esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of Node Location Imperfections on the Reliability of Single-Layer Steel Domes
###### Abstract  
This study is an attempt to assess the effect of node location imperfections on the reliability dome. The analysis concerns a single-layer steel lattice dome that is very sensitive to node snap-through. The load-displacement path of the structure was determined using the program, Finite Element Method-Krata. To determine the failure probability, reliability index, and elasticity index, the first-order reliability method approximation method was employed. The reliability analysis was conducted with Numpress Explore software, developed at the Institute of Fundamental Technological Research of the Polish Academy of Sciences, Warsaw. In this paper, it is shown how large differences in the assessment of the safety of a structure can appear when we incorrectly estimate the standard deviation of the random variable responsible for the imperfections of node locations.  
single-layer lattice domes; reliability index; elasticity index; first-order reliability method Article
# Computational Characterization of Turbulent Flow in a Microfluidic Actuator
## 7 Conclusions and Future Work  
A numerical study of the flow inside of an existing microfluidic oscillator has been carried out in this contribution. Two-dimensional simulations have been implemented to determine the oscillation frequency of such a device, which has been validated against experimental measurements and previous 2D and 3D computations in a range of Reynolds numbers. The computed flow field compares favorably with the experimentally measured velocity fields obtained with particle image velocimetry (PIV). Additionally, in order to improve the understanding of the flow dynamics inside the actuator, the pressure and turbulent kinetic energy fields along the oscillation cycle have been analyzed; such fields are not usually shown in the related literature. The main conclusion is that the 2D computations suffice to predict the operating oscillation frequency of the fluidic actuator. Moreover, it was found that the normalized mass flow curve in the oscillator versus non-dimensional time is independent of \(Re\) for the considered range; therefore, the non-dimensional frequency is also independent of the Reynolds number. Further, the effect of the modification of the feedback channels' geometry on the output frequency of the oscillator has been studied;  
Figure 15: Illustration of the turbulent behavior of the flow inside the fluidic oscillator. (**a**) Vorticity in the stream-wise direction; (**b**) velocity field in the mid plane with superimposed vortices identified by the \(Q\) criterion with \(Q=2\times 10^{5}\) s\({}^{-2}\). Both graphs correspond to the same instant of time.  
it was concluded that the rounder designs of such feedback loops implied a reduction in frequency, a fact connected with the reduction in the number of vortices appearing in the flow field. Finally, preliminary results of a full three-dimensional simulation are presented, which allows appreciating the development of coherent structures and the vorticity field, characterizing the complex turbulent flow inside the fluidic oscillator.  
In the near future, it is planned to perform a thorough analysis of the behavior of the three-dimensional vorticity and coherent structures along the complete cycle of oscillation of the fluidic actuator. Such a study would shed light on the complex flow inside this kind of device that will improve their performance in specific applications.  
Conceptualization, S.L. and J.H.L.-P.; methodology, S.L. and J.H.L.-P.; validation, J.G. and S.L.; formal analysis, S.L., J.H.L.-P. and J.G.; investigation, S.L., J.H.L.-P. and J.G.; data curation, J.G. and S.L.; writing--original draft preparation, S.L.; writing--review and editing, S.L. and J.H.L.-P.; visualization, J.G.; supervision, S.L. and J.H.L.-P.; project administration, J.H.L.-P.; funding acquisition, J.H.L.-P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Universidad Autonoma de Occidente, grant number 19 INTER-334. The APC was funded by Universidad Autonoma de Occidente.  
Not applicable.  
The authors declare no conflict of interest.
## 5 BI-LSTM Neural Network  
In short-term load forecasting, the current load value is correlated with both historical and future time information. In this paper, the BI-LSTM model is chosen for short-term load forecasting, which can consider bidirectional temporal information. The inputs of the BI-LSTM are passed through LSTM networks in different directions, and the output is the concatenation of the two directions. The network structure is shown in Figure 4.  
