esa-sceva/satcom-chunk-collection huggingface dataset:
# Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity
## 3 Proposed Scheme
### Data Embedding
```
0: Cover image \(\mathbf{I}\), watermark \(\mathbf{b}\), and data-hiding key \(K_{d}\)
0: Stego image \(\mathbf{C}\)
1: Split cover image \(\mathbf{I}\) into channel images \(\mathbf{R}\), \(\mathbf{G}\), and \(\mathbf{B}\)
2:functionTripleEmbedding(\(\mathbf{I},\mathbf{b},K_{d}\))
3: Encrypt watermark \(\mathbf{b}_{i}\) to \(\theta_{i}\) using Equation (6)
4: Group \(\theta_{i}\) to block \(\mathbf{\theta}_{j}\)
5: Copy \(\mathbf{I}\) into \(\mathbf{I^{\prime}}\) and \(\mathbf{I^{\prime\prime}}\)
6: For \(\mathbf{I^{\prime}}\) do:
7: Divide \(\mathbf{I^{\prime}}\) into blocks \(\mathbf{L}_{1j}\)
8:
9:for\(j=1\) to \(w\)do
10:\(\mathbf{x}_{j}\leftarrow\mathbf{L}_{1j}\)
11: Generate \(\mathbf{D}_{j}\) using \(K_{d}\)
12:\(\mathbf{s}_{j}=\mathbf{D}_{j}\times\mathbf{x}_{j}\)
13:\(\mathbf{\tilde{s}}_{j}=\mathbf{s}_{j}\oplus\mathbf{\theta}_{j}\)
14:\(\mathbf{H}_{j}\leftarrow\mathbf{D}_{j}\)
15:\(\mathbf{v}_{j}\leftarrow(\mathbf{H}_{j}\mathbf{v}_{j}=\mathbf{\tilde{s}}_{j})\)
16:\(\mathbf{y}_{j}=\mathbf{v}_{j}+\mathbf{x}_{j}\)
17: Blocks \(\mathbf{L}^{\prime}_{1j}\leftarrow\mathbf{y}_{j}\)
18:endfor
19: Stego image \(\mathbf{F}_{l^{\prime}}\leftarrow\mathbf{L}^{\prime}_{1j}\)
20: For \(\mathbf{I^{\prime\prime}}\) do:
21: Divided \(\mathbf{I^{\prime\prime}}\) into blocks \(\mathbf{L}_{2j}\)
22:
23:for\(j=1\) to \(w\)do
24: Assign \(\mathbf{z}_{j}\) with the first four bits of \(\mathbf{L}_{2j}\)
25:\(\mathbf{\tilde{z}}_{j}=\mathbf{z}_{j}\oplus\mathbf{\theta}_{j}\)
26: Assign \(\mathbf{\tilde{z}}_{j}\) to the first four bits of \(\mathbf{L}^{\prime}_{2j}\)
27:endfor
28: Stego image \(\mathbf{F}_{l^{\prime\prime}}\leftarrow\mathbf{L}^{\prime}_{2j}\) //The first embedding process ends
29: For \(\mathbf{F}_{l^{\prime}}\) do:
30: Repeat Steps 5-19, generate stego image \(\mathbf{S}_{l^{\prime}}\)
31: Repeat Steps 20-28, generate stego image \(\mathbf{S}_{l^{\prime\prime}}\)
32: //The second embedding process ends
33: For \(\mathbf{S}_{l^{\prime}}\) do:
34: Repeat Steps 5-19, generate stego image \(\mathbf{T}_{l^{\prime}}\)
35: Repeat Steps 20-28, generate stego image \(\mathbf{T}_{l^{\prime\prime}}\)
36: //The third embedding process ends
37:\(\mathbf{C}_{l}\) combined from \(\mathbf{T}_{l^{\prime}}\), \(\mathbf{F}_{l^{\prime\prime}}\), \(\mathbf{S}_{l^{\prime\prime}}\), and \(\mathbf{T}_{l^{\prime\prime}}\)
38:endfunction
39:\(\mathbf{C}_{R}=\textsc{TripleEmbedding}(\mathbf{R},\mathbf{b},K_{d})\)
40:\(\mathbf{C}_{G}=\textsc{TripleEmbedding}(\mathbf{G},\mathbf{b},K_{d})\)
41:\(\mathbf{C}_{B}=\textsc{TripleEmbedding}(\mathbf{B},\mathbf{b},K_{d})\)
42:Merge channel images \(\mathbf{C}_{R}\), \(\mathbf{C}_{G}\) and \(\mathbf{C}_{B}\) to color image \(\mathbf{C}\)
43:return Stego image \(\mathbf{C}\)
