esa-sceva/satcom-chunk-collection huggingface dataset:
_Article_  
An Offline Signature Verification and Forgery  
Detection Method Based on a Single Known  
Sample and an Explainable Deep Learning Approach  
[PERSON] * and [PERSON]  
Department of Forensic Science, Central Police University, Taoyuan 33304, Taiwan; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS] check for  
Received: 20 April 2020; Accepted: 25 May 2020; Published: 27 May 2020  
**Featured Application: This deep learning-based method is useful in the computer-aided detection of forgery signatures by using a single known genuine sample.**  
**Abstract:** Signature verification is one of the biometric techniques frequently used for personal identification. In many commercial scenarios, such as bank check payment, the signature verification process is based on human examination of a single known sample. Although there is extensive research on automatic signature verification, yet few attempts have been made to perform the verification based on a single reference sample. In this paper, we propose an off-line handwritten signature verification method based on an explainable deep learning method (deep convolutional neural network, DCNN) and unique local feature extraction approach. We use the open-source dataset, Document Analysis and Recognition (ICDAR) 2011 SigComp, to train our system and verify a questioned signature as genuine or a forgery. All samples used in our testing process are collected from a new author whose signatures are not present in the training or other stages. From the experimental results, we get the accuracy between 94.37% and 99.96%, false rejection rate (FRR) between 5.88% and 0%, false acceptance rate (FAR) between 0.22% and 5.34% in our testing dataset.  
**Keywords:** handwritten signature verification; signature examination; convolutional neural network (CNN); explainable deep learning; forensic science
# Algorithm for Generating 3D Geometric Representation Based on Indoor Point Cloud Data
## 4 Case Study
### Analysis of the Results  
The members which featured over one degree of geometric representation generated by the proposed algorithm are wall 3 and wall 10 of room 2, wall 10 of room 3, and wall 4 of room 4. As presented in Section 1, the topological feature of the indoor environment is that the structural members are parallel or perpendicular to other members. Based on the feature, the included angle must be zero degrees. However, the non-zero included angle has several reasons why that member is not zero. In this case study, the reasons mainly have three reasons; first, the real member is inclined. Second, the number of points located in the member is insufficient to derive the geometric representation. Lastly, the member has over two other members, which caused the error of the case study.  
First, the included angles of wall 4 of room 4 (1.260249 degrees) and wall 10 of room 3 (1.027251 degrees) are not zero because the walls are inclined. The walls are non-structural members that just separate the adjacent rooms; thus, the member did not have to be perpendicular with the floor. In particular, room 4 has a non-rigid shape composed of inclined walls since the room is a warehouse detached from the whole house, which makes the average error of room 4 (0.564577 degrees) larger than other rooms. Second, wall 3 of room 2 has 1.260596 degrees because the number of points on that wall is 2545. The reasons why the wall has insufficient points are that room 2 is connected with stairs and wall 3 is located in the encounter place, thus the real member has a minimal area among the members. In addition, the potential error caused by the movement of the scanner must be increased if the number of points are insufficient to extract the plane [21]. Finally, the included angle of wall 10 of room 2 (1.493584 degrees) was caused by the convergence of different members in one object. In that wall, the window covered with curtain is scanned, and then the points located in that curtain are captured through the scanner. Accordingly, the difference between the surfaces of the wall and curtain induced the included angle. The error can be minimized by removing the covers of opening (e.g., door, window, mirror).  
Compared to the wall, the horizontal structural members (i.e., floor and ceiling) had a lower error (0.352327 degrees) than other vertical members (0.435682 degrees). In particular, the average included angle of horizontal members except for members in room 4 was 0.294404 degrees because room 4 was detached from the house. The error means that the included angle in room 2, which had the largest width of the room (11,500 mm), causes the gap (59 mm) between the real floor and the generated floor. However, the gap is lower than the possible error caused by the potential error of the scanner (\(\pm\)30 mm); thus, it would not be an error of the proposed algorithm. Consequently, this study can generate a geometric representation of an indoor environment with consistent accuracy using PCD capture through MLS.
