esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Seaweeds from South West England as a Biorefinery Feedstock
## 3 Results and Discussion
### Lipid and High-Value Omega-3 Oil Content
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & **Total Lipid** \\ \hline \multirow{2}{*}{_U. lactuca_} & **C14:0** & **C16:0** & **C16:1** & **C18:0** & **C18:1** & **C18:2** & **C18:3 (\(\
u\))** & **C18:3 (\(\alpha\))** & **C20:4** & **C20:5** & **(mg/g)** & **(\% Dry Biomass)** \\ \hline \multirow{2}{*}{_U. lactuca_} & 0.0 & 36.4 & 0.0 & 9.1 & 18.2 & 18.2 & 0.0 & 18.2 & 0.0 & 0.0 & 18.09 & 1.8 \\  & _S. aeruginosa_ & 0.0 & 33.3 & 0.0 & 11.1 & 11.1 & 11.1 & 0.0 & 22.2 & 0.0 & 11.1 & 13.89 & 1.4 \\  & _P. Putmata_ & 10.0 & 30.0 & 0.0 & 10.0 & 10.0 & 0.0 & 0.0 & 0.0 & 20.0 & 20.0 & 12.92 & 1.3 \\  & _L. articulata_ & 14.3 & 28.6 & 0.0 & 14.3 & 14.3 & 0.0 & 0.0 & 0.0 & 14.3 & 14.3 & 12.15 & 1.2 \\  & _A. decontinisis_ & 11.1 & 22.2 & 0.0 & 11.1 & 11.1 & 0.0 & 0.0 & 0.0 & 22.2 & 22.2 & 12.36 & 1.2 \\  & _P. plantata_ & 7.1 & 21.4 & 0.0 & 7.1 & 7.1 & 0.0 & 0.0 & 0.0 & 7.1 & 50.0 & 23.25 & 2.3 \\  & _R. overlooks_ & 14.2 & 28.6 & 0.0 & 14.2 & 14.2 & 0.0 & 0.0 & 0.0 & 14.2 & 14.2 & 8.58 & 0.9 \\  & _D. earnasa_ & 9.1 & 27.3 & 0.0 & 9.1 & 9.1 & 0.0 & 0.0 & 0.0 & 18.2 & 27.3 & 18.83 & 1.9 \\  & _S. unicticum_ & 8.3 & 25.0 & 8.3 & 8.3 & 8.3 & 0.0 & 8.3 & 16.7 & 8.3 & 15.79 & 1.6 \\  & _H. dongatti_ & 7.7 & 23.1 & 0.0 & 7.7 & 15.4 & 7.7 & 0.0 & 7.7 & 15.4 & 15.4 & 19.26 & 1.9 \\  & _F. serratus_ & 12.5 & 20.8 & 4.2 & 4.2 & 25.0 & 8.3 & 0.0 & 4.2 & 12.5 & 8.3 & 29.13 & 2.9 \\  & _L. digitalta_ & 8.3 & 16.6 & 0.0 & 8.3 & 16.6 & 8.3 & 0.0 & 8.3 & 16.6 & 16.6 & 15.62 & 1.6 \\  & _S. litsitsitsan_ & 8.3 & 16.7 & 0.0 & 8.3 & 16.7 & 8.3 & 8.3 & 8.3 & 16.7 & 8.3 & 18.52 & 1.9 \\  & _C. peregrina_ & 16.7 & 16.7 & 0.0 & 16.7 & 16.7 & 0.0 & 0.0 & 0.0 & 16.7 & 16.7 & 7.50 & 0.8 \\  & _P. litifolia_ & 8.3 & 16.7 & 8.3 & 8.3 & 8.3 & 0.0 & 8.3 & 16.7 & 16.7 & 16.48 & 1.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fatty acid (FA) profiles of South West seaweed samples. Data, expressed as % of total FA content and total lipid, are given as mg/g and % of dry biomass. release agents. Given the lower economic value of these products and the low overall lipid content of _Ulwa lactuca_, it is unlikely that this seaweed would be an economical alternative source, unless integrated into a biorefinery model with at least one higher value co-product.
