esa-sceva/satcom-chunk-collection huggingface dataset:
# Sphero-Conical Modeling for the Estimation of Very Long Baseline Interferometry Invariant Point
## Appendix B Estimation of the IVP within the Gauss-Helmert Model
 \tag{13}\]  
Assuming that a target \((x_{i},y_{i},z_{i})\) is involved in both the azimuthal rotation \((r_{k})\) and the vertical motion \((c_{l})\), we arrive at the estimation model for the TLS within the GHM by combining Equations (11) and (13):  
\[\mathbf{w}_{i}=A_{i}\mathbf{\xi}+B_{i}\mathbf{e}_{i}\,. \tag{14}\]  
The (residual) observation vector is given by  
\[\mathbf{w}_{i}= \begin{bmatrix}a_{i}&b_{i}\end{bmatrix} ^{T}, \tag{15}\]  
where  
\[ \begin{split} a_{i}&=(x_{i}^{0}-\alpha^{0})^{2}+(y_{i}^{0}- \beta^{0})^{2}-(r_{k}^{0})^{2}\\ &+2(\mu_{x_{i}}^{0}-\alpha^{0})(x_{i}-\mu_{x_{i}}^{0})+2(\mu_{y_{i}}^{0}- \beta^{0})(y_{i}-\mu_{y_{i}}^{0}),\\ b_{i}&=(x_{i}^{0}-\alpha^{0})^{2}+(y_{i}^{0}-\beta^{0})^{2}+(z_{i}^{0}- \gamma^{0})^{2}-(c_{l}^{0})^{2}\\ &+2(\mu_{x_{i}}^{0}-\alpha^{0})(x_{i}-\mu_{x_{i}}^{0})+2(\mu_{y_{i}}^{0}- \beta^{0})(y_{i}-\mu_{y_{i}}^{0})+2(\mu_{z_{i}}^{0}-\gamma^{0})(z_{i}-\mu_{z_{i} }^{0}),\end{split}  \tag{16}\]and the corresponding observation error and the design matrix for the unknown parameters are represented by  
\[B_{i}\cdot\mathbf{e}_{i}= \begin{bmatrix}2(\mu_{x_{i}}^{0}-\alpha^{0})&2(\mu_{y_{i}}^{0 }-\beta^{0})&0\\ 2(\mu_{x_{i}}^{0}-\alpha^{0})&2(\mu_{y_{i}}^{0}-\beta^{0})&2(\mu_{z_{i}}^{0}- \gamma^{0})\end{bmatrix}\begin{bmatrix}e_{x_{i}}\\ e_{y_{i}}\\ e_{z_{i}}\end{bmatrix}  \tag{11}\]  
\[A_{i}\cdot\mathbf{\xi}= \begin{bmatrix}2(\mu_{x_{i}}^{0}-\alpha^{0})&2(\mu_{y_{i}}^{ 0}-\beta^{0})&0&2r_{k}^{0}&0\\ 2(\mu_{x_{i}}^{0}-\alpha^{0})&2(\mu_{y_{i}}^{0}-\beta^{0})&2(\mu_{z_{i}}^{0}- \gamma^{0})&0&2c_{l}^{0}\end{bmatrix}\begin{bmatrix}d\alpha\\ d\beta\\ d\gamma\\ d\kappa\\ dc_{l}\end{bmatrix} . \tag{12}\]  
The least-squares solution (LESS) of the GHM produces an estimate of the unknown parameters (\(\mathbf{\hat{\xi}}\)) and a prediction of the unknown random errors, that is, residuals (\(\mathbf{\hat{r}}\)) as follows:  
\[\mathbf{\hat{\xi}}= \begin{bmatrix}A^{T}(BP^{-1}B^{T})^{-1}A\end{bmatrix} ^{-1}A^{T} (BP^{-1}B^{T})^{-1}\mathbf{w} \tag{13}\]  
\[\mathbf{\hat{r}}=P^{-1}B^{T}(BP^{-1}B^{T})^{-1}(\mathbf{w}-A\mathbf{\hat{\xi}}). \tag{14}\]  
The corresponding estimated dispersion matrix for the estimated parameters and the estimated variance component are given by  
\[\hat{\sigma}_{0}^{2}=\frac{(B\mathbf{\hat{r}})^{T}(BP^{-1}B^{T})^{-1}(B\mathbf{\hat{r} })}{r}=\frac{\mathbf{\hat{e}}^{T}P\mathbf{\hat{e}}}{r} \tag{15}\]  
\[\hat{D}\{\mathbf{\hat{\xi}}\}=\hat{\sigma}_{0}^{2}\big{[}A^{T}(BP^{-1}B^{T})^{-1}A \big{]}^{-1}, \tag{16}\]  
where \(r:=\text{rk}B-\text{rk}A\) is the redundancy of the model.
