esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement in Signal Phase Detection Using Deep Learning with Parallel Fully Connected Layers
## 4 Conclusions  
In conclusion, we have investigated and evaluated a single-shot phase-detection method using deep learning. We experimentally confirmed that PxER could be significantly reduced compared with that in the previous studies by learning the light-intensity distribution around the target signal pixel. In addition, the phase-output time could be shortened by devising a network and arranging the fully connected layers in parallel. In our environment, the phase-output time of the single-pixel classification was approximately 18 times longer than that in the previous method; however, it could be reduced to 1.7 times or less when 32 pixels were simultaneously classified. Therefore, we concluded that the proposed method can significantly reduce PxER and suppress the phase-output time to almost the same level as that in the previous method. Usually, errors that occur in signal detection can be entirely corrected using an error-correction code if they are within the limits allowed by the system. Therefore, a reduced PxER can improve the recording density using tuning parameters such as aperture size and multiple recording-angle spacing. This result indicates that the proposed method can increase the recording density while maintaining the transfer rate. Thus, this method can be a promising phase-detection method to realize a large-density data-storage system in the future.  
Conceptualization, M.T. and R.F.; methodology, M.T. and R.F.; software, M.T.; validation, M.T. and R.F.; formal analysis, M.T.; investigation, M.T.; writing--original draft preparation, M.T. and R.F.; writing--review and editing, R.F.; visualization, M.T. and R.F.; supervision, R.F.; project administration, R.F.; funding acquisition, R.F. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Phase-output time using a network with parallel fully connected layers. The horizontal axis represents the number of signal pixels that are simultaneously output. The vertical axis is the phase-output time normalized by the output time of the minimum-finding algorithm. The dashed line represents the fitted curve.  
**Funding:** This research was partially funded by JSPS KAKENHI, grant number 21K04917.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Development of a Local Shielding Gas Concept
### Computational Simulation Method  
In order to obtain detailed insights into the performance of different approaches for the nozzle geometry, CFD simulations were carried out using the software ANSYS CFX 2021 (Ansys, Inc., Canonsburg, PA, USA). A 3D CFD model was utilized to investigate various arrangements of the shielding gas inlets as well as asymmetric variants of the nozzle outlet. For the numerical modeling of the turbulent flow, a shear-stress transport turbulence model was used. The fluids employed were argon for the shielding gas and air for the surrounding atmosphere. In the simulations, the gases were considered to be isothermal. Various design iterations were evaluated with regard to the given application. When comparing different nozzle geometries, uniform flow parameters were used. In all comparative cases, a shielding gas flow of 50 l/min was simulated over a flat 100 mm \(\times\) 100 mm substrate plate with a stand-off distance of 10 mm to the nozzle outlet. The performance of the respective nozzle geometry was evaluated based on the transient behavior of the flow as well as the area on the substrate plate covered with inert gas (see Section 4.1). This procedure allowed a large number of nozzle geometries to be compared qualitatively without requiring them to be manufactured and tested at great expense. The concept that proved to be most suitable for the given application is presented in the following section.
# NOVA Mobility Assistive System: Developed and Remotely Controlled with IOPT-Tools
## 4 NOVA Mobility Assistive System
### Architecture  
NWBs interact with each other, with NWCs, and with Remote Control and Monitoring Systems (RCMSs). Each NWB is a SOA component, which is a service provider and a service consumer. It is a provider of other NWBs, NWCs, and RCMSs; and a consumer of other NWBs. Each NWB receives requests from: (1) NWCs (requesting data); (2) other NWBs (sharing data); and, (3) RCMSs (sending commands and requesting data). Additionally, each NWB sends requests to other NWBs, to share data. An interaction scenario with three NWBs (NWB1, NWB2, and NWB3), nine NWCs (NWC1 to NWC9), and one RCMS, is presented in Figure 2, where each arrow connects one consumer (making requests) with one provider (replying).  
Two message exchange patterns can be used: request/reply and publish/subscribe. NWCs should request data from NWBs using a request/reply message exchange pattern, while interactions between NWBs or between RCMSs and NWBs can be done using either request/reply or publish/subscribe pattern.  
