esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Parametric Analysis  
### The Effect of the Control Points of the Mapping Function on the Accuracy of Analytic Solutions  
As shown in Figure 2, considering five different cases of control points for the mapping function:(1) point C, point F (\(m=2\)); (2) point C, midpoint of DE, point F (\(m=3\)); (3) point C, point D, point E, point F (\(m=4\)); (4) point C, point D, midpoint of DE, point E, point F (\(m=5\)); and (5) point C, midpoint of CD, point D, point E, midpoint of EF, point F (\(m=6\)), and solving \(2m\) independent equations that are derived from Equations (2) and (3), the parameter values of the mapping function can be obtained, as shown in Table 1. It can be seen that the parameter values of the mapping function are not stable enough and have poor consistency in the first case (\(m=2\)) and the second case (\(m=3\)), so they should not be used. Then, they are basically stable and have good consistency in the third case (\(m=4\)), the fourth case (\(m=5\)), and the fifth case (\(m=6\)), which can be used. When the number of control points \(m\geq 4\), the values of the parameter \(\beta_{k}\) (\(k=1\), \(2\), \(3\), \(\ldots\), \(n\)) tend to be stable with the increase in the number of control points, which indicates that the increase in \(m\) does not have much effect on the accuracy of the mapping function, and the accuracy of the third case (\(m=4\)) is basically reasonable.  
The degree of approximation of the excavation boundary under the different cases is shown in Figure 9. It can be seen that the excavation boundary can be mapped as a circle and an ellipse at the \(z\)-plane in the first case (\(m=2\)) and the second case (\(m=3\)), respectively. The third case (\(m=4\)) maps the four straight edges of the excavation boundary as four arcs and the four corners as four arcs. The approximation of the fourth case (\(m=5\)) about the straight edges and the corners of the excavation boundary is more precise, and the fifth case (\(m=6\)) is nearly perfect. The effect of the number of control points on the displacements and stresses at specific points of the stratum is shown in Figure 10. It can be seen that there is a lot of change in the values in the first case (\(m=2\)) and the second case (\(m=3\)). When \(m\geq 4\), the values of the displacements and stresses tend to be stable. The accuracy of the third case (\(m=4\)) is basically acceptable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(m=2\) & \(m=3\) & \(m=4\) & \(m=5\) & \(m=6\) \\ \hline \(\lambda\) & \(-7.07103\) & \(-7.56903\) & \(-7.79914\) & \(-7.79835\) & \(-7.79811\) \\ \(\alpha\) & \(0.17157\) & \(0.08030\) & \(0.26592\) & \(0.26573\) & \(0.26569\) \\ \(\beta_{1}\) & & \(-0.30093\) & \(-0.56187\) & \(-0.58143\) & \(-0.58425\) \\ \(\beta_{2}\) & & & \(-0.10561\) & \(-0.10527\) & \(-0.10516\) \\ \(\beta_{3}\) & & & & \(0.00139\) & \(0.00149\) \\ \(\beta_{4}\) & & & & \(-0.00002\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter values of the mapping function under different cases of control points.  
Figure 9: The degree of approximation of the excavation boundary under the different cases.
## 3 Study Methods and Evaluation Process
### Determination of Index Weight
#### 3.2.2 Method for Determining Attribute Weight Based on Rough Set and Conditional Entropy  
In the process of using rough set theory to calculate the attribute weight, sometimes the attribute importance degree is zero, that is, the attribute weight is zero. The reason for this phenomenon is that the rough set theory only considers the importance of a single attribute to the entire attribute set, without considering the importance of the attribute itself, and ignores the practical significance of the attribute. To solve this problem, [PERSON] and [PERSON] [31] introduced the concept of conditional entropy to improve the method. The calculation process is shown in Figure 3.  