```  
**Algorithm 1** Data embedding.
## 2 Related Works on Bridge Data Schemas
### Bridge Data Schemas Using EXPRESS
#### 2.1.3 Bridge Schema Elements in IFC  
While the 2013 release of IFC4 contains the necessary entities for buildings only, it does include basic high-level elements for the enhancement of the support for other infrastructures in the future. These include _IfcCivilElement_ and _IfcCivilElementType_, which were added as subtypes of _IfcElement_ and _IfcElementType_, respectively. To develop the subtypes, attributes, and PSETs for these elements, buildingSMART International (bSI) founded the Infrastructure Room (2013), which is now developing the standards for alignments (S  
detailed elements for structural analysis and design, along with a general description of the bridge. (While this can be said to be similar to the _Structural Analysis Domain_ and _Structural Elements Domain_ of the _\"domain layer\"_ in IFC, TransXML is specialized for highway bridges, unlike IFC.) However, the TransXML bridge does not consider explicit elements for the representation of the geometry of the model. While it provides elements for the geometry of the members of the entire bridge (_Bridge_ > _BeamShapes_ and its subelements), it is impossible to identify the meaning of the bridge components based on geometry. Therefore, the applications of TansXML adopt the geography markup language (GML) of OGC as a common framework. However, there are inevitable problems, since the TransXML bridge focused on engineering design while GML was developed for the geospatial domain.  
Based on the previous discussion, the following criteria were adopted as important considerations for the selection of a bridge data schema for use in this study.  
1. _Interoperable and accessible_: The schema must be software-independent, and the model data should be accessible in any situation
2. _Three-dimensional_: The schema must be able to manage the information of the constituent elements of the bridge based on 3D geometry
3. _Semantic_: The schema must include functional semantic information of the bridge components, and be capable of spatially and physically distinguishing the components
4. _Software-based data manageable_: The schema must allow information modeling on BIM authoring software  
As mentioned in Section 1, IFC is the ISO standard for BIM, satisfies all of the above criteria when applied to buildings, and is supported by an increasing number of BIM software packages. Moreover, there is a clear likelihood of extending IFC to structures other than buildings, similar to the aforementioned work of the Infrastructure Room. Accordingly, we state that the bridge information model in this study was created based on IFC4, which is the latest official version released, and explain its scope of application.
