esa-sceva/satcom-chunk-collection huggingface dataset:
# The Optimization of Covert Communication in Asymmetric Jammer - Assist Systems
## 4 Analysis of the Transmission Rate
### Derivation of Mutual Information at Bob  
For the two cases we mentioned, the mutual information in (6) can be expressed as  
\[\mathbb{I}(Zb^{n};D^{n})=n\mathbb{I}(Z_{b};D). \tag{37}\]  
Considering the codebook in Section 2.2, we have  
\[\mathrm{I}(Z_{b};D)=\mathrm{H}(Z_{b})-\mathrm{H}(Z_{b}|D) \tag{38}\] \[=-\iint p_{b}(x,y)\log p_{b}(x,y)\mathrm{d}x\mathrm{d}y+\iint \sum_{i=1}^{2}\frac{1}{2}p_{b,i}(x,y)\log p_{b,i}(x,y)\mathrm{d}x\mathrm{d}y\] (39) \[=-\iint\sum_{j=1}^{2}\frac{1}{2}p_{b,j}(x,y)\log\Big{[}\sum_{i=1} ^{2}\frac{1}{2}p_{b,i}(x,y)\Big{]}\mathrm{d}x\mathrm{d}y\] \[\qquad+\iint\sum_{j=1}^{2}\frac{1}{2}p_{b,j}(x,y)\log p_{b,j}(x,y )\mathrm{d}x\mathrm{d}y\] (40) \[=-\iint\sum_{j=1}^{2}\frac{1}{2}p_{b,j}(x,y)\log\bigg{[}\frac{ \sum_{i=1}^{2}\frac{1}{2}p_{b,j}(x,y)}{p_{b,j}(x,y)}\bigg{]}\mathrm{d}x\mathrm{ d}y, \tag{41}\]  
where  
\[p_{b,1}(x,y) =\frac{1}{2\pi\sigma_{b}^{2}}\Bigg{[}\exp\bigg{(}-\frac{(x+h_{b, x}\beta)^{2}}{2\sigma_{b,x}^{2}}-\frac{(y+h_{b,y}\beta)^{2}}{2\sigma_{b,y}^{2}} \bigg{)}\Bigg{]}, \tag{42}\] \[p_{b,2}(x,y) =\frac{1}{2\pi\sigma_{b}^{2}}\Bigg{[}\exp\bigg{(}-\frac{(x-h_{b, x}\beta)^{2}}{2\sigma_{b,x}^{2}}-\frac{(y-h_{b,y}\beta)^{2}}{2\sigma_{b,y}^{2}} \bigg{)}\Bigg{]}. \tag{43}\]  
Performing Taylor expansion, we have  
\[\log\bigg{[}\frac{\sum_{i=1}^{2}\frac{1}{2}p_{b,i}(x,y)}{p_{b,1}(x,y)}\bigg{]} =\beta\Big{(}\frac{h_{b,x}x}{\sigma_{b,x}^{2}}+\frac{h_{b,y}y}{\sigma_{b,y}^{2 }}\Big{)}+\beta^{2}\Big{(}\frac{h_{b,x}^{2}x^{2}}{2\sigma_{b,x}^{4}}+\frac{h_{ b,y}y^{2}}{2\sigma_{b,y}^{4}}+\frac{h_{b,x}h_{b,y}xy}{\sigma_{b,x}^{2}\sigma_{b,y}^{2 }}\Big{)}+\mathcal{O}(\beta^{3}), \tag{44}\]\[\log\left[\frac{\sum_{i=1}^{2}\frac{1}{2}p_{b,i}(x,y)}{p_{b,2}(x,y)}\right]=-\beta \Big{(}\frac{h_{b,x}x}{\sigma_{b,x}^{2}}+\frac{h_{b,y}y}{\sigma_{b,y}^{2}}\Big{)} +\beta^{2}\Big{(}\frac{h_{b,x}^{2}x^{2}}{2\sigma_{b,x}^{4}}+\frac{h_{b,y}y^{2}} {2\sigma_{b,y}^{4}}+\frac{h_{b,x}h_{b,y}xy}{\sigma_{b,x}^{2}\sigma_{b,y}^{2}} \Big{)}+\mathcal{O}(\beta^{3}). \tag{45}\]  
With some calculations, we can obtain  
\[\iint\sum_{j=1}^{2}\frac{1}{2}p_{b,j}(x,y)\log\left[\frac{\sum_{i =1}^{2}\frac{1}{2}p_{b,j}(x,y)}{p_{b,j}(x,y)}\right]\!\mathrm{d}x\mathrm{d}y\] \[=-\Big{(}\frac{h_{b,x}^{2}\beta}{\sigma_{b,x}^{2}}+\frac{h_{b,y}^ {2}\beta}{\sigma_{b,y}^{2}}\Big{)}\beta+(\frac{h_{b,x}^{2}}{2\sigma_{b,x}^{2 }}+\frac{h_{b,y}^{2}}{2\sigma_{b,y}^{2}})\beta^{2}+\mathcal{O}(\beta^{3}) \tag{46}\] \[=-\Big{(}\frac{h_{b,x}^{2}}{2\sigma_{b,x}^{2}}+\frac{h_{b,y}^{2} }{2\sigma_{b,y}^{2}}\Big{)}\beta^{2}+\mathcal{O}(\beta^{3}), \tag{47}\]  
