esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Establishment and Evaluation of Regression Models
#### 2.4.2 Sensitive Bands Selection  
The fusion algorithm of CARS and SPA was used to extract feature wavelengths for corn leaf samples to reduce the influence of redundant information and noise in the spectral data on model accuracy.  
The CARS algorithm [25] combines Monte Carlo sampling and PLS model regression coefficients to select the feature variables, following the principle of \"survival of the fittest.\" The wave points with short wavelengths are selected by setting the number of cyclic iterations for cyclic calculation. The optimal combination of variables is filtered according to the subset with the smallest root mean square error of interaction verification (RMSECV). The number of loop sampling is set to 30 in this experiment to screen out the variables with small covariance and significant weight.  
The SPA algorithm [26], widely used in screening and extracting feature wavelengths, is a method for an iterative search of forwarding feature variables, thereby enabling the  
Figure 6: Flow chart of the LNC prediction model design based on spectral and image fusion methods.  
screening of variable combinations with the least redundant information and the least covariance. The SPA algorithm uses the projection analysis of vectors and projects wavelengths onto other wavelengths and, by comparing the magnitude of the projection vectors, selects the wavelength with the most significant projection vector as the wavelength. The final feature wavelength is selected based on the correction model. The SPA uses RMSE as an evaluation index and determines the number of final feature variables based on the smaller RMSE value.  
The fusion of CARS and SPA algorithms can effectively eliminate the spectral space vector covariance, reduce the redundant information between feature variables, and select representative feature wavelength variables for modeling. Furthermore, it can improve the accuracy and efficiency of the model, and can more intuitively reflect the response of spectral information to the change of leaf nitrogen content.
# State-of-the-Art Review of Structural Vibration Control: Overview and Research Gaps
## 2 Overview of Structural Control Systems
### Hybrid Control System  
Hybrid control systems are developed by combining a passive system with an active or semi-active one. Hybrid systems employ multiple control devices, meaning both active control devices and passive devices are installed within the same structure. Hence, combining the systems can eliminate some of the limitations and disadvantages of each system that arise when they are used separately, thereby achieving better performance. Some of the combinations studied include base isolation and active actuators, tuned mass dampers and base isolation, and viscoelastic dampers and base isolation, among others. [PERSON] et al. [3] provided a review of the hybrid control systems and the control algorithms for the structures. The authors also noted that most research on smart structures is concentrated in the US, Japan, Taiwan, China, and Korea, with lesser attention given to Europe.  
Passive and semi-active controls have better preference than active control due to their reliability and inherent stability. An active control system may develop asynchronous control forces due to time delays, which can render the structure unstable. Out of semi-active, passive, and hybrid control, semi-active control has garnered more attention because it can offer a more effective reduction in seismic responses. Therefore, this paper reviews the advancement in semi-active control applications for earthquake mitigation. The following section presents a review of semi-active control systems for structures.
## 3 Results
### FY-4A LST Authenticity Test Using TCA
Deviation was steady and always less than the standard deviation of the true time series (\(\sim\)0.7). Snr_db, which characterizes the signal-to-noise ratio, was greater in the central-southern, central, and northern areas except  
Figure 4: Spatial distribution of Rho from TCA of FY-4A (left) and Himawari-8 (right) LSTs in Hunan Province; results are only shown for grids in which all three datasets showed a significant positive correlation and there were >100 data triplets. Figure 5: Distribution of TCA error indicators for FY-4A LST in Hunan Province; results are shown only for grids in which all three datasets showed significant positive correlation and there were >100 triplets. for Dongting Lake, where the signal showed weak interference; the signal showed some distortion in the western and southern regions. The distribution of TCA error indicators shows that FY-4A LST data is strongly influenced by topography, and parameters that indicate its accuracy are almost consistently correlated with terrain in Hunan Province, whereas parameters indicating error are inversely related to terrain. FY-4A LST has low accuracy in the western and southern mountainous areas of Hunan Province, indicated by low correlation, large deviation and strong signal distortion, especially in the northwestern and southern border areas; in the south-central, central, and northern areas other than Dongting Lake, the product is more accurate, especially in the Hengyang basin, where correlation was greater, deviation was less, and the signal-to-noise ratio was greater.
## 2 Background and Related Work
### Attention-Enhanced Module  
Attention mechanisms have gained significant traction in the deep learning community for their ability to focus on the most relevant parts of the input data, thereby improving the model's performance [30]. Attention mechanisms dynamically weigh the importance of different features, allowing the model to prioritize critical information while suppressing less relevant details. The application of attention mechanisms in image processing tasks has shown promising results. For instance, introducing the Squeeze-and-Excitation (SE) network [31] highlighted the importance of channel-wise attention in improving network performance by adaptively recalibrating channel-wise feature responses.  
