esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Parametric Analysis of the Amplitude of VC Vibration in Rolling Bearings
### Unloaded Rolling Bearing  
In the case of an unloaded rolling bearing, the amplitude of the rotor vibration depends on the size of the clearance and the angular spacing of rolling elements (Equation (7)). As the angular spacing was directly determined by the number of rolling elements in a bearing, it can be said that this amplitude depends on the total number of rolling elements and the size of the internal radial clearance and that other characteristic of bearing do not influence on its size. This means that an unloaded radial bearing with the same number of rolling elements and the size of clearance will have the same level of vibrations regardless of the type and dimensions of its constituent parts.  
The diagram in Figure 5 gives a three-dimensional dependence of the vibration amplitude on the number of rolling elements and the internal clearance in an unloaded rolling bearing.  
From the analysis of the diagram by the increase of the internal clearance, the size of vibration amplitude linearly increases, and this increment is more pronounced when the number of rolling elements in a bearing is smaller. This means that more rotation precision and lower level of rotor  
Figure 5: The vibrations amplitude of the unloaded rolling bearing depending on the size of the internal radial clearance and the total number of rolling elements.  
vibrations are accomplished by the reduction of the angle between the rolling elements i.e., with the increase of the total number of rolling elements in a bearing. However, for construction reasons, the increase in the number of rolling elements is impossible over a certain limit. That means that the rotor vibrations in an unloaded rolling bearing are unavoidable and that this is a structural characteristic of a rolling bearing, which is impossible to avoid.
# Research on Shear Behavior of Sand-Structure Interface Based on Monotonic and Cyclic Tests
## Materials and Experimental Process
### Test Apparatus  
This research used an HJ-1 ring shear apparatus (Nanjing TKA Technology Co., Ltd., Nanjing, China), as shown in Figure 3a. The test apparatus had the advantages of constant contact area during shearing, uniform stress and strain, and large shear displacement. In order to realize the interface shear test, the concave-convex blade originally located at the bottom of the shear box for fixing the specimen was taken out, and then the prefabricated ring steel of the corresponding size was put in and fixed with screws. This was to ensure that the steel ring and the base would not move relative to each other during shearing. Therefore, the HJ-1 ring shear apparatus could study the interface shear characteristics of two different materials and supported the shear test in both clockwise and counterclockwise directions, as well as controlled the shear modes of equal stress and equal displacement. These functions were not available in traditional ring shear apparatus. This research did not consider drainage conditions, and mainly studied the shear behavior of the interface between dry sand and structure. The sample size in the ring shear box was 100 mm (outer diameter) \(\times\) 60 mm (inner diameter) \(\times\) 20 mm (height). Figure 3b showed a schematic diagram of the placement of sand and steel in the ring shear box. The bottom of the ring shear box was connected with a torque sensor, the shear box was rotated by applying torque to cut, and the normal load was applied by the counterweight. The vertical displacement monitoring platform was installed above the normal stress sensor. The maximum normal stress that could be applied was 900 kPa, and the maximum torque was 300 N\(\cdot\)m. After the instrument was connected to the computer, the shearing torque could be automatically collected. The conversion relationship between shear stress and torque was as follows:  
\[\tau=\frac{3M}{2\pi(R_{2}^{3}-R_{1}^{3})} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Average Grain** & **Specific** & **Coefficient of** & **Coefficient of** & **Maximum Void** & **Minimum Void** \\
**Diameter \(D_{90}\) [mm]** & **Gravity \(G_{s}\) [-]** & **Uniformity \(C_{u}\) [-]** & **Curvature \(C_{c}\) [-]** & **Ratio \(e_{max}\) [-]** & **Ratio \(e_{min}\) [-]** \\ \hline
0.34 & 2.65 & 1.54 & 0.95 & 0.85 & 0.52 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of sand used in this study.  
Figure 2: Particle size distribution of sand.  
where \(\tau\) is the shear stress; \(M\) is the shear torque; and \(R_{1}\) and \(R_{2}\) are the inner radius and outer radius of the sand sample, respectively.
