esa-sceva/satcom-chunk-collection huggingface dataset:
# Dispersion Management and Pulse Characterization of Graphene-Based Soliton Mode-Locked Fiber Lasers
## 4 Optical Characterization of Graphene/PMMA-SA  
The optical transmission measurement of the graphene/PMMA-SA is carried out by using a broadband amplified spontaneous emission (ASE) source (Amonics ALS-CL-17-B-SC), and a Yokogawa AQ6370B optical spectrum analyzer (OSA) within a 1525 to 1656 nm wavelength range. Figure 4 shows the result of the measurement, which indicates that the transmission loss of the graphene/PMMA-SA is less than 0.6 dB across the entire wavelength range. Previous reports have proved graphene to be the broadband SA, which could cover as wide as a1000 nm to 2000 nm wavelength range [39]. However, the charac  
Figure 3: EDX spectra of the deposited GPTF on a fiber core.  
Figure 2: (**a–d**) Fabrication procedures of graphene/PMMA-SA.  
terization of graphene/PMMA-SA beyond the C-band region could not be demonstrated due to the operational wavelength limitation of the available OSA and ASE source used in this work.  
The nonlinear optical properties of the graphene/PMMA-SA are characterized by employing a balanced twin detector method, which consists of an M-fiber Menlosystem pulsed fiber laser source with a central wavelength of 1560 nm, pulse repetition rate of 250 MHz, and pulse duration of 117 fs. Figure 5 illustrates the nonlinear saturable absorbance of graphene/PMMA-SA, which can be well-fitted by a solid curve based on the nonlinear saturable absorbance formula [40], as expressed in Equation (1),  
\[\alpha(I)=\frac{\alpha_{0}}{1+\frac{I}{I_{sat}}}+\alpha_{ns} \tag{1}\]  
where \(\alpha(I)\) is the intensity-dependent absorption coefficient, \(I_{sat}\) is the saturation intensity, \(\alpha_{0}\) is the saturable absorbance (modulation depth, MD), \(I\) is the peak intensity, and \(\alpha_{ns}\) is the non-saturable absorbance. Based on the curve fitting, the modulation depth and saturation intensity of graphene/PMMA-SA are estimated to be approximately 0.8% and 2.2 MW/cm\({}^{2}\), respectively. This encourages great prospects of the graphene/PMMA-SA to perform effectively in generating ultrafast fiber lasers. In addition, the PMMA also holds graphene firmly in place on the fiber facet to ensure its interaction with the waveguided light.  
Figure 4: Transmission loss of graphene/PMMA-SA across C-band wavelength region.  
Figure 5: Nonlinear saturable absorbance properties of graphene/PMMA-SA.
## 4 Numerical Experiment and Analysis
### Impact of the Number of Measuring Points  
The number of temperature measurement points was taken as \(M=\) 2, 4, 6. The maximum number of iterations was 25, and the temperature at the measurement point and the corresponding thermal conductivity were obtained by inversion. The inversion result is shown in Table 1.  
At the measurement point \(M=\) 2, 4, 6, \(\lambda\) was calculated by the least square method, and the relative error is shown in Table 1. Results are as follows:  
* When the measurement error was 0: When \(M=\) 2, the relative error of \(\lambda\) was \(-\)0.33%; when \(M=\) 4, the relative error of \(\lambda\) was 0.04%; and when \(M=\) 6, the relative error of \(\lambda\) was 0.00%.
* When the measurement error was \(\pm\)0.1%: When \(M=\) 2, the relative error of \(\lambda\) was \(-\)0.43%; when \(M=\) 4, the relative error of \(\lambda\) was \(-\)0.25%; and when \(M=\) 6, the relative error of \(\lambda\) was \(-\)0.11%.  
Thus, it is shown that by increasing the number of measuring points, the relative error decreases and the inversion accuracy improves.