# Illegal Logging Detection Based on Acoustic Surveillance of Forest
## 2 Related Work
The results also showed a clear increase in performance with the authors' sound sensing designs that utilized sound amplitude, as well as indicated better performance for detecting sounds made by sawing wood. The authors, in [20] presented a hierarchical structured wireless sensor network which was oriented to on-site signal processing approaches that used low-cost microcontrollers. The authors introduced different time-domain methods; the first method relied on autocorrelation function, while the second method relied on TESPAR. The study results indicated that TESPAR was more sensitive to various weather effects and also pointed out that it was possible to achieve real-time, on-site, high detection performance with time domain, low complexity signal processing, with an approximately 80% true positive rate (TPR) and an almost 0% false positive rate (FPR) for different forest characteristics. The proposed system was low in cost as well as required hardware, and it could be easily used in collaborating networks of sensors in which the combination of data from different locations achieved quite good protection in large environments. The authors, in [21], introduced a system that could be used for sound detection of chainsaws and it was based on extraction of Haar-like features. The method aimed to analyze and classify signals from audio sources using frequency-domain feature extraction. More specifically, from the spectrogram, Haar-like features were specified. The method performed a two-stage thresholding approach to discriminate chainsaw from non-chainsaw sounds. The results of the study indicated that the method was very effective in recognizing chainsaw sounds and that it could effectively perform this discrimination in forests.
## 3 Dynamic Flexible Processing of Sampled Signals
### Dynamic Wavelet Threshold Denoising  
For the noisy signal after sampling, due to the noise folding effect, the sampled signal y contains the folded noise, so it is necessary to denoise \(y_{i}\) in the first place. The dynamic denoising of \(y_{i}\) is performed according to the following four steps:  
1. Select the wavelet basis function \(\psi(t)\) and the wavelet decomposition layers \(L\). Different wavelet functions have different characteristics. According to the experimental results and comprehensive considerations, the db1 wavelet is finally selected as the wavelet basis function \(\psi(t)\).
