esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Discussion  
### Validation of Equations  
In order to verify the validity of the dynamic equations obtained in this study, their dynamic characteristics in this study and those in [22] were tested under the same conditions. In [22], the out-of-plane motion is ignored in the model, thus the out-of-plane motion should be neglected in Equations (9)-(14). Substituting the out-of-plane libration angles \(\alpha_{1}=\alpha_{2}=\alpha_{1}^{\prime}=\alpha_{2}^{\prime}=\alpha_{1}^{ \prime}=\alpha_{2}^{\prime}=0\) into Equations (9)-(14), we have four degrees of freedom, and the corresponding generalized coordinates are \(\theta_{1},\theta_{2}\) and \(l_{1},l_{2}\). Therefore, the same physical model is achieved. The equations of motion were verified by comparing the dynamic responses calculated in this study with the model responses reported by [PERSON] et al. [22] with the same initial conditions and physical parameters. To facilitate the comparison, the initial conditions and relevant physical parameters of the system were set uniformly. The initial conditions were set as \(\theta_{1}(0)=20^{\circ},\theta_{2}(0)=15^{\circ},\theta_{1}^{\prime}(0)= \theta_{2}^{\prime}(0)=0\), and \(l_{1}(0)=l_{2}(0)=1\) km, which were randomly selected within reasonable limits. In order to reflect the movement of the tether, the tether velocity was set to \(l_{1}^{\prime}=l_{2}^{\prime}=1\) m/s. The physical parameters of the system were the same as those in [22]: orbit radius set at \(r=7100\) km, mass of main satellite \(m_{0}=10^{4}\) kg, mass of subsatellites set at \(m_{1}=100\) kg, \(m_{2}=500\) kg. The simulations were solved in a circular orbit in MATLAB 2017b with ODE45, an RK4 numerical integrator with variable step size.  
Figure 4a shows the numerical results of the libration angles in the present study and in [22] when the deploying velocity of tethers is 1 m/s. The libration angles calculated in this study (indicated by squares) are in good agreement with those in [22], as depicted in Figure 4a. Figure 4b shows the tether tension in the present study (dotted line) and [22] (solid line). The numerical results indicate that the tether tension in [22] was slightly higher compared to the present study. From Equations (10) and (13), the tether tension comprises Coriolis, in-plane roll, and gravitational acceleration, which are the same as the results in [22]. In this simulation, the Coriolis and in-plane roll acceleration in [22] and this study are the same. So, the difference arises from the variation in gravitational force truncation during the approximation process. In this study, only the first-order terms were retained when expanding the gravitational force using the Taylor series. It can be concluded that an accurate dynamic model of gravity helps in accurately estimating tether tension.
## 3 Problem Formulation  
Path planning is a crucial component of a UCAV mission control system, and emergencies must be taken into account. The objective of this numerical problem is to find an optimal or near-optimal flight path for the UCAV that avoids obstacles and reaches its destination safely. The 2D mathematical model for UCAV path planning used in this paper was introduced in [44]. As shown in Figure 1, there are several threats on the battlefield, such as radars, missiles, and natural barriers. The impact of each threat is represented by a circle with a particular radius, which signifies the risk area that must be avoided during the flight. If any part of the path falls within a circle, the risk of the guided UCAV being damaged increases. Hence, considering all existing threats on the battlefield and fuel consumption, the main goal of the flight mission is to find an optimal flight path that connects the starting point to the endpoint.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Optimization Techniques-Based UAV Path Planning**} \\ \hline
**Ref** & **Proposed Algorithm** & **Advantages** & **Limitations** \\ \hline \multirow{5}{*}{[43]} & The proposed method is RGSO-UAV, which is a path planning algorithm inspired by Reverse Glowworm Swarm Optimization (RGSO) for Unmanned Aerial Vehicles (UAVs) in a 3D dynamic environment. & The advantages of RGSO-UAV are that it is effective in finding optimal or near-optimal paths for UAVs in dynamic environments while also considering energy consumption, obstacle avoidance, and smoothness of the path. & The limitations of RGSO-UAV are not explicitly mentioned in the abstract, but further investigation of the paper may reveal any potential shortcomings, such as computational complexity or limited applicability to certain types of environments. \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 1: The schematic diagram for modeling the combat field. The \(X\) and \(Y\) axes represent the length and width of the horizontal battlefield, respectively. S and T indicate the starting and terminal points, respectively. All blue circles represent the threats on the battlefield. The black line connecting \(S\) and \(T\) directly represents the segment \(ST\). The blue points indicate all generated nodes, which must be linked together to construct the optimal path indicated by the red dashed line.
