esa-sceva/satcom-chunk-collection huggingface dataset:
# The Rise of Quantum Information and Communication Technologies
## 2 Challenges and Opportunities Enabling the Rise of QICTs
In quantum computing, random fluctuations have to be controlled as they occasionally flip or randomize the state of qubits during processing. Quantum software ecosystems are very active but rather fragmented: efforts are mainly directed to define languages enabling programmers to work with high-level abstractions. Among the other challenges for accelerating this trend, there is a lack of a common terminology and language, which are essential to reach standard solutions. A commonly shared terminology will enable governments, industry, and innovation communities to more effectively interact and operate towards common goals for developing industrial quantum ecosystems. Moreover, another major obstacle hindering developments and large deployments of QICT infrastructure is the fact that industry choices have not yet consolidated one type of quantum hardware. A quantum hardware abstraction layer (Quantum-HAL) would simplify and speed-up the development of quantum platforms, services, and applications. As in traditional computing systems, a hardware abstraction layer is an abstract layer of programming that allows a computer operating system to interact with a hardware device at an abstract level (rather than at a detailed hardware level); similarly, the Quantum-HAL extends this concept for quantum computing and networking. Eventually in the cybersecurity areas regarding ICT and telecommunication infrastructures, there is also a growing interest in post-quantum cryptography (PQC). PQCdoes not use any quantum technologies, but it refers to cryptographic algorithms that are thought to be secure against a cryptanalytic attack by quantum computers. QKD and PQC have different applicability scenarios in future quantum infrastructures, but their potential end-to-end integration (still under study) is very attractive. Concerning the most recent priorly stated methods, in [9], four different methods of interconnecting remote QKD networks are proposed. The proposed approaches combine, in a transparent way, different fiber and satellite physical media, as well as common standards of key-delivery interfaces. The testbed interconnections are designed to increase security by utilizing multipath techniques and multiple hybridizations of QKD and PQC algorithms.
## 2 Materials and Methods
### Trait-Trait and Juvenile-Adult Correlations  
The growth traits (Dbh and Th) were positively and highly significantly correlated (r = 0.52 *** in Rodna and r = 0.75 *** in Paltinis), and also significantly and negatively (favorable) correlated with the stem straightness (r = \(-\)0.22 *** and \(-\)0.26 *** in Rodna and r = \(-\)0.04 and \(-\)0.13 * in Paltinis), which allowed forward selection after a growth trait (Table 3). The correlations of the growth traits with the branch characteristics are unfavorable (r = 0.15 * and 0.51 *** in Rodna and r = 0.37 *** and 0.63 *** in Paltinis for Bd), which suggests the application of a two-stage breeding strategy for the trees with higher Th and lower Bd inside of the best families (the X one).  
For the CP families, significant to highly significant juvenile-adult correlations (Table 3) were registered both for Th (r = 0.42 * in Rodna and r = 0.72 *** in Paltinis) and Dbh (r = 0.43 * in Rodna and r = 0.71 *** in Paltinis), which suggests that in the future, forward selection could be practiced even from the juvenile stage, at the seedlings age of 14 years. Both for Th and Dbh, at the adult and juvenile ages, the best performing mother tree was X, with an insignificant exception for Th in the Rodna trial. At the opposite pole, mother 3 registered low results in both trials and at the two analyzed ages. The age-age trends differ significantly for the CP and SP families. Thus, if at the seedling age of 7 years, the average values for Th and Dbh were almost identical, at the adult age, CP was superior by 14-42%.