esa-sceva/satcom-chunk-collection huggingface dataset:
# GANBA: Generative Adversarial Network for
## 2 Background
### Automatic Speaker Verification (ASV)  
An ASV system is able to determine whether an utterance is uttered by the claimed speaker or not. In order to do it, it typically obtains the speaker information of the utterance by extracting either i-vector [20] or x-vector features [21]. In the verification phase, the ASV system extracts the feature vectors of the enrollment and test utterances, and they are usually mapped into a more discriminative subspace using, for example, linear discriminant analysis (LDA). Then, the ASV score of the test utterance is typically obtained using one of the following techniques:  
* Probabilistic Linear Discriminant Analysis (PLDA) [22; 23]: it is a probabilistic framework which is able to model the inter- and intra-speaker variability. There are three types of PLDA models [24]: simplified [25], standard [22], and two-covariance [26]. In all variants, the expectation-maximization (EM) algorithm [27] is used to train the PLDA model.
* B-vector [28]: it is a DNN-based model which considers ASV as a binary classification problem. Specifically, from the x-vectors \(\mathbf{x}_{\text{enroll}}\) and \(\mathbf{x}_{\text{test}}\) computed for each pair of utterances (enrollment and test utterances), a b-vector representing the relationship between \(\mathbf{x}_{\text{enroll}}\) and \(\mathbf{x}_{\text{test}}\) is computed as follows, \[\mathbf{b}=\big{[}\mathbf{x}_{\text{enroll}}\oplus\mathbf{x}_{\text{test}},|\mathbf{x}_{\text{ enroll}}\oplus\mathbf{x}_{\text{test}}|,\mathbf{x}_{\text{enroll}}\otimes\mathbf{x}_{\text{ test}}\big{]},\] (1) where \(\oplus\), \(\ominus\) and \(\otimes\) are the element-wise addition, subtraction and multiplication operations, respectively. Then, the b-vector features are fed to a binary DNN which determines whether the enrollment and test utterances are uttered by the same or different speaker.  
An ASV system is typically evaluated on a test dataset which contains utterances uttered by either _bonafide_ target speakers or _zero-effort_ impostors [29]. The equal error rate (EER) is the most common metric to evaluate it, which is the operating point at which the false rejection rate (FRR) equals the false acceptance rate (FAR). However, the EER metric does not account for either the costs of falsely accepting impostors or missing target users, nor the prior probabilities of each. In order to take these costs and priors into account, the detection cost function (DCF) metric [30] has been proposed and evaluated in the most popular speaker recognition challenges [31].
# FEM Analysis and Sensor-Based Measurement Scheme of Current Distribution for Grounding Electrode
## 4 Grounding Electrode Current Distribution Measurement
### Measurement Setup  
Furthermore, we manufactured a device to measure leakage current in the soil near the grounding electrode, and a 0-50 mA range of the Hall DC sensor was selected for current measurement.  
In Figure 13, a soil model is established by selecting a specific area in a certain environment around the grounding electrode. Firstly, we set up a simulation test platform in the laboratory, selected multiple measurement points, and buried sensors and the \"drainage wire\" at a certain depth in the soil. Secondly, the current collected by the sensor was transmitted to a single-chip microcomputer, which is the core of the weak current measuring device. Then, we added a heating module to make sure it could work normally under extremely cold conditions. Finally, the sensor data were transmitted to the server through the GSM (Global System for Mobile Communications) for establishing a complete grounding electrode database. The staff checked the detection data on the server or monitored the changes in all sensor parameters by mobile devices with remote distance.  
Figure 12: A schematic of measuring the current in the soil. (**a**) The scheme of how the Hall sensor and “drainage wire” are placed in the soil; (**b**) a magnetic field generated by a finite-length conductor at a point.  
Figure 13: Schematic of the leakage current measuring scheme.  
Here, a DC power supply with 10 A current and 1000 W power was connected to the grounding electrode through a power resistance. Then, the current was spread into the soil and flowed back to the negative pole of the power supply through the container wall.  
Moreover, we added water to each layer of soil according to Table 4 to realize soil structures with different resistivities [25; 26]. Hence, a three-layer soil model and a mixed-layer soil model were built. The scene of the experimental measurement is shown in Figure 14b,c, and the monitoring interface of current and soil environmental temperature and humidity is shown in Figure 14d. The historical data of the current measurement were stored in a memory card, and a historical data curve can be used to observe the change in the leakage current at different locations in the soil.
