esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
## 2 Related Work
### PRESENT Block Cipher
#### 2.1.4 Keyschedule of PRESENT Block Cipher  
In the version using the 80-bit key, round key \(RK\) is the leftmost 64 bits of the 80-bit key.  
\[RK=rk_{63},...rk_{0}=k_{79},...,k_{16} \tag{2}\]  
In the version using 128-bit key, round key \(RK\) is the leftmost 64 bits of 128-bit key.  
\[RK=rk_{63},...rk_{0}=k_{127},...,k_{64} \tag{3}\]  
After extracting the round key \(RK\), keys (\(K=k_{79},...,k_{0}\) for 80-bit security level or \(K=k_{127},...,k_{0}\) 128-bit security levels) are updated with Rotation, Sbox, and XOR operations. Round key generation of 80-bit security level and 128-bit security level are given in Equations (4) and (5), respectively.  
\[\begin{array}{l}k_{79},k_{78},...k_{1},k_{0}\gets k_{18},k_{17},...k_{2 0},k_{19}\\ k_{79},k_{78},k_{77},k_{76}\leftarrow\text{Sbox}(k_{79},k_{78},k_{77},k_{76})\\ k_{19},k_{18},k_{17},k_{16},k_{15}\gets k_{19},k_{18},k_{17},k_{16},k_{15} \oplus\text{round }i\end{array} \tag{4}\]  
\[\begin{array}{l}k_{127},k_{126},...k_{1},k_{0}\gets k_{66},k_{65},...k_ {68},k_{67}\\ k_{127},k_{126},k_{125},k_{124}\leftarrow\text{Sbox}(k_{127},k_{126},k_{125},k_ {124})\\ k_{123},k_{122},k_{121},k_{120}\leftarrow\text{Sbox}(k_{123},k_{122},k_{121},k _{120})\\ k_{66},k_{65},k_{64},k_{63},k_{62}\gets k_{66},k_{65},k_{64},k_{63},k_{62} \oplus\text{round }i\end{array} \tag{5}\]
# Full-Field Strain Determination for Additively Manufactured Parts Using Radial Basis Functions
## 3 Evaluation of Strain Determination Using RBFs
### Verification Examples of WAAM-Produced Cylindrical Components
#### 3.1.1 Tension and Torsion of a Cylindrical Tube
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\Delta d\) & **Gau** & **Gau + Mon** & **Wen** & **Wen + Mon** & **IMQ** & **IMQ + Mon** \\ \hline
0.15 & \(10^{29}\) & \(10^{34}\) & \(10^{4}\) & \(10^{9}\) & \(10^{6}\) & \(10^{9}\) \\
0.25 & \(10^{12}\) & \(10^{16}\) & \(10^{3}\) & \(10^{8}\) & \(10^{4}\) & \(10^{7}\) \\
0.35 & \(10^{6}\) & \(10^{10}\) & \(10^{2}\) & \(10^{7}\) & \(10^{3}\) & \(10^{6}\) \\
0.45 & \(10^{3}\) & \(10^{8}\) & \(10^{2}\) & \(10^{6}\) & \(10^{3}\) & \(10^{6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Order of condition numbers of the linear systems for different interpolation schemes and varying point distance (\(R_{0}=1.5\,\)mm, \(c_{G}=2.7\), \(c_{I}=0.2\)). Here, the expert driver routines of [29] are chosen. Figure 3: Mean relative error behavior \(e_{m}\), see Equation (37): **(left)** variation of point distance for constant shape parameter, **(right)** variation of shape parameter \(\beta_{W}\) and \(\beta_{I}\) for constant point distance \(\Delta d=0.25\,\)mm. increasing shape factor \(\beta_{I}\). However, only solutions for particular linear systems due to exceeding condition numbers for \(\beta_{I}<8\) (IMQ) and \(\beta_{I}<16\) (IMQ + Mon) are obtained. A possible approach of estimating the shape factor \(\hat{c}_{\text{IMQ}}(\Delta d)\)--in dependence of the point distance \(\Delta d\) so that accuracy and condition number are appropriate--is provided in Appendix A. The advantage of having only one function representing the deformation offers the simple possibility to evaluate--apart from the principal stretches or strains--the principal direction concerned. Here, two possibilities can be considered, see [4], either the eigenvectors of the in-plane right stretch tensor \(\hat{\mathbf{U}}\) or the in-plane left stretch tensor \(\hat{\mathbf{V}}\). The in-plane left stretch tensor stems from the polar decomposition \(\hat{\mathbf{F}}=\hat{\mathbf{V}}\hat{\mathbf{R}}\) and can be determined either by the eigenvalue problem of the left Cauchy-Green tensor \(\hat{\mathbf{B}}=\hat{\mathbf{F}}\hat{\mathbf{F}}^{T}=\hat{\mathbf{V}}^{2}\) or by rotating the eigenvectors of the in-plane right stretch tensor \(\hat{\mathbf{U}}\) by the rotation tensor \(\hat{\mathbf{R}}\). The latter is omitted for brevity. Figure 4 shows the distribution of the principal direction (eigenvectors) of the in-plane right stretch tensor \(\hat{\mathbf{U}}\) evaluated at particular positions on the sample's surface. Both possibilities are commonly not provided in DIC-evaluation programs, or are not explained in a thorough manner. However, these principal directions might provide a sound interpretation of the load distribution in the presence of inhomogeneous deformations. Thus, Figure 4 shows the known principal directions for the \"simple\" problem of tension and torsion, where the RBF-results are evaluated.
## 4 Performance Analysis
### Average Capacity  
If the state of the transmitter, the receiver and the channel are known, the average capacity of FSO system \(<C>\) is [22]:  
\[<C>=B\int_{0}^{\infty}\log_{2}(1+\frac{\gamma}{2})f(I)dI \tag{22}\]  
\(B\) is the bandwidth and is normalized to be 1 in this paper. The average capacity is also influenced by the aperture diameter. According to (12) and (13), the average capacity can be rewritten as:  
\[<C>=\int_{0}^{\infty}\log_{2}(1+\frac{R_{d}AI}{2\mu\lambda f})f(I)dI=\int_{0}^ {\infty}\log_{2}(1+A\frac{\rho_{0}}{2}I)f(I)dI=\int_{0}^{\infty}\log_{2}(1+ \frac{\gamma_{0}}{2}I)f(I)dI \tag{23}\]  
The above three criterions depend on the average SNR and the optical intensity probability density. With the receiving aperture area decreasing, the Rytov variance increases and more low SNR bits cause higher BER, higher outage rate and lower average capacity.
## 5 Discussion
In polarization-encoded LEO satellite-to-ground QKD protocols, time-dependent rotations and static polarization transformations introduce additional errors. Hence, dynamic polarization tracking and compensation systems should be considered [86]. Experimental works on active polarization tracking and compensation system have recently showed the ultra-low QBER penalties associated with the polarization bases rotation, which occur due to the satellite's motion [87]. In real QKD links, precise synchronization can also significantly increase the signal-to-noise ratio. In satellite QKD, synchronization is especially complicated due to such factors as signal fading, high loss, and the Doppler effect [88]. Synchronization can be implemented by leveraging a global navigation satellite system (GNSS) [73], but there are recent robust synchronization algorithms that have been successfully tested between the Micius satellite and OGSs [88]. In the latter experiment, it was shown that the selection of the appropriate synchronization methods can effectively decrease the QBER penalties associated with noise triggering. Besides the synchronization and polarization drift countermeasures, the amount of classical data that is required to be communicated between the satellite and ground station for implementing the key distillation process is also a critical parameter [30]. Laser communications can provide the required channel bandwidth to transmit all the relevant information, even within the temporal window of a single overpass of a LEO Cubesat [89]. Figure 15: Multiple WDM-assisted, on-board QKD source architecture. The centralized SNSPD stations allowed for a significant boost in SKR.
