esa-sceva/satcom-chunk-collection huggingface dataset:
# Frequency-Separated Attention Network for Image Super-Resolution
## 3 Method
### Frequency-Separated Attention Block  
The extracted high-frequency features, compared to low-frequency features, contain more information and thus require more processing, for which we propose the FSAB. As shown in Figure 2, the \(m\)-th FSAB consists of two branches: a high-frequency path (HF path) formed by three HFAMs to process high-frequency features and a low-frequency path (LF path) formed by a linear stack of LFAMs to process low-frequency features.  
\(H_{in_{n}}\) represents the input to the \(n\)-th HFAB, while \(H_{out_{n}}\) represents the output. There are a total of \(N\) HFABs. The equation is given by  
\[H_{out_{n}}=f_{HFAB}^{n}\big{(}H_{in_{n}}\big{)}. \tag{6}\]  
Here, \(f_{HFAB}^{n}(\cdot)\) represents the \(n\)-th HFAB, which is detailed in Section 3.3. The expression for the \(n\)-th High-Frequency Fusion Block (HFFB) is as follows:  
\[F_{HFFB}^{n}=f_{HFB}^{n}\big{(}\big{[}H_{out_{1}},\cdots,H_{out_{n}}\big{]} \big{)}, \tag{7}\]  
where \(f_{HFB}^{n}(\cdot)\) denotes the convolution in the \(n\)-th HFFB, with its output being \(F_{HFFB}^{n}\).  
In the LF path, let \(L_{in_{n}}\) be the input and \(L_{out_{n}}\) be the output of the \(n\)-th LFAB:  
\[L_{out_{n}}=f_{LFAB}^{n}\big{(}L_{in_{n}}\big{)}. \tag{8}\]  
\(f_{LFAB}^{n}(\cdot)\) represents the \(n\)-th LFAB, which is emphasized in Section 3.4. To achieve relatively high performance, we use Low-Frequency Fusion Blocks (LFFBs) for multi-level dense  
Figure 2: The architecture of the \(m\)-th FSAB.  
local feature fusion by directly connecting the output of the current and previous LFABs to the LFFB. Consequently, the expression for the \(n\)-th LFFB is as follows:  
\[F_{LFFB}^{n}=f_{LFFB}^{n}\big{(}\big{[}L_{out_{1}},\cdots,L_{out_{n}}\big{]}\big{)}. \tag{9}\]  
\(f_{LFFB}^{n}(\cdot)\) represents the \(n\)-th LFFB, with its output denoted by \(F_{LFFB}^{n}\).  
Finally, the outputs of both branches are added to merge high-frequency and low-frequency information. The output of the FSAB is generated through a deep CNN to complement the advantages of high- and low-frequency features. The \(m\)-th FSAB is as follows:  
\[\begin{split} F_{m}&=f_{FSAB}^{m}(F_{m-1}\ )\\ &=ReLU\big{(}\phi\big{(}F_{LFFB}^{N}+F_{HFFB}^{N}\big{)}\big{)}\\ &=ReLU\big{(}\phi\big{(}f_{LFFB}^{N}\big{(}\big{[}L_{out_{1}}, \cdots,L_{out_{N}}\big{]}\big{)}+f_{HFFB}^{N}\big{(}\big{[}H_{out_{1}},\cdots,H _{out_{N}}\big{]}\big{)}\big{)}\big{)},\end{split} \tag{10}\]  
Here, \(\phi(\cdot)\) represents the convolution, and \(ReLU(\cdot)\) indicates the ReLU layer.
## 5 Conclusions  
We focused on the propagation properties of OAM modes carried by the RCDPPGV carrier in absorptive, isotropic, and weak turbulent ocean link and derived the channel capacity of this link. The results show that seawater absorption has a great influence on the channel capacity of the optics communication link, and the larger the seawater absorption, the faster the absorption of channel capacity with the transmission distance. With the optics communication link with single-position power Gaussian vortex carrier, the channel capacity of the link reached an extreme value. The channel capacity increases with the increase in the \(\varepsilon\) and \(\eta\), and the change in \(L_{0}\) has little effect on the channel capacity. The channel capacity decreases with increasing \(\varpi\) and \(\chi_{t}\). The large number of OAM channels M leads to higher channel capacity. The channel capacity decreases with the increase in initial beam width and the decrease in D. Our results also show that with the increase in transmission distance \(z\), the topological charge of vortex, and \(\varepsilon\), the BER of this link increases; however, the BET of the link increases with the increase in \(\chi_{T}\) and is minimum at \(\alpha=1\) or \(-1\). Our paper also points out that we need to further study the influence of the helical direction of non-coaxial multi-helical combination vortices on the channel capacity of the communication link.  