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
###### Abstract
This paper presents two contributions: our first contribution is the evaluation of SIKE against this class of attack. Considering SIKE is one of the alternate candidates, this evaluation represents an important part of the overall assessment of this scheme within the NIST standardization process. Our second contribution is a dedicated key-recovery algorithm for SIKE in the cold boot attack setting. This contribution is in alignment with the current tendency of devising and constructing novel key-recovery algorithms for various schemes in this setting, as our review of the literature, discussed at length in Section 2.3, reveals. The organization of this research paper is as follows. In Section 2, we discuss the required background on cold boot attacks, detailing on how this attack may be carried out by an attacker. Additionally, we describe the cold boot attack model we assume throughout this paper and also review the literature relevant to our work, focusing on the evaluation of various cryptographic schemes in this setting. Finally, we detail our approach to key-recovery, i.e., the general technique we use to design our key-recovery algorithm. In Section 3, we describe the supersingular isogeny key encapsulation mechanism and its reference implementation, mainly focusing on the key generation algorithm. In Section 4, we give a detailed account of our key-recovery algorithm and our choice for key enumeration algorithms. In Section 5, we evaluate our key-recovery algorithm, focusing on the success rate of our algorithm and how to integrate a quantum key search algorithm with it to improve its overall performance. Finally, in Section 6, we conclude our paper and give insight into a research line to continue with our work.
# Burst Channel Error Reduction Based on Interleaving for Efficient High-Speed Wireless Communication
## 3 Proposed Interleavers
### Two-Dimensional Deterministic Interleaver Design  
The main idea behind this interleaver is expanding the 1-D deterministic interleaver into 2-D [33]. The proposed 2-D deterministic interleaver works as follows, assuming the case of \(N_{r}\) rows and \(N_{c}\) columns:  
* First, interleaving is split into row-wise and column-wise cases.
* The seeds for row-wise and column-wise interleavers are assumed to be two prime numbers. After interleaving, bits will be located as follows: \[\begin{array}{ccc}row-wise&&column-wise\\ 0\to 0&&0\to 0\\ 1\rightarrow(1\times p_{row})mod\ N_{r}&&1\rightarrow(1\times p_{col})mod\ N_{c}\\ 2\rightarrow(2\times p_{row})mod\ N_{r}&&2\rightarrow(2\times p_{col})mod\ N_{c}\\ \vdots&&\vdots\\ N_{r}-1\rightarrow((N_{r}-1)*p_{row})\ mod\ N_{r}&&N_{c}-1\rightarrow((N_{c}-1)*p_{col})\ mod\ N_{c}\end{array}\] where \(p_{row}\) and \(p_{col}\) are row-wise and column-wise seeds, respectively.
