esa-sceva/satcom-chunk-collection huggingface dataset:
# SPEEDY Quantum Circuit for Grover's Algorithm
## 2 Related Work
### SPEEDY: Family of Block Ciphers
#### 2.2.1 S-Box (\(SB\))  
The S-box in the SPEEDY block cipher is a 6-to-6-bit box with a 6-bit output (\(y_{0}\) to \(y_{5}\)) for a 6-bit input (\(x_{0}\) to \(x_{5}\)). It operates as a combination of NOT gate and NAND gate, as shown in Equation (1).  
\[\begin{array}{l}y_{0}=(x_{3}\wedge\overline{x}_{5}\qquad)\vee(x_{3}\wedge x_ {4}\wedge x_{2})\vee(\overline{x}_{3}\wedge x_{1}\wedge x_{0})\vee(x_{5} \wedge x_{4}\wedge x_{1})\\ y_{1}=(x_{5}\wedge x_{3}\wedge\overline{x}_{2})\vee(\overline{x}_{5}\wedge x _{3}\wedge\overline{x}_{4})\vee(x_{5}\wedge x_{2}\wedge x_{0})\vee(\overline {x}_{3}\wedge\overline{x}_{0}\wedge x_{1})\\ y_{2}=(\overline{x}_{3}\wedge x_{0}\wedge x_{4})\vee(x_{3}\wedge x_{0}\wedge x _{1})\vee(\overline{x}_{3}\wedge\overline{x}_{4}\wedge x_{2})\vee(\overline {x}_{0}\wedge\overline{x}_{2}\wedge\overline{x}_{5})\\ y_{3}=(\overline{x}_{0}\wedge x_{2}\wedge\overline{x}_{3})\vee(x_{0}\wedge x _{2}\wedge x_{4})\vee(x_{0}\wedge\overline{x}_{2}\wedge x_{5})\vee(\overline {x}_{0}\wedge x_{3}\wedge x_{1})\\ y_{4}=(x_{0}\wedge\overline{x}_{3}\qquad)\vee(x_{0}\wedge\overline{x}_{4} \wedge\overline{x}_{2})\vee(\overline{x}_{0}\wedge x_{4}\wedge x_{5})\vee( \overline{x}_{4}\wedge\overline{x}_{2}\wedge x_{1})\\ y_{5}=(x_{2}\wedge x_{5}\qquad)\vee(\overline{x}_{2}\wedge\overline{x}_{1} \wedge x_{4})\vee(x_{2}\wedge x_{1}\wedge x_{0})\vee(\overline{x}_{1}\wedge x _{0}\wedge x_{3})\\ \end{array} \tag{1}\]
# Extending UWOC System Applications through Photon Transmission Dynamics Study in Harbor Waters
## 2 Materials and Methods
### The Process of Monte Carlo Numerical Simulation
#### 2.3.3 Termination of Photon Motion or Reception of Photon  
In the simulation, the termination of a photon's motion is defined under two specific conditions. First, a photon is considered absorbed by the particles in the harbor water if its weight decreases to a threshold of \(10^{-6}\) or less. The choice of the minimum threshold will affect the calculation time of the simulation, and is also related to the link loss needed to add into the simulation, that is, the AL. The larger the link loss to be added, the smaller the threshold. Second, the photon's transmission is deemed to have ended once it reaches the receiver's photosensitive surface.  
Throughout the photon tracking process, key attributes of each photon are recorded, including its position, received angle, weight, and propagation distance. A photon is classified as 'Received' if it falls within the bounds of the receiver's photosensitive surface and if its received angle is within the specified FOV. If either of these conditions is not met, the photon is considered 'not Received', and its tracking is discontinued.  
This methodical approach allows for precise determination of photon reception, facilitating an accurate assessment of the efficiency and effectiveness of the UWOC system in various harbor water conditions.
# Privacy-Preserving and Explainable AI in Industrial Applications
## 3 Explainable Industrial AI Applications  
### The Black-Box Aspect of AI  
Artificial intelligence algorithms are, in general, regarded as black-box algorithms, i.e., it is not possible to determine or infer why the model has generated a certain output. A representative example was described in [45]; a robotics graduate student tried in 1991 to train a military vehicle to self-drive. The training of the system was accomplished by him manually driving the vehicle while the system (the algorithm was a neural network) memorized the moves for different situations. After a few training sessions, the approach seemed to be working well; however, when the vehicle reached a bridge, it did not know how to handle the situation, and the model would have crashed the vehicle if the user had not intervened. Further testing revealed that the model was relying on grassy roads to be guided along the road; hence, the appearance of the bridge caused confusion.  
