esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Protocol  
The proposed protocol consists of four stages, which are the initialization section, where the initial values are generated, the registration section, where the nodes in the network are registered, the authentication section, where the nodes in the network are verified, and the unregistration section, which is the removal of nodes leaving the network for any reason. The operations at these stages are automatic. The whole process is carried out under the command of the base station. The same operations are repeated for the relevant nodes if new nodes are added to the network. Finally, the newly joined node information is announced to the network. All necessary information for the network such as the nodes' identities and SN-CN mappings are reported to the nodes by the base station.  
Moreover, smart contracts are computer programs that can carry out transactions and agreements between anonymous parties reliably and consistently and cannot be changed retrospectively. In this study, smart contracts are written on nodes as code. In this context, the term \"smart contracts\" is used. First of all, time information is checked in all transactions. This way, it can be evaluated whether the relevant transaction is up-to-date. The current time information is always visible in the BS. The BS provides this information when needed in the CN and SN nodes.
## 3 Machine Learning-Based GNSS Residual Error Modeling Methodology
### Residual Modeling Using Machine Learning Regression
The regression modeling errors corresponding to each machine learning model considered are outlined in Table 1. The error results confirm that the proposed models are the most optimal selections. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **HPF** & \multicolumn{2}{c}{**LPF**} \\ \hline \multirow{2}{*}{**Model**} & **Regression Modeling Errors** & \multirow{2}{*}{**Model**} & **Regression Modeling Errors** \\  & **(RMSE)** & & **(RMSE)** \\ \hline Linear SVM & 0.51 mm & Tree & \\ \hline Second-order SVM & 0.49 mm & (Min. Node = 16) & 0.32 mm \\ \hline Third-order SVM & 0.47 mm & & \\ \hline Gaussian SVM & 0.39 mm & (Min. Node = 8) & 0.31 mm \\ \hline Gaussian SVM & & & \\ (Kernel = \(\sqrt{p}\)) & 0.39 mm & & \\ \hline Gaussian SVM & & & \\ (Kernel = \(\frac{\sqrt{p}}{4}\)) & 0.38 mm & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Regression modeling errors of the machine learning models with respect to the training data for the HPF and LPF components.
# Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks
## 5 Ant Colony Optimization Based Solution  
This section deals with the second solution developed for the deployment problem, the ACO technique. In ACO [22; 23; 24], to find a solution to the deployment problem, ants build solutions incrementally by walking randomly through a connected graph _G_(_P_,_E_), where \(P\) denotes the set of the deployment points and E represents a set of all undirected links connecting the deployment points and the sink node. To construct a solution to the deployment problem, each ant k starts its tour at each mobile node location and then consecutively builds the connected covers through transitioning among the deployment points in the construction graph. At each tour construction step, ant k applies a probabilistic transition rule to select which deployment point it will visit next. The probability that ant  
Figure 7: The movement operation during the time interval, t\({}_{2}\) under various parameter combinations (**a**) Case 1, (**b**) Case 2, and (**c**) Case 3.  
k, currently at deployment point p\({}_{i}\), \(i\) = 0, 1,..., \(\mid P\mid\), will select deployment point p\({}_{j}\); \(j\) = 1, 2,..., \(\mid P\mid\), to visit next is given by:  
\[P_{ij}^{k}(t)=\left\{\begin{array}{ll}\frac{\left[\tau_{ij}(t)\right]^{k} \left[\eta_{k}(t)\right]^{\beta}\left[\psi_{j}(t)\right]^{\gamma}}{\sum\limits _{l\in A}\left[\tau_{ij}(t)\right]^{\alpha}\left[\eta_{k}(t)\right]^{\beta} \left[\psi_{l}(t)\right]^{\gamma}}&if\ \delta_{sj}^{t}=0\\ 0&otherwise\end{array}\right. \tag{31}\]  
where \(\tau_{ij}(t)\) is the pheromone value between deployment point \(d_{i}\) and \(d_{j}\) at the time \(t\), \(\eta_{k}(t)\), and \(\psi_{j}(t)\) are the heuristic information; \(\alpha\), \(\beta\), and \(\gamma\) are the weight factors that control the pheromone value and the heuristic information parameters respectively.
