esa-sceva/satcom-chunk-collection huggingface dataset:
# Decoding Visual Motions from EEG Using Attention-Based RNN
###### Abstract  
The main objective of this paper is to use deep neural networks to decode the electroencephalography (EEG) signals evoked when individuals perceive four types of motion stimuli (contraction, expansion, rotation, and translation). Methods for single-trial and multi-trial EEG classification are both investigated in this study. Attention mechanisms and a variant of recurrent neural networks (RNNs) are incorporated as the decoding model. Attention mechanisms emphasize task-related responses and reduce redundant information of EEG, whereas RNN learns feature representations for classification from the processed EEG data. To promote generalization of the decoding model, a novel online data augmentation method that randomly averages EEG sequences to generate artificial signals is proposed for single-trial EEG. For our dataset, the data augmentation method improves the accuracy of our model (based on RNN) and two benchmark models (based on convolutional neural networks) by 5.60%, 3.92%, and 3.02%, respectively. The attention-based RNN reaches mean accuracies of 67.18% for single-trial EEG decoding with data augmentation. When performing multi-trial EEG classification, the amount of training data decreases linearly after averaging, which may result in poor generalization. To address this deficiency, we devised three schemes to randomly combine data for network training. Accordingly, the results indicate that the proposed strategies effectively prevent overfitting and improve the correct classification rate compared with averaging EEG fixedly (by up to 19.20%). The highest accuracy of the three strategies for multi-trial EEG classification achieves 82.92%. The decoding performance for the methods proposed in this work indicates they have application potential in the brain-computer interface (BCI) system based on visual motion perception.  
Keywords:electroencephalography; attention mechanisms; recurrent neural networks; data augmentation; brain-computer interface; visual motion perception +
Footnote †: journal: Applied Sciences
## 5 Conclusions
Particularly, since the elevation angle \(\theta\) determines the signal receiving for antennas, the finding of the paper is also helpful for the GNSS-R receiver construction and impact analysis. This finding also increases the understanding of our knowledge to the input variables and exploring the scope of the machine learning applied in GNSS-R.  
Further studies will be conducted to monitor a region for a long period of time, to take seasonal effect into account, and to evaluate the sensitivity of the different observables to SM on a regional scale. Besides, more types of terrains could be added, and the areas of the experiments should be expanded. The findings of the paper show the importance of the SNR, and further analysis of in-situ data may provide more complete insight into how the received SNR can be used to retrieve SM [38]. [PERSON] proposed the original idea, performed the experiments and organized the paper. [PERSON] provided suggestions to improve the whole framework and revised the manuscript. [PERSON] organized the ground-based measurements and revised the manuscript. [PERSON] and [PERSON] performed the XGboost algorithm. [PERSON] and [PERSON] helped for the improvement of the XGboost algorithm. This work was supported in part by the Natural Science Foundation of Jiangsu Province under Grant BK20180765, BK20170897, in part by the Nanjing Technology Innovation Foundation for Selected Oversea Scientists under Grant RK032 YZZ18003. Research on Advanced Land Surface Detection System using GNSS-R', in part by the Scientific Research Fund of Nanjing University of Posts and Telecommunications (NUPTSF) under Grant 217152, 219066, by the National Natural Science Foundation of China under Grant 41401480. The authors would thank the remote sensing group of Politecnico di Torino and [PERSON] for the ground-based experiments, also thanks [PERSON] who gives the original inspiration to do this work. The authors declare no conflict of interest.
