esa-sceva/satcom-chunk-collection huggingface dataset:
# Performing Fatigue State Characterization in Railway Steel Bridges Using Digital Twin Models
## 4 Bridge Case Study
### Description of the Bridge  
The bridge under study, named Varzea Bridge, is located in Aveiro district, Portugal, at km +59.478 along the Beira Alta international railway line (Figure 9). The bridge, constructed in 1958, is entirely steel, except for one of the columns and the abutments, which are of stone. The bridge's main structure consists of five continuous spans of \(50.5+50.5+60\times 3+50.5\) and an additional simple supported span of 20 m at one end. The bridge also has four trapezoidal-shaped steel truss columns with heights ranging from 10.10 m to 30.20 m and a stone column with 12.20 m height (on the side of the additional simple supported span) and stone abutments.  
The continuous deck is made up of two inverted warren trusses 5.95 m high and 4.40 m wide connected by transversal bracings at two levels, lower and upper, connecting the truss's lower and upper chords, respectively, and a robust upper transversal girder at the edge of each truss panel which supports the longitudinal track girders. Regarding the cross sections and connections of the various components, the structure has both simple and composite section profiles, and the connections are generally riveted. The characterization  
Figure 8: Data-flow concept.  
of the main elements that compose the truss structure of the bridge and are the target of fatigue assessment is presented in Table 1.  
The implementation of the proposed model and the results are presented in the next subcha  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Member**} & **Designation of Corresponding** & \multirow{2}{*}{**Cross Section**} \\  & **Connection Detail in FAS** & & \\ \hline \multirow{4}{*}{Inferior flanges} & sec 1 (general section) & 2 UPN400 + 13 mm flange & \\ \cline{2-4}  & sec 2 (close to intermediate columns) & 2 UPN400 + 20 mm flange & \\ \hline \multirow{4}{*}{Diagonals} & sec 3 (general section) & 2 UPN350 & \\ \cline{2-4}  & sec 9 (close to intermediate columns) & 2 UPN400 & \\ \hline \multirow{4}{*}{Vertical posts} & sec 4 (general section) & (297 \(\times\) 18) \(\times\) (348 \(\times\) 10.5) mm & \\ \cline{2-4}  & sec 10 (close to intermediate columns) & 560 \(\times\) 30) \(\times\) (348 \(\times\) 25) mm & \\ \hline \multirow{2}{*}{Cross- girders} & sec 5 & (688 \(\times\) 28) \(\times\) (297 \(\times\) 15) mm & \\ \hline Superior flanges & sec 8 & 2 UPN400 + 12 mm flange & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main members of the bridge truss structure and characterization of the respective cross-sections.  
Figure 9: Várzea Bridge: (**a**) overview of the bridge; (**b**) elevation view of the bridge; (**c**) general bridge section.
# Segmentation of Liver Tumors by Monai and PyTorch in CT Images with Deep Learning Techniques
## 3 Materials and Methods  
### ResUNet Architecture  
The UNet model is effective in liver tumor segmentation, but it has a tendency to produce negative results, resulting in an empty mask. As a result, we prefer using the ResUNet model, which is a type of neural network architecture specifically designed for medical image segmentation tasks. The ResUNet design expands upon the UNet architecture used for object detection and image segmentation in deep learning. The ResUNet model combines UNet and deep residual learning architectures, making it well-suited for medical image analysis tasks requiring precise localization and segmentation. It can handle large-scale medical images with high resolution and complex structures. It has been used in various medical image segmentation tasks such as brain tumor segmentation, organ segmentation, cell segmentation, and lesion detection. ResUNet comprises an encoding network, a decoding network, and a bridge that links those networks. Like UNet, ResUNet incorporates a gateway, encoding connections, and decoding system. The UNet employs two 3 \(\times\) 3 convolutions with ReLU activation functions. ResUNet employs pre-activated residual blocks, as shown in Figure 1. This structure maintains an encoding network, decoding network, and bridge while substituting the previously mentioned layers with pre-activated residual blocks.  