While existing watermarking models utilize attention mechanisms to enhance either invisibility or robustness, achieving both simultaneously remains challenging [26]. Our approach distinguishes from prior methods by employing a spatial attention mechanism that captures the maximum and average values at each spatial position through max and average pooling, as illustrated in Figure 3. Spatial position weights are subsequently learned via a convolutional layer followed by a sigmoid function. These weights are then applied to each spatial position on the feature map, resulting in features with enhanced spatial significance. Additionally, global features are used to create an attention mask, which identifies inconspicuous and texture-rich areas, thereby improving overall watermarking performance.
## 3 Design of a Computer Vision-Based Roadside Occupation Surveillance System (CVROSS)
### Data Preprocessing in the CVROSS
#### 3.2.1 Noise Reduction  
One of the most important stages in the entire system flow is noise reduction. This is a process of removing noise from an image, as the noise might degrade both the visual quality and the effectiveness of subsequent processing tasks [33]. In this case (according to the simulation model), on the roadside and in traffic lanes, there are different objects and signals, such as traffic indicators and instructions in traffic lanes. However, these are likely to be unrelated to vehicle and object recognition and matching, therefore they may negatively affect matching results and the effectiveness of the subsequent calculation of available parking spaces. Furthermore, even similar vehicles, such as two private cars in this case, may be the same model but different in colour. Therefore, noise reduction can ensure that unrelated objects, indicators and signals are removed before further processing of the images. This also prevents problems with colour classification. In Figure 4, an example of noise reduction is illustrated. Before noise reduction, the image obtained from vision acquisition was full of obstacles, such as a road sign, a traffic cone and yellow box markings. All these were a hindrance to vehicle and object recognition and matching. Noise reduction was achieved using an \"image mask\" to exclude irrelevant regions of the image, \"colour plane extraction\" to convert the colour image to a binary image in only black and white, and \"basic morphology\" to modify the shape of binary objects in the image, as well as to adjust the brightness. After noise reduction, the indicator, traffic cone and yellow box markings had been removed (blacked out) and only the private car remained on the screen with its shape shown in white.
## 4 Discussion  
In this work, a new ptychographic imaging correlography framework is proposed to realize long-range, high-resolution imaging. The redundant information provided by ptychography can sufficiently solve the phase retrieval challenges of conventional imaging correlography, including the apparent superimposed, upside down, and twin images. Moreover, the proposed method removes the requirement for detecting the speckle patterns within one coherence moment of the intensity fluctuations, which is impractical for the thermal source. Ptychographic imaging correlography is not constrained by the memory effect of speckles either. Therefore, there is no strict limit on the illumination probe and scattering medium. The number of speckle patterns required to recover a target is only one-sixth of the conventional imaging correlography. Therefore, the introduction of ptychography does not increase the storage burden of the system. Additionally, a flexible optimization algorithm combining the AF and RED frameworks is introduced into our reconstruction process. Given an appropriate initial value, the proposed algorithm can quickly converge on the global optimal value. Furthermore, the using of the RED regularizer can sufficiently suppress noise during reconstruction and maintain image details. A series of experiments demonstrate that the reconstruction results of the proposed imaging model are far superior to the conventional imaging correlography on both visual and quantitative metrics.  
Although the proposed work significantly improves the reconstruction resolution, it still faces some challenges. Measurement for redundant information results in low time resolution for the system. New data processing methods are required to improve the time resolution. For coherent illumination, speckles will form when the amplitude of the background is not zero. Therefore, objects with strong contrast between the foreground and background amplitudes have better reconstruction results than those where the background and foreground amplitude values are similar. In future work, an optical experiment will be performed to further verify the performance of the proposed method. Besides, we will analyze the effect of target contrast on reconstruction results and try to restore objects with low contrast.  
Conceptualization, D.W. and Z.S.; methodology, Z.L.; software, W.Z. and Z.L.; validation, G.L. and X.W.; writing--original draft preparation, Z.L.; writing--review and editing, T.J.  
This research was funded by National High Technology Research and Development Program of China 863 Program (No. Y512171800) and Youth Innovation Promotion Association (No.1188000111).  
The authors declare no conflict of interest.
## 2 Experimental Setup and Production of the Datasets  
### Experiment  
The dataset used for this study corresponds to the evoked P300 waves of nine healthy subjects (8 men, 1 woman) on an auditory BCI paradigm. A digital electroencephalogram system (Active Two, BioSemi, Amsterdam, Netherlands) was used to record the brain activity at 256 Hz. The device consists of 64 electrodes distributed over the head of the subject by means of a cap with the distribution shown in Figure 1a. This study was approved by the ethics board of the Nagaoka University of Technology. All subjects signed consent forms that contained detailed information about the experiment, and all methods complied with the Declaration of Helsinki.  
