esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Experimental Analysis
### FBG Spectrum Reconstruction and DIC Method Verification  
In this section, the reflection spectra of FBGs in an experimental test state are modeled and analyzed. The simulation reflection spectra of the FBGs obtained by the modified transfer-matrix method, as well as the bonded strain transfer coefficient (the strain transmissibility from the base aluminum alloy substrate to the fiber core) are then compared with the experimental reflected spectra. In order to test the accuracy of the FBG reflection spectrum simulation modeling method, the strain obtained by DIC simulation is used for the method validation data, as shown in Figure 4.  
In line with previous research, the strain transfer efficiency was considered as a = 0.9 when fibers were bonded using epoxy resin adhesive. The influence of strain transfer between the glue and the sensors was incorporated into the modified strain distribution. The modified strain was equal to the product of the strain transfer efficiency and the strain extracted by the DIC method. The corrected sensor strain information was then substituted into the improved transfer-matrix algorithm as an input in order to obtain a simulation spectrum of higher accuracy. Finally, the simulated spectrum (as shown in Figure 5) was compared with the experimental spectrum (as shown in Figure 6) to verify the accuracy of the strain reconstruction.  
By comparing the reflection spectrum obtained by the simulation method (Figure 5) with the reflection spectrum obtained by the actual experiment (Figure 6), it can be clearly seen that the aforementioned simulation method based on the strain transfer coefficient and improved transfer-matrix method using FBG physical parameters can effectively simulate the experimental FBG reflection spectrum curve. In light of this finding, a verification of the experimental FBG data in terms of the strain reconstruction method based on the GWO algorithm can be carried out. This method of verification provides a basis for the next step in reconstructing the experimental strain distribution of FBGs.
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 5 Evaluation
### Performance Metric Measures  
The performance was obtained from the perspective of RA delay, successful access probability (SP), and waiting time distribution. Delay measures the time difference between when users seize an RAO, and when the decision notification by the access point arrives. More technically, in a four-way RA, it is the time difference between messages 1 and 4; and in a two-way RA, it is the difference between messages 1 and 2 of the RA procedure. SP measures the chance of successful transmission of a user given that many users are competing to seize an RA request preamble (opportunity). The number of RA preambles sent gives the number of times the user has been keeping retrials before access is granted.  
A normalized cumulative reward function for a number of episodes can be formulated as follows:  
\[R=1/\sum_{i=1}^{|E|}1/D_{i}, \tag{26}\]  
where \(|E|\) represents the magnitude of the episodes per iteration.
# A Study of Landfast Ice with Sentinel-1 Repeat-Pass Interferometry over the Baltic Sea
## 3 Materials and Methods
### InSAR Methodological Approach  
In this study, the repeat-pass InSAR pair acquired by Sentinel-1 with a small perpendicular baseline is used to produce a displacement map. The Sentinel's Application Platform (SNAP) software (version 5.0) was primarily used for processing the satellite SAR data, calculating coherence, and producing the interferogram. In addition, Quantum Geographic Information System (QGIS, 2.14.11-Essen version) software is another tool used in this research.  
Interferogram processing chain starts with choosing two suitable SLC products in the study area. IW2 sub swath is selected based on our area of interest. Data products are available in VH and VV polarizations. Both polarizations are applied, although VV polarization is suitable for this research regarding high coherence magnitude level. By co-registering two SAR images (one master image and one slave image) into one stack, pixels in slave image move to align with pixels in the master image. In this step, we apply an orbit correction and use the DEM with back-geocoding co-registration. A schematic overview is shown in the co-registration block diagram (Figure 5). Next step is the interferogram formation and the coherence estimation. Interferometric phase and coherence are calculated in this step. Applying TOPSAR deburst removes demarcation area between every two bursts.  
An optimal unwrapping result and increased fringe visibility (before unwrapping) are goals in any InSAR processing. Reaching these goals is possible by applying suitable multi-looking and additional phase-filtering of the interferogram. Goldstein phase filtering is applied for phase-filtering in this study. As there are some low coherence areas (such as sea and changed land surface), extracting a high coherence area is the best option for increasing the quality of unwrapped interferogram.  
