esa-sceva/satcom-chunk-collection huggingface dataset:
# Depth Estimation from a Hierarchical Baseline Stereo with a Developed Light Field Camera
## 3 The Proposed Approach
### Hierarchical Baseline Stereo-Matching
#### 3.3.2 Constraint Enhancement  
NCC matching cost is only locally invariant to linear radiometric changes. If the target object has a textured Lambertian reflectance surface, dense coarse disparity results are obtained with high NCC scores. However, in the presence of occlusions, shadows, and specular reflectance, some macro-pixels may be mismatched with low NCC scores. To remove ambiguities and outliers, three constraints are enhanced: uniqueness, smoothness, and ordering.  
Figure 3: (**a**) The epipolar geometry of the stereo vision light field system. (**b**) The light field raw image. (**c**) The \(1-ring\) neighboring macro-pixels. (**d**) The Sobel operator.  
The uniqueness constraint ensures that if \(mp_{i}\) in \(\mathbf{LF}_{left}\) matches \(mp_{j}\) in \(\mathbf{LF}_{right}\), then \(mp_{j}\) must also match \(mp_{i}\). If \(\|d_{i}-d_{j}\|>\epsilon\), \(mp_{i}\) is occluded. A left-right consistency check is executed to detect occluded as well as mismatched macro-pixels, tolerating up to one macro-pixel.  
The smoothness constraint is based on the consistent property in a locally surrounding window, except in-depth discontinuity regions. Disparity differences between \(mp_{i}\) and neighboring macro-pixels \(mp_{i\epsilon}\) are calculated as \(diff(mp_{i},mp_{i\epsilon})=\|d_{i}-d_{i\epsilon}\|\). If more than half of these differences are larger than the threshold \(\epsilon\), \(mp_{i}\) is mismatched to an invalid disparity. Edge macro-pixels, generally having high matching confidence, are not subject to enhancement by the smoothness constraint.  
The ordering constraint means that the disparity for \(mp_{i}\) cannot exceed the disparity of its right neighbor, \(mp_{i+1}\), by more than \(\epsilon\) macro-pixels. Finally, invalid macro-pixels must be assigned new proper disparities that comply with all constraints. To perform edge-preserving filtering, a weighted median filter [25] is applied, taking into account the edge macro-pixels.
# Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna
## 4 Experiment
### Comparison with Different Traditional Methods
#### 4.3.2 Analysis of Carrier Frequency Difference on Separation Results  
A total of 2000 test samples with SNR of 5, 10, 15, 20 and 25 dB, power difference between signals of 2 dB, relative time delay of 10 us, carrier frequency of 10 MHz for one signal and 10, 10.0005, 10.001 and 10.0015 MHz for another are selected for testing. When the carrier frequency difference between the signals is 0, 500, 1000 and 1500 Hz and the SNR of the signals varies from  
As can be observed in Figure 7, the average decoding accuracy of PASA, TDBSS and MConv-TasNet algorithms is 6.37%, 29.74% and 96.8%, respectively. The MConv-TasNet algorithm is affected in 0 Hz carrier frequency difference with small SNR. However, the TDBSS algorithm has its highest decoding accuracy when the carrier frequency difference is 0 Hz. As the carrier frequencies of the two signals are the same and the difference between high and low pulses is more stable, it is more accurate to estimate the bit values of the two signals by the amplitude difference. However, the decoding accuracy of the TDBSS algorithm is 0 when the SNR is small, such as 5 and 10 dB. Therefore, the TDBSS algorithm is more affected by the SNR. The PASA is based on the carrier frequency difference to separate signals; thus, it is more affected by the carrier frequency difference. The decoding accuracy of PASA is highest when the carrier frequency difference is 1500 Hz, but it is close to 0 when the carrier frequency difference is small. In summary, the MConv-TasNet algorithm can separate more signals accurately than the PASA and TDBSS algorithm when the carrier frequency difference between overlapped signals is less than 1500 Hz.  
The average BERs of PASA, TDBSS and MConv-TasNet algorithms are 32.11%, 8.88% and 0.04%, respectively, in Figure 8. The BER of PASA is significantly higher than that of the TDBSS and MConv-TasNet algorithms, and the BER of PASA decreased gradually as the carrier frequency difference increased. The TDBSS algorithm has the lowest BER at 0 Hz. Meanwhile, the MConv-TasNet algorithm has a certain BER when the SNR and carrier frequency difference are small, and the BERs of the MConv-TasNet algorithm are all 0 for different carrier frequency differences when the SNR is greater than 10 dB. Therefore, PASA is most affected by carrier frequency difference among the three algorithms, while TDBSS and the MConv-TasNet algorithms are mainly affected by SNR.
