esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods
## 4 Applications Overview of Deep Learning in the Field of Self-Driving Cars
### Other Applications  
In addition to the applications mentioned above, there are many other applications of deep learning methods in self-driving cars, such as path planning, motion control, pedestrian detection, and traffic sign and light detection.  
Figure 17: The results of lane prediction based on the method presented in [67].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Reference** & **Method** & **Remarks** & **Performances** \\ \hline One-stage method & [PERSON] et al. (2018) [67] & CNN & The road scene features are extracted by the fine-tuned Deconvnet filters. Then scene features are used to estimate the road lanes based on a extra trees-based classification and regression framework. & Accuracy of 98.42\% on TTI dataset. \\ [PERSON] et al. (2017) [69] & CNN & The proposed framework combined CNN and a stacked extreme learning machine architecture, which can reduce computation time dramatically. & Accuracy of 98.7\% on Caltech-cordoval dataset. \\ [PERSON] et al. (2017) [71] & CNN and RNN & The proposed framework processes an input image as a sequence of the region of interests (ROIs), and applies two steps of feature extraction on each ROI by CNNs and RNNs. & AUC of 0.99 on Caltech dataset. \\ [PERSON] et al. (2018) [72] & Fast-RCNN & Multiple strategies, such as fast multi-level combination, context cues, and a new anchor generating method are employed for lane markings detection. & Precision of 83.5\% on their own dataset. \\ \hline Two-stage method & [PERSON] et al. (2018) [73] & CNN & The output of the proposed lane detection network is a lane instance map, which is based on the label of each lane pixel with a lane ID. Then the lane pixels are transformed using the transformation matrix, to finally get the lane. & Accuracy of 96.4\% on Tsingle level features are extracted based on the VGG16 encoder. Then these features are utilized for the semantic segmentation of the lanes, which can predict the high quality lane maps. & Accuracy of 93.8\% on Tsingle dataset. \\ [PERSON] et al. (2020) [75] & CNN and RNN & In the proposed method, information of each frame is abstracted by a CNN block. RNN & Accuracy of 97.3\% on Tsingle dataset. \\ [PERSON] et al. (2019) [76] & GAN & The proposed Generative adversarial networks (GANs), the source data, a prediction map and a ground truth label are input into the discriminator together for lane marking segmentation. & Accuracy of 96.39\% on Tsingle dataset. \\ \hline \hline \end{tabular}
\end{table}
Table 4: A summary of the lane detection methods based on deep learning.
## 3 Methodology  
### Clustering Analysis for Country Grouping  
Before combining the selected SPIs in an index, the 21 European countries considered in this study are first classified into several groups according to their background characteristics to make the following comparison more logical and meaningful. Moreover, those underperforming countries might be more motivated to improve themselves if being the 'best-in-class' is considered to be within reach [15].  
The subdivision of these countries into homogeneous groups is realized by applying a clustering analysis (CA). The main purpose is to divide observations in a dataset into subsets, where objects in the same subset are similar to each other with respect to a certain similarity measure [28]. Over the past decades, a large number of clustering methods such as hierarchical clustering, K-means clustering, spectral clustering, etc., have been developed and successfully applied in a wide range of fields, such as biology, geography, climate, psychology, medicine, and business (see e.g., [29; 30]).  
The clustering is normally based on the standardized data, where the raw data are first normalized by converting them into standard scores, as follows:  
\[z_{i}=\frac{x_{i}-\overline{x}}{sd} \tag{1}\]  
where \(\overline{x}\) denotes the sample mean, and \(sd\) denotes the sample standard deviation.  