## 3 THz Wave Modulation and Devices Based on ATR
### Amplitude Modulation  
Most amplitude modulation devices in the THz region are based on graphene. This is because graphene, which has ultra-wide spectral response from visible to the THz frequency range, shows remarkable properties both in optics and electrics. It has high electrical current density, tunable Fermi energy level through optics, electrics, or chemical methods, thus making it a tunable material. The conductivity of graphene could be adjusted by the applied electric field, which would affect the interaction between graphene and the THz wave. Besides, the ultra-sensitivity to the conductive interface changes from ATR geometry could further amplify these interactions. Thus, making the devices combing graphene and ATR geometry tunable with wider modulation depth, even in the wider range. Significant enhancement of THz absorption could be realized by a sandwich-structure with monolayer graphene [40]. Through this structure, the interaction between the evanescent wave and graphene layer is controlled by the incidence angle. An extremely large attenuation, up to \(\sim\)70% per reflection, was observed especially for \(s\)-polarization, and the absorptance shows a proportional relation to the Joule heating amount on the graphene surface. Another approach has been revealed for varied modulation through experimentally fabricating three graphene devices [28]. These devices couple the conductive interfaces with the evanescent waves, thus enhancing the attenuation. The modulation depth of the highly conductive ion-gel graphene (-on-quartz) device (as shown in Figure 2c) can be larger than 90% at 0.15-0.4 THz and up to 99.3% at 0.24 THz in \(s\)-polarization (as shown in Figure 2b). Results in Figure 2a,b show an obvious voltage dependence from 0.2 to 2 V due to the conductivity of graphene changed by the voltage. Further, [PERSON] et al. [23] proposed another method to enhance THz modulation, which combines the field confining of metallic gratings and sensitivity of conductive surfaces from the TIR geometry. Modulation at the range of 0.2-1.4 THz could reach 77%. Besides, a THz wave switch with Kretschmann configuration was proposed [41]. This switch consists of a prism with a high refractive index, liquid crystal, and periodically grooved metal grating. The ON-OFF state of the THz switch was realized by the different reflectivity intensity of the geometrical optics reflection of the THz wave at the specific value of the liquid crystal refractive index, which was set by the external applied bias voltage. This device showed an extinction ratio of 31.48 dB at the frequency of 1.0 THz. To conclude, deep, tunable, and broad-band amplitude modulation could be realized by combing the materials with tunable optical and dielectric properties and ATR geometry.
# A Robust Multi-Camera Vehicle Tracking Algorithm in Highway Scenarios Using Deep Learning
## 2 Related Work
### Multiple Target Tracking  
Traditional multiple object tracking (MOT) methods rely heavily on detection and tracking frameworks, where subsequent tracking is processed through candidate regions obtained from the detection results. For instance, Kalman and particle filtering are commonly employed in multi-target tracking methods, achieving a target state estimation through a combination of prediction and update steps.  
With the advent of deep learning technology, methods for tracking multiple targets using deep neural networks have witnessed significant advancements. Recent breakthroughs in object recognition, including Faster R-CNN [30], YOLO [31], and SSD [32], have enabled the accurate detection of multiple targets in single-frame images, thereby facilitating target re-identification through the utilization of deep features. Among these methods, DeepSORT [14] has emerged as one of the most effective multi-target tracking approaches.  
Figure 1: The current deployment of roadside equipment exhibits a limited detection range and high deployment costs.  
This method integrates object detection with depth matching and leverages Kalman Filter prediction to enhance accuracy and robustness.
# Breast Cancer Survival Analysis Model
## 3 Results
### Cox Analysis of Chemotherapy  
A multivariate Cox proportional risk model-validated survival analysis and risk ratio assessment were performed without chemotherapy to investigate the effect of chemotherapy on patient survival, taking into account pathological stage, age at diagnosis, gender, grade differentiation, estrogen hormone receptor test, progesterone hormone receptor test. As indicated in Table 4, the results were found to be significant (\(p\) = 0.000 \(<\) 0.05), suggesting that chemotherapy has an impact on the survival of patients with BC, and the relative risk  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multirow{2}{*}{**Significance**} & \multirow{2}{*}{**Risk Ratio**} & \multicolumn{2}{c}{**95\% Confidence Interval of Risk Ratio**} \\ \cline{3-4}  & & & **Lower Limit** & **Upper Limit** \\ \hline Surgical treatment & 0.004 * & 1.650 & 0.584 & 4.665 \\ Radiotherapy & 0.002 * & 1.155 & 0.584 & 4.665 \\ Chemotherapy & 0.000 * & 1.415 & 0.633 & 3.162 \\ Hormone therapy & 0.000 * & 3.656 & 2.051 & 6.515 \\ \hline \hline \end{tabular}  
* indicates significant difference.  
\end{table}
Table 4: Surgical treatment risk ratio.  
Figure 5: Surgical treatment of Cox survival analysis.  
HR = 1.415, indicating that the risk of death without chemotherapy is 1.415 times higher than that with chemotherapy. Figure 7 demonstrates that when all factors are included, the survival rate with chemotherapy is higher than that without chemotherapy.  
