esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
We found that the average NRCS, scattering profile, and scattering pattern are not considerably affected by a change in facet size from 1 to 10 m. However, the pdfs for backscatter are different depending on the facet size. More time is required to calculate the scattering from the sea surface with a smaller facet size. An example based on a Gaussian sea surface with an area of 2 km \(\times\) 2 km and facet sizes of 1 m \(\times\) 1 m, 5 m \(\times\) 5 m, and 10 m \(\times\) 10 m under 10 m/s wind speed at an incidence angle of 40\({}^{\circ}\) was used to demonstrate the influence of sea surface size on the NRCS simulation. The pdfs for the height, slope, and scattering of this example are shown in Figure 25. Figure 26 presents the profiles of wave elevation and the NRCS of this example, and Figure 27 shows the scattering images. From Figure 25, we can see that, for a larger facet size, the height pdf, slope pdf, and NRCS pdf are larger around the mean value and lower further from the mean value. Figure 26 shows that the ranges of fluctuation of the scattering profiles based on different sea surface resolutions are similar. Figure 27 shows that the impact of facet size on the scattering pattern is small and can be ignored to a certain extent. The analysis of the effect of sea surface resolution on the mean scattering reveals only a slight difference, which can be ignored between the average scatterings based on different facet sizes. For example, the NRCSs of the sea surface with facet sizes of 1, 5, and 10 m are \(-\)15.26, \(-\)15.24, and \(-\)15.23 dB, respectively. This result is consistent with the conclusion obtained in [10] that the average does not considerably depend on the facet size. The times required to obtain the scattering with facet sizes of 1, 5, and 10 m are 1, 30, and 4320 min, respectively, when the sea surface size is 2 km \(\times\) 2 km. In subsequent experiments, we chose a facet size of 10 m to construct the sea surface, which is the same as the resolution of Sentinel-1 IW high-resolution data. Figure 24: Comparison of scattering images based on different ocean surface sizes at the incidence angle of 40\({}^{\circ}\) and wind speed of 10 m/s in the upwind direction under VV polarization. The facet resolution is 5 m \(\times\) 5 m. (**a**) Sea surface in the area of 2 km \(\times\) 2 km; (**b**) sea surface in the area of 10 km \(\times\) 10 km; (**c**) the area enclosed by red solid line in (**b**). Figure 25: _Cont._  
Figure 25: Comparison of the distributions of sea surface with different facet sizes in the upwind (**left**) and crosswind (**right**) directions at the incidence angle of 40\({}^{\circ}\) and wind speed of 10 m/s. The surface size is 2 km \(\times\) 2 km. (**a,b**) Pdfs for elevation; (**c,d**) pdfs for slope; (**e,f**) pdfs for backscatter under VV polarization. Dashed curves, facet size is 1 m \(\times\) 1 m; solid curves, facet size is 5 m \(\times\) 5 m; solid curves with dot, facet size is 10 m \(\times\) 10 m.  
Figure 26: _Cont_.
# Unsupervised Temporal Adaptation in Skeleton-Based Human Action Recognition
## 4 Experiments
### Effects of Contrastive Prototype Learning  
#### 4.5.1 Training with Fewer Target Domain Samples  
The critical contribution of Contrastive Prototype Learning **(CPL)** is the optimization of discriminative action prototypes via supervised contrastive learning using the source domain data. It is relevant to validate the transferability of the action prototypes that are learned in the source domain while only involving a subset of the target domain data for refinement. For this experiment, we first use **CPL** to learn an encoder \(\mathcal{M}\) using the labeled data from NTU-RGB+D. Second, we fix the pre-trained \(\mathcal{M}\) and use different proportions (varying from 5% to 100%) of labeled data from PKU MMD Part I to fine-tune a classifier \(\mathcal{C}\). Note that this differs from the experiments reported in Section 4.4 that use only unlabeled data for model fine-tuning. Experimental results in Table 5 demonstrate that the model performance tends to increase monotonically with the ratio of target data samples involved but only until a saturation point. Interestingly, even using a very small number (e.g., 5%) of data samples from the target domain, the model still achieves compelling performance compared to the best model (85.06% in Table 1) when using 100% of the PKU-MMD Part I dataset.
