esa-sceva/satcom-chunk-collection huggingface dataset:
# Segmentation of Brain Tumors from MRI Images Using Convolutional Autoencoder
## 1 Introduction
Compared to these two databases, the others contain significantly fewer images, so we choose the dataset that contains 3064 images, since more data reduce the possibility of overfitting. Tumor segmentation on MRI images is mainly performed by grouping the most similar image pixels into several classes [2; 10; 16], as well as based on establishing a pixel intensity threshold [23], using super-pixel level features and kernel dictionary learning method [11], and using different CNN architectures [6; 7; 8; 9; 23; 24]. In their paper, [PERSON] et al. [23] presented the architecture of autoencoders for semantic segmentation of brain tumors from images from the same database used in this paper. An autoencoder with an image reduction of four times the input size was realized, and a pixel classification accuracy of 93.61% was achieved. For the same database, [PERSON] and [PERSON] [17] achieved a pixel classification accuracy of 95.9% using methods for extracting features from images, selecting the right features using a Genetic Algorithm, and a Simulated Annealing Algorithm. Pixel classification was performed using Support Vector Machines. In this paper, we present a new convolutional neural autoencoder (CNA) architecture for brain tumor semantic segmentation of three tumor types from T1-weighted contrast-enhanced MRI. The network performance was tested using combinations of two databases (original and augmented), and two different data division methods (subject-wise and record-wise), and two evaluation methods (5-fold cross-validation and one test). The results are presented using the histograms and mean and median values for metrics for pixel classification. A comparison with the comparable state-of-the-art methods is also presented. The best results were obtained by training the network on the augmented database with record-wise data division. The experiment shows that the proposed network architecture has obtained better results than the networks found in the literature trained on the same image database.
## 5 Simulation Results  
### Simulation Environment  
For the simulation environment, we utilized MATLAB 24a running on a Windows 11 platform with 16 GB of RAM and a Core i7-11700 processor. In our simulations, we simulated the ANN algorithm and SVM classifier (linear SVM, coarse Gaussian SVM) and utilized multiple-trained models for the ensemble learning and gossip learning framework. To fully observe the effectiveness of ensemble learning, we simulated two SVM variants and ANN algorithms, which were utilized in the voting mechanism of ensemble learning.
## 3 Numerical Examples
### Coupled Dynamic Analysis of Turbine and Support Structure  
In this example, the coupled analysis of the NREL 5 MW offshore wind turbine and the jacket support structure as mentioned in Section 3.1 was performed. The four piles supporting the jacket structure were fixed on the seabed. The support structure with 70.15 m height drove through a 50 m water depth and extended 20.15 m above the mean sea level. The environmental conditions with 2.8 m wave height and 6.07 seconds wave period were taken into account.  
The X-SEA program was used to determine the hydrodynamic forces and calculate the responses of the substructure by interchanging the displacement, velocity, acceleration, force and moment at the interface point with the FAST v8 program. Between these two programs, these responses were  
Figure 8: Comparison of the dynamic reaction moment capacity on the jacket support structure about the x-direction resulted from X-SEA and FAST v8 programs.  
Figure 6: Comparison of the dynamic lateral reaction force capacity on the jacket support structure in the y-direction resulted from X-SEA and FAST v8 programs.  
Figure 7: Comparison of the dynamic vertical reaction force capacity on the jacket support structure in the z-direction resulted from X-SEA and FAST v8 programs.  
exchanged using the present study module. The displacement of the coupled and uncoupled analysis method was monitored below the interface joint for obtaining the accurate responses of the structure in x- and y-directions as shown in Figures 11 and 12. It is obvious from these plots that, in the coupled analysis the interaction between the support structure and the tower structure was accounted for, the averaged oscillation of the structure was therefore 6.9 times less than that of the uncoupled analysis. In addition, the coupled analysis of the turbine and structure produced a result close to the realistic behavior of a wind turbine in the test site, which includes the turbine effect while the computational time was only slightly different.  
Figure 11: Comparison of displacements in the x-direction resulted from coupled and uncoupled analyses from the X-SEA program.  