## 4 Simulation Results
### Numerical Simulation Results
#### 4.1.2 Formation Tracking without Disturbance
In order to verify the advantages of the proposed method, a simulation based on the backstepping method was carried out. The formation shape and the initial positions of all the robots were consistent with that in the simulation based on the VS-MPC-ESO. The simulation results are illustrated in Figure 13. It can be seen that the three robots could form the desired shape. However, it was obvious that the formation was not able to track the desired trajectory accurately, and the real trajectory of each robot possessed slight oscillations. In addition, the control inputs of each of the robots based on backstepping are shown in Figure 14. As the backstepping could not deal with the constraints, the control input \(\tau\)1 of Robot 2 exceeded the limit (the limit of \(\tau\)1 is 6 N). According to the comparison, the proposed formation method is obviously preferred over the backstepping method in terms of the aspects of dealing with constraints and formation trajectory tracking. Figure 12: Control inputs of each robot. Figure 13: Triangle formation trajectory based on backstepping.
## 2 Design of Radial Grouting Range
### Result Analysis
#### 2.2.2 Stress Analysis on Initial Support  
As shown in Table 3, when no grouting is conducted for water plugging and reinforcement, the minimum and maximum principal stress of the first-layer initial support are 4.38 MPa and 0.69 MPa, respectively. When the grouting is conducted within 2 m around the tunnel, the minimum and maximum principal stress of the first-layer initial support are 2.88 MPa and 0.48 MPa, respectively. It represents a decrease of 34.25% and 30.43%, respectively. When the grouting is conducted within 4 m around the tunnel, the minimum and maximum principal stresses of the first-layer initial support are 1.48 MPa and 0.29 MPa, respectively. The figures are reduced by 48.61% and 39.58%, respectively. When the grouting is conducted within 6 m around the tunnel, the minimum and maximum principal stresses of the first-layer initial support are 1.34 MPa and 0.25 MPa, respectively, and the figures are reduced by 9.46% and 13.79%, respectively.  
The stress analysis on the second-layer initial support shows that when no grouting is conducted for water plugging and reinforcement, the minimum and maximum principal stress of the second-layer initial support are 3.26 MPa and 0.69 MPa, respectively; when the grouting is conducted within 2 m around the tunnel, the minimum and maximum principal stresses of the second-layer initial support are 2.15 MPa and 0.48 MPa, respectively. The figures are reduced by 34.05% and 30.43%, respectively. When the grouting is conducted within 4 m around the tunnel, the minimum and maximum principal stresses of the second-layer initial support are 1.24 MPa and 0.28 MPa, respectively. When the grouting is conducted within 6 m around the tunnel, the minimum and maximum principal stresses of the second-layer initial support are 1.12 MPa and 0.24 MPa, respectively. They are reduced by 9.68% and 14.29%, respectively. The stress comparison between the first layer and the second layer's initial support shows that the grouting range around the tunnel exceeding 4 m has an impact on the surrounding rock, and the radial grouting range on the first-layer initial support is reduced.  
Figure 5: Model displacement nephogram. (**a**) Operation condition with grouting range of 0 m. (**b**) Operation condition with grouting range of 2 m. (**c**) Operation condition with grouting range of 4 m. (**d**) Operation condition with grouting range of 6 m.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Grouting**} & \multicolumn{2}{c}{**First-Layer Initial Support**} & \multicolumn{2}{c}{**Second-Layer Initial Support**} \\ \cline{2-5}  & **Min Principal Stress** & **Max Principal Stress** & **Min Principal Stress** & **Max Principal Stress** \\  & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** \\ \hline
0 & 3.26 & 0.69 & 4.38 & 0.69 \\
2 & 2.15 & 0.48 & 2.88 & 0.48 \\
4 & 1.24 & 0.28 & 1.48 & 0.29 \\
6 & 1.12 & 0.24 & 1.34 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Minimum and maximum principal stress of initial support with different grouting ranges.