# A PSO-Based Uneven Dynamic Clustering Multi-Hop Routing Protocol for Wireless Sensor Networks
## 5 Proposed Algorithm  
Similar to the existing hierarchical routing protocols, the operation of PUDCRP also is broken up into rounds. Each round is divided into a set-up phase and a steady-state phase. In the set-up phase, an improved PSO is used to determine the circular area where the candidate CH nodes are located. The PSO-based method operates at the BS. CH nodes are selected by a multi-objective fitness function. Non-CH nodes join the cluster where the nearest CH node is located. In the steady-state phase, a connecting line aided route construction method was proposed to achieve an energy efficient routing.
## 2 Materials and Methods
### Study Area
#### 2.1.1 Prescribed Prairie Burn Sites  
The data collection for prescribed burn treatments was conducted first at three different prairie properties. Each of these praaries, symbolized as beige trees in Figure 1, contain between 8 and 60 acres of native prairie land over relatively flat terrain. The properties are segmented into multiple 1-acre plantings of native grass and forb seed mixtures, which include Big/Little Blue Stem, Black/Brown-eyed Susan, Stiff Goldenrod, Indian Grass, White/Purple Prairie Clover, Wild Rye, Side Oats Grama, Prairie Dropseed, and many others [40]. With 10-15 ft fire line paths separating the plantings, the structure of these properties allows ecology researchers to conduct prescribed fire seasonality studies, where individual plots are burned at bi-annual, annual, or 2-year intervals so that correlations between fire frequency and species regeneration can be determined [41].  
The Doak and Hermann properties (Figure 2) were used for preliminary flight testing with the UAS, and were burned on 19 September 2019 and 8 October 2019, respectively. Each of these sites was surveyed both immediately before and after the prescribed burn treatments to gather preliminary datasets with the UAS. Then, PWA (Figure 2) was burned on 2 April 2020, and was flown over multiple times; one day before the burn, one day after the burn, and multiple times thereafter (Table 1). The proximity of PWA made it possible to gather many datasets following the disturbance, achieving the best temporal resolution of the three sites. Therefore, this site was used in the final land cover assessment for prescribed burns in this project. By assessing land cover change surrounding the prescribed burn at PWA, land managers can leverage this information in conjunction with their current management procedures to assess the efficacy and ecological response of their burn treatment [3].  
Figure 1: Study area and site map for prescribed burn and selective timber harvest plots throughout the state of Indiana. Sites selected for final analysis are outlined in red. The “CHF Boundary” locator map was adapted from _The Central Hardwood Forest: Its Boundaries and Physiographic Provinces_[42].  
Figure 2: Purdue FNR prairie plots for prescribed fire seasonality research (outlined in yellow). (**A**) =Doak, (**B**) =Hermann, (**C**) =PWA. PWA was selected for final land cover analysis of prescribed burns.
# Multi-Input Convolutional Neural Networks for Automatic Pollen Classification
## 2 Materials and Methods
### Network Architecture
#### 2.2.1 Feature Extractors  
For this architecture, each of the feature extractors are very similar, with the major difference between them being that, for scattering (Figure 3) and spectrum (Figure 4), we used 2D convolutions, while, for the lifetime signal, we used 1D convolution layers, shown in Figure 5. The kernel size for the convolutional filters was 3 \(\times\) 3 for the 2D filters and 3 for the 1D filters. The number of filters used was 32 at the first layer and doubled after each max pooling layer (i.e., 32, 64 and 128 filters). After each of the feature extractors, a flatten operation was applied to prepare the feature vectors for concatenation.  
The feature extractors used in [9] have three convolution blocks, with each block composed of two convolution layers followed by a batch normalization layer and a max pooling layer. After each max pooling layer, the number of convolution filters doubles. After the last convolution block, we used dense layers that further helped to reduce the high dimensionality of the features. The presence of the fully-connected layers enabled the use of each feature extractor as a classifier on its own. This is a very important aspect of the architecture, because it allowed the feature extractors to be trained on their own. The importance of training the feature extractors independently is that this allows different learning rates, batch sizes and even optimizers for each feature type. The feature extractors can be \"frozen\" after training and a final training of the common trunk can be performed.  
Figure 2: The convolutional network architecture used for pollen classification.  