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Tial**} & \multirow{2}{*}{**Mother**} & \multicolumn{2}{c}{**Bd**} & \multirow{2}{*}{**Hg**} & \multirow{2}{*}{**Mother**} & \multicolumn{2}{c}{**Bf**} & \multirow{2}{*}{**Hg**} & \multirow{2}{*}{**Mother**} & \multicolumn{2}{c}{**Ss**} & \multirow{2}{*}{**Hg**} \\  & & & & & & \multicolumn{2}{c}{**\%-**} & & & \multicolumn{2}{c}{**-index**} & \\ \hline \multirow{8}{*}{Rodna} & 3 & 2.48 & * & & 2 & 18.8 & * & & 2 & 1.82 & * & \\  & 2 & 2.56 & * & & 3 & 18.8 & * & & X & 1.95 & * & \\  & Y & 2.62 & * & & Y & 18.9 & * & & 209 & 1.99 & * & * \\  & 209 & 2.65 & * & * & X & 19.1 & * & & 3 & 2.01 & & * \\  & X & 2.74 & & * & 209 & 19.4 & * & & Y & 2.26 & & * \\ \hline \multirow{8}{*}{Paltinis} & Y & 2.27 & * & & & Y & 14.3 & * & & 2 & 0.68 & * & \\  & 2 & 2.35 & * & * & & X & 14.3 & * & & X & 0.69 & * & \\ \cline{1-1}  & 3 & 2.41 & * & * & 2 & 14.9 & * & * & 3 & 0.71 & * & * \\ \cline{1-1}  & 209 & 2.45 & & * & 209 & 15.4 & * & * & 209 & 0.75 & & * & * \\ \cline{1-1}  & X & 2.50 & & * & 3 & 15.6 & & * & Y & 0.96 & & * \\ \hline \hline \end{tabular}
\end{table}
Table 2: Duncan test for branches’ diameter (Bd), branches’ finesse (Bf), and stem straightness (Ss) in the Rodna and Paltinis trials. Homogeneous groups (Hg, the same vertical *) for \(\alpha=5\%\).
## 1 Introduction  
Optimal power flow (OPF) is the mathematical tool used to find the optimal settings of the power system network [1]. The main target of the OPF problem is to optimize a specific objective function while satisfying feasibility and security constraints [2]. OPF has been broadly used in previous studies [3], and has served as a substantial optimization test problem because it is characterized as multidimensional, large-scale nonlinear nonconvex, and highly constrained [4; 5]. Several OPF formulations have been developed during the last few decades in order to optimize the operation of an electric power system subject to physical constraints [6]. The emerging optimization problem uses different names and different objective functions [7]. A lot of OPF solution approaches have been developed, each with distinct mathematical characteristics and computational requirements [8; 9]. In recent years, OPF optimization problems have regained importance due to the rapid adoption of distributed energy resources in the network [10]. The integration of distributed and intermittent renewable energy sources, such as photovoltaic (PV) systems and wind energy (WE), into modern power systems has introduced new types of challenges for operating and managing the power grid [11]. The stochastic nature of WE and PV units must be taken into consideration to ensure successful implementation of these intermittent energy sources to the network [12]. Solving the OPF problem has become more complicated with massive incorporation of renewable resources that impose volatile dynamics to the power grid because of their uncertainty. Conventional optimization methods, like linear (LP) and nonlinear programming (NLP) [13], quadratic programming (QP) [14], interior point method (IPM), and [PERSON]'s method [15] showed excellent convergence characteristics in solving OPF problems; however, they use theoretical assumptions not suitable for practical systems having non-differentiable, non-smooth, and nonconvex objective functions. Sometimes, the preceding approaches fail to represent the main characteristics of the fuel cost as a convex function [16]. Such a situation emerges when piecewise quadratic cost, valve points, and prohibited operating zones characteristics are presented [17]. Usually, multiple trials and accurate adjustment of associated parameters are needed to achieve the optimal solution for a specific problem. As a result, we need a faster and more robust algorithm to solve realistic OPF problems. Recently, many publications have focused on metaheuristics to solve hard optimization problems. Metaheuristics, based on a common set of principles which make it possible to design solution algorithms, may be used to overcome the abovementioned weaknesses. Most metaheuristics have the following features: they are inspired from nature, they do not use the objective function's Hessian or gradient matrix, they make use of stochastic components, and they have many parameters that need to be adapted to the problem [18].
