esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions
## 4 Experiment
### Baseline Models  
In this section, we discuss the baseline models and various parameter settings of the model. SVM (Support Vector Machine) [7] is a simple machine-learning-based method used for recognizing emotions in short texts. It categorizes the emotions into five types but does not consider the symbolic information present in the text.  
* LSTM (Long Short-Term Memory Network) [42] is a widely used variant of recurrent neural networks (RNN). It addresses the issue of gradient disappearance in traditional RNNs by introducing multiple gating units. The LSTM's hidden state is used as the feature vector for sentiment classification of documents or sentences.
* Bi-LSTM (Bidirectional Long Short-Term Memory) [25] combines Bi-LSTM with a multi-type attention mechanism. It primarily solves the problem of implicit emotion classification. However, experiments have shown that this method also yields effective results for explicit emotion classification, making it suitable for various emotion classification tasks.
* CNN+BiLSTM (Convolutional Neural Network + Bidirectional Long Short-Term Memory) [43] is a hybrid approach that combines CNN and BiLSTM to extract sentiment features from Weibo short texts, aiming to achieve sentiment classification.
* C-GRU (Context-aware Gated Recurrent Units) [44] extracts contextual information (topics) from tweets and incorporates it into an additional layer that determines the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & NLPCC2013 Dataset & & & \\ \hline Emotion category & Happy & Angry & Sad & Fear & Surprised & Disgusted & None \\ Sample size & 1562 & 575 & 670 & 92 & 248 & 800 & 6648 \\ Test sample & 391 & 144 & 168 & 23 & 63 & 204 & 1663 \\ \hline \hline \multicolumn{8}{c}{NLPCC2014 Dataset} \\ \hline Emotion category & Happy & Angry & Sad & Fear & Surprised & Disgusted & None \\ Sample size & 6511 & 1712 & 2230 & 265 & 721 & 2746 & 24,936 \\ Test sample & 1627 & 428 & 557 & 66 & 180 & 686 & 6234 \\ \hline \hline \end{tabular}
\end{table}
Table 1: NLPCC2013 and NLPCC2014 Datasets.  
sentiment expressed in the tweets. This model learns a direct association between the sentiment layer and task prediction, enabling multi-label sentiment classification.
* MF-CSEL (Multilingual Text Sentiment Analysis Model) [45] is based on training the CBOW (Continuous Bag-of-Words) model. It combines multiple languages and utilizes cost-sensitive integrated learning methods to perform multilingual sentiment analysis.
## 2 Study Area and Data Sources
### Data
#### 2.2.8 Elevation Data  
The DEM data used in this study is the GEBCO 2024 Grid [41], a globally recognized topographic model dataset that is widely used for various geospatial analyses [42].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**2021** & **Cropland** & **Forest** & **Shrub** & **Grassland** & **Water** & **Snow/Lee** & **Barren** & **Impervious** & **Weltand** & **Sum** \\ \hline Cropland & 4,794,970.59 & 119,195.01 & 3076.96 & 161,260.29 & 2303.01 & & 2.61 & & 5,0802,768.47 \\ Forest & 259,456.32 & 2,006,311.22 & 2,258.64 & 89,868.83 & 42.75 & 1.89 & & 21,375,493.65 \\ Strub & 68.64 & 8437.05 & 106,182.81 & 6209.01 & & & 121,515.35 & 12,615.35 \\ Grassland & 61,800.09 & 6667.29 & 3,342,471.57 & 1264.32 & 278.01 & 5635.44 & & 0.81 & 3,418,147.53 \\ Water & 3247.2 & & & 753.21 & 182,773.89 & 36.54 & 89.73 & 2308.32 & 0.09 & 189,208.98 \\ Snow/ Ice & & & & 6658.38 & 353.79 & 2916.72 & 32,554.08 & & & 4,428,97 \\ Barren & & & & & 0.54 & & & & 1.07 & 340,830.9 \\ Surm & 5,135,864.22 & 21,134,784.69 & 136,145.7 & 3,607,920.36 & 187,365.42 & 11,554.83 & 38,950.29 & 343,139.22 & 2.61 & 30,959,727.34 \\ \hline \end{tabular}
