esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Effects of Particle Morphology  
The differences in \(I_{pf}\) and \(V_{a}\) between crushed stone and gravel are defined in Equations (10) and (11), respectively, for understanding the effects of particle morphology.  
\[\Delta_{I_{pf}}^{CS-G}=I_{pf}^{CS}-I_{pf}^{G} \tag{10}\]  
where \(I_{pf}^{CS}\) is the \(I_{pf}\) of packed crushed stone particles, \(I_{pf}^{G}\) is the \(I_{pf}\) of packed gravel particles.  
\[\Delta_{V_{a}}^{CS-G}=V_{a}^{CS}-V_{a}^{G} \tag{11}\]  
where \(V_{a}^{CS}\) is the \(V_{a}\) of packed crushed stone particles, and \(V_{a}^{G}\) is the \(V_{a}\) of packed gravel particles.  
The \(\Delta_{I_{pf}}^{CS-G}\) and \(\Delta_{V_{a}}^{CS-G}\) of each combination for the dry-rodded test are depicted in Figures 7 and 8, respectively. As shown in Figure 7, the morphology of particles has a significant effect on the \(I_{pf}\) of smaller particles in the combination. However, the effects on different combinations are different. In the six combinations shown in the figure, the \(\Delta_{I_{pf}}^{CS-G}\) of 16 VS 4.75, 4.75 VS 1.18, and 2.36 VS 1.18 all have negative values, indicating that the \(I_{pf}\) of gravel particles in the mixture is larger than that of crushed stone. For the same combination, the contribution to the packing volume of smaller gravel particles is bigger than that of smaller crushed stone particle. For 4.75 VS 2.36 combination, the values of \(\Delta_{I_{pf}}^{CS-G}\) are all positive, indicating that in this combination, the \(I_{pf}\) of smaller gravel particles is smaller than that of smaller crushed stone particles, and the packing volume contribution of gravel particles with smaller size is smaller than that of crushed stone particles. For the combinations of 16 VS 2.36 and 16 VS 1.18, the value of \(\Delta_{I_{pf}}^{CS-G}\) is positive when the proportion of smaller size particles are small. With the increase of the proportion of smaller size particles, the value of \(\Delta_{I_{pf}}^{CS-G}\) becomes negative first and then become positive. The influence of morphology is more complex. It should also be noted that with the increase of the proportion of smaller size particles, the influence of morphology on the \(I_{pf}\) of particles tends to decrease gradually. As can be seen from Figure 8, the effect of particle morphology on \(V_{a}\) is very significant. For all combinations, the \(V_{a}\) of the crushed stone mixture is significantly higher than that of the gravel mixture. The minimum \(\Delta_{V_{a}}^{CS-G}\) is 1.59, which corresponds to 50% of \(P_{bv}\)\({}^{s}\) in 4.75 VS 1.18 combination. The maximum is 6.61, which corresponds to 40% of \(P_{bv}\)\({}^{s}\) in 4.75 VS 2.36 combination.
# Testing the Frequency Response Control Function of Level-Dependent Earplugs
## 1 Introduction
The ambiguous conclusions presented in the studies regarding the effectiveness of hearing protectors with electronic systems to improve the reception of useful signals may have resulted from the fact that most currently existing hearing protector solutions have predetermined characteristics for sound transmission frequency, with no possibility of the user influencing their shape. At the Central Institute for Labour Protection-National Research Institute (Poland), work is being carried out to develop earplugs that have, among other things, a function that allows for shaping the frequency response in octave bands of the acoustic signal emitting under the earplugs. This function implemented in the electronic system could significantly improve the reception of useful signals. The aim of this article was to present the results of research that verified the performance of filters implemented in earplugs in controlling the frequency response.