Statistical-cost Network-flow Algorithm for PHase Unwrapping (SNAPHU) is applied for phase unwrapping [19]. Further, the unwrapped phase is converted to a displacement map. Applying terrain correction, re-projection and re-sampling are final steps to obtain a geocoded product. Figure 6 shows our methodology steps.  
Figure 5: Co-registration block diagram.
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 4 Robust Watermarking Algorithms Based on Original Videos
### Video Watermarking in Transform Domain
#### 4.2.2 DWT-Based Watermarking Algorithms
In addition, the group search optimization (GSO) algorithm is applied to optimize the selected positions. In another past paper [51], scene change analysis was applied to detect the motion part of color video, and then 3D DWT was performed over these frames to obtain wavelet coefficients. By using a spread spectrum technique, the watermark is embedded into selected 3D coefficients of HL, LH, and HH sub-bands. The experimental results show that the algorithm has good transparency and robustness. [PERSON] et al. [52] proposed a robust video watermarking algorithm using significant frame selection (SFS) based on lifting wavelet transform (LWT) coefficient difference. Significant frames are selected according to the mathematical relationship among the number of original video frames, the size of coefficient blocks, and the embedding capacity. Using LWT, the luminance component Y of the selected frame is decomposed into three levels, and the LH\({}_{3}\) sub-band coefficients are obtained for watermark embedding. Although DWT-based watermarking algorithms can be better when combined with HVS, its robustness to translation attack and scaling attack is weak. The summary comparison of several watermarking algorithms in DWT domain is shown in Table 4.
## 5 Analysis of the Transmission Delay
### End-To-End Delay for One Transmission Path  
Substituting Equation (10) into Equation (9), we can obtain the expected end-to-end delay \(E[T^{\rm c}]\) for one constructed routing path as:  
\[E[T^{\rm c}]=d_{\rm SD}\frac{\rm E[T_{\rm L}]}{\rm E[D_{\rm L}]}, \tag{38}\]  
where the derivations of \(\rm E[T_{\rm L}]\) and \(\rm E[D_{\rm L}]\) are introduced in Sections 5.3 and 5.4, respectively.
# Benchmarking Anomaly Detection Methods for Extacardiac Findings in Cardiac MRI
## 4 Results and Discussion
### Unsupervised Attention-Based Methods  
Regarding attention-based methods, their low accuracy in both pixel-level and image-level classification tasks is evident in Table 2 and further supported by their anomaly maps (Figure 7). In the case of expVAE [30], the generated anomaly maps do not correlate with potential abnormality zones. Instead, they tend to highlight certain anatomical structures without a clear connection to actual anomalies. As described in Section 2, this method is based on the VAE architecture, where the KL divergence term in the loss function is expected to be minimized to ensure that the posterior matches the prior distribution. In theory, this minimization should result in latent embeddings for normal regions having values close to zero, with abnormalities appearing as deviations from this value (i.e., higher absolute values). In this setup, the KL divergence loss term serves as a form of regularization. However, in practice, minimizing the reconstruction error led to an increase in the KL divergence term, suggesting that more representative and stochastically independent latent embeddings were required to capture the wide anatomical variations present in normal samples. Consequently, higher latent embedding values may not necessarily indicate abnormalities but rather reflect the natural diversity of anatomical structures. A similar performance degradation was reported by [PERSON] et al. [18], particularly on the CheXpert dataset, where expVAE results were comparable to those of a random classifier.  
Following the same principles, AMCons [20] also failed to perform effectively, showing a tendency to highlight hyper-intense regions rather than true abnormalities. Moreover, the entropy regularization term introduced to constrain the activation values of normal feature maps may have inadvertently degraded reconstruction quality, causing oscillations between improving reconstruction fidelity and increasing entropy in activation maps. As a consequence, it performs poorly in datasets where anomalies are not characterized by high pixel intensity, as observed in this study and previously noted in [18].
