esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Classical Anomaly Detection Methods  
In the field of anomaly detection, there are some classic methods. For example, 3 sigma identifies anomalies by measuring whether current values deviate from historical averages and whether deviations meet three standard deviations. The principal component analysis (PCA) [6] method uses the weight of the eigenvalue to calculate the eigenvector distance difference corresponding to the sample point's eigenvalue so as to calculate the deviation degree of the data value from this direction. It identifies abnormalities based on the accumulation of the deviation degree for each order. The distance-based method of K-nearest neighbors (KNN) [7] determines an anomaly when the average distance of the K-nearestneighbors is more significant than the threshold. Copula-based outlier detection (CO-POD) [8] uses statistical probability functions to calculate the left and right tail probabilities. It outputs the most suitable tail probabilities according to the specific situation to perform anomaly detection. Its advantages are that it does not need to calculate the distance of the sample parts, the overhead is small, and no parameter adjustment is required. The local outlier factor (LOF) [9] method is a density-based unsupervised anomaly detection method that assigns each data point an LOF dependent on the density of the neighborhood and then compares each point to the corresponding field density to determine whether the data point is an outlier, similar to the density-based connectivity-based outlier factor (COF) [10]. One-class support vector machines (OCSVM) aim to learn decision boundaries [11; 12] for normal observations while taking some outliers into account. If the data is two-dimensional, it is to find a hyperplane to divide the normal data and some outliers, and if it is multi-dimensional data, it is to find a surface to divide. The anomalies are identified by observing whether a data point falls within the decision boundary. Ref. [13] studies various machine learning approaches for informational/noninformational classifications. A robust random cut forest (RRCF) improves the isolation forest algorithm (IF) by integrating ideas to process streaming data [14; 15; 16].  
The simplicity of the classical method has always been its advantage, but it has limitations when dealing with nonlinear, high-dimensional, and noisy data.
# Extraction of Periodic Terms in Satellite Clock Bias Based on Fourier Basis Pursuit Bandpass Filter
## 2 Fundamentals of Fourier Basis Pursuit
### Filtering  
Filtering serves the purpose of eliminating undesired components, extracting specific frequency bands, or modifying a signal's frequency content. Filters can be implemented in various forms, such as FIR filters or IIR filters [19; 20]. The output signal \(y[n]\) is calculated using the convolution of the input signal \(x[n]\) with the filter's impulse response \(h[n]\), as shown in Equation (7):  
\[y[n]=\sum_{m=-\infty}^{+\infty}x[m].h[n-m] \tag{7}\]  
According to the Convolution Theorem, convolution in the time domain corresponds to multiplication in the frequency domain, as shown in Equation (8):  
\[Y[k]=X[k].H[k] \tag{8}\]  
where \(H[k]\) represents the filter's frequency response, indicating how each frequency component will be modified. \(Y[k]\) represents the filtered frequency spectrum.  
The process of extracting a target signal from a mixed signal can present several challenges when it comes to filtering. IIR filters, while providing sharp transition bands, may introduce phase distortion due to their non-linear phase response. In contrast, FIR filters, despite offering a linear phase response and inherent stability, often struggle to achieve sharp transition bands and flat passbands [19; 20]. For finite-length signals, boundary effects can introduce ripple or attenuation near boundaries, which can result in significant distortion at the boundary of the filtered result. Additionally, if the noise overlaps with the frequency range of the target signal, certain filters may inadvertently amplify noise components, affecting the accuracy of the filtered result.
## 3 Results and Discussion  
### Normalization of Experimental Results from the Literature and Model Analysis  
First, the experimental results reported by [PERSON] et al. [6; 7] were inspected. The experiment of [PERSON] et al. was conducted using clean sand obtained during a sieve analysis of Liugui sandy soils from Kaohsiung. The initial soil specimens did not contain fines. Instead, fines were added to obtain specimens with FCs of 0-60%. The specimens with different FCs were controlled at the same relative density (Dr), void ratio (e), and peak deviator stress (290 kPa) to facilitate comparisons. The effect of low-plasticity fines on WP excitation was reflected in the PWP data. Furthermore, the changes in PWP in the plastic soils under repeated loading were compared with the results of the studies conducted by [PERSON] [54], [PERSON] et al. [55], and [PERSON] [31], in which different PI values, FCs, confining pressures, strain controls, and CSR were applied. The present study analyzed the characteristics and differences in the PWP excitation of plastic soils according to the mathematical model developed by [PERSON] et al. [42]. The experimental results and the model results were compared to obtain the WP range and model parameters of plastic and non-plastic soils. Finally, the reconstituted soil specimens produced in the present study were compared to those described in the literature to investigate the effect of plastic fines on WP excitation.
