esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Mcp1-/Ccl2  
In subsequent measurements, the concentration of MCP-1/CCL2 increased; however, there were no statistically significant differences between the compared measurements. Analysis of the correlation showed no relationship between the initial serum concentration of MCP-1/CCL2 and the age of the patients (\(r=-0.066;\)\(p\) value = 0.774) or the BVA (\(r=0.356;\)\(p\) value = 0.104). Serum MCP-1/CCL2 concentration did not correlate significantly with the concentrations of other parameters at the baseline and after the initial loading phase.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Pairs of**} & \multicolumn{6}{c}{**PGFP-A**} & \multicolumn{6}{c}{**PLGF**} & \multicolumn{6}{c}{**MCP-1/CCL2**} & \multicolumn{2}{c}{**IL-17A**} \\ \cline{2-11}  & \multicolumn{2}{c}{**MV**} & \multicolumn{1}{c}{**SE**} & \multicolumn{1}{c}{\(p\)} & \multicolumn{1}{c}{**MV**} & \multicolumn{1}{c}{**SE**} & \multicolumn{1}{c}{\(p\)} & \multicolumn{1}{c}{**MV**} & \multicolumn{1}{c}{**SE**} & \multicolumn{1}{c}{\(p\)} & \multicolumn{1}{c}{**MV**} & \multicolumn{1}{c}{**SE**} & \multicolumn{1}{c}{\(p\)**} \\  & **(pg/mL)** & **SE** & **Value** & **(ng/L)** & **SE** & **Value** & **(ng/L)** & **SE** & **Value** & **(pg/mL)** & **SE** & **Value** \\ \hline
1 vs. 2 & 271.819 & 38.721 & 0.886 & 440.884 & 101.786 & 349.082 & 39.306 & 0.797 & 139.088 & 53.938 & 0.653 & 0.653 \\
264.608 & 38.411 & 360.349 & 105.293 & 366.747 & 46.689 & 0.797 & 146.329 & 55.155 & 0.653 \\
1 vs. 3 & 271.819 & 38.721 & 0.969 & 440.884 & 101.786 & 349.082 & 39.306 & 139.088 & 0.5398 & 0.888 \\
270.760 & 35.376 & & 356.953 & 103.707 & 0.216 & 386.029 & 48.258 & 143.556 & 52.383 & 0.888 \\
1 vs. 4 & 271.819 & 38.721 & 0.320 & 440.884 & 101.786 & 349.082 & 39.306 & 139.088 & 0.5398 & 0.888 \\  & 278.053 & 32.983 & 302.151 & 103.042 & **0.023** & 390.617 & 41.371 & 0.428 & 151.233 & 55.887 & 0.016 \\
2 vs. 3 & 264.608 & 38.411 & 0.722 & 360.349 & 105.293 & 390.674 & 46.689 & 0.694 & 146.329 & 55.155 & 0.971 \\
2 vs. 4 & 270.760 & 35.376 & 356.953 & 103.707 & 0.996 & 386.029 & 48.258 & 0.694 & 143.556 & 52.383 & 0.971 \\
2 vs. 4 & 264.608 & 38.411 & 0.348 & 360.349 & 105.293 & 366.747 & 46.689 & 0.683 & 146.329 & 55.155 & 0.216 \\
278.053 & 32.983 & & 302.151 & 103.042 & 0.839 & 390.617 & 41.371 & 0.683 & 151.233 & 55.887 & 0.216 \\
3 vs. 4 & 270.760 & 35.376 & 356.953 & 103.707 & 386.029 & 48.258 & 0.687 & 143.556 & 52.383 & 0.091 \\
278.053 & 32.983 & 0.747 & 302.151 & 103.042 & 0.737 & 386.029 & 48.258 & 143.556 & 52.383 & 0.091 \\ \hline \multicolumn{11}{c}{MV—mean value; SE—standard error; VEGF—vascular endothelial growth factor; PLGF—placental growth factor; MCP-1/CCL2—monocyte chemoattractant protein 1/chemokine (C-C motif) ligand 2; IL-17A—interleukin 17A—IVA, intravitreal aflipercept. \({}^{\bf 2}\) Measurement: 1—just before the first IVA injection; 2—2–3 days after the first IVA injection; 3—immediately before the third IVA injection; 4—2–3 days after the third IVA injection.} \\ \end{tabular}
\end{table}
Table 2: Results of determinations of PIGF, VEGF, MCP-1/CCL2 and IL-17 concentrations in tested blood samples.