# RCLED Optimization and Nonlinearity Compensation in a Polymer Optical Fiber DMT System
## 5 Results and Discussions  
The block diagram of the transmission system is shown in Figure 1 based on the off-line processing. A 10-bit DAC and 8-bit analog-to-digital conversion (ADC) are used in the signal generator and the signal demodulator, respectively. The measured 3 dB bandwidth of RCLED is around 135 MHz when the system works at \(I_{\text{DC}}=20\) mA. The PD gives a current output proportional to the detected optical power at a responsivity of 0.3 A/W. The background noise power spectral density (PSD) is measured at around -110 dB/Hz. Since only the static case is modeled in this paper, the frequencies below the RCLED's 3 dB bandwidth have a close and similar power transfer function as the DC region. Accordingly, the signal bandwidth must be less than the RCLED's 3 dB bandwidth. Therefore, the parameters of the following DMT modulation are designed based on Table 1. In addition, a bit-loading allocation for the DMT signal described in Figure 7 is also adopted in our transmissions. For fair comparison, all DMT schemes own the fixed clipping ratio [16] of 12 dB.
## 4 Results
### Calculation and Analysis Model of Frost-Heave Force
#### 4.2.1 Frost-Heave Force Calculation Model  
At present, the frost-heave force of local water-retaining cavities is mostly calculated by referring to the theoretical definition of the tunnel's overall freeze/thaw cycle. The frost-heave force calculated from the field tests ranged from 0.06 to 0.30 MPa [27], and the frost-heave force calculated analytically could reach more than 10 MPa [28]. The results of the two are quite different. There is currently no reliable basis for calculating the frost-heave force caused by the freezing of trapped water behind the tunnel lining.  
Some scholars have conducted research on the frost-heave force of the local water storage space around tunnels [29]. Based on the cavity caused by the uneven early excavation surface of the tunnel, a frost-heave force calculation model considering the elastic resistance of the lining has been proposed. On this basis, [PERSON] [13] modified and re-derived the original local water frost-heave model by considering the elastic resistance of ice body deformation, using three series of springs to simulate the cavity deformation after the frost-heave force was generated. At the same time, the surrounding rock and the deformation coordination conditions for the simultaneous displacement of the lining structure were used to derive the frost-heave force calculation formula.  
However, the above models all ignore the freezing process of water in the cavity and the supply and discharge conditions of groundwater during freezing. They assume that the water-retaining cavity is closed, which is different from the actual situation. Therefore, we propose a frost-heave force computational model that considers the excretion conditions of the empty cavity. Since tunnel vault voids often occur at the highest point of each mold lining near the end of the formwork, and the void cavities formed by concrete pouring aremostly circular wedge-shaped voids, the frost-heave force was calculated based on circular wedge-shaped voids, as shown in Figure 11.
# Radial and Oblique Impact Testing of Alpine Helnets onto Snow Surfaces
## 5 Conclusions  
The current study found that the recreational alpine sports helmet with RDS and the ski racing helmet with a smooth outer shell significantly reduced mean peak angular acceleration compared to helmets with no RDS in oblique impact tests. Snow samples collected during later times with higher ambient temperatures were associated with significantly lower mean peak linear headform accelerations for radial impact tests. The current study helps to bridge the knowledge gap between real helmet impacts on alpine snow slopes and laboratory helmet impacts onto rigid surfaces, which is the current method to assess the impact attenuation performance of helmets for alpine sports. In addition, the current study informs stakeholders in standards tests for alpine sports helmets regarding the potential to include oblique impact performance criteria. Future studies should continue to explore oblique impact tests in situ on snow slopes and use foams with similar material properties to snow in laboratory oblique impact tests to avoid the consistency issues identified in the current study.  
Conceptualization, D.A.P., P.H., S.K. and A.S.M.; methodology, D.A.P. and P.H.; formal analysis, D.A.P. and R.M.; investigation, D.A.P., R.M. and P.H.; resources, P.H. and S.K.; writing--original draft preparation, D.A.P. and R.M.; writing--review and editing, P.H., S.K. and A.S.M.; supervision, S.K. and A.S.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** D.A.P. was funded by an Endeavour Research Fellowship from the Australian Government. S.K. and R.M. were supported by research funds from Tillvakterket (Swedish Agency for Economic and Regional Growth) for the project Augmented Sports and European Union's Horizon 2020 research and innovation program under the Marie-Curie Grant Agreement No. 642662.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** Data are available upon reasonable request.  