By using the out of the head sound localization method [26], the subjects were presented with stimuli (100 ms of white noise) from six different virtual directions via earphones followed by an interval in which no sound was produced (silent interval). Figure 1b shows the six virtual direction positions relative to the subject.  
We refer to one stimulus and one corresponding silent interval as a trial. Four different trial lengths (200, 300, 400, and 500 ms) were considered in order to analyze the impact that the speed of the stimuli presentation could have on the identification of the P300 wave.  
For the creation of this dataset, each subject completed a task, which was comprised of a collection of 12 sessions, for each of the proposed trial lengths. Figure 1c illustrates the conformation of a task. Each session had as the attention target a fixed sound direction that changed clockwise from one session to another starting from Direction 1 (see Figure 1b). Subjects were asked to attend only to the stimuli perceived to be coming from the target direction and to count in silence the number of times it was produced. The subjects performed this experiment with their eyes closed.  
In each session, around 180 pseudo-randomized trials were produced, meaning that for every six trials, sound from each direction was produced at least once and that stimuli coming from the target direction were never produced subsequently to avoid overlapping of the P300 wave. Thus, of the approximately 180 trials contained in each session, only a sixth of these would contain the P300 wave corresponding to the target stimuli.
## 6 Conclusions  
This study introduces a novel weighted loosely coupled algorithm for fusing data from IMUs and visual measurements and demonstrates its observability. The algorithm, of which the coupled part possesses a computational complexity of \(O(n)\), retains the high  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Translation RMSE** &
\begin{tabular}{c} **Translation Mean** \\ **Error** \\ \end{tabular} & **Translation STD** \\ \hline ICEKF & 0.08922 m & 0.009765 m & 0.08869 m \\ Monocular & 0.735 m & 0.533 m & 0.506 m \\ Monocular & & & \\ ORB-SLAM V3 & 0.001366 m & 0.004981 m & 0.01272 m \\ with IMU & & & \\ Monocular MSCKF & 0.2689 m & 0.09905 m & 0.25 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Norm errors of the translations between the ground truth and the results of the dataset experiments (MainHall02).  
Figure 17: Comparison of the partial trajectory from the ICEKF against the visual measurement with leap noise.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Translation RMSE** &
\begin{tabular}{c} **Translation Mean** \\ **Error** \\ \end{tabular} & **Translation STD** \\ \hline ICEKF & 0.04153 m & 0.00574 m & 0.04112 m \\ Monocular & 0.0393 m & 0.0355 m & 0.01678 m \\ ORB-SLAM V3 & 0.003841 m & 0.002973 m & 0.002433 m \\ with IMU & & & \\ Monocular MSCKF & 0.1305 m & 0.05366 m & 0.119 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Norm errors of the translations between the ground truth and the results of the dataset experiments (ROOM01).  
frequency update capability inherent to the EKF framework, thus enabling high-accuracy active localization. Both simulation and dataset tests reveal that the ICEKF achieves deep integration of the IMU and the visual data on the velocity layer throughout the updating process, which benefits the localization performance. By conveniently tuning the weights assigned to different data sources, the framework improves both the fusion accuracy and the resilience to abrupt noise. In future studies, we will aim to apply the approach across multiple devices by analyzing the sensitivity of the coupling coefficients in challenging real-life scenarios.  
Conceptualization, Z.L. and P.T.; methodology, C.L.; software, Z.L. and T.W.; validation, C.L.; formal analysis, C.L.; investigation, Z.L. and P.T.; resources, Z.L. and P.T.; data curation, C.L. and T.W.; writing--original draft preparation, Z.L. and C.L.; writing--review and editing, T.W. and P.T.; visualization, Z.L. and C.L.; supervision, P.T.; project administration, Z.L. and P.T.; funding acquisition, T.W. All authors have read and agreed to the published version of the manuscript.  
This research was sponsored by the National Key Research and Development Program of China [Grant number 2022 YFC3320503] and the Foundation for Innovative Research Groups of the National Natural Science Foundation of China [Grant number 12221002], funded by [PERSON].  
Not applicable.  
The source code presented in this study is available upon request from the corresponding author.  
The authors acknowledge the anonymous reviewers for their helpful comments on the manuscript.  
The authors declare no conflicts of interest.
