esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Operation Principles and Analysis Methods  
The detailed operating principle of the fundamental U-shaped resonator is discussed in this section, followed by parameter studies. Next, a multi-layer meta-surface composed of two identical U-shaped resonators, a common ground, two coupling apertures, and two dielectric substrates is provided. The equivalent circuit model of the proposed meta-surface is then analyzed and validated via comparison with the results of the full-wave simulations. Finally, the response under oblique incidence is investigated, and a comprehensive design flowchart is summarized.
## 4 Conclusions  
Two categories of aluminum-based metal matrix composites (Al MMCs) were synthesized by the PM process: a hybrid composite Al/SiC/Al2O3 and an Al/7 vol.% SiC composite. The influence of the part modulus and sintering time on the density and hardness of the Al/7 vol.% SiC composite was investigated at a sintering temperature of 700 \({}^{\circ}\)C. In addition, the density, compressive strength and hardness of different Al/SiC/Al2O3 MMCs were examined for various combinations of sintering conditions. The following conclusions were drawn from the current experimental study and analysis.  
1. The addition of particulate reinforcements such as SiC and alumina to an aluminum matrix improved the compressive true strength. An excess in the addition of reinforcements (beyond 7 vol.% of SiC and 3 vol.% of Al2O3) caused poor sinterability for the chosen sintering condition.
2. An increase in the sintering time contributed less to the compressive strength of the composite material (Al/SiC/Al2O3 MMCs). However, a higher densification was evident for the sintered density, hardness and metallographic analysis when the sintering time was increased from 30 min to 90 min for the Al/7 vol.% SiC composite.
3. The sintering condition of 700 \({}^{\circ}\)C for 60 min was suitable for the aluminum metal matrix composites (Al MMCs) to achieve a higher density and improved mechanical properties for the hybrid composites: (a) Al/2 vol.% SiC/3 vol.% Al2O3 and (b) Al/7 vol.% SiC/3 vol.% Al2O3.
4. A marginal variation in the sintered properties was seen when varying the part modulus. Further, the parts with the 1.67 modulus produced the best combination of sintered properties. Interfacial bonding between the reinforcing SiC particles and the aluminum metal matrix was not observably influenced by changing the part modulus, but was significantly altered by the sintering parameters (sintering temperature and sintering time).
5. At 650 \({}^{\circ}\)C and below, the matrix phase and the reinforcement phase did not uniformly transfer the compressive load.  
Further research is required for an in-depth microstructural analysis to examine the additional compounds formed in Al MMCs due to a higher sintering temperature and their contribution to the overall properties of Al/SiC/Al\({}_{2}\)O\({}_{3}\) MMCs.  
Conceptualization, K.C.N. and K.K.R.; methodology, K.C.N.; software, K.C.N.; validation, K.C.N., K.K.R. and P.P.D.; formal analysis, K.C.N.; investigation, K.K.R.; resources, P.P.D.; data curation, T.S.S.; writing--original draft preparation, K.C.N.; writing--review and editing, K.K.R.; visualization, K.K.R.; supervision, P.P.D.; project administration, T.S.S.; funding acquisition, P.P.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Materials and Methodology
### Mann-Kendall (MK) Test  
The MK test (non-parametric test) can be used to spot trends in an abiding scale of data [47]. Equation (1) is used to find the trend rate \(m_{1}\) using linear regression:  
\[y=m_{1}x_{t}+c_{o}, \tag{1}\]  
where \(x\) represents the temperature, precipitation, and snow cover. By a \(t\)-test, the significance of \(m_{1}\) was confirmed. The rising and falling patterns in various time series of snow cover, precipitation, and temperature are indicated by the positive and negative of \(m_{1}\)[48]. The strength of a trend is influenced by its adjusted significance level, size, the sample size and the number of changes over a durable scale of data [49]. Equation (2) yields the MK test statistics (\(S\)):  
\[S=\sum_{k=1}^{n-1}\sum_{j=k+1}^{n}sgn\big{(}X_{j}-X_{k}\big{)}, \tag{2}\]  
\[sgn\big{(}X_{j}-X_{k}\big{)}=\left\{\begin{array}{ll}\mathrm{if}\big{(}X_{j} -X_{k}\big{)}>0,&+1\\ \mathrm{if}\big{(}X_{j}-X_{k}\big{)}=0,&0\\ \mathrm{if}\big{(}X_{j}-X_{k}\big{)}<0,&-1\end{array}\right\}, \tag{3}\]  