# Dynamic Analysis of Fuzzy Systems
## 5 Application
### Simple Pendulum Model  
In this example, the second-order fuzzy model described in Section 4.1 is used to model a simple pendulum. A normalised sinusoidal function modifies the membership function of the angle \(\theta\) to demonstrate that, by an appropriate choice of membership function, the model is identical to the one obtained mathematically.  
A practical example of the dynamic model of a simple pendulum is provided. The pendulum's differential equation is:  
\[J\ddot{\theta}(t)+B\dot{\theta}(t)+mglsin(\theta(t))=\tau(t) \tag{38}\]  
When \(J=ml^{2}\) is the inertia of the pendulum+ball set, B is the friction on the axis of rotation, \(l\) is the length of the pendulum and \(\tau(t)\) is the applied torque. See Figure 24.  
Figure 23: Performance comparison of different models.  
If it is written in standard form as (22), the pendulum model remains;  
\[\frac{l}{g}\ddot{\theta}(t)+\frac{B}{mgl}\dot{\theta}(t)+sin(\theta(t))=\frac{1}{ mgl}\tau(t) \tag{39}\]  
The normalised input sets, whose universe of discourse are \([-1,1]\), and the centre of gravity of the output set in \(\{-1,-0.5,0,0.5,1\}\) (see Figure 25) is as follows:  
Figure 26 shows the input and output scaling factors.  
Figure 24: Simple pendulum.  
Figure 25: Fuzzy sets; (**a**) input set \(\theta\), (**b**) input sets \(\dot{\theta}\)_and_\(\tau\), (**c**) output set \(\ddot{\theta}\) centre of gravity and (**d**) output surface between \(\ddot{\theta}\), \(\dot{\theta}\) and \(\theta\).  
The resultant Mamdani model is as follows:  
\[R^{(1)}:\text{IF }\theta\text{ is }N\text{ AND }\dot{\theta}\text{ is }N\text{ THEN }\ddot{\theta}=4\frac{g}{l}\] \[R^{(2)}:\text{IF }\theta\text{ is }N\text{ AND }\dot{\theta}\text{ is }Z\text{ THEN }\ddot{\theta}=2\frac{g}{l}\] \[R^{(3)}:\text{IF }\theta\text{ is }N\text{ AND }\dot{\theta}\text{ is }P\text{ THEN }\ddot{\theta}=0\] \[R^{(4)}:\text{IF }\theta\text{ is }Z\text{ AND }\dot{\theta}\text{ is }N\text{ THEN }\ddot{\theta}=2\frac{g}{l}\] \[R^{(5)}:\text{IF }\theta\text{ is }Z\text{ AND }\dot{\theta}\text{ is }Z\text{ THEN }\ddot{\theta}=0\] \[R^{(6)}:\text{IF }\theta\text{ is }Z\text{ AND }\dot{\theta}\text{ is }P\text{ THEN }\ddot{\theta}=-2\frac{g}{l}\] \[R^{(7)}:\text{IF }\theta\text{ is }P\text{ AND }\dot{\theta}\text{ is }N\text{ THEN }\ddot{\theta}=0\] \[R^{(8)}:\text{IF }\theta\text{ is }P\text{ AND }\dot{\theta}\text{ is }Z\text{ THEN }\ddot{\theta}=-2\frac{g}{l}\] \[R^{(9)}:\text{IF }\theta\text{ is }P\text{ AND }\dot{\theta}\text{ is }P\text{ THEN }\ddot{\theta}=-4\frac{g}{l}\] \[R^{(10)}:\text{IF }\tau\text{ is }N\text{ THEN }\ddot{\theta}=-2\frac{g}{l}\] \[R^{(11)}:\text{IF }\tau\text{ is }Z\text{ THEN }\ddot{\theta}=0\] \[R^{(12)}:\text{IF }\tau\text{ is }P\text{ THEN }\ddot{\theta}=2\frac{g}{l}\]  
The result of the fuzzy model applied to the simple pendulum can be seen in Figure 27. The graph was elaborated upon using different friction coefficients: B = [0, 0.2, 0.4, 0.6, 0.8, 1], \(m=0.25\) kg, \(l=0.8\) m and \(g=9.81\) m/s\({}^{2}\). It was also compared with the dynamic model.
