esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Biological vs. Solitonic Neural Networks  
As mentioned above, here, we implement a Soliton Neural Network (SNN) interconnecting several solitonic units together. The transmission of incoming signals to the network depends on the \"strength\" with which the channels that make up the synaptic connections have been written. The simplest neural unit [18] is built with an X-Junction structure in which two channels, created by the propagation of photorefractive solitons, intersect at the center of the neural geometry. Among all spatial solitons and their associated waveguides, the photorefractive ones are particularly interesting for application, being generated by low-power light beams, as intense as microwatts or even nanowatts [20; 21; 22]. For neural purposes, we use optical signals that, propagating within soliton waveguides, can modify them by means of a local variation of the refractive index--where the refractive contrast is greater, the light will remain more confined, reinforcing that specific synaptic trajectory within the neural graph. In fact, the information will deviate towards one of the two channels of each junction as a function of the induced refractive index variation, which depends on the photogenerated electric charges. In photorefractive SNN, the photogenerated electric charges assume the functional role that the neurotransmitters play in the Biological Neural Networks (BNN): regulator of synaptic intensity. The synapse also plays another fundamental role: memory. As a connection strengthens, the neural system begins to learn an input sequence. The repetition of information results in a synaptic strengthening, which is synonymous with information memorization. Therefore, structural change (synaptic strengthening or weakening) means a memorization or an obliviation. The SNN indeed strengthens or weakens its channels according to the information (input signals) it is learning.  
In summary, BNNs and SNNs learn by changing their structure according to the incoming information; a neural path is strengthened mainly based on two factors:  
* Intensity of input signals;
* Persistence of signals along a specific path.  
Then, the information processing is stored in the self-modification process of the structure. Figure 1 shows a summary diagram of the functionality of the two neural models, biological and solitonic, highlighting the analogous logical-structural flow that leads from input to learning and its memorization.
# Quantum Conditional Strategies and Automata for Prisoners' Dilemmata under the EWL Scheme
## 3 Definitions and Background  
Game theory involves players that interact with each other, the standard assumption being that this is done in a rational way. The famous minimax theorem of [PERSON] is widely considered as the starting point of game theory. [PERSON] defined a game as something \"involving two players who play against each other and their gains add up to zero\" (which is now known as zero-sum game). Since then, game theory has been developed extensively in many fields and branches of scientific research. Evolutionary game theory studies the evolution of strategies over time and has been applied to biology [29].  
Quantum game theory can be considered as an extension or as a variation of classical game theory. In the simplest form of a classical game between two players with two actions for each of them, both of them can use a bit (a '0' or a '1') to express their choice of strategy. In the quantum version of the classical game, the bit is replaced by the qubit, which, generally, is in a superposition of the two basis \"kets\" \(|0\rangle\) and \(|1\rangle\), which are the quantum analogues of the classical 0 and 1, respectively. When two or more qubits are present, they may be entangled, which means that the quantum state of each qubit cannot be described independently of the state of the others, something that further complicates the expected payoffs of the game.  
Games can be either non-cooperative or cooperative. Cooperative game theory describes the structure, strategies, and payoffs of a set of players (coalitions) [25]. In a non-cooperative game, cooperation may be incorporated only through the choice of strategies the players make. The strategies chosen by each player are of particular interest to us. We are also interested in the negotiation and formation of the coalition in the cooperative case. Full or incomplete information may also exist for games.  
We define the strategic form of a general game following the definition of [25].  