The proposed architecture for NWBs, composed by six modules, is presented in Figure 3:  
* NOVA-WiFi-Client Provider (NWCP)--responsible for receiving and sending data to NWCs;
* NOVA-WiFi-Beacon Provider (NWBP)--responsible for receiving data from other NWBs;
* NOVA-WiFi-Beacon Consumer (NWBC)--responsible for sending data to other NWBs;* NOVA-WiFi-Beacon DataBase (NWBDB)--containing data about the NWB, its neighbors, and about the last connected NWCs;
* RCMS Provider (RCMSP)--responsible for receiving commands and sending data to RCMSs; and,
* Dynamic Beacon Controllers (DBCs)--controls the dynamic/behavior of the infrastructure (for example, the lights of a traffic light sign) and the NWCP, NWBP, and NWBC modules.  
The NWCP module is a wireless access point for NWCs, requesting data from the NWB and simultaneously providing its own data, for example, the wheelchair current position. The NWCP module saves the received data in the NWBDB and sends data from the NWBDB. The NWBP module is an access point for other NWBs that send data, for example, warning that a mobility scooter is on the road 50 m away. The NWBP module saves the received data in the NWBDB. The NWBC module is a client of other NWBs and connects to their NWBP modules. NWBC sends data from the NWBDB. The RCMSP module interacts with the DBCs module, writing the received commands and  
Figure 3: NOVA-WiFi-Beacon architecture.  
Figure 2: Interaction scenario between NOVA-WiFi-Beacons, NOVA-WiFi-Clients, and a remote control and monitoring system.  
reading the current controller state and IOs (inputs and outputs). Finally, the DBCs module controls the infrastructure behavior and the NWCP, NWBP, and NWBC modules. The DBCs module of each NWB must be developed according to the intended behavior for that NWB.
## 3 Network Scenario and QoT Model
### Quality of Transmission Model
As a result, we obtain the following relation:\[SNR_{l}^{-1}=\sum_{e\in p}{(\beta(e)+\gamma(e)\cdot K_{e})}, \tag{3}\]  
where  
\[\beta(e) =\sum_{i\in e}\left(P_{i}^{\rm ASE}+P_{i}^{\rm NL}\right)\cdot P_{ \rm ch}^{-1}, \tag{4}\] \[\gamma(e) =L_{e}\cdot 10^{\left(XT^{\rm REF}+XT^{\rm MARGEN}\right)/10}. \tag{5}\]  
Due to the inverse operation, we consider that the QoT of a lightpath is acceptable whenever the value calculated using Equation (3) does not exceed the inverse of the required SNR value, which we denote as \(Q\), where \(Q=(SNR^{\rm REQ})^{-1}\). In Figure 1, we compare Model (1), referred to as _OSNR-based_, with the QoT model considered in previous networking studies in SS-FONs (e.g., in [11; 12; 13; 14]) that are based on the estimation of XT only [9] and which we refer to as the _XT-based_ approach. In particular, we show transmission reach values estimated using both models (on the left side) and a relative difference in the estimated TR values (on the right side) for different numbers of active adjacent cores (\(K\)) in a function of the reference XT level in MCF (\(XT^{\rm REF}\)), assuming the QPSK modulation format. We can see that the TR obtained using the XT-based model may be much higher than when applying the OSNR-based model. Indeed, the relative difference in TRs may reach up to between 80% and 100% for some \(XT^{\rm REF}\) values (around \(-61\) dB/km for \(K=6\) and \(-57\) dB/km for \(K\in\{2,3\}\)). This difference is significant and may result in improper network operation if the less conservative XT-based model is used. Therefore, in the remainder of the paper, we focus on application of the OSNR-based QoT model in the optimization of SS-FONs, namely, on mathematical modeling of the OSNR-aware lightpath planning problem and on developing an effective optimization method for solving it.
