esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Development and Management of the Victorian Resources Online Website  
### Development Background  
The Victorian Resources Online website was launched in 1997 when hosted by the former Department of Natural Resources and Environment (NRE). It was originally conceived to replace the hardcopy Atlas of Victoria [20] and provide access to statewide overview maps of natural resources information for the knowledge domains of \"climate\", \"landform\", \"land use\", \"water\" and \"soil\". When launched in 2007 it consisted of around 200 web pages and 50 image-based maps. Additional domains of \"biodiversity\", \"marine\", and \"land and water management\" have been included in ensuing years. \"Land and water management\" was established as a domain to manage a range of natural resource theme areas not directly relevant to the \"soil\", \"landform\", \"water\" or \"land use\"; and that have a broader catchment and land management focus--including \"salinity management\", \"catchment management\", \"irrigated agriculture\", \"land degradation\" and \"invasive plants\".  
Central to Victoria's catchment management framework are ten Catchment Management Authorities (CMAs) established under Victoria's main NRM act, the _Catchment and Land Protection Act_ (1994) [21]. Regional VRO sub-sites were established in 2000 to provide access to more regionally contexted information based on these 10 catchment management regions. Links have been established, wherever relevant, between regionally contexted information and relevant statewide and overview information.  
In January 2003, most of the functions of the former NRE were moved into the newly created Department of Sustainability and Environment (DSE) and Department of Primary Industries (DPI). The hosting of the VRO website has since been undertaken by DPI, with DSE providing co-funding support. The Future Farming Systems Research Division (FFSR) within DPI is the custodian of Victorian data related to soils and land use and is responsible for managing the VRO project. DSE became the custodian of key data related to water, vegetation and biodiversity, necessitating some changes to VRO content, particularly in the \"water\" section, that now largely signposts users to relevant information on the DSE website. VRO currently consists of around 11,000 web pages as well as 1900 maps and 1000 downloadable documents. Information is provided at a range of scales--from statewide to more detailed catchment management region and sub-catchment levels. The current VRO homepage is shown in Figure 1.
# Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS
## 6 Discussion
### Comparison with the Existing Literature  
The comparison of our findings with previous research on spot instance pricing holds significance, regardless of the inherent disparities between the two pricing models that necessitate consideration. Furthermore, it is imperative to emphasize that our research represents one of the initial attempts to specifically investigate price forecasting for reserved instances. Consequently, the scope for direct comparisons with prior studies is constrained.  
Spot instances and reserved instances exhibit distinct characteristics in relation to their pricing dynamics and utilization patterns. Spot instances are subject to real-time pricing that is determined by current supply and demand conditions. Consequently, spot instances can experience significant fluctuations in price. In contrast, reserved instances are acquired at a predetermined cost for a specified duration, providing a consistent pricing structure but limited adaptability.  
In order to provide a comprehensive comparison, several studies have examined spot instance pricing and reported MAPE values. These studies [11; 12; 13] have found that short-term forecasts, typically ranging from 1 to 3 hours ahead, exhibit MAPE values between 10% and 20%. Conversely, longer-term forecasts, specifically those made 24 h in advance, demonstrate MAPE values ranging from 20% to 40%. The observed values for spot instance pricing generally exhibit higher values compared to those obtained for reserved instance pricing, indicating the greater price volatility associated with spot instances.  
Although previous research has extensively examined the prediction of spot instance prices, our study distinguishes itself by encompassing a diverse array of offers within each instance family. The prevailing body of literature has primarily focused its examination on a limited number of case types, potentially neglecting the complex and multifaceted aspects of pricing dynamics in cloud services. The comprehensive methodology employed in our study enables a deeper understanding of pricing trends among various instance families, thereby offering a more holistic perspective of the pricing environment in cloud computing. The extensive range of services offered is especially advantageous for consumers who aim to maximize their utilization of cloud resources and minimize costs across various services, rather than concentrating solely on a specific instance type.
