esa-sceva/satcom-chunk-collection huggingface dataset:
# Elevator Fault Diagnosis Method Based on IAO-XGBoost under Unbalanced Samples  
[PERSON]  
1 School of Electrical Engineering, Xinjang University, Urumqi 830017, China; [EMAIL_ADDRESS] (C.Q.)[EMAIL_ADDRESS] (M.L.); [EMAIL_ADDRESS] (P.Z.); [EMAIL_ADDRESS] (X.Z.)[EMAIL_ADDRESS] (X.Z.)1  
to achieve sample balancing based on decision making mechanism. Reference [9] considered the possibility that oversampling may generate new noise samples; so they used the MeanRadius-SMOTE algorithm to reduce the generation of noise samples and add more samples that have the ability to influence the decision boundary. Reference [10] considered the effect of redundant samples, and the SMOTE algorithm was improved by removing the noisy samples in the dataset before oversampling. The above methods solve the problem of sample imbalance but do not consider the effect of sample features on oversampling.  
Reference [11] proposed XGBoost, an integrated learner based on Boosting. XGBoost trained multiple individual learners based on the negative gradient of the loss function. Reference [12] used the XGBoost algorithm to model wind turbine fault classification. The method had better diagnostic results compared to SVM. The selection of hyperparameters of the XGBoost model has a significant impact on the performance of the model, so tuning the hyperparameters of the model is a critical process. Manual tuning requires practical experience, and the complicated steps are not easy to realize. For this reason, many scholars propose the optimization of model hyperparameters through intelligent optimization algorithms. Reference [13] used genetic algorithm (GA) to optimize the hyperparameters of the XGBoost model to establish a transformer fault diagnosis model, and the results showed that this method effectively improved the accuracy and stability of transformer fault diagnosis.  
Aiming at the sample imbalance and hyper-parameter tuning of the XGBoost model, this study proposes an elevator fault diagnosis method by recursive feature elimination (RFE)-SMOTE-Tomek and IAO-XGBoost. First, RFE is utilized for feature screening to remove redundant features while reducing the dimensionality of the data. Second, SMOTE is applied to generate new samples, followed by the application of Tomek-Link to remove the noisy samples in the new samples to obtain the sample-balanced dataset. This method enhances the proportion of valid features in the samples while reducing the effect of noise samples in the newly generated samples. Finally, the hyperparameters of the XGBoost model are optimized by the IAO algorithm to construct the IAO-XGBoost elevator fault diagnosis model. The results show that the method is accurate and effective for elevator fault diagnosis.
###### Abstract  
In order to study the effects of the microstructure parameters of cement modified subgrade silty soil (CMSS) in a frozen area under freeze-thaw (F-T) cycles on the macroscopic mechanical properties, the static triaxial test, scanning electron microscopy (SEM), and grey relation analysis (GRA) were implemented on silty soil modified with 0% and 2% cement at optimum moisture content from the northwest in Jilin Province in China. The results showed that the shear strength, the cohesion of 0% and 2% CMSS, decreased with the increase of F-T cycles, while the internal friction angle was not obviously changed. The shear strength and its parameters of 2% CMSS doubled compared to that of 0% CMSS. The micro-parameters, representing the particle morphological characteristics, particle arrangement, and pore characteristics of CMSS, changed differently under F-T cycles. If the cement was not added, the cohesion and the internal friction angle were most sensitive to the average particle diameter (D\({}_{\text{P}}\)) and the average particle abundance (C), respectively. When the cement content was 2%, the cohesion was chiefly affected by the particle size fractal dimension (D\({}_{\text{ps}}\)), while the internal friction angle was mainly related to the average pore diameter (D\({}_{\text{h}}\)). The main principle of cement improvement was to decrease D\({}_{\text{h}}\) of soil under F-T cycles.  