## 3 The Application of a Linear Array for the Identification of LE and TE Noise Source  
### Experimental Setup  
The noise measurements are conducted in the low-speed open-jet wind tunnel in Northwestern Polytechnical University. The wind tunnel has a rectangular exit with dimensions of 0.3 m \(\times\) 0.09 m. The maximum inflow velocity is 100 m/s with turbulence intensity below 1%. The uncertainty of the inlet mean velocity is within 0.5%. A NACA65(12)-10 blade with a chord of 150 mm and a span of 300 mm is employed as the baseline blade. The blade is fixed by two plexiglass side-plates, as shown in Figure 7(a).  
A linear array with 32 unequally spaced microphones is employed to identify and characterize the noise source of the blade. The array is 0.69 m below the airfoil with the array center located at the mid-chord of the blade. The microphones are installed on a hard-wall surface. The measured sound pressure levels (SPL) are assumed to be 6 dB larger than that of their actual values assuming a total reflecting. It must be noted that, in order to remove the background noise, the correction methods for the effects of reverberation and shear layer on sound propagation developed by our group [41] was used in this study. The \(1/4\) inch BSWA microphones are utilized in the experiment. All the microphones are calibrated by a standard noise source with a frequency of 1000 Hz and a SPL of 94 dB. The acoustic time signals are recorded with a sampling rate of 32,768 Hz for 10 s. The narrowband spectra are computed with a Hanning window of 50% overlap and a frequency resolution of 32 Hz. The so-called \"_BT_ product\" is 320, where \(B\) is the frequency bandwidth and \(T\) is the sampling time. As a result, the autospectral random uncertainty is approximately \(\varepsilon_{r}=3.95\%\), which results in a random SPL uncertainty of \(u_{r,\text{SPL}}\in\{-0.18,0.17\}\) dB [42].
## 3 Determination of Parameters for Asphalt Concrete Material
### Softening Parameters \(b_{1}\), \(b_{2}\)  
The softening parameters (\(b_{1}\), \(b_{2}\)) shown in Equations (12) and (13) control the material softening behaviour after peak stress. These parameters are obtained from experiments, as detailed below.  
* Value of \(b_{1}\) from the uniaxial compressive test: The uniaxial compressive test was conducted for AC according to ASTM 1074. The detailed test results and setup can be further referred to [11]. Based on the test results, it is found that the corresponding strain at peak stress (\(f_{c}\) = 4.6 MPa) was about 0.018, and the final failure strain was about 0.1, which was higher than that of concrete. The Young's modulus obtained from stain gauges attached at the middle height of the sample was 598 MPa. Based on experimental results, the compressive energy \(G_{c}\) was calculated at 15.1 MPa\(\cdot\)mm. Hence, for example, the \(b_{1}\) value for a 10-mm mesh size was calculated as 3.45 using the method stated in Section 2.1.
* Value of \(b_{2}\) from the fractural test: The value of \(b_{2}\) is determined by fracture energy \(G_{f}\), which can be obtained from the uniaxial tensile test or the three-point single-edge notched beam test (SNB). In the current study, the SNB test was carried out to evaluate fracture energy \(G_{f}\) for the AC material. The detailed theory about the SNB test can be found in the established literature [19]. Therefore, only the test result is presented here. In the SNB test, the compacted AC beam was fabricated with a dimension of  
Figure 5: Equation of state (EOS) for AC with \(f_{c}\) = 4.6 MPa.  
400 \(\times\) 100 \(\times\) 100 mm\({}^{3}\) depth. A mechanical notch was sawn with a depth of 20 mm, which gave a ratio of notch to beam depth of 0.2. The simply supported sample with a span length of 340 mm was tested under a 35 \({}^{\circ}\)C temperature. From the test, fracture toughness \(K_{IC}\) can be obtained according to the formula suggested by [PERSON] and [PERSON] [19]. Then, the fracture energy \(G_{f}\) is calculated using:  
\[G_{f}=\frac{\left(1-v^{2}\right)K_{IC}^{2}}{E} \tag{18}\]  
in which \(E\) is the elastic modulus and \(\
u\) is [PERSON]'s ratio.  
The parameter \(b_{2}\) is further determined by assigning fracture energy \(G_{f}\) in the use of single element simulation of the uniaxial tensile test. The \(b_{2}\) is then obtained via an iterative procedure until the area under the stress-stain curve from the single element simulation coincides with the value of \(G_{f}/w_{c}\). The parameters obtained from SNB and single element simulation for AC (\(f_{c}\) = 4.6 MPa) are summarized in Table 2.