# Distributed Weighted Coverage for Multi-Robot Systems in Non-Convex Environment
## Scenario 2 Multi-robot motion control based on BUVC under constant density function
By introducing communication distance constraints, the number of iterations in Scenario 3 is more than that in Scenario 2. This is because following the introduction of the communication distance constraint, the Voronoi regions of the robots are constantly reduced. Thus, the positions of the robots' centroids are constrained, and the final number of iterations is increased. \begin{table}
\begin{tabular}{c|c|c|c|c|c} \hline \hline  & **Algorithm** & **Scenarios** & \begin{tabular}{c} **The Number of** \\ **Iterations when the** \\ **System Is Stable** \\ \end{tabular} & \
\begin{tabular}{c} **Cost Function** \\ **when the System** \\ **is Stable** \\ \end{tabular} &
\begin{tabular}{c} **Average Position** \\ **When the** \\ **System Is Stable** \\ \end{tabular} \\ \hline \multirow{3}{*}{Convex environment} & CVT & Scenarios 1 & 140 & 0.074 & 0.00033 \\ \cline{2-6}  & & Scenarios 2 & 123 & 0.0079 & 0.00034 \\ \cline{2-6}  & BUVC+Communication & Scenarios 3 & 189 & 0.0078 & 0.00012 \\ \cline{2-6}  & Scenarios 4 & 267 & 0.0019 & 0.00089 \\ \hline \multirow{2}{*}{Non-convex environment} & distance constraints & Scenarios 5 & 146 & 0.0036 & 0.00110 \\ \cline{2-6}  & Scenario 6 & 119 & 0.0047 & 0.00121 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of simulation results of multi-robot formation based on BUVC in six scenarios. Figure 13: Multi-robot coverage control based on BUVC in non-convex environment. One of the reasons for the reduction in the number of iterations in Scenarios 5 and 6 relative to that in Scenario 4 is that the introduction of obstacle areas in the task environment reduces the workspace. As the working area of the robots is reduced, the convergence speed of the robot-covering system is increased. Another reason is that the final to-be-covered area is partially occluded by obstacles. Therefore, the robot coverage system needs to adjust less frequently when it is almost stable.
# AoI Analysis of Satellite-UAV Synergy Real-Time Remote Sensing System
## 2 System Model
### Communication Model
#### 2.2.3 Sink  
Since the \(m_{2}\) collected by UAV is more correlated to the location of GU \(k\) compared to \(m_{1}\) collected by SAT, the reception quality of \(m_{2}\) at GU is prioritized. Therefore, GUs adopt the successive interference cancellation (SIC) to first decode \(m_{1}\) and then decode \(m_{2}\). The SINR of decoding \(m_{1}\) at the GU \(k\) in \(A_{SAT}\), which is denoted as \(\gamma_{k}^{s}\), can be expressed as  
\[\gamma_{k}^{s}=\frac{\left|\sqrt{p_{s}}h_{k}^{s}\right|^{2}}{\left|\sqrt{p_{u} }h_{k}^{u}\right|^{2}+\sigma^{2}},\;\;\;k\text{ in }A_{SAT}, \tag{5}\]  
where \(p_{s}\) and \(p_{u}\), respectively, denote the transmit power of SAT and UAV, and \(\sigma^{2}\) is the power of additive white Gaussian noise (AWGN). The SINR of decoding \(m_{2}\) at the GU \(k\) in \(A_{UAV}\), which is denoted as \(\gamma_{k}^{u}\), can be expressed as  
\[\gamma_{k}^{u}=\frac{\left|\sqrt{p_{u}}h_{k}^{u}\right|^{2}}{\sigma^{2}},\;\; \;k\text{ in }A_{UAV}, \tag{6}\]  
To ensure that all GUs can correctly decode the packets from the UAV and the SAT, the transmit rate of \(m_{1}\) should be no larger than  
\[R_{s}=B\log_{2}(1+\text{min}\{\gamma_{k}^{s}\mid k\text{ in }A_{SAT}\}), \tag{7}\]  
and the transmit rate of \(m_{2}\) should be no larger than  
\[R_{u}=B\log_{2}(1+\text{min}\{\gamma_{k}^{u}\mid k\text{ in }A_{UAV}\}), \tag{8}\]  
where \(B\) is the FFR bandwidth allocated to SAT and UAV for transmission.
## 4 Model Simulations with FEM-3D
### Simulation Results in Standstill Conditions
#### 4.3.4 Effect of Varying the Width of the Transversal Slots  
Model 4 redesigns the width of the transversal slots of the primary part increasing its dimension along the x-axis (see Figure 7). To this end, a sweep parameter was used which found the optimal value of this dimension. This change into the opening of the transversal slots was necessary to show how the thrust force increased and the electric field over the lateral teeth decreased when the opening of the slot increased. The initial value of the transversal slots was 35 mm (see Figure 2) and the final value was 75 mm. Figure 22 shows the thrust force for the different openings in a transitory state. As shown, the thrust force did not change significantly.  
