esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulations and Results
### Orbit Determination Simulation
#### 3.2.3 Statistical Analysis with Various Satellites
For the satellite with NORAD ID 58837 launched in the same batch, the 24 h predicted RMS position error after OD can reach approximately 13 km, with a maximum value of about 60 km, as shown in Figure 14a. Sudden jumps are observed in the predicted position and semi-major axis errors, which are caused by discontinuities in the \"true orbit\". Such discontinuities are systematically present because the \"true orbit\" is generated through stitching multiple ephemeris files, where discontinuities may occur at file transition epochs. Taking the satellite with NORAD ID 58839 as an example, Figure 15a,b demonstrate that our method achieves robust orbit determination, despite the presence of orbital discontinuities. The 24 h prediction RMS position error is about 1.54 km. However, excessively large jumps may potentially indicate orbital anomalies. For Starlink satellites in the orbit-raising phase, such jumps are typically associated with significant changes in the rate of the semi-major axis (orbital altitude) variation. The variation in the \"true\" semi-major axis with time during the OP period for the satellite with NORAD ID 58837 is illustrated in Figure 16. The red dashed lines in the figure divide the evolution of the semi-major axis into four segments, each characterized by different rates of semi-major axis change. This change in the orbital behavior of Starlink 58837 around 8 February 2024 is also clearly captured in Figure 1a, where the TLE-derived orbital altitude profiles show significantly different rates of change. Under such circumstances, the constant along-track acceleration assumption employed in our reduced orbital dynamics model becomes invalid, leading to significant OD and OP errors. Figure 14: Predicted position and semi-major axis errors over 24 h for Starlink satellite with NORAD ID 58837: **(a)** position errors; **(b)** semi-major axis errors. Figure 15: Predicted position and semi-major axis errors over 24 h for Starlink satellite with NORAD ID 58839: **(a)** position errors; **(b)** semi-major axis errors.
## 2 OMA of Linear Time-Invariant Structures Based on LPP  
### Problem of OMA of Linear Time-Invariant Structures  
According to the dynamic theory of the structure, the dynamic equation of \(n\) degrees of freedom (DOF) linear time-invariant vibration structure in the physical coordinate system is:  
\[\mathbf{M}\ddot{\mathbf{X}}(t)+\mathbf{C}\dot{\mathbf{X}}(t)+\mathbf{K} \mathbf{X}(t)=\mathbf{F}(t) \tag{1}\]  
where \(\mathbf{M}\in\mathbb{R}^{n\times n}\), \(\mathbf{C}\in\mathbb{R}  
by \(d\) order modal response \(\overset{\rightarrow}{q}_{i}(t)\in\mathbb{R}^{1\times T}(i=1,2,\cdots,d)\). The main idea of OMA is to identify the modal shape matrix \(\mathbf{\Phi}\in\mathbb{R}^{n\times d}\) and modal response matrix \(\mathbf{Q}(t)\in\mathbb{R}^{d\times T}\) of the structure only from the vibration displacement response signal \(\mathbf{X}(t)\in\mathbb{R}^{n\times T}\). Finally, the natural frequency \(f\) and damping ratio \(\xi\) are identified by using the single degree of freedom (DOF) technique [37] from the modal response matrix \(\mathbf{Q}(t)\in\mathbb{R}^{d\times T}\). For example, the natural frequency \(f\) was identified by fast Fourier transform (FFT), and the damping ratio \(\xi\) was identified by the random decrement technique (RDT) and the Hilbert transform (HT) from the modal response matrix \(\mathbf{Q}(t)\in\mathbb{R}^{d\times T}\). The idea was proposed in [38; 39]. When the value of each natural frequency is different, The main modal shape \(\overset{\rightarrow}{\Phi}_{i}\) satisfies normalized orthogonality and the modal response \(\overset{\rightarrow}{q}_{i}(t)\) of each other is independent.  