## 6 Results
### Energy Consumption  
The diagram presents the outcomes of simulated conditions, energy consumption, and the baseline scenario, which represents the existing state prior to any improvements (Figure 2). After conducting dynamic simulations, the following values were derived:  
\[\begin{array}{l}\text{Q}_{\text{H, nd}}=1021.69\text{ MWh/a}\\ \text{q}_{\text{h, nd}}=\text{Q}_{\text{H, nd}}/\text{A}_{\text{f}}\\ =1021.69/6098.85\text{ MWh/m2a}=0.168\text{ MWh/m2a}\\ \text{Q}_{\text{H, nd, rel}}=(168/90)\times 100=186.7\%\end{array} \tag{1}\]  
\(\text{Q}_{\text{H, nd, rel}}\leq 200\) for buildings designated for sports and recreation, representing class E.  
The diagram shows the annual consumption of total energy for the SC1 facility (Figure 3) calculated using the software package IES VE, Apache module, VistaPro.  
Figure 3: Final annual energy consumption for the SC2 Vozdovac Sports Centre.  
Based on the calculation, the annual electricity consumption amounts to 156,000 kWh. Simulation conditions yielded a consumption of 154.35 MWh annually, which is considered as a very satisfactory deviation.
## 4 Results
### Detection of the Most Influential Factors  
Although the purpose of this work is to propose a method for obtaining reliable predictive models from imbalanced datasets in the specific HAI application domain, we have expanded the study in order to obtain deeper knowledge from the medical point of view. To this end, some feature selection methods have been applied to determine which are the most influential factors in infection acquisition. We have considered both methods based on the gain of information provided by the feature with respect to the class [54] and the CFS (correlation-based feature subset selection) approach [55] which also considers the correlation between the factors or features. CFS evaluates the significance (merit) of a subset of features (attributes), taking into account the individual predictive ability of each feature and the degree of redundancy between them. This method selects the subsets of attributes that are highly correlated with the class while having low inter-correlation between them.  
The method of information gain ratio yielded the following ranked list of attributes, where the values in parentheses are the ratios of information gain: extrarenal deprivation (0.115), parenteral nutrition (0.093), emergency surgery (0.030), neutropenia (0.028), APACHE (0.023), immunodeficiency (0.018), central venous catheter (0.016) and mechanical ventilation (0.016). Through the CFS technique, 89 subsets of attributes were automatically evaluated. The best of which, with a merit of 0.14, was formed by the following attributes: neutropenia, central venous catheter, parenteral nutrition and extrarenal deprivation. Although gain ratio and merit values range from 0 to 1, values obtained in the context of this study are common in classification problems. Analyzing the output of the methods, we can observe a coincidence in 4 factors, although there is no exact coincidence in the order of importance of these features. These are: neutropenia, central venous catheter, parenteral nutrition and extrarenal deprivation. This indicates that the presence of invasive devices is one of the major causes of infection in an ICU.  
In addition, we have examined some models generated by the machine learning algorithms. Figure 14 shows some rules extracted from a decision tree classifier. This type of model is formed by nodes where attributes are tested, branches for the different values of the attributes and leaves for the classes, one in each leaf. The tree represents a set of rules that are checked when an instance needs to be classified. In Figure 14, the nested conditions are shown, and dashed lines are used to delimitate the levels. For example, the first rule of the tree is the following: IF extrarenal deprivation =  
Figure 13: True positive rate (TPR) and true negative rate (TNR) for all tested algorithms and sampling strategies.  
N and Parenteral nutrition = Y and APACHE \(\leq\) 17 THEN Infection = NO. This portion of the model contains 11 rules. We can also see that the attributes in the top levels of the tree (the best) are some of the selected by the feature selection methods discussed above.
# Study on Mechanism of Stick-Slip Vibration Based on Torque Characteristics of PDC Bit
## 6 Conclusions  
1. The working torque of PDC bit has little influence on parameters such as tooth density and blade number. However, it is greatly affected by the depth of intrusion, strength of the rock, and size of the drill bit. The change in the depth of intrusion (or the sudden change in drilling pressure) has a significant impact on the speed of the drill bit.
2. The fundamental reason for SV of the bit is that the torque on the bit is insufficient to overcome the resistance of the rock and so the dynamic balance is broken. The main factors affecting SV include the change in stratum rock strength, changes in drill string system length and stiffness, change in the WOB load, and vibration of the drill string system.
3. When the bit resists torque, the bit speed drops or the bit can even stop running; that is, SS occurs. The drill string distorts and starts to accumulate energy. During the slippage stage, the bit speed is notably higher than the rotary table speed.
