esa-sceva/satcom-chunk-collection huggingface dataset:
# Quotient Network-A Network Similar to ResNet but Learning Quotients
## 4 Experiments
### Visualization  
To verify the motivations proposed in the introduction, we visualize the intermediate feature (quotient for quotient network and residual for ResNet) maps calculated in the first three stacked modules of the 110-layer networks trained on CIFAR10. The feature maps when the input image is a frog are shown in Figure 4, and the feature maps when the input images are other categories are shown in the Appendix B. As can be seen from the figure, the quotient feature maps are clearer than the residual feature maps, and it is easier to see the complete structure of a frog from the quotient feature. This phenomenon is in line with our conjecture. The quotient of new and old features is more likely to be an independent and meaningful feature that can reflect a particular aspect of the characteristics of the frog. Therefore, it generates more clearly identifiable feature maps. On the contrary, ResNet learns the arithmetic difference of different types of features and lacks independent attribute meaning, so its feature maps are blurrier and more difficult to recognize. Moreover, our network learns relative difference (i.e., quotient), which is not sensitive to the size of old features, and it can exert a stable and effective influence on feature values of different sizes. That, in turn, makes our feature map information richer compared with ResNet's feature map information. It can even be seen that the feature maps of some ResNet channels are approximately pure colors.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**\#Params**} & \multicolumn{2}{c}{**Quotient Network**} & \multicolumn{2}{c}{**ResNet**} \\ \cline{2-4}  & **Network** & **Accuracy (\%)** & **Network** & **Accuracy (\%)** \\ \hline
0.68 M & quotient network44 & 96.17 \(\pm\) 0.12 & Resnet44 & 95.98 \(\pm\) 0.04 \\
0.87 M & quotient network56 & **96.20 \(\pm\) 0.11** & Resnet56 & 95.96 \(\pm\) 0.06 \\
1.75 M & quotient network110 & 96.12 \(\pm\) 0.05 & Resnet110 & **96.03 \(\pm\) 0.01** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with ResNets of different layers on the SVHN dataset. Results are expressed as “mean \(\pm\) std”.  
Figure 4: The middle feature maps when the input image is a frog. The left is for the quotient network, and the right is for ResNet. From top to bottom, it is for the first, second, and third stacked modules in order.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**\#Params**} & \multicolumn{2}{c}{**Quotient Network**} & \multicolumn{2}{c}{**ResNet**} \\ \cline{2-5}  & **Network** & **Accuracy (\%)** & **Network** & **Accuracy (\%)** \\ \hline
2.72 M & quotient network44 & 73.25 \(\pm\) 0.27 & Resnet44 & 72.66 \(\pm\) 1.24 \\
3.50 M & quotient network56 & **73.53 \(\pm\) 0.18** & Resnet56 & **73.07 \(\pm\) 0.24** \\
6.99 M & quotient network110 & 73.00 \(\pm\) 0.55 & Resnet110 & 72.34 \(\pm\) 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with ResNets of different layers on the CIFAR100 dataset. Results are expressed as “mean \(\pm\) std”.
# Multi-Level Site Selection of Mobile Emergency Logistics Considering Safety Stocks
## 2 Model Construction
### Mobile Emergency Logistics Multilevel Site Selection Model  
Based on the population distribution, road traffic and administrative boundaries of the coverage area, the site selection model selects the number and location of logistics' centers and warehouses. In order to solve the multilevel siting model for mobile emergency logistics, the improved immuno-optimization algorithm (IIA) has strong global search capability, adaptability and robustness. Compared with other models, the mobile emergency logistics model allows the distribution warehouses to transfer to each other. To minimize the total cost of emergency logistics, a multilevel siting model of mobile emergency logistics is constructed for emergency events:  
\[\min F=\sum_{k\in K}F_{k}a_{k}+\sum_{j\in I}\left(f_{j}x_{j}+\sum_{j\in I} \sum_{k\in K}\hat{I}_{jk}x_{jk}+\sum_{i\in I}\sum_{j\in J}\hat{d}_{ij}\mu_{i} \right)+S+T \tag{1}\]  
\[S=ah_{1}\sum_{j\in I}\sigma_{j}\sqrt{L_{j}}+ah_{2}\sum_{k\in K}\sqrt{P_{k}( \sum L_{j}\sigma_{j}^{2})} \tag{2}\]  
\[T=(\alpha\sum_{j\in J}\sigma_{j}\sqrt{L_{j}}-\alpha\sqrt{\sum_{j\in I}L_{j} \sigma_{j}^{2}})\sum_{i,j\in K}d_{ij}/n \tag{3}\]  