## 3 Results and Analysis of the Ground Test
### Performance Test of the Orthographic Stabilization Platform
#### 3.1.2 Test Method and Results of the Stability Accuracy
In the ground test, the swing environment of the orthographic stabilization platform fully compares the swing environment during the flight process, and a margin of over 50% is reserved for the motor capacity, which means that the stabilization platform can maintain stability accuracy in flight maneuvers that are more severe than the ground test environment. In addition, the components used in the orthographic stabilization platform are all wide temperature devices, and the machined parts have undergone surface anodizing or chrome plating treatment, which can adapt to external environments ranging from \(-30\)\({}^{\circ}\)C to \(65\)\({}^{\circ}\)C. Moreover, the entire equipment was placed in a high and low temperature box for high and low temperature operation testing and assessment, and it was able to maintain normal working conditions, indicating that the equipment has the adaptability to the onboard environment.
# Antiference: New Concept for Evolutive Mitigation of RFI to GNSS +
## 3 Models  
The Antiference system uses two NN models for the detection and classification of different RFI types. One model is concerned with the detection and classification of different jammer types, and the other detects the existence of spoofing signals. Figure 1 shows the two models used for jamming detection and classification (left) and spoofing detection (right).  
Different features were used for the detection of jamming and spoofing. This implied that separate models would need to be used (otherwise the models would either not be efficient or their size would increase dramatically), which, in turn, also led to having two different FPs and FP databases. The FP of the incoming signals will be created and matched against the FP database for each model separately. If no matches are found, the needed features are extracted from the signal and sent for detection and classification through the NN. The results will be both inserted in the FP database and also sent to the mitigation unit for parameter extraction and mitigation of the signal.  
The jamming detection/classification NN is built using transfer learning. It is illustrated in Figure 1, and is composed of two parts: (i) In green: this part extracts features from the spectrograms. Its main component is a pre-trained 50 layers ResNet architecture (see [24] for details about this type of architecture), available through the Keras toolbox (see [25]). The final pooling layer is used to reduce the dimension of the feature map between this step and the decision layer. This reduces the required computations down the network while acting as a \"summary\" of the most prominent features. In addition, a dropout layer is inserted between the extracted features and the rest of the network to ensure the robustness of the training. (ii) In blue: features are processed by the decision layer, using a fully connected (FC) decision layer. This layer combines the features extracted by the ResNet50 with additional information (the kurtosis and AGC value of the sample).  
The ResNet50 V2 architecture was selected by comparing the training speed and classification performance of several architectures commonly used for transfer learning on a subset of the available training data. It is the one that showed the best compromise between the complexity of training and a good prediction of all the classes.  
In addition, the parameters of this architecture and of the training process were tuned to enable the highest performance, using a process of Bayesian optimization (see [26; 27] for an overview of the tool used to perform the optimization).
## 6 Outlook: Reinforcement Learning Approach  
Since we have no access to any team and are dependent on publicly available data, our future work will focus on a reinforcement learning approach. The race simulation will therefore be used as an environment for training an agent to make race strategy decisions. Here, as in real-world races, the agent can choose between four actions at the end of each lap: no pit stop, pit stop (hard), pit stop (medium), pit stop (soft). Thus, the decision is not divided into two stages, as in the methodology presented above. After rendering a decision, a lap is simulated under consideration of the chosen action. Before the agent chooses its next action, it is rewarded for its previous action, in two parts. The first part is determined by the difference between the current lap time and an average lap time calculated for the driver in a pre-simulation without opponents. Thus, the agent learns that it can increase its reward by fitting a fresh set of tires. The second part considers the number of positions that the driver has won or lost since the previous decision. Consequently, the agent will learn that a pit stop costs positions. By optimizing the strategy, the agent autonomously maximizes its reward.  
In its current state, the agent is trained specifically for each race. During training, the other drivers' strategies in the simulated race can be controlled by the VSE or be predefined by the user. To ensure that the agent can be used universally for all drivers and situations, the driver controlled during training is randomly selected before each training episode.  
In initial tests, the agent achieved better results in the race simulation than the VSE. For the 2019 Chinese Grand Prix, in which there were 20 drivers, it achieved an average position of 8.46 over 1000 simulated races, each with a randomly selected driver. The VSE only manages an average position of 9.51. Various factors can explain the better performance of the agent. Firstly, the VSE is trained on many different real races and learns to make reasonable decisions 'on average', which may, however, not be a perfect fit for the driver, car, track, and race situation. The agent, in contrast, is trained specifically for the race in question. Secondly, the agent makes a pit stop decision in combination with the next compound, which enables better timing of the pit stop compared to the two-stage approach of the VSE. Thirdly, in contrast to the VSE, the agent does not need the intended number of pit stops for the race. This simplifies handling and allows him to amend the number of pit stops during the race if the situation changes.  
The results of the initial tests seem promising, but must be evaluated in further races. Furthermore, we have to analyze whether the agent is able to learn and make active use of advanced tactical opportunities, such as the undercut. It is also necessary to check whether the agent's strategies seem reasonable, since in training based purely on simulation, it will also learn the simulation and parameterization errors.