esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Calculation of Bonding Strength and Critical Lapping Length  
### Grey Correlation Analysis of Bonding Factors  
Grey correlation theory [34] was used to quantitatively analyse the correlation of the steel bar diameter \(d\), lapping length \(l\), relative lapping length \(l\)/\(d\), sleeve inner diameter \(D\), sleeve wall thickness \(t\), steel content \(\rho_{\text{v}}\), and simplified steel content \(t\)/\(D\) with the bonding strength. Table 5 shows the correlation results of each factor with the bonding strength. It is mainly described by the order of the correlation degree, not only the magnitude of the correlation \(r\). Because the correlation \(r\) of each factor was higher than 0.7, each factor was closely related to the ultimate bonding strength.  
As shown in Table 5, the correlation between the simplified steel content \(t\)/\(D\) and the ultimate bonding strength was the highest. The correlation between the sleeve inner diameter and the ultimate bonding strength was also relatively high. The correlation between the wall thickness \(t\) of the sleeve and ultimate bonding strength was relatively weak. This was because the constraint ability of the sleeve increased rapidly with the increase in the wall thickness when the wall thickness of the sleeve was relatively small (less than 2.5 mm). The increase in the wall thickness had little effect on improving the constraint ability of the sleeve when the wall thickness of the sleeve was large (no less than 2.5 mm) [35]. The wall thicknesses of most of the sleeves were large (3 mm) in the data analysed, resulting in a relatively weak correlation between \(t\) and the ultimate bonding strength \(\tau_{\text{u}}\). Among the parameters of the steel bar, that is, the diameter \(d\), lapping length \(l\)  
Figure 25: Analysis of the bonding stress of the sleeve in the higher-loading period under compression. (**a**) Bonding stress distribution of steel bar, (**b**) Bonding stress of sleeve.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Influencing Factors** & \(d\) & \(l\) & \(t\) & \(D\) & \(lId\) & \(D\)/\(d\) & \(t\)/\(D\) & **Steel Content \(\rho_{\text{v}}\)** \\ \hline Correlation \(r\) & 0.7624 & 0.7195 & 0.7466 & 0.7782 & 0.7255 & 0.7559 & 0.7921 & 0.7903 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Correlation of each factor with the bonding strength.  
and relative lapping length \(l/d\), the steel bar diameter \(d\) was most highly correlated with ultimate bonding strength. The relative lapping length \(l/d\) had a better correlation with the ultimate bonding strength compared with the single factor \(l\).
## 3 Methodology
### Scientometric Analysis  
The scientometric analysis began by merging missing bibliometric information (assignees, dates, and inventor names) using Google Patents based on the USPTO patent numbers. With complete records, the analysis proceeded through four steps: First, temporal trend analysis mapped patent activity by year and country to reveal the growth and activity patterns. Second, organization mapping identified the leading assignees and their focus areas. Third, inventor tracking measured the innovation thrust intensity, international participation, and category diffusion by complexity. Finally, latency evaluation assessed the time from filing to grant, offering insight into patent complexity and examination speed. These analyses provide critical signals of technological momentum, organizational leadership, and the maturity of innovation within hydrogen-powered aviation.
## 7 Conclusions  
In this study, we introduced a novel spatiotemporal diversification scheme to enhance the security of end-to-end key distribution in quantum key distribution (QKD) networks. The proposed strategy dynamically alternates key distribution paths and incorporates an adaptive path recovery process. By employing a penalty-based mechanism, the system detects and excludes suspicious relay nodes, ensuring the integrity of the key distribution process while maintaining high efficiency. Our results demonstrate that the proposed method significantly improves system performance by leveraging multipath key distribution and dynamic path recovery. Diversifying transmission routes reduces the risk of attacks by lowering the adversary's success rate while transmitting multiple keys simultaneously introduces randomness that further strengthens the system's security. These findings underscore the importance of integrating dynamic path selection and penalty-based node evaluation in designing secure QKD systems. The proposed approach offers a promising solution for improving the security and robustness of QKD networks, paving the way for practical and reliable implementation across diverse environments.  