2. Using \(\psi(t)\) to perform the wavelet transformation of \(y_{i}\) at the \(L\) layer.
3. Determine the threshold \(T\) and handle wavelet coefficients below \(T\). The determination of \(T\) is crucial. If \(T\) is on the large side, more wavelet coefficients are handled; hence, the characteristics of the signal are lost; if \(T\) is on the small side, the denoising effect is not guaranteed. Therefore, we incorporate dynamic factors \(\eta\) into noise variance estimation to determine the threshold. \[T_{i}=\eta_{i}\sigma_{i}\sqrt{2 nL}\] (12) where \(\sigma_{i}\) is the noise standard deviation estimation of the signal. \[\sigma_{i}=\frac{M_{ei}}{0.6745}\] (13) \[M_{ei}\] is the median of the approximate component wavelet coefficients after decomposition for each channel. Considering that the \(p_{i}(t)\) of each channel is different, the SNR of the sampled signal \(y_{i}[n]\) obtained is also different, so \(T\) is mainly determined by the SNR of each channel. Meanwhile, a large amount of noise and signals are mixed, making it difficult to distinguish between them. Therefore the determination of \(T\) also requires \(\sigma\) to be dynamically adjusted. When the channel SNR is low, \(\eta\) should be larger, so that noise can be removed to the maximum extent. When the channel SNR is high, \(\eta\) should be scaled down appropriately to retain more signal details [24]. The noise power \(P_{noise}\) is determined based on the noise standard variance \(\sigma\), and the value of signal power \(P_{signal}\) is derived from \(P_{noise}\) and the overall power of the signal. Based on the \(P_{noise}\) and \(P_{signal}\) of each channel, the scaling factor \(\eta\) is defined. \[\eta=\left\{\begin{array}{rl}0.8&P_{noise}<P_{signal}\\ 1&P_{noise}=P_{signal}\\ 1.1&P_{noise}>P_{signal}\\ \end{array}\right.\] (14) After the threshold is determined, the wavelet coefficients below \(T\) are set to zero.
4. The wavelet inversion of the processed signal gives the denoised signal \(\hat{y}_{i}\).
## 3 Principals of Routing Protocols and Their Breakdowns
### Energy Aware Multi-Path Routing Protocols
#### 3.1.6 Multi-hop ARQ  
In [29], [PERSON] et al. proposed a multi-hop automatic repeat request (ARQ) protocol. ARQ is required to manage the re-transmission of lost packets. The author uses a half-duplex mode for the protocol. Each packet needs to be acknowledged before transmission; it is known as the Stop and Wait (S&W) process. The authors used a hybrid ARQ scheme that chooses the implicit acknowledge packet (ACK) when the transmission is isotropic and explicit otherwise.  
* Comparison Both multi-hop ARQ and EH-ARCUN use vertical layer and harvesting-based system models, respectively. They do not require localization and Hello packet. Multi-hop ARQ has a single sink, whereas EH-ARCUN has multiple moving sinks, and the simulation was  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Protocol** & **Hello Packet** & **Localization** & \begin{tabular}{c} **Simulation** \\ **Tool** \\ \end{tabular} & **No. of Sink** & **System Model** & \begin{tabular}{c} **Forwarding** \\ **Node Selection** \\ \end{tabular} \\ \hline SPRINT & ACK packet & Not needed & MATLAB & Single & Opportunistic & RSS \\ PA-EPS-CASE & \
\begin{tabular}{c} Data from \\ source \\ \end{tabular} & Not needed & Not given & Multiple &
\begin{tabular}{c} Vertical \\ layering \\ \end{tabular} & Depth \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison Proactive routing protocols.  
performed in MATLAB, it uses maximum for selecting forwarding node selection, and the L2 protocol is used for multi-hop ARQ. In Table 4, we compare the hybrid routing protocols.  
* Cluster-based routing protocols  
The cluster-based routing technique is one of the most popular routing techniques for WSN. In this network, the nodes are grouped into a cluster, and each cluster has its own CH. Each CH collects the data from the member nodes and transmits it. CH uses more energy than others as it propagates and collects information from the node. Hence, it is crucial to choose CH carefully. Clustering-based protocols are further divided into two subtypes, layer-based, and grid-based clustering protocols. In layer-based, the seafloor is divided into layers and the nodes in each layer are divided into multiple clusters. Whereas, in grid-based, the seafloor is divided into grids, and clusters are created within grids. The CH gathers the member nodes' information in both types and passes it on to the following grids or layers CH. We review a few energy-efficient cluster-based routing protocols for UWSNs in depth in this section.