Then, a distance measure should be selected and computed. A distance measure is an estimate of the degree of similarity between cases in the set. A small distance means a strong similarity. Some of the most commonly used distance measures include for example the Euclidean distances, the squared Euclidean distances, the Chebychev distances, and so on [31]. In this study, the squared Euclidean distance is chosen as a distance measure, because this measure is less affected by the addition of new objects such as outliers. It can be computed as follows:  
\[D(m,n)=\frac{\sum\limits_{i=1}^{N_{d}}{(m_{i}-n_{i})^{2}}}{N_{d}} \tag{2}\]  
where \(D(m,n)\) denotes the distance measures for individual indicators between two objects' (countries in this study) \(m\) and \(n\) over \(N_{d}\) dimensions.  
Bearing some basic ideas about the clustering analysis in mind, a certain clustering method can be applied for country grouping, e.g., the hierarchical clustering analysis, which can be employed directly in SPSS 26.0. Meanwhile, to test the validity of the clustering results, a hypothesis testing (for two groups) or ANOVA (for three or more groups) could be conducted.
## 2 Materials and Methods
### Western Blot Analysis  
After treatment, human SM cells were lysed in an SDS-PAGE sample buffer, and proteins were analyzed by Western blotting. Membranes were incubated overnight with antibodies against pan-actin, HO-1, p62, Pser349-p62 (P-p62), CS, CoxIV, sod1, catalase, Akt, Pser473-Akt (P-Akt) (all from Cell Signaling Technology, Ozyme, Saint-Quentin-en-Yvelines, France), glutathione reductase (GR) (AbFrontier, VWR, Rosny-sous-Bois, France), 4-HNE (Abcam, Paris, France) and then with species-directed secondary antibodies conjugated to IRDye800 or IRDye680 (LI-COR Biosciences, Bad Homburg, Germany). Membranes were analyzed with the Odyssey CLx Imaging System (LI-COR Biosciences, Bad Homburg, Germany). Protein expression was quantified using ImageJ software (Version 1.42q/Java1.6.0_14, National Institutes of Health, New York, NY, USA, [[http://rsbweb.nih.gov/ij/index.html](http://rsbweb.nih.gov/ij/index.html)]([http://rsbweb.nih.gov/ij/index.html](http://rsbweb.nih.gov/ij/index.html)), accessed on 31 January 2024). Protein quantification of each sample was corrected by the quantification of the corresponding pan-actin, analyzed on the same sample and same membrane. Data are expressed relative to the human SM cells mean level of protein expression in palmitate conditions without GPP extract treatment, which was set at 1.
# Amplitude and Phase Information Interaction for Speech Enhancement Method
## 1 Introduction  
In people's daily lives, speech signals are easily contaminated by external factors, such as background noise and reverberation. The purpose of speech enhancement is to attenuate noise and reverberation from noisy speech and to preserve clean speech. In recent years, deep learning-based speech enhancement has achieved significant progress. This method can be broadly categorized into two approaches: time domain method and time-frequency domain method. The former method directly processes the speech waveform in the time domain [1; 2; 3], while the latter method employs short-time Fourier transform (STFT) to convert the time-domain speech into a frequency spectrum represented in the time-frequency domain and processes the spectrum as input features [4; 5; 6]. The latter approach can enhance the noisy speech by predicting masks (e.g., IBM [7], IRM [8], CRM [9], and PSM [10]) required for speech enhancement. These masks come in different types and serve various purposes.  
FullSubNet is a time-frequency domain-based speech enhancement model [11] that takes the magnitude spectrum of the noisy speech as input features. Through a deep learning structure consisting of two parts, full-band and sub-band, it generates a mask to achieve speech enhancement. The model has achieved good results, but there are points for improvement in the approach it uses. Firstly, the original model does not utilize the phase spectrum of the noisy speech, which can result in deviations in the phase of the enhanced output compared to clean speech. Additionally, during the sub-band division, the width of each sub-band also affects the model's performance. Choosing an appropriate sub-band width hyperparameter allows the model to better capture the local frequency characteristics of speech. The Lookahead hyperparameter influences the model's consideration of future information. Selecting suitable values for these two hyperparameters will allow the model's capabilities to be more fully utilized. The proposed method in this paper addresses these two aspects and improves upon the original model.