where \(X_{j}\) and \(X_{k}\) are the data values at times \(j\) and \(k\), respectively, n is the length of the dataset, and \(sgn\) is the sign function which accepts values of \(-1\), \(0\), and \(+1\). The function from the \(t\) value of \(S\) displays trends in the climate datasets, which are either positive or negative:  
\[Var(S)=\frac{n(n-1)(2n+5)-\sum_{k=1}^{p}t_{k}(t_{k}-1)(2t_{k}+5)}{18}, \tag{4}\]  
where \(t_{k}\) is the number of observations in the \(k\)th group and \(p\) is the linked group. Equation (5) yields the (STS) standardized test statistic (\(Z_{S}\)), which can be calculated by  
\[Z_{S}=\left\{\begin{matrix}\frac{5-1}{\sqrt{Var(S)}},&S>0\\ 0,&S=0\\ \frac{5+1}{\sqrt{Var(S)}},&S<0\end{matrix}\right\}, \tag{5}\]  
where \(Z_{S}\) illustrates how important the trend is. If \(Z_{s}>Z_{\alpha/2}\), the STS is then used to test the null hypothesis, \(H_{0}\), and displays the confidence level. The trend of snow cover, precipitation and temperature datasets is statistically significant at the significance levels of \(\alpha=0.01\) (or 99% confidence intervals), \(\alpha=0.05\) (or 95% confidence intervals), and \(\alpha=0.1\) (or 90% confidence intervals), and the trend is considered to be real. The null hypothesis of no trend is rejected if \(Z_{s}>1.45\), \(Z_{s}>1.96\), and \(Z_{s}>2.56\), at the 1%, 5%, and 10% significance levels, respectively. Detailed information on the modified Mann-Kendall test can be found in our previous study [47].
# Underwater-Acoustic-OFDM Channel Estimation Based on Deep Learning and Data Augmentation
## 2 The UWA-OFDM Communication System  
We consider a downlink UWA-OFDM communication system, and a schematic representation of the traditional system model is shown in Figure 1. The information \(X(k)\) to be transmitted passes through the baseband signal processing to obtain the transmitted signal \(x(t)\). Then, \(x(t)\) passses through the UWA channel to the receiving device, to obtain \(y(t)\). After a series of reverse transformations, the frequency-domain information \(Y(k)\) can be obtained. For UWA channels, the general expression is as follows:\[h(t,\tau)=\sum_{l=1}^{L}A_{l}(t)\delta(\tau-\tau_{l}(t)). \tag{1}\]  
Here, we make the assumption that the UWA channel exhibits linear invariance within an OFDM transmission signal. This allows for a satisfactory approximation of the UWA channel as the channel impulse response of the \(L\) principal discrete paths. \(A_{l}(t)\) is the decay coefficient of the constant path \(l\), and \(\tau_{l}(t)\) is the time delay corresponding to the path \(l\).  
The propagation of sound waves in the ocean is affected by the wave fluctuations of the sea surface, the uneven stratification of the seabed, and the scattering and refraction effects caused by the heterogeneity of the seawater medium. In addition, the complexity of a UWA channel is reflected because it changes over time and through space, and these changes occur randomly, so an additional statistical-modeling stage is required. However, some efforts are still being made to build standard models to succinctly describe the statistics of UWA channels [23]. Therefore, in this paper, we used the powerful fitting ability of the DL method to reconstruct the UWA channel of the data block, based on the channel estimated by the pilot block.  
At the transmitter, the transmission symbol of the inserted pilot undergoes initial conversion into parallel data streams. Subsequently, it is modulated onto various subcarriers through a Discrete-Fourier-Reverse-Transform-(IDFT) unit. Thereafter, a Cyclic Prefix (CP) is inserted, to mitigate inter-symbol interference. The transmission signal passes through parallel-to-serial conversion and then is sent to the water-acoustic channel. The signal received at the receiver is  
\[y(t)=\sum_{l=1}^{L}A_{l}(t)x(t-\tau_{l}(t))+w(t), \tag{2}\]  
where \(w(t)\) represents the Additive-White-Gaussian Noise (AWGN). After removing the CP and performing the DFT, the received frequency-domain signal becomes  
\[Y(k)=X(k)H(k)+W(k). \tag{3}\]  
In the conventional UWA-OFDM system, the pilot signal is extracted and utilized for channel estimation. The LS estimate of \(H(k)\) can be represented as [9]  
\[\hat{H}(k)=\frac{Y_{pilot}(k)}{X_{pilot(k)}}=H(k)+\frac{W(k)}{X_{pilot}(k)}. \tag{4}\]  
Here, we assume that the pilot is distributed in a comb-like structure, to better estimate the effect of the Doppler.  
