esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study proposed a novel autoencoder-based signal compression model that combines RVQ and a CM to improve the transmission efficiency and reconstruction performance of acoustic signals in the DIFAR sonobuoy environment. The proposed autoencoder model achieved a high compression ratio and excellent signal reconstruction performance. By applying a pruning technique to reduce the computational complexity of the model, it was tailored for battery-operated sonobuoy environments. In addition, performance evaluation using a realistic air channel model based on the Curved-Earth Two-Ray Model demonstrated that the proposed autoencoder outperformed existing methods in signal reconstruction metrics, such as Spectrogram MSE, LSD, and SNR, under conditions where the BER remained below approximately 0.05%. Specifically, under SNR conditions of 0 dB or higher, the proposed method achieved up to a 60.58% reduction in Spectrogram MSE and significant improvements in LSD and SNR compared to existing DIFAR sonobuoy-based neural network. However, performance degradation was observed in extreme SNR conditions with BER exceedingly approximately 0.2%, highlighting the need for additional error correction techniques to alleviate these problems.  
This study suggests that the efficient and improved design of autoencoder can enhance the real-time data transmission and reconstruction capabilities of sonobuoy systems in military and oceanographic applications. Future research will aim to validate the scalability of the proposed method across various types of underwater acoustic signals and more complex communication.  
Conceptualization, Y.P. and J.H.; Data curation, Y.P.; Funding acquisition, J.H.; Investigation, Y.P.; Methodology, J.H.; Project administration, Y.P. and J.H.; Software, Y.P.; Writing--original draft, Y.P.; Writing--review and editing, Y.P. and J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the 'Student-Initiated Creative Research Project' at Changwon National University in 2024 and in part by the National Research Foundation of Korea (NRF) grant funded by the Korean Government [Ministry of Science and ICT (MSIT)] under Grant 2022R1G1A1008798.  
Not applicable.  
Dataset available on request from the authors.  
The authors declare no conflicts of interest.
## 3 System and Methodology
### Object Classification  
As mentioned in the segmentation network section, segmentation networks often cannot accurately predict segmentation masks for objects with similar textures but different geometries.  
Figure 5 shows that the left image is a cup with a handle, and the right image is the other side of the cup, in which the handle cannot be seen. Although the handle of the cup has the same pattern as the cup body, it cannot be separated using color information alone. We designed a new classification network that introduced depth information to classify two objects.  
According to the segmentation results of the previous step, the target regions were cropped as inputs into the classification network. The structure of the classification is shown in Figure 6: two convolutional networks extracting color and geometric features, a fuse layer for the two types of features, and a fully connected layer to obtain the result. In each branch, the features were first extracted through the convolutional and max-pooling layers. Subsequently, after the fourth layer of convolution, the color feature vector and depth feature vector of the same dimension are obtained. They are multiplied element by element to fuse the final features and feed them into a fully connected layer. The outputs were concatenated and eventually fed into the pose predictor.  
Figure 5: Objects with the same textures and different shapes, the **left** object is a cup with a handle and the **right** object is another side of the cup with the handle hidden. The depth image is next to the color image.  
Figure 6: The overall framework of the classification network. Two convolutional networks are designed to extract color and geometric features, respectively, then fuse those features, and utilize fully connected layers to obtain results.
## 3 Method  
To address the shortcomings of the existing depth estimation algorithms, we present a light field depth estimation method that considers blur features and planar geometric structure priors. The main components of this approach are as follows.  
(1) Neighborhood Selection for Microimages: To address the limited ability to select neighborhoods in microimages, a multiconstraint relationship involving lighting consistency, geometric consistency, and blur features is established. First, feature point matching is conducted based on local photometric feature consistency constraints. Then, geometric consistency constraints are employed to eliminate feature point matches that do not satisfy the imposed epipolar constraints. Simultaneously, the overlapping constraints are used to define the neighborhood range. Finally, within the determined neighborhood range, the blur score of each microimage is computed via its gradient magnitude, allowing for the identification of neighboring microimage sets with varying degrees of blur. This approach enables depth information to be acquired across different depth-of-field ranges, thereby enhancing the accuracy of depth estimation.  