E.M.G.: simulation and software, E.M.G.: validation, M.A.: conceptualization, methodology, system design; E.M.G.: formal analysis, E.M.G.: investigation, M.A.: resources, D.G.: data curation, E.M.G. and M.A.: writing--original draft preparation, M.A.: writing--review and editing, E.M.G.: visualization, M.A.: supervision, M.A. and D.G.: project administration, M.A. and D.G.: funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This project is partially sponsored by the Commonwealth Cyber Initiative, an investment in the advancement of cyber R&D, innovation, and workforce development.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Graphical and Analytical Quantitative Comparison
## 4 The Case Study of San Francesco di Paola
### Geometrical Safety Factors  
In this section, we show the geometrical safety factor computed with the different methods. As mentioned in Section 3, for TLA it is easy to span all the possible curves and find the one that is the closest to the midsurface of the vault.  
For the case study of San Francesco di Paola, in Figure 13 we show the most internal possible thrust line and the virtual intrados and extrados that mark the maximum possible thickness reduction in the vault. In this case, the maximum thickness reduction is \(d~{}=~{}62.4\%\), which corresponds to a geometrical safety factor \(GSF=2.65\).  
Figure 11: Membrane Equilibrium Analysis: minimum (**a**) and maximum (**b**) principal values of the Pucher stresses.  
Figure 12: Radial and circumferential Pucher stresses along a meridian using MEA.  
The computation of the geometrical safety factor with MTLM requires a more complicated optimization algorithm, capable of finding the best combination of hoop forces at each voussoir to minimize the distance of the thrust line from the midsurface.  
The higher flexibility of MTLM with respect to TLA allows for a higher geometrical safety factor. Our optimization algorithm is able to reduce the overall thickness of the vault of \(d=80\%\), with a corresponding safety factor \(GSF=5.00\), as shown in Figure 14.  
Concerning MEA, the algorithm presented in the present paper does not permit access to non-concave membrane surfaces. For non-concave surfaces, the problem could be approached by assigning the stress potential, which is convex with the definition of No-Tension material, and solving it for the shape, as recently shown in [44].  
Therefore, we limit our study the evaluation of the geometrical safety factor with MEA to concave shapes. We use a trial-and-error algorithm to explore some of the possible  
Figure 14: Thrust line obtained using MTLM in the safest case.  
Figure 13: Thrust line obtained using TLA in the safest case.  
membrane surfaces to which the negative semidefinite stress tensor corresponds. Finally, we obtain the shape shown in Figure 15, which fills into a thickness reduced by 67% with respect to the original, which corresponds to a geometrical safety factor of \(GSF=3.03\).
# Photovoltaic Prediction Software: Evaluation with Real Data from Northern Spain
## 2 Methodology
### Pvsyst  
PVSYST software is a European-based PV system predictor developed by the University of Geneva for the European Energy Center. It is suitable for studying, simulating and analyzing stand-alone and grid-connected PV systems. Additionally, it is also useful for solar-thermal systems and solar applications. PVSYST is a well-designed tool with an extensive number of features for detailing the PV system and includes an extensive database of different PV related products. PVSYST includes algorithms for thermal and wind effects calculation, wire losses and incidence angle modifier losses, albedo values, and a horizon adjustment capability. This software has received significantly high scores and recommendations in various comparative studies [15; 21; 22; 23].  
PVSYST imports meteorological and global irradiation data from Meteoromr and NASA-SSE. In addition, other external databases can be introduced in the study. PVsyst implements theoretical models for the calculation of both global irradiation on an inclined plane and diffuse irradiation [24; 25]. The database of available PV panels, inverters, and batteries is very broad, is directly updated by the manufacturers, but can also be imported from the PHOTON database and even permits the definition of \"ad hoc\" panels of different technologies: mono-Si, poli-Si, and thin film. It is compulsory to introduce the design of the system: distribution of PV panels, and connection with the inverter, tilted angle and sun-tracking system specification. The incidence angle can be optimized and shadows and other elements can be introduced with PVSYST that may have an impact on the performance of the installation. Although the PV plant can be precisely described, when looking for a preliminary study, some parameters are not pre-defined, which complicates any definition of the basic study.  