## 5 Results
### Motor Working under Rated Operating Conditions  
The first study focuses on the detection of eccentricity faults when the analyzed fPMa-SynRM operates under rated conditions, that is, 4 \(A_{\text{RMS}}\), 3.5 kW, 240 V\({}_{\text{RMS}}\), 5000 rev/min, and 65\({}^{\circ}\) current angle (between \(d\)-0\({}^{\circ}\) and \(q\)-90\({}^{\circ}\) axes). Figure 5 shows the spectral content of the line currents of the analyzed fPMa-SynRM under healthy and 40% static eccentricity conditions.  
Figure 4: Connection realized to measure the ZSVC using a five-phase balanced resistor network to generate the artificial neutral point.  
The results presented in Figure 5 show minor differences between the spectral content of healthy and faulty machines, and it is thus difficult to detect eccentricity faults from the analysis of the line currents when the motor operates under rated conditions. The low discrimination attained between healthy and faulty conditions by the method based on the stator currents is attributed to the high reluctance path of the magnetic flux due to the combined effect of the saturation of the rotor segments placed between the flux barriers and the flux crossing the flux barriers. This reluctance takes similar values to those of the air gap, thus hindering the discrimination of the method based on the analysis of the stator currents spectra.  
Figure 6 shows the spectral content of the ZSVC of the analyzed fPMa-SynRM under healthy and 40% static eccentricity conditions.  
It is noted that both the stator currents and ZSVC spectra are normalized with respect to the maximum value of the respective waveform. The results presented in Figure 6 clearly show a better sensitivity of the ZSVC-based method compared to the method based on the analysis of the stator currents.  
Figure 5: Rated operating conditions. Spectral content of the line currents. Healthy machine and faulty machine with 40% eccentricity.  
Figure 6: Rated operating conditions. Spectral content of the ZSVC. Healthy machine and faulty machine with 40% eccentricity.  
However, to attain a better discrimination, it is suggested that other operating conditions in which the machine and specifically, the rotor segments, work under less saturated conditions are analyzed.
## 3 Results
### Ammonia (NH\({}_{3}\))  
Ammonia can be measured using FTIR, QCL or LDS from the tailpipe.  
A Quantum Cascade Laser (QCL) element emits an intermittent mid-infrared laser beam in a sample cell at a very specific wave number by applying a pulsed current [128, 147, 148]. During a pulse, the element temperature varies, and, consequently, the actual wave number continuously shifts within a narrow range. Compounds that absorb energy in this oscillation range will decrease the light intensity and will be detected at the relevant wave numbers.  
The measurement principle of a Laser Diode Spectrometer (LDS) is based on single line molecular absorption spectroscopy [149]. A diode laser is used for emitting a near-infrared (compared to QCL mid-infrared) light beam through the sample gas, and the light is detected by a receiver. The laser diode output is tuned for a gas-specific absorption line. Cross-sensitivities of the method is insignificant as, spectrally, the laser light is much narrower than the gas absorption line.  
Table 1 summarizes the studies that FTIR was assessed in measuring NH\({}_{3}\). The calibration gases readings were within 5% of the specified values. In general, the agreement between FTIR was good. Steady-state engine tests with NH\({}_{3}\) dosing showed the importance of the FTIR response time and the sampling line conditioning. The differences in dynamic response indicated buffering of NH\({}_{3}\) (known to \"stick\" on walls) within the sampling line before breakthrough to the analyzer cell [150].  
The comparison of FTIR with QCLs was also good on average. In one case, examining real-time graphs, higher and sharper peaks were obtained with the QCL due to the 10-Hz frequency [151]. Comparison of an FTIR with chemical ionization mass spectrometry (CI-MS) gave good agreement [152], and comparisons with an in-situ (cross-duct, without sampling line) LDS was also in good agreement [149].  
There were only a few studies comparing tailpipe with dilution tunnel [152, 153]. However, as pointed out by these studies, severe losses of ammonia can occur in the dilution tunnel resulting in lower emissions.  
The sampling location and the sampling conditions are very important for NH\({}_{3}\) measurements. As it was mentioned in the NOx section, the formation of ammonium nitrate is also possible in the presence of HNO\({}_{3}\). Low sample line temperature can result in condensation inside the heated line, which can lead to loss of NH\({}_{3}\). On the other hand, upstream of the SCR in the presence of HNCO and H\({}_{2}\)O, a high sample line temperature can produce NH\({}_{3}\)[150]. For such cases, a temperature of 113 \({}^{\circ}\)C was recommended. Downstream of the SCR, no such phenomena were observed.