In the decision information table \(S=(U,C,D,V,f)\), the conditional entropy of the decision attribute set \(D(U/D=\{D_{1},D_{2},\ldots,D_{k}\})\) relative to the conditional attribute set \(C(U/C=\{C_{1},C_{2},\ldots,C_{m}\})\) is expressed as:  
\[I(D|C)=\sum\limits_{i=1}^{m}\frac{|C_{i}|^{2}}{|U|^{2}}\sum\limits_{j=1}^{k} \frac{\left|D_{j}\cap C_{i}\right|}{|C_{i}|}\left[1-\frac{|D_{j}\cap C_{i}|}{|C _{i}|}\right] \tag{6}\]  
In the decision information table, \(\forall C_{i}\in C\), and the importance degree of conditional attribute (index) \(C_{i}\) is expressed as:  
\[\mathit{New}\;\mathit{Sig}(C_{i})=I(D|C-\{C_{i}\})-I(D|C) \tag{7}\]  
where \(\mathit{New}\;\mathit{Sig}(C_{i})\) indicates the importance of the conditional attribute \(C_{i}\) in the entire conditional attribute set, and \(I(D|\{C_{i}\})\) indicates the importance of the conditional attribute \(C_{i}\) itself in the system.  
Considering these two aspects comprehensively and carrying out standardization processing, the weight of each conditional attribute can be obtained:  
\[W(C_{i})=\frac{\mathit{New}\;\mathit{Sig}(C_{i})+I(D|\{C_{i}\})}{\sum\limits_{ i=1}^{m}\{\mathit{New}\;\mathit{Sig}(C_{i})+I(D|\{C_{i}\})\}} \tag{8}\]  
Figure 3: Technical flow chart of underground space suitability evaluation based on rough set and conditional entropy: (a) flow chart of weight calculation based on rough set and conditional entropy; (b) flow chart of fuzzy comprehensive evaluation.  
However, when the value of conditional attribute \(C_{i}\) is less than three, the weight is always 1/2 according to the above method, which is too average. In this paper, it will be corrected and the importance degree of conditional attribute (index) \(C_{i}\) is expressed as:  
\[\textit{New}\;\textit{Sig}(C_{i})=I(\textit{D}|C-\{C_{i}\})-I(\textit{D}|C)+ \frac{\sum\limits_{a\in\mathbb{C}}|a(x)|-\sum\limits_{a\in\mathbb{C}-\{C_{i} \}}|a(x)|}{\sum\limits_{a\in\mathbb{C}}|a(x)|} \tag{9}\]  
where the conditional entropy and the normalized weight of \(C_{i}\) are still calculated according to Equations (6) and (8), respectively.
## 4 Discussion
With respect to the time scale of integration, the correlation between ground-based data and IMERG data improves with increasing summing time scales from hourly to yearly, which is in line with others' findings [25; 64; 68; 70]. However, the strength of the correlation at a yearly timescale is constrained by the short duration of the study period (2 years). In addition, IMERG data have difficulty reproducing the spatial variability of rainfall within the catchment (Figure 7a,b,e). This may at least partly result from the spatial smoothing inherent to GPPs, daily extreme events not identified by the satellite (Figure 5), as well as the small number of rain gauges used as the bias correction for IMERG [53]. At the event time scale, there is good agreement in terms of PDF of duration and depth (Figure 8), which is consistent with the results of [59] in Brazil. Finally, the poor performance of IMERG on an hourly scale has also been reported by other studies [74; 75]. This is particularly due to the inferior performance at locations where the estimate is derived by morphing, which occurs when no overpasses by any of the passive microwave instruments in the GPM constellation are available at a specific half-hour [53]. This is also due to the temporal resolution of inputs (>1 h), cited above (Section 2.3.4), used to calculate the IMERG product [53; 74]. In this paper, we only focused on IMERG for the assessment at different time scales due to its better performance at a daily scale. However, it is important to note that the poor performance of some products at a finer scale does not necessarily suggest that they will be bad at longer time scales. As a matter of fact, the accuracy of gridded precipitation products increases as the time of accumulation increases [25; 64; 76].