Conceptualization, Y.Z. ([PERSON]) and Q.Y.; methodology, Y.Z. ([PERSON]); software, Q.Y.; validation, Y.Z. ([PERSON]), Q.Y. and Y.Z. ([PERSON]); formal analysis, Q.Y.; investigation, Q.Y.; resources, Y.Z. ([PERSON]); data curation, Q.Y.; writing--original draft preparation, Y.Z. ([PERSON]); writing--review and editing, Y.Z. ([PERSON]); visualization, Q.Y.; supervision, Y.Z. ([PERSON]); project administration, Y.Z. ([PERSON]); funding acquisition, Y.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (61871202).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 10: Channel capacity of the link with the random double-position power Gaussian vortex carrier optics communication system versus dissipation rate of kinetic energy per unit mass of fluid \(\varepsilon\) and dissipation rate of the mean-squared temperature \(\chi_{T}\).
## 3 FLARoute: Fair Locality Aware Routing  
Achieving a balance between routing fairness and network performance requires minimizing the use of popular nodes through the maximum utilization of less popular nodes in a manner that does not incur significant degradations in throughput. This requires exploiting the regularity embedded in node movement to design a forwarding utility that reflects a node's potential to be part of a series of relays that form a path to the destination through time-separated links. For instance, one of the concepts of spatial locality is that users regularly visit only one or few regions, and these often correspond to where their home or workplace is located [66; 71]. It is possible to exploit this regularity to increase the chances of finding less popular nodes that can carry messages progressively towards the destination's location in multiple hops. Although noticeable delays may be incurred in the process, it is preferred to degrading throughput, as long as the delay is within an acceptable range with respect to the application scenario. A suitable means of identifying the burden on nodes without using buffer occupancy is also required. As discussed in Section 2.4.3, relying on buffer occupancy alone may lead to scenario-specific performance, as fluctuations in data traffic, variations in buffer capacity and dynamic user behaviour may cause over-burdened nodes to either go undetected or be wrongly identified as under-burdened nodes. In this section, we present FLARoute, and tackle the following points in the process:  
* A forwarding utility that can assign forwarding responsibilities to nodes according to their spatial connectivity, in order to allow more nodes to participate in routing even under higher degrees of spatial locality;
* A non-scenario-specific means of identifying the burden on nodes, whose performance is not degraded by fluctuations in data traffic, variations in buffer capacity and dynamic user behaviour;
* A mechanism that can maximize the participation of less popular nodes by allowing them to forward messages with minimum involvement from popular nodes, especially when the latter is over-burdened; and
* A forwarding algorithm that makes fair forwarding decisions with the available knowledge. By addressing these points, FLARoute is able to achieve the features shown in Table 2.
## 2 Research Background
### Biomass Resources  
Among the different kinds of biomass, lignocellulosic biomass is the most abundant in the world, with an estimated production of 181.5 billion tons annually [22]. However, the availability of biomass in terms of quantity, quality, and price is affected due to the absence of regulation for a specific market for biomass resources.  
Multiple types of lignocellulosic biomass are currently suitable for gasification. There are little experimental results available in the literature so far. One of them is presented by [PERSON] et al., who tested pongamia residue (shells), which is one of the most abundant biomasses in developing countries, obtaining pellets with a bulk density of 146 kg/m\({}^{3}\)[23]. On the other side, [PERSON] et al. carried out a study in Qatar demonstrating the benefits of a biomass blend composed of date palm residues, sewage sludge, and livestock manure, which helped to overcome the handicap of seasonal biomass availability [24].  
There are recent examples of research conducted with different kinds of biomass to produce syngas. Thus, [PERSON] et al. [16], investigated the behavior of a fixed-bed gasifier fed with different types of biomass resources, including eucalyptus woodchips, pine pellets, and eucalyptus charcoal. On the other side, [PERSON] et al. [25] successfully tested a commercial small-scale open-top gasifier to produce syngas from forest woodchips. Other kinds of traditional biomass such as sawdust have also been used, mixed with personal care waste like sanitary napkins, as presented by [PERSON] et al. [26] for the production of syngas in a downdraft gasifier for thermal applications.  