## 3 Results and Discussion  
### Morphology  
Figure 1A shows the morphology of the GO, which appears to have some platelets present in the image and some large ball-like cluster structures. It is also observed in Figure 1A that some very thin sheets are present on the top left of the image, showing the typical crinkled sheets of graphene oxide. The modified [PERSON]'s synthesis method has shown similar results [19; 54]. [PERSON] et al. reported folded ultra-thin films with crinkled edges [55]. Figure 1B shows the decoration of 25% Zn (OH)x on the GO sheets. The appearance of the 25% ZnO-GO composite shows a structure that is very similar to that of the GO, with the presence of large ball-like clusters, and some small plate-like structures are also present. The SEM image of the 50% Zn (OH)x GO composite sample (Figure 1C) shows a broken sheet-like structure. There are some bright spots present on the sample, which are visible NPs on the surface of the GO sheets, which may be due to the readiness of preferable sites for the growth of Zn(OH)x [47]. Alternatively, the precipitation of the Zn (OH)2 on the surface may be large particles/more crystalline and may be clustered. The 75% Zn (OH)x-GO composite material (Figure 1D) shows a structure similar to that of the 50% Zn (OH)x-GO composite/broken sheet structure. In addition, the edges of the broken sheets in the layers are visible on the surface, as are the small clusters of the Zn (which are the bright spots).  
In fact, different samples are shown in Figure 1. The SEM image of the GO without Zn (OH)x is shown in Figure 1A, while the SEM images of GO coated with 25%, 50%, and 75% of Zn(OH)x are shown in Figures 1B, 1C and 1D, respectively. One would expect that the morphology of the GO will change as it reacts with Zn and OH ions. However, in all the samples, one can see some plate-like structures indicating the existence of graphene oxide. Figure 1A shows the graphene oxides extracted from the solution. In fact, the extracted graphene oxide from the solution would then be reacted with Zn and OH, where we would probably lose the smaller particles, and some digestion of the GO would happen to change the appearance somewhat. The possible digestion and loss of the smaller particles of GO would be to larger extents as the Zn and OH concentration was increased or the amount of Zn and OH increased. Thus, one would expect more large visible structures after the reaction of GO with higher concentrations of Zn and OH.
# Distribution of Operating Costs Along the Value Chain of an Open-Pit Copper Mine
## 2 Materials and Methods  
### The Open-Pit Mine  
This case study focuses on an open-pit mine in the southeast Democratic Republic of the Congo (DRC), producing copper (Cu) within the Central African Copperbelt. The deposit is hosted in the Neoproterozoic metasedimentary rocks of the Katangan Supergroup, which is closely associated with the external fold-and-thrust belt of the Lufilian Arc, a prominent arc-shaped structural zone. This belt extends approximately 2000 km in a northeast-southwest direction, traversing Meso-to-Neoproterozoic sedimentary basins across the continent from the Atlantic Ocean in the southwest. The Katangan Supergroup is stratigraphically divided into three distinct groups, listed from top to bottom: Kundelungu (Ku), Nguba (Ng), and Roan (R). The Roan Group is further subdivided into four subgroups, including the Mines (R2) and Dipeta (R3) subgroups, which host the primary Copper-Cobalt mineralization. The deposit itself is situated within a geologically complex setting that includes a variety of rock formations, such as dolomitic, graphitic, albitized shale, conglomerate, and quartzite. The mineralization is distributed across three distinct zones: the oxide zone, the mixed or transitional zone, and the sulfide zone. The oxide zone is characterized by copper oxide minerals like chrysocolla and malachite. The mixed zone, on the other hand, contains both oxide and sulfide mineralized ore bodies. In the case of the sulfide zone, it is dominated by copper sulfide minerals such as chalcopyrite, chalcolcite, and bornite [38,39].  
