esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Architecture
### Description of IFS-CMR Subsystems
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Term Sets** \\ \hline Data Complexity (DC) & Low (Lo), Moderate (Mo), High (Hi) \\ Time Sensitivity (TS) & Low (Lw), Middle (Md), High (Hg) \\ Number of Neighboring Vehicles (NNV) & Sparse(Sp), Medium Density (Me), Dense (De) \\ Avg. PC per Neighbor Vehicle (APCpNV) & Low (L), Moderate (M), High (H) \\ Layer Selection Decision (LSD) & Decision Level 1 (DL1), DL2, DL3, DL4, DL5, DL6, \\ DL7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters and term sets for FS-CFELS. Figure 5: Membership functions of FS-AQoS. (**a**) Link Latency, (**b**) Radio Interference, (**c**) Effective Reliability, (**d**) Update Info. for Vehicle Position, and (**e**) Quality of Service. Figure 6: Membership functions of FS-ANVPC. (**a**) Available Computing Power, (**b**) Available Storage, (**c**) Predicted Contact Duration, (**d**) Quality of Service, and (**e**) Neighbor \(i\) Processing Capability. In Tables 4-6, we show the Fuzzy Rule Base (FRB) of FS-AQoS, FS-ANVPC, and FS-CFELS, respectively. The FRB forms a fuzzy set of dimensions \(\mid T(x_{1})\mid~{}\times~{}\mid T(x_{2})\mid~{}\times~{}\cdots~{}\times~{} \mid T(x_{n})\mid\), where \(\mid T(x_{i})\mid\) is the number of terms on \(T(x_{i})\) and \(n\) is the number of input parameters. Therefore, since each subsystem has four input parameters with three linguistic terms, each FRB consists of 81 rules. The control rules have the form: IF \"conditions\" THEN \"control action\". For instance, for FS-AQoS, for Rule 20: \"IF LL is Lo, RI is Ha, ER is Nef and UIVP is Mo, THEN QoS is Md\". \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline
**No** & **LL** & **RI** & **ER** & **UIVP** & **QoS** & **No** & **LL** & **RI** & **ER** & **UIVP** & **QoS** & **No** & **LL** & **RI** & **ER** & **UIVP** & **QoS** \\ \hline
1 & Lo & Pe & Nef & Fw & Hg & 28 & Me & Pe & Nef & Fw & Vl & 55 & Hi & Pe & Nef & Fw & El \\  
f & Fw & Hg & 52 & Me & Ha & Ef & Fw & Lv & 79 & Hi & Ha & Ef & Fw & El \\
26 & Lo & Ha & Ef & Mo & Vh & 53 & Me & Ha & Ef & Mo & Md & 80 & Hi & Ha & Ef & Mo & El \\
27 & Lo & Ha & Ef & MaH & Hg & 54 & Me & Ha & Ef & Ma & Lv & 81 & Hi & Ha & Ef & Ma & El \\ \hline \hline \end{tabular}
\end{table} 
Table 4: FRB of FS-AQoS. Figure 7: Membership functions of FS-CFELS. (**a**) Data Complexity, (**b**) Time Sensitivity, (**c**) Number of Neighboring Vehicles, (**d**) Avg. PC per Neighbor Vehicle, and (**e**) Layer Selection Decision.
## 2 Packet-Based Traversal Time Hop Count Analysis (TTHCA)
### TTHCA Wormhole Detection Algorithm and Extended AODV Route Discovery  
While TTHCA defines a similar threshold to WAP for the maximum link delay per HC of a healthy route, it relaxes the fundamental assumption that network nodes have negligible packet processing delays by replacing RTT with PTT in the detection process, so a wormhole is suspected in a route if:  
\[\frac{PTT}{HC}>\frac{R}{S} \tag{2}\]  
where \(R\) is the maximum radio range and \(S\) is the propagation speed of a wireless signal. This ratio sets the upper bound for all PTT per HC values, which means that no FP occurrences are feasible in TTHCA (see Section 3). Instead of performing the wormhole existence check on each intermediate node as in WAP, TTHCA uses a similar approach to that employed in DelPHI, to concentrate the check to the source node for minimising the route discovery delay.  
The flowchart of the complete TTHCA extended AODV route discovery procedure is displayed in Figure 2.  