This architecture, with multiple inputs and outputs, allows pollen to be classified in two ways. A classification can be made by using a single feature type or all of the feature types. In order to evaluate the importance of each of the features in the classification task, intermediate output layers were added after each of the feature extractors. These outputs use the latent representation of the features to make a classification using just one of the features. This was necessary, because each of the datasets contained different classes of pollen that have different degrees of similarity between classes for each of the feature types. The advantage in having intermediate output layers is that each of the feature extractors can be trained independently should they require a different learning rate, batch size, or number of training epochs.
# Structure of Argon Solid Phases Formed from the Liquid State at Different Isobaric Cooling Rates
## 4 Discussion  
The study of crystallization and glass transition processes on models of argon atoms has been conducted for more than half a century. Thus, in [17], the results of molecular dynamics experiments on isochoric cooling of particle systems with a Lennard-Jones potential are described, in which signs of crystallization are observed at cooling rates from \(10^{12}\) K/s and below. In [16], a simulation of isobaric cooling of argon with different cooling rates was performed, while signs of crystallization were observed at cooling rates from \(10^{10}\) K/s, which is confirmed by the results of our calculations. In contrast to the above noted very fast cooling rates characteristic for molecular dynamic simulations the non-computer experimental fast cooling techniques are typically characterized by much lower rates which result in complete crystallization rather than glass formation in both inorganic [30; 31; 32; 33] and biological systems [34]. Unfortunately, in relation to argon, there are no results of empirical experiments in the literature that investigated the processes of rapid cooling although methods of rapid quenching of melts have been practiced for a long time, such as the common rapid solidification processing (RSP) method [35; 36], which provides cooling at rates up to \(10^{5}\)-\(10^{6}\) K/s, and the fastest picosecond pulsed laser quenching method [37], which provides a cooling rate of up to \(10^{14}\) K/s. When using such methods, the formation of metallic glasses has been experimentally confirmed [30; 31; 32; 33; 38; 39].  
The most common practice of studying the structure of molecular media in a computer experiment is the calculation and analysis of the shape of the functions of the radial  
Figure 4: Configurations of particles and axes of symmetry of FCC cells in solid phases of argon formed by cooling from the liquid phase at rates: (**a**) \(10^{8}\) K/s, (**b**) \(10^{9}\) K/s, (**c**) \(10^{10}\) K/s and (**d**) \(10^{11}\) K/s at a pressure value of 3 MPa.  
distribution of particles. Such a study was carried out by us in [40]. It is worth noting that the analysis of the radial distribution functions allows us to give a qualitative assessment of the structure [41]. The method proposed in this article provides a quantitative assessment of the concentration of crystal structures in solid phases. This approach opens up prospects for calculating the conditions of thermobaric synthesis of materials with specified structural characteristics: crystals with the required concentration of crystal lattice defects, composite materials representing a glassy medium with clusters of crystals of specified sizes immersed in it e.g., liquid crystalline and glassy systems [10].
# Point-Kernel Code Development for Gamma-Ray Shielding Applications
## 3 Gamma Flux Solution for Simple Source Geometries
### Source Slab with a Parallel Slab Shield  
This subsection presents an infinite slab of thickness \(t\) (cm) that is shielded by an infinite slab of thickness \(b\) (cm). The linear attenuation coefficients of the source and shield materials are \(\mu_{1}\) and \(\mu_{2}\), respectively. The source slab has a constant volume emission rate of \(S_{V}\) (phot/cm\({}^{3}\)/s) and material with self-absorption. The point detector flux on the surface of the slab shield (P) has the arrangement given in Figure 4.  
The volume source can be interpreted as the sum of differential slabs of thickness \(dx\) corresponding to infinite plane sources of specific intensity \(Svdx\) (phot/cm\({}^{2}\)/s), for which the gamma flux solution is presented in Equation (10). The contribution to the point detector P from the infinite isotropic plane at position \(x\) is given as follows:  
\[d\phi=\bigg{(}\frac{S_{V}dx}{2}\bigg{)}E_{1}(\mu_{1}x+\mu_{2}b). \tag{26}\]  
The uncollided flux at detector P is thus obtained by integrating the source thickness variable \(x\) from 0 to \(t\), giving in turn an analytical solution based on the \(E_{2}\) function as follows:  
\[\phi=\frac{S_{V}}{2\mu_{1}}\cdot[E_{2}(\mu_{2}b)-E_{2}(\mu_{1}t+\mu_{2}b)]. \tag{27}\]  
The straightforward generalization of the buildup flux based on the Taylor \(B\)-factor is not possible, since the gamma-ray buildup is produced in composite shield, using two layers of different materials. However, approximation to buildup flux can still be obtained by means of calculating the composite \(B\)-factor based on several methods. For the purpose of this work, we have implemented methods of [PERSON], [PERSON], [PERSON] [23] and Bowman-Trubey [4].  
