esa-sceva/satcom-chunk-collection huggingface dataset:
# A Feasibility Study for a Hand-Held Acoustic Imaging Camera
## 4 Materials and Methods
A key goal is extending as much as possible the minimum detectable frequency to improve directivity with fewer elements. While reducing array aperture, we must balance performance trade-offs from decreased low-frequency directivity and potential undersampling artefacts. This work details simulations on irregular aperiodic subsampling to concentrate high-frequency information while avoiding grating lobes (Figure 4) and exploring upper harmonic reconstruction to determine whether intelligibility is retained without fundamental frequencies reducing the device's bandwidth to optimize the simulation metrics. Following prototype optimization and evaluation using audio test signals, we compare metrics like signal-to-noise ratio to the original large-scale system. This study aims to progress acoustic imaging capabilities from constrained laboratory settings towards real-world applications through engineered mobile platforms. This work aims to re-engineer a compact, portable prototype (Figure 7) that transmits synchronized audio and video data streams to a commercial tablet or laptop. The audio is captured by an array of microphones on the primary module, while the video is acquired by a thermographic or conventional camera. These peripheral modules interface with the central unit via multiple USB connections. Embedding an FPGA onboard the central module alongside an ARM processor enables straightforward interfacing leveraging their integrated architecture (Figure 8). The system operates on battery power with LED indicators and debugging ports and can dock to the tablet mechanically. A remote internet link via WiFi, LTE, or 5G facilitates control and data sharing. The tablet/laptop display provides a visualization interface to process the multimodal data streams using algorithms, machine learning, and deep neural networks. This integrated design retains the core functionality of the original laboratory prototype while minimizing size and maximizing portability for real-world deployment. Ongoing work focuses on implementation challenges including power optimization, heat dissipation, enclosure design, calibration, and field testing. By progressing acoustic imaging capabilities from constrained lab settings to handheld adaptable platforms, this research aims to unlock new applications in machine condition monitoring, spatial sound mapping, and other domains limited by current large-scale wired systems.
## 4 Strain Results and Analysis
### Strain Analysis
#### 4.2.4 Dynamic Strain Spectrum Analysis  
In general, the frequency of a blast wave is higher than that of a natural earthquake wave. However, due to the strong damping of the soil, the response frequency of the pipe-soil structure is relatively low. The effective signals by spectrum analysis are mainly concentrated in the low-frequency band (0-200 Hz). Here, the environment of the experiment site was complex, the sampling frequency of the dynamic strain indicator was high, and the cut-off frequency filter was not set, and thus, the noise was severe. Therefore, the signal was low-pass filtered, and the typical waveform and spectrum are shown in Figure 11.  
Take the dynamic strain spectra of the four test points, as shown in Figure 12. Each spectrum is a single-peak or multi-peak curve with a low peak frequency. The peak frequency of strain in this experiment was less than 10 Hz.  
Figure 11: Typical strain waveform before and after filtering: (**a**) The original and filtered signals; (**b**) the fast Fourier transform (FFT) spectra of the original and filtered signals.  
Figure 10: The PCS of 33H (\(R=2.7\) m or \(R=3.75\) m).  
Whether the hoop strain or the axial strain, there was an obvious peak frequency, and the peak frequency was superior to other extreme values; the value was from 4 Hz to 9 Hz. The peak frequency contains limited spectrum information, and there is no obvious law comparing the peak frequency of each charge mass. Therefore, in this paper, the average frequency is defined as [33]:  
\[f_{c}=\sum_{i=1}^{n}A_{i}f_{i}/\sum_{i=1}^{n}A_{i} \tag{10}\]  
where \(f_{i}\) is the frequency of fast Fourier transform (FFT) spectrum and \(A_{i}\) is the amplitude.  
Since the effective signal was mainly concentrated in the low-frequency band, the cut-off frequency was set to 200 Hz when calculating the average frequencies (\(R=2.7\) m). All the average frequencies were lower than 50 Hz. The law of attenuation of the average frequency with the increase of the charge mass was very obvious, showing a power attenuation relationship, as shown in Figure 13.
## 3 Methodology
### Direct Inversion Method  
The direct inversion method is an inversion technique based on the GMF, which derives sea surface wind speed directly by solving the relationship equation between NRCS observation and wind speed, under the constraint of known wind direction [10]. In practical applications, the priori wind direction data are mainly derived from NWP models, atmospheric reanalysis data, or scatterometer measurements.  
