esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Correlation between CUE and Climate Change Factors  
GPP, NPP and CUE under three future climate scenarios representing different radiative forcings have substantial correlations with the climate variables MAT, MAP and CO2 concentration (Figure 3). GPP and NPP are highly positively correlated with MAT and CO2 concentration, respectively, and there is little substantial difference among scenarios. The positive correlation between MAP and GPP and NPP is slightly lower in the low radiation forcing scenario than in the medium and high radiation forcing scenarios. CUE and MAT, MAP, and CO2 concentrations show different degrees of negative correlation under the three scenarios. CUE has a low negative correlation with MAT and CO2 concentration under the SSP1-2.6 scenario (Figure 3a), increasing under the SSP3-7.0 scenario (Figure 3b), and has the highest negative correlation with them under the SSP5-8.5 scenario (Figure 3c). Thus, the negative correlation between CUE and MAT and CO2 concentration increases with the increase in radiative forcing; that is, the higher the temperature and CO2 concentration are, the CUE shows an increasingly obvious decreasing trend. The negative correlation between CUE and MAP is slightly weaker than that between CUE and MAT and CO2 concentration, especially in the low radiation forcing scenario. Likewise, the negative correlation between CUE and MAP becomes increasingly substantial with increasing radiative forcing.  
Figure 3: Correlations among GPP, NPP, CUE, MAT, MAP, and CO2 concentration in the three scenarios. Correlations under the SSP1-2.6 scenario are shown in (**a**). Correlations under the SSP3-7.0 scenario are shown in (**b**). Correlations under the SSP5-8.5 scenario are shown in (**c**). The size of the circle and the depth of the color indicate the level of correlation; red indicates a positive correlation, and blue indicates a negative correlation. * means the correlation is likely not = 0.
# Heat Transfer Enhancement of Nanofluids with Non-Spherical Nanoparticles: A Review
## 3 Convective Heat Transfer of NSN Nanofluids
### Numerical Predictions and Mechanism Discussion
#### 3.2.2 Particle Rotational Diffusion and Orientation Distribution  
The morphology and orientation of the dispersed solids are complex in particle suspensions. [PERSON] et al. [10] suggested four possible particle configurations in nanofluids, including the parallel series, Hashin-Shtrikman (or H-S), and EMT configurations. Researchers assume the H-S configuration when the TCE is estimated [64,72]. Nevertheless, the alignment of non-spherical nanoparticles is apparent in internal flows, specifically near the wall. Researchers give less consideration to the orientation distribution of nanoparticles. [PERSON] et al. [112, 113] found the highest overall heat transfer coefficient when nanofluids with cylindrical shapes were used for nanofluid flow in a shell and tube heat exchanger.  
Moreover, non-spherical nanoparticles rotate, which delays and disturbs the thermal boundary layer [102]. The rotational diffusion coefficient of nanoparticles has recently attracted research attention. [PERSON] et al. [109, 110, 111] and [PERSON] et al. [108] analyzed the turbulent and laminar NSN nanofluid flows in straight or turbulent pipes by considering the Brownian rotational diffusion and orientation distribution. They found that the orientation distribution of cylindrical nanoparticles was less concentrated as the Reynolds number increased in the turbulent regime. Particle orientation components at the cross-section are nearly 1/3, implying that the nanorod orientation is nearly random in space at the channel center. The nanorod is more likely to align with the flow direction with the increasing shear near the wall. A nanorod with a higher aspect ratio is likely to be rotating towards the flow direction.
## 3 Mechanical Vibration Tests
### Electromagnetic Fastening  
In the charts presented in Figure 20, the results of the experimentally obtained transmittance functions for the electromagnetic chuck at measurement point 2 are presented. The ordinate axis represents transmittance as a dimensionless quantity, and the abscissa axis represents the frequency in Hz.  
Figure 19: The arrangement of vibration sensors during vertical vibration testing.  
