esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Case Western Reserve University Bearing Data Set (CWRU)  
CWRU test apparatus (Figure 1) consists of a 2-hp electric motor, a torque transducer/encoder, dynamometer and control electronics. The motor shaft is supported by test ball bearings, indicated as fan end (FE) and drive end (DE) bearings. The specifications of the tested components are reported in Table 1.  
Localized faults were introduced by means of an electro-discharge machining (EDM) on the inner race, outer race and balls with fault diameters varying from 0.007 to 0.040 in (0.18-1.02 mm). Defect depth was 0.011 in (0.28 mm) for diameters between 0.007 and 0.021 in, whereas it was 0.050 in (1.27 mm) for diameters of 0.028 and 0.040 in. The details of all the fault specifications are reported in [55]. In the present work, the case of an inner race damage introduced in the DE bearing was analyzed. In these circumstances, the damages with diameters of 0.007, 0.014 and 0.021 in (0.18, 0.35 and 0.53 mm) were considered, since they referred to the same bearing type (SKF 6205-2 RS JEM). Tests were carried out for each damage level with the absorbed powers of 0, 1, 2 and 3 hp (0, 0.735, 1.470 and 2.205 kW). In these tests, the running speed \(N\) ranged between 1721 and 1796 rpm. Even though SI units (International System of Units) were preferred by the author of this work, they also chose to preserve coherence with the original work of the CWRU and with the extensive literature that relates to them.  
Vibration data were collected using accelerometers fixed to the motor housing by means of a magnetic base at the 12 o'clock position (opposite to the load zone--6 o'clock position). The radial load, as pointed out by [PERSON] and [PERSON] [38], is mostly given by the static gravitational load. Baseline and fault-bearing signals were acquired using a 16-channel DAT recorder. Digital data handled in this study were sampled at 48 kHz (Table 2). Samples were preprocessed by extracting parts with equal time durations in order to take advantage of the automatable procedure adopted in this study. In this context, it was verified that the frequency resolution provided by the choice of the number of samples \(M\) was able to correctly represent the fast Fourier-transform (FFT) spectra. Furthermore, a reasonable number of fault impulses (more than 200) was contained in the time duration \(T\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bearing** & **Designation** & **BPFI—Multiple of Shaft Speed** & \(P_{u}\)—Fatigue Limits (kN)** \\ \hline DE & SKF 6205-2 RS JEM 1 & 5.415 & 0.335 \\ FE & SKF 6203-2 RS JEM & 4.947 & 0.200 \\ \hline \hline \end{tabular}  
* NTM-equivalent bearings were used for 0.028 and 0.040 in drive end (DE) damages. FE: fan end and _BPFI_: Ball Passing Frequency on the Inner race.  
\end{table}
Table 1: Bearing specifications.  
Figure 1: Case Western Reserve University (CWRU) bearings test apparatus [38; 55].
# A Novel Computational Imaging Algorithm for Electrical Capacitance Tomography
## 2 Imaging Methodology Based on Regularized Extreme Learning Machines  
Imaging quality is crucial for ECT applications, so we must find image reconstruction algorithms with strong imaging capabilities. The idea of the RELM algorithm mainly comes from the actual ECT experimental procedure. Through many experiments, it is found that there is a certain relationship between the capacitance correlation coefficient and the imaging target. Therefore, it is particularly important to find the mapping relationship between the two and use this mapping relation to solve the problem of low reconstruction accuracy for the ECT. In this paper, based on the existing numerical methods, we propose a RELM-based computational image reconstruction algorithm that fully exploits the mapping relationship between capacitance correlation coefficients and imaging targets, and achieves high reconstruction accuracy. The specific implementation process can be carried out as follows:  
1. The training sample set between the capacitance correlation coefficient and the imaging target is obtained through numerical simulation or experiment.  
2. The mapping model between the capacitance correlation coefficient and the imaging target is obtained by the RELM method.
3. The capacitance correlation coefficient corresponding to the measured capacitance value is calculated. The calculation method is the same as that of the training sample set.