The C-band Model (CMOD) function is commonly used for VV-polarized SAR ocean surface wind field inversion. It establishes a semi-empirical GMF that relates NRCS, radar incidence angle, ocean surface wind speed, and wind direction at 10 m height. Its general form can be expressed as [17]  
\[\sigma_{vv}^{\circ}=B_{0}(\theta,V)[1+B_{1}(\theta,V)\text{cos}\varphi+B_{2}( \theta,V)\text{cos}2\varphi]^{n} \tag{6}\]  
where \(\sigma_{vv}^{\circ}\) represents the VV-polarized NRCS, \(V\) is the wind speed at 10 m height above the ocean surface, \(\varphi\) is the relative wind direction (defined as the angle between the wind direction and the radar-looking angle), and \(\theta\) is the radar incidence angle. \(B_{0}\) characterizes the dependence of the SAR backscattering coefficient on wind speed and incidence angle, while \(B_{1}\) and \(B_{2}\) describe its modulation effect on wind direction. The parameter \(n\) varies according to different CMOD functions. CMOD5.N is a model function derived from ECMWF numerical wind field data under neutral atmospheric boundary conditions, suitable for wind field retrieval under neutral atmospheric boundary conditions, with \(n\) taking the value of 1.6 [17]. This study uses wind direction information provided by ERA5 reanalysis data as input, combined with CMOD5.N for direct wind speed retrieval. It should be noted that CMOD5.N assumes that the air-sea interface is under neutral stability conditions, which constitutes a potential limitation of the method, as atmospheric stability in actual sea conditions may deviate from neutrality with seasonal and diurnal variations, especially during periods of significant seasonal temperature differences, potentially affecting wind speed retrieval accuracy. Despite this limitation, considering that the study area remains under near-neutral conditions most of the time, and that the model demonstrates high robustness in practical applications, CMOD5.N remains a reasonable choice for this research.
## 4 Results
### Vertical Distribution Patterns  
A total of 3085 wells have been drilled in the study area, resulting in a high well density. In particular, within the intensive development zones, well spacing ranges from approximately 100 to 300 m, providing a solid foundation for delineating the distribution characteristics. Based on sedimentary facies analysis, detailed interpretations of single-well and multi-well sedimentary facies profiles (Figures 8 and 9) were conducted to summarize the formation characteristics and spatial distribution patterns of sand bodies in the study area (note: for clarity, a subset of wells is shown due to space limitations). The findings offer important guidance for accurately understanding the areal distribution of delta front sand bodies and lay the foundations for predicting their spatial extension [33; 34].  
Figure 8: The characteristics of single-well core facies in the Qing 1 Member of the Dagingzijing area.  
Through detailed stratigraphic correlation of the Qing 1 Member in the Daqingzijing area, it was found that the drilling geological data are relatively detailed, and the sequences of each sand group have high integrity. Sand Group IV and even Sand Group III are absent in some wells. A multi-well profile from Hei 184 to Qianshen-11 was plotted to analyze the vertical development characteristics of sand bodies. The following features are summarized: (1) The Qing 1 Member strata are thicker in the southwest and thinner in the northeast, and the lake basin develops gradually. (2) In the southwest part of the study area, subaqueous distributary channel microfacies and estuary dam microfacies are mainly developed, with relatively thick layers, constituting the main sands of the delta front zone. In the central part, estuary dam marginal microfacies and sheet sand microfacies are developed, with relatively thin layers. In the northeast part, pro-delta microfacies and deep lacustrine facies are developed. (3) Sand bodies are distributed in a strip-like pattern, with thicknesses ranging from 2 to 8 m. (4) Multi-stage subaqueous distributary channel sand bodies are superimposed vertically and have good connectivity horizontally, developing in a contiguous manner. The multi-well profile shows that channel sand bodies are mostly developed in the shallow-water delta front and deteriorate from the bottom to the top of the profile.
## 3 System Model
### Network Model
Computational tasks may originate from IoT devices and require offloading to relay satellites for processing due to limited device capabilities. IoT devices create computational tasks and transmit them either to relay satellites or directly to the ground station for processing. Computational tasks encompass processing activities generated by IoT devices or satellites, requiring computational resources to execute functions like data analysis, image processing, and complex calculations. Accordingly, IoT devices establish connections with relay satellites through an association process, where each device selects an appropriate relay satellite for communication and task offloading based on factors such as signal quality, available resources, and connectivity status. Let \(OS_{n}\) denote the \(n\)-th observation satellite, \(RS_{k}\) denote the \(k\)-th relay satellite with a computational capacity of \(\phi_{k}\), and \(ID_{m}\) denote the \(m\)-th IoT device. Each relay satellite's sub-channel offers a bandwidth of \(B\), allowing multiple IoT devices to connect with relay satellites via Orthogonal Frequency Division Multiple Access (OFDMA) technology. The specific system layout is depicted in Figure 1.  