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 2 Related Work
### Image Classification Methods
#### 2.3.3 GP-Based Image Classification Methods
As shown in Tables 4 and 5, some studies have achieved success in limited types of image classification tasks by designing new program structures and function sets. Others have proposed new GP methods for classifying degraded images affected by blurriness, low contrast, or noise. However, the designed program structures of these methods are not sufficiently effective, limiting the classification performance of the algorithm. As shown in Table 6, some studies have focused on improvements in genetic operators, with the aim of obtaining GP individuals with excellent classification performance. Nonetheless, these enhancements are mainly limited to a few datasets and may still face fitness stagnation in broader or more complex scenarios due to the high similarity among individuals. In cases with fewer image samples and adverse factors like blurriness and noise, the best individuals or solutions derived by GP algorithms are prone to overfitting, a problem that has not received adequate attention in current research. Therefore, this study proposes a new GP-based method for degraded image classification. This method employs a new program structure that facilitates the transmission of information acquired by nodes across layers through an information transmission mechanism, thereby evolving efficient individuals with discriminative features. Additionally, the accompanying evolution strategy proposed accompanies multiple GP individuals during the evolutionary process and guides the algorithm to explore more effective GP individuals \begin{table}
\begin{tabular}{p{142.3 pt}} \hline \hline [PERSON] et al. [35] introduced a unique adaptive mutation method, as well as a new crossover technique to control tree bloat during the evolutionary process, thereby enhancing classification performance. \\ \hline [PERSON] et al. [36] proposed a new mutation operator specifically targeting the control of program tree bloat. This operator dynamically adjusts the size of the program tree during the evolutionary process. Experimental results indicate that the method of dynamically adjusting the size of the program tree effectively enhances classification performance. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Improvement methods for GP genetic operator.
# Data Security-Based Routing in MANETs Using Key Management Mechanism
## 1 Introduction
### Motivation  
For secure communication in MANETs, various secret keys are used. In most of the existing cryptography-based routing protocols, each node is responsible for the generation and distribution of its own secret keys. The main objective of this paper is to prevent energy dissipation due to the individual key generation and distribution. Therefore, an efficient energy-aware secure key management system is proposed. Asymmetric key cryptography is used, which involves two specialized nodes, labeled Calculator Key (CK) and Distribution Key (DK). These two nodes are responsible for the generation, verification, and distribution of secret keys. As a result, other nodes need not perform any kind of additional computation for building the secret keys. These nodes are selected using the energy consumption and trust values of nodes.
# Temperature Effects on the Natural Frequencies of Composite Girders
## 2 Temperature Effects on a Composite Bridge  
Bridges are exposed to various temperature conditions generated by environmental factors such as solar radiation, air temperature, and irregular winds. These environmental factors induce heat transfer in bridges, resulting in a temperature field. Heat transfer is also affected by thermal properties such as specific heat and thermal conductivity. Three main mechanisms contribute to heat transfer within and between a bridge and its surrounding environment. These mechanisms include the following ones: (1) thermal conduction through the sections of a bridge, which occurs as a result of temperature differences between materials; (2) convection, which is involved in the exchange of heat between the bridge and the surrounding air; and (3) radiation, which encompasses both incoming and outgoing radiation, including solar radiation, longwave radiation from the atmosphere and surrounding surfaces, and radiation emitted by the bridge itself [15].  
The temperature field in a bridge section can be uniform or non-uniform, depending on environmental conditions. Furthermore, a non-uniform distribution or temperature gradient can vary, depending on the thermal properties of bridge members in addition to environmental conditions [16; 17]. Variations in temperature along temperature fields can influence the dynamic properties of bridges in two main ways, namely by changing the elastic moduli and sizes of members [5; 7]. Therefore, it is necessary not only to evaluate temperature fields accurately, but also to account for changes in elastic moduli and member sizes to investigate the effects of temperature on the natural frequencies of bridges. The following subsections discuss thermal properties, elastic moduli, and size variations of members to elucidate the effects of temperature on composite bridges.
## 3 Verifications and Results
### Target Recognition Methodology  
Following the reduction in average detection error, the data stored in AID tags can be more effectively leveraged for target recognition. The proposed methodology for target recognition is described below, with its detailed workflow illustrated in Figure 9.  