\end{table}
Table 2: Land use transition matrix for the Qinba Mountain area in 2018 and 2021 (Unit: \(\text{hm}^{2}\)).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**2021** & **Cropland** & **Forest** & **Shrub** & **Grassland** & **Water** & **Snow/Lee** & **Barren** & **Impervious** & **Weltand** & **Sum** \\ \hline Cropland & 4,794,970.59 & 119,195.01 & 3076.96 & 161,260.29 & 2303.01 & & 2.61 & & 5,0802,768.47 \\ Forest & 259,456.32 & 2,006,311.22 & 2,258.64 & 89,868.83 & 42.75 & 1.89 & & 21,375,493.65 \\ Strub & 68.64 & 8437.05 & 106,182.81 & 6209.01 & & & 121,515.35 & 12,615.35 \\ Grassland & 61,800.09 & 6667.29 & 3,342,471.57 & 1264.32 & 278.01 & 5635.44 & & 0.81 & 3,418,147.53 \\ Water & 3247.2 & & & 753.21 & 182,773.89 & 36.54 & 89.73 & 2308.32 & 0.09 & 189,208.98 \\ Snow/ Ice & & & & 6658.38 & 353.79 & 2916.72 & 32,554.08 & & & 4,428,97 \\ Barren & & & & & 656.38 & 353.79 & 2,67.66 & 11.07 & 340,830.9 & 359,183.16 \\ Intervious & 15,673.59 & 841.41 & & & 0.54 & & 1.71 & 2.25 \\ Surm & 5,135,864.22 & 21,134,784.69 & 136,145.7 & 3,607,920.36 & 187,365.42 & 11,554.83 & 38,950.29 & 343,139.22 & 2.61 & 30,595,727.34 \\ \hline \end{tabular}
\end{table}
Table 1: Land use transition matrix for the Qinba Mountain area in 2018 and 2020 (Unit: \(\text{hm}^{2}\)).
# Computed Tomography-Driven Design and Fused Filament Fabrication of Graded Density Bone Scaffolds
## 1 Introduction  
Tissue engineering and regenerative medicine are interdisciplinary fields that have undergone significant advancements in recent decades. These fields aim to develop biological and synthetic substitutes that replicate the structure and function of natural tissues. The conventional method is the autograft method but it is not always feasible, mainly due to the availability of donor tissues [1]; therefore, the adoption and demand for artificial substitutes are increasing [2].  
In this context, two different strategies are commonly employed, involving prostheses and scaffolds. Prostheses are permanent or semi-permanent artificial devices designed to restore the structural or functional role of damaged tissues or organs. They are typically fabricated from biocompatible materials such as titanium, ceramics, or high-strength polymers and do not actively promote tissue regeneration.  
Scaffolds, by contrast, are temporary, porous, interconnected structures designed and engineered to enhance cellular interactions, mimicking the extra-cellular matrix [3]. Theysupport cell attachment, proliferation, and tissue regeneration, and are biodegradable, gradually degrading as the natural tissue forms and replaces the scaffold.  
Among the traditional methods for producing scaffolds--such as electrospinning, soluble solid templating, the decellularization of natural tissues, particulate leaching, gas foaming, sintering, and freeze-drying--additive manufacturing (AM) has emerged as an approach that can overcome the limitations of these methods, allowing full engineering of the final scaffolds [1; 4].  