* Finally, the new locations are mapped back into the two-dimensional interleaver, resulting in two-dimensional interleaved bits. For instance, consider an \(8\times 8\) 2-D deterministic interleaver with \(p_{row}=3\) and \(p_{col}=5\). The new locations of the bits will be as follows: \[\begin{array}{ccc}row-wise&&column-wise\\ 0\to 0&&0\to 0\\ 1\rightarrow(1\times 3)mod\ 8=3&&1\rightarrow(1\times 5)mod\ 8=5\\ 2\rightarrow(2\times 3)mod\ 8=6&&2\rightarrow(2\times 5)mod\ 8=2\\ 3\rightarrow(3\times 3)mod\ 8=1&&3\rightarrow(3\times 5)mod\ 8=7\\ 4\rightarrow(4\times 3)mod\ 8=4&&4\rightarrow(4\times 5)mod\ 8=4\\ 5\rightarrow(5\times 3)mod\ 8=7&&5\rightarrow(5\times 5)mod\ 8=1\\ 6\rightarrow(6\times 3)mod\ 8=2&&6\rightarrow(6\times 5)mod\ 8=6\\ 7\rightarrow(7\times 3)mod\ 8=5&&7\rightarrow(7\times 5)mod\ 8=3\\ \end{array}\] The arrangement of the bits prior to interleaving is seen in Figure 3(a). By applying the new order of column- and row-wise interleaved bits acquired from the precedingcalculation and mapping the locations, we get the bit arrangement shown in Figure 4b after interleaving.
# Comparison of Temperature Dependent Carrier Transport in FinFET and Gate-All-Around Nanowire FET
## 5 Conclusions  
In this work, the temperature dependent characteristics of NMOS GAA NW-FETs have been investigated comparing with FinFET. In GAA NW-FET, the experimental \(I_{DS}\)-\(V_{GS}\) characteristics show higher \(I_{DS}\) and better \(SS\) than in FinFET from 25 to 125 \({}^{\circ}\)C. The modulation trend of \(V_{TH}\) and \(SS\) with temperature is not significantly different between GAA NW-FET and FinFET. At weak \(N_{inv}\), \(\mu_{eff}\) is mainly limited by phonon scattering for both GAA NW-FET and FinFET. However, at strong \(N_{inv}\), the \(\mu_{eff}\) of GAA NW-FET is less impacted by the SR scattering. FinFET shows smaller \(d\mu_{eff}\)/\(dT\) (\(\sim\)\(-\)0.162 cm\({}^{2}\)/V-s/\({}^{\circ}\)C) and higher mobility degradation coefficient \(\alpha\) (0.96), compared to that of GAA NW-FET (\(\sim\)\(-\)0.016 cm\({}^{2}\)/V-s/\({}^{\circ}\)C and 0.6, respectively). This means that the carrier transport in GAA NW-FET is not mainly limited by SR scattering but phonon scattering at high \(N_{inv}\). In the case of FinFET, the SR scattering mainly limits the carrier transport at high \(N_{inv}\).  
Data curation, S.K., D.J. and J.W.L.; Formal analysis, S.K., D.J., J.K., R.R., B.P., J.M., H.M., T.C., N.H. and J.W.L.; Funding acquisition, B.P. and N.H.; Investigation, R.R., J.M., H.M. and T.C.; Project administration, B.P. and N.H.; Validation, R.R., J.M., H.M. and T.C.; Visualization, S.K., D.J., J.K. and J.W.L.; Writing--original draft, S.K., D.J. and J.W.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MOTIE (Ministry of Trade, Industry & Energy (10067808)) and KSRC (Korea Semiconductor Research Consortium) support program for the development of the future semiconductor device, and the National Research Foundation of Korea (NRF) grant funded by the KOREA government (MSIT) (2019R1F1A1060687).  
The imec sub-14 nm program members and the amsimec (test lab) are acknowledged for their support.  
The authors declare no conflicts of interest.
## 4 Results and Discussion  
### Initial Parameters  
To validate the method proposed in Section 3, this paper analyzes a case in Ningbo, Zhejiang Province, China. The total area of the study area is 3.75 square kilometers. The area map is shown in Figure 4. The original regional map and its geographic coordinates are collected from Open Street Map and edited by JOSM. The total heating load of this area is 18.5 MW, including 29 load points. Two energy stations are planned to be built. The information of each load point is shown in Appendix A.  
