esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Results
### Evaluation Settings  
To demonstrate the validity of our proposed framework for sentiment analysis, we evaluate the framework against several baseline methods, including methods using traditional features, CNN-based methods, and CNN-based methods combined with instance segmentation.  
* The global color histograms (GCH) consists of 64-bin RGB histogram, and the local color histogram features (LCH) divide the image into 16 blocks and generate a 64-bin RGB histogram for each block [31].
* [PERSON] et al. [28] propose SentiBank to describe the sentiment concept by 1200 adjectives noun pairs (ANPs), witch performs better for images with rich semantics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Images Number** & **Source** & **\#Annotators** & **Emotion Model** \\ \hline FI & 23,308 & social media & 225 & [PERSON] \\ Flickr & 484,258 & social media & - & [PERSON] \\ TwitterI & 1269 & social media & 5 & Sentiment \\ TwitterII & 603 & social media & 9 & Sentiment \\ EmotionROI & 1980 & social media & 432 & [PERSON] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Released and freely available datasets, where #Annotators respectively represent the number of annotators.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Learning Rate** & **Drop Factor** & **Croped Size** & **Momentum** & **Optimizer** \\ \hline FI & 0.01 & 20 & 224 \(\times\) 224 & 0.9 & SGD \\ Flickr & 0.01 & 5 & 224 \(\times\) 224 & 0.9 & SGD \\ TwitterI & 0.02 & 30 & 224 \(\times\) 224 & 0.9 & SGD \\ TwitterII & 0.03 & 20 & 224 \(\times\) 224 & 0.9 & SGD \\ EmotionROI & 0.03 & 30 & 224 \(\times\) 224 & 0.9 & SGD \\ \hline \hline \end{tabular}
\end{table}
Table 2: Setting of training parameters on the dataset of FI, Flickr, EmotionROI, Twitter I, Twitter II.  
DeepSentibank [32] utilizes CNNs to discover ANPs and realizes visual sentiment concept classification. We apply the pre-trained DeepSentiBank to extract the 2089-dimension features from the last fully connected layer and employ LIBSVM for classification.
* [PERSON] et al. [29] propose to select a potentially cleaner training dataset and design the PCNN, which is a progressive model based on CNNs.
* [PERSON] et al. [9] employ object detection technique to produce the \"Affective Regions\" and propose three fusion strategy to generate the final predictions.
* [PERSON] et al. [8] utilize saliency detection to enhance the local features, improving the classification performance to a large margin. And they adopt an ensemble strategy, which may contribute to performance improvement.
## 2 Introduction to Experiments  
### Test Equipment and Parameters  
Tests were carried out in the CA-01 atmospheric boundary layer wind tunnel at Chang'an University, with a work section size of 15 m \(\times\) 3 m \(\times\) 2.5 m. The test model was designed based on an actual project, where the geometric scale ratio is 1:70. The length of the model is 1.80 m, the width is 0.72 m, and the height is 0.057 m. The heaving and torsional frequencies are 3.42 Hz and 8.30 Hz, respectively, and the remaining test parameters are shown in Table 1.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Parameter** & **Symbol** & **Unit** & **Real Bridge Value** & **Scale Ratio** & **Test Value** & **Error (\%)** \\ \hline Width & B & m & 50.4 & 1/70 & 0.72 & - \\ Height & H & m & 4 & 1/70 & 0.057 & - \\ Length & L & m & - & - & 1.80 & - \\ Mass per unit length & m & kg/m & 37,000 & 1/70\({}^{2}\) & 7.72 & \(-\)2.2 \\ Mass moment of inertia per unit & J\({}_{\text{m}}\) & kg\(\cdot\)m\({}^{2}\)/m & 8,490,000 & 1/70\({}^{4}\) & 0.34 & 2.9 \\ Heaving frequency & f\({}_{\text{b}}\) & Hz & 0.1432 & — & 3.42 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Motion parameter.  
