esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Survey of GNSS Signals and Receiver Architectures
#### 1.1.2 GNSS Signals Modulations  
One of the most complex modulations involves two sub-carriers--namely SC1 & SC2--in the Multiplexed BOC (MBOC) scheme, which is defined as a spectrum (\(G\)) involving \(\text{BOC}\left(1,1\right)\) and \(\text{BOC}\left(6,1\right)\) in a 10 to 1 power ratio:  
\[G_{\text{MBOC}}\left(f\right)=\frac{10}{11}G_{\text{BOC}\left(1,\;1\right)} \left(f\right)+\frac{1}{11}G_{\text{BOC}\left(6,\;1\right)}\left(f\right) \tag{2}\]  
This power ratio allows for a smaller bandwidth to be processed in low-end receivers, while still achieving lock. MBOC is found in two different implementations. In Galileo, the Composite BOC (CBOC) data and pilot signal components first sub-carriers end up in counter-phase, while their second carriers remain in phase, after being combined \(s_{E1}=s_{E1-B}-s_{E1-C}\)[10], with:  
\[\begin{array}{c}s_{E1-B}\left(t\right)=\;PC_{E1-B}\left(t\right)\cdot d\left( t\right)\cdot\text{CBOC}\left(6,1,1/11,+\right)\left(t\right)\\ s_{E1-C}\left(t\right)=\;PC_{E1-C}\left(t\right)\cdot SC_{E1-C}\left(t\right) \cdot\text{CBOC}\left(6,1,1/11,-\right)\left(t\right)\end{array} \tag{3}\]  
with the Primary Code (\(PC\)), Secondary Code (\(SC\)), navigation data (\(d\)), and:  
\[\text{CBOC}\left(6,1,1/11,\pm\right)\left(t\right)=\sqrt{\frac{10}{11}}\text{ BOC}\left(1,1\right)\pm\sqrt{\frac{1}{11}}\text{BOC}\left(6,1\right)\left(t\right) \tag{4}\]  
In GPS, the Time-Multiplexed BOC (TMBOC) only involves the second sub-carrier in the pilot signal component, enabled four times within a 33-chip long pre-determined sequence [11]:  
\[\begin{array}{c}s_{L1C_{D}}\left(t\right)=\;PC_{L1C_{D}}\left(t\right)\cdot d \left(t\right)\cdot\text{BOC}\left(1,1\right)\left(t\right)\\ s_{L1C_{D}}\left(t\right)=\;\sqrt{3}PC_{L1C_{D}}\left(t\right)\cdot SC_{L1C_{D }}\left(t\right)\cdot\text{TMBOC}\left(6,1,\;4/33\right)\left(t\right)\end{array} \tag{5}\]
# Dynamic Modeling of the Dissipative Contact and Friction Forces of a Passive Biped-Walking Robot
###### Abstract  
This work presents and discusses a general approach for the dynamic modeling and analysis of a passive biped walking robot, with a particular focus on the feet-ground contact interaction. The main purpose of this investigation is to address the supporting foot slippage and viscoelastic dissipative contact forces of the biped robot-walking model and to develop its dynamics equations for simple and double support phases. For this investigation, special attention has been given to the detection of the contact/impact between the legs of the biped and the ground. The results have been obtained with multibody system dynamics applying forward dynamics. This study aims at examining and comparing several force models dealing with different approaches in the context of multibody system dynamics. The normal contact forces developed during the dynamic walking of the robot are evaluated using several models: Hertz, Kelvin-Voight, Hunt and Crossley, Lankarani and Nikravesh, and Flores. Thanks to this comparison, it was shown that the normal force that works best for this model is the dissipative Nonlinear Flores Contact Force Model (hysteresis damping parameter - energy dissipation). Likewise, the friction contact/impact problem is solved using the Bengisu equations. The numerical results reveal that the stable periodic solutions are robust. Integrators and resolution methods are also purchased, in order to obtain the most efficient ones for this model.  
