esa-sceva/satcom-chunk-collection huggingface dataset:
# Global Buckling Resistance of Cold-Formed Steel Beams with Omega-Shaped Sections
## 4 Numerical Models
### Development and Validation of the ABAQUS Model
The loading data were obtained from the point of load application, while the displacements were measured at the points where the LVDTs (Linear Variable Differential Transformers) were located. The validation of the models for the three analyzed profiles involved a comparison of the load and displacement values recorded during the tests with those obtained through numerical simulation. Additionally, the observed failure modes were compared to those obtained through numerical simulation. Figure 12 presents a comparison of the load and displacement values obtained experimentally and analytically for the 170 \(\times\) 2.0 profile. The analysis of Figure 12 yielded the following observations: in Figure 12a, the variation obtained for the force applied by the actuator was \(-4.07\%\), while the displacement was merely \(-1.45\%\), thus validating this model. Further validation is evidenced by the variation in Figure 12b, where the applied force exhibited a \(-3.4\%\) change, while the displacement showed a \(-5.7\%\) variation. Figures 13 and 14 provide a comparison between the observed failure modes and those obtained from numerical simulation under downward and upward loading, respec  
Figure 11: Boundary conditions: (**a**) small profiles such as 70 \(\times\) 1.5 and 170 \(\times\) 2.0 and (**b**) larger profiles such as 300 \(\times\) 2.5. Figure 12: Comparing the results between numerical models and experimental tests on the 170 \(\times\) 2.0 profile: (**a**) downward loading and (**b**) upward loading. tively. The numerical model demonstrates a high level of accuracy in capturing the experimental results. Due to their capability to achieve larger clear spans, omega-shaped profiles are commonly utilized in industrial structures, often with cladding sheets attached. These sheets stabilize the profiles by being fixed to them. However, this beneficial effect was not considered both in the experimental campaign and in the numerical analysis.
# Key Issues in Using the Freezing Method in Power Tunnel Rehabilitation Projects
## 3 Establishment of Finite Element Numerical Simulations
### Geometric Modeling and Parameter Selection  
(1) A three-dimensional transient model was established using COMSOL finite element software, with the geometric model's dimensions determined by the freezing range and the impact of groundwater flow on the permafrost curtain. The model, with dimensions of L(X) \(\times\) W(Y) \(\times\) H(Z) = 50 m \(\times\) 30 m \(\times\) 50 m, was meshed using a physical field control model to ensure accurate representation of the temperature field and minimize boundary error. The model was surrounded by an adiabatic boundary, initially set at 18 \({}^{\circ}\)C, with impermeable boundaries on the top, bottom, left, and right sides, and permeable boundaries on the front and back. The meshing and boundary conditions are illustrated in Figures 4 and 5, respectively.  
(2) In this study, the soil parameters chosen were derived from the least favorable soil layer of the project, drawing upon relevant permafrost experimental research [25; 26] to ascertain the thermal conductivity, the permeability coefficient of the soil, and other  
Figure 4: Grid division diagram.  
Figure 5: Boundary condition diagram.  
pertinent properties. The values of the selected parameters for the physical model are presented in Table 1.  
(3) During the freezing period, the soil undergoes a phase change into frozen soil due to the influence of low temperatures. The thermal conductivity, permeability coefficient, specific heat, and density of the soil are temperature-dependent, with the unfrozen temperature range extending from 0 \({}^{\circ}\)C to 30 \({}^{\circ}\)C and the frozen temperature range from \(-\)30 \({}^{\circ}\)C to 0 \({}^{\circ}\)C. The design specifications dictate that the brine temperature serves as the temperature load. The model employs transient analysis, with the wall of the frozen pipe serving as the thermal load boundary condition, and the surrounding environment set to an adiabatic boundary. The freezing process was discretized into 60 day-long time steps, each of 24 h. The brine cooling schedule was achieved through interpolation of a function. Specifically, within the first 5 days of freezing, the refrigerant temperature was reduced to \(-\)15 \({}^{\circ}\)C, and between days 10 and 60, the refrigerant temperature dropped to \(-\)28 \({}^{\circ}\)C. The temperature reduction schedule during freezing is detailed in Table 2.
