esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 2 Background
### Sdn  
Network programmability, manageability, and flexibility are to be enhanced by SDN [16], a novel idea that separates the control plane from the data plane. SDN centralizes control logic in a software-based controller, whereas the data plane consists of simple forwarding devices that follow controller commands. The [PERSON] et al.-proposed OpenFlow protocol [17] has become the de facto standard for data plane device-to-controller communication in SDN.  
There are many advantages to the SDN architecture in terms of network security. Because the control logic is centrally located, SDN allows for a global view of the network and facilitates the adoption of standardized security standards throughout the network. Because SDN is programmable, it is feasible to dynamically alter security measures in response to changing network circumstances and to swiftly implement countermeasures against known dangers, including DDoS assaults. Figure 1, which shows the basic architecture of a SDN network, emphasizes the division between the control plane and the data plane.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attack** & **Description** & **Mitigation** \\ \hline
**NS Flooding** & Overflowing target with NS messages & Rate limiting, validation \\ \hline
**NA Spoofing** & Faking NAs to poison cache & Cryptography like SEND \\ \hline
**RA Flooding** & Excess RAs consume resources & RA rate limiting \\ \hline
**Malicious RAs** & Wrong configuration and routes & RA filtering and monitoring \\ \hline
**Replay Attacks** & Reusing captured messages & Timestamps, sequence numbers \\ \hline
**IPv6 Reconnaissance** & Discovering devices via NDP & Isolation, firewall rules \\ \hline \hline \end{tabular}
\end{table}
Table 4: NDP attacks and mitigations.
# Complementing Solutions to Optimization Problems via Crowdsourcing on Video Game Plays
## Implementation
### Game 1: Scheduling Problems  
#### 3.2.1 Background for the Scheduling Problems  
Scheduling problems consist of finding assignments, called schedules, for a set of jobs on a set of machines. Each machine can process at most one job at a time, and each job can be processed on at most one machine at a time. There is a wide range of scheduling problems, with different characteristics and levels of complexity ([[http://www2.informatik.uni-osnabrueck.de/knust/class/](http://www2.informatik.uni-osnabrueck.de/knust/class/)]([http://www2.informatik.uni-osnabrueck.de/knust/class/](http://www2.informatik.uni-osnabrueck.de/knust/class/))). They can be divided into a three-field classification, in terms of the nature of the machines, that of the jobs, and optimality criteria [34]. The first field has two components: the machine type, and the number of machines, m. The machine type, in turn, can be either:  
1. Identical parallel machines, where any machine can process any job without impacting processing time.
2. Open shop, where each job has an operation for each machine, but where the order of operations within a job is irrelevant.
3. Job shop, where any machine can process any operation, but where operations must be completed in an specified order. The second field characterizes the execution of jobs; it can be either:  
1. Preemption, which indicates whether the processing of a job can be interrupted and later resumed.
2. Presence of limited resources, which means that the processing of a job is constrained to the use of limited resources.
3. Precedence constraints, which imposes ordering constraints in the execution of jobs.
4. Bound on the number of operations, which indicates there is a constant upper bound on the number of operations per job.
5. Bound on processing time, which conveys whether the processing time for each job is constant, bounded, or unbounded.  
The third field, optimality criteria, provides a way to compare various schedules. Optimality criteria can be based on either the maximum value (optimizing the performance of each machine) or the sum (optimizing the performance of all machines overall) of metrics such as completion time, lateness, or tardiness.  
For the scheduling game, participants solved a variety of example problems with different numbers of machines and with jobs that consist of more than one operation. Operations could be executed only on a specific machine.
# Clinical Applications of Optical Coherence Angiography Imaging in Ocular Vascular Diseases
## 6 Iris Neovascularization
### Limitations of OCTA for Iris Neovascularization  
Adapting OCTA for the anterior segment to detect iris neovascularization has a few limitations. Firstly, current OCTA systems are not optimized for the anterior segment [10; 80]. The scanning protocols have to be altered and specialised anterior segment adaptive lenses have to be used [78; 80]. The field of view is also limited compared to angiography techniques at present [78].  
Secondly, anterior segment OCTA is incapable of registering scans and providing localization required for comparison of serial scans [1; 81]. Additional image processing software is still to overcome this limitation.  