(2) Addressing Mismatches in Regions with Weak Textures: To address the issue of mismatches in regions with weak textures, a depth estimation algorithm that incorporates blur features and prior knowledge is proposed. The microimages with the least blur propagate disparities to those with greater levels of blur, resulting in a higher probability of acquiring successful disparity matches. By introducing geometric structure prior knowledge, the initial disparity results are optimized based on the positional relationships between the images and the local planar characteristics of the scene, consequently improving the accuracy and robustness of the depth estimation results obtained in regions with weak textures. The workflow of the proposed method is illustrated in Figure 1.
# Influence of Surface Tilt Angle on a Chromatic Confocal Probe with a Femtosecond Laser
## 4 Experimental Investigation Results for the Tracking Local Minimum Method  
### Experimental Configuration for the Tracking Local Minimum Method  
A schematic diagram of the confocal optical system used in the experiment is shown in Figure 6. Supercontinuum light, which is a mode-locked femtosecond laser, emitted from a single-mode fiber was collimated using a collimating lens. After passing through a polarizing beam splitter (PBS) and a quarter-wave plate (QWP), the light was focused onto the object to be measured using a plano-convex lens. Then, the reflected light from the surface of the object was again collimated by a plano-convex lens, passed through the QWP and PBS, and was split into two light beams by a beam splitter (BS). Each beam was treated as a measurement light and a reference light; the two beams were focused onto the fiber end-face using the same objective lens and then analyzed using an optical spectrum analyzer. The reference fiber was given an optical axis offset of \(z_{d}=200\) m. As mentioned above, the normalized intensity ratio \(I_{\mathrm{TLM}}\) was obtained and analyzed by dividing the measurement light spectrum \(I_{\mathrm{Mea}}\) obtained from the fiber-detector for the measurement light by the reference light spectrum \(I_{\mathrm{Ref}}\) obtained from the fiber-detector for the reference light [22, 23]. The configuration of this optical system was the same as that in the previous studies [24, 27]. The measurement object was fixed to a \(z\)-axis stage and a rotation stage. It should be noted that only one rotary stage (\(\theta_{r}=\theta x\)) was mounted since the response  
Figure 5: Numerical calculation results regarding the effect of the change in the object tilt angle \(\theta_{X}\) on the TL method based on our developed dual-fiber-detector chromatic confocal probe; (**a**), the axial response curve of the reference light \(I_{\mathrm{R}}\) (**b**), the relationship between \(\theta_{X}\) and \(z\)-position in \(I_{\mathrm{R}}\) (**c**), the relationship between \(\theta_{X}\) and \(z\)-position about \(I_{\mathrm{TLM}}\) (**d**) the relationship between centroid wavelength \(\lambda_{\mathrm{focused}}\) and \(z\)-position in different \(\theta_{X}\).  
to the tilt angle of the measurement object was circularly symmetrical with respect to the optical axis.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 3 LIMS System: fs-Laser Ablation Ion Source and Time-of-Flight Mass Analyser
### Selected Femtosecond-LIMS Systems  
The historic development of LIMS instrumentation is well-documented in a recent review and includes a brief overview of the first-generation instruments and more recent designs developed between 1960 and 1990 [1]. The limited number of femtosecond-LIMS instruments so far is a result of the high price of the fs-laser system and perhaps of the prevailing experience from the nanosecond predecessor of this method about the qualitative rather than quantitative capabilities of LIMS. However, already early femtosecond LIMS indicated many improved capabilities of this technique. A TOFMS equipped with a commercial Ti:Sapphire femtosecond-laser (75 fs, 800 nm, 1 kHz) and a coaxial ion extraction system, a two-stage reflectron and microscope system, demonstrated high capabilities for depth profiling analysis [19]. In this instrument, focused laser radiation is positioned at a 60\({}^{\circ}\) incident angle and an ion funnel was implemented to transport ions. The low mass resolution of less than 300 was still sufficient to analyse most of the ions. In later applications, this instrument was applied to laser desorption post-ionisation for investigations of molecular species.  
