esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
## 2 Proposed Method
### Problem Formulation
#### 2.1.1 Technical Background  
Gearboxes are perhaps the most known component where modulations phenomena are encountered. The diagnosis of gears is well established in the literature. It is known that the vibration of the gears is mainly generated by the contact between two teeth; which leads to a periodic signal at the meshing period of the teeth. According to the Fourier series theory, this translates into a set of harmonics associated with the meshing frequency and its integer multiples [44].  
Often, gears are subject to manufacturing errors, leading to (i) a non-uniform spacing between the teeth and (ii) irregularities on the contact surface of the teeth. These create local vibratory events during the contact phase. Because of the rotating motion of the gear, each of these events will reproduce after each complete revolution of the same gear. These (slower) periodic events interact with the (fast) meshing phenomenon through periodic modulations. We distinguish two types of modulations, namely amplitude and phase modulations, generated by different physical phenomena. Periodic amplitude modulations are often due to an uneven distribution of the stiffness along the teeth surface. Phase modulations are often the result of a change in the meshing period, defining the contact duration between two meeting teeth, which can be physically associated with the change in the spacing among the teeth. Amplitude and phase modulation are well modeled in the literature and they are characterized in the spectral domain by lateral bands around the meshing frequencies, as illustrated by Figure 1 [45].  
In the presence of a defect, like a tooth spill or pittings, the vibratory event is stronger and more impulsive, and so are the modulations, resulting in stronger and richer sidebands around the meshing frequency and its harmonics. This explains why the information on the gears health state is mainly contained in the modulations which are traditionally estimated thanks to a demodulation around the meshing frequency [45]. It is worth noting that periodic amplitude modulations result in a perfectly symmetrical sidebands, as opposed to periodic phase modulations. However, amplitude modulations are more dominant in gearbox vibration analysis; thus, still leading to an inner symmetry in the sidebands. This property will be integrated in the proposed hierarchical model of the propose Bayesian CS approach.  
Eventually, it should be remembered that meshing phenomena are generally fast and their frequency is often very high. For example, the meshing frequency of the driving gear of an ARRIEL helicopter powerturbine gearbox would reach 16 kHz, which means that a  
Figure 1: Spectrum of a typical modulated signal.  
sampling frequency of 200 kHz is needed to'see' the first five meshing harmonics. This explains why a compressive sensing approach is needed to reduce the amount of data.
# Detection of Cracks in Arch Dams Using Monitoring Data and Numerical Models
## 4 Discussion and Conclusions
### Ability to Detect Cracks  
The proposed methodology correctly indicated whether or not a crack was present in 80% of the cases if the dam had monitoring devices with an accuracy of 0.1 mm. The cases in which the proposed methodology failed to detect the crack correspond to low reservoir levels. The following results were obtained:  
* The prediction was correct in 100% of the cases considering where the devices provided an accuracy of 0.02 mm;
* The use of additional monitoring points in addition to those already installed in the dam did not improve detection capability;
* The more accurate alternative methodology did not improve detection capability.
# Identification of Damage in Beams by Modal Curvatures Using Acoustic Beamformers
## 2 Application of MFP Algorithms to Damage Identification
### Bartlett Beamformer  
The Bartlett beamformer is the simplest of all beamformers. It has been utilized in almost all studies on MFP, mostly for validation and comparison purposes. It is the projection of the data vector onto the replica vector  
\[B_{Bart,i}(\mathbf{x})=|\bar{\mathbf{\chi}}_{i}^{T}(\mathbf{x})\mathbf{d}_{i}|^{2}, \tag{1}\]  
for the \(i\)-th mode. Note that in Equation (1), we used the unit-normalized replica vector \(\bar{\mathbf{\chi}}_{i}(\mathbf{x})=\mathbf{\chi}_{i}(\mathbf{x})/||\mathbf{\chi}_{i}( \mathbf{x})||\). This is necessary in beamformers because spatial sampling alters the modulus of the replica. Using a unit-normalized replica serves to avoid errors causedby vectors with different lengths and different angles to have the same projection onto the data vector. Using the cross-spectral density matrix of data (CSDM) [29], \(\mathbf{K}_{i}=\mathbf{d}_{i}(\mathbf{d}_{i})^{T}\), the Bartlett beamformer can be expressed in quadratic form as  
\[B_{Bart,i}(\mathbf{x})=\bar{\boldsymbol{\chi}}_{i}^{T}(\mathbf{x})\mathbf{K}_{ i}\bar{\boldsymbol{\chi}}_{i}(\mathbf{x}). \tag{2}\]  
It is known that this processor has many side lobes, even when using noise-free pseudo-experimental data [29], that is, numerically generated data which simulate experimental data. To overcome the drawback of sidelobes, different processors operating with various types of filters, called weights in imaging, have been proposed and utilized.