## 2 Materials and Methods
### Bidirectional Long Short-Term Memory Method  
To capture long-term dependencies and sequence information in text, this study also employs Bidirectional Long Short-Term Memory networks (Bi-LSTM). Bi-LSTM consistsof two LSTM (Long Short-Term Memory) layers: one processes the text sequence in the forward direction, and the other processes it in the backward direction, thereby capturing both forward and backward contextual information simultaneously. We set the number of hidden states in the Bi-LSTM units to 128 and feed the word embedding vectors of the text data as inputs to the Bi-LSTM layers. As information passes through each layer, the LSTM units update their internal states at each time step to capture relationships between the current word and its preceding and succeeding words. By stacking multiple LSTM layers, we can capture more complex sequence patterns. Finally, we apply an average pooling over the entire sequence output to obtain a fixed-length feature vector, which represents the encoded form of the text sequence.
# Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors
## 3 Methodology  
### Autoencoders vs. Variational Autoencoders  
Data compression is the method widely used in applications where the dimensionality of the primary data must be reduced. The motive of such techniques is to share the same amount of information with minimum transmission bits. The autoencoders and variational autoencoders are the basic approaches implemented for data compression in machine learning- based applications. This approach transforms the data from higher to lower dimensional space through compression technique. Autoencoders can learn information efficiently from the unlabeled data through its encoder and decoder blocks. The encoder compresses the data from higher dimensional space to latent space, and the decoder is implemented to convert the latent space information back to higher dimensional space. However, the autoencoders have the limitation of generating valid information from the non-regularized input data. Variational autoencoders were developed to address this issue by improving its generative capability on the whole latent space created from the encoders. The encoders of VAE create output parameters with a predefined distribution for its every input created from the latent space. To regularize the latent space, VAEs are imposed with a constraint on the latent distribution, making it to provide a normal distribution [19]. Figures 2 and 3 represent the architectural overview of autoencoder and the variational autoencoders.
## Implementation of a Vector-based Tracking Loop Receiver in a Pseudolite Navigation System
VTL trackingcontrol input is generated from pseudorange and range-rate estimates that are estimated from navigation results, while the navigation results are calculated from the tracking results. Tracking results, _i.e._, discriminator output, are not directly connected to the tracking control inputs, but are used in estimating pseudorange and range rate from receiver position, velocity and satellite position. These in turn are used to generate tracking control inputs. In this case, even though some satellite signals are attenuated or blocked at times, the receiver can track them using the navigation results obtained from undisturbed visible satellites. It can make use of the redundancy of visible satellites. This is a well-known technique for improving tracking robustness. [PERSON] [1] commented that in the nonlinear conditions in PL navigation systems, a VTL should improve tracking performance. However, the application of VTL to PL systems has not yet been properly studied. We propose a VTL algorithm applicable to asynchronous PL navigation systems, and assess its ability to mitigate the near-far problem and improve PL navigation availability. This paper starts with a brief review of the VTL concept, comparing it with a conventional ITL in Section 2. Section 3 describes the construction of a vector delay/frequency lock loop (VDFLL) based on the extended Kalman filter (EKF). In Section 4, a measurement model and a navigation algorithm for asynchronous PL navigation systems are reviewed. Section 5 proposes a VTL algorithm for an asynchronous PL navigation system and its receiver implementation. In Sections 6 and 7, a simulation and test results will be described. The test was performed using the Seoul National University GNSS Laboratory (SNUGL) indoor navigation system. The results show that VTL could be a good solution for the near-far problem and improve PL navigation availability.
# Time-Varying Communication Channel High Altitude Platform Station Link Budget and Channel Modeling
## 3 HAPS Link Budget and Channel Modeling
### Channel Modeling  
For the given time slot \((k,l)\), the corresponding sub-channel \(h_{r,q}[k,l]\) can be arranged in a \(M_{R}\times M_{T}\) matrix \(\mathbf{H}[k,l]\), its component is. \(\left[\mathbf{H}[k,l]\right]_{r,q}=h_{r,q}[k,l]\) The I/O correspondence of MIMO is  
\[\mathbf{r}[k,l]=\mathbf{H}[k,l]\mathbf{s}[k,l]+\mathbf{w}[k,l] \tag{14}\]  
where \(\mathbf{w}[k,l]\) is \(M_{R}\) dimension noise vector for every time slot \((k,l)\). \(\mathbf{s}[k,l]\) is the input vector with \(M_{T}\) dimension; \(\mathbf{r}[k,l]\) is the output vector with \(M_{R}\) dimension.  