Figure 9: Comparison of the dynamic reaction moment capacity on the jacket support structure about the y-direction resulted from X-SEA and FAST v8 programs.
## 2 Related Work  
### Bias-Corrected Fuzzy C-Means (BCFCM)  
A brain MRI can be easily corrupted by noise, so an FCM model that does not consider spatial information cannot accurately segment such images. [PERSON] et al. [22] modified FCM by introducinga spatial constraint as the regularization term to allow central pixel \(x_{k}\) to be determined by pixels \(x_{r}\) in its immediate neighborhood. The objective function of BCFCM is given by  
\[J_{BCFCM}=\sum\limits_{i=1}^{c}\sum\limits_{k=1}^{N}u_{ik}^{m}\|x_{k}-v_{i}\|^{2 }+\frac{\alpha}{N_{R}}\sum\limits_{i=1}^{c}\sum\limits_{k=1}^{N}u_{ik}^{m}\sum \limits_{r\in N_{k}}\|x_{r}-v_{i}\|^{2}, \tag{1}\]  
where \(u_{ik}^{m}\) represents the membership function, \(v_{i}\) is the cluster center, \(N_{k}\) stands for the set of neighbors, and \(\alpha\) is a parameter that needs to be manually adjusted to control the influence of the regularization term. The objective function can be minimized in a way that is similar to the standard FCM model, so the minimization of \(u_{ik}\) can be expressed as  
\[u_{ik}=\frac{\left(\|x_{k}-v_{i}\|^{2}+\frac{\alpha}{N_{R}}\sum\limits_{r\in N _{k}}\|x_{r}-v_{i}\|^{2}\right)^{-\frac{1}{(m-1)}}}{\sum\limits_{j=1}^{c} \left(\left\|x_{k}-v_{j}\right\|^{2}+\frac{\alpha}{N_{R}}\sum\limits_{r\in N _{k}}\left\|x_{r}-v_{j}\right\|^{2}\right)^{-\frac{1}{(m-1)}}}. \tag{2}\]  
Then, the minimization of \(v_{i}\) can be expressed as  
\[v_{i}=\frac{\sum\limits_{k=1}^{N}u_{ik}^{m}\Bigg{(}x_{k}+\frac{\alpha}{N_{R}} \sum\limits_{r\in N_{k}}x_{r}\Bigg{)}}{(1+\alpha)\sum\limits_{k=1}^{N}u_{ik}^{ m}}. \tag{3}\]  
Though BCFCM integrates spatial information, noise pixels still destroy the central pixel because of the same weight for each pixel in the immediate neighborhood. Further, the regularization term needs to be computed during each iteration, which takes much more time than other models.
# Reconstructed Prototype Network Combined with CDC-TAGCN for Few-Shot Action Recognition
###### Abstract
Since action samples are represented as time sequences, this study follows the approach used in DASTM [2], employing Soft-DTW [3] distance. Soft-DTW approximates the discrete values in the DTW formula with continuous values, allowing distance metrics to be trained and yield smoother results. In terms of the encoder, its role is to transform sample features from high-dimensional to low-dimensional while preserving semantic information as much as possible. The action sequences encompass spatial attributes in the form of coordinate information and temporal attributes in the form of frame information. Traditional encoders (such as RNNs [4], CNNs [5; 6], and LSTMs [7]) employ single-step convolutions, resulting in limitations in effectively fusing spatial and temporal features. Thus, this study selects the spatiotemporal encoder comprising spatial and temporal convolution processes. Regarding time convolution, previous research often employed simple 2D-CNN convolutions. However, vanilla convolution can only extract surface-level temporal features from the sequences. This study introduces the spatiotemporal encoder CDC-TAGCN, which leverages the advantages of CDC [8] principles, redefining the time convolution operator, known as CDC-TCN. CDC-TCN can compute the discrepancies between each feature within a convolution window and the window's mean value while performing vanilla convolution operations. These discrepant values capture the variations in the current convolution window's features. The method of mean-based computation enhances the model's ability to resist interference from temporal noise. For action recognition datasets, they fall into two categories: video datasets and skeleton datasets. Skeleton datasets have the following advantages compared to video datasets: (1) Their data volume is smaller than the video dataset but contains rich and critical semantic information. (2) They remain unaffected by background and lighting conditions. This study chose NTU-RGB + D 120 [9] and Kinetics [10] as datasets; they are currently the most commonly used and challenging data sets. The contributions of this paper can be summarized as follows:  
1. The SRC module of RC-PN leverages cosine similarity between samples to reconstruct the weight coefficients of samples, reducing the interference of high-noise samples. 2. A novel loss is proposed to adjust the distances between different classes and within the same class, which solves the misclassification problem arising from minimal distributional differences. 3. Introducing a novel spatiotemporal encoder called CDC-TAGCN, in which the time convolution operator, CDC-TCN, retains the vanilla convolutional feature information and extracts discrepant features during convolution.