In the dynamic LEO satellite scenario explored in this paper, to more clearly illustrate the system's link dynamics, we consider that the system time \(T\) is divided into continuous intervals of duration \(\tau\). Given the nature of inter-satellite links in the LEO network, each LEO satellite is equipped with four interconnections with other satellites. The links between satellites within the same orbit are comparatively stable, while those between satellites in adjacent orbits achieve relative stability through steerable beam technology. Therefore, the inter-satellite network topology is assumed to remain stable. To describe the connection status of the inter-satellite communication links, we define \(x^{o}_{n,k}\in\{0,1\}\) as the physical link indicator between the observation satellite \(OS_{n}\) and the relay satellite \(RS_{k}\). If \(x^{r}_{n,k}=1\), a physical link exists between \(OS_{n}\) and \(RS_{k}\); otherwise, \(x^{o}_{n,k}=0\). Similarly, \(x^{r}_{k,k^{\prime}}\in\{0,1\}\) represents the link indicator between relay satellites \(RS_{k}\) and \(RS_{k^{\prime}}\), where \(x^{r}_{k,k^{\prime}}=1\) indicates that a link exists, and \(x^{r}_{k,k^{\prime}}=0\) indicates no link. The satellite-to-ground network topology is assumed to change with each time slot, yet it remains constant within a single time slot. To describe the satellite-to-ground link status, \(x^{i}_{m,k,t}\in\{0,1\}\) is used to indicate the existence of a physical link between the IoT device \(ID_{m}\) and the relay satellite \(RS_{k}\) at time slot \(t\). Here, \(x^{i}_{m,k,t}=1\) indicates a link exists, otherwise \(x^{i}_{m,k,t}=0\). Likewise, \(x^{g}_{k,t}\in\{0,1\}\) serves as the indicator for a link between the relay satellite \(RS_{k}\) and the ground station at time \(t\), where \(x^{g}_{k,t}=1\) symbolizes an existing link, and \(x^{g}_{k,t}=0\) symbolizes its absence. Figure 1: System model.
## Appendix A The Bias of \(\ell_{2,1}\) Regularizer in Group Sparse Classification  
Using \(\ell_{2,1}\) regularizer in classification problems not only minimizes the number of the selected classes, but also minimizes the \(\ell_{2}\)-norm of coefficients within each class. The later may adversely affect the classification result, since the optimal representation of a test sample by training samples of the correct subject may contain large coefficients. Moreover, in many classification applications, the number of training samples from different classes is not the same. We argue that the bias of \(\ell_{2,1}\) regularizer makes it unfair for classes of different sizes.  
**Example A1**.: _Suppose that a test sample \(\mathbf{y}\in\mathbb{R}^{m}\) can be represented by a combination of all \(n_{i}\) samples from class \(i\) without error, i.e., \(\mathbf{y}=\mathbf{A}_{i}\mathbf{x}_{i}\) and \(\|\mathbf{x}_{i}\|_{1}=1\), where \(\mathbf{A}_{i}\in\mathbb{R}^{m\times n_{i}}\) and \(\mathbf{x}_{i}\in\mathbb{R}^{n_{i}}\)._  
1. _If the number of samples in this class is doubled by duplication, the training set of class_ \(i\) _becomes_ \(\mathbf{\tilde{A}}_{i}=[\mathbf{A}_{i},\mathbf{A}_{i}]\in\mathbb{R}^{m\times 2n_{i}}\)_. Obviously,_ \(\mathbf{y}\) _can also be well represented by_ \(\mathbf{y}=\mathbf{\tilde{A}}_{i}\mathbf{\tilde{x}}_{i}\)_, where_ \(\mathbf{\tilde{x}}_{i}=[\eta\mathbf{x}_{i}^{\top},(1-\eta)\mathbf{x}_{i}^{\top}]^{\top} \in\mathbb{R}^{2n_{i}}\) _(_\(0\leq\eta\leq 1\)_) and_ \(\|\mathbf{\tilde{x}}_{i}\|_{1}=1\)_. However,_ \(\|\mathbf{x}_{i}\|_{2}^{2}-\|\mathbf{\tilde{x}}_{i}\|_{2}^{2}=2\eta(1-\eta)\|\mathbf{x}_{i} \|_{2}^{2}\geq 0\)_. That is,_ \(\ell_{2,1}\) _value of the first representation (before duplication) is greater than that of the second one (after duplication)._