## 4 Results
### Gear Tooth Sample  
An evaluation of a gear tooth with an axial extension greater 5 mm is presented in this Section. Thirty equally spaced propagation distances from 0 mm to 10 mm have been calculated. Figure 4 shows our experimental setup, including the gear test sample.  
Figure 5 shows the phase \(\varphi\) (top) and phase smoothness \(s_{z}\) (bottom) maps for two propagation distances \(z=3.0\) mm and \(z=6.0\) mm, respectively. For areas of low noise and thus focused parts, the phase shows sharp stripes, whereas the unfocused areas are superimposed by noise. In the left panels, a propagation distance of \(z=3.0\) mm is pictured with the focused point and the associated phase smoothness on the right area of the measurement. The right panels were evaluated at a propagation distance of \(z=6.0\) mm and show a clear maximum band and horizontally decreasing focus on the left of the measurement.  
Figure 4: Experimental setup including the gear tooth sample and holography sensor.  
Figure 5: Phase \(\varphi\) (left/right) and phase smoothness \(s_{z}\) for two propagation distances \(z\). Left: \(z=3\) mm, right: \(z=6\) mm. Sharp stripes can be observed in areas of low phase noise, whereas areas of high phase noise appear as random phase distribution.  
As a next step, the synthesis of phase maps with an extended depth of field has to be addressed--the calculated propagation stack holds a best phase-noise distance for each pixel coordinate \((x,y)\). Figure 6 shows the progression of the phase-noise signal at different reconstruction distances \(z\) for equally distributed points on the gear flank. Two thumbnails at \(z=3.50\) mm (left) and \(z=6.33\) mm (right) illustrate the position of the crosses on the sample near the focus or the orange cross (left) and the red cross (right).  
In the following sections, we propose two methods for the focus point selection with different applications.
## 6 Conclusions  
The QTP is a region prone to stable landslide dams, yet the understanding of the stabilization mechanism of individual stable landslide dams remains insufficient. This study focused on the Cuonaxue Co landslide dam, a representative stable landslide dam developed along the eastern margin of the QTP. Investigating the stabilization mechanisms of typical landslide dams along the eastern margin of the QTP holds significant scientific and practical implications for disaster mitigation of similar dams formed in this region.  
Field investigations and comprehensive understanding revealed that the Cuonaxue Co landslide dam formed through high-speed and long-runout landsliding damming the river. The inverse grading structure generated by this landslide plays a decisive role in stabilizing the dam. Specifically, coarse particles in the upper dam section exhibit high erosion resistance, shielding underlying fines from fluvial erosion. Long-term fluvial scour formed a step-pool system in the spillway, which effectively dissipates flow energy: Quantitatively, 68% of hydraulic energy is consumed by this step-pool system. In addition, 3D modeling revealed that limited migration of fine-grained particles does not compromise the overall stability of the dam. Two-dimensional modeling demonstrated significantly more pronounced seepage fields in the coarse-grained layer than in the fine-grained layer, indicating that the upper coarse layer resists fluvial erosion and thereby prevents erosion of underlying fine particles.  