The black-box problem [46] has represented a concern since the very beginnings of neural network research. Currently, very complex neural network architectures are employed, which deepen the black-box problem. The advancements of the technology and of computing power have also further increased its importance. It has become obvious both as a developer and as a user that, to trust an algorithm for making important decisions, one needs to make sure that the algorithm relies on the right properties and reasons.
## 1 Introduction
As evidenced by missions like the European Space Agency's (ESA) Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) [4] and the Japan Aerospace Exploration Agency's (JAXA) Super Low-Altitude Test Satellite (SLATS), VLEO satellites offer advantages such as reduced communication latency and enhanced spatial resolution, though atmospheric drag can pose additional challenges. This study aims to compare and analyze the combinations of satellite constellations and LPWAN technologies in an IoT-NTN environment. Specifically, by applying a genetic algorithm under identical conditions, we evaluate which communication method and orbital configuration achieve superior optimization results. Through this analysis, our goal is to propose the most effective satellite network configuration in terms of both cost efficiency and communication performance, thereby contributing a novel strategy for future IoT communication networks. In light of these evolving technological trends, this study explores the potential of LPWAN technologies in VLEO environments, comparing their communication efficiency and latency improvements against conventional LEO satellites. Using a genetic algorithm, this study derives optimal satellite constellations tailored to various LPWAN technologies and IoT-NTN environments. By comparing these configurations, we proposes the most suitable combination of LPWAN technologies and satellite constellations to meet the targeted performance criteria.
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 1 Introduction
### Contributions  
This research primarily focused on cellular network coverage enhancement approaches and conducted a literature review on the topics of coverage enhancement techniques, coverage definitions, and coverage requirements. Techniques for improving coverage capacity, spectrum efficiency, throughput, latency, and data rates are analysed. The contributions of this paper are summarised as:  
1. This survey focused on the 5G network coverage. Starting from the coverage definitions, requirements, and its enhancement techniques;
2. The evaluations and advancements of mobile networks from 1G to 5G, are discussed;
3. 3 GPP releases are explored towards the coverage enhancements, spectrum bands, multiplexing techniques, number of channels aggregated, and their bandwidths;
4. In this survey, we highlighted the 5G network key parameters, applications and advantages;
5. The coverage degradation sources are identified and the enhancement techniques are explored;
6. The coverage enhancement techniques such as small cells, carrier aggregation, device to device communication, non-orthogonal multiple access, multiple input and multiple output, and optimization approaches are discussed;
7. The pros and cons and the future challenges of each enhancement technique are highlighted.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture
### Power System State Estimation (PSSE)  
PSSE is one of the fundamental applications in smart grids, which estimates accurate system state by collecting and analysing the meter measurements and power system models to prevent cyber-physical attacks [9; 21]. Many important applications like contingency response and optimal flow calculation highly rely on state estimation. After receiving the measurement data, PSSE is usually finished in three stages: observability analysis, state estimation (SE), and bad data detection (BDD).  
Observability analysis is to determine whether the state variables of a power system can be estimated using measurements from available sensors in the network. The state vector that SE usually refers to voltage magnitudes and phase angles on the power grid buses, which is used to evaluate system performances and reliability. State vector can also be formed as a three-phase model in polar coordination [48]. The BDD process usually comes after the state estimation, to check especially the false data attacks (FDI, replay attack, and zero-dynamic attack). The BDD process usually comes up with a residual signal based on the measurements and then compares the value with the predefined threshold. If the residual value exceeds the threshold value, it can be considered an attack. The state estimation on the AC power system is considered computationally complex and expensive due to the nonlinear model and calculating both real and reactive power [21]. As a result, many power systems use stable DC power systems to represent the AC model.  
The observability analysis, measurement data, state estimation algorithms, and bad data detection algorithms for different state estimation systems (static state estimation, dynamic state estimation, and distribution system state estimation) are studied in detail.
## 6 Utilizing MRSO for Solving Engineering Design Issues
### Comparative Analysis of MRSO and RSO in Engineering Applications  
This subsection presents a comparative analysis of the MRSO versus the original RSO across seven engineering design problems. The objective is to emphasize the enhancements and benefits of the MRSO in solving constrained engineering design problems more efficiently. The comparison highlights the practical improvements brought by the MRSO in terms of performance and reliability.  