# The Bimodal Neutron and X-ray Imaging Driven by a Single Electron Linear Accelerator
## Appendix A Electron Linear Accelerator
### Shielding and Collimator  
A 10-meter-long vacuum tube is designed for neutron transport and collimation. Eleven ring-shaped neutron absorbers made of boron carbide ceramics are placed inside the vacuum tube with an interval of 1 m between each other to collimate the neutrons traveling inside the tube. The tube's outer surface is covered by boron-containing rubber with 60 wt.% boron to absorb the neutrons that may escape from inside the tube. The detector is placed in a shielded container, from inside to outside, by 10 cm lead, 0.5 cm boron-containing rubber, and 30 cm boron-containing polyethylene. The S/B (signal neutrons versus background neutrons, where signal neutrons are the neutrons that travel directly from the heavy water converter, and background neutrons stand for those undergo scattering in the circumstances) ratio of this system is larger than 200.
## 2 Related Work
It is very different from the previous consideration of the number of hops on the transmission path. In the non-duty cycle network, the sender can select the nearest neighbor node from the sink to forward the data every time. There is a different situation in the network based on the duty cycle. When the sender has data to transmit, the nearest neighbor node from the sink in its forwarding nodes set is not necessarily in the active state. At this point, the sender has two options. One is to continue to wait for the nearest neighbor node from the sink in the forwarding nodes set until it wakes up. In this way, the distance of transmission is far each time, so the number of hops needed for the data transmission to the sink is less, but each hop takes a long time to wait. The two are not waiting, but are transferring the data to a neighbor node that is in the active state and is closer to the sink than the sender. Although the distance of one hop in the transmission is not very large, more hops are needed in the transmission path to the sink. But compared to the first choice, the delay of each hop is smaller. It can be seen how to select the appropriate forwarders is a very complex problem. From another point of view, [PERSON] et al. solved the problem of the forwarding node selection in the delay minimization [42]. They abstracted the selection of forwarding nodes in the sleep-wake cycle WSNs into the Asset Selling Problem (ASP) [42], and proved that this is a NP complete problem. It can be seen that it is a challenge issue to reduce delay in duty cycle based WSNs. A novel solution is proposed in this paper from a new point of view. We notice that when the sender sends data, there are multiple candidate forwarding nodes, therefore only one node in these nodes wakes up so that data can be forwarded. This inspires the following idea: if the duty cycle of a part of the forwarding nodes is set to 1, then they will always be in active state. Although the energy consumption of the node with a duty ratio of 1 is large, the duty cycle of most nodes is still smaller because of the small proportion of the nodes set to 1. Therefore, the energy consumption of the network is not high and it can effectively reduce the sleep delay. More importantly, we found that in WSNs the energy consumption in the area near the sink is high and the energy consumption of the nodes far away from the sink is small. According to the related research, the area far away from the sink left eighty percent of the network energy. Therefore, we make full use of the energy of the nodes far away from the sink to select a certain proportion of nodes and set the duty cycle of them to 1. It is not fixed that the duty cycle of some nodes is always 1. Thus, this method makes full use of the network energy, balances the energy consumption of the network, effectively reducing the delay, and does not affect the life of the network, which is very important for the resource strained network [62; 63; 64]. The method proposed in this paper is also innovative.
# Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines
## 5 Experiment and Evaluation
### Performance of the Proposed Method
#### 5.2.3 Multi-Level Threshold Segmentation  
At this stage, the process separates the iced transmission line from a complex background containing objects and other noise. Pixels are generally grouped into various regions (objects and backgrounds) in this method. Figure 23 shows the results of the multi-threshold segmentation.  
Figure 23 shows that our method successfully clusters pixels into different regions (object and background). After dividing several regions in the multi-threshold stage, the image successfully recognizes transmission line icing objects. At the same time, the background at this stage is eliminated.
