esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Properties Analysis of the Functions \(\varphi_{m}^{s}\)
### Single-Resource-Block Power Allocation
```
1:Input :\(\mathbf{\alpha},\mathbf{T},\mathbf{B}_{\mathbf{S}},\mathbf{G}^{s}_{t}(\mathbf{t}\in\mathbf{T}),\mathbf{\sigma }^{s}_{t}(\mathbf{t}\in\mathbf{T}),\mathbf{A}_{s}*,\mathbf{P}s_{max}\)
2:Global variable :\(\widetilde{p}^{s}_{1},\widetilde{p}^{s}_{2},\ldots,\widetilde{p}^{s}_{|A_{s} *|_{s}}\)
3:Function 1 \(\mathbf{\alpha},\mathbf{T},\mathbf{B}_{\mathbf{S}},\mathbf{G}^{s}_{t}(\mathbf{t}\in\mathbf{T}),\mathbf{\sigma }^{s}_{t}(\mathbf{t}\in\mathbf{T}),\mathbf{A}_{s}*,\mathbf{P}^{s}\)
4:For\(n=1\)to\(|A_{s}*|\)do
5:\(\#\)Calculate the optimum value of \(\vartheta^{s}_{m,m}\) in (30)
6:\(\widetilde{p}_{s}*=argmax(n,n,\mathbf{\alpha},\mathbf{T},\mathbf{B}_{S},\mathbf{G}^{s}_{t}(t \in\mathbf{T}),\mathbf{\sigma}^{s}_{t}(t\in\mathbf{T}),A_{s}*,p^{s})\)
7:\(\#\)Modify \(\widetilde{p}_{s}\) if this allocation dissatisfies condition \(w4*\)
8:\(n\gets m-1\)
9:While\(\widetilde{p}^{s}_{n_{s}}<\widetilde{p}_{s}*\) and \(n\geq 1\)do
10:\(\widetilde{p}*\leftarrowargmax(\mathbf{n},\mathbf{m},\mathbf{\alpha},\mathbf{T},\mathbf{B}_{\mathbf{S} },\mathbf{G}^{s}_{t}(\mathbf{t}\in\mathbf{T}),\mathbf{\sigma}^{s}_{t}(\mathbf{t}\in\mathbf{T}),\mathbf{A} _{s}*,p^{s})\)
11:\(n\gets n-1\)
12:endwhile
13:\(\widetilde{p}^{s}_{(n+)_{s}}=\cdots=\widetilde{p}^{s}_{m_{s}}\leftarrow \widetilde{p}*\)
14:Endfor
15:Return\(\widetilde{p}^{s}_{1},\widetilde{p}^{s}_{2},\ldots,\widetilde{p}^{s}_{|A_{s} *|_{s}}\)
16:End Function 1
17:\(\widetilde{p}^{s}_{1},\widetilde{p}^{s}_{2},\ldots,\widetilde{p}^{s}_{|A_{s} *|_{s}}\gets function1(\mathbf{\alpha},\mathbf{T},\mathbf{B}_{\mathbf{S}},\mathbf{G}^{s}_{t}(t \in\mathbf{T}),\mathbf{\sigma}^{s}_{t}(t\in\mathbf{T}),\mathbf{A}_{s}*,\mathbf{P}_{max}\)
18:Function 2 (\(p^{s}\))
19:Return\(min\big{\{}\widetilde{p}^{s}_{1},p^{s}\big{\}},\ldots,min\Big{\{}\widetilde{p}^{s} _{|A_{s}*|},p^{s}\Big{\}}\)
20:End Function 2
```  
**Algorithm 2** Single-resource-block power allocation scheme (RBPA)  
The maximum calculating complexity for power allocation is \(\mathcal{O}(A^{2})\), and the subsequent evaluation work costs \(\mathcal{O}(A)\). Suppose that there are \(Q\) different power budgets given in the multi-resource-block optimization. In that case, RBPA is used as the basic module for calculating the corresponding optimal power allocation with the complexity of \(\mathcal{O}(A^{2}+\mathcal{O}A)\).
