esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization and Finite Element Modeling of Microperforated Titanium Grade 2
###### Abstract  
Hybrid Laminar Flow Control (HLFC) is a promising technology for reducing aircraft drag and, therefore, emissions and fuel consumption. The integration of HLFC systems within the small space of the wing leading edge, together with de-icing and high lift systems, is one of the main challenges of this technology. This challenge can be tackled by using microholes along the outer skin panels to control suction without the need for an internal chamber. However, microperforations modify the mechanical properties of titanium sheets, which bring new challenges in terms of wing manufacturability. These modified properties create uncertainty that must be investigated. The present paper studies the mechanical properties of micro-drilled titanium grade 2 sheets and their modeling using the Finite Element Method (FEM). First, an experimental campaign consisting of tensile and Nakajima tests is conducted. Then, an FEM model is developed to understand the role of the anisotropy in sheet formability. The anisotropy ratios are found by combination of Design of Experiments (DoE) and the Response Surface Method (RSM); these ratios are as follows: 1.050, 1.320, and 0.975 in the directions Y, Z, and XY, respectively. Some mechanical properties are affected by the presence of microholes, especially the elongation and formability that are significantly reduced. The reduction in elongation depends on the orientation: 20% in longitudinal, 17% in diagonal, and 31% in transversal.  
titanium; Finite Element Method; micro-drilling; tensile test; Nakajima test; anisotropy +
Footnote †: journal: Applied  
doi:10.3390/  
accepted: 29 August 2024  
Published: 5 September 2024
## 5 Challenges and Future Research Trends
### Lightweight Networks for Channel Estimation  
From extensive previous research, it is evident that large-size deep learning models can learn more complex data features due to more parameters and a deeper network structure. These factors enable them to achieve higher channel estimation performance. However, the performance improvement is often accompanied by higher computational complexity and resource consumption, which may be a significant limitation in resource-constrained practical applications. In light of this challenge, lightweight networks offer promising prospects for channel estimation. These networks are characterized by optimized structures and a reduced parameter count. Specifically, the design of these networks aims to balance the high estimation performance with reduced complexity and resource demands.
# Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information
## 4 Experiment
### General Setting  
The polarimetric features considered in this paper are summarized in Table 1. For morphological feature extraction, opening, closing, opening by reconstruction, and closing by reconstruction are considered and are summarized in Table 2. For each of those filters, an SE whose dimensions unit increased from 5 to 19 with a step of 2 pixels is used. In the classification stage, the involved parameters are parameters in the SVM, and the weight parameter \(\eta\) that can tune the contribution of two kernels. And the kernel we used is Radial basis function (RBF) which fulfills Mercer's condition.  
In the training process, the overall accuracy curve in Figure 5a reaches the top when \(\eta=0.6\). So, we may conclude that set \(\eta=0.6\) in Flevoland data set can make the best performance. Similarly, in Figure 5b, setting \(\eta=0.6\) in San Francisco Bay data set had best outcome. Therefore, we set \(\eta=0.6\) in the experiment for the Flevoland and San Francisco Bay data sets.
# Time-Dependent Performance of a Multi-Hop Software Defined Network
## 4 Transient Analysis of Multiple SDN Switches
Packets leaving the node \(j\) according to the distribution \(f_{Dj}(x,t)\) choose any node \(i\) with probability \(r_{ji}(t)\) and the times between two packets routed from node \(j\) to \(i\) has pdf \(f_{ji}(x,t)\)  
\[f_{ji}(x,t) = f_{Dj}(x,t)r_{ji}(t)+f_{Dj}(x,t)*f_{Dj}(x,t)[1-r_{ji}(t)]r_{ji}(t)+ \tag{24}\] \[f_{Dj}(x,t)*f_{Dj}(x,t)*f_{Dj}(x,t)[1-r_{ji}(t)]^{2}r_{ji}+\cdots\]  
For example, a packet leaving station \(j\) goes to station \(i\) with probability \(r_{ji}(t)\) or with probability \(1-r_{ji}(t)\) it goes elswhere but the second packet goes to \(i\) with probability \(r_{ji}(t)\), hence the gap has has pdf \(f_{Dj}(x,t)*f_{Dj}(x,t)\) with probability \([1-r_{ji}(t)]r_{ji}(t)\), etc., or, after Laplace transform:  
\[\tilde{f}_{ji}(s,t) = \tilde{f}_{Dj}(s,t)r_{ji}(t)+\tilde{f}_{Dj}(s,t)^{2}[1-r_{ji}(t)]r _{ji}+\tilde{f}_{Dj}(s,t)^{3}(1-r_{ji}(t))^{2}r_{ji}+\cdots\] \[= \frac{r_{ji}(t)\tilde{f}_{i}(s,t)}{1-[1-r_{ji}(t)]\tilde{f}_{i}(s,t)}\,\]  