Since scaffolds must have specific characteristics related to the biological aspect, structure, and chemical composition, AM seems the perfect approach for their production. AM enables high customization to match the patient-specific anatomy, precise control over the internal architecture and porosity, and an ever-growing range of available materials. These include materials such as synthetic polymers (e.g., polylactic acid (PLA), polyglycolic acid (PGA), polycaprolactone (PCL)) and natural polymers (e.g., collagen, fibrin, chitosan and glycosaminoglycans (GAG)), or bioceramics (e.g., hydroxyapatite (HA), \(\beta\)-tricalcium phosphate (\(\beta\)-TCP), \(\alpha\)-TCP, calcium silicate, bioactive glasses) and hybrid materials [1; 2; 5; 6; 7; 8]. For syntheses, metals are often used, such as pure titanium, Ti6 Al4V alloys, stainless steel AISI 316L, cobalt-chromium, and cobalt-chromium-molybdenum (especially used in dental prosthetics), as well as magnesium alloys such as Mg-Ca-Zn. The common AM technologies used in scaffold fabrication include selective laser sintering (SLS), material extrusion (MEX), and vat photopolymerization (VPP) [1; 4].  
Enabling the AM workflow, patient-specific scaffolds can be designed based on biomedical imaging techniques such as CT or magnetic resonance imaging (MRI), and their internal architecture and porosity are engineered and customized using computer-aided manufacturing (CAD) software.
## 4 Discussion
### Enhancing Performance  
#### 4.2.1 Task Completion Time  
The analysis of task completion time for TMT-VR Parts A and B revealed significant differences among the interaction modes. Specifically, the headset mode consistently outperformed the controller mode, with participants completing tasks more quickly. This finding aligns with previous research that suggests head movement-based interaction offers a more intuitive and less cognitively demanding method for interacting with virtual environments, as it mimics natural head movements used in real-world tasks [79]. Moreover, the lack of significant differences between the headset and eye tracking modes suggests that both are efficient for task completion, with the headset being slightly quicker, likely due to its more stable and precise control in VR environments [80]. The absence of significant interactions between gaming skill and interaction mode further indicates that the advantages of the headset and eye tracking modes are consistent across different levels of gaming experience, reinforcing the versatility of these interaction methods in VR cognitive assessments.
## 3 Results and Discussion
### Modeling of Material Properties
#### 4.1.3 GFR Laminate-Concrete Interface  
The GFRP laminate-concrete interface can be more effectively modeled using the cohesive model in ABAQUS. When dealing with debonding issues of strengthening materials, cohesive elements and surface-based cohesive behavior are preferable alternatives for this type of modeling. Surface-based cohesive behavior requires a linear elastic traction separation as well as damage initiation and evolution laws, and it has very comparable formulas and constitution laws to cohesive elements. A graphic representation of a standard bilinear traction-separation rule expressed in terms of effective traction \(\tau\) and effective opening displacement (Interfacial slip) \(\delta\) is shown in Figure 20. The initial stiffness \(K_{0}\) is specified as [56] and is modeled as a rich zone of small thickness at the interface. The initial stiffness can be expressed as follows:  
\[K_{0}=\frac{1}{\frac{t_{i}}{C_{i}}+\frac{t_{c}}{C_{c}}} \tag{1}\]  
where \(t_{i}\) and \(t_{c}\) represent the thickness of adhesive and the concrete thickness, respectively. \(G_{i}\) and \(G_{c}\) are the shear modulus of the adhesive and concrete, respectively. The values of shear stress of each resin \(\tau_{\text{max}}\) according to the experimental results used for B1-0-GPP, B2-0.6-GPP, B3-1.2-GPP, B4-0-EP, B5-0.6-EP, and B6-1.2-EP are 4.2 MPa, 4.26 MPa, 4.31 MPa, 3.95 MPa, 4.1 MPa, and 4.2 MPa, respectively (See Figure 3). The damage initiation criterion was described by using the quadratic nominal stress function:  
\[\left\{\frac{\sigma_{n}}{\sigma_{n}^{0}}\right\}+\left\{\frac{\tau_{n}}{\tau_ {s}^{0}}\right\}+\left\{\frac{\tau_{t}}{\tau_{t}^{0}}\right\}=1 \tag{2}\]  
where \(\sigma_{n}\) is the cohesive tensile, \(\tau_{s}\) and \(\tau_{t}\) are the interface's shear stresses, and \(n\), \(s\) and \(t\) denote the direction of the stress component. Damage was considered to initiate when the traction function reached one. The maximum normal stress \(\sigma_{n}^{0}\) in comparable studies is equal to 2.1 MPa, while the maximum shear stresses \(\tau_{s}^{0}\), \(\tau_{t}^{0}\) are taken according to the experimental results (see Figure 3). Energy release was used to assess the development of interface damage. The description of this model can be found in the ABAQUS material library. Previous investigations have found values for the fracture energy, \(G_{\sigma}\), ranging from 300 J/m\({}^{2}\) to 1500 J/m\({}^{2}\)[57]; for this investigation, the values taken were calculated based on experimental results.
