esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation of Column Separation Using Rapid Closure of an Upstream Valve
## 3 Observations and Results
### Column Separation Behaviors and Synchronous Pressure Surges
It should be noted that the shapes of pressure histories at PT5# were similar to the results reported by [PERSON] [14]. Figure 4: Pressure heads along the pipeline for the test in Figure 3: (**a**) pressures at PT6# to PT9#; (**b**) pressures at PT1# to PT5#. Figure 6: Pressure oscillation patterns for column separation on the downstream side of BV1#: (**a**) Type 2; (**b**) Type 3; (**c**) Type 4. The position and timing of the maximum pipeline pressure is uncertain, as depicted in Figures 5 and 6, and it does not always coincide with the collapse of cavities. In Figure 5a, a maximum pressure of 167.17 m (30.9% higher than the maximum value at PT6#) occurs at PT7# during Phase-1, where no cavities formed due to sustained pressure above the vapor pressure before \(H_{max}\) appeared. This could be attributed to the interaction between the pressure waves resulting from valve closure and the collapse of cavities adjacent to the valve and the stress wave caused by the pipeline's movement. Similarly, in Phase-1 of the Figure 6a test, \(H_{max}\) was observed at PT4# (1.2% higher than the maximum pressure at PT3#), while in the test displayed in Figure 6c, it occurred at PT7# (12.3% higher than the maximum pressure at PT3#). However, in the case of Figure 5c, \(H_{max}\) occurred at PT7# (11% higher than the maximum pressure at PT6#) during Phase-2, in which the pressure at PT7# decreased to the vapor level for approximately 0.021 s, indicating possible cavity formation at this position. The resulting wave from the collapse of cavities at PT7# may have further contributed to the interaction effect observed here. The duration of cavities near the valve during Phase-4 for the tests shown in Figure 5b,c and Figure 6b,c was longer compared to that observed during Phase-3 with proportions being recorded as follows: 17.6%, 51.3%, 26.8%, and 101.1%, respectively. This complex interaction between the water hammer wave, collapse wave of cavities, and stress wave induced by pipeline movement likely serves as a principal contributing factor.
# A Class of Perfectly Secret Autonomous Low-Bit-Rate Voice Communication Systems
## 1 Introduction
### Related Works
However, from the perspective of information-theoretic security, any chaotic algorithm can be considered a deterministic dynamic system fully defined by its initial states. Consequently, the equivocation of secret keys in such systems cannot exceed the length of their binary representation. Therefore, similar to classical cryptographic algorithms with finite secret keys, this class of algorithms cannot provide perfect secrecy. Notably, no prior work has proposed a perfectly secret autonomous system where secret key generation is derived from a synthesized speech signal, independent of the telecommunication channel and without external key distribution infrastructure.
# Variable Multiple Interleaved Bi-Directional DC/DC Converter with Current Ripple Optimization
## 6 Conclusions  
The main contribution of this paper is to propose a variable multiplicity control method of multiple interleaving bidirectional DC/DC power converters to optimize the current ripple, that is, dynamically adjust the number of parallel multiplicities in the actual operation of the circuit according to the duty cycles, so that the output current ripple is as small as possible. First, the relationship between the current ripple and the number of parallel multiplicities and the duty cycle is analyzed and deduced and the switching logic that can generate the minimum current ripple is designed. Second, the average value model and the AC small signal model of variable multiplicity interleaving bidirectional DC/DC power converter are established, and the transfer function of the system is obtained. Third, according to the transfer function of the system, the control system of the converter is designed. Simulation and experimental results show that the control method proposed in this paper can effectively further reduce the current ripple of the traditional multiple DC/DC converter, and to a certain, reduce the switching loss caused by a blind increase of switching frequency to optimize current ripple, so it is of great significance. All in all, the  
Figure 19: Current ripples of the first three channels under partial duty cycle when the parallel multiplicity is 4, 5, and 6. (**a,c,e**) The current ripples of the first three channels with the duty cycle of 0.25 when the parallel multiplicity is 4, 5, and 6; (**b,d,f**) The current ripples of the first three channels with the duty cycle of 0.5 when the parallel multiplicity is 4, 5, and 6.  
current ripple optimization algorithm proposed in this paper is suitable for occasions that require low current ripple, such as battery energy storage systems, electric vehicles, etc. However, due to multiple interleaving DC/DC converters using more parallel branches, the cost is relatively high. Further research on improving reliability and reducing costs is needed in the future.  
