esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multitask-Based Transfer Framework for Cooperative Multi-Agent Reinforcement Learning
## 3 Preliminaries  
### Decentralized Partially Observable Markov Decision Process  
The decentralized partially observable Markov decision process (Dec-POMDP) [57] can be used to model partially observable cooperative multi-agent tasks. A Dec-POMDP consists of a tuple \(G=\langle S,U,P,r,\mathcal{O},O,n,\gamma\rangle\). \(S\) is the state space, where \(s\in S\) represents the global environment state. Every agent \(a\in A\equiv\{1,...,n\}\) picks an action \(u^{a}\in U\) from the action space \(U\) at each timestep and forms the joint-action \(\mathbf{u}\in\mathbf{U}\equiv U^{n}\) to interact with the environment. Then, a state transition will occur according to a state transition function \(P(s^{\prime}|s,\mathbf{u}):S\times\mathbf{U}\times S\rightarrow[0,1]\). Agents will receive a reward signal from the reward function \(r(s,\mathbf{u}):S\times\mathbf{U}\rightarrow\mathbb{R}\) with a discount factor \(\gamma\). In the execution stage, every agent draws a local observation \(o\in\mathcal{O}\) by an observation function \(O(s,a):S\times A\rightarrow\mathcal{O}\). Every agent stores an observation-action history \(\tau^{a}\in T\equiv(\mathcal{O}\times U)\), based on which agent \(a\) derives a policy \(\tau^{a}(u^{a}|\tau^{a})\). Joint-action value \(Q^{n}(s,\mathbf{u})=\mathbb{E}[R_{t}|s_{t}=s,\mathbf{u}_{t}=\mathbf{u}]\) is the expectation of the discounted reward sum \(R_{t}\) under joint-policy \(\pi\). In this article, we use \(x\in\mathcal{X}\equiv(S\times S)\) to represent a state pair (state transition) consisting of two consecutive states: \(x_{t}=(s_{t},s_{t+1})\). We consider Dec-POMDPs, where agents cooperate to fulfill a common purpose, and each agent has a finite discrete action set.
## 2 Mathematical Foundation of Generalized Algorithm
### Cylindrical Shaped Enclosure  
Enclosures of electronic equipment elements can have not only a rectangular shape, but also a cylindrical one. Accordingly, a modified analytical model was proposed in [36] to evaluate the SE of such enclosures. It assumes that the aperture is located at the base of the cylindrical enclosure (see Figure 6), and SE is calculated using the same equivalent circuit as in [20].  
To take into account the cylindrical shape of the enclosure, expressions for calculating the characteristic impedance and propagation constant in [36] are replaced by:  
\[Z_{g\,mn}=Z_{0}/\sqrt{1-\left(\lambda/\lambda_{c\,mn}\right)^{2}}, \tag{28}\]  
\[k_{g\,mn}=k_{0}\sqrt{1-\left(\lambda/\lambda_{c\,mn}\right)^{2}}, \tag{29}\]  
where; \(\lambda_{c\,mn}\) is the cut-off wavelength for the mode with indexes \(m\) and \(n\), which can be calculated as [37]:  
\[\lambda_{c\,mn}=2\pi R/\xi_{mn}\,, \tag{30}\]  
where; \(R\) is the enclosure radius, \(\xi_{mn}\) is the \(n\)-th root of equations \(J^{\prime}{}_{m}(\alpha_{mn})=0\) (for \(TE_{mn}\) modes), and \(J_{m}(\alpha_{mn})=0\) (for \(TM_{mn}\) modes), where \(J_{m}(\alpha_{mn})\) is the Bessel function of order \(m\), and \(J^{\prime}{}_{m}(\alpha_{mn})\) is the first derivative of this function.