The simulation parameter settings are shown in the table [16]. In Table 1, the life cycle of a DES is 20 years. In the paper, the investment cost of energy station is calculated according to the load demand and the unit capacity investment cost of energy station equipment is 10,000 RMB. This paper does not study the influence of pipe diameter on the result, so the value of pipe diameter is 500 mm. The electricity price is 0.8 RMB per kilowatt hour, the thermal conductivity of the insulation material is 0.06 W/(m\(\cdot\)\({}^{\circ}\)C), and the depreciation rate is 1%.
## 2 Mathematical Modeling of HESS
### DC Bus  
The lithium battery and supercapacitor are connected in parallel to the _DC_ bus through a bidirectional _DC/DC_ converter. The model is shown in Figure 6.  
As shown in Figure 6, \(R_{Bat}\) is the internal resistance of the lithium battery, and \(R_{SC}\) is the internal resistance of the supercapacitor, state-space method is used to establish a continuous system model to derive the differential equations for the lithium battery and supercapacitor, as indicated in Equations (4) and (5).  
\[\frac{dI_{Bat}}{dt}=\frac{1}{L_{Bat}}[V_{Bat}-R_{Bat}I_{Bat}-V_{DC}(1-\alpha_{Bat })] \tag{4}\]  
\[\frac{dI_{SC}}{dt}=\frac{1}{L_{SC}}[V_{SC}-R_{SC}I_{SC}-V_{DC}(1-\alpha_{SC})] \tag{5}\]  
where \(\alpha_{Bat}\) is the duty cycle of the _DC/DC_ converter connected to the lithium-ion battery, and \(\alpha_{SC}\) is the duty cycle of the _DC/DC_ converter connected to the supercapacitor.
## 2 Results and Discussion
### Phenolic Antioxidants and Structure-Activity Relationships in Goji Juice  
#### 2.3.1 Total Phenolics and Flavonoid Contents  
The contents of total phenolics increased in the goji juice samples after fermentation (\(p<0.05\)) (Figure 2a). The concentration of total phenolics was 467.13 \(\pm\) 1.73 mg/100 mL in the raw juice but increased to 1.27, 1.11, and 1.07 times in the goji juice samples fermented with SLV, SZP, and SZVP, respectively. Previous studies have shown that fermentation generally increased the content of phenolics. For example, [PERSON] et al. [30] fermented elderberry juice with \(L\). _Hannosus_, \(L\). _plantarum_, and \(L\). _casei_, and found that the content of total phenolics increased after fermentation. The acid substances produced by \(L\). _plantarum_ during fermentation increased the content of total phenolics in broccoli [31]. The finding is consistent with the results obtained in the present study. It is proposed that enzymes and carboxylic acids produced by the fermentation process may have destroyed the structure of the cells to release phenolic substances, thereby increasing the total phenol content after fermentation.  
Figure 2: Contents of total phenols (**a**) and flavonoids (**b**) in raw juice and fermented goji juices. Different superscript letters (a–d) in the columns indicate significant difference (\(p\leq 0.05\)).  
The flavonoid contents varied among the raw juice and the juice samples fermented with SZP and SZVP, and the flavonoid content was the highest in the juice fermented with SLV compared with the other samples (Figure 2b). It was reported that fermentation increased the contents of flavonoids in wheat and corn straw extracts, which may be related to the \(\beta\)-glucosidase produced by \(L\). _plantarum_ during fermentation [32]. However, in the present study, the flavonoid contents in the juice samples fermented with SZP and SZVP containing \(L\). _plantarum_ differed clearly from that in the raw juice, possibly due to the interactions among metabolites produced during fermentation by each strain.
