esa-sceva/satcom-chunk-collection huggingface dataset:
# Interference Mitigation for Metro Train Location Based on Karhunen-Loeve Decomposition
## 2 Signal Model and Interference Modes
The time domain expression of the pulse signal is  
\[J(t)=bp(t). \tag{4}\]  
where \(b\) is signal amplitude, \(p(t)\) is an impulse signal with duty cycle \(\tau_{\text{j}}/T_{\text{j}}\). \[p(t)=\left\{ \begin{array}{ll}1,nT_{\text{j}}\leqslant t\leqslant nT_{\text{ j}}+\tau_{\text{j}}&n=0,1,2,\cdots.\\ 0,\text{ else}\end{array} \right.. \tag{5}\]  
The frequency of a chirp signal varies linearly with time, which can be expressed as follows. \[J(t)=b\sin\biggl{[}2\pi\biggl{(}f_{0}+\frac{k}{2}t\biggr{)}t\biggr{]},0\leq t \leq T_{\text{j}}. \tag{6}\]  
where \(c\) is the amplitude of the chirp signal, \(f_{0}\) is the initial frequency, \(k\) is the sweeping frequency rate, and \(T_{\text{j}}\) is the sweeping time. The jamming-to-signal-and-noise-ratio (JSNR) is used in this paper to measure the impact of interference on the positioning signal, which is calculated as the average power of \(J(t)\) divided by the sum of the average power of \(r_{CPIS}(t)\) and \(\omega(t)\). When the receiver  
Figure 2: Two modes of interference. (**a**) The interfering signal source is stationary relative to the train positioning terminal; (**b**) The interfering signal source moves relative to the train positioning terminal. receives the interference signal, the carrier noise ratio \((C/N_{0})\) will change, which will affect the positioning accuracy. The carrier to noise ratio is calculated by the following formula. \[\left[C/N_{0}\right]_{\text{eq}}=\bigg{(}\frac{1}{C/N_{0}}+\frac{JSNR}{QR_{ \text{c}}}\bigg{)}^{-1}. \tag{7}\]  
where \((C/N_{0})\) is the undisturbed carrier noise density, \(Q\) represents the spread spectrum processing gain adjustment factor, and \(R_{\text{c}}\) is the positioning signal code rate. With the increase of JSNR, the carrier noise ratio decreases, which will cause acquisition failure and increase the time for capturing and searching the BS signal. For the tracking process, the ranging accuracy is also related to the carrier noise ratio. The calculation formula of the error mean square error (\(\sigma_{IDLL}\)) of the code phase is as follows. It can be seen that with the increase of the interference power, the carrier noise ratio decreases and the positioning accuracy decreases. \[\sigma_{IDLL}=\sqrt{\frac{B_{L}}{2\cdot C/N_{0}}\frac{1}{B_{f}T_{\text{C}}} \bigg{(}1+\frac{1}{T_{coh}\cdot C/N_{0}}\bigg{)}}. \tag{8}\]  
where \(B_{L}\) is noise bandwidth of code track loop, \(B_{f}\) is RF front-end bandwidth, \(T_{C}\) and \(T_{coh}\) represent pseudo code period and time for integration, respectively. Figure 3 shows the structure of the train positioning terminal, and the interference suppression module is placed between the RF front end and the baseband signal processing unit. The signal containing interference is down-converted to a digital intermediate frequency signal and then enters the interference suppression module. After executing the interference mitigation algorithm proposed in this paper, it is sent to the baseband signal processing unit to obtain accurate location.
# Bike-Sharing Travel Demand Forecasting via Travel Environment-Based Modeling
## 2 Literature Review  
### Bike-Sharing Trip Demand Forecasting  
In general, the factors that influence the demand for bicycle sharing are multifaceted. Thus far, the existing research has explored the impact of various factors, including departure time, weather conditions, location, and population distribution [5; 6; 7]. Additionally, different land use patterns also have the potential to influence cycling demand [8; 9]. Non-motorized transport facilities emerge as a crucial factor in determining bicycle demand. Studies have revealed a complex nonlinear relationship between the demand for bike-sharing and factors such as the distance from the street to the city center, the proportion of non-motorized lanes, transportation facilities, population density, and Point of Interest (POI) density [10; 11]. Advocates of bicycle-friendly cities propose the construction of robust bicycle road networks to foster increased bicycle usage [12]. Echoing this sentiment, a notable number of cyclists have expressed interest in cycling but expressed dissatisfaction with the current bicycle facilities [13; 14].  