## 3 DL-Based Ontology
### Entities for Support Generation
For example, a lattice support structure optimisation method based on Taguchi experiment was presented in [71]; a lattice support structure optimisation framework based on genetic algorithm was established in [72]; in [59], a topology optimisation method was used to optimise lattice support structure to prevent residual stress induced build failure; in [73], the topology of lattice support structure is optimised to maximise its thermal conductivity and to mitigate the effect of thermal stress on the dimensional and geometric accuracy of an LPBF part; a method for optimising the topology of lattice support structure is presented in [74]; a hybrid optimisation framework for topology, build orientation, and support structure is established in [75]. These methods provide practical tools for optimisation of the support structure of an LPBF part. They can be selected according to specific optimisation objects and objectives. To represent the knowledge in support generation, a concept named _SupportedArea_, five relations listed in Table 5, and seven instances named _latticeStructure_, _cellularStructure_, _unitCellStructure_, _pinStructure_, _yStructure_, _iyStructure_, and _treeStructure_ (instances of _SupportStructure_) were created. Using these entities, an ontological view of support generation for LPBF is delineated in Figure 5.
# Address Space Layout Randomization Next Generation
## 8 Evaluation
### Absolute Address Entropy
Note that because ASLR is applied only when the system is rebooted, remote attackers can do a brute force attack against the libraries without requiring a forking server since the libraries will be always mapped to the same addresses. On average, to remotely bypass the ASLR in 64-bit OS X systems will require \(\frac{2^{16}}{2}\) = 32,768 attempts which clearly is not enough to deter attackers. **ASLR-NG:** In 32 bits, libraries and mapped objects have almost 20 bits of entropy, which is comparable with the minimum randomized objects in 64-bit Linux (vDSO and ARGV). Because of the small VM space in 32 bits the entropy is intrinsically limited, but thanks to the ability of the ASLR-NG \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**32-Bits**} & \multicolumn{4}{c}{**64-Bits**} \\ \cline{2-9}
**Object** & **OS X** & **Linux** & **PaX** & **ASLR-NG** & **OS X** & **Linux** & **PaX** & **ASLR-NG** \\ \hline ARGV & 8 & 11 & 27 & 31.5 & 16 & 22 & 39 & 47 \\ Main stack & 8 & 19 & 23 & 27.5 & 16 & 30 & 35 & 43 \\ Heap (brk) & 8.7 & 13 & 23.3 & 27.5 & 15.9 & 28 & 35 & 43 \\ Heap (mmap) & 7.7 & 8 & 15.7 & 27.5 & 16 & 28 & 28.5 & 43 \\ Thread stacks & 11 & 8 & 15.7 & 27.5 & 16 & 28 & 28.5 & 43 \\ Sub-page object & - & - & - & 27.5 & - & - & - & 43 \\ Regular mmaps & 7.7 & 8 & 15.7 & 19.5 & 16 & 28 & 28.5 & 35 \\ Libraries & 7.7 & 8 & 15.7 & 19.5 & 16 & 28 & 28.5 & 35 \\ vDSO & 7.7 & 8 & 15.7 & 19.5 & 16 & 21.4 & 28.5 & 35 \\ Executable & 8 & 8 & 15 & 19.5 & 16 & 28 & 27 & 35 \\ Huge pages & 0 & 0 & 5.7 & 9.5 & 7 & 19 & 19.5 & 26 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative summary of bits of entropy. to use the full address range to allocate any object, it increases entropy by up to 20 more bits than Linux and 12 more than PaX. Although ASLR-NG provides the highest randomization for huge pages, the alignment constraint (which resets the lowest 22 bits) only leaves the possibility of randomizing the highest 10 bits. In 64 bits, ASLR-NG provides up to 15 more bits than Linux and 14 more than PaX. Regarding huge pages, Linux and PaX have 1 million possible places to load huge pages compared with the 67 million of ASLR-NG. This increment in entropy, jointly with the specific-zone for huge pages, increases the cost for an attacker to guess where they are placed and at the same time prevents using them in correlation attacks. Hence, ASLR-NG outperforms Linux and PaX ASLR in both 32- and 64-bit systems.