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 5 Proposed Work Overview
### Asset-Specific Bridge Selection (ABS)
#### 5.2.2 Overview of Bridge Selection  
The selection of an asset-specific bridge within CrossDeFi is a critical process that ensures optimized, secure, and efficient transfers of assets across different blockchain networks. This process involves several steps:  
1. Identification of Transferable Assets: Identify the specific digital assets that need to be transferred across different DeFi systems, ranging from cryptocurrencies to other digital assets unique to specific networks. This step is essential for tailoring the bridge selection to these specific asset requirements.
2. Evaluation of Bridge Protocols: Evaluate various bridge protocols available within the DeFi ecosystem, assessing, for each asset, compatibility and network support.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Bridges** & **Asset Support** & **Integration with DeFi Apps** & **Speed and Efficiency** & **Network Adaptability** & **Validators** & **Bi-Directional Transfer** & **Security Level** & **Transaction Fees** \\ \hline Binance & Yes & No & High & Medium & Yes & Yes & Medium & Very high \\ \hline Portal & Yes & Yes & High & High & Yes & Yes & Medium & High \\ \hline Plenty & Yes & Yes & High & High & No & Yes & High & High \\ \hline Avalanche & Yes & Yes & Medium & Very high & No & Yes & Very high & High \\ \hline Stargate & Yes & No & Medium & Medium & No & Yes & Medium & High \\ \hline Zeroswap & Yes & Yes & Medium & High & No & Yes & High & High \\ \hline cbridge & Yes & Yes & Medium & High & No & Yes & High & High \\ \hline \hline \end{tabular}
\end{table}
Table 6: Understanding of different bridges with their characteristics.  
Figure 3: CrossDeFi asset transfer process.  
This evaluation includes a thorough analysis of each bridge's features, capacities, and constraints, ensuring that the selected bridge aligns perfectly with the asset's transfer requirements.
3. Consideration of Liquidity: Assess the liquidity available for the asset pairs at both the source and destination networks. Adequate liquidity is crucial for handling the volume of transfers without causing market disruption or experiencing slippage, thus supporting efficient and smooth asset transitions.  
By following these structured steps, CrossDeFi ensures that asset transfers are not only possible but are conducted with maximum efficiency and security, reflecting the robustness of CrossDeFi's operational framework.
## 3 Results and Discussion

Table 3: Change in the bacteria counts of dry-salted and brined anchovies prepared from fresh raw material (control group) in comparison to frozen and thawed raw material (experimental group) during refrigerated storage at \(4\pm 1\)\({}^{\mathrmAccording to the International Commission for Microbiological Standards of Foods [52], initial counts on fish may vary from \(10^{2}\) to \(10^{7}\) cfu/g, a total aerobic count that can only be used as an indication of time-temperature conditions during storage if sufficient data are known about a particular fish species. These values can change during handling and processing. The highest TVAMB counts corresponded to the lowest brine concentration (10%) of the experimental group on the 1 st week of ripening and significantly decreased during storage (\(p<0.05\)) (Table 3). Various researchers reported low bacterial loads during different stages of ripened anchovies [51; 53]. In our previous study with commercially brined and dry-salted products, TVAMB counts varied between 2.17 and 2.75 log cfu/g and between undetectable and 3.08 log cfu/g, respectively [42]. The spoilage of salted and dried fish during storage is mainly due to the activity of microorganisms, especially the salt-tolerant halophilic bacteria (HB). Past studies demonstrated low or absent HB counts for these types of products at the beginning of the ripening period, with counts usually increasing during storage [50], which was also supported
# Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G
## 1 Introduction
Further, we automate adapting to the best possible modulation scheme using the K-means algorithm, an unsupervised ML algorithm.
## 2 Experimental Model and Facilities  
### Double Membrane Wing Model  
The double-membrane wing model originates from a solar UAV with a wingspan of 15 m (in Figure 1). The flight altitude of this aircraft fluctuates between 0-15,000 m, indicating different flight speeds and Reynolds numbers. The primary parameters of the aircraft are portrayed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Take-off mass (kg) & 35.0 \\ Wing load (kg/m\({}^{2}\)) & 2.6 \\ Cruise speed at 0 m (m/s) & 6.4 \\ Cruise speed at 15,000 m (m/s) & 16.5 \\ Flight latitude (\({}^{\circ}\)) & 40.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The primary aerodynamic parameters at different heights.  
