esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter
## 3 Time Series Forecasting Experiments
### Presentation of Prediction Results and Analysis  
#### 3.5.1 Comprehensive Comparison of Models  
RNN, LSTM, GRU, and BiLSTM all adopted the structure of three hidden stakes, and simpleRNN was set to have only one hidden stake. All hidden stakes were set to have the same number of units, and the models were trained for 200 times with an input time step of 10. The evaluation indexes of the prediction results for several models are referred to in Figure 14 and Table 2.  
Figure 14: Schematic diagram of comparison of prediction effect of basic model.  
Figure 12: Schematic diagram of prediction error changing trend with epochs and units.  
Figure 13: Diagram of the variation trend of fitting time with epochs and units.  
The RNN series do not perform well in prediction. Although a neural network with multiple hidden stakes can theoretically fit any continuous function, the basic RNN model requires more training time and optimization improvements. The prediction errors of long short-term memory neural networks are all small and close to each other. Among them, BiLSTM is more time-consuming due to the two-way fitting calculation. Comprehensively comparing the accuracy and computational time of different methods, the long short-term memory neural network performs better in this time series information-mining task.
## 3 Proposed Method
### Attention-Guided Transformer Module (AGTM)
#### 3.2.3 Channel-Wise Multi-Head Transformer Block (CMTB)
It contains a \(3\times 3\) depth-wise convolution layer with a stride of \(1\) followed by a GELU layer [56] and another \(3\times 3\) depth-wise convolution layer with a stride of \(1\). Finally, the output feature maps \(\mathbf{X}_{out}\in\mathbb{R}^{H\times W\times C}\) can be calculated by reshaping the result of Equation (6). Additionally, we introduce GDFN [57] to effectively learn local image structures by encoding information from spatially neighboring pixel positions. Given feature maps \(\mathbf{X}_{in}\in\mathbb{R}^{H_{\times}W\times C}\) as the input of the GDFN, the output \(\mathbf{X}_{out}\in\mathbb{R}^{H\times W\times C}\) can be obtained by:  
\[\mathbf{\hat{X}}=\mathbf{H}_{\mathit{dconv}}^{3\times 3}(\mathbf{H}_{fc}( \mathbf{X}_{in})),\quad\mathbf{X}_{out}=\mathbf{H}_{fc}(\mathbf{\hat{X}} \cdot\sigma(\mathbf{\hat{X}})) \tag{7}\]  
where \(\mathbf{H}_{\mathit{dconv}}^{3\times 3}(\cdot)\) and \(\mathbf{H}_{fc}(\cdot)\) denote the \(3\times 3\) depth-wise convolution layer and the full connection layer, respectively. \(\sigma(\cdot)\) represents the GELU non-linearity. The AGB and CMTB are two components that work in tandem to enhance facial features and strengthen the inner feature map relationship. The AGB is responsible for extracting and guiding the key features from the inner feature maps, while the CMTB aggregates and refines the previously extracted feature information. By leveraging both these blocks, the AGTM can concurrently enhance local facial details and global facial structures, making it a promising solution for face image reconstruction tasks.
## 3 Grid-Based Event Detection
### Grid-Based Fault-Tolerant Event Detection
Our proposed event detection scheme can be depicted as follows: \begin{tabular}{l} \hline Grid-based distributed event detection \\ Step 1. Given sensor reading \(x_{i}^{k}\), each sensor node computes \(b_{i}^{k}\). \\ Step 2. Each sensor node with \(b_{i}^{k}=1\) reports a 1 and each cluster head counts \(e_{i}^{0},e_{i}^{1},e_{i}^{2},e_{i}^{3}\), and apply a threshold test. \\ Step 3. Obtain \(e_{j}^{0},e_{j}^{1},e_{j}^{2},e_{j}^{3}\) of each of its neighboring cluster-head \(H_{j}\). \\ Step 4. Count the number of 1's for each of the SRs and apply a threshold test with \(\theta\). \\ Step 5. Update the confidence levels. \\ \hline \end{tabular}   
An illustration of the proposed event detection scheme is given in Figure 2, where there are only 4 grids, A through D. A small dotted circle in the center represents an event region placed across the four grids. The number within each subgrid, represents the number of sensor nodes (in the subgrid) reporting a 1. The total number of nodes in the subgrid is in the corresponding parenthesis. Among the four grids, grid A has only two 1's out of 13 nodes. By performing inter-cluster (or grid) communications, the dotted SR (_i.e._, SR\({}_{ABCD}\)) in the center can be found to have five 1's out of 13 nodes, more likely to pass the threshold test. Although we described that each cluster head applies the threshold test for eight SRs for convenience, in reality, it needs to apply the test to three SRs at most, in E, S, and SE directions. At the cluster head in grid A, for example, it needs to apply the test to SR\({}_{AB}\), SR\({}_{ABCD}\), and SR\({}_{AC}\). The cluster head at grid B needs to apply the test only to the SR\({}_{BD}\), and so on. Hence redundant threshold tests can easily be removed.