## 3 Results and Discussion
### External Validation of the Instrumental Screening Tool
This may be because oxidation VOC are present in virgin olive oils of all categories, even in different concentrations. Samples with fusty-muddy and, in particular, musty-humid-earthy features as MPD generally performed better in terms of percent of uncertain samples and classification as non-EVOO, although we cannot make a clear statement about the classification into VOO and LOO classes of the latter due to the limited number of samples with this MPD. We can conclude that, although the classification efficiency depends on the type of MPD present in virgin olive oils, the correct classification obtained for samples presenting one of the three evaluated MPD was satisfactory. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**MPD**} & **Validation** & \multirow{3}{*}{**Uncertain**} & \multirow{3}{*}{**Samples Assigned**} & \multirow{3}{*}{**Non-EVOO Samples Assigned to a Category**} \\ \cline{4-7}  & **Set 1** & & & & **Correct (\%)** & **Correct (\%)** \\ \cline{4-7}  & & & **Total (\%)** & & **Non-EVOO 2** & **as VOO 3** & **as LOO 3** \\ \hline \multirow{6}{*}{**RANCID**} & 1 (\(n=14\)) & 0 (0/14) & 100 (14/14) & 71.4 (10/14) & 40.0 (4/10) & 50.0 (2/4) \\  & 2 (\(n=13\)) & 46.2 (6/13) & 53.8 (7/13) & 85.7 (6/7) & 100 (2/2) & 80.0 (4/5) \\ \cline{1-1}  & 3 (\(n=12\)) & 16.7 (2/12) & 83.3 (10/12) & 90.0 (9/10) & 80.0 (4/5) & 80.0 (4/5) \\ \cline{1-1}  & 4 (\(n=14\)) & 14.3 (2/14) & 85.7 (2/14) & 75.0 (9/12) & 57.1 (4/7) & 60.0 (3/5) \\ \cline{1-1}  & 5 (\(n=14\)) & 35.7 (5/14) & 64.3 (9/14) & 88.9 (8/9) & 60.0 (3/5) & 75.0 (3/4) \\ \cline{1-1}  & 6 (\(n=12\)) & 33.3 (4/12) & 66.7 (8/12) & 100 (8/8) & 75.0 (3/4) & 100 (4/4) \\ \cline{1-1}  & 7 (\(n=10\)) & 50.0 (5/10) & 50.0 (5/10) & 100 (5/5) & 100 (2/2) & 100 (3/3) \\ \hline \multicolumn{2}{c}{**Weighted mean**} & **26.9** & **73.0** & **84.6** & **62.9** & **76.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: External validation classification results for non-EVOO samples (\(n=36\)), according to the main perceived defect (MPD) by the panel test: uncertain (%) and correctly classified samples (%).
## Materials and Methods
### Principle of H-Doppler Flow Imaging
Each signal component is composed of the ordered singular values \(\lambda_{i}\), spatial signal components \(U_{i}\) and temporal signal components \(V_{i}\):  
\[s(z,x,t)=\sum_{i=1}^{\text{rank}(S)}\lambda_{i}\cdot U_{i}(z,x)V_{i}(t) \tag{17}\]  
After performing the SVD, tissue signals typically found in high singular values as their high speckle strength and spatiotemporal coherence ensure that a large number of spatial pixels will exhibit the same time profile. In contrast, blood signals should be typically residing in medium-to-low singular values, as they exhibit much lower speckle brightness and spatiotemporal coherence. 4. Low-order singular value thresholding (L-SVT)  
To differentiate blood flow signal from tissue signal, a low-rank matrix projection \(1^{f}\) (i.e., singular value thresholding, SVT) is designed to be multiplied by the singular value matrix \(\Delta\):  
\[S^{f}=U\Big{(}\Delta I^{f}\Big{)}V^{*} \tag{18}\]  
where \(S^{f}\) is the filtered dataset. Typically, \(I^{f}\) is a diagonal identity matrix with zeros for the first couples of diagonal elements, leading to a truncated singular value matrix corresponding to the removal of tissue motion. To accurately differentiate the blood signal, one needs to determine the L-SVT for \(I^{f}\). According to [9], the predefined tissue motion frequency can be utilized to determine the cutoff value of L-SVT: first, the Fourier-transform of the temporal singular vectors \(V\)\({}^{*}\) is calculated, and the amplitude is averaged to estimate the spectral content of the Region of Interest (ROI). Then, the singular vector Doppler frequency curve is used to search for the threshold beyond which the Doppler frequency exceeds the predefined tissue motion frequency (e.g., 19 Hz), as shown in Figure 2.  
