esa-sceva/satcom-chunk-collection huggingface dataset:
# Key-Recycling Strategies in Quantum-Key-Distribution Networks
## 5 QKR Mechanism and Strategy
### Partial Recycling  
As shown in the Algorithm 1, when a data service arrives, a QKD service is generated, and then the key-relay path P\({}_{\text{set}}\) is generated by the Dijkstra algorithm. When the relay process proceeds to node_current, the relay process fails, that is, the 'node_begin-node_current' key pair encrypted by the  
Figure 6: QKP communication message format: broadcast message format and ordinary message format.  
Figure 7: QKR mechanism and three recycling methods: (a) partial recycling, (b) all recycling, and (c) mixed recycling.  
key pair 'node_current-node_next' cannot be decrypted successfully at node_next. However, at this time, we do not know whether the relay failure process is caused by the eavesdropper's activity, and we either do not know whether the eavesdropper successfully obtained both the 'node_begin-node_current' and 'node_current-node_next' keys.  
```
1:For each data service Ser (V\({}_{\text{s-d}}\), R\({}_{\text{sec}}\), t\({}_{\text{s-s}}\), t\({}_{\text{d}}\)) do
2: Generate QKD (V\({}_{\text{s-d}}\), t\({}_{\text{s-d}}\), N\({}_{\text{key\_relay}}\), P\({}_{\text{failure}}\), Tp);
3:For each QKD service QKD (V\({}_{\text{s-d}}\), t\({}_{\text{s-q}}\), N\({}_{\text{key\_relay}}\), P\({}_{\text{failure}}\), Tp) do
4:P\({}_{\text{set}}\leftarrow\)routing computation with D algorithm;
5:If flag bit shows relay failure &&KV\({}_{\text{b-c}}\) >= KV\({}_{\text{c-n}}\) then
6:Recycle key number(b,c) = N\({}_{\text{key\_relay}}\);
7:Else if flag bit shows relay failure & KV\({}_{\text{b-c}}\) < KV\({}_{\text{c-n}}\) then
8:Recycle key number(c,n) = N\({}_{\text{key\_relay}}\);
9:End if
10:End for
11:End for
```  
**Algorithm 1** Quantum Key Partial Recycling  
It is undoubtedly unsafe to directly recycle two pieces of keys at the same time. In this case, if the partial-recycling algorithm is used, it is necessary to define a parameter denoted by KV called 'key value' to evaluate which segment of the key needs to be recycled, which is expressed as:  
\[\text{KV}_{\text{s-d}}=\sum_{i=s,j=s->next}^{i=d->pre,j=d}W_{(i,j)} \,,(i,j)\in P_{set} \tag{3}\]  
The more relay nodes a key pair pass, the more relay keys are consumed, and therefore, the more the key pairs pass through the number of relay nodes, the more precious it is. Therefore, it is obviously wise to recycle the key pair with a high key value and destroy the other pair.  
The advantage of this algorithm is that it has less impact on other services with encryption requirements, the recycling process is easy to implement, and the security of the recovery key is less dependent on the design of the algorithm. The disadvantage of this algorithm is that the security of the recovered key is low and some of the keys are not recycled.  
Partial-recycling algorithm calls [PERSON]'s algorithm once and then performs a summation calculation with complexity O (1). Thus, Partial-recycling algorithm has a complexity of O(V\({}^{2}\)).
## 3 Results
### Classification Model  
In this experiment, we used the logistic regression, random forest, Naive Bayes, SVM and KNN algorithms to perform the classification, and the features fed into the classifiers were the LF/HF, ST and QT. We performed the hyperparameter optimization before training the classifier models, as it plays a vital role in the classification accuracy of machine-learning models. The models are evaluated using performance metrics such as accuracy, precision and recall. Accuracy is measured in terms of the number of correctly classified data points out of all the data points. The receiver operating characteristic (ROC) curve is the most effective tool for measuring the accuracy [38]. However, in most of the classification problems, imbalanced class distribution exists, and the F1-score is used to evaluate the model. We used the following parameters: true positive, true negative, false positive and false negative to calculate the performance metrics. True positive represents the correctly classified stroke rate, whereas true negative represents the correctly classified nonstroke rate. The false negative and false positive provide the incorrectly classified nonstroke and stroke rates, respectively. The accuracy, precision, recall and F1-scores were calculated as follows [8]:  
\[\textit{Precision = True positive/(True positive + False positive)} \tag{5}\]  
\[\textit{Recall = True positive/(True positive + False negative)} \tag{6}\]  
\[\textit{F1-score = 2 \times((precision \times recall)/(precision + recall))} \tag{7}\]  
\[\textit{Accuracy = (True positive + True negative)/(True positive + True negative + False positive + False negative)} \tag{8}\]  
Table 5 summarizes the computational results obtained from the classifier models and listing of the four performance parameters. Considering all the models, the highest classification accuracy 96.6%, precision 94.3% and recall 99.1% were acquired by the KNN  
Figure 6: Boxplot illustration of the distribution of QT (\(p\) = 0.029) and ST intervals (\(p\) = 0.014) between the stroke and the control subjects.  
