esa-sceva/satcom-chunk-collection huggingface dataset:
### Information Dissemination Models  
#### 3.2.1 Independent Cascade Mode  
Influence propagation models describe how nodes in a social network transition from an inactive state to an active state based on the influence of their neighbors' states. As nodes that were activated in the previous time step go on to activate other nodes in the next time step, the number of activated nodes in the network increases, eventually leading to the  
Figure 4: Schematic diagram of network constraint coefficient calculation.  
Figure 3: Structural hole diagram. A–F are different nodes, with node B highlighted in yellow to represent the key node involved in bridging structural holes between different subgroups of nodes.  
activation of all nodes that can be influenced. This process is known as influence diffusion within the network. Many researchers have investigated this process and proposed various models. Among them, the independent cascade (IC) model and linear threshold (LT) model are two of the most classic influence propagation models.  
The IC model is a classic model for discretizing the propagation process. Extensive research has been conducted on this model by scholars worldwide. The model formalizes the social network as a directed graph \(G=(V,E,W)\), where \(V\) represents the set of nodes and \(E\) represents the set of directed edges between nodes. For each directed edge (\((u\to v)\in E\)), \(p_{uv}\in[0,1]\in W\) denotes the probability of the activation of node \(v\) by its incoming neighbor node (\(u\)). This probability indicates the likelihood that node \(v\) will be successfully activated by node \(u\).  
The IC model follows a process whereby activated nodes attempt to activate their unactivated neighbors at discrete time steps. Algorithm 1 details the process, which begins by selecting an initial set of activated nodes, known as the seed set (\(S_{0}\)), and proceeds iteratively. At each time step (\(t\)), nodes activated in the previous time step attempt to activate their neighboring nodes, with each attempt succeeding based on a predefined probability (\(p_{uv}\)). The process continues until no further activations occur in the network.  
```
Input: Social network graph \(G=(V,E)\), activation probability \(p_{uv}\), seed set \(S_{0}\), user activation threshold \(\theta_{v}\), current time step \(t_{c}\), end time step \(t_{e}\) Output: Set of activated nodes \(S_{t}\) Initialize:\(AS\gets S_{0}\), \(S_{t}\gets S_{0}\) while\(t_{c}\leq t_{e}\)do for each user \(u\in S_{t-1}\setminus S_{t-2}\)do for each neighbor \(v\in N_{\text{out}}(u)\setminus S_{t-1}\)do if\(p_{uv}>\theta_{v}\)then  Activate node \(v\) \(v\in S_{t}\setminus S_{t-1}\) \(AS\gets AS\cup\{v\}\), \(S_{t}\gets S_{t}\cup\{v\}\) elseif\(p_{uv}<\theta_{v}\)then \(v\in V\setminus S_{t}\), node \(v\) remains inactive endif endfor endfor  Increment time step \(t_{c}\gets t_{c}+1\) endwhile return\(S_{t}\)
```  
**Algorithm 1** Independent Cascade Model
# Blockchain Applications in the Military Domain:
## 5 Discussion
### Enhancing Military Logistics with Blockchain Technology: Benefits and Transformations
Blockchain technology, therefore, gives the military real-time tracking, a secure way of sharing data, and record-keeping that is tamper resistant, which all point to making it a transformational tool for managing complex and high-stakes military supply chains. With continued development, blockchain will likely mature into an integral component of military logistics systems and change how resources are managed and deployed in major operations.
# Comparative Analysis of Viscous Damping Model and Hysteretic Damping Model
## 2 Comparative Analysis of Transient Responses Based on Different Damping Models
### The Transient Response of Hysteretic Damped System  
Based on the hysteretic damping model, the time-domain equation for an SDOF system is expressed as [21]  
\[m\ddot{x}(t)+f_{\mathrm{h}}+kx(t)=0 \tag{9}\]  
The hysteretic damping force is expressed as  
\[f_{\mathrm{h}}=-\eta kx^{\mathrm{H}}(t) \tag{10}\]  
where \(\eta\) is the loss factor; \(x^{\mathrm{H}}(t)\) is Hilbert transform of the displacement.  