To study the evolution of the forces for each dimension of the transversal slots, the average values of \(F_{y}\) are summarized in Table 3. Besides, this table contains the average values obtained with model 3, whose results are a reference value. Model 4 incorporated all changes from models 2 and 3.  
Figure 21: Transverse magnetic field density.  
Figure 22: Thrust force in a transitory state for different values of the transversal slots.  
Figure 23 collects all data from Table 3. The optimal thrust force was reached in model 4 for an increment of 20 mm in the transversal slots. This force was around 138 N and consequently, the opening of the transversal slots has been redefined with a value of 55 mm. Compared to model 3, the thrust force represents an improvement of around 10%. Not all increments of the transversal slots were valid to improve the thrust force because with an increment greater than 20 mm the leakage flux began to be relevant, and the linkage magnetic flux fell.  
The levitation force presented the opposite behavior when the transversal slots increased. If model 3 was considered as a reference value, with the new dimension of the transversal slots fixed in 55 mm, model 4 operated under conditions of attraction forces as shown in Figure 24. Figure 25 plots the average values for this attraction force. Moreover, the evolution of F\({}_{x}\) did not present an optimum value when the transversal slots were increased 20 mm in contrast with the thrust force. For this value, model 4 developed an average attraction force of \(-\)56.
## 4 Experiment and Results
### Evaluation Metrics  
The most important detection metric for object detection is mean average precision (\(mAP\)), which is the mean value of \(AP\) for each class. The results of object detection were presented in binary classification, and there were four categories: true positive (\(TP\)), false positive (\(FP\)), true negative (\(TN\)), and false negative (\(FN\)). Based on the number of the four categories, precision and recall were defined as  
\[P=\frac{TP}{TP+FP} \tag{9}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Training Parameters** & **Parameter Value** \\ \hline Input\_shape & [416; 416] \\ Initial learning rate & 0.01 \\ Min learning rate & Initial learning rate * 0.01 \\ Optimizer\_type & sgd \\ Learning rate\_decay\_type & cos \\ CPU & Intel(R) Xeon(R) CPU ES-2640 v3 @ 2.60 GHZ (4 processors) \\ GPU & GeForce GTX 1080 Ti/PCIe/SSE2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training parameters and environment.  
\[R=\frac{TP}{TP+FN} \tag{10}\]  
\(AP\) is the area under the precision-recall(\(PR\)) curve:  
\[AP=\int_{0}^{1}P(R)dR \tag{11}\]  
\(mAP\) is defined as  
\[mAP=\frac{1}{N}{\sum_{N}AP} \tag{12}\]  
where \(N\) represents the number of classes.
## 3 Results and Discussion  
The organosolv process is a delignification process that consists of breaking the internal lignin and hemicellulose bonds of the lignocellulosic material; in this case, a corncob. Table 1 shows the chemical composition of corncob before performing the organosolv pretreatment: 45% cellulose, 34% hemicellulose, 16% lignin, and 6% extractives. These values are comparable to the data published by several authors, who indicated that the cellulose, hemicellulose and lignin ratios were 45%, 35% and 15%, respectively [23; 24]. The high percentage of hemicelluloses in the corncob makes it a suitable material for thermochemical pretreatments such as organosolv. The pretreated corncob had 22% cellulose, 72% hemicellulose, 5% lignin, and 2% extractives, from which it could be verified that the organosolv treatment solubilised and de-polymerised lignin and hemicelluloses, dividing them into separate liquid streams (hydrolysate) and leaving behind solid fractions rich in cellulose for other applications [25; 26].  