and the mutual information can be expressed as  
\[\mathbb{I}(Z_{b};D)=\Big{(}\frac{h_{b,x}^{2}}{2\sigma_{b,x}^{2}}+\frac{h_{b,y }^{2}}{2\sigma_{b,y}^{2}}\Big{)}\beta^{2}+\mathcal{O}(\beta^{3}). \tag{48}\]
## 3 Optimization System  
Ensuring the chemical and thermal homogeneity of the processed bath is of crucial importance for the processes carried out in electrode furnaces. When developing the furnace optimization system discussed in the paper, the authors assumed that the target optimization criterion that would well describe this feature of the furnace would be the average bath flow velocity. Of course, as intuition suggests, increasing the flowing current intensity will intensify the phenomena of natural and forced convection affecting this quantity. However, the optimization problem described in this paper concerns the modification of the furnace design (more precisely, the geometry of electrodes), assuming an unchanged electrical power supplied to the furnace.  
The standard, full optimization loop (Figure 5, the green path) for such a task shown in the figure includes, in the first step, the creation of an electromagnetic submodel of the analyzed furnace solution for a given point of the solution space (provided in the form of parameters).  
Once the electromagnetic submodel has performed its calculations, the distributions of the Joule heat power density and Lorentz force fields are passed to the hydrodynamic model. On this basis, the HD model calculates the velocity field in a steady simulation. The spatial velocity distribution is subjected to a simple analysis providing a target criterion: the average bath velocity. This value is passed to the optimization algorithm, which modifies the parameters starting the next optimization cycle. However, this relatively simple solution is hindered by the substantial computational time required for hydrodynamic calculations, which can exceed several hours for the 3D process under consideration. Even when restricting the optimization to local algorithms, several hundred iterations are typically needed to find an optimal solution. A simple calculation tells us that optimizing the furnace with this approach will take months.  
Figure 5: Standard and accelerated optimization loops.  
The solution to this problem proposed in this paper is to use a surrogate criterion whose value can only be determined based on the electromagnetic submodel. Since the computation time in such a model is several dozen seconds, a radical reduction of the entire optimization time is achieved. Such an accelerated optimization loop is shown in Figure 5.