**Definition 1**: _A game in strategic form is a triple \(\Gamma=(N,(S_{i})_{i\in N},(u_{i})_{i\in N})\) in which_  
* \(N=\{1,2,\ldots,n\}\) _denotes a finite set of players;_
* \(S_{i}\) _denotes the set of strategies of player_ \(i\)_, for each player_ \(i\in N\)_;_
* \(u_{i}\colon S_{1}\times S_{2}\times\cdots\times S_{n}\to\mathbb{R}\) _is a function that associates each vector of strategies_ \(s=(s_{i})_{i\in N}\) _with the payoff_ \(u_{i}(s)\) _to player_ \(i\)_, for every player_ \(i\in N\)_._
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
## 1 Introduction
The interference characteristics of NGSO satellites are characterized by selecting a large number of representative pointings, and the field of view of the NGSO satellite is divided into cells. The cumulative probability distribution of the equivalent isotropic radiated power (EIRP) in each cell is obtained through the simulation of a large number of pointings. For the aggregate interference analysis, this paper utilizes the interference calculation methods outlined in ITU Rec. RA. 1323 [25] to convolve the probability density functions of the equivalent power flux density (EPFD) from different systems and obtain the probability density function of aggregate interference. The main contributions of this paper include:  
1. A method is proposed for characterizing interference in the RAS band by partitioning the field of view of the NGSO satellite into cells and selecting representative pointings for each cell, and then determining the probability distribution of the EIRP in each cell by sampling a large number of beam pointings according to the user distribution. 2. The concept of beam activation probability is introduced to account for the fact that satellite beams are not always active, and three definitions of beam activation probabilities are provided. 3. A new method for calculating the EPFD for RAS telescopes is proposed, which combines the probability density function of the EIRP in the field of view of the satellite with the activation probability of the satellite beams. 4. A method for calculating the aggregate interference from multiple NGSO constellations to the RAS is presented. The aggregate EPFD at the RAS receiver is obtained by summing the EPFDs of individual NGSO systems, and the interference generated by different NGSO systems is assumed to be independent of each other. The aggregate EPFD probability density function can be expressed as the convolution of the EPFD probability density functions of the NGSO systems. The rest of the paper is organized as follows. In Section 2, the interference models of the NGSO constellation and RAS stations are presented, and the satellite models and radio astronomical telescopes are described in detail. In Section 3, the spatial distribution of the satellite's EIRP and the activation probabilities of the satellite beams are presented, and the computational model of the EPFD provided by the ITU is modified on the basis of this model. Finally, a method to evaluate the total interference of multiple NGSO satellite constellations is given. In Section 5, a detailed and complete discussion of the interference is given.
# Concealed Conduit Routing in Building Slabs
## 2 Materials and Methods
### Pipe Path Routing
#### 2.2.1 Abstract Pipe Path Calculation  
The flowchart of the routing process is shown in Figure 6. The routing process starts with the abstract path routing. The _abstract path_ of a pipe is the shortest path generated in the top-level representation. It is utilized to schedule the pipe and generate a subspace to lay out the real pipe path. The abstract path is produced by the following steps:  
1. Locate the vertices (slabs) in \(G\), which contain the terminals of this pipe.
2. Generate a shortest path to connect these vertices using [PERSON]'s algorithm.
3. Duplicate the slabs containing multiple terminals: If a slab contains \(k\) terminals (\(k\geq 2\)), the associated vertex is duplicated \(k\) times in the shortest path.
4. Compute the path length by accumulating the weights of the edges in the shortest path. If the two ends of an edge are the same, its weight is set to one-half of the width of the slab. (We assume that the slab width \(>\) the slab height.)
5. Output the length and vertices of this shortest path.  
The abstract path is a sketch of the real pipe path. Its vertices represent the slabs that would be penetrated by the pipe, and its length is an estimation of the length of the real pipe path. These two pieces of data are used in the incoming computations.
# A Review of Transformer-Based Approaches for Image Captioning
## 5 Vision-Language Pre-Training in Captioning
### VLP in Image Captioning
#### 5.2.12 PaLI: A Jointly-Scaled Multilingual Language-Image Model  
The Pathways Language and Image (PaLI) model [55] combines inputs from the vision and language domains and generates outputs in the language domain. A key feature of this model is that is uses a very large-scale encoder-decoder language models and an enormous Vision Transformer (ViT-e) image model; the models in these two domains have been pre-trained separately. The reuse of the large unimodal backbones for language and vision modeling enables a significant reduction in training cost.  