2. _If the number of samples in this class is increased to_ \(dn_{i}\) _by copying_ \(d-1\) _times (d > 1), the training set of class_ \(i\) _becomes_ \(\mathbf{\tilde{A}}_{i}=[\mathbf{A}_{i},\cdots,\mathbf{A}_{i}]\in\mathbb{R}^{m\times dn_{i}}\)_. Obviously,_ \(\mathbf{y}=\mathbf{\tilde{A}}_{i}\mathbf{\tilde{x}}_{i}\) _is a representation of_ \(\mathbf{y}\)_, where_ \(\mathbf{\tilde{x}}_{i}=[\frac{1}{d}\mathbf{x}_{i}^{\top},\cdots,\frac{1}{d}\mathbf{x}_{i}^ {\top}]^{\top}\in\mathbb{R}^{dn_{i}}\) _and_ \(\|\mathbf{\tilde{x}}_{i}\|_{1}=1\)_. Then_ \(\|\mathbf{\tilde{x}}_{i}\|_{2}=\frac{1}{\sqrt{d}}\|\mathbf{x}_{i}\|_{2}<\|\mathbf{x}_{i}\|_ {2}\)_._  
Example A1 tells us that the group size affects the value of \(\ell_{2,1}\) regularizer. Even if the new training sample is only a copy of the original samples (without adding any new information), the value of \(\ell_{2,1}\) regularizer will decrease. Therefore, \(\ell_{2,1}\) regularizer is unfair for classes of different sizes. It has the tendency to refuse the class has relatively few samples, because the coefficient vector is more likely to have a large \(\ell_{2,1}\) regularizer value. Please note that \(\ell_{2,0}\)-regularizer is independent of group size and it does not have such unfairness.
## 4 Receiver-Triggered Handshake Protocol
### Receiver-Triggered Handshake Protocol
#### 4.2.2 Inter-Carrier Terminal Communication  
If a carrier terminal that has received information from victim terminals needs to go to a shelter and send the information to its shelter terminal, many carriers are needed to cover a wide disaster area. To reduce the number of carriers required, we allow a carrier to pass information to another carrier. Information may be relayed in many hops: for example, from a victim terminal to a carrier terminal to another carrier terminal and still another carrier terminal until the last carrier terminal sends information to a shelter terminal.  
Since a carrier terminal collects bundles as it transmits an \(\mathrm{S_{M}}\), it can determine whether another carrier terminal is nearby. Each of the two nearby carrier terminals receives an \(\mathrm{S_{M}}\) from each other, and based on \(\mathrm{S_{M}}\) received, determines which of them should receive information from the other terminal. An \(\mathrm{S_{M}}\) contains information about the time when the carrier terminal communicated with  
unit overflows. To avoid this, in determining whether a carrier terminal should receive or send information, it takes the available storage capacity of the carrier terminals concerned into consideration. The algorithm that incorporates the above and satisfies (3) and (5) in Section 4.1 is as follows:  
Step 1: Carrier terminal C\({}_{\text{j}}\) transmits an S\({}_{\text{M}}\).  
Step 2: Carrier terminal C\({}_{\text{i}}\) transmits an S\({}_{\text{M}}\).  
Step 3: C\({}_{\text{i}}\) compares its time with that of C\({}_{\text{j}}\).  
Case 1: If time of C\({}_{\text{i}}\) < time of C\({}_{\text{j}}\), go to Step 4-1.  
Case 2: If time of C\({}_{\text{i}}\) > time of C\({}_{\text{j}}\), go to Step 4-2.  
Step 4: 4-1: C\({}_{\text{i}}\) sends an R\({}_{\text{M}}\) to C\({}_{\text{j}}\).  
\(\text{4-2: C}_{\text{i}}\) sends an S\({}_{\text{M}}\) to C\({}_{\text{j}}\), go to Step 2.  
Step 5: C\({}_{\text{j}}\) sends back an A\({}_{\text{M}}\).  
C\({}_{\text{i}}\) receives the A\({}_{\text{M}}\) from C\({}_{\text{j}}\) and deletes the R\({}_{\text{M}}\).  
In this way, a bundle is relayed by multiple carrier terminals until it reaches a shelter terminal. The sequence of C\({}_{\text{j}}\) sending copies to C\({}_{\text{i}}\) (Step 4) is shown in Figure 5. The sequence of C\({}_{\text{i}}\) sending copies to C\({}_{\text{j}}\) (Step 5) is shown in Figure 6.  