Limited average power and peak power the upper and lower boundaries of the MIMO channel capacity of Equation (15) can be expressed as:  
\[\frac{1}{T}E\left\{\sum_{k=0}^{K-1}\sum_{l=0}^{L-1}\|\mathbf{s}[k,l]\|^{2} \right\}\leq KP \tag{15}\]  
where \(T\) denotes the entire transmission time period. \(E\{\cdot\}\) is the mean value, \(P\) expresses the transmission power. \(K\) indicates the maximum number of slot time. \(\mathbf{s}[k,l]\) is the input vector:  
\[\frac{1}{T}\|\mathbf{s}[k,l]\|^{2}\leq\zeta\frac{P}{L} \tag{16}\]  
where \(\zeta\) is the adjustment factor. \(L\) is the maximum value of the slot time with \(M_{T}\) dimension.  
Under the assumption that the input signal satisfies the average power limited Equation (16) and the peak power limited Equation (17), the non-coherent capacity of channel Equation (17) has an upper boundary of \(C\leq U_{1}^{\text{mimo}}\), and wireless communication channel capacity \(U_{1}^{\text{mimo}}\) can be denoted by the following equation:  
\[U_{1}^{\text{mimo}}\ (B)\overset{\text{def}}{=}\sup_{0\leq p\leq\text{$ \rho$max}}\sum_{r=0}^{M_{R}-1}\left[\frac{B}{TF}\ln(1+\frac{\wp\lambda_{r}}{B} )-\alpha\psi(B)\right] \tag{17}\]  
where \(B\) represents the channel bandwidth. sup indicates the minimum upper bound of one set. For example, \(\sup\{\mathbf{x}\}\) is the minimum upper bound of the set \(\mathbf{x}\). \(\wp\) denotes the jitter factor of HAPS platform. \(\wp_{\text{max}}\) expresses the jitter maximum value.  
Without loss of generality, \(\psi(B)\) can be expressed as:  
\[\psi_{r}(B)\overset{\text{def}}{=}\zeta\frac{B}{\alpha_{\text{max}}}\log_{2} (1+C/N) \tag{18}\]  
where \(C\) denotes the wireless communication signal power. \(N\) is the noise power. \(\zeta\) is belong to \([0,1]\). If related parameters are obtained, the value of \(\psi_{r}(B)\) could be calculated.
# Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation
## 4 Experiments and Results
### Experimental Setup
#### 4.1.3 Training Setup  
For all experiments, we use the U-Net architecture [31] to train the models and split the datasets into training, validation, and test set. To find good fitting training hyperparameters (e.g., learning rate and weight decay) for our in-house datasets, we performed Random Search over ten configurations until model convergence. As a preprocessing step, we apply with 50% probability either random crop or downsize operations before passing the data to the different augmentation strategies for efficient memory and computing use. For KITTI and EM datasets, we use a similar training setup as in [28]. To speed up memory intensive processes, we use mixed precision training with 16 bits. For our experiments, we made use of four GeForce GTX 1080 GPUs. For better reproducibility, we list the training parameters for each of the datasets for a detailed view in Table 3.  
Furthermore, we performed early stopping on the validation set. To save computing and still obtain enough samples on the validation set for early stopping, we evaluate every 10% of the total epochs on the validation set. This ensures that for each dataset, independent of the number of epochs needed until convergence, the number of epochs evaluated on the validation set is proportional to the total number of epochs. We run these experiments three times for the different data augmentation approaches and take the mean of the test IoU to ensure a fair comparison. In the case of RandAugment and SmartAugment, we evaluated 50 configurations for each method and report the mean test IoU of the three best performing configurations on the validation set.  
For all our experiments, we use Stochastic Gradient Descent (SGD) optimizer [32] and Cosine Annealing [33] as our learning rate scheduler, and anneal the learning rate over the total number of epochs.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Dataset** & **Resolution** & \begin{tabular}{l} **Batch** \\ **Size** \\ \end{tabular} & \begin{tabular}{l} **Learning** \\ **Rate** \\ \end{tabular} & \begin{tabular}{l} **Epochs** \\ **\end{tabular} & \begin{tabular}{l} **\# Data** \\ \end{tabular} & \begin{tabular}{l} **\#Train** \\ \end{tabular} & \
\begin{tabular}{l} **\#Val** \\ \end{tabular} &
\begin{tabular}{l} **\#Test** \\ \end{tabular} \\ \hline KITTI & \(185\times 612\) & 4 & 0.001 & 4000 & 200 & 140 & 30 & 30 \\ RaVeNNA & \(180\times 180\) & 3 & 0.001 & 2000 & 1684 & 1107 & 216 & 361 \\ EM & \(512\times 512\) & 2 & 0.01 & 500 & 30 & 20 & 5 & 5 \\ ErfASst & \(864\times 864\) & 2 & 0.05 & 5000 & 50 & 30 & 10 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training parameters for each dataset. Train, val, test denote the data split used during training.  