In terms of operational requirements, the mine employs a drill-and-blast technique alongside a truck-and-shovel system. Out of 350 scheduled operating days per year, 50 are dedicated to maintenance and repairs, resulting in 300 effective working days. Mining activities run on two 10 h shifts per day aimed at extracting a run-of-mine (ROM) of rock density 2.5 t/m\({}^{3}\). The processing plant includes three units (i.e., crushing, milling, and flotation) designed to produce a concentrate grading 28% Cu. The plant has an average annual capacity of 10,350,000 metric tons of ore and is operated in two 12 h shifts per day over 345 effective days each year. With an availability rate of 94.5%, the plant processes ore with an average head grade of 2.5% Cu.
# CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation
## 4 Experiments and Results
### Basic Settings
#### 4.1.3 Evaluation Index  
To evaluate the model's performance, we employ the following metrics:  
\[\text{Accuracy}\ =\ \frac{TP+TN}{TP+TN+FP+FN} \tag{9}\]  
\[\text{DR (Recall)}\ =\ \frac{TP}{TP+FN} \tag{10}\]  
\[\text{Precision}\ =\ \frac{TP}{TP+FP} \tag{11}\]  
\[\text{TNR}\ =\ \frac{TN}{FP+TN} \tag{12}\]  
\[\text{F1-score}\ =\ 2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{13}\]  
\[\text{G-Mean}\ =\ \sqrt{\text{DR}\times\text{TNR}} \tag{14}\]  
\[\text{Macro F1}\ =\ \frac{1}{n}\sum_{i=1}^{n}\text{F1-score}_{i} \tag{15}\]  
Accuracy indicates the ratio of correctly classified instances to the total number of instances. Detection Rate (DR), also referred to as Recall, measures the proportion of intrusion instances correctly identified out of all actual intrusion instances. Precision calculates the fraction of detected genuinely intrusive intrusion instances. The F1-score, a harmonic mean of Precision and Recall, balances these two metrics effectively. True Negative Rate (TNR), or Specificity, evaluates the proportion of correctly classified normal instances among all normal instances. The G-mean metric assesses the model's ability to handle imbalanced datasets by incorporating both sensitivity and specificity. Lastly, Macro F1 provides an overall assessment by averaging the F1-scores across all classes, offering a broad view of the model's classification performance, as shown in Table 1.
# Discrimination of Smoke-Exposed Pinot Noir Wines by Volatile Phenols and Volatile Phenol-Glycosides
## 2 Results and Discussions  
### Concentration of Free-Form and Total VPs in Non-Smoke-Exposed and Smoke-Exposed Wines  
Quantification of free-form and total volatile phenols (VPs) is crucial for assessing the smoke-taint risk associated with smoke-impacted vintages. Calibration standard curves demonstrated good linearity (R\({}^{2}\) > 0.9990) for all analytes (Table S1). The concentrations of the free and total VPs of the 14 non-smoke-exposed samples and 21 smoke-exposed ones were shown in Table S2 and Figure 1a. The free and total VPs concentration levels in the non-smoke-exposed wine provide baselines for the evaluation of smoky effect. The mean value of free-form guaiacol, 4-methylguaiacol, \(\circ\)-cresol, \(p\)-cresol, and \(m\)-cresol concentration for non-smoke-exposed wines was 6.8, 1.8, 1.5, 1.5, and 1.6 \(\mu\)g/L, respectively. Meanwhile, the mean value of total VPs concentration in non-smoke-exposed wines was 18.4, 2.3, 4.0, 7.8, and 1.0 \(\mu\)g/L, respectively, slightly higher than the reference baseline reported [30; 31]. In comparison, for smoke-exposed wines, the mean concentrations of free-form guaiacol, 4-methylguaiacol, \(o\)-cresol, \(p\)-cresol, and \(m\)-cresol were 51.9, 13.3, 14.5, 7.4, and 12.7 \(\mu\)g/L, respectively, while the concentration of total VPs in smoke-exposed wines was 117.2, 38.9, 26.9, 24.4, and 12.8 \(\mu\)g/L, respectively.  