The results from Table 9 show that the MRSO consistently outperforms the original RSO in terms of average performance across all seven engineering design problems [49]. The average performance metrics in Table 9 clearly demonstrate the MRSO's superiority over the RSO. In the Pressure Vessel Design problem, the MRSO achieves an average cost of 6.956 \(\times\) 10\({}^{+3}\), which is significantly better than the RSO's 1.720 \(\times\) 10\({}^{+4}\). Similarly, in the
In terms of consistency, reflected by standard deviation, the MRSO also demonstrates superior performance in most cases. For instance, in the Pressure Vessel Design, the MRSO shows a significantly lower standard deviation (4.479 \(\times\) 10\({}^{+2}\)) compared to the RSO (1.031 \(\times\) 10\({}^{+4}\)), indicating that the MRSO is not only more efficient but also more stable across iterations. In the Gear Train Design, the MRSO exhibits much smaller variability with a standard deviation of 2.739 \(\times\) 10\({}^{-12}\), compared to the RSO's higher variation of 6.392 \(\times\) 10\({}^{-3}\). However, there are instances where the RSO shows slightly better consistency, such as in the Three Bar Truss problem, where the RSO achieves a lower standard deviation (4.829) compared to the MRSO (6.522). This suggests that while the MRSO offers substantial improvements in most cases, there are still some applications where further refinement could help improve its consistency.  
While the MRSO demonstrates significant improvements over the RSO, there are still cases where both algorithms exhibit weaknesses, particularly in handling complex constraints or highly nonlinear functions. For example, in the Three Bar Truss problem, despite the MRSO having a slightly better average value, both algorithms show relatively close performance with only marginal differences. This suggests that both the MRSO and the RSO may face challenges in solving certain structural design problems with high dimensionality or multiple constraints. Additionally, in the Welded Beam problem, while the MRSO outperforms the RSO, the higher standard deviation for both algorithms indicate instability, suggesting that further refinement may be necessary to improve convergence and reliability in these types of problems. These failure points highlight the need for the continued optimization of the MRSO's parameters and potential hybridization with other algorithms to ensure robust performance across all types of engineering design applications.
# Service Interface Translation. An Interoperability Approach
## 4 Performance Evaluation
### Metrics  
The evaluation of the scenario was performed using the most representative times and sizes of the generated code. Figure 7 shows the sequence diagram of the interaction between the service consumer and provider making use of the consumer interface generator. The generation was triggered by the orchestration system. The consumer requested the provider endpoint to consume its service despite the interoperability mismatches between them. After the interface was generated, the consumer was able to communicate with the provider via the newly generated interface. The generation process was only necessary once. The blue diamonds in the figure indicate when the samples were taken. In Table 4, a comprehensive summary of the selected metrics is presented.  
\begin{table}
\begin{tabular}{c l c} \hline \hline
**Notation** & **Meaning** & **Calculations** \\ \hline \(T_{G}\) & Time taken to generate the interface & \(t_{b}-t_{a}\) \\ \hline \(T_{B}\) & Time taken to compile and built the interface project & \(t_{c}-t_{b}\) \\ \hline \(T_{I}\) & Time taken to perform the service translation by the generated interface instance & \(t_{f}-t_{g}\) \\ \hline \(T_{S}\) & Total time taken to consume the service & \(t_{g}-t_{d}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Metrics used for the evaluation.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Specifications** & **Provider \& Consumer** & **Core Systems \& Interface** \\  & **(Raspberry Pi)** & **Generator (Laptop)** \\ \hline Memory & 4 GB & 16 GB \\ \hline Processor & Quad core Cortex-A72 (ARM v8) & Intel(R) Core (TM) i7-7500U CPU \\  & 64-bit SoC @ 1.5 GHz & @ 2.70 GHz \\ \hline OS & Raspbian & Windows 10 \\ \hline Disk & 16 GB & 413 GB \\ \hline \hline \end{tabular}
\end{table}
Table 3: Testbed setup specifications.