It is important to note that the damping force of the hysteretic damping model can be equivalently evaluated by using the rate-independent damping model [27].  
Based on the complex plane method, the general solution of Equation (9) is assumed as  
\[x(t)=\psi\mathrm{e}^{(\alpha_{\mathrm{h}}+\mathrm{i}\omega_{\mathrm{h}})t} \tag{11}\]  
where \(\psi\) is the amplitude of the vibration response.  
Then  
\[\left\{\begin{array}{ll}x^{\mathrm{H}}(t)=-\mathrm{i}\psi\mathrm{e}^{( \alpha_{\mathrm{h}}+\mathrm{i}\omega_{\mathrm{h}})t},&\omega>0\\ x^{\mathrm{H}}(t)=\mathrm{i}\psi\mathrm{e}^{(\alpha_{\mathrm{h}}+\mathrm{i} \omega_{\mathrm{h}})t},&\omega<0\end{array}\right. \tag{12}\]  
\[\ddot{x}(t)=-\omega_{\mathrm{h}}{}^{2}\psi\mathrm{e}^{(\alpha_{\mathrm{h}}+ \mathrm{i}\omega_{\mathrm{h}})t} \tag{13}\]  
Substituting Equations (11)-(13) into Equation (9), the damped natural frequency and the attenuation coefficient are obtained.  
The damped natural frequency is  
\[\omega_{\mathrm{h}}=\frac{\sqrt{2}}{2}\omega\sqrt{\sqrt{1+\eta^{2}}+1} \tag{14}\]  
Equation (14) shows that the hysteretic systems are in an underdamping state and the loss factor is limited. However, the law of the damped natural frequency for the hysteretic damping model is different from the one for the viscous damping model. The damped natural frequency is greater than the natural frequency.  
The attenuation coefficient is  
\[\alpha_{\rm h}=-\frac{\sqrt{2}}{2}\omega\sqrt{\sqrt{1+\eta^{2}}-1} \tag{15}\]  
Equation (15) shows that the attenuation coefficient is negative and the stability of the free vibration response is guaranteed based on the hysteretic damping model.  
The transient response is expressed as  
\[x(t)=\mathrm{e}^{\alpha_{\rm h}}(A\sin\omega_{\rm h}t+B\cos\omega_{\rm h}t) \tag{16}\]
## 3 Results
### Uncertainty Analysis under Different Noise Conditions  
Noise is unavoidable in the analysis of rock physics. Hence, the uncertainty under noise conditions is an essential ability of the proposed methods. Noise can be ambient or source generated, coherent or random [20]. In a broader sense, noise can be from the uncertainty of the measurement or seismic inversion results. The real data, including well log data, seismic data and inversion data, cannot be noise-free.  
Seismic inversion, simultaneous inversion in particular, is a useful tool in quantitative interpretation, although it was not involved in the previous discussion. This technique converts the data from the reflectivity (or interface) domain to the impedance domain, which is more geologically meaningful. Simultaneous inversion can provide information on the P-wave and S-wave velocities and the density in a large area. The accuracy of simultaneous inversion is limited, although it can  
Figure 8: AUC curve of \(\lambda\rho\).  
Figure 7: The performances of \(\lambda\rho\) in different water saturation scenarios: (**a**) ROC curves. (**b**) metric parameters.  
achieve high accuracy in theory. Its accuracy is dominated by the quality of the input pre-stack seismic data and the well log, the seismic-well tie, and the extracted wavelet together. In a real application, the correlation coefficient between the inverted and real log maybe is not as high as the expectation. Hence, there are errors between the inverted data and the real value. The errors can be regarded as noise when applying the inverted data as the input.  