The main data outputs are annual PV production (MW) and the specific PV production (\(\mathrm{kWh/kW_{p}}\) year), as well as the performance factor. A monthly diagram of standard production per installed \(\mathrm{kW_{p}}\) is generated, including the following information: power loss, total energy produced, the effective power to the output of the generator, the energy injected into the network, the electrical efficiency of the facility, and a table with the solar irradiation data (horizontal global irradiation, incident irradiation at the receptor). In addition, the tool generates a diagram of losses during the year, indicating the estimated losses due to temperature, wiring, inverter system, and shadows. It is a payment program, but a complete version is free for one month. Subsequently, the program functions in DEMO mode, with very limited options.
## 5 Comparison and Discussion Between KANs and MLPs
### Prediction Errors and Discussion
#### 5.2.3 KAN-C vs. MLP-C  
KAN-C features 20 nodes in the hidden layer. KAN-C's spline basis function combination space expanded. This theoretically enhances its approximation capability. However, the test set errors increased compared to KAN-A/B, indicating a heightened overfitting risk. MLP-C, with the hidden nodes expanded to 20, saw its parameter space jump from 12 (MLP-B) to 80 dimensions (3 \(\times\) 20 + 20 \(\times\) 1). The training and test R\({}^{2}\) improved from 0.6644/0.6608 (MLP-B) to 0.9830/0.9831 (MLP-C). This is aligned with [PERSON]'s theorem on width compensation. MLP-C's test/training MSEs both stabilized at 0.0064, suggesting 20 nodes reached the architecture's generalization boundary.  
KAN-C exhibited a precision-generalization paradox. While training, the MaxAE dropped to 0.3513, and the test MaxAE surged to 0.7884. This anomaly stems from input perturbation amplification in high-dimensional parameter spaces and the absence of dropout regularization in KAN-C's complex structure. These factors lead to a reduced regularization capacity.  
MLP-C demonstrated improved stability post-expansion. Nearly identical error averages across datasets and small standard deviations (from 20 runs) indicated consistent performance. Its minimized MaxAE (compared to MLP-A/B) benefited from ReLU's sparsity-induced implicit regularization in the complex architecture, making MLP-C the top-performing MLP variant.  
The runtime of KAN-C increased from 299.53s (KAN-B) to 747.66s, surpassing expectations based on linear parameter growth. This was driven by two factors: 1140 basis function interaction combinations (C\({}_{20}^{3}\)) and computational demands from B-spline second-order continuity constraints. Conversely, MLP-C's runtime decreased to 6.86 s (vs. MLP-B's 7.12 s), likely due to reduced Hessian condition numbers from increased parameters. This accelerates convergence in moderately complex networks.
## 2 Proposed Method
### The Proposed Model
#### 2.2.3 Training Scheme  
Unstable training is a major concern when using GANs, and it can occasionally result in mode collapse or low-quality output. In auto-encoder-based GAN models in particular, the reconstruction performance of the discriminator is a decisive factor in establishing the visual quality of an output image. However, excellent reconstruction performance cannot be guaranteed because the importance of the reconstruction error for a real image decreases as \(k_{t}\) increases, as can be seen in Equation (3). Training a discriminator on relatively large images (e.g., \(128\times 128\)) is slow and difficult, and \(k_{t}\) becomes larger because the discriminator does not effectively function as an auto-encoder. Motivated by PGGAN [17], our model attempts to overcome this problem by starting the training process with low-resolution images. In other words, the size of the training images increases as training progresses (Figure 2). When the size of an image is larger, new layers are added to both the generator and discriminator to adjust the size of the input and output correctly. While PGGAN starts training with \(4\times 4\) images, our model begins with \(32\times 32\) images because our model performs sufficiently without threatening stability when generating images with sizes of \(32\times 32\) or lower, thereby reducing the training time. After a few epochs of training, the size of the training images is doubled, and new layers are added to the generator, encoder, and decoder while maintaining the weights in the conventional layers. By progressively training the generator and the discriminator in this manner, our model achieves better reconstruction performance than when \(k_{t}\) remains constant. Because the discriminator is trained to some extent in the previous stage, the training process becomes more stable and the visual quality is higher than when training with \(128\times 128\) images directly. In addition, the layers of the generator and the decoder can accurately reflect the spatial properties of their input.