\[\hat{I}_{jk}=d_{jk}\sum\mu_{i}y_{ij} \tag{4}\]  
\[\hat{d}_{ij}=d_{ij}\mu_{i} \tag{5}\]  
\[\mu_{j}=\sum_{i}\mu_{i},\sigma_{j}=\sqrt{\sum_{i}\sigma_{i}^{2}} \tag{6}\]  
\[\sum_{j\in I}x_{jk}\leq q,x_{j}=\{0,1\},\forall k\in K \tag{7}\]  
\[\sum_{j\in I}\sum_{i\in I}d_{ij}\leq(D_{i}^{r}+d_{jc}),\forall j\in J,\forall i\in I \tag{8}\]  
The objective Function (1) represents the minimization of the location cost of mobile emergency logistics. The total cost includes the fixed costs of logistics centers and distribution warehouses, transportation costs at all levels, and safety stock costs. Constraint (2) indicates that the safety stock cost consists of the logistics center safety stock cost and the distribution warehouse safety stock cost. Constraint (3) suggests that the retransfer transportation cost equals the average distance between the distribution bins multiplied by the safety stock difference between the distribution bins. Constraint (4) indicates that the transportation cost between the logistics center and the distribution warehouse is related to the transportation volume and price. Constraint (5) indicates the relationship between transportation cost and demand volume. Constraint (6) shows that the emergency demand expectation obeys a normal distribution. Constraint (7) indicates that, at most, one facility is in the set of candidate sites for the distribution warehouse. Constraint (8) is used to constrain the maximum coverage of the distribution area of the distribution warehouse.
## 4 Case Study and Applications
### A Classification Problem: Binary Prediction of Smoker Status Using Bio-Signals  
In a manner analogous to the preceding case study, which was based on the solution of a regression problem, we will demonstrate the correct functioning of the library of neural networks created in Scala for the PSO algorithm and the DSPSO and DAPSO variants. The implemented classes build three neural networks that solve a classification problem using the Smokers dataset from Kaggle and construct three classifiers with the parameter values in Table 5. The first network is trained with PSO, the second with DSPSO, and the third with DAPSO and four SuperRDDs. This allow us to observe that the implemented networks function correctly and compare the efficiency of DAPSO and DSPSO with that of their sequential version.  
Accordingly, a dataset was selected from the Kaggle platform, corresponding to a competition where the objective is to predict the binary variable \"smoking\" using a set of biological characteristics, including the presence of caries, the levels of haemoglobin or triglycerides. The metrics to be used were accuracy, precision, recall, and F1-score.
# A Hybrid Adaptive Particle Swarm Optimization Algorithm for Enhanced Performance
## 3 Improved PSO Algorithm
### Complex Chaotic Mapping Initialization Population
Figure 1 illustrates the distribution of the composite chaotic mapping initialized population alongside that of the pseudo-random sequence initialized population. The distribution map clearly indicates that the method of initializing the population using composite chaos effectively integrates the advantages of Sine mapping and Logistic mapping. This approach significantly enhances the uniform distribution of the spatial probability density of the population and improves its spatial traversability [29]. In contrast, initializing the population with a traditional sequence of random numbers, despite demonstrating a high degree of randomness, results in particle aggregation. This aggregation can negatively affect the initial iterations of the algorithm, leading to premature convergence of the particles and potentially trapping them in local optimal solutions. Consequently, the use of composite chaotic mapping for population initialization markedly enhances the efficiency of the algorithmic optimization search and increases the diversity of the population.
## 7 Discussion  
The most distinctive feature of the proposed method in this paper is that the system automatically changes the file mode between the online mode and the local mode while monitoring the client's directory activation ratio. By controlling various parameter values, it is possible to flexibly adjust the time to change to the online and the local modes and the range of files to be changed. Table 4 summarizes the role of each parameter in the active file mode transition mechanism.  
