esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 2 Literature Review
### Research Gaps and Future Directions  
Research gaps in quantum-enhanced deep learning for 6G networks include the need for larger-scale quantum hardware to demonstrate practical advantages over classical methods in real-world network scenarios. Future directions involve developing more efficient quantum algorithms tailored specifically to 6G network challenges, such as dynamic resource allocation, ultra-low-latency processing, and copyright detection techniques. Integrating effective copyright detection methods into quantum frameworks can enhance the safeguarding of intellectual property rights in an environment characterized by rapid content generation. Potential avenues for combining quantum and classical approaches include hybrid architectures that utilize the strengths of both systems, with quantum processors managing specific computationally intensive tasks within larger classical network management frameworks. Additionally, there is a need for more comprehensive benchmarking studies to clearly identify scenarios where quantum methods offer significant benefits over classical techniques in 6G environments, particularly regarding copyright detection and adherence to legal standards.
## 4 Discussion
### Oxidative Stress Marker  
Considering that the aging process is associated with increased oxidative stress, which has been implicated in the pathogenesis of neurodegenerative diseases [58], we investigated whether our RT protocol could modulate oxidative stress by assessing the circulating levels of MDA, a biomarker of lipid peroxidation. No significant changes in MDA concentrations were observed following the intervention, nor were there any differences between the 1 SET and CON groups at the end of the study. The lack of increase in lipid peroxidation suggests that the RT protocol was not detrimental in promoting oxidative damage, an important consideration when working with older populations. Some studies in the literature report a decrease in oxidative stress levels after 12 or 18 weeks of RT [59; 60], while others report no changes even with antioxidant supplementation over 24 weeks [61]. Exercise intensity may also impact oxidative stress. [PERSON] et al. (2002) investigated the effects of a 6-month RT program with varying intensities [62]. Their results showed a 14% reduction in MDA levels in a group that trained at 50% 1 RM, while the group that used 80% 1 RM showed a reduction of 18%. In summary, although our RT protocol did not significantly alter the MDA levels, the lack of increased lipid peroxidation suggests that the intervention was safe from an oxidative stress standpoint. Taken together with the previous findings, it is plausible that both exercise duration and intensity play critical roles in modulating oxidative stress. It is essential to acknowledge that although MDA is a widely utilized marker of lipid peroxidation, its limited specificity and inability to detect oxidative damage to other molecules, as well as the variations induced by external factors, represent significant limitations. Therefore, to achieve the more comprehensive assessment of oxidative stress, future studies should include a broader panel of both oxidative and antioxidant biomarkers. The previous studies have shown that resistance exercise can either increase or decrease oxidative stress, depending on the specific characteristics of the training protocols, and possibly the individual's training status [63; 64; 65; 66]. Therefore, the impact of our low-volume RT protocol on oxidative stress was evaluated by measuring the serum levels of MDA, a by-product of lipid peroxidation and a widely used marker of oxidative stress in cells and tissues (Figure 6). These results suggest that an 8-week low-volume RT protocol does not increase oxidative stress and may be considered a safe training approach for the population under study.
# Fast Sound Source Localization Based on SRP-PHAT Using Density Peaks Clustering
## 4 Obb-Srp-Phat  
SRP-PHAT searches all candidate points in the objective space, since the probability of each point being sound source is regarded as the same. Such an algorithm can guarantee a global optimal solution, however, the resulting huge computation makes its application impossible in a real-time situation. In practice, the sound source is typically located at a specific position, instead of an equal probability of showing up at every point of the space. For example, in a video conference room, the voice of a speaker can only be emitted from the area above the seats, while it is almost impossible to be given from other places. If all the possible sound source positions are localized to construct an ODB in advance, only the positions in the database need to be searched in real-time sound source localization.  
Thereby, the shortcoming of the heavy computational load due to searching points one by one in a large space can be overcome, and real-time localization becomes practical.  
Based on this, the authors put forward an improved procedure called ODB-SRP-PHAT to decrease the computational load and maintain the robustness. The new procedure consists of two steps, i.e., offline database construction and online sound source localization, as shown in Figure 1.  