During the AODV route discovery process \(\mathit{AT}_{\mathit{TOT}}\) is measured and included in the RREP packet. When the source node receives a RREP packet, the PTT is calculated using Equation (1) and the threshold (2) applied to ascertain whether the identified route is potentially wormhole infected. If a  
Figure 2: The TTHCA extended AODV route discovery algorithm.  
wormhole is suspected, a graylist is broadcasted in an analogous manner to MHA to find a new unique route between the source and destination nodes. This iterative process of locating new routes is repeated until a safe route is found which unlike MHA, importantly makes TTHCA effective in HM wormhole detection. Furthermore, the network overhead is significantly reduced as only one route discovery iteration is needed if the MANET is wormhole free.
## 4 Results
### Accuracy Comparison for the Fusion Results Using 250 m and 500 m MODIS Data  
To analyse the influence of spatial resolution differences, we applied STDFA and ESTARFM using MOD09 GQ data in Kuche and Luntai, and compared the fusion results with the results obtained using  
Figure 3: Comparison of MODIS, GF-1 WFV and synthetic data generated by ESTARFM and STDFA acquired on 7 October 2013: (**a**) MODIS data; (**b**) synthetic GF-1 WFV data generated by STDFA; (**c**) synthetic GF-1 WFV data generated by ESTARFM; (**d**) actual GF-1 WFV data.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Kuche**} \\ \hline \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**STDFA**} & \multicolumn{4}{c}{**ESTARFM**} \\ \cline{2-10}  & Blue & Green & Red & NIR & Blue & Green & Red & NIR \\ \hline \multirow{2}{*}{\(r\)} & 0.9658 & 0.9687 & 0.9778 & 0.9513 & 0.9669 & 0.9705 & 0.9790 & 0.9563 \\  & 0.0001 & 0.0001 & 0.0002 & 0.0001 & 0.0001 & 0.0001 & 0.0001 & 0.0001 \\ MAD & 0.0079 & 0.0089 & 0.0093 & 0.0081 & 0.0076 & 0.0085 & 0.0088 & 0.0078 \\ RMSE & 0.0264 & 0.0279 & 0.0254 & 0.0222 & 0.0220 & 0.0209 & 0.0180 & 0.0168 \\ bias & \(-\)0.0240 & \(-\)0.0252 & \(-\)0.0222 & \(-\)0.0188 & \(-\)0.0193 & \(-\)0.0174 & \(-\)0.0135 & \(-\)0.0124 \\ \hline \multicolumn{10}{c}{**Luntai**} \\ \hline \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**STDFA**} & \multicolumn{4}{c}{**ESTARFM**} \\ \cline{2-10}  & Blue & Green & Red & NIR & Blue & Green & Red & NIR \\ \hline \multirow{2}{*}{\(r\)} & 0.9334 & 0.9199 & 0.9315 & 0.8654 & 0.9338 & 0.9251 & 0.9314 & 0.8643 \\ Var & 0.0002 & 0.0003 & 0.0004 & 0.0006 & 0.0002 & 0.0003 & 0.0004 & 0.0006 \\ MAD & 0.0083 & 0.0105 & 0.0102 & 0.0098 & 0.0082 & 0.0096 & 0.0102 & 0.0105 \\ RMSE & 0.0287 & 0.0245 & 0.0287 & 0.0317 & 0.0272 & 0.0274 & 0.0261 & 0.0289 \\ bias & \(-\)0.0249 & \(-\)0.0165 & \(-\)0.0211 & \(-\)0.0195 & \(-\)0.0231 & \(-\)0.0211 & \(-\)0.0175 & \(-\)0.0141 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the correlation analysis between synthetic and actual GF-1 WFV data.
## 6 Results
### Efficiency Tables  
The effectiveness of contaminant removal, as stated earlier, was described using the concentration-(contact) time product (CT) and the reported (removal) percentage as shown in Table 2. The analysis of model pollutant, phenol gives CT values 5570 mg/L-min (chlorination) and 1200 mg/L-min (ozonation). The reported corresponding removal efficiencies are 99%(chlorination) and >99% (ozonation). The equivalent representative quantity under plasma technology for the same model contaminant (phenol) is the PT  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Contaminant**} & \multicolumn{4}{c}{**Chlorination Technology**} & \multicolumn{4}{c}{**Oxonation Technology**} \\ \cline{3-8}  & & **CT (mg/L \(\times\) min)** & **Conc\({}^{*}\)\(\times\) Time \({}^{*}\)** & \% Removal** & **CT (mg/L \(\times\) min)** & **Conc\({}^{*}\)\(\times\) Time \({}^{*}\)** & \% Removal** \\ \hline Organic: EDC & Phenol & 5570 [(90)] & 5570 \(\times\) 1 & 99 & 1220 [(91)] & 80 \(\times\) 15 & \textgreater{}99 \\ Organic: EDC & Carthanazepine & 1065 [(92)] & 172.5 \(\times\) 60 & 40 & 2.2 [(93)] & 0.44 \(\times\) 5 &  
(power \(\times\) time) value of 2430 W\(\cdot\)min (as in Table 3) with associated removal efficiency of 98%. The breakdown of other contaminants by the three technologies may be compared in a similar way. This analysis of phenol removal and comparison among technologies gives basic knowledge for further investigations and scaling up to pilot schemes with respect to the plasma technology.