Then we compute the squared coefficient of variation:  
Figure 8:0  
Hence:  
Figure 8:1  
where the parameters \(\lambda_{0i}\) and \(C_{0i}^{2}\) refer to the flows coming to station \(i\) from outside of the network. The parameters of the input flow at station \(i\) are given by (21) and (25). Equations (23) and (25) form a system of linear equations yielding \(C_{Ai}^{2}(t)\) and also the diffusion parameters \(\beta_{i}(t),\alpha_{i}(t)\) for every node \(i\). At each interval \(\delta\), the functions \(f_{i}(x,t;\psi_{i})\) providing the queue length distributions at every station \(i\) for \(t\in\delta\) are computed. Their values at the end of the interval yield, among others, the current utilizations \(\phi_{i}\) used to determine the flow parameters and diffusion parameters for the next interval \(\delta\). The pdf \(f_{Ri}(x,t)\) of the time-dependant response time (waiting time plus service) is determined using the first passage time from the end of the queue to zero, as defined by Equation (18). If \(f_{Ri}(x,t)\) is the response time pdf at node \(i\), then the response time pdf \(f_{R}(x,t)\) for the path \(1,\ldots,n\) of \(n\) stations is:  
Figure 8:2  
or:  
Figure 8:3  
The loss probability \(p_{loss}(t)\) for same entire path may be computed from:  
Figure 8:4  
where \(p_{Ni}(t)\) is the probability that the queue at station \(i\) is saturated at time \(t\)--i.e., the diffusion process for this station is at time \(t\) at the barrier \(x=N\).
# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
###### Abstract  
Specific emitter identification (SEI) is a professional technology to recognize different emitters by measuring the unique features of received signals. It has been widely used in both civilian and military fields. Recently, many SEI methods based on deep learning have been proposed, most of which assume that the training set and testing set have the same data distribution. However, in reality, the testing set is generally used later than the training set and lacks labels. The long time span may change the signal transmission environment and fingerprint features. These changes result in considerable differences in data distribution between the training and testing sets, thereby affecting the recognition and prediction abilities of the model. Therefore, the existing works cannot achieve satisfactory results for a long time span SEI. To address this challenge and obtain stable fingerprints, we transform the long time span SEI problem into a domain adaptive problem and propose an unsupervised domain adaptive method called LTS-SEI. Noteworthily, LTS-SEI uses a multilayer convolutional feature extractor to learn feature knowledge and confronts a domain discriminator to generate domain-invariant shallow fingerprints. The classifier of LTS-SEI applies feature matching to source domain samples and target domain samples to achieve the domain alignment of deep fingerprints. The classifier further reduces the intraclass diversity of deep features to alleviate the misclassification problem of edge samples in the target domain. To confirm the effectiveness and reliability of LTS-SEI, we collect multiple sets of real satellite navigation signals using two antennas with 13 m- and 40 m-large apertures, respectively, and construct two available datasets. Numerous experiments demonstrate that LTS-SEI can considerably increase the recognition accuracy of the long time span SEI and is superior to the other existing methods.  
specific emitter identification; long time span; unsupervised domain adaptation; source domain; target domain; satellite navigation signal +
Footnote †: journal: remote sensing  
Article  
A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
# Influence of the Motion of a Spring Pendulum on Energy-Harvesting Devices
## 7 Energy-Harvesting Device Performance  
Based on the mentioned dynamical models, when piezoelectric and electromagnetic devices are connected with the 2 DOF spring pendulum systems, one can expect to obtain an energy harvester with enhanced power output.  
EH devices can employ vibration energy and produce electricity, as we mentioned before. In our work, we used the considered dynamical system to obtain the necessary mechanical vibrations to power the two different models (_a_) and (_b_). As shown in model (_a_), the piezoelectric device is a transducer, which can convert mechanical stress into electricity using the advantages of piezoelectric materials. The electromagnetic device mechanism in the model (_b_) is made up of a magnet that is wrapped in a coil to create a magnetic flux. When a dynamical system vibrates, it cuts the magnetic flow, resulting in the generation of electricity.  
