esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Burst Containments for FESS
### Energy Storage Capabilities of Rotor Materials  
In order to optimally design FESS burst containments it is important to fully understand how energy is actually stored in the rotor. As described in Equation (1), the kinetic energy is proportional to the rotor's moment of inertia, \(I\), and the rotational speed, \(\omega\), squared.  
\[E_{k}=\frac{1}{2}I\omega^{2} \tag{1}\]  
The maximum _kinetic energy_\(E_{k\_max}\) that can be stored in the flywheel is limited by the maximum admissible centripetal stresses, \(\sigma_{max}\), which occur in the rotor and can be described as  
\[E_{k\_max}=K_{shape}\frac{\sigma_{max}}{\rho} \tag{2}\]  
\(K_{shape}\) is the so-called shape factor, a geometric design variable ranging from 0.3 to 1 depending on rotor geometry [10]. Hence, from a material point of view, the function of \(\sigma\)/\(\rho\) (the ratio between tensile strength and rotor material density) determines the storable specific energy, indicating that light, yet strong materials hold the greatest potential. At the current state of the art and under consideration of a safety factor of 2 (50% exploitation of the rotor material) 31 kg of high-strength steel or 7 kg of high-strength carbon fiber composite (_TG1000_) are required to store 1 kWh of energy. Still, as long as there are advances in material science, the specific energies (and energy densities) of FESS will increase further over time. Though only expected in the distant future, flywheel rotors made of carbon nano-tube technology could result in a radical change in the energy storage sector, revealing a theoretical potential of up to 15 kWh/kg [5]. A comparison of FESS rotors with fossil fuels, which offer an energy density of \(\sim\)12 kWh/kg and Li-Ion batteries (at \(\sim\)0.25 kWh/kg) is given in Figure 3.  
It must be stressed that the performance of FESS safety housings needs to increase at the same rate as the specific energy content of rotors does! This is not only important in order to guarantee perfect safety of this energy storage technology, but also to allow full exploitation of both the rotor and housing material, as described in the subsequent section.  
Figure 2: (a) Impressions of the accidents at Beacon Power in Stephentown [8]; (b) Quantum Technologies flywheel-plant in San Diego, USA [9].
## 5 Machine Learning Algorithms
### Speech Emotion Recognition Models
#### 5.1.1 Convolutional Neural Networks  
CNNs are deep learning architectures suited to grid-structured data such as images, using convolutional layers to automatically capture spatial patterns and feature hierarchies. They are highly effective at tasks such as image classification, object detection, and face recognition, due to their ability to efficiently identify patterns and features in images. Based on four datasets RAVDESS, TESS, SAVEE, and CREMA-D, a convolutional network architecture was developed [32]. Using CNN treats Mel spectrograms as images, leveraging CNN's strength in feature extraction from 2D data. The model architecture includes several convolutional layers (Conv2D), each followed by pooling and batch normalization to reduce complexity and maintain stability. After multiple convolutional layers, a GAP (global average pooling) layer is used to reduce the number of parameters, followed by a dropout layer to prevent overfitting, and finally a dense layer with a softmax activation function to classify emotions. This approach allows for efficient feature learning from audio data without the need for extensive preprocessing.
# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 4 Experimental Results and Analysis  
This section consists of parameter tuning, complete experimental results, and analysis. Crop classification results are evaluated against standard metrics.
## 3 Results
### Expression and Activity of GLO1 in the Uterus of Young and Old Rabbits on Day 6 of Pregnancy  
The uterus, especially the endometrium, are crucial organs for embryo implantation and the establishment of a successful pregnancy. We analysed the GLO1 expression and activity in the pregnant uterus at day 6 p.c. In the endometrium of old rabbits, we observed significant lower GLO1 mRNA and protein levels (Figure 4A,B), whereas the specific GLO1 activity was not altered (Figure 4C).  
In the uterus, GLO1 was localised in the myometrium, as well as in the glands and epithelium of the endometrium. The stroma of the endometrium was weakly stained. No differences in GLO1 localisation and staining intensity were observed between young and old rabbits (Figure 4D). The GLO1 protein decrease detected by qPCR and Simple Western (Figure 4A,B) was not evident in immunohistochemical sections.  