5. Background noise subtraction  
To remove the high-frequency noise coupled with the blood flow signal, we propose a Doppler subtraction imaging method to suppress the high-frequency noise to avoid the robustness problems caused by the complex high-order threshold selection method [15]. The same SVD clutter filtering (i.e., low-order SVT of 26) is separately applied to all channels of the H-scan. Since there is little blood flow signal assigned in the R channel (mostly large layer scattering), the majority of the remaining signal after SVD clutter filtering should be noise. Since the R and B channel data are derived from ultrasonic backscattered signal within the same ROI at the same time period, we can subtract the noise field (i.e., R channel SVD filtering results) from the power Doppler result (i.e., B channel SVD filtering results) to increase the contrast-to-noise-ratio (CNR) of blood flow Doppler imaging.
# Architectural Design for Digital Twin Networks
## 7 Conclusions  
Digital Twin Networks represent an interesting approach to network management, offering real-time insights and predictive capabilities. Throughout this paper, we have underscored the complexities and ambiguities surrounding DTN implementation, emphasizing the lack of standardized definitions and clear implementation guidelines in the existing literature.  
It is crucial for organizations that venture into DTN projects to define their specific needs and goals upfront. This proactive approach ensures that the chosen tools and techniques align effectively with the desired outcomes, optimizing both implementation efficiency and operational effectiveness.  
Looking forward, the establishment of an official definition for DTNs holds promise for enhancing interoperability between systems. A standardized framework would streamline communication protocols and data exchange formats, facilitating seamless integration across diverse network environments. This standardization, coupled with the availability of existing tools and methodologies discussed in this paper, would accelerate the design and implementation phases of DTN projects significantly. In addition to this standardized framework and tools, the DTN would greatly benefit from a unified cybersecurity framework tailored specifically for Digital Twin Networks.  
As DTNs continue to evolve and gain traction in various industries, future research efforts should focus on refining these definitions and expanding the repository of practical tools. By doing so, the network community can collectively advance the capabilities and applicability of DTNs, paving the way for more resilient and efficient network infrastructures.  
J.W.: conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, visualization; M.B.: writing--review; J.H.: writing--review and editing, supervision, project administration. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Acknowledgments:** During the preparation of this manuscript/study, the author(s) used chatpgt-4o for the purposes of spellcheck and sentence restructuring. The authors have reviewed and edited the output and take full responsibility for the content of this publication.  
Figure 8: Conceptual mapping illustrating the similarities between an organizational cybersecurity framework and a potential framework for DTNs, inspired by the Cyberfundamental Framework [56] from the Centre for Cyber Security Belgium (CCB). Levels \(L_{1}\) to \(L_{4}\) represent the different tiers of security maturity.
# Analytical Electromechanical Modeling of Nanoscale Flexoelectric Energy Harvesting
## 4 Numerical Results and Discussion  
In this section, polyvinylidene difluoride (PVDF) is taken as an example material to demonstrate the effects of size and the electric resistance on flexoelectric energy harvesting. The PVDF is a flexible piezoelectric polymer material, which has the following properties: mass density \(\rho=1.78\times 10^{3}\) kg/m\({}^{3}\), elastic modulus \(c_{1111}=3.7\) GPa [37], piezoelectric coefficient \(e_{311}=-0.01\) N/V-m, dielectric permittivity \(\kappa_{33}=8.15\times 10^{-11}\) C\({}^{2}\)/\(\left(\)N-m\({}^{2}\)\(\right)\), and flexoelectric coefficient \(\mu_{3113}=2.3\times 10^{-2}\) uC/m [44]. In all analyses, the length/width/thickness aspect ratio of the beam, \(100:1:1\), was maintained constant over the entire frequency range. For the tip mass, \(M_{\rm t}=0.2\times m\times L\) was chosen in the following simulations. The damping ratios were given by \(\xi_{1}=\xi_{2}=0.0285\), with which the two damping coefficients (\(c_{\rm s}\) and \(c_{\rm a}\)) can be obtained [42]. For convenience, all the simulation results are given in form of FRFs, which were normalized by the base excitation acceleration \(\ddot{w}_{\rm b}=-\omega^{2}W_{0}\mathrm{e}^{\mathrm{i}\omega t}\).