Figure 1: Traditional UWA-OFDM communication system.
## 2 Materials and Methods
### Dataset
#### 2.2.2 Satellite Data Acquisition and Pre-Processing  
The Landsat 8 OLI satellite data with a nominal 30-m spatial resolution used in this study were accessed from the United States Geological Survey (USGS) portal ([[https://glovis.usgs.gov](https://glovis.usgs.gov)]([https://glovis.usgs.gov](https://glovis.usgs.gov)) (accessed on 1 January 2021)). The images (path-row: 125-045) covered the coastal waters of the Beibu Gulf of Guangxi to the maximum extent, and the 13 automatic monitoring stations established by the Marine Environmental Monitoring Center of Guangxi are in the coverage. 34 scene images with low cloud cover were selected, ranging from 2013 to 2020 (Table 1).  
Figure 1: Study region. The colored rectangles represent the mouths of the bays in the Beibu Gulf.  
The Landsat 8 OLI satellite images were radiologically calibrated and atmospherically corrected before further processing. By radiometric calibration, the digital number (DN) recorded by the sensor could be converted into the spectral radiance and to the Top of the Atmosphere (TOA) reflectance. The surface reflectance may have changed after the atmospheric transmission, so the atmospheric correction was required. The error of reflectance reduced after atmospheric correction and could be used for the retrieval of Chl-a concentration.  
In this study, all of the images were processed for radiometric calibration and atmospheric correction using the FLAASH model in the ENVI 5.3.1 software package. The corrected image reduced the influence of water vapor particles in the air and was clearer than the image before correction. The spectral curve of the pixel after atmospheric correction was closer to the actual spectral curve of the ground object and more in line with the requirements of inversion as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date** & **Cloud Cover** & **Date** & **Cloud Cover** \\ \hline
7 December 2020 & 22.26 & 28 October 2017 & 0.13 \\
5 November 2020 & 12.66 & 2 March 2017 & 0.09 \\
2 September 2020 & 18.40 & 14 February 2017 & 0.28 \\
27 April 2020 & 14.75 & 28 December 2016 & 1.07 \\
23 February 2020 & 9.22 & 9 October 2016 & 3.60 \\
5 December 2019 & 9.63 & 3 June 2016 & 15.18 \\
18 October 2019 & 15.99 & 23 October 2015 & 0.64 \\
2 October 2019 & 5.56 & 7 October 2015 & 11.35 \\
15 August 2019 & 15.80 & 1 June 2015 & 21.82 \\
11 May 2019 & 34.00 & 14 April 2015 & 1.01 \\
20 February 2019 & 33.79 & 1 August 2014 & 16.62 \\
18 December 2018 & 26.23 & 14 June 2014 & 14.28 \\
31 October 2018 & 0.03 & 21 January 2014 & 0.05 \\
29 September 2018 & 4.98 & 5 January 2014 & 1.24 \\
11 July 2018 & 19.78 & 20 December 2013 & 0.42 \\
9 June 2018 & 1.89 & 4 December 2013 & 0.03 \\
1 February 2018 & 1.89 & 2 November 2013 & 5.83 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dates of remote sensing images.  
Figure 2: Distribution of sampling points.
## 3 Materials, Methods, and Equipment
### Classical Bearing Capacity Methods
#### 3.6.2 [PERSON]'s (1961) Method  
[PERSON]'s method is instrumental in calculating bearing capacity for circular piles in sand, combining empirical data with theoretical considerations to account for soil-pile interactions. The approach centers on the principle that a pile's ultimate bearing capacity depends on the soil's internal friction angle and pile geometry [28]. The method introduces a bearing capacity factor \(\text{N}_{q}\) that represents frictional resistance contributions. This factor varies with the angle of internal friction and the pile's depth-to-width ratio, as illustrated in graphical representations (Supplementary Material S1). These graphs demonstrate how bearing capacity factors increase with higher friction angles, reflecting enhanced soil resistance against shear failure. Additionally, deeper piles show higher bearing capacities due to increased soil confinement [28]. When applying this method, engineers must first determine the soil's angle of internal friction, which governs the bearing capacity factors. After calculating the pile's depth-to-width ratio, appropriate \(\text{N}_{q}\) values can be interpolated from [PERSON]'s graph. The ultimate bearing capacity is then computed using Terzaghi equations for both shaft resistance along the pile's lateral surface and end-bearing resistance at the pile tip. This methodology provides a comprehensive framework for predicting the load-bearing capabilities of circular piles in sandy environments.  