# Estimating Significant Wave Height from SAR with Long Integration Times
## 2 Estimating Significant Wave Height from SAR with Long Integration Times
### Significant Wave Height Estimation  
Combining the azimuth cutoff \(\lambda_{c}\), the wavelength \(\lambda_{p}^{n}\) of the \(n\)th wave system, the wave propagation direction \(\phi^{n}\) and the radar parameters, the significant wave height is calculated by [14]  
\[SWH^{n}=\frac{0.3608}{\beta\sqrt{G^{n}g}\sqrt{\tanh\left(2\pi d\mathord{\left/ \vphantom{\lambda_{c}^{n}}\lambda_{p}^{n}\right.\kern-1.2 pt}\lambda_{p}^{n} \right)}}\lambda_{c}\sqrt{\lambda_{p}^{n}} \tag{16}\]  
where,  
\[G^{n}=1-0.5\mathrm{sin}^{2}\theta\Big{[}1+\frac{\pi}{B}\Big{/}\mathrm{sinh} \left(\frac{\pi}{B}\right)\cos(2(\phi^{n}+90^{\circ}))\Big{]} \tag{17}\]  
\[\beta=\frac{R}{V} \tag{18}\]  
where \(d\) is the water depth, \(B=2.44\), \(R\) is the slant range, and \(V\) is the platform speed. When \(d\gg\lambda_{p}^{n}\Big{/}2\), \(\sqrt{\tanh\left(2\pi d\mathord{\left/\vphantom{\lambda_{c}^{n}}\lambda_{p}^{ n}\right.\kern-1.2 pt}\lambda_{p}^{n}\right)}=1\), Equation (16) can be written as  
\[SWH^{n}=\frac{0.3608}{\beta\sqrt{G^{n}g}}\lambda_{c}\sqrt{\lambda_{p}^{n}} \tag{19}\]  
After the estimating significant wave height of all ocean wave systems is completed, significant wave heights of all systems are output  
\[SWH=[SWH^{n}],n\in[1,Num] \tag{20}\]
## 2 Results and Discussion
### Sorption Properties
#### 2.2.1 The pH Effect
The slopes for AO-APEI are higher than for APEI, and the pH has a greater impact on sorption performance for the amidoximated sorbent. For ion-exchange processes, the slope of the curve is usually correlated to the exchange ratio between protons and metal ions. For AO-APEI, this molar ratio is close to 7; this is not consistent with the charge of Sr(II) and its expected interaction with reactive groups (-NH and -NOH). Therefore, other side-reactions are responsible for this high molar ratio.
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 4 Modulation Recognition Based on Deep Learning in Small Sample Environment
### The Small Sample AMR Algorithm Based on Signal Data Enhancement
#### 4.1.2 Data Augmentation Method Based on Semi-Supervised Learning  
Semi-supervised learning can jointly optimize unlabeled samples and labeled samples, improve the performance of the model, and improve the recognition accuracy. Therefore, semi-supervised learning is often used to solve the modulation identification problem under few-shot conditions [101]. [PERSON] et al. designed a semi-supervised learning framework based on Generative Adversarial Networks (GAN) that can directly process IQ signal data and make full use of unlabeled samples to achieve end-to-end accurate classification of a small number of electromagnetic signals [102].
## 3 Methodology
### Traditional Weighting Matrix Model  
In the literature, numerous weighting models rely on either the satellite's elevation (\(EL\)), carrier-to-noise density (\(C/N_{0}\)), or a combination of both these parameters to give a weight for each satellite signal. Additionally, this step acts as an input to the satellite selection/rejection techniques. An overview of the traditional weighting matrix models is explained as follows:  
1. Elevation-based model (\(EL\) model): In this particular model, the variance, carrier-phase observation model is dependent on the satellite's elevation angle (\(EL\)) and utilizes mathematical functions such as \(\sin^{2}(EL)\) or \(\sin(EL)\)[35; 36; 37]: \[\sigma_{Pr}^{2}=\frac{\sigma_{o}^{2}}{\sin^{2}(EL)}\] (1) \[\sigma_{Pr}^{2}=\frac{\sigma_{o}^{2}}{\sin(EL)}\] (2)  
Figure 2: The coordinate frames.  
where \(\sigma_{o}^{2}\) is the error variance of the pseudorange.
2. Carrier to noise density-based model (\(C/N_{o}\) model): In [35; 36], the model was structured around the signal-to-noise ratio \(C/N_{o}\), expressed as follows: \[\sigma_{p_{r}}^{2}=b\cdot 10^{\frac{-C/N_{0}}{10}}\] (3) where b is constantly defined empirically and equals \(10^{4}\) m\({}^{2}\). In [12; 38; 39], the proposed standard deviation of code observation \[\sigma_{pr}=a+b\ 10^{\frac{-C/N_{0}}{20}}\] (4) In this context, the coefficient \(a\) represents the root mean square value (RMS) of pseudorange observation residuals, while \(b\) corresponds to the pseudorange coarse acquisition (\(C/A\)-code) chipping length, which was defined as 293 m for L1 measurements.
