esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 2 Materials and Methods
### Lithology Classification Based on Deep Learning
#### 2.3.2 Lithology Feature Extraction  
Despite using advanced remote sensing tools, classification of lithology is challenging due to high similarity caused by weathering and coverage, which can produce confusion in the classification. Thus, in this study, we extract new features to increase the separability among different classes and to improve the classifier performance.  
The volume and surface scattering power obtained from Freeman decomposition is used in this study to indicate the differences in structures. The cross polarized ratio measures the roughness of the surface, and the co-polarized correlation coefficient is related to the permittivity. The lithology can be well characterized by combining these features. The lithological differences are sensitive to  
Figure 1: Flow chart of lithology classification.
## 3 Results
### County Level Productivity Estimation
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \multicolumn{1}{c}{} & \multicolumn{3}{c}{**Kemper**} & \multicolumn{3}{c}{**Brunswick**} & \multicolumn{3}{c}{**Brantley**} \\ \hline
**Series** & **SI** & **Adjusted SI** & **FR** & **Series** & **SI** & **Adjusted SI** & **FR** & **Series** & **SI** & **Adjusted SI** & **FR** \\ \hline Bibb & 30.5 & 20.7 & 0.36 & Appling & 25.9 & 17.0 & 0.18 & Albany & 29.0 & 19.4 & 0.29 \\ Daleville & 29.0 & 19.4 & 0.29 & Ashlar & 22.9 & 14.6 & 0.11 & Bladen & 28.7 & 19.2 & 0.28 \\ Freest & 27.4 & 18.2 & 0.23 & Badin & 24.4 & 15.8 & 0.14 & Bonifiy & 25.9 & 16.7 & 0.18 \\ Jena & 30.5 & 20.7 & 0.36 & Cecil & 25.3 & 16.5 & 0.16 & Centenary & 25.9 & 16.7 & 0.18 \\ Kinston & 30.5 & 20.7 & 0.36 & Chewacha & 25.6 & 16.7 & 0.17 & Ealonia & 27.4 & 18.2 & 0.23 \\ Kijpling & 27.4 & 18.2 & 0.23 & Emporia & 25.9 & 17.0 & 0.18 & Florla & 27.4 & 18.2 & 0.23 \\ Kirkville & 29.0 & 19.4 & 0.29 & Enon & 20.4 & 12.8 & 0.07 & Foxworth & 24.4 & 15.8 & 0.14 \\ Mantachie & 29.9 & 20.2 & 0.33 & Fluvanna & 23.2 & 14.8 & 0.11 & Fuquay & 25.9 & 17.0 & 0.18 \\ Mayhew & 27.4 & 18.2 & 0.23 & Georgeville & 24.7 & 16.0 & 0.15 & Hurricane & 27.4 & 18.2 & 0.23 \\ Mooreville & 29.0 & 19.4 & 0.29 & Goldston & 22.6 & 14.4 & 0.10 & Kingston & 30.5 & 20.7 & 0.36 \\ Okitbeha & 23.2 & 14.8 & 0.11 & Helena & 25.6 & 16.7 & 0.17 & Lakeland & 22.9 & 14.6 & 0.11 \\ Ora & 25.3 & 16.5 & 0.16 & Herndon & 24.4 & 15.8 & 0.14 & Leefield & 25.6 & 16.7 & 0.17 \\ Prentis & 26.8 & 17.7 & 0.21 & Iredell & 20.4 & 12.8 & 0.07 & Leon & 22.9 & 14.6 & 0.11 \\ Quitman & 28.0 & 18.7 & 0.25 & Lignum & 23.2 & 14.8 & 0.11 & Lynn Haven & 24.4 & 15.8 & 0.14 \\ Ruston & 25.6 & 16.7 & 0.17 & Madison & 22.0 & 13.9 & 0.09 & Mascotte & 21.3 & 13.4 & 0.08 \\ Savannah & 24.7 & 16.0 & 0.15 & Mattaroni & 24.4 & 15.8 & 0.14 & Meggett & 30.5 & 20.7 & 0.36 \\ Smithdale & 26.2 & 17.2 & 0.19 & Pacolet & 23.8 & 15.3 & 0.12 & Medirim & 25.9 & 17.0 & 0.18 \\ Sweatman & 25.3 & 16.5 & 0.16 & Rion & 24.4 & 15.8 & 0.14 & Ogeechee & 27.4 & 18.2 & 0.23 \\ Wilcox & 24.7 & 16.0 & 0.15 & Riverview & 30.5 & 20.7 & 0.36 & Olustee & 24.4 & 15.8 & 0.14 \\ \hline \end{tabular}
\end{table} 
Table 6: Soil series, site index at base age 50, site index adjusted for base age 25 using [41], and FR values derived from site index using Equation (3) in Kemper County, Mississippi; Brunswick County, Virginia; and Brantley County, Georgia based on SSURGO database.