passive model; biped walking; Impact and contact; friction force; dissipative force +
Footnote †: journal: Applied Sciences
# An FDM-Based Dynamic Zoning Method for Disturbed Rock Masses above a Longwall Mining Panel
###### Abstract
However, in this method, the empirical formula is used to pre-set the maximum height of the caved zone, which may be strongly affected by human intervention. In the fourth method, caving criteria are established according to experience. In the simulation of the mining process, the boundary of the caved zone is determined dynamically according to the caving criterion, and the mechanical parameters of the rock masses in the caved zone are adjusted according to experience. [PERSON] and [PERSON] thought that failure of the strata was the preliminary requirement for its caving [19; 20; 21]; therefore, the criterion for caving of the failed strata (failure in tension) to occur in the model was a maximum shear strain of 0.25 or a downwards displacement of 1 m. Then, the cohesion value of caved materials was set to zero. This method does not need to pre-set the height of the caved zone; thus, it can avoid the calculation error caused by human intervention. However, the zoning criteria also have limitations. In reality, the critical shear strain is strongly site-dependent, and the vertical displacement of the caved rock masses is associated with the size of the mined-out area. In most of the currently used methods for zoning simulation, specific attention is usually paid to modeling the process of roof rock falling and the adjustment of caved rock mass parameters, while in contrast, the strength deterioration of rock masses in other zones is neglected. Although the rock masses in the fractured zone, continuous deformation zone, and surface cracking zone are not broken or collapsed, the strength deterioration of rock masses varies with the extent of damage. In addition, the four zones are developed during the mining process, and the mechanical properties in different regions correspondingly differ. The dynamic zoning characteristics of disturbed rock masses should not be neglected; otherwise, inexact numerical results may be achieved. In this paper, an FDM-based dynamic zoning method for disturbed rock masses above a longwall mining panel is proposed. This method is mainly composed of four stages: (1) establishing the simplified complete stress-strain curve; (2) determining the zoning criteria; (3) adaptively adjusting the mechanical parameters of disturbed rock masses; and (4) numerically modeling longwall mining based on the FDM. To demonstrate the effectiveness of the proposed dynamic zoning method, a numerical application case is presented with the use of FLAC\({}^{3\text{D}}\) software. FLAC\({}^{3\text{D}}\) is inherently capable of solving nonlinear and large-strain problems; in particular, it can be well applied to modeling the distribution patterns of the stress, strain, and displacement of surrounding rock masses in underground mining [22].
## 3 Methodology
### Component Complementarity Analysis  
The semi-supervised single-image rain removal algorithm based on multi-scale wavelet transform proposed in this paper integrates several specialized components, each of which contributes to the overall rain removal performance. The following is a formal derivation of how the rain removal effect can be enhanced by the complementary nature of each component.
## 6 Experimental Setup  
### Hardware Setup  
The WSN devices selected for the experiments are Crossbow's TelosB motes CA2400 [39], equipped with Texas Instrument CC2420 transceiver [40]. The devices are programmed to collect a continuous set of RSSI samples with a sampling frequency of 2 kHz, over a sampling window that is selected according to the specific experiment. The RSSI value for each sample is fetched from the first 8 bits of register 0x16 of the CC2420 transceiver and represents the incident RF power in the selected 5 MHz-wide channel averaged over 128 \(\upmu\)s (hence, eight IEEE 802.15.4 O-QPSK symbols). The RSSI data, fetched from the register in the form of an 8-bit signed integer, are buffered in the RAM and periodically saved to the internal flash memory. At the end of the sampling process, the content of the flash memory is sent over the USB port to a laptop, which logs the received data. The choice of this method for collecting RSSI data, in place of the direct sample-and-send over USB approach, was due to the insufficient bitrate (i.e., 115,200 baud including serial message overhead) available at the serialinterface. In order to validate the performed measurements, we time stamp all of the observations and measure the delay of the instructions and task implemented in Tiny OS. This aspect will be further discussed in Section 8.2.2. Since [PERSON] et al. [41] reported a consistent offset among RSSI readings performed with CC2420-equipped radio devices, we have also profiled our devices by means of a simple calibration process. We will discuss the effect of node calibration as well as the effect of this process on the performance of the proposed solution in Section 8.2.1.
## 2 General Structure of the Methodology
### Multi-Source Data Input Module
#### 2.2.1 Feature Optimization Based on Transfer Learning  
In this study, model-based transfer learning [20] is employed to accelerate model convergence and enhance both model accuracy and generalization. In the task of rail surface condition identification, the primary advantage of transfer learning lies in its ability to improve task-specific feature learning by transferring the feature representations learned by the teacher model (GoogLeNet) and the student model (MobileNet) from a large-scale image dataset. This process ultimately enhances the model's identification accuracy and generalization performance.  
The implementation process begins with pre-training both GoogLeNet and MobileNet on the ImageNet dataset, followed by the transfer of their parameters to the rail surface condition identification task. Two target networks, GoogLeNet-Rail and MobileNet-Rail, are subsequently created by copying the structure and parameters of the pre-trained models, with the exception of the output layer. These networks are used as rail surface image feature extractors, and an output layer is added to match the number of categories in the rail surface condition dataset. The parameters of this output layer are initialized randomly. Finally, effective transfer learning is achieved by training the target networks, GoogLeNet-Rail and MobileNet-Rail, to learn features specific to rail surface condition images. The realization process is illustrated in Figure 3.