4. The calculated capacitance correlation coefficient is used as the input of the mapping model obtained in the second step, and the final reconstruction image is predicted.  
The proposed RELM-based image reconstruction algorithm is different from the regular ELM algorithm, and their imaging process block diagrams are presented in Figures 1 and 2, respectively. The RELM algorithm has the following advantages: (1) the algorithm effectively uses the prior information obtained from numerical simulations or experimental results, which greatly improves the quality of imaging results; (2) when the mapping model is obtained from the training sample set, the algorithm does not directly establish the imaging model between the measured capacitance and the imaging target, but rather establishes the mapping relationship model between the capacitance correlation coefficient and the true images of targets; and (3) based on the TR technology, the conventional ELM method is improved, which effectively enhances the stability of the numerical solution, and the solution efficiency is greatly improved by the ISB algorithm.  
The RELM model has some limitations, such as resolution and number of sensors. When the number of sensors is different and the measured area is divided into different numbers of microelements, the capacitance correlation coefficients obtained are different, which means that the input of the model is different. Therefore, it is necessary to retrain the mapping model.
# Integrated Geotechnical Analysis of Allophanic Volcanic Ash Soils: SDMT and Laboratory Perspectives
## 1 Introduction  
Masonry cracks in housing and enclosures in the Caupicho and Garrochal neighborhoods in a volcano-tectonic zone [1] and soft soils with lacustrine evidence motivated this study.  
The current research includes thin film mineralogy, Rx diffraction, scanning electron microscopy (SEM), triaxial CU, and permeability research not previously performed, obtaining in situ geotechnical parameters by the Marchetti SDMT test at 10 m depth (soil specific gravity ( \(\gamma\) ), material index (ID), lateral soil pressure coefficient (Ko), ratio between precon-solidation pressure \(\sigma\)c and present effective vertical pressure \(\sigma\)o (OCR), angle of internal friction ( \(\phi\) ), cohesion of undrained unconsolidated soil (Cu), horizontal permeability (Kh), and seismic wave velocity (Vs)) and physical-mechanical geotechnical characterization (granulometry, Arterberg limits, specific gravity, moisture content, organic content, oedocentric consolidation test, and drained consolidated triaxial test). All these tests were carried out to understand the geological, chemical, mineralogical history, and physical-mechanical characteristics applicable to engineering studies in soft soils of the Caupicho sector, southof Quito. This study was compared with others carried out in the area by Mayanquer [2], Albuja [3, 4], Perfafiel [5], and Seismic Quito [6], finding valuable correlations.  
The structural geology in the study area indicates anticlines and a reverse geological fault generated by tectonic compression between the valley of Tumbaco and the valley of Quito. The soils formed were of volcanic origin from sources such as Atacazo and El Corazon [7, 8, 9]. The allophanes found in soils of volcanic origin, or andosols, with a diameter of 3-5 nm, are composed of silica tetrahedra (Si) and aluminum octahedra (Al) [10]. In Ecuador, andosols are distributed in the north-central highlands in high and humid zones [10]. [PERSON], 1982 [11], stated that the coexistence of diatoms with allophanes in andosols may indicate an early stage of soil development. Porous particles (allophane, imogolite, diatoms, plagioclase, and volcanic glass) largely explain the high porosity of the volcanic ash, with maximum void ratios of approximately 5. [PERSON], 2019 [12], believed that the reasons for the resistance to liquefaction lie largely in the nature of the allophane particles (which form the \"fines\"), in particular, the surface properties of the allophane particles and the strong electrostatic bonding between them. Sludges and mires depend on the amount of organic matter to be considered as peat [13, 14]. Diatomaceous soils are significantly more compressible than fine soils with a similar geotechnical classification [15].  
Owing to the accelerated urbanization in southern Quito, additional geotechnical studies are required to evaluate the bearing capacity of the soil, liquefaction, and consolidation settlement, among others.