The calculation formula for the BI-LSTM is shown in the following equations.  
\[\overset{\rightarrow}{h}_{t}=\underset{\text{LSTM}}{\overset{ \rightarrow}{h}_{t-1}}(h_{t-1},x_{t},C_{t-1})\quad t=1,2,\ldots,n \tag{14}\]  
\[\overset{\leftarrow}{h}_{t}=\underset{\text{LSTM}}{\overset{ \leftarrow}{h}_{t+1}}(h_{t+1},x_{t},C_{t+1})\quad t=1,2,\ldots,n \tag{15}\]  
\[y_{t}=a_{t}\overset{\rightarrow}{h}_{t}+b_{t}\overset{\leftarrow}{h}_{t}+c_ {t}\quad t=1,2,\ldots,n \tag{16}\]  
where \(\overset{\rightarrow}{h}_{t}\) and \(\overset{\leftarrow}{h}_{t}\) are the predicted load values of the forward and backward LSTM units at time \(t\), respectively; \(h_{t-1}\) is the predicted load value at time \(t-1\); \(\underset{\text{LSTM}}{\overset{\rightarrow}{h}_{t}}()\) and \(\underset{\text{LSTM}}{\overset{\leftarrow}{h}_{t}}()\) represent the forward and backward calculation processes of the LSTM, respectively. \(x_{t}\) is the input characteristic at time \(t\); \(a_{t}\) and \(b_{t}\) are the forward and backward output weights at  
Figure 4: The structure of the BI-LSTM neural network.  
Figure 3: To calculate the MIC value of the \(i\)-th sampling point.  
time \(t\), respectively; \(C_{t}\) is the memory unit of \(t\) moments; \(y_{t}\) is the predicted load value of the final output of BILSTM unit at time \(t\).
# Propagation Mechanism of Pressure Waves during Pulse Hydraulic Fracturing in Horizontal Wells
## 5 Conclusions  
In this paper, a transient flow model for variable-flow-rate injection in horizontal wells was developed while considering the impact of unsteady friction. Based on this model, the effects of wellbore section lengths and liquid injection schemes on downhole pressure fluctuations were analyzed. The results of the analyses are as follows:  
The vertical section length of the wellbore affects the magnitude of the downhole pressure and also has an impact on the amplitude of downhole pressure fluctuations. The horizontal section length, however, only affects the amplitude of downhole pressure fluctuations. Furthermore, the attenuation rate of pressure waves is faster in pure horizontal wells, with the average fluctuation amplitude of pressure reduced by about 13% compared to pure vertical wells.  
The viscosity of the fracturing fluid and the amplitude of the injection flow rate have a significant impact on the amplitude of downhole pressure fluctuations. To improve the fracturing effectiveness, it is recommended to minimize the viscosity of the fracturing fluid and maximize the amplitude of the injection flow rate in engineering practice. Utilizing rectangular wave injection is more prone to induce fatigue failure in reservoir rocks.  
The influence of the mean injection flow rate and injection frequency on downhole pressure fluctuations is not linear, but rather, there exists an optimal solution that is related to the wellbore parameters. This optimal solution can be calculated in advance before operation, thereby improving the operational efficiency.  
Writing--original draft preparation, Y.Z.; writing--review and editing, J.Z.; supervision, X.F.; funding acquisition, S.D. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China with the grant number 51974276.  
Not applicable.  
The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to [EMAIL_ADDRESS].  
The authors declare no conflicts of interest.  
Figure 19: Influence of the frequency on the amplitude of downhole pressure fluctuations: (**a**) 1000 m deep; (**b**) 3000 m deep.