## 3 Tissue Engineering Approach
### 3D Bioprinting
The technology did not affect the survival and the growth of rat RGCs and glial cells compared with cells seeded onto tissue culture plates [123]. This result opens the way for developing a printed construct to be used in retinal regeneration. Recently, a 3D in vitro retina model comprising of RPE cells and photoreceptors was fabricated using inkjet-based technology to study the interaction between the layers in AMD disease [124]. According to the authors, after bioprinting both cell types were correctly positioned in a layered structure and expressed specific proteins such as tight junction-associated protein ZO-1 in the RPE layer and light-sensitive proteins in the photoreceptor layer [124]. The use of inkjet-printed structures for tissue engineering is limited by their low mechanical properties and their long term durability. Moreover, due to the presence of the nozzle, clogging issues are common with viscous bioink [122]. The extrusion-based technology relies on the extrusion of continuous filaments of bioink through a nozzle using a driving force, i.e., a piston, a screwing system or pneumatic pressure. It is the most versatile bioprinting process as it enables the printing of the broadest range of bioink viscosities [122]. Additionally, it allows clinically relevant constructs to be obtained in terms of size and shape. However, the rheological requirements of the bioink are stringent [125]. Extrusion-based bioprinting was investigated to produce a 3D in vitro model of the RPE and photoreceptor layers [126]. The RPE cell line was bioprinted with a precise pattern and allowed to form a monolayer in 14 days followed by the bioprinting of the photoreceptor cell line [126]. Such a bioprinted construct could be meaningful for biomedical applications such as disease research and high-throughput screening. So far, the bioprinted retinal construct has been used only as in vitro models that could be used in the future for basic research and drug screening (Table 4). However, this technology is very promising also for retinal tissue engineering due to the possibility of generating layer-by-layer a 3D complex multicellular stratified structure, which mimics the retina architecture and could be transplanted as potential therapies for retinal diseases involving the degeneration or dysfunction of multiple retinal layers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fabrication Technique** & **Scaffold Structure** & **Cell Type** & **Research Stage** & **Reference** \\ \hline Inkjet bioprinting & Not applicable & Rat RGCs and retinal glia & In vitro & [123] \\ Inkjet bioprinting & 3D bilayer retina model & Human cell line ARPE-19 and pig & In vitro & [124] \\ Extrusion bioprinting & 3D bilayer retina model & Human cell line ARPE-19 and Y79 & In vitro & [126] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A summary of 3D bioprinting techniques used in retinal applications. This table highlights the fabrication technique, the structure of the scaffold and the cell type used. RGC: retinal ganglion cells.
# Machine-Learning-Based Side-Channel Evaluation of Elliptic-Curve Cryptographic FPGA Processor
## 2 Background and Related Terminologies  
### Power-Analysis Attacks  
The PA is a strong passive attack, meaning that the attacker does not need to manipulate the device in any way to extract the secret key. In fact, whenever a command is executed by the device, the consumed power is measured by putting a resistor between \(V_{ss}\) or \(V_{dd}\) and the true \(V_{dd}\), for processors implemented in CMOS technology. The voltage drop by the current through the resistor is recorded. The voltage measurements are then analyzed using statistical methods to recover the secret key. The details of CMOS leakage can be found in [27].  
PAs can be categorized into simple (SPA) and DPA. The feasibility of a simple PA depends upon the assumption that each instruction will have a unique power trace, which is normally caused by key-dependent branching. For scenarios where traces are not related to the key and instructions but are related to the data key, such attacks are categorized as differential power-analysis attacks. In DPA, the results of hypothetical models are compared with the actual experimental results.
# Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications
## 4 EVSP Process
### Multiple Emergency Requests  
A conflict requests handling mechanism is designed to deal with the case when multiple prioritized vehicle requests are received at the same time. If EV and TSP requests are received in RSU at the same time, EV requests should be handled as prioritized rather than TSP requests because EV has the authority of signal preemption. In the case of multiple EV requests from different approaches, we give emergency vehicles (ambulances, fire engines, police cars) different weights. For example, if the weight of ambulance is higher than the police vehicle, RSU will give priority to the ambulance service when both requests are received at the same time. The first-come first-served (FIFO) operation strategy is adopted if two EV requests from different approaches received the same weight.