## 1 Introduction
### Structure of the Paper  
The remainder of this paper is organized as follows: Section 2 reviews related work on optimization algorithms, emphasizing the history of Gradient Descent (GD), its evolution, and the developments leading to modern variants. Section 3 presents the preliminaries, including the fundamentals of GD and the Adam optimization algorithm. Section 4 introduces the proposed MAGD algorithm in detail. Section 5 describes the experimental setup and results, comparing MAGD's performance with GD, Adam, and Swarm-Based Gradient Descent (SBGD) on benchmark optimization problems and neural network training tasks. Finally, Section 6 concludes the paper by summarizing the theoretical and empirical findings, discussing research limitations, and providing recommendations for future work.
# Quality of Experience in 6G Networks: Outlook and Challenges
## 2 Quality of Experience in the Cellular Evolution
### QoE in the First Four Generations
#### 2.1.3 Fourth Generation Networks and QoS Legacy  
Before 4G, a migratory transition called long term evolution (LTE) was introduced with critical changes at the access level interface and architecture and at the core [33]. These changes allowed for a fully packet-switched core and up to 300 Mbps (under a 4 \(\times\) communications through 4 Multiple-Input Multiple-Output (MIMO) at a 20 MHz allocation) in the downlink. This evolved packet core (EPC) enabled a fully IP-based cellular network with mobility and billing management across multiple radio interfaces.  
True 4G, called LTE-Advanced, is currently at 338 deployments worldwide and continues to be rolled out [1]. LTE-Advanced saw significant improvements over LTE in terms of access layer technology that significantly improved downlink rate (up to 1 Gbps).  
Transitioning to 5G, 3 GPP Releases 13 and 14 introduced what is called LTE-Advanced Pro. These releases also allowed for a more capable carrier aggregation, yielding a possible 3 Gbps data rate in the downlink. Meanwhile, the releases allowed for the use of an unlicensed spectrum and provided the means for supporting IoT access to the cellular network. Further details on these releases can be found in [34; 35].  
A key observation to be made from the evolution discussed above is a focus on improving mobile user experience through enhancing data rates, which meant coupling QoE with a QoS-based view in managing the network, i.e., in isolation of the user perception.  
The 4G enhancements in data rates came with supporting services such as multiradio access and offloading, location-based services, etc. While 5G networks will continue to observe order-of-magnitude-increase-per-generation growth, a strong emphasis has been made on supporting operational flexibility and new communication modes. These and other aspects of 5G networks are discussed next. Further details on cellular networks' evolution and expanded descriptions of 4G networks can be found in [33; 36].
# Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Network
## 3 Modeling And Problem Formulation
### Delay Model
#### 3.2.4 Service Migration Delay  
The delay in service migration is similar to the delay in task offloading. This is mainly related to the service capacity and the transmission speed. The migration decision time is negligible compared to the transmission delay [44]. The migration delay can be expressed as:  
\[d_{cm}^{i,t}=\frac{H_{i}.bytes}{V_{tr}^{j,t}}+d_{r} \tag{5}\]  
where \(H_{i}.bytes\) is the service capacity, \(V_{tr}^{j,j,t}\) is the transmission speed between original edge server \(j\) to the migration target edge server \(j^{\prime}\), and \(d_{r}\) is the service reconfiguration time.
# Survey of Blockchain-Based Applications for IoT
## 4 Identity and Access Control
### Authorization  
Permissioned blockchain networks provide a secure and decentralized approach to authorizing and registering IoT devices, ensuring that only verified and trusted devices can participate in the network. Unlike public blockchains, which allow anyone to join and interact, permissioned blockchains require explicit approval for devices to be registered and authorized, reducing the risk of malicious actors infiltrating the network.  
Before an IoT device can be fully integrated into the network, it must go through a verification process where its identity, origin, and integrity are confirmed. This involves validating unique device credentials, such as cryptographic keys, digital signatures, or hardware-based security identifiers like PUFs. Once verified, the device is formally approved and granted authorization, allowing it to communicate, perform transactions, and interact securely within the ecosystem.  
Organizations can adopt either centralized or decentralized identifier (DID) approaches. In a centralized model, a trusted entity or certificate authority (CA) is responsible for issuing and managing device credentials, ensuring that only authorized devices can join the network. While this approach provides a controlled and structured authentication process, it also introduces a single point of failure and scalability limitations. Alternatively, DID frameworks, built on a blockchain or DLT, allow devices to maintain self-sovereign identities, reducing reliance on central authorities. For large-scale deployments, this process becomes increasingly challenging.  