## 3 Results and Discussion
### Crystal Structure and Magnetic Properties
Therefore, the covalent interactions, i.e., antiferromagnetic coupling, of the Cr \(d\) electrons are not direct. Instead, the \(p\) electrons of the Z atom interact with the Cr \(d\) electrons, which results in ferromagnetic coupling between the Co and Cr atoms. It is clear to see, in Table 1, that the spin magnetic moments for Co, Cr and Al are 0.77, 1.58 and \(-\)0.093 \(\mu_{B}\), respectively. In the \(XA\) Co\({}_{2}\)CrAl compound, the Cr atom sits at the C site, and the Cr atom is not surrounded by eight Co atoms. Instead, the Cr atom is surrounded by four Co and four Cr atoms; see Figure 1b. The crystal structure of bulk Cr is body-centered-cubic (bcc) and the experimental lattice constant is 2.88 A. In bulk Cr, the Cr is surrounded by four Cr atoms, and bulk Cr is known as an antiferromagnetic material. The interatomic distance between Cr atoms is smaller compared with that in bulk bcc Cr, suggesting stronger antiferromagnetic coupling between Cr and Co atoms. Hence, the spin magnetic moments for Co, Cr and Al in \(XA\) Co\({}_{2}\)CrAl Heusler become 0.93 (1.33), \(-\)1.32 and \(-\)0.035 \(\mu_{B}\), respectively. Let us examine the scalar relativistic band structures and density of states for Co\({}_{2}\)CrAl in the regular and inverse structures, as shown in Figure 2. It is clear that Co\({}_{2}\)CrAl in the regular structure is half-metal because there is a spin-down bandgap at the Fermi level. However, Co\({}_{2}\)CrAl in the \(XA\) structure is a normal metal. Indeed, the spin-up or spin-down gap disappears in the energy range between \(-\)6 and 2 eV in the \(XA\) structure of Co\({}_{2}\)CrAl. In the regular Co\({}_{2}\)CrAl, the Cr atom is spin-spitted and at the Fermi level, and the Cr atom makes a larger contribution to the density of states than the Co atom in the regular structure. However, in the \(XA\) structure of Co\({}_{2}\)CrAl, the Co atom (at the C site) makes the largest contribution to the spin-down density of states at the Fermi level. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\mathbf{X_{2}YZ}\) & **Structure** & \(\boldsymbol{a}\) & \(\boldsymbol{m^{tot}}\) & \(\boldsymbol{m_{s}^{X1}}\) & \(\boldsymbol{m_{s}^{X2}}\) & \(\boldsymbol{m_{s}^{Y}}\) & \(\boldsymbol{m_{s}^{AI}}\) \\ \hline Co\({}_{3}\)Al & regular & 5.672 & 4.00, 3.90 [1] & 1.25 & 1.25 & 1.71 & \(-\)0.087 \\ Co\({}_{2}\)CrAl & regular & 5.701, 5.727 [2] & 3.00, 1.55 [2] & 0.77 & 0.77 & 1.58 & \(-\)0.093 \\  & \(XA\) & 5.723 & 0.82 & 0.93 & 1.33 & \(-\)1.32 & \(-\)0.035 \\ Cr\({}_{2}\)CoAl & regular & 5.885 & 5.75 & 2.18 & 2.18 & 1.17 & 0.020 \\  & \(XA\) & 5.721, 5.79 [3] & 0.00 & - & - & - & - \\ Cr\({}_{3}\)Al & regular & 5.908, 5.83 [4] & 2.92, 2.88 [4] & \(-\)1.60 & 2.16 & 2.16 & 0.033 \\ \hline \hline \end{tabular}  
* Experimental results, see [26].  
\end{table} 
Table 1: Calculated lattice constant a (Å), total spin magnetic moments (\(m^{tot}\)) (\(\mu_{B}\)/f.u.) and atomic (\(m_{s}\)) (\(\mu_{B}\)/atom) spin magnetic moments of all considered Co\({}_{3-x}\)Cr\({}_{x}\)Al Heusler compounds.
# An Efficient Relayed Broadcasting Based on the Duplication Estimation Model for IoT Applications
## 5 Conclusions  
In this paper, we proposed an efficient flooding mechanism aimed at the fast and reliable dissemination of traffic for IoT services. By combining two schemes of duplication suppression and re-queuing and using the duplication ratio, the proposed mechanism selectively discards duplicate frames in the transmission buffer to avoid unnecessary redundant transmissions and at the same time, it compensates for the delivery failure of frames due to excessive suppression, collisions, and/or interference. As a result, it considerably decreased the time of traffic dissemination without compromising its reliability. Moreover, we proposed a practical method of approximating the duplication ratio based on the number of adjacent nodes and verified that the outstanding performance of the proposed mechanism was maintained even with the approximated duplication ratio. For future work, we will extend the proposed mechanism by considering diverse traffic and multiple traffic source nodes in the realistic IoT scenarios and rapid topology change in vehicular ad hoc networks or flying ad hoc networks.  