We assumed that the sound source is in the far field, and the localization algorithm only estimates the direction of arrival (DOA) of the sound source. In a spherical coordinate system, the search ranges of the azimuth (\(\theta\)) and elevation (\(\varphi\)) are 0-359\({}^{\circ}\) and 0-90\({}^{\circ}\) respectively, where the search space mesh has increments of 1\({}^{\circ}\), and each mesh point is a candidate orientation \(q(\theta,\,\varphi)\). Figure 1(a) shows an eight-element uniform ring microphone array with a diameter of 20 cm. Figure 1(b) illustrates the SRP-PHAT energy map, i.e., SRP values, obtained by analyzing a certain frame of the signal collected by the microphone array. In Figure 1(b), the darker the color is, the greater the SRP value becomes. The position of the darkest color (47\({}^{\circ},33^{\circ}\)) is estimated as the sound source location. For the convenience of further analysis, we use \((\hat{\theta}_{i,j},\,\hat{\varphi}_{i,j})\) to represent the estimated location of the \(i\)-th sound source by analyzing the \(j\)-th frame of the signal, and use \((\theta_{i},\,\varphi_{i})\) to represent the actual location of the \(i\)-th sound source.  
Figure 1: ODB-SRP-PHAT flowchart.  
Figure 2: Microphone array model and SRP-PHAT energy map.
## 1 Introduction
The makespan criterion is a firm-oriented performance measure, which aims at minimizing the idle times on the machines, thus resulting in a maximization of machine utilization. However, total flowtime and total tardiness criteria are both customer-oriented performance measures, which aim at minimizing the waiting times of jobs amongst machines in order to finish the jobs as early as possible, thus resulting in a maximization of the customer satisfaction. Since the tardiness is measured by the difference between due date and flowtime, both performance measures are equivalent. For the total tardiness criterion, a few papers can be found in [34; 35]. Regarding the total flowtime criterion, a few papers can be found in the literature. A hybrid harmony search is presented in [36]. A discrete artificial bee colony algorithm is presented in [37]. An iterated greedy algorithm is developed in [38], and a branch and bound is presented in [39] for solving small size of instances. Very recently, a GRASP and a discrete artificial bee colony (DABC_RCT) algorithm are developed in [40; 41], which outperformed the existing algorithms from the literature. In this paper, we present a variable block insertion heuristic (VBIH) algorithm to solve the BFSP with the total flowtime criterion. Through extensive computational analyses on [PERSON]'s well-known benchmark suite, we demonstrate that the proposed VBIH algorithm outperforms the recent best performing DABC_RCT algorithm from the literature. Ultimately, 52 out of 150 problem instances are further improved with substantial margins. The rest of the paper is organized as follows. In Section 2, the blocking flowshop scheduling problem with speed-up method is formulated. Section 3 presents the components of the VBIH algorithm. Section 4 presents the design of experiment approach for parameter tuning. The computational results and comparisons are provided in Section 5. Finally, Section 6 gives the concluding remarks.
# Autonomous Underwater Vehicle Localization Using Sound Measurements of Passing Ships
## 4 MATLAB Simulations
### Three Ships Scenario  
We next consider the scenario with three ships. The speed of every ship is 2 m/s, and that of the AUV is 2 m/s. The AUV starts from the origin. In Figure 3, the starting point of each ship is marked with a diamond. At every minute, the trajectory of every ship is plotted with red asterisks. At every minute, the trajectory of the AUV is depicted with blue circles.  
Consider the case where the proposed localization method is utilized, while using three ships. In Figure 4, we plot \(meanE_{k}\), \(maxE_{k}\), and \(minE_{k}\) with respect to sample-index \(k\). See that the estimation error decreased considerably, compared to Figure 2. This is due to the fact that the AUV localization uses three bearing lines emitted from three ships, instead of one bearing line emitted from one ship. To run one Monte-Carlo simulation takes about 2.6 s utilizing MATLAB simulations. See that the computational load of the proposed localization scheme is very low.  