## 3 Results
### Influence of Geomechanical Parameters
#### 3.2.1 Influence of the Rock Type (Parameter m\({}_{\text{i}}\))  
The parameters UCS = 10 MPa and GSI = 50 have been fixed for a specific geometric configuration (2a = 6 m, 2b = 3 m, b/a = 0.50, B = 4 m, H/b = 3) adopting an ample range of rock types (m\({}_{\text{i}}\) = 5 to 30). As previously indicated, the dimensionless load eccentricity E/a has been varied from zero to a value for which its influence becomes negligible.  
The values that define the geometry of the cavern, in all the cases, are:  
\[2\text{a}=6\text{ m},\quad\text{ }2\text{b}=3\text{ m},\quad\text{ b/a}=0.50,\quad\text{ B}=4\text{ m},\quad\text{ H/b}=3\]  
The three following cases are studied:  
\[\text{ITR-1:}\quad\text{ m}_{\text{i}}=5\] \[\text{ITR-2:}\quad\text{ m}_{\text{i}}=17\] \[\text{ITR-3:}\quad\text{ m}_{\text{i}}=30\]  
The failure mechanisms obtained in each case studied have been omitted; however, they are the same as those shown in Section 3.1, following the same pattern based on the eccentricity E/a.  
Figure 10 only represents the influence of the rock type (parameter m\({}_{\text{i}}\)) on the relative ultimate bearing capacity (q\({}_{\text{u}}\)/q\({}_{\text{u}\text{H}\text{G}\text{I}}\)) when varying the load eccentricity (E/a).  
By analysing the curves of the above Figure 10, the following conclusions can be drawn:  
As can be observed, for eccentricities E < 3a, the variation in the relative ultimate bearing capacity (q\({}_{\text{u}}\)/q\({}_{\text{u}\text{H}\text{G}\text{I}}\)) is negligible (lower than 5%) for all the lithologies analysed. There are only  
Figure 10: Influence of rock type (parameter m\({}_{\text{i}}\)) on the ultimate bearing capacity when varying the load eccentricity.  
differences ranging between 10% and 17% for eccentricities higher than 4a, for which the value of the relative ultimate bearing capacity is greater than 80%, and, therefore, the influence of the cavern is greatly limited.  
In conclusion, it can be stated that the influence of the affected rock type is negligible when the load eccentricity with respect to the cavern axis is limited; therefore, the reduction in the ultimate bearing capacity is substantial (as shown in previous sections) and the existence of the cavern is decisive. However, a greater influence can be observed, with variations between 10% and 17%, when the eccentricity is higher, and consequently, the existence of the cavern is less important.
## 5 Perceived Distance Control in Sound Field Approximation and Psychaocustic-Based Methods  
These methods are used in many practical spatial sound techniques and systems, such as conventional two-channel stereophonic sound and various multichannel sounds. Because the reproduced sound field by this type of methods is a rough approximation of the target sound field, at most, these methods are unable to accurately create all spatial perception cues. These methods rely completely on the psychoacoustic principle to recreate various spatial auditory events and perceptions. For example, the psychoacoustic principles of summing directional localization with two or more loudspeakers are usually used with these methods to create perceived virtual sources in various directions [25, 26, 27, 28, 29]. By feeding decorrelated signals to multiple loudspeakers, it is also possible to create a sensation of envelopment in reproduction [53]. Based on these principles, various microphone and signal panning/mixing techniques for stereophonic and multichannel sounds have been designed [32,54,55].  
Because no psychoacoustic principle of summing distance perception with multiple loudspeakers is available, the methods for perceived distance control in stereophonic and multichannel sound are limited. In addition to controlling the level and spectral cues in reproduction, a common practice to create different distance sensations in stereophonic and multichannel sound is to control the proportion of reflections/reverberations in the reproduced signals using different signal-mixing or microphone-recording techniques. Adding reflection and reverberation make the perceived virtual source farther away from the listener, although this method can not make the perceived virtual source closer to the listener. However, stereophonic and multichannel sound reproductions are unable to accurately recreate reflection/reverberation sound fields, and thereby may be unable to recreate all reflection-related distance information. Moreover, reflections in a listening room, which are difficult to be compensated for in stereophonic and multichannel sound reproduction, may influence the reproduction of reflection-related target distance information. Therefore, the effect of the existing methods of perceived distance control in stereophonic and multichannel is limited. As the number of channels increases, some sound-field-based methods (such as the HFC-HOA method) are able to recreate the distance perception information and compensate for the listening room reflections in multichannel sound reproduction. This is an example of the mixed use of spatial audio techniques based on different principles.  