## 6 Conclusions
### Result Overview
For example, Croatia  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **SMOTE** & **Scenario** & **Parameters** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Logistic Regression & - & 3 & - & 0.71429 & 0.70238 & 0.71429 & 0.65306 \\  & + & 4 & - & 0.65714 & 0.65714 & 0.65714 & 0.65714 \\ \hline Support Vector Machine & + & 2 & C = 1, gamma = 1 & 0.68571 & 0.65369 & 0.68571 & 0.65432 \\  & + & 3 & C = 10, gamma = 1 & 0.62857 & 0.57767 & 0.62857 & 0.59147 \\  & - & 5 & C = 10, gamma = scale & 0.77143 & 0.82857 & 0.77143 & 0.72245 \\ \hline  & - & 2 & k = 1, p = 1 & 0.65714 & 0.65714 & 0.65714 & 0.65714 \\ K-Nearest Neighbors & - & 3 & k = 3, p = 1 & 0.71429 & 0.70238 & 0.71429 & 0.65306 \\  & + & 4 & k = 5, p = 1 & 0.71429 & 0.69388 & 0.71429 & 0.69353 \\  & + & 6 & k = 1, p = 1 & 0.77143 & 0.78561 & 0.77143 & 0.77598 \\ \hline  & - & 4 & - & 0.71429 & 0.70238 & 0.71429 & 0.65306 \\ Gaussian Naive Bayes & - & 5 & - & 0.77143 & 0.82857 & 0.77143 & 0.72245 \\  & - & 6 & - & 0.77143 & 0.82857 & 0.77143 & 0.72245 \\ \hline  & - & 1 & * & 0.76923 & 0.64615 & 0.76923 & 0.70234 \\  & + & 2 & * & 0.76923 & 0.73133 & 0.76923 & 0.74563 \\ Neural Network & + & 3 & * & 0.80769 & 0.79371 & 0.80769 & 0.79924 \\  & + & 4 & * & 0.80769 & 0.90385 & 0.80769 & 0.82675 \\  & + & 5 & * & 0.84654 & 0.84615 & 0.84615 & 0.84615 \\  & + & 6 & * & 0.80769 & 0.85897 & 0.80769 & 0.82249 \\ \hline Decision Tree & + & 4 & - & 0.71429 & 0.69143 & 0.71429 & 0.67659 \\  & - & 6 & - & 0.74286 & 0.72976 & 0.74286 & 0.73012 \\ \hline  & - & 1 & ccp\_alpha = 0.0065 & 0.65714 & 0.60571 & 0.65714 & 0.61190 \\  & + & 2 & ccp\_alpha = 0.019 & 0.74286 & 0.72976 & 0.74286 & 0.73012 \\ Pruned Decision Tree & + & 3 & max\_depth = 2 & 0.74286 & 0.72976 & 0.74286 & 0.73012 \\  & + & 5 & ccp\_alpha = 0.004 & 0.77143 & 0.82857 & 0.77143 & 0.72245 \\  & + & 6 & ccp\_alpha = 0.003 & 0.74286 & 0.72976 & 0.74286 & 0.73012 \\ \hline  & - & 1 & 3 models * & 0.71429 & 0.69143 & 0.71429 & 0.67659 \\  & + & 2 & 3 models * & 0.77143 & 0.77714 & 0.77143 & 0.74127 \\ Voting Classifier & - & 3 & 3 models * & 0.74286 & 0.72972 & 0.74286 & 0.71717 \\  & + & 4 & 3 models * & 0.71429 & 0.70208 & 0.71429 & 0.70571 \\  & - & 5 & 3 models * & 0.77143 & 0.82857 & 0.77143 & 0.72245 \\  & + & 6 & 3 models * & 0.82857 & 0.82466 & 0.82857 & 0.82343 \\ \hline  & - & 1 & estim = 7 & 0.65714 & 0.60571 & 0.65714 & 0.61190 \\  & + & 2 & estim = 50 & 0.71429 & 0.69388 & 0.71429 is highly centralized around its capital, Zagreb, with relatively few large urban centers. This centralization may influence contestants' representation and behaviors in ways that differ significantly from those in countries with more evenly distributed populations and multiple major cities. Therefore, although some insights may be broadly applicable, we refrain from asserting that our conclusions hold universally.
## 3 Results  
The means and standard deviations of the height obtained with MARS 4.0 and with the ad hoc Excel/VBA spreadsheet were 36.5 \(\pm\) 7.3 cm and 37.2 \(\pm\) 7.2 cm when calculated using take-off velocity and 36.5 \(\pm\) 6.6 cm and 35.8 \(\pm\) 6.6 cm when calculated using flight time. The mean propulsive impulse was 207 \(\pm\) 20 N \(\cdot\) s and 209 \(\pm\) 20 N \(\cdot\) s.  
For jump height calculated based on take-off velocity, the MAE was 1.79 cm (PMAE was 4.92%). In contrast, for jump height calculated based on flight time, the MAE and PMAE were lower, 0.69 cm and 1.89%, respectively. For the propulsive impulse, the MAE was 5.5 N \(\cdot\) s (PMAE = 2.66%). All Lin's CCC values indicated a very strong relationship. The statistical parameters, including the 95% confidence interval for the Lin CCC, the SEM, and the minimal detectable changes, are included in Table 4.  