## 3 Application in Age-Development fMRI Data
### Dataset and Preprocessing  
The fMRI data utilized in this study were sourced from the age-development (children versus adults) movie-watching dataset released by [14]. The selection of this passive stimulus fMRI dataset, as opposed to task-evoked or resting-state fMRI datasets, was driven by two primary considerations. Firstly, passive stimulus perception has been observed to significantly reduce head motion, resulting in enhanced imaging quality compared to task-evoked fMRI setups [22]. Secondly, given that all subjects viewed the same movie clip, we assumed near-identical stimulus perception across all subjects, facilitating group-wise analysis without necessitating intricate between-subject alignment techniques typically employed in resting-state fMRI datasets [23].  
In the age-development study, a total of 155 subjects participated, comprising 33 adults and 122 children. During the MRI acquisition session, all subjects were instructed to watch a silent 5.6-min animated movie titled \"Partly Cloudy\" [24]. Structural and functional MRI data were acquired using a three-Tesla scanner equipped with a standard 32-channel head coil. The anatomical images of each subject were subsequently normalized to the Montreal Neurological Institute (MNI) template. Following the removal of motion-correlated artifacts, each subject's neuroimaging time course consisted of 149 time intervals with a repetition time (TR) of 2 s. To facilitate preprocessing, skull stripping was performed using the individual T1-weighted (T1w) reference image, followed by spatial normalization to the widely used nonlinear MNI152 template, specifically, the MNI152 nlin2009 ascym template (the default spatial template used in the fMRI-prep application for fMRI image preprocessing: [[https://fmriprep.org/en/stable/spaces.html](https://fmriprep.org/en/stable/spaces.html)]([https://fmriprep.org/en/stable/spaces.html](https://fmriprep.org/en/stable/spaces.html)) (accessed on 1 May 2022)).
## 3 Results and Discussion
### Cytocompatibility Analysis  
Cell proliferation of electrospun membranes at different degrees of crosslinking was determined by CCK assay after culturing for 7 days. As shown in Figure 7, for all samples, the cell density increased with prolonged culturing time. No statistical significance on the first day of culturing was observed between the crosslinked samples and the control of TCP substrate, which implied no observable initial inhibition of cell proliferation for all the samples. This was most likely due to the washing step by glycine aqueous solution [43]. After 3 days of culturing, the number of human umbilical vein endothelial cells (HUVECs) on the electrospun membranes after 18 and 24 h crosslinking was remarkably higher than that after 6 and 12 h crosslinking. It was hypothesized that the significantly increased cell adhesion was a result of a higher degree of crosslinking. The higher degree of crosslinked membranes showed a lower extent of swelling and a corollary increased modulus. It is reported that increased modulus enhances cell adhesion [47]. In addition, the surface hydrophilicity of material will affect the attachment, proliferation, migration, and viability of many different cells. It was reported that surfaces with moderate hydrophilicity (30\({}^{\circ}\) to 70\({}^{\circ}\) related to cells and materials) are most conducive to cell adhesion and spreading [48]. The surface water contact angles were measured and are shown in Figure S4. As crosslinking time increased from 6 h to 24 h, the average water contact angle increased from 26.6\({}^{\circ}\) to 39.0\({}^{\circ}\) which may support the cell adhesion. Moderate hydrophilicity (Figure S4), increased modulus (as shown in Figure 4c), along with the interstitial spaces (as shown in Figure 3c), would better resemble the extra cellular matrix and provide better physical support and surface, thus upgrading the cell proliferation. It has been reported that the potential cytotoxic of GA may be due to the residues of unreacted GA and leaching out as the materials degrade. In this paper, the thorough glycine quenching followed by crosslinking, which can maximally inactivate the residual aldehyde groups of GA molecules, was proved to be helpful in alleviating the potential cytotoxic of GA crosslinking [18]. The high adhesion under 60.6% crosslinking indicates that the morphology of collagen fibers is retained and cellular interaction is enhanced.