seasonally frozen area; freeze-thaw cycles; microscopic parameters; macroscopic mechanical properties; cement modified subgrade silty soil
# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 1 Introduction
### Previous Studies  
Several studies show the advantages of multi-wavelength LiDAR data in different applications, such as atmospheric processing [8; 9], geological analysis [10], fire detection [11], topographic mapping [12; 13; 14], map updating [15], and plant monitoring [16; 17]. [PERSON], (2015) [18] analyzed the accuracies of digital terrain models (DTMs) from Optech Titan and showed that the mean differences among DTM generation from three channels were less than 0.03 m, and the composition of the three intensities could be considered an alternative orthoimage product. [PERSON] et al. (2016) [19] also used the Optech Titan multispectral LiDAR and maximum likelihood classifier in land covers classification, achieving an overall accuracy of 90% for six classes. [PERSON] et al. (2016) [20] presented the capabilities of assessment and performance metrics for the multi-wavelength LiDAR to quantify the performance of Optech Titan multi-wavelength LiDAR system in land cover classification, bathymetric mapping, canopy characterization, and geometrical accuracy. [PERSON] et al. (2016) [21] used Optech Titan multi-wavelength LiDAR data to perform object-based land cover classification and found that a pseudo normalized difference vegetation index (pNDVI) generated from a multi-wavelength LiDAR system may improve vegetation identification, achieving an overall accuracy higher than 90% and kappa coefficient reaching 0.89. [PERSON] et al. (2017) [22] compared the reflectance of active multispectral LiDAR, active hyperspectral LiDAR, and passive spectrometer for leaf nitrogen concentration; their coefficient of determination (R\({}^{2}\)) for spectrometer (R\({}^{2}\) = 0.73) and hyperspectral LiDAR (R\({}^{2}\) = 0.74) showed a high correlation with leaf nitrogen content. Based on previous studies, the multi-wavelength LiDAR not only increases the number of 3D points but also provides backscattered energies in different wavelengths.  
Geospatial object-based image analysis (GEOBIA) [23] has been widely applied in multispectral images to merge similar pixels into a region; the geometrical and radiometrical properties of regions are then extracted to separate different land covers. The advantages of object-based classification compared with pixel-based classification have been established in previous studies [24; 25]. Through image segmentation, the related image pixels are combined into image objects that provide the objects' attributes and shape to identify different land covers [26]. Object-based classification is therefore more flexible than traditional pixel-based classification. A similar process can be applied to object-based LiDAR classification [27]. To obtain useful information in LiDAR classification, the LiDAR data are clustered into an object, and its features are then selected to separate different land covers in object-based LiDAR classification.
# Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning
## 5 Experimental Analysis
### Identification of Regimes
#### 5.1.2 Identification Results Analysis  
To observe the statistical characteristics of the traffic flow associated with the different regimes, the mean of each measure at each detector station for each regime was calculated, as depicted in Table 4. Meanwhile, the second column of the table provides the corresponding regime in Figure 2, which is associated with each hidden state in the identified model. From the table, we can see that each regime has distinct traffic flow characteristics. Regime 1 has