esa-sceva/satcom-chunk-collection huggingface dataset:
# Accelerating Image Classification using Feature Map Similarity in Convolutional Neural Networks
## 3 Feature Map Caching Method
### Similarity Measurement  
The similarity of the input feature vector \(V_{query}\) (\(U\)) with the cached feature vector \(V_{cached}\) (\(T\)) can be defined as a cosine similarity. We use the following cosine distance to express similarity as distance:  
\[D(U,\ T)=1-\frac{\sum_{i}(U_{i}\times T_{i})}{\sqrt{\sum_{i}(U_{i})^{2}} \times\sqrt{\sum_{i}(T_{i})^{2}}} \tag{6}\]  
Figure 2: Feature vector extraction process. Mean and standard deviation of each feature map are calculated and listed to generate a feature vector \(V\).  
This is a real value between 0 and 1. A distance value of zero means the two feature vectors are equal. From this, the input feature vector can be classified into the corresponding class of the cached feature vector with the highest similarity. However, in this case, the input feature vectors are classified as the most similar feature vectors among the cached feature vectors, which can lead to incorrect answers. Hence, if all input feature vectors are classified using only the cache, then all results will be a _Cache Hit_, which contains true and false positives. If a false positive is obtained, it is better to classify it using the CNN without the cache. Thus, even though the most similar cached feature vector is obtained for the input feature vector, this value should be set to a _Cache Miss_ using the threshold value, if this result is a false positive.  
Therefore, the threshold is defined as the cosine similarity between \(V_{new}\) (\(U\)) and \(V_{cached}\) (\(T\)) when \(V_{cached}\) is extracted.  
This threshold is also updated every time the cache is accumulated. Therefore, the distance threshold \(D_{cached}\) for _Cache Hit_ is defined as follows:  
\[D_{cached}=\ \frac{(D_{cached}\times n)+D_{new}}{n+1} \tag{7}\]  
where  
\[D_{new}=1-\frac{\sum_{i}(U_{i}\times T_{i})}{\sqrt{\sum_{i}(U_{i})^{2}}\times \sqrt{\sum_{i}(T_{i})^{2}}} \tag{8}\]  
However, if the cumulative count of \(V_{cached}\) is one, the distance cannot be obtained. Therefore, to specify a threshold, at least two images in one class must be cached. Each time the cumulative count increases, a mean of the cached distance is refreshed. In addition, the cache of each convolution layer contains class, cumulative count (\(\geq\)2), mean distance, and mean feature vector.  
Finally, when classifying the input image using the cache, the matching function \(f\) for determining _Cache Hit_ from the input feature vector \(V_{query}\) and cached feature vector \(V_{cached}\) based on distance threshold \(\varepsilon\) is defined as:  
\[f_{\varepsilon}(V_{query},\ V_{cached})\left\{\begin{array}{ll}Hit&if\ \min\{D(V_{query},V_{cached})\}\leq\varepsilon\\ Miss&otherwise\end{array}\right. \tag{9}\]
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 3 Trajectory Data Sparsity and Anomaly Detection
### Sparse and Abnormal Trajectory Points Detection
#### 3.2.1. Detection Method Based on Time Elements  
In a single voyage mission, the acquisition of AIS trajectory data may be affected by the inconsistency of the acquisition frequency, resulting in inconsistent time intervals. This inconsistency may cause two problems: a larger time interval may cause sparse trajectory, resulting in the loss of critical vessel trajectory information, and a smaller time interval leads to data redundancy. To properly solve this problem, ensure the integrity and continuity of the trajectory data, and eliminate the potential impact of time interval anomalies, this study deals with time intervals as follows. Firstly, the time interval percentage in the trajectory data was counted as shown in Figure 2, through which it was found that the amount of time interval data within 180 s accounted for more than 90% of the total amount. Based on this finding, 180 s was determined as the interpolation time. Then, the time difference between two adjacent points is judged; if this time difference is greater than twice the interpolation time, i.e., greater than 360 s, it means that the trajectory segment is a sparse trajectory restoration, i.e., data is missing; is marked as a trajectory restoration point; and the number of restoration points is \(\text{int}(\Delta t/180)\).