The determined transfer function functions show clear deviations from the requirements of the industry standard IEC 60068-2-6. In each case, the transmittance increases with frequency. Graph 20 (a) illustrates the changes in transmittance for acceleration equal to 1 g. Under these conditions, the transmittance changes in a predictable manner--it increases non-linearly. The situation is different for higher acceleration amplitudes. It was observed that it changes randomly and unpredictably without a trend in the low frequency range (up to 100 Hz). It was hypothesized that the cause of such a transfer function curve was the detachment of the sample from the holder due to insufficient holding force.  
To confirm this assumption, an additional experiment was prepared (Figure 21). Paper tape was attached to the side surfaces of the holder. Before the vibrational experiment commenced, a line was drawn so that the dimension \"\(\times\)\" from the drawing in Figure 21 was equal to 0. After conducting vibrational studies, a displacement of 2-3 mm was observed. Thus, the assumption was confirmed. This means, at the same time, that the electromagnetic holder in the presented configuration should not be used for vibrational tests.  
A hypothesis was proposed that the steel plate placed under the holder was too thin. This prevented the proper development of the electromagnetic field and the generation of the desired clamping forces. Further increasing the thickness of the steel plate has an exceptionally adverse effect on the mass balance of an already heavy holder. Increasing the mass necessitates the installation of an exciter with a greater dynamic force, which in turn significantly affects investment, service, and operating costs. Additional research work is required in this area.  
Figure 21: View of paper tape with a mark indicating the susceptibility of the clamp to detaching during vibration.  
Figure 20: Transmittance function for the electromagnetic chuck in the frequency domain for sensor no. 2: (a) test 1 g, (b) test 2 g, (c) test 4 g, and (d) test 8 g.
# Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection
## 5 Discussion
### Comparison with Previous Work  
The following section provides a comparison with previous work in online misogyny detection. Although the comparative analysis presented in Table 3 does not utilize identical  
Figure 12: Performance comparison of experimental deep-learning models on original and augmented datasets, evaluated using: (**a**) Precision, (**b**) Recall, (**c**) F1-Score, (**d**) Accuracy, (**e**) ROC AUC. datasets across all studies, it assesses methodologies and their limitations. This approach offers a significant evaluation of the effectiveness of various techniques in different contexts, demonstrating the adaptability and scalability of advanced NLP methods for tackling the complex challenge of detecting online misogyny in varied data settings. The study presents significant advancements by employing sophisticated NLP techniques, such as emoji decoding, contextual expansion using Sentence-Transformers, and LDA-based topic modeling. These methods deepen our understanding of nuanced and contextual aspects of language, crucial for identifying complex forms of misogyny that conventional models may overlook. By expanding the training datasets through data augmentation, the study addresses the commonly observed limitation of inadequate and non-diverse data, which often impairs the efficacy of models in capturing the full spectrum of misogynistic expressions. This is notable in previous research such as [25; 26], where limited dataset diversity constrained model performance. Furthermore, the research involved a distinct exploration of machine-learning, deep-learning, and Transformer-based models, assessing each model's strengths and weaknesses separately to enhance accuracy in detecting various forms of misogynistic content. This meticulous approach helps address several challenges highlighted in earlier studies. For instance, ref. [19] struggled with interpreting irony and sarcasm and adapting to new lexicons, while [21] pointed out difficulties related to the subtlety and context-dependency of misogynistic language. The proposed framework effectively mitigates these issues through improved contextual understanding and robust data augmentation strategies. Additionally, the limitations of static lexicon-based methods, as demonstrated by [24], which fail to keep pace with the evolving language on social media, are adeptly countered in the new study. The implementation of dynamic and adaptive methods, especially the real-time context awareness provided by Sentence-Transformers and LDA, allows the proposed
## 5 Results
### Evaluation of the Artifacts
#### 5.2.1 Feasibility Evaluations  
Iteration 1: Feasibility of predictions in decision making regarding multi-access routing was evaluated utilizing offline ML with a RF regressor, MLFlow and RedisAI. The following lessons were learned:  
* RF regressor was trained with sklearn and integrated with MLFlow for visualization of performance (10-fold cross validation) with MLFlow UI. MLFlow had to be executed on the same node with a model training process (sklearn) for realizing performance evaluation with MLFlow UI.  
Figure 12: Deployment diagram of emulated mesh network environment.  