For these influencing factors, commonly employed methods encompass machine learning and neural network models for forecasting bike-sharing demand. [PERSON] et al. [15] devised a forecasting model using a back-propagation (BP) neural network, which was based on a non-negative matrix factorization algorithm, to predict bike-sharing trip demand. [PERSON] et al. [16] formulated a prediction model leveraging a BP neural network and selected historical bike-sharing usage data from the case area. Through programming and model resolution using Matlab software, [PERSON] ascertained the predicted future bike-sharing usage volume in the designated area. [PERSON] et al. [17] crafted a BP neural network model that integrates user tidal riding patterns and geographical spatial point-of-interest data to anticipate bike-sharing riding volume influenced by weather factors. Meanwhile, [PERSON] et al. [18] developed six deep-learning models sharing the same architecture to forecast demand for a station-based bike-sharing service in Seoul, South Korea.  
However, machine learning and neural network algorithms prove inadequate for all regions because the demand for bike-sharing trips commonly shows spatial variation. Furthermore, these algorithms are plagued by issues encompassing poor interpretability and substantial operational complexity [19; 20].
## 2 Materials and Methods
### Chemistry
#### 2.1.2 Experimental Section
: 224-225 \({}^{\circ}\)C; R\({}_{\rm f}\) = 0.23 (EtOAc/\(n\)-Hexane = 1:2); \({}^{1}\)H NMR (500 MHz, DMSO-d\({}_{6}\)) 8 9.46 (s, 1H), 7.88 (s, 1H), 7.07 (dd, 1H, \(J\) = 1.5, 7.5 Hz), 7.01 (brtd, 1H, \(J\) = 1.6, 7.7 Hz), 6.78 (dd, 1H, \(J\) = 1.0, 8.0 Hz), 6.70 (brtd, 1H, \(J\) = 1.1, 7.4 Hz), 6.61 (d, 1H, \(J\) = 2.3 Hz), 6.48 (d, 1H, \(J\) = 2.3 Hz), 3.86 (s, 3H), 3.81 (s, 3H), 3.52 (s, 2H); \({}^{13}\)C NMR (200 MHz, DMSO-d\({}_{6}\)) 8 175.8, 164.1, 160.9, 160.0, 155.4, 151.9, 130.5, 127.9, 125.7, 123.8, 119.6, 115.6, 108.7, 96.4, 93.3, 56.5, 56.3, 25.6; IR (thin film, neat) \(\
u_{\rm max}\) 1643, 1595, 1566, 1454, 1373, 1207, 1152, 1078, 824, 752 cm\({}^{-1}\); LR-MS (FAB+) _m/z_ 313 (M + H\({}^{+}\)); HR-MS (FAB+) calculated for C\({}_{18}\)H\({}_{17}\)O\({}_{5}\) (M + H\({}^{+}\)) 313.1076; observed 313.1078. _Oleracone D_ (1). To a solution of olerone F (2) (150 mg, 0.48 mmol) in methylene chloride (5 mL), 1.92 mL of BCL\({}_{3}\) (1 M solution in CH\({}_{2}\)Cl\({}_{2}\), 1.92 mmol) was slowly added at 0 \({}^{\circ}\)C. After stirring for 1 h at the same temperature, the reaction mixture was quenched with H\({}_{2}\)O, diluted with methylene chloride, stirred for an additional 1 h, and extracted with methylene chloride. The combined organic layer was washed with brine, dried over anhydrous magnesium sulfate, and concentrated in vacuo. Purification of the concentrated residue by flash column chromatography (EtOAc: \(n\)-Hexane = 1:5) afforded 114 mg (80%) of olerone D (1) as a yellowish powder. The spectral data of 1 were consistent with previous data (Table S3). m.p. : 154-155 \({}^{\circ}\)C; R\({}_{\rm f}\) = 0.31 (EtOAc: \(n\)-Hexane = 1 : 5); \({}^{1}\)H NMR (500 MHz, DMSO-d\({}_{6}\)) 8 12.76 (s, 1H), 9.47 (brs, 1H), 8.06 (s, 1H), 7.08 (dd, 1H, \(J\) = 1.4, 7.5 Hz), 7.03 (brtd, 1H, \(J\) = 1.6, 7.7 Hz), 6.81 (dd, 1H, \(J\) = 0.8, 8.0 Hz), 6.71 (brtd, 1H, \(J\) = 1.4, 7.4 Hz), 6.61 (d, 1H, \(J\) = 2.3 Hz), 6.38 (d, 1H, \(J\) = 2.3 Hz), 3.84 (s, 3H), 3.61 (s, 2H); \({}^{13}\)C NMR (200 MHz, DMSO-d\({}_{6}\)) 8 181.6, 165.6, 161.6, 158.2, 155.4, 155.3, 130.4, 128.0, 125.0, 121.4, 119.5, 115.4, 105.5, 98.4, 92.8, 56.4, 25.0; IR (thin film, neat) \(\