# Implementation of Fault-Tolerant Control for
###### Abstract  
In this paper, an active fault-tolerant control for a robot manipulator based on synchronous sliding mode is proposed. As the synchronization errors approach zero, the joint errors tend to become equal and also approach zero. Therefore, the synchronization technique is inherently effective for a fault-tolerant controller. To demonstrate such a system, the following implementation is presented. First, an estimator was designed with an extended state observer to estimate uncertainties/disturbances along with faults/failures. The estimator signal was used for an online compensator in the controller. A fault-tolerant controller with a combination of synchronous sliding mode technique and estimator was proposed. The stability of the system was established using Lyapunov theory. Finally, fault tolerant control was implemented in a three degree-of-freedom robot manipulator and compared to the conventional sliding mode control. This comparison shows the effectiveness of the proposed active fault-tolerant control with synchronous sliding mode technique.  
Keywords:fault-tolerant control; active fault-tolerant control; sliding mode control; synchronous sliding mode control; extended state observer; fault estimation; fault +
Footnote †: journal: Applied
# Online Foot Location Planning for Gait Transitioning Using Model Predictive Control
## 4 Foot Location Selection Based on MPC
Equation (23) selects the foot that is currently in the swing period. By using Equations (19) and (20) can be converted into the following quadratic optimization form. \[\min_{\mathbf{x}}\frac{1}{2}\mathbf{p}_{\mathbf{f}}^{\mathbf{T}}\mathbf{H} \mathbf{p}_{\mathbf{f}}+\mathbf{p}_{\mathbf{f}}^{\mathbf{T}}\mathbf{g} \tag{24}\]  
where \(\mathbf{H}=2\big{(}\mathbf{\bar{B}}^{\mathbf{T}}\mathbf{L}\mathbf{\bar{B}}+ \mathbf{K}\big{)}\), \(g=2\mathbf{\bar{B}}^{\mathbf{T}}\mathbf{L}(\mathbf{\bar{A}}\mathbf{X}_{0}- \mathbf{X}_{d})-2\mathbf{K}\mathbf{p}\). By limiting the foot location of each time step to be equal, the dimension of the matrix to be computed is greatly reduced and the efficiency of the computation is improved. The dimensionality of the final matrices \(\mathbf{H}\) and vector \(\mathbf{g}\) is independent of the prediction length and is only related to the number of variables. Therefore, it can greatly reduce the amount of computation to improve the computation speed and increase the frequency of prediction. \[\mathbf{\bar{p}}=\frac{\sum_{i=1}^{4}\mathbf{f}^{(i)}}{m}-\mathbf{g} \tag{25}\]  
\(\mathbf{g}\in\mathbf{R}^{3\times 1}\) is the acceleration of gravity. Equations (11) and (25) form the complete dynamics model of the quadruped robot. In Equation (11), angular acceleration is determined by the foot location and GRFs. The GRFs also determines the linear acceleration in Equation (25). The user not only wants the quadruped robot to be able to keep up with the desired speed, but also to ensure that the posture is stable around the desired value. This places demands on both linear and angular acceleration. The MPC controller will select the GRFs based on Equations (11) and (25) and the foot location. Therefore, the choice of foot location will directly influence the outcome of the GRFs. The HF selects the foot location only according to the current state so that the gain value greatly affects the result. The UPMPC considers not only the current state but also the influence of the foot location on the future state. It improves adaptability by selecting a foot location where the future state is more in line with the desired one, rather than just based on the current state. Therefore, the UPMPC can adapt to multiple gaits and complete gait transitioning between different periods.
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 3 The Proposed Method
### Classification and Regression  
The classification and regression sub-network consists of two parts: the classification branch and the regression branch. In this paper, the classification loss function and regression loss function are designed based on varifocal loss \(L_{vfl}\)[35] and Generalized Intersection over Union (GIoU) loss \(L_{GIoU}\)[36]. This aids in learning feature representations and position estimation suitable for object tracking tasks, ultimately obtaining accurate tracking results.