The commonly used unit of noise level is decibel (dB). By definition, the noise level (\(N_{L}\)) can be derived by the ratio of energy (\(\frac{E_{noise}}{E_{signal}}\)) or amplitude (\(\frac{A_{noise}}{A_{signal}}\)) between noise and signal as follows:  
\[N_{L}=10\log_{10}\left(\frac{E_{noise}}{E_{signal}}\right)=20\log_{10}\left( \frac{A_{noise}}{A_{signal}}\right), \tag{13}\]  
In this section, the noise levels are set at \(-20\) dB, \(-10\) dB and \(-7\) dB which correspond the energy ratio is 0.01, 0.1 and 0.2. The noise analysis is performed with different water saturation conditions.
# Linear Programming-Based Non-Probabilistic Reliability Bounds Method for Series Systems
## 3 Proposed LPNRBM
### Identification of Zero Design Variables
#### 3.2.2 Identification of Zero Design Variables for the Tricomponent System
\begin{aligned} \mathbf{P}_{12}&=\frac{(b_{0} \cos\gamma_{12}-b_{0})}{\sin^{2}\gamma_{12}}a_{i}+\frac{(a_{0}\cos\gamma_{12}- b_{0})}{\sin^{2}\gamma_{12}}b_{i}\\ \mathbf{P}_{13}&=\frac{(c_{0}\cos\gamma_{13}-b_{0})}{ \sin^{2}\gamma_{13}}a_{i}+\frac{(a_{0}\cos\gamma_{13}-c_{0})}{\sin^{2}\gamma_ {13}}c_{i}\\ \mathbf{P}_{23}&=\frac{(c_{0}\cos\gamma_{23}-b_{0})}{ \sin^{2}\gamma_{23}}b_{i}+\frac{(b_{0}\cos\gamma_{23}-c_{0})}{\sin^{2}\gamma_ {23}}c_{i}\end{aligned} \right. \tag{15}\]  
Figure 5: Five different cases of a tricomponent system. (**a**) Non-empty MECE event case. (**b**) Two empty MECE events case. (**c**–**e**) One empty MECE event cases. \[ \begin{array}{l}\mathbf{P}_{123}=\frac{a_{0}-h_{0}\cos\gamma_{12}-c_{0}\cos\gamma_{ 13}-a_{0}\cos^{2}\gamma_{23}+c_{0}\cos\gamma_{12}\cos\gamma_{23}+b_{0}\cos \gamma_{13}\cos\gamma_{23}}{\cos^{2}\gamma_{12}+\cos^{2}\gamma_{13}+\cos^{2} \gamma_{23}-1-2\cos\gamma_{12}\cos\gamma_{13}\cos\gamma_{23}}a_{i}\\ \quad\quad\quad+\frac{b_{0}-a_{0}\cos\gamma_{12}-c_{0}\cos\gamma_{23}-b_{0}\cos ^{2}\gamma_{13}+c_{0}\cos\gamma_{12}\cos\gamma_{13}+a_{0}\cos\gamma_{13}\cos \gamma_{23}}{\cos^{2}\gamma_{12}+c_{0}\cos^{2}\gamma_{13}+c_{0}\cos\gamma_{12} \cos\gamma_{13}+a_{0}\cos\gamma_{13}\cos\gamma_{23}}b_{i}\\ \quad\quad\quad\quad+\frac{c_{0}-a_{0}\cos\gamma_{12}-c_{0}\cos\gamma_{23}-c_{ 0}\cos^{2}\gamma_{12}+b_{0}\cos\gamma_{12}\cos\gamma_{13}-c_{0}\cos\gamma_{1 3}\cos\gamma_{23}}{\cos^{2}\gamma_{12}+c_{0}\cos^{2}\gamma_{13}+c_{0}\cos^{2} \gamma_{13}+c_{0}\cos^{2}\gamma_{13}+c_{0}\cos^{2}\gamma_{13}\cos\gamma_{23}} {\cos^{2}\gamma_{12}+c_{0}\cos^{2}\gamma_{13}+c_{0}\cos^{2}\gamma_{13}+c_{0} \cos^{2}\gamma_{13}+c_{0}\cos^{2}\gamma_{13}\cos\gamma_{23}}c_{i}\end{array}  \tag{16}\]  
In general, \(\mathbf{P}_{123}\) and \(\mathbf{P}_{12}\) exist. If those failure surfaces are parallel to each other, \(\mathbf{P}_{123}\) and \(\mathbf{P}_{12}\) do not exist and can be seen at infinity but do not affect the subsequent identification. The five cases in Figure 5 can be identified by these special points and the relative positions between the failure surfaces. Taking Figure 5a as an example, the point \(\mathbf{P}_{123}\) is in the unit sphere, and the point \(\mathbf{P}_{12}\) does not coincide with the point \(\mathbf{P}_{123}\). There are no empty events in the system. The recognition criterion is as follows:  
\[\left\{ \begin{array}{l}\|\mathbf{P}_{123}\|\leq 1\\ \mathbf{P}_{123}\
eq\mathbf{P}_{12}\end{array} \right. \tag{17}\]  
Similarly, Table 1 shows the identification of the remaining cases and the empty events they produce. Design variables whose MECE events are subsets of the identified empty events should be zero. Additionally, the number of subsystems mentioned above is not greater than \(\frac{m(m-1)(m-2)}{6}\).