Residuals depicted in the Bland-Altman plots appeared homoscedastic for all three variables (and the associated regression lines did not exhibit any linear relationship), indicating that the differences between MARS 4.0 and the ad hoc Excel/VBA spreadsheet were not dependent on the magnitude of the measurement (see Figures 5-7).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Statistical Parameter**} \\ \cline{2-6}
**Variable** & **Reliability** & \multicolumn{2}{c}{**Error (cm)**} & \multicolumn{2}{c}{*** MCD (cm)**} \\ \cline{2-6}  & **Lin CCC (95\% CI)** & **MAE** & **SEM** & **MDC\({}_{90}\)** & **MDC\({}_{95}\)** \\ \hline Jump height from take-off velocity & 0.932 (0.902–0.953) & 1.79 & 0.55 & 1.28 & 1.52 \\ Jump height from flight time & 0.984 (0.977–0.989) & 0.69 & 0.07 & 0.16 & 0.19 \\ Propulsive impulse & 0.940 (0.914–0.959) & 5.5 & 1.95 & 4.55 & 5.41 \\ \hline \hline \end{tabular}  
* Lin’s concordance correlation coefficient with the lower and upper ends of the 95% confidence interval;
* MDC: minimal detectable change; MAE: Mean Absolute Error; SEM: Systematic Error of Measurement.  
\end{table}
Table 4: Statistical parameters used to evaluate the spreadsheet’s validity.  
Figure 5: Bland–Altman plot of the height computed from take-off velocity.
_Applied_  
_sciences_  
**Soliton Molecules and Multisoliton States in Ultrafast Fibre Lasers: Intrinsic Complexes in Dissipative Systems**  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 2, [PERSON] 2, [PERSON] 3, [PERSON] 2 and [PERSON] 2,***  
1 4 th Physics Institute and Research Center SCoPE, University of Stuttgart, Pfaffenwaldring 57, 70569 Stuttgart, Germany  
2 State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments, Tsinghua University, Beijing 100084, China; [EMAIL_ADDRESS] (P.W.); [EMAIL_ADDRESS] (Y.D.); [EMAIL_ADDRESS] (K.Z.); [EMAIL_ADDRESS] (X.X.)  
3 Watson Laboratory of Applied Physics, California Institute of Technology, Pasadena, CA 91125, USA; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS] (L.G.); [EMAIL_ADDRESS] (C.Y.); Tel.: +86-10-627-2824 (C.Y.)  
Received: 14 December 2017; Accepted: 23 January 2018; Published: 29 January 2018  
**Featured Application: High-capacity telecommunication, advanced time-resolved spectroscopy, self-organization and chaos in dissipative systems.**  
**Abstract:** Benefiting from ultrafast temporal resolution, broadband spectral bandwidth, as well as high peak power, passively mode-locked fibre lasers have attracted growing interest and exhibited great potential from fundamental sciences to industrial and military applications. As a nonlinear system containing complex interactions from gain, loss, nonlinearity, dispersion, etc., ultrafast fibre lasers deliver not only conventional single soliton but also soliton bunching with different types. In analogy to molecules consisting of several atoms in chemistry, soliton molecules (in other words, bound solitons) in fibre lasers are of vital importance for in-depth understanding of the nonlinear interaction mechanism and further exploration for high-capacity fibre-optic communications. In this Review, we summarize the state-of-the-art advances on soliton molecules in ultrafast fibre lasers. A variety of soliton molecules with different numbers of soliton, phase-differences and pulse separations were experimentally observed owing to the flexibility of parameters such as mode-locking techniques and dispersion control. Numerical simulations clearly unravel how different nonlinear interactions contribute to formation of soliton molecules. Analysis of the stability and the underlying physical mechanisms of bound solitons bring important insights to this field. For a complete view of nonlinear optical phenomena in fibre lasers, other dissipative states such as vibrating soliton pairs, soliton rains, rogue waves and coexisting dissipative solitons are also discussed. With development of advanced real-time detection techniques, the internal motion of different pulsing states is anticipated to be characterized, rendering fibre lasers a versatile platform for nonlinear complex dynamics and various practical applications.  
**Keywords:** soliton molecules; bound states; fibre lasers; nonlinear interaction; ultrafast nonlinear optics; fibre optics