## 3 Proposed Method
### Objective Functions
First and foremost, a quantizer \(Q_{c}\) is created using the discretization scheme \(\mathcal{L}_{c}\) and the feature selection vector \(\mathbf{p}_{c}\). An LM-WLCSS classifier can thus be trained on the training dataset. Although the objective function \(f_{5}\) is completely independent of the classifier construction, an infeasible solution situation may be encountered due to the negativity of the rejection threshold \(\omega_{c}\), as stated in (19). In contrast, evaluation procedure continues, and from the elected class template \(T_{c}\) and the rejection threshold, it follows the objective function \(f_{3}\). As previously mentioned, the decision variable \(h_{c}\) must be locally investigated. When the coefficient of variation \(\frac{\mu^{(c)}}{\sigma^{(c)}}\) is different from zero, the procedure increments the value of \(h_{c}\) from 0 to \(\frac{\mu^{(c)}}{2\times\sigma^{(c)}}\) with a step of \(\frac{\mu^{(c)}}{2\times 10\times\sigma^{(c)}}\) because a high amplitude of the coefficients can nullify the rejection threshold. For each coefficient value, the previously constructed LM-WLCSS classifier is not retained. Only updating the SearchMax threshold, clearing the circular buffer (variable \(B_{c}\)), and resetting the matching score are necessary. Here, the greater objective function \(f_{1}\) obtained value (i.e., the best-obtained classifier performance) and its associated \(h_{c}\) are preserved, and the evaluated solution \(\mathrm{x}\) and objective function \(\mathbf{F}(\mathrm{x})\) are updated in consequence.
# Research on Hard, Transparent and Hydrophobic Coating on PMMA Sheet
## 4 Conclusions  
In this paper, targeting the application of a self-cleaning coating on PMMA substrate for deep-sea optical windows, nano SiO\({}_{2}\) particles modified organic silane coatings were successfully prepared. The chemicals, surface microstructure, wettability, hardness, adhesion, transparency, water scouring resistance as well as microorganism attachment behavior of the coatings were investigated. The results showed that adding SiO\({}_{2}\) nanoparticles into the organic silicon coating can effectively improve the hydrophobicity due to generating a micro-nano structure surface. However, excessive addition would result in a decrease in hydrophobicity, adhesion as well as transparency, due to the inorganic SiO\({}_{2}\) particle destroying the integrity of the organic coating. The optimal coating was the S05 coating, which possessed a water contact angle of 114.2\({}^{\circ}\), hardness of 4H, adhesion level of 0, and visible light transmittance of 0.886. After 40-h water scouring, both the water contact angle and the visible light transmittance were slightly decreased, which were 108.3\({}^{\circ}\) and 0.839 respectively, implying good water scouring resistance. Moreover, the microorganism attachment rate of the S05 coating was about half that of the PMMA substrate, suggesting good application prospects for deep-sea optical windows.  
Conceptualization, G.W.; methodology, X.X. and X.C.; formal analysis, P.Z., S.G. and W.C.; Writing--review and editing, X.Z.; Project administration, R.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Early-Stage Key Technology Research Project of Cold Seepep Equipment from the Chinese Academy of Science (LQ-GJ-01).  