## 2 Materials and Methods  
### Establishment of DaMDHAR-Expressing Yeast  
Total RNA was isolated from the leaves of _D. antarctica_ plants using the RNeasy plant mini kit (Qiagen, Hilden, Germany). cDNA was synthesized by reverse transcription-polymerase chain reaction (RT-PCR). The _DaMDHAR_ coding region was amplified from the cDNA by PCR using _Pfu_ polymerases (Roche, Basel, Switzerland). The reaction conditions were as follows: initial denaturation at 94 \({}^{\circ}\)C for 3 min, followed by 30 cycles of 94 \({}^{\circ}\)C for 30 s, 58 \({}^{\circ}\)C for 30 s, and 72 \({}^{\circ}\)C for 1.5 min, and a final extension at 72 \({}^{\circ}\)C for 7 min. The sense and antisense primer set used for PCR cloning of the _DaMDHAR_ gene are as follows: 5\({}^{\circ}\)-ATTCTAGAACTAGTTGATCCATGGCAGCAGAGAACACTTC-3\({}^{\prime}\) and 5\({}^{\prime}\)-TCGACGGTATCGATAAGCTTTCAGATTCGCGCGAAACAGG-3\({}^{\prime}\). The nucleotide sequences of the p426 GPD vector corresponding to endonucleases (_BamHI_ and _Hind_III) are underlined. The primer sequence involved in the _DaMDHAR_ gene is boldfaced. The purified PCR product was cloned into the yeast expression vector p426 GPD under the control of the _glyceroldehyde-3-phosphate dehydrogenase_ (_GPD_) promoter (Euroscarf, Frankfurt, Germany) using the sequence- and ligation-independent cloning (SLIC) method with T4 DNA polymerase [44]. After sequence confirmation, the vector construct of p426 GPD:DaMDHAR was transformed into _S. cerevisiae_ BY4741 cells using the PEG/LiCl method [45]. Transformants were selected by plating cells on an SD agar medium (0.67% yeast nitrogen base without amino acids and with ammonium sulfate, 0.192% yeast synthetic dropout medium lacking uracil, 2% glucose, and 1.5% agar) at 28 \({}^{\circ}\)C for 3 days. A single colony was streaked, cultured under the same conditions, and then used for subsequent experiments. Yeast strains used in this study were represented in Table S1.
## 2 Numerical Model  
### Submerged Floating Tunnel  
We considered SFT with 28 mooring lines as an example, as shown in Figure 1. Material properties and design parameters are presented in Table 1. The tunnel, 20 m in diameter and 800 m in length,is made of high-density concrete. BWR (buoyancy weight ratio) is set as 1.3. We assume that the tunnel has fixed stations at both ends and water depth is constant at 100 m. The submergence depth, which is a vertical distance between the MWL (mean water level) and the tunnel centerline, is set at 61.5 m. As can be seen in Figure 1, four 60-degree inclined mooring lines made of stuldess chains are installed with 100-m interval along the longitudinal length. The lengths of the mooring lines are 50.2 m for lines #1 and #2 and 38.7 m for lines #3 and #4. In addition, accelerometers are located along the tunnel's centerline, as shown in Figure 1c. We conduct a systematic sensitivity test with respect to the numbers and arrangements of accelerometers, as discussed in Section 4.3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Tunnel length & 800 & m \\ Tunnel diameter & 20 & m \\ Interval of mooring lines & 100 & m \\ End boundary condition & Fixed-fixed & - \\ Material of tunnel & High-density concrete & - \\ Length of mooring lines & 50.2 (line \#1,2), 38.7 (line \#3,4) & m \\ Added mass coefficient & 1.0 & - \\ Nominal diameter of mooring lines & 0.18 & m \\ Drag coefficient & 0.55 (tunnel), 2.4 (mooring lines) & - \\ Bending stiffness (_EI_) & \(1.34\times 10^{11}\) (tunnel), 0 (mooring lines) & kN-m\({}^{2}\) \\ Axial stiffness (_EA_) & \(3.23\times 10^{9}\) (tunnel), \(2.77\times 10^{6}\) (mooring lines) & kN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameter of the SFT (\(E\) is Young’s modulus, \(I\) is area moment of inertia, \(A\) is cross-sectional area).  