Figure 3: Performance (mean IoU) analysis for different numbers of operations for RandAugment++ on EM. These results indicate that the number of applied operations should optimally not be limited to three, as in classical RandAugment.
### Objectives and Scope of the Paper  
This paper aims to provide a comprehensive survey of the enabling technologies, challenges, trends, and future prospects of FSO communication in the context of 5G and beyond. The key objectives include the following:  
1. Analysis of Current Challenges: examination of the major challenges faced by FSO systems, including atmospheric turbulence (AT), weather-induced signal degradation, and alignment issues.
2. Review of Enabling Technologies: exploration of critical technologies such as adaptive optics (AO), advanced modulation schemes, aperture averaging, spatial diversity, cooperative relaying, hybrid FSO system, and error correction codes (ECCs) that are instrumental in overcoming the challenges associated with FSO communication.
3. Exploration of Emerging Trends: discussion of emerging trends, such as the integration of FSO with RF, millimeter-wave (mmWave), and Terahertz (THz) technologies, as well as the use of artificial intelligence (AI) and machine learning (ML) to optimize FSO performance.
4. Prospects for 5G and Beyond: assessment of the potential of FSO communication to meet the demands of next-generation networks, including its scalability, reliability, and deployment strategies.
5. Future Research Directions: identification of open challenges and future research directions necessary to realize the full potential of FSO in the evolving landscape of communication technologies.  
The aforementioned objectives highlight the involvement of numerous phenomena in FSO communication, which must be meticulously analyzed to ensure a reliable communication system.
# Complex Network Modeling and Analysis of Microfracture Activity in Rock Mechanics
## 4 Results and Discussion  
### Characteristics of Rock Microfracture Networks  
Numerous research findings indicate that real networks typically exhibit the following characteristics [44]: (1) Statistical behavior: Networks with many nodes often display certain statistical properties. (2) Sparsity of connections: Most networks feature numerous nodes with sparse connections. (3) Uncertainty of connections: Connections within networks tend to lie between complete regularity and randomness. (4) Complexity of dynamic behavior: Each node within the network demonstrates nonlinear dynamic behaviors such as bifurcation and chaos. Space-time evolution: The evolution of networks over space and time manifests rich and complex dynamic behaviors. Generally, research on network models concentrates on examining some of these characteristics.  
This study focuses on the fracture point network at peak stress, aiming to analyze the distribution pattern of node degrees within the sample and validate the characteristics of the rock fracture network. Based on the aforementioned analysis, rupture points situated at excessively long temporal and spatial distances from other points can be deemed isolated rupture points. These rupture points are discrete and have a degree of 0. Conversely, a few fracture points serve as pivotal nodes facilitating crack propagation, exhibiting a \"Hub\" function by connecting multiple microcracks. These fracture points demonstrate high clustering with other points and consequently exhibit a significantly large degree of connectivity.  
In the fracture point network of sample GC1 under peak stress, the average node degree is 1.67, with the highest node degree recorded at 8. Specifically, 2 nodes exhibit a degree of 8, whereas 11 nodes have a degree of 7, collectively accounting for about 2% of the total fracture points. Moreover, a significant portion of the nodes (479) have a degree less than or equal to 2, accounting for more than 75% of the total. Overall, most rupture points show small degrees, indicative of the \"extension type\" or isolated category, whereas a minority exhibit high degrees, representing core nodes generated by \"aggregation\". As shown in Figure 14, both the number of nodes and the cumulative distribution probability  
Figure 13: Complex network with GC1 at peak stress (Green dots represent acoustic emission events, and red lines indicate the connections (edges) between these events).  
decrease consistently with increasing degree, with the rate of decrease gradually diminishing. This pattern aligns with a power-law distribution (f(x) = X\({}^{-\alpha}\), (\(\alpha>0\))), highlighting the scale-free properties characteristic of a typical complex network [36].
