esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works  
### Vector Map Data Security  
The main idea of the watermarking schemes for GIS vector maps is to embed the watermark into the vector map by modifying the coordinates of vertices of geometric objects in the spatial domain [2; 3], or embedding the watermark into the spectrum coefficients of a sequence of vertices or topologies in the frequency domain [4]. Thus, vector map watermarking is not suitable for preventing attacks and illegal copying.  
The security techniques for GIS vector map data have been proposed in recent years [5; 6; 7; 8; 9]. Authors have mostly explained various methods to define an access control system for spatial data on the Web, or mentioned the security requirements for geospatial database management systems and privacy policies. However, access control on the Web and the management of databases do not prevent attacks, illegal duplication, and distribution. [PERSON] et al. [10] proposed a compound chaos-based encryption algorithm for vector data by considering the storage characteristics and the parameters of a chaos-based system; however, this method is not responsive to the various data formats of vector maps. [PERSON] et al. [11] encrypted the spatial index of a set of vector data in an external Oracle database management system when it is transmitted to the client; however, this algorithm does not ensure the security of vector maps because the key length is very short. [PERSON] et al. [12] also described an encryption algorithm that combined AES and RSA (Rivest-Shamir-Adleman) cryptography with a simple watermarking technique in order to protect the copyright protection of vector maps in online and offline services. This method encrypts all parts of a shape-file using an AES block cipher operator of 256 bits. This approach is typical because it encrypts the data stream of a shape-file using the AES cipher, and the computation time is very long. [PERSON] et al. [13] proposed a perceptual encryption method that is combined with the compression process for vector map data. This method only encrypted the direction and position of data units in the compression process. This method has low security.  
[PERSON] et al. [14] proposed a selective encryption method for vector map data based on a chaotic map in the frequency of discrete wavelet transform (DWT); however, this method only selects some objects and encrypts DC (Direct Current) values in the DWT domain by a common secret key. This method is very simple and weak because it does not encrypt all contents of vector maps and uses a common key. Moreover, this research did not present performance or security evaluations. In summary, the weakness of previous methods is solved by the proposed algorithm, which is presented in Section 3.
# Bearing Fault Diagnosis Based on Vibration Envelope Spectral Characteristics
## 4 Experimental Results and Analysis
### Analysis of Experimental Results
#### 4.3.3 Comparison of Fault Diagnosis Using Different Classification Algorithms  
To validate the simplicity and efficiency of the Naive Bayes algorithm, accuracy and classification time were compared using CWRU 0 hp data and MFPT 150 lb bearing data against machine learning and deep learning algorithms, as shown in Figure 8. Fault diagnosis algorithms include random forest (RF), Support Vector Machine (SVM), convolutional neural network (CNN), convolutional neural network combined with Bidirectional LSTM (CNN-BiLSTM), Back Propagation (BP), and Long Short-Term Memory (LSTM).  
From Figure 8, it can be observed that in the CWRU dataset, the accuracy of various classification algorithms reaches 99%, with LSTM, RF, and NB achieving a high accuracy of 100%. On the MFPT dataset, CNN and BP achieve an accuracy of 99.58%, while other algorithms achieve 100% accuracy. However, NB exhibits the shortest diagnostic time, significantly lower than all algorithms except SVM.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Acc (\%)** & **Sen (\%)** & **Spe (\%)** \\ \hline
150 lb & 100.00 & 100.00 & 100.00 \\
200 lb & 99.53 & 99.54 & 99.85 \\
250 lb & 100.00 & 100.00 & 100.00 \\
300 lb & 99.08 & 99.07 & 99.69 \\ average & 99.65 & 99.65 & 99.89 \\ \hline \end{tabular}
\end{table}
Table 2: Evaluation of fault detection results in MFPT.  
Figure 8: Diagnostic results of different classification algorithms on the (**a**) CWRU and (**b**) MFPT datasets.