## 5 Results and Discussion
### Effects of Rotor Speed on Sealing Performance  
When the inner and outer rotors are in counter-rotation, we discuss the sealing performance under three conditions: (1) where only the inner rotor speed changes, (2) where only the outer rotor speed changes and (3) where the speed of both the inner and outer rotors changes synchronously (with unchanged absolute speed difference).
## 3 Loading Modes and Exposure Conditions
### Workmanship and Adhesion Property of Waterproofing Sheet Membrane Overlap Joint  
During the installation of waterproofing membranes, overlap joints naturally form [7]. If the waterproofing membranes are not sufficiently cured, or if the concrete surfaces are not cleaned prior to installation, gaps can form along the interface of these overlap joints. Thicker waterproofing sheets have a higher elastic modulus, and elastic recovery applies additional straining force on the adhesion [8].  
In most cases, overlap joint adhesion is a highly technical procedure, and poor workmanship can easily lead to the gaps forming. As illustrated in Table 5, a second rolling operation on the lower portion of the overlap joint is required to ensure that the membranes securely bond to the concrete substrate [9].  
In order to account for these factors, the proposed test method includes a visual inspection of the overlap joints for signs of leakages as a measure of workmanship.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Width Range** & **Description** \\ \hline
2.5 mm & Lower end of dry shrinkage range of normal weight concrete (400 to 800 microstrains, 1 microstrain = 1 \(\times\) 2.54\({}^{-5}\) mm) [6]. Waterproofing membrane systems unable to withstand this range of concrete displacement can be considered to be unsuitable for any below-grade concrete structure. \\ \hline
5.0 mm & Higher end of dry shrinkage range of normal weight concrete. Waterproofing membranes that are able to withstand this range of concrete displacement can be considered to be suitable for well reinforced concrete structures without any cracks present. Direct installation over concrete joints should be avoided. Example of suitable application area: below grade wall and floor installation. \\ \hline
7.5 mm & Higher than normally expected movement range of concrete structures. These types of movements could be caused by exposure to complex sources including freeze-thaw, dry shrinkage, structural settlement, water pressure and/or vibration from loads. Waterproofing membranes that are able to withstand this range of concrete displacement can be considered to be suitable for below-grade concrete structures. Examples of suitable application area: walls and floors with micro cracks, contraction joints. \\ \hline
10.0 mm & Very high movement range of concrete structures. These types of movements could be caused due to extreme exposure to complex sources including freeze-thaw, dry shrinkage, structural settlement, water pressure and/or vibration from loads. Such length of movement can also be expected from expansion joints of large scale concrete slabs. Waterproofing membranes that are able to withstand this range of concrete displacement can be considered to be excellent for below-grade concrete structures. Examples of suitable application area: walls and floors with micro cracks, contraction joints, expansion joints, construction joints. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Crack/joint movement range in “Proposal on Composite Waterproofing Method Evaluation Method”.
