esa-sceva/satcom-chunk-collection huggingface dataset:
# DOA Estimation of GNSS Signals Based on Deconvolved Conventional Beamforming
## 5 Conclusions  
DOA estimation is essential for GNSS channel modeling, assessing multipath effects, and mitigating multipath errors. In order to achieve high-precision blind estimation of multiple signal components with low computational complexity, this study introduces a super-directive beamforming-based DOA estimation method. Firstly, the correlation value vector is generated by multiplying the array signal with a locally replicated signal. Secondly, the array response pattern is formulated within the correlation domain, which is tantamount to the convolution of the fundamental function with the impulse response function of signal incident directions. Thirdly, leveraging the principles of deconvolution, precise estimation of DOA parameters is accomplished using the Richardson-Lucy algorithm. This paper further substantiates the proposed method's performance in scenarios involving both single and multiple signal sources. When there is only one signal source and the \(C/N_{0}\) ratio is greater than \(40\) dBHz, the proposed method can achieve similar performance to OMP. When there are multiple targets and the number of array elements is small, such as when \(N=8\), the new method significantly outperforms the OMP algorithm. In both cases, the proposed method significantly outperforms the MUSIC and Capon methods. The findings indicate that, compared to traditional approaches, our method delivers superior estimation accuracy under low signal-to-noise ratios and with a limited number of array elements, without the need for prior knowledge of the signal source number. The experimental results validate the rationality and effectiveness of the proposed method, making it suitable for integration into compact, low-power GNSS array antenna systems for precise real-time DOA parameter estimation. Additionally, it has the potential for wider application in DOA estimation within the realms of communication, radar, and other related fields.  
Conceptualization, J.W., F.W., and X.T.; methodology, [PERSON], X.T., and C.L.; software, [PERSON], H.L., and C.L.; validation, [PERSON] and X.T.; formal analysis, J.W. and H.L.; investigation, J.W.; resources, [PERSON]; data curation, J.W.; writing--original draft preparation, [PERSON]; writing--review  
Figure 10: Estimation statistic results under different \(C/N_{0}\) ratios: (**a**) \(N=8\); (**b**) \(N=12\).  
and editing, C.L., H.L., and X.T.; visualization, [PERSON]; supervision, [PERSON]; project administration, X.T.; funding acquisition, F.W. and J.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number U20A0193, and the National Natural Science Foundation of China, grant number 62303482. Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest. The authors declare no conflicts of interest. References
# Deep Representation of EEG Signals Using Spatio-Spectral Feature Images
###### Abstract
Specifically, in [26], spectral and spatial information of EEG signals were combined in a three-channel RGB image and used for training a DNN, and deep representations were analyzed. Finally, some studies have used transfer learning approaches based on deep representations for domain adaptation [32; 33]. Despite these efforts, deep representations of EEG signals remain poorly understood due to the lack of interpretability. Improving our understanding of EEG deep representations would be useful not only for explaining the decision process of a trained model, but also for effectively realizing transfer learning and in general gaining insight into the mechanisms of the brain. In this paper, we propose an approach for analyzing deep representations of EEG signals. We base our approach on the common assumption that different frequency bands in EEG signals and brain areas are associated with different brain activities. For each deep representation in a DNN, our approach produces a spatio-spectral feature image (SSFI) which is visualized as a topographic map and identifies the brain areas and different frequency bands associated with the target deep representation. We evaluate our approach on DNN models trained on EEG signals from the Physiology of Auditory Attention (PhyAAt) dataset [34] and in addition carry out an inter-subject dependency (ISD) analysis to explore generalized representations across subjects [35; 36]. This paper is organized as follows. Section 2 introduces the PhyAAt dataset, which we use to evaluate our proposed approach. In Section 3, we describe the methods used in this paper, which include generation of SSFIs from multi-channel EEG signals, CNN architecture definition and training, deep representation analysis, and ISD analysis. Section 4 presents the results from our analysis, and finally, Section 5 includes the conclusions and a discussion.