# Developing and Field Testing Path Planning for Robotic Aquaculture Water Quality Monitoring
###### Abstract  
Marine food chains are highly stressed by aggressive fishing practices and environmental damage. Aquaculture has increasingly become a source of seafood which spares the deleterious impact on wild fisheries. However, continually monitoring water quality to successfully grow and harvest fish is labor intensive. The Hybrid Aerial Underwater Robotic System (HAUCS) is an Internet of Things (IoT) framework for aquaculture farms to relieve the farm operators of one of the most labor-intensive and time-consuming farm operations: water quality monitoring. To this end, HAUCS employs a swarm of unmanned aerial vehicles (UAVs) or drones integrated with underwater measurement devices to collect the in situ water quality data from aquaculture ponds. A critical aspect in HAUCS is to develop an effective path planning algorithm to be able to sample all the ponds on the farm with minimal resources (i.e., the number of UAVs and the power consumption of each UAV). Three methods of path planning for the UAVs are tested, a Graph Attention Model (GAM), the Google Linear Optimization Package (GLOP) and our proposed solution, the HAUCS Path Planning Algorithm (HPP). The designs of these path planning algorithms are discussed, and a simulator is developed to evaluate these methods' performance. The algorithms are also experimentally validated at Southern Illinois University's Aquaculture Research Center to demonstrate the feasibility of HAUCS. Based on the simulations and experimental studies, HPP is particularly suited for large farms, while GLOP or GAM is more suited to small or medium-sized farms.  
aquaculture; robotics; monitoring; machine learning; path planning; vehicle routing problem; drones +
Footnote †: journal: Applied  
doi:10.3390/
## 4 Example Analysis  
### Example Setting  
This paper uses the modified IEEE 30-bus power system as an illustration. PV was added at buses 12 and 22. Figure 3 depicts the structure of the power system. And numbers indicate the bus numbers and arrows indicate the load.  
The Gurobi solver was used under MATLAB R2018a. The example was carried out on a computer equipped with Intel (R) Core (TM) i5-10500 CPU @ 3.10 GHz and 16.0 GB RAM.  
The PV output of two localities in Belgium during the month of March and April is used as historical data, while the output during the first 4 days of May is used as the forecasted value [20]. The PV output during the first 4 days of May is depicted in Figure 4:  
In order to demonstrate the validity of the method proposed in this paper, the results are contrasted against the results of the SE method, which enumerates the same critical system states. The calculation process of the SE method presented in this paper is as follows:  
1. Enumerate the PV output sequence.
2. Enumerate the system critical states at time t.
3. Calculate the reliability levels of the power system.  
Figure 4: The PV output forecasting for 4 days.  
Figure 3: Structure of the power system.  
The relative error of the reliability levels obtained by the two methods is contrasted in order to demonstrate the validity of the proposed method. The formula for calculating relative error is given in Equation (44):  
\[err=\frac{1}{T}\sum_{t=1}^{T}\frac{\mid Y_{t,SE}-Y_{t,PCE}\mid}{Y_{t,SE}}\times 1 00\% \tag{44}\]  
where \(Y_{t,SE}\) represents the reliability levels values calculated by the SE method at time \(t\); \(Y_{t,PCE}\) represents reliability levels values calculated by the PCE-base method at time \(t\).  