Anterior segment OCTA is also prone to artefact. Motion artefacts are common in anterior scans due to saccadic eye movements. There is currently a lack of motion correction software and still an unmet need for specialised eye-tracking systems built specifically for the anterior segment [10; 82]. Current OCTA software is also designed to image the posterior segment, which can result in non-parallel segmentation and artefacts when used for the anterior segment due the curvature of the cornea [83]. Additionally, anterior OCTA is also prone to artefacts from hyper-reflective structures such as corneal fibrosis or scarring [80]. Vessels in the superficial layers can also create projection artefacts in the deeper layers, which can be misinterpreted by the software as abnormal or additional vessels [5]. Therefore, it is important to compare the scans with serial scans, corresponding structural OCT, and slit lamp photographs.  
In OCTA, dense iris pigmentation affects visualization of deeper stromal vessels, as also seen in FA and ICG [5]. As NVI develops on the surface of the iris, distinction of NVI by OCTA is much easier in eyes with darker irises. Conversely, light irises require more careful evaluation of en face angiographic images to differentiate between physiological and pathological vessels. Out of these options, ICG still has better penetration through pigment as it is performed using near-infrared wavelengths [6].  
Lastly, anterior segment OCTA is also unable to visualise deeper vessels in eyes with corneal opacities, dense iris pigmentation, or vessels in thick iris tumours [81]. The systems also have poorer detection of vessels with minimal flow since erythrocytes are slower in small calibre vessels and may be below the threshold of detection, which is dependent on the A-scan rate of the system [80]. OCTA is also unable to differentiate between arteries and veins [84]. As OCTA algorithms are optimized for the posterior segment, which has mainly transverse flow in vessels, anterior segment vessels with axial flow may also not be detected [85].
## 3 Application to Gimpo Airport
### Three Types of Topology
#### 3.2.2 Satellite Topology  
A satellite topology has one or more TLOF pads connected to a gate distributed on a circumference of the TLOF pad. In this layout, gates below the flight path cannot be used and are suitable for installation on rooftops and grid-type districts of cities and suburb square spaces [22]. As shown in Figures 19 and 20, the satellite topology can have a  
Figure 17: Linear topologies for the Gimpo Airport parking lot that comply with the FAA’s new regulations: (**a**) current parking space; (**b**) expanded parking space.  
Figure 18: Linear topologies for the Gimpo Airport parking lot that comply with the EASA’s new regulations: (**a**) current parking space; (**b**) expanded parking space.  
relatively large number of gates per TLOF pad compared to the linear topology, and the aircraft throughput in the TLOF pad is high because the guidance distance is shorter than that of the pier topology.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
###### Abstract  
Erbium-Doped Fiber Amplifiers (EDFAs) are fundamental to optical communication networks, providing signal amplification while introducing noise that affects system performance. Accurate noise figure estimation is critical for optimizing link budgets, monitoring optical Signal-to-Noise Ratio (OSNR), and enabling real-time network optimization. Traditional analytical models, while computationally efficient, often fail to capture device-specific variations, whereas machine-learning-based approaches require large training datasets and introduce high computational overhead. This paper proposes a polynomial regression model for real-time EDFA noise figure estimation, striking a balance between accuracy and computational efficiency. The model leverages Generalized Least Squares (GLS) regression to fit a multivariate polynomial function to measured EDFA noise figure data, ensuring robustness against measurement noise and dataset variations. The proposed method is benchmarked against experimental measurements from multiple EDFAs, achieving prediction errors that are within the measurement uncertainty of Optical Spectrum Analyzers (OSAs). Furthermore, the model demonstrates strong generalization across different EDFA architectures, outperforming analytical models while requiring significantly less data than deep-learning approaches. Computational efficiency is also analyzed, showing that inference time is below 0.2 ms per evaluation, making the model suitable for real-time digital-twin applications in optical networks. Future work will explore hybrid modeling approaches, integrating physics-based regression with Machine Learning (ML) to enhance performance in high-variance spectral regions. These results highlight the potential of lightweight polynomial regression models as an alternative tocomplex ML-based solutions, enabling scalable and efficient EDFA performance prediction for next-generation optical networks.  