As expected, results showed that the free-form and total VPs in smoke-exposed wines were higher than in non-smoke-exposed wines. The concentration of free-form guaiacol, 4-methylguaiacol, \(o\)-, \(p\)-, and \(m\)-cresol in smoke-exposed wine were 7.6, 7.5, 9.4, 5.1, and 8.4-fold, respectively, higher than that of non-smoke-exposed wines. Whereas total guaiacol, 4-methylguaiacol, \(o\)-, \(p\)-, and \(m\)-cresol concentrations in smoke-exposed wine were 6.4, 17.0, 6.8, 3.1, and 13.3-fold of those in non-smoke-exposed wine, respectively. Although VPs have been identified as potential marker compounds, it has not been unequivocally  
Figure 1: The bar chart of VPs and VP-glycos for smoke-exposed and non-smoke-exposed wines (referred to as Control). Each dot corresponds to a sample. The yellow dot in each group corresponds to the mean. (**a**) Concentration of VPs (\(\upmu\)g/L) was analyzed by SPME-GC-MS. (**b**) The relative abundance of VPs-glycos was estimated from HPLC-HRMS/MS. H: hexose, P: pentose.  
demonstrated that they are the root cause of wine smoke taint. The contribution of VPs to smoke taint has been discussed in our previous publication, and free-form cresols have been identified as significant factors by statistical analyses [25].
## 2 Materials and Methods
### Mechanical Property Tests  
Three-point bending tests (TPBT), referring to the RILEM Technical Committee 50-FMC [35], were conducted using a 100 kN electronic universal testing machine (SHIMADZU). The beam specimen had a dimension of 100 mm \(\times\) 100 mm \(\times\) 400 mm. The notched specimens were loaded until failure at a crosshead displacement rate of 0.1 mm \(\cdot\) min\({}^{-1}\). A 2000 kN electro-hydraulic testing machine was used for compression and splitting tests. According to GB/T 50081-2002 [36], the stress rate was set to 0.5 and 0.05 MPa \(\cdot\) s\({}^{-1}\) in compression and splitting tests, respectively. The cubic specimens were cut to a size of 100 mm \(\times\) 100 mm \(\times\) 100 mm from the fractured prism specimens, which was to ensure that the corresponding compression and splitting tensile results were obtained from concrete samples with the same properties [37]. It should be noted that the shear forces near the supports in the bending test were all less than 4 kN, which means that there was hardly any influence on the measured strength of the cubic specimens.  
The fracture energy of concrete, \(G_{F}\), is a fundamental fracture parameter used to indicate its crack resistance in fracture and in crack analysis. It can be defined as the amount of energy required to produce a unit area of a crack. Following the RILEM [35] recommendations, fracture energy can be determined from the experimental load-deflection curve using Equation (4):  
\[G_{F}=(W_{0}+mg\delta_{0})/A \tag{4}\]  
where \(W_{0}\) is the total area under the load-deflection curve, \(m\) is the mass of the beam between the supports, \(\delta_{0}\) is the mid-span deflection of the beam upon failure, \(g=9.81\) m \(\cdot\) s\({}^{-2}\) is the gravitational acceleration and \(A\) is the area of the ligament. Here, \(A=t\cdot h\), where \(t\) is the width of the beam and \(h\) (equal to 70 mm) is the depth of the ligament.
## 3 Results and Discussion
### Study Steps  
One of the main objectives of this research is FOS optimization based on intelligent methods and optimization algorithms. In this methodology, different SVR models are implemented to obtain the best model for FOS simulation. These models are analyzed with different statistical criteria in order to identify the model that has more flexibility than other models by proper comparison. In the next step, the two optimization algorithms HHO and WOA are connected to the superior SVR model. The purpose of this work is to optimize the identified parameters to maximize the FOS value. This was done by coding in the MATLAB environment. For each analysis, a series of different designs of intelligent models and optimization algorithms are performed to achieve the best conditions. The steps outlined in the flowchart are shown in Figure 3.