esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Network Layer: The Main Building Block of Fixed Networks
### Five Generations of Access: From PSTN to 50G EPON
#### 4.1.3 F1G--1990's (Wideband Era)  
In the 1990s, although backbone networks could provide high-capacity links based on SONET/SDH systems (over 10 Gb/s), access networks were the limiting factor for the provision of broadband services to users, e.g., video on-demand, interactive/video games and video conferencing. Access networks, such as DSL, could provide relatively low bandwidth, especially in the upstream direction (in the order of a few hundred kb/s). Moreover, even if some versions of DSL could provide a higher bandwidth (50 Mb/s) in the downstream direction, the access distance is short enough (up to 6 km, or, in the case of 50 Mb/s, significantly less) [5]. It is worth mentioning that DOCSIS systems used by cable television operators provided not only cable television (CATV--Community Antenna Television) but also internet access over the existing infrastructure of HFC, covering a much longer distance (up to 160 km) and offering a broadband access (up to 1 Gb/s in the downstream direction). However, since these systems have been designed for broadcasting, their use as access networks by allocating bandwidth to users according to demands, it is not the most proper solution for broadband access. Nevertheless, HFC showed that troubleshooting existing access networks could be accomplished by the extensive use of fiber optics, which offer ample bandwidth with very low losses [6].
# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
## 3 Methods
### Evaluation Metrics  
Two different metrics are used to evaluate the success of each method, _mean absolute error_ (MAE) and Pearson correlation coefficient. Mean absolute error is a common method for evaluating a regression method and is in units that are directly understandable [27]. Pearson correlation coefficient provides additional insight into regression performance by giving an indication of the linearity of the regression [28]. For each method studied, five fold cross validation is used with randomly selected groupings. In each table of reported results, the mean of the five folds is reported alongside the standard deviation, which is in parentheses. A one-sided, two sample \(t\)-test is used to determine if any other methods are statistically similar to the method with the lowest error. The method with the lowest error is bolded and any other method that is not significantly different (\(p>0.05\)) is also bolded.
## 2 QSAR--A Brief Overview
### Activities in QSAR and QSPR  
Quantitative Structure-Property Relationships (QSPRs) are closely related to QSAR, with the primary distinction being the type of activity studied. While QSAR focuses on biological activities such as K\({}_{\text{d}}\), K\({}_{\text{i}}\), ED\({}_{50}\), IC\({}_{50}\), and binary representations like active/inactive, QSPR investigates chemical or physical properties of compounds, including melting point, vapor pressure, pKa, logP, and more.  
In QSAR/QSPR analysis, the measured observation is often referred to as an \"end-point\", indicating that the value has been obtained through a series of experimental steps, ultimately yielding data suitable for analysis. End-points are typically classified into the following categories:  
* Physicochemical end-points: these include properties such as logP, logD, pKa, logS, and water stability.
* ADMET end-points: these involve parameters like permeability through passive transport measured via PAMPA (Parallel Artificial Membrane Permeability Assay) [44], blood-brain barrier permeability (PAMPA-BBB) [45], absorption [46], drug transporters assessed through P-glycoprotein transporter assays [47], and metabolites [48].
* Toxicological, drug interaction, and secondary pharmacology end-points: this category covers cytotoxicity [49], carcinogenicity [50], genotoxicity [51; 52], metabolism induction/inhibition [53; 54], and drug transporter inhibition [55].  
Figure 2: The workflow of Statistical Molecular Design is illustrated as follows: characteristics or descriptors of selected compounds are calculated to form the X matrix, which is then reduced using Principal Component Analysis (PCA). This results in two independent (orthogonal) principal components (PCs) in the given example. The scores of these components, referred to as principal properties (PPs), serve as the starting data for a full or fractional factorial design. In this design, the PPs are evaluated at two levels (low and high), resulting in 2(levels)2(PPs/factors) = 4 possibilities, as shown in the table on the right. A scatterplot provides a graphical representation of the PP scores. By aligning the PPs with the settings of the design, a diverse set of compounds is selected. In the given example, the “corner” selected compounds (1, 8, 13, and 20) are denoted in red circles on the scatterplot, with their values highlighted in red and bold in the PCA table.  