\(\overline{EENS}\) is used to represent the mean of EENS. The calculation method is as follows:  
\[\overline{EENS}=\frac{1}{T}*\sum_{t=1}^{T}EENS(t) \tag{45}\]
## 3 Analysis and Correction of Main Errors
### Separation and Correction of Non-Orthogonal Errors  
According to the layout of our developed micro CMM, the surfaces of three reflection mirrors should be exactly orthogonal to each other. If it is not, non-orthogonal errors occur and increase the final measured error directly. Firstly, the guideways of the \(X\)-, \(Y\)- and \(Z\)-stages are adjusted to be orthogonal exactly by a calibrated square gauge. Then the interferometers are adjusted to keep the laser beam  
Figure 6: Calibration of the wavelength of one interferometer.  
Figure 7: Schematic diagram of the surface flatness calibration of \(X\)-interferometer’s reflection mirror.  
parallel with the \(X\)-, \(Y\)- and \(Z\)-guideways respectively. After that, the reflection mirrors are adjusted to be orthogonal according to the guideways. For example, Figure 8 shows the schematic diagram of the orthogonal error between reflection mirrors of \(X\)- and \(Y\)-interferometers. Provided that \(\Theta_{xy}\) is the non-orthogonal angular error between \(X\)- and \(Y\)-mirrors and the stage is moved by a displacement of \(x_{0}\) in the \(X\) direction, the \(Y\)-interferometer will read an output error of \(\Delta y\) (\(\Delta y=x_{0}\cdot\tan\Theta_{xy}\)). This error will add to the \(Y\) coordinate of the measured point. In order to separate the non-orthogonal errors among three mirrors, the stage is moved in one direction only. Then, the outputs of other two interferometers are the combination of the flatness errors and non-orthogonal errors. Since systematic flatness errors of the mirrors are known because they have been corrected in the above section, then the non-orthogonal errors are obtained from the interferometers' outputs after the systematic flatness errors are subtracted. All the other non-orthogonal errors can be separated and corrected according to the same method. After the calibration and correction, all of the non-orthogonal errors are less than about 40 nm (2\(\sigma\)).  
Besides the above errors, there are other remained errors. One is the thermal expansion error. The temperature control accuracy in the measuring area is \(\pm\)0.5 \({}^{\circ}\)C. If the material of the ample (gauge block) is 12 \(\times\) 10\({}^{-6}\) and its length is 50 mm, then the thermal expansion error is about 30 nm (2\(\sigma\)).  
The second is the residual Abbe error. The diameter of the probe ball used here is 1 mm. In Section 2.3, the center of the probe's tip-ball can not be adjusted at the right center of the laser beams of the three interferometers. This offset is within \(\pm\)0.5 mm. These two offsets are Abbe offset. The motion angular error of \(X\)-, \(Y\)-stages is within \(\pm\)6\". Then, the Abbe error caused by this two offsets is about 30 nm (2\(\sigma\)). In \(Z\) direction, the maximum Abbe offset is the maximum measuring length 6 mm of the probe stylus. The motion angular error of \(X\)-, \(Y\)-stages is within \(\pm\)2.1\". Then, the Abbe error in \(Z\) direction is about 61 nm (2\(\sigma\)).
# Dependence Representation Learning with Convolutional Neural Networks and 2D Histograms
## 4 Feature Generation
### Experimental Setup  
For reliable evaluation of feature generation, we have followed the experimental setup used in the previous study 'AutoLearn' as much as possible.  
Datasets: we used the same datasets and splits that ware used for the correlation regression (see Table 1). Table 4 lists the 13 test datasets used for the evaluation of feature generation and comparison with AutoLearn. The IDs listed in Table 4 are task IDs of OpenML. Of the 13 datasets, we selected 10 datasets which ware used in the AutoLearn study.  