Keywords:EDFA; noise figure; polynomial model; machine learning; optical networks
# Towards Large-Scale Fast Reprogramable SOA-Based Photonic Integrated Switch Circuits
###### Abstract  
Due to the exponentially increasing connectivity and bandwidth demand from the Internet, the most advanced examples of medium-scale fast reconfigurable photonic integrated switch circuits are offered by research carried out for data- and computer-communication applications, where network flexibility at a high speed and high connectivity are provided to suit network demand. Recently we have prototyped optical switching circuits using monolithic integration technology with up to several hundreds of integrated optical components per chip for high connectivity. In this paper, the current status of fast reconfigurable medium-scale indium phosphide (InP) integrated photonic switch matrices based on the use of semiconductor optical amplifier (SOA) gates is reviewed, focusing on broadband and cross-connecting monolithic implementations, granting a connectivity of up to sixteen input ports, sixteen output ports, and sixty-four channels, respectively. The opportunities for increasing connectivity, enabling nanosecond order reconfigurability, and introducing distributed optical power monitoring at the physical layer are highlighted. Complementary architecture based on resonant switching elements on the same material platform are also discussed for power efficient switching. Performance projections related to the physical layer are presented and strategies for improvements are discussed in view of opening a route towards large-scale power efficient fast reprogrammable photonic integrated switching circuits.  
photonic integrated circuits; SOA; fast optical switching; re-programmability; packet switching +
Footnote †: journal: Applied Sciences
# Characterization and Finite Element Modeling of Microperforated Titanium Grade 2
## 3 Results and Discussion  
### Tensile Test Results  
Microperfated specimens with longitudinal and transversal orientations show similar cracks, but specimens with a diagonal orientation look different. It can be concluded that cracks always move along the shortest path between two microholes (Figure 8).  
Figure 8: Comparison of crack surfaces.  
A comparison of microperforated and non-microperforated tensile test results is shown in Figure 9. Based on these results, it can be concluded that microperforation significantly reduces the elongation and the elastic modulus. These reductions can be explained by the reduction in resistant area and the concentration of stresses caused by the holes. It is important to highlight that elongation reduction depends on the orientation: 20% in longitudinal, 17% in diagonal, and 31% in transversal.  
Counterintuitively, yield stress and ultimate tensile strength increase slightly in microperforated material in the three orientations. Yield stress increases by 3-5% depending on orientation, and ultimate tensile strength near 3%. The most relevant attributes of stress-strain curves are summarized in Table 3. The explanation for these results is unclear. Only three repetitions per case were conducted, and the increments are small; thus, it could be a consequence of random variation rather than a real effect of the microholes.  
Titanium grade 2 is anisotropic, and the introduction of microperforation does not change this fact. Figure 10 shows the average tensile test results for perforated material  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Orient.** & \begin{tabular}{c} **Elastic Modulus** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **UTS** \\ **(MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Yield Stress** \\ **(MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Elongation** \\ \end{tabular} \\ \hline \multirow{3}{*}{BM} & Long. & & 460 \(\pm\) 4 & 330 \(\pm\) 3 & 0.420 \(\pm\) 0.010 \\  & Trans. & 115,000 & 439 \(\pm\) 4 & 397 \(\pm\) 7 & 0.435 \(\pm\) 0.033 \\  & Diag. & & 426 \(\pm\) 2 & 365 \(\pm\) 3 & 0.440 \(\pm\) 0.003 \\ \hline \multirow{3}{*}{PM} & Long. & & 481 \(\pm\) 3 & 342 \(\pm\) 4 & 0.335 \(\pm\) 0.005 \\  & Trans. & 82,000 & 461 \(\pm\) 1 & 410 \(\pm\) 1 & 0.300 \(\pm\) 0.005 \\  & Diag. & & 439 \(\pm\) 3 & 373 \(\pm\) 4 & 0.365 \(\pm\) 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tensile properties of titanium grade 2.  
Figure 9: Average tensile test results. Engineering values.  
in the three orientations. These results indicate substantial differences between the flow curves, especially in terms of yield stress, ultimate tensile strength, and elongation. For example, the maximum tensile strength is reached in the longitudinal direction and the highest elongation in 45\({}^{\circ}\) specimens. These results are consistent with those obtained by [PERSON] et al. (2016).