Methodology, L.S. and Y.W.; Software, L.S.; Formal analysis, Y.S. and T.L.; Investigation, L.S., Y.W., T.L., Z.X., Y.Z., T.T. and S.L.; Data curation, T.L.; Writing--original draft, L.S.; Writing--review & editing, Y.S.; Funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Key Laboratory of Xinjiang Coal Resources Green Mining, Ministry of Education, Xinjiang Institute of Engineering, Urumqi 830023, China (Grant No. KLXG-Z2513); the Third Xinjiang Scientific Expedition Program (Grant No. 2022 zjkk1305); Xinjiang Uygur Autonomous Region \"Tianchi Talent\" Introduction Program (Grant No. 2025 XGYTCYC07); and Doctoral Research Initiation Fund of Xinjiang Institute of Engineering (Grant No. 2025 XGYBQJ08).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Camera Geolocation Using Digital Elevation Models in Hilly Area
## 6 Conclusions and Future Work  
Skyline localization is an important branch of visual geolocation, especially in the field where the feature points are sparse. When a GPS signal is unavailable or disturbed, skyline localization is an important auxiliary localization method in the field environment, but there is little research on skyline localization in hilly areas. This paper analyzed the difficulties of skyline locating in hilly areas, including lush vegetation and closeness to skyline. Therefore, a new method of skyline localization was proposed in this paper, which includes two new skyline features: Enhanced Angle chain code and skyline lapel point, and a new matching method which incorporates the skyline pyramid and the skyline distance heatmap. The experimental results show that this method has high localization accuracy in hilly areas. The proposed method also has some limitations. When the nearby trees significantly block the skyline, or when the mountain in front of the camera is too close, the accuracy of the method is low.  
Figure 32: The effect of rotation on a skyline.  
Figure 33: The performance of different rotations.  
In the future, we will investigate enhanced lapel point, which adds the ridge line lapel point (the lapel point formed between ridge lines) to existing skyline lapels, so as to improve the accuracy of geolocation. Although the interference of atmospheric environment is not within the scope of this paper, the skyline geolocation under multiple visibilities is important, because the photo will change due to haze and other reasons.  
[PERSON], [PERSON] and [PERSON] contributed to the conception of the study; [PERSON] performed the experiment; [PERSON] and [PERSON] contributed significantly to analysis and manuscript preparation; [PERSON], [PERSON] and [PERSON] performed the data analyses and wrote the manuscript; [PERSON] and [PERSON] helped perform the analysis with constructive discussions. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Conflicts of Interest: Conflict of interest Authors [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] declare that they have no conflict of interest.
# Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation
###### Abstract  
The sixth-generation (6G) network is supposed to transmit significantly more data at much quicker rates than existing networks while meeting severe energy efficiency (EE) targets. The high-rate spatial modulation (SM) methods can be used to deal with these design metrics. SM uses transmit antenna selection (TAS) practices to improve the EE of the network. Although it is computationally intensive, free distance optimized TAS (FD-TAS) is the best for performing the average bit error rate (ABER). The present investigation aims to examine the effectiveness of various machine learning (ML)-assisted TAS practices, such as support vector machine (SVM), naive Bayes (NB), \(K\)-nearest neighbor (KNN), and decision tree (DT), to the small-scale multiple-input multiple-output (MIMO)-based fully generalized spatial modulation (FGSM) system. To the best of our knowledge, there is no ML-based antenna selection schemes for high-rate FGSM. SVM-based TAS schemes achieve \(\sim\)71.1% classification accuracy, outperforming all other approaches. The ABER performance of each scheme is evaluated using a higher constellation order, along with various transmit antennas to achieve the target ABER of \(10^{-5}\). By employing SVM for TAS, FGSM can achieve a minimal gain of \(\sim\)2.2 dB over FGSM without TAS (FGSM-NTAS). All TAS strategies based on ML perform better than FGSM-NTAS.  