## 3 Methods  
### Preparation of Modified Bitumens  
Mixer of model IKA EUROSTAR 20 DIGITAL (Figure 2) was used for preparation of modified bitumens in laboratory conditions. Frequency of shaft rotation for this mixing device is regulated automatically by microprocessor within the interval of 30 and 2000 rotations per minute. Shaft rotation frequency has been regularly compared with the target one and it is automatically corrected at deviation. This ensures constant rotation rate even at binder viscosity variation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Name of Polymer**} & \multicolumn{4}{c}{**Value of Characteristics**} \\ \cline{2-5}  & **Air Voids, \%** & **Voids in Mineral** & **Density, g/cm\({}^{3}\)** & **Water Saturation,** **\% by Volume** \\ \hline - & 3.8 & 15.7 & 2.39 & 3.0 \\ Calprene 501 & 3.0 & 16.0 & 2.41 & 2.0 \\ Butonal NS 198 & 4.1 & 18.1 & 2.39 & 3.5 \\ Elvlay 4170 & 2.6 & 16.3 & 2.41 & 1.5 \\ SBS & 3.7 & 17.1 & 2.40 & 2.2 \\ SBS SR & 3.3 & 16.8 & 2.40 & 1.85 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mix design characteristics of the stone mastic concretes.  
Figure 1: Granulometric curve of mineral part of the stone mastic asphalts.  
Preparing of the modified bitumens by the mixing device was performed in the following sequence:  
1. The neat bitumen was heated up to the temperature of 175-180 \({}^{\circ}\)C in the mixing device.
2. A polymer was gradually added in bitumen during mixing.
3. Full mixing time was 2 h.
4. Bitumens modified with polymer Elvaloy after mixing (duration for 2 h) were kept at the mixing temperature (175-180 \({}^{\circ}\)C) for 12 h.
## 2 The Personalized Diagnosis Methodology
### The Personalized Diagnosis Method Using WPT and SVM  
#### 2.2.1 The Basic Principle of WPT  
Wavelet transform (WT) is the process of the decomposition and reconstruction of the original signal both in the time and frequency spaces, which is widely used to extract the fault feature. However, at the high frequency bands, WT fails to give a good performance. Wavelet packet transforms (WPT) are the better choice to decompose the high-frequency bands. The typical binary tree structure is shown in Figure 2. Index \((i,j)\) represents the decomposition signal, where \(i\) is the layer and \(j\) (\(j=2i-1\)) is the node in the \(i\)-th layer, respectively. For instance, (0,0) represents the primary signal; (1,0) is the low frequency component in the first layer; and the corresponding high frequency component is (1,1).
# Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification
## 2 Overview of ABSA
### Aspect Based Sentiment Analysis  
The fine-grained analysis of sentiments for extracting various aspects and detecting the polarities of the extracted aspects [12] is referred to as Aspect Based Sentiment Analysis (ABSA). ABSA can be further divided into mainly three main sub-tasks [13]: (a) Aspect Extraction (AE) or Opinion Target Extraction (OTE), (b) Detecting the Aspect Category, and (c) Determining the sentiment polarity of aspect. Many studies have been carried in recent years, dealing with one, two, or all three subtasks of ABSA.  
In this case, e.g., consider the sentence in Figure 2, \"Awesome Thai food, price friendly but poor ambience\", Thai food, price, and ambience are aspects where 'Thai food' belongs to category {food}, 'price' belongs to category {cost} and 'ambience' belongs to category {miscellaneous}. Further, the sentiment polarity of 'Thai food', 'price', and 'ambience' is positive, positive, and negative, respectively.  
Since this study deals with the statistical comparison of various deep learning methods proposed in ALSC literature, thus in the subsequent section, the ALSC task is explained in detail.