\[\mathbf{\Phi}^{T}\mathbf{\Phi}=\mathbf{I}_{d\times d} \tag{3}\]  
\[\mathbf{Q}(t)(\mathbf{Q}(t))^{T}=\mathbf{\Lambda}_{d\times d}=\left[\begin{array}[ ]{ccccc}\overset{\rightarrow}{q}_{1}(t)(\overset{\rightarrow}{q}_{1}(t))^{T }&\cdots&0&\cdots&0\\ \vdots&\ddots&\vdots&\ddots&\vdots\\ 0&\cdots&\overset{\rightarrow}{q}_{i}(t)(\overset{\rightarrow}{q}_{i}(t))^{T }&\cdots&0\\ \vdots&\ddots&\vdots&\ddots&\vdots\\ 0&\cdots&0&\cdots&\overset{\rightarrow}{q}_{d}(t)(\overset{\rightarrow}{q}_ {d}(t))^{T}\end{array}\right]=\left[\begin{array}{ccccc}\boldsymbol{ \kappa}_{k}&\cdots&0&\cdots&0\\ \vdots&\ddots&\vdots&\ddots&\vdots\\ 0&\cdots&\boldsymbol{\kappa}_{i}&\cdots&0\\ \vdots&\ddots&\vdots&\ddots&\vdots\\ 0&\cdots&0&\cdots&\boldsymbol{\kappa}_{d}\end{array}\right] \tag{4}\]
## 3 Results
### Simulation
To quantitatively analyze the performance of the proposed method and quantify the image resolution, a dimensionless metric termed the array performance indicator (API) [11] was adopted and defined as  
\[\text{API}=A_{-6\text{dB}}/\lambda_{c}{}^{2} \tag{31}\]  
where \(A_{-6\text{dB}}\) is the image area in which the amplitude is higher than \(-6\) dB of its maximum value in the defined imaging area, and \(\lambda_{c}\) is the wavelength at the center frequency. As well as the API, the full width at half maximum (FWHM) was introduced to represent the \(-6\) dB lateral resolution of the results. Table 2 lists the APIs and FWHMs of five representative SDHs, where the number of the SDH represents the order of the SDH from left to right. From the table we can see that the average API of the result reconstructed by the TFM was 0.7264, while that of the result of the proposed method was 0.7070. In addition, the FWHM of the proposed method's result was 0.9738 mm, which is about 4.5% less than the result of the TFM. The table shows that the proposed method has a small advantage compared to the TFM in lateral resolution. Table 2 also lists the time costs for the image reconstruction with these two methods. The TFM took about 258.12 s, and the  
Figure 5: Simulation results reconstructed by TFM and proposed method. A in (**a,b**) represents the imaging result of the tenth SDH of the specimen from left to right. (**a**) The image reconstructed by TFM. (**b**) The image reconstructed by the proposed method. (**c**) The local enlarged image of SDH A in (**a**). (**d**) The local enlarged image of SDH A in (**b**). proposed method took only 4.31 s to reconstruct the image. The proposed method shows a huge computational efficiency advantage compared with the time-domain TFM. Therefore, the simulation results show that compared to the TFM, the proposed method can avoid the distortion of the reflectors and improve the SNR of the images while achieving a relatively low API. More importantly, it has a huge efficiency advantage over the time-domain TFM.
## 4 Results Analysis
### Sensitivity of Wave Velocity to Material Properties and Volumetric Parameters  
A sensitivity analysis was conducted to evaluate (1) if the wave velocity was sensitive enough to capture the materials with varied mechanical properties (i.e., dynamic modulus), (2) to find out how the wave velocity changes (i.e., increase or decrease) with increased/decreased magnitude of material properties, (3) to determine the effects of different depth positions on wave velocity, and (4) to evaluate the effects of different load pressures on wave velocity.  
Figure 6a shows that when all the other parameters (i.e., density and pavement structure) remained constant, the ultrasonic wave velocity was sensitive enough to the change in the dynamic modulus; the ultrasonic wave velocity kept increasing with the increased dynamic modulus value with robust correlation (R\({}^{2}\) was close to 1). Figure 6b shows the influence of density on wave propagation by changing its value with an increment of 1000 kg/m\({}^{3}\), starting from 2100 kg/m\({}^{3}\), while keeping the other  
Figure 5: Relationship between simulated and measured ultrasonic wave velocity. (**a**) Non-destructive testing (NDT)-measured and simulated and (**b**) laboratory-measured and simulated.  
parameters constant (i.e., dynamic modulus and pavement structure). It can be seen that, generally, the wave velocity decreased with the increase in density, but this decrease was not large and the wave velocity value tended to be stable. This shows that density had less influence on ultrasonic wave propagation as compared with dynamic modulus.  
To analyze the effect of thickness on wave velocity, model AC-13 was selected and analyzed with 8 different depth locations, as shown in Figure 7a.  
From Figure 7b, it is observed that simulated wave velocities generally rose with increasing depth. However, at relatively deep locations, ultrasonic wave velocity tended to plateau with the same magnitude of vibration. This was due to ultrasonic waves interfering with each other due to reflected waves at the bottom of the model during wave propagation.