# Dense Pure Tungsten Fabricated by Selective Laser Melting
## 2 Materials and Methods  
### Powder Spheroidization and the Selective Laser Melting Process  
The raw tungsten powders used for this work were commercially available polyhedral powders (Xiamen Tungsten Co. Ltd., Xiamen, China). These powders were processed using plasma spheroidization equipment (TekSphero-40, Tekna Co. Ltd., Sherbrooke, QC, Canada), with a maximum power of 40 kW. Argon was used as the shield gas in the spheroidization process. During the spheroidization process, the powders were carried by gas at a fixed speed and passed vertically through a hot plasma zone. Within this plasma zone, the polyhedral powders were melted and subsequently shrunken to assume a spherical shape by the effect of surface tension.  
The selective laser melting experiments were conducted with a Renishaw AM 400 machine (Renishaw Plc., New Mill, UK), equipped with a 75 \(\upmu\)m spot diameter, 400 W fiber laser. The laser was operated in pulse mode and focused at one point for a set period (ET, exposure time) at a defined power (P, power) and then immediately moved to the next point and the exposure process was repeated. The distance between the adjacent points was defined as the point distance (pd). The thickness of the deposited powder layer was set to 30 \(\upmu\)m in this study. The laser scanning strategy operated in a zig-zag pattern, with the distance between these parallel lines defining the hatch distance (hd). In order to minimize the internal residual stress, the laser scanning direction was rotated by 67\({}^{\circ}\) between adjacent layers. To limit the oxidation of the tungsten, the fabrication process was conducted in a chamber that was filled with argon gas to maintain the oxygen content below 100 ppm. For our analysis, blocks of dimensions 10 mm \(\times\) 10 mm \(\times\) 5 mm were built for characterization, as shown in Figure 1.
# MapReduce Algorithm for Variants of Skyline Queries: Skyband and Dominating Queries
## 4 Skyband and Dominating Query Processing
### DC Sets Computation  
The coordinator distributes the output pairs to the workers according to the worst rank attribute. Figure 6 presents the \"\(DC\) sets computation\" procedure. As shown in the figure, pairs of \(O_{2,1}\), \(O_{2,2}\), \(O_{3,2}\), and \(O_{3,3}\) are distributed to worker \(DC\)\(Comp_{1}\) for \(a_{1}\). Similarly, pairs of \(O_{1,1}\), \(O_{1,2}\), and \(O_{3,1}\) are distributed to worker \(DC\)\(Comp_{2}\) for \(a_{2}\).  
Each worker outputs \(DC\) sets for each object. In Figure 6, because object \(O_{2,1}\) has the worst rank of 5 in \(a_{1}\), \(DC\)\(Comp_{1}\) outputs {\(O_{2,2}\), \(O_{3,2}\)} as the \(DC\) set for \(O_{2,1}\). It should be noted that \(O_{2,2}\) and \(O_{3,2}\) have a greater rank than that of \(O_{2,1}\) in \(a_{1}\). Object \(O_{1,1}\) has the worst rank of 6 in \(a_{2}\) and \(DC\)\(Comp_{2}\) outputs the \(DC\) set member of \(O_{1,1}\) as {\(O_{3,1}\)}. We calculate the \(DC\) sets for other objects similarly.  
Figure 5: Worst rank computation process.  
Figure 6: Domination check (\(DC\)) sets computation process.
# A Robust RANSAC-Based Planet Radius Estimation for Onboard Visual Based Navigation
## 5 Conclusions  
In this paper, we presented a robust image processing pipeline for estimating the center and radius of planets and moons that can be integrated into an Autonomous Navigation System for modern spacecraft. We evaluated the resilience of our method to various sources of noise and reported appreciable results handling images with significant amount of noise, frame distortions, artifacts, and partial occlusions. We reported the pixel-level accuracy and the effect of noise on the estimated target parameters. We aimed at a computationally low-demanding pipeline able to be run on an onboard computer with limited resources.  
The presented method allows performing radius and center estimation of planets on board space-qualified processors, such as LEON3, with acceptable latency. This fact is fundamental to perform Visual-Based Navigation for missions having strict requirements in terms of radiation tolerance, such as Medium-Earth Orbit, Geostationary Earth Orbit, or long-term Low Earth Orbit missions [24; 25; 26].  