## 3 Power Allocation Optimization and Problem Formulations
### Power Allocation Optimization Process  
This subsection presents the methodology of power allocation to maximize the DASR of an FDD system. While several power allocation approaches have been investigated in the literature, see, e.g., [64; 65; 66; 67; 68; 69; 70], the concentrate was based on the TDD transmission mode. Here, we investigate the power allocation process to maximize the DASR in the FDD transmission mode using a simple straightforward approach. As mentioned earlier, the conventional method divides the power for channel training and data transmission equally without considering any optimization criteria. Equitable power allocation with respect to the training sequence phase and data phase is achieved through uniform power allocation. The work of [18] tried to characterize the tradeoff between DASR maximization and MSE minimization in FDD MMIMO systems. In [18], a uniform power allocation was assumed, and the DASR was not optimized with respect to power allocation but rather was optimized with respect to the training sequence length only. The authors in [18] clearly stated that optimizing the training power and data power with respect to DASR maximization could be investigated in the future. However, there is no characterization for the DASR in FDD MMIMO systems with respect to optimum power allocation. Therefore, our paper investigates the DASR performance with this desired scenario, and the results show that a considerable improvement can be achieved using the proposed power allocation approach, as we can see later in Section 5. To this end, a nonuniform power allocation strategy is performed, which allows for the maximization of the DASR of MMIMO FDD systems with LCT, which is the main objective function of this paper. In this case, the available coherence time \(\tau_{\text{c}}\) and the available total transmit power \(\rho\) are combined and divided nonuniformly between the data and training phase, as well as smartly allocated in such a way as to maximize the DASR per each transmission block. In this case, we are targeting the energy allocated per each coherence block and managing the power allocated to each phase (data transmission and channel estimation). This implies that allocating more power to the channel estimation while preserving some time duration for data transmission could help in improving the channel estimation quality while maximizing the DASR. This is because the training sequence length is more valuable due to the prelog fraction that is dedicated to transmitting useful data to the user.
# Approaches for Dealing with Seasonality in Clinical Prediction Models for Infections
## 3 Materials and Methods
### High Dimensionality  
A common problem related to clinical datasets is that of having a high number of possible features and a modest number of observations. This may make the training process difficult and lead to less accurate models.  
This effect is usually alleviated by using filters. One common approach consists of performing univariate analyses between each candidate predictor and the outcome class [32]. In these cases, experts recommend the use of the chi-squared test or [PERSON]'s exact test for nominal variables and a univariate logistic regression model or two-sample Student's \(t\)-test for continuous variables. Those predictors that have a low \(p\) value, commonly \(p<0.05\), are then considered interesting for the training process.  
Other more complex filters are available as well; for example, the Fast Correlation-Based Filter (FCBF) [33] estimates the relevance of each feature with regard to the target outcome along with whether it is redundant for any other relevant feature. Only those features considered highly relevant and non-redundant are used to train the model.  
Furthermore, it is a common practice to use the Least Absolute Shrinkage and Selection Operator (LASSO) approach when working with logistic regression. LASSO seeks a balance between model complexity and accuracy by imposing a constraint on the sum of the absolute size of the regression coefficients [34]. This allows certain coefficients to reach zero during the search for the optimal solution, allowing them to be removed from the model. This results in less overfitted models.  
The modern algorithms used to create decision trees, such as C5.0, include the option of _winnowing_, or removing the predictors that are considered to be unimportant before creating the model [31].  
