esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Modeling Method
#### 2.2.6 Calculating the Anisotropic Parameters of Fractured Saturated Rock  
After obtaining all the stiffness elements of the saturated rock, we can calculate the Thomsen-style anisotropic parameters using the following equations [45; 46]:  
\[\varepsilon^{(v)}=\frac{C_{11}^{sat}-C_{33}^{sat}}{2C_{33}^{sat}} \tag{15}\]  
\[\gamma^{(v)}=\frac{C_{66}^{sat}-C_{44}^{sat}}{2C_{44}^{sat}} \tag{16}\]  
\[\delta^{(v)}=\frac{\left(C_{13}^{sat}+C_{55}^{sat}\right)^{2}-\left(C_{33}^{ sat}-C_{55}^{sat}\right)^{2}}{2C_{33}^{sat}(C_{33}^{sat}-C_{55}^{sat})} \tag{17}\]  
where \(\varepsilon^{(v)}\) can be used to describe the anisotropy of P-wave velocity in HTI medium, \(\gamma^{(v)}\) can be used to describe the anisotropy of S-wave velocity in HTI medium, and \(\delta^{(v)}\) is the change rate of P-wave NMO velocity.  
By the relationship between the Thomsen-style anisotropic parameters and the fracture weaknesses, Equations (15)-(17) can be linearized, under the assumption of weak anisotropy [47]:  
\[\varepsilon^{(v)}=-2g(1-g)\Delta_{N} \tag{18}\]  
\[\gamma^{(v)}=-\frac{\Delta_{T}}{2} \tag{19}\]  
\[\delta^{(v)}=-2g[(1-2g)\Delta_{N}+\Delta_{T}] \tag{20}\]  
where \(\Delta_{N}\) and \(\Delta_{T}\) are the normal and tangential weaknesses parameters of fractured saturated rock. The parameter \(g=V_{s}^{2}/V_{p}^{2}\) is the S-wave to P-wave velocity ratio to the square, where \(V_{p}\) and \(V_{s}\) are the P- and S-wave velocities in the background medium.  
Since the vertical P-wave and S-wave velocities are parallel to the fracture direction in HTI media, the formula for calculating the P-wave and S-wave velocities of background rock are as follows, under the assumption of weak anisotropy [48]:  
\[V_{p}=\sqrt{\frac{C_{33}^{sat}}{\rho}} \tag{21}\]  
\[V_{\rm s}=\sqrt{\frac{C_{44}^{sat}}{\rho}} \tag{22}\]  
Additionally, we can get the fracture density \(e\) from the tangential weaknesses using the following equation [49]:  
\[e=\frac{3(3-2g)}{16}\Delta_{T} \tag{23}\]  
Therefore, all the anisotropic parameters and elastic modulus of formation can be derived from the well logging data.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 4 Applications of AI in Early Fault Diagnosis of Rotating Machinery
### Other Methods  
This section describes other AI-based early fault diagnosis methods. [PERSON] et al. [171] used dictionary learning to automatically derive signal features for characterizing different operational conditions and faults of rolling bearings. [PERSON] et al. [172] used time-domain features as input of generic multi-layer perceptron for bearing fault identification. [PERSON] et al. [173] used the non-dimensional symptom parameters after wavelet transformation to extract fault features, and then colony optimization was used to classify fault types at an early stage. [PERSON] et al. [174] used the wavelet transform to decompose the vibration signals, then the standard deviation of average energy and the logarithmic energy entropy were used as the fault features. Finally, the quadratic classifier was used for early fault detection and diagnosis in bearings. [PERSON] et al. [175] used statistical parameters in both the time domain and the frequency domain to extract the fault features. Then the features are put into fuzzy lattice  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Authors** & **Methodologies** \\ \hline [PERSON] et al. [158] & Statistical feature + SVM \\ [PERSON] et al. [156] & Impact time frequency dictionary + SVM \\ [PERSON] et al. [157] & Band-pass filters and Hilbert Transform + v-SVM \\ [PERSON] et al. [160] & EEMD + multi-scale fuzzy entropy + SVM \\ [PERSON] et al. [162] & WPD + EEMD + SVM \\ [PERSON] et al. [163] & Continuous wavelet transform+ SVM \\ [PERSON] et al. [155] & Statistical parameters + PCA + SVM \\ [PERSON] et al. [165] & Singular value decomposition+ SVM \\ [PERSON] et al. [164] & CWT + GA + SVM \\ [PERSON] et al. [159] & Spectral kurtosis + SVM \\ \hline \hline \end{tabular}