3.6.3. [PERSON] (1966) Method  
The [PERSON] method (1966) provides a systematic approach to estimating the ultimate bearing capacity of pile structures in sandy soils, where load-bearing capacity primarily derives from frictional resistance [29]. The methodology combines empirical data with theoretical principles to address soil-pile interactions. Key to this approach are the bearing capacity factors \(N_{q}\) and \(N_{\gamma}\), which represent contributions from surcharge and skin friction. These factors vary with the soil's angle of internal friction (\(\phi\)) and sand density, as illustrated in Supplementary Material S1. The graph demonstrates that as \(\phi\) increases, both \(N_{q}\) and \(N_{\gamma}\) rise, indicating enhanced soil resistance against shear failure. This relationship emphasizes the importance of accurately determining soil frictional properties when applying the method. The graph also shows how sand density affects bearing capacity factors. The standard penetration test value (\(N_{\alpha\sigma}\)), indicating sand density, correlates with higher values of \(N_{q}\) and \(N_{\gamma}\) as density increases, highlighting soil compaction's role in enhancing pile load-bearing capacity. For cohesionless soils in deep foundations, the ultimate bearing capacity follows the classical Terzaghi formulation, with maximum base, tip, or point-bearing resistance capped at 11,000 kN/m\({}^{2}\).
# AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
## 3 Subjective Listening Tests Using the MUSHRA Methodology
### Experiment 2-Multiple Point Audio Sources  
The results from experiment 1 showed that quality could be predicted from analysis of ACN 0 (the omnidirectional channel) but as expected, that localization accuracy was influenced by the subsequent channels. In order to further investigate localization accuracy, a second MUSHRA test was designed using multiple point sources. In contrast to experiment 1 where single point audio sources were used, this time only localization accuracy was evaluated.  
A different cohort of listeners took part in experiment 2, with some overlap with the listeners from experiment 1. In total 20 participants performed the test and 13 results were taken into account (7 listeners gave the hidden reference a score of less than 90 twice or more times). The participants included 5 experienced listeners and 8 semi-experienced listeners with an average age of 33 ranging from 21 to 50. The exclusion of inexperienced listeners who failed to identify the reference after initial training reinforced the importance of using experienced listeners in spatial audio testing.  
\begin{table}
\begin{tabular}{l r r r r r r r r r} \hline \hline Elevation & \(\pm\)35 & \(\pm\)35 & \(\pm\)45 & \(\pm\)45 & \(\pm\)90 & 0 & 0 & 0 & 0 \\ Azimuth & \(\pm\)135 & \(\pm\)45 & 0 & 180 & 0 & 0 & \(\pm\)135 & 180 & \(\pm\)45 & \(\pm\)90 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Azimuth and elevation angles in degrees used for 26-point Lebedev Quadrature layout.  
\begin{table}
\begin{tabular}{l r r r} \hline \hline  & **Ambisonics** & **Bit Rate** & **Bit Rate Per** \\
**Type** & **Order** & **(kbps)** & **Channel (kbps)** \\ \hline Reference & 3 & 12,288 & 768 \\
3 OA 512 & 3 & 512 & 32 \\
3 OA 256 & 3 & 256 & 16 \\ FOA 128 & 1 & 128 & 32 \\ FOA 32 (anchor) & 1 & 32 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Encoding/compression schemes used with single point audio sources.