Moreover, we examined the impact of different selected values of the physical parameters on the system to obtain high performance. The time histories of the output voltages and output powers of the two models of piezoelectric and electromagnetic devices are plotted in Figures 30 and 31. It is noticed that the maximum value of voltage, current, and even the output power decreases with the increase of the damping coefficients (\(c=c_{1}=c_{2}\)). The increasing excitation amplitude increases the output voltage, current, and power, as indicated in Figures 32 and 33. The piezoelectric device's output power is much greater than the electromagnetic one of the model (_b_), as shown in Figures 31 and 33.  
Figure 31: The time histories of the output power from the studied two energy-harvesting models with different selected values of damping coefficient.  
Figure 30: The time histories for the voltage \(v\) from the piezoelectric device and the current \(q\) from the electromagnetic device with different values of damping coefficient.  
The response of the excitation frequencies is represented in the curves of Figures 34 and 35 for different selected values of the damping coefficients. High voltage, current, and power are generated at a frequency of approximately 10 Hz. It is observed that when the value of the damping coefficient increases, the generated voltage and power of the piezoelectric device decrease, as seen in parts (_a_) of Figures 34 and 35. The current and power of the electromagnetic device remain unchanged due to the current's approximate equations, as seen in parts (_b_) of Figures 34 and 35.  
Figure 34: The output voltage and the current versus excitation frequency with the variation of the damping coefficients.  
Figure 32: The time histories of the output voltage due to the existence of the piezoelectric device and the current from the electromagnetic device with different chosen values of excitation amplitudes.
# Intraoperative Imaging of Cortical Blood Flow by Camera-Based Photoplethysmography at Green Light
###### Abstract  
Intraoperative evaluation of blood perfusion in the brain cortex is an important but hitherto unresolved problem. Our aim was to demonstrate the feasibility of cerebral microcirculation assessment during open brain surgery by using camera-based photoplethysmography (cbPPG) synchronized with an electrocardiogram. Cortical blood flow was monitored in five patients with different diagnoses. Two cases (tumor resection and extra-intracranial bypass grafting) are presented in detail. Blood-flow parameters were visualized after processing cortex images recorded under green-light illumination before and after surgical intervention. In all cases, blood flow was successfully visualized in >95% of open brain. Distributions of blood pulsation amplitude, a parameter related to cortical blood perfusion; pulse arrival time; and blood-pressure-pulse shape were calculated with high spatial resolution (in every pixel). Changes in cerebral blood supply caused by surgical intervention were clearly revealed. We have shown that the temporal spread of pulse arrival time and the spatiotemporal variability of pulse shape are very sensitive markers of brain circulatory disturbances. The green-light cbPPG system offers a new approach to objective assessment of blood-flow changes in the brain during surgical intervention. The proposed system allows for contactless monitoring of cortex blood flow in real time with high resolution, thus providing useful information for surgery optimization and minimization of brain tissue damage.  
neurosurgery; blood flow imaging; intraoperative monitoring; cerebral microvessels; imaging photoplethysmography; blood microcirculation 2020
# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 2 Literature Review
### Comparative Studies on Quantum vs. Classical Approaches  
Comparative studies between quantum and classical approaches in deep learning for 6G networks have shown promising results, with quantum methods demonstrating potential advantages in certain computational tasks. Performance metrics commonly used in these comparisons include processing speed, accuracy, resource utilization, and adaptability to network fluctuations. Recent research has indicated that quantum-enhanced algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA) [51; 52] and quantum neural networks (QNNs) [53], can outperform classical methods in specific scenarios, particularly for complex optimization problems and feature extraction tasks in high-dimensional data spaces.  
QAOA, in particular, utilizes a hybrid quantum-classical-classical approach to tackle combinatorial optimization problems by iteratively refining solutions through quantum gates, making it effective for tasks like the Multiple Knapsack Problem. This capability allows QAOA to explore multiple solutions simultaneously, offering significant advantages in scenarios where classical algorithms struggle to find optimal solutions efficiently.  
On the other hand, QNNs leverage the principles of quantum mechanics to enhance neural network architectures, enabling them to process information in fundamentally different ways than classical neural networks. This unique processing capability allows QNNs to capture complex patterns in data more effectively, potentially leading to improved performance in tasks such as image recognition and classification.  