* The RF regressor-based model was converted to ONNX format (with sklearn-onnx library [29]) for transferring to RedisAI. PMML-based models were also experimented with (converted with sklearn2 pmml library [30]), but initial inference performance was slower (~19 ms vs. ~1 ms with ONNX-based model in RedisAI).
* A new MLFlow plugin for RedisAI was needed for realizing model deployment (to RedisAI), because the existing RedisAI plugin only supported Tensorflow models. The new MLFlow plugin enabled transfer of ONNX models to RedisAI. The trained ONNX-based model was deployed from MLFlow to RedisAI by executing the plugin from the command line.
* RedisAI was executed as a Docker image and provided predictions for the emulator (via redisai-py library [31]).
* RedisAI had to be placed near the decision-making point. Initially, ~30 ms inference latency was observed, when RedisAI was placed further from the decision-making point (emulator). This led to large OWD peaks in experiments. OWD predictions were needed for each AP for making a decision regarding the most suitable AP for routing (total prediction latency = number of APs
* RedisAI prediction latency). Thus, to perform timely/real-time decisions, prediction latency needed to be minimized (~1 ms latency) by placing RedisAI close to the emulator, which led to improved OWD performance.  
Iteration 2: Feasibility of online decision making was evaluated utilizing continuous ML with River. The following lessons were learnt:  
* River-based training could not be integrated with MLFlow. Instead, the training process was managed manually.
* Online training was experimented with using River by updating and retraining the model periodically with new data samples (every 2 s). The trained model was kept in memory.
* The model was locked periodically for both training and inference process with multi-threading. This approach was feasible, since training takes a short time, and the model is available most of the time for prediction purposes (~1.5-1.8 s/2 s). However, this approach also caused occasional inference peaks when the model was trained during inference requests. Prediction delays in the emulator were typically ~1 ms, with occasional peaks (~10-50 ms) during model updates, when predictions were fetched every 200 ms.
* Utilization of the model in online emulation experiments was hindered by not having real time OWD measurements available for training purposes (future work).
## 3 Proposed ATMALS-GSK Algorithm
### Atmals-Gsk
#### 3.2.5 Adaptive \(P_{ls}\)  
In the proposed ATMALS-GSK algorithm, an adaptive local search mechanism is incorporated to improve solution quality through controlled mutation. This local refinement is designed to balance exploitation and exploration dynamically as the algorithm progresses. At each iteration \(G\), \(N_{LS}\) solutions are randomly chosen from the entire population, and their variables are considered for local changing. The probability of mutating each decision variable in a selected individual (i.e., \(P_{LS}\)/_ProbSize_) is governed by the same Gaussian-based probability function used in Equations (3)-(7), considering a variation range of [1, 2] with a discretized value of 0.1.  
Simultaneously, the range of local search, denoted \(Range_{LS}\), shrinks linearly over generations, as shown in Equation (8), which means that mutations at the beginning are more exploratory, enabling the algorithm to explore broader areas of the search space. Over time, the range contracts, guiding the search toward local regions around promising solutions, which enhances the exploitation capabilities of the algorithm. This dynamic adjustment promotes intensive search and fine-tuning near convergence.  
\[Range_{LS}(G)=Range_{LS,\text{max}}-(Range_{LS,\text{max}}-Range_{LS,\text{ min}})\times\frac{G}{MaxGen} \tag{8}\]  
Together, these two adaptive mechanisms (i.e., auto-tuning adaptiveness of the local search probability and shrinking the local search range) help the proposed ATMALS-GSK algorithm evolve from automatic adaptive broad global search to focused local refinement, improving its ability to find high-quality, well-converged solutions.
