esa-sceva/satcom-chunk-collection huggingface dataset:
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 4 Simulation Experiment and Theoretical Analysis
### Performance Comparison
The method in ([PERSON] et al., 2019), the data are first embedded into the pixel position close to the peak. The method is that three encrypted adjacent pixels are selected as a group, and each group calculates two absolute differences, which are sorted according to the sum of two absolute differences of each group. The image distortion is reduced, thus its performance is generally higher than the scheme in our paper. The method in ([PERSON] et al., 2019) uses the histogram shrinking to reserve the embedding space. However, the distortion caused by this method increases with the increase of the embedding capacity. The method in ([PERSON] et al., 2019) is the same as the one in ([PERSON] et al., 2019). As the embedding capacity increases, the number of histogram shifts increases when the data are self-embedded. In addition, the distortion introduced by constructing MCGs is also getting larger and larger, which leads to faster performance degradation of the algorithm. In general, the performance of our scheme is better than the one in ([PERSON] et al., 2019; [PERSON] et al., 2019) when the embedded capacity is high. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Literature**} & \multicolumn{3}{c}{**Features**} \\ \cline{2-4}  & **Separable** & \
\begin{tabular}{c} **Computational Complexity** \\ **of Data Hiding** \\ \end{tabular} &
\begin{tabular}{c} **Protection Mechanism of** \\ **the Hidden Data** \\ \end{tabular} \\ \hline Method in ([PERSON] et al., 2019) & Yes & \(O(k^{3})\) & WPC mechanism \\ Method in ([PERSON] et al., 2019) & Yes & \(O(k)\) & Homomorphic and probabilistic \\ Method in ([PERSON] et al., 2019) & Yes & \(O(k)\) & Mechanism of Paillier \\ The proposed scheme & Yes & \(O(k)\) & SHE(R-LWE-based) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of RDH-HEI.
# YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition
## 4 Experimental and Results Analysis
### Results Analysis
#### 4.2.3 Ablation Test  
To evaluate the impact of various modules in the proposed method, we conducted ablation experiments on the SIMD dataset. The experimental results are shown in Table 5.  
Exp 1 represents the original YOLOv8 model without any modifications. Exp 2 introduces the SEF module on top of YOLOv8. Exp 3 incorporates the SPPFE module, Exp 4 enhances the WIoU loss function, and Exp 5 integrates the TPH module, all built upon the YOLOv8 framework. The results indicate that the SEF module, the SPPFE module, the transformer predict head (TPH), and the introduced WIoU loss function all contribute to improving the algorithm's detection performance.  
We conducted a total of four sets of experiments, and from Table 5, it is evident that the SEF module has the most significant impact on improving the algorithm's detection performance, with an increase of 0.9% in AP50. The SPPFE module has improved by 0.2%. The WIoU loss function and TPH module led to improvements of 0.2% and 0.3% in AP50, respectively.
# Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms
## 3 Hierarchical Task Assignment and Path Finding
### HTAPF: Principles and Methods
#### 3.2.2 Decentralized Area Assignment
(2018), we have weighted interactive and spontaneous processes by two free parameters, respectively, \(h\) and \(k\), used to define the ratio \(h/k\) between interactive and spontaneous processes. As shown by [PERSON] et al. (2018), in the simple non-hierarchical case, changing this ratio allows switching between a utility-proportional deployment where robots are allocated to areas proportionally to their utility when the ratio is low, to a utility-responsive collective decision where the optimal number of robots is allocated to the area that currently has the highest utility (higher values for \(h/k\)). Indeed, when \(h=0\), there is no recruitment or inhibition, and only commitment or abandonment are considered, which are driven directly by knowledge of the area utility. Relying more on interactive transitions allows to make a collective decision towards areas of higher utility, while balancing the deployment to avoid overcrowding, thanks to self-inhibition. In this study, we consider \(h/k=0.25\) (\(h=0.2\), \(k=0.8\)), which favors spontaneous transitions while still exploiting interactions to collectively choose the best area where to service tasks.