# Variable Scale Pruning for Transformer Model Compression in End-to-End Speech Recognition
## 2 Related Work
### Pruning Structure  
Pruning methods can be broadly categorized into two main groups [10]:  
* Unstructured pruning [2], which is a pruning technique that involves removing individual weights from a weight matrix. When a weight value is replaced with zero, it effectively prunes a connection. This type of pruning is commonly referred to as sparse pruning [23], as it results in sparse matrices (as shown on Figure 1b).
* Structured pruning [25] is a pruning technique that specifically targets the removal of entire blocks of weights, rather than individual weights, within a neural network. This approach enables pruning at a higher level of granularity, allowing for the elimination of larger structural components. For instance, in fully connected layers [26], it is possible to zero out blocks of connections (refer to Figure 1d), or even entire rows or columns of a weight matrix (as shown on Figure 1c), resulting in the removal of entire neurons. Similarly, in convolutional neural networks (CNNs), filters or channels can be removed, and in transformer models [27; 28], even attention heads can be pruned.  
Pruning can be incorporated into the training process as an additional step between training epochs (iterative pruning), applied all at once after the model training is complete (one-shot pruning) [13], or applied between fine-tuning steps [25]. The importance of the weights can be determined by their magnitude [29], their gradients [30], or a custom measurement [31].  
Our focus lies on unstructured pruning, specifically targeting the removal of connections. This approach centers on pruning the smallest independent elements of the model, which allows for pruning in large quantities without significant impacts on performance.
## 2 Materials and Methods
### Sample-Size Calculation and Randomization  
A priori power analysis (two-tail t-test between two independent samples with equal group sizes) was used for sample size estimation, based on the results of a pilot study. Previously, it was reported that the osseodensification technique improves implant stability by approximately 25% [25]; thus, an effect size of 25% on implant stability was assumed. A sample of at least 17 dental implants in each group would be required to achieve 0.80 power at a significance level of 0.05 (SPSS version 22.0; IBM Corporation, Armonk, NY, USA). Considering an expected drop-out rate of 30%, a minimum sample size of 23 implants was selected in the present study. Opaque envelopes were used as a randomization method to assign each implantation site to receive one of two bone preparation techniques: osseodesnification drills with healing chamber (test group--G1) or standard undersized drilling (control group--G2).
## 2 Data Quality Analysis
### DQA2: Satellite Clock Correction Difference When Ephemeris Is Updated  
The satellite clock correction for a given satellite is broadcasted in the ephemeris, and the difference of the satellite clock correction between the ephemeris update should be small if the GNSS satellite control ability is stable. It is necessary to analyze the satellite clock correction difference between each ephemeris update, and the DQA2 method is therefore proposed in this work. The results for DQA2 are summarized in Table 3. In this analysis, GPS has stable performance for controlling the satellite clock correction, and all GPS satellites clock correction differences are less than 0.35 m. On the other hand, the differences of the BDS satellite clock correction between ephemeris updates are larger than those of GPS. The clock corrections for BDS satellites need to be improved. As a result, a BDS-GPS user could follow the satellite clock correction estimation method provided in [20] to reduce satellite clock error effect on positioning.