The resulting model is pre-trained on a very large-scale dataset, the Web Language Image (WebLI) dataset [55], which has the distinction of being very large-scale and multi-lingual. WebLI encompasses over a hundred languages and contains more than 10 B image-text pairs, which surpasses all the other datasets commonly used for vision-language pre-training. With this approach, the authors try to achieve a jointly scaled model and overcome the limitation which often results from the fact that language models tend to be much larger and trained on larger datasets compared to the vision models. The model performs well on several downstream language, vision and vision-language tasks, including image captioning and visual question answering.  
The architecture of PaLI is simple and scalable. It consists of an enormous vision transformer that encodes an image into tokens which are then fed, together with encoded text tokens, into a large scale transformer-based encoder-decoder to produce the textual output. Tasks are therefore framed using an \"image + query to answer\" modeling interface. The high performance of the model comes in large part from the sheer size of the model as well as of the dataset on which it is trained; although the WebLI dataset has 10 B image-text pairs, they train their model on a subset of 1 B examples (so as to only use high-quality examples).
# A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN
## 3 Multipath/NLoS Bias Estimation Using a Clustering Algorithm
### Statistical Characterization of the Leftover Terms
Based on the previous analysis, clustering algorithms can be utilized to identify the largest cluster without multipath/NLoS, subsequently enabling the determination of leftover terms affected by multipath/NLoS. If the assumption is that at least two satellites are free from multipath/NLoS effects, the method will be unable to form a cluster consisting of \(L^{s}(t)\) without multipath/NLoS. Consequently, the multipath/NLoS estimation method will declare a failure rather than providing an inaccurate estimate. Additionally, the clustering method may make it difficult to identify small-value \(F^{s}(t)\) because the quantities are close to \(e^{s}_{r}(t)\). However, considering the fact that, as previously recalled, \(e^{s}_{r}(t)\) is expected to range in the order of about \(\pm 1\,\text{m}\), those \(F^{s}(t)\) values close to \(e^{s}_{r}(t)\) will only produce minor impact positioning errors due to their small values. As a result, the main focus of this research is on those sufficiently large values of \(F^{s}(t)\) that will induce detrimental loss of accuracy in the navigation solutions.
## 2 Materials and Methods
### CNN-SVM Network Enhanced by the Black-Winged Kite Algorithm  
Convolutional Neural Networks (CNNs) are a crucial architecture in the field of deep learning. Their basic structure typically includes an input layer, convolutional layers, activation functions, pooling layers, fully connected layers, and an output layer, along with regularization techniques to prevent overfitting.  
Support Vector Machines (SVMs) are a significant algorithm in machine learning, widely used for classification and regression tasks. They effectively handle both linearly separable and non-linearly separable data, constructing optimal decision boundaries in high-dimensional spaces.  
The CNN-SVM approach combines the strengths of deep learning and machine learning [44]. CNN acts as feature extractors, learning high-level feature representations from spectral data, which are then classified by SVMs. The overall workflow is illustrated in Figure 3. SVMs offer excellent generalization and robustness, effectively handling high-dimensional feature spaces with numerous spectral bands. This approach avoids the excessive parameters and overfitting issues associated with traditional CNN fully connected layers. For spectral data, CNN can extract features from both global and local regions through convolution operations, preserving the spatial structure of spectral bands. SVM kernel functions then map these features to high-dimensional spaces, capturing nonlinear relationships more effectively and simplifying computations in the original feature space.  
Optimization is performed using the BKA, leveraging its robust search capabilities and balance between local and global optima to find the optimal parameters. A model is then constructed based on these optimal results, and the data are trained to obtain feature parameters. These extracted feature parameters are input into the SVM network to complete the prediction, as illustrated in Figure 4.  