An additional consideration here is to find the optimal parameter values to maximize the overall performance. In this paper, we investigate the file mode change performance in terms of two aspects of local storage usage ratio and file access delay, which are in a trade-off relationship with each other. For example, as the _OMT_ value increases, the local storage usage ratio improves because local mode files are easily changed to the online mode. On the other hand, as the number of online mode files increases, the average access delay of these files increases. Conversely, as the _OMT_ value decreases, the local storage usage ratio increases, but the access delay decreases. When the user considers these two criteria with equal weight, we need a method for finding optimal parameter values. Additionally, the user may wish to assign different weights to the two criteria. Some users may deem access delay as the more important factor compared to the local storage usage ratio, and vice versa. If an appropriate parameter value can be presented according to the weight of each performance criterion, we can extend the active file mode transition mechanism to a user customized service.
# Fractional Frequency Reuse Optimal SINR Threshold Selection Using NIR and ISODATA
## 2 Literature Review
### The Iterative Self-Organizing Data Analysis (ISODATA) Method  
The iterative self-organizing data analysis (ISODATA) is an unsupervised classification method useful in categorizing pixels of an undefined image. The pixels for a given image are grouped into specific categories based on their spectral values [16].  
Generally, in the ISODATA technique, an arbitrary threshold value is selected. Secondly, each pixel is classified according to where it falls on either side of the chosen threshold value. The mean values of each class, one class being above the threshold and the other below the threshold, are then estimated using a Gaussian distribution. Finally, the second and third steps are iterated until the change between the looping is infinitesimal [17].
## 3 Autostereoscopy-Raman Spectrometry-Based Measurement System
### Data Collection and Processing Procedure  
The digital reconstruction of the autostereoscopic 3D measurement method is actually a reconstructed process of the pixels from EIs, and it is able to provide a set of focus stack images reconstructed along the output plane. These images contain clearly focused images and defocused images. Depth information of objects can be obtained by the extraction of clearly focused information of the focal stack.  
In this proposed method and its configuration, accurate calibration of the autostereoscopic 3D system is required to obtain the absolute depth of the object. First, the resolution plate is placed at the working distance of the objective, and the elemental images are obtained. Use the above method to reconstruct the light field image. The clearest position is the position of the primary image plane of the microscope system, and its conjugate plane is the object plane at the working distance of the microscope objective. This position is determined to be the initial zero position. The autostereoscopic system calibration is completed. The positional relationship between the object plane and the image plane of the microscope system can be approximated by this formula [31]:  
\[\Delta X\approx\Delta V/M^{2}, \tag{6}\]  
where \(\Delta X\) is the moving distance of object surface, and \(\Delta V\) is the moving distance of image plane.  
The data collection and processing procedure of the proposed ARS system is shown in Figure 4. As the first step, the object to be detected needs to be placed within the working distance of the objective. The second step is to obtain the elemental images, and then obtain image sequences of depth slices. the clear parts of depth slices are extracted by calculating sharpness function of each pixel. Usually, there are multiple peaks in the sharpness evaluation value due to the influence of noise. When there are multiple peaks, it is necessary to traverse all the peak positions, collect multiple spectra and select the position with the strongest spectral signal as the target existence position. The optimal spectrum is then analyzed to obtain the substance composition. As shown in Figure 1, when there is only one peak, the spectrum of this position is directly collected for analysis.  
Figure 4: The data collection and processing procedure of the proposed ARS system.  
The proposed ARS system has a fast and large-depth detection scheme without scanning.  
Meanwhile, different from the Raman spectrometry system, there is no need to scan the whole detection depth to find out excitation position, which will be guided by the autostereoscopic signal of z-axis positioning. Accordingly, the material information of the foreign body can be quickly obtained, so as to predict the source of the substance. Different from the traditional autostereoscopic 3D measurement system, there is no need to use additional complex image process techniques to find out the fine-focused depth information, Raman spectrometry signal can provide effective and robust additional information to rule out the false peaks from noise.
## 3 Decremental Antenna Selection Algorithms  
Since the \(\ell_{0}\)-norm is non-convex and it has no accurate approximations, it is difficult to solve the optimization problem \(\mathcal{P}_{1}\). To our best knowledge, there exists no approach to minimize the linear combination of \(\ell_{0}\)-norm and \(\ell_{2}\)-norm. The optimal solution can be found by exhaustive searching \(2^{N}\) antenna subsets, _i.e._, solving the following NP-hard problem with \(2^{N}\) times,  
\[\mathcal{P}_{2}:\ \min_{\mathbf{w}\in\mathbb{C}^{N}} \|\mathbf{w}\|_{2}^{2}\] s. t. \[|\mathbf{w}^{H}\mathbf{h}_{i}|^{2}\geq\bar{\gamma}_{i},\quad i=1, \ldots,M \tag{4}\]  
The complexity is enormous when the BS is equipped with large-scale antenna arrays. An iterative SCA algorithm has been proposed by [PERSON] _et al._ to solve the problem \(\mathcal{P}_{2}\) effectively [PERSON] _et al._ (2015). In this sections, we try to find the near-optimal solution by jointly optimizing the antenna subset and its beamforming vector.  