## 2 Theoretical Analysis
### Watermark Embedding and Extraction Based on Cellular Automata Transform (CAT)  
#### 2.2.1 Cellular Automata  
Cellular automata is a dynamic model with discrete time, space and state. It is composed of cell units arranged according to certain rules. The state of each unit is synchronized with the cell state of the previous time and kept updated. The current moment is related to the evolution of cellular automata. The one-dimensional formula of CAT and the two-dimensional formula of CAT are defined as follows:  
\[f=\sum_{k}{c_{k}A_{ik_{i}}} \tag{9}\]  
\[\sum_{k=0}^{N-1}{\sum_{k=0}^{N-1}{c_{k}A_{ijkl}}} \tag{10}\]  
Among _i,j,k,l_ = 0,1,2,..., \(N-1\), \(f_{i}\) and \(f_{ij}\) represent one-dimensional and two-dimensional data sequences, respectively, \(A_{ik}\) and \(A_{ijkl}\) represent basis functions (also known as a transformation bases), \(c_{k}\) and \(c_{kl}\) represent the transformation coefficients. The above formula takes the basis function as the transfer function and the data sequence from the physical domain to map transformation coefficients in the CA domain. \(A_{ik}\) is called one dimension generated by one-dimensional CA; the basis function (_i,k_ = 0,1,..., \(N-1\)) of one-dimensional CA with N cells is a function of cell state (_i,t_ = 0,1,2,..., \(N-1\)), there are an infinite number of such function representations. Life lists the sub categories \(\beta\) Class \(\rho\) Seven orthogonal wiki functions of class and class R (type1-type7), the orthogonal basis can find the transformation system (_i,j,k,l_ = 0,1,..., \(N-1\)) in a recursive way, which is called bivariate, it can be generated by the evolution of a two-dimensional CA containing N \(\ast\) N cells.  
It can be seen that generating the basis function with CA is the prerequisite for cat. The construction of CA is realized by setting the parameters of the dynamic system according to its dynamic model. The typical system parameters of CA are different, the constructed CA is different, the basis functions generated by different CA are different and the CAT transformation coefficients obtained by different basis functions are different. Using the base key can generate a second wiki as shown in Figure 3.  
Life's research shows that CAT can produce thousands of orthogonal bases, semi orthogonal bases, biorthogonal bases and non-orthogonal bases with different properties, some of which have similar properties to the bases of known transforms (such as Walsh transform, wavelet transform, etc.). Some bases reflect the self-organization of data sets or functions, which are the rich and complex transformation properties of CA and cat. The main function of the transformation of cellular automata is to obtain a large number of basis functions with different properties.  
The nature of the self-generating function shown by some basis functions is unmatched by other transformations.
###### Abstract  
Slope deformation prediction is one of the critical factors in the early warning of slope failure. Establishing an accurate slope deformation prediction model is important. Time-series displacement data of slopes directly reflect the deformation characteristics and stability properties of slopes. The use of existing data analysis approaches, such as statistical methods and machine learning algorithms, to establish a reasonable and accurate prediction model based on the monitored time-series displacement data is a common solution to slope deformation prediction. In this paper, we conduct a comparative investigation of machine learning approaches for slope deformation prediction based on monitored time-series displacement data. First, we established eleven slope deformation prediction models based on the time-series displacement data obtained from seven in situ monitoring points of the Huanglianshu landslide using machine learning approaches. Second, four evaluation metrics were used to comparatively analyze the prediction performance of all models at each monitoring point. The experimental results of the Huanglianshu landslide indicated that the long-short-term memory (LSTM) model with an attention mechanism and the transformer model achieved the highest prediction accuracy. The comparative analysis of model characteristics suggested that the Transformer model is better adapted to predict nonlinear landslide displacements that are affected by multiple factors. The drawn conclusion could help select a suitable slope deformation model for early landslide warnings.  
slope deformation; time-series data; machine learning; long-short-term memory (LSTM); transformer 2023
## 3 Nicotinamide Riboside as a Tool to Mitigate Metabolic Disorders
### Nicotinamide Riboside and Diabetes  
NRS can be used to mitigate type 2 diabetes (T2D) and neuropathy in HFD-fed prediabetic and diabetic male C57 BL/6] mice. NR treatment significantly restored liver NADP\({}^{+}\) and NADPH amounts and reduced weight gain, blood glucose and HS [166]. NAD\({}^{+}\) precursors (NMN, NR) improved MT function in diabetes and prevented experimental diabetic peripheral neuropathy (DPN). The experiments were carried out in streptozotocin (STZ)-induced diabetic rats or mice. NMN was administered for two months (50 or 100 mg/kg i.p. injection on alternate days). HFD-fed mice were supplemented by NR at 150 or 300 mg/kg for two months. NR treatment in HFD-fed mice led to the normalization of adult dorsal root ganglion (DRG) neurons' functionality [167]. From the point of view of cellular and molecular mechanisms, a recent study in NRK1 KO mice showed that NRK1 protected against HFD and age-induced pancreatic \(\beta\)-cell failure [168]. Also, NRS promoted mitofusin 2 (MFN2)-mediated MT fusion in diabetic hearts through the SIRT1-PGC-1\(\alpha\)-peroxisome proliferatorator-activated receptor alpha (PPAR\(\alpha\)) pathway [169].  