# Incorporating Epistemic Uncertainties in Ship Operability Study
## 4 Discussion
### Application in DSS for Ship Operation  
The probabilistic approach can be used in ship operation for tactical DSS. When a ship encounters heavy weather, a probabilistic operability plot could be a useful tool to help the ship master evaluating the consequences of potential maneuvers, i.e., changing the course and/or reducing the ship speed. This is illustrated in Figure 6, where the effect of maneuvering in heavy weather is presented. On the right-hand side of the polar plot, results are presented for a nominal ship speed of 17 kn, while results on the left-hand side refer to the reduced ship speed of 5 knots. Although differences in this example are not very large, the shipmaster could evaluate what would be the consequences of the potential speed or course changes.  
If the costs related to the delay specified in Section 4.1 are considered, then it may be stated that for the 5% exceeding probability, the risk involved, obtained by multiplying the costs of delay with 0.05, is rather small and hardly altering the sailing route would be justified. This is not the case for the 95% exceeding probability, where risk reduction by modifying the sailing parameters would be cost-efficient and a different combination can be considered.
# Vision-Based Distance Measurement in Advanced Driving Assistance Systems
## 6 Experiments and Results
### Distance Measurement of the Target  
#### 6.3.1 Distance Measurement of the Pedestrian  
In this subsection, we evaluate our distance measurement method for pedestrians. Our method computes the depth average of all pixels whose depth values are in the peak range of the depth histogram. To verify the effectiveness of our measurement method, we compared it with the method that calculates the average depth of all pixels belonging to the pedestrian region. In the experiment, under the premise of fixing the camera position, we arranged the human to stand at the positions \(L\) and \(2L\), away from the camera position, and conducted quantitative comparative experiments under four different \(L\) values, as shown in Figure 8. Figure 8a is a schematic diagram of our experimental device, Figure 8b,c shows four original images corresponding to different \(L\) values (2.82, 3.93, 5.87, and 7.86 m) and their depth maps using the proposed depth map estimation network.  
The distance measurement results of the pedestrian are given in Table 2. According to these results, we came to the following two conclusions: (1) the pedestrian distance measurement error of our method is significantly smaller than the error obtained using the average depth value; (2) the measurement results of our method effectively reflect the relative distance between two pedestrians, i.e., the distance between the second pedestrian and the camera is twice the distance between the first pedestrian and the camera.  
Figure 8: Distance measurement of the pedestrian. (**a**) Schematic diagram of the experimental device. (**b**) Four original images corresponding to different \(L\) values: \(L\) = 2.82 m in (**b-1**); \(L\) = 3.93 m in (**b-2**); \(L\) = 5.87 m in (**b-3**); \(L\) = 7.86 m in (**b-4**). (**c**) Depth maps corresponding to four input images of (**b**) using the proposed depth map estimation network.  
Figure 7: Results of depth map estimation in real road scenarios. (**a**) The first row shows forward-looking images captured from the real road scenarios. (**b**) The second row shows the depth maps of the images in the first row using the proposed method of depth map estimation.
# Primitive Shape Fitting in Point Clouds Using the Bees Algorithm
## 3 Primitive Fitting Methods
### The Bees Algorithm  
The bees algorithm (BA) is a popular intelligent optimisation technique that found wide application in optimisation problems [29]. Inspired by the honeybees foraging behaviour, it performs multiple simultaneous local searches at different sites of the solution space. The bees algorithm considers candidate solutions as food sources, and employs artificial bees to evaluate their quality (fitness) and exploit the most promising regions of the search space.  
The algorithm begins sending \(ns\) artificial scout bees to randomly sampled locations (candidate solutions) in the search space. The scout bees evaluate the quality of the food sources where they landed using Equation (1). Each visited solution becomes the centre of a neighbourhood delimited by a hypercube of side \(ngh=2\delta^{l}\). The algorithm then enters the main loop, which consists of a number of steps. The first step is called waggle dance, in analogy with the waggle dance behaviour of honey bees where foragers are recruited for harvesting the richest food sources. In this step, the neighbourhoods around the fittest \(nb\) solutions visited by the scouts are selected for local search.  