**Theorem 1**.: _Let \(\mathrm{Y}\) and \(\Phi\) denote two antenna subsets with the relationship \(\mathrm{Y}\subset\Phi\). \(\mathbf{w}_{\mathrm{Y}}^{\text{opt}}\) and \(\mathbf{w}_{\Phi}^{\text{opt}}\) are the optimal beamforming vectors regarding to \(\mathrm{Y}\) and \(\Phi\), respectively. The minimal transmission power consumed by the antenna subset \(\mathrm{Y}\) is no less than that of \(\Phi\), i.e., \(\left\|\mathbf{w}_{\mathrm{Y}}^{\text{opt}}\right\|_{2}^{2}\geq\left\|\mathbf{w}_{ \Phi}^{\text{opt}}\right\|_{2}^{2}\)._  
Proof.: Let \(\mathbf{w}_{\Phi}=(\mathbf{w}_{\mathrm{Y}}^{\text{opt}};\mathbf{0})\), then the elements of beamforming vector corresponding to the antenna subset \(\Delta\)=\(\Phi-\mathrm{Y}\) equal zero. It is obvious that \(\left\|\mathbf{w}_{\mathrm{Y}}^{\text{opt}}\right\|_{2}^{2}=\left\|\mathbf{w}_{\Phi} \right\|_{2}^{2}\geq\left\|\mathbf{w}_{\Phi}^{\text{opt}}\right\|_{2}^{2}\).  
As can be seen from Theorem 1, with the shrinking of the antenna subset, the minimal transmit power consumption gets larger, while the circuit power consumption gets smaller. Therefore, we can determine a tradeoff between the circuit power and the transmit power through designing the decremental antenna selection algorithms. The initial antenna subset is defined as \(S_{(0)}\) with all the antennas selected. In each iteration, a new antenna subset is obtained from the previous subset according to different methods. The iteration of antenna selection continues when the total power consumption of the new antenna subset is less than the previous antenna subset. So, in each step of the antenna selection, we can make sure that the obtained new subset is superior to the previous subset and it is thus updated. Otherwise, when the total power consumption of the current iteration is no less than the previous subset, the antenna selection process is terminated. In this process, the total power consumption decreases step by step. Inspired by this fact, we proposed three decremental antenna selection algorithms based on the SCA approach.
# An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks
###### Abstract  
In Body Area Networks (BANs), how to achieve energy management to extend the lifetime of the body area networks system is one of the most critical problems. In this paper, we design a body area network system powered by renewable energy, in which the sensors carried by patient with energy harvesting module can transmit data to a personal device. We do not require any a priori knowledge of the stochastic nature of energy harvesting and energy consumption. We formulate a user utility optimization problem. We use Lyapunov Optimization techniques to decompose the problem into three sub-problems, i.e., battery management, collecting rate control and transmission power allocation. We propose an online resource allocation algorithm to achieve two major goals: (1) balancing sensors' energy harvesting and energy consumption while stabilizing the BANs system; and (2) maximizing the user utility. Performance analysis addresses required battery capacity, bounded data queue length and optimality of the proposed algorithm. Simulation results verify the optimization of algorithm.  
body area networks; energy management; energy harvesting; online resource allocation algorithm; Lyapunov optimization +
Footnote †: journal:  
_Article_  
An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks
# Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems
## 2 Vehicle Modeling
### Guidance Law
#### 3.2.2 Modified Proportional Law  
The guidance for the terminal phase of flight is formulated as a modified proportional law ruled by expression (20). Equation (21) calculates time to target, \(t_{g0}\). Guidance is activated only when the vertical coordinate of the line of sight vector is greater than a given constant (\(C_{2}\)) [1].  