## 2 Problem Definition  
### Power Curves  
A wind turbine power curve depicts the relationship between the hub wind speed and output power, and it is an important index for evaluating the wind turbine performance. As shown in Figure 1, the power curve can be divided into four regions according to the operational features. In Region A, where the wind speed is lower than the cut-in speed \(v_{\text{in}}\), no electric power generation occurs. In Region B, the wind speed \(v\in[v_{\text{in}},v_{\text{rated}}]\), and \(v_{\text{rated}}\) is rated wind speed. The maximum wind power tracking can be attained. According to Betz's Law, the theoretical maximum utilized wind power is called the Betz theoretical limit value, the coefficient of which is 0.593. To produce maximum power, several types of control strategies are applied, such as pitch angle control, generator torque control and yaw control, to approximate the Betz curve as much as possible. In Region C, where in which the wind speed \(v\in[v_{\text{rated}},v_{\text{out}}]\), the wind turbine remains at the rated power. As the wind speed increases, the tip speed ratio decreases more rapidly than that in the constant rotation region, and the wind turbine runs at a constant power with a smaller wind turbine power coefficient C\({}_{P}\). In Region D, where the wind speed is faster than the cut-out speed, the pitch angle is adjusted to 90\({}^{\circ}\); consequently, the turbine blades stop rotating, and wind power is no longer generated. By analyzing the operational features of the power curve, Regions B and C can be noted as the main working regions, and these regions are selected as the research objects for assessing the operation condition of a wind turbine. For convenience, Region B is called the maximum wind power tracking region (MWPTR), while Region C is called as the rated power output region (RPOR).
# A Novel Structure of Rubber Ring for Hydraulic Buffer Seal Based on Numerical Simulation
## 5 Discussion
### Evaluation of Butterfly-Ring on Sealing Performance  
Compared with O-ring, the proposed butterfly-ring improves the contact peak pressure at groove side. Because the stiffness of the rubber ring varies with its geometry, and the pre-deformed butterfly-ring improves the compression stiffness and is prone to lateral deformation, so the contact peak pressure of the butterfly-ring is higher than that of the O-ring at groove side. This is also confirmed by the calculation results, as shown in Figure 11. Under installation mode, the contact peak pressure of the butterfly-ring is increased by 30% at groove side, and the contact peak pressure increases by 14-16% under working mode.  
In addition, the increase of contact peak pressure of butterfly seal to load is lower than that of O-ring. It shows that the sensitivity of butterfly seal is less than that of O-ring.
# A Broadband Analog Predistortion Linearizer Based on GaAs MMIC for Ka-Band TWTAs
## 5 Discussion  
The experimental results show that the APD MMIC with independent adjustment capability for TWTAs can be realized through appropriate diode size selection and adoption of the double-branch vector synthesis circuit structure. The MMIC design can be independent of the TWTA design. For the case of multiple LTWTA operating simultaneously in a MIMO scenario, because of the wide gain and phase expansion adjustment ranges,  
Figure 12: The C/IM3 performance before and after the proposed APD MMIC circuit.  
Figure 13: NPR performance of LTWTA with a signal width change when OBO = 3 dB.  
one-to-one adaptation can be realized according to the gain and phase characteristics of each TWTA, so the nonlinear indices of LTWTA can be improved effectively. Comparing the C/IM3 of the LTWTA and the TWTA, the output power fallback of the LTWTA under the same C/IM3 value of 28 dBc was 5 dB lower than that of the TWTA, which means that the LTWTA can work at a higher power state and its efficiency will be higher, which is a key energy consumption index for satellite communication systems.  
The APD MMIC design adopts the NFMA to realize the broadband TWTA phase compression change with the operating frequency, which grants the APD MMIC phase expansion the same characteristics as the TWTA. The proposed APD MMIC achieves good compensation for the TWTA phase compression in the broadband range and improves the nonlinearity of the broadband signal. From the perspective of the broadband nonlinearity index of NPR, a change in the signal bandwidth from 100 MHz to 500 MHz does not lead to a serious deterioration of the NPR, which to some extent shows the effectiveness of NFAM in improving the broadband characteristics of APD MMIC.  