## 3 Case Study and Experimental Results
We have realized that each time the wind speed increased, the aerial vehicle was displaced from the desired aerial trajectory, forcing the vehicle to constantly rectify this route, which has consumed more time and efforts from the system. A sole 2,100 mAh battery was used for drone electronics, motors, mote and communications. During the experiments, the drone was able to fly with this battery along 5,000 m at a 5 m/s maximum velocity. This value substantially depends on the number of waypoints and the hovering time required to obtain all the data as well as the wind conditions.
# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
## 4 Classification Methodology
### Datasets
Non-flooded areas were characterized by the lowest values (on the order of 136-142 dB for peak SNR and \(-45\) to \(-30\) dB for GLO1) while flooded vegetation was characterized by higher values (on the order of 142-148 dB for peak SNR and \(-30\) to \(-20\) dB for GLO1) and open water was characterized by having even higher values (of the order of 148-156 dB for peak SNR and \(-20\) to \(-5\) dB for GLO1). The selection of the GNSS-R observables used by the MDTR algorithm to assess the GNSS-R inundation classification is further discussed in Section 4, analyzing their impact on inundation detection performance. In addition to the GNSS-R observables, the static ancillary datasets were also evaluated to assess their contribution to the classification. These ancillary inputs included: (1) the Shuttle Radar Topographic Mission (SRTM) 90 m resolution global digital elevation model (SRTM90 DEM) [51], Figure 6a, and (2) the Global Forest Watch Aboveground live woody biomass (AGB) density (Mg ha-1) dataset at 30 m resolution for the year 2000 (GFW AGB) [45], Figure 6b. The SRTM90 DEM provides 90 m resolution data at near-global coverage between 56\({}^{\circ}\)S and 60\({}^{\circ}\)N, obtained from an 11-day mission in February 2000 from a radar system onboard the Space Shuttle Endeavor. The inaccuracies of the SRTM90 DEM dataset are on the order of \(8.2\pm 0.7\) m and \(6.9\pm 0.5\) m, for X-band and C-band respectively, as compared to data from the International GNSS Service (IGS) [52]. GFW AGB was generated from more than 700,000 quality-filtered Geoscience Laser Altimeter System (GLAS) LIDAR observations using allometric equations to estimate AGB based on lidar-derived canopy metrics [45; 53]. The global set of GLAS AGB estimates was used to train random forest models that predict AGB based on spatially continuous data and provided global scale estimates. The main uncertainties on the GFW AGB dataset come from errors from allometric equations, the LiDAR based model, and the random forest models. According to their website, the error map for the current version of the biomass density map is not available yet. Both ancillary maps were re-gridded to \(0.005^{\circ}\times 0.005^{\circ}\) (\(\sim\)500 m \(\times\) 500 m) to match the CYGNSS data and used as inputs to the classification algorithm. Figure 6: Ancillary data used in this study, (**a**) digital elevation model (DEM) from the Shuttle Radar Topographic Mission 90 m resolution (SRTM90) in meters, and (**b**) Global Forest Watch (GFW) biomass density (Mg/ha) map obtained from GFW. Both maps have been re-gridded to \(0.005\times 0.005\) deg. lat/lon to match the grid resolution of the observables.
# Research on Spaceborne Target Detection Based on Yolov5 and Image Compression
## 3 Remote Sensing Target Detection
### Yolov5 Network
#### 3.1.3 Neck  
An FPN and PAN structure is used to combine the characteristics of the target on different layers, as shown in Figure 6. The FPN structure delivers strong semantic features  
Figure 4: The structure of CSP 1.  
Figure 5: The structure of CSP 2.  
from top to bottom, whereas the PAN structure delivers strong positioning features from the bottom upwards. It is beneficial for improving the accuracy of target identification.