The model was suspended from the inner support using eight springs during the testing phase. The model skeleton was composed of aluminum alloy to ensure adequate stiffness and strength at high wind velocities. To minimize the impact of the end effect, the model's aspect ratio was set to 2.5 and a binary end plate was placed at the end of the model, as illustrated in Figure 1. Three Panasonic laser displacement meters were employed to monitor and collect the model's heaving and torsional motion. These signals were recorded at a sampling frequency of 200 Hz for a duration of 40 s. Additionally, the pressure characteristics of the section were monitored using an MPS4164 scanner from Scanivalves (Liberty Lake, WA, USA). The signals were sampled at a frequency of 850 Hz for 40 s. The model was divided into ten distinct regions based on its shape to capture pressure variations. To guarantee accuracy, 78 pressure taps were used to continually monitor the sectional surface pressure. Figure 2 clearly illustrates the model's layout and zones.
# Identifying Synthetic Faces through GAN Inversion and Biometric Traits Analysis
## 3 Inversion-Based Detection  
The key idea of the devised forensic strategy is to discriminate between real and GAN-synthesized face images by retro-projecting the image under analysis into the GAN latent space, synthesizing the image associated with the relevant point in the latent space, and comparing the generated image with the actual one.  
This concept is represented in Figure 1. We start from an input image under investigation \(\mathbf{x_{l}}\) (the picture on top of image space), and we feed it into a GAN inversion process to obtain the corresponding point \(\mathbf{z_{R}}\) in the GAN latent space. We then run the GAN generator to produce a reconstructed version \(\mathbf{x_{R}}\) of the original image, associated with \(\mathbf{z_{R}}\) (bottom image in the image space). At this point, we have two copies of the image under analysis, the original and the reconstructed one, and we want to compare them to measure their similarity: the greater the similarity, the higher the probability that the image is GAN-generated. In fact, if the image under analysis comes from this generator, independently of the fact that it was part of the training set or not, a point in the latent space should exist that closely generates the same image. This is not true in the case of a real image, for which that point, in general, will not exist, and the inversion will just provide a more or less accurate approximation but not a perfect reconstruction. Indeed, in practice, we will never obtain a pointwise-equal image due to the limited accuracy of the inversion process, but nevertheless, we expect that GAN-generated images will much more closely match the target than real images.  
Thus, properly comparing \(\mathbf{x_{l}}\) and \(\mathbf{x_{R}}\) is an important part of the process, as the differences among the two images cannot be just modeled as random noise. For this reason, we jointly perform two types of analysis: one based on standard image similarity measures, and the other on more specific face similarity features, and we analyze the relevant performances.  
In the following sub-sections, we further illustrate the two main processes concerned with the above scheme: the inversion process, and the comparison and classification processes.
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 4 Materials and Methods
### Methodological Innovations
#### 4.4.4 Non-Stationary Markov Chain (NMC) with \(t\)-Copula  
The NMC module dynamically models market state transitions, incorporating real-time exogenous variables such as the Sentiment Index and Illiquidity Index. As demonstrated by [PERSON] (2021) [38], the use of \(t\)-copulas to handle serial dependencies among event times allows for more accurate modeling of state transitions under varying market conditions. This dynamic approach differentiates the framework from static or time-invariant Markov models.