free distance optimized transmit antenna selection (FD-TAS); fully generalized spatial modulation (FGSM); machine learning (ML); support vector machine (SVM); transmit antenna selection (TAS) +
Footnote †: journal: Future Internet
## 2 Correlation Method  
### Continuous Wavelet Transform  
Suppose the function \(\psi\in L^{2}(R)\cap L^{1}(R)\) and \(\psi^{\prime}(0)=0\), a series of wavelet basis functions can be obtained from \(\psi\) by stretching and shifting, as shown in Formula (1),  
\[\psi_{a,b}(t)=|a|^{-\frac{1}{2}}\psi\left(\frac{t-b}{a}\right), \tag{1}\]  
where \(a,b\in R,a\
eq 0,\psi_{a,b}\) is a continuous wavelet function, \(\psi(t)\) is the mother wavelet function, and \(a\) is the scaling factor. When \(a>1\), it indicates stretch in \(\psi(t)\), which is beneficial for extracting low-frequency features from the signal. When \(0<a<1\), it indicates a compression in \(\psi(t)\), which is beneficial for extracting high-frequency features from the signal.  
The Morlet wavelet is suitable for capturing the rapid oscillation characteristics of signals, as it exhibits similar pulse features to those of bearing faults [34]. Therefore, in this paper, the Morlet wavelet was adopted as the mother wavelet function. The mathematical expression of the Morlet wavelet is [35]:  
\[\psi(t)=\pi^{-\frac{1}{2}}e^{-\frac{t^{2}}{2}}\cos(w_{0}t). \tag{2}\]  
For any function \(f(t)\in L^{2}(R)\), its CWT is defined as [16],  
\[W_{f}(a,b)=\left\langle f(t),\psi_{a,b}(t)\right\rangle=|a|^{-\frac{1}{2}} \bullet\int_{-\infty}^{+\infty}f(t)\overline{\psi}\left(\frac{t-b}{a}\right)dt, \tag{3}\]  
where \(W_{f}(a,b)\) represents the wavelet transform coefficient, \(\left\langle f,\psi_{a,b}(t)\right\rangle\) denotes the inner product of two functions, and \(\overline{\psi}(t)\) denotes the complex conjugate function of \(\psi(t)\). The wavelet transform coefficient represents the degree of matching between the signal and the wavelet basis function, that is, the amplitude information at different times and frequencies. The more similar the signal is to the wavelet basis function, the greater the coefficient value.  
After transforming the mother wavelet function through different scales, the wavelet coefficient matrix \(W_{f}(a,b)\) can be finally obtained through the above calculation process, which can be used to represent the energy distribution of the signal at different scales and frequencies. In the process of using the python programming language, the value of wavelet coefficient matrix \(W_{f}(a,b)\) was displayed in the image through color filling, and the time-frequency graph was obtained.
# Investigation on Eddy Current Sensor in Tension Measurement at a Resonant Frequency
## 1 Introduction
Although EME sensor improves measurement sensitivity, it is rarely applied in practice due to its high cost. This paper presents a novel eddy current sensor with a single-coil structure to overcome the above problems. The eddy current sensor excited by a swept frequency signal involves a multi-turn wire coil. When the swept frequency signal is applied to the sensor coil, it produces a varying magnetic field around it and the eddy current is formed on the surface of the test specimen. The eddy current produces a secondary magnetic field in a direction opposite to that of the initial magnetic field. The amount of magnetic field is reduced, which is directly correlated to the change in the impedance parameters of the sensor. The theoretical model of the eddy current sensor with a single-coil structure has been established to obtain the explicit relation between the impedance parameters and the external tension. The paper is organized as follows: Section 2 proposes the equivalent model of eddy current sensor and establishes the theoretical derivation of sensor behavior. In Section 3, the magnetic field distributions of sensor with and without the sleeve structure are simulated using finite element software. Then, the experimental system is built; impedance analysis is carried out and the influence of sleeve structure is discussed in Section 4. Finally, some conclusions and future work are detailed in Section 5.
