esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions
## 3 IATN for Bearing Fault Diagnosis under Variable Working Conditions
### Transfer Loss  
As mentioned in Section 2, the most used transfer loss function is currently MMD. One of the most important concepts of MMD is the kernel function \(\phi(x)\). In MMD, \(\phi(x)\) is fixed. The Gaussian kernel function is usually chosen as the kernel function because the Gaussian kernel function can map data onto an infinite-dimensional space:  
\[k\big{(}x,x^{\prime}\big{)}=\exp\Bigl{(}-\parallel x-x^{\prime}\parallel^{2}/ \left(2\sigma^{2}\right)\Bigr{)} \tag{7}\]  
Theoretically, any selection of a kernel function can determine the MMD of two different distributions. But in some extreme cases, this kernel function is determined to make the MMD distance of two different distributions exceedingly small. To avoid this situation, MK-MMD is proposed.  
\[\mathcal{K}\triangleq\Big{\{}k=\sum_{u=1}^{m}\beta_{u}k_{u}:\sum_{u=1}^{m} \beta_{u}=1,\beta_{u}\geqslant 0,\forall u\Big{\}} \tag{8}\]  
By using multi-kernel functions, this paper can better represent the differences in data distribution in high-dimensional space and improve the representation ability of model features.  
\[\text{MK}-\text{MMD}(X,Y)=\parallel\frac{1}{n}{\sum_{i=1}^{n}\mathcal{K}(x_{i })}-\frac{1}{m}{\sum_{j=1}^{m}\mathcal{K}\big{(}y_{j}\big{)}}\parallel_{H}^{2} \tag{9}\]  
Moreover, to further enhance the domain adaptation performance of the transfer loss, this paper designs a novel domain difference measure combining MK-MMD and feature center distance (FCD). FCD refers to calculating the distance between the target domain feature clustering center and the source domain feature clustering center. Assuming that there are \(m\) samples \(X^{k}=[X_{1},X_{2}\cdots X_{m}]\) of the \(k\)-th category in the source domain, and  
Figure 3: IATN for bearing fault diagnosis under variable working conditions.  
there are n samples \(Y^{k}=[Y_{1},Y_{2}\cdots Y_{m}]\) in the target domain judged to be the \(k\)-th category by the model, the definition of FCM is shown in (10):  
\[L_{FCM}=\frac{1}{k}{\sum_{k=1}^{K}{\dot{s}_{k}}}=\frac{1}{k}{\sum_{k=1}^{K}{ \left|{\overline{X^{k}}-\overline{Y^{k}}}\right|}} \tag{10}\]  
where \(K\) is the number of categories. By combining (9) and (10), the novel transfer loss is defined as (11):  
\[L_{T}=L_{\text{MK-MMD}}+L_{FCM} \tag{11}\]  
The combination of MMD and FCD enables the model to use different feature alignment strategies for feature transfer. FCD promotes local feature alignment, and MMD is used for global feature alignment. The integration of these two loss functions combines local and global feature alignment, thereby enhancing domain adaptation performance.
## 4 Discussion
Two types of noise signals with different parameters in the frequency and time domains were selected for analysis. Nonstationary impulse noise with a broadband spectrum and the duration of impulse components shorter than the duration of the useful AE signal, as well as stationary noise with continuous components and a narrowband low-frequency spectrum, were studied. In both cases, a significant intersection of the frequency ranges of noise and useful signal was observed. The differences between noise and useful signals were determined using wavelet transformation. The choice of wavelet analysis as a method for presenting data is explained, firstly, by the high time and frequency resolution in the analysis of impulse signals and, secondly, by the possibility of universalizing the feature extraction procedure for various types of noise [28]. An effective solution was the use of wavelet kurtosis. Kurtosis is the fourth standardized moment of probability distribution, often used to detect a signal and determine the time of arrival [29]. In this study, kurtosis is used more effectively since it analyzes the coefficients of wavelet decomposition in time and frequency projections, which allows more reliable detection of the presence of a useful signal since the signal-to-noise ratio increases with wavelet decomposition. Secondly, the simultaneous assessment of time kurtosis and frequency kurtosis allows the detection of change points associated with the presence of useful signals, both in the time and in the frequency domain. The effectiveness of using wavelet kurtosis for signal analysis and detection of disorder moments is confirmed by a number of researchers [26; 27]. A network with backpropagation of errors was used as a neural network, and an alternative solution could be to use a convolutional neural network to recognize a wavelet spectrogram without additional feature extraction. However, the use of classical neural network architecture made it possible to achieve the desired classification result due to the effective selection of features considering the useful signal parameters.