## 3 Results
### Simultaneous Quantification of the Nine Compounds in JGT
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Compound** & **Linear Range** & \multicolumn{2}{c}{**Regression Equation * ** & **Location** **LOD * ** & **Location** **(\(\upmu\)g/mL)** & **(\(\upmu\)g/mL)** & **Location** **(\(\upmu\)g/mL)** & **Location** **(\(\upmu\)g/g)** \\ \hline Gallic acid & 3.125–100 & 28,401 & \(-\)9804.2 & 0.9999 & 0.042 & 0.126 & 8.665 \(\pm\) 0.008 \\ Albiflorin & 1.5625–50 & 13,499 & \(-\)895.32 & 1.0000 & 0.096 & 0.289 & 5.089 \(\pm\) 0.030 \\ Paeoniflorin & 12.5–400 & 16,167 & 177.8 & 1.0000 & 0.587 & 1.780 & 55.292 \(\pm\) 0.111 \\ Liquirtien apioside & 3.125–100 & 14,801 & 236.14 & 1.0000 & 0.112 & 0.340 & 5.977 \(\pm\) 0.031 \\ Liquirtien & 3.125–100 & 18,962 & 2978.4 & 1.0000 & 0.150 & 0.453 & 6.279 \(\pm\) 0.010 \\ Benzoic acid & 1.5625–50 & 52,232 & 6580.1 & 1.0000 & 0.150 & 0.455 & 3.497 \(\pm\) 0.002 \\ Liquirtigenin & 0.93–125 & 36,568 & 528.46 & 1.0000 & 0.014 & 0.041 & 0.022 \(\pm\) 0.001 \\ Benzoylbeaniflorin & 1.5625–50 & 25,762 & 762.53 & 1.0000 & 0.087 & 0.264 & 1.603 \(\pm\) 0.009 \\ Glycyrhizin & 3.125–100 & 6315.4 & 125.64 & 1.000 & 0.103 & 0.311 & 10.492 \(\pm\) 0.030 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Regression parameters, LOD, LOQ, and concentrations of the nine compounds in JGT. Figure 2: Simultaneous quantification of nine compounds in JGT. **(A)** HPLC chromatograms of the water extract of JGT at 230, 250, and 275 nm. **(B)** Chemical structures of the nine compounds. 1, gallic acid; 2, albiflorin; 3, paeoniflorin; 4, liquirtien apioside; 5, liquirtiin; 6, benzoic acid; 7, liquirtigenin; 8, benzoylpeaoniflorin; 9, glycyrrhizin.
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 6 Discussion  
The effectiveness of the proposed SCAE model in recovering noise-affected data is shown via experimental analysis. Table 5 presents the performance comparison results for PLSR, RNN, and SCAE. While the training error of PLSR is more than three times that of the SCAE, that of RNN is more than two times greater. Similarly, PLSR, RNN, and SCAE have testing errors of 3.42, 2.69, and 1.09, respectively. It is evident that the SCAE demonstrates superior performance compared with currently available data reconstruction approaches. This is the result of using the data of multiple sensors simultaneously, which not only preserves the temporal component of the data of each sensor but also reveals the relationship between the sensors. In addition, the use of a convolutional autoencoder architecture enhances the efficacy of data extraction, particularly when dealing with two-dimensional data. The improvement in the ability to identify and analyze data patterns leads to increased efficiency in the recovery of affected data.  
Furthermore, the comparison between the SCAE and CAE is conducted in different scenarios. The experimental results indicate that, despite their shared use of a hybrid CNN and AE framework, the SCAE exhibits superior performance than the CAE across all evaluated comparisons. In the context of using the PM 2.5 dataset, it was observed that the training error of the CAE reached 1.53%, while that of the SCAE was shown as 1.06%. Table 5 displays the testing errors of the CAE and SCAE, which are 1.5% and 1.09%, respectively. Moreover, the employing of additional actual datasets, such as temperature, AQI, O3, or PM10, demonstrates that the SCAE exhibits enhanced efficacy in the reconstruction of data. In investigations with varying Gaussian noise variances, training and testing errors are substantially reduced. When the variance of the noise increases from 0.1 to 1, there is an observed increase in the gap between the testing error of the CAE and the SCAE. Specifically, the difference in testing error between the two models rises from 1.38% to 2.73%, as clearly shown in Figure 9.  