# A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network
## 4 Partitioned CenterPose Network
### Network Architecture  
In the PCP Network, a convolutional backbone network is applied for feature extraction. Then, we use three sets of prediction heads (body center prediction head, offset prediction head, and body joint prediction head) to process the output features. First, we will discuss the structure of the offset prediction head. In PPR, the offset vector is the key to connecting an instance of a person with their body joints; as such, it is very important to obtain an accurate offset vector. Directly regressing the value of an offset vector is inefficient as it is a highly non-linear task and difficult to learn the mapping [3]. Inspired by [13], we use two associative embedding maps to record the vector value of each offset. As shown in Figure 3, the output of the backbone is passed through two parallel branches. The output channel of the first branch is twice the number of part centers, which focus on the 2D vector value of the offset from the body center to the part centers. The second branch looks at the offset of the remaining joints to the part center. Then, we concatenate the output of these two branches and pass it through a simple convolutional module to acquire the final embedding maps. When the coordinates of the body center or part center are obtained, the feature value of the embedding map at this position can be regarded as the corresponding offset vector value. In the body center prediction head, follow the approach used by CenterNet [17], we use a simple convolutional module, which contains only a separate 3 \(\times\) 3 convolution, ReLU, and a 1 \(\times\) 1 convolution, to predict the body center and the bounding box using two parallel branches. The body joint prediction head  
Figure 3: The architecture of the Partitioned CenterPose Network. A convolutional backbone network applies three sets of prediction heads to predict instance location, joint offset, and joint heatmap. The final output is generated by combining these three prediction results.  
estimates a heatmap of each body joint \(\left(x_{n}^{k},y_{n}^{k}\right)\) using the same structure as used for the body center prediction head to reduce computational complexity.
## 3 Proposed Antenna
### Measurement Results with and without the Superstrate
Figure 7: Normalized elevation cuts for the single element at 14 GHz in the measurements (solid curves) and simulation (dashed curves) without the superstrate (left) and with the superstrate (right) for (**a**) Elevation cut \(\Phi=0^{\circ}\), (**b**) Elevation cut \(\Phi=0^{\circ}\), (**c**) Elevation cut \(\Phi=90^{\circ}\), and (**d**) Elevation cut \(\Phi=90^{\circ}\). elevation cuts at \(\Phi=0^{\circ}\) and \(\Phi=90^{\circ}\). In general, there is a good correlation between the experimental and simulation results in this case.
## 3 Method
### Retriever Consensus Confidence Fusion (RCCF)
#### 3.3.4 Algorithm Implementation  
Algorithm 1 presents the complete RCCF fusion procedure, demonstrating the practical implementation of our consensus-based approach.  
```
0: Query \(q\), retriever result lists \(\{R_{1},R_{2},\ldots,R_{n}\}\), parameters \(\{\alpha,\beta,\gamma\}\)
0: Fused document ranking list with confidence scores
1: Initialize candidate document set \(D=\bigcup R_{i}\)
2: Normalize scores for each retriever
3:for each document \(d\in D\)do
4: Compute rank consistency \(C_{\text{rank}}(d)\)
5: Compute score consistency \(C_{\text{score}}(d)\)
6: Compute overall consensus \(C(d)=\alpha\cdot C_{\text{rank}}(d)+(1-\alpha)\cdot C_{\text{score}}(d)\)
7: Compute adaptive weights \(w_{i}(d)\) for \(i=1\ldots n\)
8: Compute RCCF score \(\text{Score}_{\text{RCCF}}(d)\)
9: Compute confidence Confidence(\(d\))
10:endfor
11: Sort documents by \(\text{Score}_{\text{RCCF}}(d)\) in descending order
12:return Ranked document list with corresponding confidence scores
```  
**Algorithm 1** RCCF fusion algorithm.
## 2 Materials and Methods  
### Preparation of Samples  
Wild red deer (_Cervus elaphus_) meat used in this study was sourced from the local processing plant \"VADEX\" Mezofoldi Zrt. in Hungary. The wild red deer, which were hunted in Western Hungary, included both males and females and were aged between 4 and 6 years. Fresh meat samples were packed in low-density polyethylene pouches, transported to the laboratory under chilled conditions, and stored at 4 \(\pm\) 1 \({}^{\circ}\)C for one day. _Semimelranosus_ muscles were dissected from 12 individual deer. Each muscle was cut into four steaks of similar sizes (approximately 10 \(\times\) 5 \(\times\) 1.5 cm), resulting in a total of 48 steaks. These steaks were randomly divided between two treatment groups: 24 steaks were treated with a 2% lactic acid and 2% ascorbic acid mixture, and 24 steaks were left non-treated as control samples.  