Figure 1: 2D and 3D schematic drawings (accelerometers are marked with red dots in (**a**); mooring line numbers are given in (**b**); sensor numbers (1–9) are given in (**c**)).
# Gaussian Process Modeling of Specular Multipath Components
## 5 Conclusions  
We demonstrate the applicability of Gaussian process regression (GPR) for the large-scale amplitude and phase modeling of specular multipath components (SMCs). UWB or mm-wave radio systems provide sufficient temporal and/or angular resolution to resolve such SMCs at the receiver side. The GPR model describes the amplitude and phase fluctuations of SMCs due to changing reflection characteristics of building materials and due to the angle-dependent gain of the TX and RX antennas. Our experimental results--performed with UWB channel measurements--show that the mean amplitude and phase information of SMCs can be predicted reliably, while some uncertainty results from the dense multipath component (DMC) that interferes with the SMCs. The variance predicted by the GPR model reflects the impact of this DMC.  
Figure 7: GP prediction of the SMC phases for (**a**) the SMC amplitude of VA at \(\mathbf{a}_{2}^{(1)}\) associated with anchor 1 (east plaster board wall), (**b**) the SMC amplitude of VA at \(\mathbf{a}_{2}^{(2)}\) associated with anchor 2 (east plaster board wall), (**c**) the SMC amplitude of VA at \(\mathbf{a}_{5}^{(1)}\) associated with anchor 1 (north glass window).  
Large-scale predictability of the radio channel is achieved through the combination of the GPR model (to describe the SMC amplitudes and phase shifts) and a geometric model of the SMCs to describe the arrival times and angles. That is, based on a set of training data and hyperparameters of the models learned from it, these core features of the channel impulse response can be predicted at any point in the surveyed environment. We obtain a new level of environment-awareness, useful to predict performance characteristics of wireless networks over large spatial scales.  
Conceptualization, K.W. and E.L.; Formal analysis, A.H.N., E.L. and M.R.; Investigation, A.H.N., M.R. and E.L.; Software, Validation, Visualization, A.H.N. and M.R.; Supervision, K.V.N. and K.W.; Writing--original draft, A.H.N.; Writing--review & editing, M.R., E.L. and K.W. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the financial support from the OEAD (Autrian Exchange Service) (for funding [PERSON] to conduct research at Graz University of Technology), from Graz University of Technology (for funding [PERSON] within the Research Program \"Dependable Things\"), and from the Austrian Science Fund (FWF) (for funding [PERSON] under the Schroedinger Fellowship program, grant number J4027).  
The authors declare no conflict of interest.
# Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm
## 4 Proposed Method
### Hybrid PSOFF Algorithm  
A population-based refutable algorithm, developed by [PERSON] and [PERSON], is Particle Swarm Optimization [31]. This algorithm is developed based on the inspiration of the blocking behavior of birds or the schooling behavior of fish. This efficient algorithm hybridized with Firefly algorithm is used here for the stability enhancement of the ANC system that has been designed based on the MFsLMS algorithm. In PSO, each particle is moved with a particular velocity and position. The velocity and position of each particle are ameliorated according to the objective function, and sometimes this change depends on the behavior of neighboring particles.  
Figure 2: Active noise control with modified FsLMS.  
The parameters must be initialized are number of particles = 4; dimension of the problem = 2; upper bound = 2; lower bound = 0; \(x_{1}=0.7\); \(x_{2}=0.7\); \(y_{1}=0.5\); and \(y_{2}=0.5\).  