Encoder: This part of the network is used to increase the number of feature maps and reduce the input spatial dimensions of the input image by capturing high-level features of  
Figure 1: ResUNet architecture representation.  
the image. The encoder is responsible for capturing features from the input data. It consists of multiple convolution layers and max pooling layers.
* Residual Connection: This component is added to the traditional UNet architecture to address the issues of invisible gradients and enhance network stability. The residual connections allow gradients to flow more easily through the network, improving network accuracy.
* Decoder: This network section is tasked with up-sampling from the encoder feature map and producing segmentation maps corresponding to the input image data. Numerous convolutional and up-sampling layers are produced by the decoder.
* Skip Connections: In the ResUNet architecture, skip connections allow information from the encoder to bypass the residual connection and be directly concatenated with decoder feature maps, enabling the network to maintain the fine features of the input image.  
The ResUNet model produces a segmentation map, where each pixel is labeled, indicating whether there is a liver or a background image. The training network uses a supervised learning approach to predict accurate labels for a set of annotated images. The network weights are adjusted iteratively to minimize the difference between the predicted labels and ground-truth labels.
# Asymmetric Quantum Multicast Network Coding: Asymmetric Optimal Cloning over Quantum Networks
## 2 Materials and Methods
### Optimal Asymmetric Quantum Universal Cloning Machine
\begin{split} U_{1\to 3}^{(\alpha,\beta,\gamma)}|\psi\rangle=& \sqrt{\frac{d}{2d+2}}[\alpha|\psi\rangle_{A}(|\[PERSON]_{d}^{+}\rangle_{BR}|\[PERSON]_ {d}^{+}\rangle_{CS}+|\[PERSON]_{d}^{+}\rangle_{BS}|\[PERSON]_{d}^{+}\rangle_{CR})\\ &+\beta|\psi\rangle_{B}(|\[PERSON]_{d}^{+}\rangle_{AR}|\[PERSON]_{d}^{+} \rangle_{CS}+|\[PERSON]_{d}^{+}\rangle_{AS}|\[PERSON]_{d}^{+}\rangle_{CR})\\ &+\gamma|\psi\rangle_{C}(|\[PERSON]_{d}^{+}\rangle_{AR}|\[PERSON]_{d}^{+} \rangle_{BS}+|\[PERSON]_{d}^{+}\rangle_{AS}|\Phi_{d}^{+}\rangle_{BR})].\end{split}  \tag{10}\]  
Here, \(\alpha,\beta,\gamma\) are non-negative real parameters which are used instead of the weight \(\{w_{i}\}_{i=1}^{3}\) and satisfy the following constraint [71; 79; 80]:  
\[\alpha^{2}+\beta^{2}+\gamma^{2}+\frac{2}{d}(\alpha\beta+\beta\gamma+\alpha \gamma)=1. \tag{11}\]  
In terms of \(U_{ABCRS}\), a \(1\to 3\) optimal asymmetric UQCM \(\varepsilon_{1\to 3}^{(\alpha,\beta,\gamma)}\) can be written as:  
\[\varepsilon_{1\to 3}^{(\alpha,\beta,\gamma)}(|\psi\rangle\langle\psi|):= \operatorname{Tr}_{RS}\Bigl{(}U_{1\to 3}^{(\alpha,\beta,\gamma)}|\psi\rangle \langle\psi|_{A}U_{1\to 3}^{(\alpha,\beta,\gamma)\ \dagger}\Bigr{)}. \tag{12}\]  
The fidelities between an input state and each reduced density matrix, which were proved to be optimum [71; 79; 80], are given as follows:  
\[ \begin{split} F_{A}&=1-\frac{d-1}{d}\biggl{(}\beta ^{2}+\gamma^{2}+\frac{2\beta\gamma}{d+1}\biggr{)},\\ F_{B}&=1-\frac{d-1}{d}\biggl{(}\alpha^{2}+\gamma ^{2}+\frac{2\alpha\gamma}{d+1}\biggr{)},\\ F_{C}&=1-\frac{d-1}{d}\biggl{(}\alpha^{2}+\beta ^{2}+\frac{2\alpha\beta}{d+1}\biggr{)}.\end{split}  \tag{13}\]  
We refer [79] for the figure depicting behavior of \(F_{A},F_{B}\), and \(F_{C}\) with respect to \(\alpha,\beta\) and \(\gamma\).