Overall, recreating various auditory distance perceptions in stereophonic and multichannel sound reproduction remains a challenging task; more psychoacoustic research on this issue is needed.
# Dynamic Modeling and Full-Process Simulation of the Core Engine Test Rig Main Test System
## 2 Physical Model of the System  
Figure 1 shows the schematic diagram of the core engine test rig system, which can be divided into three parts: the main test system, the fuel supply and ignition system, and the engine system. The main test system is to supply gas to the test engine and can be roughly divided into three lines by test operating conditions: normal-temperature line, low-temperature line, and high-temperature line, which are, respectively, used for environmental simulation tests of different operating conditions; the fuel supply and ignition system is to supply fuel to the engine and ignite the aviation kerosene and air mixture to enable burning.  
The research object of this paper is the main test system. In the normal-temperature-condition test, the regulator valve F3 is used to regulate the total air flow rate, F9 and F10 are used separately to control the air flow into the engine main line and the exhaust bypass, and F4, F6, F7, and F11 are closed all the time; in the low-temperature condition, F4 is used to regulate the normal-temperature air flow rate, the low-temperature air flow rate is controlled by the low-temperature air source and its corresponding regulator valve (F11 only plays an auxiliary regulation role) which is not involved in this test rig,  
Figure 1: Schematic diagram of the core engine test rig system.  
the functions of F9 and F10 are the same as those in the normal-temperature condition, and F3 and F8 remain closed. The ultimate goal of the two operating conditions is to control the total pressure \(p_{\text{tm}}\), total temperature \(T_{\text{tm}}\), and the flow rate \(Q_{\text{ma}}\) of the engine inlet gas by combined adjusting various valves so as to provide the required air working medium for the engine test.  
It can be seen from the physical model and working characteristics of the system that the gas flow pipeline of the test rig is complex in constitution, there are numerous regulating devices, and the intake state of the engine is controlled by the combined regulation of multiple valves. As different operating conditions have different requirements on the engine inlet air, there are big differences in the regulating scheme of various valves, the combination of multiple valves makes the time sequence design and regulation more complex, and these valves are regulated manually based on experience and limited technical specifications and features. All these factors together make the current debugging of the test rig an arduous task for engineers. By contrast, the computer numerical simulation method can be used to conduct numerical modeling for the rig-regulating process without starting up the real hardware to study the effect of the opening and closing time sequence of the valves on the system dynamic characteristics. Then, an appropriate regulating scheme can be concluded for a specific operating condition.
## 4 Decision Trees and Random Forests
### Stop Criteria  
Overfitting refers to the situation where the model fits the training data too much, leading to larger errors in the test data. To avoid this problem stopping criteria can be set. This set of rules can be regulated to achieve trees that are neither too short (too general) nor too long (overfitting problem).  
The tree will stop its development by itself in two cases:  
* When the objective values are homogeneous in all samples of the node.
* When the input values are constant in the node.  
In the previous cases, it is not possible to gain purity in the nodes by continuing the divisions, so the algorithm stops. These nodes that are not divided are called leaf nodes. In addition to these situations, other criteria can be added. The most common approaches are:  
* Set a node as a leaf node if it contains less than a minimum number of samples.  
Figure 2: Decision tree example for site 1, limited to 4 leaf nodes.  
* Set a maximum node depth. In this way, the number of divisions that the algorithm performs is limited.
* Establish a node as a leaf node when the impurity decrease is less than a certain threshold.
* Establish a node as a leaf node if it is not possible to establish dependent nodes with a minimum number of samples.  
All these criteria must be defined by the user looking for the most adequate balance. Such balance can be difficult to achieve, which is why specially dedicated models are commonly used at the expense of a greater computational load.  
These stopping criteria can be understood as a method of pruning the tree. Specifically, they are pre-pruning methods that are carried out during the growth of the tree.  