In this work, we used LASSO to carry out experiments with logistic regression and activate the winnowing option for decision trees. We additionally tested the use of only those features with \(p<0.05\) after a Fisher test or \(t\)-test and the use of FCBF to discard highly redundant features for both logistic regression and decision tree models.
## 6 Simulation Result
### Evaluation of Reward-Based Performances  
Table 9 presents a comparative analysis of the reward-based performance of each algorithm in terms of Episode Reward Mean, Step Reward Mean, and Reward Standard Deviation.  
TQC consistently outperformed all other algorithms across these three metrics. It achieved the highest Episode Reward Mean of 977.7 and the highest Step Reward Mean of 0.62. Additionally, it recorded the lowest Reward Standard Deviation of 0.28, indicating that the learned policy was capable of acquiring rewards in a stable and consistent manner at each time step. This suggests that TQC's distributional reinforcement learning framework effectively minimized reward volatility while maximizing reward gains, even under uncertain autonomous driving scenarios.  
SAC ranked second in both Episode Reward Mean and Step Reward Mean, closely following TQC. It also maintained a relatively low Reward Standard Deviation of 0.33, demonstrating good stability in reward acquisition throughout each episode.  
TD3 performed moderately well in the reward-related metrics. Although its Reward Standard Deviation (0.31) was slightly lower than SAC's, it fell behind in terms of total reward accumulation, reflected in its lower values for Episode Reward Mean and Step Reward Mean. PPO produced a similar level of mean rewards to TD3 but exhibited a higher Reward Standard Deviation of 0.40, indicating greater variability and suggesting that PPO's on-policy nature led to less stable policy updates during training.  
CrossQ and DDPG recorded relatively poor performance across all reward-based metrics. However, CrossQ outperformed DDPG in all three indicators, Episode Reward Mean, Step Reward Mean, and Reward Standard Deviation, implying that CrossQ achieved relatively improved policy optimization over DDPG despite its overall low Route Completion performance.  
\begin{table}
\begin{tabular}{l c c c} \hline  & **Episode Reward** & **Step Reward** & **Reward Standard** \\  & **Mean** & **Mean** & **Deviation** \\ \hline DDPG & 64.5 & 0.21 & 0.71 \\ TD3 & 693.8 & 0.46 & 0.31 \\ SAC & 823.9 & 0.52 & 0.33 \\ TQC & **977.7** & **0.62** & **0.28** \\ CrossQ & 144.7 & 0.35 & 0.68 \\ PPO & 644.1 & 0.46 & 0.40 \\ \hline \end{tabular}
\end{table}
Table 9: Performance comparison of reward-based metrics over 1 M steps of learning (Bold text indicates the method that achieves the best performance for each metric).
## 3 Results
### Results of Individual Studies  
The study shows that various machine learning algorithms were used for transformer monitoring using DGA data; the support vector machine (SVM), artificial neural networks (ANN), and k-Nearest Neighbor (kNN) stand out as the commonly used and adapted algorithms with percentages of 32%, 17%, and 12%, respectively, (excluding the hybrid algorithms of the aforementioned). Some of the studied algorithms have hybrid algorithms adapted with an attempt to improve their performance such as those of ANN found in this study which includes CNN, GRNN, improved BP neural network, deep believe network, and Adaptive Neuro-Fuzzy Inference System, all of which contribute an additional combined 10%, which bring the overall total of neural networks contribution to 27%. Based on the evaluated studies, the noted contributing factors behind the dominant application of the abovementioned algorithms include their accuracy, simplicity, and computational speed when compared to other algorithms.  
Other machine learning algorithms that had fewer contributions and are being explored in the studied literature include the k-Means Clustering algorithm, improved fuzzy c-means clustering, Bayesian Networks, hybrid gray wolf optimization algorithm, Gaussian Process Multi-classification, Lasso regression, Hidden Markov model, and Logistic Regression, among others. The combined contribution of these algorithms is 26%. Figure 9 shows the distribution of machine learning algorithms used for transformer monitoring using DGA data.  