For the AGE argpyrimidine, immunohistochemical analysis showed a cytosolic as well as a nucleoplasmatic localisation, particularly in the myometrium, glands and epithelium. In contrast to GLO1, argpyrimidine was also localised in stromal cells. Comparing the staining pattern to that of GLO1, the argpyrimidine staining showed more variety across the endometrium, with a very intense staining in some distinct endometrial cells. In the endometrium of old rabbits, more of these intensively stained endometrial cells were detected than in young rabbits (Figure 4D), indicating an increased accumulation of argpyrimidine in the endometrium of old animals.  
Figure 4: Quantification of GLO1 mRNA, protein and enzymatic activity in the endometrium of young and old rabbits at day 6 p.c. (**A**) GLO1 mRNA levels were quantified by qPCR. The Mann–Whitney U test was used for statistical analysis (* \(p\) = 0.033). (**B**) GLO1 protein analysis was carried out by the Simple Western method. Relative mRNA and protein amount are normalised to the levels of vinculin (* \(p\) = 0.038). (**C**) Enzymatic GLO1 activity was measured spectrophotometrically in 10 \(\upmu\)g endometrial protein. Specific GLO1 activity was calculated relatively to GLO1 protein amount within the same sample. Results are shown as box plots. All values are expressed in per cent, with young = 100% (_n_ \(\geq\) 9). (**D**) Immunohistochemical detection of GLO1 and argpyrimidine in cross-sections through the uterine wall. M = Myometrium. En = Endometrium.
# A Connected and Autonomous Vehicle Reference Architecture for Attack Surface Analysis
## 2 Related Work
### Reference Architectures
#### 2.1.1 Non-CAV Reference Architectures  
Before exploring the existing CAV reference architecture it is useful to examine reference architectures for different fields. In doing so they raise interesting ideas for ways in which CAV reference architectures can be improved.  
A common architectural framework for the development of interoperable industrial internet systems was presented in Reference [26]. The Industrial Internet Reference Architecture (IIRA) is divided into four viewpoints, namely Business, Usage, Functional and Implementation. While and last two viewpoints are of utmost importance in the identification of a system's threats and vulnerabilities, as they are concerned with a system's functional requirements, interdependencies and technological implementations. The IIRA also describes a system's business objectives and expected usage, both of which go beyond the scope of a reference architecture for attack surface analysis.  
A Smart Grid Reference Architecture was developed in Reference [27] which uses Business, Functional, Information and Communication viewpoints. Explicit considerations of _information security_, are included (i.e., confidentiality, privilege escalation), however, the methodology of how to perform a security analysis of the system is not described. The systems described are complex and include many implementation details, including the scenario a component is operating in and what actions the component is involved with. From a security analysis perspective the reference architecture could be simplified (e.g., by removing business cases) to reduce the scope for which cyber security needs to be considered. This means that while the reference architecture states that it is useful for a cyber security analysis, due to it describing aspects of a Smart Grid which do not have cyber security considerations, performing a cyber security analysis is difficult. The conclusions from this are that reference architectures for cyber security analysis, should focus on the aspects of the system for which cyber security is relevant.
## 7 Solution of The Digital Beamforming Matrix and The Analog Beamforming Matrix
### Solution of The Hybrid Beamforming Matrix  
Before calculating \(\mathbf{F}_{BB}\) and \(\mathbf{F}_{RF}\), it is necessary to obtain the hybrid beamforming matrix \(\mathbf{F}\). For the solution of \(\mathbf{F}\), we can adopt the EVD algorithm based on the previously obtained optimization variable \(\mathbf{W}\). According to the relationship between \(\mathbf{W}\) and \(\mathbf{F}\), i.e., \(\mathbf{W}_{l}\triangleq f\mathbf{f}_{l}^{H}\), the solution of \(\mathbf{F}\) can be modeled as follows:  
\[\min_{f_{l}}\Big{\|}\mathbf{W}_{l}-f\mathbf{f}_{l}^{H}\Big{\|}_{\mathbf{F}^{\prime}}^{2} \tag{80}\]  
where the hybrid beamforming vector \(\mathbf{f}_{l}\) can be given by  
\[\mathbf{f}_{l}=\sqrt{\upsilon_{l}}\mathbf{u}_{l},\forall l, \tag{81}\]where \(v_{l}\) is the maximum eigenvalue of the matrix \(W_{l}\) and \(u_{l}\) is the maximum eigenvector of the matrix \(W_{l}\).
# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 5 Experiments
### Algorithm Performance Comparison
#### 5.3.2 Calculation Efficiency Analysis  
To accurately evaluate the efficiency of CPS-MOEA, comparative testing was conducted between CPS-MOEA and three other algorithms, PICEA-g, MOPSO, and GDE3. The test results are presented in Table 4. Note that considering the randomness of intelligent optimization algorithms, the average computation time of each algorithm in 30 independent runs is used as the computation time in Table 4.  
From Table 4, it can be seen that CPS-MOEA has better computational efficiency than other algorithms. In addition, by using more advanced computer hardware and optimized code, the computational efficiency of our method can be further improved. From these results, it can be argued that the method can meet the real-time requirements  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **Calculation Time (s)** \\ \hline CPS-MOEA & 138.38 \\ PICEA-g & 143.27 \\ MOPSO & 153.65 \\ GDE3 & 143.81 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculation time of the algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **Metrics** & **Average Value** & **Best Value** & **Worst Value** \\ \hline \multirow{4}{*}{CPS-MOEA} & GD & 2.08 & 2.06 & 2.11 \\  & spread & 0.97 & 0.95 & 0.99 \\  & IGD & 20.46 & 20.44 & 20.48 \\  & GD & 2.09 & 2.06 & 2.21 \\ PICEA-g & spread & 0.97 & 0.95 & 1.19 \\  & IGD & 20.50 & 20.46 & 20.59 \\  & GD & 2.13 & 2.07 & 2.25 \\ MOPSO & spread & 0.98 & 0.96 & 0.99 \\  & IGD & 20.67 & 20.53 & 20.95 \\  & GD & 2.08 & 2.06 & 2.11 \\ GDE3 & spread & 0.99 & 0.95 & 1.01 \\  & IGD & 20.46 & 20.47 & 20.49 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The values of measurement indicators GD, spread, and IGD.  
in practical applications and is superior to other methods used in this study in terms of computational efficiency.
# Study on Shielding Effect of the Pile Group in a Soft-Soil Foundation
## 2 Materials and Methodology
### Methodology  
Figure 4 demonstrates the most typical installation sequence in engineering practices that was adopted for the numerical pile group, where the piles were driven sequentially from the negative X-axis direction to the positive X-axis direction, totaling 9 piles (P1-P9) in a 3 \(\times\) 3 distribution. Due to the symmetrical distribution of the numerical model, the influence of various piling sequences on soil can be easily realized by observing at various directions. Therefore, to clearly observe the shielding effects, two opposite piling sequences were determined to be compared by setting monitoring points to the west and the east off the pile group. Thus, one sequence (W direction) indicated that piles were penetrated and moved away; the other sequence (E direction) indicated that the piles were penetrated and moved closer. Additionally, two parallel monitoring profiles, A-A\({}^{\prime}\) and B-B\({}^{\prime}\), were established along the X and Y axis to conduct further analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & \begin{tabular}{c} **Young’s** \\ **Modulus (E/MPa)** \\ \end{tabular} & \begin{tabular}{c} **Poisson’s Ratio** \\ **(v)** \\ \end{tabular} & \
\begin{tabular}{c} **Bulk Modulus** \\ **(K/MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Shear Modulus** \\ **(G/MPa)** \\ \end{tabular} \\ \hline Value & 257.1 & 0.286 & 200 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pile properties used in the numerical model.  
Figure 3: Pile body grids and expansion vectors of the cylinder/semi-sphere.  
According to our early numerical simulation tests, during expansion, the vertical interaction between the pile and soil is weak, and the shielding effect should be explained by focusing on the lateral interaction between the pile and soil. Thus, the lateral displacement and lateral stress of the soft soil foundation were assumed as the disturbance indicators. The response characteristics of the displacement field, stress field as well as strain field of the soil will be simulated and discussed to verify the applicability of the model. Subsequent investigations will explore the shielding effects caused by pile squeezing, and ultimately make clear the shielding mechanism of the pile group.