The effects appear to be broad. Supplementation with NR reduces brain inflammation and improves cognitive function in diabetic mice. NR treatment for six weeks of HFD-fed diabetic Institute of Cancer Research (ICR) male mice significantly reduced amyloidosis (amyloid beta (\(\Delta\beta\)) precursor protein, presenilin 1) and neuroinflammation (IL-1, IL-6, and TNF-\(\alpha\) markers) [10]. In STZ-induced diabetic rats, i.p. administered NR every 48 h for six weeks improved enteric neuropathy through myenteric plexus neuroprotection [170].  
Other benefits of NRS in experimental murine diabetes refer mainly to the (i) alleviation of hepatic metaflammation by modulating NLRP3 inflammasome in a rodent (8-week-old KK/HIJ) male mice) model of T2D [171], (ii) enhancement of endothelial progenitor cell (EPC) function to promote refractory healing of diabetic wounds through mediating the SIRT1-AMP-activated protein kinase (AMPK) pathway [172], and (iii) improvement in fetal growth under hypoglycemia previously induced in pregnant mice [173].
## 5 Discussion of the Results
### Federations While Releasing the Balloons
Although those communications difficulties, the federations enable downloading data from Balloon A, being able to properly empty its buffer. Figure 18 presents the buffer usage of both balloons during this scenario. Note how Balloon A empties its buffer using the different federations, until the communication is no longer feasible with Balloon B. This communication loss is mainly caused by the high elevation angle between them. In this case, the achieved throughput changes between the federation and downlink communications to the previous scenarios. In particular, the downlink remains during all the time with a PER of 0.0%, which enables achievement of a throughput of 621.2 bps, i.e., 34.4% of the maximum throughput. Meanwhile, the PER of federation communications is 38.5%, larger than in the other cases. Therefore, its throughput also is reduced to 96.2 bps, which corresponds to 49.2% of the maximum throughput (Equation (6)). Figure 16: Buffer usage of Balloon A (blue) and Balloon B (red) in data packet unit while balloons are released.
# Video Error-Resilience Research Based on Error-Resilient Screen Content
###### Abstract  
In order to make video transmission more stable, various error-resilient mechanisms are proposed on video coding in the literature. However, the redundancy mechanism behind classical redundant coding algorithms is relatively simple and is not suitable for the network environment and video content in the context of screen content sequence with multiple abrupt frames and still frames. Motivated by this, a frame-level coding selection mechanism is proposed in this paper for the error-resilience transmission of screen content, where additional code stream or redundant information is considered to improve error-resilient performance with redundant coding and acceptable video quality is obtained in the case of frame transmission error. In addition, selective allocation redundancy is conducted to take the importance of the video frame ROI (region of interest) area into account in the co-encoding process. As a result, the redundancy insertion efficiency and the reliability are improved in return. The corresponding experiments validate the effectiveness of the schemes proposed in this paper.  
screen content coding; error-resilience; redundant coding; HEVC +
Footnote †: journal: Applied  
Article  
Video Error-Resilience Research Based on Error-Resilient Screen Content
## 3 Results and Discussion
### Effects of Operational Parameters Photo-Fenton like Catalyst
#### 3.3.4 Effect of Initial Dye Concentration and Kinetics of Degradation of RR198  
The effect of different initial RR198 concentrations between 5 and 100 mg/L were investigated on RR198 and COD removal efficiency.  
The highest RR198 removal efficiency and COD removal efficiencies were 98.9% and 97.28% respectively, as shown in Figure 8; increased concentration of RR198 would decrease the probability of reaction between free radicals and dye molecules.  