The percentages (%; w/w) obtained for C: 46.6 and N: 0.4, as shown in Table 1, are in agreement with data reported in the literature (C: 41.7% and N: 0.1%) [22], suggesting that corncob is a material that can be used in an anaerobic process. However, the C:N ratio of corncob is 61:0.5, which is considerably higher than the optimal value reported in the literature for other biomasses used in anaerobic digestion [3]. The decomposition of materials with high carbon content, greater than 35:1, occurs more slowly, because the multiplication and development of bacteria are low, due to the lack of nitrogen, but the biogas production period is longer. On the other hand, with a C:N ratio less than 8:1, bacterial activity is inhibited due to the formation of an excessive ammonium content, which in large quantities is toxic and inhibits the process [4].
# Finite Element Method (FEM) Modeling of Laser-Tissue Interaction during Hair Removal
## 3 Results
### Optimization of the FEM Model  
The FEM models were constructed so that the density of the tetrahedral elements close to the hair shaft and follicle was much higher than the density on the periphery. A high density of tetrahedral elements was also present in the whole epidermis, while in the dermis, far from the hair, the density of tetrahedral elements was lower, with the lowest in the subcuits. The reason for this is that the description of a homogeneous volume, for example the subcuits far from the hair, does not require as many elements as, e.g., a thin epidermis or a tiny hair shaft and follicle. We found that the following ratio in the number of tetrahedral elements between the hair shaft/follicle (Nhair), epidermis (Nepidermis), dermis (Ndermis), and subcuits (Nsubcuits) would be appropriate: Nhair:Nepidermis:Ndermis:Nsubcuits = 10:10:10:0:1.  
To estimate the minimal number of tetrahedral elements, we simulated the average temperature within the hair follicle using a fluence of 5 J/cm\({}^{2}\) and a sequence of three consecutive pulses with a duration of 2.5 ms and a pause of 200 ms between each pulse. The average temperature was calculated in the same manner as described above.  
Figure 4: Impact of the number of photons on the absorbance of the hair follicle, averaged over 10 simulation runs.  
Figure 5 shows the dependence of the average temperature of the hair follicle immediately after the first, second, and third pulses, respectively, on the number of tetrahedral FEM elements. We can conclude that the average temperature of the hair follicle starts converging at roughly 500,000 elements, which was taken as the minimal number of elements in our simulations. FEM Model #1 thereby had 422,742 elements, FEM Model #2 had 453,775 elements, and FEM Model #3 had 586,633 elements.  
For the selected three models, the simulation speed of the laser-tissue interaction on the Intel i5-8250u CPU was approximately 50,000 photons per second, while the single heat diffusion simulation took approximately 3 h to complete.
## 5 Simulation Framework and Experimental Scheme  
The simulations were conducted by integrating the InTAS scenario within the Artery-C framework, selected for its advanced capability to handle both vehicular mobility and 5G communications in urban environments. Artery-C, specifically designed for vehicular communication (V2X) in 5G networks, supports real-time communication scenarios in highly dynamic environments and is compatible with the SUMO platform. SUMO was employed to manage detailed traffic simulations, renowned for accurately modeling complex urban traffic behaviors and interactions. This combination provides an optimal environment for studying the impact of human uncertainty on network resource allocation in 5G networks.