u_{\rm max}\) 1647, 1585, 1510, 1435, 1368, 1240, 1163, 1051, 822, 754 cm\({}^{-1}\); LR-MS (FAB+) _m/z_ 299 (M + H\({}^{+}\)); HR-MS (FAB+) calculated for C\({}_{17}\)H\({}_{15}\)O\({}_{5}\) (M + H\({}^{+}\)) 299.0919; observed 299.0915.
# Deep Learning (Fast R-CNN)-Based Evaluation of Rail Surface Defects
###### Abstract  
In current railway rails, trains are propelled by the rolling contact between iron wheels and iron rails, and the high frequency of train repetition on rails results in a significant load exertion on a very small area where the wheel and rail come into contact. Furthermore, a contact stress beyond the allowable stress of the rail may lead to cracks due to plastic deformation. The railway rail, which is the primary contact surface between the wheel and the rail, is prone to rolling contact fatigue cracks. Therefore, a thorough inspection and diagnosis of the condition of the cracks is necessary to prevent fracture. The Detailed Guideline on the Performance Evaluation of Track Facilities in South Korea specifies the detailed requirements for the methods and procedures for conducting track performance evaluations. However, diagnosing rail surface damage and determining the severity solely rely on visual inspection, which depends on the qualitative evaluation and subjective judgment of the inspector. Against this backdrop, rail surface defect detection was investigated using Fast R-CNN in this study. To test the feasibility of the model, we constructed a dataset of rail surface defect images. Through field investigation, 1300 images of rail surface defects were obtained. Aged rails collected from the field were processed, and 1300 images of internal defects were generated through SEM testing; therefore, a total of 1300 pieces of learning data were constructed. The detection results indicated that the mean average precision was 94.9%. The Fast R-CNN exhibited high efficiency in detecting rail surface defects, and it demonstrated a superior recognition performance compared with other algorithms.  
rolling contact fatigue; rail surface defects; rail internal defects; deep learning; fast R-CNN; rail surface defect detection +
Footnote †: journal: Applied Sciences
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
## 3 Interleaver
### Proposed Interleaver  
#### 3.7.1 Chaotic Map Utilized  
The Duffing map is a two-dimensional map and a discrete version of the Duffing equation. This is a differential and nonlinear equation of the second order used to model certain damped and driven oscillators.  
Duffing maps describe a dynamic system that exhibits chaotic behavior. From a point \((x_{n},\ y_{n})\) on the plane, a new point is assigned, which is given by  
\[x_{n}=y_{n-1}; \tag{19}\]  
\[y_{n}=-b\cdot x_{n-1}+a\cdot y_{n-1}-y_{n-1}^{3}; \tag{20}\]  
The map depends on the two constants \(a\) and \(b\). In general, \(a=2.6\) and \(b=0.8\) are defined to produce a chaotic behavior. For the experimental tests, the initial point considered is \(x_{1}=1\) and \(y_{1}=1\).