## 3 Results
### Model Simulation for Seasonal and Inter-Annual Dynamics of RE at the Site Scale  
Using the model parameters in Table 2, we simulate time-series \(RE\) of all 57 site-years and then further compared them to the flux tower-observed \(RE\) for each vegetation subtype in the alpine and temperate grasslands. In most cases, the temporal variation of the \(RE\) is tracked robustly by our model (Figure 6). The agreement between the modeled and observed \(RE\) is the best for the desert steppe and meadow steppe (\(R^{2}>0.9\)), followed by the alpine shrub meadow (\(R^{2}=0.872\)), the alpine meadow steppe (\(R^{2}=0.805\)), the alpine _Kobresia_ meadow (\(R^{2}=0.778\)), the typical steppe (\(R^{2}=0.743\)), and the alpine swamp meadow (\(R^{2}=0.715\)). Ground covered by water in the alpine swamp meadow would  
Figure 5: Comparison between the observed and predicted ecosystem respiration (\(RE\)) in the Tibetan alpine grasslands (**left**) and the Inner Mongolian temperate grasslands (**right**).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Vegetation Type** & **Types of Parameter Estimation** & **Types of Cross-Validation** & \(\mathbf{R^{2}}\) & **RMSE** & \(\boldsymbol{p\)**Value**} \\ \hline  & SH\_SW\_ST & KO & 0.844 & 0.841 & \(<\)0.01 \\  & KO\_SW\_ST & SH & 0.756 & 1.047 & \(<\)0.01 \\ Alpine & KO\_SH\_ST & SW & 0.917 & 0.927 & \(<\)0.01 \\ Grasslands & KO\_SH\_SW & ST & 0.784 & 1.544 & \(<\)0.01 \\  & Joint-sites (AR\_GL\_HBCO\_GL) & / & 0.778 & 0.774 & \(<\)0.01 \\ \hline  & TS\_DS & MS & 0.803 & 0.405 & \(<\)0.01 \\ Temperate & MS\_DS & TS & 0.575 & 0.919 & \(<\)0.01 \\ Grasslands & TS\_MS & DS & 0.643 & 1.021 & \(<\)0.01 \\  & Joint-sites (TS\_MS\_DS) & / & 0.749 & 0.605 & \(<\)0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Joint-sites estimation and leave-one-out cross validation for each vegetation type.  
affect the spectral reflectance and further disturb the model drivers (_EVI_ and _LSWI_), and this may be the main reason for the relatively low performance in this vegetation type.  
The seasonal variation of _RE_ in China's grasslands could be described as a unimodal curve peaking in July or August. Across the seven vegetation types, the mean annual peak respiration rate ranges from 1.10 to 5.84 g C m\({}^{-2}\) day\({}^{-1}\). The highest value occurring in the alpine meadow or the meadow steppe is approximately four times higher than that of the alpine meadow steppe or the desert steppe. There are also significant inter-annual fluctuations in the _RE_ at some sites; for instance, the observed _RE_ decreased remarkably as precipitation decreased in 2011 in SZWQ (Figure 6f), 2005 and 2006 in NM (Figure 6e), and 2009 in HBKO (Figure 6a). In addition, our model could accurately reproduce the inter-annual dynamics of _RE_ due to the variation in precipitation across years.
# Robotized Mobile Platform for Non-Destructive Inspection of Aircraft Structures
## 3 Testing and Experimental Validation
### Initial and Laboratory Tests  
After assembling the prototype, the thrust force measurements of the propeller drive as a function of power consumption were first measured. For this purpose, two digital scales with a range of up to 5 kg, the UNI-T UT203+ clamp multimeter, which measured the current consumption, and the MASTECH MS8211 multimeter, which measured the voltage supplying the vehicle, were used in the experiment. The maximum measured thrust was 2.7 kg with a power consumption of 658 W. The diagram of the thrust in function of the consumed power is presented in Figure 8.  
In the next step, the static coefficient of friction between the wheel tread and the ground was measured. An inclined plane was used for this purpose, which was made of birch plywood and MDF board. The test surface, on one side, was covered with a steel sheet painted with high-gloss aerosol varnish to simulate the aircraft skin surface. The other side was left in a shell state.  
Figure 8: The thrust of the propeller in function of the consumed power for the MP.  
Up to a certain value of the angle, the component of G force is balanced by the friction force and the body is at rest. By increasing this angle, the limit at which the body starts moving is reached. This angle is called the friction angle, and the static friction coefficient is equal to the tangent of this angle.  
A GEKO digital protractor was used for angle measurements. The results of these measurements were as follows. The friction angle for a painted steel sheet and rough plywood was 17.7\({}^{\circ}\) and 31\({}^{\circ}\), respectively, which corresponds to the static friction coefficient of 0.32 and 0.6. Next, the MP's holding angle when the propeller drive was operating at maximum available power was measured. For the painted steel sheet, the angle was 53\({}^{\circ}\), while for the raw surface of the birch plywood, the angle was 80\({}^{\circ}\).  
The last laboratory test was related to the measurement of the noise generated by the propeller drive unit operating at full power. A Bruel & Kjaer Type 2239 meter was used to measure the sound intensity. The measured equivalent continuous sound level was 114 dBA.  