# Investigation on Cutting Blasting Efficiency of Hard Rock Tunnels under Different Charge Diameters
## 2 Destruction Range of Single Blasting Hole  
When a cylindrical charge was detonated in the blasting hole, a shock wave with strong pressure was first aroused in the rock medium. The rock near the blasting hole was crushed into powdery rock particles under the shock wave, inducing the crushed zone. During the formation of the crushed zone, the stress amplitude of the shock wave decreased rapidly, and then the shock wave turned into a stress wave at the edge of the crushed zone. Due to stress attenuation, the stress wave could not lead to the crushed destruction of rock. However, the compressive stress exerted by the stress wave made the rock move outward along the radial direction of the blasting hole, and the rock outside the crushed zone would be subjected to the tangential tensile stress due to the Poisson effect. Then, because the tangential tensile stress was greater than the tensile capacity of the rock, radial fractures radiating around and penetrating the fracture zone would be generated [34]. After that, the stress wave attenuated to an earthquake wave at the edge of the fracture zone, which no longer induced the direct destruction of the rock and could only cause the elastic vibration of rock particles in the distant region. A diagram of the cylindrical charge blasting is presented in Figure 2.  
Since the tensile strengths of brittle media such as rock are usually only about 5-10% of their own compressive strength, the range of the blasting fracture zone was bound to be much greater than that of the blasting crushed zone [35]. Therefore, it was believed that the range of the blasting fracture zone was the key basis of a practical blasting design, therefore only the ranges of the blasting fracture zone under the different charge diameters are compared here. Moreover, in practical rock blasting constructions, the radial decoupled charge structure was often employed to facilitate the loading of the cartridges. Thus, based on the tensile stress failure criterion, [PERSON] et al. [36] have provided a formula for calculating the radius of the fracture zone after a radial decoupled cylindrical charge blasting, as shown below:  
\[R=\left(\frac{\rho_{\mathrm{e}}D_{\mathrm{e}}^{2}K_{\mathrm{r}}^{-6}Nb}{8S_{ \mathrm{t}}}\right)^{\frac{1}{2}}r_{\mathrm{b}} \tag{1}\]  
where \(R\) represents the radius of the fracture zone; \(\rho_{\mathrm{e}}\) represents the explosive density; \(D_{\mathrm{e}}\) represents the detonation velocity; \(K_{\mathrm{r}}\) represents the decouple coefficient, and \(K_{\mathrm{r}}=d_{\mathrm{b}}/d_{\mathrm{c}}\); \(d_{\mathrm{b}}\) and \(d_{\mathrm{c}}\) represent the diameters of the blasting hole and the cartridge, respectively; \(N\) represents the stress enhancement factor, taken as \(N=8\); \(b\) represents the lateral stress coefficient, and \(b=\mu/(1-\mu)\); \(\mu\) represents the Poisson ratio; \(\alpha\) represents the attenuation exponent of the stress wave, taken as \(\alpha=2-b\); \(S_{\mathrm{t}}\) represents the tensile strength; \(r_{\mathrm{b}}\) represents the radius of the blasting hole.
## 4 Test and Evaluation Methods
### Laboratory Binder Performance Test  
The tensile tests were performed on ESA35, ESA40, ESA45, and ESA50 binders using the WDW-100 universal material tensile machine manufactured by Jinan Fangyuan Testing Instruments Co., Ltd. (Jinan, China), and the tensile strength (_TS_) and elongation at break (_EB_) of the specimens were calculated. The test rate and temperature were 500 mm/min and 23 \({}^{\circ}\)C, respectively. For the same binder, at least three parallel tests were performed.  