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 6 Security Analysis
### Information Integrity
#### 6.1.1 Compromised Drones  
In our UAV communication model, there are two different types of drones. The first type is the monitoring drones \(D\); these drones are enrolled in the monitoring groups to scan specific subareas and mainly collect useful information for the rescue operations. There are \(k\) number of groups that are responsible for each task in every subarea, where \(k\) is varied according to the size and importance of the subarea; \(CS\) receives several copies from the same piece of data from different groups. If an adversary \(\mathcal{A}\) compromises a drone \(D_{a}\) from group \(G_{u}\), there are several other drones in the same group that are sending the same data to the cluster heads. Thus, \(CS\) still can distinguish the false data from the received chain of messages.  
If that compromised drone by the chance is one of the cluster heads for the reading round, the two messages received from the two heads are not identical. \(CS\) in that case rejects the two sets and reports that group as a malicious one. However, \(CS\) still obtains the required information from the other honest groups in the subarea. The \(CS\) follows the same procedure if \(\mathcal{A}\) compromises both cluster heads or even compromises the whole group. If \(\mathcal{A}\) compromises several groups, i.e., \(CS\) receives different sets of values for the same reading, then \(CS\) accepts the majority. We assumed that \(\mathcal{A}\) can only compromise a limited number of groups and the probability that the attacker compromises a large number of groups is low.  
The second group of drones is the relays \(R\), which are drones flying back to the station, i.e., to recharge their batteries. Relays are forwarding the messages from the cluster heads to \(CS\). If \(\mathcal{A}\) compromises a relay drone \(R_{x}\), he/she should know all IDs for the monitoring drones and cluster heads for all transmitted messages from different groups to be able to fabricate the forwarded messages, i.e., \(\mathcal{A}\) has to decrypt all the messages and extract the concatenated IDs, which is an NP-hard problem. If \(\mathcal{A}\) attempts to destroy the relay \(R_{x}\) and prevent it from reaching \(CS\), or block the forwarded messages from it, there are plenty of other relays to perform the same job. For example, on average, the drone scans the area for 15 min before flying back to recharge.  
In addition, all drones have to follow their assigned location and data features; if \(\mathcal{A}\) attempts to redirect the drone to another location, its \(LF\) is different than that assigned by the operator. Thus, a malicious action is detected, while, if \(\mathcal{A}\) compromises the drone and inserts his/her fake information, he/she does not follow the determined routine by the operator, i.e., does not insert a data feature \(DF\), and just encrypts the fake data by \(CS\)'s public key. Consequently, the drone is declared as compromised device and the operator does not relay on its information.
## 1 Introduction  
Named entity recognition (NER) is a fundamental step in information extraction that recognizes and classifies certain predefined linguistic expressions, mainly proper nouns such as people, place names, and organization names, in unstructured text to determine the range of entity names and classify which entity names belong to that range [1]. These NERs are often composed of a nested structure. Traditional NER techniques are limited in their ability to capture hierarchical entity relationships, particularly from an information extraction perspective. Flat NER techniques have mainly been studied for technical reasons.  
Nested NER is an approach that analyzes the nested structure in which a named entity representation contains another named entity representation to overcome traditional flat NER [2]. However, the problem is that NER techniques are mainly studied for flat NER, and few training data have been built for nested NER.  
To address these issues, this study proposes a label description embedding (LDE) model and a nested NER model robust to few-shot learning environments. The LDE model modifies the output layer of the existing biaffine nested NER model into a label attention layer that can use the semantic information of labels by encoding their descriptions.
## 3 Results
### Scanning Electron Microscopy  
Figure 3 shows the scanning electron microscopy (SEM) images of the morphology of the DPSCs on the surfaces of the different materials after 120 h of culture. These images revealed that the DPSCs were able to attach to each material. They covered the surface of PRM, BIO, and WRP with spindle-shaped, flattened fibroblastic morphologies  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **(U/L Protein)** \\ \hline
**Group** & **2D** & **ULA** \\ \hline Basal media & 1.68 (\(\pm\)0.02) \({}^{\text{A}}\) & 1.42 (\(\pm\)0.02) \({}^{\text{A}}\) \\ Osteogenic media & 3.21 (\(\pm\)0.78) \({}^{\text{C}}\) & 2.75 (\(\pm\)0.04) \({}^{\text{BC}}\) \\ PRM & 3.01 (\(\pm\)2.78) \({}^{\text{C}}\) & 3.25 (\(\pm\)0.15) \({}^{\text{C}}\) \\ BIO & 2.03 (\(\pm\)0.14) \({}^{\text{AB}}\) & 2.63 (\(\pm\)0.44) \({}^{\text{B}}\) \\ WRP & 2.69 (\(\pm\)0.52) \({}^{\text{BC}}\) & 2.54 (\(\pm\)0.01) \({}^{\text{B}}\) \\ \hline \end{tabular} Values are written as means (\(\pm\) standard deviation). Values with different capital and lowercase letters within each column and row represent statistically significant differences (\(p<0.05\)).  