## 1 Introduction
This causes some problems such as node and link failure due to unbalanced depletion of nodes' batteries across the network. Applying multi-path routing [14, 15] in WSNs could result in traffic and energy load balancing over the network. Furthermore, it is not necessary to update the route information periodically, which wastes a remarkable amount of the nodes' power [16]. The sensor nodes are used to forward the data and control packets to the next hop at a maximum power level, which results in fast energy exhaustion. In this state, by employing a power control scheme [17, 18, 19] in routing protocols in which the nodes are able to adjust the transmission power level based on the distance from the next hop, the relay nodes can conserve much more energy. Finally, bio-inspired algorithms [20] have recently been added to the above category as an important class since they can optimize the route construction phase. Bio-inspired protocols which are designed based on insect sensory systems try to construct the shortest path between the source and the destination so that it can conserve much more energy. Our aim in this paper is to help readers better understand the fundamental energy-aware mechanisms applicable to routing algorithms in wireless sensor networks and point out the potential for improving network lifetime making use of these techniques. We present a comprehensive classification for these mechanisms and discuss a variety of the state-of-the-art energy-efficient routing and cross-layer protocols under this taxonomy. As mentioned before, multi-path methods can avoid network partitioning by distributing traffic loads on most of the sensor nodes while multiple sink and mobile sink methodologies overcome this problem by changing the sink's neighbors periodically and balancing the energy consumption in the sink vicinity. Power control schemes can save nodes' energy by decreasing the power needed to transmit data packets to the next hop in the routing protocols while bio-inspired algorithms can optimize the route construction phase by finding the shortest path for data routing. We categorize the protocols using power control techniques as cross-layer schemes, while the rest are classified as simultaneous mechanisms in the network layer. To the best of our knowledge, our work is the first effort to categorize lifetime improvement strategies applied in routing for WSNs. The rest of this paper is organized as follows: we present the related work in Section 2. Background and preliminaries is presented in Section 3. In Section 4, we discuss various mechanisms using multiple sinks and mobile sinks to balance the energy consumption of the network. We continue in this section with a presentation of some routing mechanisms using multiple paths, power control and bio-inspired protocols to save node energy and prolong network lifetime. A comparison among different mechanisms is shown in Section 5. Discussion and open issues for future studies is given in Section 6. Finally, Section 7 concludes the paper.
# Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar
## 4 Comparative Performance Analysis
### Evaluation of Doppler Shift Distortion of Range Profiles
#### 4.2.2 Peak-to-Sidelobe Level Ratio
Further exceptions are the combinations of the complementary sequences Golay A and B, which tend to behave similarly as the remaining PRBSs of similar \(N_{\text{PRBS,usable}}\) for low and high \(|f_{\text{D}}/\Delta f|\), but suffer severe PSLR degradation in the middle range of the considered \(|f_{\text{D}}/\Delta f|\) values (between 0.2 and 0.3, approximately) due to the mainlobe degradation effect described in Section 4.2.1. Focusing on \(|f_{\text{D}}/\Delta f|\leq 0.1\), which is typically assumed, e.g., in OFDM radar systems, it is seen that all considered PRBSs experience negligible PSLR degradation and most of them present nearly the same PSLR of around \(-13.27\,\text{dB}\), which is related to the sinc-like shape of the oversampled PACFs. Among the exceptions are the Gold sequences, which present PSLRs approximately \(0.36\,\text{dB}\) to \(1.87\,\text{dB}\) higher than the aforementioned \(-13.27\,\text{dB}\) depending on the sequence length, and Kasami sequences, whose variants with \(N_{\text{PRBS}}=255\), \(N_{\text{PRBS}}=1023\), and \(N_{\text{PRBS}}=4095\) present a PPLR \(1.65\,\text{dB}\) lower, \(0.86\,\text{dB}\) higher, and \(0.21\,\text{dB}\) lower than the trend for the remaining sequences of achieving similar \(N_{\text{PRBS,usable}}\), respectively.