A comparison with some recent analog linearization works for PA is given in Table 2. Considering the linearization performance and operating band, the APD MMIC design is comparable to state of the art. while the APD MMIC is small in size and highly integratable, which is very suitable when combined with other control or temperature compensation functions at the front-end of the power amplifier to form a linear channel amplifier with complex functions.  
In future work, the memory effect caused by the influence of broadband complex modulation signals on the diode supply voltage of the APD circuit will be considered, and its influence will be reduced through APD circuit design modifications. Furthermore, the integration of analog APD and digital APD technology will be considered from the aspect of power amplifier linearization, where APD is used to compensate for nonlinearity and DPD to eliminate the memory effect with the aim of proposing a broadband power amplifier linearization solution while reducing the complexity of the DPD algorithm.
# Evaluating Satellite Sounding Temperature Observations for Cold Air Aloft Detection
## 3 Results
### Diagnosing Causes for True and False CAA Detection
Lower _dof_temp_ values increased the odds of false positive state by \(\sim\)60%, while higher _eta_rej_ values increased the odds of falsely detecting CAA by \(\sim\)150%. This can mean that the false positive state is a result of a poor cloud clearing fit. _eta_rej_ values can be large because of instrument noise, a poor a priori, or from undetected clouds. Instrument noise can potentially be ruled out because the lapse rates appear to have little impact on the false positive odds ratio. The odds of the false positive state increases with both low information content (from low _dof_temp_) and a lower presence of clouds (low _cloud_frac_), so a poor a priori may be the cause of false positives. However, it is also possible that there is cloud contamination (leading a low _cloud_frac_), thereby increasing _eta_rej_ for the case. In either case, the salient feature of the false positive state is a high _eta_rej_ and a low _cloud_frac_. In contrast to the true detection states, the two false detection states have prominent features. The odds of the false negative state are strongly increased for cases with a high _cloud_frac_ and low _eta_rej_, while the converse is true for the false positive. The odds of both false states increase with lower _dof_temp_ values, especially for the false positive state. In summary, each detection state has a very different relationship with the diagnostic parameters that are available in operational NUCAPS. In the next section, we will discuss some ways that this information can be useful in operational forecasting of CAA. Figure 6: Results for a logistic regression on diagnostic parameters for the four CAA detection states described in Table 1. X indicates diagnostic parameters that do not meet statistical significance requirements.
# BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network
## 2 Related Research
### Fast Recovery Method of Link Failure Based on Control Plane  
The fast recovery method for control plane link failures requires the controller to be involved in the recovery process. After the link failure is detected, the controller computes a new forwarding path and configures forwarding rules in the switch. [PERSON] et al. employed reinforcement learning techniques to assess link performance within the network topology and formulated backup paths based on the impact of faulty links, thereby diminishing the likelihood of congestion on backup routes [11]. [PERSON] et al. introduced a hybrid fast path recovery algorithm (HFPR-A). Upon the failure of a communication link, this algorithm swiftly identifies the shortest or an approximately shortest path between nodes to facilitate rapid recovery [12]. [PERSON] et al. introduced a backup path search algorithm that takes into account both backup path length and link load balancing. This approach aims to minimize backup path length and optimize link utilization during fault recovery processes [13]. [PERSON] et al. introduced a path risk metric designed to reroute end-to-end traffic on failed links with minimal operational disruption, thereby ensuring rapid traffic recovery [14]. The control plane-based method for recovering from fault links begins by identifying the fault link within the network topology. Subsequently, a fault link recovery algorithm is devised to satisfy constraints such as the shortest recovery path and optimal link load. Finally, the SDN controller computes a new data forwarding path within the network topology and updates the switch with a new flow table entry. This type of method may introduce significant delay during the interaction between the controller and the switch, rendering them unsuitable for WAMS where strict limits on end-to-end data transmission delay are critical.