## 6 Numerical Simulation  
In this section, the numerical simulation results are given to verify the validity of the proposed analysis model. As mentioned above, \(W_{min}\), \(n\), \(m\), \(p_{c}\), \(T_{s}\), and \\(T_{c}=Data+DIFS+\delta\) and \(T_{s}=Data+SIFS+DIFS+ACK+2\delta\). Using the above parameters, we consider three transmission scenarios (as shown in Table 2), and the parameter settings of each scenario are from [13]. That is, exactly one set of data is taken for each of the three different scenarios set in [13] to achieve the goal of ensuring that the proposed model is valid over a wide range of parameters while also avoiding the complexity of the simulation process.  
Figures 4 and 5 show the functional relationship between the average service delay and collision probability, \(p_{c}\), in all the scenarios, where Figure 4 is in Scenario 1, and Figure 5 is in Scenario 2 and Scenario 3. Obviously, the average service delay of the Nav model in all the scenarios is bigger than that of the analysis model due to  
In Figure 6, we obtain the comparison graphs of the successful transmission probability, \(p_{s}\), between the analysis model and the Nav model in three scenarios. The following is a brief explanation, with Scenario 2 as an example. We can observe that as the collision probability increases, the successful transmission probability of our model is much larger than that of the Nav model because it comprises the ICS procedure. Indeed, since a DIFS duration is generally much shorter than a random backoff duration, our model saves the bandwidth and improves the transmission efficiency.  
Figure 7 shows the standard deviation of the service delay in terms of the collision probability in all the scenarios. In any scenario, when the collision probability is smaller, the standard deviation of the service delay for the analysis model is smaller than that of  
Figure 5: The average service delay with respect to the collision probability in Scenarios 2 and 3.  
Figure 6: Successful transmission probabilities in all scenarios.  
the Nav model. That is because the ICS procedure added to the analysis model saves the transmission delay. When the collision probability reaches a certain value, the standard deviation of the service delay for our model is much larger than that of the Nav model. That is because the duration of the ICS procedure is much smaller than the backoff procedure. Thus, as the collision probability increases, the service delay of the analysis model is bigger as the frequency of entering the backoff procedure increases, and the gap of the service delay standard deviations is larger.
## 3 Improvements in WPAN MAC
### K-Dly-ACK-AGG Policy  
To reduce the overhead of the IEEE 802.15.3 MAC, we use the concept of frame aggregation.The idea of frame aggregation is to aggregate multiple MAC frames into a single (or approximately single) transmission [9], as shown in Figure 4(a).  
In this paper we combine the frame aggregation concept and Dly-ACK mechanism with some modification and we define this new mechanism as K-Dly-ACK-AGG, as shown in Figure 4(b), where K is the burst size of data frames, [10]. Imm-ACK with aggregation method act same as K-Dly-ACK-AGG (where K = 1) so there is no point in considering Imm-ACK policy individually with aggregation. The main advantage of K-Dly-ACK-AGG policy is that it can make redundant inter-frame spaces and replace MAC headers with smaller size headers in Dly-ACK policy. This scheme is similar to the Blk-ACK policy in IEEE 802.15.3c. However, the Blk-ACK policy is only used with the aggregation whereby our K-Dly-ACK-AGG scheme does not have such a restriction. Furthermore, the K-Dly-ACK-AGG scheme offers much simpler ACK mechanism and frame format (it can easily switch between Dy-ACK, K-Dly-ACK-AGG, and Imm-ACK) [10].  
Figure 4: Aggregation Method and K-Dly-ACK-AGG Policy. (a) Aggregation Method; (b) K-Dly-ACK-AGG Policy (K = 3).
# Composite Foundation Settlement Prediction Based on LSTM-Transformer Model for CFG
## 3 The Establishment of a CFG Compound Foundation Settlement Model Based on LSTM-Transformer
### The LSTM Model  
LSTM (long short-term memory) is a type of sequence data processing model that addresses the issues of vanishing and exploding gradients encountered in training long sequences within RNNs (recurrent neural networks) by introducing 'gates' mechanisms [31]. The LSTM model implements selective information flow through these gates--the forget gate, input gate, and output gate--which control the retention, input, and output of  
Figure 2: Multi-head mechanism.  