Using an orthogonal ion extraction method and a linear TOFMS, [PERSON] et al., with a home-built LIMS system, investigated the performance of both femtosecond and nanosecond lasers for laser ablation/ionisation [18]. The sample is placed vertically at the side of the entrance to the drift tube and the laser beam is focused on the sample at normal incidence. The instrument had a mass resolution (m/\(\Delta\)m) of 300 for \(m/z=65\). The instrument also showed that acceptable spectral quality can be achieved by applying low laser fluences. A few systems introduced here have demonstrated high instrument performance in delivering high-resolution mass spectra (2200, >10,000), highly sensitive measurements (sub-ppm), and successfully conducted chemical composition analysis. Their high performance was demonstrated in numerous performance and application studies relevant to geochemistry, material science, and bio-relevant applications [19; 21; 22; 131]. We introduced two LIMS instruments designed in our group: a miniature LIMS instrument, (LMS) for space research, and a large laboratory LIMS system, LMS-GT [21; 24; 25; 132]. Recently, another miniature LIMS system called ORIGIN equipped with ns-laser desorption source was developed for detection of amino acids on planetary surfaces [12]. All these instruments use a characteristic collinear or axial ion extraction method. The third instrument is high irradiance LIMS, LI-O-TOFMS equipped with gas collision cell and applying the orthogonal ion extraction method [20; 23].
## 3 Results
### Color Measurement (L, a, and b Values)
#### 3.1.2 a Values  
The results of statistical analysis of the red-green color component, or a values, are shown in Figure 5. The three crown materials, E-max, Lisi, and Celtra, had positive a values, demonstrating red components. The a value was significantly higher in Celtra (\(p<0.05\)) than in E-max and Lisi, as shown in Figure 5A.  
Regarding a values after immersion in saliva/coffee and after brushing, all the groups showed a positive red component. The changes in the red component values were significant only for Celtra (\(p<0.05\)), as shown in Figure 5A,B.
## 2 Background
### WBASN Topologies  
The network topologies describe the data communication structure among the network's nodes. In WBASNs, different types of topologies are used, including peer-to-peer (P2P), mesh, cluster tree, hybrid and star. According to the requirement of applications, a topology is selected. These requirements include scalability, robustness, energy, reliability, latency and mobility. According to the IEEE 802.15.4 standard, functionality-wise sensor nodes are divided into two types, i.e., full-function sensor nodes (FFSNs) and reduced-function sensor nodes (RFSNs). The FFSNs are capable of routing functions, whereas RFSNs can only do peer-to-peer communication. Usually, RFSNs are deployed when energy is a critical issue. The advantages and disadvantages of these topologies are discussed in [53; 54].
## 2 Prototype System and Methods
### Aircraft Segmentation from LiDAR Scans  
The first step of object tracking is the separation of the dynamic and static content of the scene, and then identification of the dynamic objects. A reference or baseline scan of the background can be obtained from scans that include no aircraft, and then, an occupancy grid is created from the average background scan. During tracking, this grid is compared to an incoming scan, and thus, the dynamic content could be easily filtered. The performance is further improved by defining a region of interest from the scans, such as the runway or taxiway in view.  
In our tests, only one airplane presents at any epoch, and therefore, this segmentation is a relatively easy task. It is noteworthy that almost always just one plane is in the network's field of view for landing scenarios due to mandatory aircraft separation. Obviously, there can be more than one plane in the case of taxiing. In the latter case, the airplanes can be segmented from each other by simply tracking their motion in the region of interest. At the end of the segmentation process, 3D coordinates of the aircraft body points with their timestamps are available for each scan.  