# A Multicast Routing Scheme for the Internet: Simulation and Experimentation in Large-Scale Networks
## 6 Experimental Analysis
### Scenario  
The PIM-SSM and GCMR experimental tests are carried out in a network of 207 Autonomous Systems (ASes) as illustrated in Figure 9. The behaviour of each AS has been simplified as a single router since we are analysing the construction of a multicast service in an inter-domain scenario. Hence, these 207 ASes represent only a section of the internet in which one AS offers this multicast service to the remaining 206 ASes.  
We perform ten runs of the identical experiment: initially, one multicast server in one AS is chosen, and then ten receivers from ten distinct ASes are sequentially added to the MDT. The server and nine receivers are selected at random, while one of the receiver is always the client located at the UPC premises in Barcelona and connected to the Virtual Wall by means of an openvpn tunnel. These runs allowed us to evaluate the average stretch, the routing table size, and the communication cost of PIM-SSM and GCMR.  
\begin{table}
\begin{tabular}{l r r r r r r r r} \hline \hline  & \multicolumn{4}{c}{**Stretch**} & \multicolumn{4}{c}{**Memory Space Ratio**} \\  & **16k** & **26k** & **32k** & **46k** & **16k** & **26k** & **32k** & **46k** \\ \hline
**ST** & 1 & 1 & 1 & 1 & 23.51 & 29.47 & 32.67 & 46.5 \\
**PIM-SSM** & 1.058 & 1.072 & 1.082 & 1.105 & 146.81 & 160.6 & 202.2 & 270.38 \\
**BIER (best)** & 1.083 & 1.143 & 1.250 & 1.364 & 9.54 & 14.98 & 20.17 & 25.7 \\
**GCMR** & 1.031 & 1.038 & 1.044 & 1.049 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stretch and memory space consumption considering 2000 leaf nodes and the 16k, 26k, 36k and 46k networks.  
In addition, we are also interested in evaluating the recovery time experimentally. For this particular test, once the MDT is complete with one video server and the 10 clients, we start the transmission of a Full High Definition video and, after 5 min, we emulate a failure in the link connecting AS1 and AS6 (see Figure 10). Thus, we are able to calculate the time needed to receive back the video at the client in Barcelona after the failure in the MDT. To stress the so-called path exploration problem of BGP [37], we consider a specific inter-domain scenario, where two blocks of ASes are interconnected by means of the link between AS1 and AS6. Path exploration implies that certain BGP routers may explore a number of transitory paths in reaction to path failures or routing policy changes before adopting a new best path or announcing the destination unreachable. As a result, it may take a long time until the entire network converges on a final choice, resulting in poor routing convergence. When we discuss the results below, we will look into this phenomena.
## 5 Reachability Checking
### Reachability Analysis
}^{k-2}\big{(}\mathcal{T}_{\tau}(\overline{\mathbf{w}}_{j},\mathbf{d},\overline{\mathbf{w}}_{j+1})\wedge\mathcal{T}_{\sigma}(\overline{ \mathbf{w}}_{j+1},\overline{\mathbf{a}}_{j},\overline{\mathbf{w}}_{j+2}) \big{)}.\]  
Using the above formula and a quantifier-free first-order formula \(\mathit{reach}(\overline{\mathbf{w}})\), which encodes the set of states that satisfy a given property, we aim to determine whether any state satisfying \(\mathit{reach}(\overline{\mathbf{w}})\) is reachable. It is achieved by verifying the satisfiability of the following formula:  
\[\psi_{k}=\mathit{path}_{k}(\overline{\mathbf{w}}_{0},\ldots,\overline{ \mathbf{w}}_{k})\wedge\bigvee_{j=0}^{k}\mathit{reach}(\overline{\mathbf{w}}_{ j}).\]  
The method described relies on the following theorem. **Theorem 1**. : _Let \(\hat{\mathcal{M}}\) be a model and \(s\) a state. Then, for every \(k\in\mathbb{N}\), the state \(s\) is reachable in \(\hat{\mathcal{M}}\) via a path of length \(k\) if and only if the formula \(\psi_{k}\) is satisfiable._  
The proof by induction on \(k\) is straightforward. We terminate the unfolding of the transition relation if either the formula \(\psi_{k}\) is satisfiable or if the SMT-solver cannot determine the satisfiability of the formula in question. Alternatively, termination could occur if the value of \(k\) equals the reachability diameter of the system-the minimum number of steps required to reach all reachable states. However, the reachability diameter cannot be calculated for many systems, and the available estimates often need more precise. This limitation results in BMC needing to be completed in practice.