Steaks in the control group were vacuum-packed and kept in a refrigerated cabinet at 4 \(\pm\) 1 \({}^{\circ}\)C. For the treatment group, individual meat steaks were placed inside vacuum bags, and a water-based mixture containing 2% lactic acid and 2% ascorbic acid was sprayed inside each bag using a manual sprayer. The applied solution concentration was 1% in relation to the initial meat weight, with 4 g of solution used for every 400 g of meat. After treatment, all samples were vacuum-packed using a C200 vacuum packer (Multivac Ltd., Geprufte Scherhert, AGW, Wolfertschwenden, Germany) and stored at 4 \(\pm\) 1 \({}^{\circ}\)C for 21 days. The vacuum packaging material used was PA/PE 90, with a moisture vapor permeability of 2.6 g/m\({}^{2}\) per day, oxygen permeability of 50 cm\({}^{3}\)/m\({}^{2}\), carbon dioxide permeability of 150 cm\({}^{3}\)/m\({}^{2}\), and nitrogen permeability of 10 cm\({}^{3}\)/m\({}^{2}\). The assessment of quality parameters occurred on days 1, 7, 14, and 21 during the storage period.
# Multi-Connectivity for Multicast Video Streaming in Cellular Networks
## 8 Conclusions  
In this paper, we propose leveraging multi-connectivity (MC) for multicast transmissions and prove that it results in significant performance enhancements for multicast streaming services. We address the resource allocation problem in MC multicasting, aiming to maximize the number of concurrently served users and prove its NP-hardness. Our proposed centralized greedy approximation (CGA) algorithm for MC multicast resource allocation achieves an approximation ratio of \((1-1/e)\). For delay-sensitive applications where centralized resource allocation might become infeasible, we propose a distributed greedy allocation (DGA) algorithm that enables MC multicasting without coordination between base stations. We show that, despite its distributed nature, the DGA algorithm results in just 0.3% more packet loss compared to the centralized policy. Using rigorous simulations, we conclusively demonstrate that employing multi-connectivity in multicast transmissions results in increased user coverage and reduced packet losses. Furthermore, we evaluated the efficacy of our algorithms in real-time video streaming applications, utilizing traces from authentic video streams to generate realistic traffic patterns. Performance comparisons of the CGA algorithm's solution with the optimal solution obtained for a smaller problem size using brute force show that they match. We also demonstrated that  
Figure 7: Comparisons of (**a**) the average number of packets successfully delivered (out of 133,121) and (**b**) the average number of users left unserved under MC multicasting and MBSFN while transmitting a real-time video stream. Realistic video traffic patterns were generated using traces of a video of the Tokyo Olympics [11].  
MC multicasting outperforms MBSFN, eliminating the need for strict synchronization and extended cyclic prefixes.
# Comparing the Measured and Thermodynamically Predicted AFm Phases in a Hydrating Cement
## 6 Conclusions  
Results from 27 cement samples with three w/c ratios at 1, 3 and 28 days has shown that, due to a number of reasons, such as kinetics, thermodynamic equilibrium and the faster dissolution of calcite than measured, monocarbonate is often predicted to form over hemicarbonate, regardless of w/c ratio. Additionally, the overriding stability of monocarbonate in thermodynamic models also leads to its predicted formation, even though hemicarbonate is observed in experimental measurements. Furthermore, while hemicarbonate is predicted to form in OPC models, it only appears in systems with <2 wt.% of calcite, where it is replaced by monocarbonate. In the XRD readings above, hemicarbonate is shown to exist in a CEM cement with a calcite content of just under 5 wt.%.  