The enhanced performance shown by the SCAE in comparison with the CAE may be attributed to enhancements in both the initialization of model weights and the underlying model architecture. The utilization of a more complex structure may give rise to the issue of vanished gradients in the backpropagation process. The use of random weight initialization might also contribute to the possibility of vanishing gradients, hence restricting the model to learn effectively. Furthermore, the presence of inadequately initialized weights might lead to the model getting stuck in local optimum situations. The SCAE employs a stacked fashion, which serves the dual purpose of reducing the size of subnets and optimizing the weight initialization process via the use of a weight sharing mechanism. The efficiency of this approach has been clearly proven through intensive experiments with real-world data.
# Long-Term Creep Mechanical and Acoustic Emission Characteristics of Water-Immersed Coal Pillar Dam
## 2 Methodology
### Testing Apparatus and Procedure  
The creep specimens were obtained from Daliuta Coal Mine in the Shendong Mining Area (Yulin, China). Testing was performed using the long-term uniaxial creep testing machine with a maximum axial load capacity of 600 kN and specimen dimensions of \(\varphi\)50-100 mm \(\times\) H100-200 mm. The constant-weight loading system was applied, ensuring constant stress during deformation. The concurrent acoustic emission testing was carried out by the PCI-II acoustic testing system manufactured by Physical Acoustics Company (Princeton Junction, NJ, USA), which has an 18-bit AD A/D converter and a 1 kHz\({}^{-3}\) MHz bandwidth frequency range and can continuously record waveforms at 10 MS/s. The AE detection threshold is set at 24 dB to avoid the environmental noise affecting the effectiveness of the data.  
To simulate the prolonged field immersion conditions of coal pillar dams, the test setup incorporated a water supply-regulation system with a reservoir chamber integrated into the loading base (Figure 3). This system maintained continuous water saturation throughout the creep deformation. Concurrently, the microcrack propagation dynamics during creep were monitored using the PCI-II Acoustic Emission System with synchronized data acquisition.  
Figure 3: Schematic of the uniaxial creep testing apparatus for water-immersed coal sample.  
Figure 2: UCS of Daliuta coal samples at different immersion times [20].  
To investigate the long-term water immersion effects on the mechanical behavior of coal pillar dams, uniaxial creep tests were conducted on two standard coal specimens, one under both natural conditions and one under water-immersed conditions at a constant 10 MPa loading level over approximately six months. Coal specimens (\(\up\)50 mm \(\times\) H100 mm) were drilled perpendicular to bedding from Daliuta CMUR coal seams. All samples met the ISRM standards for parallelism (\(\pm\)0.05 mm) and flatness (\(\pm\)0.02 mm). Water-immersed samples underwent vacuum saturation (48 h) to achieve full saturation. The experimental protocols were executed as follows:  
Water-Immersed Sample: The experiment is carried out under continuous water immersion; the applied axial load level is set as 65% UCS (10 MPa). At 171.5 days, the axial load was stepwise loaded by one level (5% UCS) every 8 h. When the load level reached 100% UCS, the specimen completely failed under this load level.  
Natural Sample: The specimen was not water-immersed. The axial load level is set as 65% UCS (10 MPa) and was tested for 213 days under this load level.