To address large-scale and distributed blockchain-based deployment for IoT networks, [PERSON] et al. [30] suggested that IoT devices should be registered using a blockchain-based digital certificate (blockCert) during the manufacturing stage to ensure secure identity management. The device's unique identifier (PUF-based ID), MAC address, and manufacturer details are included in this blockCert. Each device then undergoes a multi-tier verification process, where distributors and customers validate its authenticity before deployment. This approach prevents tampering, counterfeiting, and unauthorized device inclusion in the network by leveraging blockchains' decentralized and immutable nature for trusted device authentication.
# Visual Tracking with FPN Based on Transformer and Response Map Enhancement
## 4 Results
### Algorithm Comparison
#### 4.2.3 GOT-10K Benchmark  
GOT-10K is a challenging large-scale benchmark. To test the generalization of the tracking algorithm, there is no class overlap between the training subset and testing subset. GOT-10K employs Average Overlap (AO), Success Rate (SR 0.5, 0.75 mean threshold) and Frames Per Second (FPS) as the measurement. If FPS > 30, the algorithm meets real-time property. We evaluated TR-Siam on GOT-10K and compared it with other state-of-the-art trackers.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Tracker** & **Precision \(\uparrow\)** & **Normalized Precision \(\uparrow\)** & **Success \(\uparrow\)** \\ \hline SINT [4] & 0.295 & 0.354 & 0.314 \\ ECO [14] & 0.310 & 0.338 & 0.324 \\ SiamFC [5] & 0.339 & 0.420 & 0.336 \\ MDNet [33] & 0.373 & 0.460 & 0.397 \\ SiamMask [34] & 0.469 & 0.552 & 0.467 \\ SiamRPN++ [7] & 0.491 & 0.569 & 0.495 \\ ATOM [31] & 0.497 & 0.605 & 0.499 \\ SiamCAR [18] & 0.507 & 0.600 & 0.507 \\ Ours & 0.527 & 0.606 & 0.528 \\ \hline \end{tabular}
\end{table}
Table 1: Results on LaSOT benchmark. Red fonts indicate the top tracker \(\uparrow\). Indicates that the larger the parameter value, the better the tracker performance.  
Figure 7: Precision scores of different attributes on the UAV123 data set. Take Overall as an example. In (0.719,0.832), 0.719 represents the algorithm with the lowest precision under this attribute, and 0.832 represents the algorithm with the highest precision under this attribute.  
As demonstrated in Table 2, the proposed method achieved the best performance among all comparison algorithms. TR-Siam achieved a 2.7% higher performance on AO and 3.8% on \(SR_{0.75}\) compared with the baseline tracker SiamCAR. Additionally, the FPS is 45, which meets the real-time property in 1080P resolution ratio.
# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
###### Abstract  
The use of unmanned aerial vehicles (UAVs) is increasing in transportation applications due to their high versatility and maneuverability in complex environments. Search and rescue is one of the most challenging applications of UAVs due to the non-homogeneous nature of the environmental and communication landscapes. In particular, mountainous areas pose difficulties due to the loss of connectivity caused by large valleys and the volumes of hazardous weather. In this paper, the connectivity issue in mountainous areas is addressed using a path planning algorithm for UAV relay. The approach is based on two main phases: (1) the detection of areas of interest where the connectivity signal is poor, and (2) an energy-aware and resilient path planning algorithm that maximizes the coverage links. The approach uses a viewshed analysis to identify areas of visibility between the areas of interest and the cell-towers. This allows the construction of a blockage map that prevents the UAV from passing through areas with no coverage, whilst maximizing the coverage area under energy constraints and hazardous weather. The proposed approach is validated under open-access datasets of mountainous zones, and the obtained results confirm the benefits of the proposed approach for communication networks in remote and challenging environments.  