# Experimental Evaluation of Unicast and Multicast CoAP Group Communication
## 2 Group Communication
### Hybrid Group Communication
#### Group Membership Management  
Another important feature that our solution provides is the multicast group membership management. This means that whenever a multicast entity is created, the EM assigns an IPv6 multicast address to the entity and automatically requests the individual members to join the multicast group. The EM verifies that the members have joined the group and informs the client about the assigned multicast address. Since the assigned multicast addresses can be globally routable, the client can communicate with the multicast group directly without contacting the EM. In order for members to be able to join the group, the members' constrained CoAP implementation has been extended with a new resource for group management. This approach essentially implements group communication as proposed by IETF CoRE in the groupcomm draft, but with the addition of group management functionality through the entity manager. Once the group configuration has taken place, any client can interact with the group using its multicast address without the need to be aware of the presence of the EM. In addition, once all management has taken place, the EM can disappear from the network, leaving behind an operational network with configured multicast group communication possibilities. A mixed form is also possible, where clients use unicast to go to the EM, but the EM uses multicast to go the members. This way, a client can still benefit from other EM features, such as response processing. Details about the group membership management implementation will be provided in Section 4.1.
## 3 Results and Discussion
### Validity of LC Measurement  
The linearity of the calibration curves for each concentration of glucose, allose, and raffinose and the corresponding correlation coefficient (R) were calculated (Table S1). The LOD and LOQ were calculated from the calibration curves. The peak separation was evaluated by calculating the resolution (Rs). For reproducibility and precision evaluation, repeated measurements (\(n=12\)) were performed, and the relative standard deviations (RSD) were calculated. The calibration curves for glucose, allose, and raffinose had R\({}^{2}\) values of 0.998, 0.999, and 0.999, respectively, with good linearity. The LOD (s/a = 3.3) and LOQ (s/a = 10) were glucose: 0.29 ng/mL and 0.89 ng/mL, respectively; allose: 0.24 ng/mL and 0.72 ng/mL, respectively; and raffinose: 0.68 ng/mL and 2.07 ng/mL, respectively.
## 4 Results
### Rate of Change  
As shown in the new extracted shoreline of chart 6428 (Figure 9), the chart needs to be updated with new shorelines. Since the river is very dynamic in this location, a second aspect of the project is to evaluate the rate of change on the river to better understand the frequency at which the chart would need to be updated. As illustrated in Figure 11, by using data from the Landsat archive from 1972, 1982, 1992, 2003, 2011 and a Sentinel-2 image from 2018, a rate of change of approximately 220 m per decade was determined in the most dynamic areas within chart 6428. The rate of change was calculated by measuring the maximum distance from the vectors of the shoreline extracted from the 1972 and 2018 images. For this 46-year period, the maximum distance from the two vectors is around 1 km, which represents close to 22 metres of change per year. To better predict future changes in the river, data for every decade (1972 to 2018) were also estimated. This approach is needed to ensure that the shoreline changes were not introduced by one major event, such as a landslide. Based on the extracted shorelines, we can see in Figure 11 that although some decades, 1982 to 1992 for example, exhibit more changes, there is a gradual change in the river shoreline between each decade. Therefore, we can suspect that this general trend will continue to occur in the future and thus special attention will be required in those areas of the river to ensure that the charts are up to date. The detail at which a cartographer can update a map/chart is half a millimetre at chart scale. The scale of chart 6428 is 1:50,000; as such, 0.5 mm on the chart represents 25 m on the ground. With a rate of change of approximately 22 m/year, this chart would need to be revaluated at least every two years.  
Figure 10: Demonstration of how optical and radar imagery are used to illustrate dry areas and islands in charts, with (**a**) the optical Landsat-8 image from 28 July 2017 displaying an island and the RADARSAT-2 image from 9 October 2015 displaying a dry area; (**b**) segmentation produced using the optical Landsat-8 image overlaid on the RADARSAT-2 image; and (**c**) a classification of the combined images displaying the Mackenzie River, lakes, land and drying areas. Imagery © 2015, U.S. Geological Survey and RADARSAT-2 Data and Products © MacDonald, Dettwiler and Associates Ltd. (2014/2016) – All Rights Reserved. RADARSAT is an official mark of the Canadian Space Agency.