information, respectively. This architecture allows the LSTM to maintain long-term dependencies and handle information flow more effectively compared to traditional RNNs. The basic structure of an LSTM network, as depicted in Figure 3, showcases its recurrent nature. Through its distinctive 'gates', the LSTM can manage and control the cell state [32; 33]:  
\[z_{f}=sigmoid(w_{f}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{4}\]  
\[\left\{\begin{aligned} & z_{i}=sigmoid(w_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \\ & z=\tanh(w\cdot[h_{t-1},x_{t}]+b)\\ &\qquad\qquad c_{t}=z_{f}\cdot c_{t-1}+z_{i}\cdot z\end{aligned}\right. \tag{5}\]  
\[\left\{\begin{aligned} & z_{0}=sigmoid(w_{0}\cdot[h_{t-1},x_{t}]+b_{0}) \\ & h_{t}=z_{0}\cdot\tanh(c_{t})\end{aligned}\right. \tag{6}\]  
In the equations, \(z_{i}\) represents the LSTM update value (input gate); \(z\) is the new candidate value vector (candidate value); \(c_{t-1}\) and \(c_{t}\) represent the cell states of the previous and current rounds, respectively; \(h_{t-1}\) and \(h_{t}\) represent the hidden states of the previous and current rounds, respectively; w and b represent the weight matrices and bias terms of the output gate, input gate, and forget gate, respectively.  
In summary, the current time step's hidden layer output (\(h_{t}\)) and cell state (\(c_{t}\)) in the LSTM are jointly determined by the previous time step's hidden layer output (\(h_{t-1}\)), cell state (\(c_{t-1}\)), and the current time step's input (\(x_{t}\)). Additionally, according to relevant research, when exploring the complex nonlinear relationship between foundation settlement and influencing factors, it is possible to construct LSTM models with two or more stacked hidden layers for training.
# FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks
## 2 Related Work
### The Fuzzy Logic-Based Routing Technologies in ULANs  
To optimize end-to-end latency, [PERSON] et al. [25] implemented an optimized vector protocol based on fuzzy logic (FLOVP) that provides better routing performance than vector-based forwarder (VBF) algorithms. [PERSON] et al. [26] performed a weight-based fuzzy logic (WBFL) algorithm for underwater acoustic networks. Experiments showed that this mechanism for selecting relay nodes is efficient and accurate. [PERSON] et al. [27] proposed an energy-efficient routing protocol that used fuzzy logic to select DNs in hydroacoustic networks. [PERSON] et al. [28] proposed a fuzzy-based routing protocol to determine node locations and energy levels in UANs. [PERSON] et al. [29] developed a fuzzy algorithm to assist in cooperative opportunity routing in UANs, effectively reducing energy consumption, survival time, and delay. [PERSON] et al. [30] recommended an energy-efficient cooperative opportunity routing (EECOR) protocol for hydroacoustic networks, which used a fuzzy logic-based scheme to select the most suitable relay node considering energy consumption. [PERSON] et al. [31] presented a fuzzy logic-based grid routing algorithm that extended the survival time and reduced the end-to-end latency of UANs.  
[PERSON] et al. [32] used a routing protocol that considered the residual energy and deployment depth of underwater nodes. Based on various fuzzy logic metrics, this protocol outperformed similar protocols regarding end-to-end latency and energy consumption. [PERSON] et al. [33] proposed a direction-sensitive routing protocol (DSR) that used fuzzy reasoning to select the best candidate for hydroacoustic networks. The simulation results showed that the algorithm had better energy consumption, packet delivery rate (PDR), and average end-to-end latency. [PERSON] et al. [34] proposed an energy-efficient packet protection scheme based on fuzzy logic. The simulation results showed that the proposed routing protocol is efficient with a minimum number of routing table entries. [PERSON] et al. [35] proposed a fuzzy logic-based clustering algorithm that constructed optimal CHs based on fuzzy logic.
