esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Radar Jamming Signal Analysis and Modeling
### Deception Jamming  
Deception jamming primarily involves generating false targets that resemble real targets based on the signals emitted by actual targets. When the radar system detects these targets, the radar receiver may receive information from the false targets, potentially leading to the loss of information regarding the real targets.
# Cubic \(q\)-Bezier Triangular Patch for Scattered Data Interpolation and Its Algorithm
## 3 Scattered Data Interpolation Using \(q\)-Bezier Triangular Patch
### Derivative Estimation  
The partial derivatives \(f_{x}(V_{i})\) and \(f_{y}(V_{i})\) at the vertices \((x_{i},y_{i}),i=1,2,3,\ldots,n\) of the triangle are estimated using the method proposed in [16]. These partial derivatives are used to find derivatives in the direction of \(\varepsilon\), which is an edge of the triangle that is shown in Figure 4.  
Figure 3: Control points for cubic \(q\)-Bezier triangular patch.  
Figure 2: Cubic \(q\)-Bezier triangular with different values of \(q\).  
The derivative along the side \(\varepsilon_{i}\) is given by  
\[\frac{\partial f}{\partial\varepsilon_{i}}=(x_{i-1}-x_{i+1})\frac{\partial f}{ \partial x}+(y_{i-1}-y_{i+1})\frac{\partial f}{\partial y} \tag{8}\]
# An Improved Empirical Wavelet Transform and Its Applications in Rolling Bearing Fault Diagnosis
## 2 Exposition of Traditional EWT Method
### EWT Method
The empirical wavelet transform \(W_{\rm f}^{\varepsilon}\) is defined by a method similar to the traditional wavelet transform, the Fourier transform is \(\rm F(\cdot)\), and the inverse Fourier transform is \(\rm F^{-1}(\cdot)\)  
\[W_{\rm f}^{\varepsilon}(n,t)=\ \langle f(t),\Psi_{\rm n}(t)\rangle\ =\int f(\tau) \overline{\Psi_{\rm n}(\tau-t)}d\tau=F^{-1}\Bigl{(}f(\omega)\ \widehat{\Psi}_{\rm n}(\omega)\Bigr{)} \tag{6}\]
Generate the approximation coefficients \(W_{f}^{\varepsilon}(n,t)\) by the inner product of the signal with the empirical scaling function:  
\[W_{f}^{\varepsilon}(0,t)=\ \langle f(t),\varnothing_{1}(t)\rangle\ =\int f(\tau) \overline{\varnothing_{1}(\tau-t)}d\tau=F^{-1}\big{(}f(\omega)\widehat{ \varnothing}_{1}(\omega)\big{)} \tag{7}\]  
where \(\widehat{x}(\omega)\), \(\widehat{\varnothing}_{1}(\omega)\), \(\widehat{\Psi}_{n}(\omega)\) can be expressed as Fourier transforms of \(x\), \(\varnothing_{1}(\omega)\), and \(\Psi_{n}(\omega)\). The signal can be rebuilt as follows:  
\[ \begin{split}& f(t)=W_{f}^{\varepsilon}(0,t)*\varnothing_{1}( \omega)+\sum_{n=1}^{N}W_{f}^{\varepsilon}(n,t)*\Psi_{n}(t)\\ &=F^{-1}\Bigg{(}\ \widehat{W}_{f}^{\varepsilon}(0,\omega) \widehat{\varnothing}_{1}(\omega)+\sum_{n=1}^{N}\ \widehat{W}_{f}^{\varepsilon}(n,\omega)\Psi_{n}(\omega)\Bigg{)}\end{split}  \tag{8}\]  
where \(\widehat{W}_{f}^{\varepsilon}(0,\omega)\) and \(\ \widehat{W}_{f}^{\varepsilon}(n,\omega)\) can be expressed as Fourier transforms of \(W_{f}^{\varepsilon}(0,t)\) and \(W_{f}^{\varepsilon}(n,t)\). The empirical mode can be given by  
\[\left\{ \begin{array}{l}f_{0}(t)=W_{f}^{\varepsilon}(0,t)*\varnothing_{1}(t )\\ f_{k}(t)=W_{f}^{\varepsilon}(n,t)*\Psi_{k}(t)\end{array} \right. \tag{9}\]  
After obtaining the empirical mode of the signal, the Hilbert transform can be used to calculate instantaneous frequency and instantaneous amplitude or to diagnose faults.