There is some overlap among these categories, as certain end-points may influence more than one classification.
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 3 5G Coverage Enhancement Techniques
### Non Orthogonal Multiple Access
#### 3.4.4 Security Issues  
In NOMA, multiple user messages are superimposed on a single RB. The SIC receiver is used to decode the overlaid signals on the receiver. In the SIC receiver, the strong users are allocated with lower powers and the weak users are allocated with stronger powers for channel fairness. At the receiver, strong users have to subtract the low-power signals to get
# A Multi-Frame PCA-Based Stereo Audio Coding Method
## 2 Multi-Frame PCA-Based Coding Method  
### Framework of PCA-Based Coding Method  
The encoding process can be described as follows: after time-frequency transformation such as MDCT, the frequency coefficients are used in the module of PCA, which includes the multi-frame technique. Several matrices are generated after PCA is quantized and encoded to bitstream. The decoder is the mirror image of the encoder, after decoding and de-quantizing, matrices are used to generate frequency domain signals by inverse PCA (iPCA). Finally, after frequency-time transformation, the encoder can export audio. Flowcharts of encoder and decoder for mono signals are shown in Figures 1 and 2. The part of MDCT is used to concentrate energy of signal on low band in frequency domain, which is good for the process of matrix construction (details are shown in Section 2.4). Some informal listening experiments have been carried out on the performance applying PCA without MDCT. The experimental results show that without MDCT, the performance of PCA has slight reduction, which means more bits are needed by the scheme without MDCT in order to achieve the same output quality of the scheme with MDCT. Thus, in this paper MDCT is assumed to enhance the performance of the PCA, although it will bring more computational complexity.
## 3 Results
### Time Analysis  
The temporal efficiency of the Slime Mold Algorithm in optimizing the UAV's neural network was a big aspect of this study. The initial iteration was the most time-consuming due to the intricate process of defining and tuning the neural network's parameters, which was critical for establishing a robust foundation for the algorithm's subsequent operations.  
A consistent decrease in processing time was observed across iterations, highlighting the neural network's improving computational efficiency and accelerated learning rate. This trend reflects the algorithm's capacity to adapt and optimize more effectively with each iteration.  
The balanced approach between exploration and exploitation within the algorithm underscores its adaptability to the neural network's evolving learning needs. The initial time investment, though substantial, was justified by the efficiency gains in subsequent iterations.  
The Slime Mold Algorithm showed promise in optimizing the neural network for a solar-powered UAV. Its ability to balance exploration and exploitation led to enhanced performance over successive iterations, with the initial investment in time proving crucial to the optimization process.  
Future studies should consider expanding the scope by increasing the number of iterations and incorporating a wider range of performance metrics. Additionally, applying the Slime Mold Algorithm to different types of neural networks or UAV applications could provide further insights into its adaptability and efficiency in various contexts.  
This study contributes to the field of AI-powered control systems for UAVs, particularly those relying on solar energy. By utilizing the Slime Mold Algorithm for neural  
Figure 4: Optimization results: (**a**) exploration vs. exploitation and (**b**) runtime.  
Figure 3: VLM discretization scheme (x, y, z in meters).  
network optimization, this study offered an alternative approach to traditional methods. The results demonstrated the potential of such algorithms in fine-tuning the weights and biases of neural networks, thereby enhancing the functionality and autonomy of solar-powered UAVs. The successful application of this algorithm in developing an AI-driven solar UAV control system supports its potential effectiveness in similar contexts.  
The success of the Slime Mold Algorithm in this study suggests new avenues for integrating hyper-heuristic algorithms into UAV control systems, especially in challenging environments. As current systems approach performance limits, exploring multiple optimization algorithms could further improve UAVs' efficiency and decision-making capabilities. This direction is promising for the future of UAV technology, particularly in scenarios requiring autonomous operation in complex environments.