\end{table}
Table 11: Applications of support vector machine (SVM) method in early fault diagnosis of rotating machinery.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Authors** & **Methodologies** \\ \hline [PERSON] et al. [169] & 1D convolutional neural networks \\ [PERSON] et al. [166] & CWT + multilayer perceptron network \\ [PERSON] et al. [170] & Multi-layer neural networks \\ [PERSON] et al. [167] & Wavelet packet transform+ EMD + BP neural network \\ [PERSON] et al. [168] & Chaotic behavior features + neural network \\ \hline \hline \end{tabular}
\end{table}
Table 12: Applications of neural network method in early fault diagnosis of rotating machinery.  
neurocomputing classification model to detect gear faults in an early stage. [PERSON] et al. (2017) used discrete wavelet analysis to extract features, and then binary classification tree was used to identify different damage levels of rotor bars under different load conditions. [PERSON] et al. (2017) put time domain features into one-class classifier for the early fault detection of rotating machinery. The above mentioned methods are summarized in Table 13.
# Inertial Propulsion of a Mobile Platform Driven by Two Eccentric Bodies
## 7 Conclusions  
In this paper, an original concept for the propulsion of a mobile platform is presented. The system uses the inertial force generated by two eccentric masses, which are rotating in opposite directions. Verification of the analytically derived kinematic parameters and validation of the concept were performed in SolidWorks by means of motion simulation.  
Experimental tests were also performed to demonstrate the ability of the system to produce linear movement. By operating the system over five different distances, it was observed that the differences between the measured average speed of the IPS and the simulated results are under 8%.  
Moreover, the influence of the eccentric's driving speed on the vehicle's speed was investigated. The speed of the platform can be increased by increasing the driving mass's speed. That is, by doubling the speed of the driving masses (from 500 to 1000 rpm), the average speed of the platform grows 3.99 times. This result confirms the dependence between these two parameters deduced by analytical means.  
Additionally, the influence of the driving body's mass on the speed of the IPS was examined. Incorporating four pairs of eccentric bodies with increasingly higher masses, the platform's average speed was established. It was noticed that by multiplying the weight of each driving mass by 2.5 times (from 0.18 to 0.45 kg), the speed of the platform increases 2.28 times (from 93.72 to 214.13 mm/s). The non-linear dependence between these two parameters was explained by the previously deduced analytical relation.  
Finally, the present study confirms that the IPS developed by the authors is functional and capable of generating unidirectional linear movement, thus presenting a possible propulsion system for driving boats or submarines. In this case, the major advantage of the developed system, compared to the current propulsion systems of ships and submarines, is that the propellers and their sealing systems are eliminated.  
In addition, the proposed system is the only alternative to running vehicles on very slippery surfaces (mud or ice), where no tire provides the grip necessary to travel in such conditions.  
Figure 8: Influence of the eccentric’s mass on the platform’s displacement speed.  
Future research is being planned that will focus on aspects related to the dynamic behavior of the system, with an emphasis on displacement speed uniformity and measures to improve efficiency and power consumption.  