## 2 Advantages and Design Procedure of Capacitive Isolated Series Resonant Converter
If the battery voltage is less than half the PV voltage, the configuration in Figure 1c can be used because it has the ability to step down to less than half, operating very close to the resonant frequency. First, the battery and PV voltages and the power range must be known. Then, the maximum and minimum voltage gains can be calculated. After that, the operating frequency range can be defined as a normalized value relative to the resonant frequency. By knowing what frequency range is acceptable for control, the minimum quality factor must be determined to include the full range of the voltage gain. This means that, at the minimum-Q curve, the  
Figure 3:5 Simplified AC resonant circuit using first harmonic approximation. maximum and minimum voltage gains must be achieved within the allowable frequency range. If this is achieved in the minimum-Q curve, it will naturally be achieved in the higher-Q curve as the frequency versus voltage gain change is sharper. After defining the minimum quality factor and the resonant frequency, the resonant tank parameters can be found directly. A flow chart for a systematic procedure to design the parameters of the proposed series resonant converter is shown in Figure 3:6
## 2 Methodology
### Outlier Judging Metric  
In order to distinguish the outlier dynamically, a stochastic outlier judging criterion is used. One of the sophisticated methods to detect outliers, the evaluation means used in the safety-related sensor system of the safety standard IEC-62998, is referenced [40]. The core idea of the coverage interval is to determine the threshold to validate that the measured samples are kept inside the boundary for a certain probability as follows [27]:  
\[C_{p}\leq 1-\frac{PFH_{\text{u}}}{d}, \tag{22}\]  
where \(C_{p}\) is coverage probability, \(PFH_{\text{u}}\) is the probability of failure in an hour, and \(d\) is the demand rate, which is how frequently the functionality of the system is required to operate. Finally, the judgment criterion for the ODs can be written as:  
\[P(\gamma(T_{\text{c}})\geq\chi_{\alpha})=\alpha, \tag{23}\]  
where \(\chi_{\alpha}\) is the predetermined \(\alpha\)-quantile of the Chi-square distribution. In other words, \(\alpha\) can be written as a significance level. In this framework, outliers will be detected based on the computed probability threshold that refers to \(\alpha\). In this approach, based on the  
Figure 5: Proposed method to detect outlier delay (OD) using Gaussian mixture model.  
safety integrity level (SIL) of 2, it is assumed that the target system which makes \(\alpha\) should be higher than \(1-2.5\times 10^{-7}\). \(\chi_{\alpha}\) is therefore set as 26.62 [41]. In addition, \(\gamma\) denotes the Mahalanobis distance.  
\[\gamma(T_{\mathrm{c}})=M^{2}=(T_{\mathrm{c}}-\mu)^{T}(\sigma)^{-1}(T_{\mathrm{ c}}-\mu), \tag{24}\]  
where \(M\) is the Mahalanobis distance criterion. If judging index \(\gamma(T_{\mathrm{c}})\) is larger than \(\chi_{\alpha}\), the outlier is rejected because the safety criterion is violated. It should be noted that, according to the dimension of the measured physical parameters, the domain of the \(\chi_{\alpha}\) will be changed.
## 3 Results
### Graphene-Based Sensors  
#### 3.2.1 Fabrication Approaches and Growth Techniques  
Graphene, a single layer of carbon atoms arranged in a two-dimensional honeycomb lattice, has garnered significant attention in the field of nanosensors due to its exceptional electrical, mechanical, and sensing properties [42]. Several fabrication approaches and growth techniques have been developed to create graphene-based sensors with precise control over their properties [43].  
One common method for graphene fabrication is the mechanical exfoliation technique, also known as the \"Scotch tape\" method [44]. In this approach, a piece of adhesive tape is used to repeatedly peel off thin layers of graphene from a graphite source. This technique allows the creation of high-quality graphene flakes with a thickness of just one atomic layer. However, the scalability and efficiency of this method are limited. CVD is also another widely used technique for large-scale graphene production.  