\[\left[\begin{array}{c}\psi_{dem}\\ \theta_{dem}\end{array}\right]=\left\{\begin{array}{c}\frac{\overrightarrow {LOS_{w}}-\overrightarrow{\omega_{b}}t_{g0}}}{t_{g0}^{2}}\cdot\left[ \begin{array}{c}\overrightarrow{k_{w}}\\ -\overrightarrow{i_{w}}\end{array}\right]\text{ if }atan\frac{LOS_{3_{NED}}}{ \sqrt{LOS_{1_{NED}}^{2}+LOS_{2_{NED}}^{2}}}\leq C_{2}\\ \left[\begin{array}{c}0\\ 0\end{array}\right]\text{ else }\end{array}\right. \tag{20}\]\[t_{go}=max\left[\begin{array}{c}\frac{1}{\delta}\left(\overrightarrow{v_{w}} \rightarrowrightarrow\overrightarrow{j_{w}}+\sqrt{(\overrightarrow{v_{w}} \rightarrow\overrightarrow{j_{w}})^{2}+2 gLOS_{2_{w}}}\right)\\ \frac{1}{\delta}\left(\overrightarrow{v_{w}}\rightarrow\overrightarrow{j_{w} }-\sqrt{(\overrightarrow{v_{w}}\rightarrow\overrightarrow{j_{w}})^{2}+2 gLOS_ {2_{w}}}\right)\end{array}\right] \tag{21}\]  
Here, \(\overrightarrow{i_{w}},\overrightarrow{j_{w}}\), and \(\overrightarrow{k_{w}}\) represent the orthonormal basis of the working axes, and \(\overrightarrow{LOS_{w}}=[LOS_{1_{w}},LOS_{2_{w}},LOS_{3_{w}}]^{T}\) and \(\overrightarrow{LOS_{NED}}=[LOS_{1_{NED}},LOS_{2_{NED}},LOS_{3_{NED}}]^{T}\) are the vectors of the line of sight and its components in working and NED axes, respectively.
## 4 Results and Discussion
### Ablation Study of Core Components  
To evaluate the importance of individual components within the proposed localization framework, we conducted an ablation study by progressively removing or altering key modules. Each ablation scenario was tested under identical conditions using the LiDAR-based point cloud testing dataset described earlier (415 frames). The average localization error was recorded for each setting and compared to the full system.  
The results in Table 5 show that both the MJPF and the combined dictionary substantially enhance localization accuracy (the best result is highlighted in bold). Removing the MJPF leads to increased error due to the lack of stochastic reasoning across cluster transitions, while omitting the combined dictionary weakens the system's ability to handle simultaneous track observations and sensor dropout scenarios. The highest error occurs when both components are disabled, confirming their combined necessity.  
These findings reinforce the robustness and necessity of our proposed system architecture in ensuring reliable localization across diverse real-world scenarios.
## 2 Materials and Methods
### CFD Model Setup  
For the CFD simulation, the commercial software Ansys Fluent v.14.5 was utilized [28]. This software is widely recognized within the scientific community due to its reliability and precision. Its key features include a 3D double-precision solver, a pressure-based solver, steady-state analysis, and RANS (Reynolds-averaged Navier-Stokes) equations combined with the standard RNG k-\(\epsilon\) model [29]. The PISO algorithm [30] was employed for pressure-velocity coupling. Pressure interpolation schemes were chosen for the convection and viscous terms of the equations, utilizing second-order discretization schemes. During the simulation, continuity, the turbulence kinetic energy (k), and momentum (in the x, y, and z directions) were computed until all scaled residuals stabilized at a minimum value of \(10^{-5}\). Velocity and temperature were monitored at various points throughout the solving process to ensure convergence.  
The model and its boundary conditions accurately reflect the actual architectural structure in terms of shape and size. As illustrated in Figure 5, the computational domain is represented as a rectangular prism with dimensions of 4.05 \(\times\) 8.35 m and a vaulted ceiling with a maximum height of 4.68 m. A uniform velocity field was applied at the inlet (the building's door), with no-slip conditions enforced on all solid walls. The boundary conditions on walls adjacent to the external environment were set to variable temperatures within a stabilized periodic thermal regime. The flooring was treated as adiabatic, given its direct contact with the ground without any underground cavities. Lastly, an outlet boundary condition with uniform zero relative pressure was applied at the exit plane (the roof light of the building). The main characteristics of the building materials were also defined: the opaque walls have a density of 2800 kg/m\({}^{3}\), a specific heat capacity of 1000 J/kgK, and a thermal transmittance of 1.4 W/mK; the transparent roof light has a density of 120 kg/m\({}^{3}\), a specific heat capacity of 1000 J/kgK, and a thermal transmittance of 1 W/mK.