## 3 Effect of Inhomogeneous Subgrade Support
### Stiffness Degradation of Pavement Slab  
The damage plastic constitutive model is used to simulate the nonlinear behavior of pavement concrete, further analyzing the effect of inhomogeneous subgrade support on the stiffness degradation of rigid pavement slabs.  
The characteristics of the damage factor under the wheel load of the A320 aircraft are shown in Figure 7. It can be seen that the stiffness degradation of the pavement slab becomes more severe with the increasing extent of the inhomogeneous subgrade support. Under the condition of uniform subgrade support (\(\beta\) = 0%), the tensile and compressive damage factors of rigid pavement (with the thickness is 0.36 m) are 3.796 \(\times\) 10\({}^{-2}\) and 8.729 \(\times\) 10\({}^{-4}\). With the increase in the inhomogeneous support degree (\(\beta\) = 71.43%), the tensile and compressive damage factors reach 5.197 \(\times\) 10\({}^{-2}\) and 1.326 \(\times\) 10\({}^{-3}\), which have improved 36.90% and 51.91%, respectively.  
Figure 8 illustrates the tensile and compressive damage distributions of various aircraft loads with a constant condition (thickness of 0.36m and condition 1). It can be seen that the stiffness degradation of the pavement slab is located at the bottom of the rigid pavement slab under the load of the A320 aircraft. While for the B737-800 aircraft, the stiffness degradation caused by the tension of the pavement is mainly located on the bottom of the slab, and the stiffness degradation caused by the compression mainly locate on the top of the slab. In addition, the degradation of pavement stiffness caused by tension is significantly larger than that caused by compression.  
Figure 8: Damage distributions of the pavement slab. (**a**) Tensile damage under A320 action. (**b**) Compression damage under A320 action. (**c**) Tensile damage under B737-800 action. (**d**) Compression damage under B737-800 action.  
Figure 7: Damage factors of the rigid pavement slab under the A320 action (**a**) condition 1, (**b**) condition 2, and (**c**) condition 3.
# Current Status of Optical Systems for Measuring Lycopene Content in Fruits: Review
## 4 Current Systems for Estimating Lycopene
### Ultraviolet-Visible (UV-Vis) and Infrared (NIR) Spectroscopy
#### 4.3.1 Raman Spectroscopy  
Raman spectroscopy has been used to measure the lycopene content. The technique involves the use of high-energy monochromatic light, such as a laser, which disperses over the molecules and interacts with the photons of the sample. The study matrix does not require a complex preparation and they can be studied in either glass or polymer packaging [74; 75; 76; 77]. Typical Raman measurements employ the highest intensity that is related to the longest wavelength and Stokes scattering [77]. The measurement is carried out using the vibrations of the stretches of the C-C bonds (v1) of the polyene chain. The other stretch of interest is the bond C=C and the deformation C-CH3, respectively known as (v2) and (v3) [78; 79; 80; 81; 82; 83].  
[PERSON] et al. [83] reported two peaks of greater magnitude for the estimation of carotenoids in tomato. The location of these is in the spectral regions of 1100-1200 and 1400-1600 cm\({}^{-1}\). In Figure 9, the study of the behavior of the maturity of the tomato is presented. It can be seen that the peaks coincide with those reported by [PERSON] et al. [83]. Measurement of the lycopene content was achieved using a portable Raman spectroscopy system and the maximum concentration of lycopene occurred in the state of deep red maturity [84].  
[PERSON] et al. [85] developed a spatial compensated Raman spectroscopy system (SORS), for a non-destructive evaluation of the internal maturity of the tomatoes. The system shown in Figure 10 consists of a 785 nm laser in the spectra range 4000 to 50,000 nm, a 16-bit camera with 1024 \(\times\) 256 pixels, and a Raman imaging spectrometer. This system accepts light by means of an input slot 5 mm long by 100 mm wide. The spectrometer works in the range of 779-1144 nm. The focusing unit consists of a band pass filter, a focus lens, and an optic fiber collimator. The biological materials' auto-flowering difficulty was solved by the curve adjustment method called modified polynomial means [86]. The method used to identify the lycopene was through the divergence of spectral information (SID) and relative entropy.