## 1 Introduction
A special emphasis was placed on integrated solution methods, which are becoming more significant for terminal management [5]. [PERSON] et al. (2015) reviewed the existing survey articles on container terminal operations. They mostly focused on two things: firstly, modern container terminal skills, and secondly, new operational research (OR) guidelines and models for present research fields [6]. In a study by [PERSON] and [PERSON] (2010), two kernel-based ML methods were presented as Gaussian processes and \(\varepsilon\)-support vector machines. To assess their relative performance, they were contrasted with the multilayer feedforward neural network (MLFNN) model, which was applied in earlier investigations. Data from the Port of Houston's Bayport and Barbours Cut container terminals were used to build the model [7]. [PERSON] et al. (2011) investigated the use of Linear Regression and Multilayer Perceptron to forecast future container throughput at Bangkok Port. The Bank of Thailand, the Office of the National Economic and Social Development Board, the World Bank, the Ministry of the Interior, and the Energy Policy and Planning Office were contacted to identify the factors impacting cargo throughput at the Bangkok Port. These variables were included in the forecasting MLP and linear regression models, which produced a projection of cargo throughput. The outcomes were then evaluated using mean absolute error and root mean squared error [8]. AI and ML are necessary for port management in many ways, from berth scheduling to quay allocation. According to the literature, there are few publications on the topic, and the most common application of machine learning techniques is to predict various port characteristics. Meanwhile, growing examples of prescriptive and autonomous machine learning approaches are seen in the literature. However, to the best of our knowledge, we find no study involving machine learning and feature engineering to overcome the challenge of predicting the vessel size based on container capacity. In this paper, we used neighborhood component analysis (NCA) as a tool for feature selection and state-of-the-art machine learning algorithms for multiclass classification. The paper proposes a novel two-stage approach for estimating and predicting vessel size based on container capacity. To the best of our knowledge, for the first time, NCA is employed to select the best set of features in the area of port management. Alongside this, our proposed two-stage model is also novel in its kind.
## 3 Design Concept and Characteristics
### Simulation of the System  
In this part, damping effectiveness and the level of vibration reduction of the proposed ATMID are elucidated. To this end, the passive-controlled, adaptive-controlled, and uncontrolled models were simulated so that the values of the structure's parameters were consistent with the feasible realization of the experiment as shown in Table 1. Furthermore, the PTMD with its set of parameters of stiffness (\(k_{\rm d}\)) and damping (\(c_{\rm d}\)) in addition to auxiliary mass (m) was designed based on the fixed-point method as mentioned in Table 2[47].  
Regarding the design of the ATMID, as CVT operates in neutral mode (\(\phi=1\)) the inertance ratio of \(b/\rm M=0.2\) and the magnification factor of \(b/\rm m_{f}=60\) are provided. Nevertheless, in CVT operational modes of overdrive and underdrive, the changeable inertance is obtained (\(b_{\rm min}<b<b_{\rm max}\)), depending on the range of CVT ratio (0.45-2.2). Likewise, the device stiffness and the range of CVT ratio ought to be set in such a manner that the resonance frequency of the primary structure falls in the ATMID's frequency range (\(\omega_{\rm d_{min}}<\omega_{\rm s}<\omega_{\rm d_{max}}\)). The ATMID is able to produce different characteristics of its own (\(b_{\rm i}\),\(\omega_{\rm d}\)) in each frequency of the excitation (\(\omega_{\rm e}\)) while other parameters are constant. In other words, each set of the device parameters possesses its own specific inertance and frequency at which the device can optimally work in a related excitation frequency.
## 3 Results
### Impacts of Land Use and Land Cover Dynamics on Ecological Functions  
#### 3.3.1 Impacts of Land Use and Land Cover Types on Ecological Functions  
The cultivated land has the smallest NPP, SOM, and TN, and the largest ERO in 2010. Among the different ecosystem types in 2010 (Figure 5), NPP changed from 72.94 g C m\({}^{-2}\) a\({}^{-1}\) in cropland to 173.05 g C m\({}^{-2}\) a\({}^{-1}\) in forest; ERO changed from 1028.89 g m\({}^{-2}\) a\({}^{-1}\) in grassland to 4988.62 g m\({}^{-2}\) a\({}^{-1}\) in cropland; SOM varied from 7.54 g kg\({}^{-1}\) in cropland to 9.26 g kg\({}^{-1}\) in forest; and the change in TN ranged from 1118.97 mg kg\({}^{-1}\) in cropland to 1245.92 mg kg\({}^{-1}\) in forest.  