However, these studies also highlight that the practical advantages of quantum approaches are still limited by current hardware constraints and the need for further development of quantum algorithms tailored to 6G network challenges.
# Finite Element Analysis of Geogrid-Incorporated Flexible Pavement with Soft Subgrade
## 3 Results
### Tensile Force in the Geogrid  
Figure 7 illustrates the tensile force generated in the geogrid due to the wheel load, focusing on a single tyre imprint, with the normalized wheel path specified within the figure. Like geogrid strain, the magnitude of tensile force is influenced by the subgrade strength, with weaker subgrades resulting in higher tensile forces in the geogrid while stronger subgrades yield comparatively lower forces. However, this tensile force predominates primarily beneath the tyre imprint and gradually diminishes as the wheel path extends towards the pavement model's edge. A notable reduction of approximately 16% in geogrid tensile force is observed as the CBR value of the subgrade soil increases from 1% to 3%. In contrast, an increase of about 35% and 41% is noted if the CBR value changes to 5% and 8%, respectively.  
Figure 6: Strain and deflection developed in the geogrid.  
Figure 7: Variation of tensile force in the geogrid with several CBR values.
# Coaxial Fibres Incorporated with Phase Change Materials for Thermoregulation Applications
## 2 Materials and Methods
### Fibres' Physical, Chemical, Thermal, and Mechanical Characterization
#### 2.4.5 Differential Scanning Calorimetry (DSC)  
To evaluate the phase change behaviour of the samples and verify the phase change temperature, melting enthalpy, and endothermic changes in the materials (specifically CA (Mn 30,000 and 50,000), recycled CAt, and PEG2000) and the fibre samples, DSC analyses were carried out using Mettler Toledo equipment, model DSC-822 (Columbus, OH, USA), and a cooling accessory (Labplant RP-60 cryostat, Huddersfield, UK). The polymer material samples were placed in an aluminium crucible (Mettler Toledo, ME-26763, Columbus, OH, USA) and exposed to a heating rate of 10 \({}^{\circ}\)C/min over a range of 25 to 500 \({}^{\circ}\)C for all samples under an N\({}_{2}\) atmosphere of 200 mL/min.
# Estimating Interference with a Two-Dimensional Viterbi Algorithm for Bit-Patterned Media Recording
## 2 Estimating Interference with 2D VA
### 2.2 Da Va  
In BPMR systems, the received signal is disturbed by 2D interference. Therefore, we needed to remove or mitigate 2D interference into 1D interference to apply 1D VA. In this section, we introduce 2D VA. Based on the 2D VA, we propose an interference estimator for converting 2D interference into 1D interference in the next section (Section 2.3).  
As shown in Figure 1, the output of the equalizer \(z[j,k]\) can be written as:  
\[\begin{split} z[j,k]=& d[j,k]+w_{\mathbf{F}}[j,k]\\ =&\sum_{n=-1}^{1}\sum_{m=-1}^{1}a[j-n,k-m]g_{n,m}+w_{\mathbf{F}}[j,k]. \end{split} \tag{19}\]  
where \(w_{\mathbf{F}}\) [\(j,k\)] denotes the colored noise. By minimizing the mean square error (MSE), we \(w_{\mathbf{F}}\) [\(j,k\)] can be ignored while analyzing the estimated signal. The interfered signal can be rewritten as:  
\[\begin{split}\sum_{n=-1}^{1}\sum_{m=-1}^{1}a[j-n,k-m]g_{n,m}=\\ +ta[j-1,k-1]+pa[j-1,k]+ta[j-1,k+1]\\ +ra[j,k-1]+a[j,k]+ra[j,k+1]\\ +ta[j+1,k-1]+pa[j+1,k]+ta[j+1,k+1].\end{split} \tag{20}\]  
When we compare the target, \(\mathbf{G}\), and Equation (20), interference [\(r\) 1 \(r\)] is from the main track and interference [\(t\) \(p\) \(t\)] is from the upper and lower tracks. In this study, we defined [\(t\) \(p\) \(t\)] as ITI, [\(t\) \(r\) \(t\)]\({}^{T}\) as ISI, [\(p\) 1 \(p\)]\({}^{T}\) as vertical interference (VI), and [\(r\) 1 \(r\)] as horizontal interference (HI) in \(\mathbf{G}\). ITI affects the current symbol based on the symbols in neighboring (upper and lower) tracks, and ISI affects the current symbol based on the symbols in neighboring (previous and next) sample times.  