## 3 Cases and Measurements  
### Case Definition  
Three cases are chosen to test all the extrapolation methods as shown in Table 5. The terrains include mountain, plateau and plain, and the surface types include wasteland, shrubbery and farmland.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stability Conditions** & **Mountain** & **Plain** \\ \hline A & \(L>-0.032\) & \(L>-0.316\) \\ B & \(-3.162<L\leq-0.032\) & \(-3.162<L\leq-0.316\) \\ C & \(-316.228<L\leq-31.623\) & \(-63.246<L\leq-3.162\) \\ D & \(L\leq-316.228\), \(L>158.114\) & \(L\leq-63.246\), \(L>158.114\) \\ E & \(-154.952<L\leq 158.114\) & \(-154.952<L\leq 158.114\) \\ F & \(L\leq-154.952\) & \(L\leq-154.952\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification of stability based on \(L\) in different terrains.  
Figure 1 shows the topographic maps of the three cases. The wind tower site SJB is located at an edge of a plateau (Figure 1a,d). The elevation of SJB in the plateau is 1678 m, and the surface type is wasteland. There is a hillside in the southwest of SJB, and flat grassland in the northeast. The GFC wind tower sits on the mountaintop, which has an altitude of 713 m (Figure 1b,e). The surface type of GFC is shrubberg. Due to the special weather conditions in the area, underlying shrub is evergreen throughout the year. The HNH wind tower is located at the elevation of 66 m, where the terrain is flat (Figure 1c,f). The land surface type is farmland and there are a large number of villages in the area.  
Figure 1: Topographic maps of three cases: (**a**) SJB; (**b**) GFC and (**c**) HNH; Contour maps of the three cases: (**d**) SJB; (**e**) GFC and (**f**) HNH.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Site Name** & **Terrain** & **Surface** & **Elevation (m)** \\ \hline
1 & SJB & Plateau & Wasteland & 1678 \\
2 & GFC & Mountain & Shrubbery & 713 \\
3 & HNH & Plain & Farmland & 66 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Wind tower site information.
# Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
## 5 Results and Discussion
### Examination of Data
Concerning the distributions of the district divisions (Figure 7), it was noted that the majority of interventions were recorded in circumscription No. 8 which is near the seaport as previously noted in Section 4. The most prevalent neighborhood was no.10 which is the  
Figure 5: Distribution of feature—Road Length. Figure 6: Distribution of interventions across zones. The plots allowed for analysis on when the interventions were taking place. For the circumscriptions, the interventions were split in time with the majority of interventions taking place within the first 4 year groups for circumscriptions 1, 5, 7 and 8. On the other hand, activities were concentrated in the later years for circumscriptions 2, 4 and 6; with circumscription 3 being the only one with a statistically significant overlap of activities across the period. This is important because it again gives an insight into time intervals that can be set up for monitoring activities within various districts based on the historical activities. It essentially demonstrates timelines of when neighborhoods and circumscriptions need attention and thus intervention. The next important data analytic was the year of the intervention. A violin plot was constructed to highlight when in time the activities were being carried out. This is shown in Figure 10. Within the plot, it can be seen that the majority of interventions occurred in the middle at year group 4, which represents interventions at year 7. This gives the study another important discussion point as it showcases that the interventions are concentrated around this time and thus a larger number of resources could be allocated in the future for this period. The violin plot is used to easily highlight the peaks in the distribution. Figure 10: Violin plot of the distribution of interventions in time. Figure 9: Boxplot of maintenance intervention in the neighbourhood with respect to time. For the other features within the dataset, another interesting feature to highlight was the presence of commercial activities. This is important as commercial activities are likely to lead to higher traffic and general activity and therefore the roads in these areas are likely to suffer from more damager quicker. A boxplot of this feature versus time was plotted and is displayed in Figure 11. This indicates that the areas without commercial activities required maintenance at a later stage than those with the businesses. This was expected but it is important to confirm to validate the use of this data based on typical trends in traffic and road maintenance on pavement deterioration. Once the initial examination of the data were completed, the next phase was the feature selection as it is more difficult to understand which of the other features should be utilized in a modelling framework using only a statistical examination of the distributions of features and this is where the use of the deep learning becomes key.
# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
## 3 Results
### Statistical Results
#### 3.2.1 Satellite Removal Strategy
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**Original RMSE**} & \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Removed**} & \multicolumn{2}{c}{**Satellite-Removed RMSE**} \\ \cline{3-4} \cline{6-6}  & & **Height** & **3D** & & **Parameters** & **Satellite** & **Height** & **3D** \\ \hline \multirow{6}{*}{18 February 2016} & \multirow{6}{*}{0.1699} & \multirow{6}{*}{0.2148} & \multirow{6}{*}{0.2148} & MP1 & G14 & 0.1799 & 0.2247 \\ \cline{5-6}  & & & & MP2 & G15 & 0.1726 & 0.2192 \\ \cline{5-6}  & & & & ROTI & G24 & 0.1769 & 0.2243 \\ \cline{5-6}  & & & & \(\sigma_{\Phi}\) & G2 & 0.1750 & **0.2145** \\ \hline \multirow{6}{*}{2 April 2016} & \multirow{6}{*}{0.0087} & \multirow{6}{*}{0.0329} & MP1 & G12 & **0.0071** & 0.0330 \\ \cline{5-6}  & & & & MP2 & & **0.0066** & **0.0326** \\ \cline{5-6}  & & & & \(\sigma_{\Phi}\) & G14 & 0.0090 & 0.0329 \\ \hline \multirow{6}{*}{13 April 2016} & \multirow{6}{*}{0.8233} & \multirow{6}{*}{0.8521} & MP1 & G9 & 1.0379 & 1.0559 \\ \cline{5-6}  & & & & MP2 & G21 & **0.5325** & **0.6039** \\ \cline{5-6}  & & & & ROTI & G28 & **0.6823** & **0.7235** \\ \cline{5-6}  & & & & \(\sigma_{\Phi}\) & G15 & **0.7308** & **0.7599** \\ \hline \multirow{6}{*}{**SNA0P**} & \multirow{6}{*}{9 May 2016} & \multirow{6}{*}{0.4466} & \multirow{6}{*}{0.5796} & MP1 & G30 & **0.4087** & **0.5206** \\ \cline{5-6}  & & & & MP2 & G31 & **0.4252** & **0.5588** \\ \cline{5-6}  & & & & ROTI & G8 & 0.4642 & 0.6033 \\ \cline{5-6}  & & & & \(\sigma_{\Phi}\) & G21 & 0.4616 & 0.5839 \\ \hline \multirow{6}{*}{6 June 2016} & \multirow{6}{*}{0.5636} & \multirow{6}{*}{1.1350} & MP1 & G16 & 0.6796 & **1.0541** \\ \cline{5-6}  & & & & MP2 & G1 & 0.6431 & **1.1060** \\ \cline{5-6}  & & & & ROTI & & & \\ \cline{5-6}  & & & & \(\sigma_{\Phi}\) & G21 & 0.7575 & 1.3633 \\ \hline \multirow{6}{*}{28 July 2016} & \multirow{6}{*}{0.0099} & \multirow{6}{*}{0.0207} & MP1 & G24 & **0.0089** & 0.0212 \\ \cline{5-6}  & & & & MP2 & & **0.0085** & 0.0303 \\ \cline{5-6}  & & & & ROTI & G21 & 0.0112 & 0.0217 \\ \cline{5-6}  & & & & MP1 & G13 & 0.0024 & 0.0266 \\ \cline{5-6}  & & & & MP2 & & & \\ \cline{5-6}  & & & & \(\sigma_{\Phi}\) & G17 & 0.0010 & 0.0231 \\ \cline{5-6}  & & & & ROTI & G1 & 0.0014 & 0.0245 \\ \hline \multirow{6}{*}{13 October 2016} & \multirow{6}{*}{0.0010} & \multirow{6}{*}{0.0228} & MP2 & & & & \\ \cline{5-6}  & & & & ROTI & G1 & 0.0014 & 0.0245 \\ \cline{5-6}  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Change of the height and 3D positioning errors (m) through the satellite removal strategy as represented by the RMSE at SNA0P. Improved RMSEs (m) are highlighted in bold.