Conceptualization, J.D. and S.W.; methodology, S.F.; validation, Y.X.; formal analysis, K.Z. and L.S.; writing--original draft preparation, J.D., S.W. and Y.X.; writing--review and editing, J.D., S.W. and Y.X. 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, grant number 52177211, and by the Heilongjiang Postdoctoral Research Starting Fund, grant number LBH-Q20020.  
Not applicable.  
The authors declare no conflict of interest.
# Fracture Prediction in Weldox 700E Steel Subjected to High Velocity Impact Using LS-DYNA
## 3 Results and Discussion
### Mesh Convergence Study  
The mesh convergence study is performed to optimize the number of elements, minimize CPU time, and cost-effectiveness with acceptable results. The mesh convergence study has been performed based on the aspect ratio (ratio of largest to smallest edge length) of the element in the target plate. It is known that deformation and stresses have steep gradients around the impact zone under blunt nose-shaped projectiles, i.e., the contact region. Therefore, the mesh size of the elements in the contact region is very fine. The FE mesh for the rest of the plate is coarser with a one-way bias towards the contact region. Furthermore, the elements along the impacting face are kept square and the numbers of the elements along the thickness direction are varied to vary the aspect ratio of the elements. The analysis model with finite element mesh is presented in Figure 3b. The total number of elements and nodes with respect to different values of aspect ratio is given in Table 3. The results on residual velocity of the projectile with respect to aspect ratio are presented in Figure 4. The initial velocity of the projectile is taken as 275.7 m/s. It has been observed that there is variation in residual velocity at higher values of aspect ratio (greater than 2). Therefore, the subsequent analysis is performed with an aspect ratio value of 2.  
Figure 4: Aspect ratio vs. residual velocity.
## 5 Results and Discussion
### Cooperative Task between UAV and UGVs
#### 5.2.2 Experiment #2  
In the second experiment, the weight of the load is increased to 97 g. Consequently, the total weight to be lifted corresponds to about 37% of the weight of the UAV. The additional 25 g are used to evaluate the controller response under the same conditions of Experiment #1. The whole evaluation can be seen at [[https://youtu.be/OUIOjn5_a5s](https://youtu.be/OUIOjn5_a5s)]([https://youtu.be/OUIOjn5_a5s](https://youtu.be/OUIOjn5_a5s)) (accessed on 6 December 2022).  
Table 3 presents the instants of the events of each action referring to Experiment #2. All the analysis and discussion of Experiment #1 also apply to Experiment #2. Notice that this time the landing of B1 on T1, after the delivery of C1, occurred on the first attempt. This can be seen in the altitude variable of B1 around 80 s in Figure 12.  
Comparatively, during the transport stage, the load showed greater longitudinal and lateral oscillations in Experiment #2 than in #1. This is a consequence of the additional mass of the load causing the oscillation amplitude to increase. However, the controller proved to be able to handle this disturbance.  
Both experiments showed that it is possible to successfully coordinate a heterogeneous squadron to together perform a cargo transport mission, with the possibility of the UAV taking off and landing on a trailer connected to one of the UGVs. Furthermore, this work showed the application of an electromagnetic actuator connected to an overhead cable as an option to grab and release the cargo at the opportune moments of the transport  
Figure 12: Results of Experiment #2. Position time evolution of the cargo (C1), the electromagnet effector (E1), and the Bebop UAV (B1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Seq** & **Time (mm:sss)** & **Region** & **Action** & **Snapshot (mm:ss)** \\ \hline
1 & 00:01 & Transportation & P1 carries (C1 + B1) to the combined point & 03:15 \\
2 & 00:22 & Transportation & P1 arrives at the agreed point and triggers & 03:33 \\  & & & P2 which starts moving & \\
3 & 00:42 & Docked \(\mid\) Lifting & P2 reaches the combined point and triggers & 03:53 \\  & & & B1 to take off & \\
4 & 00:55 & Lifting & B1 picks up C1 using the electromagnet E1 and go to P2 & 04:07 \\
5 & 01:08 & Lifting \(\mid\) Returning & [PERSON] releases C1 the under P2 and returns & 04:21 \\  & & & to the other side of the platform & \\
6 & 01:18 & Returning \(\mid\) Transportation & [PERSON] positions himself above the trailer and lands & 04:30 \\
7 & 01:40 & Transportation & The formation returns to their respective & 04:34 \\  & & & address and the task is successfully completed & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sequence of events of the experiment 2.  
mission. Finally, being an applied science project, the results demonstrated the stability of the controllers proposed in previous works, as well as their tolerance to the rejection of disturbances caused by load transportation.