## 4 Experiments and Result Analysis
### The Power Industrial Control System Dataset  
In Figure 5 we show the power system framework used in this evaluation [25], a complex mix of supervisory control systems interacting with various smart electronic devices complemented by network monitoring devices such as Snort and Syslog systems. The power system components are as follows: G1, G2 are two generators, R1-R4 are four intelligent electronic devices (IEDs) that can open or close circuit breakers, and BR1-BR4 are four circuit breakers. Each IED automatically controls a circuit breaker: R1 controls BR1, R controls BR2, and so on. When a fault is detected, the IED uses a distance protection scheme that trips the circuit breaker; however, because the device lacks abnormal monitoring capabilities, it is impossible to tell whether the fault is real or fake. The operator can also manually trip circuit breakers BR1-BR4 with the IED. The manual override is used when performing maintenance on the lines or other system components.  
The five types of power system status are as follows: (1) malfunctioning short circuit (2) maintaining the line (3) remote trip command injection (attack) (4) changes to the relay settings (attack) (5) injection of data (attack).  
Figure 4: Principle of improved grid search algorithm (IGSA).  
The dataset has a total of 128 features, including electrical physical quantity features and network traffic features [26]. The phasor measurement unit measures the electrical physical quantity characteristics (PMU). The system employs four PMUs, each measuring 29 electrical characteristics, for a total of 116 electrical characteristics in the dataset. The index of each column is in the form of \"R#-Signal Reference\" that indicates a type of measurement from a PMU specified by \"R#\". Each feature's name and description are listed in Table 1. For example, R1-PA1: VH is the phase angle of the phase A voltage as measured by PMU R1. There are 12 features after the PMU measurement data feature, which are the control panel log alerts, Snort alerts, and relay status (relays and PMU integrated) corresponding to the 4 IEDs. The last column is the label, which represents whether a sample is normal or under attack.
###### Abstract  
In the near future, international space agencies have planned to achieve significant milestones in investigating the utilization of Global Navigation Satellite Systems (GNSS) within and beyond the current space service volume up to their application to lunar missions. These initiatives aim to demonstrate the feasibility of GNSS navigation at lunar altitudes. Based on the outcomes of such demonstrations, dozens of lunar missions will likely be equipped with a GNSS receiver to support autonomous navigation in the lunar proximity. Relying on non-invasive, consolidated differential techniques, GNSS will enable baseline estimation, thus supporting a number of potential applications to lunar orbiters such as collaborative navigation, formation flight, orbital manoeuvers, remote sensing, augmentation systems and beyond. Unfortunately, the large dynamics and the geometry of such differential GNSS scenarios set them apart from current terrestrial and low-earth orbit use cases. These characteristics result in an increased sensitivity to measurements time misalignment among orbiters. Hence, this paper offers a review of baseline estimation methods and characterizes the divergences and limitations w.r.t. to terrestrial applications. The study showcases the estimation of the baseline length between a lunar CubeSat mission, VMMO, and the communication relay Lunar Pathfinder mission. Notably, real GNSS measurements generated by an Engineering Model of the NaviMoon receiver in the European Space Agency (ESA/ESTEC) Radio Navigation Laboratory are utilized. A radio-frequency constellation simulator is used to generate the GNSS signals in these hardware-in-the-loop tests. The performed analyses showed the invalidity of common terrestrial differential GNSS ranging techniques for space scenarios due to the introduction of significant biases. Improved ranging algorithms were proposed and their potential to cancel ranging errors common to both receivers involved was confirmed.  