The NPP of cropland is the least while that of forest is the highest under the same scenario (Figure 5). Additionally, among the different scenarios, both land use types change greatly while the grassland is relatively stable; the maximum NPP of cropland is expected to appear under the B1 scenario with 169.43 g C m\({}^{-2}\) a\({}^{-1}\) in 2100.  
In cropland, ERO is the strongest in the same climate scenario, whereas grassland only suffers from a lower rate of ERO (Figure 5). The A2 scenario has the largest soil erosion, followed by the B2 scenario. Different ecosystem types will result in different ERO because of the close relationship between vegetation and ERO.  
Under the same climate scenario, among different ecosystem types, SOM in forest is the largest and it is the smallest in cropland (Figure 5). Comparing SOM under different scenarios in the same ecosystem type, SOM in grassland is relatively stable. In forest and cropland, the SOM and TN are the largest under the B1 scenario and are the smallest under the A2 scenario.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Baseline** & **A1B** & & **A2** & & **B1** & & **B2** & \\ \hline  & **2010** & **2050** & **2100** & **2050** & **2100** & **2050** & **2100** & **2050** & **2100** \\ \hline NPP & 154.1 & 163.0 & 179.9 & 148.1 & 125.3 & 163.8 & 186.3 & 161.9 & 136.2 \\ ERO & 1796.1 & 1432 & 933.6 & 2105.1 & 2930.2 & 1393.5 & 734.1 & 1493.1 & 2365.5 \\ SOM & 8.6 & 8.8 & 9.2 & 8.5 & 7.9 & 8.8 & 9.3 & 8.8 & 8.2 \\ TN & 1122.9 & 1134.2 & 1155.1 & 1114.6 & 1086.6 & 1135.9 & 1163.0 & 1132.0 & 1100.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Averaged NPP (unit: g C m\({}^{-2}\) a\({}^{-1}\)), ERO (unit: g m\({}^{-2}\) a\({}^{-1}\)), SOM (unit: g kg\({}^{-1}\)), and TN (unit: mg kg\({}^{-1}\)) in the landscape level under different scenarios.
## 3 Results
### Dynamic Model Parameters  
The parameter values for the processes mortality, photosynthesis and recruitment of M\({}_{1}\) were compared with a dynamically averaged parameter of M\({}_{6}\). Dynamically averaged parameters of M\({}_{6}\) are calculated for each time step by averaging the six different parameter values for each PFT, weighted by the current simulated biomass fraction for each PFT. In the early successional phase, the forest is dominated by pioneer species, so the averaged parameter value for M\({}_{6}\) is comparable to the parameter values of pioneer species. Therefore, it is evident that the averaged parameter of M\({}_{6}\) differs significantly from M\({}_{1}\) (Figure 5), especially before the early-successful phase (\(<\)100 years). Mean mortality rate of M\({}_{6}\) is twice as high in this phase, maximum photoproductivity almost four times as high and number of seeds five times higher compared to M\({}_{1}\) (Figure 5). From the mid-successful phase onwards (\(>\)100 years), the dynamically averaged parameters of M\({}_{6}\) are similar to the values of M\({}_{1}\). In this phase, the forest is dominated by climax species, which is comparable to the parameter values of the climax species, which resulted in a very similar parameter value of M\({}_{1}\) and averaged parameter of M\({}_{6}\).  
Figure 4: Simulated carbons stocks (**a–c**) and net ecosystem exchange (**d–f**) for a tropical forest for different model parameterization versions. Positive net ecosystem exchange values indicate that the forest acts as a carbon sink, negative values a carbon source. Mean values and standard deviations of the net ecosystem exchange in late successional phases are presented on the right-hand side of (**d–f**).
# Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning
## 3 Research Design
### Data Collection
#### 3.1.1 Twitter Data  
To connect to Twitter's API, a developer account was generated. We gathered and sorted Tweets in English using keywords such as #Microsoft, #MSFT, and #Microsoft365. During the timeframe from 16 July 2020 to 31 October 2020, a total of 90,000 tweets were crawled from Twitter.  