Conceptualization, Z.-I.K. and A.G.; methodology, Z.-I.K.; software, C.-O.M., and V.C.; validation, C.-O.M. and Z.-I.K.; formal analysis, S.T.; investigation, S.T.; resources, V.C.; data curation, C.-O.M.; writing--original draft preparation, S.T.; writing--review and editing, A.G.; visualization, A.G.; supervision, Z.-I.K.; project administration, Z.-I.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Construction of a Geological Fault Corpus and Named Entity Recognition
## 2 Related Work  
### Geological Named Entity Recognition  
Geological Named Entity Recognition (GNER) is an important research area in geological information extraction, aiming to identify relevant geological entities from geological literature, reports, and databases. According to the development history of entity extraction, it can be mainly classified into three categories of methods, which are early rule-based and dictionary-based extraction methods, statistical learning-based methods, and deep learning-based methods. Different named entity recognition models and their advantages and disadvantages are shown in Table 1.  
In recent years, with the surge in the amount of geoscientific data, traditional rule-based, dictionary-based, and statistical learning-based methods have become insufficient to meet the growing demands, leading neural network-based models to gradually dominate the field. Early models, such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs), efficiently process sequence data, improving named entity recognition in geological texts. However, as research has advanced, it has been observed that RNNs and LSTMs suffer from longer training time and produce static word vectors that do not capture contextual information, thus leading to a decrease in their entity recognition ability. To address this issue, [PERSON] et al. [21] from the Google team proposed a BERT language pre-training model, which better captures syntactic and semantic information in various contexts. The model achieved state-of-the-art results in \(11\) text analysis tasks in the general domain at that time, becoming a focal point in natural language processing both domestically and internationally. [PERSON] et al. [22] combined geological domain knowledge with the BERT model to perform the NER task in GeoBERT-based geological reports, using a small number of sample size. [PERSON] et al. [23] introduced a NER model based on deep learning in the mineral domain. [PERSON] et al. [24] integrated the BERT model with the Geological News Named Entity Recognition (GNNER) BiLSTM- CRF model to identify named entities in geological news texts from China Geological Bureau; [PERSON] et al. [25] proposed Semi-GDNER, a semi-supervised geohazard named entity recognition method, which effectively extracts six types of geohazard entities using both manually labelled and unlabeled data. [PERSON] et al. [9] constructed a geological entity recognition model, which added geological naming rules to correct the model results and improve the recognition of complex geological entities. However, most of the existing BERT models are pre-trained on general domain text, limiting their direct applicability to specialized domains that contain a large number of technical terms and domain-specific grammatical structures. Further research is needed to
## 4 Analytical Model: Signal-to-Crosstalk Ratio  
In this section, we introduce an analytical model of the SCR of a switch network. Then, we show how to apply it to Benes, dilated Benes, switch and select, and double-layer network.
## 3 Experimental Setup and Experimental Results
The red curves represent the BER performance of the 4D modulated Slepian sequences with 32 samples; the blue curves are the BER performances of 4D modulated Slepian sequence with 24 samples; the cyan plots show the BER performance of regular 16 QAM modulation after DSP; and non-DSP 16 QAM BER performance is illustrated in the green plot. The solid plots represent the post-LDPC BER performance, while dashed curves are for pre-LDPC BER performance. The BER performance corresponding to the back-to-back configuration is shown in Figure 7a, we can see the proposed 4D modulation signals are successfully transmitted using a 4D modulator with the SS-based basis functions and the proposed LDPC-coded SS-based 4D modulation scheme significantly outperforms the regular LDPC-coded 16 QAM modulation format with DSP, achieving 3.8 dB and 3.4 dB gain in OSNR when BER = \(10^{-5}\) for 32 samples and 24 samples, respectively. Figure 7b shows the BER performance of different modulation formats after 40 km transmission. Obviously, without DSP the LDPC-coded 16-QAM exhibits early error floor and any reliable transmission is not possible. The DSP-free LDPC-code SS-based 4D modulation scheme is able to achieve error-free transmission. On the other hand, we can clearly see that the regular LDPC-coded 16 QAM with DSP now outperforms the LDPC-coded DSP-free 4D SS signals for about 2.2 dB in OSNR at BER = \(10^{-5}\), but this comparison is not fair. If we compare our results of back-to-back performance with those of 40 km link transmission, we can find that with the DSP, regular 16 QAM does not have too much performance loss after 40 km transmission, but the performance of our proposed DSP-free 4D SS signal deviated a lot after 40 km transmission. This is because we are using the phase to carry information, but we do not apply any phase recovery. Researchers in [34; 35; 36] have already put efforts to the DSP-free transmission for simple modulation formats, such as PAM4, which is non-phase-sensitive, along with a very short transmission distance. Figure 6: Comparison of received and transmitted waveforms. If we use different orders of waveforms to transmit all information, the performance could be improved. In [36], the researchers implemented a DSP-free transmission without equalization. If we can try to apply some simple phase recovery, the performance will be promisingly improved.