However, despite the high immunity to outliers and noise, the applicability of the methods is limited to planets, whose projection on the image plane is a circle. A complex task for future research involves the pose estimation of non-collaborative objects having an arbitrary shape. The complexity of this task is enhanced by the lack of information on the attitude and positions, and on the noise, outliers, occlusions, motions, and others. In addition, the complexity of the algorithms used in these applications is often prohibitive given the strict power consumption and memory footprint requirements of space-qualified platforms [24]. For instance, one of the most promising approaches involves the use of Deep Neural Networks [27], which demonstrated promising potentials for this kind of application. However, the high computational cost required to perform the inference of Deep Neural Networks often forces the use of modern Commercial off-the-shelf hardware accelerators featuring higher computational efficiency than space-qualified devices. Although Deep Neural Networks  
\begin{table}
\begin{tabular}{l c} \hline
**Pipeline Step** & \% \\ \hline Background threshold & 0.57 \\ Bounding box & 17.32 \\ Morphological Edge mask & 15.48 \\ Edge detection & 17.71 \\ Masked edge & 1.67 \\ Polar transf. and Sector binning & 1.33 \\ RANSAC & 45.92 \\ \hline \end{tabular}
\end{table}
Table 2: Relative amount of execution time for each step in the processing pipeline.  
proved to perform complex tasks and can be easily accelerated with COTS hardware accelerators, their applicability in long-term missions is still under extensive investigation, and it currently poses an important open research question [24; 28; 29].
# Ultra-Wideband Bandpass Filters Using Tapered Resonators
## 5 Results and Discussion  
The filters proposed and analyzed in the previous section were simulated using the CST software tools with a time-domain solver and waveguide ports. A Roger RT5880 substrate with a 2.2 relative permittivity, 0.78 mm thickness, and 0.0009 loss tangent was used for this purpose. Then, the designed filters were fabricated and experimentally characterized. The proposed microstrip UWB bandpass filter shown in Figure 6 was fabricated, with the parameters listed in Table 2, using a laser etching system. The experimental results were obtained using the Anritsu Vector Network Analyzer. The simulated and measured results were compared for various \(k/\beta_{0}\) ratios. The simulated and measured responses of \(k/\beta_{0}=0.25\) filter with ETTL short-circuited resonator are shown in Figure 9a. However, the simulated and measured responses of the filter with equivalent LTTL are shown in Figure 9b. The measured return loss (S\({}_{11}\)) of both designed filters is better than 17.5 dB over the bandwidth extended from 3.24 GHz to 10.88 GHz (the fractional bandwidth of 108.22%), and the insertion loss (S\({}_{12}\)) is better than 1.25 dB over the entire bandwidth for both the exponential and the linear stubs filters.  
Figure 8: Simulated S–parameters results of the lumped element filter represented in Figure 7.  
Figure 7: UWB filter with LC equivalent resonators.  
Furthermore, the simulated and measured results of \(k/\beta_{0}=0.375\) filters with ETTL and LTTL are shown in Figure 10a,b, respectively. Here, the measured return loss is better than 17.3 dB over the bandwidth extended from 3.11 GHz to 10.95 GHz (the fractional bandwidth of 111.52%) and the insertion loss is better than 1 dB over the entire bandwidth for both tapered stubs.  
In addition, for \(k/\beta_{0}=0.50\), the return loss is better than 17 dB and the bandwidth extends from 3.09 GHz to 10.99 GHz (the fractional bandwidth of 112.22%) with an insertion loss less than 0.8 dB over the entire bandwidth for both stubs, as shown in Figure 11.  
Moreover, the obtained measurement results show a group delay that is almost flat over the entire passband bandwidth, as can be seen in Figure 12. The measured group delay is less than 0.8 ns and measured directly using the vector network analyzer. The photograph of the fabricated filter with \(k/\beta_{0}=0.25\) is shown in Figure 13.  
Figure 10: Simulated and measured S-parameters of the proposed filters (represented in Figure 6). (**a**) ETTL of \(k/\beta_{0}=0.375\). (**b**) Equivalent LTTL.  
Figure 9: Simulated and measured S-parameters of the proposed UWB filters (represented in Figure 6). (**a**) ETTL of \(k/\beta_{0}=0.25\). (**b**) Equivalent LTTL.  
Figure 12: Measured group delay of the designed UWB ETTL filter of \(k/\beta_{0}=0.50\).  