# Biomass Dynamics in a Fragment of Brazilian Tropical Forest (Caatinga) over Consecutive Dry Years
###### Abstract  
Increases in water scarcity due to climate change, especially in dry regions, can affect the dynamics of successional species. In view of the longest sequence of dry years (2010-2019) to have occurred in the Brazilian semi-arid region, with a consequent reduction in water availability, the influence of rainfall distribution on the production of above-ground plant biomass was investigated in a Dry Tropical Forest (DTF). This natural change monitoring experiment was conducted over 11 years (2009-2019) in a fragment of DTF under regeneration for 40 years, in the district of Iguatu, Ceara, Brazil. All living individuals of the woody component with a Diameter at Ground Level (DGL) \(\geq\)3 cm and a height (h) \(\geq\)100 cm were measured during 2009-2010, 2015-2016, 2018-2019. Biomass production was calculated using an allometric equation defined for DTF species. A mean mortality rate of 134 ind. ha\({}^{-1}\) yr\({}^{-1}\) was registered, with a recruitment of 39 ind. ha\({}^{-1}\) yr\({}^{-1}\), generating a mean deficit of 95 ind. ha\({}^{-1}\) yr\({}^{-1}\). The mean reduction in biomass was 3.26 Mg ha\({}^{-1}\) yr\({}^{-1}\). Climate conditions during consecutive dry years have a direct effect on the mortality and recruitment of woody species, with a recruitment/mortality ratio of 0.11. Shrubby-tree individuals of smaller diameter showed less resilience to the cumulative effect of drought.  
climate change; semi-arid region; floristic diversity; mortality; recruitment +
Footnote †: journal: Applied Sciences
# Determination of the Crack Propagation Direction in Mixed-Mode Missions due to Cyclic Loading
## 4 Calculation of the Propagation Direction Due to a Mission
The reasoning behind this is that cycle extraction should be performed on a quantity that is as close as possible to what the outcome of the process is: the crack propagation rate (due to the mission). Therefore, it is reasonable not to base the extraction on a stress intensity factor history, but rather the crack rate history, which includes the effect of the stress intensity factors on crack propagation through the temperature-dependent crack propagation parameters. Each extracted cycle has a maximum and a minimum value of \(K_{eq}\), leading to \(\Delta K_{eq}=K_{eq}^{max}-K_{eq}^{min}\). These two extremes are characterized by the temperatures \(T_{K_{eq}^{max}}\) and \(T_{K_{eq}^{min}}\), respectively. The propagation rate due to this cycle is now taken to be the maximum of \(\frac{da}{dN}(T_{K_{eq}^{max}})\) and \(\frac{da}{dN}(T_{K_{eq}^{min}})\), i.e., \(da/dN\) for \(\Delta K_{eq}\) is once calculated at \(T_{K_{eq}^{max}}\) and once at \(T_{K_{eq}^{min}}\), and the maximum is taken. This approach allows remaining on the conservative side since there are some materials that show a decrease of the crack growth rate for increasing temperature in certain temperature ranges. In the end, the crack growth rate due to the mission is given by the sum of the crack growth rate for each extracted cycle. The essence of the present method is the concept of a dominant loading step enforcing its crack propagation direction on the complete mission. Alternatively, one comes in the literature across concepts using the propagation direction of all loading steps in the mission [37]. The idea is to consider the deflection angle as a weighted average of the crack deflection angles of each step. One approach is to take the equivalent K-factors as weighting factors. However, here, the crack propagation rate in accordance to Equation (5) is proposed as the weighting factor, in agreement with the philosophy to use quantities related to the growth rate rather than the loading. Thus, the new deflection angle satisfies the following equation:  
\[\varphi=\frac{\sum_{i=1}^{Nstep}\varphi_{i}\Big{(}\frac{da}{dN}\Big{)}_{i}}{ \sum_{i=1}^{Nstep}\Big{(}\frac{da}{dN}\Big{)}_{i}} \tag{6}\]  
where \(i\) is the \(i\)-th static step and \(Nstep\) is the total number of static steps used to discretize the mission. This alternative approach to calculate the crack propagation direction will be compared with the dominant step approach in the following section. Figure 8: Crack growth rate evolution over time. The continuous black lines represent the crack growth rate for the given mission. The red dashed lines represent the crack growth rate after the mission reevaluation.