## 2 Design, Materials, and Methods  
### Device Structure  
For the MS transmission line in this study, the strip conductor was separated from ground plane by a dielectric substrate, as shown in Figure 1. Polyimides (PIs) are known for their orange color, good thermal stability, decent mechanical properties, and relatively low dielectric loss. Therefore, PIs were naturally chosen as the substrate material for RF devices [17]. The strip conductor on the top of the substrate was fabricated by laser writing with a certain sheet resistance. The ground plane was fabricated using conductive silver paste to ensure sufficient conductivity and flexibility for the test. The width and length of the strip conductor and the thickness of the substrate are marked as \(w\), \(l\), and \(t_{\mathrm{s}}\), respectively. Table 1 lists the parameters of the MS line. Note that the characteristic impedance depends on the geometric dimensions of the MS lines and the electrical properties of the materials. Here, we turn the geometric dimensions to ensure an approximately 50 \(\Omega\) characteristic impedance to match the impendence of the test system.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Width of the Strip conductor, \(w\) & 0.45 mm \\ Length of the Strip conductor, \(l\) & 30 mm \\ Thickness of the Strip conductor & 4 \(\mu\)m \\ Thickness of the PI substrate, \(t_{\mathrm{s}}\) & 0.2 mm \\ Dielectric constant of the PI substrate & 3.5 \\ Loss Tangent of the PI substrate & 0.0027 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the 50 \(\Omega\) MS line.  
Figure 1: The structure of the laser-scribed MS line. \(w\), \(l\), and \(t_{\mathrm{s}}\) are the width of the strip conductor, the length of the strip conductor, and the thickness of the polyimide substrate, respectively.
# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
###### Abstract  
Wind turbines are a crucial part of renewable energy generation, and their reliable and efficient operation is paramount in ensuring clean energy availability. However, the bearings in wind turbines are subjected to high stress and loads, resulting in faults that can lead to costly downtime and repairs. Fault detection in real time is critical to minimize downtime and reduce maintenance costs. In this work, a simple neural network model was designed and implemented on a Raspberry Pi for the real-time detection of wind turbine bearing faults. The model was trained to accurately identify complex patterns in raw sensor data of healthy and faulty bearings. By splitting the data into smaller segments, the model can quickly analyze each segment and generate predictions at high speed. Additionally, simplified algorithms were developed to analyze the segments with minimum latency. The proposed system can efficiently process the sensor data and performs rapid analysis and prediction within 0.06 milliseconds per data segment. The experimental results demonstrate that the model achieves a 99.8% accuracy in detecting wind turbine bearing faults within milliseconds of their occurrence. The model's ability to generate real-time predictions and to provide an overall assessment of the bearing's health can significantly reduce maintenance costs and increase the availability and efficiency of wind turbines.  
wind turbines; neural network; real-time implementation; bearing fault detection +
Footnote †: journal: Applied sciences
###### Abstract  
This study investigated the impact coefficient of a large-span steel truss arch railroad bridge under moving train loads, with the Nanning Three Banks Yongjiang Special Bridge serving as the case study. Field tests were conducted to measure the bridge's self-vibration characteristics, dynamic deflection, and strain. A coupled vehicle-bridge vibration model was developed using the finite element software ABAQUS 2022 for the bridge and multi-body dynamics software SIMPACK 2022 for the CRH2 train. The two models were integrated to simulate the dynamic interaction between the train and bridge under different speeds and single-/ double-track operations. The results demonstrate that the joint simulation of SIMPACK and ABAQUS was an effective method for the vehicle-bridge coupled vibration analysis. The key findings include the following: the deflection and stress impact coefficients increased with the train speed, where the main span exhibited larger deflection coefficients than the side span. The stress impact coefficients varied significantly across different bridge components, where the lower chord of the side span and the ties of the main span showed the highest values. While there was no substantial difference in the deflection impact coefficients between the single- and double-track operations, the stress impact coefficients showed deviations, particularly in the side span's lower chord and ties, highlighting their sensitivity to vehicle-induced deflection. This study concluded that the bridge's deflection impact coefficient met design specifications, but the stress impact coefficient exceeded the specified values, suggesting that stress amplification should be carefully considered in the design of similar bridges to ensure operational safety.  
b
# Speaker Diarization: A Review of Objectives and Methods
## 4 Use of Speech Features for Speaker Diarization  
The speech features described above (primarily MFCCs and FBEs, often in a form called a Mel spectrogram) are used in most speech classification applications. For SD, they serve both for time stamp decisions, based on spectral continuity, and for labelling speech by speaker between each successive pair of time stamps. We now discuss how to do this for localized speaker diarization.  