## 5 Simulation Analysis
### Simulation Results of S21 Transmission Coefficient With and Without Block  
The S21 transmission coefficient findings for both designs, with and without the block, illustrate the mutual coupling behavior between the antenna components spanning the frequency range of 1 GHz to 15 GHz (Figure 16: (a) with the block and (b) without the block). Mutual coupling denotes the electromagnetic interaction among antenna components, potentially resulting in interference and diminished performance. The mutual coupling coefficient continuously stays below \(-15\) dB in all arrangements, indicating no electromagnetic interference among the antenna parts. A coupling value of \(-15\) dB suggests that less than 3.16% of the power is transmitted between components, hence maintaining adequate isolation and mitigating substantial signal deterioration. The antenna with the block exhibits minimal mutual coupling, indicating that the block facilitates improved isolation and minimizes undesirable electromagnetic interactions among the components. This enhanced isolation guarantees that each antenna element functions autonomously, unaffected by signals from adjacent components, thereby enhancing the antenna's performance across the frequency spectrum.  
In the design without the block, the mutual coupling stays below \(-15\) dB, indicating that the antenna retains enough isolation and little interference but somewhat elevated coupling values relative to the configuration with the block. In conclusion, both antenna topologies provide superior isolation with little mutual coupling over the frequency spectrum. The block's presence further augments this isolation, reducing signal interference and enhancing overall performance, especially at elevated frequencies. The uniformity throughout the frequency spectrum guarantees the antenna's efficient operation, making it appropriate for applications requiring little electromagnetic interference among components.
# Multipath Interference Impact Due to Fiber Mode Coupling in C+L+S Multiband Transmission Reach
## 1 Introduction
There are several sources of coherent MPI in optical communications systems, such as the leakage signals in reconfigurable optical add-drop multiplexer (ROADM) nodes [17], the mode coupling in bend-insensitive fibers (G.657) used in passive optical networks [18], quasi-single-mode fibers [19], and G.654E fibers used for long-haul transmission. Coherent MPI modeling is a complex task, since the signal replicas are correlated, and, consequently, the Gaussian statistics cannot be considered a rigorous tool, as shown in [20; 21]. Nevertheless, most of the works in the literature that deal with the impact of coherent MPI in system Quality of Transmission (QoT) use Gaussian statistics so that the simplistic signal-to-noise metric can be used, e.g., [14]. In [14], the signal-to-noise ratio penalty, due to coherent MPI, has been assessed for the MB transmission scenario using the G.654E SMF and considering several modulation formats. It has been concluded that to mitigate the MPI impact, the transmission distance should be reduced, but this reduction was not quantified. Recently, in [22], we computed the transmission distance reach in a C+L+S MB scenario, considering the impact of MPI, and concluded that for the S-band scenario, an approximately 25% reach decrease is observed when the MPI per span increases to \(-\)26 dB/span for quadrature phase-shift keying (QPSK) signals. In this paper, we will further develop the work in [22] by presenting and discussing in more detail the analytical formalism, based on the generalized signal-to-noise ratio (GSNR), used to assess the impact of the MPI on the transmission reach of C+L+S MB systems using the G.654E SMF. We study the maximum transmission reach as a function of the MPI for each one of the three bands (C, L, and S) for a broader range of transmission scenarios than in [22]. For example, we consider two modulation formats--QPSK and 16-quadrature amplitude modulation (QAM)--and three symbol rates--32, 64, and 128 GBaud--thereby assessing the influence of the modulation format and symbol rate on the MPI's impact. This paper is organized as follows: In Section 2, we present the MPI characterization in G.654E SMF transmission. In Section 3, the QoT model is presented and discussed. In Section 4, we show and discuss the results of the MPI's impact on the transmission reach of C+L+S MB systems for several transmission scenarios. Finally, in Section 5, some concluding remarks are drawn.