# Two-Dimensional Permutation Vectors' (PV) Code for Optical Code Division Multiple Access Systems
## 4 2-Dimensional PV System Description  
### Design of 2D-PV System  
The basic 2D-PV-based OCDMA system comprises numerous W-T encoders/decoders at the transmitter and receiver parts, respectively. Each encoder/decoder structure is made up of FBG-based delay lines, each with a certain bit period. FBGs as delay lines are preferred due to low circuit complexity and reduced delay time when compared to first generation 2D TW implementations (tunable filters and delay lines) [24]. Due to recent advancement in thin film technology, thin film filters (TFFs) are other alternative implementation approaches [25].
## Monitoring and Analysis of the Roadway Forming Effect
### Monitoring Results and Analysis of Road Forming Effect  
#### 5.2.1 Monitoring Results and Analysis of Roadway Deformation  
(1) Monitoring Results of Roadway Surface Displacement  
As shown in Figure 17, at the first test point of the first cable tape, the measured convergence of each position and the advance distance of the working face are as follows:  
Figure 16: Schematic diagram of monitoring scheme.  
Figure 14: Schematic diagram of the layout of the cross measuring point method.  
Figure 15: YAD400 mine bolt cable dynamometer.  
Analysis of monitoring results  
As shown in Figure 17, for the left side of the roadway (gob side): with the advancement of the working face, the amount of deformation increases sharply. When the working face has been advanced for about 15 m, the deformation slows down as the broken rock in the gob is restrained by the side props and stabilizes after two cycles of pressure.  
For the right side of the roadway (solid coal side): With the advancement of the working face, 0-80 m from the working face, the deformation increases at a speed \(v_{d}\) of 4-25 mm/d; beyond 80 m from the working face, the convergence of the right side stabilizes.  
For the roadway floor: The heave of the roadway floor increases continuously with the advancement of the working face without any slowdown. As the floor is quite soft, the floor heave is obvious, exceeding 600 mm at last. Further floor treatment will be needed.  
For the roadway roof: With the advancement of the working face, after 80 m, the roof subsidence gradually decreases and stabilizes; the subsidence remains within 200 mm. By then, the fallen gangue in the gob is in contact with the main roof and has filled up the gob, helping support the main roof. This suggests that the roadway forming effect is good.
## 3 System Model
### Communication Model
#### 3.1.2 Wayside-to-Wayside (W2W) Link  
At time slot \(t\), it is possible that more than one wireless W2W link transmits information simultaneously; thus, there is interference between W2W links [16]. During the packet transmission, the one-hop link from wayside relay \(i\) to wayside relay \(j\) at time slot \(t\) is denoted as \(l_{ij}(t)\), where \(i,j\in\{R_{1},R_{2},\ldots,R_{m},\ldots R_{M}\}\). Moreover, \(\rho_{ij}(t)\) represents the \(i\to j\) link transmission status. \(\rho_{ij}(t)=1\) denotes that wayside node \(i\) is transmitting with node \(j\). Therefore, the SNR for the transmission of wayside node \(i\) and wayside node \(j\) can be represented as  
\[\gamma_{i,j}=\frac{P_{i}\left|h_{ij}\right|^{2}}{N_{0}+\sum\limits_{l_{ij}(t )\
eq l_{i,j}(t)}\rho_{i^{\prime},l^{\prime}}P_{i^{\prime}}\left|h_{i^{\prime },l^{\prime}}\right|^{2}}, \tag{5}\]  
Figure 1: Next-hop relay selection in T2T communication.  
where \(l_{i^{\prime},j^{\prime}}(t)\) is the interference link during the time slot \(t\).  