### Research Contributions  
Motivated by previously described advancements, in this paper, we present a novel 3D encoder-decoder based architecture with variational autoencoder regularization. Our intention is to achieve maximum optimality in training performance, efficiency, and final segmentation result accuracy for the whole heart segmentation task. The work contributions can be summarized as:  
1. We propose a new connectivity structure of residual unit that we refer to as feature merge residual units (FM-Pre-ResNet). The FM-Pre-ResNet unit attaches two convolution layers at the top and at the bottom of the pre-activation residual block. The top layer balances the parameters of the two branches, while the bottom layer reduces the channel dimension. This allows the construction of a deeper model with a similar number of parameters compared to the original pre-activation residual unit.
2. We present a 3D encoder-decoder based architecture that efficiently incorporates FM-Pre-ResNet units and is additionally guided with variational autoencoders (VAE). The architecture includes three stages. First, in an encoding stage, FM-Pre-ResNet units learn a low-dimensional representation of the input. Second, in the VAE stage,an input image is reduced to a low-dimensional latent space and reconstructs itself to provide a strong regularization of all model weights and is only used during the network training. Finally, the decoding stage creates the final whole heart segmentation.
3. The proposed approach obtains highly comparable dice scores to the state-of-the-art for whole heart segmentation tasks on CT images while outperforming the current state-of-the-art on the MRI images.
# Enhancing Sensitivity of Double-Walled Carbon Nanotubes with Longitudinal Magnetic Field
###### Abstract  
In this study, the behavior of double-walled carbon nanotubes (DWCNTs) used as mass sensors is explored under various boundary conditions; particular attention is paid to the crucial topic of resonant nanomechanical mass sensors. In the presented approach, nanotubes are subjected to a distributed transverse magnetic force and supported by an elastic foundation. The impacts of the longitudinal magnetic field, elastic medium, and diverse physical parameters on the responsiveness of the sensors are assessed. Using the energy method, governing equations are formulated to determine the frequency shifts of the mass nanosensors. Our findings reveal significant variations in the frequency shifts due to a longitudinal magnetic field, which depends on the applied boundary conditions. This research holds significance in the design of resonant nanomechanical mass sensors and provides valuable insights into the interplay of factors affecting their performance. Through exploring the intricate dynamics of DWCNTs used as mass sensors and thus contributing to the broader understanding of nanoscale systems, the implications for advancements in sensor design are offered and applications are introduced.  
Keywords:nanoresonators; mass sensor; magnetic field; double-walled carbon nanotube; frequency shift +
Footnote †: journal: Applied
# Reliability of Supply and the Impact of Weather Exposure and Protection System Failures +
###### Abstract  
Extreme weather is known to cause failure bunching in electrical transmission systems. However, protection systems can also contribute to the worsening of the system state through various failure modes--spontaneous, missing or unwanted operation. The latter two types of failures only occur when an initial failure has happened, and thus are more likely to happen when the probability of failure of transmission lines is high, such as in an extreme weather scenario. This causes an exacerbation of failure bunching effects, increasing the risk of blackouts, or High Impact Low Probability (HILP) events. This paper describes a method to model transmission line failure rates, considering both protection system reliability and extreme weather exposure. A case study is presented using the IEEE 24 bus Reliability Test System (RTS) test system. The case study, using both an approximate method as well as a time-series approach to calculate reliability indices, demonstrates both a compact generalization of including protection system failures in reliability analysis, as well as the interaction between weather exposure and protection system failures and its impact on power system reliability indices. The results show that the inclusion of protection system failures can have a large impact on the estimated occurrence of higher order contingencies for adjacent lines, especially for lines with correlated weather exposure.  