Figure 3: Gradation design results for the asphalt concrete: (**a**) SMA-13 and (**b**) EA-10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test** & \begin{tabular}{c} **Specimen** \\ **Size/mm** \\ \end{tabular} & **Evaluation Index** & \
\begin{tabular}{c} **Test** \\ **Temperature/\({}^{\circ}\)C** \\ \end{tabular} & **Specification** & **Test Standard** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Rutting test \\ Beam bending test \\ Immersion Marshall test \\ Freeze–thaw splitting \\ tensile strength test \\ Pendulum test \\ \end{tabular} } & 300 \(\times\) 300 \(\times\) 50 & Dynamic stability (_DS_) & 70 & \(\geq\)6000 times/mm & T0719-2011 \\  & 250 \(\times\) 30 \(\times\) 35 & Maximum flexural & \multirow{2}{*}{\(-\)10} & \multirow{2}{*}{\(\geq\)3000 \(\mu\)c} & \multirow{2}{*}{T0715-2011} \\  & & strain (\(\epsilon_{\text{B}}\)) & & & \\ Immersion Marshall test & \(\Phi\)101.6 \(\times\) 63.5 & Residual stability (_RS_) & 23 & \(\geq\)85\% & T0709-2011 \\  & & Tensile strength & & & \\ tensile strength test & & ratio (_TSR_) & 23 & \(\geq\)80\% & T0729-2011 \\  & & British pendulum & & & \\  & number (_BPN_) & 20 & \(\geq\)45 & T0964-2008 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Basic information on concrete performance tests.  
The NDJ-1C asphalt Brinell viscometer manufactured by Shanghai Meiyu Testing Instruments Co., Ltd. (Shanghai, China) was used to conduct viscosity tests on ESA35, ESA40, ESA45, and ESA50 binders. The 27\(\#\) rotor and 50 r/min rotational speed were selected, and the test temperature was 170 \({}^{\circ}\)C to fit the actual construction.
# UAV IoT Framework Views and Challenges: Towards Protecting Drones as \"Things\"
## 3 Proposed Framework and Methodology
### Potential Security-Sensitive UAV IoT Applications
#### 3.3.1 Power-Line Monitoring  
In this case, we exploit topics related to anonymity of IoT mobile devices, vision-based security using scene analysis, secure communications, and safe landing locations. The proposed scenario involves power-line monitoring and inspection, mainly over rural areas, as illustrated in Figure 5. Power-line monitoring is essential for all high- and medium-power operators/distributors. Today, most inspections are carried out with aerial methods with the use of helicopters, while sometimes terrestrial methods are used as well (e.g., ground patrols). Both methods are expensive and time consuming without guaranteeing successful results. Operators spend a lot of time and money to repair damages that were not detected during inspections. According to the proposed application, unmanned multicopters would be equipped with a set of visual, infrared, and localization sensors. All data would be acquired simultaneously, and a flight path would be scheduled based on power-pylon positions. This includes X, Y, and Z co-ordinates that would be used as a reference for the calculation of mission waypoints. A flight route would be designed in a way that multicopter flies several meters above the power pylons, several meters beside them in one direction, and then back to the other side of the power line. This would allow for capturing oblique inspection images of both sides of the power infrastructure, as well as point clouds and nadir images with a large side overlap, resulting in doubled density of point clouds and images suitable for accurate orthophoto building and stereoscopic analyses. After landing, all acquired data and flight logs would be downloaded. They would be analyzed for quality and  
Figure 4: Proposed UAV IoT architecture.  
completeness. The following flights would be planned retaining a buffer in order to allow overlapping with the previous mission to assure continuity of data along the power lines.
# Benefit Analysis of Grid-Connected Floating Photovoltaic System on the Hydropower Reservoir
## 3 Economy-Financial Analysis
### Main Calculation
#### 3.1.5 Net Present Cost--NPC  
The net present cost of a cost analysis remark component taking into account all costs and revenues incurred over the plant's lifespan. The total NPC is determined by Equation (7) [8; 10; 13; 15; 16; 17; 31; 48; 49].  
\[\text{NPC}=\frac{\text{C}_{\text{tot, ann}}}{\text{CRF}\left(\text{i},\text{ T}_{\text{p}}\right)} \tag{7}\]  
where \(\text{T}_{\text{p}}\) is the plant lifetime (year), \(\text{C}_{\text{tot,ann}}\) is the total annualized cost ($/year), i is the real interest rate (percent), and CRF is the capital recovery factor which is calculated by Equation (8) [8; 10; 13; 15; 16; 17; 31; 48; 49]:  
\[\text{CRF}\left(\text{i},\text{ n}\right)=\frac{\text{i}\left(1+\text{i} \right)^{\text{n}}}{\left(1+\text{i}\right)^{\text{n}}-1} \tag{8}\]  
where n refers to the number of years.  