Figure 13: Photograph of the designed UWB BPF.
## 4 Results  
### Training and Validation Loss  
Figure 4a illustrates the training accuracy across epochs. It is evident that the CV-CNN-TCN-DCC model achieves the highest accuracy throughout the training process (55.16%), closely followed by the CV-CNN-TCN (55.09%) and CNN (54.82%) models, while SCRNN (49.42%) and ResNet (51.72%) lag behind. Figure 4b depicts the corresponding training loss, where the CV-CNN-TCN-DCC model demonstrates the lowest loss across epochs, indicating superior convergence. The CNN and CV-CNN-TCN models also show competitive loss values, whereas SCRNN and ResNet exhibit relatively higher losses, reflecting slower optimization.  
Figure 3: The proposed model architecture.
# Time-Series Classification Based on Fusion Features of Sequence and Visualization
## 4 Experiment
### Sub-Model Evaluation  
Figure 6 depicts the accuracy and loss curves of two sub-models trained on the data set StarLightCurves. From the figure, we can see that, although the accuracy rate and loss fluctuated, the overall accuracy rate gradually increased and the loss gradually decreased, finally reaching a stable state, and that the results on the training set were slightly better than the results of the verification set, which shows that the model has good generalization ability. Figure 4 shows an example of transformed Area Graphs and features from the 'BeetleFly' data set. In the data set BeetleFly, the records are divided into two categories. We randomly selected one record from the validation set of each category and show the corresponding Area Graphs (Figure 4a,b), sequence features (Figure 4c,d), and visualization features (Figure 4e,f) extracted by LSTM-A and CNN-A, respectively. Using the area in the red box in Figure 4a,b for comparison, the numbers in this area in the original record were quite different, which may be the main trend used to distinguish record categories. Correspondingly, in the learned sequence features, the corresponding positions were the wave crest and wave trough; that is, they were quite different. In the visualization features, we can see that most of the features of the corresponding positions were similar, as the numbers around the red box in the original record are similar, and only one part of the visual features corresponding to the value of the red box position is different, which proves that CNN-A has learned the features of similar parts, as well as the features of the different parts, of the record. We also compared the accuracy of the pre-trained model with that of the integrated model. Table 6 shows the accuracy comparison of the two sub-models and the integrated model on 112 data sets. The bold values in TSC-FF denote model wins or ties with sub-models, while those in LSTM-A and CNN-A denote model wins or ties with LSTM and CNN, respectively. Among them, the integrated model TSC-FF achieved a higher accuracy on 72 data sets than the two sub-models. This shows that the combination of the two features generally improved the classification performance. By comparing  
Figure 6: Accuracy and loss curves of training two sub-models on the data set StarLightCurves. the accuracy of the sub-models with and without the attention mechanism, we can see that LSTM-A achieved higher accuracy on 95 data sets than without the attention mechanism, and the same for CNN-A was 103. This indicates that, for the two sub-models, when using the attention mechanism, the model can learn more effective features. In addition, the difference in performance between CNN-A and CNN also illustrates the effectiveness of the CT-Attention method proposed in this paper.
# Crack Detection of Concrete Based on Improved CenterNet Model
###### Abstract  
Cracks on concrete surfaces are vital factors affecting construction safety. Accurate and efficient crack detection can prevent safety-related accidents. Using drones to photograph cracks on a concrete surface and detect them through computer vision technology has the advantages of accurate target recognition, simple practical operation, and low cost. To solve this problem, an improved CenterNet concrete crack-detection model is proposed. Firstly, a channel-space attention mechanism is added to the original model to enhance the ability of the convolution neural network to pay attention to the image. Secondly, a feature selection module is introduced to scale the feature map in the downsampling stage to a uniform size and combine it in the channel dimension. In the upsampling stage, the feature selection module adaptively selects the combined features and fuses them with the output features of the upsampling. Finally, the target size loss is optimized from a Smooth L1 Loss to IoU Loss to lessen its inability to adapt to targets of different sizes. The experimental results show that the improved CenterNet model reduces the FPS by 123.7 Hz, increases the GPU memory by 62 MB, increases the FLOPs by 3.81 times per second, and increases the AP by 15.4% compared with the original model. The GPU memory occupancy remained stable during the training process and exhibited good real-time performance and robustness.  
crack detection; attention mechanism; feature fusion; frameless; CenterNet +
Footnote †: journal: Applied Sciences  
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