# Smart Grid Theft Detection Based on Hybrid Multi-Time Scale Neural Network
## 3 Proposed Method
### Dataset Pre-Processing
#### 3.2.2 Abnormal Value Processing  
Abnormal value handling is an important part of data pre-processing that reduces or eliminates errors and biases caused by the inclusion of outliers in the data. Outliers are usually discrete points that appear in the dataset that do not conform to the normal distribution or trend. In the data analysis and modeling process, outliers often have a negative impact on the results and therefore need to be handled accordingly. Common outlier processing methods include deletion, replacement, and interpolation techniques, among which deletion is the simplest and most direct method, although useful information may be lost. Replacement can retain data samples, but a suitable replacement strategy needs to be selected according to the specific situation. Interpolation estimates missing or abnormal values based on existing data points. In practical applications, it is necessary to consider what kind of abnormal value processing method is used in combination with various factors such as data distribution and feature analysis to ensure the accuracy and reliability of the data. In this paper, the abnormal values were processed according to Equation (2).  
\[\mathrm{F}(x_{i})=\begin{cases}\mathrm{avg}(\mathrm{X})+2\cdot\mathrm{std}( \mathrm{X})&x_{i}>\mathrm{avg}(\mathrm{X})+2\cdot\mathrm{std}(\mathrm{X})\\ x_{i}&\mathrm{other}\end{cases} \tag{2}\]  
where X is the vector composed of \(x_{i}\), \(\mathrm{avg}(\mathrm{X})\)is the mean of X, and \(\mathrm{std}(\mathrm{X})\)is the standard deviation of X.
# High Performance GAA SNWT with a Triangular Cross Section: Simulation and Experiments
## 6 Conclusions  
We demonstrated a gate-all-around silicon nanowire transistor (GAA SNWT) with a triangular cross section by simulation and experiments. Through three-dimensional simulation tool Sentaurus, it was found that with the same nanowire width, the triangular cross-sectional SNWT exhibited the lowest subthreshold swing, the highest on/off ratio, and the most excellent SCE immunity compared with the circular and quadrate ones, which may be attributed to the smallest equivalent distance from the nanowire center to the surface in triangular SNWTs. Then, P-type triangular GAA SNWTs with L\({}_{\rm g}=140\) nm and D = 20 nm were fabricated by the TMAH wet etching technique. Thanks to the anisotropic etching rate of the TMAH solution, a self-stopped etching behavior on a (1 1 1) crystal plane can be achieved, ensuring the accurately controlled size and cross-sectional shape of the channel. Finally, we measured and modelled the carrier mobility of fabricated devices with varying temperatures so as to extract the surface roughness mobility of 629 cm\({}^{2}\)/Vs, confirming the benefit of a stable and smooth cross section obtained by TMAH treatment.  
Conceptualization, M.L.; Methodology, M.L.; Software, G.C.; Formal Analysis, M.L. and G.C.; Data Curation, G.C.; Writing--Original Draft Preparation, G.C.; Writing--Review & Editing, M.L.; Project Administration, M.L. and R.H. This research received no external funding. This work was supported in part by the National Key Research and Development Plan (No. 2016 YFA0200504), Program of National Natural Science Foundation of China (No. 61474004 & No. 61421005). The authors would like to thank the staff of National Key Laboratory of Micro/Nano Fabrication for the assistance in device fabrication. The authors declare no conflict of interest.
## 2 Approach and Implementation
### Homogeneous Datastructure for Inhomogenous Datasets
The majority of the existing structure remains in the updated implementation with key elements included to satisfy the requirements of data source context in relation to distributed inhomogeneous data sources. Included in the structure, session information provided indexing for session context of the data source specific to both the sensor, and the context for the whole session of a collection of sensors. For example, the context of a sensor includes the definition of spatial placement in relation to the testing subject, such as the placement on the human body. Modifications to the \"sensor\" structure were made to ensure that the update structure was inclusive of varying data source collection methods, moving away from specific device structures. The majority of modifications made to the athlete data (Athdata) structure saw the inclusion of two additional structures referred to as \"Metadata\" and\"Segments\" (Figure 1). Both were developed to provide indexing interfaces of inhomogeneous data sources. Therefore, allowing the ability to associate searchable references to metadata tags in the context of a whole session, dataset and specific regions of time series data. Figure 1: Unified Modelling Language (UML) top-down depiction of the revision for athlete homogeneous wearable sensor data structure.