Figure 3: CNN-SVM flowchart.
# Simulation Tool for Winter Navigation Decision Support in the Baltic Sea
## 9 Computational Experiments
In practice, the vessel speed threshold for icebreaker assistance depends on the ice and traffic situation and the availability of icebreakers. The lower the threshold, the further the vessels must travel without assistance. Consequently, the higher the threshold, the busier the icebreakers. The experiments were carried out for N = 50 and 116. The range of the icebreaker assistance threshold was from 3 to 6 kn. This threshold varied in steps of 1 kn. \[C_{on}=1,\ C_{max}=2,\ s_{c}=6\ \text{kn},t_{max}\ =12\ \text{hr}\]  
From Table 8, it can be observed that for N = 50 system performance was almost similar for all values of \(s_{n}\). However, for N = 116 the system performance varied for higher values of \(s_{n}\). For \(s_{n}=6\ \text{kn}\), the system performed significantly worse than the other configurations. A higher value of \(s_{n}\) implies that the icebreakers need to start assisting the vessels sooner and hence are busier. This also means that vessels must wait longer for icebreakers to become available.
# Three-Dimensional Trajectory Optimization for UAV-Based Post-Disaster Data Collection
## 5 Simulation Results  
In this section, numerical analyses are performed to evaluate the performance of the proposed algorithms. Each of the results shown in the following part of the manuscript are derived from 100 simulation runs, each using a different randomly generated pattern of UE distribution.
# The Ionospheric Scintillation Effects on the BeiDou Signal Receiver
## 5 Effect on Tracking Process for BeiDou Receiver  
Once the acquisition is achieved, the signal will be sent to the tracking loop in which the several kinds of information for positioning could be figured out. The tracking process mainly consists of the carrier tracking loop, including phase lock loop (PLL) and frequency lock loop (FLL), and code tracking loop (DLL). Since ionospheric scintillation will not influence the signal frequency, only PLL should be taken into consideration. The output of PLL is the carrier phase. If the error of carrier phase measurements is too high, the accuracy of carrier-phase-ranging will decrease and there is a high probability of the signal losing its lock. Even if the signal transmitted from a satellite is not lost, it can alter the position precision in DLL. One of the DLL functions is the measurement of the delay between the code carried by the signal and the receiver local code. This delay is an estimation of the time needed by a signal to reach the receiver. Then the receiver is able to compute the distance of the satellite. If the error of DLL is too high, it will have a great influence on pseudo-ranging, so considering all the factors mentioned, this paper uses the correlation peak, acquisition probability, PLL error, loss of lock probability and DLL error to evaluate the influence of scintillation on receiver performance [27].
## 3 RUL Prediction Methodology
### Bayesian RUL Prediction Based on Extended Kalman Filter
#### 3.4.3 State Process Model for Bearing Degradation Estimation  
Due to the accelerating deterioration of the bearing as defects accumulate, the behaviour of the bearing health indicator is often approximated as an exponential function of time. For example, in the work of [16], \(a_{t}e^{b_{t}t}\) is used for modelling the variance of vibration signals during the bearing lifespan and was shown to be more suitable than the form \(a_{t}+b_{t}e^{c_{t}t}\) for RUL prediction. Note that the model parameters (i.e., \(a_{t}\), \(b_{t}\), and \(c_{t}\)) are time-variant and need to be updated dynamically in the EKF. To reduce the need for prior assumptions on the initial conditions, the bearing degradation model with the Markovian property is formulated as  
\[x_{t}=f(x_{t-1})=e^{b_{t}\Delta t}x_{t-1}, \tag{23}\]  
where \(x_{t}\) denotes the bearing degradation state indirectly observed via the proposed ESI. This can be regarded as a simplification of [PERSON]'s model [16] by fixing the parameter \(a_{t}\), leading to a multiplication relationship between two consecutive degradation levels \(x_{t-1}\) and \(x_{t}\), as \(e^{b_{t}\cdot\Delta t}\) is equivalent to the form \(\frac{e^{b_{t}t}}{e^{b_{t}-1(t-1)}}\) with \(\Delta t=1\).  