## 4 Discussion
The new protocol established 5 coverage classes, but the fifth class does not refer to a specific range, it includes coverage area over 20% and no further classes can be distinguished due to overlap. It means that the coverage area could be higher than 20% in standard conditions. From the results obtained on the droplet size analysis, the frequency function about the distribution of the diameter range has been analyzed through a plot (Figure 11). The highest concentration of the number of droplets is in the diameter range of 0.1-0.3 mm. In vineyard  
Figure 10: Frequency plot describing WSP frequency both in the LiDAR test (in blue) and standard test (in yellow) for different coverage classes (expressed as percentage range). treatments, the optimal diameter of droplets should be about 100 \(\upmu\)m. Therefore, the distribution of the number of droplets in the figure below can be considered representative of an optimal distribution on field conditions. Nowadays, vines protection from GPM and GDM is still based on massive preventive applications of sulfur and copper but evidence on the dangers of these compounds emerged, making their use no longer in line with the principles of sustainable agriculture. Recently public attention has been focused especially on the risks associated to copper [45], since it is classified as heavy metal and its repeated use results in negative consequences for environment, human health, wine quality and soil fertility [46; 47; 48; 49; 50]. For these reasons, European Union inserted copper into the list of active ingredients candidates for substitution and limited the quantity allowed in agriculture up to an average of 4 kg/ha per year [51]. Technologies that lead to exclusive distribution to targets represent one of the strategies to reduce the amount of PPPs sprayed. Treatments performed with LiDAR system showed in some case a reduction of incidence, severity, and McKinney Index of GPM and GDM comparable to those obtained with conventional sprayer, in plants located before or after vine gaps. Results showed that it is possible to reach satisfactory levels of protection by distributing a lower quantity of agrochemicals per unit of surface area. On the other side, doses per hectare below the minimum of label are not allowed and thus legislation need to be adapted to technical innovations. Farm to Fork objectives can only be achieved by integrating all the available tools to reduce the amount of dangerous pesticides, from technologies for the optimization of distribution to the use of alternative compounds with proven efficacy, such as some basic substances [52; 53]. This category of compounds is not considered in the Harmonised Risk Indicator 1 [54] calculation.
## 3 The Use of Personalized Ontologies for Query Expansion
### Future Directions for Query Expansion Based on Personalized Ontologies  
In the literature, a wide number of query expansion strategies has been defined. In Section 3 some approaches based on an ontological user profile to query expansion have been presented.  
In the query expansion strategy, a possible future investigation is to consider multiple ontologies to create a tailored user's profile. Several works have analyzed the joint usage of a domain ontology with a global ontology. For example, in [55] the authors have taken into account the problem of expanding queries with terms satisfying spatial constraints. To this aim a tourism ontology and a global ontology with geographic information are used. In [56] the authors present _Miology_, a Web-based application that helps users to automatically select the right ontologies (from an ontology repository) related to the topics of given Web results. Future investigations can be addressed to the use of these topic-ontologies for adding more focused terms to the user's query.  
Another future direction could be to allow users to interact with the knowledge model (the ontology in this context) to increase the effectiveness of query expansion techniques [57]. In [58] a study about the effectiveness of interactive query expansion within the context of a relevance feedback system has been performed. In this study, it emerges that about one-third of the terms directly chosen by the users from a list of candidate terms is potentially useful to improve the phase of query expansion. For example, in [59] the Concept based Information Retrieval Interface (CIRI) ontology has been developed to help the user in formulating his/her queries. The experiment was a pilot study of the Finnish Web ontologies project into the _Food industry_ domain. By using CIRI, a user can navigate the ontology by choosing the concepts up to the desired number of expansion levels. A promising research topic could be to make use of personal ontologies to better exploit an interactive search task. This _navigational query expansion_ allows the user to have an active role during her/his searches. In [60] the authors define the so-called ontology query model to produce an Information Retrieval query from a set of concepts selected by a user browsing the considered ontology. Experiments are performed on a biomedical ontology merging several well-known biomedical sources, such as the Gene Ontology, MeSH and Swissprot databases.