## 4 Results
The duration of the winter fuel consumption norms is set by the management of each specific transportation company. Therefore, for accurate fuel consumption planning, a corresponding winter period must be established in the TMSs of Russian transportation companies. Figure 7: Business processes of dispatchers in the TMS “Autobase” reconstructed with Process Mining technologies (“spaghetti processes”). Another issue related to ensuring the reliability of the transportation process during the winter season is associated with tracking the mileage for winter tires. For trucks, winter tires are practically never used, while for light vehicles, winter tires are mandatory. In the TMS, the period of their use is also determined by the corresponding winter period, similar to fuel consumption. However, due to cost savings or other reasons, there are instances where the installation of winter tires is delayed, which is also reflected in the TMS. In our example, the transportation company has a mixed fleet of over 300 vehicles, including both trucks and light vehicles. In the TMS, fuel consumption is planned by the manager with the \"Solovyova\" account, whose business processes are illustrated in Figure 8. The TMS tire accounting is the responsibility of the manager with the \"Sorokina\" account (Figure 9), while fuel consumption planning is ensured by the manager with the \"Solovyova\" account, whose business processes are shown in Figure 8. From these models, it is evident that both managers arbitrarily set the winter period in the TMS. Moreover, for unknown reasons, manipulations of the winter period in the TMS were also carried out in January, as seen in Figure 10. Consequently, such manipulations affected the calculation of fuel consumption during transportation planning. However, in this context, it is also essential to consider weather conditions. In the case of a mild winter with positive temperatures, adhering to winter fuel consumption norms during planning may contribute to increased costs. However, in actual transportation operations, fuel consumption may not rise. So, when creating a digital twin of the organization using Process Mining, it is crucial to correlate the activities of transportation planning managers with weather and climatic conditions. Currently, for building this mathematical model, research efforts are directed towards utilizing Markov chains [66]. Figure 8: Fragment of the “Solovyova” dispatcher actions in TMS “Autobase” during fuel consumption planning.
# Adaptive Kinetic Scrolling: Kinetic Scrolling for Large Datasets on Mobile Devices
## 4 Experiment
### Implementation  
We implemented AKS for the user study and in-depth analysis. In this section, we will present and explain the pseudocode based on the callback GUI function frequently used in mobile web or mobile application frameworks. The proposed code is shown below (see Box 1). Comparing with the statement machine of AKS, which is presented in Figure 1, will aid in clearer understanding. This code snippet was written by referring to the structure of the source implemented in [30].  
```
var offset //variable for measuring scrolling distance var velocity //variable for velocity of scrolling var timestamp //variable for timestamp to calculate scrolling velocity var remainingData //variable for size of remaining data var clutchtimeThreshold = C //Constant for triggering the AKS function tap() { //tap() is a callback function that is called when the screen is touched. var velocity = 0 timestamp = Date.now(); //Date.now() means the 'get current timestamp' offset = currentPos() }
```  
_function_ release()| //release() is a callback function that is called when the finger is released. var delta = offset - currentPos() var elapsed = timestamp - Date.now() velocity = delta/elapsed remainingData = screen.Size() - currentPos() if(velocity > clutchtimeThreshold){ AKS(delta, elapsed, remainingData) } else{ kineticScroll(delta, elapsed) } } ```  
The _tap_ function is called when the user taps a finger on the screen to start scrolling. At this time, the timestamp and current position at which scrolling starts are recorded in the _timestamp_ and _offset_ variables, respectively. Thereafter, the _release_ function is called when the user releases their finger, which implies stoppage of scrolling. In the release function, the velocity is calculated based on the movement distance (_delta_) and the consumption time (_elapsed_) of the scroll just performed. At this time, AKS is activated if the scrolling speed is above the _clutchtimeThreshold_ (constant C in Figure 3). In AKS, the speed of autoscrolling is amplified as shown in (Figure 1). Moreover, the parameter that determines the amplification is _remainingData_, which implies the amount of item in the scroll to be navigated. In our implementation, the remaining data in the scroll was measured in the following manner. First, we calculate the relative position of the current viewport to the total scrollable page length. Thereafter, the number of items displayed in the viewport was multiplied by the ratio. In this way, we can measure the remaining scrollable items. If you use the APIs provided by Android applications, you can measure the number of items left without additional calculations and calculate them more easily. AKS is a widget that operates using only the user's behavior and GUI environment without additional data preprocessing. It can be conveniently applied to the part corresponding to the view of the Model-View-Controller (MVC) pattern with only a simple implementation.