4. When the bit resistance torque is constant, the drill string torque from the bottom hole to the wellhead increases gradually. When the bit reaches a certain critical value, the bit will stop for a long time; that is, SS will occur. When the drill string accumulates enough energy, it will break and reach the dynamic balance again.
5. To minimize damage to the bit from SV, the following three measures can be taken: 1. The auxiliary cutting structure characteristics of the bit (e.g., the compound cutting structure of backup teeth) can be modified to limit the penetration depth of the cutting teeth within a certain range. 2. The bit structure can be optimized by, for example, using a spiral blade to optimize the transverse unbalanced force of the bit. 3. The drilling parameters can be optimized to control the WOB speed under drilling of heterogeneous formations to prevent the occurrence of SV.  
Figure 16: Variation in drilling speed of drill bits under different drill string lengths.  
**Author Contributions:** Conceptualization, L.L.; Methodology, C.Z.; Writing--original draft, A.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Open Fund (PLN2023-33) of National Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University), Sichuan Provincial Science Foundation Project [2023 YFQ0063], and Science Foundation of Yibin University [2019 QD19].  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** Thanks to Southwest Petroleum University for its support of the research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Story-Based Random Structure Modeling Method for Regional Buildings Earthquake Loss Estimation
## 1 Introduction
Building information obtained by census data is usually rough and rarely involves detailed building construction information, such as HAZUS, which divides buildings into 36 categories according to their structural characteristics and use functions and determines the ratio of the number of each type of building in the region based on zoning census data, and determines the difference in losses between different individuals of the same kind of building by floor area, which cannot consider the impact of the differences in the internal construction on the seismic performance and earthquake losses. The GIS-based approach faces the same problem. [PERSON] et al. [26] conducted field surveys to gather architectural information on masonry structures, and the collected information was classified into two categories: deterministic parameters and uncertain parameters. Archetypes, which are ideal representations of existing building stock, were created to model the geometrical and mechanical uncertainties of buildings. Although the process based on field surveys or design data can obtain detailed building construction information, it is more labor-intensive and time-consuming. It can be used for small-scale regional building information surveys in neighborhoods and streets, but it is challenging to apply it to an urban-regional scale. The method based on empirical data is similar to that founded on census data, which gives the proportion of the number of various types of buildings in the region and can only differentiate the differences between buildings by structural kind, but cannot distinguish the individual differences between similar buildings without knowing the specific structural design. Therefore, although the elastoplastic analysis method based on the structural analysis model can obtain relatively intuitive building damage and loss distribution, which is beneficial for disaster prevention, mitigation, and relief decision making and provides support for post-disaster recovery and reconstruction work [2, 5], the need to obtain detailed building construction information to establish an exemplary finite element model and the enormous workload of information collection have led to the difficulty of adopting this method to estimate regional building losses [9, 10, 27, 28]. Therefore, a solution to the current problem of the difficulty in fine-modeling buildings on a regional scale when lacking detailed structural design information is proposed. The proposed method is the construction of a story-based random structure (SBRS) model, which is based on the probabilistic approach and requires less readily available building information. Thismodel can efficiently simulate the structural arrangement and component information of a building, making it possible to estimate earthquake loss based on the seismic capacity of the structure.
## 6 Discussion and Conclusions
### Limitations and Future Research  
In this study the researchers faced two main limitations. First was the lack of access to YouTube statistics to compare with the research questionnaire responses. Such studies and statistics are not available through YouTube itself. Second, due to the COVID-19 pandemic, lockdowns, and social distancing, face to face interviews and direct watching of respondents was not possible.  
Still, this research drew the attention of the researchers to several factors that are very interesting to pursue for future research. Shortness and ease of explanation are also two important factors that leaners look for in educational YouTube content with animation that draws learners to YouTube over another provider. As stated by an interviewee, it must be \"short and sweet\". Hence, the question of the length of videos is that shorter YouTube videos are more attractive than longer ones. Moreover, the quality of the production of the YouTube film is important.  
Does showing the teacher's face affect the number of views? Seeing the presenter of the education material may affect the understanding of the lesson, and the teaching manner and use of animation, pen and paper, and board in the presentation are other factors that need to be investigated.  