## 2 Results
### Overexpression of Entire MVA Pathway Genes  
Enzymes responsible for (\(-\))-\(\alpha\)-bisabolol biosynthesis in _E. coli_ DH5\(\alpha\)-pTSN-Bisa-Mm strain are controlled by IPTG-inducible promoters; trc promoter for _MrBBS_, and _ispA_ genes, and lac promoter for all MVA pathway genes (Figure 2A). To this end, we explored the effect of IPTG amount on (\(-\))-\(\alpha\)-bisabolol production and MVA accumulation in batch culture. When 0.025 mM IPTG was used for induction, (\(-\))-\(\alpha\)-bisabolol production increased by 1.8-fold along with a 1.4-fold decrease of MVA accumulation compared to those of the control that were not induced by IPTG (Figure 3A). To scrutinize the effect of IPTG on (\(-\))-\(\alpha\)-bisabolol production, a pSEVA231-Bisa-Mm was generated using a medium copy number plasmid, pSEVA231 (pBBR1 ori) (Figure 3B). Interestingly, the _E. coli_ DH5\(\alpha\)-pSEVA231-Bisa-Mm strain produced 926 mg/L of (\(-\))-\(\alpha\)-bisabolol without the accumulation of MVA under the induced condition (0.025 mM IPTG), which is 3.7-fold higher than the uninduced condition (Figure 3B). When the IPTG amount increased up to 0.1 mM, both pTSN-Bisa-Mm (high copy number), and pSEVA231-Bisa-Mm (medium copy number) showed a dramatic decrease (90%) in (\(-\))-\(\alpha\)-bisabolol production compared to those in the presence of 0.025 mM IPTG (Figure 3A,B).  
Figure 2: Introduction of MvaK1 from _M. mazei_ for the improvement of (\(-\))-\(\alpha\)-bisabolol production. (**A**) Plasmid constructs for expressing the entire MVA pathway, FPP synthase and (\(-\))-\(\alpha\)-bisabolol synthase. The plasmid pTSN-Bisa-Sa and pTSN-Bisa-Mm have _muxk1_ gene of _S. aureus_ and _M. mazei_, respectively. (**B**) Improvement of (\(-\))-\(\alpha\)-bisabolol production in the engineered _E. coli_ DH5\(\alpha\) harboring pTSN-Bisa-Mm compared with the strain expressing pTSN-Bisa-Sa. Cells were grown in TB medium containing 10 g/L glycerol and 20% (v/v) of _n_-dodecane at 30 \({}^{\circ}\)C for 72 h without the addition of isopropyl \(\beta\)-p-1-thiogalactopyranoside (IPTG). The error bars represent the standard deviation of the concentrations of (\(-\))-\(\alpha\)-bisabolol and MVA from three biological replicates. (**C**) Fed-batch fermentation of _E. coli_ DH5\(\alpha\) harboring pTSN-Bisa-Mm. The fed-batch fermentation was performed in TB medium and 20% (v/v) of _n_-dodecane using two-phase culture in the absence of IPTG at 30 \({}^{\circ}\)C and pH 7.0. After depletion of glycerol initially added, glycerol was fed intermittently into the bioreactor during fermentation. An agitation speed of 280 rpm and an aeration rate of 1 vessel volume per minute (vvm) were maintained throughout the cultivation.
# A Survey on Immersive Cyber Situational Awareness Systems
## 6 Discussion
### Industrial Implications of ICSA Systems  
Our study highlights the growing potential of immersive technologies in the cybersecurity industry, following their success in domains such as gaming, education, healthcare, and entertainment. Technologies like virtual reality and augmented reality are now being applied to cybersecurity awareness and technical training. These immersive systems create engaging, hands-on learning environments that improve the skills and preparedness of cybersecurity professionals by simulating realistic threat scenarios. Beyond training, immersive technologies enhance how security teams visualize and interpret complex cybersecurity data. This includes network traffic patterns, alerts, threat indicators, and infrastructure topologies. By presenting data in an intuitive and spatial manner, ICSA systems help reduce cognitive load and improve decision-making. They also support real-time collaboration within Security Operations Centers and Network Operations Centers, enabling teams to coordinate responses more effectively during incidents.  