## 4 III-V-on-Silicon Devices for Optical Communication Applications
### III-V-on-Silicon Laser Sources for Optical Communication Applications
#### 4.1.2 1550 nm Distributed Feedback Lasers Integrated on Silicon Photonic Integrated Circuits  
Distributed feedback lasers are important optical sources for integrated transceivers because they provide single mode emission, have a small footprint, high optical output power and relatively narrow linewidth. Their small footprint also makes direct modulation of these laser sources attractive. Direct modulation of laser diodes for high-speed transceivers has significant advantages over the use of external modulators in terms of power consumption, fabrication complexity and compactness [21; 22; 23; 24; 25], especially for short distance optical interconnects. High-speed directly modulated lasers on the InP platform and bonded to a silicon substrate have been demonstrated recently [21; 22; 23]. Given the mentioned advantages of silicon photonics, it would be desirable to have directly modulated lasers with a high modulation bandwidth, heterogeneously integrated on and coupled to silicon PICs. This was recently demonstrated [26]. The device layout and a device SEM cross-section are shown in Figure 6. The laser structure consists of the amplifier structure elaborated in Section 4.1.1, with a silicon distributed feedback grating implemented underneath the III-V mesa. The grating coupling strength is determined by the spacing between the III-V and silicon layer. The distributed feedback (DFB) grating period in the experiment is 245 nm and has a 50% duty cycle. The length of the DFB laser is 340 \(\upmu\)m and two 220 \(\upmu\)m long tapers are used to realize a high efficiency and low reflection coupling from the laser to the silicon waveguide. The grating coupling strength is 135 cm\({}^{-1}\). The laser operates single mode and has 6 mW single facet waveguide coupled output power at 100 mA bias. The device has a 3 dB electro-optic bandwidth of 15 GHz at 100 mA bias current as shown in Figure 7a. 28-Gbps non-return-to-zero (NRZ) direct modulation with 2.1 dB extinction ratio is demonstrated as shown in the inset of Figure 7a. After transmission of over 2 km of dispersion shifted single mode fiber, only 1 dB power penalty is incurred (see Figure 7b).  
Figure 5: **(a)** Output on chip as function of the input power on chip. **(b)** Gain as function of input power for different injection current. **(c)** Noise figure of the amplifier as function of on-chip input power for different injection currents. **(d)** Gain as function of output power on-chip in gain-clamped operation.  
Figure 6: **(a)** Layout of the distributed feedback laser structure. **(b)** SEM device cross-section of the fabricated devices.
## 3 Methodology
### Construction of Information Propagation Graphs
#### 3.1.2 Extraction of Structural Features of Information Propagation  
Given the extensive node count within the propagation graph \(G_{i}\), the DropEdge technique is employed during model training to randomly eliminate edges in the original graph. This approach aims to circumvent over-fitting and mitigate information loss due to excessive smoothing. In the DropEdge algorithm, we first randomly sample a subset \(E_{drop}\) from the edge set \(E_{i}\), with a size of \(\lfloor p\cdot\left|E_{i}\right|\rfloor\). Subsequently, we generate a mask matrix \(M\in\{0,1\}^{n_{i}\times n_{i}}\), where \(M_{ij}\) is defined by the Equation (2). Finally, we use the Hadamard product (\(\odot\)) to modify the adjacency matrix \(A\), resulting in \(A^{\prime}=A\odot M\).  