## 3 Product Service System Case Study
### Product Service System Simulation Model  
The first step in the proposed process is to create a high fidelity PSS model. The Anylogic(tm), software was used to create the PSS model to simulate PSS usage and usage-based spare parts failures. Figure 1 shows the basic structure of the PSS discrete event simulation model. The PSS selected for the case study is typically sold in pairs (quantities of two) to customers, and used in serial configuration to process customer orders for the duration of the contract period. There are work in process (WIP) product inventory as shown in the model. When the simulation starts, document orders start to arrive into the facility. Each order contains a request for certain quantities of a finished product, which, in this case, is the document produced in the desired quantity. The production line initiates product manufacturing, and utilizes the PSS that is part of the total production system. Once requested product quantity is manufactured, the order is complete and the finished document products are processed for delivery to final recipients. The simulation model records PSS usage, which is equal to the total quantity of the products manufactured. When the PSS usage reaches a threshold for a particular spare part with a specific usage based failure rate, the part fails, and the PSS shuts down. The failed part is replaced with the part from the field repair kit inventory. Once the spare part is replaced, the usage count for that specific spare part is reset and the production resumes. The replaced part is replenished from the central warehouse to fill the field repair kit. If the failed part is out of stock at the customer's site, then an emergency order is placed, and the part is shipped from the central warehouse immediately, using one-day express shipment. When this happens, it is recorded as a down incident for the PSS.  
Figure 2 shows the Input-Process-Output (IPO) diagram for the PSS simulation model shown in Figure 1. Required input data are spare part life, quantity of spare part onsite and the monthly usage of PSS. Once the simulation ends, the model yields three specific outputs: (1) total demand for the  
Figure 1: Simulation model flow chart for industrial printing product service system.  
spare part required by PSS, (2) the total number of PSS down incidents due to spare part shortage, and (3) the PSS availability.
# Multi-Layered Satellite Communications Systems for Ultra-High Availability and Resilience
###### Abstract  
Satellite communications systems provide a means to connect people and devices in hard-to-reach locations. Traditional geostationary orbit (GEO) satellite systems and low Earth orbit (LEO) constellations, having their own strengths and weaknesses, have been used as separate systems serving different markets and customers. In this article, we analyze how satellite systems in different orbits could be integrated together and used as a multi-layer satellite system (MLSS) to improve communication services. The optimization concerns combining the strengths of different layers that include a larger coverage area as one moves up by each layer of altitude and a shorter delay as one moves down by each layer of altitude. We review the current literature and market estimates and use the information to provide a thorough assessment of the economic, regulatory, and technological enablers of the MLSS. We define the MLSS concept and the architecture and describe our testbed and the simulation tools used as a comprehensive engineering proof-of-concept. The validation results confirm that the MLSS approach can intelligently exploit the smaller jitter of GEO and shorter delay of LEO connections, and it can increase the availability and resilience of communication services. As a main conclusion, we can say that multi-layered networks and the integration of satellite and terrestrial segments seem very promising candidates for future 6G systems.  
non-terrestrial networking; 6G networks; network architecture +
Footnote †: journal: Electronics Electronics
###### Abstract  
The rapid development of sixth-generation (6G) mobile broadband networks and Internet of Things (IoT) applications has led to significant increases in data transmission and processing, resulting in severe traffic congestion. To better allocate network resources, predicting network traffic has become crucial. However, satellite networks face global imbalances in IoT traffic demand, with substantial variations in satellite density and load distribution within the same constellation. These disparities render traditional traffic prediction algorithms inadequate for dynamically changing satellite network topologies. This paper thoroughly examines the impact of adaptive time stepping on the prediction of dynamic traffic load. Particularly, we propose a high-speed traffic prediction method that employs machine learning and recurrent neural networks over the 6G Space Air Ground Integration Network (SAGIN) structure. In our proposed method, we first investigate a variable step size-normalized least mean square (VSS-NLMS) adaptive prediction method for transforming time series prediction datasets. Then, we propose an adaptive time stepping-Gated Recurrent Unit (ATS-GRU) algorithm for real-time network traffic prediction. Finally, we compare the prediction accuracy of the ATS-GRU algorithm with that of the fixed time stepping-Gated Recurrent Unit (FTS-GRU) algorithm and compared the prediction results of three different step sizes (FSS, VSS, and ATS) based on normalized least mean square (NLMS). Numerical results demonstrate that our proposed scheme can automatically choose a suitable time stepping to track and predict the traffic load curve with acceptable accuracy and reasonable computational complexity, as its time stepping dynamically adjusts with the traffic.  