## 4 Experimental Validation and Analysis  
### Experimental Validation  
#### 4.1.1 Experimental Setup  
The experimental subject of this study is a specialized vehicle (Figure 5). Three such vehicles, labeled Vec1, Vec2, and Vec3, were used in the experiments. Vec1 and Vec2 served as transmitting ends in the communication experiments, while Vec3 acted as the receiving end. Figure 6 shows the EMC performance evaluation indicator hierarchy system of the vehicular wireless communication system. Among these indicators, \(u_{11}\), \(u_{12}\), \(u_{13}\), and \(u_{14}\) are determined based on transmitter performance; \(u_{21}\), \(u_{22}\), and \(u_{23}\) are determined by receiver performance; \(u_{24}\) is established through practical engineering experience; \(u_{31}\) and \(u_{32}\) are defined by antenna characteristics; \(u_{41}\) and \(u_{42}\) are specified according to relevant EMC standards.  
Figure 5: Three-dimensional model of the vehicle.  
The experimental scene is in a specific urban area of Beijing. Figure 7 shows the satellite remote sensing image of the urban area, with an effective resolution of 5 m after fusion processing.  
This section investigates vehicular voice communication within the frequency band spanning 30 MHz to 1 GHz, encompassing the complete Very-High-Frequency (VHF) range and partial Ultra-High-Frequency (UHF) spectrum. Within this frequency regime, electromagnetic waves primarily propagate via line-of-sight (LOS) transmission along the terrestrial surface. Figure 8 illustrates three radio propagation paths (communication links), where signal quality is predominantly governed by  
Figure 6: Hierarchical system of EMC performance evaluation indicators.  
Figure 7: Satellite remote sensing image of the urban area.  
Vec1 conducted two randomized communication trials at Position A (designated V1-PA-1 and V1-PA-2) and one trial at Position C (V1-PC). Vec2 performed one randomized trial each at Position B (V2-PB) and Position C (V2-PC). These experimental configurations are sequentially numbered as evaluation schemes 1 through 5. These five trials form four comparative experimental groups:  
1. Same-position, same-vehicle control group: V1-PA-1 and V1-PA-2.
2. Different-position, same-vehicle control group: V2-PB and V2-PC.
3. Same-position, different-vehicle control group: V1-PC and V2-PC.
4. Different-position, different-vehicle control group: V1-PC and V2-PB; V1-PA-1 and V2-PC (and other possible combinations).