The multivariable Cox proportional hazard model test survival analysis and hazard ratio assessment were performed with and without hormone therapy, taking into account the pathological stage, diagnosis age at diagnosis, gender, stage differentiation, estrogen hormone receptor test, and PRA, the results found that the survival rate of the group with hormone therapy, as shown in Table 4, had significant differences (\(p=0.001<0.05\)), indicating that hormone therapy has an impact on the survival rate of patients with BC, and the relative risk is HR = 3.656, which means that the risk of death without hormonal treatment is 3.656 times higher than that of hormonal treatment. Figure 8 indicates that when all factors are included, the survival rate with hormonal treatment is higher than that without hormonal treatment.
## 2 Materials and Methods  
### Portable AMC Detector Deploying Soft X-Ray Radiolysis  
Figure 1a shows a schematic diagram of the portable detector for AMC in the cleanroom air deploying soft X-ray radiolysis [7; 8; 11]. A cylindrical stainless steel chamber was prepared for converting AMC into nanoparticles under soft X-ray irradiation (soft X-ray radiolysis chamber) and sealed using a copper gasket to prevent airborne contaminants in the cleanroom air from entering the chamber. The dimension of the chamber was \(\Phi\) 127 mm \(\times\) 203 mm (5 in \(\times\) 8 in) and the bulk residence time of AMC in the chamber was 100 s at 1.5 L per minute (LPM). The inner surface of the chamber was electro-polished and baked at 200 \({}^{\circ}\)C before the measurements in the cleanroom for minimizing background signals caused by outgassing from the chamber wall. A soft X-ray emitter (SXN-10F, Sunje Ltd., Busan, Republic of Korea) was installed to the top of the chamber and irradiated soft X-ray inside the soft X-ray chamber through a Kapton film window, which allows only soft X-ray to penetrate through it. A small-size high-efficient particulate air (HEPA) filter capsule was connected to the inlet of the soft X-ray radiolysis chamber for removing the error caused by primary particulate contaminants  
in the cleanroom air and monitoring only gaseous AMC. In the soft X-ray chamber, AMC coming from the cleanroom air was converted into nanoparticles through the soft X-ray radiolysis. The nanoparticles synthesized in the chamber were charged with a Boltzmann distribution by a neutralizer (Po-210) and then transported to the SMPS for measuring the size distributions of the synthesized nanoparticles. The aerosol inlet and sheath flow rates for the SMPS were set at 1.5 and 15 LPM, respectively. In addition, the scan and the retrace times were set at 120 and 15 s, respectively). Based on the SMPS setting, the number size distribution of nanoparticles synthesized in the soft X-ray radiolysis chamber was measured from 2.5 to 65 nm. To make a portable detector for investigating AMC hot spots in the cleanroom, the components of the AMC detection method were organized on a portable cart with three stages as shown in Figure 1a. Due to the size of the cart, the UCPC was stored below the nano DMA and the tubing length between two instruments was kept as short as possible to minimize the particle loss between them. A small-sized laptop PC on the electrostatic classifier (Model 3082, TSI Inc., Minneapolis, MN, USA) was deployed for controlling the SMPS and acquiring measured data.
# Design and Performance Analysis of Navigation Signal Based on OFDM
## 2 Mathematical Model of OFDM Navigation Signal Modulation System  
OFDM is a very widely used multicarrier modulation technique. OFDM converts serial data into parallel data, and then performs an M-order modulation of the data, such as by using BPSK, QAM, M-QAM, etc., and transforms the modulated data into an inverse Fourier transform. The OFDM adds a guard period to suppress inter-symbol interference and subcarrier interference (inter-carrier interference).  
Referring to the classical OFDM communication signal modulation mode, the modulation method of the OFDM navigation signal is proposed. The mathematical expression of an OFDM symbol is as follows [14]:  
\[x(t)=\frac{1}{K}\sum_{k=0}^{K-1}c_{n,k}e^{2\pi k\Delta ft} \tag{1}\]  
where \(K\) is the number of subcarriers, \(c_{n,k}\) is the information modulated by the \(k\)th subcarrier in the \(n\)th symbol in a frame of OFDM, and \(\Delta f\) is the subcarrier interval.  