Practical shields are often laminated, with layers of different materials, have finite thicknesses and have an outer boundary. Such multilayered or composite shields introduce difficulties for \(B\)-factor determination, since the propagating gamma radiation will change its angular and energy distribution inside the shield, as well as in the vicinity of regional boundaries. This somewhat carries a memory effect since incident gamma rays depend on previous layers, and their ordering is important due to high-Z dependence of photoelectric absorption and pair-production. The explicit modeling using the elaborate methods of radiation transport (MC or \(S_{N}\)) is the proper way to conduct such simulations, but accumulated empirical data over history suggests \"accurate enough\" approximations for most practical  
Figure 4: Geometry arrangement of the volume source slab and parallel slab shield [23].  
purposes. The user-based intuition in synthesizing the composite \(B\)-factor based on simple averaging or multiplication over different layers is out of the question, so most common methods \"that work\" are implemented and briefly mentioned in the following.
## 3 Dynamic Multi-Hop Routing (DMR) Technique  
The DMR protocol dynamically switches between available routes through the WSN. The results of applying the DMR protocol have revealed that distributing the load across different sensor nodes along the route can improve the WSN's performance, extend its lifetime, and increase its overall stability. The protocol divides the network's lifetime into successive rounds, each comprising four phases: initialization, announcement, table preparation, and routing. The DMR protocol makes several assumptions about the WSN architecture used within it, as follows: the sensor nodes are deployed randomly across the network area and are initially charged with the same amount of energy; the network includes a BS; and the distances of the CHs are measured relative to it. The first section introduces the energy dissipation model used by the DMR protocol, followed by an explanation of the protocol's phases.
## 4 Synthesis of Studies across the 360-Degree Video and 3D Virtual Environment  
### Studies Using the 360-Degree Video  
Studies using 360-degree video as VR mode is tabulated in Table 3. In the 360-degree video group, 2 out of 5 studies reported that virtual nature produces similar psychological benefits as nature in the real world. In the study by [PERSON] and [PERSON] [36], participants experienced similar emotions when exposed to a contemplative natural scene in both real life and virtual reality, with the exception of higher levels of anger in the real-life condition and higher amusement in VR. The sense of presence reported was also similar in both scenarios, suggesting that VR can closely mimic real life in terms of emotional response and presence.  
[PERSON] et al. [42] found that 6 min of outdoor exposure to nature (sitting in a foldable chair in an outdoor nature environment) had a greater effect on improving positive mood, while 6 min of virtual exposure to nature via 360-degree videos of the same environment with recorded soundscape only maintained positive mood before and after using VR to expose to nature.  
In the [PERSON] et al. [43] study, they found that while participants reported similar restorativeness between the real-world condition and virtual condition, actual enjoyment of a walk and change in effect were higher in the real-world condition than in the virtual condition. However, they attribute a cause to cybersickness from using the HMD. Such a problem can be overcome by user self-selection or improvement in HMD technology to alleviate cybersickness.  
[PERSON] et al. [44] found that while there was a difference in encouraging creativity in participants in a VR condition when comparing nature vs. urban stimuli, that difference vanished when switching to a real-world condition where participants sat on a chair in an outdoor scene. However, in the second study where they reported that virtual and real conditions had different results, [PERSON] et al. [44] actually compared 2D videos shown on iPad flat-screens with the real environment and not the videos experienced through HMD devices, which may account for the discrepant results. An HMD may be a better comparison and be more similar.  
Thus, there is mixed evidence of whether virtual nature through 360-degree videos can have the same psychological benefit as nature in the real world. However, it may be partly attributed to practical details with the VR setup, like cybersickness.
# Safety Assessment of the Cover-and-Cut Method Under Blasting Vibration Induced by Tunnel Excavation
## 5 Determination of Safe Vibration Thresholds and Development of Mitigation Strategies
### Determination of Safe Vibration Thresholds
#### 5.1.2 Establishment of Safe Thresholds  
By comparing field and simulation data with relevant safety standards, the following conclusions were drawn.  