differential GNSS; baseline estimation; inter-spacecraft range; lunar orbiters; GNSS in space 2024162755. [[https://doi.org/10.3390/rs16152755](https://doi.org/10.3390/rs16152755)]([https://doi.org/10.3390/rs16152755](https://doi.org/10.3390/rs16152755))  
1
## 3 Measurement Setup and Ferrofluidic Properties
### Ferrofluidic Properties  
Ferrofluid is a type of material that can act like a liquid and a magnetic solid. It is made of nanoscale ferromagnetic particles and suspended in a liquid (such as oil). When no magnet is nearby, the material acts like a liquid, but when a magnet is nearby, the particles become temporarily magnetized and the ferrofluid acts like a solid. When the magnet is removed, it returns to its liquid state. The particles, which have an average size of about 100 A (10 nm), are coated with a stabilizing dispersing agent (surfactant) which prevents particle agglomeration, even when a strong magnetic field gradient is applied to the ferrofluid. In this work, the ferrofluid was supplied from Ferrotec Co. Ltd. (www.ferrotec.com). As shown in Table 2, in this work, four ferrofluids, referred to as EMG901,  
Figure 3: (**a**) The calibration kit for inductor characterization. (**b**) The measurement setup for inductor characterization.  
EMG905, EMG909 and EMG911, were applied to, respectively, initial magnetic permeabilities of 5.4, 3.1, 1.9 and 1.3; magnetic saturation equal to 66 mT, 44 mT, 22 mT and 11 mT, and magnetic particle concentration 11.8%, 7.8%, 3.9% and 2%.
###### Abstract  
In many noise scenarios, it is necessary to ensure ventilation and noise suppression. In this paper, a ventilated acoustic metamaterial labyrinth plate (VAMLP), formed by an array of labyrinth cells (LCs), is presented. Each labyrinth cell contains four labyrinth waveguide units (WUs). Based on the impedance series principle, an analytical model of the WU was developed and validated by a numerical model and impedance-tube experiments to determine the sound transmission loss of the WU and the LC. The mechanism of the influence of thermo-viscous loss was quantitatively analyzed, and it was clarified that the VAMLP produced sound absorption due to thermo-viscous loss. The change law of impedance at the entrance of the waveguide was analyzed, revealing the noise-reduction mechanism of the labyrinth unit. Combining a BP network and an improved sparrow search algorithm (ISSA), a BP-ISSA optimization model is proposed to optimize the ventilation capacity of the labyrinth cells. The BP-network model can accurately predict the resonance frequency from the structural parameters to form the fitness function. The ISSA optimization model was constructed using the fitness function as the constraint of an equation. Finally, the combination of structural parameters with optimal ventilation capacity was obtained for a given noise frequency.  
acoustic metamaterials; labyrinth structures; impedance series; parameter optimization; thermo-viscous loss +
Footnote †: journal: Applied  
0000-0001-9993-3880][PERSON]  
1 School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China; [EMAIL_ADDRESS] (D.Z.); [EMAIL_ADDRESS] (C.C.); [EMAIL_ADDRESS] (X.S.) 1
## 3 Experiments and Results
### Experimental Results
#### 3.4.3 Ablation Study  
To assess the advantages of different modules contained in the proposed AEUNet++ model, in this section, we compared the performance of UNet++, AUNet++ (UNet++ with the attention block), and AEUNet++ (UNet++ with attention block and multi-task learning) based on the WHU aerial image data set. As shown in Table 3, the two modules could improve the prediction accuracy; compared with multi-task learning, the combination with the attention block boosted the segmentation performance dramatically. The reasons may be that while the attention module could adaptively refine the four prediction feature maps output by UNet++, feature maps were learned that should have been emphasized or suppressed, which was most important for the final result. Hence, combination with the attention block was able to improve the accuracy further. Although  
layer feature maps in light of their relative importance, and to multi-task learning for integrating the boundary geometric information of buildings into the proposed AEUNet++ network. To test the performance of AEUNet++, experiments on two data sets representing the distribution of buildings at different scales were conducted. Compared with the existing U-Net and UNet++ models, AEUNet++ was more accurate in visual and quantitative evaluations. Therefore, AEUNet++ is an effective method for extraction of buildings from high spatial resolution imagery. In future research, we will explore better ways of expressing multiscale features and boundary constraints and expanding the data sets.  