## 1 Introduction
Raman spectroscopy is another type of vibrational spectroscopy using an intense light beam, such as a laser, to excite the sample molecules by inducing Raman-active vibrational modes and measuring inelastically scattered photons [13]. Since FT-IR measures the absorption of light, it is effective in measuring colored and fluorescence samples. At the same time, the presence of fluorescence creates optical noise for Raman measurements, which easily obscures the spectral fingerprint of the sample [14]. In addition, Raman scattering is based on polarizability changes in functional groups during molecule vibration [15]. Therefore, nonpolar bonds tend to give an intense Raman signal, while water in samples could be virtually disregarded due to a weak Raman signal [15]. Based on the reasons above, FT-IR and Raman are often used as complementary technologies for broader chemical identification. However, limited studies have employed vibrational spectroscopy for the authentication of maple syrups, and there is a gap in knowledge on the performance of portable/handheld devices for the detection of adulteration in maple syrups. [PERSON] and others [8] reported the use of benchtop FT-IR, NIR, and FT-Raman systems to detect corn syrup adulteration in maple syrup. [PERSON] and others [16] used FT-IR to contrast the carbohydrate composition of maple syrups against other sweeteners. In addition, chemometrics or multivariate analysis techniques have been proven to be successfully applied in the study of food matrices [17; 18]. The objective of this research was to evaluate portable mid-infrared and Raman devices in generating predictive models for the non-destructive and fast fingerprinting of traditional and BBL maple syrups, allowing for product authentication and detection of potential ingredient tampering. This is the first study that characterizes a premium maple syrup aged in oak bourbon barrels, as there is no standard of identity or any other study reporting on this novel product. The use of miniaturized vibrational spectroscopies in maple syrup authentication can provide the industry with field-deployable devices for quality control and for preventing adulteration with cheaper ingredients.
## 3 Scene Reduction Method Based on Improved Clustering Algorithm
### Validity Test  
#### 3.2.1 Scene Reduction Process Based on Improved Clustering Algorithm  
Scene reduction is the process of classifying and merging objects to be clustered. According to the results of past research and the analysis of scenario characteristics in Section 2, the wind farm scenario set is divided into four scenarios corresponding to spring, summer, autumn and winter. The photovoltaic scenic set is divided into 12 scenarios in spring, summer, autumn, winter and three weather types: cloudy, sunny and rainy. When scene reduction of DG is carried out, scene reduction is carried out with day as the basic unit of clustering. Assuming that the total number of individual scenarios \(n(1,2,\ldots,N)\) is \(N\). A single scenario has \(T\)-period scenario data. The data contained in all scenarios can be represented by matrix \(N*T\). By improving the clustering algorithm to merge the same kind of scene into \(K\) scenes, the reduced scene data can be represented by \(K*T\). Typical scenesobtained after reduction have the same temporal characteristics as the original scenes, so as to ensure the temporal characteristics of the scenes before reduction. Scenario reduction of two types of DGs and loads is carried out by using the above method. This paper takes the wind power generation scenario as an example to test the effectiveness of the improved clustering algorithm proposed.  