The wing of the aircraft consists of one rectangular section in the middle and one trapezoidal section on each side. As portrayed in Figure 1, a rectangular wing with four-rib spacing is taken as the force measuring section. The chord length of the section is 0.91 m and the span length is 2.018 m. The leading edge consists of the outer carbon fiber shell and the internal PMI foam. There is an isolation section on each side of the experiment section to minimize the impact of vortices generated at the end of the wing.  
The skin materials used by this UAV vary according to different flight stages. In the short-endurance test flight stages, to reduce the flight cost, the polyimide membrane containing longitude and latitude glass fiber is used as the skin material. This type of skin is relatively smooth, but it does not have power generation capacity. During long-endurance flight, the wing skin is the flexible photovoltaic cell membrane based on polyurethane material. As portrayed in Figure 2, the experimental wing covers three skin materials: A. carbon fiber skin wing, which is used as the benchmark for the experiment; B. polyimide membrane wing; C. photovoltaic membrane skin wing.  
Both membrane materials contain multilayer structures. Through the tensile experiment, the equivalent mechanical properties of the membranes are obtained, as demonstrated in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Polyimide Membrane** & **Photovoltaic Membrane** \\ \hline Elastic modulus (MPa) & 6450 & 320 \\ Poisson’s ratio & 0.34 & 0.32 \\ Thickness (mm) & 0.012 & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The equivalent mechanical properties of the membranes.  
Figure 1: Double membrane wing of the solar UAV.  
Figure 2: Three kinds of experimental models.
## 7 Discussion
Calculating volume using PIX4D can be part of these processes where three-dimensional models of projects are created using data derived from drones. In this way, the results obtained from case studies can align with the previous literature to demonstrate how the ideas and concepts existing in the literature can be applied in specific contexts such as case studies, and how this can lead to improving the effectiveness of project management and its outcomes. Finally, the incorporation of VR and AR in construction projects not only enhances efficiency and accuracy but also fosters improved collaboration and informed decision making throughout the project lifecycle. The integration of aerial imagery, facilitated by advanced tools like drones, signifies a cutting-edge technological advancement that has significantly enhanced the efficiency and accuracy of distribution operations and container design. This approach has yielded numerous benefits, positively impacting engineering operations and decision-making processes, as follows:  
* Comprehensive Site Analysis: Aerial imagery enables a comprehensive understanding of terrains, available spaces, and potential obstacles, aiding in better site comprehension. * Decision Guidance: Data from aerial imagery support informed decision making regarding container distribution based on factors such as access and terrain. * Increased Precision and Efficiency: Precise analysis of aerial imagery results in more accurate distribution, reducing space wastage and enhancing workflow efficiency. * Time and Effort Savings: Utilizing aerial imagery reduces the need for manual surveying, saving time and effort. * Monitoring and Performance Improvement: Regular monitoring of distribution operations enhances performance analysis, facilitating continuous improvement. * Strategic Planning Enhancement: Identifying high-traffic areas streamlines strategic planning for optimizing container distribution. * Safety and Security Enhancement: Identifying safe areas and potential risks enhances operational safety and risk mitigation. * Emergency Response Planning Improvement: Strategic area identification aids in emergency response planning, facilitating efficient evacuation and rescue operations. By adopting this approach, operational efficiency improves, and on-site space distribution becomes more accurate, aligning with sustainability objectives and overall performance enhancement goals.