## 4 Mathematical Analysis  
### Robustness  
In RRSS, after sending a data packet to the best next-hop, the sender waits for a reply message from the receiver. If the receiver fails to forward the data packet, it will not send the reply packet. If the sender does not receive the reply message, it will delete the routing, then select the best next-hop among the candidates again. When all the candidate nodes fail to send reply messages, we think the delivery fails. We assume the density of nodes is \(n\) (the number of nodes per square meter). The volume of the candidate area is related to the communication range of the sender and the distance from the sender to the sink node. In order to facilitate analysis, we put the area of interest into a 3D coordinate system.  
As in Figure 8, the coordinate of the sink node is (0, 0, 0), and the coordinate of the sender is (0, 0, \(-L\)). Candidate nodes are in the shaded part; the number of candidate nodes is \(N\). The volume of the shaded part can be divided into two parts, \(V_{1}\) and \(V_{2}\). \(h_{1}\) and \(h_{2}\) are the height of two ball-lackings. Then we obtain Equation (9).  
\[V=V_{1}+V_{2}=\frac{1}{3}\pi(3R-h_{2})\times h_{2}^{2}+\frac{1}{3}\pi(3L-h_{1 })\times{h_{1}}^{2} \tag{9}\]  
Figure 7: Void-avoiding algorithm.  
The average distance from the sender to the next-hop node is \(l\). The distance from the source node to the sink node is denoted by \(S\). The hop number from source to sink is \(N_{H}=\frac{S}{d}\). \(N_{i}\) denotes the number of candidate nodes at the \(i\)th hop. Therefore, the probability of a data packet being successfully delivered to a sink node is \(P\).  
\[P=\prod_{i=1}^{N_{H}}p_{i}^{\prime}=\prod_{i=1}^{N_{H}}\left(1-\left(1-p\right) ^{N_{i}}\right) \tag{15}\]
## 2 Experimental Investigation
### Experimental Test Setup and Loading Procedure  
The experiment setup was illustrated in Figure 3. The CFST column was horizontally connected to the reaction wall through two hinge supports. For simulating seismic loading, one MTS hydraulic actuator with a 500 KN capacity was adopted to apply the cyclic load to the beam end. For reflecting the axial pressure of the CFST column in actual engineering, the axial load was applied at the top of the column by a hydraulic jack. The axial load level (n) is 0.3 for the CFST column, which is defined as n = N/N\({}_{\text{u}}\), where N is the axial load applied to the column and \(N_{\text{u}}\) is the nominal ultimate axial compressive load of the CFST column predicted by the specification [19]. The distance from the loading point on the beam to the column flange was 1.2 m. The cyclic load is controlled through the layer angular displacement, which is the ratio of the horizontal displacement of the beam end to the loading point distance, which was 1.2 m, as mentioned above. The loading procedure suggested by the SAC Joint Venture (1997) [20] for cyclic testing was adopted and is summarized in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Steel Coupons** & **Steel Wall Thickness(mm)** & \(E_{\text{s}}\) **(GPa)** & \(f_{\text{y}}\) **(MPa)** & \(f_{\text{u}}\) **(MPa)** & \(\delta\) **(\%)** \\ \hline End plate-1 & 12 & 197.29 & 399.2 & 562.8 & 25.7 \\ \hline End plate-2 & 24 & 198.26 & 408.5 & 534.5 & 28.7 \\ C-shaped channel & 7 & 192.29 & 395.2 & 567.8 & 27.7 \\ Steel tube & 5 & 208.75 & 477.1 & 609.2 & 21.3 \\ Beam web-1 & 7 & 202.23 & 412.6 & 593.6 & 24.83 \\ Beam web-2 & 6 & 192.05 & 254.1 & 397.0 & 30.0 \\ Beam flange-1 & 11 & 202.02 & 412.4 & 591.5 & 18.6 \\ Beam flange-2 & 9 & 197.41 & 269.7 & 405.7 & 26.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of the steel components.  
Figure 3: Test setup. (**a**) Schematic of test setup; (**b**) picture of test setup.
# Leaking Gas Source Tracking for Multiple Chemical Parks within An Urban City
## 2 Generation of Sensors Data by AERMOD
### Basic Features of AERMOD  
The complete AERMOD modeling system consists of two preprocessors and the diffusion model itself. AERMOD meteorological preprocessor (AERMET) is a stand-alone program which provides the state of the surface and the mixed layer, and the vertical structure of the PBL (planetary boundary layer). The AERMOD mapping program (AERMAP) is a stand-alone terrain preprocessor, which is used to both characterize the terrain and generate receptor grids for AERMOD.  