ICSA systems can further assist in troubleshooting and operational monitoring. Their interactive interfaces allow analysts to explore data collaboratively and identify issues more efficiently. However, despite these advantages, the use of immersive technologies introduces new challenges, particularly in terms of security and privacy. Immersive platforms often capture sensitive data, such as user behavior, gaze patterns, and biometric identifiers. If not properly secured, these data may be vulnerable to misuse or attack [71]. It is therefore essential to implement strong safeguards. These include encryption of data in transit and at rest, multifactor authentication, endpoint protection, and well-defined privacy policies. Ensuring secure configurations of immersive environments is also critical to reducing the attack surface.  
A compelling real-world example that reflects the foundational objectives of ICSA systems is SCOUT [72], a Critical Infrastructure Protection framework that delivers intelligent cyber situational awareness for industrial systems. Although SCOUT does not employ immersive interfaces, it exemplifies how integrated cyber-physical monitoring, real-time threat detection, and coordinated response capabilities can support critical environments. The architectural principles of SCOUT, particularly its fusion of security and operational data, mirror the core goals of ICSA. Integrating such systems with immersive technologies could further enhance operator situational awareness by enabling interactive exploration of attack paths, anomalies, and system states within 3D virtual environments.  
In summary, immersive technologies offer significant value to cybersecurity operations. When paired with robust security and privacy measures, ICSA systems can enhance train ing, improve threat comprehension, and enable faster, more informed incident response, while minimizing associated risks.
# An Artificial Intelligence-Based Algorithm for the Assessment of Substitution Voicing
###### Abstract  
The purpose of this research was to develop an artificial intelligence-based method for evaluating substitution voicing (SV) and speech following laryngeal oncosurgery. Convolutional neural networks were used to analyze spoken audio sources. A Mel-frequency spectrogram was employed as input to the deep neural network architecture. The program was trained using a collection of 309 digitized speech recordings. The acoustic substitution voicing index (ASVI) model was elaborated using regression analysis. This model was then tested with speech samples that were unknown to the algorithm, and the results were compared to the auditory-perceptual SV evaluation provided by the medical professionals. A statistically significant, strong correlation with r\({}_{s}\) = 0.863 (\(p\) = 0.001) was observed between the ASVI and the SV evaluation performed by the trained laryngologists. The one-way ANOVA showed statistically significant ASVI differences in control, cordectomy, partial laryngectomy, and total laryngectomy patient groups (\(p\) < 0.001). The elaborated lightweight ASVI algorithm reached rapid response rates of 3.56 ms. The ASVI provides a fast and efficient option for SV and speech in patients after laryngeal oncosurgery. The ASVI results are comparable to the auditory-perceptual SV evaluation performed by medical professionals.  
convolutional neural networks; deep learning; laryngeal carcinoma; substitution voicing; ASVI 2022129748.
## 5 Results
### Exergy Analysis
The cycle utilizing MgCl\({}_{2}\)-KCl salts achieves the lowest exergy destruction of 0.76 MW at a pressure ratio of 2.9. In comparison, the minimum exergy destruction for the cases using solar salts is 0.90 MW at a pressure ratio of 2.9. The use of the MgCl\({}_{2}\)-KCl salts reduces the exergy destruction on the IHE and precooler by 46.15% and 26.7%, respectively. Figure 5: Comparison of exergy destruction in recuperators at different pressure ratios. Figure 6: Comparison of exergy destruction in intermediate heat exchanger and precooler at different pressure ratios. As illustrated in Figure 7, the exergy destruction in the turbine and compressor grows with the higher pressure ratio in the case of solar salts. In the case of the recompression compressor (Figure 7c), the exergy destruction achieves a maximum at an intermediate pressure ratio higher in solar salts. For the MgCl\({}_{2}\)-KCl salts, the minimum exergy destruction on the turbine, main and recompression is 0.48 MW, 0.18 MW and 0.16 MW, respectively, at the minimum pressure ratio. In the case of solar salts, the minimum exergy destruction on the turbine, main compressor and recompression compressor are 0.69 MW, 0.25 MW and 0.25 MW, respectively, at the minimum pressure ratio. Consequently, the chloride molten salts reduce exergy destruction in the turbine, main compressor, and recompression compressor by 57%, 28%, and 36%, respectively.