Upon acquiring the noise-containing time-domain echo signal, the corresponding AID tag can be generated. This tag is converted into its binarized form after host retrieval. The binarized data is then compared with its noise-free counterpart using the Jaccard similarity  
Figure 8: Detection error rates for 3570 material combinations under 3 dB SNR: including interfering material pairs (e.g., PMMA-PVC) results in a 12 % error; after excluding low-contrast combinations, the error drops to 0.41%. Error bars represent 95% confidence intervals from 10,000 Bootstrap resamples.  
Figure 9: Flowchart of the Target Recognition Methodology.  
coefficient, yielding a column vector of Jaccard similarity coefficients. Subsequently, cosine similarity [28; 29] is computed between this vector and the Jaccard matrix, as expressed in Equation (17). To further enhance recognition accuracy, the TOP-K query optimization method [30] is applied.  
\[\text{Cosine}(A,B)=\frac{\sum_{i=1}^{n}A_{i}\cdot B_{i}}{\sqrt{\sum_{i=1}^{n}A _{i}^{2}}\sqrt{\sum_{i=1}^{n}B_{i}^{2}}} \tag{17}\]  
To validate recognition stability, a column vector of Jaccard similarity coefficients was compared with 20 additional Jaccard matrices under 3 dB SNR noise interference for similarity recognition trials. Validation shows that the computation time for the Jaccard column matrices referenced in Figure 9 is approximately 0.005 s. The full Jaccard matrix computation time remains below 0.5 s (matrices are precomputed and stored in the database, eliminating additional computational overhead). Finally, the processing time for column-to-matrix matching in target recognition is around 1.5 s. The calculation is carried out based on the matrix of the maximum dimension, which can be further reduced when matching with lower-dimensional matrices.  
As demonstrated in Figure 10, the detection error rate exhibits a significant reduction with increasing matching dimensionality, with the overall detection success rate reaching 99%.
# Design and Evaluation of Dynamic Topology for Mega Constellation Networks
###### Abstract  
Due to numerous Low Earth Orbit (LEO) satellites, urgent analysis of many temporary intersatellite links (ISLs) is necessary for mega constellation networks. Therefore, introducing a dynamic link in topology design is crucial for increasing constellation redundancy and improving routing options. This study presents one class of static topology of satellites (STLS) and two types of dynamic topology of satellites (DTLS). Firstly, a call model based on global population density distribution is determined using world population density by provincial administrative divisions. Then, using a common simulation platform, the Dijkstra algorithm obtains random paths between 10,000 pairs of urban ground stations, adopting a time slice division strategy. Finally, 3 indexes are obtained within 66-time slices: average call distance, number of hops, and total time delay. Results show that DTLS1 reduces these indexes by 3.58%, 3.72%, and 3.57%, respectively, compared with DTLS2 under the same conditions, indicating that DTLS1 has the best network performance, transmitting traffic quickly in any direction through the reverse track, thereby verifying the related hypothesis.  
dynamic topology; mega constellation; inter-satellite link; network performance +
Footnote †: journal: Electronics  
0
Footnote 0: Copyright** © 2023 by the authors. Liconsee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# A Channel Measurement-Based Listen-Before-Talk Algorithm for LTE-LAA and WLAN Coexistence
## 2 Related Work
### Recent Studies on the Coexistence Between LTE-LAA and WLAN Systems  
Recent studies have explored various approaches to address the coexistence between LTE-LAA and WLAN systems. Ref. [1] formulated a non-linear integer programming problem to allocate resource blocks at the LTE-LAA eNB, aiming to ensure the fair use of the unlicensed spectrum for both LTE-LAA eNB and WLAN systems. Ref. [11] proposed a medium access control protocol leveraging physical-layer interference suppression techniques to mitigate cross-technology interference and enhance spectrum efficiency and fairness. Ref. [12] developed sensing techniques to accurately estimate operational parameters, such as the contention window size and CCA threshold, for LTE-LAA transmissions  
Figure 1: LB-LBT scheme for LTE-LAA and WLAN coexistence.  
through the APs of WLAN systems. Ref. [13] developed multi-antenna multi-user transmit precoding and power control techniques performed at LTE-LAA eNBs to regulate the transmissions of WLAN APs. Ref. [14] formulated a problem to jointly optimize the transmission probability and rate of the LTE-LAA system, aiming to maximize throughput while ensuring fair usage of unlicensed channels between LTE-LAA and WLAN systems. Ref. [15] analyzed the performance of LB-LBT in the LTE-LAA system, taking into account the GAP period, which represents the required waiting time for the LTE-LAA eNB before data transmission.