Different from an ordinary Gaussian diffusion model, AERMOD incorporates a building downwash algorithm called PRIME. In this way, AERMOD has the ability to handle diffusion problems within complex terrain with fast inference speed. Its features are summarized as follows:  
* AERMOD is easy to be implemented on computing devices.
* AERMOD provides robust and reasonable concentration predictions under plenty of circumstances.
* AERMOD can effectively handle gas diffusion problems with complex terrain such as factories and buildings.
## 2 Methodology
Since the electricity generating sources have a significant influence on indirect emissions and consequently on TEWI, four countries where the electricity is generated using different sources are selected in this study. These countries include Brazil, Sweden, Canada and Poland, where \(\beta\) values are 0.074, 0.012, 0.130 and 0.791 kgCO\({}_{2}\)-eq. kWh\({}^{-1}\), respectively [28]. As shown in Figure 2, in Sweden, the main sources of electricity generation are clean and renewable energies, resulting in much lower emissions than countries such as Poland, where coal is the main source of electricity generation. To investigate the warming impact of the heat pump system, the TEWI metric is employed in this study. The TEWI, a commonly used metric to investigate the GHGs missions in RACHP, was developed by the US Department of Energy and Oak Ridge National Laboratories [13]. The TEWI is comprised of two parts, including the direct and indirect emissions of GHGs. The accidental leakage of refrigerants cause direct emissions, and the electricity consumption causes indirect emissions during the refrigeration system operation. The TEWI is calculated as shown in Equation (1) [30]:  
\[TEWI=Direct\ emissions+Indirect\ emissions \tag{1}\]  
\[Direct\ emissions=GWP\cdot L\cdot n+GWP\cdot m\cdot\left(1-\alpha_{recovery}\right)\]  
\[Indirect\ emissions=n\cdot E_{annual}\cdot\beta\]  
where \(L\), \(n\), \(m\), \(\alpha_{recovery}\), \(E_{annual}\), and \(\beta\) are leakage (kg per year), system operating time (year), refrigerant charge (kg), recovery factor (%), annual energy consumption (kWh) and carbon emissions factor (kgCO\({}_{2}\)-eq. kWh\({}^{-1}\)) related to electricity generation, respectively. It is noted that the TEWI is subjected to a certain amount of uncertainty [31; 32]. It is due to the large degree of uncertainty and assumptions related to the TEWI parameters. Figure 1: Pressure-enthalpy diagrams of the alternative low-GWP refrigerants and reference fluids for (**a**) MTHP and (**b**) HTHP.
# Routing Attacks Detection in 6 LoWPAN-Based Internet of Things
## 4 Proposed Combination Approach for an IoT Intrusion Detection System
### Feature Selection and Extraction  
The IoT is composed of smart devices that have limited processing power, memory, energy, and communication range. One of the main challenges for IDS in this ecosystem is dealing with numerous irrelevant features that can cause overhead on the system. It is widely known that redundant and irrelevant features can lead to a low detection rate. Therefore, the objective of feature selection is to identify the most significant features that can be used in the IDS to detect various attacks effectively. The features are analyzed for both normal and abnormal behaviors using the extracted labels, and an information gain method is applied for feature selection. The information gain technique has a fast execution time, and it extracts the best-performing feature set for the specific type of model. In previous studies, information gain has been regularly used to assess how well each distinct attribute separates the given dataset. The overall entropy \"\(I\)\" of a given dataset \"\(S\)\" is calculated using this method.  
According to [PERSON]'s work from 2010, the total entropy, denoted as \(I\), of a particular set of data, \(S\), can be defined as follows. The following formula represents the entropy of a system \(S\), where \(c\) is the number of classes in the system, and \(pi\) is the proportion of elements belonging to the \(i\)th class. The entropy measures the uncertainty or randomness of the system, and this is defined as the sum of the probabilities of each class multiplied by their logarithm base 2.  
\[I(S)=-\sum_{i=1}^{c}p_{i}\text{log}_{2}p_{i}\]  
\[IG(S,A)=I(S)-\sum_{v\in A}\frac{|S_{A,v}|}{|S|}I(S_{v})\]  
This formula represents the information gain (\(IG\)) of feature \(A\) concerning a set of data \(S\) in a decision tree. It is calculated by subtracting the sum of the weighted entropies of the possible subsets of \(S\) that result from splitting \(S\) on feature \(A\) from the entropy of \(S\).  