# Semantic Features Based N-Best Rescoring Methods for Automatic Speech Recognition
## 4 N-Best Rescoring with Coordination Scores
### Sentence Coordination Scores  
#### 4.1.1 Sentence Probability Score Using LDA  
To discriminate whether a word is suitable in a document, we could use the probability of a word given the topic estimation of the document. The probability can be computed by a weighted average of the probability of this word given each topic, for which the weight is the topic distribution of the document, i.e.,  
\[P(w_{i}|s)=\sum_{j=1}^{|T|}P(w_{i}|z_{j})P(z_{j}|s), \tag{6}\]  
where \(|T|\) is the hyper-parameter of topic numbers in the LDA model training progress, and \(z_{j}\) represent the \(j\)-th topic. We use the item in Row i Column j of the \(\beta\) matrix for \(P(w_{i}|z_{j})\) calculated by the LDA tool kit mentioned in the previous section and the corresponding row in \(\gamma\) for the hypothesis sentence \(s\) by the infer procedure in the tool kit for \(P(z_{j}|s)\). In re-ranking tasks, we regard the hypothesis sentences as the given document.  
This probability is regarded as LSM [3] and it is used to measure the coordination of a sentence for the first time here.  
The probability of a word sequence, which is regarded as a bag-of-word (BOW) in this word, given the sentence topic distribution of a sentence is the product of the probabilities for each words in the sentence given the sentence topic distribution when simplified that the words are independent. It can be written as:  
\[P(w_{1},\dots,w_{|L|}|s)=\prod_{i=1}^{L}P(w_{i}|s)=\prod_{i=1}^{L}[\sum_{j=1} ^{|T|}P(w_{i}|z_{j})P(z_{j}|s)]. \tag{7}\]where \(L\) is the length of the document, namely the hypothesis sentence. We use the logarithm of the word sequence probability as the sentence coordination score:  
\[Score_{s}=logP(w_{1},\ldots,w_{|S|}|s). \tag{8}\]
# Systematic Analysis and Design of Control Systems Based on [PERSON]'s Direct Method
## 4 Controller Design
### ISS Control Lyapunov Function  
The concept of the control Lyapunov function can also be applied to the ISS with respect to a disturbance. For this purpose, consider a system which, in addition to the control input \(u\), is also influenced by a disturbance \(w\). In the case with affinity to the input, the system can be described as follows  
\[\dot{x}=f(x)+g_{1}(x)w+g_{2}(x)u \tag{56}\]  
by the vector fields \(f,g_{1},g_{2}:\mathbb{R}^{n}\rightarrow\mathbb{R}^{n}\). Definition 1 can be generalized as follows [7; 66; 67]:  
**Definition 2** (ISS Control Lyapunov Function).: _A continuously differentiable, positive definite and radially unbounded function \(V:\mathbb{R}^{n}\rightarrow[0,\infty)\) is called ISS control Lyapunov function (in short: ISS-CLF) if a function \(\gamma\) of class \(\mathcal{K}_{\infty}\) exists such that for all \(x\
eq 0\) and all \(w\in\mathbb{R}\)_  
\[|x|\geq\gamma(|w|)\implies\inf_{u}(L_{f}V(x)+L_{g_{1}}V(x)w+L_{g_{2}}V(x)u)<0 \tag{57}\]  
_holds._  
Analogous to the condition (52) and (57) can be represented via  
\[L_{g_{2}}V(x)=0\implies L_{f}V(x)+|L_{g_{1}}V(x)|\gamma^{-1}(|x|)<0 \tag{58}\]  
as an equivalent formulation ([66] Section II).  
Once a corresponding ISS-CLF has been found, it is possible to use feedback (53) with \(a=L_{f}V(x)+|L_{g_{1}}V(x)|\gamma^{-1}(|x|)\) and \(b=L_{g_{2}}V(x)\), an ISS closed-loop can be generated with respect to the disturbance \(w\).  