protection systems; failure bunching; extreme weather; reliability; HILP; extraordinary events
# Real-Time FPGA-Based Balance Control Method for a Humanoid Robot Pushed by External Forces
## 4 Experimental Results
### Balance Control with Stepping Forward  
In this experiment, the TKU-X robot is hit from the back by a baseball or a volleyball at 0.75 s. When the balance control method is disabled and enabled, the experimental results are respectively shown in Figures 13 and 14. The initial values of the inclination angle \(\phi\) (pitch-axis) and ZMP (x-axis) are \(7^{\circ}\) and 0, respectively. When the balance control method is disabled, the experimental results of the TKU-X robot hit from the back by a baseball and a volleyball are shown in Figure 13a,b, respectively. As shown in Figure 13a, the data reveal that the robot wavers back and forth substantially and needs about 0.6 s to converge to its stable state. As shown in Figure 13b, because the impact force produced by the volleyball is 0.93 N, the data reveal that the robot instantly falls down to the ground and fails to remain standing and balance. When the balance control method is enabled, the experimental results of the TKU-X robot hit from the back by a baseball and a volleyball are shown in Figure 14a,b, respectively. As shown in Figure 14a, because the external force is from the back of the robot, the values of the inclination angle \(\phi\) (pitch-axis) and ZMP (x-axis) increase from \(7^{\circ}\) and 0, respectively. When the safety threshold \(\varepsilon\) is exceeded by the inclination angle \(\phi\), a capture point \(x_{Sup}\) is calculated. During the period when the robot stretches out its foot to the capture point and steps on the capture point, the values of the inclination angle \(\phi\) (pitch-axis) and ZMP (x-axis) decrease and return to \(7^{\circ}\) and 0, respectively. Because the impact force produced by the baseball is 0.47 N, the TKU-X robot takes a step forward about 1.36 cm to maintain balance. In this experiment of the TKU-X robot is hit by a baseball from the back; the recovery time of the enabled control method takes about 0.25 s, which is 2.4 times faster than the disabled control method to return to a stable state. Similarly, as shown in Figure 14b, because the impact force produced by the volleyball is 0.93 N, the TKU-X robot takes a step forward 4.15 cm to maintain balance. In this experiment of the TKU-X robot being hit by a volleyball from the back, the recovery time of the enabled control method is about 0.25 s (for a successful return to a stable state), which is not realized when the control method is disabled. Some experimental results are shown in Table 4 when the proposed balance control method with the stepping forward is enabled or disabled for the TKU-X robot. The video can be viewed on the website: [[https://youtu.be/h2](https://youtu.be/h2) psoR5T3 eo]([https://youtu.be/h2](https://youtu.be/h2) psoR5T3 eo)  
Figure 16: Experimental results of the TKU-X robot hit from the front by a baseball or a volleyball at 0.75 s when the balance control method is enabled. (**a**) Baseball. (**b**) Volleyball.
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 5 Proposed Future Security Mechanisms
### Artificial Intelligence for 6G
#### 5.3.2 Proposed Security Features  
Security in 6G will need to address the existing vulnerabilities identified in 5G, as NFV and SDN will continue to be a central and pivotal aspect to 6G system design [140]. 6G will also further embrace the use of AI/ML for aspects such as QoS and Quality of Experience (QoE), which expose another attack surface to malicious actors that can directly impact the end user applications and user experience. To combat the threats of this expanded attack surface, predictive models for QoS and QoE have been proposed [141] as well as intrusion detection models [142] and anomaly detection models [143].  
These methods take different approaches to attack identification and prevention. The predictive QoS models analyze current network information and predict QoS challenges through regular user traffic but also any malicious traffic that may occur on the network. Such predictive analysis methods aim to prevent an attack from even occurring, while intrusion and anomaly detection are used in response to an attack occurring. While intrusion and anomaly detection both analyze current network traffic to identify a possible attack, their methodology is slightly different. Intrusion detection focuses mainly on outside access to network operations that should not have been authorized during normal activities, while anomaly detection takes a holistic approach to network traffic and aims to detect any malicious traffic from new or existing connections.
