esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Background  
### Roadway Layout and Geological Conditions of Test Points  
The test point was located in working face 21405 of the Hulusu coal mine. This working face was the first mining face in the fourth panel of coal seam 2-1 of the mine. Figure 1 shows the roadway layout of working face 21405. The daily progress of the working face was 5.6 to 9.6 m. The two entries were abandoned behind the longwall progress. The withdrawal roadway was arranged near the stop-mining line for the withdrawal of the production equipment after the completion of mining. A protective coal pillar with a width of 360 m was left between the withdrawal roadway of working face 21405 and the main roadway in the panel area. The value of the pillar width was greater than that of the conventional value (half the width of the working face), which facilitated the final layout of a longitudinal longwall face after all of the working faces to the right were completed.  
The buried depth of the working face was 650 m, and the thickness of the coal in the final mining area was 3.4 m. The cross-sectional shape of the withdrawal roadway was rectangular, with a width of 4.8 m and a height of 3.4 m. It was driven using a roadheader for high efficiency construction. In the withdrawal roadway, the roof of the coal seam was composed of medium sandstone, fine sandstone, and coarse sandstone, and the floor was composed of mudstone or sandstone. Figure 2 shows the occurrence situation of the rock strata, and Table 1 shows the mechanical parameters of the rock layer. As depicted in Figure 2, the withdrawal roadway will experience the most significant mining impact during the final mining period, that is, the working face will be continuously advanced until it connects with the withdrawal roadway. During this period, the front abutment pressure of the working face will gradually approach the withdrawal roadway, and the impact on the stability of the surrounding rock in the withdrawal roadway will become increasingly significant.  
Figure 1: Roadway layout and mining environment.  
Figure 2: Profile view of the rock strata and the relationship between the withdrawal roadway and the working face.  
In order to effectively control the stability of the surrounding rock in the withdrawal roadway, the following three key tasks need to be accomplished: (1) understanding the causes behind the strong rock pressure and the significant deformation of the surrounding rock in the withdrawal roadway; (2) selecting a reasonable construction time to ensure that the excavation of the withdrawal roadway is not affected by the mining; and (3) implementing a scientific support plan to control the deformation of the surrounding rock of the withdrawal roadway during the mining activities.
## 3 Results and Reports
### Classification Performances
#### 3.4.1 Initial Classification Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Systolic** & **Classifier** & **Classes** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline \multirow{4}{*}{Dts\_1} & SVM (RBF) & Class 0 & 0.97 & 1 & 0.98 \\  & & Class 1 & 1 & 0.92 & 0.96 \\  & LDA & Class 0 & 0.83 & 0.93 & 0.88 \\  & & Class 1 & 0.78 & 0.57 & 0.66 \\  & KNN & Class 0 & 0.93 & 1 & 0.97 \\  & & Class 1 & 1 & 0.84 & 0.91 \\  & XGBoost & Class 0 & 1 & 1 & 1 \\  & RF & Class 0 & 1 & 1 & 1 \\  & & Class 1 & 1 & 1 & 1 \\ \hline \multirow{4}{*}{Dts\_2} & SVM (RBF) & Class 0 & 0.97 & 0.99 & 0.98 \\  & & Class 1 & 0.97 & 0.9 & 0.93 \\  & LDA & Class 0 & 0.88 & 1 & 0.94 \\  & & Class 1 & 1 & 0.56 & 0.72 \\  & KNN & Class 0 & 0.96 & 0.99 & 0.98 \\  & & Class 1 & 0.97 & 0.87 & 0.92 \\  & XGBoost & Class 0 & 1 & 1 & 1 \\  & RF & Class 1 & 1 & 1 & 1 \\  & & Class 1 & 1 & 1 & 1 \\ \hline \multirow{4}{*}{Dts\_3} & SVM (RBF) & Class 0 & 0.98 & 0.97 & 0.97 \\  & & Class 1 & 0.96 & 0.97 & 0.97 \\  & LDA & Class 0 & 0.82 & 0.86 & 0.84 \\  & & Class 1 & 0.8 & 0.75 & 0.77 \\  & KNN & Class 0 & 0.95 & 0.99 & 0.97 \\  & & Class 1 & 0.99 & 0.93 & 0.96 \\  & XGBoost & Class 0 & 1 & 1 & 1 \\  & & Class 1 & 1 & 1 & 1 \\  & RF & Class 0 & 1 & 1 & 1 \\  & & Class 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative classification results using the standards performance.