U
## 2 Maximum Performance Evaluation
### Numerical Results
The MPE results at the end-effector position \(\mathbf{p}_{e}\)= (200 mm, \(-150\) mm) can be represented in the form of polygons (the so-called allowable polygons in [25]), as shown in Figure 5. The performance results of the DAM-3R and JAM-3R are expressed in solid and dotted lines, respectively. It can be clearly seen that the end-effector velocity of the DAM-3R was greater for all 16 base directions than that of the JAM-3R with no payload. As explained in our previous work [24], this is due to an appropriate change in the gear ratio depending on the position of the sliders. Figure 6 shows the MPE results for both the DAM-3R and JAM-3R at nine target positions equidistantly distributed in the workspace (Figure 6). Although the DAM-3R used a motor with 1.7% less power (Table 1), the maximum end-effector velocity was up to 31.9% higher at (200 mm, \(-150\) mm). Figure 4: Flowchart of the optimization framework using the analysis module (RecurDyn) and the optimization module (MATLAB). Figure 5: The maximum end-effector velocity evaluated for the DAM-3R (red solid line) and JAM-3R (magenta dotted line) at the end-effector position (200 mm, \(-150\) mm). The MPE results obtained above can be considered as the upper bounds of the maximum end-effector velocity at each end-effector position. Under such velocity constraints, the dynamics of the end-effector position can be expressed by using a first-order model in MPC (see Section 3.2 for more details), which can significantly simplify the MPC formulation for time-optimal trajectory planning without a significant loss of accuracy. We note that it is difficult to precisely evaluate the maximum performance of a redundant manipulator such as a DAM without the aid of the MPE.
This means that our proposed model can effectively learn features from data with complex structures and reduce the data dimensions, whereas it can use the spatial and meteorological features from previous timestamps to predict the location at the next timestamp, thereby improving the prediction accuracy. Our motivation is to construct a prediction model that accounts for the complexity of massive tropical cyclone track data and meteorological variables. More specifically, we provide a fusion deep learning model that combines an auto-encoder layer with a GRU layer to predict tropical cyclone tracks. The main contributions are summarized below:  
(1) The proposed AE layer of our model tends to learn and compress the preprocessed data to reduce the complexity, whereas the outputs of the AE layer are taken as the input data to the GRU layer for training, increasing both the prediction efficiency and accuracy. (2) A novel customized batch method is proposed to process the non-equalized input dataset for the GRU model. (3) Our proposed model is implemented in a real-world tropical cyclone dataset, and the experiment results demonstrate that the proposed AE-GRU model performs better than some existing traditional methods and deep learning methods. The rest of the paper is organized as follows: Section 2 reviews studies related to this subject. Section 3 introduces the proposed model based on an AE neural network and a GRU neural network. Section 4 presents the experimental results, and Section 5 summarizes this study and offers suggestions for future research.
# A Proposal for a Federated Learning Protocol for Mobile and Management Systems
## 3 The Proposed Framework Components  
A fundamental prerequisite in our design is the establishment of a reliable communication channel between the terminal devices and the central server. This channel is crucial for transmitting loss calculations and model weights. Our design accounts for the versatility of this system to be applied across various technologies and includes provisions for backup communication methods. Furthermore, we must address the challenge of preventing the central server from becoming overwhelmed by multiple client requests. To meet this requirement, we accommodate the deployment of multiple independent central servers that synchronize during the learning process. The conceptual framework for connecting these central servers and end clients is depicted in Figure 1.  
Figure 1: The components of the conceptual framework.  
In this design framework, multiple central servers are employed simultaneously, and communication is facilitated using a combination of Apache Kafka [20] and a real-time database. A real-time database is a data repository that actively gathers, processes, and enhances data immediately upon their creation, ensuring that the database remains up-to-date in real time. Kafka serves as an auxiliary communication source rather than the primary one. This choice is motivated by the need to mitigate potential data loss in the event of communication failures, necessitating the implementation of a mechanism for data retransmission. It is worth noting that our design departs from the common practice of using the protobuffer method for data transfer, instead opting for text-based communication as per the specified requirements.  
For the real-time database component, we have chosen the Firebase Realtime Database [21] for the development. Additionally, on the end clients, we utilize the default TensorFlow Lite [22] framework provided by Google, while the servers employ TensorFlow as their primary framework. These technology choices align with our intention to leverage the capabilities and compatibility offered by Google technologies to ensure the efficient and effective operation of the system.