To determine adequate feedback employing quantifier elimination, one uses a positive definite approach function \(V(x,q)\) and a suitable approach for the comparison function \(\gamma(|w|,c)\) with a parameter vector \(q\) or \(c\). The suitability of the approach functions used can then be determined by applying quantifier elimination to the prenex formula  
\[\exists q,c\,\forall x\,\forall w\,\exists u:\quad|x|\geq\gamma(| w|,c)\] \[\implies L_{f}V(x,q)+L_{g_{1}}V(x,q)\,w+L_{g_{2}}V(x,q)\,u<0,\]  
respectively,  
\[\exists q,c\,\forall x:\quad\left(x\
eq 0\wedge L_{g_{2}}V(x,q)=0\right)\] \[\implies L_{f}V(x,q)+|L_{g_{1}}V(x,q)|\gamma^{-1}(|x|,c)<0\]  
can be determined. There may be other constraints to consider to the parameters, for example, to ensure positive definiteness of \(V\) or monotonicity of \(\gamma\). For example, \(q>0\) must hold for \(V(x)=qx^{2},\ x\in\mathbb{R}\) to be positive definite.
# FVR-Net: Finger Vein Recognition with Convolutional Neural Network Using Hybrid Pooling
###### Abstract  
In this paper, we present FVR-Net, which is a novel finger vein recognition network using a convolutional neural network (CNN) with a hybrid pooling mechanism. The scheme is based on the use of a block-wise feature extraction network to extract discrete features from interclass vein image samples, regardless of their visual quality. Input images to FVR-Net are subjected to preprocessing prior to being fed into the network in order to segment the vein patterns from the background. We designed a feature extraction network in which each block consists of a convolutional layer followed by hybrid pooling, whose output activation maps are concatenated before passing features to another block within the network. In the hybrid pooling layer, two subsampling layers of maxpooling and average pooling are placed in parallel where the former activates the most discrete features of the input, and the latter considers the complete extent of the input volume so better localization of features can be accessed. After the features are extracted, they are passed to three fully connected layers (FCLs) for classification. We conduct several experiments on two publicly available finger vein datasets based on visual quality of the images. When compared to conventional studies, the proposed model achieves outstanding recognition performance with accuracies of up to 97.84% and 97.22% for good and poor-quality images, respectively. By varying multiple network hyperparameters, we obtain optimal settings such that the model can guarantee the best recognition accuracy for a finger vein biometric system.  
finger vein; biometrics; Gabor filters; deep neural networks +
Footnote †: journal: Medical  
Generally, vein images are acquired by utilizing near infrared radiation (NIR) or thermal infrared-based optical imaging systems [5]. Such imaging techniques do not require close proximity to sensors, which makes them suitable for finger vein imaging systems. In addition, they can facilitate imaging of deeper tissues at the microvascular level. However, during real-time acquisition, the captured image might be inherently susceptible to quality degradation, including image shading due to uneven thickness of muscles inside the finger, irregular shadowing, positional misalignment, scattering of light when imaging finger tissue, and physiological changes such as inaccurate placement of the finger. During real-time recognition, these factors pose nontrivial threats due to irrelevant and ambiguous separability between the vein patterns and the background. Therefore, many consider finger vein recognition with high accuracy a challenging task. Despite these challenges, in recent years, obtaining accurate vein recognition techniques is in demand.
## 3 Materials and Methods
### Phytosterol and Fatty Acid Analysis
#### 3.12.3 GC-MS Analysis of Fatty Acids  
The fatty acid portion obtained in Section 3.9.1 was acidified with 1 M HCl and was extracted with 10 mL of hexane. The organic portion was segregated, whereas the extraction solvent was removed with a vacuum rotary evaporator. Methylation was performed by adding 4 mL of 14% BF\({}_{3}\)/MeOH (_v/v_) at 80 \({}^{\circ}\)C for 10 min. The produced fatty acid methyl ester (FAME) was subsequently extracted with 2.5 mL of hexane, dried using MgSO\({}_{4}\) and finally filtered with a cotton plug and silicate powder. The FAME profile was analysed using a GC-MS equipment (GCMS-QP 2020, Shimadzu, Kyoto, Japan). Separation was performed using a Zebron ZB-WAX capillary column (30 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness; Phenomenex, Torrance, CA, USA). The scanning was conducted at a mass range of 50-400 _m/z_ at an EI of 70 eV and at a 5 scan s\({}^{-1}\) mode. The carrier gas used was helium with a flow rate of 1.0 mL min\({}^{-1}\) at a split ratio of 1:10. The temperature gradient programmes used were as follows: (a) 45 \({}^{\circ}\)C for 3 min, (b) 45 \({}^{\circ}\)C to 220 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C min\({}^{-1}\) and (c) 220 \({}^{\circ}\)C-250 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C min\({}^{-1}\) and finally maintained at 250 \({}^{\circ}\)C for 2 min. The injector was held at a temperature of 260 \({}^{\circ}\)C.