# Enhanced Control Strategy for Three-Level T-Type Converters in Hybrid Power-to-X Systems
## 5 Verification  
### Simulation Validation  
Simultaneously, simulations were performed using Powersim software to validate the feasibility of the proposed control. The proposed control was applied on a three-level, three-phase T-type inverter, as shown in Figure 4, with designed parameters listed in Table 1. Simulation results are shown in Figures 10-13. Figures 10 and 11 demonstrate the capability of the proposed design of precise tracking. The reference and output \(\alpha\) and \(\beta\) control loops of the capacitor/output voltage outer loop (Figure 10) and the inverter side inductor current (Figure 11). Three phase signals of the output voltage (top) and inverter current (bottom) are shown in Figure 12. It may be concluded that tracking performance remains satisfactory  
Figure 9: Block diagram of the DC-link capacitors voltage balancing control.  
at all times, demonstrating the validity and robustness of the proposed controller. Figure 13 demonstrates the DC-link capacitors' voltage balancing control. Figure 13a shows zoomed-in results of the DC-link capacitors voltage \(v_{\text{C1}}\) and \(v_{\text{C2}}\). Results clearly show that voltage balancing loop control stabilizes the voltage difference between the DC-link capacitors. Figure 13b shows the modulation signals of the three phases before \(v_{mx}\) (top) and after \(v_{mx1}\) (bottom), the correction of the three phases. Figure 14 illustrates the ability of the proposed design to accurately track sudden step changes in the reference signal magnitude, even under worst-case conditions caused by abrupt load variations. The simulation results further confirm that the system effectively maintains precise tracking of these step changes, achieving near-zero steady-state error. To conclude, the simulation results obtained have clearly proved the solid performance of the proposed controller.  
Figure 11: Simulation results of the inverter side inductor current reference and output signals of both \(\alpha\) (top) and \(\beta\) (bottom) loops.  
Figure 10: Simulation results of the capacitor voltage reference and output signals of both \(\alpha\) (**top**) and \(\beta\) (**bottom**) loops.  
Figure 14: Simulation results three phase signals of output voltages and inverter side inductors currents.  
Figure 12: Simulation results of the three-phase signals of output voltages (**top**) and inverter side inductors currents (**bottom**).  
Figure 13: Simulation results of **(a)** DC-link capacitors voltages and (**b**) modulation signals of the three phases before (**top**) and after (**bottom**) the correction.
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 3 Preliminary
### Problem Description  
The emergency communication network serves as the core supporting system for post-disaster rescue operations, where its deployment efficiency and operational reliability  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Model** & **Method** & **Core Idea** \\ \hline AlphaGo, AlphaZero [16; 17] & Neural Networks + Monte Carlo Tree Search & Achieve game-playing capabilities without human prior knowledge. \\ \hline DRL-EC [18] & DQN Algorithm & Optimize UAV hovering positions to maximize communication coverage and endurance. \\ \hline DRL-Urban-Planning [19] & GNN-based Reinforcement Learning & Allocate land and road positions to enhance urban space utilization. \\ \hline TorchCraft AI [20] & FCN-based Reinforcement Learning & Select optimal building sites based on terrain features. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typical applications in intelligent deployment.  
directly impact life-saving effectiveness and the control of disaster losses. In complex and dynamic disaster scenarios, such networks must possess rapid networking capabilities, multi-service support, and high robustness to enable diverse communication services--including messaging, audio, and video--while meeting multi-dimensional requirements for on-site command coordination, medical collaboration, and resource allocation [2].  
As illustrated in Figure 1, the network architecture of an emergency communication system is jointly composed of a transmission network and an access network [21]. The access network focuses on rapid connectivity of field terminal devices, utilizing technologies like trunked intercoms and 5G mobile networks to provide rescuers with messaging, voice, and low-latency data services, ensuring real-time command at the front line. The transmission network adopts a redundant architecture integrating multiple links such as satellite, microwave, and fiber-optic cables, and can forward data through public network to establish an efficient and stable high-speed information transmission channel between the rear command center and the disaster site [7]. This multi-dimensional transport mode not only significantly enhances video transmission quality but also ensures communication continuity in extreme environments through redundant link backup mechanisms.  