satellite network; dynamic traffic load prediction; adaptive time stepping (ATS); Gate Recurrent Unit (GRU); machine learning Article
# Formation Mechanism and Response Strategies for Urban Waterlogging: A Comprehensive Review
## 3 Analytical Framework
### Numerical Simulation  
Numerical simulation plays a crucial role in the study of urban flooding, effectively predicting flood behavior and providing a theoretical foundation for urban planning and disaster prevention and mitigation [1; 72; 73; 74]. In recent years, with advancements in computational power and simulation accuracy, numerical models have become indispensable tools for analyzing urban flooding, addressing various aspects from rainfall simulation and flow simulation to flood damage assessment [8; 47; 75; 76; 77; 78]. Despite significant progress in inundation simulation and risk assessment with existing models, parameters' sensitivity, accuracy, and computational efficiency still require further optimization. [PERSON] et al. [76] indicated that rainfall distribution and urban representation methods significantly impact simulation results, and both water levels and urban representation techniques introduce substantial uncertainties into flood flow patterns. Since urban flooding is primarily caused by heavy rainfall and issues with drainage systems, our discussion is divided into two parts: hydrodynamic models and simulation models.
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 3 Calculation Model and Method of the Main Circuit Parameters
### Calculation Formulas of Main Circuit Parameters
#### 3.2.2 Calculation of the DC Voltage Difference  
The pole to ground voltage difference \(\Delta U\) between the rectifier and the inverter is defined as:  
\[\Delta U=U_{\text{dLR}}-U_{\text{dLI}}=R_{\text{d}}I_{\text{d}} \tag{32}\]  
where \(R_{\text{d}}\) is the DC line resistance on the pole level.  
For the monopolar ground return mode, the relationship between \(U_{\text{dR}}\) and \(U_{\text{dLI}}\) is:  
\[U_{\text{dR}}-U_{\text{dI}}=U_{\text{dLR}}-U_{\text{dLI}}+(R_{\text{eR}}+R_{ \text{gR}}+R_{\text{eI}}+R_{\text{gI}})I_{\text{d}} \tag{33}\]  
For the monopolar metallic return mode, the relationship is:  
\[U_{\text{dR}}-U_{\text{dI}}=2R_{\text{d}}I_{\text{d}} \tag{34}\]  
For the bipolar mode, the relationship is:  
\[U_{\text{dR}}-U_{\text{dI}}=U_{\text{dLR}}-U_{\text{dLI}} \tag{35}\]
## 3 Design of Physical Layer Security Communication System Using Proposed LPD Signal
### Sampling Clock Offset Compensation  
The sampling clock offset for the same frequency \(\omega_{c}\) between [PERSON] and [PERSON] is defined as \(\kappa_{\omega}=\omega_{B,c}-\omega_{A,c}\), where \(\omega_{A,c}\) and \(\omega_{B,c}\) indicate the actual clock frequency of [PERSON] and [PERSON], respectively. The sampling clock offset can be estimated nearly perfectly only if the SNR is sufficiently high or the number of pilot symbols is sufficiently large. According to the secure transmission protocol, \(\kappa_{\omega}\) can be estimated in step 2. Then, the LPD signal is modified as  
\[x_{s}(t)=\sum_{k=L}^{L+N-1}s(t)\cdot\alpha_{k}\exp[k\cdot j(\omega_{A,c}+\kappa _{\omega})t] \tag{5}\]  
The central frequencies of sub-band signals are shifted according to \(\kappa_{\omega}\), which is considered to be a shared key between [PERSON] and [PERSON]. As the sampling clock offset has been compensated at the transmitter, the sampling clock synchronization between [PERSON] and [PERSON] would be realized. When [PERSON] sampled the received signal with sampling clock \(\omega_{B,c}\), sub-band signals in the transmitted signal would be aligned naturally, as shown in Figure 3. Then, modulated signal \(s(t)\) may be recovered. Taking the weighted factor \(\alpha_{k}\) and the channel coefficients \(h_{k}\) into account, the SNR of sampled signal \(\tilde{s}[n]\) at Bob is given by  
Figure 4: Secure communication system model.  