New biomass sources have also been explored. Among them, water hyacinth has resulted in one of the most appropriate sources of primary fuel due to its wide availability, being an invasive species all over the globe, and has demonstrated favorable characteristics for cheap biofuel production. In fact, water hyacinth contains a large quantity of cellulose that can be converted into bioethanol by enzymes to power vehicles and motors, as well as biogas to generate electricity by using thermal gasification (>500 \({}^{\circ}\)C) [27; 28].  
Recently, new research lines focused on maximizing hydrogen content in the biomass gasification process have been developed. The objective is to produce syngas enriched with hydrogen as a result of thermochemical gasification [15; 29; 30].  
In spite of that, there is a lack of appropriate literature investigating the relation between the properties of biomass, the type of gasifier, and thermodynamic parametersof the gasification process, which justifies the scope of the present research to promote biomass as a renewable fuel for the future.
## 4 Engineering Application
### Model Performance Analysis
#### 4.3.1 Comparison of Anomaly Detection Algorithms  
In comparative experiment 1, we compared the performance of different anomaly detection algorithms when using energy consumption as the monitoring parameter. The detection results and a comparison of the different threshold settings are shown in Figure 7. To provide clearer visualization of threshold settings and anomaly detection results, we add magnified insets of the detection results of VAE-LSTM and DT_VAE-LSTM for abnormal events 2 and 3 in Figure 7. The detection performance of each algorithm is presented in Table 3. The proposed DT_VAE-LSTM model exhibits significantly higher recall values for various abnormal scenarios than other methods. This indicates that the proposed method can more accurately identify abnormal information in the shield tunneling data, resulting in a lower false negative rate. In particular, during the detection process of abnormal event 2 and abnormal event 3, the DT_VAE-LSTM detected anomalies significantly earlier than the AE-LSTM and VAE, demonstrating more stable and reliable anomaly detection performance. Although the VAE-LSTM, which set a fixed threshold in the comparative experiment, somewhat reduces the false positive rate, it lacks adaptability to data in different scenarios, resulting in decreased detection performance when the construction scenario changes. The dynamic threshold method proposed in this article can reduce the influence of noise data and set more accurate thresholds by adapting to data from different shield tunneling scenarios. The VAE-LSTM exhibited noticeable false negatives in the detection of abnormal event 2 and abnormal event 3, with a reduction of 21% and 8% in recall values, respectively, compared to the DT_VAE-LSTM based on dynamic threshold setting. Therefore, the anomaly detection method we propose, which is based on dynamic threshold setting, can achieve a better balance between recall and precision. The \(F1\) score consistently remains the highest, thereby enhancing the overall detection capability of various abnormal scenarios.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparative Model** & **Model Number** & **Model** \\ \hline \multirow{3}{*}{1} & 1-1 & VAE \\  & 1-2 & AE-LSTM \\  & 1-3 & VAE-LSTM \\  & 1-4 & DT\_VAE-LSTM \\ \hline \multirow{3}{*}{2} & 2-1 & DT\_VAE-LSTM (torque) \\  & 2-2 & DT\_VAE-LSTM (thrust) \\  & 2-3 & DT\_VAE-LSTM (torque) \\ \hline \multirow{3}{*}{3} & 3 & AD\_SI \\ \cline{2-3}  & & \\ \end{tabular}
\end{table}
Table 2: Comparative model settings.  
Figure 7: Energy consumption and results of different anomaly detection algorithms.
# Applying Deep Learning to Medical Imaging: A Review
## 3 Applications in Medical Imaging
### Image Segmentation
#### 3.1.2 Challenges and Future Directions  
Despite the success of DL-based segmentation methods, several challenges remain. These include the need for large, annotated datasets, the limited interpretability of the models, and the robustness of the algorithms to variations in image quality, acquisition protocols, and patient populations [45; 112]. Future research directions may focus on developing more efficient annotation techniques, incorporating domain knowledge into DL models, and improving the generalization capabilities of these models to unseen data or rare pathologies [113].