To enhance the deployment efficiency and communication reliability of emergency communication networks, scientific planning of the deployment locations for critical components--including field command centers, ATM switches, satellite communication vehicles, microwave relay vehicles, optical terminal equipment, 5G base stations, and trunked base stations--is required. The deployment strategy should comprehensively account for geographical environments and communication methods to achieve a dynamic balance between communication quality and coverage efficiency.
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 5 Distributed Machine Learning for 6G
### Communication-Efficient Federated Learning
#### 5.1.2 Federated Learning Training Method Design  
In addition to using wireless transmission techniques, new training methods can be used to increase the learning parameters, allowing FL to be applied to wireless networks more effectively.  
As wireless devices have limited processing capabilities and energy, the number of the model parameters that can be trained and sent by them is often limited, as is the time frame in which they can be employed for training. When creating a FL training method, all of these factors must clearly be taken into account. Furthermore, the FL training methods determine the topologies of the networks produced by the devices, which has an impact on both the training complexity and the convergence time. As a result, not only the characteristics of the wireless channel, but also the location and mobility of the devices, must be taken into account while designing FL training techniques.  
The authors of [51] used decentralized averaging approaches to update each device's local ML model in order to include more devices in FL participation and lessen their reliance on PS. In particular, with these methods, each device needs to transmit its parameters only to the devices close to it to make an estimate of the global model. In this way, the communication load is considerably reduced, and provides the possibility to connect and give one's contribution to the FL even to devices limited by scarcity of resources or low connectivity.  
The performance of FL with centralized averaging can be seen in the example considered in [51], where six devices interacting with a BS were involved. In a first implementation of the original FL, only 4 of the 6 devices would be able to contribute, due to insufficient requirements in terms of latency. On the other hand, with this decentralized architecture, even devices that would have been excluded in the prior situation are allowed to contribute by just connecting with the device closest to them. This type of architecture gives a significant improvement in terms of accuracy.  
As already highlighted, centralized algorithms will not be able to meet the latency and privacy requirements needed for cellular network applications in post-5G technologies. For this reason, approaches that allow data to remain at the edge of the network (smart-phones or IoT devices) and implement a learning algorithm without having to be transferred are rousing increasing interest, both in telecommunications and in the IoT industry.
# Transmitter Diversity Gain Technique Aided Irregular Channel Coding for Mobile Video Transmission
## 5 IRCCs Channel Code Design
Therefore, the employed IRCC is capable of providing different error protections to the coded bitstream and it can be varied, depending on the specific order of concatenation of the three H.264/AVC partitions per video frame. The IRCC percentage vector derived using the IRCC optimization procedure reveals a IRCC subcode \(r\) with code-rate \(=0.30\) encode \(0.6\%\) of the stream, while that of other subcodes encodes the corresponding portion of the video stream according to the input percentage vector. Figure 4 shows the EXIT chart of the 17 subcodes of IRCC. It is justified from the figure that different code-rates produces different curves on the EXIT chart, used to find the intersection with the inner precoder curve, at which turbo cliff is achieved.
# Dynamic Analysis of Underwater Torpedo during Straight-Line Navigation
## 4 Numerical Model
### Results of Torpedo Direct Navigation under Munk Moment of 0  
With a fixed thrust T = 50 KN, the torpedo's Munk moment coefficients are changed to 0, 0.02, 0.04, 0.06, 0.08, and 0.10, respectively, and the torpedo-related motion parameters can be achieved by simulating the torpedo direct sailing motion and observing the change of its sailing state. Under the simulation condition that the Munk moment coefficient is 0, then the velocity diagram in the \(x\)-direction, horizontal displacement diagram in the \(x\)-direction, the depth-fixed navigation diagram and the lateral displacement diagram of the model are drawn to verify the effectiveness of the torpedo direct navigation motion model.  
It can be seen from Figure 2 that during the simulation time, the speed of the torpedo reached a stable state, and the steady speed was about 20.76 m/s, which is consistent with the sailing speed of general torpedoes. The sailing distance of the torpedo reaches about 2000 m in the \(x\)-direction and basically shows a linear growth. The torpedo can maintain a stable navigation depth at this speed, which conforms to the actual working situation of the torpedo. The lateral displacement of the torpedo is basically 0 within the simulated time of 100 s. That is to say, the torpedo basically maintains straight-line navigation in the \(x_{0}O_{0}y_{0}\) plane. The above conclusions well verify the effectiveness of the torpedo direct navigation model; therefore, the following simulation can be carried out based on this model.  