The density of the captured scans depends on the object-sensor distance. Figure 3 shows segmented data captured by five LiDAR sensors at 23 m and 33 m object-sensor distance. Various colors indicate the times, and thus, the point cloud in the figures shows all scans of the plane while crossing the sensor network's field of view. Clearly, the shape of the airplane is barely recognizable in Figure 3a, and not recognizable in Figure 3b. These scans contain a very small number of points of the aircraft body due to the large object-sensor distance, and thus, dedicated algorithms are needed to extract the motion information or trajectories.  
Figure 3: Top-down views from an airplane captured at different times by the sensor network. The two subfigures illustrate the point densities at different object–sensor distances: color differentiates scans: **(a)** at 23 m object–sensor distance, the average number of points per scan is 130 (26 per sensor), and **(b)** at 33 m object–sensor distance, the average number of points per scan is 20 (4 points from each sensor).
## 2 Background
### Lumped-Element Approximation (Single Neck, Multi-Neck)  
For a single neck Helmholtz resonator, according to the lumped element approximation [46], the resonance frequency can be estimated as (using nomenclature from [PERSON] et al. [33]):  
\[f_{0}=\frac{c_{0}}{2\pi}\sqrt{\frac{\rho_{0}S_{1}}{V_{0}M_{1}}} \tag{10}\]  
where \(c_{0}\) and \(\rho_{0}\) are the speed of sound and density of the fluid inside the cavity and \(V_{\mathrm{o}}\) is the cavity volume. \(S_{1}\) is the cross-sectional area of each neck. Finally, \(M_{1}\) (as presented in Section 2.1) can be seen as the inertial mass of the neck air volume. In the case of a cylindrical neck with diameter \(d_{1}\) and length \(\ l_{1}\), the neck air volume mass \(M_{1}\) can be approximated as: \(M_{1}=\rho_{0}(l_{1}+a_{1}d_{1})\).  
For multi-neck Helmholtz resonators, both in the studies by [PERSON] et al. [33] and [PERSON] et al. [34], calculations were performed to account for the additional necks by simply combining the surface areas of the extra necks (\(S_{2}\)) to that of the primary neck. For these calculations, the common lumped element approximation formula was expanded to accommodate for these changes. According to this approach, the fundamental acoustic resonant frequency can be calculated as (using nomenclature found in [PERSON] et al. [33]):  
\[f_{0}=\frac{c_{0}}{2\pi}\sqrt{\rho_{0}\frac{S_{1}+N_{leaks}S_{2}}{V_{0}M_{1}}} \tag{11}\]
# Typhoon Loss Assessment in Rural Housing in Ningbo Based on Township-Level Resolution
## 3 Methods
### Construction of Typhoon Loss Assessment Model for Rural Housing  
The flow chart for the construction of a typhoon loss assessment model for rural housing is shown in Figure 4. As can be seen, the simulated typhoon wind speed was selected as input layer data, and the output layer data were the insured loss rate of each township due to typhoons, and the statistical data covered 139 townships in Ningbo. Based on the available data of the typhoon causal factors and the losses of the disaster-bearing bodies, the typhoon loss assessment models of rural housing based on the RBF neural network and BP neural network can be established, respectively. Among them, the construction of the RBF neural network is simpler than a BP neural network. Based on the existing data samples, a neural network with a radial basis function expansion factor of 1.2 was created by the \"newrb\" function, and the mean square error target was set to 0.001. The training algorithm of RBF neural network can be divided into two steps: the first step is to decide the number of nodes in the hidden layer and the center \(c_{i}\) of the basis function according to the distribution of training samples; and the second step is to obtain the connection weights \(\omega_{ij}\) based on the determined network parameters.  
The BP neural network was designed as a typical 3-layer network model. The number of neurons in the hidden layer was determined as five by the empirical formula and trial-and-error method, and the transfer functions were tangent S-type transfer function \"tansig\" and linear transfer function \"purelin\", respectively. The system default \"trainlm\" function was used for training, and the training number was set to 1000, the training target was 0.001, and other parameters were taken as default values.