# Enhancement of In-Plane Seismic Full Waveform Inversion with CPU and GPU Parallelization
## 1 Introduction
### Importance, Scope and Limitations of the Study  
FWI is a handy non-destructive method to obtain accurate information about the subsurface parameters of the earth of critical infrastructure, and its importance is still increasing. However, FWI primarily has two significant scopes for improvement to find widespread applicability in exploration geophysics and engineering. They are: (a) the improvement of the optimisation and parameter search algorithms, which is not the scope of this study, and (b) the improvement in computational efficiency. FWI is an iterative algorithm that demands high computational costs and high memory, which is generally out of the scope of general computers. If possible, the computation for the given hardware often takes days or months, which makes it practically inefficient.  
The present study mainly focuses on enhancing in-plane seismic full waveform inversion with CPU and GPU parallelisation. The existing numerical algorithms are implemented in serial and parallel codes, and the computational efficiency achieved by parallelisation is studied. Thus, the computational enhancement by parallelisation is limited to the specific algorithm used in the programming. The study was conducted about in-plane elastic waves in the time domain, and the full waveform inversion was conducted using the step-gradient method (See [31]). Other advanced algorithms, where faster and more robust search methods enhance the FWI algorithms, are not within the scope of this study. Additionally, the test and validation are conducted for the limited number of hardware available; thus, the performance boost due to CPU and GPU parallelisation may vary depending upon the hardware configuration.
## 3 Results and Discussion
### Path-Planning Simulation  
Following the indication of relevant anatomical information by the neurosurgeon, including the entry region, obstacles to avoid, sulci, blood vessels, and tract information, the algorithm is executed to obtain the optimal trajectory to reach the destination point. In Figure 6, the optimal trajectory obtained from an algorithm execution with only 100 iterations is visualized. With the aim of minimizing tissue damage during the procedure, a straight trajectory is selected from the entry region to the area surrounding obstacles. The RRT algorithm is then executed to complete the trajectory to the target point while avoiding obstacles. The obtained trajectories are then compared with the paths generated using the RRT algorithm from the entry point to the goal (see Figure 7). Subsequently, all trajectories are evaluated by the cost function (safety, effort, and entry angle) to select the optimal path. The provided solution features a straight trajectory that reaches approximately halfway to the destination point, after which a modification in direction is made to avoid proximity to the tracts identified in the forceps minor, ensuring that the least amount of tissue possible is affected while avoiding obstacles.  
Figure 7 shows the algorithm's execution for the evaluation of the different entry points. In order to obtain this Figure, the RRT algorithm was applied from the entry point to the end point, resulting in trajectories that minimized the score obtained from the binary volumetric dataset, which is consistent with a more restrictive execution of the algorithm in terms of security, but this includes surgical tools with navigation capabilities that currently do not exist. Figure 7 also shows the trajectory selected as a result (blue).  
Figure 5: Volumetric view of the entry point and angle information obtained in Slicer3D: (**a**) Three-dimensional view. (**b**) Sagittal plane.
# Dolphin Sounds-Inspired Covert Underwater Acoustic Communication and Micro-Modem
## 6 Conclusions  
In this paper, we have designed a portable micro-modem based on the bionic covert UAC algorithm for underwater sensor networks. Specifically, it is the first time that a modem has been used to communicate using real dolphin-call signals. It opens the door for secure and covert communication--the message between the divers or UUVs and control center cannot be identified. Additionally, the compact design of the modem has many advantages such as being portable for divers, it can be placed on UUV and it can also be used as a standalone unit for a particular application. In particular, it is suitable for combat divers' information transmission at short distances and other military applications with covert operations. In the pool experiment, we tested the covert UAC algorithm by using the time delay between dolphin sounds and verified that the modem could efficiently perform reliable data transmission over short distances. At a distance of 10 m, a data rate of 27.1 bits per second is achieved. However, work is still in progress and our current work only uses transducers for lab communication. We plan to test our modem in the sea or a lake shortly. We will try to increase the communication distance as well as the data rate. We will also enhance the circuit of the modem, improve its stability, and reduce its output noise. The existing dolphin sounds library  
Figure 16: The time–frequency analysis and demodulation results; (**a**) Transmitted dolphin clicks; (**b**) Correlation result of transmitted clicks; (**c**) Received clicks at 7.2 m; (**d**) Correlation result of received clicks at 7.2 m; (**e**) Received clicks at 10 m; (**f**) Correlation result of received clicks at 10 m.  
can be expanded, and more algorithms need to be developed to imitate dolphin sounds and realize covert communication.  