Figure 17: The comparison of flooding reliability in terms of \(R_{val}(n\%)\) when (**a**) the number of nodes is 100 and the network radius is \(2D_{tx}\), (**b**) the number of nodes is 200 and the network radius is \(3D_{tx}\).  
**Author Contributions:** Authors contributed equally to this work.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2017R1A2B4009458).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Works and Motivations
### Motivations for the Status-Byte-Assisted RDMA Transmission Mechanism
To offer a comprehensive understanding of the key characteristics, advantages, and limitations of existing RDMA transmission mechanisms, Table 1 presents a comparative analysis of traditional RDMA transmission methods and the sliding window-based approach. This comparison highlights the inherent trade-offs and challenges associated with each mechanism, providing a foundation for discussing potential improvements. Building on this analysis, we propose to utilize status bytes to indicate the status of each memory block and introduce a status-byte-assisted RDMA transmission mechanism. By introducing the status byte, we achieve fine-grained control over memory blocks, reduce the need for frequent memory status exchanges, and enhance multi-task transmission efficiency and system scalability. Moreover, the proposed status-byte-assisted transmission mechanism better manages memory block status, thereby overcoming the limitations of the sliding window transmission mechanism and providing higher stability and scalability in multi-device environments.
# Analyzing Six Indices for Online Short-Term Voltage Stability Monitoring in Power Systems
###### Abstract  
In recent years, the importance of online monitoring short-term voltage stability has been considerably increased. Unstable cases due to response of fast dynamic loads such as induction motors to the serious disturbances can be avoided by online monitoring of voltage transients and activating fast and appropriate controls to encounter deep and prolonged voltage drop. In this paper, a set of indices based on the phasor measurement unit (PMU) measurements are introduced and compared for short-term voltage stability. To increase efficiency of the indices, a special algorithm for each index is proposed by using the investigation of the results of applying them to six disturbances' scenarios simulated on the IEEE 39 bus system. The disturbance scenarios are representative of different cases of stable, unstable, and deep and prolonged voltage drop associated with short-term voltage transients. The performance of these indices are studied in terms of the time and accuracy required for determining the short-term voltage stability/instability cases. It is shown that the proposed method has better performance in comparison with other techniques that can be applied to power systems in reality.  
voltage stability index; short-term instability; real-time monitoring; PMU +
Footnote †: journal: Applied Sciences
## 3 Application in the Reconstruction of the HWRR Decommissioning Model  
### Main Technical Parameters  
Due to the direct impact of point cloud data accuracy on the subsequent quality of 3D model reconstruction, ensuring the precision of laser-scanning data is crucial for achieving high-precision reconstruction of nuclear facilities. Taking various factors into account, including the decommissioning simulation requirements, the workload associated with scanning the HWRR decommissioning area, computer hardware capabilities, the model retrieval speed, and point cloud data management, laser scanning over a large spatial range was selected for the HWRR decommissioning area. To expedite the process, it is imperative to enhance the efficiency of data acquisition, thereby reducing the scanning time. The obtained raw point cloud data, serving as valuable and reusable digital assets, should encompass color information on the surfaces of buildings and equipment within the HWRR decommissioning area. In summary, considering the specific requirements for acquiring point cloud data in a nuclear facility, the following technical parameters for laser scanning are specified: the 3D laser-scanning range covers the entire decommissioning area, approximately 140 m \(\times\) 90 m \(\times\) 40 m; the single-station point cloud data acquisition error is less than 0.5 mm; and the point cloud data should include color information, with an efficiency of no less than 200,000 points per second.  