the minimum flow rate mean and occupancy mean, but the maximum speed mean. Compared with Regime 1, Regime 2 shows a higher flow rate mean and an occupancy mean, but a lower speed mean. In Regime 3, the traffic flow shows the maximum flow rate mean and the mild occupancy mean and speed mean. In Regime 4, the flow rate mean begins to drop down while the occupancy mean continues to increase and the speed mean continues to drop down. Finally, in Regime 5, the occupancy mean achieves the maximum and the speed mean comes to the minimum. Meanwhile, the flow rate mean reaches a similar level to that of Regime 2. Figure 4 provides the regime identification results at each detector station. In each subfigure, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Station ID** & & **State 1** & **State 2** & **State 3** & **State 4** & **State 5** \\ \hline \multirow{4}{*}{400976} & State 1 & 0.9438 & 0.0498 & 0.0000 & 0.0064 & 0.0000 \\  & State 2 & 0.0088 & 0.9793 & 0.0000 & 0.0021 & 0.0098 \\  & State 3 & 0.0000 & 0.0000 & 0.9839 & 0.0010 & 0.0150 \\  & State 4 & 0.0035 & 0.0059 & 0.0018 & 0.9848 & 0.0039 \\  & State 5 & 0.0000 & 0.0137 & 0.0132 & 0.0025 & 0.9706 \\ \hline \multirow{4}{*}{400081} & State 1 & 0.9773 & 0.0000 & 0.0139 & 0.0086 & 0.0002 \\  & State 2 & 0.0000 & 0.9022 & 0.0054 & 0.0923 & 0.0000 \\  & State 3 & 0.0096 & 0.0006 & 0.9739 & 0.0037 & 0.0123 \\  & State 4 & 0.0229 & 0.0143 & 0.0019 & 0.9609 & 0.0000 \\  & State 5 & 0.0000 & 0.0000 & 0.0130 & 0.0000 & 0.9870 \\ \hline \multirow{4}{*}{400329} & State 1 & 0.9829 & 0.0000 & 0.0000 & 0.0171 & 0.0000 \\  & State 2 & 0.0000 & 0.9359 & 0.0556 & 0.0045 & 0.0040 \\ \cline{1-1}  & State 3 & 0.0000 & 0.0133 & 0.9703 & 0.0000 & 0.0164 \\ \cline{1-1}  & State 4 & 0.0195 & 0.0010 & 0.0003 & 0.9574 & 0.0219 \\ \cline{1-1}  & State 5 & 0.0003 & 0.0005 & 0.0145 & 0.0129 & 0.9718 \\ \hline \multirow{4}{*}{400825} & State 1 & 0.9849 & 0.0024 & 0.0000 & 0.0000 & 0.0127 \\  & State 2 & 0.0074 & 0.9754 & 0.0059 & 0.0000 & 0.0113 \\ \cline{1-1}  & State 3 & 0.0000 & 0.0003 & 0.9794 & 0.0083 & 0.0119 \\ \cline{1-1}  & State 4 & 0.0000 & 0.0000 & 0.0314 & 0.9686 & 0.0000 \\ \cline{1-1}  & State 5 & 0.0127 & 0.0009 & 0.0159 & 0.0000 & 0.9705 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Determined \(A\) in four regime identification models. the data points with each color represent an identified regime. Comparing Figure 2 with Figure 4, we can see that the proposed approach can properly identify different regimes of traffic flow.
## 3 Results and Discussion
### Evaluation of Physicochemical Properties of cosmetic Macroemulsions Tested
#### 3.1.2 Viscosity  
The rheological properties of a cosmetic product determine its consistency and the application parameters, form of dosage, ease of spreading on the skin and rate of absorption. Significant changes in the viscosity of a given cosmetic product at each stage of the technological process may indicate undesirable changes related to instability [21]. The viscosity of the macroemulsions studied was measured on day 0 and on day 30. Analysis of the results of measurements showed that the viscosity of macroemulsion 1 insignificantly decreased by about 2.5%, from 23,100 \(\pm\) 346 mPas (day 0) to 22,543 \(\pm\) 223 mPas (day 30), measured with spindle R6 at a rotation rate of 30 rpm. The viscosity of macroemulsion 2 (serum for the body) significantly increased over the 30-day period, from 3604 \(\pm\) 116 mPas (day 0) to 5983 \(\pm\) 151 mPas (day 30), measured with spindle R3 at a rotation rate of 12 rpm. Such a great change in viscosity suggested the possible instability of the sample and impelled us to perform careful verification of the results of the stability tests described below.