## Case Study  
### Problem Description and Its Parameters  
In the section, the Qinzhou-Sanyangchuan soft rock tunnel located in Gansu Province, People's Republic of China, is investigated as an illustrative example. Surrounding rock of Section K3 + 038-100 of the tunnel is strongly weathered granite gneiss. The buried depth of Section K3 + 038-100 of the tunnel is 135-152 m. The aggregate score of the rock mass rating (RMR) is 33, and its rock mass classification with RMR is determined to be IV. The tunnel has a height of 10.23 m and a span of 12.46 m, shown in Figure 4.  
Figure 3: Flowchart of XGBoost-NSGA-II-based back analysis.  
Design parameters of the initial support of the tunnel: length of the rock bolt \(\phi\)25-5 = 3.5 m; spacing of the grille steel frame \(\phi\)25 = 0.6 m; thickness of sprayed concrete = 25 cm; steel mesh \(\phi\)8 = 20 \(\times\) 20 cm. Design parameters of the second lining of the tunnel: thickness of reinforcement = 33-45 cm. The buried depth of the tunnel is 148 m. Horizontal geostress in the area of the tunnel is 4.125-4.411 MPa. The design parameters of initial support and second lining are shown in Table 1.  
The initial support of Section K3 + 038-050 of the tunnel was completed on 9 October 2019. Since the completion of the initial support, the deformation of tunnel initial support structure had been increasing, leading to the collapse of Section K3 + 038-050 of this tunnel on 12 November 2019, show in Figure 5. The tunnel was monitored by the infrared rangefinder during the whole period before the disaster. On the collapse of these sections of the tunnel, the maximum value of the vault settlement and the horizontal displacement of one of side walls are 46.2 cm and 40.0 cm, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Initial Support**} & \multicolumn{3}{c}{**Concrete Lining**} & \multicolumn{2}{c}{**Rock Bolt**} \\ \hline \(\mathbf{E_{IS}/GPa}\) & **Poisson’s Ratio \(\boldsymbol{\
u}\)** & \(\mathbf{E_{CL}/MPa}\) & **Poisson’s Ratio \(\boldsymbol{\
u_{CL}}\)** & \(\mathbf{E_{bol}/MPa}\) & \(\boldsymbol{\
u_{bol}}\) \\ \hline
26.311 & 0.22 & 28,000 & 0.27 & 210,000 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tunnel support mechanical parameters.  
Figure 4: Initial support and second lining of tunnel (unit: cm).
# Evaluation of Applications for Mobile Devices on the Practice of Physical Exercise in Adolescents
## 3 Results  
### Descriptive Dimension  
With respect to localization, we refer to the digital dissemination platforms where the applications are distributed and promoted; here, 61.29% of the apps are developed for Google Play, 16.12% for Apple and 22.58% are developed for both sources (Figure 2).  
The agents responsible for the development of the apps are mostly companies. Leap Fitness Group developed 29%, while the remaining 71% are entities that create one or two apps, as in the case of AllTrails, Adidas or Nike.  
As for the last update of these apps for mobile devices; most of the apps were updated in 2021, for a total of 30 apps (97%), while only one (3%) was last updated in 2020.  
All selected apps had to meet the requirement of being free to download. Of these, a total of 19 (61%) are absolutely free, while 12 (39%) offer a micropayment service to enjoy a number of extra features or services.  
The age ranges of the apps for mobile devices are mostly set as adapted to various ages. There are no apps specifically recommended for teenagers between 12-17 years old.  
A total of 16 apps (57%) have no age restriction or age guideline. A total of 10 apps (36%) are set for ages four and over, while one app (4%) is for ages nine and over, and another single app (4%) is for ages 17 and over.  
With respect to language, most of the localized apps are in Spanish (48.38%), although 45.16% are published in multilanguage. Only 3.22% are only in English, and the same percentage are in Spanish and English.  
The rating of the apps by the users is very homogeneous, as expected since only apps with a score higher than 4 stars were chosen. In total, an average score of 4.62 points was obtained. Figure 3 shows how the frequencies are distributed, and no app reaches a score of 5 stars.
# A Study on Design Requirement Development and Satisfaction for Future Virtual World Systems
## 4 Virtual World Architecture Inspection
### Client/Server  
In the C/S architecture (Figure 8a), simulation logic is processed at the server end, while clients only render updates, which are sent from servers to users. A C/S virtual world has a definitive platform owner (e.g., Mozilla owning Mozilla Hubs (hubs.mozilla.com), a VR-friendly social virtual world platform running in browsers). The online services (e.g., authentication, inventory management, and region management in Second Life) and application data (e.g., user items, user inventory, and virtual property in Second Life) belong to the platform owner. C/S architecture has predominated the virtual world industry (see the list of sampled virtual worlds in the Supplementary Materials).