model, with the F1-score as 96.6%. Thus, the KNN algorithm provided the best performance results, followed by random forest with 94.4% accuracy, 91.7% precision, 97.7% recall and a 94.6% F1-score. Further, the SVM showed the performance results of 85.4% accuracy, 81.5% precision, 91.7% recall and a 86.3% F1-score. The Naive Bayes algorithm attained 72.7% accuracy, 64.2% precision, 87.8% recall and a 74.1% F1-score, whereas the least performance results were given by the logistic regression model as 66.9% accuracy, 57.1% precision, 91.7% recall and a 70.3% F1-score. The results showed that the KNN classifier model outperformed the other models.
# Parameter Design for Inter-Satellite Laser Link Acquisition Under Weak Light Conditions
## 3 Analytical Model
### Acquisition Failure Probability Calculation
#### 3.1.1 Receiving SC out of the Pre-Estimated UC  
In the first case, the acquisition failure probability is the probability that the receiving SC is located outside the scanning area. The uncertain distribution of the receiving SC follows the zero-mean Gaussian distribution with a variance of \(\sigma_{uc}^{2}\) in both the pitch and yaw directions, which are independent of each other. The radial distribution follows a Rayleigh distribution. Therefore, the acquisition failure probability of the first case is provided by the following:  
\[P_{lost,1}=e^{\frac{-r_{max}^{2}}{2r_{uc}}}. \tag{13}\]
## 4 Discussions
### Yield Strength  
As mentioned in the Results section, the normalized yield stress relationships based on [PERSON] and Euler-Bernoulli theories are identical for BCC, diamond, and truncated cube structures and both theories have good agreement with the numerical results. However, for the hexagonal packing, truncated octahedron, and rhombicuboctahedron structures, the Euler-Bernoulli and Timoshenko yield strength results are quite different (particularly for the hexagonal packing case), and the Timoshenko analytical results show much better overlapping with numerical results as compared to that for the Euler-Bernoulli analytical curve. The reason why the Timoshenko and Euler-Bernoulli yield strengths are identical for some geometries and different for some other is described extensively in [40], but it is explained here briefly. As introduced above, \(T=\frac{12E_{S}l}{l^{3}}\) and \(V=\frac{6E_{S}l}{l^{2}}\) for Euler-Bernoulli beam theory, and \(T=\frac{1}{\frac{1}{l2E_{S}l}+\frac{1}{xE_{GS}}}\) and \(V=\frac{1}{\frac{1}{6E_{S}l}+\frac{1}{xE_{GS}}}\) for Timoshenko beam theory. For both the theories, \(\frac{V}{T}=\frac{l}{2}\). Therefore, as the analytical yield strength relationship for both the diamond and BCC in the final stage of derivation is a mere function of \(\frac{V}{T}\), both  
Figure 9: Unit cells stress contour for (**a**) BCC, (**b**) diamond, (**c**) hexagonal packing, (**d**) rhombicuboctahedron, (**e**) truncated cube, and (**f**) truncated octahedron unit cells.  
the theories give identical relationships in these two unit cells. As for the truncated cube case, the normalized yield stress in the critical strut could be obtained from the equilibrium of forces and moments, and the failure is caused merely due to axial stress. Therefore, the Euler-Bernoulli and Timoshenko theories give the same results again. Nevertheless, for the three other unit cells, first, the displacement caused by bending needs to be taken into account in the calculations, and second, the formulas in the final stages of derivation are not a mere function of \(\frac{V}{T}\), and the V and T terms are present independently and not in a fraction form in such a way that one is a factor of the other. Therefore, the results differ for Euler-Bernoulli and Timoshenko theories for the three other unit cells.