During early hydration, AFt and AFm phases are predicted to precipitate. This is confirmed thermodynamically as the immediate dissolution of SO3, Na2 SO4 and K2 SO4 partly precipitates as gypsum in the first hours of hydration. After approximately two days, monosulfate and hemicarbonate phases are expected to form due to their sulfate (SO3/Al2O3) and carbonate CO2/Al2O3) ratios. However, the thermodynamic model only predicts monocarbonate is calculated to equilibrate.  
Figure 16: Typical thermodynamic phase assemblages for (**a**) 0%, (**b**) 2% and (**c**) 5% calcite.  
**Author Contributions:** Conceptualization, N.H. and M.T.; methodology, M.R. and G.D.; software, N.H.; validation, M.T. and M.R.; formal analysis, N.H., M.T. and M.R.; investigation, N.H.; data curation, N.H. and G.D.; writing--original draft preparation, N.H.; writing--review and editing, M.T. and M.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Funding to publish this work was from a US-Ireland grant tri-funded by the National Science Foundation (NSF, 1805818), Science Foundation Ireland (SFI, 17/US/3424), and the Department for the Economy of Northern Ireland (DfE, USI 127).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to acknowledge the valuable advice provided by [PERSON] during the production of this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Methodology  
### Analytical Model  
The dynamic tunnel-soil-tunnel interaction model, as shown in Figure 1, consists of twin flexible tunnels, infinitely long in the z-direction, embedded in a half-space. The z-axis of the rectangular coordinate system is perpendicular to the paper (points to the reader). The twin tunnels are circular with the centers at points of o\({}_{1}\) and o\({}_{2}\), and the outer radius and inner radius are \(a\) and \(b\) (\(b=0.9a\)), respectively. With the points of o\({}_{1}\) and o as the origin, there are two sets of coordinate systems. The rectangular coordinates and the corresponding cylindrical coordinates are drawn. The half-space and the two tunnels are assumed to be linearly elastic, uniform, and isotropic, and the boundary between the tunnel and the half-space is assumed to be tightly connected. The half-space is marked with shear wave velocity \(\beta\), mass density \(\rho\), and shear modulus \(\mu\). The tunnels are marked with shear wave velocity \(\beta_{0}\), mass density \(\rho_{0}\), and shear modulus \(\mu_{0}\). The distance between the center \(o_{1}\) and \(o_{2}\) is s. The burial depth of the tunnels is \(D\). An excitation is an incident plane SH-wave with circular frequency \(\omega\), incidence angle \(\gamma\) measured from the vertical z-axis, and unit amplitude. To reduce the complexity of the problem and obtain the analytical solution to the problem, the influence of underground water is ignored.
# A New Passive Islanding Detection Solution Based on Accumulated Phase Angle Drift
## 5 Simulation Test
### Sensitivity-Reactive Power Imbalance  
The reactive power imbalance sensitivity tests are based on the default settings: \(\Delta Ang\_set=1.0^{\circ}\), \(\sum Ang\_set=18^{\circ}\), \((df/dt)\_set=0.5\) Hz/s.  
Before the islanding condition, the active power is well balanced between SM and local load. The reactive power imbalance varies from 100% surplus to \(-150\%\) deficit. The same test cases are also applied into the conventional VVS relay with angle setting as \(6^{\circ}\) and ROCOF relay with settings as 0.5 Hz/s and 0.5 s time delay, to compare the sensitivity of different passive islanding detection methods. Totally 8 test cases are created and the test results based on the sensitive settings of PAD solution are shown in Table 4.  