# Parameter Conversion between Controlled Pass-By Method and Alternative Close Proximity Method
## 3 Results and Correlation Analysis
### Parameter Conversion of Noise Experiment Results between CPB and A-CPX  
#### 4.3.1 Ignoring the Shielding Effect of the Vehicle Body on Noise Propagation  
Taking the two different curves in Figure 6 as the correction basis (\(\Delta\)L), by subtracting the correction difference of the corresponding frequency from SPL\({}_{\text{CPX1}}\) and SPL\({}_{\text{CPY3}}\) the noise spectrum prediction values SPL\({}_{\text{CPB1}}\) and SPL\({}_{\text{CPB3}}\), respectively, were obtained.  
\[\text{SPL}_{\text{CPB1}}=\text{SPL}_{\text{CPX1}}-\Delta\text{L}_{1} \tag{6}\]  
\[\text{SPL}_{\text{CPB3}}=\text{SPL}_{\text{CPY3}}-\Delta\text{L}_{3} \tag{7}\]  
In the above, we assumed that the statistical characteristics of the sound sources of the four tires of the entire vehicle were the same if ignoring the shielding effect of the vehicle body on noise propagation. Then, the noise values for the left and right tire noises transmitted to the S2 measurement point would be the same. The noise spectrum prediction values SPL\({}_{\text{CPB2}}\) and SPL\({}_{\text{CPB4}}\) for the T2 and T4 tire at the S2 measuring point can be expressed as follows:  
\[\text{SPL}_{\text{CPB2}}=\text{SPL}_{\text{CPB1}}=\text{SPL}_{\text{CPX1}}- \Delta\text{L}_{1} \tag{8}\]  
\[\text{SPL}_{\text{CPB4}}=\text{SPL}_{\text{CPB3}}=\text{SPL}_{\text{CPY3}}- \Delta\text{L}_{3} \tag{9}\]  
By using Equations (8) and (9), the noise prediction values of each tire at the S2 measuring point were obtained by subtracting the sound attenuation values in the propagation path from the measured near-field noise values corresponding to the four tires. The spectrum distribution of the total tire noise prediction value was obtained by superposing the tire noise prediction values at the S2 measuring point according to the same frequency. Then, the total tire noise prediction value at the S2 experiment point was obtained using the noise superposition equation. The difference between the measured and predicted values of far-field tire noise is shown in Table 5 when the shielding effect of the vehicle body on tire noise propagation is ignored.  
From the difference between the measured value and the predicted value of the far-field tire noise in Table 5, we found that the difference between the measured and the predicted values was slightly different according to the different tire pattern, and the average difference was about 1.1 dB.
## 3 Simulation Results
### Performance of KLQVI-CFAR Based on Real Clutter Data  
McMaster IPIX radar clutter data ([[http://soma.ece.mcmaster.ca/ipix/dartmouth/](http://soma.ece.mcmaster.ca/ipix/dartmouth/)]([http://soma.ece.mcmaster.ca/ipix/dartmouth/](http://soma.ece.mcmaster.ca/ipix/dartmouth/)), accessed on 20 April 2025) is used to test the performance of KLQVI-CFAR. Set \(P_{FA}=10^{-3}\), target and interferences follow the Swerling II model, and the parameters of model are the same as (12).  
The detection probabilities of various detectors in a homogeneous environment are shown in Figure 10.  
From Figure 10, the comparison results of the detection performance based on real data are similar to those based on simulation data mentioned above. Therefore, the FOSTA algorithm used in KLQVI-CFAR can improve the detection performance.  
Figure 10: The detection probabilities of various detectors in a homogeneous environment based on the McMaster IPIX radar clutter data.  
Figure 9: False alarm control capability of each detector in a clutter edge environment.  
When 6 interferences are added to the reference window, the detection probabilities of various detectors in a multiple-target environment are shown in Figure 11.  
Similarly, from Figure 11, in multiple-target environments, the comparison results of the detection performance based on real data are also similar to those based on simulation data mentioned above. Thus, it can be seen that KLQVI-CFAR has good potential application capabilities.