## 2 Materials and Methods
### Data Analysis and Evaluation  
Data from the field and laboratory measurements were checked for quality by correlating selected major ions from randomly duplicated samples, of which the correlation values are all above 0.9, indicating low to insignificant analytical errors. Total dissolved solids (TDS), pH, electrical conductivity (EC) temperature and major ions were analysed using Geochemist Workbench to determine groundwater types for the study area. Piper diagrams for both seasons were also plotted using the same software. Results of water types were used in relation to the major ions for other plots in Excel. At the same time, the spatial distribution maps of the stable isotopes were generated in ArcGIS v10.6.
# Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry
## 3 Methodology
### Odometry Algorithm  
Regarding the LiDAR odometry algorithm used in this study, we chose to evaluate LOAM [3]. The reasons to use LOAM are threefold. Firstly, it is a well-known method that has been constantly used and cited since its publication. Secondly, it ranks as the top method in the KITTI odometry leaderboard in both translation and rotation. Finally, there is an open-source implementation available [16].  
Figure 2: Point cloud downsizing with each of the tested configurations.  
LOAM algorithm divides the SLAM problem into odometry generation and map registration. These two processes run in parallel at different rates. The odometry generation algorithm runs at 10 Hz and computes a low fidelity motion estimation. This estimation is integrated into the registration algorithm, which runs at 1 Hz and optimizes the obtained odometry while building the map. To perform these computations with the point clouds, the authors proposed to extract two sets of features based on the smoothness of the points: so-called edge and planar features. Afterwards, these sets of features are processed separately, and a matching algorithm is applied to find the relative transformation between the extracted features and the map of the environment. This principle is applied by most SLAM algorithms; therefore, the collected results can be extrapolated to other LiDAR-based methods.
## 4 Classification of Routing-Related Surveys
### Least Focused Areas
#### 4.3.3 Other Routing Surveys  
The authors in [49] have discussed in-network fusion techniques under routing-driven, coding-driven, and fusion-driven categories. The authors in [57; 89] have discussed distributed hash table (DHT) based routing in WSNs. In [57], a general model of DHTs-based routing is presented while [89] delineates a more comprehensive classification of DHT based routing protocols in WSNs which consider structure, identifier-assignment and auto-deployment of concerned protocols. Moreover, it is clear from Table 12 that this categorization embodies only one high count survey and  
\begin{table}
\begin{tabular}{c l c} \hline \hline \multirow{2}{*}{**Survey Type**} & \multicolumn{2}{c}{**Categorization and Design Requirements Based Analysis of Geographic Routing Surveys**} \\ \hline \multirow{2}{*}{**Mobile**} & _Hg_ & [43; 44; 94] \\ \cline{2-3}  & _Md_ & [109] \\ \hline \multirow{2}{*}{**Location based**} & _Md_ & [91] \\ \cline{2-3}  & _Lw_ & [55] \\ \hline \multirow{2}{*}{**Partial based**} & _Hg_ & [137; 142] \\ \hline \hline \end{tabular}  
* Hg* = High Count, Md* = Medium Count and Lw* = Low Count. Note: Various categories of count correspond to Count Number Index (CNI) in Tables 2 and 3.  
\end{table}
Table 11: Design analysis of geographic routing surveys in WSNs.  
three medium count surveys which clearly exhibits a great potential of research even in these least focused areas of survey publication.  
In [71], the authors have discussed the functionality of various routing protocols for underwater wireless sensor networks (UWSNs) and classified them mainly into structural and operational based categories. The author in [102] have discussed and compared various non-cross-layered traditional cross-layered and intelligent cross-layered routing protocols in UWSNs. The authors in [108] have discussed the advantages and disadvantages of various data forwarding routing approaches in UWSNs. In [110], the authors have discussed various localization-oriented and localization-free routing protocols for UWSNs. In [144], the authors have evaluated various UWSNs based MAC and Routing protocols using comparison and simulation mechanisms. The design issues and challenges are discussed in [71; 102; 108; 110; 144]. Future research directions are outlined in [71; 102; 108; 144].  