Figure 1: The AUV starts from the origin. The ship starts from (2000,0) in meters, and the trajectory of the ship at every minute is plotted with red asterisks. The starting point of the ship is marked with a diamond. At every minute, the trajectory of the AUV is depicted with blue circles (one ship scenario).  
Figure 5 is the enlarged figure of Figure 4. Since we utilize the bearing measurements of three ships, the estimation error is small from the beginning of the scenario.
## 7 Conclusions
The measured HbR and HbO concentration changes across different clinical phases during the 8-h continuous measurements were consistent with our expectations based on physiological principles. Although still preliminary data, the CCO concentration changes provide new insights into the swine metabolic reactions during the different phases of HS and the treatment using pREBOA with and without VPA. Thus, the SCISCCO system provides a noninvasive tool for monitoring changes in metabolism as well as hemodynamic response. Using the all-fiber integrated SCL as the key enabling technology, the SCISCCO system has the potential of being a practical, cost-effective, portable, and noninvasive means of monitoring brain and organ oxygenation and metabolism in the emergency department, operating room, or other medical facilities. Applications of the SCISCCO system could range from a new tool for screening concussion patients (such as being used in the cognitive attention test results) to use in an intensive care unit to gauge a patient's organ response to treatments (such as the cart-based system used in the animal trials). Conceptualization, M.N.I, T.Z., I.K., S.B., D.A.B., J.R.J., Y.S.S., R.R. and H.B.A. ; methodology, M.N.I, T.Z., I.K. and H.B.A. ; software, T.Z. and X.H. ; validation, T.Z. ; formal analysis, T.Z. ; investigation, T.Z. and C.N.M. ; resources, M.N.I., T.Z., I.K., R.R. and H.B.A. ; data curation, T.Z. ; writing--original draft preparation, M.N.I. ; writing--review and editing, M.N.I, T.Z. and A.D.; visualization, T.Z. ; supervision, M.N.I. ; project administration, M.N.I. ; funding acquisition, M.N.I., I.K., S.B., J.R.J., Y.S.S., R.R. and H.B.A. All authors have read and agreed to the published version of the manuscript. This investigation was supported in part by the Medical Technology Enterprise Consortium (MTEC) (MTEC-17-08-MultiTopic-340B (PI: [PERSON], [PERSON], and [PERSON])), the Exercise and Sport Science Initiative (ESSI) at the University of Michigan (PI: [PERSON], [PERSON], [PERSON], and [PERSON]), and NIH Grant R01 HD092498 (PI: [PERSON]). **Institutional Review Board Statement:** This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of University of Michigan (# HUM00147128, approved on 16 July 2018). **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Conflicts of Interest:** M.N.I. is also the Founder and Chief Technology Officer of Omni Sciences, Inc. Omni Sciences makes super-continuum lasers and holds the patents on the super-continuum technology. The University of Michigan has filed a patent application that at least in part covers the SCISCO system and its applications.
## 6 Evaluation and Experimental Results
### End-to-End Transmission Delay Reduction  
In this section, we present the experimental results of the handshake regarding the improvement of the total E2E transmission delay after the compression of HIP DEX packets. For doing so, we evaluated all delays involved in the transmission delays of the packets' handshake regarding the transmission delay of the handshake process between the Initiator and the Responder, the propagation, the processing, and the queuing delays. A granular comparison with previous works that evaluate the same delays composing E2E transmission delays are presented in the following sections.