Do teachers' accents affect the views of an educational YouTube? Accent when spoken in English or Arabic is another factor that influences the number of views. Some accents are more popular than others, for example, the Egyptian accent in Arabic is well understood due to TV shows and movies; hence, it is better understood than other Arabic accents. Furthermore, American and British accents are more popular than other English-speaking countries. A suggestion for future research is to investigate whether AI can be involved in changing the accents of the speaker to make the accent more understandable.  
In addition, as future work, the ML methods can be developed further to predict the information adoption (IA) of YouTube as a learning tool and its influence on academic achievement (AA). Furthermore, this can be expanded to include the proposed model and other constructs.
## 4 Experiments and Results  
### Experiment Setup  
A commercial-grade receiver, u-blox ZED-F9P, was connected to a microcomputer, Nvidia Jetson TX2. A total of four satellite constellations with a single frequency were enabled during the experiments: GPS L1, GLONASS G1, Galileo E1, and Beidou B1. We modified the open-source library RTKLIB [52] for the GNSS-related processes, the main program structure can be found in Appendix B. Google Ceres Solver [53] was used for the nonlinear least squares (NLS) and FGO processes. Several experiments took place on the lower east side of NYC (Murray Hill, Manhattan), map plot can be found in Appendix A. In these experiments, two team members walked fixed navigation routes as if commuting between an origin (NYU Medical Center, New York City) and specific target destinations (storefronts) in a 1 mile radius. A total of 11 trips were made and used for analysis.  
The ground-truth reference trajectory was obtained via post-processing. The pedestrian subjects who collected the data walked straight lines and made their best attempts not to veer. Starting and ending locations and locations in-between were labelled manually. We also equipped a smartphone during the experiment and recorded the device location output. We used the smartphone output location to interpolate the longitudinal speed and project the vector between starting and ending location.
## 6 Security and Performance Analysis
### Computational Analysis
The computing time of the decryption process in our proposed scheme remains constant, even if the number of \(AA\) figures and number of attributes/\(AA\) increases, as pointed out in Figure 5(a),b. This constant value that does not increase linearly is due to outsourcing the decryption operation to the fog nodes. The end-user decryption needs only one division and power operation to obtain and use the end-user key to solve the final data. The linear secret-sharing scheme (LSSS) matrix calculation is not involved in end-user decryption, i.e., the part about attributes is not involved [48; 49]. Therefore, the decryption time does not increase with the number of \(AA\) figures and number of attribute/\(AA\). The ABE-based attribute revocation remains constant, even if the number of \(AA\) figures and number of attributes/\(AA\) increase. The reason is that the scheme in [13] also proposed an outsourcing mechanism to fog nodes in the decryption operation. Finally, we compare the attribute-revocation algorithm's computational overhead between the ciphertext update (\(CTUpldate\)) and key update (\(KeyUpdate\)). The comparison is depicted in Figure 7. It can be readily seen from Figure 7 that the revocation algorithm's operation time increases with the number of attributes to be revoked. The \(CTUpldate\) time is longer than the \(KeyUpdate\) time. The reason is that only the attribute-group key needs to be updated. However, the entire ciphertext must be updated when the key is updated. However, in both cases, the computational time is lower than 0.6 s to ensure the attribute revocation's efficiency in our proposed scheme. Figure 5: Computing overhead of the encryption algorithm.