## 2 Experimental Study
### Test Characterization Method
#### 2.3.2 Characterization of Fiber Microstructure  
A Phenom Pro desktop scanning electron microscope (SEM) and atomic force microscope (AFM) were employed to characterize the surface morphology and fracture morphology of aramid fiber before and after modification. The PPTA fibers were flatly fixed onto metal circular sample holders using conductive adhesive. After gold sputtering treatment, the surface microstructure of the PPTA fibers was observed using SEM. A scanning electron microscope/X-ray energy dispersive spectrometer (SEM/EDS) and an atomic force microscope (AFM) were employed to determine the surface element content and surface roughness of the fiber. For AFM analysis, the fibers needed to be trimmed to appropriate sizes and fixed onto the sample holder using double-sided adhesive. Care was taken to ensure that the entire surface of the sample remained at the same level. Tests were conducted using the tapping mode, with a scanning range of 3 \(\times\) 3 \(\upmu\)m.  
X-ray diffraction (XRD) was used to analyze whether the modification treatment affected the crystal structure of aramid fibers, with a scanning speed of 8\({}^{\circ}\)/min and a scanning angle range from 5\({}^{\circ}\) to 80\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Fiber Layers** & **Modling Temperature (T\({}_{0}\))\({}^{\circ}\)C** & **Modling Pressure (P\({}_{0}\)) MPa** & **Modling Time (t) h** \\ \hline
7 & 60 & 5 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Molding process parameters.  
Figure 2: Partial testing equipment: (**a**) 025 MN flat vulcanizing machine; (**b**) MTS-CMT-4204 universal mechanical testing machine.  
X-ray photoelectron spectroscopy (XPS) was utilized to determine the surface elements, functional groups, and their content changes before and after modification of aramid fibers.
# Hybrid RF/VLC Network Architecture for the Internet of Things
## 7 Conclusions  
In this work, as a major achievement, a hybrid cellular architecture has been developed that allows inter-operability between different technologies in the world of Internet of Things. The main application is to provide coverage to remote areas that lack mobile coverage or that require a long range and high data rates. The results obtained using the proposed network architecture suggest multiple applications such as long range communication, localization, monitoring, or even downloading content from a remote user. In order to address the aims of this work, a hybrid network architecture has been developed including the following aspects:  
* A cell prototype has been implemented, and it includes a gateway between the ZigBee and LoRaWAN technologies that actsas a bridge in a transparent way to the user (human or machine) of the network. In addition, a central node that uses VLC technology capable of transmitting data on the downlink has been implemented in this cell. This technology also allows its use as a location mode in cases where other technologies such as GPS are not allowed.
* Moreover, a protocol-abstraction layer based on an HNF has been developed. The HNF allows routing, node identification, and discovery. Moreover, this frame format allows for segmentation of the transmitted data, so a download of larger content can be implemented between the IP network and each of the cells, and vice versa.  
Furthermore, several field tests were carried out. These tests were based on demonstrating the feasibility and performance of the proposed network architecture. It was observed that the defined fragmentation scheme allows for long-length data transmissions regardless of the wireless technology. The proposed hybrid network architecture allows close-range technologies (ZigBee and VLC) to be used in isolated scenarios, such as rural environments, using the LoRa backbone in a transparent way.  
Conceptualization, F.D.-R. and A.M.-S.; Data curation, R.P.-J.; Formal analysis, V.G. and D.S.-R.; Funding acquisition, F.D.-R.; Investigation, A.M.-S.; Methodology, F.D.-R. and D.S.-R.; Software, A.M.-S.; Supervision, R.P.-J. and D.S.-R.; Validation, R.P.-J.; Writing--original draft, F.D.-R. and V.G.; Writing--review & editing, F.D.-R., V.G., R.P.-J. and D.S.-R. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Spanish Research Administration (MUCIU, Project OSCAR, Ref. TEC2017-84065-C3-1-R) and the Canary Islands Regional Government (CEI2018-15). The authors declare that there is no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.