Figure 1: Schematic diagram of torpedo model.
## 6 Methodology and Performance Metrics Measurement
### Normal Condition
#### Ospf  
OSPF plays a critical role in enabling flexible and efficient routing decisions within the network. As a dynamic routing protocol, OSPF is particularly effective in disseminating routing information across an autonomous system involving multiple connections. In the current configuration, OSPF is responsible for directing data traffic along the most efficient paths. The data traffic traverses the overlay network (Tunnel), while internet traffic is managed by the underlay network. Tunnel-1 serves as the primary connection for data traffic, with Tunnel-2 acting as the backup. Additionally, OSPF manages internet traffic by designating ISP-B as the primary connection and ISP-A as the backup. This arrangement facilitates load sharing, provides link-failover capabilities, and optimizes network performance by distributing traffic across multiple links. In the event of a primary link failure, OSPF dynamically redirects traffic to the secondary link, thereby minimizing downtime and ensuring uninterrupted connectivity. The responsive and adaptive routing capabilities of OSPF enable the load-sharing and link-failover system to effectively utilize network resources while maintaining stable connectivity for both data and internet traffic. Figure 3 illustrates the ELGRP topology with dual ISPs, and Figure 6 presents the OSPF topology with dual ISPs under normal operating conditions.  
Figure 5: Traceroute from Branch-2.  
Figure 6: OSPF normal topology.  
The traceroute results for data and internet traffic originating from Branch-1 with OSPF enabled in the topology are shown in Figure 7. A closer inspection reveals that internet traffic is routed via ISP-B, while data traffic from Branch-1 to Branch-2 is transmitted through the primary tunnel assigned to ISP-A. This approach ensures efficient load sharing by distributing both incoming and outgoing traffic across the two ISP connections.  
The traceroute results for data and internet traffic originating from Branch-2 while OSPF is active in the topology are depicted in Figure 8. The results show that data traffic from Branch-2 to Branch-1 is routed through the primary tunnel assigned to ISP-A, while internet traffic is directed via ISP-B. This configuration effectively distributes both incoming and outgoing traffic across the two ISP connections, optimizing network resource utilization.
# Modelling of Failure Behaviour of 3D-Printed Composite Parts
## 3 Results and Discussion
### Isotropic Material (ABS Polymer)  
In this section, 3D-printed parts with ABS polymer are considered. The developed computational models are applied for damage modelling four different test coupons subjected to uniaxial tensile loading. The stress-strain curves are obtained using the present computational models and are then compared with existing experimental work.  
The simulation results based on the computations models of CM-1 and CM-2 are in good agreement with the stress-strain curves of experimental work. Figure 6 shows the comparison of results for the printed parts with cross-ply and angle-ply printing strategies. The results obtained from the computational model of CM-1 are higher than those of other computational models and experimental work [6]. However, the stress-strain curve obtained from the results of CM-2 is comparable with the experimental results. The above discrepancy in the computational models is due to the CM-1 utilizing the virgin material  
Figure 5: Finite element models of RVEs of 3D-printed parts for computational methodology-2 (CM-2) (a) ABS polymer, (b) ABS + sCF composite material.  
properties for damage modelling. In contrast, the CM-2 utilizes testing data of 3D-printed parts for modelling nonlinear behaviour. Further, the difference in the experimental and computational models is due to the assumptions made in the finite element models.  
Further, the results of the present computational models are compared with Tsai-Hillply-based failure theory. For this, we calculated \(U_{t}\)-strength, \(E_{x}\)-stiffness along the x-axis, and \(\epsilon_{t}\)-strain to failure from the results of computational models. Then, these properties are then compared with both experimental work and Tsai-Hillply failure theory in Table 4. The Tsai-Hill failure criterion predicted lower stiffness and strength for 3D-printed parts. This difference in the results is due that the failure criterion employing the properties that were obtained from the mechanical testing results of uniaxially 3D-printed test coupons. The present computational models estimated higher results because of the assumptions in the FE models. However, the computational model based on CM-2 predicted accurate values because the nonlinear data for this is obtained from the mechanical testing data of uniaxially 3D-printed test coupons. The difference between the computational results of the two printing strategies (cross-ply, angle-ply) is not significant, but from the experimental work, it is observed that the angle-ply 3D-printed parts sustained higher strain before undergoing a complete fracture when compared to the cross-ply 3D-printed parts.