# Machinery Fault Signal Detection with Deep One-Class Classification
###### Abstract
* In the deep OCC methods, more useful features for fault signal detection can be simultaneously extracted along with minimizing loss function on anomaly detection tasks. By doing so, the fault signal detection performance can be improved. * Finally, we applied the deep OCC methods to the widely used benchmark fault signal datasets and the signal dataset collected from our own rolling element experimental platform. By doing so, the effectiveness and applicability of the deep neural network-based methods to real fault signal detection problems can be confirmed. The remainder of this paper is organized as follows. Section 2 presents the related works on the proposed study. In Section 3, we present a deep neural network-based fault signal detection framework. The experimental settings, baseline of the comparison method, and results are reported in Section 4. The concluding remarks are provided in Section 5.
# Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
## 4 Results and Discussion
### Recognition and Computational Time Analysis for CURE-OR
#### 4.3.1 Object Recognition Accuracy  
Table 5 shows the object recognition accuracy of CNN by using traditional and embedded denoising approaches with median, Gaussian, and without a filter. For instance, traditional denoising with a median filter has maximum object recognition accuracy for lens blur, which is 52.6%. Similar results were seen in the CURE-TSR dataset for lens blur. For impact contrast, the traditional denoising approach with a median filter has maximum accuracy, i.e., 60.4%. Contrast refers to the difference in brightness or color between various parts of an image. A Gaussian filter may actually make contrast problems worse by blurring the borders between contrasting regions.  
In overexposure, the traditional denoising approach with a Gaussian filter has maximum object recognition accuracy, i.e., 47.6%. When an image's brightness is too high, overexposure happens and features are lost in the highlights. When an image is overexposed, the brightest pixels may all have the same high value, which will act as the region's median value. This means that applying a median filter will not change the value of these pixels and will not recover the lost details. Similarly, in underexposure, the maximum object recognition accuracy uses an embedded denoising approach with a Gaussian filter (50.7%). Median filters do not work in underexposure because when an image's brightness is too low, underexposure happens and features are lost in the shadows. The pixels in the darkest regions of an underexposed image might all have the same low value, which will serve as the region's median value. A median filter cannot restore the lost features because it will not alter the value of these pixels. Table 5 shows the traditional median and Gaussian produce maximum accuracy for the maximum number of impacts (4/6 impacts). When we compare the mean accuracy and SD, embedded Gaussian is the best, with mean accuracy and SD as 49.6 and 2.9, respectively, while the embedded median appears to be the worst (its respective mean accuracy and SD are 34 and 3.6). However, if we contrast traditional denoising against embedded denoising, traditional denoising, especially traditional Gaussian, shows superior results overall.
## 3 Fabrication and Integration  
One of the incentives behind CubeSats is maximizing the commercial off-the-shelf (COTS) components that can be used to build the satellite. This means that ideally developers of a CubeSat are to fabricate minimum possible amount of parts. This idea is supported by assuming that some parts of sufficient quality can be obtained at costs less that their in-house fabricated counterparts, even if those COTS components are not space qualified.  