This work was supported by the National Natural Science Foundation of China (Grant No. 61771152), and the Natural Science Foundation of Heilongjiang, China (Grant No. F2016010). All authors contributed significantly to the work presented in this manuscript. [PERSON], [PERSON] and [PERSON] proposed the new idea described in this paper; [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON] provided some valuable advice and contribution.  
The authors declare no conflict of interest.
# Efficient Data Exchange between WebAssembly Modules
## 3 Related Work
### Conclusions  
Considering that different standards have different objectives, they are difficult to compare directly. However, all popular solutions define specific functions to be imported by the module and exported by the host. A common issue among these systems is potential incompatibility, which is notably seen in WASI. As standards evolve and new functions are added, outdated hosts may not support these functionalities. This potentially renders the module inoperable. By contrast, the component model attempts to handle evolution and extend the communication between modules, but it is still under development and requires copying and transforming data between each module.  
When analyzing existent serialization formats, JSON is versatile and popular due to its wide support across programming languages. However, it is inefficient for inter-process communication (IPC) and does not allow for partial reading. MessagePack and Protobuf are efficient in compression and performance, requiring a description language and generating specific code for each data field. They excel in scenarios with full data reads and efficient memory management.  
Zero-copy serializers such as FlatBuffers, Cap'n'Proto, and Karmem enable direct data reading, making them useful for random access without full message decoding. FlatBuffers support the omission of empty fields, unlike Karmem and Cap'n'Proto. However, Flat  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Feature** & **JSON** & **MessagePack** & **ProtoBuf** & **FlatBuffers** & **Cap’n’Proto** & **Karmem** \\ \hline Requires schema description & No & No & Yes & Yes & Yes & Yes \\ Schema evolution & N/A & N/A & Yes & Yes & Yes & Yes \\ Compression & None & Low & High & Low & None & None \\ Supports random access & No & No & No & Yes & Yes & Yes \\ Security & High & High & High & Low & High & Medium \\ Languages coverage & High & High & High & Medium & Medium & Low \\ Generator integration & N/A & N/A & External & Built-In & External & Built-In \\ Reference implementation & JS & C & C++ & C++ & C++ & Go and Zig \\ Maximum Size & N/A & N/A & N/A & 2 GB & 16 EB & 4 GB \\ Supports custom default & N/A & N/A & Yes & Yes & Yes & No \\ value & N/A & N/A & Yes & Yes & No & Yes \\ Supports fixed-size vector & N/A & N/A & Yes & Yes & No & Yes \\ Supports vector of vectors & N/A & N/A & No & No & Yes & No \\ Supports map/dictionary & N/A & N/A & Yes & No & No & No \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between serializers.  
Buffers' default lacks data verification, which poses security risks, and its generated code may be less efficient in some languages. Additionally, table lookups and multiple pointer traversals can introduce performance penalties, especially with large amounts of data.
# Open-Set Signal Recognition Based on Transformer and Wasserstein Distance
## 4 The Proposed Algorithm
### Algorithm Framework  
In order to summarize, the overall algorithm, namely the open-set signal recognition based on the transformer and Wasserstein distance (OSSG\({}_{TW}\)), can be proposed as:  
\[\left\{\begin{array}{l}(1)\text{ Initialize the parameters }\theta,\left\{p^{k}\right\}, \mathcal{R}\text{ respectively; set}\\ \alpha,\,\beta,\,\eta,\,\gamma,\,\text{as the learning rate }\mu\text{ and the iteration number }t;\\ (2)\text{ Perform }t\to t+1;\\ (3)\text{ Calculate the loss by }\mathcal{L}_{all}^{t}\left(\theta^{t},\left\{\left(p^{k} \right)^{t}\right\},\mathcal{R}^{t}\right);\\ (4)\text{ Update the parameters }\left\{p^{k}\right\}\\ \left(p^{k}\right)^{t+1}=\left(p^{k}\right)^{t}-\mu\frac{\partial\mathcal{L} _{all}^{t}}{\partial p^{k}}\left(\theta^{t},\left\{\left(p^{k}\right)^{t} \right\},\mathcal{R}^{t}\right);\\ (5)\text{ Update the parameters }\mathcal{R}\text{ by}\\ \mathcal{R}^{t+1}=\mathcal{R}^{t}-\mu\frac{\partial\mathcal{L}_{all}^{t}}{ \partial\mathcal{R}}\left(\theta^{t},\left\{\left(p^{k}\right)^{t}\right\}, \mathcal{R}^{t}\right);\\ (6)\text{ Update the parameters }\theta\text{ by}\\ \theta^{t+1}=\theta^{t}-\mu\frac{\partial\mathcal{L}_{all}^{t}}{\partial \theta}\left(\theta^{t},\left\{\left(p^{k}\right)^{t}\right\},\mathcal{R}^{t} \right).\end{array}\right.\]  
Figure 2: The principles of the proposed center loss and the central feature. The center loss drives the embedding features of each category to accumulate to the corresponding central feature \(\mathfrak{c}\).