Assume that the ESI since the FDT follows an accelerated incremental trend before reaching the end-of-life threshold; the true bearing degradation level can be described by the state vector \(\mathbf{x}_{t}\), and the measurements \(\mathbf{z}_{t}\) are the available historical observations of ESI at the prediction time. The state process model for EKF can be expressed as  
\[\mathbf{x}_{t}=f(\mathbf{x}_{t-1},\mathbf{v}_{t})=\begin{pmatrix}e^{b_{t-1}\Delta t}\mathbf{x }_{t-1}(1)+v_{t}\\ b_{t-1}\end{pmatrix}, \tag{24}\]where the interval between two adjacent time steps is one minute (\(\Delta t=1\)) and the input \(\mathbf{u}_{t}\) is omitted as there is no active control. Also, the measurement model for the ESI observations is given by  
\[\mathbf{z}_{t}=h(\mathbf{x}_{t},\mathbf{w}_{t})=\mathbf{x}_{t}(1)+w_{t}. \tag{25}\]  
The local linearization of the state model and the measurement model is achieved based on the Jacobian matrices given in Equation (18) to (21), which writes  
\[\mathbf{F}_{t}=\
abla f_{x}=\begin{pmatrix}e^{b_{t-1}\Delta t}&e^{b_{t-1}\Delta t} \mathbf{x}_{t-1}(1)\\ 0&1\end{pmatrix}, \tag{26}\]  
\[\mathbf{G}_{t}=\
abla f_{0}=\begin{pmatrix}1\\ 0\end{pmatrix}, \tag{27}\]  
\[\mathbf{H}_{t}=\
abla h_{\mathbf{x}}=\begin{pmatrix}1&0\end{pmatrix}, \tag{28}\]  
\[\mathbf{W}_{t}=\
abla h_{\mathbf{w}}=1. \tag{29}\]
## 4 Results and Discussion
### Estimation of Water Yield between 2020 and 2029
#### 4.4.2 Water Yield Estimation under Wet Year Scenario
Furthermore, the monthly water yield estimation under the wet year scenario is reported in Table 19. The results show that the average monthly water yield in the summer season (December-March) varies from a minimum of 4.95 million m\({}^{3}\) in March to a maximum of 58.92 million m\({}^{3}\) in December. The average monthly water yield in the rainy season (April-November) varies from a minimum of 2.80 million m\({}^{3}\) in April to a maximum of 286.47 million m\({}^{3}\) in October.
## 3 Results and Discussion
### Parameter Sensitivity Analysis
#### 3.4.1 Sensitivity Analysis of Putting Model Parameters  
With respect to the unaged parameters on the G*/sin\(\delta\), which was the scope of the Case Study-1, Figure 5 shows that the temperature, softening point and test frequency are the most influencing parameters. Further, the influence of phase angle showed a unique trend with the phase angle at T58 \({}^{\circ}\)C recording the highest STi value. The results are in agreement with the standard laboratory values; increase in test temperature and frequency decrease the G*/sin\(\delta\) values of asphalt binder. Further, the importance of viscous and elastic properties of asphalt binder that influences the rutting behaviour observed in the phase angle and softening point values.  
Figure 5: Sobol indices obtained for rutting model parameters.
## 3 Results
### Model Validation  
To verify the accuracy of the correlation model established in this study, three soil samples with different clay contents, moisture contents, and densities that were not used in the model setup were applied to validate the model. The computed cohesion and internal friction angle were compared to the measured values from direct shear tests (Table 5). The PBIASs between the averaged predicted and measured cohesion and internal friction angle were both within 15%, and the RRMSEs were less than 11%, indicating satisfactory estimations of the soil shear strength indicators.