## 3 Results and Discussion
### Results of the Optimisation of the CPM Mechanism Links  
This section will present the results of the optimisation of the design of the main positioner mechanism links of the CPM device. They concern the analysis of the main links 3 and 4, which provide the main support for the rehabilitated lower limb, and the driving kinematic pair of the positioner mechanism, consisting of links 1 and 2. The choice of test materials from which new link structures were generated concerned the material used in the base structure based on commercial aluminium structural profiles, made from AL 6061. Structures were also generated using Generative Design methods made from MG-AL-ZN ALLOY and MG-AL ALLOY alloys. During preliminary research, attempts were also made to use plastics, ABS and PEEK, which due to their lower material density and stiffness could not realistically contribute to reducing the mass of the solution while maintaining an acceptable level of deformation. The first attempts to generate structures showed that the mass of the links was significantly lower in relation to the initial mass, but the deformations of the links forming the kinematic pairs studied disqualified the results obtained from the practical application. Among the other metallic materials initially selected for numerical analysis, titanium was also chosen. This material, with its very good mechanical properties, offered the hope of obtaining a generated structure which, although much denser than aluminium and its alloys, could ultimately form a high-strength, low-mass structure. In the case of titanium, the initial tests showed that the high density of the material led to a rejection of the results, as the final mass of the solution exceeded the initial mass of the positioner links. However, for the optimisation (creation of a new design) of links 1 and 2, only one material was selected--316 Stainless Steel, analogous to the basic solution of the CPM device. The results of the optimisation of each link with the Creo Parametric Generative Design tool will be presented later in this chapter.
## 1 Introduction
(2022) proposed a load classification Long Short-Term Memory with Self-Attention Mechanism (SA-LSTM) model in response to the problems of category imbalance and insufficient performance of the classification model in user load classification [18]. [PERSON] et al. (2024) presented a method based on the self-attention long short-term memory network to investigate the problem of dam deformation prediction, and the results indicated that this approach achieved relatively good prediction effects on the actual dam deformation data [19]. [PERSON] et al. (2024) employed a new model combining the long short-term memory network and the self-attention mechanism to address the accuracy problem of photovoltaic power generation output prediction, especially under the influence of weather conditions [20]. [PERSON] et al. (2023) presented a new type of statistical model called non-linear function-on-function regression and found it is effective for analyzing complicated data through simulations and real-world examples [21]. However, relevant research in the shipping carbon emission reduction field is still rather rare [22; 23; 24; 25; 26; 27]. Since the prediction of fuel consumption and carbon intensity possesses temporal characteristics [28], the LSTM model can capture the short-term dependency relationship in the sequence, but there may be certain limitations in handling long-term dependency relationships. The self-attention mechanism can effectively capture the long-distance dependency relationship between different positions in the sequence. Introducing the self-attention mechanism increases the model's ability to model the overall information of the sequence, making it more suitable for processing ship operation data that contains long-term dependencies. Therefore, the SA-LSTM model with the introduction of the self-attention mechanism may have the potential to further improve the accuracy of ship fuel consumption prediction. The main objectives of this study are to investigate the impact of the self-attention mechanism on the LSTM model and to evaluate the performance of the SA-LSTM model in predicting ship fuel consumption using multi-source heterogeneous data, including AIS data, fuel flow sensor data, meteorological data, and sea condition data. This study is one of the few studies that investigates the application of the self-attention mechanism and LSTM model in predicting ship fuel consumption. It considerably improved the prediction accuracy as compared to most machine learning methods and traditional LSTM models. The organization of the paper is as follows: Section 1 introduces the research background of this paper and a review of relevant literature. Section 2 introduces the main data and processing of this research. Section 3 introduces the main methodological approach in this research. Section 4 takes a container ship equipped with multiple sensing devices as an example to comparatively analyze the fuel consumption and carbon intensity prediction results of various methods. Finally, relevant discussions are conducted and the conclusions of this paper are summarized.