# Elastoplastic Constitutive Model for Energy Dissipation and Crack Evolution in Rocks
## 3 Elastoplastic Representation of Energy Dissipation and Crack Evolution
### Energy Dissipation and Strength Parameters  
Assuming that there was no heat exchange with the external environment during the loading and failure process of the rock, according to the first law of thermodynamics, the total energy \(U\) input by the external work will be converted into elastic strain energy \(U^{e}\) and dissipated energy \(U^{d}\)[29; 30]:  
\[U=U^{e}+U^{d} \tag{18}\]  
\[U^{e}=(\sigma_{1}\varepsilon_{1}^{e}+\sigma_{2}\varepsilon_{2}^{e}+\sigma_{3} \varepsilon_{3}^{e})/2 \tag{19}\]  
Substituting Equation (1) into Equation (19) gives the following:  
\[U^{e}=\Big{[}\sigma_{1}^{2}+2\sigma_{3}^{2}-2\mu\Big{(}2\sigma_{1}\sigma_{3} +\sigma_{3}^{2}\Big{)}\Big{]}/2 \tag{20}\]  
Meanwhile, during the conventional triaxial compression of rocks, the total input energy \(U\) can also be expressed as follows:  
\[U=U_{0}+U_{1}+U_{3} \tag{21}\]  
\[U_{0}=3(1-2\mu)\sigma_{3}^{2}/2E \tag{22}\]  
\[U_{1}=\int_{0}^{\varepsilon_{1}}\sigma_{1}d\varepsilon_{1} \tag{23}\]  
\[U_{3}=2\int_{0}^{\varepsilon_{3}}\sigma_{3}d\varepsilon_{3} \tag{24}\]where \(U_{0}\) is the elastic strain energy stored during the confining pressure loading phase, \(U_{1}\) is the positive work performed by the axial stress, and \(U_{3}\) is the negative work performed by the circumferential stress. Substituting Equation (21) into Equation (18) gives the following:  
\[U^{d}=(U_{1}+U_{3})-(U^{e}-U_{0}) \tag{25}\]  
Based on Equations (20) and (22)-(24), Equation (25) indicates that the dissipated energy is equal to the integral of the stress-strain curve minus the elastic strain energy. Therefore, in the elastoplastic constitutive model, the cohesion \(c(\gamma_{p})\) and internal friction angle \(\varphi(\gamma_{p})\), which are associated with the stress-strain curve in Equation (3), can be introduced to quantitatively characterize the energy dissipation.
###### Abstract  
Surface texture characterization of components built using additive manufacturing (AM) remains a challenge. The presence of various asperities and random roughness distributions across a surface poses several challenges to users in selecting an appropriate cut-off wavelength (\(\lambda_{\text{c}}\)), evaluation length (\(\mu_{\text{n}}\)), and measurement area. This paper investigates a modified framework for surface texture characterization of AM components. First, the surface asperities in an AM component were identified through scanning electron microscope (SEM) analyses. The maximum diameter (\(\varphi_{\text{m}}\)) of the surface asperities were determined through image processing and were used as cut-off for surface texture evaluation. Second, another set of surface texture results were extracted using standard measurement procedures per ISO 4287, 4288, 25178-1, -2, and -3. Third, the investigative measurement framework's effectiveness and suitability were explored by comparing the results with ISO standard results. Last, the effects of using non-standard cut-off wavelength, evaluation length, and measurement area during surface texture characterization were studied, and their percentage deviations from the standard values were discussed. The key findings prove that (a) the evaluation length could be compromised instead of cut-off, (b) measurement area must be 2.5 times the maximum asperity size present in the surface, and (c) it is possible to identify, distinguish, and evaluate specific features from the AM surface by selecting appropriate filters, thereby characterizing them specifically. The investigations and the obtained results serve as valuable data for users to select appropriate measurement settings for surface texture evaluation of AM components.  