The OFDM signal frame structure is shown in Figure 1[26]. A frame OFDM signal is usually composed of multiple OFDM symbols, and each OFDM frame is composed of OFDM symbols and protection intervals in the time domain. The time domain expression \(s(t)\) of OFDM navigation signals with protection intervals is:  
\[s(t)=x_{u}(t)+x_{g}(t), \tag{2}\]In the formula, \(x_{u}(t)\) represents the OFDM symbol and \(x_{g}(t)\) represents the protection interval.  
The OFDM modulation time domain signals \(x_{u}(t)\) and \(x_{g}(t)\) can be derived from \(x(t)\):  
\[\begin{array}{l}x_{u}(t)=\frac{1}{K}\sum\limits_{n=0}^{N-1}\xi_{1}\big{(}t-T_{ g}-nT_{S}\big{)}x\big{(}t-T_{g}-nT_{S}\big{)}\\ x_{g}(t)=\left\{\begin{array}{l}\frac{1}{K}\sum\limits_{n=0}^{N-1}\xi_{2}(t- nT_{S})x(t+T_{u}-nT_{S}),\text{CPModel}\\ 0,\text{ZPModel}\\ \end{array}\right.,\\ g_{1}(t)=\left\{\begin{array}{l}1,0\leq t<T_{u}\\ 0,\text{other}\\ \end{array}\right.\\ g_{2}(t)=\left\{\begin{array}{l}1,0\leq t<T_{g}\\ 0,\text{other}\\ \end{array}\right.\end{array} \tag{3}\]  
In the formula, \(n\) is the \(n\)th signal in a frame OFDM, \(T_{u}\) is the useful time length of a symbol, \(T_{u}=1/\Delta f\), \(T_{g}\) is the length of the protection interval, \(T_{s}\) is the time length of a symbol, \(T_{s}=T_{u}+T_{g}\), \(N\) is the number of symbols contained in a frame OFDM, and \(g_{1}(t)\) and \(g_{2}(t)\) represent the time domain window function, which is used to control the influence of the OFDM symbol and protection interval.  
A further finishing of the above formula yields:  
\[\begin{array}{l}x_{n}(t)=\frac{1}{K}\sum\limits_{n=0}^{N-1}\sum\limits_{K=0 }^{K-1}c_{n,k}g_{1}\big{(}t-T_{g}-nT_{S}\big{)}e^{j2\pi t\Delta f(t-T_{g}-nT_ {S})}\\ x_{g}(t)=\left\{\begin{array}{l}\frac{1}{K}\sum\limits_{n=0}^{N-1}\sum \limits_{k=0}^{K-1}c_{n,k}g_{2}(t-nT_{S})e^{j2\pi t\Delta f(t-2T_{g}-(n-1)T_{ s})},\text{CPModel}\\ 0,\text{ZPModel}\\ \end{array}\right.,\\ \end{array} \tag{4}\]  
Substituting Equation (4) into Equation (2) yields the time domain expression of the OFDM navigation signal.
## 3 Results  
### Measurement Configuration and Verification  
The standard operation procedure (SOP) described here follows the material preparation presented in the previous section. First, we ensure a stable lab climate in a controlled environment (temperature, humidity) to enable the stable operation of the SMM with minimum mechanical and electrical drift. The measurements are performed at 30 GHz using a modified 25 PT300A AFM tip from Rocky Mountain Nanotechnology(r). A PNA Keysight(r) E8364B VNA with the RF power source set to 2 dBm and the intermediate frequency bandwidth (IFBW) to 50 Hz is used. Highly stable coaxial cables and feedthrough coaxial transitions are used to connect the VNA to the probe. Nanonis(r) Signal Conditioning (SC5) and Real-Time Controller (RC5) modules are used to drive the AFM measurements. The cantilever deflection voltage is set to 90 mV, the approach-retract factor is about 6 nm/mV and the resulting force is estimated to be 9.7 \(\upmu\)N. The images were scanned over 40 \(\times\) 40 \(\upmu\)m\({}^{2}\) with 256 pixels with a scanning time (forward and backward) of 5119 s. Prior to the measurement, SEM imaging of the apex tip is performed to check the tip shape (Figure 5a). Topography, together with both real and imaginary parts of the measured complex reflection coefficient \(\Gamma_{M}\) images, is acquired simultaneously (Figure 5b,c).  