After acquiring the point cloud data, the data can be processed to generate a 3D model of the HWRR. Due to limited system resources and to ensure smooth platform operation without extensive computations, low-precision modeling is applied to the buildings, various floor corridors, offices, doors, windows, and ground within the HWRR decommissioning area. The reconstruction error for feature surfaces is maintained below 5 mm. For internal equipment within buildings, high-precision modeling is utilized, with a reconstruction error for feature surfaces below 2 mm. The reconstructed 3D model is compatible  
Figure 5: Schematic diagram of coordinate transformation.  
with simulation software such as ANSYS ([[https://www.ansyss.com/zh-cn](https://www.ansyss.com/zh-cn)]([https://www.ansyss.com/zh-cn](https://www.ansyss.com/zh-cn)) (accessed on 24 February 2024) and DELMIA ([https://www.3 ds.com/zh-hans/products/delmia](https://www.3 ds.com/zh-hans/products/delmia) (accessed on 24 February 2024)). To maximize resource utilization, temporary and movable equipment is generally not required to undergo modeling.
# ZSM Framework for Autonomous Security Service Level Agreement Life-Cycle Management in B5G Networks
## 4 ETSI PoC
### Security Assets
#### 4.1.1 Smart Traffic Analyzer (STA)  
The STA is an ML-trained security asset designed to retrieve communication flows and classify their behavior. The STA is built with flexibility in mind, allowing different ML models to be loaded and configured based on TSAT [44] values, as outlined in [34]. The STA's configuration defines the type of traffic to be analyzed, the ML model for pattern detection to be loaded, and the possible classification outputs. In the PoC, the STA is utilized for 5G core protection by monitoring and analyzing 5G signaling traffic. The ML model loaded into the STA is trained to analyze monitored flows and classify them as either crypto-mining or genuine traffic. When anomalous traffic is detected, the STA reports an alert, providing a malicious flow classification along with a confidence level.  
The STA plays an important role in the presented architecture by handling the Security Data Collection (monitoring) and Security Analytics Engine. STA life-cycle management follows the three steps presented in the introduction of the section. The policy model is extended to include new security analysis and monitoring capabilities, along with additional configuration actions. These extensions allow the inclusion of additional monitoring parameters, such as specifying Service Based Interface (SBI) traffic and defining analysis parameters. These analysis parameters indicate to the trained ML model how to classify abnormal behavior in SBI traffic. Based on the 5G core context and the type of protection specified in the policy, the translation plugin selects the ML model to be used, the TSAT values required by the model to classify traffic types, and the potential classification outputs for the traffic. This ensures alignment between the policy and the STA's configuration. Finally, the configuration driver interacts with the STA API, which operates alongside the 5G core NFs and retrieves its URI from the Data Services. The driver identifies the interfacethat requires analysis and sends the appropriate configuration requests to the STA API. Upon receiving these requests, the STA applies the configuration and automatically begins analyzing the traffic.  
This process enables the STA to efficiently monitor and analyze traffic in real time, providing a dynamic and robust layer of protection adapted for the 5G core.
## 3 Related Works
### Watermarking algorithms based on singular vectors  
In recent years, several watermarking schemes, which embed the watermark bits into the left or right singular vectors (matrices \(U\) and \(V\)), have appeared. The first algorithm based on singular vectors was proposed by [PERSON] [39]. The host image is a grayscale image, whereas the watermark is a binary image. The embedding process can be briefly described as follows: the host image is divided into non-overlapping blocks of size 8\(\times\)8, and blocks with higher ranks (i.e., complex blocks) are selected for embedding using a secret key. Each watermark bit is embedded by modifying the relation between the second and third coefficient of the first column of matrix \(U\). The algorithm is robust to common attacks and the original image is not required in the detection process. However, using this algorithm an exact watermark extraction can't be achieved [29]. This is caused by the values of the \(U\) matrix which belong to the interval [-1..1]. Another defect of this technique is related to the selection of the higher rank blocks, since rank is not a reliable parameter [40; 41].  
In [40], the authors propose a similar watermarking scheme except for the use of the \(V\) matrix in the embedding process. To improve the security, the components from the first column of the matrices \(U\) and \(V\) are randomly selected using a secret key. It is shown that the algorithm is robust against common attacks. Nonetheless, the bit error rate (BER) between the original watermark and the recovered one is different from zero.