Conceptualization, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and X.Z.; methodology, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); software, X.Z.; validation, X.Z.; formal analysis, [PERSON] ([PERSON]), [PERSON] ([PERSON]), X.Z.; investigation, [PERSON] ([PERSON]), [PERSON] ([PERSON]), X.Z.; resources, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); data curation; writing--original draft preparation, [PERSON] ([PERSON]) and X.Z.; writing--review and editing, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); visualization, X.Z.; supervision, [PERSON] ([PERSON]) and H.Z. ([PERSON]); project administration, H.Z. ([PERSON]); funding acquisition, H.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation, China (No. 41971400), and in part by the Fundamental Research Funds for the Central Universities under Grant 2019 ZDPY09.  
Not applicable.  
The data and the code of this study are available from the corresponding author upon request.  
No potential conflict of interest was reported by the author.
# Development of the First Portuguese Radar Tracking Sensor for Space Debris
## 2 Implemented System
### Reference  
The reference module, depicted in Figure 1 in orange, generates reference signals at 100, 400, and 400.4 MHz. The main reference oscillator is a 5 MHz oven-controlled crystal oscillator (OCXO) from MTI [31]. The 5 MHz reference locks a pair of 100 MHz XPLO's that are then multiplied by 4 to generate the 400 and 400.4 MHz. This module is connected to the controller board in order to switch between IQ detection at base band or with 400 KHz offset.
# Mixed Reality Enhanced User Interactive Path Planning for Omnidirectional Mobile Robot
## 6 Experimental Results and Analysis  
### The Design of the Interactive System  
The interactive system designed in this paper included the interactive interface of HoloLens, the control of the mobile robot, and the communication between the two parts.  
The interface was a virtual panel and some buttons with Unity3D software combined with HoloToolKit, as shown in Figure 6. The panels can show the map and the mobile robot's trajectory, and the buttons can interact with the movement of the mobile robot. These buttons in the interface can be used to establish communication with the mobile robot and receive the map and robot position, control the start and stop of the robot, and set the position of the target point and obstacles through gesture interaction. This control mode gives full play to the advantages of HoloLens, which can not only see the real motion scene of the robot through the lens but also operate the projected virtual panel only through gestures without the help of other devices.  
We then built the project in a Visual Studio solution file in Unity3D, and used Visual Studio to deploy the project to HoloLens via USB cable. When HoloLens and the mobile robot are on the same LAN and their IP addresses are matched, HoloLens can control the mobile robot independently.  
The communication mode in this system adopted the flow shown in Figure 7. Communication between the interface in HoloLens and robot operating system (ROS) was conducted through ROSbridge WebSocket. After receiving the information, the ROS system would send it to the ROS plugin related to the path planning algorithm and map construction. Finally, the calculated motion control information would be sent to the mobile robot that subscribed to it through the ROS topic. HoloLens received information from ROS in the same way. Compared with other communication protocols, WebSocket can frequently carry out two-way data transmission and maintain a persistent connection between communication parties, which is more suitable for real-time control between robot and HoloLens during robot movement in this system.  
The complete system operation process is as follows:  
1. Establish a connection to synchronize the pre-acquired map and the initial coordinates of the mobile robot;
2. Set the moving target point and start the robot;
3. In the process of robot movement, choose whether to add virtual obstacles and their positions;
4. Transmit the current position of the real robot to the virtual panel in real-time until it reaches the target point.  
Because of the network transmission delay, the position of the robot in the virtual panel will be slightly delayed from its actual position, but the operator can see the robot's movement in the real scene at the same time, so the impact of this small delay is not great.