## 2 Materials and Methods
### Metal Ion Extraction and Recovery  
To test the extraction capacity of the synthesized materials, 50 mg JNP-bPEI or 250 mg colloidosomes were suspended in 5 mL of a metal salt solution with \(c\) = 10 mmol/L at the natural pH of UPW (pH 5.5 \(\pm\) 0.5). After mechanical shaking at 5000 rpm for 10 min, the samples were left undisturbed for approx. 12 h. The JNP suspensions were centrifuged at 10,000 rpm for 20 min and the supernatant was collected for analysis. The particles were washed through three cycles of centrifugation/resuspension in UPW. The colloidosome samples were filtered (10 \(\upmu\)m pore size) and the filtrate was collected for analysis. The colloidosomes were washed with copious amounts of UPW. The supernatant and filtrate obtained from experiments using JNPs and colloidosomes, respectively, were diluted toa metal ion concentration of approx. 50 mg/L and acidified to prevent the formation of metal hydroxides during analysis. Standard solutions for calibration were prepared from the corresponding metal salts in UPW.  
The metal ion concentration in the diluted supernatant and filtrate were analyzed using Inductively coupled plasma-optical emission spectrometry (ICP-OES) (ICP-OES 5100, Agilent Technologies, Basel, Switzerland) and compared to the initial concentration. The metal ion extraction capacity \(q_{\mathrm{e}}\) (mg/g) was calculated by:  
\[q_{\mathrm{e}}=\frac{(c_{\mathrm{i}}-c_{\mathrm{e}})\ V}{m_{\mathrm{P}}} \tag{1}\]  
where \(c_{\mathrm{i}}\) (mg/L) is the initial concentration, \(c_{\mathrm{e}}\) (mg/L) is the extracted concentration, \(V\) (L) is the volume of the sample, and \(m_{\mathrm{P}}\) (g) is the dry mass of the sorbent.  
The washed JNPs and colloidosomes were then resuspended in 5 mL 0.5% H\({}_{2}\)SO\({}_{4}\) by means of mechanical shaking at 5000 rpm for 10 min. To aid redispersion, the JNP samples were also sonicated for 10 min. The samples were then left undisturbed for approx. 12 h. The particles were separated from the aqueous medium as described previously. Supernatant and filtrate were analyzed using ICP-OES, and the metal ion recovery capacity \(q_{\mathrm{r}}\) (mg/g) was calculated by:  
\[q_{\mathrm{r}}=\frac{c_{\mathrm{r}}\ V}{m_{\mathrm{P}}} \tag{2}\]  
where \(c_{\mathrm{r}}\) (mg/L) is the concentration of metal ions recovered from the particles, \(V\) (L) is the volume of the sample, and \(m_{\mathrm{P}}\) (mg) is the dry mass of the particles.  
The JNPs and colloidosomes were washed as described previously, and the entire procedure was repeated at least three times. All ICP-OES measurements were performed in triplicate.
## 6 Conclusions
### Conflicts of Interest: The authors declare no conflict of interest.  
#### Nomenclature  
\begin{tabular}{l l} \(A\) & error state matrix \\ \(a_{m}\) & control term \\ \(B\) & equivalent viscous friction coefficient \\ \(\beta\) & estimated equivalent viscous friction coefficient \\ \(\tilde{B}\) & estimation error of the equivalent viscous friction coefficient \\ \(b\) & uncertain input vector \\ \(ctrerr\) ( \(\cdot\) ) & contour error calculation function \\ \(D_{B}\) & blade diameter \\ \(D_{i}\) & distance from \(H_{c}\) to \(H_{i}\) \\ \(D_{i-1}\) & distance from \(H_{c}\) to \(H_{i-1}\) \\ \(D_{M}\) & major diameter \\ \(d_{i}\) & distance from \(R\) to \(H_{i}\) \\ \(d_{i-1}\) & distance from \(R\) to \(H_{i-1}\) \\ \(\delta\) & Stribeck shape factor \\ \(\delta a_{m}\) & additional term \\ \(e\) & following error vector \\ \(\epsilon\) & contour error \\ \(\epsilon_{j}\) & contour error of the \((j)\)th pitch cycle \\ \(\epsilon_{j,x}\) & axial component of the contour error \(\epsilon_{j}\) on X-axis \\ \(\epsilon_{j,y}\) & axial