# Characterization of Inter-System Biases in GPS + BDS Precise Point Positioning
## 4 Characterization of ISB
### Discussion  
Figure 3 shows that the ISBs obtained with even the same receiver and antenna type, they had little difference. Therefore, in order to investigate the receiver and antenna type affected the ISB characterization, we collected the data from ultra-short baseline (about several meters) stations to ensure the same ambient environments. We discuss the implication of receiver and antenna type in more detail below.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Station** & **Receiver type** & **Antenna type** \\ \hline CEDU & SEPT POLARX5 & AOAD/M\_T \\ HOB2 & SEPT POLARX5 & AOAD/M\_T \\ KARR & TRIMBLE NETR9 & TRM59800.00 \\ PNGM & TRIMBLE NETR9 & TRM59800.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Stations selected for the investigation of ISB characterization affected by the ambient environment.  
Figure 2: GPS/BDS ISB series from stations HKSL, KARR and PNGM stations estimated by using Wuhan University (WHU), Center for Orbit Determination (CODE) and Deutsches GeoForschungsZentrum (GFZ) precise products during DOY 043–036, 128–130 and 227–229, 2018.
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
###### Abstract  
Satellite network is considered a prominent architecture for future space-air-ground integrated networks, where the relay satellite network (RSN) plays an essential role in data transmission. However, the relay satellite network faces practical challenges. First, due to the dynamic network topology and limited resources such as storage capacity, antennas, and bandwidth, optimizing data transmission and resource allocation in a resource-constrained stochastic system is challenging. Second, satellites intermittently enter the eclipse zone, which leads to an unstable energy supply. Therefore, optimal energy management is required to balance energy supply and consumption. In this paper, we proposed an online optimal control algorithm, based on Lyapunov stability theory, to optimize data transmission and energy management jointly. Furthermore, the performance of our proposal is analyzed comprehensively, including the reasonable bounds for data and energy and the optimality of our proposed algorithm. Finally, extensive performance evaluation simulations are performed based on real-world satellite parameters. The simulation results indicate that the proposed method can achieve long-term network stability and energy sustainability. Moreover, the proposed algorithm shows an improvement of 9.6% and 20.3% in system utility compared to the non-optimized transmission method and non-optimized energy management algorithm, respectively.  
relay satellite network; data transmission; resource allocation; stochastic optimization +
Footnote †: journal: remote sensing  
02023152629. [[https://doi.org/10.3390/rs15102629](https://doi.org/10.3390/rs15102629)]([https://doi.org/10.3390/rs15102629](https://doi.org/10.3390/rs15102629))
## 4 Structural Sensitivity Analysis
### Sensitivity Analysis of the Maximum Deflection to the Internal Prestress  
When anchoring the steel bundle, the actual internal prestress during the construction process is different from the designed value due to non-standard operation, which will lead to an inevitable impact on the linear shape of the main beam. Therefore, sensitivity analysis of the internal prestress is conducted in this section.  
As plotted in Figure 9, it can be seen that at the state of the maximum cantilever, the effect of the internal prestress increases with the increasing value. Therefore, the end of  
Figure 8: Relationship between the bulk density and maximum variation value of deflection per span.  
Figure 7: Variation of the deflection per span at maximum cantilever end.  
the closure segment of each span is tickled upward with the increasing internal prestress, and the upward torsion trend is linearly varied. After changing the internal prestress, the maximum variation of the deflection of each span occurs at the mid-span position, as plotted in Figure 10. Under the maximum cantilever condition, the variation of maximum deflection at the mid-span position of each span presents a linear relationship with the variation of the internal prestress. The deflections of the first and second spans are less sensitive to the variation of internal prestress compared to the other three spans. Among them, the deflection of the fourth span is most sensitive to the variation of internal stress. When the internal prestress changes by 5%, the deflection changes by 5%. Above all, the change in internal prestress has a certain influence on the linear shape of the bridge, which reflects the problems of structural stability and design optimization. These issues are of great significance to the safety and sustainability of the structure, and by analyzing the data laws, the prestressed design can be optimized and the performance and service life of the structure can be improved.