## 2 Background
### Background on RPL  
All transmissions (upward, downward, and point-to-point) in RPL happen within a set of links referred to as a _Destination-Oriented Directed Acyclic Graph_ (DODAG). RPL nodes construct a collection of DODAGs, each rooted at a border router. For each DODAG, the root node starts the process by sending a _Destination Information Object_ (DIO) indicating its presence. Upon receiving a DIO, nodes can start sending data to the root and set a Trickle timer to further advertise the network by sending more DIOs. Nodes also send _Destination Advertisement Object_ (DAO) packets to the root node to enable downward traffic to be routed.  
Numerous RPL extensions, both centralized [12] and distributed [13], have been proposed in the research community to address various challenges such as network density, mobility, and interference. While centralized approaches offer certain advantages, such as centralized control, they often introduce additional control traffic, which can be undesirable in dense networks. Therefore, to mitigate these concerns, ACTOR proposes enhancements to the standard in a distributed manner.  
RPL was not initially tuned to cope with channel contention. In the event of packet loss due to high traffic, RPL tries to initiate local or global repairs by sending more DIOs, thereby further congesting the medium. Local repair refers to the scenario in which a node starts searching for a parent with no implication on the global routing state. This is triggered when the node does not have any active link toward the root node. A global repair, however, can only be initiated via the root node, and it rebuilds the whole routing graph on all the nodes from scratch. It has been shown that, under a heavy traffic load, RPL shows unstable topology maintenance, unfairness, and high routing overhead. Also, handling the hidden terminal problem is flawed since nodes do not use their capacity to adapt their transmission power.  
RPL nodes advertise the quality of their upward connection using an _Objective Function_ (OF). The _Objective Function 0_ (OF0) and _Minimum Rank with Hysteresis Objective Function_ (MRHOF) are well-known, famous OFs mostly used with different routing metrics, including the expected transmission count (ETX), RSSI, and nodes' remaining energy. Among the metrics, ETX is the most commonly used, as it captures both the long-term link reliability and energy consumption. The calculation of the routing metric assumes constant transmission power in the standard RPL.
# Optimal Control of an Ultraviolet Water Disinfection System
## 2 A Disinfection UV System  
### Structure of the UV System  
This work adopted a typical UVDS, i.e., the pilot disinfection water system described in [5] and illustrated in Figure 1. In the proposed modification, in addition to the equipment's technical parameters, we also used data presented in the paper mentioned above. The treated water can be recycled through a recycling circuit that allows several passages through the irradiation room.  
In most cases, the UVDS uses tubular germicidal lamps surrounded by a quartz tube submerged in a chamber through which the water flows. This is a cylindrical stainless reactor with an annular section, a length of 70 cm, and an internal diameter of 6 cm. The volume of the irradiation chamber is V = 2 L. The UV radiation source is usually a low-pressure mercury arc lamp that produces a UV shortwave with a wavelength of 253.7 nm. An electronic ballast ensures the starting of the discharge lamp and controls the current through the discharge lamp. The experiments used water whose UV transmittance was around 90% to diminish the influence of the decrease in the UV radiation intensity due to the growing distance from the UV lamp (see [7]).  
The disinfection unit was precisely designed to facilitate the modification of UV intensity and freshwater flow rate. In this study, only UV intensity (\(I\)) and the freshwater flow rate (\(Q\)) were considered to be control inputs, meaning that they achieve the control action, which is to irradiate the water with a certain UV dose. The power module can establish the setpoints for the UV light intensity and the flow rate inside the minimum and maximum values as below:  
\[I_{\rm min}\ =\ 5\:\rm mW/cm^{2};\ I_{\rm max}\ =\ 10\:\rm mW/cm^{2};\ Q_{\rm min }\ =\ 0.2\:\rm L/s;\ Q_{\rm max}\ =\ 0.8\:\rm L/s;\]  
These physical parameters are manually controlled because the basic automation level does not exist; namely, there are no control closed-loops that adjust the control inputs to equal the setpoint values. This study regards the optimal control level that establishes the setpoint values for the control inputs of the UVDS taking into account optimum criteria.