The impact of blasting vibrations on tunnel structures follows the order central pillar > diaphragm wall > roof. All PPVs are below the stipulated safety threshold of 2.0-2.5 cm/s, ensuring structural safety.  
Through this analysis, safe vibration thresholds suitable for this project have been established, providing valuable data and reference for similar future projects.
## 2 Theory and Model
### The 16-Coil 3D EMT Model  
Because multi-layer sensors can provide more projections between different layers, such sensors are more suitable for 3D imaging. Taking scalability into account, we referenced the classic 8-coil EMT model and constructed a dual-layer, 16-coil model with 8 coils per layer, as illustrated in Figure 2. In Figure 2, the green parts represent the coils, while the gray sections indicate the tube.  
By sequentially exciting and detecting through 16 coils, a two-dimensional matrix \(D_{16\times 16}\) can be created with 256 projections. Suppose considerations for the removal of information redundancy are taken into account. In that case, the Reciprocity Theorem stipulates that the outcome of coil i acting as the exciter and coil j as the detector is equivalent to that when coil j is the exciter and coil i is the detector.  
\[d_{p_{i-j}}=d_{p_{j-i}} \tag{5}\]  
Upon excluding all self-induction signals \(d_{p_{i-i}(i=1:16)}\), a total of \(\frac{n\times(n-1)}{2}=120\) independent detection signals are obtained.
# A Novel CDMA-Based Secret Message Multiplexing Scheme by Image Data Hiding +
## 2 CDMA Multiplexing
Generate Hadamard codes using Hadamard matrix. \[H_{1}=[1],H_{2}= \begin{bmatrix}1&1\\ 1&-1\end{bmatrix} ,H_{2^{k+1}}= \begin{bmatrix}H_{2^{k}}&H_{2^{k}}\\ H_{2^{k}}&-H_{2^{k}}\end{bmatrix} \]  
The \(4\times 4\) Hadamard matrix \(H_{4}\) is as follows. \[H_{4}= \begin{bmatrix}1&1&1&1\\ 1&-1&1&-1\\ 1&1&-1&-1\\ 1&-1&-1&1\end{bmatrix} \]  
Each row represents a distinct spreading code. Those four mutually orthogonal spreading codes are assigned to four users in order. \[C_{A}=[1\;1\;1\;1],\;C_{B}=[1\;-1\;1\;-1],\;C_{\mathrm{C}}=[1\;1\;-1\;-1],\;C_{ D}=[1\;-1\;-1\;1]\]  
Step 3. For each user, perform the multiplication between the user data and the spreading code bit by bit. \[ \begin{array}{l}\mathrm{A_{C}}{=[1\;1\;1\;1,\;-1\;-1\;-1\;-1,\;1\;1\;1\;1], \;B_{C}}{=[1\;-1\;1\;-1,\;1\;-1\;1\;-1,\;-1\;1],\;C_{C}}{=[-1\;-1\;1\;1,\;-1 \;-1\;1,\;1\;-1\;-1],\;D_{C}}{=[-1\;1\;1\;-1,\;1\;-1\;-1\;1,\;1\;-1\;1]}\end{array} \]  
Step 4. Add the spread data together to form the CDMA signal. \[M=A_{c}+B_{c}+C_{c}+D_{c}=[0\;0\;4\;0,\;0\;-4\;0\;0,\;2\;2\;-2\;2]\]  
At the receiving side, the steps for CDMA demultiplexing are listed below. Step 1. Divide the CDMA signal into three groups. Each group is composed of four digits. Perform inner product operation of CDMA codeword groups and spreading codes \(C_{A},C_{B},C_{C},\) and \(C_{D}\) individually. The results are \([4,\;-4,\;4],\)\([4,\;4,\;-4],\)\([-4,\;-4,\;4],\) and \([-4,\;4,\;4].\) Step 2. Make decisions with the threshold set to zero. Accordingly, \([4,\;-4,\;4],\)\([4,\;4,\;-4],\)\([-4,\;-4,\;4],\) and \([-4,\;4,\;4]\) are decoded as \([1\;\;0\;\;1],\)\([1\;\;1\;\;0],\)\([0\;\;0\;\;1],\) and \([0\;\;1\;\;1],\) respectively. It is evident that all users' data are retrieved successfully.