# _Bacillus_ sp. GLN Laccase Characterization: Industry and Biotechnology Implications
## 3 Results
### Influence of Organic Solvents, Inhibitors, and Metal Ions on Lacase Stability  
The organic solvent stability of GLN lacase was studied using acetone, ethanol, methanol, and propanol at two concentrations for 1 h, 8 h, and 16 h; the results showed that the GLN lacase maintained at least 80%, 78%, and 82%, respectively, of its original activity in the various solvents (Table 2). The results further demonstrated, in some cases, that the enzyme regained stability after prolonged preincubation with the solvents. For example, acetone treatment at 10% showed 80.18 \(\pm\) 0.73% after 1 h, 84.41 \(\pm\) 0.09% after 8 h, and 92.53 \(\pm\) 6.06% after 16 h. At the same time, methanol at 20% displayed a similar pattern of stability with 81.81 \(\pm\) 1.56% at 1 h, 87.52 \(\pm\) 3.77% at 8 h, and 93.11 \(\pm\) 2.66% at 16 h (Table 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Organic** & **Concentration** & \multicolumn{3}{c}{**Residual Activity (\%) a**} \\ \cline{3-5}
**Solvent** & **(\%)** & **1 h** & **8 h** & **16 h** \\ \hline Control & - & 100 \(\pm\) 1.01 & 100 \(\pm\) 1.32 & 100 \(\pm\) 1.08 \\ Acetone & 10 & 80.18 \(\pm\) 0.73 & 84.41 \(\pm\) 0.09 & 92.53 \(\pm\) 6.06 \\  & 20 & 90.77 \(\pm\) 4.14 & 81.42 \(\pm\) 1.01 & 86.61 \(\pm\) 0.83 \\ Ethanol & 10 & 84.99 \(\pm\) 1.1 & 78.95 \(\pm\) 1.19 & 82.0 \(\pm\) 1.29 \\  & 20 & 91.94 \(\pm\) 0.46 & 88.56 \(\pm\) 2.11 & 82.26 \(\pm\) 1.10 \\ Methanol & 10 & 86.35 \(\pm\) 1.01 & 89.67 \(\pm\) 4.59 & 84.47 \(\pm\) 0 \\  & 20 & 81.81 \(\pm\) 1.56 & 87.52 \(\pm\) 3.77 & 93.11 \(\pm\) 2.66 \\ Propanol & 10 & 89.21 \(\pm\) 3.58 & 84.47 \(\pm\) 0 & 82.33 \(\pm\) 2.30 \\  & 20 & 86.55 \(\pm\) 1.84 & 88.17 \(\pm\) 3.03 & 86.29 \(\pm\) 5.70 \\ \hline \end{tabular}
\end{table}
Table 2: Influence of organic solvents on GLN lacase stability.  
Figure 7: Thermoactivity (**A**) and thermostability (**B**) profile of _Bacillus_ sp. GLN lacase. The results were presented as mean values of three experimental analyses. Error bars show when they are greater than the marker.  
The enzyme stability was adversely affected after treatment with potential inhibitors at a higher concentration of 100 mM (Table 3). GLN 12.2 (10.67 \(\pm\) 0.13%, 58.33 \(\pm\) 0.51%, 65.14 \(\pm\) 1.26%, 63.08 \(\pm\) 3.17, and 63.98 \(\pm\) 0% of the original activity against SDS, EDTA, NaN\({}_{3}\), urea, and NaCl, respectively.  
The influence of metal ions on the lacase's catalytic property was assessed, and the results indicated that the enzyme maintained high residual activity (>85%), with CuSO\({}_{4}\) slightly enhancing the enzyme activity (110.68 \(\pm\) 2.17%) compared to the control after 30 min of incubation (Table 4).
## 2 Datasets  
In this study, the data used include InIRA measurements [21; 22; 24] and collocated WaveWatch III (WW3) waves [33], traditional altimeter SWH (JASON2, JASON3, SARAL, and HY2A), and ETOPO1 ocean depth data [34]. The InIRA and traditional altimeter data are remote sensing data, whereas the WW3 and ETOPO1 are model data. WW3 provides the SWH and wave direction, whereas ETOPO1 provides the ocean depth. All these data were used for analyzing the model dependence and validating the retrievals. Figure 1 shows the location map of the InIRA data and the collocated data of the four traditional altimeters. As indicated in the figure, the datasets are mainly concentrated in the coastal areas of Southeast Asia. A brief introduction of the various data mentioned above is given below.