# Volatile, Microbial, and Sensory Profiles and Consumer Acceptance of Coffee Cascara Kombuchas
## 2 Materials and Methods
### Sensory Analysis
#### 2.6.2 Rate All That Apply (RATA)  
After marking the hedonic scales, assessors were given a pre-prepared checklist with 34 sensory attributes related to appearance, aroma, flavor, and mouthfeel, which were identified in a preliminary session performed by a trained panel. The sensory panel consisted of nine trained assessors (aged 28-58) with a minimum of 200 h of experience in evaluating different food products and 30 h of experience in evaluating fermented beverages and infusions. In order to generate sensory descriptors, six samples of kombucha (three from this study and three traditional ones) were presented to assessors. First, assessors were asked to generate their individual descriptors using a modified grid method [33]. Via open discussion with the panel leader, assessors agreed on the best descriptors to fully describe the samples, their definitions, and how to evaluate those [34]. They were organized according to alphabetical order as follows: for aroma, citric, fermented, floral, herbal, honey, raisin, red fruits, rose wine, syrup, woody, and yellow fruits; for taste, acid/sour, bitter, and sweet; for flavor, alcoholic, apple vinegar, beer yeast, hibiscus, prune, and vinegar; and for mouthfeel, texture and aspect, astringent, brown color, clear, fizzy, full-bodied, opaque/matte, refreshing, sparkling, and watery. Assessors were required to select all terms they considered appropriate to describe the infusions. Considering that kombucha samples exhibited different intensities of aroma, taste, and flavor attributes, we investigated whether assessors perceived such differences by asking them to score the attributes according to their intensity (RATA scores: 1 = low intensity; 2 = medium intensity; and 3 = high intensity).
## 3 Results
### Assessment of the Consolidation Settlements  
In this part, clay soil samples were subjected to a series of incrementally applied static loads at magnitudes of 50 kPa, 100 kPa, 150 kPa, 200 kPa, 250 kPa and 300 kPa to evaluate their consolidation behavior over an extended duration of 3600 min (60 h). Throughout the loading period, vertical deformation corresponding to consolidation settlements was precisely recorded to characterize the time-dependent compressibility and secondary consolidation effects inherent to the soil matrix. The experimental program incorporated the stabilization of these soil samples with nano-calcium carbonate (nano-CaCO\({}_{3}\)) and Glass Fiber Chopped Strand (GFCS) additives at their respective optimum dosages, determined from preliminary tests. The addition of nano-CaCO\({}_{3}\), characterized by its high surface area and reactivity, enhances the soil fabric by promoting particle bonding and filler effects at the micro-scale, thereby reducing pore volume and permeability. Simultaneously, the GFCSs provide a flexible, three-dimensional reinforcement network that increases tensile strength and stiffness, helping to resist deformation under load. The results demonstrated a marked decrease in consolidation settlements for all applied stress levels in the stabilized specimens compared with untreated clay samples. Quantitatively, the synergistic effect of combined nano-CaCO\({}_{3}\) and GFCSs application produced a reduction in the time-dependent consolidation settlements by a factor of up to 6.80. This substantial improvement is primarily attributed to two mechanisms:  
Load Transfer and Distribution: The flexible GFCSs network acts as a bridging reinforcement within the soil matrix, distributing external loads more uniformly and preventing localized excessive deformation.  
Figure 12: (**a**) Bearing capacity values for different ratios of GFCSs and nano-CaCO\({}_{3}\). (**b**) Bearing capacity ratio (BCR) for different ratios of GFCSs and nano-CaCO\({}_{3}\).  
Particle Bonding and Fabric Improvement: Nano-CaCO\({}_{3}\) particles chemically interact with clay minerals, enhancing interparticle bonding and reducing compressibility by limiting soil particle rearrangement during loading.  
Together, these mechanisms effectively dissipate the stress transmitted from the superstructure to the subsoil, limiting the magnitude and rate of consolidation settlements. The consolidation settlement curves, which capture the deformation behavior under varying loads, and the quantitative comparisons between stabilized and untreated samples are presented in Figure 13.