3. \(EL-C/N_{0}\)-model: In [35; 37; 40; 41], a combination model was proposed that depended on both \(El\) and \(C/N_{o}\): \[\sigma_{pr}^{2}=k\ \frac{10^{\frac{-C/N_{0}}{10}}}{\sin^{2}El}\] (5) where \(k\) is 1 if the line-of-sight (LOS) signal is available and 2 or \(\infty\) if no LOS signal is available: \[\sigma_{pr}^{2}=x+y\ \frac{10^{\frac{-C/N_{0}}{10}}}{\sin El}\] (6) \[\sigma_{pr}^{2}=x+\frac{y}{\sin El}+z\ 10^{\frac{-C/N_{0}}{10}}\] (7) where x, y, and z model a nonlinear regression problem that relates the mean of Inverse-Gamma estimated distributions and the corresponding \(EL-C/N_{o}\) pair [40].
4. Google proposed sigma: In the smartphone's RGNSSM, there is a provided value referred to as Received Space Vehicle Time Uncertainty Nanos (RSVTUN). This value signifies the estimated discrepancy in the received GNSS time. \[\sigma_{pr}^{2}=\text{RSVTUN}\ \times 10^{9}\times c\] (8) Google advises filling the weighting matrix with this value, yet neither the Android developer's guide nor the accompanying white paper offers guidance on its calculation [16].
# AI Makes Crypto Evolve
## 7 AIEC
### Homomorphic Neural Cryptography  
Homomorphic encryption is a recent trend in cryptography [206; 207; 208]. Homomorphic encryption allows trusted third parties to process encrypted data without knowledge of the data. Data can be encrypted before transmission. A Fully Homomorphic Encryption (FHE) scheme would allow people to perform any arbitrary manipulation on encrypted data such as addition or multiplication at the same time. This section studies how AI-based techniques, specifically NNs, could be used to enhance homomorphic encryption.  
The authors of [209] argued that homomorphic encryption is susceptible to errors because of the calculations performed by the algorithm in each step. They attempted to solve this problem by separating the computational operations while encrypting and decrypting the data at each stage. Combining this with NNs provides a robust encryption algorithm that minimizes the errors and offers a strong encryption system.  
Further, the authors of [210] firstly argued that the complexity caused by a modification in encrypted data increases the difficulty of the implementation of FHE. In their method, noises would be added to the plaintext at the time of encryption using NNs. It would be hard to predict/evaluate when the modification procedures also modify the noise. Secondly, they argued that it takes a lot of time and computing power to perform decryption through bootstrapping. One possible solution is to limit the time of modification for the encrypted data.  
While FHE is still in progress, Partial Homomorphic Encryption (PHE) can be implemented in real-world applications. The Paillier encryption algorithm, as a PHE algorithm, was used in [211] with the support of DNNs for face recognition.
# IoT Blockchain Data Vercity with Data Loss Tolerance
## 4 Discussion  
The objective of this research was achieved and proven with the experimental evaluation, simulation and result analysis. This research confirms that IoT blockchain data veracity with data loss tolerance can be accomplished by the proposed IoT blockchain. It is apparent from the simulation results that blockchain complemented IoT data veracity with its peer-to-peer (P2P) connectivity and secured distributed storage. They have created redundancy and provided greater Byzantine fault tolerance (BFT) in ensuring high data veracity with data loss tolerance. The edge computing of IoT blockchain technology is also conceptually workable with intelligent small computing resources, and it opens up a new era of bringing the intelligence of data collection, connectivity, computation and storage into the edge/device layer. Additionally, instead of a single-purposed IoT device, the IoT blockchain was equipped with intelligence to perform more smart decisions such as optimized protocol in connectivity, storage and auto-recovery when necessary.  
The data veracity can be further enhanced with data veracity strength monitoring, this is proved with experimental simulation by using capacitance as its strength indicator. More simulation and evaluation models can be carried out to make it a practical and workable model.
# Inter-Unit Consistency and Validity of 10-Hz GNSS Units in Straight-Line Sprint Running
## 4 Discussion  
The aim of the study was to investigate the inter-unit reliability and validity of multiple 10-Hz Catapult GNSS units during straight-line sprint running. Inter-unit consistency was  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**GNSS Variables** & **ICC** & **95\% Confidence Intervals** & **SEM** \\ \hline Total distance & 0.131 & [\(-\)0.024, 0.556] & 8.8 m \\ Peak speed & 0.323 & [0.101, 0.736] & 1.3 m/s \\ \hline \hline \end{tabular} Note. ICC denotes intraclass correlation coefficients; SEM denotes standard error of measurement.  