Not applicable.  
The data that were used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.
# The Multiband over Spatial Division Multiplexing Sliceable Transceiver for Future Optical Networks
## 3 MB(oSDM) S-BVT Architecture and Definition
architecture, that can be based on programmable filters capable to aggregate/disaggregated different MB slices [43]. However, due to low level of maturity of this technology, alternative solutions based on static/non-reconfigurable band pass filters (BPFs) can be considered. The receiver of the proposed MB S-BVT can be based on different implementations, such as DD or coherent detection, trading-off performance versus complexity/cost [22; 42]. Analogue-to-digital converters (ADC) are included in the transceiver architecture to digitize the OFDM analogue signal, to perform the receiver DSP operations. These include RF down-conversion, data parallelization, CP removal, fast Fourier transform implementation, equalization, symbol de-mapping, and serialization. The presented sliceable architecture will enable to dynamically allocate its available MB bandwidth into smaller, independently manageable contributions, that can also be transmitted through different fibre spatial channels (i.e., fibre cores, modes) enhancing overall flexibility, scalability, and providing an efficient utilization of the MB and spatial resources. Thanks to the transceiver modularity and sliceability the MB flow can be disaggregated in an intermediate network node to transmit different set of bands/sub-bands, corresponding to different users/slices, to different fibre cores and destinations. The available MB and spatial resources can be dynamically adjusted and reallocated as needed, and the different contribution of the MB S-BVT can be sliced to different users or applications based on their specific requirements. Hence, capacity and performance will be maximized, leveraging the spatial and frequency domains, while meeting the diverse needs of various users and services. On the top of that, the adoption and implementation of a software defined networking (SDN) control plane and the corresponding SDN agents will be crucial to successfully adapt and configure the proposed transceiver solution [40].
## 2 General Design and Features of the Instrument v2021
### Satellite Communications  
After the recorded data are processed, the data (composed of the Welch wave acceleration spectrum between \(f_{min}\) and \(f_{max}\); the estimates for \(Hs\), \(T_{z}\), and \(T_{c}\); as well as UTC timestamp information) are packed into a binary packet. In order to reduce the size of the binary packet, the estimates for \(Hs\), \(T_{z}\), and \(T_{c}\) are transmitted as 32-bit floats, while the Welch frequency bins are discretized into renormalized 16-bit unsigned integers. The renormalization of the Welch bins is performed relative to the peak value of the spectrum, which is transmitted as a 32-bit float within the packet. This allows to significantly reduce the size of the binary packets and the overall iridium costs while keeping a high accuracy for the data transmitted.  
In addition to the wave spectrum, geographical positioning is obtained with a simple GNSS module, and is also used to generate accurate UTC reference times and to periodically recalibrate the real time clock of the microcontroller to avoid time drift. The GNSS data are packed, buffered, and transmitted using an efficient binary encoding, similar to what is done for the wave data.  
The sample rate of both the GNSS and the wave measurements can be adapted through 2-way iridium communication. In addition, the real time clock present on-board the microcontroller is used to make sure that the measurements are performed at fixed hours and minutes. In order to reduce iridium costs and energy consumption, the firmware can pack several binary packet packages together before transmitting these as a single iridium message. As all iridium communications are buffered on the microcontroller, any data not transmitted due to failure in iridium communication can be retransmitted at the next transmission attempt to prevent data loss. When an iridium communication is established, the instrument transmits a burst of several messages, sending back to the user all the information stored in the binary message buffers.  
A simple binary protocol decoder, available as a Python module, is provided alongside the code for the instrument firmware. This, together with the web interface or web API offered by modern Iridium providers (in our case, we use Rock Seven Mobile Services Ltd, though other providers would be possible), allows great flexibility and ease-of-use for the end user. Those tasks can easily be automated, and we provide a custom bash script for performing https requests directly to the server of RockSeven, which allows us to retrieve all the iridium messages received over a user-selected time span as a csv database.