## 2 Related Work
### Reactive Approaches  
Before the introduction of OpenFlow groups, including fast failover groups, many studies adopted recovery mechanisms to handle this issue. [PERSON] [33] et al. computed routing paths using VLAN. [PERSON] [34] et al. compared their fast failover system with the NOX controller's learning switch, learning PySwitch, and routing mode. [PERSON] [35] et al. implemented fault tolerance in wide area networks (WANs) using SDN, as routing protocols such as BGP and OSPF encounter severe problems during failures. For instance, while BGP has a longer path convergence time, OSPF has a longer recovery time. [PERSON] [36] et al. developed a recovery method that reduces path computation time by using a locally optimal fault-tolerance method. [PERSON] [37] et al. considered link failure and controller placement issues, studying network resilience of OpenFlow-based centralized networks. They first formulated controller placement metrics and failure types including links, switches, and failures between switches and controllers. They then performed resilience analysis for the centralized network architecture. Experimental results showed that controller location significantly affects the resilience of the network. [PERSON] [38] et al. focused on real-time flows, considering their own fault tolerance limitations, and used adaptive path recovery. They implemented a multi-constraint path-finding algorithm that can reroute traffic according to time budgets. In the research by [PERSON] [39] et al., the authors considered the fault recovery of service function chains. Based on the service requirements of each service, they used recovery as a type of fault-aware routing. [PERSON] [40] et al. designed a backup path selection algorithm for the network that achieves low interruption rates for flows based on a recovery mechanism and heuristic algorithm.  
Some studies have also considered this reliability. In the research by [PERSON] [41] et al., they designed a system based on the Byzantine model to tolerate faulty switches to enhance reliability. Moreover, [PERSON] [42] et al. focused on the reliability of the control path, which is an important consideration for out-of-band controllers whose network view might be affected by data plane failures. In addition, [PERSON] [43] et al. considered reliability in an SDN-enabled VANET environment. They adopted a network coding-based approach for reliable data propagation. [PERSON] [44] et al. proposed a reactive method for fault tolerance in hybrid SDN. They utilized a stochastic network calculus framework for dynamic resource allocation in MEC systems. This method ensures QoS while addressing network faults reactively, aligning network resource management with current network conditions and demands.
## 2 Related Work
### Underwater Data Muling  
Underwater data muling using AUVs and DTNs can overcome the limitations of current underwater communications technologies [15; 38; 39; 40]. In [15; 41; 42], the authors demonstrated that data muling in underwater sensor networks using AUVs is a very useful approach for long-term environmental monitoring and surveillance. Using small and agile  
Figure 3: Bit rate over range for different underwater acoustic, optical and RF systems and the area filled by the GROW solution (based on [31]).  
AUVs combined with short-range high throughput communications, the solution proposed in the GROW project [6] enables a broadband virtual link between a fixed or mobile SU and a CSU.  
Despite the long delays and disruption generally experienced in data muling processes, the interest in several complex applications such as video streaming solutions has increased in recent years. It has already been proven that streaming large amounts of data is possible [43]. Moreover, there is even a DTN framework specifically designed for video streaming purposes, called Bundle Streaming Service (BSS) [44; 45]. BSS supports two different types of connection: (1) a best-effort option for close to real-time video streaming; and (2) a reliable transport option, giving the user the possibility of replaying the video later in its integral form. Recently, the concepts of data muling, underwater wireless communications and video streaming were combined in [46], addressing the issue of long-range video transfer in underwater scenarios with promising results.
###### Abstract  
With growing interest in Industry 4.0, machine-to-machine communication (M2M) will become the key enabler for low-power wide area networks (LPWANs) in connecting machines and sensor nodes distributed across a distance in the industrial environment. The choice of modulation and diversity techniques, and the selection of spectrum (licensed/unlicensed) will impact and influence the requirements of wireless M2M systems. Link reliability is one of the most important requirements for LPWAN deployment in industrial scenarios. Rotating Polarization Wave (RPW) system has been recently proposed as an LPWAN solution for reliable M2M communication in high clutter environment and it deploys BPSK modulation with polarization diversity (PD). This paper proposes a new multi-level Rotating Polarization Phase-Shift Keying (RP-MPSK) modulation to provide high data rate and energy efficiency. A novel quaternion model for RPW system (Q-RPW) is also proposed to reduce the complexity in modeling, simulation, and implementation. Results using Q-RPW model show that RP-MPSK modulation offers a high diversity gain over BPSK with second-order diversity. Bit error rate (BER) performance of RP-MPSK modulation compared against other LPWAN modulation like MPSK, FSK and QAM has shown high reliability and substantial improvement in SNR. To overcome the degradation in error performance caused by the proposed higher-order modulation, sampling rates are recommended based on BER performance. BER performance of RP-MPSK under multipath and interference conditions is also investigated.  
polarization diversity; LPWAN; machine-to-machine communication; phase-shift keying: industrial communication systems; sub-gigahertz band +
Footnote †: journal: Sensors
## 3 Aberration Theory Analysis
### Aberration Simulation Analysis  
After the above demodulation model is established, the simulation analysis of the experiment can be carried out in combination with the beam model of the first part. To facilitate understanding, we will start from the primary phase to verify the theory. Among the common aberrations, the spherical aberration and the coma are typical of RSA and NRSA, their shapes are shown in Figure 4 (spherical aberration is (a), coma is (b)). Therefore, we use Zernike polynomials to fit spherical aberrations and coma that vary from \(\lambda/100\) to \(\lambda\), then substitute them into the above model to get the resulting graph as follows:  
From the results in Figure 5, it can be seen that as the aberration increases, the influence of spherical aberration on the phase is greater than that of the coma. Under the same aberration change, the phase change corresponding to spherical aberration is about five times that of a coma. This is consistent with the above theory.  