# Metaheuristic Approaches to Solve a Complex Aircraft Performance Optimization Problem
## 4 Genetic Programming Aided Optimization of Conceptual Aircraft Design
### Optimization Formulation, Design Variables and Constraints  
Designing aircraft structures is a challenging procedure because the weight as an objective should be minimized and the stiffness provided should supply the required structural rigidity. The structural stiffnesses are expressed as \(El_{xx}\) (flexural rigidity in the \(x\)-direction), \(El_{yy}\) (flexural rigidity in the y-direction), \(G\)) (torsional rigidity), and the mass is denoted by \(\rho S\), where \(\rho\) is the density of the air and \(S\) represents a fixed cross-section. For the constants \(E\) (Young's modulus) and \(J\) (polar second moment of area), the stiffness is proportional to the mass:  
\[\Big{(}El_{xx}+El_{yy}+G\Big{)}l\propto\rho SI \tag{10}\]  
where \(l\) is the reference length of the wing.  
Thus, in the aircraft conceptual design phase, the optimization formation can be defined as:  
\[\text{Objective : minimize }\sum\Big{(}\Big{(}El_{xx}+El_{yy}+G\Big{)}l \Big{)} \tag{11}\]  
Design Constraints: Four aeroelastic constraints and one aerodynamic constraint:  
Figure 5: (**a**) A parametric wing structure; (**b**) lifting surface in the aerodynamic model; (**c**) model for structural analysis.  
The maximum z-direction displacement at the wingtip was less than 7% of the magnitude at the semi-span. The wing deformation of the large aircraft will still obey the linear theory of elasticity in the cruise flight condition;  
The maximum torsion at the wingtip was less than 2.14\({}^{\circ}\). The elastic torsional deformation that might lead to a decrease of the local angle of attack;  
The aileron efficiency was greater than 60%;  
The flutter speed at sea level was greater than 320 m/s;  
The total drag was less than the drag obtained by the baseline design.  
Design Variables: The lower and upper bounds of design variables were determined by 90% and 110% of the baseline values [44], respectively, which are shown in Table 1.  
The optimization flowchart shown in Figure 6 demonstrates GP metamodel-assisted multidisciplinary optimization process for an aircraft conceptual design. Firstly, the Latin optimal hypercube design of experiments was used to determine the best distribution of sampling points for metamodel building. According to these design points, various jig shape models of the representative wing were generated for aeroelastic and stability analyses. Thus, the training data for GP metamodel building was obtained. By comparison of the predicted values from metamodels with the true results, more sampling points will be used to improve the accuracy of metamodels in the next iteration if the accuracy quality is not satisfactory. Finally, parametric optimization on the metamodels using SQP was performed to seek the optimal aircraft design.
# Smart Irrigation Systems from Cyber-Physical Perspective: State of Art and Future Directions
## 3 Problem Space
### IoT Layer Dimension
#### 3.1.1 Perception Layer
The rain sensor, also called _Rain Switch_, is used to shut down the irrigation system when detecting rainfall to save water. Using a rain sensor ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018) can utilize natural water resources to reduce water usage. [PERSON] et al. (2018) even recommended using a rain sensor to stop unwanted rain. According to the performance test by [PERSON] et al. (2018), a rain sensor can save around 27% of the water. In addition to these three types of sensors, other sensors (e.g., pH, water flow, light, wind, and pressure sensors) can be used in smart irrigation systems to gather more data for precise purposes. To connect with the sensors, extract measurement reading data from the sensor, and send it to the user application/API, it is necessary to select either a microcontroller or microprocessor. A microcontroller (also called MCU) consists of one or multiple CPUs (processor cores) and memory and IO peripherals. In the investigated irrigation systems, except for ([PERSON] et al., 2018), which used a Pycon LoPy4 microcontroller, and ([PERSON] et al., 2018), which used an Atmega 328 SOC Nano, 40% of the papers chose the Arduino Uno R3. NodeMCU (ESP32) is another smaller and cheaper option that can be a good choice when only three to four sensors are used. Table 3 summarized the microcontroller chosen for the IoT irrigation system.