## 3 Results
### Video Consumption  
Figure 7 shows an enlargement of the axe about the \"Video consumption\" of Figure 3. Students watched the videos until the end in the first visualization: \"just in case the questions and doubts that appeared would be solved later in the video\". Videos are watched in order: \"Follow the order established by the course guide and see them one after the other according to the time available at each moment\"; and videos are watched several times, \"at least twice per video (one first shot and one review) but in more complicated parts of the course, up to four times\".  
On the other hand, videos are consumed as another element of study and usually watched while studying. For example, one comment was, \"I did not frequently watch the videos, I watched them when I studied the module\". Watching the videos was also related to the delivery of activities or exams. One student said, \"Since I have already watched the videos, I reviewed the videos when having to deliver an activity\". Activities were what we call \"Continous Assessment Activities\" (CAT).  
Figure 6: Enlargement of the box “video quality”.  
An important behavior is that students take notes while watching the video: \"Well, for example, I take notes, if there is something that interests me, I write it down, I always do that\". And regarding videos of problems, they use videos as a support tool who may give them feedback and clues when needed. They usually try to do the problem before watching the video, and in case they do not know how to continue, they see the part of the video until that point. One student gives a reason for that behabiour: \"Because it gives me the option to stop. Solve the problem by myself and then see how far I get by myself. When I arrive to a milestone, I can stop and watch the video to see my performance\".  
It is important to note that students liked the interactivity capabilities of videos, such as the possibility of stopping and jumping directly to several parts of the video. Usually, they interacted with the videos while watching them. \"I use the back button and pause to return to a part that was not well understood or to take notes,\" said one student.  
There are more interactions when reviewing the videos because in the first visualization students usually watch the video until the end. According to one comment, \"In the following reviews, yes, you go more specific and go to specific points... because you already know the content... you go to the place you think you want to see, or you are looking for... or you do a random search because you know that what you are looking for is in that area... then there comes a time when you say, 'it is not giving me anything' and you start moving forward and skipping things to see if something that you did not catch appears\".  
Finally, in general, students do not change the video speed when watching a video.
## 3 Materials and Fabrication Techniques
### Fabrication Techniques
#### 3.3.2 Screen-Printing  
Screen-printing has also been successfully used for the fabrication of OECTs-based biosensors. In 2015, [PERSON] et al. [111] reported on the fabrication of glucose and lactate sensors on a flexible, plastic substrate (polyethylene naphthalene, PEN). These devices were obtained by using three different inks: PEDOT:PSS (fabrication of the transistors active channel, final thickness of 500 nm), poly(vinylidene fluoride) (PVDF, a dielectric material used to insulate the electrical contacts and define the contact area between the channel and the electrolyte, final thickness of 2 um) and finally a silver-based ink (used to realize the electrical contacts, final thickness of approximately 8 um). After printing, each layer was annealed at 130 \({}^{\circ}\)C (for PEDOT:PSS) or at 100 \({}^{\circ}\)C (for PVDF and silver contacts) for a few minutes (Figure 10). More recently, [PERSON] et al. [112] employed screen-printing for the fabrication of an OECT-based sensor for the detection of ascorbic acid in food. In this case, the whole device (active channel, gate, source and drain contacts) were realized by screen-printing a PEDOT:PSS ink on a flexible, polyester substrate. After printing, the transistors were annealed in air at 100 \({}^{\circ}\)C for 10 minutes; the printed layers had a final thickness of about 55 um.
## 2 Methodology
### Control System Diagram  
The model was next imported into a MATLAB-Simulink environment to facilitate an ADAMS-Simulink co-simulation. The model was exported from ADAMS through the plant-export tool and integrated into Simulink as a block, featuring input and output variables as outlined in the preceding sub-chapter. A closed-loop control system has been defined (Figure 7, Table 5), with a specific section of the diagram focused on assessing pointing accuracy.  