In [101], the authors have discussed and compared some opportunistic routing protocols in WSNs. Moreover, some brief future directions are also outlined. The authors in [114] have extensively discussed numerous single/multi-path and single/multi-radio multichannel routing protocols in WSNs under a novel taxonomy and summarize the pros and cons of discussed protocols. It discusses various applications of multichannel routing, design issues/challenges and future research directions in a comprehensive manner. This information is very useful for providing a thorough information regarding understanding the multichannel routing approach and designing novel multichannel routing protocols in WSNs.
## 2 Materials and Methods
### Selection of the Optimal SPME Fiber  
Standard addition calibration plots were obtained for all 25 analytes using the four most common commercially available SPME fibers: 85 \(\upmu\)m Carboxen(Car)/polydimethylsiloxane(PDMS), 100 \(\upmu\)m PDMS, 65 \(\upmu\)m PDMS/divinylbenzene(DVB) and 50/30 \(\upmu\)m DVB/Car/PDMS (all-from Supelco, Bellefonte, PA, USA). The calibration process was the same as described by [PERSON] et al. (2015). Calibration samples were prepared by adding 1.00 \(\upmu\)L of a standard solution of analytes (0.50, 1.00, 2.00 and 4.00 ng \(\upmu\)L\({}^{-1}\) for benzene, toluene and alkanes; and 0.050, 0.100, 0.200 and 0.400 ng \(\upmu\)L\({}^{-1}\) for other analytes) into the 20 mL crimp-top headspace vial (HTA, Brescia, Italy) filled with laboratory air. Ranges of concentrations of VOCs added to calibration samples were chosen in order to cover their real concentrations in ambient air (according to the preliminary screening results). Added concentrations of benzene, toluene, and alkanes in the calibration samples were 25, 50, 100 and 200 \(\upmu\)g m\({}^{-3}\). Added concentrations of ethylbenzene, _m-_, _p-_, _o-x_planes, polycyclic aromatic hydrocarbons and other analytes were 2.5, 5, 10 and 20 \(\upmu\)g m\({}^{-3}\). Extraction was conducted at room temperature (22 \({}^{\circ}\)C) for 10 min; desorption time was 1 min.  
From the calibration plots, relative standard deviations (RSDs) of slopes and limits of detection (LODs) were determined. RSDs of slopes were determined using the LINEST function of Microsoft Excel software (Microsoft(r) Excel for Office 365, Version 1909, Redmond, WA, USA). LODs were calculated using:  
\[\text{LOD}=\frac{\left(\frac{b}{a}\ +\ C_{add}\right)\times\ 3}{S/N} \tag{1}\]  
where \(b\) is the intercept of a calibration plot; \(a\) is the slope of a calibration plot; _Cadd_ is the standard addition concentration (\(\upmu\)g m\({}^{-3}\)); and _S/N_ is the signal-to-noise ratio.
## 6 Applications of UAV Computing
### B5G Networks  
Many attempts have been made in the networks sector to fully realize the potential of UAV communications for cellular and wireless communications. The research community investigated the possibility of UAV-mounted usage via flying relays and BSs that can dynamically relocate themselves to extend network coverage, increase spectral efficiency, and improve user QoS. UAVs can have low-altitude support beyond LoS control and reliable communications [188]. Furthermore, UAVs can provide various services to cellular IoT-based networks by offering processing facilities for consumers close to the ground.
## 4 Examined Distributed Consensus Gossip-Based Algorithms
### Geographic Gossip Algorithm  
Another analyzed approach is \(GG\), the principle of which lies in the combination of gossiping with geographic routing mechanisms [28]. As in the case of the previous algorithm, at each time instance \(k\), one of the agents is woken up and selects one of the agents from the whole network (except for itself) uniformly at random [26; 28]. Subsequently, these two agents perform the pairwise averaging operation (3). Thus, the main idea of this approach is that an agent can perform the pairwise averaging operation with agents further than one hop [28]. Thus, one of the most serious drawbacks of this approach is  
Figure 5: Comparison of inner states/arithmetic mean estimates before and after consensus is achieved in multi-agent system.  
the necessity to know the geographical information about the agents [26; 28]. However, the diversity of pairwise exchanges is significantly increased compared to the previous algorithm whereby data aggregation is assumed to be optimized [26]. In our experiments, we assume the optimal routing, i.e., the messages are always transported to the addressee by the shortest path.