# Evaluation of NOx Reduction Effect and Impact on Asphalt
###### Abstract  
Nitrogen oxide (NOx), emitted at the highest rate among automobile exhaust gases, is the main cause of air pollution, and various construction technologies are being developed to reduce NOx emissions. In this study, the NOx reduction effect of surface treatment technology for road pavements, and the effect of the photocatalytic reaction on asphalt pavements, were evaluated using a photocatalyst. Three types of titanium dioxide (TiO\({}_{2}\)) were used as photocatalysts, and an asphalt rejuvenator used to recover aged asphalt was applied as a surface treatment agent. To evaluate the NOx reduction effect, a test device capable of testing large-sized specimens was manufactured and compared with the ISO method, which only allowed the testing of small specimens. In addition, the effect of TiO\({}_{2}\) and the asphalt rejuvenator on the asphalt mixture was analyzed through chemical analysis. The test results of the newly manufactured mixed-tank photo reactor showed the same trend as the ISO test results concerning the evaluation of its NOx removal performance. As a result of the performance evaluation of the surface treatment using TiO\({}_{2}\), the NO removal rate was up to 7.83% when Anatase-type TiO\({}_{2}\) with excellent light efficiency was applied. In addition, when the rejuvenator was used, the oxidation of asphalt, caused by the photoreaction of TiO\({}_{2}\), was reduced.  
Keywords: titanium dioxide; asphalt surface treatment; nitrogen oxide; photocatalytic reaction +
Footnote †: journal: Applied Sciences
# Lightning Interferometry with the Long Wavelength Array
## 3 Results
### Sensitivity  
As shown in Equation (6), if we normalize by the integration period, the image amplitude is proportional to the average incident radio power at the antenna from the direction of the source. Similar to [21], the image amplitude can be expressed in terms of the galactic background power received by an antenna. The reference power was found by averaging the total power received in multiple integration windows from all antennas when no lightning was present.  
The probability density for the received power of a number of different antenna configurations is shown in Figure 5b. Here, the probability density is normalized against the number of integration periods imaged, rather than the number of point sources located. This means that the cumulative sum of each distribution will be less than unity if, on average, less than one source is located for each integration window (when a small number of antennas is used) and greater than unity if, on average, more than one point source is located per window (when a large number of antennas is used). Unsurprisingly, the number of low-power sources located increases rapidly as antennas are added to the array.  
With 32 antennas in the array, the distribution peaks around 12.5 dB\({}_{\text{GB}}\); this peak remains constant as antennas beyond 32 are added to the array. This happens because essentially every integration window has at least one locatable VHF point source if 32 or more antennas are used to image the source, as shown in Figure 5a.  
As more antennas are used to image the sky, the noise floor of the images becomes continually lower. Sources with received power below 0 dB\({}_{\text{GB}}\), i.e., sources radiating less power than the galactic background, are routinely located. Indeed, there is a secondary peak in source powers less than 0 dB\({}_{\text{GB}}\) seen in the 249-antenna distribution. These low-power sources are not simply noise solutions from side lobes; approximately 85% of them are located on lightning channels. Sources this faint can be located because the galactic background is not a point source, and so the maximum image amplitude of the galactic background is significantly lower than the reference power.
# A Hybrid Hidden Markov Model for Pipeline Leakage Detection
###### Abstract  
In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hidden Markov model (HMM). The hybrid HMM, i.e., DNN-HMM, consists of a deep neural network (DNN) with multiple layers to exploit the non-linear data. The DNN is initialized by using a deep belief network (DBN). The DBN is a pre-trained model built by stacking top-down restricted Boltzmann machines (RBM) that compute the emission probabilities for the HMM instead of Gaussian mixture model (GMM). Two comparative studies based on different numbers of states using Gaussian mixture model-hidden Markov model (GMM-HMM) and DNN-HMM are performed. The accuracy of the testing performance between detected state sequence and actual state sequence is measured by micro \(F_{1}\) score. The micro \(F_{1}\) score approaches 0.94 for GMM-HMM method and it is close to 0.95 for DNN-HMM method when the pipeline is divided into three sections. In the experiment that divides the pipeline as five sections, the micro \(F_{1}\) score for GMM-HMM is 0.69, while it approaches 0.96 with DNN-HMM method. The results demonstrate that the DNN-HMM can learn a better model of non-linear data and achieve better performance compared to GMM-HMM method.  