Additionally, the intermediates which were produced during the reaction may cause the generation of the secondary radicals, thus consuming SO\({}_{4}\)\({}^{-\bullet}\). This may have an effect on the competition between dye molecules and its byproducts in reacting with reactive radicals [47]. Therefore, the removal of dye declined at high dye concentrations in the process of the PF like- AC/CFO; kinetics of degradation is necessary for the optimization of conditions. All the experiments were performed in different systems at temperatures of 10 to 35 \({}^{\circ}\)C (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dye** & **Kinetic Model** & **Dye** & **Kinetic Model** & **Dye** & **Kinetic Model** \\ \hline Intra-particle diffusion & & pseudo-second order & pseudo-first order \\ \hline \(\mathrm{q_{t}=k_{t}t^{\frac{1}{2}}+C_{i}}\) & \(\frac{1}{\mathrm{q_{t}}}=\frac{1}{k_{\mathrm{Eq}}t^{\frac{1}{2}}}+\frac{1}{ \mathrm{q_{t}}}\) & \(\mathrm{q_{t}=q_{e}\left(1-e^{-k_{t}t}\right)}\) \\ \hline
0.5 & \(\mathrm{k_{i}}\) & 24.2 & \(\mathrm{q_{e,\,\mathrm{Cal}}\,(mg/g)}\) & 22 & \(\mathrm{q_{e,\,\mathrm{Cal}}\,(mg/g)}\) \\ \hline
16.7 & \(\mathrm{C_{i}}\) & 0.009 & \(\mathrm{k_{1}(min^{-1})}\) & 0.07 & \(\mathrm{k_{1}(min^{-1})}\) \\ \hline
0.6 & \(\mathrm{R^{2}}\) & 0.99 & \(\mathrm{R^{2}}\) & 0.98 & \(\mathrm{R^{2}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kinetic models for RR198 adsorption on activated Carbon/CoFe\({}_{2}\)O\({}_{4}\) (AC/CFO).  
Figure 8: Effect of initial dye on RR198 and COD removal efficiencies, optimum conditions: [PS = 5 mM, catalyst dosage = 0.3 g/L, reaction time: 25 min, and pH= 6.5].  
The results show a straight line In (C\({}_{0}\)/C\({}_{\text{t}}\)) when plotted against time confirming the assumed pseudo-second-order kinetic; enhancement of the catalyst activity and oxidation reactions occur with the increase of temperature, and consequently, higher removal efficiency of RR198.
# Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips
## 4 User Experience Model
### Mode Transfer Cost  
When a user changes transportation mode, he incurs different costs. For instance, when changing to a private hired vehicle, companies usually practice a hiring fee that increases the monetary cost of the trip. Furthermore, some transportation modes require a waiting time, until a vehicle arrives to take the user, which increases the total duration of the trip. Hiring fees and waiting time for private hired vehicles and busses are both assessed in our experiments; they are obtained from real data through different APIs, e.g., Uber and Google Directions. Besides these costs, we use a mode transfer penalty to model the direct costs in terms of experience that the user incur by having to change the transportation mode. We introduce an exponential depreciation operator in Equation (8) to account for mode transfer penalty cost in experience for the user. We count the number \(n_{r}\) of changes in the route and use it to compute the total impact in a given route option as:  
\[\lambda_{r}=\begin{cases}0,\text{ if }n_{r}=0\\ \text{e}^{an_{r}}/100,\text{ otherwise}\end{cases}. \tag{8}\]  
where \(\lambda_{r}\) is the impact due to the number of mode changes. In our study, we did not obtain supporting data to evaluate \(a\), this parameter could vary according to user preferences. Yet, \(a=1\) results in a reasonable impact considering traditional urban trips standards. Table 1 shows the impact according to the number of mode changes for \(a=1\).  
Using Equation (8), the final generalized time of a route in Equation (7) changes to Equation (9).  