After completion of the prototype, a test on moving the MP along a vertical wall was performed (see Figure 9). The surface on which the vehicle moved was wall panels covered with wood-like veneer. Passing this test successfully, the MP was prepared for the tests in real operational conditions.
# Dynamic Relevance-Weighting-Based Width-Adaptive Auto-Encoder
## 2 Literature Review
### Discussion  
Despite recent progress in evolving architectures (DEVDAN [18], SAQN [21], and NDL [17]), most methods still rely on manual thresholding, fixed pruning schedules, or heuristic-based evaluations of node importance. These approaches often suffer from instability or lack of theoretical grounding. In contrast, our method introduces a continuous, relevance-weight-based adjustment guided by an inner-product-preserving objective, allowing both expansion and pruning without manual intervention. This positions our model as a principled and flexible alternative to existing fixed and evolving autoencoders.  
Compared to prior work, our Width-Adaptive Autoencoder (WAAE) introduces a novel strategy to dynamically control the latent width during training. This offers a complementary mechanism to pruning; rather than removing weights post hoc, we proactively adapt the model architecture in response to training signals. Our method supports flexibility during inference and can match model capacity to resource constraints without separate retraining. Thus, our approach lies at the intersection of adaptive compression and architecture search, contributing a new dimension to model efficiency by enabling width-scalable representations in the autoencoder framework.  
Unlike prior methods such as DEVDAN, SAQN, and NDL that rely on heuristic-based or threshold-driven pruning, our approach introduces a principled relevance-weighting mechanism based on an inner-product-preserving objective. This formulation allows for continuous and data-driven adaptation of network width, ensuring stable learning dynamics without reliance on empirical thresholds or task-specific tuning. As a result, our model offers a more generalizable and theoretically grounded framework for width-adaptive representation learning.  
To ground our approach theoretically, we build on the principle of inner-product preservation as a mechanism for evaluating and maintaining the relevance of neurons during training. Specifically, we define a relevance score for each neuron based on its contribution to preserving the pairwise inner products of hidden representations, which are indicative of the local geometry of the feature space. This allows for a continuous, data-driven adaptation of network width, offering a principled alternative to discrete pruning heuristics or static architectural decisions commonly found in previous work.
# Flaw Size Quantification for Cable Flaw Inspection System with Inductive Search Coil Sensor
## 4 Experimental Result Analysis
### Analysis of Response Amplitude Based on Flaw Cross-Section Area  
This section presents a comparative analysis of the influence of the flaw cross-sectional area on the response amplitude. Experimental conditions for the investigation are outlined in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Axial Length (mm)**} & \multicolumn{2}{c}{**Averaged**} & \multicolumn{2}{c}{**Averaged**} & \multirow{2}{*}{**Amplitude**} & \multirow{2}{*}{**Proportion of Difference**} \\  & **Amplitude** & & & \\  & **under** \(D_{1-1}\) & **under** \(D_{2-1}\) & **under** \(D_{2-1}\) & **Difference** (\(\mu\)**H) & **Difference** \\  & (\(\mu\)H) & (\(\mu\)H) & & & \\ \hline
10 & 4.342 & 4.828 & 0.486 \(\pm\) 0.075 & 10.1 \(\pm\) 1.5\% \\
20 & 8.094 & 8.466 & 0.372 \(\pm\) 0.093 & 4.4 \(\pm\) 1.1\% \\
30 & 9.850 & 10.554 & 0.704 \(\pm\) 0.103 & 6.7 \(\pm\) 1.0\% \\
40 & 11.519 & 12.525 & 1.006 \(\pm\) 0.039 & 8.0 \(\pm\) 0.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Inductance amplitude comparison under \(D_{1-1}\) and \(D_{2-1}\).  
Figure 15: Compensated response comparison of flaws with different lengths at 2500 Hz excitation frequency.  
Figure 16a displays the offset response values of 10 mm flaws with different cross-sectional areas at 2500 Hz excitation frequency on Layer 2. To quantify the amplitude of each situation, the absolute amplitudes were calculated on the basis of Equation (6) and averaged. The results were presented in Figure 16b. The figure shows a clear trend in the response amplitude of the self-inductive coil as the flaw cross-sectional area decreases under \(D_{2-i}\). The response amplitude experiences significant attenuation, indicating a linear relationship between the flaw cross-sectional area and the self-inductive coil response amplitude. These findings reveal a positive correlation between the flaw cross-sectional area and the self-inductive coil response amplitude for flaws located at the same depth inside the cable.
# Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification
###### Abstract  
Fault Detection and Diagnosis of electrical machine and drive systems are of utmost importance in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. Multi-label classification has recently gained popularity in various application domains as an efficient method for fault detection and monitoring of systems with promising results. The contribution of this work is to propose a novel methodology for multi-label classification for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. In this research, the Electrical Signature Analysis as well as traditional vibration data have been considered for modeling. Furthermore, the performance of various multi-label classification models is compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.  
Keywords:multiple fault detection; rotating electrical machines; drive systems and power electronics; multi-label classification; machine learning; data science; big data; energy conversion; soft computing; fault severity; fault classifiers; electric machine +
Footnote †: journal: Applied Sciences
## 3 Method Description
### SNR Gain
The comparative analysis in this section covers noise environments within different SNR ranges and aims to demonstrate the new algorithm's theoretical correctness. Experimental results firmly establish the new algorithm's effectiveness in handling in-band noise and its superiority in demodulation accuracy, especially its application potential under low SNR conditions.
# MQC-MB: Multiphoton Quantum Communication Using Multiple-Beam Concept in Free Space Optical Channel
## 4 The Implementation of MQC-MB
### Multiple Beam Quantum Communication Phase
#### 4.2.3 Step 3: Detection of Quantum Signal  
[PERSON] will then receive the light beam passing from the receiver array, fine pointing assembly, and point ahead angle assembly before arriving at the HWP. There, [PERSON] applies his secret polarization using the same angle \(\theta\) set at the initial stage, and the beam is passed through the beam splitter. The detector will detect whether the bit is 0 or 1 when the beam passes through the \(0^{\circ}\) and \(90^{\circ}\) polarizer.
# LDM-Ex-FDM: A Novel Multi-Service Transmission Scheme for the ATSC 3.0 System
## 4 Simulation Results and Analysis
### DIC Performance for the Fixed Reception  
In this subsection, the BER performance comparisons of the LDM-Ex-FDM scheme with the proposed DIC scheme and the traditional SIC scheme are shown in Figures 19 and 20, respectively. Note that the proposed DIC could also work with traditional _LDM_ used in ATSC 3.0. As shown in Figure 19, the BER performance of the proposed LDM-Ex-FDM scheme with NU\(-\)16 QAM constellation mapping and a DIC detector under the multipath channel when different power injection levels of _LDM_ are allocated. It can be observed that when the power injection level is 10 dB, there exists a gap of about 0.1 dB @BER = \(10^{-6}\) between the low-complexity DIC detector and the SIC scheme. Moreover, when the power injection level is higher than 10 dB, the performance loss reduces to within 0.1 dB. In Figure 20, when a higher-order modulation, NU\(-\)64 QAM, is applied in the fixed service, the proposed DIC scheme is capable of achieving almost the same BER performance as that of the traditional SIC. It can be inferred from Figures 19 and 20 that the higher the power injection level and/or the modulation order adopted by the fixed service, the better performance that the DIC scheme can achieve.  
Figure 19: BER performance of fixed service in the LDM-Ex-FDM scheme with direct interference cancellation (DIC) and successive interference cancellation (SIC) with perfect channel impulse response (CIR) under the multipath channel, where the parameters of the ATSC 3.0 system were set to \(\Delta\) = 10 dB, 12 dB, 15 dB, the _CL_ constellation was set to QPSK, the _EL_ constellation was set to NU\(-\)16 QAM, and the puncturing rate was set to 1/12.  
The basic principle of DIC is that it regards the \(CL\) symbols as the constellation offset of the \(EL\) symbols of the \(LDM\) symbols, and demodulates the \(CL\) symbols by making a hard decision on the composite constellation of the \(LDM\) symbols. Therefore, the composite constellation of \(LDM\) symbols has nothing to do with the signal coding type in each layer of \(LDM\), but is affected by the modulation type and the power injection level of the signal in each layer of \(LDM\).  
As shown by these results, compared to the SIC scheme, the proposed DIC scheme can achieve lower processing complexity and lower delay with almost no performance loss in the case of a relatively high power injection level.