* (\(S\)) is the entropy of set \(S\). It measures the impurity of set \(S\). The higher the entropy, the more mixed the labels are in the set;
* \(A\) is the feature being considered for splitting the data;
* \(v\) is a possible value of feature \(A\);
* \(S\_(A\),\(v\)) is the subset of \(S\) in which feature \(A\) has value \(v\);
* \(S\_v\) is the subset of \(S\) in which feature \(A\) takes value \(v\), and \(I(S\_v)\) is the entropy of \(S\_v\).  
The formula can be interpreted as the reduction in entropy achieved by splitting the data on feature A. It selects the feature with the highest information gain as the root of the decision tree and recursively applies the same procedure to the subsets resulting from the split until a stopping criterion is met.
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 6 Results and Discussions
### Performance Comparison between Federated Learning and Machine Learning  
This section presents the performance analysis of the AI model trained using federated learning in comparison to machine learning principles in the absence of channel loss. The effects of channel loss are reported in the next section.
## 3 Numerical Simulations and Results
### Results
We wanted to point out that the focal spot which is similar to the focal spot represented in Figure 5b is also radially polarized before the focusing element, and it is likely that the underlying phenomenon that generates this certain focal shape is the same. The configurations that can be produced using elliptical/linear input polarization are not evidenced in the paper, but they are likely to be different from our case since the linearly polarized beam cannot become circularly symmetric after the radial analyzer. [PERSON] et al. [32] also showed that the Gouy phase plays an important role in the change of the intensity profile of a vector beam near the focal point. In Figure 8, we show the focal spot produced by variation 2 of the optical system proposed herein. The optical layout is similar to the layout for variation 1A, except that MC has slightly different characteristics and SPP is not present. Unlike variation 1A, the beam is composed of a helical mode \(-2\) with left circular polarization and a non-helical mode with right circular polarization, while the mode conversion efficiency is set around 80%. The profile flatness is good and is maintained for 5-10 \(\upmu\)m. The edges are not sharp, but this is not a design problem; it is caused by the fact that the  
Figure 7: Intensity profile in the focal plane for variation 1 of the system, using the aspheric lens with lower numerical aperture. Variation 1A (**a**), variation 1C (**l**), and intermediate configurations (**b**–**k**). spot is almost diffraction-limited and the length in the transversal plane on which the intensity drops from the maximum value to zero is approximatively given by the radius of the diffraction-limited spot. The polarization state is circular near the optical axis. One of the major advantages is the fact that the spot is radially polarized near the edges of the flat profile; radial polarization, as pointed in Reference [7], is ideal for Si/SiO\({}_{2}\) processing, especially for drilling boreholes.
## 2 Methods  
### Anatomical Relevant Space  
The relevant FOV for functional cardiac imaging can be defined from a 3D dataset as follows:  
1. Automatic real-time segmentation of myocardial boundaries A fully automatic real-time segmentation of the left ventricular myocardium in a volumetric ultrasound recording was performed using the B-spline Explicit Active Surfaces (BEAS) framework [18; 19]. More specifically, BEAS uses two explicit functions, one to represent the endocardial surface and another to represent the myocardial thickness. This allows to fully characterize the endo- and epicardial surfaces. These surfaces can then be used to define a binary mask identifying voxels belonging to the myocardium only.
2. Coverage function Using these binary images, a \"coverage function\" was defined as follows. First, based on the ray-tracing principle, the path of a given scanline within the volumetric image volume can be traced. The pixels belonging to that scanline are compared with the binary mask, in order to compute the percentage of pixels of the given scanline belonging to the myocardium. Finally, this procedure is repeated for all scan lines in the original pyramidal volume leading to a \"coverage function\".