We retrieved the user ID, keyword, user account, tweet text, and date of creation, as shown in Table 1.
# Power-Efficient Resource Allocation for Active STAR-RIS-Aided SWIPT Communication Systems
## 2 System Model and Problem Description
### Problem Description
Therefore, for the ES protocol, the communication rate achievable by the \(i\)-th information receiver is as follows:  
\[R_{i}^{\mathrm{ES}}=lb\left(1+\frac{\Big{|}f_{t,i}^{H}P\Theta_{t}^{\mathrm{ES }}Gw_{i}\Big{|}^{2}}{\sum\limits_{j\
eq i,j\in K_{I}}\Big{|}f_{t,i}^{H}P\Theta_ {t}^{\mathrm{ES}}Gw_{j}\Big{|}_{2}+\Big{|}\Big{|}f_{t,i}^{H}P\[PERSON]_{t}^{ \mathrm{ES}}\Big{|}\Big{|}2\sigma_{RIS}^{2}+\sigma_{i}^{2}}\right) \tag{3}\]  
If the noise power is ignored, the power is accepted at the \(j\)-th energy receiver:  
\[E_{j}^{\mathrm{ES}}=\sum\limits_{k\in K_{I}}\Big{|}f_{r,j}^{H}P\[PERSON]_{r}^{ \mathrm{ES}}Gw_{k}\Big{|}^{2}+\sum\limits_{k\in K_{E}}\Big{|}f_{r,j}^{H}P\Theta _{r}^{\mathrm{ES}}Gv_{k}\Big{|}^{2},\forall j\in K_{E} \tag{4}\]  
The objective of this paper is the joint optimization of the active beamforming variable \(\{w_{i},v_{j}\}\), the passive beamforming variable \(\Theta_{k}^{\mathrm{ES}}\), \(\forall k\in\{t,r\}\), and the amplification multiplier matrix \(P\) for a given ES operation protocol under the communication rate of the information receiver and the energy-harvesting constraints of the energy receiver so as to minimize the AP power consumption. The optimization problem can be thus stated:  
\[\min\limits_{\{w_{i},\{v_{j}\},\emptyset\},\Theta_{k}^{\mathrm{ES}}}\sum \limits_{i\in K_{I}}\left|\left|w_{i}\right|\right|^{2}+\sum\limits_{j\in K_{E} }\left|\left|v_{j}\right|\right|^{2} \tag{5}\]  
\[s.t.\;\;R_{i}^{\mathrm{ES}}\geq\overline{R}_{i},\forall i\in K_{I} \tag{6}\]  
\[E_{j}^{\mathrm{ES}}\geq\overline{E}_{j},\forall j\in K_{E} \tag{7}\]  
\[\Theta_{k}^{\mathrm{ES}}\in\mathbb{F}^{\mathrm{ES}},\forall k\in\{t,r\} \tag{8}\]where \(\overline{R}_{i}\) in Equation (6) denotes the minimum rate requirement for user \(i\), \(\overline{E}_{j}\) in Equation (7) denotes the minimum energy harvesting requirement for user \(j\), and \(\mathbb{P}^{\rm ES}\) in Equation (8) denotes the feasible set corresponding to the emission and reflection coefficient matrix. Because of the coupling between variables, this paper uses the algorithm of alternating iteration to optimize the solution for different variables. We first stabilize the passive beamforming variable \(\Theta_{k}^{\rm ES},\forall k\in\{t,r\}\) and the magnification matrix \(P\), optimizing variable \(\{w_{i},v_{j}\}\). Once the most optimal solution of \(\{w_{i},v_{j}\}\) is available, we fix the value unchanged and then optimize the passive beamforming variable \(\Theta_{k}^{\rm ES},\forall k\in\{t,r\}\) and magnification matrix \(P\) and so on iteratively until the system converges.