# A Machine-Learning-Based Cyberattack Detector for a Cloud-Based SDN Controller
## 4 Integration of an ML-Based Cybersecurity Detector and Mitigator in the TeraflowSDN Controller
### Integration of the Cybersecurity Components in the TeraFlowSDN Architecture
#### 4.2.1 Distributed Attack Detector  
The Distributed Attack Detector (DAD) component monitors the network data plane for the presence of malicious network flows by receiving IP traffic from co-located packet processors. The DAD is deployed at the edge of the network (e.g., at the central office or edge data centre) to improve the scalability and response time in the attack-detection process and enable the real-time detection of malicious traffic. For this purpose, a packet processor is used to generate statistical summaries of the network flows by aggregating packets into flow-level statistics, where each flow is an aggregate of packets belonging to  
Figure 3: Cybersecurity component architecture.  
the same packet flow (same source IP address, source port, destination IP address and destination port).  
This approach of sending summary statistical characteristics of monitored traffic to the CAD also favours scalability, as it eliminates the need to send full traffic information to the centralized controller. Monitoring traffic at the IP layer is expected to use a considerable amount of bandwidth between the packet processors and the DAD, but avoiding the transmission of huge amounts of telemetry to the TeraFlowSDN controller is a major improvement in terms of preserving the network bandwidth to the controller and, therefore, improving the scalability. In addition, processing traffic at the edge of the network allows for reduced delays in the attack-detection process.  
Unlike the other components, the DAD does not expose a gRPC server but rather runs a script to obtain and process network traffic. In the current deployment, the packet processor is emulated by re-injecting network packets previously stored in a PCAP file using tcpreply, which is a standard tool available on Linux systems. The injected packets are processed with the Tstat [34] tool to generate statistical summaries of the network flows by aggregating all new packets arriving within a specific time window that can be configured.  
In addition, Tstat obtains additional information about the status of the connection and stores this information in the log file. The DAD continuously reads the information generated from this log file, processes it to extract the information in a structured way and adjusts it to apply the gRPC message format expected by the CAD. Once this is completed, the DAD communicates with the CAD to send this information.  
In the current implementation, it uses unary gRPC messages to report the traffic monitoring summary information collected by Tstat to the Centralized Attack Detector component, which makes inferences with the ML model to detect IP-level attacks. In later versions, flow messages will be implemented to avoid the delay limitations that can occur with current unary messages.
## 3 Results and Discussion
### Surface Characterization of MgO/Mg(OH)\({}_{2}\) NPs through SEM  
The surface topology of MgO/Mg(OH)\({}_{2}\) NPs was analyzed by SEM. The results showed that the average size of the MgO NPs was \(<\)50 nM, whereas the average size of the Mg(OH)\({}_{2}\) NPs was \(<\)100 nM, as shown in Figures 1 and 2, respectively. The results in Figure 1 confirm that the average particle size of the MgO NPs was \(<\)50 nM (particle sizes of 39.69 nM at a 5 \(\upmu\)m resolution and 41.67 nM at a 1 \(\upmu\)m resolution are shown). However, the average particle size of Mg(OH)\({}_{2}\) NPs was \(<\)100 nM (particles sizes of 59.53 nM at a 5 \(\upmu\)m resolution and 92.60 nM at a 1 \(\upmu\)m resolution are shown), as shown in Figure 2. The color of the MgO/Mg(OH)\({}_{2}\) NPs was white; however, the particles were irregular in shape. A particle size analysis was conducted using Image-Pro Plus based on the SEM image. Briefly, we randomly selected three regions followed by histogram-based particle selection and particle counting.  
Figure 1: SEM image of MgO nanoparticles (NPs) at (**a**) low magnification and (**b**) high magnification.
# Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency
## 3 System Models and Problem Formulation  
### Network Model  
The network architecture adopts a satellite ground network structure. In this architecture, there is a low-orbit satellite where edge computing nodes can be deployed. In the area covered by low-orbit satellites, multiple users are randomly distributed in the area. Satellite edge servers directly providing services to end users can reduce the response latency of end devices. In the network scenario, various devices such as user terminals, data monitoring devices, and IoT devices are connected to the LEO satellite network. The  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Literature** & \begin{tabular}{l} **Independent** \\ **Task** \\ \end{tabular} & \begin{tabular}{l} **Dependent** \\ **Task** \\ \end{tabular} & \begin{tabular}{l} **Satellite** \\ **Computing** \\ \end{tabular} & \begin{tabular}{l} **Local** \\ **Computing** \\ \end{tabular} & \
\begin{tabular}{l} **Ground** \\ **Computing** \\ \end{tabular} &
\begin{tabular}{l} **Resource** \\ **Allocation** \\ \end{tabular} & **Task Delay** \\ \hline
[8; 11; 12] & \(\surd\) & & \(\surd\) & \(\surd\) & & \(\surd\) & \(\surd\) \\
[10; 14; 18; 19] & \(\surd\) & & \(\surd\) & & \(\surd\) & \(\surd\) & \(\surd\) \\
[13; 15; 16; 17; 20] & \(\surd\) & & \(\surd\) & \(\surd\) & \(\surdLEO satellite undertakes the task of computing services. Due to the fact that each DAG task has different time tolerance delays, it has different priorities. Different processing methods are selected based on these priorities. As shown in Figure 1:
# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 2 Literature Review  
### Deep Learning in Image Processing with Copyright Detection  
#### 2.1.1 Overview of Classical Deep Learning Models for Image Processing  
Classical deep learning models have revolutionized image processing in recent years, with convolutional neural networks (CNNs) and generative adversarial networks (GANs) [27; 28] being at the forefront of this transformation. CNNs have become the go-to architecture for various image-related tasks, including classification, object detection, and segmentation [29; 30]. Their ability to automatically learn hierarchical features from raw pixel data has led to unprecedented performance in tasks like facial recognition and medical image analysis. GANs, on the other hand, have opened new frontiers in image generation and manipulation. By pitting a generator network against a discriminator in a minimax game, GANs can produce highly realistic synthetic images and perform tasks like style transfer and image-to-image translation [31; 32; 33]. These models have not only pushed the boundaries of what is possible in image processing but have also found applications in diverse fields such as autonomous vehicles, security systems, and creative arts. Furthermore, the ongoing advancements in these architectures continue to inspire innovative solutions for complex challenges, such as real-time image processing in dynamic environments. As researchers explore hybrid models that combine classical techniques with emerging technologies like quantum computing, the potential for even greater breakthroughs in image analysis becomes increasingly promising.
## 3 Experiment and Result
### General Windshear Observations  
In addition to the microburst, general windshears occur frequently at airports. These kinds of conventional windshears have weaker intensity compared with microbursts, but their impact on aircraft cannot be ignored.  
Two general windshear cases are introduced, which were detected on 6 May, 5:44-5:52 (UTC) and 26 May, 10:23-10:41 (UTC). These two windshears mainly affected the glidepath and runway, respectively. Figure 6 shows the wind field and windshear ramp distribution with an elevation of 3\({}^{\circ}\) on 6 May, 05:49:47-05:50:19 UTC, when a windshear event was reported by the crew 3 km away from the landing spot of 35L. There was a distinct low speed zone across the south side of the glide path, as shown in Figure 6a,b. The north wind is dominant, with the maximum wind speed exceeding 15 m/s. Figure 6b-d shows windshear center distribution on the runway, and 2 MF of 35L/17R, 3 MF and 2 MF of 35R/17L, 2 MF of 01L/19R were affected by the windshear, which led to windshear alert messages. The windshear center distribution matches the low-speed zone. The windshear alert message at 2 MF of 35L/17R was consistent with the windshear reports from the crew.  
Figure 7 shows the wind field and windshear ramp distribution with an elevation of 0\({}^{\circ}\) on 26 May, 10:33:38-10:34:21 (UTC), when two windshear events were continuously reported by the crew at the runway of 01L at 10:33 and 10:34. The radial speed and horizontal speed are shown in Figure 7a,b. The background wind was mainly westerly with the maximum wind speed exceeding 10 m/s. The runway of 01L/19R was mainly affected by the windshear. A large number of ramps was detected on the runway monitor area, as shown in Figure 7c. It was consistent with the windshear reports from aircrews. The windshear ramp center distributions of 35L/17R and 35R/17L were not drawn, for few windshear ramps were counted.  
Figure 5: Windshear center distribution on the runway due to the microburst, on 28 April, 15:54:12-15:54:42 (UTC). (**a**) 35L/17R, (**b**) 35R/17L, (**c**) 01L/19R. N is the number of windshear ramps that exceed 7 m/s. 3 MF, 2 MF, and 1 MF in the X axis mean the three monitor subareas in the landing sides, while 3 MD, 2 MD, and 1 MD mean the monitor subareas in the departure sides.  