## 3 Methods
### Research Instrument  
The questionnaire consisted of three parts with 79 questions. The first two parts were compulsory. Teachers' background information was collected in the first part with 7 questions including gender, age, and the school grades they taught. The second part was a scale for the survey of readiness and the factors that influenced their behavioral intention to implement STEM teaching in K12. The scale was mainly adapted from the DTPB scale constructed by [PERSON] et al. [25] and the scale constructed by [PERSON] et al. to measure the readiness of STEM teachers [36]. Altogether, there were 61 adapted DTPB scale contents and 10 readiness scale contents, among which 67 were originally coded scale items and 4 were reverse-coded scale items. These 71 items constructed 15 potential variables, including behavioral intention, attitudes, perceived usefulness, perceived ease of use, compatibility, subjective norms, superior influence, peer influence, student influence, parental influence, perceived behavioral control, self-efficacy, facilitating conditions, behavioral readiness, and cognitive readiness. In order to avoid the antagonistic psychology of the participants, and to reduce the possibility of distortion of the questionnaire contents, we applied reverse-coded scale items for items of perceived ease of use. Most of these variables have been proven to be reliable and efficient in previous research on teachers' behavioral intentions [28; 29; 30; 52]. At the same time, we applied a 5-point Likert scale for grading, ranging from 1 (\"strongly disagree\") to 5 (\"strongly agree\"). The third part was1 optional item. Here, we applied open-ended questions to collect teachers' opinions about STEM education. Detailed information about the research instrument can be found in Appendix A.  
In order to ensure the content validity of the instrument, we invited experts and high-performance teachers of STEM education research to check the appropriateness and clarity of the items and structure of the questionnaire. Modifications were made according to their suggestions. [PERSON]'s \(\alpha\) reliability coefficient was used to evaluate the internal consistency of the research instrument, and the test results can be found in Table 1. With regards to the 15 latent variables in the scale, their Cronbach \(\alpha\) value range was 0.848-0.947, all meeting the standard of \(\alpha\) > 0.7. Moreover, [PERSON]'s \(\alpha\) of the whole scale was 0.980, indicating a high reliability and a reliable measurement index content. In order to verify the applicability of the factor analysis, we conducted the KMO measure test and [PERSON]'s test. The KMO value was 0.969 (greater than 0.9), which meant that the questionnaire data were appropriate for the factor analysis. [PERSON]'s test value was 0.000 (less than 0.05), which meant that it met the standard, the data were distributed spherically, and the variables were inter-independent to a certain degree.
## 4 Results and Discussion
### Experimental Setup
#### 4.1.2 Baseline Methods  
For the evaluation of RCCF, we made a comparison against established fusion methods, including linear weighted combination, reciprocal rank fusion (RRF), and CombSUM. For the evaluation of AM-HDGAT, baseline methods included traditional CNN-based classifiers, BERT-based models, graph convolutional networks (GCNs), and standard graph attention networks (GATs).
# An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT
###### Abstract  
Monitoring and early warning technology for forest fires is crucial. An early warning/monitoring system for forest fires was constructed based on deep learning and the internet of things. Forest fire recognition was improved by combining the size, color, and shape characteristics of the flame, smoke, and area. Complex upper-layer fire-image features were extracted, improving the input conversion by building a forest fire risk prediction model based on an improved dynamic convolutional neural network. The proposed back propagation neural network fire (BPNNFire) algorithm calculated the image processing speed and delay rate, and data were preprocessed to remove noise. The model recognized forest fire images, and the classifier classified them to distinguish images with and without fire. Fire images were classified locally for feature extraction. Forest fire images were stored on a remote server. Existing algorithms were compared, and BPNNFire provided real-time accurate forest fire recognition at a low frame rate with 84.37% accuracy, indicating superior recognition. The maximum relative error between the measured and actual values for real-time online monitoring of forest environment indicators, such as air temperature and humidity, was 5.75%. The packet loss rate of the forest fire monitoring network was 5.99% at Longshan Forest Farm and 2.22% at Longyandong Forest Farm.  
forest fire monitoring; deep learning; convolutional neural network; back propagation neural network; internet of things (IoT) 2023