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 4 Improved Saturation Attack Method
### The Triggering Phase
#### 4.4.2 Adaptive Timeouts
The solid arrow represents that the packet can hit the inserted flow rule. After generating a flow rule with the initial timeout value, we wait for 2 s after the previous one and send a probing packet, which triggers a new flow rule installation. Now, we do not know if its timeout is larger than the initial value, so the state is marked as unknown. We wait 2 s and then send a testing packet. The testing packet can hit the flow rule, proving that the new flow rule has a larger timeout than the initial one. After that, we send packets at increasing intervals (3 s and 4 s), and both can hit. A new table-miss occurs when the packet interval is increased to 5 s. The testing packet with 5 s indicates that the assigned timeout value of the new rule is larger than 5 s. The following packets with 6 s and 7 s can match the flow rule, which indicates that the timeout can cover packets with 7 s as intervals. Then the third table-miss occurs because the timeout cannot cover 8 s. Similarly, testing packets with 8 s, 9 s, and 10 s verify that the third triggered flow rule has a larger timeout value. Finally, after we wait 11 s and send a probing packet, the triggered flow rule cannot cover the next testing packet with the same intervals. We can conclude that the maximum timeout is 10 s. Note that the attacker only needs to perform this process one time to figure out the state transition, and this process only takes tens of seconds in this case. The above steps incur only a few packet-in messages to probe the maximum timeout, as well as acquiring one transition path to reach the state of maximum timeout. During the attack, steps can be further simplified from the acquired state transition diagram. Testing packets matching corresponding flow rules can be skipped to trigger flow rules, which are marked with solid arrows in Figure 7. For instance, the attacker can induce the controller to trigger a flow rule with the maximum timeout value quickly by sending only three packets with intervals of 2 s, 5 s, and 8 s. To prevent continuous packet-in messages, an attacking flow can intentionally generate a series of data packets with short inter-arrival times to  
Figure 6: An example of state transition diagram for a flow table entry. refresh the idle timer, thereby maintaining the flow rule state for a certain period of time before generating a table-miss message.
## 4 Discussion
Otherwise, regardless of the fact that the molecules are covalently bonded to the substrate, molecular and/or pseudomolecular ions would be generated by ToF-SIMS. Only organosilane molecules are capable of the homocondensation reaction [46], leading to -Si-O-Si- polymerization in addition to a condensation reaction for a portion of their silanols that will be anchored to the SiO\({}_{2}\) substrate [18; 19]. Our ToF-SIMS results verified that the -Si-O-Si- network eliminates the formation of ions with the alkyl group bonded to either Si or SiO\({}_{n}\). A plausible explanation for this is that the -Si-O-Si- network is strong enough so that alkyl groups are fragmented before the network can be fragmented. As a result, both hydrocarbon ions (Figures 4c, 5c and 7c) and silicon oxide clusters (Figure 7c) were detected. If all silanols were anchored to the substrate (i.e., without the formation of the cross-linked -Si-O-Si- network), ions with an alkyl group bonded to Si or SiO\({}_{n}\) would be observed, perhaps more or less similar to that of the OPA SAMs case [33; 34]. FTIR spectroscopy has also been used to study both anchoring and polymerized Si-O bonding at the interface of organosilane SAMs on silicon oxide substrates [25; 47]. Molecular simulations have been carried out to understand the interplay between the anchoring Si-O bonding and the polymerized -Si-O-Si- network, as well as its impact on the orderliness of SAMs [37; 48; 49]. Differentiating between these two scenarios is not straightforward for organosilane SAMs formed on silicon oxide. ToF-SIMS is considered capable of providing unique information about the interface chemistry of organosilane SAMs in that the ion fragmentation patterns may be different. This leads to a ToF-SIMS analytical approach that not only detects diagnostic ions, but also identifies a lack of them if the system has a reference to compare with. Future work includes experiments on OTMS SAMs formed on non-silicon oxides so that the silicon oxide cluster ions Si\({}_{n}\)O\({}_{2n+1}\)HH\({}^{-}\) (\(n\) = 2-5) serve as markers for the polymerized -Si-O-Si- network, while ions containing Si, O and the constituent element of the substrate can be regarded as the anchoring bond. This will thus provide opportunities for gaining more specific information towards a better understanding of the condensation and homecondensation reactions of silanols of the hydrolyzed OTMS molecules, as well as the development of more ToF-SIMS analytical approaches to explore the interface chemistry of organosilane SAMs.