###### Abstract  
In this study, we aimed to create heat sinks with higher heat dissipation capabilities for a compact light-emitting diode (LED) recessed downlight (CLRDL) under jet impingement cooling. We desired to use the sinks in limited space to maintain lower junction temperature and allow higher LED power. Perforated-finned heat sinks (PTFHSs) and metal-foam-like heat sinks (MFLHSs) fabricated using selective laser melting (SLM) were compared with a traditional funnel heat sink (TTFHS). Two cooling fans with higher and lower velocity at Reynolds numbers of 16916 and 6594 were individually installed on each heat sink. Numerical simulations were performed using COMSOL rotating machinery and nonisothermal flow interface with the standard \(k\)-\(\varepsilon\) turbulence flow model. Validations were performed on this apparatus. The SLM heat sinks exhibited higher Nusselt numbers and lower thermal resistance than traditional heat sinks because of a relatively higher heat transfer coefficient and larger heat transfer area. For the proposed SLM heat sinks with larger surface areas, complex flow channels, and ventilation holes under jet impingement cooling, the PTFHS exhibited the highest heat transfer enhancement followed by MFLHS and TTFHS. The results contribute to solving the problems of heat dissipation of higher light output LED lighting.  
LED lighting; forced convection; air jet impingement cooling; heat sink; selective laser melting; numerical simulation +
Footnote †: journal: Applied Physics Letters
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 4 Performance Evaluation
### Federated Reinforcement Learning
#### 4.1.2 Learning Process  
As explained in Sections 3.2-3.4, we performed FRL on the implementation described in Section 4.1.1. Episode is a unit of learning, and each episode ends after a certain number of steps forward. At the end of the episode, we records the sum of the reward values obtained by the agent in the episode as the score of the episode. We trained the agent with periodically changing the traffic distribution on the map whenever a certain number of episodes are finished as explained in Section 3.4.1. Thus, the agents were able to perform learning in various environments, alleviating any bias which can be formed from a specific environment.  
In the learning process, the agent continues to update the policy so that a better action can be selected, and the purpose of the agent is to learn a policy that maximizes the expected compensation values. Thus, we monitored the sum of the scores obtained in the last 100 episodes during learning, and Figure 5 shows the result. As shown in the figure, the average of score values increases as the episode passes, which means that the more learning were repeated, the better the agent performed the mission. The average value continues to increase up to about 600 episodes and reaches the saturation, so we decided to perform learning until 1000 episodes.  
We periodically conducted an evaluation where UAVs make their own judgments in consideration of traffic and move according to policy based on the neural network being trained during performing learning. In Figure 5(a)-e, in the upper row shows the traffic map of each episode, and the lower subfigures, Figure 5(f)-j, show the final deployment of UAVs in each episode. As shown in the figure, in episode 0 where learning was not performed at all, UAVs are located close to each other and do not respond appropriately to traffic. This phenomenon can also be seen in the case of episode 100, where learning was not sufficiently performed. However, as the episode proceeds and the agent performs learning, UAVs are located depending on the distribution of traffic as shown in results of episodes 200, 600, and 1000. In addition, UAVs spread appropriately without clumping together to provide network services in a wide range.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Horizon value & 20 \\ Minibatch size & 5 \\ Number of epochs & 4 \\ Learning rate & 0.0003 \\ Generalized advantage estimator & 0.95 \\ Clipping parameter & 0.2 \\ Discount factor gamma & 0.99 \\ Value function coefficient & 0.5 \\ Optimizer algorithm & Adam \\ Critic network dimension & 16\(\times\)256\(\times\)256\(\times\)256\(\times\)5 \\ Actor network dimension & 16\(\times\)256\(\times\)256\(\times\)256\(\times\)5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters and values used for learning.
## 2 Materials and Methods
### Localization of the Human Operator  
Tests were conducted to learn the trade-offs between UPD localization approaches that aggregated data from an internal low-cost GNSS system, the wireless network signal strength, and an external DGNSS receiver with an EGNOS correction signal. The proposed hybrid approach is based on combining data from two sources: (1) satellite-based positioning and (2) positioning based on the RSSI from the wireless routers used in the RHEA scenario.