# Performance Analysis of Time Synchronization Protocols in Wireless Sensor Networks
## 5 Evaluation Result
### Effect of Packet Loss  
Packet loss might occur frequently in a practical network, especially in wireless networks. Packet loss can be caused by many factors, such as collision during transmission, signal quality, hardware, and software issues [44]. In this simulation scenario, we do not focus on the cause of packet loss in WSNs. Instead, the performance of each protocol is evaluated for different packet loss ratios. It is predicted that the packet loss will deteriorate the performance of the time synchronization protocols. Figure 13(a) shows that the number of rounds (convergence time) increases gradually with the packet loss ratio. Similarly, the network synchronization error increases with the packet loss ratio, as shown in Figure 13(b). Generally, the network synchronization error of E-FTSP is still smaller than those of GTSP and FTSP under the same conditions. It is noteworthy that the network still achieves synchronization even in high-error environments such as one with 30% packet loss. This is because the time synchronization protocols send messages periodically; hence, it is acceptable if packets are lost in some synchronization rounds. In other words, packet loss does not cause abnormal errors in E-FTSP (and in FTSP and GTSP). This proves the robustness of these protocols.  
Figure 13: (**a**) Average network error of FTSP, GTSP, and E-FTSP according to the network size (number of nodes). (**b**) Average number of rounds required to synchronize entire network of FTSP, GTSP, and E-FTSP according to the network size (number of nodes).
# Application of Gas Foil Bearings in China
## 2 Applications of Gas Foil Bearings
### Application in Centrifugal Blowers  
The centrifugal blowers are important in industrial ventilation, which are widely used in many situations, such as medical, chemical, and sewage treatment. Due to the advantages of the high adaptability of the gas foil bearings, the service life and reliability of the blowers are greatly improved under extreme conditions, such as in a dusty environment. On the other hand, flower with gas foil bearings have the merits of low noise, high efficiency, and no pollution. In view of above advantages, centrifugal blowers supported by gas foil bearings have become the first choice for energy-efficient blower. Since the 1970s, foreign blower companies have tried to apply foil bearings in centrifugal blowers. By the end of the last century, highly efficient, energy-saving and highly integrated gas foil bearing blower products have come out on the market. Although domestic theoretical research on gas foil bearings has already been carried out, due to constraints such as materials and process levels, mature products are not yet available on the market. The main domestic research work in this area is the Institute of Machinery Manufacturing Technology, CAEP (China Academy of Engineering Physics) and Shijiazhuang Kingston Bearing Company. [PERSON] developed a high-speed air foil bearing with a load capacity of 12.5 kg and tested  
Figure 5: Airborne turbo cooler by Xi’an Jiaotong University [50].  
the bearing performance [53]. By assembling air foil bearings, high-speed motors, and fan impellers, a prototype centrifugal blower was designed and manufactured. The centrifugal blower is shown in Figure 6. The diameter of the rotor is 80 mm and the maximum speed of the original machine is 30 krpm.
## 4 Design Considerations
#### Estimate the duty cycle  
The objective of the proposed AC electronic load is used to generate a load current close to positive and negative symmetry, i.e., the entire resonance behavior must be completed in one switching cycle (in other words, \(f_{s}\leq f_{r}\)). When ignoring the errors and losses caused by all components, in the ideal case the resonant frequency \(f_{r}\) will be equal to the switching frequency \(f_{s}\).  
To provide sufficient energy to resonate continuously for the main circuit, the power switch of the resonant circuit must have sufficient conduction time. However, if this value is too large it will cause a positive and negative half-cycle asymmetry, which will reduce the energy recovery rate. The aforementioned description can be expressed as  
\[\frac{1}{f_{s}}\geq\frac{1}{f_{r}}+D_{\text{max}}T_{s} \tag{39}\]  
where \(D_{\text{max}}\) is the maximum duty cycle of the power switch.  
Equation (39) can be rewritten, and we can obtain \(D_{\text{max}}\) as  
\[D_{\text{max}}\leq 1-\frac{f_{s}}{f_{r}} \tag{40}\]  
According to Table 1, the selected switching frequency \(f_{s}\) ranges 33.5 kHz to 38.6 kHz, and when substituting into (40), the maximum duty cycle \(D_{\text{max}}=13.2\%\).