# Numerical Simulation of the Influence of Spur Dikes Spacing on Local Scour and Flow
## 1 Introduction
[PERSON] and [PERSON] [16] studied the effect of spur dike length on bed shear stress distribution. They found that the large bed shear stress values and pressure standard deviation values observed around the tip of the spur dike, beneath the upstream part of the main horseshoe vortex and beneath the separated shear layers, increase with increasing spur dike length. There have been many studies into the flow field and local scour around single spur dikes, but fewer into grouped spur dikes. Moreover, most studies in the literature focus on spur dikes with fixed spacing, with comparatively few studies into the variation of the spacing. In this study, the local scour morphology and flow field characteristics of spur dikes with different spacings are investigated by a combination of the flume experiment and numerical simulation. Further, the variations in velocity, turbulent kinetic energy, and shear stress associated with variations in spur dike spacing are discussed.
## 2 Problem Formulation
### Fractional Maxwell Model  
We start our consideration from the simplest yet commonly used two-component fractional model with basic elements connected in series (fractional Maxwell model, Figure 2b). The constitutive equation for this model reads as [39]:  
\[\sigma(t)+\tau^{\alpha-\beta}aD_{t}^{\alpha-\beta}\sigma(t)=E\tau^{\alpha}aD_{t} ^{\alpha}\varepsilon(t),\quad 0\leq\alpha\leq 1,\quad 0\leq\beta\leq 1,\quad \alpha\geq\beta, \tag{7}\]  
where \(\alpha\) and \(\beta\) stand for the fractional orders, \(\tau,E\) are defined as:  
\[\tau=\Big{(}\frac{E_{1}\tau_{1}^{\alpha}}{E_{2}\frac{\beta}{\tau_{2}}}\Big{)}^ {\frac{1}{\tau+\beta}},\quad E=E_{1}\Big{(}\frac{\tau_{1}}{\tau}\Big{)}^{\alpha },\quad E=E_{2}\Big{(}\frac{\tau_{2}}{\tau}\Big{)}^{\beta}, \tag{8}\]  
and \(E_{1},\tau_{1},E_{2},\tau_{2}\) are the [PERSON]'s moduli, relaxation times for elements \"1\" and \"2\", respectively. Constitutive equation of fractional Maxwell model (7) can be reduced to classical one, if \(\alpha=1\) and \(\beta=0\). Moreover, if we set \(\alpha=1\) and \(\beta=1\), Newtonian fluid can be obtained. The ranges set for fractional orders \(\alpha\) and \(\beta\) have both mathematical and physical meaning. Non-negative values of fractional orders and their difference reflect the fact that the dynamics of processes considered are described by fractional derivatives not fractional integrals. The upper limit of fractional orders range is set to get the corresponding classical viscoelastic model as a limiting case of a fractional one. For \(\tau\) as it was defined above, \(\alpha=\beta\) represents a special case. Indeed, it corresponds to relaxation time blowing up. Physically it means that a fluid becomes a critical gel. This special case is, however, beyond the scope of the current study. It is worth mentioning that relaxation time blows up only provided that the base of \(\tau\) is greater than unity. If, however, that is not the case, i.e., \(\tau\) base is less than unity, \(\tau\) itself remains bound.  
Figure 2: Fractional models: (**a**) basic element; (**b**) [PERSON]; (**c**) Kelvin–Voigt; (**d**) [PERSON]; (**e**) Poynting–Thomson; (**f**) [PERSON].
# Role of Bimodal Water Retention Curve on the Unsaturated Shear Strength
## 1 Introduction
Hence, there are many shear strength prediction equations that use WRC with the saturated shear strength parameters to estimate the unsaturated shear strength of soil. One of the most widely utilized models is by correlating shear strength with pore size distribution of soil, which actually depends on WRC [32]. Numerous research works have been performed on bimodal soil, but those studies are generally limited to WRC and permeability of soil. Therefore, this study investigates the characteristics of the unsaturated shear strength of soil with bimodal WRC. In addition, the new mathematical equation is proposed to estimate the unsaturated shear strength of soils with a bimodal WRC.