Additionally, the operating cost is calculated by subtracting the total annual cost from the initial cost, as shown by Equation (9) [20].  
\[\text{OC}=\text{C}_{\text{ann,t}}-\text{C}_{\text{ann,capital}} \tag{9}\]  
The COE is the mean cost of fruitful electrical energy per kWh, and is one of the most important parameters for comparing the cost effectiveness of hybrid systems and for finding the optimal size of the system [8; 9; 10; 13; 15; 17; 20; 50].  
\[\text{COE}=\frac{\text{C}_{\text{ann,t}}}{\text{E}_{\text{ served}}} \tag{10}\]where, \(\rm C_{ann,t}\) is the annualized total cost (including the component-related expenses such as capital, replacement, and maintenance ($)) and \(\rm E_{served}\) is the annual served energy (kWh).
# Research on the Prediction of Operator Users' Number Portability Based on Community Detection
### Organization of the Paper  
In this paper, we discuss how to improve the existing research regarding the prediction of user number portability based on community detection technology. Firstly, in this study,we selected information from real customers in the operator's local network, extracted attribute information related to the target customer for five consecutive months as sample data, and adopted the neural network method, Word2 Vec [11; 12], to the continuous data of model users, which not only solves the limitation of the missing time-series change in the source data but also more effectively expresses the similarity and analogy among different input features of users.  
Secondly, in the process of data preprocessing, we add geographical attributes of customers by creatively building a multilayer community network; this method solved the limitation of the lack of source data that can reflect the associated attributes among users. Furthermore, the geographic attributes used in this project are accurately narrowed down to the community level after desensitization, which does not invade the privacy of the user, yet simultaneously compensates for the irretrievability of the attributes of the users' phone calls.  
Thirdly, in this study, we adopted an improved network embedding method based on the matrix factorization framework to extract new features of the community network, which solved the limitation of dimension explosion caused by the direct one-hot encoding of the customers' geographic attributes.  
Finally, considering the large number of input users and features in the prediction model, in this study, we compared three feature selection methods to assess their advantages and disadvantages. Additionally, we evaluated the processing performance of five classifiers using the data from this project to identify the most suitable classification model, which can improve accuracy and facilitate practical applications for operators and enterprises.
# Matched-Filter Thermography
## 6 Summary  
This paper reviews the basic principles and experimental results of matched-filter thermography such as the thermal-wave responses to matched-filter thermography using two major pulse compression techniques (i.e., linear frequency modulation and binary phase coding) from both theoretical and experimental perspectives. Both simulations and experimental works reported to date suggest that incorporation of matched-filtering in thermography allows one to maintain good axial resolution while inspecting deep into opaque and turbid media, overcoming the classic limitation of conventional pulsed and lock-in thermography. Moreover, recent works in the field suggest that scanning the matched-filter allows one to create iso-delay images, and thus enables deconvolution of thermal responses of defective regions from those of the non-defective regions.  
**Acknowledgments:** N.T. is grateful to the Natural Sciences and Engineering Research Council of Canada for the award of a Discovery Grant (RGPIN-2015-03666), to the Canadian Institutes of Health Research for the award of a Collaborative Health Research Projects Grant (DAN381313), and to the Lassonde School of Engineering and the York University for their financial support.  
**Conflicts of Interest:** The author declares no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
Figure 11: **(a)** teeth matrix with hidden inter-proximal early caries. The rectangle shows the imaged area; **(b)** conventional LIT and **(c)** BPC phase images; TCT images obtained at **(d)** 2.7 ms; **(e)** 29.7 ms; and **(f)** 45.9 ms delay times. The white color depicts the pixels coherent to the delayed matched filter. The arrow indicates the hidden interproximal caries [22].