The second step is where the simultaneous exploitative searches are performed, that is the neighbourhoods are harvested (local search). Namely, \(nre\) forager bees are sent to exploit the neighbourhood of the very best \(ne\leq nb\) visited solutions, and \(nrb\leq nre\) foragers are sent to the remaining \(nb-ne\) sites. Each forager lands on a food source in the assigned neighbourhood, and evaluates its quality. The landing site of the foragers is determined using the procedure described in Section 3.3. That is, in the local search step the bees algorithm concurrently samples the neighbourhoods around the most promising solutions. The forager that visited the fittest solution within a neighbourhood becomes the new scout, and the centre of the neighbourhood is moved to that solution.  
If no forager finds a solution that is fitter than the centre of the neighbourhood, the scout remains unchanged, and the local search is said to stagnate. In this case, the size of the neighbourhood is reduced (neighbourhood shrinking procedure). After \(stlim\) consecutive stagnation cycles the neighbourhood is abandoned and the scout is re-initialised at a new randomly picked location in the search space (site abandonment procedure).  
Global explorative search (third step) is performed by the remaining \(ns-nb\) scouts, which keep on randomly sampling the solution space looking for new promising regions. At the end of one cycle of the main loop, \(nb\) scouts mark the neighbourhoods resulting from local search, and \(ns-nb\) scouts mark the neighbourhoods found through global search. The algorithm terminates after a given number of iterations returning the best solution found.  
For more details on the bees algorithm and its capabilities, the reader is referred to [7; 30; 31].
## 3 LoMoB Localization Scheme
### Location Estimation
#### 3.3.2 Estimating the Location of a Sensor Node Using the Weighted Mean
In LDB, when a sensor node has four projected beacon points \(F_{1}\), \(L_{1}\), \(F_{2}\), and \(L_{2}\), as shown in Figure 3(b), two possible points are obtained based on \(F_{1}\) and \(L_{1}\) and one point between the two is estimated as the location of the sensor node based on \(F_{2}\) and \(L_{2}\). Here, the error in the estimated location depends largely on the distance between \(F_{1}\) and \(L_{1}\)[16]. If \(F_{1}\) and \(L_{1}\) are located very close to each other, the location error is expected to be large. In this case, a better choice may be that \(F_{2}\) and \(L_{2}\) (or another two projected beacon points) are used for the two possible points, rather than \(F_{1}\) and \(L_{1}\). However, the selection of two projected beacon points for the possible points has not yet been studied. If the errors of the beacon points \(F_{1}\) and \(L_{1}\) increase because of some factors such as irregularities in the radius of the circle formed by the transceiver's beam and the location error of a mobile beacon, the errors of the two possible points also increase, which results in an increase of the location error. Even though more beacon points, in addition to \(F_{1}\), \(L_{1}\), \(F_{2}\), and \(L_{2}\), are obtained, the localization accuracy is not improved because the additional beacon points are not used for localization. As in LDB, LoMoB obtains two intersection points based on two beacon points and determines one point between the two as a potential location for the location of the sensor node based on the other beacon points. However, compared to LDB, LoMoB estimates the location of a sensor node based on all the potential locations, not just one potential location. In addition, potential locations with a high weight contribute more to the estimation of the location of the sensor node than other potential locations. For these reasons, LoMoB is expected to improve LDB in challenging underwater environments even though LoMoB uses just four beacon points, as does LDB. If more than four beacon points are used, the localization accuracy is expected to improve further.
# Spectrum and Energy Efficiency of Uplink Massive MIMO System with D2D Underlay
## 3 Spectrum Efficiency Analysis  
In this section, we propose the approximate probability density function (PDF) of the SINR, based on which, we analyze the outage probabilities and the achievable rates for both the D2D and the CUEs by considering the mutual interference between the two systems.