Figure 5: Sequence of inter-carrier terminal communication (from C\({}_{\text{j}}\) to C\({}_{\text{i}}\)).  
Figure 6: Sequence of inter-carrier terminal communication (from C\({}_{\text{i}}\) to C\({}_{\text{j}}\)).
## 5 Conclusions  
The following conclusions can be drawn from the work presented in this paper:  
* TSA has the ability to polarise coated steel to potentials more negative than \(-\)800 mV vs. Ag/AgCl (saturated KCl) at 25 \({}^{\circ}\)C. This behaviour was observed even when a damage or holiday (up to \(\sim\)18%) was present.
* The calculated corrosion rates were found to be below 0.01 mm/year after 3 months of immersion testing even when a holiday (up to \(\sim\)18%) was present in TSA-coated steel specimens. The extrapolated long-term TSA coating corrosion rate was found to be \(<\)0.005 mm/year.
* Deposits were found in the damage region of TSA-coated steel. The deposits comprised a layer of brucite [(Mg(OH)\({}_{2}\)] close to the steel substrate and a layer of aragonite (CaCO\({}_{3}\)) away from the substrate. This layer of deposits formed on the cathodically polarised steel surface reduced the exposed steel (effective cathode) area to some extent. This possibly resulted in lowering the corrosion rate of TSA.
* Natural seawater contain salts of Mg and Ca. These dissolved salts are responsible for the formation of calcareous deposits on cathodically polarised surfaces. Therefore, the presence of these salts are important in the electrolyte when simulating seawater in the laboratory. Although 3.5 wt% NaCl solution has the same chloride equivalent as seawater, it should not be used in the laboratory as a substitute for natural or synthetic seawater to evaluate marine corrosion performance of TSA coatings.  
This research was funded by TWI's Industrial Members via the Core Research Programme (CRP). The Author would also like to thank MDPI for full APC waiver.  
Not applicable.  
The author would like to thank [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] for their technical assistance.  
The author declares no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 Current Status of Development of Geophysical Exploration Techniques
### Exploration Techniques Based on Elastic Wave Velocity
#### 2.2.4 Elastic Wave Computerized Tomography Method  
Computerized tomography (CT) is a nondestructive detection technique based on the principle of laminar imaging, which can rapidly detect hidden problems inside structures [42]. The fundamental principle of the elastic wave CT method is projection imaging, and if there is a hidden problem inside the medium and the nature of the medium changes, the CT imaging of the wave velocity in the subsurface medium is achieved through the detection of the wave velocity using different wave velocities of elastic waves in different media, with high accuracy. Elastic wave CT can be divided into seismic wave CT [43] and ultrasonic CT, both having the same principle. Elastic waves propagate in a medium to produce ray beams, which form a section in the detection area, and based on the changes in the physical parameters of the initial signal rays of each seismic wave passing through the detection area on the section, the image is reconstructed using mathematical methods to determine the location of geological anomalies and hidden dangers. Figure 7 shows the principle of the elastic wave CT method.  
Figure 7: Schematic of cross wiring obtained by the elastic wave CT method.  
[PERSON] et al. [44] used the elastic wave CT method for the quality inspection of dam cutoff walls, and the results were found to be accurate and effective. However, its anomalies could not be distinguished from some unique anomalies such as those in top-down longitudinal profiles, and require more in-depth study. [PERSON] et al. [45] applied the ultrasonic shear wave CT method to detect cracks in railroad bridges, and the detection accuracy reached 35 mm, verifying the high accuracy of the method. [PERSON] [46] adopted the exploration technology of elastic wave CT combined with ultrasonic waves for accurate measurements of the crack location, depth, and width, which played a practical role in the detection of cracks in dams. [PERSON] et al. [47] applied the elastic wave CT method to inspect continuous prestressed concrete beams and proved that the method can effectively reflect the internal conditions of the inspected body. The elastic wave CT method is a destructive exploration technique that requires multiple boreholes with depths up to the bottom of the dam and must satisfy the condition that the leakage channel of the dam passes between the boreholes.
## 2 Materials and Methods
### Trajectory Prediction
#### 2.2.1 Free Flight Trajectory Prediction (F-FTP)  
The flight is performed freely by maintaining current motion states (velocity). Waypoint sequence is parsed from the flight plan to determine the change of the flight heading. As described before, the F-FTP is implemented by building the transition of the temporal sequence of flight trajectory, i.e., \(t\rightarrow(x,y,z)\). Historical trajectories of a given flight serve as the training data, in which the flight time \(t\) is converted into the relative time (based on the takeoff time) to align the input time sequence.