## 2 Materials and Methods
### Methodology
#### 2.3.4 Event Transition Identification Method  
In fact, not all meteorological droughts lead to ecological events. There are many factors that cause ecological droughts, and the transition of meteorological drought is only one of them. The correlation between meteorological and ecological drought events is fundamental to the transition of droughts. As shown in Figure 5, there are several main causes of event delivery:  
(1) During a meteorological drought (A), the onset of an ecological drought (a) implies that the former has generated the latter.  
(2) If two discontinuous ecological (b1, b2) droughts occur within a meteorological drought (B), they are considered to be the same ecological drought.  
(3) If the termination of an ecological drought (c) occurs after the initiation of a subsequent meteorological drought (D), and no ecological drought occurs during that meteorological drought, it is deemed that only one ecological drought has been recorded; conversely, if an ecological drought does occur, it is regarded as a distinct ecological drought event [49]. The gray area here represents the transition from a reasonable meteorological drought to an ecological drought.  
According to the association between meteorological drought and ecological drought occurrence, the characteristics of meteorological drought and ecological drought in the Shendong mining area were further analyzed, including drought frequency, duration, and intensity. The frequency and correlation between meteorological droughts and ecological droughts are shown in Figure 5.  
Figure 4: Drought events and their characteristic identifications. (**a**) Recognition pixel (**b**) Mark continuous pixels (**c**) Identification event.  
droughts were also analyzed. The transition frequency is the number of ecological drought events divided by the number of meteorological drought events, and the transition effect is expressed by the Pearson correlation coefficient of meteorological and ecological drought characteristics [50].
# Fault Diagnosis Algorithm of Beam Pumping Unit Based on Transfer Learning and DenseNet Model
## 2 Fault Diagnosis Model and Application Method
### Fault Diagnosis Model
#### 2.2.1 Convolutional Neural Network and Transfer Learning
Transfer learning is a method in the field of big data, used to apply a known model to a new field and is often used in deep learning tasks in computer vision and natural language processing. The implementation process is to train the existing model as the basis of the new model to achieve the purpose of reducing the computing cost and speeding up the computing speed. Transfer learning imitates the human visual system, making full use of prior knowledge from different but related domains when performing new tasks in a given domain and solving related cross-domain learning problems. In transfer learning, extracting representative information and applying it to a new task can effectively improve the computational efficiency of the new task model. For learning tasks with scarce feature data, transfer learning can retain existing features while learning new features to avoid feature extraction errors caused by insufficient samples. In 2014, [PERSON] et al. [19] studied the transferability of each layer feature in deep learning. The experimental results are as follows: When deep learning is applied to image processing, the features extracted by the first layer are basically similar to the Gabor filter and color spots. In general, the first layer is not clearly related to the specific image dataset, while the last layer of the network is closely related to the selected dataset and its mission objectives. [PERSON] refers to the first layer of features as general features and the last layer as specific features, and summarizes them as follows:  
1. Feature transfer can improve the generalization performance of the model, even when the target dataset is very large. 2. When the parameters are fixed and the number of layers increases, the transfer accuracy between two tasks with low similarity increases faster
## 4 Experiments
### Data Augmentation  
To address the problem of emotional speech data scarcity in SER, a data augmentation technique was employed, focusing on simulating reverberation effects using the Room Impulse Response (RIR) datasets [62]. RIR consist of recordings from various acoustic environments, such as rooms, halls, and auditoriums, each capturing the unique acoustic characteristics of the space.  
In our approach, we augmented the original clean audio recordings by convolving them with randomly selected impulse responses from the RIR. This process introduced reverberation effects into the speech signals, effectively simulating the acoustics of different environments. Importantly, this convolution preserved the temporal alignment of the audio, ensuring that emotional expressions in the recordings were not distorted. The resulting augmented dataset provided a diverse range of acoustic conditions, which enriched the training data. This augmentation process not only increases the amount of training data but also introduces variability, helping the models better handle different acoustic environments during inference. Ultimately, this approach can lead to more reliable and accurate emotion recognition from speech.