\end{table}
Table 1: Reliability statistical outputs to assess inter-unit consistency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**GNSS Units** & **Mean (SD)** & \(p\)**-Value** & \multicolumn{2}{c}{**Effect Size (\(d\))**} \\ \hline Unit 1 (n = 13) & 49.77 (5.92) & 0.030 * & 8.41 & Large \\ Unit 2 (n = 13) & 46.69 (10.62) & 0.747 & 4.49 & Large \\ Unit 3 (n = 10) & 44.50 (8.55) & 0.663 & 5.29 & Large \\ Unit 4 (n = 13) & 52.23 (10.11) & 0.039 * & 5.17 & Large \\ Unit 5 (n = 11) & 52.00 (10.13) & 0.067 & 5.13 & Large \\ Unit 6 (n = 12) & 50.83 (8.57) & 0.063 & 5.93 & Large \\ Unit 7 (n = 12) & 47.50 (8.06) & 0.460 & 5.89 & Large \\ Unit 8 (n = 13) & 52.69 (12.18) & 0.061 & 4.33 & Large \\ \hline \hline \end{tabular} Note. * Significant differences detected using one-sample \(t\)-tests (\(p\) < 0.05).  
\end{table}
Table 2: Validity of GNSS distance measurements against known distance of 45.72 m.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**GNSS Units** & **Mean (SD)** & \(p\)**-Value** & \multicolumn{2}{c}{**Effect Size (\(r\))**} \\ \hline Unit 1 (n = 13) & 7.04 (1.15) & 0.057 & 0.604 & Large \\ Unit 2 (n = 13) & 6.89 (1.98) & 0.127 & 0.495 & Medium \\ Unit 3 (n = 10) & 6.92 (1.03) & 0.064 & 0.673 & Large \\ Unit 4 (n = 13) & 7.11 (0.89) & **0.010 * & 0.780 & Large \\ Unit 5 (n = 11) & 7.37 (1.55) & 0.416 & 0.303 & Medium \\ Unit 6 (n = 12) & 8.40 (2.53) & 0.970 & 0.026 & Negligible \\ Unit 7 (n = 12) & 6.86 (1.34) & 0.064 & 0.615 & Large \\ Unit 8 (n = 13) & 7.37 (1.05) & 0.057 & 0.604 & Large \\ \hline \hline \end{tabular} Note. * Significant differences detected using Wilcoxon signed-rank tests (\(p\) < 0.05). Group mean (SD) of video-based peak speed was 7.78 (0.90) m/s.  
\end{table}
Table 3: Validity of GNSS peak speed measurements against video analysis.  
assessed among eight GNSS units worn on each participant, and validity was tested by comparing total distance and peak speed against criterion-referenced values. The most prevailing outcomes were that despite all GNSS units belonging to the same system, low inter-unit reliability and varied accuracies in distance and speed measurements were found during fast speed running.
# Research on Grouting Pressure of Earth-Rock Dams Reinforced by Polymer Based on Discrete Elements
## 6 Simulation Result Analysis
### Influence of Different Grouting Pressures
#### 6.2.1 Variation of Slurry Diffusion Range  
Under different grouting pressures, the diffusion range of the slurry is affected variably.  
Analysis of Figure 13 reveals that at a constant grouting pressure, the diffusion range in the Z direction exceeds that in the X direction. As grouting pressure increases, changes in the Z direction are more pronounced than in the X direction, and this difference becomes increasingly significant. Specifically, when grouting pressure rises from 0.1 MPa to 0.3 MPa, the expansion in both the X and Z directions becomes more noticeable, with an enhanced \"U\" shaped opening in the X direction. This increase in grouting pressure can overcome internal resistance within the rockfill body, facilitating slurry diffusion over greater distances.  
However, as grouting pressure escalates from 0.3 MPa to 0.7 MPa, the change in slurry diffusion range stabilizes. During this phase, although the increase in grouting pressure continues to positively affect slurry diffusion, the degree of influence gradually diminishes. This is because, as grouting pressure increases, the voids within the rockfill body are progressively filled, and the pathways for slurry diffusion become restricted.  
Further increase in grouting pressure from 0.7 MPa to 0.9 MPa results in a diminished change in the slurry diffusion range, especially below the grouting hole where the effect is minimal. In this high-pressure stage, the additional grouting pressure has a limited impact on diffusion range expansion. At this point, most gaps within the rockfill body have been filled, and the channels for slurry diffusion are further restricted, leading to less pronounced changes in diffusion range.  
Figure 12: Detection zoning. (Like Figure 6, the color represents the particle size).  
Figure 13: Diffusion range of slurry under different grouting pressures.  