## 2 Principle of Rotational Speed Estimation Based on In Situ MEMS Accelerometer  
The estimation principle of the rotational speed based on gravity signals that are decoupled from an in situ accelerometer is shown in Figure 1. The core idea of the proposed method is to sense the variation in the gravity acceleration component of an in situ accelerometer as the disk rotates. Assuming that the in situ accelerometer is a bidirectional sensor, the sensitive directions are tangential and radial. Thus, the signal composition function in the two sensitive directions can be written as  
\[S_{T}(t)=g\bullet\cos\theta(t)+f_{1}\big{[}\ddot{\theta}(t),r\big{]}+f_{2} \big{[}\ddot{x}(t),\theta(t)\big{]}+f_{3}\big{[}\ddot{y}(t),\theta(t)\big{]} \tag{1}\]  
\[S_{V}(t)=-g\bullet\sin\theta(t)+\omega^{2}(t)\bullet r+f_{4}\big{[}\ddot{x}(t), \theta(t)\big{]}+f_{5}\big{[}\ddot{y}(t),\theta(t)\big{]} \tag{2}\]  
where \(g\) refers to the gravity acceleration. \(\theta\) denotes the rotation angle. \(\ddot{\theta}/\ddot{x}/\ddot{y}\) stand for the vibration acceleration in the torsional, horizontal and vertical directions, respectively. \(f_{1}[]\), \(f_{2}[]\) and \(f_{3}[]\) are the projection functions of \(\ddot{\theta}/\ddot{x}/\ddot{y}\) in the tangential direction of the in situ accelerometer. \(r\) is the radius of the in situ accelerometer. \(\omega\) means the rotating speed of the disc or shaft. \(f_{4}[]\) and \(f_{5}[]\) are the projection functions of \(\ddot{x}/\ddot{y}\) in the radial direction of the in situ accelerometer.  
Figure 1: A schematic plot of the estimation principle based on an in situ MEMS accelerometer.  
Based on the observation of Equations (2) and (3), it can be found that the change process of gravity acceleration is included in both the tangential and radial directions. If gravity acceleration decoupling is achieved, the one-to-one correspondence relationship between \(\theta\) and \(t\) can be obtained, and then, the rotational speed can be estimated based on the phase calculation of the acquired gravity acceleration component. However, it seems that the centrifugal acceleration component in the radial direction will be greatly affected by the instantaneous speed change and has a square relationship. It is expected that the accurate extraction of gravity acceleration will be disturbed in the case of variable speed in the radial direction of the in situ accelerometer. Therefore, this paper mainly focuses on an accurate extraction method for the tangential gravity acceleration component.  
The key problem to be solved by this paper is to decouple the gravity acceleration component as accurately as possible, because the phase angle of the gravity acceleration component directly determines the estimation accuracy of the rotational speed.
## 3 Results and Discussion  
### Volumetric Characterisation  
The volumetric characterization is supported by the analysis of the air voids content of each specimen which was determined according to EN 12697-8 [61]. Figure 5 presents average bulk density values and air voids content (Vm) values for each AC mixture and set of specimens (dry/wet and unaged/aged). The vertical error bar represents the standard deviation of four specimens.  
Figure 4: Tecnico accelerated ageing (TEAGE) simulation.  
From Figure 5, it is possible to observe the following:  
* For each AC mixture, all sets have similar volumetric properties.
* The air voids contents of the tested AC mixtures are relatively low, which can be justified by the impact energy compaction used (75 blows) and by the fact that it is a dense-graded AC mixture.
* There was a decrease in the bulk density, and consequently an increase in air voids content, with the addition of plastic, as would be expected, since the plastic was used in replacement of bitumen, which is a denser material than the plastic.
* The specimens with plastic and nanoclay present higher air voids content. This can be partially explained by the increase of bitumen viscosity caused by the nanoclay.