## 4 Results and Discussions
### Measurement of the Synthesized Sound Pressure Field  
To verify the effectiveness of employed sound field synthesizing method, the generated pressure distribution is measured by scanning the sound field using the hydrophone. The sampling interval is 50 \(\upmu\)m, smaller than \(\lambda/4\) (185 \(\upmu\)m), which satisfies the requirements for ensuring the accuracy of the acoustic field reconstruction. Figure 7 shows the acoustic pressure distribution within a \(\lambda~{}\times~{}\lambda\) domain located at the center area of the pedestal. The elongated acoustic potential well is formed with the orientation angle 0\({}^{\circ}\) and 45\({}^{\circ}\) in Figure 7a,b, respectively. Modulating the excitation signals, the pressure node is shifted by 0.5\(\lambda\) in the direction of 45\({}^{\circ}\) and 135\({}^{\circ}\) (Figure 7c,d), respectively. In the above process, there are always four anti-nodes round the node as expected. When compared with the predicted pressure field in Figures 3 and 4, the pressure distribution around the node seems to  
\begin{table}
\begin{tabular}{c c c} \hline
**Name** & **Density (g/cm\({}^{3}\))** & **Sound Speed (m/s)** \\ \hline Deionized water & 1.0 & 1480 \\ Strip-shaped silica particle & 2.33 & 5340 \\ \hline \end{tabular}
\end{table}
Table 1: Acoustic parameters of deionized water and silica bead.  
Figure 6: The measured pressure and the phase difference of each transducer at various excitation voltage. (**a**) The measured pressure. (**b**) The measured phase difference.  
be distorted. This deviation may be caused by: (1) the deviation of incident waves from the ideal plane wave as assumed in the theoretical calculation; (2) the movement errors and electromagnetic noises of step motor when scanning the sound field; (3) the installation error between theoretical and actual positions of PZT transducer; or, (4) inevitable reflected waves. However, the deviation will not obstruct the device functionality, since the long and narrow potential well is effectively formed and transformed, showing good agreement with the theoretical prediction.
# A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network
## 5 Performance Evaluation
However, the performance SORE is nearly constant with increase in the rate threshold. This near constant behavior indicates that the performance of SORE mainly depends upon the leftover Wi-Fi rate and not the rate threshold of UEs. SCRE outperforms SORE for higher QoS requirement, as associating with multiple SBS helps in using their resources (spectrum, power), which is more beneficial than using the leftover capacity of a single Wi-Fi AP. Thus, we can conclude that the performance of Wi-Fi offloading is mainly influenced by the Wi-Fi leftover capacity, whereas cooperation is more beneficial when the data rate requirement is higher in a less dense network. After executing both SCRE and SORE, the value of switching parameter for rate threshold \(\alpha\) is passed to SCO that performs switching. The green dotted line in Figure 6 indicates that for \(\alpha<=100\) (lower data rate demand), the performance of SORE is better and for \(\alpha>100\) (higher data rate demand), the performance of SCRE is better. Hence, SCO switches to the better rate enhancement technique. Figure 7 shows a relationship between average data rate per UE and total number of RBs per SBS used in network. We analyze cooperation while varying the number of UEs i.e., \(N=10\), 20 and 30. For Wi-Fi offloading we consider \(N=20\). The rate threshold is 100 bits/s and 3 SBS are considered. An increasing trend is observed with the increase in number of RBs in both SCRE and SORE. Highest increase in data rate is observed for SCRE with \(N=10\) users. On the other hand, SORE still shows a significant increase with RBs. These results are for'snapshot-based' static load conditions and may not be representative of the real situation. However, this quantification of results for various network parameters provides useful insight on set of conditions for network offloading. As we have simulated both high density as well as low density networks under different traffic requirements, the general conclusions drawn from this study are still valid when we have dynamic traffic. It is true that offloading decisions cannot be based on per slot basis because it requires a lot of overhead, and a long-term approach is useful to make such decisions; however, the conditions to select offloading method remain the same. Figure 6: Averaged Achievable data rates per user by changing Rate threshold values in Cooperative Communication and by taking 20, 40, 60 leftover capacity in Wi-Fi offloading and overall 3 SBSs, 10 UEs and 10 RBS.