## 3 Results
### Coastal Flood Analysis of Ria of Arosa
Overall, it can be observed that the tombolo of El Grove (where the Umia-O Grove intertidal complex is located), the Isla de Arosa, and the surroundings of the Umia  
Figure 9: (**A**) Flood risk map of SE sector of Ria of Arosa. (**B**) Flood risk map of the SE sector of the Ria de Arosa in which the urban areas have been added. River have a larger inundation area. Additionally, upon closer examination of the map, it is noteworthy that El Grove is at risk of the tombolo becoming submerged, effectively separating this municipality from the mainland and turning it into an island. Similarly, the Isla de Arosa is at risk of potentially becoming three separate islands in the future. Creating detailed maps that focus on urban centers and urbanized areas in general allows for an examination of how different degrees of flood risk affect these zones (Figure 10). In this case, one can observe the urban centers of Villanueva de Arosa, Cambados, El Grove, and Sangenjo, which are areas with the highest population density (Table 2). These areas are also subject to the flood risk that encompasses the same zones explained previously. Using the flood risk map and the 2018 land use map, it is possible to assess exposure to risk by considering several parameters: municipality area, population size, population density, elevation, and distance from the coast of each municipality (Table 2). First, the urbanized areas, including urban centers, industrial areas, and infrastructure, can be identified using the land use map. Geographic information system (GIS) tools can be employed to delineate these areas and calculate the square kilometers of urbanized land. By applying geospatial techniques, the exposed area can be easily determined. Figure 10: Detailed maps of the risk of flooding in the urban centers of each municipality. The results of flood risk exposure in the Ria of Arosa for different scenarios indicate that the coastal sector with the largest urbanized area exposed to the risk of marine flooding is 1.90 km\({}^{2}\) in El Grove, where the population density is 494.46 inhabitants/km\({}^{2}\), and the flood risk could potentially affect 944 people. However, the greatest population exposed to the risk is on the Isla de Arosa, where 947 people could be affected by flooding in an exposed urbanized area of 1.36 km\({}^{2}\) (Figure 9B). Summing up all the results, it is estimated that in the studied area of the Ria of Arosa, there is a total of 7.45 km\({}^{2}\) of urbanized area exposed to the risk of flooding, putting 3707 inhabitants at risk. This represents approximately 6.46% of the total population in the studied area.
## 5 Conclusions  
We propose a Neural Network-based approach inspired by the operation of the feedback control system to improve the localisation of autonomous vehicles and robots alike in challenging GPS deprived environments. The proposed approach is analytically compared to the INS specifically in scenarios characterised by hard braking, roundabouts, quick changes in vehicular acceleration, motorway, sharp cornering and successive left and right turns. By estimating the displacement and orientation rate of the vehicle within a GPS outage period, we showed that the Neural Network-based positioning approach outperforms the INS significantly in all investigated scenarios, by providing up to 89.55% improvement on the displacement estimation and 93.35% on the orientation rate estimation.  
Nevertheless, we encountered the problem of poor model generalisation due to the varying characteristics of the sensor noise and
# Seismic Risk Analysis of Existing Link Slab Bridges Using Novel Fragility Functions
## 2 Seismic Reliability Framework
### Fragility Functions
#### 2.2.2 Fragility Functions of Concurrent Failure Mechanisms  
Being a multi-component system, a bridge can manifest diverse damage mechanisms involving different structural elements and these can combine to produce a variety of damage scenarios. For instance, a seismic event might produce the attainment of the ultimate conditions on both piers and bearings; thus, one might be interested in quantifying the likelihood associated with such a combined failure event, in particular expressing its probability conditional to a given intensity level (IM = \(im\)). Such a particular type of fragility function can be mathematically expressed as follows:  
\[F_{M_{l}}(im)=P[M_{l}|\text{IM}=im] \tag{3}\]  
where \(M_{l}\) (with \(l=1,\ldots N_{M}\)) is one of the \(N_{M}\) potential failure scenarios including one or more damage mechanisms. In other terms, \(M_{l}\) are disjoint partitions of the global failure set, \(F\), and the probability of the latter, \(P[F]\), is given by the sum \(\sum_{l}P[M_{l}]\).