One can distinguish two cases: (1) an input conversation for SD with a small set of subscribed speakers (known to the classification system via prior training, e.g., a common scenario for meetings), and (2) arbitrary unknown audio with multiple speakers (where the system has no knowledge about speakers). SD systems may assume that the number of speakers is known a priori or is at least bounded.  
In the first case, for each segmented speech portion, the system chooses the speaker (from among the subscribed set) that is the closest match to the data. If no match is found (e.g., a spectral distance that the system estimates for each speaker exceeds a pre-set threshold), the system may allow an outcome of \"no match\". In both cases, spectral distance can be measured against a general speaker model called a UBM (Section 5.2). We discuss the case where a portion of speech has multiple speakers later.  
Speaker labelling errors include _false rejections_ (FR) and _false acceptances_ (FA). FR (a _miss_) occurs when a classifier rejects incorrectly a true speaker. FA occurs when the system identifies the wrong speaker. In SV, a frequent evaluation criterion is _equal-error rate_, a low percentage that balances FA and FR. Many classifiers decide relative to a _threshold_, accepting or rejecting when a distance or similarity measure lies above or below such a value; so, FAs decrease and FRs increase as a function of thresholds.
# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 2 Literature Review
### 6G Networks: Characteristics and Challenges with Copyright Detection  
6G networks are envisioned to provide unprecedented capabilities, including ultra-high bandwidth (potentially reaching terabits per second), extremely low latency (sub-millisecond), and massive connectivity (supporting millions of devices per square kilometer) [48; 49]. These networks are expected to integrate advanced technologies such as artificial intelligence, terahertz communications, and large-scale satellite constellations to enable new applications like holographic communications and extended reality. However, the implementation of 6G networks presents significant challenges for data processing and image analysis, particularly due to the massive increase in data volume and the need for real-time processing. While offering increased bandwidth, the high-frequency bands used in 6G also face issues with signal propagation and penetration, potentially requiring dense network deployments. Additionally, integrating AI and machine learning algorithms directly into the network infrastructure poses challenges in terms of computational efficiency, energy consumption, and the need for distributed processing capabilities to handle the immense data loads in real time.  
In this paper, we employ a hybrid quantum-classical deep learning model specifically designed to address these challenges. This model combines the rapid edge detection and feature extraction capabilities of quantum computing with the image fidelity strengths of classical methods.  
The implementation of copyright detection in 6G networks presents unique characteristics and challenges due to the dynamic nature of these advanced telecommunications systems. With the ability to handle vast amounts of data at unprecedented speeds, 6G networks facilitate real-time monitoring and analysis of digital content, enabling prompt identification of potential copyright infringements. Advanced algorithms can analyze visual features and metadata to detect unauthorized use across various platforms. However, the sheer volume of content generated in 6G environments complicates the development of efficient detection systems, as traditional methods may struggle to keep pace with the rapid influx of new material. This necessitates adopting more sophisticated techniques, such as machine learning and image hashing, which improve accuracy by recognizing subtle variations in content and identifying similarities with existing copyrighted works. Additionally, the evolving nature of digital content--such as alterations made through cropping or filtering--poses further difficulties for copyright detection systems, requiring solutions that can adapt while maintaining high levels of accuracy. Addressing these challenges will be essential for ensuring compliance with copyright laws and protecting the rights of content creators in the fast-evolving landscape of 6G networks, ultimately promoting responsible use of digital content while safeguarding intellectual property rights.