## 4 Result and Discussion
### Experiment Setup  
Any cloud scheduling heuristic's performance is evaluated using a variety of methodologies, including analytical, experimental, and simulation techniques. The challenge with experiments on actual clouds is the cost and frequent need for a specialist for the setup. Furthermore, using the cloud platform for simulations results in high monitoring costs. The analytical methods' drawback is that they cannot assess the suggested scheduling schemes in their entirety. Tools for simulation are frequently used to evaluate the effectiveness of contemporary approaches. This study used Cloudsim to compare the suggested technique to the four available contemporary approaches (EEFO, DEERA, ELBS, and DEBTS). The simulations were run on a system outfitted with an Intel Core i5-8500 (TM) CPU (3.00 GHz) and 20 GB of RAM.
## 4 Results Analysis
### Analysis of Calculation Results
#### 4.2.3 General Prevention and Control Zoning for Flash Floods  
The general prevention and control zoning for flash floods includes a total of 1443 small watersheds, with a total area of 239,413.68 km\({}^{2}\), accounting for 63.47% of the total area of the Yunnan administrative division. Among these small watersheds, there are 889 small watersheds with flash flood prevention and control villages and a total area of 158,203.89 km\({}^{2}\). From the perspective of spatial distribution, among the 16 cities under the jurisdiction of Yunnan, general prevention and control zoning for flash floods in 10 cities accounted for more than 60% of the administrative area, and general prevention and control zoning for flash floods in five cities accounted for more than 70%. This is because the flash flood control capacity of the villages along the river in most cities is higher than the 20-year return period and the population in the flash flood danger area is relatively small; therefore, the general prevention and control zoning in the flash flood is larger.
_Article_  
**Assessment of Wide-Sense Stationarity of an Underwater Acoustic Channel Based on a Pseudo-Random Binary Sequence Probe Signal**  
**[PERSON](r)**  
Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology,  
Narutowicza 11/12, 80-233 Gdansk, Poland; [EMAIL_ADDRESS]  
Received: 28 December 2019; Accepted: 5 February 2020; Published: 11 February 2020  
**Featured Application: The method described can be used to assess whether the signal received in the underwater acoustic communication system can be modelled as a stochastic wide-sense stationary process. The fulfillment of this condition is necessary both to calculate the basic transmission parameters of the communication channel, such as coherence bandwidth and coherence time, and to correctly detect information transmitted in a UAC systems using modern modulation and coding schemes, such as orthogonal frequency-division multiplexing or spread spectrum techniques. A fast and energy efficient method of assessing whether the received signal meets the WSS assumption can be implemented in a UAC adaptive system, enabling the change of modulation parameters, in particular, the duration of the modulation symbol for which the WSS assumption is fulfilled, thereby increasing the UAC system's reliability.**  
**Abstract: The performances of Underwater Acoustic Communication (UAC) systems are strongly related to the specific propagation conditions of the underwater channel. Designing the physical layer of a reliable data transmission system requires a knowledge of channel characteristics in terms of the specific parameters of the stochastic model. The Wide-Sense Stationary Uncorrelated Scattering (WSSUS) assumption simplifies the stochastic description of the channel, and thus the estimation of its transmission parameters. However, shallow underwater channels may not meet the WSUS assumption. This paper proposes a method for testing the Wide-Sense Stationary (WSS) part of the WSSUS feature of a UAC channel on the basis of the complex envelope of a received probe Pseudo-Random Binary Sequence (PRBS) signal. Two correlation coefficients are calculated that can be interpreted, together, as a measure that determines whether the channel is WSS or not. A similar wide-sense stationarity assessment can be performed on the basis of the Time-Varying Impulse Response (TVIR) of a UAC channel. However, the method proposed in this paper requires fewer computational operations in the receiver of a UAC system. PRBS signal transmission tests were conducted in the UAC channel simulator and in real conditions during an inland water experiment. The correlation coefficient values obtained using the method based on the envelope of a probe signal and the method of analysing the TVIR estimates are compared. The results are similar, and thus, it is possible to assess if the UAC channel can be modelled as a WSS stochastic process without the need for TVIR estimation.**  
**Keywords: underwater acoustic communications; wide-sense stationary; pseudo-random binary sequence**