In this sense, there have been serious attempts to catalog state-of-the-art CubeSat components [38], and report the expected evolution of technology applicable to CubeSats [39]. The selection criteria for such COTS components should rely on flight heritage and the results of a thorough verification and validation process. Regarding the first factor, the more flight hours that a component can demonstrate without a failure, the more reliable it is. Component suppliers do not usually provide detailed reliability statistics, but they do provide the number of successful missions. Such a number can be used as a proxy figure to the reliability of the component, especially considering the high rate of CubeSat infant mortality. A statistical analysis and a reliability estimation model for CubeSats are discussed in Reference [40]. With respect to the second factor, the development teams may increase the reliability of the components by testing them in an appropriate environment at early stages of the development process. For this purpose, the utilization of hardware-in-the-loop simulation techniques for nanosatellites is proposed in References [41; 42]. In such simulations, prototyped components can be integrated with other emulated components, and tested against their specifications through fault injection. In the later stages, a fully integrated system can also be validated in a similar manner.  
The following sections describe the development of SE01 as a reference for guiding prospective developers toward the most common materials, components and practices for fabrication and integration of CubeSats. For more detailed investigations, ESA has published a database with material specifications for outgassing, corrosion, flammability, etc. [43]. NASA has also made available a database with the results of outgassing experiments [44].
## 4 Analysis of the Existing Datasets
### Comparison of Surveillance Datasets
#### 4.8.2 Based on Color Space  
Previous research has demonstrated that, when constructing a three-channel model, it is crucial to select an appropriate dataset carefully. Specifically, it has been noted that only the UCSD and Subway datasets are represented in grayscale. In contrast, the remaining datasets (UMN, Avenue, UCF-Crime, and ShanghaiTech) are represented in the RGB color space. This is an important consideration when selecting a dataset for use in constructing a three-channel model, as the dataset's color space distribution can significantly impact the overall accuracy and performance of the model. Table 12 shows the color space distribution of the datasets.
## 5 Model Validation
The calculation formula is shown in Equation (17), and the calculation results are shown in Table 3. The improved Knothe time function model's maximum relative standard deviation was only 5.7%, and the average relative standard deviation was only 4.8%. In contrast, the Knothe time function model maximum relative standard deviation was as high as 24.8%, and the average relative standard deviation was 23%, much higher than the improved Knothe time function model, which confirms the improved Knothe time function model accuracy and reliability established in this paper. \[\left\{ \begin{array}{l}m=\sqrt{\frac{\sum_{i=1}^{k}\left(W_{m}-W_{p}\right) ^{2}}{k-1}}\\ f=\frac{m}{W_{0}}\end{array} \right. \tag{17}\]  
where \(m\) is standard deviation; \(f\) is relative standard deviation; \(W_{m}\) is monitored value; \(W_{p}\) is predicted value; \(k\) is monitoring data volume. Figure 8: _Cont._  
Figure 8: Comparison of two time-function models for surface subsidence prediction: (**a**) z38; (**b**) z41; (**c**) z44; (**d**) z47. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model Parameter**} & \multicolumn{4}{c}{**Monitoring Points**} & \multirow{2}{*}{**Average Value**} \\ \cline{2-2} \cline{4-5}  & **z38** & **z41** & **z44** & **z47** & \\ \hline C & \(1.62\times 10^{-10}\) & \(1.30\times 10^{-10}\) & \(0.85\times 10^{-10}\) & \(1.16\times 10^{-10}\) & \(1.23\times 10^{-10}\) \\ \(n\) & 3.52 & 4.01 & 4.04 & 3.94 & 3.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Time influence parameters of different main strike profile monitoring points. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Monitoring Points**} & \multicolumn{2}{c}{**Improved Knothe Time Function Model**} & \multicolumn{2}{c}{**Knothe Time Function Model**} \\ \cline{2-5}  & \(m\)/mm & _ff_\% & _m_/mm & _f_\% \\ \hline z38 & 37.6 & 4.6 & 174.5 & 21.3 \\ z41 & 42.5 & 4.3 & 219.7 & 22.3 \\ z44 & 61.9 & 5.7 & 259.7 & 23.7 \\ z47 & 52.9 & 4.6 & 286.3 & 24.8 \\ Average value & 48.7 & 4.8 & 235.1 & 23 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Error between monitored and predicted values. Figure 9: Prediction of surface subsidence velocity curve by the improved Knothe time function model: (**a**) z38; (**b**) z41; (**c**) z44; (**d**) z47.