In terms of the software adopted in the literature, MATLAB 2019b is the most commonly used software for modeling and computing machine learning algorithms for transformer DGA applications with a percentage of 31%, surprisingly, Python (v.3.8) is used by only 2% of the papers studied, and LabView (v.2024) just 1%. The rest of the docu  
Figure 9: Machine learning algorithms used for transformer monitoring.  
ments which contributed 66% did not specify the software or engineering tool used for computation. Figure 10 shows the distribution of the software used.  
On the industrial standards utilized, many of the papers studied do not specify the globally adopted standards used in the analysis. This causes concern as having an accurate depiction of standards would aid in understanding which ones are the most effective and accurate. From the collected studies, 36% of the papers used IEC 60599, while 9% specified C57 IEEE 104 standards, and the remaining 55% of papers did not specify the standard used. Figure 11 shows the distribution of standards used for DGA interpretation.  
This study shows that a majority of the DGA datasets used to conduct the simulations are obtained from the literature (34%) and the IEC TC10 Database (32%), and a combined percentage of 25% is obtained from transformers used in 16 different industrial power utilities, manufacturers, substations, and private companies. The rest of the 9% of sources of the datasets are not specified in papers. Figure 12 shows the distribution of the sources.  
Figure 11: Standards used for DGA interpretation.  
Figure 10: Software used for DGA analysis.
## 3 Results  
### Implementation Details  
We implement the proposed OGE model as well as the relevant pre- and post- processing using the Python framework which is open source. The most important packages involved in the programming experiments include but not limited to Pytorch, Pytorch Geometric, Numpy, Networkx, and so on. OGE was conducted on a single NVIDIA Tesla V100 with 32 GB of GPU memory and train for 2000 epochs to make the model converge. We use the Adam optimizer to train our models from scratch, and the initial learning rate is set to 0.005, and the weight decay coefficient is set to 0.0005 By the way, the primary purpose of regional geochemical exploration is to delimit areas of interest for further exploration, and thus, the performance of OGE can be evaluated or described from two perspectives: the percentage of discovered mineral deposits to the total mineral deposits--the recognition rate, and the size of the anomalous area.  
For a better illustration, taking the I-series elemental association as example, Figure 8 gives the recognition rate versus loss curves during the training phase. As can be seen, before completing 160 epochs, both the recognition rate and the training loss decrease sharply. And then, the decreasing trend of loss become slow, eventually stabilizing at 0.124 or so; meanwhile, the growth of the recognition rate rises and became oscillating, centered at 0.837. So, in order to jump over the oscillation area, here we set the training epoch as 2000.  
Figure 7: The fractal schema In N(\(\geq\)\(r\)) versus \(\ln r\).
# Research Progress on Router Devices for the OAM Optical Communication
## 2 OAM Channel Multicasting
To experimentally demonstrate the proposed concept, [PERSON] and coworkers established 1-to-40 multicasting with 16-ary shift keying in OAM communication and achieved the encoding and decoding of 40 images with 16 gray values with a BER of 0 following a 3 m free space transmission. Using the design of various OAM-RDs and related technologies, starting from the one-to-many multicast link in an ideal environment to the multicast link under diverse practical conditions, the OAM channel's multicast technology has gradually matured. This is highly significant for reducing data congestion and enhancing efficiency and performance through multi-user data multicast. In addition, with the continuous development of hardware technology, the number of OAM multicasting channels has greatly increased, which is advantageous for its application in the network [110; 111  
Figure 2: Working principle and experimental setup of OAM-RDs based on OAM channel multicasting (II) [109]: **(a)** principle of OAM neural communication, and **(b)** experimental setup. multicasting routers have become one of the important devices in today's complex optical communication networks and have broad development prospects. Table 2 summarizes the representative works reported to date for the channel multicasting routers in OAM-based optical communication.