Figure 6: The windshear event detected by the CDWL on May 6, 05:49:47–05:50:19 (UTC). (**a**) Radial speed, (**b**) horizontal speed and wind vector, (**c–e**) windshear ramps center distribution of 35L/17R, 35R/17L, 01L/19R. N is the number of windshear ramps that exceed 7 m/s.  
Figure 7: The windshear event detected by the CDWL on May 26, 10:33:38–10:34:21 (UTC). (**a**) Radial speed, (**b**) horizontal speed and wind vector, (**c**) windshear ramps center distribution. N is the number of windshear ramps that exceed 7 m/s.
# A High Performance 0.3 V Standard-Cell-Based OTA Suitable for Automatic Layout Flow
## 6 Design of the Standard-Cell-Based ULV OTA
### Automatic Layout Flow within the Cadence Innovus Tool  
The schematic of the OTA in Figure 6, made up of several \(\times\)2 and \(\times\)20 inverter gates taken from the standard-cell library, has been coded in structural Verilog. The Verilog netlist has then been imported in the Cadence Innovus tool, together with the technology files needed for the automatic place-and-route flow. A simplified non-timing-driven place-and-route flow consisting of the design import, floorplanning, placement, and routing steps performed automatically through _tcl_ scripts was then carried out to generate the layout shown in Figure 9. The area of the OTA is about 598 \(\upmu\)m\({}^{2}\) (width = 30.34 \(\upmu\)m, length = 19.68 \(\upmu\)m). To improve the matching of standard-cells implementing the input stage, the input pins have been placed close to each other at floorplan stage, thus driving the place-and-route tool to place the standard cells of the input stage close to each other.
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 4 Implementation and Analysis of Real-Time SR Algorithms in IoT Context
### Comparative Analysis  
To evaluate the applicability, we present a comparison of real-time SR implementations in IoT systems using key metrics presented in Table 2.  
This comparison highlights the trade-offs between performance metrics across different SR methods implemented on a common IoT platform. Here, the ESPCN offers a better balance, surpassing the SRCNN in image quality (PSNR: 26.9, SSIM: 0.852) while consuming the same amount of energy (750 mW) and exhibiting shorter latency. The approach from [PERSON] et al. adopted a balanced approach, achieving an improved SSIM (0.869) with 95 ms latency and 700 mW energy usage while maintaining a PSNR of 25.6. NeuriCam performed better than the other approaches (PSNR: 26.5, SSIM: 0.872) albeit with the highest latency (200 ms) and energy usage (850 mW).
## 3 Experimental Setup
### Experimental Description  
In our experiments, we do not need the phrase transcription to obtain the corresponding alignment because one phrase dependent HMM has been trained with the background partition using a left-to-right model with no skip states, which has been developed with models of 10, 20, 40 and 80 states for each phrase. With these models, we can extract statistics from each utterance of the database and use this alignment information inside our DNN architecture. As inputs to train the alignment models and to the DNN, we employ 20-dimensional MFCC with their first and second derivatives as features to obtain a final input dimension of 60. Then, an energy based voice activity detector is used over them. On these input features, we apply a data augmentation method called Random Erasing [23], which helps us to avoid overfitting in our models due to the scarceness of data in this database.  
We have evaluated different configurations of the DNN architecture, which we refer to as the front-end part of the system. In addition, we also have evaluated the interaction of these architectures with the alignment and the utterance duration by means of several HMM sizes. Finally, we have extracted supervectors as a combination of front-end and alignment with a flattened layer, and, with them, we have obtained speaker verification scores by using a cosine similarity metric without any normalization technique.  
A set of experiments was performed using Pytorch [24] to evaluate our system. We compare a front-end that uses an average pooling similar to [4; 5], and the acoustic feature input directly aligned with the HMM alignment; thus, we obtain the traditional supervector. Additionally, we have evaluated several front-end architectures with four different configurations: one convolutional layer with a kernel of dimension 1 equivalent to a dense layer but keeping the temporal structure and without adding context information, one convolutional layer with a kernel of dimension 3, three convolutional layers with a kernel of dimension 3, and four convolutional layers with a kernel of dimension 3. These configurations for the kernel size and the number of layers have been selected as simple as possible since, as we mentioned in Section 2.2, deep neural networks have not achieved good results in this task.  
In addition, in this work, we have used as a reference system the traditional supervector instead of an i-vector based system due to the limitations of i-vectors for text-dependent tasks in the RSR2015 database, as pointed out in [25; 26], where the authors concluded that these kinds of datasets do not have enough data to train the traditional i-vectors extractors properly.