The closed control loop incorporates satellite coordinates (transformed from ECEF to ENU reference frame as introduced in Equations (1)-(7)), ship motions, and the previously introduced errors referred to, satellite coordinates and ship motions (Tables 1 and 4), as inputs. Both types of errors are introduced as random errors in the intervals considered. The satellite coordinates with the added errors are multiplied with the rotation matrix from the ENU reference frame to the Pointer reference frame (Figure 8).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Block** & **Content/Description** \\ \hline Sat. C. & Satellite coordinates in ENU reference frame \\ Sat. C errors & Satellite coordinates errors \\ X & Matrix multiply \\ Ship motions errors & Ship motions errors \\ Az-El fcn & Function to calculate azimuth and elevation angles \\ Az DoF control & PID controller for azimuth DoF \\ Model & ADAMS model \\ El Dof control & PID controller for elevation DoF \\ Ship motions & Euler’s angles of ship motions \\ \(R_{p}^{E}\to R_{p}^{E}\)_fcn_ & Function to generate the rotation matrix from ENU r.f to Pointer r.f \\ Error _fcn_ & Function to calculate the pointing error \\ X–Y graph & Pointing error plot \\ \hline \hline \end{tabular}
\end{table}
Table 5: Control diagram blocks descriptions.  
Figure 6: ADAMS model plant layout.  
Figure 7: Control diagram overview.  
This multiplication allows us to express the coordinates of the satellite in the Pointer reference frame.  
The azimuth-elevation function (Figure 9) calculates the satellite's displacement in terms of azimuth and elevation and accounts for the error quantities resulting from the acquisition of ship motions presented in Table 4.  
Azimuth and elevation are calculated as presented in Equations (8) and (9):  
\[\mathrm{Az}=atan2(\mathrm{X},\mathrm{Y}) \tag{8}\]  
\[\mathrm{El}=atan2(\mathrm{Z},(\mathrm{X}^{2}+\mathrm{Y}^{2})^{1/2}) \tag{9}\]  
considering S the position of the satellite as introduced in Equation (6).  
The satellite's displacement's proportional value is then utilized by two PID controllers to generate a force signal to set in motion the azimuth and elevation ATP system axes.  
The model's outputs are the Pointer's Euler's angles, and then, a function transforms them into a rotational matrix and the loop restart.  
From this rotational matrix and the satellite coordinates, it is possible to calculate the pointing accuracy in terms of error along the azimuth and elevation axes, which will be presented in Section 2.7.
## 7 Conclusions  
The paper presents numerical procedures for modeling particle transport in tunnel water and mud inrush using the coupled LBM-DEM. The following main conclusions are drawn:  
(1) Numerical tests have confirmed the feasibility of the coupled LBM-DEM for solving fluid-particle interaction problems dominated by the presence of many particles and the occurrence of channels. The TSP was utilized to identify karst water inrush ahead of the Xiaozhai Tunnel, with subsequent excavation confirming the forecast's accuracy. Additionally, the coupled LBM-DEM was employed to simulate water flow processes in karst pipelines with a small amount of sediment. Particles are transported through water flow to the outlet of karst pipelines under hydraulic gradient loading. When the hydraulic gradient exceeds 6, the Darcy velocity gradually tends to be constant.  
(2) The mud outburst on the tunnel face can be summarized into three stages: initiation stage, acceleration stage, and the stable flow stage. Especially during the acceleration stage, rapid collapse of the tunnel working face occurs as bonding strength between particles increases, leading to a large number of particles being washed away. This highlights the importance of reinforcing advanced support and providing timely support during tunnel construction.  
(3) Tunnel mud-burst damage is attributed to combined effects from various factors including geological conditions, water pressure, and excavation activities. Only when these three components reach certain conditions can a tunnel experience mud-burst disasters.  
The methodology is described for 2D problems, but extending it to 3D cases has proven straightforward. The overall computational cost can be quite significant as a sufficiently fine lattice is frequently demanded and millions of time steps also have to be carried out.  
**Funding:** This work was supported by the National Natural Science Foundation of China (Grant No.: 42101089).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request. All data shown in the figures and tables can be provided on request.  
Figure 14: Position distribution of three solid particles.