## 5 Results and Discussion
### Linear Models
These high localized stresses are primarily attributed to the discretization method of the constraint elements. Specifically, rigid-body element 2 (RBE2) introduced considerable stiffness, generating stress hot spots just outside the application points. To ensure that yielding does not occur in these areas, further analysis of the potential indentation of the bolted connections is recommended. Overall, the results demonstrate the need for mapped thickness models to better capture the deformation and stress behavior of the stamped sheet-metal component. While the mapped model showed improvements over the uniform thickness model, particularly at Points A and C, further refinements in the FEM setup are necessary to address discrepancies in areas with complex stress and strain distributions. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Point A**} & \multicolumn{3}{c}{**Point B**} & \multicolumn{3}{c}{**Point C**} \\ \cline{2-10}  & **Measured** & **Simulated** & **Deviation** & **Measured** & **Simulated** & **Deviation** & **Measured** & **Simulated** & **Deviation** \\  & **[mm]** & **[mm]** & **[\%]** & **[mm]** & **[mm]** & **[\%]** & **[mm]** & **[mm]** & **[\%]** \\ \hline Uniform & \multirow{2}{*}{5.60} & 4.88 & 12.90 & \multirow{2}{*}{1.26} & 1.35 & 7.30 & \multirow{2}{*}{0.413} & 0.175 & 57.60 \\ Mapped & & 5.11 & 8.70 & & 1.42 & 12.60 & & 0.194 & 53.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Difference between Measured and Simulated data in points A, B, C in Uniform and Mapped model.
# Analysis of Permanent Magnet Motors in High Frequency--A Review
## 2 High-Frequency Phenomena in Electrical Machines  
In low-frequency operation range the parameters such as resistance and inductance hardly depend on the frequency, so they can be considered to be constants. However, in high frequencies, some new phenomena arise leading to variations in these parameters. Thus, as the frequency increases, the resistance increases and the inductance decreases.  
Before analyzing any electromagnetic device in high-frequency, a specific frequency range must be defined depending on the application standards, as shown in this International Special Committee on Radio Interference (CISPR) guideline [74]. EMI conducted standards cover the frequency range from 150 kHz to 30 MHz, whereas the radiated standards cover the frequency range of 30 MHz to 100 MHz. However, in most cases, it is difficult to obtain models to achieve good accuracy in the whole range of frequencies, especially beyond 10 MHz [17; 20; 28; 39; 51]. Moreover, in [75], it is stated that the model of a transformer is not accurate beyond 10 MHz due to the current coupling between Differential-mode (DM) and Common-mode (CM) paths.
# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 2 Related Works
### Matching-Based VOS Networks  
Recent research in matching-based video object segmentation (VOS) [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23] has primarily focused on improving network structures, including building more efficient memory [5; 6], adopting local matching [7; 8; 9], and incorporating background context [3; 4]. Among them, STM [1] is the pioneer in the field of video object segmentation, which first proposed the design of the memory bank, enabling VOS methods to effectively utilize the temporal information in video sequences. STCN [2] optimized the memory retrieval strategy based on STM, improving the performance. RDE [5] improved the memory update mechanism, avoiding the problem of memory expansion over time. [PERSON] [24] borrowed the technique of knowledge distillation, making the video object segmentation method more suitable for running on mobile devices. However, there are still challenges that need to be addressed. On the one hand, existing methods often overlook crucial shape information when segmenting object edges, leading to reduced accuracy in edge-region segmentation. Inspired by [25], this paper proposes an object shape extraction module to enhance the network's accuracy in segmenting object edges. On the other hand, the common approach to addressing misalignment of similar objects primarily relies on pixel-level motion information. As a result, it often proves inaccurate in cases involving occlusion and fast-moving objects. Inspired by [20], this paper introduces an object motion prediction module, which relies primarily on object-level motion information not constrained by image features. This performs better in addressing challenges such as occlusion and rapid object movement compared to pixel-level motion.