We keep raw data (Nanonis(r) *.sxm format) and postprocess data separate and make sure to not overwrite raw data during analysis treatment, presented in the following sub-section.
## 3 Design
### Optical Design of MEPI
#### 3.2.1 Design Approach  
In the design process of the sub-aperture polarimeter, it is of great significance to deeply consider the radius of the Earth and the key position of the L1 point of the Earth. With the help of the geometric relationship model constructed by the radius of the Earth and the position of the Earth-Moon L1 point, we accurately derived the key parameters such as the field of view, the height of the image radius, and the focal length of the system. According to these parameters, we have designed the sub-aperture polarimeter so that it can stably and efficiently present the clear image of the whole Earth at the L1 point of the Earth and Moon and achieve the overall detection target. This achievement highlights the importance of the research and application of relevant parameters, and provides strong support for the research and monitoring of earth science.  
The role of the sub-aperture imaging system is to divide the optical path into four channels and convert the same object into four images. In the design consideration of the sub-aperture system, the scale parameter selection of the system aperture \(d_{2}\) should be close to the single image plane diameter d, aiming to minimize the insufficient utilization of the image plane. At the same time, the spacing of the aperture deviation needs to be controlled within a reasonable range to ensure that the system energy can be efficiently utilized so as to optimize the overall optical performance and energy conversion efficiency of the sub-aperture system and meet the accuracy and stability requirements of the system in practical applications. As shown in Figure 6, given a single channel aperture \(d_{2}\) and a reserved distance m, the outer tangential aperture \(D_{2}\) of the subchannel can be calculated:  
\[D_{2}=\Big{(}\sqrt{2}+1\Big{)}d_{2}+\sqrt{2}m \tag{9}\]  
where the total focal length of the system is f, the focal length of the main system is f\({}_{1}\), and the focal length of the sub-channel system is f\({}_{2}\). The focal length formula between the two systems can be obtained as follows:  
\[\frac{1}{f}=\frac{1}{f_{1}}+\frac{1}{f_{2}}-\frac{d}{f_{1}f_{2}} \tag{10}\]  
Then, according to the ideal object-image relationship formula:  
\[\frac{1}{l^{\prime}}-\frac{1}{l}=\frac{1}{f} \tag{11}\]  
\[l_{2}=l^{\prime}_{1}-d \tag{12}\]  
We can calculate the total length of the system:  
\[L=d+l_{2} \tag{13}\]  
Figure 6: (**a**) Aperture segmentation diagram; (**b**) System optical path diagram.  
In the process of system design, it is necessary to comprehensively weigh the geometric scale parameters of the whole system, scientifically plan the focal length distribution scheme of the main system and the sub-channel system, and achieve the expected goals and technical requirements of the system design.
## 4 Results  
### Selection of Landslide Conditioning Factors  
There is no global guideline for the selection of landslide conditioning factors, but they should be selected based on the characteristics of the case study area, data availability, and literature review [58]. One of the most important steps in spatial prediction modeling is the selection of appropriate and effective conditioning factors among all available factors. Not all conditioning factors have an equal impact on landslide occurrences; thus, the most and least important conditioning factors must be identified and the least effective factors should be removed from modeling, as they can reduce the prediction power of the models.  
In the present study, the least support vector machine (LSSVM) [59] and average merit (AM) criteria [60; 61] were applied for the selection of the proper conditioning factors as well as determining their importance. The results showed that all selected conditioning factors have an impact on landslide occurrences in the Shangnan area, China. Altitude has the greatest impact on landslide occurrences (AM = 14.5), followed by distance to road (AM = 13), distance to fault (AM = 12.6), lithology (AM = 12.6), land use (AM = 11.1), distance to river (AM = 10.5), STI (AM = 8.5), SPI (AM = 7.2), TWI (AM = 6.7), profile curvature (AM = 6.1), slope angle (AM = 4.7), NDVI (AM = 4.5), soil (AM = 3.7), plan curvature (AM = 3.2), and slope aspect (AM = 1.5) (Figure 4). Thus, the most effective conditioning factors are altitude, distance to road, and distance to fault, which aligns with results of [PERSON] et al. [33] and [PERSON] et al. [39]. As all 15 landslide conditioning factors positively contribute in the study area, all were used for further analysis.