###### Abstract  
In the era of the global pandemic caused by the COVID-19 virus, 3D digitisation of selected museum artefacts is becoming more and more frequent practice, but the vast majority is performed by specialised teams. The paper presents the results of comparative studies of 3D digital models of the same museum artefacts from the Silk Road area generated by two completely different technologies: Structure from Motion (SfM)--a method belonging to the so-called low-cost technologies--and by Structured-light 3D Scanning (3D SLS). Moreover, procedural differences in data acquisition and their processing to generate three-dimensional models are presented. Models built using a point cloud were created from data collected in the Afrasiyab museum in Samarkand (Uzbekistan) during \"The 1 st Scientific Expedition of the Lublin University of Technology to Central Asia\" in 2017. Photos for creating 3D models in SfM technology were taken during a virtual expedition carried out under the \"3D Digital Silk Road\" program in 2021. The obtained results show that the quality of the 3D models generated with SfM differs from the models from the technology (3D SLS), but they may be placed in the galleries of the virtual museum. The obtained models from SfM do not have information about their size, which means that they are not fully suitable for archiving purposes of cultural heritage, unlike the models from SLS.  
structure from motion (SfM); structured-light 3D scanning (3D SLS); 3D models; cultural heritage; Silk Road; Afrasiyab museum; virtual museum +
Footnote †: journal: Applied Sciences
## 5 Discussion
### Reaction Time  
Even though TI and VI are commonly used in our daily life, G-HI was the fastest input interaction and significantly faster than VI, as shown in Figure 5. To use G-HI, drivers stare at the windshield and use a quick eye movement for selection and a head nod to trigger the selection. On the other hand, when using TI, drivers must divert their eyes from the windshield to a touch panel and stretch or adjust their body posture to reach the panel; that physical effort takes time. When using VI, participants used sentences of varying length to trigger their selection: some said \"the first one\" or \"select the first object\". The time the user takes to decide which words correctly represent the desired selection likewise affect total reaction time.  
The automated driving platform responded in two ways for each of our two scenarios, as shown in Figure 1b. In the slow car ahead scenario, the vehicle overtakes the slow car or slows down behind it. In the accident ahead scenario, the vehicle changes lanes to pass the accident on the right side or changes the trip route to avoid passing near the accident. We found that using G-HI enables faster reaction time for the three-input interactions in all vehicle actions (overtaking, slowing down, changing lanes, and changing route). However, for lane changes, reaction time using G-HI is significantly faster than using TI or VI, as shown in Figure 6. When changing lanes, the driver will pass the accident in the nearest lane, mimicking human nature where drivers often continue to look at an accident. Therefore, using VI and TI are not optimal input interactions in such cases, as both input interactions create high cognitive and physical loads, as shown in Figure 6. Moreover, the usage of G-HI shows a consistency even during the motion of the driving platform, users were able to reach and trigger their selection comfortably.
## 3 Overview of the Selected Techniques
### Global Adaptive Observer (GAO)
#### 3.1.2 Observer Design
From the signals \(U_{a}\), \(U_{b}\), and \(U_{c}\) and their derivatives (i.e., quadrature signal) obtained through the observer (16), the sequences can be easily estimated through the Equations (7)-(9).
## 3 Results
### Sequencing Data and Quality Control  
Quality control of sequencing raw data showed effective reads of 41,651,524-43,366,270 across all samples (Supplementary Materials, Table S4). Q20 and Q30 base percentages were 97%-98% and 94%-98% respectively, with GC content at 46%-56%. These high-quality data were suitable for subsequent bioinformatic processing and analysis.
# A Novel Hybrid CSP-PV Power Plant Based on Brayton Supercritical CO\({}_{2}\) Thermal Machines
## 2 Methodology
### Heat Pump  
The heat pump receives a low-temperature heat input ranging from 300 \({}^{\circ}\)C to 390 \({}^{\circ}\)C from the thermal oil of the solar field. It then releases high-temperature heat into the molten salt loop, with temperatures ranging from 405 \({}^{\circ}\)C to 589 \({}^{\circ}\)C. A reverse recuperated Brayton cycle has been adopted to achieve a sensible heat transfer profile in the working fluid of the heat pump. This cycle operates far from the critical point, leading to a nearly ideal gas behaviour.  