## 2 Theory and Models
### Cyclostationary Description
Conversely, CCCF at a particular cyclic frequency can be treated as a coefficient in the Fourier series expansion of CCF:  
\[\mathcal{R}_{vi}(t,\tau)=\sum_{m=-\infty}^{+\infty}\mathcal{R}_{vi}^{mF}(\tau )\exp\Big{(}j2\pi\frac{m}{T}t\Big{)}. \tag{26}\]  
At the same time, CCCF \(\mathcal{R}_{vi}^{mF}(\tau)\) at cyclic frequency \(\alpha=mF\) is the inverse Fourier transform of CSCF \(\mathcal{S}_{vi}^{mF}(f)\) at the same cyclic frequency:  
\[\mathcal{R}_{vi}^{mF}(\tau)=\int\limits_{-\infty}^{+\infty}\mathcal{S}_{vi}^{ mF}(f)\exp(j2\pi f\tau)d\tau. \tag{27}\]  
The substitution of the explicit representation (24) into (27) leads to the representation of CCCF in the form of the Fourier series:  
\[\mathcal{R}_{vi}^{mF}(\tau)=\sum_{k=-\infty}^{+\infty}\mathcal{S}_{vi}^{(m,k) }\exp\bigg{[}j2\pi\bigg{(}k+\frac{\big{(}(m)\big{)}_{2}}{2}\bigg{)}F\tau\bigg{]}. \tag{28}\]  
CCCF (28) is a periodic function of \(\tau\). However, the value of its period depends on whether \(m\) is an even or odd integer. Thus, if \(m\) is even, the period is \(T=1/F\), but if \(m\) is odd, the period gets the double value, which is \(2T\). The latter follows from the fact that the greatest common divisor of the sequence \(F_{k}=[k+1/2]F,\ k\in\mathbb{Z}\) is \(F/2\), whose reciprocal determines the period of the Fourier series (28).
# An Algebraic Approach to Identifiability
## 3 Observability and Identifiability
### Locally Identifiability  
For some systems, there might be initial conditions that prevent the unique identification of parameters from the corresponding output trajectories. Common examples are equilibria of autonomous systems. The method described herein allows computing such subsets when the indentifiability is not given. Apart from those subsets, the map between the initial state and parameter \((x;p)\) and the output trajectory may still be injective within a neighbourhood of a point \((x_{0},p_{0})\). This means that varieties \(\mathbf{var}^{\mathbb{R}}\big{(}\mathfrak{J}\big{)}=\overline{\mathcal{I}}\) and \(\overline{\mathcal{J}}\) and the Zariski closures of indistinguishable and equal extended states, respectively, are equal in an open neighbourhood of a point \(z_{0}=(x_{0},x_{0};p_{0},p_{0})\in\mathcal{Z}\). The equality in this neighborhood can be tested geometrically by removing all irreducible components of the indistinguishable set \(\mathcal{I}\) that are not contained in this neighborhood. On the other hand, the points \(z_{0}\), where the varieties are not equal, are easily found as they are either contained in more than one irreducible component of \(\overline{\mathcal{I}}\) or such a component strictly contains \(\overline{\mathcal{J}}\), i.e., has a higher dimension.  
Recall that the Zariski closure of the indistinguishable extended states is given by \(\mathbf{var}^{\mathbb{R}}\big{(}\mathfrak{J}\big{)}\), where  
\[\hat{\mathfrak{J}}=\hat{\mathfrak{J}}_{1}\cap\cdots\cap\hat{\mathfrak{J}}_{k} \subseteq\mathfrak{J}\]  
and all ideals \(\hat{\mathfrak{J}}_{i}\) are relatively prime. If there are some \(\hat{\mathfrak{J}}_{i}\) that are strictly contained in \(\mathfrak{J}\), the system is at no point locally observable or identifiable. Instead, there may be a locally or globally identifiable subspace. Otherwise, one of the prime components in the minimal decomposition must equal \(\mathfrak{J}\) (because the indistinguishable states always include the equal ones). In this case, the not locally observable points \(z_{0}\) are found by intersecting \(\mathcal{J}\) with the corresponding real varieties \(\mathbf{var}^{\mathbb{R}}\big{(}\hat{\mathfrak{J}}_{i}\big{)}\) different from the one of the equal extended states. Note that the extended point \(z_{0}\) includes two copies of the same original point \((x_{0};p_{0})\), which is just a projection of the latter.  
While there were different definitions for equal and indistinguishable pairs for different global problems in the preceding sections, the same can be used to discuss local properties, as described herein. Note that for the identifiability test for all or a subset of the parameters, as described in Sections 3.3 and 3.4, the projection has to be done for each irreducible variety independently.