 20,001 & 39 \(\times\) 10,001 \\ \hline Tracking Faults & 313 \(\times\) 20,001 & 40 \(\times\) 10,001 \\ \hline Mechanical Faults & 17 \(\times\) 20,001 & 16 \(\times\) 10,001 \\ \hline Normal Faults & 13 \(\times\) 20,001 & 12 \(\times\) 10,001 \\ \hline Total Size & 17.5 Mega-Byte (MB) & 11.3 Mega-Byte (MB) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The samples of datasets for arcing and non-arcing in the time and frequency domains.  
\[x_{o}^{l}=f\left[max\left(\sum_{im}x_{i}^{l-1}\right)+b^{l}\right] \tag{2}\] \[x_{o}^{l}=f\left(x_{i}^{l-1}*d_{io}^{l}+b^{l}\right) \tag{3}\]
## 3 Design of Data Collaboration Mode Based on Improved Framework
### Data Collaboration Method Based on ITS
#### 3.1.3 Collaboration between GPS Information and BIM  
This paper aims to carry out a 3D visual display on the basis of establishing the data coordination framework of the intelligent transportation system and using geographic data to enhance the BIM model. Therefore, this paper used the interactive way of GPS information and BIM technology to collect data. In this paper, through desensitization of GPS terminal service providers, we obtained the activity data of personal vehicles equipped with GPS tracking system in Wuxi, Jiangsu Province in 2020, including vehicle identification, pick-up time, longitude and latitude of pick-up location, and vehicle speed. This provides reliable support for follow-up interactive research. This paper carried out the following processing for GPS information: First, data filtering. Delete irrelevant data, low-sampling data, error data, etc. (such as unreal coordinate repeated timestamp, too fast speed, too long waiting time, too small trajectory, etc.); Second, map matching; Third, calculate the road speed; Fourth, coordinate change and eliminate error, applied in BIM traffic model; Fifth, use algorithms to mine travel hotspots and peak times. However, as the data are limited to private cars equipped with the supplier's location system, the data are still one-sided.