# An Optimization Routing Algorithm Based on Segment Routing in Software-Defined Networks
## 3 Algorithm Design
### Comprehensive Evaluation Model
#### 3.2.4 Calculate the Value of Performance Evaluation  
\(A=\left(a_{ij}\right)_{3\times k}\) denotes the value of the performance evaluation matrix of the three types of network business at each moment of each link \(a_{ij}\) is given by:  
\[a_{ij}=W_{i}\times D_{j}^{T};\;0\leq a_{ij}\leq 1 \tag{19}\]  
where \(W_{i}\) (\(i\) = web, voice, stream) represents the weight vector of the \(i\) business scheme; and \(D_{j}^{T}\) represents the vector of function fraction of \(j\) link at the corresponding time.
# Sparse Representation and SVM Diagnosis Method for Inter-Turn Short-Circuit Fault in PMSM
## 1 Introduction
The sparse representation is used to extract the first and second largest sparse coefficients of both current signal and vibration signals, and they are composed into four-dimensional feature vectors, which are input into the SVM for training. At last, the trained SVM model is used for fault diagnosis. Section 2 introduces the principle and solution algorithm of sparse representation used in this paper, as well as the features selected in this work. Section 3 introduces the principle of SVM. In Section 4, two PMSM were used in the experiment: one motor was manually set the inter-turn short-circuit fault and the other was normal. The experiment results showed that the method is feasible.
# Research on Gangue Detection Algorithm Based on Cross-Scale Feature Fusion and Dynamic Pruning
## 4 Experimental Validation and Analysis
### Comparative Experiments  
To further validate the improved algorithm's detection performance on coal and gangue, comparative experiments were conducted using a self-created dataset specifically designed for coal and gangue detection. The experiments involved comparing the algorithm proposed in this paper with current mainstream object detection algorithms. Analyzing Table 2 reveals that, compared to SSD, Faster R-CNN, YOLOv3, and YOLOv5s, the algorithm proposed in this paper shows an improvement in mAP by 20.4%, 3.7%, 0.7%, and 1.8%, respectively. The two-stage object detection algorithm, Faster R-CNN, has the highest model weight, and the detection time for a single image is the longest, reaching 76.4 ms. Therefore, it exhibits a slower inference speed. For the remaining single-stage object detection algorithms, the shortest detection speed was 15.3 ms. Despite being faster than Faster R-CNN, there is still a need for improvement to meet the real-time detection requirements for coal and gangue. The algorithm proposed in this paper achieves a detection time of 9.8 ms per single image, showing a 56.2% improvement in inference speed. The model weight is reduced by 35.7% to 8.8 Mb. Therefore, the improved model exhibits significant advantages in both detection speed and accuracy, showcasing good performance while also reducing subsequent hardware deployment costs.
## 3 Results
### Identification of the Reaction Products in the Gas-Phase
#### 3.2.2 Detection by PTR-ToF-MS  
The PTR-ToF-MS detects protonated molecular ions for non-chlorinated organic compounds. All the reaction products observed by SPME/GC-MS, except 3-chloro-2-butanone, were also observed by PTR-ToF-MS: acetaldehyde (C\({}_{2}\)H\({}_{4}\)OH\({}^{+}\), \(m\)/\(z\) = 45.03), acetic acid (C\({}_{2}\)H\({}_{4}\)O\({}_{2}\)H\({}^{+}\), \(m\)/\(z\) = 61.02), methylglyoxal (C\({}_{3}\)H\({}_{4}\)O\({}_{2}\)H\({}^{+}\), \(m\)/\(z\) = 73.02), 2,3-butanedione (C\({}_{4}\)H\({}_{6}\)O\({}_{2}\)H\({}^{+}\), \(m\)/\(z\) = 87.05), and 2-methyl-2-butenoic acid (C\({}_{5}\)H\({}_{8}\)O\({}_{2}\)H\({}^{+}\), \(m\)/\(z\) = 101.6). In addition, other products were observed such as formaldehyde (CH\({}_{2}\)OH\({}^{+}\), \(m\)/\(z\) = 31.01), methanol (CH\({}_{4}\)OH\({}^{+}\), \(m\)/\(z\) = 33.03), ketene (C\({}_{2}\)H\({}_{2}\)OH\({}^{+}\), \(m\)/\(z\) = 43.01), propene (C\({}_{3}\)H\({}_{6}\)H\({}^{+}\),  
Figure 2: (**a**) Chromatogram obtained after 60 min of the chlorine (CI) reaction of _trans_-2-methyl-2-butenal (T2M2B). (**b**) Mass spectra of the most intense peaks of the chromatogram that were attributed to products.  