## 4 Analytical Methods
### Logistic Regression  
Regression analysis is a statistical technique for predicting the value of dependent variables from independent variables by understanding the causal relationship between variables. It is used to analyze the relevance of dependent variables to independent variables. A typical multiple linear regression (MLR) formula is equivalent to Equation (5).  
\[p_{MLR}(y_{i}|x_{i})=v+w^{T}x_{i},\ \ \ i=1,2,\ldots,m \tag{5}\]  
where \(X=[x_{1},\ldots,x_{m}]^{T}\in\mathbb{R}^{m\times n}\) is a set of training data and \(Y=[y_{1},\ldots,y_{m}]\in\mathbb{R}^{m}\) is a set of labels. \(w\in\mathbb{R}^{n}\) are weighting values and \(v\) represents the intercept. \(p_{MLR}(y_{i}|x_{i})\) is the predicted value of \(y_{i}\) when the independent variable \(x_{i}\) attains a certain value. A typical regression analysis can acquire any value depending on the independent variable; thus, the \(p_{MLR}(y_{i}|x_{i})\) value can extend to infinity. If the dependent variable is a binary categorical variable, linear regression does not properly represent the relationship between the independent and dependent variables.  
Therefore, logistic regression (LR) [25] can be used instead of linear regression if the dependent variable is binary (i.e., \(y_{i}\in\{-1,+1\}\)). Using logistic regression, the value of the dependent variable can be represented as a value between zero and one. Expressing logistic regression as a formula is equivalent to Equation (6).  
\[p_{LR}(y_{i}|x_{i})=\frac{exp\big{(}(v+w^{T}x_{i})y_{i}\big{)}}{1+exp((v+w^{T} x_{i})y_{i})} \tag{6}\]  
When the independent variable \(x_{i}\) acquires a certain value, the predicted value of \(p_{LR}(y_{i}|x_{i})\) has the concept of probability between 0 and 1.  
The average logistic loss function is calculated from the negative log-likelihood of the logistic model with respect to all samples.  
\[\mathcal{L}_{avg}(w,v)=\frac{1}{m}\sum_{i=1}^{m}\log\Bigl{(}1+\exp\Bigl{(}-y _{i}\Bigl{(}w^{T}x_{i}+v\Bigr{)}\Bigr{)}\Bigr{)} \tag{7}\]  
The model parameters w and v are determined in the direction of minimizing the average logistic loss function by a maximum likelihood estimation.  
\[minimize\ \ \mathcal{L}_{avg}(w,v) \tag{8}\]  
By adding weight-regulating terms, which is a standard technique for preventing overfitting, to the mean logistic loss function, we can limit the weights from increasing in value and improve the generalization performance of our models.  
\[minimize\ \ \mathcal{L}_{avg}(w,v)+R(C) \tag{9}\]Here, \(R(C)\) is the regularization function, which can have different forms depending on the regularization method. The \(\ell_{1}\)-regularized logistic regression problem is  
\[minimize\ \ \ell_{avg}(w,v)+\frac{1}{C}\sum_{i=1}^{n}|w_{i}| \tag{10}\]  
The \(\ell_{2}\)-regularized logistic regression problem is  
\[minimize\ \ \ell_{avg}(w,v)+\frac{1}{C}\sum_{i=1}^{n}w_{i}^{2} \tag{11}\]  
where \(C\) is a regularization parameter used to adjust the balance between the magnitude of the weight vector and the average logistic loss measured by the \(\ell_{1}\)-norm or \(\ell_{2}\)-norm.
# Pursuit-Evasion Problem of Unmanned Surface Vehicles in a Complex Marine Environment
## 5 Experimental Results  
In order to further validate the effectiveness of the interception algorithm proposed in this paper, the pursuit-evasion experiment is presented in this section. Two USVs with the same principal dimensions are defined as the evader (yellow) and the pursuer (orange). The mass of the USV is 12 kg, the length is 0.8 m, and the width is 0.24 m. The maximum speed of the evader and the pursuer are 0.5 m/s and 0.6 m/s, respectively. The position is measured by the ultra-wideband (UWB), and the positioning accuracy is 0.1 m. In addition,  
Figure 8: The estimated value of uncertain unknown \(\tau_{ur}\).  
Figure 6: Distances between pursuer and evader.  
Figure 7: The estimated value of uncertain unknown \(\tau_{uw}\).  
the pursuer is equipped with cameras to hunt the evader. The composition of the USV is shown in Figure 9.  