3. Ring-shaped template matching To find a spatially continuous FOV that covers a given percentage of the total amount of myocardium (i.e., prospectively defined by the user as \"\(T\)\"), a ring-shaped template matching was used. This shape was chosen as an approximation of the left ventricular geometry when looking down from the apex, i.e., when the transducer is placed in an apical position. In 3D, this FOV therefore defines a hollowed cone, Figure 1 (left). We express the amount of myocardial coverage \(T\) as a function of the inner radius of the ring template (\(R_{i}\)) and its thickness (\(\Delta R\)). In order to effectively gain frame rate, a compromise has to be made between the amount of myocardial coverage, i.e., \(T\), and the extent of the FOV. From all \(R_{i}\) and \(\Delta R\) combinations that provide \(T\) myocardial coverage, the one with minimal \(\Delta R\) was chosen, as this would keep the volume to be scanned minimal. In this way, it is ensured that the desired \(T\) coverage is obtained using the least amount of lines possible (i.e., at the highest frame rate). In turn, these radii are used to determine the parameters (opening angle, \(\Phi\), and thickness, \(d\Phi\)) for a conical scan, as represented in Figure 1 (right). These radii give directly the inner and outer image lines that define the inner and outer surface of the cone. Then, using the angular inter-beam spacing, the line numbers can be converted to the respective angles.  
Figure 1: Graphical representation of a conical scan for anatomical imaging in 3D (**left**) and 2D (**right**), where \(\Phi\) and \(\Delta\Phi\) represent the opening angle and thickness of the cone, respectively.
# Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin
## 4 Results and Discussion
### Statistical Evaluation of GPPs
#### 4.1.2 Monthly Comparison
With monthly time series, it can be concluded that first, the TRMM 3B42 has the best performance at the basin scale, and CMORPH has the largest errors. Second, conducting analyses at the pixel scale further confirms the outperformance of the other models by TRMM 3B42, which is characterized by stable performance with observations in all of the indicators. In contrast, GSMaP, CPC, and CMORPH show inferior performances, especially in the south-east part of the basin.
## 4 Conclusions  
A three-dimensional flow solver based on IBM was developed and applied to the flows around multiple particles colliding with a wall. The performance of the present flow solver was compared with those of previous studies of the two types of collisions; a moving particle colliding with a flat wall, and collision between two moving particles. Differences between the two-way and one-way schemes were identified for multiple particles colliding with the wall. The main results are summarized below:  
1. The drag coefficient of a fixed particle computed by the proposed solver agreed well with the drag coefficient of previous studies. The skew-symmetric scheme could capture the unsteady flow characteristics such as the wake structure.
2. The wake structures of the moving particle colliding with the flat wall, the curved wall and another moving particle exhibited the same trends as the wake structures computed in previous studies. We demonstrated that the present flow solver using a simple collision algorithm appropriately solves the collision problem.
3. When the number of particles is large, the flow structure becomes complex because particle-particle collisions caused frequent lateral movements of the particles.
4. For many particles, the one-way scheme could not accurately predict the flows, because it overestimated the number of collisions and ignored the influence of the particles on the fluid, which altered the flow phenomena.  
The interaction of a fluid and multiple colliding particles remains as an open problem, requiring more details to be able to compare the results with experimental results in a large region. Moreover, the difference of the flow characteristics between the particles-flat wall and particles-curved wall collisions will need to be investigated in the case of the large number of particles.  
Conceptualization, Y.M, and S.T.; Methodology, S.T.; Software, Y.M. and S.T.; Formal analysis, Y.M.; Investigation, Y.M.; Resources, S.O.; Project Administration, S.T., K.F. and S.O.  
The research received no external funding.  
Part of the present simulations were implemented by the High Performance Computer Infrastructure (HPCI) hp150130, hp160150 and hp170111. This work was supported by JSPS KAKENHI Grant Number 16K18018, 17K06167 and 18K03937. Part of the work was carried out under the Collaborative Research Project J15052 \"Study for accurate prediction of unsteady aerodynamic characteristics around moving objects\" with the Institute of Fluid Science, Tohoku University. This study was partially supported by the Research Project of Tokai University \"Development of measurement system for unsteady flows around moving objects by numerical simulations and experiments\".  
The authors declare no conflict of interest.