## 3 Results
### Significance Evaluation of Effective Connectivity  
Figure 4 shows the significant effective connectivity provided using BMR and BMA. The \"Group Mean\" panel illustrates the results of commonalities across all subjects. We could see that some connections were removed for a posterior probability of 0, indicating  
Figure 3: Results of the effective connectivity analysis using spectral Dynamic Causal Modeling (DCM) and Parametric Empirical Bayes (PEB). The “Group Mean” panel illustrates the posterior beliefs of all connections for the group commonality. The positive posterior mean indicates the corresponding effective connectivity is excitatory, whereas the negative mean indicates inhibitory connectivity. The “Group Difference” panel shows the posterior beliefs of all connections for the group difference, where the positive posterior mean reveals that the corresponding effective connectivity is enhanced more in drivers than in controls, and the negative value means the connectivity is reduced in drivers. The numbers next to the arrowhead label the corresponding connections, in accordance with the figures on the abscissa.  
these connections are not critical for the functional network construction. The others establish a directed connectivity network of either excitatory (marked in red) or inhibitory (blue) connections among the four brain regions (the connection with a posterior probability of 100% is shown in bold). Interestingly, inhibitory connections are established from the pre-SMA to the remaining three regions, while the connections from these brain regions to the pre-SMA are excitatory.  
The \"Group Difference\" panel shows the group difference results provided using BMR and BMA. We can now make a clear distinction that the drivers establish a total of seven enhanced effective connections relative to the controls. The posterior probabilities of all connections are shown in a bar chart, ranging from about 57% to 100%. The self-connection of the pre-SMA and the pre-SMA\(\rightarrow\)JSFG connection have 100% posterior probabilities, indicating that these two connections exhibit the most significant group difference.
## 5 Discussion
Beyond individual student benefits, it can aid educators by pinpointing areas within a class or student group that require additional attention based on exercise or test performances. Educators can thereby, accordingly, refine instructional strategies, identify curricular gaps, and ensure that teaching methodologies align with students' evolving needs, which actively contribute to the pedagogical landscape. It can also be used by digital textbook creators in assessing the coverage of exercises with respect to textbook content and help to inform the design of exercises to align with chapter content. Thus, linking learning resources and building such AI-enhanced educational recommendation systems are foundational for intelligent textbook and technology-enhanced teaching and learning. However, to bridge the gap between technological expectations and user experiences, thorough testing, evaluation, and validation in real-world settings with educators and learners are imperative. Future work should expand testing to cover multiple subjects, languages, cross-cultural users, and diverse educational contexts to further enhance the pedagogical value of the proposed technical solution. In this study, exercises were linked to textbook content at the level of textbook sections. This choice was primarily a result of the structure of the digital textbooks that were used, where there was a 1:1 mapping between exercises and textbook sections. While this arguably seems reasonable in terms of utility, one could consider recommending textbook content at different levels of granularity, e.g., in the form of chapters or sub-sections. The performance of linking exercises to textbook content at the chapter level would probably increase due to it likely being an easier task, while utility would most probably decrease. On the other hand, utility could potentially increase if recommendations were made on the sub-section or even paragraph level, depending on the nature of the exercise. In future work, we aim to evaluate our approach at different levels of abstraction, which will require manual annotation of the data or the involvement of human evaluators. Beyond different structural levels within textbook content, this technique can also be used to recommend and link various learning materials, such as curriculum goals, lecture slides, concept dictionaries, and external supporting resources. With the aid of an AI-enhanced content recommender, these learning materials can be harnessed effectively to cater to the requirements of both educators and students. Our first attempt to evaluate Swedish knowledge graph-based ConceptNet Number-batch model reveals that the model is trained under way too low a coverage of sources in the Swedish language, which results in an uncomplete Swedish vocabulary, leading to unstable and underperforming results. This may have an impact when using ConceptNetNumberbatch alone or integrating with other models. The absence of a well-established pre-trained Swedish knowledge graph model leaves space for future research.