# Effects of Stretching and Resistance Training on Psychophysical Awareness: A Pilot Study
###### Abstract  
Muscle-joint flexibility is defined as the ability of a muscle to stretch in a controlled manner, allowing a wide range of movement at the joints. While numerous methodologies exist for improving flexibility, few studies have investigated the role of athletes' perceptual processes and awareness related to their own body and movement control during such training. In this pilot study, we explored how two different training protocols--static and dynamic stretching (control group, CON) and multi-joint resistance training (experimental group, EXP)--influence both flexibility and psychophysical awareness, understood as a multidimensional construct involving perceived flexibility improvements, self-assessed control over exercise execution, and cognitive-emotional responses such as engagement, motivation, and satisfaction during physical effort. The study involved 24 male amateur track-and-field athletes (mean age 23 \(\pm\) 2.5 years), randomized into two equal groups. Over 12 weeks, both groups trained three times per week. Flexibility was assessed using the Sit and Reach Test at three time points (pre-, mid-, and post-intervention). A \(2\times 3\) mixed ANOVA revealed a significant group \(\times\) time interaction (F = 20.17, \(p\) < 0.001), with the EXP group showing greater improvements than the CON group. In the EXP group, Sit and Reach scores increased from pre = 28.55 cm (SD = 4.91) to mid = 29.39 cm (SD = 4.67) and post = 29.48 cm (SD = 4.91), with a significant difference between pre and post (\(p\) = 0.01; d = 0.35). The CON group showed minimal changes, with scores of pre = 28.66 cm (SD = 4.92), mid = 28.76 cm (SD = 5.03), and post = 28.84 cm (SD = 5.10), and no significant difference between pre and post (\(p\) = 0.20; d = 0.04). Psychophysical awareness was assessed using a custom questionnaire structured on a 5-point Likert scale, with items addressing perception of flexibility, motor control, and exercise-related bodily sensations. The questionnaire showed excellent internal consistency ([PERSON]'s \(\alpha\) = 0.92). Within the EXP group, psychophysical awareness increased significantly (from 3.50 to 4.17; \(p\) = 0.01; d = 0.38), while no significant change occurred in the CON group (\(p\) = 0.16). Post-hoc power analysis confirmed small to moderate effect sizes within the EXP group, although between-group differences lacked sufficient statistical power. These results suggest that resistance training may improve flexibility and concurrently enhance athletes' psychophysical self-awareness more effectively than traditional stretching. Such findings offer practical implications for coaches seeking to optimize flexibility training by integrating alternative methods that promote both physical and perceptual adaptations.  
**Keywords:** flexibility; multi-joint resistance training; stretching; psychophysical awareness
## 4 Applications of the Time-Correlated Localization Measurement Errors Model  
The proposed time-correlated localization errors model can be beneficial for location prediction when GPS is not available due to variations in urban structure (e.g., entry into tunnels). The model can also improve the localization accuracy because it incorporates the time dependency of measurements into prediction. The model does not need information beyond the initial position measurements of a vehicle. This reduces the complexity of processing in terms of data filtering and fusion. The \(p\) -order Gauss-Markov model can also be used as a prediction tool that enhances the performance of existing localization schemes, such as the Kalman filter [27]. In the following section, we illustrate through simulations and real vehicle mobility traces how the model can be used standalone as well as a prediction tool for the Kalman filter.