\[SNR_{B,d}=\frac{\left|\sum\limits_{k=L}^{L+N-1}\alpha_{k}h_{k}\exp(j2\Delta k\pi) \right|^{2}\cdot P_{s}}{NN_{0}\omega_{B}} \tag{6}\]  
For Eve, the sampling frequency can hardly be the same as that of Bob. Although the receiving method is known to [PERSON], sub-band signals cannot always be aligned at the baseband after sampling. The spectrum of the sampled signal at Eve would be the same as that in Figure 3. The sampled signal is a summation of sub-band signals with different carrier frequency offsets, which can hardly be eliminated. Such a sampled signal cannot be used for demodulation, and interception by [PERSON] cannot be realized.
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 6 Energy Efficiency and Resource Allocation Algorithms
### Selected Papers
An SDN application called energy management and monitoring application, and a power consumption optimizer is developed in [81] to optimize the energy consumption of a C-RAN infrastructure with energy consumption estimation based on flow rates. An SDN controller-based power control framework with an adaptive ant colony optimization algorithm is proposed in [49] to avoid the frequent deactivation and reactivation of the lightpaths when new traffic request arrives, thus saving switching power. Finally, a joint caching, computing, and bandwidth resource allocation is designed for SDN in [83] to minimize the energy consumption consumed by content caching, data computing and traffic transmission.
###### Abstract  
This paper investigates the trajectory tracking control problem for underactuated underwater vehicles, for which a model is expressed in terms of quasi-velocities arising from the inertia matrix decomposition. The control approach takes into account non-modeled dynamics and external disturbances and is suitable for symmetric vehicles. It is shown that such systems can be diagonalized using inertial quasi-velocities (IQVs). The strategy consists of the velocity controller and two adaptive integral sliding mode control algorithms. The proposed approach, introducing velocity transformation and using backstepping methods and integral sliding mode control, allows trajectory tracking for vehicles in described models with symmetric inertia matrix. Proof of the stability of the closed system was carried out using IQV. The proposed scheme has been verified on two 3 DOF models of underwater vehicles with thruster limitations. A brief discussion of the results is also given.  
underactuated marine vehicle; trajectory tracking; backstepping; integral sliding mode control; robustness; velocity transformation; quasi-velocities; simulation +
Footnote †: journal: Applied sciences
## 5 Performance Evaluation
### Performance Metrics  
We used the following metrics to evaluate the performance of TLQoS.  
_Average End-to-End Latency._ End-to-End latency of a packet is measured as the time difference between the packet generation time and the time when it is received by the BC. Delays experienced by distinct delay-sensitive data packets (Cr and Dc) are averaged over the total number of delay-sensitive packets received by the BC.  
_On time Packet delivery ratio._ It is the ratio of the total number of delay-sensitive packets received by the BC within the deadline to the total number of delay sensitive packets generated by the WBAN nodes.  
_Reliability._ It is the ratio of the total number of unique reliability-sensitive packets (Cr and Rc) received by the BC to the total number of reliability-sensitive packets generated by the WBAN nodes.  
_Average Temperature Rise_. The average temperature rise of the nodes presents the average change in temperature of the nodes from that at the initial simulation period.  
_Average Energy Consumption_. This parameter considers the average energy consumption of the nodes due to transmission and reception of packets. In our simulation, each packet transmission by a node consumes 0.2 units of energy and each reception consumes 0.1 units of energy.