**Acknowledgments:** The authors would like to thank Flottsbro Alpin ski resort for allowing snow sample collection. At the time of the study, D.A.P. and A.S.M. were affiliated with the Australian Centre for Research into Injury in Sport and its Prevention (ACRISP), which is one of the International Olympic Committee Research Centres for Prevention of Injury and Protection of Athletic Health.  
**Conflicts of Interest:** [PERSON] is the Chief Science Officer and Co-Founder of MIPS AB (Stockholm, Sweden).
## 4 Software Tools
### Recording the Network Traffic  
From the point of view of testing a network's security and performance, it is necessary to record the network traffic. In this work, we consider the following two software tools: Wireshark (ver. 4.0.1) and Tcpdump (ver. 4.99.5).  
The free tool Wireshark is one of the best-known and widely-used tools for capturing and analyzing network traffic [56]. It is available for many operating systems, including Windows, Linux, and macOS. It provides in-depth packet analysis at the protocol level and offers a convenient graphical user interface. It supports capture and display filters, which allows one to easily search for packets of interest. TShark is a version of Wireshark that uses only command-line communication with the user. In contrast to Wireshark, more effort is required to configure and use this tool. Secondly, it is a rather resource-intensive program, which can cause problems when only low-performance or old machines are available. Therefore, for simple tasks on servers where no graphical user interface (GUI) is possible, tcpdump is recommended.  
Tcpdump is a command-line-style tool for capturing packets on Unix/Linux systems [57]. It is also available for Windows, under the name WinDump (ver. 4.3). It is renowned for its low system resource requirements and high speed. As a result, it is a great solution when we use servers without a graphical user interface. It saves captured packets to.pcap files and allows for filtering during capture.  
In this work, tcpdump is used to capture and log network traffic on Linux servers. Data collection is performed automatically. Next, Wireshark is used for further analysis, understanding the dependencies and selecting the special data fragments.
## 3 Results and Discussion
### Single-Photon Nature  
The HBT experiment setup was used to verify the single photon nature of our waveguide coupled on-chip source. The second-order correlation function g\({}^{(2)}(\tau)\) was measured both from the emission at the position of the quantum dot (a) and at the point of output coupler (b). Caused by low coupling efficiency, the count rate is not very high, only about 2000 counts per second. In order to achieve reasonable photon count rates, the power was set to have the strongest exciton line. The signal-to background ratio decreased leading to a diminished contrast of the antibunching with increasing power levels [30]. Depicted in Figure 4, from a fit to the raw data, we can see that g\({}^{(2)}(\tau=0)\) was clearly below 0.1 in the configurations and revealed excellent single photon behavior. As shown in Figure 4c, the second-order cross correlation function g\({}^{(2)}(\tau)\) between the emission from the QD and the output coupler was also measured, which proved that photons collected at the end of the waveguide originated from the QD. The results unambiguously proved that a single photon emission from GaAs QD was successfully generated and routed along the on-chip waveguide with preservation the single photon behavior. To clarify, we chose the output coupler with a higher emission photon counts for the measurements.  
Figure 4: \(Cont.\)  
Figure 3: Spectra collected at positions of the quantum dot (black dash line) and out-coupler at the end of the waveguide (red line).