## 3 Antibiotic Degradation and Fate Kinetics in Surface Water
### Photodegradation
Additionally, the pH variation in water changes the ionic strength of pollutants and affects the electron transfer capacity and their reaction activities [56]. The change in pH plays a decisive and positive role in the photolysis removal of antibiotics. Tetracyclines are relatively stable in acidic conditions and unstable in alkaline conditions, attributed to the inter and intramolecular proton transfer in excited states [39]. Fluoroquinolones can exist in various pH forms (cationic, zwitterionic, or anionic form), affecting their direct photolysis rate. Under sunlight, the rapid removal of fluoroquinolones occurs in neutral and slightly alkaline conditions. Minimal antibiotic degradation rate kinetics from direct photolysis and volatilization is evident during cold seasons, especially in winter, due to low water temperatures and cloud cover [63]. This is attributed to low organic matter production from bacterial and algal biomass and limited photochemical generation of reactive species from photosensizers. Deep water bodies have limited temperature penetration throughout the water level to the bed material. As a result, antibiotics in deep water bodies have decreased incidences of photodegradation. The presence of nitrate ion concentration inhibits the indirect photodegradation of sulfathiazole, while dihydrogen carbonate enhances the photodegradation of sulfathiazole [58]. Nitrate ion concentration does not affect sulfarrazine degradation, while hydrogen carbonate inhibits the photodegradation of sulfarrazine [58] and promotes the degradation of sulfisoxazole [56]. The presence of halogen ions (Cl\({}^{-}\) and Br\({}^{-}\)) inhibits the degradation of most sulfonamide antibiotics [56]. Therefore, understanding the natural removal pathway and establishing a reliable estimation mechanism for an accurate photodegradation rate is critical for the environmental fate modeling of antibiotics.
## 4 Results Analysis and Discussion
### Prediction Error Analysis  
In this study, we applied Decision Tree, Random Forest, and Gradient Boosting Regressor models to predict the target variables. To evaluate the performance of these models, we analyzed the distribution of prediction errors for each model. The prediction error plots reveal the relationship between actual values and predicted values, providing a visual representation of the accuracy and fit of the models on the test set, as shown in Figure 13.  
Figure 13: Prediction Error Scatter Plots: (**a**) Longitude Prediction Error; (**b**) Latitude Prediction Error; (**c**) SOG Prediction Error; (**d**) COG Prediction Error.  
The prediction error plots demonstrate that the Random Forest model exhibits a strong linear relationship when handling the data, providing accurate predictions for longitude and latitude. Although some deviations are observed in the predictions for Speed Over Ground (SOG) and Course Over Ground (COG), the overall prediction accuracy of the Random Forest model surpasses that of the other models. Overall, the Random Forest model displays the best predictive performance. The Decision Tree model's prediction results, as seen in the scatter plots, exhibit more dispersed points, indicating that its predictions are less accurate than expected. Compared to the Random Forest and Gradient Boosting Regressor models, the Decision Tree model performs poorly when handling this data. The Gradient Boosting Regressor model shows exceptionally high accuracy in predicting target variables LON and LAT, with predicted values closely aligning with actual values, indicating excellent predictive performance. In predicting SOG, although there are some points that deviate from the ideal red prediction line, the overall error remains small, demonstrating the model's stability and accuracy in this dimension. However, in predicting COG, the points in the scatter plot are more dispersed, indicating a certain degree of prediction error. The Mean Squared Error (MSE) and Adjusted \(r^{2}\) are used to quantify the prediction errors of different models, as shown in Table 2. Despite the favorable performance of both the Decision Tree and Random Forest models on the learning curves, the Decision Tree model suffers from overfitting and single-model limitations, resulting in poorer performance when predicting new data. In contrast, the Random Forest model, by integrating multiple decision trees, effectively reduces the risk of overfitting, enhancing the model's stability and prediction accuracy.