In summary, at lower pressures, the slurry diffusion range increases significantly with rising pressure; under medium pressures, the change in the slurry diffusion range tends to stabilize; and under higher pressures, the expansion of the slurry diffusion range becomes limited. Additionally, experimental results also indicate that the diffusion range in the Z direction is larger than in the X direction, and changes in the Z direction are more sensitive to variations in grouting pressure.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 3 Potential Applications
### Data Collection, Dissemination and Analysis  
Data collection in vehicular environments aims at supporting real-time interactions for an improved safety and convenience. Smart traffic lights (STLs) illustrate an example of real-time interactions support. STL can directly interact with nearby sensors for pedestrian detection, speed monitoring and location data collection. A set of cooperating STLs can play a key role in traffic management by coordinating the green traffic wave [29]. Moreover, equipping STLs with edge computing capabilities can effectively contribute to predicting potential crashes, with the aid of real-time data analysis and learning algorithms. Consequently, approaching vehicles can be warned in advance to take proper actions. The collected data can be aggregated and summary values can be sent to the Cloud for global, long-term analytics.  
An example application that benefits from edge computing can be found in [9], where edge nodes continuously collect traffic data from nearby vehicles and STLs. Collected data is processed in two layers for acquiring high-level knowledge. At the first layer, the immediate processer captures various event patterns from the collected data. For example, when an edge node identifies an accident event from an analysis of a crashed car data, it notifies other edges in addition to vehicles nearby the accident location. The second layer contains processing components to derive high-level knowledge from the data processed at the first layer.
# Numerical Analysis of Vibroacoustic Response of Timber Floor Panels Damped with Porous Materials
## 2 General Structural Design  
### Floor Panel Description  
A timber floor panel consisting of glued laminated elements (beams, joins, and floor) is investigated in this study. The dimensions of the panel have been defined to ensure appropriate implementation and transport conditions so its width is fixed at 2.40 m, and its length at 7.8 m. Each panel consists of two main glulam beams (75 \(\times\) 225 mm) in the longitudinal direction, thirteen glulam joists in the transversal direction (75 \(\times\) 175 mm) with a spaceman of 0.60 m, and a 25 mm of timber floor that connects the beams (Figure 1). The beams of this panel are the parts that are connected with the vertical elements of the structure (columns, facades, etc.). In this study, the panel is investigated for two connection types (see detail 1 of Figure 1), as the connection's stiffness can vary [4,18]. In addition, the case study examines the effects of the porous material on the floor of the panel in order to damp the vibroacoustic response of the structure (see detail 2 of Figure 1). The timber material of the main parts of this floor panels is categorized in the class GL24h, but experimental tests have been conducted by the authors of the present study (see Section 2.2).
## 3 Results and Discussion
### XRD Characterization  
The phase of the PCL/HA composite filaments was analyzed by means of XRD, as shown in Figure 4. HA exhibited weak intensity peaks, due to its relatively poor crystallinity [13], but the observed intensity peaks still corresponded to the characteristic peaks of HA. On the other hand, relatively strong intensity peaks were observed in PCL, compared to HA, due to its semi-crystalline nature. PCL exhibits two strong diffraction peaks at Bragg angles 20 = 21.3\({}^{\circ}\) and 23.6\({}^{\circ}\)[28], and our peaks demonstrated the same result. Thus, in the XRD patterns of the PCL/HA composites, both characteristic peaks can be clearly observed. However, it can be seen that as the wt.% of HA in the PCL/HA composite increased, the intensity peaks corresponding to PCL decreased. Therefore, the XRD analysis indicates that the PCL/HA composite filaments were correctly synthesized and there was no change in their properties during the filament fabrication and 3D printing processes.
# A Weighted Turbo Equalized Multi-Band Underwater Wireless Acoustic Communications
###### Abstract  
The multi-band UWAS (Underwater Wireless Acoustic Sensor) communication techniques are effective in terms of performance and throughput efficiency because they can overcome selective frequency fading by allocating the same data to different frequency bands, in an environment of rapidly changing channel transfer characteristic. However, the multi-band configuration may have a performance worse than the single-band one because performance degradation in one particular band affects the output from all the other bands. This problem can be solved by using a receiving end that analyzes the error rates of each band, sets threshold values and allocates the lower weights to the inferior bands. There are many methods of setting threshold values. In this paper, we proposed an algorithm to set the threshold value by using the preamble error rates, which are known data that have to be transmitted and received. In addition, we have analyzed the efficiency of multi-band transmission scheme in the UWAS communication by applying 1-4 number of multi-bands, using turbo pi codes, with a coding rate of 1/3. We evaluated the performance of the proposed multi-bands transmission model in real underwater environments. Experimental results showed that the performance increased as the number of multiple bands increased. Furthermore, the performance of multi-band was improved when the proposed threshold algorithm was applied.  
underwater wireless acoustic communications; multi-band; turbo pi code; threshold +
Footnote †: journal: Applied Sciences