Another method would be to develop the entire tree and perform the pruning eliminating those nodes that provide the worst results in a different data set than that used to generate the tree. This method usually provides better results than pre-pruning.
## 3 Results and Discussion
### Electromagnetic Field on Different European Roads
#### 3.1.3 Croatia  
In Croatia, EMF measurements were carried out in the car cabin during driving on motorway A3 (in two road sections between points P1 HR-P2 HR and P2 HR-P3 HR), as shown in Figures 7 and 8. The selected driving road is part of the International E-road network--E 70.  
Figure 7: Croatia: (**a**) Boxplot showing total electric field (\(E_{total}\)) and electric field on each axis (\(E_{x}\), \(E_{y}\), and \(E_{z}\)). The central dotted line in the box shows the median, and the bottom and top edges are the 25 th and 75 th percentiles, respectively; (**b**) Map showing the selected road on which measurements were taken.  
During driving on the second road section (P2 HR-P3 HR), the maximal value of the total electric field was 8.96 V/m. The obtained value is approximately twice as high as that in the section P1 HR-P2 HR. We assume that obtained results are because P3 HR is part of the Zagreb Ring Road. Another factor that contributes to the higher electric field value is the deployment of the base station antennas.  
The median of \(E_{total}\) is 0.24 V/m for road section P1 HR-P2 HR and 0.25 V/m for road section P2 HR-P3 HR. The lowest \(E_{total}\) value in the two road sections was 0.11 V/m. We see that the \(E_{total}\) upper quartile (Q3 = 0.31 V/m) is about the same for the two distributions. In addition, from Figures 7 and 8, we can see that the electric field on the probe \(y\)-axis has a slightly higher median in the two road sections. We assume that the obtained results are caused by the distribution of the E-field inside the car, which varies due to the many different routes of propagation of electromagnetic waves from the transmitter/s to the LSProbe. The electromagnetic waves penetrate through windows and reflect off the car chassis. In addition, some electromagnetic waves penetrate the car's seats. We suppose that measured single higher values of the electric field are due to the passage of the vehicle near EMF sources.
# Precipitable Water Vapor Retrieval Based on DPC Onboard GaoFen-5 (02) Satellite
## 3 Results  
### Global Distribution of PWV Retrieved from DPC Data  
Figure 3 shows the mean values of global water vapor from 20 January 2022 to 29 January 2022. The blank areas denote no retrieval results, which happens in cloud-contaminated regions. During the last winter season, most of the regions were dry in the Northern Hemisphere. The inland areas of North America, Europe, Asia, and northern Africa had low water vapor contents (<2 cm). Coastal and off-shore regions such as southeastern India and southwestern Africa had relatively high water vapor values, about 2 cm. The PWV contents were high (>3 cm) in Myanmar and Thailand, which have considerable rainforest coverage. Places such as Brazil, Congo, and northern Australia with  
rainforest land cover had high water vapor contents. In this period, the DPC PWV data derived from our developed algorithm illustrate the climate-influenced global water vapor content distribution well.  
Since PWV has obvious zonal distribution characteristic, we calculated the zonally averaged absolute PWV values, which are depicted in Figure 4. As expected, the PWV content was higher at the mid-lower latitudes of the southern summer hemisphere. The mean PWV content was higher than 2 cm at latitudes from \(-40^{\circ}\) to \(10^{\circ}\). Two obvious peaks of PWV were found at \(-7^{\circ}\) and \(-21^{\circ}\), with PWV content being higher than 4 cm. Rainfall, continuous cloud cover, and the transpiration of plants contribute to rainforest moisture in these mid-lower-latitude regions.  
Figure 5 shows the variation in the statistical results of global mean PWV derived using the DPC. The highest global mean PWV value was 2.032 cm on 20 January 2022, and  
Figure 4: The 10-day zonally averaged PWV values observed using the DPC.  
Figure 3: Global mean PWV values derived from DPC data for 20 January 2022 and 29 January 2022.  
the lowest value was 1.541 on 29 January 2022. A clear downward trend could be seen, except for two instances of PWV value increment on 25 January 2022 and 27 January 2022.