The scene reduction process based on improved clustering algorithm is shown in Figure 9.
# Analysis of Building Platform Inhomogeneities in PBF-LB/M Process on Alloy 718
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Differential Pressure** & **Position** & **Exposed Area** & \multicolumn{2}{c}{**Depth (\(\mu\)m)**} & \multicolumn{2}{c}{**Width (\(\mu\)m)**} \\
**(mbar)** & & **(\%)** & **Avg.** & **SD** & **Avg.** & **SD** \\ \hline Nominal & 1 & 59 & 5.8 & 11.5 & 34.25 & 68.5 \\ Nominal & 1 & 48 & 8.2 & 16.5 & 40.0 & 80.0 \\ Nominal & 1 & 37 & 42.0 & 21.0 & 37.0 & 74.0 \\ Nominal & 1 & 26 & 78.5 & 7.9 & 141.2 & 6.9 \\ Nominal & 2 & 14 & 0.0 & 0.0 & 0.0 & 0.0 \\ Nominal & 2 & 70 & 21.5 & 18.3 & 79.0 & 53.1 \\ Nominal & 2 & 59 & 31.8 & 24.2 & 92.7 & 62.1 \\ Nominal & 2 & 48 & 51.7 & 13.9 & 119.1 & 13.2 \\ Nominal & 2 & 37 & 58.5 & 7.9 & 133.7 & 11.1 \\ Nominal & 2 & 26 & 69.25 & 9.8 & 134.2 & 4.6 \\ Nominal & 3 & 14 & 0.0 & 0.0 & 0.0 & 0.0 \\ Nominal & 3 & 70 & 21.5 & 14.4 & 86.0 & 57.4 \\ Nominal & 3 & 59 & 31.3 & 21.1 & 86.0 & 57.4 \\ Nominal & 3 & 48 & 67.0 & 11.8 & 135.7 & 7.3 \\ Nominal & 3 & 37 & 64.2 & 9.1 & 131.2 & 3.4 \\ Nominal & 3 & 26 & 76.1 & 9.0 & 141.5 & 7.2 \\ Nominal & 4 & 14 & 3.8 & 7.5 & 16.2 & 32.5 \\ Nominal & 4 & 70 & 52.5 & 11.1 & 114.7 & 8.3 \\ Nominal & 4 & 59 & 60.0 & 9.6 & 127.0 & 14.8 \\ Nominal & 4 & 48 & 66.5 & 6.8 & 130.7 & 10.4 \\ Nominal & 4 & 37 & 65.2 & 5.6 & 131.7 & 6.4 \\ Nominal & 4 & 26 & 78.5 & 4.1 & 138.5 & 6.2 \\ Nominal & 5 & 14 & 18.7 & 23.2 & 62.5 & 73.3 \\ Nominal & 5 & 70 & 61.2 & 9.1 & 125.5 & 5.9 \\ Nominal & 5 & 59 & 62.0 & 3.7 & 126.5 & 5.1 \\ Nominal & 5 & 48 & 59.5 & 6.2 & 125.2 & 3.4 \\ Nominal & 5 & 37 & 62.8 & 2.7 & 134.7 & 3.9 \\ Nominal & 5 & 26 & 73.1 & 6.5 & 131.2 & 5.1 \\ High & 1 & 14 & 2.5 & 5.0 & 20.2 & 40.5 \\ High & 1 & 70 & 12.2 & 8.6 & 70.0 & 48.3 \\ High & 1 & 59 & 51.8 & 16.9 & 132.5 & 16.5 \\ High & 1 & 48 & 55.5 & 12.8 & 129.2 & 11.6 \\ High & 1 & 37 & 71.1 & 16.8 & 138.8 & 13.4 \\ High & 1 & 26 & 75.5 & 7.04 & 145.7 & 7.1 \\ High & 2 & 14 & 47.8 & 16.1 & 116.7 & 10.4 \\ High & 2 & 70 & 65.1 & 8.2 & 125.2 & 21.1 \\ High & 2 & 59 & 58.2 & 10.2 & 116.1 & 13.0 \\ High & 2 & 48 & 63.6 & 6.1 & 134 & 13.5 \\ High & 2 & 37 & 74.8 & 10.6 & 138.4 & 3.8 \\ High & 2 & 26 & 67.6 & 8.8 & 139.6 & 7.3 \\ High & 3 & 14 & 50.5 & 13.2 & 113.5 & 8.9 \\ High & 3 & 70 & 60.1 & 4.6 & 123.4 & 10.3 \\ High & 3 & 59 & 74.8 & 10.8 & 128.6 & 5.9 \\ High & 3 & 48 & 67.4 & 15.3 & 125.4 & 9.5 \\ High & 3 & 37 & 73.6 & 9.3 & 126.8 & 5.6 \\ High & 3 & 26 & 79.6 & 8.2 & 131.8 & 6.6 \\ High & 4 & 14 & 52.2 & 8.1 & 115.5 & 8.3 \\ High & 4 & 70 & 57.2 & 13.2 & 123.5 & 6.5 \\ High & 4 & 59 & 65.2 & 9.3 & 120.4 & 4.3 \\ High & 4 & 48 & 76.4 & 4.7 & 127.4 & 7.2 \\ High & 4 & 37 & 76.9 & 10.4 & 135.6 & 2.7 \\ High & 4 & 26 & 74.2 & 8.8 & 135.2 & 4.9 \\ High & 5 & 14 & 59.8 & 13.6 & 133.2 & 1.9 \\ High & 5 & 70 & 67.8 & 8.1 & 132.1 & 5.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Cont.