The transmission throughput between wayside relay \(i\) and wayside relay \(j\) is  
\[C_{i,j}=B\log_{2}(1+\gamma_{i,j}). \tag{6}\]
# Activation of Persulfate for Groundwater Remediation:
## 4 Factors Affecting the Application of Persulfate in ISCO
### pH  
The reaction of persulfate with pollutants in soil and groundwater is closely related to pH. The pH of the solution is impacted by persulfate reactions, which makes it difficult to quantify. Sulfate, a very weak conjugate base, is the main inorganic result of persulfate reactions. As a result, the pH drops when persulfate reactions produce protons, as has been reported in many persulfate systems [73; 122; 123]. [PERSON] et al. (2020) reported that, as a result of proton generation, persulfate caused the pH to drop to acidic levels (3-4) [123]. A similar  
Figure 2: Factors affecting the application of persulfate in ISCO.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Target** & **Activation** & **Persulfate Dosage** & **Duration** & \begin{tabular}{c} **Removal** \\ **Efficiency (\%)** \\ \end{tabular} & **pH** & \
\begin{tabular}{c} **Temperature** \\ (°C)** \\ \end{tabular} & **Ref.** \\ \hline TCE, TCA, DCE & Alkaline (KOH), & Na\({}_{2}\)S\({}_{2}\)O\({}_{8}\)-4221.9 L & 4 months & 100 & - & - & [114] \\ Diesel \& & & (10\%wt) & & & & \\ Gasoline LNAPL & Peroxide & - & - & 99.33 & - & - & [117] \\ PAHs, BTEX & Surfactant-iron & - & 70 days &
\begin{tabular}{c} BTEX -92 \\ VOCs -91 \\ \end{tabular} & - & - & [118] \\ BTEX, & CaO & - & - & 42-93 & - & - & [112] \\ napthalene & TPH & none & Na\({}_{2}\)S\({}_{2}\)O\({}_{8}\)-20 g/L & 120 days & 98 & - & - & [119] \\ TCA, DCE, DCA & electrokinetically & 40 g/L & 208 days & 80 & - & 5-40 & [56] \\ PCE, TCE, BTEX & Alkaline (NaOH) & Na\({}_{2}\)S\({}_{2}\)O\({}_{8}\)-26\% (wt) & 12 weeks & over 90 & 4.9–9.1 & - & [110] \\  & & NaOH-25\% (wt) & & & & & \\  & & Na\({}_{2}\)S\({}_{2}\)O\({}_{8}\):NaOH & & & & & \\  & & = 2:1 & & & & \\ TCE & & 5500 L of 5\% & 75 days & 95–100 & 6.70–7.22 & 30.4–31.6 & [120] \\  & & persulfate solution & & & & & \\ TPHs, PAHs & CaO & Na\({}_{2}\)S\({}_{2}\)O\({}_{8}\)-249.76 t & - & 60–99\% & 1.9–9.3 & - & [121] \\  & & CaO-404.6 t & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: \(Cont.\)result was observed by [PERSON] (2008), wherein pH decreased from 7 to roughly 2 (except alkaline activation) in unbuffered treatment systems [122]. The pH variations observed in the field were not as significant as those in the laboratory research. [PERSON] al. (2002) observed a drop in pH from 5.7 to 5.3 in monitoring wells [124]. Results obtained from [PERSON] (2018) showed that when the persulfate was injected, the pH was firstly decreased from 7.0 to 6.2, but recovered to 7 in the next few days and the groundwater in each injection well remained at a near-neutral pH (6.70-7.22) [120].
## 1 Introduction
Consequently, we propose a multi-step process that ultimately analyses and tests several optimization algorithms and combines them into ensembles of optimizers using three proposed strategies: hybrid cascade, hybrid single elite solution, and hybrid multiple elite solutions. We also use an initial search procedure to determine the best parameter values for each algorithm considered in this paper. Based on the result provided by the search procedure, we identify the three best-performing algorithms and use them in each proposed ensemble strategy. Each individual algorithm is run for a longer period, while the algorithms in an ensemble are run for a significantly shorter time. For each simulation scenario, we collect performance statistics at specific iterations to perform a fair comparison between strong individual optimizers and the proposed ensembles of weak optimizers. As we demonstrate in Section 4, the most suitable model for our problem results from a combination of three biologically inspired optimizers using the hybrid multiple elite solutions strategy. To our knowledge, this is the first instance when multiple biologically inspired optimization algorithms have been combined in such a manner, for the study of complex chemical processes such as MMA. The paper is structured as follows: Section 2 presents a brief review of some of the more notable results from the related literature; in Section 3 we provide a detailed description of our method: the dataset containing the experimental values pertaining to our problem, the encoding approach for transforming the neural network parameters into a format usable by an optimization algorithm, as well as the strategies for tuning, selecting and combining several algorithms into ensembles; Section 4 presents our experimental results, where we compare the best-performing algorithms with their various ensemble arrangements so as to find the approach that provides the best regression models for our problem; the paper ends with a Conclusions Section where we discuss our contribution and findings and point out the main directions for future work.