Other growth techniques, such as epitaxial growth, chemical synthesis, and molecular beam epitaxy, have also been explored to produce graphene with specific properties tailored for sensor applications [45; 46]. These techniques offer additional control over graphene's crystal structure, doping levels, and functionalization, allowing for the customization of sensing properties. Figure 3 showcases the various techniques employed in the fabrication and growth of graphene-based sensors. These estimates aim to reflect a general trend in the research field and should be interpreted with caution. Mechanical exfoliation and CVD are observed to be among the most common methods for producing graphene for sensor applications, attributed to their ability to yield high-quality graphene sheets. On the other hand, epitaxial growth, chemical synthesis, and molecular beam epitaxy, while utilized in certain applications, appear less frequently in the surveyed literature. Future work with a systematic meta-analysis approach may provide a more quantified and statistically validated distribution of these techniques.
## 3 Results and Discussion
### Antibiotic Resistance of the Selected Strains  
The MIC was determined by a broth microdilution assay and the results in Table 1 indicated that, among LAB, only _L. brevis_ CNTA 1374 demonstrated sensitivity to all antibiotics tested according to the referenced cut-off values [30]. However, it should be noted that kanamycin resistance has been described as intrinsic in pediococci and most lactobacillus species (including _L. plantarum_) [61; 62; 63]. This has been observed in numerous previous works and has been explained by the high rate of spontaneous chromosomal mutations conveying resistance to aminoglycos, having a low potential for horizontal spread [64; 65; 61].  
In the case of _B. subtilis_ CNTA 517, [PERSON] et al. [66] already pointed out that the cut-off antibiotic values defined by SCAN [31] more closely reflect the intrinsic resistances of _B. subtilis_, in contrast to those set by FEEDAP [30]. In this regard, _B. subtilis_ CNTA 517 was deemed susceptible to all tested antibiotics. Furthermore, no mobile genetic elements were found after sequencing this strain, and all the detected antibiotic resistance genes (detailed in Table S5) are also found in the type of strain _B. subtilis_ subsp. subtilis_ str. 168 (NCBI Reference Sequence NC_000964.3). These findings support the hypothesis that _B. subtilis_CNTA 517, like the three tested LAB strains, does not harbor any acquired antimicrobial resistance which could be transferred to other bacteria.  
Evaluation of the Cytotoxicity of the Cell-Free Supernatant of B. subtilis CNTA 517 on Epithelial Cells  
The effect of different concentrations of the CFS of _B. subtilis_ CNTA 517 on Caco-2 and HT-29 (Figure S3) cell lines was determined by the MTT method. At the assayed conditions, the concentrations of the tested CFSs were found to be non-toxic. These results coincide with the research presented in the works of [PERSON] et al. (2017) and [PERSON] et al. (2018), affirming the non-toxic characteristics of this micro-organism in epithelial cells. According to these observations, the CFS of the strain _B. subtilis_ CNTA 517 could, therefore, be considered as an ingredient for industrial use in food.
# Frequency-Domain and Spatial-Domain MLMVN-Based Convolutional Neural Networks
###### Abstract  
This paper presents a detailed analysis of a convolutional neural network based on multi-valued neurons (CNNMVN) and a fully connected multilayer neural network based on multi-valued neurons (MLMVN), employed here as a convolutional neural network in the frequency domain. We begin by providing an overview of the fundamental concepts underlying CNNMVN, focusing on the organization of convolutional layers and the CNNMVN learning algorithm. The error backpropagation rule for this network is justified and presented in detail. Subsequently, we consider how MLMVN can be used as a convolutional neural network in the frequency domain. It is shown that each neuron in the first hidden layer of MLMVN may work as a frequency-domain convolutional kernel, utilizing the Convolution Theorem. Essentially, these neurons create Fourier transforms of the feature maps that would have resulted from the convolutions in the spatial domain performed in regular convolutional neural networks. Furthermore, we discuss optimization techniques for both networks and compare the resulting convolutions to explore which features they extract from images. Finally, we present experimental results showing that both approaches can achieve high accuracy in image recognition.  