Moreover, we defined a vector, \(\mathbf{v}\), as follows:  
\[\mathbf{v}=\left[\begin{array}{ccc}v[j,k-1],&v[j,k],&v[j,k+1]\end{array} \right], \tag{21}\]  
where  
\[v[j,k-1]=ta[j-1,k-1]+ra[j,k-1]+ta[j+1,k-1], \tag{22}\]  
\[v[j,k]=pa[j-1,k]+a[j,k]+pa[j+1,k],\text{ and} \tag{23}\]  
\[v[j,k+1]=ta[j-1,k+1]+ra[j,k+1]+ta[j+1,k+1]. \tag{24}\]With the assignments in Equations (22)-(24), the ISI estimator can be designed. Similarly, if \(\mathbf{v}\) is assigned along the horizontal direction, as in Equations (25)-(27), the ITI estimator can be designed.  
\[v[j-1,k]=ta[j-1,k-1]+pa[j-1,k]+ta[j-1,k+1]. \tag{25}\]  
\[v[j,k]=ra[j,k-1]+a[j,k]+ra[j,k+1]. \tag{26}\]  
\[v[j+1,k]=ta[j+1,k-1]+pa[j+1,k]+ta[j+1,k+1]. \tag{27}\]  
Based on Equations (21)-(24), a trellis can be designed to detect \(\mathbf{v}\) using VA. Because the input, \(a[j,k]\), is \(1\) or \(-1\), the values of \(\mathbf{v}\) can be calculated, as listed in Table 1. Thus, the trellis has 36 states for \([v[j,k-1],v[j,k]]\), and each state has six output branches for \(v[j,k+1]\).
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 1 Introduction
### Background and Previous Work
#### 1.1.2 EDI Messaging  
EDI consists of five critical components:  
1. Message specification;
2. Syntax and format;
3. Partner agreements;
4. Timing;
5. Addressing.  
We make use of some subsidiary components under the message specification, syntax and format components, including a message's file size, source file size, bytes, maps, etc., from the attached XML documents. We also take into account a message's action and category. We use these as a novel approach to identify bursty and non-bursty messages. We also use these to partition the dataset to facilitate modelling of our queueing system.
## 6 Challenges and Future Directions  
### Dynamic Network Management  
Dynamic network management plays a pivotal role in enhancing communication performance across factory floors to handle the dynamic and often unpredictable changes in the environment caused by metallic surfaces and the movements of various objects, including robots, automated guided vehicles, and other metallic objects, as well as the dense deployment of wireless devices. Dynamic network management is critical in facilitating the optimal allocation of network resources, ensuring quality of service (QoS), and adapting to changing network conditions to enable efficient and reliable industrial operations [66].  
The evolution of 5G technology is expected to focus on further enhancing network slicing capabilities to cater to the diverse requirements of Industrial IoT use cases. This involves tailoring network slices to specific application needs, such as ultra-low latency, ultra-high dependability, and the ability to handle massive connections [67]. Additionally, the integration of edge computing capabilities into 5G networks and the 5G LAN-type service offer the potential for low-latency processing and an improved user experience. In this context, dynamic network management and fast packet switching play a critical role in efficiently allocating resources between the central cloud and edge nodes, thereby supporting latency-sensitive applications [68].  
As WiFi 7 is anticipated to introduce improved multi-user MIMO methods, enabling multiple devices to transmit data simultaneously, dynamic network management becomes essential for optimizing scheduling and resource allocation for numerous users. This optimization process takes into account channel conditions, traffic patterns, and user priorities. To maximize spectrum utilization and alleviate congestion, existing spectrum access strategies, such as dynamic spectrum sharing or cognitive radio, may be adopted in WiFi 7, and new techniques need to be discovered. In this scenario, dynamic network management will facilitate the dynamic selection of the most suitable spectrum bands and their allocation to different WiFi services. Furthermore, future WiFi networks are expected to incorporate enhanced QoS methods to support a wide array of applications with varying requirements. Enhanced dynamic network management will be required to provide differentiated QoS based on application types, latency sensitivity, bandwidth demands, and traffic prioritization. The discovery of such adaptive approaches to network management will ensure the efficient utilization of resources and the seamless delivery of services tailored to the specific needs of different applications [66].