deep neural network; hidden Markov model; leakage detection; pipeline +
Footnote †: journal: Applied Physics Letters
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 3 Methodology
### Design of the Dual-Encoder Module  
The encoder structure used in the Dual-Encoder module is inspired by the encoder structure in Transformer [37]. The self-attention mechanism enables the encoder structure to pay attention to important information such as channel difference and channel fading through dot product calculation between antennas in CSI. We adopt two encoder structures for parallel computation, and the weights of the two encoders are independent of each other, which allows them to flexibly choose the attention direction. Finally, the outputs of the Dual-Encoder structure are summed to increase the robustness of the model. Additional design details for Dual-Encoder are as follows.
# Blockchain and Smart Contracts for Digital Copyright Protection
## 1 Introduction and Motivations
This means that the WCA has limited involvement in the protocol, just enough to support both a high-security level of the protocol and an easy involvement of buyers in the purchase transactions. Although they represent a good balance between usability and security, the watermarking protocols based on a limited involvement of WCAs still need to be improved to meet the requirements of the current web context [1]. The first aspect to intervene in is the removal of WCAs without reducing the usability of the protocols. To this end, solutions can be found in the field of blockchains used to protect digital copyright [10; 11; 12]. A blockchain can be considered a distributed ledger that can record information on commercial or network transactions in blocks chained using several specific technologies. In particular, each block is digitally signed and chained after the insertion of two tokens: a timestamp and a cryptographic hash calculated on the previous block. As a result, the chained blocks cannot be modified. They are validated by running a decentralized \"consensus algorithm\" on computing web nodes to determine whether a transaction is valid. Moreover, blockchains can take advantage of a specific facility represented by the so-called \"smart contracts\". Such contracts consist of code that is automatically run when preset conditions and requirements are fulfilled without the need for a central authority. The code unalterably implements both the conditions set out in an agreement reached by distinct web parties and the rules and events that can trigger the automatic execution of the contracts [13; 14; 15]. This paper describes a new watermarking protocol that exploits blockchain technology to avoid using WCA. The protocol is based on the \"buyer-friendly\" design approach [16; 17], which has already proven effective in meeting the usability requirements documented in [1]. Moreover, it employs blockchain and smart contracts to implement and efficiently register verifiable transactions among the entities negotiating digital content on the web. The result is a protocol that is both usable and secure without resorting to TTPs, and this peculiarity makes it suited for the current web context.
# Quality Evaluation Algorithm for Chest Compressions Based on OpenPose Model
## 2 System Architecture
### Key Motion Feature Assessment
#### 2.3.3. Frequency of Compression  
We obtained an initial video with a 30 fps frame rate and judged the compression frequency by the order numbers of frames in which the minimum point appears, i.e., the end of one single compression. For example, assuming that the abscissa is \(e\) and that there is a minimum \(y\) value, the frequency \(f=1800/e\). To eliminate the influence of local minimum points during the calculation, the \(y\) value is taken every two frames. Thus, in  
Figure 5: Illustration of the depth of CCs.  
Figure 6: Example of solution to the local minimum point problem: (**a**) The local minimum point problem for the first, second and fourth time; (**b**) The \(y\) values are selected every two frames to find unique compression depth. And the orange dots mean the selected maximum after processing.  
Figure 7: Illustration of the rebound of compression.  
practice, the frequency \(f=900/e\). As shown in Figure 8, if the minimum values appear at the three horizontal axis points \(a\), \(b\) and \(c\) from the starting position, it means that the three compressions end at the \(a\), \(b\) and \(c\) frames. Then, we can calculate the corresponding compression frequencies \(f1=900/a,f2=900/(b-a)\) and \(f3=900/(c-b)\).  
If the calculated frequency is lower than 100 times/min, the algorithm regards the operation as too slow; if the frequency is higher than 120 times/min, the operation is deemed to be too fast. If it is between 100 and 120 times/min, the operation is judged as correct.