Figure 4: The spherical aberrations (**a**) and coma (**b**) map fitted by Zernike polynomials.  
Figure 3: Schematic diagram of the quadrature demodulation algorithm. The incident light is converted into an electrical signal by the phase meter, the NCO emits a signal with a heterodyne frequency and the interference signal is multiplied and integrated to obtain a phase signal.  
In actual telescope systems, aberrations are extremely complex, including not only primary aberrations but also advanced aberrations. Therefore, we simulated a more general aberration composition, and to see the effect more clearly, we used the Monte Carlo algorithm to randomly assign the first 15 terms of the Zernike polynomial and fix its total aberration size at \(\lambda/60\). Then, we obtained a comparison chart (Figure 6) of removing the non-rotationally symmetrical aberrations and not removing them.  
The abscissa in Figure 6 represents the Monte Carlo random number, and the ordinate represents the size of the demodulation phase. It can be seen that the red part (including rotational symmetry aberration) has a greater influence than the blue part (not included). Their variances are V\({}_{\rm r}\) = 1.9560 \(\times\) 10\({}^{-06}\), V\({}_{\rm b}\) = 2.4549 \(\times\) 10\({}^{-14}\). Therefore, it once again confirms the above theory in this paper. The effects of RSA and NRSA on phase demod  
Figure 5: The abscissa refers to the magnitude of the aberration from \(\lambda/100\) to \(\lambda\). The ordinate represents the final demodulated phase size. Red represents the phase that changes with the magnitude of the spherical aberration. Blue represents the phase that changes with the magnitude of the coma.  
Figure 6: The effect of random aberration on phase demodulation with total aberration fixed at \(\lambda/60\), blue does not contain rotational symmetry aberrations, and red does.  
ulation in space gravitational wave detection are different, and it is obvious that RSA is more important.
## 3 Particle Swarm Optimization
### Time and Space Complexity of PSO  
In terms of time complexity, the main computational cost of PSO lies in evaluating the objective function for each particle in each iteration. The objective function represents the problem to be optimized and can vary in complexity depending on the problem domain. Therefore, the time complexity of PSO is closely related to the evaluation time of the objective function [75]. In each iteration, all particles need to evaluate their positions, update their personal bests and the global best, and adjust their velocities and positions. This process continues until a termination condition is met. The number of iterations required for convergence depends on various factors such as the problem complexity, the size of the search space, and the convergence speed of the swarm [76]. Generally, the time complexity of PSO is considered to be moderate, as it typically requires a reasonable number of iterations to converge to an acceptable solution [77]. The greater number of iterations leads to the requirement of large memory. PSO requires memory to store the positions, velocities, personal bests, and global best of each particle in the swarm [78]. The amount of memory required is proportional to the population size, which is typically determined by the problem being solved. PSO may also require memory to store auxiliary variables, such as acceleration coefficients and parameters controlling the swarm behavior. The space complexity of PSO is, therefore, determined by the memory requirements for storing the swarm's state and other relevant variables. The space complexity is generally considered to be reasonable, as it scales linearly with the population size and does not depend on the size of the search space. Figure 3 shows the improvements in PSO over time to quickly converge to the optimal solution.  
Figure 3: Convergence improvements of different variants of PSO.
## 5 Results and Discussion
### Ablation Study  
In order to evaluate the importance of depth information in spatial distance estimation, tests were conducted by selecting random frames of different angles from similar LSUs, and distance was estimated with and without depth information. For example, as shown Figure 12, distance and depth were measured for two different frames. Depth-based distance using Equation (9) and normal Euclidean distance between the person object and the vase object were estimated. On comparing the depth-based distance and Euclidean distance between the two frames, it was seen that the error of the depth-based distance metric is much less than the error of the Euclidean distance metric. The experiment was repeated for 10 different scenarios from 10 different episodes; depth-based distance error was estimated to be at least six times smaller than the Euclidean distance error, on average.