## 3 HPLDDC Control Loop
### Constant-Current Source Driving and CLC Using PIAP Control
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Notation** & **Transfer Function (TF) Parameter for Buck Mode** & **TF Parameter for Boost Mode** \\ \hline \(nu_{2}\) & 0 & \(-L_{1}r_{c}/r_{\eta\eta d}\) \\ \(nu_{1}\) & \(r_{c}C_{o}\) & \(r_{c}(1-D_{\eta\eta d})^{2}-L_{1}/(r_{\eta\eta d}C_{o})-r_{c}r_{L}/r_{\eta\eta d}\) \\ \(nu_{0}\) & 1 & \(-r_{L}/(r_{\eta\eta d}C_{o})+(1-D_{\eta\eta})^{2}/C_{o}\) \\ \(de_{2}\) & \((L_{1}C_{o})(r_{\eta\eta d}+r_{c})/(r_{\eta\eta d}+r_{L})\) & \(L_{1}(r_{c}/r_{\eta\eta d}+1)\) \\ \(de_{1}\) & \(C_{o}r_{c}+r_{\eta\eta d}r_{L}/(r_{\eta\eta d}+r_{L})+L_{1}/(r_{\eta\eta d}+r_{ L})\) & \(r_{c}(1-D_{\eta})^{2}+r_{L}+r_{c}r_{L}/r_{\eta\eta d}+L_{1}/(r_{\eta\eta d}C_{o})\) \\ \(de_{0}\) & 1 & \(r_{L}/(r_{\eta\eta d}C_{o})+(1-D_{\eta})^{2}/C_{o}\) \\ \(k_{cca}\) & \(V_{in(max)}/[r_{\eta\eta d}\ (r_{\eta\eta d}+r_{L})]\) & \(V_{o}/[r_{\eta\eta d}\ (1-D_{\eta})]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters in Equation (11). Figure 9: Current-loop controller (CLC) using proportional-integral associating proportional (PIAP) control: (**a**) Circuit configuration diagram; (**b**) start-up phase principle. \[G_{cellr}(s)=\widetilde{d}/\widetilde{v}_{cea}=k_{ca}. \tag{14}\]  
The \(v_{ca}\) can exponentially increase in the start-up transient phase to accomplish the soft-start control for the CLC output voltage, \(v^{\prime}_{ca}\); hence, the TF of the soft-start control can be expressed as follows:  
\[G_{ss}(s)=\widetilde{v}_{ca}/\widetilde{v}^{\prime}_{ca}=1000/(s+1000). \tag{15}\]  
From Figure 10, open-loop TFs \(G_{op(ht)}\) (s) and \(G_{op(ht)}\) (s) can be obtained using (11) and (14), which can be expressed as follows:  
\[G_{op(ht)}\ (s)=G_{cellr(ht)}(s)\ G_{lt(ht)}\ (s), \tag{16}\]  
\[G_{op(ht)}\ (s)=G_{cellr(ht)}\ (s)\ G_{kt(ht)}\ (s), \tag{17}\]  
where \(G_{lt(ht)}\) (s) and \(G_{cellr(ht)}\) (s) are the SBBC TF and SBBC controller in the boost mode operation. \(G_{lt(ht)}\) (s) and \(G_{cellr(ht)}\) (s) are in the buck mode operation. By substituting Equations (16) and (17), and using the MATLAB software, the frequency response to design the CLC for the current loop compensation of HPLDDC can be obtained. Therefore, the HPLDDC requirements for the steady-state operation, including an adequate phase margin, a high low-frequency gain (LFG), and a feasible crossover frequency, can be achieved. The TF of the CLC using the PI control is expressed as follows:  
\[G_{cea(PI),}(s)=g_{p}(s+g_{i}/g_{p})/s=g_{p}+g_{i}/s \tag{18}\]  
where \(g_{i}\) and \(g_{p}\) are the integral and proportional gains.
## 3 Signal Analysis Method
### Signal Enhancement and Denoising  
Next, the signals were further processed using pre-emphasis and Hampel filtering techniques. Firstly, pre-emphasis was applied to adjust the spectral distribution of the signal, enhancing the high-frequency components that were more susceptible to attenuation during signal acquisition and transmission, making them more prominent throughout the signal. The pre-emphasis function is shown in Equation (1).  
\[y(n)=x(n)-\alpha\cdot x(n-1), \tag{1}\]  
where \(x(n)\) represents the signal at the \(n\)-th sampling point, and \(y(n)\) denotes the signal after pre-emphasis. The pre-emphasis coefficient \(\alpha\) is typically set to 0.97 [34].  
Subsequently, Hampel filtering, based on the median absolute deviation estimation method, was applied to attenuate impulsive noise in the signal, ensuring that the final passive acoustic signal achieves higher quality and accuracy. The expression for the Hampel filter is given in Equation (2).  
\[y_{i}=\text{median}(x_{i-k_{0}},\ldots,x_{i+k_{0}}), \tag{2}\]  
Here, \(k_{0}\) represents the window size. If the input value deviates from the median of the window by more than a selected threshold \(n_{\sigma}\), the median is used as the output; otherwise, the original value is output. Figure 7 shows the MFCC spectrogram of a passive acoustic signal after Hampel filtering. It can be observed that with the application of the Hampel filter, the intensity of anomalies in the audio is significantly reduced. The peak intensity of the noise was reduced by 59.83%.