The cycle can be described as follows (Figure 3): the low-pressure and -temperature stream (4) exiting the turbine (HPT) absorbs heat from the low-temperature reservoir in the thermal oil/CO\({}_{2}\) heat exchanger (TOHX). A second stage of heating (5-6) takes place in the recuperator (REC), utilizing the high temperature of the stream leaving the compressor (2-3). Subsequently, the low-pressure stream, now at a high temperature after these two heating stages, provides thermal energy to the hot sink (6-1) via the CO\({}_{2}\)/molten salt heat exchanger (MSHP). After that, the low-pressure stream enters the compressor (HPC), which increases its pressure and temperature (1-2). After the compressor, the high-pressure stream releases heat in the recuperator (2-3), returning to the turbine inlet conditions (3).  
Figure 3: Proposed layout.  
The isentropic efficiencies of the turbine and compressor have been set to 92% and 88%, respectively [25]. The recuperator's temperature approach is assumed to be 5 K, and 10 K in the low-temperature molten salt/CO\({}_{2}\) heat exchanger. The compressor's outlet pressure is 300 bar, with an inlet pressure of 85 bar and a temperature of 415 \({}^{\circ}\)C. Pressure drops of 2% have been considered for each stream in the heat exchangers [21]. Table 1 outlines the characteristics of the state points for the heat pump. To visually depict this information, Figure 4 illustrates the p-h diagram. Notably, the behaviour of the fluid might be approximated to that of an ideal gas operating in a reverse Brayton cycle.
## 1 Introduction
Whereas in quantum key distribution (QKD) based protocols, which are popular in optical fiber networks [19], there is the need for a coherent light source (e.g., from laser sources) [20]. These schemes cannot be adopted in indoor VLC systems. Alternatively, the more recent approach of chaos, emerging from an interdisciplinary branch of mathematics, is a promising candidate. Thus, high security-based VLC links could be implemented based on chaos communications [21]. It should be noted, however, that not all chaotic cryptosystems proposed in the literature can be considered cryptographically robust by default [22; 23]. Nonetheless, the chaotic inclusion (or embedding) method, which uses continuous-time chaotic models, is considered secure and implementable in practice [24]. For its practical implementation, chaotic inclusion requires total synchronization between the chaotic oscillators involved. In chaos theory terminology, this kind of synchronization is referred to as complete, total, or identical; defined as overall convergence between Tx and Rx over time under different initial conditions. That is, for a chaotic Tx \(\dot{x}(t)=f(x(t))\) and a Rx \(\dot{\hat{x}}(t)=f(\hat{x}(t))\), complete synchronization is achieved when \(\lim\limits_{t\to\infty}||x(t)-\hat{x}(t)||=0\) for \(x(0)\
eq\hat{x}(0)\)[3]. Considering the high sensitivity of chaotic systems to the initial conditions, the prospect of achieving total synchrony is non-trivial, especially under the effect of wireless channel conditions [25; 26]. The end purpose is to study the suitability of chaotic inclusion schemes in VLC. In the context of chaos synchronization, it is not clear that the coupling methodology, via a chaotic signal acting as a carrier, can handle the variable delay induced by the multipath effect. Furthermore, these delays vary depending on both the Rx position and the frequency of the carrier signal. As result, complete or total chaotic synchronization is a function of both factors. In this way, this work finely analyzes the upraise and impact of multipath delays in chaotic synchronization while suggesting a region limit in the carrier frequency, progressively lifted with time normalization procedures. We study the degree of complete synchronization based on cross correlation coefficients for continuous-time Colpitts chaotic synchronization. In more detail, we investigate chaos synchronization in a multiple input single output (MISO) VLC for both line of sight (LOS) and non-LOS propagation schemes. The results presented are based on numerical simulation models. The paper is structured as follows. Section 1 has introduced the problem statement. Section 2 provides a description of the working models for the Colpitts model employed with its related chaotic coupling mechanism in an ideal channel and the VLC multipath model implemented. Section 3 presents the numerical simulation results obtained. Finally, conclusions are drawn in Section 4.