convolution neural networks; CNNMVN; complex-valued neural networks; multi-valued neuron; MLMVN; image recognition; frequency domain +
Footnote †: journal:
# An Update of Carbazole Treatment Strategies for COVID-19 Infection
## 4 Carbazoles Treatment
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \multicolumn{2}{p{113.8 pt}}{**Compound**} & **Name** & **Reference** \\ \hline SARS-CoV-2 M-pro inhibitors & & \\ \hline Viral-entry inhibitors targeting human ACE2 & & Edotecarin & \\  & 6-((1,3-)Thydroxyproman-2-yl)amino)-2,10-dihydroxy-12- & & \\
8 & ((2,8,3,4,55,6,6)-3,4,5-trihydroxy-6-(hydroxymethyl))tetrahydro-2H-pyram-2-yl)-12,13-dihydro-5H-indol[2,3-4]pyrrolo[3,4-]carbazole-5,7(6H)-dione & \\ \hline
9 & & 7-HydroxStaurosporine (methylamino-5,6,7,8,9,16,16)-16-hydroxy-6-methoxy-5-methyl-7- (methylamino-5,6,7,8,9,15,16-hexah)hydro-17-oxa-4b,9a,15-triaza-5,9-methanolbenz[0,b],b]cyclonon[jk][cyclopentate]-as-indacen-14(5H)-one & [56] \\ \hline
10 & & CIMSSNa sodium 3-4-(((S)-5,(55-5,8,95)-8,95)-methoxy-9-methyl-16-oxo-6,7,8,9,15,16-hexah)hydro-5H,14H-b,9a,15-triaza-5,9-methanolbenz[0,b],b]cyclonon[jk][cyclopentate]-as-indacen-7-yl]-4-oxohexanamidomethyl-1H-1,2,3-triazol-1-yl)propane-1-sulfonate & [57] \\ \hline
11 & 11 & 6-Formylindolo(3,2-b)carbazole & [58] \\ \hline NPC1 inhibitor & & & \\ \hline
12 & 2-((2-(1-Benzylpiperidin-4-y)ylethyl)amino)-\(N\)-(9H-carbazol-9-yl)acetamide & [59] \\ \hline Antiviral against PLpro & & & \\ \hline
13 & 6-Cyano-5-methoxy-12-methylindolo [2, 3A] carbazole & [60] \\ \hline Immunotherapy treatment & & & \\ \hline
14 & Ramatroban 3-[(3-)-3-[(4-fluorophenyl)sulfonylamino)-1,2,3,4-tetrahydroocarbazol-9-yl]propanoic acid & [53,61,62] \\ \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)in coronavirus is PLpro (papain-like protease), which performs an essential action in the processing mechanism of viral polyproteins [73] and has fundamental action against human immunity through post-translational modifications on human proteins [60; 74]. Below, we report the drugs with a carbazole structure **(1-14)** that have been studied and selected to date, which can be inserted into the therapeutic equipment of the battle against COVID-19, or their scaffolds can be used as skeletons for the design of new antiviral compounds.
# Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning
## 3 Materials and Methods
### Problem Statement  
We consider a typical few-shot learning problem. Namely, we are given a _base_ dataset \(\mathbf{D}_{base}\) and a _novel_ dataset \(\mathbf{D}_{novel}\) such that \(\mathbf{D}_{base}\cap\mathbf{D}_{novel}=\emptyset\). \(\mathbf{D}_{base}\) contains a large number of labeled examples from \(K\) different classes and can be used to train a generic feature extractor. \(\mathbf{D}_{novel}\), also referred to as a task or episode in other works, contains a small number of labeled examples (support set \(\mathbf{S}\)), along with some unlabeled ones (query set \(\mathbf{Q}\)), all from \(n\)_new_ classes that are distinct from the \(K\) classes in \(\mathbf{D}_{base}\). Our goal is to predict the classes of unlabeled examples in the query set. The following parameters are of particular importance to define such a few-shot problem: the number of classes in the novel dataset \(n\) (called \(n\)-way), the number of labeled samples per class \(s\) (called \(s\)-shot) and the number of unlabeled samples per class \(q\). Therefore, the novel dataset contains a total of \(l+u\) samples, where \(l=ns\) are labeled, and \(u=nq\) are unlabeled. In the case of an inductive few-shot, the prediction is performed independently on each one of the query samples. In the case of a transductive few-shot [20; 38], the prediction is performed considering all unlabeled samples together. Contrary to our previous work [1], we do not consider knowing the proportion of samples in each class in the case of a transductive few-shot.