\[M_{ij}=\left\{\begin{array}{ll}0,e_{ij}\in E_{drop}\\ 1,\quad\textit{others}\end{array}\right. \tag{2}\]  
As shown in Figure 3, in this paper, the bidirectional Graph Convolutional Network (GCN) is used as a feature extractor to extract the propagation structure features of information. Since the original post contains the most abundant information, in the calculation process the characteristics of each node are combined with the characteristics of its root node to strengthen the information content of the source post. The procedure for extracting propagation characteristics is illustrated by Equations (3)-(6):  
\[H_{\text{I}}^{\textit{TD}}=\sigma(\hat{D}^{-\frac{1}{2}}\widetilde{A}^{ \textit{TD}}\hat{D}^{-\frac{1}{2}}XW_{0}) \tag{3}\]  
\[\widehat{H}_{\text{I}}^{\textit{TD}}=concat(H_{\text{I}}^{\textit{TD}},X_{ root}) \tag{4}\]  
\[H_{\text{Z}}^{\textit{TD}}=\sigma(\hat{D}^{-\frac{1}{2}}\widetilde{A}^{ \textit{TD}}\hat{D}^{-\frac{1}{2}}\widehat{H}_{\text{I}}^{\textit{TD}}W_{1}) \tag{5}\]  
\[H^{\textit{TD}}=concat(H_{\text{Z}}^{\textit{TD}},H_{\text{ITD}}^{\textit{ root}}) \tag{6}\]  
where \(\hat{D}\) is the degree matrix corresponding to the propagation graph, \(\widetilde{A}=A^{\textit{TD}}+I_{N}\), \(W_{0}\), \(W_{1}\) and are all learnable parameters, and the obtained \(H^{\textit{TD}}\) is the top-down propagation characteristics of false information. Similarly, the bottom-up propagation characteristics  
Figure 2: Information event propagation diagram.  
\(H^{BU}\) can be obtained, and the representation vector \(h_{p}\in R^{1\times d_{p}}\) of information propagation characteristics can also be obtained by average pooling after splicing the two. The pooling process is shown in Equation (7).  
\[h_{p}=Meanpool(concat(H^{TD},H^{BU})) \tag{7}\]
## 4 Results
### Natural Frequencies
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\Omega_{1}\) & \(\alpha_{1}\) & \(\Omega_{2}\) & \(\alpha_{2}\) & & \multicolumn{6}{c}{**Nondimensional Frequency Parameters**} \\ \cline{5-8}  & & & \(\beta_{1}\) & \(\beta_{2}\) & \(\beta_{3}\) & \(\beta_{4}\) & \(\beta_{5}\) \\ \hline \multirow{2}{*}{0} & \multirow{2}{*}{0} & \multirow{2}{*}{0} & \multirow{2}{*}{0} & \multirow{2}{*}{0} & \multirow{2}{*}{2.3587} & \multirow{2}{*}{3.9918} & \multirow{2}{*}{5.8927} & \multirow{2}{*}{8.4328} & \multirow{2}{*}{11.3698} \\  & & & & \(\beta_{1}\) & \(\beta_{2}\) & \(\beta_{3}\) & \(\beta_{4}\) & \(\beta_{5}\) \\ \hline \multirow{2}{*}{0.5} & \multirow{2}{*}{0.1} & \multirow{2}{*}{0.5} & \multirow{2}{*}{0.1} & \multirow{2}{*}{2.0652} & \multirow{2}{*}{3.0312} & \multirow{2}{*}{5.3734} & \multirow{2}{*}{8.2727} & \multirow{2}{*}{11.3075} \\  & & & & 1 & & & & \\  & & & & 1 & & & & \\ \hline \multirow{2}{*}{0.5} & \multirow{2}{*}{1} & \multirow{2}{*}{0.5} & \multirow{2}{*}{0.1} & \multirow{2}{*}{0.5} & \multirow{2}{*}{0.1} & \multirow{2}{*}{1.6155} & \multirow{2}{*}{2.8248} & \multirow{2}{*}{5.3220} & \multirow{2}{*}{8.2578} & \multirow{2}{*}{11.3016} \\  & & & & 1 & & & & \\ \hline \multirow{2}{*}{0.5} & \multirow{2}{*}{0.1} & \multirow{2}{*}{1} & \multirow{2}{*}{0.1} & \multirow{2}{*}{2.3046} & \multirow{2}{*}{3.9818} & \multirow{2}{*}{5.8622} & \multirow{2}{*}{8.3642} & \multirow{2}{*}{11.3060} \\  & & & & \(\beta_{1}\) & & & & & \\ \hline \multirow{2}{*}{0.5} & \multirow{2}{*}{0.1} & \multirow{2}{*}{1} & \multirow{2}{*}{0.1} & \multirow{2}{*}{2.0594} & \multirow{2}{*}{2.9811} & \multirow{2}{*}{5.2816} & \multirow{2}{*}{8.1918} & \multirow{2}{*}{11.2410} \\  & & & & 1 & & & & & \\ \hline \multirow{2}{*}{0.5} & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{0.1} & \multirow{2}{*}{1.7069} & \multirow{2}{*}{3.8892} & \multirow{2}{As can be seen from Figure 2, the change in the first two dimensionless frequency parameters was higher than the others. It is seen that the change in the upper frequencies was much less. It is seen that there was no change in the fourth and fifth frequency parameters when the \(\alpha_{2}\) value approached 1. As a result, it can be said that the increase in the rotational inertia of the end masses was more effective, especially at low frequencies of the structure.