\[T(r)=(1+\lambda_{r})\sum_{\forall s\in S_{r}}T(s). \tag{9}\]
# Modeling, Simulation, and Cruise Characteristics of Wingtip-Jointed Composite Aircraft
## 2 Dynamic Modeling of Wingtip-Jointed Composite Aircraft
### Aerodynamic Database Establishment  
The relative rolling movement of wings and fuselages produces an unsteady aerodynamic force in the aircraft system, especially lateral and internal forces. In general, the unsteady aerodynamic influence on the drag force, lift force, and pitch moment of a conventional rigid-body aircraft can be ignored. In this study, the aerodynamic drag force, lift force, and pitch moment were obtained under the quasi-steady assumption [12], while the unsteady aerodynamic effect was not considered; the drag force, lift force, and pitch moment of the wingtip-jointed composite aircraft under a certain configuration were nearly equal to those of the corresponding static configuration. The aerodynamic force and moment of the wingtip-jointed composite aircraft system can be expressed as:  
\[\left\{\begin{array}{l}D=qS(C_{D_{0}}+C_{D\alpha}\alpha+C_{DV}\cdot\Delta V/V+C _{D\varphi_{12}}\varphi_{12}+C_{D\delta}\cdot\delta\\ L=qS(C_{L\delta}+C_{L\alpha}\alpha+C_{LV}\cdot\Delta V/V+C_{L\delta}q/(2V/ \bar{c})+C_{L\dot{\alpha}}\dot{\alpha}/(2V/\bar{c})+C_{L\varphi_{12}}\varphi_{1 2}+C_{L\dot{\delta}}\cdot\delta\\ C=qSb\big{(}C_{\xi D}\beta+C_{CP}p/2V+C_{Cr}r/2V+C_{Cp_{12}}p_{12}+C_{Cp_{12}} \varphi_{12}+C_{C\delta}\cdot\delta\big{)}\\ l=qSb\big{(}C_{l\beta}\beta+C_{lp}pb/2V+C_{lr}rb/2V+C_{l\varphi_{12}}\varphi_{1 2}+C_{lp_{12}}p_{12}b/2V+C_{l\delta}\cdot\delta\big{)}\\ m=qS\bar{c}(C_{m0}+C_{ma}\alpha+C_{mV}\cdot\Delta V/V+C_{mq}q/(2V/\bar{c})+C_{ m\dot{\alpha}}\dot{\alpha}/(2V/\bar{c})+C_{mp_{12}}\varphi_{12}+C_{m\dot{\beta}} \cdot\delta)\\ n=qSb\big{(}C_{n\beta}\beta+C_{mp}pb/2V+C_{mr}rb/2V+C_{mp_{12}}p_{12}+C_{mp_{1 2}}\varphi_{12}+C_{n\dot{\delta}}\cdot\delta\big{)}\\ l_{1}=-l_{2}=qSb\big{(}C_{l1_{0}}+C_{l\beta}\beta+C_{l1_{p12}}\varphi_{12}+C_{ l1_{p12}}p_{12}b/2V+C_{l\delta}\cdot\delta\big{)}\end{array}\right., \tag{19}\]  
where \(\delta\) is a six-dimensional array consisting of the control surface deflection, which can be expressed as \(\delta=(\delta_{a_{1}},\delta_{a_{2}},\delta_{e_{1}},\delta_{e_{2}},\delta_{r _{1}},\delta_{r_{2}})\).  
Establishment of the aerodynamic database included the acquisition of aerodynamic derivatives of relative roll motion, aerodynamic derivatives of composite control surface deflection, and general aerodynamic parameters. In Figure 6, the field of calculus is a cylindrical flow field with 10 times forward fuselage length, 20 times body length backward, and 10 times span diameter. The Y axis points back along the axis of the fuselage. The unstructured grid of the wingtip-jointed aircraft system is shown in Figure 7, while detailed symbols and the corresponding data acquisition methods are listed in Table 2.  
Figure 6: Wingtip-jointed aircraft system unstructured grid.
## 3 Synthetic Data Test
### Low-Conductivity Model  
In this part, we design a low-conductivity model, with the model information listed in Table 2. C-response data are generated at 27 logarithmically spaced periods from 1 day to 114 days by adding Gaussian distributed errors with 5% standard deviation.  
Similar to the first case, we finally retain 90,000 samples for later inference. The inversion results are shown in Figure 4. As indicated in Figure 4, all the interfaces are well resolved by the data, with sharp peaks centered at the true interface depths. Again, the deeper interface manifests larger uncertainty than that of the shallower one. In general, the three-layer model is well recovered. However, the uncertainty of conductivity for the second layer is significantly larger than that of the rest of the layers. This physically makes sense, since the geomagnetic fields are insensitive to lower-conductivity structures. Due to the high conductivity, the deepest layer has been slightly better constrained than the first layer, indicating our algorithm can recover a physically meaningful model with information contained in the data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **1** & **2** & **3** \\ \hline \(\sigma\) (S/m) & 0.5 & 0.01 & 1 \\ \(z\) (km) & 300 & 400 & 2090 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Conductivity and interface depth information for the low-conductivity model.  
Figure 3: Sampling results for nonphysical parameters at \(T=1\): (**a**) the sampling history of misfit, (**b**) 1D marginal density of misfit, and (**c**) 1D marginal distribution for the interface number.  
Figure 5a,b shows the sampling history and 1D marginal density of the sampled Markov chain at \(T=1\) for data misfit, indicating good mixing of the sampled Markov chain and narrow constraint on the data misfit. Figure 5c shows 1D marginal density at \(T=1\) for the interface number. As in the previous case, the data information can constrain the interface number in a relative narrow range peaking at the true interface number.