## 2 Related Work
They proposed a series of distributed multi-hop localization algorithm, such as Pattern-Driven localization Scheme (PDS) [17], Reliable Anchor-based Localization (RAL) [18], Anchor Supervised (AnSuper) [19], and Selective Multilateration (SM) [20]. Based on the anchor node information, the family of algorithms divides the deployment area into a number of uniformly distributed set of nodes. Taking the SM algorithm as an example, it uses the angle between the anchor nodes to measure the distance between the anchor nodes and divide the area. However, the SM method also requires human intervention in the selection of the number of divided regions and the regional center nodes. Figure 2 shows the localization results of the SM algorithm, which selects different regional centers and the number of regions. The figure randomly distributes 400 nodes (as shown in Figure 2a), of which 40 are anchor nodes (shown in Figure 2b). Figure 2c,e,g are the anchor nodes partition map, and Figure 2d,f,h are the corresponding localization results. Figure 2d,h are centered on node 12 and the nodes are divided into three and two areas, respectively. Figure 2f is centered on node 17 and the area is divided into three regions. It is easy to find, with different nodes as the center and divided into different numbers of regions obtained by localization, that results are not the same, which confirms the instability of the algorithm. Figure 2: _Cont._  
The latest study based on the number of packets between nodes and estimated distance residuals, uses Multi-objective Optimization to carry out the node position estimation [21; 22]. But the bionic methods employed in the localization algorithm use pseudo-random number, so the results are not same each time. In addition, the optimal solution requires multiple iterations, which affects the efficiency of the algorithm. The Extreme Learning Machine (ELM) is a new learning method of single-hidden-layer feedforward neural networks (SLFNs) proposed by [PERSON] [23]. In the ELM, the input weights (connecting the input nodes to the hidden nodes) and the hidden layer nodes are randomly selected, and the output weights (connecting to the output node of hidden layer node) are determined by calculating the Moore-Penrose (MP) generalized inverse of the hidden layer output matrix. This learning algorithm ensures the generalization performance of the network, and significantly enhances the convergence as well. Moreover, the additional consumption on the tunes for learning rates, and the curses of local minimizers can be avoided [24]. Because of satisfactory precision and high efficiency, ELM has a very wide application. In this paper, a new distributed multi-hop method, ML-RELM, is proposed by combining ELM and distributed multi-hop localization algorithm. This method constructs the optimal relation (model) by the information of the actual distance and the number of inter-node hops, and then uses this model to predict the distance between the unknown node and the known node.
## 2 Three-Closed-Loop Control of an Unbalanced Three-Phase Four-Wire Inverter
### Control Analysis of Three-Phase Four-Wire Inverters
#### 2.3.3 Voltage RMS Outer Loop Control  
To ensure that the output of the inverter has good voltage regulation and that the load voltage amplitude remains in balance when the three-phase load is unbalanced, the RMS control loop can be added to the voltage loop, as shown in Figure 7. The collected three-phase voltages are subjected to an RMS calculation to obtain \(u_{\text{rms}}\) and then compared with the reference value of the RMS value \(u_{\text{rms}}\)\({}^{*}\). After PI adjustment, the amplitude correction signal \(u_{\text{m}}\) is generated. \(u_{\text{m}}\) is then multiplied by the unit sine wave \(\sin\omega t\) to obtain the three-phase instantaneous command voltages, which are transformed by coordinates to obtain the command voltages \(u_{\text{d}}\)\({}^{*}\) and \(u_{\text{q}}\)\({}^{*}\).
## 4 Numerical Model and Materials
### Soil-Tunnel System
#### 4.1.4 Boundary Conditions  
Since the numerical model of the soil-tunnel system only has a limited range of soil instead of the practical infinite space, it is essential to include an artificial boundary to make sure that the scattering waves can be eliminated on the boundary. In this paper, the viscous-spring boundary proposed by [PERSON] et al. [43] is adopted, whose efficiency has been greatly verified in many publications [44; 45]. When using the viscous-spring boundary, a  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Uniaxial Compressive** & **Uniaxial Inelastic** & **Compressive** & **Uniaxial Tensile** & **Uniaxial Cracking** & **Tensile** \\
**Stress/(MPa)** & **Compressive Strain** & **Damage \(d_{\mathrm{c}}\)** & **Stress/(MPa)** & **Tensile Strain** & **Damage \(d_{\mathrm{t}}\)** \\ \hline
7.239 & 0 & 0 & 2.720 & 0 & 0 \\
22.122 & 6.77 \(\times\) 10\({}^{-5}\) & 0.032 & 2.216 & 8.28 \(\times\) 10\({}^{-5}\) & 0.293 \\
32.14  
great number of springs and dampers should be added on the boundary nodes. For each boundary node, the viscous-spring boundary is shown as Figure 6.  