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 4 Network Traffic Forecasting Based on ARIMA-BPNN
### IGWO-Based BPNN Parameter Optimization
#### 4.2.1 Standard GWO Algorithm  
GWO is a group intelligence optimization algorithm derived from simulating the social hierarchy and hunting mechanisms of grey wolves [12]. In GWO, wolves are divided top-down into four groups based on fitness values: \((\alpha,\beta,\delta,\omega)\). The former three groups \((\alpha,\beta,\delta)\) denote the leader class (optimal solution). Candidate solutions are positioned around \((\alpha,\beta,\delta)\) for updating. The position update of \((\alpha,\beta,\delta,\omega)\) consists of three stages.  
* Encirclement phase. Based on the hunting encirclement mechanism of grey wolves, the encirclement phase is defined as \[Q=\left|EX_{p}(t)-X(t)\right|\] (12) \[X(t+1)=X_{p}(t)-LQ\] (13) where Equation (11) defines the distance between the grey wolves and the prey. Equation (12) defines the position updating of grey wolves. \(t\) denotes the number of iterations. \(X(t)\) denotes the position of the grey wolves, \(X_{p}(t)\) denotes the position of the prey and \(Q\) denotes the distance between the grey wolves. \(LE\) is the coefficient vector that is defined as follows \[L=2 ar_{1}-a\] (14) \[E=2r_{2}\] (15) \[a=2-2t/T_{\max}\] (16) where the convergence factor \(a\) decreases linearly with the number of iterations within [2, 0], \(T_{max}\) is the maximum number of iterations and the variables \(r_{1},r_{2}\) are randomly distributed within [0, 1].
* Hunting phase. During the hunting phase, \(\alpha,\beta,\delta\) lead the search process and gradually approach the prey. The tracking process can be described as follows \[\left\{\begin{array}{l}Q_{\alpha}=\left|E_{1}\cdot X_{\alpha}-X\right|,Q_{ \beta}=\left|E_{2}\cdot X_{\beta}-X\right|\\ Q_{\delta}=\left|E_{3}\cdot X_{\delta}-X\right|\end{array}\right.\] (17) where \(Q_{\alpha},Q_{\beta},Q_{\delta}\) denote the distance between \(\alpha,\beta,\delta\) and \(\omega\), \(X_{\alpha},X_{\beta},X_{\delta}\) denote the current positions of \(\alpha,\beta,\delta\), \(X\) is the current position of \(\omega\) and \(E_{1},E_{2},E_{3}\) are random vectors. \[\left\{\begin{array}{l}X_{1}=X_{\alpha}-L_{1}Q_{\alpha},X_{2}=X_{\beta}-L_{ 2}Q_{\beta}\\ X_{3}=X_{\beta}-L_{3}Q_{\delta}\end{array}\right.\] (18) \[X(t+1)=\frac{X_{1}+X_{2}+X_{3}}{3}\] (19) where Equation (17) defines the direction of the approximation of \(\omega\) to \(\alpha,\beta,\delta\) and the corresponding step size. Equation (18) defines the final position of \(\omega\).
* Attacking phase. As \((\alpha,\beta,\delta,\omega)\) approach the prey, the convergence factor \(a\) decreases linearly and \(|L|\) varies within \([-a,a]\). When \(|L|<1\), the algorithm converges and the prey position is obtained.