additive manufacturing; surface texture characterization; surface roughness; surface measurement; profile and areal roughness measurement +
Footnote †: journal: Applied Sciences
## 1 Introduction
Figure 1: Phase attractors of the unified hyperchaotic oscillator when \(a_{4}=[13,27,36]\). Figure 3 illustrates the encryption/decryption scheme using an N-levels cascaded chaotic system. In this scheme, on the transmitter side, the voice signal is initially masked by the output of the first 4D oscillator. To further enhance the security and randomness of the encrypted signal, the output from each subsequent level of the cascaded oscillators is used to successively mask the signal. This process generates the final encrypted signal, which is then transmitted through the communication channel. Based on the encryption scheme, the encrypted signal can be described as follows:  
\[X_{t}(t)=m(t)+c_{1}X_{1}+c_{2}X_{2}+c_{3}X_{3}+\ldots+c_{N}X_{N} \tag{2}\]  
where m(t) is the original information message, \(X_{1},X_{2},X_{3}\ldots X_{N}\) are the state vectors generated from each level in the cascaded system and \(c_{1},c_{2},c_{3}\ldots c_{N}\) are selectable gains. On the receiver side, to unmask the original voice message, the output of the first chaotic system is subtracted from the cascaded chaotic system recursively till the last system produces the recovered information message that is similar to the original one. In this study, we use the following assumptions to ensure that our secure communication system will recover the information signal correctly:  
**Assumption 1**. : _The cascaded systems on the transmitter side are similar to the systems on the receiver side._  
The cascaded systems at both ends must exhibit similar dynamics for successful signal decryption. If the systems are not similar, the chaotic signals will differ, leading to a mismatch in the decryption process and possible communication failure. Figure 3: Block diagram of N-level cascaded chaos-based secure communication system. Figure 2: Block diagram of N-levels cascaded chaotic oscillators. **Assumption 2**: _Each hyperchaotic system on the receiver side is synchronized to its opposite one on the transmitter side._  
Since the chaotic oscillators are sensitive to the initial conditions, the synchronization is essential to make the generated chaotic signals on the receiver side synchronized, allowing for successful decryption. Without synchronization, the encrypted signal cannot be accurately interpreted by the receiver, leading to errors in the recovered data. _Based on the previous assumptions and the decryption scheme, the recovered signal can be described as follows:_  
\[m_{r}(t)=X_{t}(t)-c_{1}Y_{1}-c_{2}Y_{2}+c_{3}Y_{3}-\ldots-c_{N}Y_{N} \tag{3}\]  
where \(m_{r}(t)\) is the recovered voice signal, \(X_{t}(t)\) is the received encrypted signal, \(Y_{1},Y_{2},Y_{3}\ldots Y_{N}\) are the state vectors generated from each level in the cascaded system in the receiver side which are similar to \(X_{1},X_{2},X_{3}\ldots X_{N}\) state vectors, respectively, and \(c_{1},c_{2},c_{3}\ldots c_{N}\) are selectable gains.
# A Comprehensive Review of Open Caisson Modeling Technology: Current Practices and Future Prospects
## 5 Challenges and Future Directions
### Future Directions  
The scarcity of significant-quality literature in field measurements from shaft construction was most likely due to the high cost of monitoring schemes particularly for deep shaft construction projects. These costs resulted from the need for deep drilling to install casings for inclinometers and extensometers below the shaft plug level in order to gather representative data. Furthermore, shaft construction sites were frequently associated with high levels of activity, which might interfere with measurement accuracy, resulting in data that are not genuinely typical. Furthermore, the existence of pre-existing subsurface structures might alter soil deformation mechanisms, confounding measurement results and reducing the availability of valid field data. Future research should focus on addressing the existing gaps in understanding earth pressure distribution and ground movements during open caisson shaft construction, particularly in complex, heterogeneous soil conditions. Large-scale experiments and advanced physical modeling techniques, such as centrifuge modeling, are crucial for achieving more accurate insights into soil behavior and the effects of gravity in geotechnical events. The development of ultra-deep shafts in composite soil strata required further exploration, with emphasis on soil-structure interactions, caisson geometry, and construction sequences. These factors significantly influence ground movements and earth pressure mechanisms, yet they remain insufficiently understood.  
To overcome the limitations of modeling techniques, future efforts should improve the stress representation in 1-g models while addressing scaling challenges in n-g centrifuge models. Hybrid modeling approaches that combine the advantages of both techniques, alongside advancements in material simulation and boundary condition replication, hold promise for achieving more reliable and realistic results. Key future research directions are given in the following sections.