## 2 Constellation Station Maintenance Analysis
In this context, this paper summarizes and compares the literature on constellation orbit-control strategies from around the world. The existing research results, existing problems, and areas to be further studied are analyzed, and the geometric configuration maintenance law, orbit-control methods, and constellation orbit-control strategies of large-scale low-orbit constellations are studied. The aim is to realize the optimal design of large-scale constellation orbit control in low orbit and to provide a reference for the future engineering planning and control operation of the Internet of Constellations.
## 2 Design of Proposed Bluetooth-Beacon-Based PWS
### Design of BLE Transmitter Units Using Bluetooth Beacons  
The transmission characteristics of Bluetooth beacons may vary depending on their orientation installation (Figure 2). For example, in the case of RECO beacons developed by Perples Inc. (Seoul, Korea), signals with transmission intensity set to \(-4\) dBm can be propagated up to \(70\) m in front, \(40\) m to the back, and \(30\) m to the side of the beacon [20]. Signals propagating to the front and back of the beacon exhibit directionality, whereas those propagating to the side exhibit omnidirectionality. The proposed BBUNPROWS was designed to use omnidirectional signals generated from the sides of beacons, accomplished by attaching Bluetooth beacons in a horizontal orientation.  
Figure 3 depicts examples of Bluetooth beacons installed on a worker's helmet, on the back of a vehicle, and at a potentially hazardous location. Bluetooth beacons attached to workers' helmets help drivers recognize workers approaching the caution and warning zones, even in dark workspaces, thereby preventing collisions. Bluetooth beacons installed at potentially hazardous locations provide alerts to drivers of the approach of vehicles towards a dangerous zone, thereby prompting safe driving. Inside underground tunnels, where vehicles move linearly along a fixed path, Bluetooth beacons can be installed on the rear of each vehicle, with a smartphone installed at the front to prevent collisions between vehicles.  
Figure 1: Conceptual diagram depicting Bluetooth low energy (BLE) transmitter and receiver units of the proposed Bluetooth-beacon-based underground proximity warning system (BBUNPROWS).
## 2 Principles
### Wavelet Packet Energy Method  
#### 2.3.1 Wavelet Packet Decomposition  
Wavelet packet decomposition (WPD), also known as optimal subband tree structuring, is a further optimization of wavelet transform. On the basis of the wavelet transform, both low-frequency and high-frequency subbands are decomposed at each level of signal decomposition. A cost function is then minimized to calculate the optimal signal decomposition path, which determines the decomposition of the original signal. The three-layer wavelet packet decomposition tree is shown in Figure 4.  
Figure 4: Wavelet packet decomposition diagram.  
By using wavelet packet decomposition, the original signal \(x(t)\) can be expressed as [49]  
\[x(t)=\sum\limits_{i=1}^{2^{j}}x_{j}^{i}(t) \tag{10}\]  
where \(x_{j}^{i}(t)\) represents the \(i\)-th subband of the \(j\)-th decomposition layer, and can be expressed as  
\[x_{j}^{i}(t)=\sum\limits_{-\infty}^{+\infty}c_{j,k}^{i}(t)\psi_{j,k}^{i}(t) \tag{11}\]  
where \(\psi_{j,k}^{i}(t)\) represents the wavelet packet function and \(c_{j,k}^{i}(t)\) represents the wavelet packet coefficient.  
The relationship between the \(j\)-th decomposition layer and the \((j+1)\)-th decomposition layer is  
\[\begin{array}{l}x_{j}^{i}(t)=x_{j+1}^{2^{j}-1}(t)+x_{j+1}^{2i}(t)\\ x_{j+1}^{2^{j}-1}(t)=x_{j}^{i}(t)\sum\limits_{k=-\infty}^{+\infty}h(k-2t)\\ x_{j+1}^{2^{j}}(t)=x_{j}^{i}(t)\sum\limits_{k=-\infty}^{+\infty}g(k-2t)\end{array} \tag{12}\]  
where \(h(k)\) represents the low-pass filter, \(g(k)\) represents the high-pass filter, and \(k\) is the translation parameter.