## 2 The Problem of Crack Measurement Based on Machine Vision
### Inaccurate Crack Identification Method  
Canny edge detection has been considered to be an objective mathematical method [31,32] as the edges are defined as the gradient in the \(x\) and \(y\) directions generated by the one-order finite difference:  
\[P_{x}[i,j]=(I[i,j+1]-I[i,j]+I[i+1,j+1]-I[i+1,j])/2, \tag{5}\]  
\[P_{y}[i,j]=(I[i,j]-I[i+1,j]+I[i,j+1]-I[i+1,j+1])/2, \tag{6}\]  
where \(i\) and \(j\) are the locations of each pixel, and \(I\) is the pixel value. Then, its gradient amplitude can be computed as  
\[M[i,j]=\sqrt{P_{x}[i,j]^{2}+P_{y}[i,j]^{2}}, \tag{7}\]  
and the direction as follows:  
\[\theta[i,j]=\arctan(P_{y}[i,j]/P_{x}[i,j]), \tag{8}\]  
However, for cracks in reality, redundant edges are always generated and make the results undesirable, and the blurring of crack edges may cause the detected results to be discontinuous. The Canny algorithm calculates the position of the target edge based on the image gradient, which is calculated by referring to the gray values on both sides of the edge, meaning that the places with a large gray difference in the image may be judged as edges. However, the cracks not only have grayscale features but also morphological features. Only using gradient expression will cause many target edges that are not cracks to be identified as crack edges, resulting in noise. Meanwhile, when the gray difference of the crack is small, the Canny algorithm cannot calculate the edge position, as shown in Figure 2b.  
Given the irregular shape of the cracks and the interference from background noise, deep learning algorithms are selected by most scholars as the most practical crack detection method. However, for an object as narrow as a crack, each pixel size can affect the accuracy of the results, especially when photographing from a long distance [17,33]. The accuracy of crack segmentation can be analyzed by some indicators, but it still should be refined further during measurement application. Meanwhile, since the proportion of crack pixels in the image is less than the background, this can cause sample imbalance, and the ordinary loss function cannot effectively describe the difference between the predicted value and the ground truth [11,34]. Therefore, combining the advantages of the improved U-net  
Figure 2: Problems of machine vision measurement methods. (**a**) Images of calibration board from side direction; (**b**) edges detected by Canny method, red circle is the discontinuous part.  
algorithm to automatically extract cracks and the Canny method to accurately calculate the characteristics of crack edges would be a suitable crack measurement approach.
## 4 Results and Discussion
### Comparison of Computational Results
Table 4 shows the variation in the wind farm power output predicted by the 40 \(\times\) 60 grid method and the coordinate method relative to the 20 \(\times\) 30 grid method. As the wind turbine number increases from 40 to 46, the power increase by using the 40 \(\times\) 60 grid method varies roughly from 1% to 1.5%, while the  
Figure 4: Wind farm cost of energy results (in million dollars per megawatt) for different design methods. power increase by using the coordinate method varies from 1.5% to 2%, depending on the number of installed wind turbines.
## 5 Taxonomy of Soft Computing Techniques Aware Clustering-Based Routing Protocols in VANETs
### Fuzzy Logic  
Fuzzy Logic (FL) is a numerical tool developed to convey approximate human thought. FL produces intermediary standards centered on inference rules and linguistic variables, instead of a conventional set theory in which the outputs are either real or untrue. There are four fundamental components of a fuzzy logic scheme: fuzzification, defuzzification, a fuzzy rule base, and a fuzzy inference engine, as shown in Figure 6. The fuzzification component plans the device responses to the appropriate fuzzy sets. It designates every fuzzy set, which is defined by a language word, such as \"high\", \"low\", \"moderate\", \"small\", and \"large\" membership degree.  
Figure 6: Fuzzy Logic System.  
If-then rules are stored by the fuzzy inference engine, from which the fuzzified values with the aid of the fuzzy rule are mapped to linguistic output variables. The results obtained from the inference scheme are translated using defuzzification, such as the averaging technique and centroid technique, into crisp values [34].  
[PERSON] et al. [35] suggested a stable multilevel clustering routing protocol in VANETs. It is an expansion of the AODV protocol at the first level, which has been strengthened by using fuzzy logic to establish efficient routing between members of the cluster. For routing among CHs and destinations, Tabu search was used at a higher level. This approach is used to solve problems with hybrid optimization and utilizes a cost function to choose a resolution from a range of potential solutions. Node size, node velocity, node angle, and reliability are the efficient metrics utilized for selecting the optimum path. The simulation result showed an improved transmission rate of packets and reduced average E2E delays. The number of packet failures contrasted with previous protocols.  
The proposed protocol uses fuzzy logic to improve the routing between the cluster members. The parameters taken for the research work are distance, direction, and velocity. However, more parameters may be considered for future work, looking toward the dynamic scenario in VANET.