\end{table}
Table 4: Alkaline phosphate activity in the experimental groups.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **(Percentage of Control, \%)** \\ \hline
**Group** & **1 Day** & **3 Day** & **7 Day** \\ \hline IRM & 106.09 (\(\pm\)12.01) \({}^{\text{A}}\) & - & - \\ PRM & 107.46 (\(\pm\)4.39) \({}^{\text{AB}}\) & 130.04 (\(\pm\)9.06) \({}^{\text{Ab}}\) & 184.75 (\(\pm\)4.48) \({}^{\text{BC}}\) \\ BIO & 135.59 (\(\pm\)17.75) \({}^{\text{Ba}}\) & 181.62 (\(\pm\)15.05) \({}^{\text{B}}\) & 228.81 (\(\pm\)6.78) \({}^{\text{Cc}}\) \\ WRP & 91.31 (\(\pm\)10.40) \({}^{\text{A}}\) & 144.09 (\(\pm\)13.34) \({}^{\text{Ab}}\) & 127.83 (\(\pm\)17.87) \({}^{\text{Ab}}\) \\ \hline \end{tabular} Values are written as means (\(\pm\) standard deviation). Values with different capital and lowercase letters within each column and row represent statistically significant differences (\(p<0.05\)).  
\end{table}
Table 3: Cell viability in ULA groups.  
Figure 2: Alizarin red S staining of the DPSCs that were cultured in 2D and ULA culture plates for 21 days.  
(red asterisks in Figure 3). However, in contrast to PRM and BIO, on the WRP surface, fibrobl morphology was barely visible, and cloud-like clustering of dead DPSCs was observed extensively under low magnification (red arrow in Figure 3). The DPSCs on PRM appeared flatter and broader in shape than the other groups.
## 3 Parameter Selection Experiment
### Extraction of Feature Parameters  
The types of traditional time-domain feature parameters in AE signals are multitudinous, among which the energy, duration, count numbers, rise time, arrival time, and peak amplitude have been widely studied (Figure 3). Thus, they are chosen to explore the variation regulation with moisture content. The definitions of each AE parameter are as follows:  
* Energy: The area under the signal envelope.
* Duration: The time interval between the signal first coming over the threshold until it attenuates to the threshold.
* Count numbers: The number of times the signal comes over the threshold.
* Rise time: The time interval between the signal first coming over the threshold and reaching amplitude.
* Arrival time: The time point at which the signal first comes over the threshold.
* Peak amplitude: The maximum amplitude of the signal.  
The AE wave amplitudes after the signal processing board and AD conversion module were mainly concentrated between 800-1000 mV. Wave amplitudes exceeding a defined threshold should be considered a useful signal, and the signals below the threshold should be considered noise [34]. By measurement, the amplitude of electromagnetic interference noise on the signal processing board was 160 mV approximately, and the threshold voltage was set to 180 mV. Hence, the noise can be filtered, and the AE can be validated.  
Figure 2: Experimental system.  
There were 100 samples of each rock sample with different moisture content, which were numbered 1-100. The samples' number was taken as abscissa, and the parameters were taken as ordinate. The feature parameters of samples with different moisture contents are shown in Figure 4.  
Figure 4: Feature parameters of samples with different moisture contents. (**a**) is Energy parameter; (**b**) is Duration parameter; (**c**) is Counts Number parameter; (**d**) is Rise Time parameter; (**e**) is Arrival Time parameter; (**f**) is Peak Amplitude parameter.  
Figure 3: Feature parameter of the acoustic emission (AE) signal.