The UWB position measurement system is composed of three base station modules at fixed positions, and the final positioning data are generated only on the \"base station\" module connected to the controller. The \"label\" module is fixed on the moving ship. The \"tag\" can be easily added or reduced. Figure 10 shows the structure of UWB position.  
UWB is used to obtain and predict the position of the pursuit vehicle and the target vehicle through the Kalman filtering algorithm. Since the movement speed of the pursuer and target in this test changes slowly, and the sampling frequency of UWB is greater than 10 Hz, the ship can be regarded as moving at a uniform speed within the sampling frequency. Due to the noise and error of the data directly obtained by the UWB sensor in real time, the data need to be processed by the filtering algorithm. First, obviously wrong UWB data are eliminated. Data points beyond the scope of the tested water area or beyond the radius of 2 m of the current position obtained by the least squares method will be regarded as abnormal measurement data points. Then, the least squares method is used to perform second-order fitting on 20 data points that were recently saved. The fitting result is taken as the prediction value, and the Kalman filter algorithm is used to correct the latest measurement value to obtain the optimal target motion information.  
In the test, the evader always moves at a heading angle of 10\({}^{\circ}\). The pursuer senses the target and executes an interception in order to show the trajectories of the evader and the pursuer more clearly. Figure 11 shows their trajectory curves according to the UWB data.  
Figure 10: Structure of UWB position measurement system.  
Figure 9: Composition diagram of the USV.  
Although the escaping target moves along a curve, it can still be seen that the proposed encirclement algorithm can intercept the escaping target.
## 3 Proposed Optimal Strategy
### KE Optimization Method
#### 3.2.2 Optimal KE Solution  
The KE optimization problem in this study is solved using PSO [29], which is adopted for optimal tuning of the KE reserve value for each wind farm. Maximizing the additional KE can be translated into optimizing for rotational speed in the de-loaded control. The purpose of the proposed KE optimization method is to maximize the KE reserve defined in Equation (19). Here, because the optimal rotational speed, \(\omega_{m,opt,i}\) is a constant value determined from wind speed, the objective function of the KE optimization problem is defined as  
\[Max\left[\sum_{i=1}^{N}0.5J_{i}\left(\omega_{del,i}^{2}\right)\right] \tag{22}\]  
The objective function is aimed at ensuring that generators maximize their KE reserve from the stored rotor, while performing the active power control to support the frequency of the power system. For enhancing the frequency support capability of wind farms, the main issue of this paper realizes KE optimization method to determine the de-loading power value reference for each PMSG systems [15; 16], and maximizes the sum of KE reserve value through Equation (22). When the wind farm operates according to the system operator's output command \(P_{ref,uf}\), the de-loaded power reference \(P_{del,i}\) and de-loaded rotational speed \(\omega_{del,i}\) of the individual PMSG wind turbine determined in the control system must satisfy the following constraints.  
Figure 8: The triggering scheme and frequency detection.  
Figure 7: Modified \(q\)-axis rotational current control loop.  
Total power output constraints of the wind farm:  
\[P_{ref,wf}=\sum_{i=1}^{N}P_{del,i} \tag{23}\]  
Power output constraints of PMSG wind turbines:  
\[P_{min,i}\leq P_{del,i}\leq P_{max,i} \tag{24}\]  
Rotational speed constraints of PMSG wind turbines:  
\[\omega_{m,opt,i}\leq\omega_{del,i}\leq\omega_{m,limit} \tag{25}\]  
When the wind turbines operate at the proposed de-loaded control, the output of the PMSG wind turbine is kept constant according to the de-loaded power reference. Thus, the sum of the de-loaded references of individual wind turbine generators, \(P_{del,i}\) should be equal to the output of the wind farms according to the output command of the system operator \(P_{ref,wf}\), which sets the output of the wind farm for stable system operation. The minimum possible power is defined in the range that can maintain stable wind power operation for a given wind speed. When a wind turbine is operated in the de-loaded control mode, the rotational speed reference must be determined within the range of constraints. From Equation (25), the minimum rotational speed is limited from the optimal rotational speed in the MPPT control mode, and the maximum rotational speed of all wind turbines is set to the rotational speed reference of pitch angle control.