# Sensing in Inland Waters to Promote Safe Navigation: A Case Study in the Aveiro's Lagoon
## 3 Low-Cost Maritime Data-Sharing System Design and Evaluation
The case study is a common approach in maritime navigation studies, which aims to improve human-machine cooperation and enhance situational awareness and decision-making for sailors [51]. Nonetheless, as the sharing system will be used by humans, it is important to evaluate its usability in an outdoor and maritime context. For that reason, the Contextual Design (CD) approach was used [27], which integrates the design into a participatory and iterative process and analyzes it in a specific context. CD allowed this research to create a centralized UI design process to be integrated into the data sharing system based on the sailors' needs.
# Adaptive Damping Log-Domain Message-Passing Algorithm for FTN-OTFS in V2X Communications
## 5 Conclusions  
In this paper, we propose a novel FTN-OTFS modulation scheme that integrates FTN signaling with SEFDM for high-mobility V2X communications. We derive analytically tractable input-output models in both the DD and TF domains, providing a solid foundation for efficient receiver design. To tackle the challenges of non-orthogonal transmission and mitigate the resulting interference, we propose an advanced ADL-MP detection algorithm. This algorithm was specifically designed to improve numerical stability, ensuring robust performance even in complex communication environments. Simulation results not only demonstrate the superiority of the proposed FTN-OTFS scheme over OFDM in high-mobility V2X communication, but also verify the effectiveness of the proposed ADL-MP algorithm in both the EVA channel model and HST scenarios. In conclusion, the proposed FTN-OTFS modulation scheme, combined with the ADL-MP detection algorithm, offers a powerful and reliable solution for future high-mobility V2X communication systems. Its superior performance and robustness make it a promising candidate to meet the stringent demands of next-generation vehicular networks.  
Conceptualization, H.X. and B.K.N.; methodology, H.X. and B.K.N.; software, H.X.; validation, C.Z. and Q.W.; formal analysis, H.X. and B.K.N.; writing--original draft preparation, H.X.; writing--review and editing, C.-T.L.; project administration, C.-T.L. and B.K.N.; funding acquisition, C.-T.L. and B.K.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Development Fund, Macau, SAR, under Grant 0044/2022/A1.  
The original contributions presented in this study are included in the paper.  
The author declares no conflicts of interest.
## 3 Results and Discussion
### Surface Area and Porosity Analysis  
Compared with unmodified manganese sands, the specific surface area, the pore volume, and the average pore size of the modified manganese sand samples in Table 2 increased by 26.9%, 41.7%, and 26.9%, respectively. During the modification process, the influence of the potassium permanganate solution on the internal thin layer and impurities of manganese sand samples changed the micropore structure of the manganese sand samples themselves, and through the connection of small pores, intermediate pores were formed. With the increase in the specific surface area and pore diameter, modified manganese sand was more beneficial to the diffusion and adsorption of iron and manganese ions.  
The adsorption volume of manganese sand increased with the increase in relative pressure. When the relative pressure was about 0.5 in Figure 2, the curve was divided into two smooth curves. The hysteresis loop occurred at high pressure, and capillary condensation occurred. According to the figure, the nitrogen adsorption-desorption isotherms of manganese sand and modified manganese sand belonged to type IV isotherms in the BDDT  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **BET Surface Area** & **Total Pore Volume** & **Average Pore Size** \\  & **m\({}^{2}\)/g** & **cm\({}^{3}\)/g** & **nm** \\ \hline Unmodified & & & \\ manganese sand & 19.281 & 0.060 & 12.451 \\ Modified manganese & & & \\ sand & 24.459 & 0.085 & 15.797 \\ \hline \hline \end{tabular}
\end{table}
Table 2: BET test results of manganese sand.  
Figure 1: SEM of unmodified manganese sand (a) and modified manganese sand (b).  
classification system. The adsorption-desorption curve revealed an obvious hysteresis phenomenon, which was the result of nitrogen capillary condensation in the mesopore. The adsorption effect of the modified manganese sand was improved, which showed that the pore expanded after being corroded by potassium permanganate.