component of the contour error \(\epsilon_{j}\) on Y-axis \\ \(\epsilon_{j,z}\) & axial component of the contour error \(\epsilon_{j}\) on Z-axis \\ \(\epsilon_{k}\) & contour error at the \((k)\)th iteration \\ \(\epsilon\) & estimated CEV \\ \(\epsilon_{x}\) & axial component of the estimated CEV \(\epsilon\) on X-axis \\ \(\epsilon_{y}\) & axial component of the estimated CEV \(\epsilon\) on Y-axis \\ \(\epsilon_{z}\) & axial component of the estimated CEV \(\epsilon\) on Z-axis \\ \(\eta\) & uncertainty \\ \(G(s)\) & transfer function matrix of feed drive axes \\ \(G_{x}(s)\) & transfer function of the feed drive \(X\)-axis \\ \(G_{y}(s)\) & transfer function of the feed drive \(Y\)-axis \\ \(G_{z}(s)\) & transfer function of the feed drive \(Z\)-axis \\ H & helical path \\ H\({}_{j}\) & helical cycle of the \((j)\)th pitch cycle \\ \(H_{c}\) & position on \(\overrightarrow{H_{i-1}H_{i}}\) vector \\ \(H_{i}\) & \((i)\)th interpolated position on a helical path \\ \(I\) & identity matrix \\ \(\tilde{I}\) & equivalent moment of inertia \\ \(\tilde{\tilde{J}}\) & estimated equivalent moment of inertia \\ \((j)\) & estimation error of the equivalent moment of inertia \\ \(K_{0}\) & control gain with \(K_{0}>0\) \\ \(K_{1}\) & control gain with \(K_{1}>0\) \\ \(K(s)\) & transfer function matrix of the iterative learning contour controllers \\ \(K_{x}(s)\) & transfer function of the iterative learning contour controller for the feed drive \(X\)-axis \\ \(K_{y}(s)\) & transfer function of the iterative learning contour controller for the feed drive \(Y\)-axis \\ \(K_{z}(s)\) & transfer function of the iterative learning contour controller for the feed drive \(Z\)-axis \\ \((k)\) & iteration number \\ \(\lambda_{\min}(Q)\) & minimum eigenvalue of the matrix \(Q\) \\ \(\omega_{s}\) & Stribeck velocity \\ P\({}_{j}\) & command cycle of the \((j)\)th pitch cycle \\ P\({}_{k}\) & command path at the \((k)\)th iteration \\ \(p\) & matrix with \(P=P^{T}>0\) \\ \(P_{H}\) & helical pitch \\ \(Q\) & matrix with \(Q=Q^{T}>0\) \\ \end{tabular}
# Forward and Inverse Studies on Scattering
###### Abstract  
The Rayleigh wave has been frequently applied in geological seismic inspection and ultrasonic non-destructive testing, due to its low attenuation and dispersion. A thorough and effective utilization of Rayleigh wave requires better understanding of its scattering phenomenon. The paper analyzes the scattering of Rayleigh wave at the canyon-shaped flaws on the surface, both in forward and inverse aspects. Firstly, we suggest a modified boundary element method (BEM) incorporating the far-field displacement patterns into the traditional BEM equation set. Results show that the modified BEM is an efficient and accurate approach for calculating far-field reflection coefficients. Secondly, we propose an inverse reconstruction procedure for the flaw shape using reflection coefficients of Rayleigh wave. By theoretical deduction, it can be proved that the objective function of flaw depth \(d(x_{1})\) is approximately expressed as an inverse Fourier transform of reflection coefficients in wavenumber domain. Numerical examples are given by substituting the reflection coefficients obtained from the forward analysis into the inversion algorithm, and good agreements are shown between the reconstructed flaw images and the geometric characteristics of the actual flaws.  
surface flaw; Rayleigh wave; scattering; modified BEM; reconstruction +
Footnote †: journal: Applied Mechanics and Control of Mechanical Structures/College of Aerospace Engineering,  
Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; [EMAIL_ADDRESS] (B.W.); [EMAIL_ADDRESS] (Y.D.)  
12 February 2018; 3 March 2018; Published: 12 March 2018