The coefficients of the springs and dampers can be obtained from the following equations:  
\[K_{BT}=\alpha_{T}G/r,\ K_{BN}=\alpha_{N}G/r \tag{3}\]  
\[C_{BT}=\rho c_{s},\ C_{BN}=\rho c_{p} \tag{4}\]  
where, \(K_{BT}\) and \(K_{BN}\) are the spring coefficients in the two different directions, respectively; \(C_{BT}\) and \(C_{BN}\) are the damping coefficients in the two different directions, respectively; \(\alpha_{T}\) and \(\alpha_{N}\) are modified coefficients for the spring coefficients; \(r\) is the distance from calculated nodes to the input wave; \(G\) is the soil shear modulus; \(c_{s}\) is the velocity of shear wave, while \(c_{p}\) is the velocity of the P wave.
###### Abstract  
The continuous changes of the market and customer demands have forced modem automation systems to provide stricter Quality of service (QoS) requirements. This work is centered in automation production system flexibility, understood as the ability to shift from one controller configuration to a different one, in the most quick and cost-effective way, without disrupting its normal operation. In the manufacturing field, this allows to deal with non-functional requirements such as assuring control system availability or workload balancing, even in the case of failure of a machine, components, network or controllers. Concretely, this work focuses on flexible applications at production level, using Programmable Logic Controllers (PLCs) as primary controllers. The reconfiguration of the control system is not always possible as it depends on the process state. Thus, an analysis of the system state is necessary to make a decision. In this sense, architectures based on industrial Multi Agent Systems (MAS) have been used to provide this support at runtime. Additionally, the introduction of these mechanisms makes the design and the implementation of the control system more complex. This work aims at supporting the design and development of such flexible automation production systems, through the proposed model-based framework. The framework consists of a set of tools that, based on models, automate the generation of control code extensions that add flexibility to the automation production system, according to industry 4.0 paradigm.  
flexible automation production systems; model driven engineering; multi agent system; I4.0 components
## 2 Interdependent Network Construction
### Control Network  
In the process of control command, the control sector is the smallest control unit, and only the transfer of control rights exists between neighboring sectors. Therefore, the control network is constructed by using control sectors as nodes and control right handover relations as connected edges. Since the transfer of control rights is mutual, and there is no other influence between adjacent sectors due to the transfer of control rights, the whole control network is a non-directional and powerless network. The control sectors and the control network are shown in Figure 5.  
Figure 4: Schematic diagram of flight conflict network. Numbers 1–6 indicate aircraft designators.
## 4 Numerical Experiments
### Numerical Experiment 2  
Next, in this experiment, we assume that \(p_{m}\) is nearly equal to zero. This implies that if an FL algorithm in a group is compromised by poisoning attacks, the prediction accuracy of its global model will become low, which leads to predicting outputs incorrectly. A notable example of such a federated algorithm is FedAvg, which is vulnerable to poisoning attacks. As a result, the prediction accuracy of a global model derived from FedAvg can drop to 0 even with a single attack. For a normal group, the prediction accuracy of its global model is set to \(p_{n}\). In this experiment, we aim to demonstrate the output prediction accuracies of the overall collaborative FL framework for both FLcert-P and our grouping scheme, as the number of malicious edge clients increases.  
To facilitate comparison with FLcert-P, we adopt accuracy as the evaluation metric. This metric represents the fraction of inputs correctly classified by the FL algorithm when \(m\) malicious clients are involved. We set \(N\) to 100, \(n\) to 300, and \(p_{n}\) to a random range between 0.92-0.95. According to the first experiment, it was revealed that the number of normal groups must exceed that of malicious ones in our framework. For FLcert-P, since \(\binom{n}{k}\) is too large, we sample \(N\) groups according to our grouping rule, with each group containing \(k\) clients sampled uniformly at random from the \(n\) clients. As shown in Figure 3, the accuracy of our collaborative FL surpasses that of FLcert-P. When the number of malicious edge clients approaches 55, the prediction accuracy of FLcert-P decreases dramatically and is close to 0 while \(n_{m}\) approaches 75.  
Figure 3: The accuracy of a collaborative FL as \(n_{m}\) increases.