# Algebraic Point Projection for Immersed Boundary Analysis on Low Degree NURBS Curves and Surfaces
## 3 Background on Algebraic Level Sets
### Implicitization of a Parametric Curve  
Given a rational parametric curve \(\mathbf{C}(X(u),Y(u),W(u))\) of degree \(p\) with \(x=\frac{X(u)}{W(u)},y=\frac{Y(u)}{W(u)}\), one can construct two auxiliary polynomials:  
\[g_{1}(x,u) =W(u)x-X(u)=0 \tag{4a}\] \[g_{2}(y,u) =W(u)y-Y(u)=0 \tag{4b}\]  
The above polynomial equations can be rearranged in descending power of \(u\) as follows:  
\[g_{1}(u) =a_{p}u^{p}+a_{p-1}u^{p-1}+\cdots+a_{1}u+a_{0} \tag{5a}\] \[g_{2}(u) =b_{p}u^{p}+b_{p-1}u^{p-1}+\cdots+b_{1}u+b_{0} \tag{5b}\]  
From the above, the following resultant system may be obtained through algebraic manipulations [25]:  
\[\begin{bmatrix}(a_{p}b_{p-1})&\cdots&(a_{p}b_{0})\\ \vdots&\ddots&\vdots\\ (a_{p}b_{0})&\cdots&(a_{1}b_{0})\end{bmatrix}\begin{pmatrix}u^{p-1}\\ u^{p-2}\\ \vdots\\ 1\end{pmatrix}=\begin{bmatrix}\mathbf{M}^{B}\end{bmatrix}_{p\times p}\begin{pmatrix} u^{p-1}\\ u^{p-2}\\ \vdots\\ 1\end{pmatrix}=0 \tag{6}\]  
where \((a_{i}b_{j})=a_{i}b_{j}-a_{j}b_{i}\), \(\mathbf{M}^{B}\) is Bezout matrix and is a function of \(x\) and \(y\) having the following important property:  
\[\mathbf{M}^{B}(x,y)=\mathbf{M}^{B}_{x}x+\mathbf{M}^{B}_{y}y+\mathbf{M}^{B}_{w} \tag{7}\]  
where \(\mathbf{M}^{B}_{x},\mathbf{M}^{B}_{y}\), and \(\mathbf{M}^{B}_{w}\) depend on control point coordinates and weights. Therefore, these matrices can be pre-computed for a given rational parametric curve and re-used given any new physical point \(\mathbf{x}\). The determinant, \(\det(\mathbf{M}^{B}(\mathbf{x}))\), is defined as the Bezout resultant. Since all the allowable parameter values \(u\) for curve \(\mathbf{C}(X(u),Y(u),W(u))\) are roots of Equation (6), \(\det(\mathbf{M}^{B}(\mathbf{x}))=0\) gives the equation of the implicitized curve. Thus, the algebraic level sets corresponding to a rational parametric curve (e.g., Bezier curve) are given by:  
\[\Gamma(\mathbf{x})=\det(\mathbf{M}^{B}(\mathbf{x})) \tag{8}\]  
An example of algebraic level sets is shown in Figure 3.  
Figure 3: Implicitization of a quartic Bézier curve. Level set \(\Gamma(\mathbf{x})=\det(\mathbf{M}(\mathbf{x}))\) can be used as a measure of distance.
# MFHE: Multi-View Fusion-Based Heterogeneous Information Network Embedding
## 2 Related Work  
### Network Embedding  
Early embedding learning was based on spectral analysis, representing the network as a low-dimensional representation of the adjacency matrix input model to obtain nodes [31; 32]. However, this method does not take into account the information inside the node, and the quality of the learned network embedding is poor. With the development of deep learning, representative models have gradually emerged in the field of network embedding, such as Deepwalk [6] and LINE [7] models. The above two models are inspired by word2 Vec [33], a classic model in natural language processing. DeepWalk and LINE models replace the input of word2 Vec model with network input from a text corpus. DeepWalk regards nodes as words and random walk paths as sentences, and learns network representation based on random walk strategy. Given the shortcomings of DeepWalk that are not applicable to the weighted network, then the LINE model is proposed. However, LINE focuses too much on the critical characteristics and does not consider the features of the meta-path. Therefore, [PERSON] et al. [8] propose Node2 Vec, which combines a depth-first search and breadth-first search to design a more flexible random walk strategy. In addition to the above methods based on the random walk strategy, SDNE [34] designs the autoencoder dimensionality reduction vector representation. GraphGAN [35] applies the idea of GAN to graph representation learning, allowing the generator to fit or estimate the true probability of the connection distribution as much as possible, and the discriminator calculates the possibility of edges between nodes. If considering the network more carefully, we will find that many nodes in the network carry attribute information, such as commodity category, citation text information, etc. Thus, [PERSON] et al. [36] propose GCN jointly to learn the attributes of vertices and the topology of the graph. GCN first proposes a convolutional approach to fuse graph structure features, providing a whole new perspective. However, GCN only learns the representation of nodes in a given network, and its scalability is poor. Therefore, [PERSON] et al. [37] propose GraphSAGE, which represents unknown vertices in the training by using the attribute information of vertices, that is, inductive learning. However, its sampling increases exponentially with the increase in order depth. The number of samples needs to be limited; however, this will result in the loss of some node information. To solve the problem that GCN has the same weight value when fusing neighbor nodes, [PERSON] et al. [38] propose a GAT model and design a multi-head attention mechanism. However, the above methods are all designed for homogeneous information network, and cannot be directly used for HIN embedding.