The velocity of the particle \(D_{n}\) is defined by  
\[D_{n}=D_{0}+(x_{1}*y_{1}*best1\cdot Z_{ii})+(x_{2}*y_{2}*best2\cdot Z_{ii}). \tag{6}\]  
In Equation (6), \(D_{0}\) is the old velocity; \(best1\) and \(best2\) are the local and global best, respectively. \(Z_{ii}\) is the position of a particle. The variables \(x_{1}\) and \(x_{2}\) are positive constants; which holds some value depending on convenience. Likewise, \(y_{1}\) and \(y_{2}\) are random variables in the range (0, 1).  
One of the fastest optimization techniques is Firefly (FF) algorithm [32]. It was developed by [PERSON] [33], who developed this algorithm by taking flashing behavior of fireflies as inspiration. The PSO algorithm is hybridized with the FF algorithm to enhance the stability of the ANC system. The hybridizing of these two algorithms is achieved through the position updating of the particles via the movement of fireflies.  
The target function (Fitness Function) of the algorithm is  
\[Fitness=\frac{1}{[5*(2N+1)*P_{r}]} \tag{7}\]  
where \(N\) is the number of error microphones (\(N\) = 4) and \(P_{r}\) is the probability of the error noise (0.2).  
The movement of fireflies or position of particles will be decided by  
\[Z_{ii}=Z_{ii-1}+D_{new}e^{-\gamma r^{2}}+\alpha\varepsilon \tag{8}\]  
where \(\gamma\) is the light absorption coefficient (holds the value 1); \(r\) is the relative distance of fireflies; \(\alpha\) is the randomization parameter (\(\alpha\) = 0.2); \(\varepsilon\) is a random number between 0 and 1 (\(\varepsilon\) = 0.1).
## 5 Performance Evaluation
### Simulation Setup
#### 5.1.4 Estimation Model
As SVR cannot, in practice, handle the input mentioned in Section 3.3, we provide SVR with \((d_{t},\;sunny,\;cloudy,\;rainy)\) as input, in which the five-dimensional distance information \((lat_{t},\;lan_{t},\;x_{t},\;y_{t},\;z_{t})\) is processed as distance \(d_{t}\). The disadvantage of this is the loss of the ability to identify different locations around the globe. \begin{table}
\begin{tabular}{l l} \hline \hline
**Name** & **Value** \\ \hline Neurons in Weather Embedding Layer & \(n_{W}\) \\ Neurons in Distance Embedding Layer & \(n_{D}\) \\ Consider Past Moment Number & \(n\) \\ Estimation Model Learning Rate \(\alpha_{E}\) & \(0.01\) \\ Estimation Model Training Epochs \(E_{max}\) & \(400\) \\ Estimation Model Batch Size \(b_{E}\) & \(128\) \\ Estimation Model Loss Criterion & MAE \\ Estimation Model Training Data Set & \(70\%\) \\ Estimation Model Validation Data Set & \(20\%\) \\ Estimation Model Test Data Set & \(10\%\) \\ \hline Decision Model Training Data Set & \(80\%\) \\ Decision Model Test Data Set & \(20\%\) \\ Decision Model Memory Size \(||\mathbf{M}||\) & \(8192\) \\ Decision Model Batch Size \(b_{D}\) & \(2048\) \\ Decision Model Repeat Time \(N\) & \(40\) \\ Decision Model Maximum Step \(S_{max}\) & \(60\) k \\ Decision Model Learning Rate \(\alpha_{D}\) & \(0.001\) \\ Decision Model Forget Factor \(\gamma\) & \(0.3,0.5,0.7,0.9\) \\ Baseline (BER) Method Margin & \(10^{-5}\) \\ Baseline (PER) Method Margin & \(0.1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters in estimation model and decision model.
# Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems
## 1 Introduction
### Notations  
Boldface capital letters denote matrices and the boldface lowercase letters denote column vectors. The function \(\mathit{diag}(\mathbf{a})\) correspond to a diagonal matrix \(\mathbf{A}\) with diagonal entries equal to vector \(\mathbf{a}\), and \(\mathit{diag}(\mathbf{A})\) gives a diagonal matrix with entries equal to the diagonal entries of the matrix \(\mathbf{A}\). The operations \((.)^{*}\), \((.)^{T}\), and \((.)^{H}\) represent the conjugate, transpose, and Hermitian transpose of a matrix, respectively. \(\{\alpha|_{l}^{L}\}_{l=1}^{L}\) denotes an \(L\) length sequence. The vector \(\mathbf{a}=\left[\mathbf{a}_{q}\right]_{1\leq q\leq Q_{1}}\in\mathbb{C}^{Q_{1} Q_{2}}\) and the matrix \(\mathbf{A}=\left[\mathbf{A}_{q}\right]_{1\leq q\leq Q_{1}}\in\mathbb{C}^{Q_{1} Q_{2}\times L}\) are the concatenation of vector \(\mathbf{a}_{q}\in\mathbb{C}^{Q_{2}}\) and matrix \(\mathbf{A}_{q}\in\mathbb{C}^{Q_{2}\times L}\), respectively. \(\mathbf{A}(i,j)\) denotes the element at row \(i\) and column \(j\) of the matrix \(\mathbf{A}\). The matrix \(\mathbf{I}_{M}\) and \(\mathbf{Z}_{M}=M^{-1/2}\exp(-j2\pi ij/M)\), \(i,j=0,\ldots,M-1\), are the identity matrix and the discrete Fourier transform (DFT) matrix, with size \(M\times M\). Finally, the indices \(k\), \(m\), and \(u\) refer to the subcarrier, time slot, and UTs, respectively.
## 5 Numerical Evidence and Noise Model
### Noise Analysis  
#### 5.2.1 Noise on the Raw Data  
The following example demonstrates the change of behavior of the probability distribution of raw data units and the CDF-transformed units, and serves only demonstration purposes.  
For an illustrative example, let \(N=1000\) units, the amount of sample raw data units \(X_{j,i}\), \(X^{\prime}_{j,i}\) (the units are resulted from random quadrature measurements) taken from [PERSON]'s and [PERSON]'s raw data, respectively. The Gaussian random units \(X_{j,i}\) are characterized with zero mean and variance \(\sigma_{X}^{2}=100\).  
In Figure 9a, the distribution of the \(X_{j,i}\) Gaussian random raw data units is shown. Figure 9b depicts the result of the \(C(\cdot)\) Gaussian CDF function applied on \(X_{j,i}\). The Gaussian random behavior is eliminated and is changed into uniform.  
The distribution of the Gaussian noise vector \(\Delta_{j,i}\in\mathbb{N}\Big{(}0,\sigma_{X_{2}}^{2}\Big{)}\) of the quantum channel \(\mathcal{N}_{2}\), at \(\sigma_{X_{2}}^{2}=4\) is shown in Figure 10.  
Figure 9: (**a**) The distribution of [PERSON]’s raw data units. The units follow Gaussian random distribution; (**b**) The distribution of the Cumulative Distribution Function (CDF)-transformed units. The probability distribution has changed into uniform in the range of \([0,1]\).  
At [PERSON]'s side, the received noisy units \(X^{\prime}_{j,i}\) and the CDF-transformed \(C\big{(}X^{\prime}_{j,i}\big{)}\) units have a modified distribution with variance \(\sigma_{X^{\prime}}^{2}=\sigma_{X}^{2}+\sigma_{X_{2}}^{2}=104\), as depicted in Figure 11. The Gaussian noise on the units is added by \(\Delta_{j,i}\in\mathbb{N}\Big{(}0,\sigma_{N_{2}}^{2}\big{)}\).  
This example showed that the uniform distribution of the Gaussian random raw data could be achieved by trivial operations, without any multidimensional calculations or coding.