## 3 UAV AG Communication Channel Modeling and Characterization
### A UAV AG Multi-Mobility Multi-Trajectories Channel Model
#### 3.1.3 Generation of Cluster Trajectories  
When a ground BS cannot meet the communication requirements of a great deal of vehicles as well as users, a UAV-assisted emergency network is considered. UAVs used as mobile BSs are deployed rapidly, and the AG channel at this time needs to be further investigated: thus, the single cluster model was employed for simulation of an AG channel model for military and urban emergency communication scenarios. It was assumed that the interference of many vehicles near the Rx could be abstracted into multiple clusters; that such clusters would have the same trajectory with the Rx; and that the appearance and disappearance of vehicles in the range were characterized by the birth and death process of clusters; therefore, the coordinates of the cluster were expressed as  
\[\mathbf{C}_{t_{k^{\prime}}^{\prime}}(t)=\mathbf{A}_{q}^{R}(t)=\mathbf{A}_{ini,q}^{R}+\mathbf{V}_{Q}t+\Delta D \tag{18}\]  
\[D_{p,t_{k^{\prime}}^{\prime}}^{T,SB}(t)=||\mathbf{C}_{t_{k^{\prime}}^{\prime} }(t)-\mathbf{A}_{p}^{T}(t)|| \tag{19}\]  
\[D_{q,t_{k^{\prime}}^{\prime}}^{R,SB}(t)=||\mathbf{A}_{q}^{R}(t)-\mathbf{C}_{t _{k^{\prime}}^{\prime}}(t)||, \tag{20}\]where \(\mathbf{C}_{l^{\prime}_{k^{\prime}}}(t)\) represented the coordinates of a single cluster, \(D^{T,SB}_{p^{\prime},l^{\prime}_{k^{\prime}}}(t)\) and \(D^{R,SB}_{q,l^{\prime\prime}_{k^{\prime}}}(t)\) were the distance of the \(l^{\prime}\)th ray between the Tx/Rx and the \(k^{\prime}\)th cluster, and \(\Delta D\) was a relative distance.
# A Finite Regime Analysis of Information Set Decoding Algorithms
## 1 Introduction
We note that, in addition to being the most efficient strategy to perform general random linear code decoding, ISD techniques can also be employed to recover the structure of the efficiently decodable code from its obfuscated version for the LDPC, QC-LDPC and QC-MDPC code families. Recently, the National Institute of Standards and Technology (NIST) has started a selection process to standardize asymmetric cryptosystems resistant to attacks with quantum computers. Since decoding a random code is widely believed to require an exponential amount of time in the number of errors, even in presence of quantum computers, code-based cryptosystems are prominent candidate in the NIST selection process [24]. Hence, having accurate and shared expressions in the finite length regime, both in the classical and in the quantum computing setting, for the work factor of attacks targeting such schemes, it is important to define a common basis for their security assessment. A work sharing our intent is [25], where a non-asymptotic analysis of some ISD techniques is performed. However, a comprehensive source of this type is not available in the literature, to the best of our knowledge.
# A Method for Processing Static Analysis Alarms Based on Deep Learning
## 2 Background
### Automatic Static Analysis Tools  
The initial generation of ASATs for Java, e.g., FindBugs [6; 7], PMD (found at [[https://pmd.github.io](https://pmd.github.io)]([https://pmd.github.io](https://pmd.github.io)) (accessed on 3 January 2024)), and Checkstyle (found at [[https://checkstyle.org](https://checkstyle.org)]([https://checkstyle.org](https://checkstyle.org)) (accessed on 3 January 2024)), were developed to detect potential issues or violations of coding standards. These ASATs made significant contributions when they were initially presented but also had serious limitations, particularly in terms of generating numerous false alarms [8; 9]. FindBugs is a widely recognized open-source static analysis tool that specializes in identifying potential defects for Java code, such as null pointer dereferences, resource leaks, and violations of coding conventions. Although FindBugs is now an abandoned project, its successors are still adopted by major software companies, e.g., Google [10; 11; 12], Facebook [13; 14]. These ASATs were typically designed as an analysis framework based on various forms of static analysis, which allows them to effectively analyze complex programs and scale to handle large codebases. According to the study conducted by [PERSON] et al. [15], 71% of developers pay attention to different warning categories depending on the development context. However, the abundance of false alarms generated by ASATs has led to the unwillingness of developers to configure and adopt them [16]. [PERSON] et al. [17] have compared three popular ASATs to detect bugs, including Google's Error Prone [11], Facebook's Infer [13], and SpotBugs (accessed at [[https://spotbugs.github.io](https://spotbugs.github.io)]([https://spotbugs.github.io](https://spotbugs.github.io)) (accessed on 3 January 2024)). We chose SpotBugs as the static analysis tool after comprehensive consideration; this is a fork of FindBugs that carries on from the point at which it left off.  
Figure 1: Overview of our proposed approach.