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 4 Case Study
### Conceptual Model
#### 4.1.1 Comfort Conditions and Energy Saving  
Among the comfort conditions that the system must guarantee is that the temperature of a room is pleasant for those who occupy it. In other words, the temperature is between 15 and 25 \({}^{\circ}\)C, being these limits are subject to configuration adjustments according to personal preferences. However, energy-saving conditions are also considered, stating that energy must be only consumed when the room is occupied. Consequently, the conditions to be jointly met are that:  
* The most recently updated temperature is low (comfort condition);
* The room is occupied by at least one resident (energy saving condition);
* The heater or fan (air conditioning) in the room is turned off (condition of reduced network traffic and consequently emergency saving).  
The comfort conditions are the conditions noted in the corresponding transitions in the instances of the service patterns, as analyzed in the computational models as shown in Sections 4.2 and 4.3.
## 3 Potential Security Implications of IoT in 6G Environment
### Network- or Device-Level Vulnerabilities
#### 3.1.7 AI-Related Security Issues  
AI-related vulnerabilities are a growing concern in 6G networks, as AI systems are susceptible to unique attacks. These attacks can include adversarial manipulations such as digital noise or physical changes to environments that mislead AI-based systems. For instance, altering objects can confuse object recognition systems or disrupt or eavesdrop sensor data and information in IoT networks [8]. Furthermore, data poisoning can skew training sets, while model poisoning compromises AI models' accuracy and predictions. AI-generated attacks also mimic legitimate traffic to bypass security, while privacy breaches expose or misuse sensitive data through AI systems [5].
# Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data
###### Abstract  
This paper introduces a database-based design methodology aimed at optimizing a 26 GHz MIMO antenna system through machine learning (ML) techniques. The procedure is divided into two primary phases. Initially, a rectangular microstrip patch antenna is designed and enhanced using analytical models alongside ML algorithms that are trained on a detailed dataset of geometric parameters. This yields effective impedance matching (S11 \(<\) -45 dB) and a high gain (-6.64 dBi), which serve as the foundation for the MIMO structure. In the second phase, split ring resonator (SRR) configurations are integrated between the antenna elements to reduce mutual coupling. A specialized dataset, featuring varied dimensions of SRR, quantities of unit cells, and spatial placements, is utilized to train Random Forest models that forecast arrangements achieving optimal isolation (S21 \(<\) -40 dB) while maintaining low reflection losses. Additionally, a secondary dataset is constructed to investigate the best strategies for SRR placement, ensuring an optimal balance between isolation and return loss. The ultimate MIMO design is validated via comprehensive full-wave electromagnetic simulations and experimental measurements. The proposed system exhibits noteworthy performance enhancements, including an envelope correlation coefficient (ECC) \(<\) 0.005, diversity gain (DG) \(\approx\) 9.99 dB, channel capacity loss (CCL) \(<\) 0.3 bits/s/Hz, total active reflection coefficient (TARC) \(<\) -30 dB, radiation efficiency exceeding 80%, and a maximum gain increase up to 10.22 dB. The close correlation between predicted and measured outcomes validates the effectiveness of the ML-driven approach in expediting antenna optimization for 5G and future applications.  
machine learning; isolation; MIMO array antennas; 26 GHz band; SRR; 5G/6G applications 2025
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 6 Perceptual Experiment 1 (Video-Only)
Figure 6b presents the \(\text{MCS}_{\text{HRC}}\) values for each HRC, corresponding to both frame-freezing and packet-loss groups. It is clear that the range of \(\text{MCS}_{\text{HRC}}\) is much smaller than the range of \(\text{MQS}_{\text{HRC}}\), which fluctuates around '3' (\"Neutral\" Content). The small differences between the HRCs and ANCs (anchor sequences without distortions) show that participants did not perceive great differences, in terms of content, between degraded and original sequences. There are, however, small variations in the \(\text{MCS}_{\text{HRC}}\) values that somehow follow the \(\text{MQS}_{\text{HRC}}\) behaviour, at a smaller range. This suggests that there is a certain correspondence between quality and content, which is in agreement with the results obtained in previous studies [64; 65].