\(m\)/\(z\) = 43.05), formic acid (CH\({}_{2}\)O\({}_{2}\)H\({}^{+}\), \(m\)/\(z\) = 47.01), propanal (C\({}_{3}\)H\({}_{6}\)OH\({}^{+}\), \(m\)/\(z\) = 59.08), butyric acid (C\({}_{4}\)H\({}_{8}\)O\({}_{2}\)H\({}^{+}\), \(m\)/\(z\) = 89.05), and 2-butrenal (C\({}_{4}\)H\({}_{6}\)OH\({}^{+}\), \(m\)/\(z\) = 71.03). Given that molecular ions are detected, it is possible that an isomer of the mentioned products would be present. In Figure 3, it is observed that the major products detected by PTR-ToF-MS are acetaldehyde, ketene, and formaldehyde, with product yields of (40.8 \(\pm\) 0.6)%, (23.5 \(\pm\) 0.4)%, and (11.0 \(\pm\) 0.2)%, respectively. For the minor reaction products, the yields were determined to be less than 5%. The three major products, acetaldehyde, ketene, and formaldehyde, were also formed during the exposure of T2M2B to the UV light in the test experiments performed prior the Cl reaction, but their amount was negligible compared with the observed concentration after the Cl reaction.
# Automatic Word Spacing of Korean Using Syllable and Morpheme
###### Abstract  
In Korean, spacing is very important to understand the readability and context of sentences. In addition, in the case of natural language processing for Korean, if a sentence with an incorrect spacing is used, the structure of the sentence is changed, which affects performance. In the previous study, spacing errors were corrected using n-gram based statistical methods and morphological analyzers, and recently many studies using deep learning have been conducted. In this study, we try to solve the spacing error correction problem using both the syllable-level and morpheme-level. The proposed model uses a structure that combines the convolutional neural network layer that can learn syllable and morphological pattern information in sentences and the bidirectional long short-term memory layer that can learn forward and backward sequence information. When evaluating the performance of the proposed model, the accuracy was evaluated at the syllable-level, and also precision, recall, and f1 score were evaluated at the word-level. As a result of the experiment, it was confirmed that performance was improved from the previous study.  
spacing correction; syllable embedding; morpheme embedding; convolutional neural network; bidirectional long short-term memory +
Footnote †: journal: Medical  
as additional features. Then, a method of correcting the word spacing error was proposed through an architecture in which bidirectional LSTM (Bi-LSTM) and CRF were combined [6]. There was a study that proposed a method of correcting word spacing errors using a sequence-to-sequence model, specialized in processing complex and large sequence data by stacking LSTMs [9].  
Most previous studies construct the word spacing system using one of the features of syllables, words, and morphemes in sentences. In addition, the model of previous studies was constructed using convolutional neural networks (CNN), recurrent neural networks (RNN), and CRF. There are studies that show better performance using both CNN and LSTM in combination than to construct a model using them individually [10; 11]. In addition, there is a study in which the structure that combines CNN and LSTM outperforms the structure that combined LSTM and CRF in the part-of-speech tagging task that detects metaphors in sentences [12]. Therefore, we constructed a model of the architecture that combines CNN and Bi-LSTM. We extracted local features of syllables and morphemes using multiple filter CNN and extracted order information through Bi-LSTM. It concatenates the LSTM output of syllables and morphemes information, passes through the fully connected layer, and finally outputs the space tag.  
The rest of this paper is organized as follows. Section 2 describes the characteristics, collection, and preprocessing of the Korean text dataset. In Section 3, we describe the model architecture used for word spacing correction. In Section 4, we compare the experimental results with the proposed model with existing studies. Finally, we present the conclusion and future work of this study in Section 5.