## 5 Laboratory Verification for PMSM Drive
### Laboratory Stand  
A mobile setup was employed for the research, consisting of two identical low-voltage three-phase permanent magnet synchronous machines connected by a rigid coupling (Figure 12).  
The Permanent Magnet Synchronous Motors used, of type _Teknic M-2310P-LN-04K_, are industrial-grade _NEMA 23_ machines designed to exhibit natural PMSM characteristics at a low supply voltage. These motors are equipped with incremental encoders with a resolution of 4000 pulses per revolution. For all tests, the current controller parameters remain constant, with only the velocity controller structure changing. The second motor functions as a controlled load. Both machines are independently powered by three-phase  
Figure 12: General view of the test site. In the foreground, the sandwich-like connection with the control system and two inverters can be seen. In the background, two PMSMs connected by a shaft can be seen.  
MOSFET inverters. The control system includes two Texas Instruments Booster Packs Evaluation modules, namely _BOOSTXL-DRV8305_, and a real-time launch kit, _Texas Instruments LAUNCHXL-F28379D_. The inverters used are designed to operate at low voltages, below 45 V (during the tests performed, the motors were supplied with 24 V), and handle high currents, up to 20 A. Control is implemented using a dual-core, fast, real-time processor _TMS320F28379D_, with built-in Control Law Accelerator _ITAG_ and _USB_ ports. The system has only two available full DAC converters (besides PWM-type converters). The control system utilizes default structures for the \(d\)- and \(q\)-axes' current controllers, which are implemented through library functions provided by the kit manufacturer.  
The selected important parameters of the tested machine are as follows:  
\(R_{s}=0.72\,\Omega\);  
\(L_{s}=0.40\,\mathrm{mH}\);  
\(\Psi=4.6\,\mathrm{V}@1000\,\mathrm{rpm}\);  
\(U_{max}=40\,\mathrm{V}\);  
\(U_{supp}=24\,\mathrm{V}\);  
\(n_{n}=6000\,\mathrm{rpm}\);  
\(T_{n}=0.274\,\mathrm{Nm}\);  
\(I_{n}=7.1\,\mathrm{A}\);  
\(I_{max}=13.5\,\mathrm{A}\).
## 3 Tvdi-Based Drought Index Monitoring
### Tvdi Drought Monitoring Index
#### 3.1.2 Constructing the Feature Space  
In practical applications, it is challenging for the study area to exhibit both extremely dry and extremely wet areas, resulting in the dry and wet edges in the NDVI\(-\)LST feature space not aligning perfectly as straight lines. To enhance the rationality of feature space construction, it is necessary to conduct reasonable data screening on NDVI and LST data. Specifically, we select monthly data from 2021 to 2023, carefully screen the quantity on a monthly basis, covering a total of 36 months, and employ the maximum synthesis method for data synthesis. Eliminating discrete point values and NDVI anomalies in the LST data is essential to enhance the alignment of the dry and wet edge equations in the NDVI\(-\)LST feature space.  
Linear fitting was performed to obtain the NDVI-LST feature space. According to the definition, it can be observed that the surface moisture content becomes drier with the increase of surface temperature and wetter with the decrease of surface temperature. Additionally, the soil moisture content increases from top to bottom in the NDVI-LST feature space. The feature space, constructed based on the month-by-month data of NDVI and LST in Laibin City, is shown in Figure 5. Based on the constructed NDVI-LST feature space, it is evident that the linear relationship between NDVI and LST is more pronounced, and the scatter plot exhibits a trapezoidal distribution.  
Figure 4: Feature space of vegetation index and surface temperature (Brown represents arid areas, green represents humid areas).