Target function implementation: as for a performance metric of this experiment, it is difficult to directly evaluate the quality of newly generated features. Since AutoLearn used classification accuracy over the original feature space and the augmented feature space to evaluate its empirical results of feature generation, we have also used the same performance metric to compare the results to our results. For more reliable evaluation, we split the samples of the feature vectors into a training set and a test set using the default splits provided by OpenML for each dataset. We also trained the feature generators (LRR and KRR) only using the training samples.  
Evaluation method of feature generation: we evaluated feature generation performance using seven classification algorithms, each of which is used in AutoLearn: k-nearest neighbors (KNN), logistic regression (LR), support vector machine (SVM) with linear kernel (SVM-L), SVM with polynomial kernel (SVM-P), random forest (RF), AdaBoost (AB), and decision tree (DT). We also report the average accuracy of seven classifiers. Following AutoLearn, we used the default hyperparameters used by scikit-learn for the classification algorithms. We also report the original accuracy of our algorithm, without feature generation.  
Implementation details: The learning rate of the optimizer is \(10^{-4}\). We added dropout layers with a ratio of 0.2 after every convolutional layers and did not decay the weights. To evaluate the newly generated features for the target function, \(target_{gen}\), we simply used the logistic regression functionality provided by scikit-learn with the default hyperparameters. The threshold \(\alpha\) was set to 0.2 after a small grid search.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Name** & **Instances** & **Classes** & **Features** \\ \hline
37 & Diabetes & 768 & 2 & 8 \\
38 & E.coli & 336 & 8 & 7 \\
39 & Sonar & 208 & 2 & 60 \\
53 & Vehicle & 846 & 4 & 18 \\
57 & Ionosphere & 351 & 2 & 34 \\
58 & Waveform & 5000 & 3 & 40 \\
2075 & Abalone & 4177 & 28 & 8 \\
2079 & Eucalyptus & 736 & 5 & 19 \\
2382 & Wine & 178 & 3 & 13 \\
3057 & Spectf & 349 & 2 & 44 \\
3940 & Leukaemia & 72 & 2 & 7129 \\
10092 & Arcene & 200 & 2 & 10,000 \\
10093 & Banknote & 1372 & 2 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Description of test datasets used for evaluation of feature generation.
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 3 Methods and Materials
### Mathematical Preliminaries  
This section contains the mathematical underpinnings of the suggested encryption technique, including the criteria for evaluating security and the chaotic maps for generating keys.
## 4 Discussion
### Response of WUE to Seasonal Drought
In humid areas, vegetation is usually less adaptable to water stress, and the response to drought is different from those of physiological mechanisms operating in arid biomes on short time scales. On the contrary, the response of biomes to drought in semi-arid and semi-humid regions occurs on long time-scales because plants can resist water shortages [13]. Some studies have also found a complicated relationship between vegetation activity and drought in humid regions with surplus water. The response of plants in humid regions to drought is affected by phenology aspects, such as vapor pressure and the period of active leaf flushing [60]. Moreover, the tissue structure of plants in humid regions is very likely damaged by drought [61]. However, once the dry spell is over, vegetation in humid regions can rapidly recover to its previous state [13]. Therefore, vegetation in arid regions is more sensitive to drought than vegetation in humid regions. In the current study, it was found that seasonal WUE responses to drought of different magnitudes varied among different ecosystems. Spring and winter mild drought increased WUE in arid and semi-arid regions, whereas WUE decreased in humid regions. Similarly, moderate drought in the summer decreased WUE in arid and semi-arid areas but increased WUE in semi-humid areas. Similar results were found in previous studies [13; 22; 24], such as a study by [PERSON] et al. [24], who found that the response of WUE to drought stress differed in arid, semi-arid, semi-humid, and humid regions. Furthermore, some researchers found that a drought in autumn and spring in Southwest China continued from 2009 to 2010, resulting in a decrease in primary productivity and carbon absorption, whereas a drought in early summer promoted the growth of Amazon vegetation and increased carbon storage [62]. Therefore, seasonal drought significantly influenced vegetation, especially in terms of ecosystem WUE.