# Robust Localization of the Mobile Robot Driven by Lidar Measurement and Matching for Ongoing Scene
## 3 Localization of Mobile Robot in Ongoing Scene
### Localization of Artificial Landmarks and Generated Buildings Based on Adaptive Weights  
The artificial landmarks that set in ongoing scene are gradually blocked by the generated buildings, which leads to a poor localization or failure for mobile robot. So, we regard the generated buildings as the observation features, and adopt the matching method based on ICP to achieve localization of mobile robot. Figure 5 has shown a robust localization of mobile robot at a designated location. The ongoing scene has four artificial landmarks and generated buildings, and the reflector 1 is blocked by the generated building. At this time, mobile robot can be located through the other three reflectors and the generated buildings. Therefore, the paper proposes a robust localization model that based on the fusion of reflectors and ongoing buildings. Firstly, according to the coordinate of the reflectors in the prior scene, the trilateration method is used to estimate the initial position of mobile robot. Secondly, with the gradual generation of buildings under construction in the scene, geometric features of the reflectors and generated buildings are simultaneously extracted by Lidar. Then, the two geometric primitives are regarded as observation features, and the ICP algorithm is used to achieve the data association between two adjacent locations to complete robust localization of mobile robot. Considering the influence of measurement noise and matching errors to the localization model, the normalized weighted factors are set to adjust the localization accuracy and efficiency adaptively, and the localization with robustness of mobile robot is also improved.
## 4 Effect of External Cross-Frame on Bridge's Ultimate Behavior
### Girder Displacement Responses  
Figure 7 shows the load-displacement responses after one girder fracture was applied at the midspan. In Case 1, the ultimate load-carrying capacity was approximately 3.85-times of the HS-20 truck. This capacity was less than that estimated in the verification analysis. This discrepancy could be caused by differences in the live-load configurations. As mentioned in Section 2.2, the simulated live loads used for verification were modeled with point loads and distributed loads corresponding to the test setup. However, for these analysis cases, point loads matching the HS-20 truck were used, and thereby, a higher loading effect than the simulated loads was exerted. The collapse in Case 1 was initiated by the pullout failure of the stud connections in the fractured girder; therefore, the bridge lost the load-transferring resistance exerted by the concrete deck.  
The cases with external cross-frames (Cases 2-4) resulted in a higher load-carrying capacity than that in Case 1. Case 2 exhibited approximately five-times the ultimate capacity of an HS-20 truck. Case 3 showed a similar load-displacement behavior as Case 2, but its capacity (5.46-times of the HS-20 truck) was 9.2% higher than that of Case 1. This discrepancy may be caused by the buckling effect of the diagonal. In Case 2, the right diagonal member of the external cross-frame near the midspan (Frames 2 and 3) buckled as the applied load reached its maximum capacity. Therefore, the external cross-frame loses its resistance to transverse external loads. Meanwhile, the elastic behavior of the diagonals was assumed in Case 3; therefore, the external cross-frames could provide resistance beyond the ultimate load estimated in Case 2.  
Case 4 exhibited somewhat complicated behaviors compared to the other cases, although its ultimate capacity was similar to that of Case 2 (5.09-times of the HS-20 truck). As the normalized load reached approximately 40% of the HS-20, girder displacement  
Figure 7: Load-displacement responses at midspan.  
suddenly increased. This tendency was repeated when the applied load reached approximately 2.2-times of the HS-20 truck. Considering that the chords behaved elastically in Case 4, the expected failure mode was buckling of the diagonals. Therefore, these sudden increases could be attributed to buckling as the applied load increased. Further details on the members of the cross-frames are provided in the following section.
# A Proposed Soft Computing Model for Ultimate Strength Estimation of FRP-Confined Concrete Cylinders
## 1 Introduction
Finally, a new formulation has been proposed to estimate the ultimate compressive capacity of FRP-confined concrete cylinders. It should be noted that the correlation coefficient of the proposed formula is equal to 0.9809, which shows a good agreement with the actual values. A comparison has been performed between the results obtained by FFBPNN and the results of the other existing models to demonstrate the ability of the proposed method. The results show that the values of the mean percentage of error (3.49%), root mean square error (3.99), and average absolute error (0.035) for the proposed method are less than other studied methods. It means that, for the proposed formula, more than 96% of the simulated results are entirely consistent with the experimental results, and also that the proposed method is very accurate compared to other existing methods. Furthermore, it is shown that the FFBPNN is a formula that can be used for all types of FRP (carbon, aramid, and glass). The proposed method can be easily employed using a calculator with high precision while, in the case of neuro-fuzzy, neural network and other known methods, a computer and sophisticated software are usually needed.