The reactive power imbalance does not have direct influence on the frequency of SM. However, reactive power imbalance has influence on the terminal voltage of SM and hence has the indirect  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reactive Power** & **PAD Trip Time (ms)** & **VVS Trip Time (ms)** & **ROCOF Trip Time (ms)** \\
**Imbalance** & **with Default Settings** & & \\ \hline
100\% & 260 & No trip & No trip \\
90\% & 280 & No trip & No trip \\
80\% & 300 & No trip & No trip \\
70\% & 340 & No trip & No trip \\
60\% & 380 & No trip & No trip \\
0–50\% & No trip & No trip & No trip \\ \(-120\%\)–0 & No trip & No trip & No trip \\ \(-130\%\) & 380 & No trip & No trip \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sensitivity performance comparison with default settings under reactive power imbalance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Active Power** & **Trip Time (ms)** & **Trip Time (ms)** & **Minimum Angle** & **Duration (ms)** \\
**Imbalance** & **Default Settings** & **Stable Settings** & **Change (\({}^{\circ}\))** & **for 1 ROCOF I** \\  & & & & **> 0.5 Hz/s & **> 1.0 Hz/s** \\ \hline
50\% & 180 & 280 & 6.9 & 540 & 480 \\
40\% & 200 & 320 & 5.5 & 520 & 440 \\
30\% & 240 & 360 & 4.12 & 480 & 380 \\
20\% & 280  
influence on the dynamic behavior of voltage-dependent loads, which causes the dynamic frequency response of SM. So, the passive methods, PAD, VVS, and ROCOF, are all not very sensitive to the reactive power imbalance. Compared with VVS and ROCOF relays, the proposed PAD solution has the better sensitivity and faster tripping time.
# Adaptive Virtual RSU Scheduling for Scalable Coverage under Bidirectional Vehicle Traffic Flow
## 6 Performance Evaluation
### RSU Allocation
#### 6.2.1 Impact of RSU Numbers  
To analyze the impact of RSU distribution, we evaluate these strategies with RSU numbers \(m=10\), \(m=20\), and \(m=40\), respectively. As to the vehicle density, we assume that the number of vehicles in each region \(\{\lambda_{1},\lambda_{2},...,\lambda_{100}\}\) follows the normal distribution with \(\sigma=10\) and expectation \(\mu\) from 100 to 300. The relative connectivity is used as the performance metric to compare the three strategies. In Figure 9a, we can see that the difference among these strategies is minor with a few number of RSUs. As the number of RSUs grows, the performance gap is increased. In Figure 9c, the number of RSU is \(m=40\), and the performance our proposed bidirectional allocation strategy is much better than the other two strategies, especially with the low vehicle density. Generally, the Top-K strategy has a better performance compared with the uniform distribution strategy, as the Top-K strategy always allocates the RSUs in the regions with the highest vehicle density. Nevertheless, the performance difference is reduced as the number of RSUs keeps growing. We can conjecture that there would be more opportunity for vehicle-to-vehicle communication when the region has a higher vehicle density. Therefore, our Top-K strategy can hardly improve the global connectivity through naively allocating the RSUs in the regions with highest vehicle density.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Configuration** \\ \hline Number of regions & 100 \\ Area of each region & \(1000\times 1000\) m\({}^{2}\) \\ Number of vehicles expectation in each region & \(100\leq\mu\leq 300\) \\ Number of vehicles variance in each region & \(5\leq\sigma\leq 30\) \\ Communication range & 400 m \\ Number of replicas & 1000 \\ Skewness range & \(0.271\leq s\leq 1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Region-level parameter configuration.  
Figure 8: Skewness with different parameter \(s\).
# Peak Energy Reduction in Flow Shop including Switch-Off Policies and Battery Storage
## 2 Literature Review
Therefore, this paper studies the impact of the battery characteristics to reduce the influence of energy use over the peak power constraint by first asking:  
RQ1: _What is the impact of the energy storage system characteristics to reduce the energy use over the peak power constraint?_  
The second research question regards the integration of the energy storage system with the main switch-off policies proposed in the literature, asking:  
RQ2: _Can the switch-off policies significantly improve the use of the energy storage system to reduce the energy use over the peak power constraint?_  
Simulation models are developed to answer our two research questions. By considering different battery characteristics (capacity and charge rate), two switch-off policies and three levels of power fluctuations of the manufacturing operations provide information to support decisions in energy-saving costs. The following sections outline the reference context, the simulation model, and the numerical experiments conducted.