esa-sceva/satcom-chunk-collection huggingface dataset:
# A Gradient-Based Cuckoo Search Algorithm for a Reservoir-Generation Scheduling Problem
## 3 Cuckoo Search Algorithm
### Implementation of Improved Cuckoo Search (ICS)  
With the improvements above, the improved CS can be described as follows:  
**Step 1**: Initialize the algorithm parameters  
This step specifies the desired parameters, including size of population (\(ns\)), abandon probability (\(p_{a}\)), disturbance weight (\(w\)), step size (\(sl\)), and Levy distribution parameters \(u\) and \(c\).  
**Step 2**: Initialize the population  
In this step, new random nests (\(\mathbf{x}_{1},...,\mathbf{x}_{ns}\)), whose total number is \(ns\), are generated as in Equation (14):  
\[x_{i}^{d}=(UB^{d}-LB^{d})\cdot rand(0,1)+LB^{d},d=1,2,...,D \tag{14}\]  
Then each vector will be evaluated by the objective function and stored in the population.  
**Step 3**: Generate a new solution by Levy flights  
Here a source nest \(\mathbf{x}_{i}\) and target nest \(\mathbf{x}_{j}\) from the population are randomly chosen. A new cuckoo from nest \(i\) flight to nest \(j\) obeys the features of Levy flight. Then a new solution is generated from Equation (12).  
If the new generated solution is out of proper range, we would adjust the solution from Equation (10).  
**Step 4**: Update the host nest Evaluate the fitness of the new solution \(f(\mathbf{x}^{\prime}_{i})\) and compare it with the fitness of source nest \(f(\mathbf{x}_{i})\). If the new solution is better, replace the source nest with the new solution.  
**Step 5** Abandon the worst nest  
If the random value is smaller than \(p_{a}\), a new solution is generated randomly and it will replace the worst nest.  
**Step 6** Check the stop criterion  
Repeat Step 3 to Step 5 until the termination criterion is satisfied. In this paper, the termination criterion is the maximum number of evaluation times.  
The pseudo-code of the ICS algorithm is presented in Algorithm 2.  
```
Objective function \(f(\mathbf{x}),\mathbf{x}=\left(x_{1},...,x_{d}\right)^{T}\) Initialize default parameters Generate initial population of \(n\) host nests \(\mathbf{x}_{i}(i=1,2,...,n)\) While(\(t<MaxEvaluation\)) or (stop criterion)  Select two solution \(\mathbf{x}_{i},\mathbf{x}_{j}\) from host nests randomly For\(d\)=1,...,D do \(x_{i}^{dt}=x_{i}^{d}+\left(sl\times Levy(u,c)\times\left(x_{j}^{d}-x_{i}^{d} \right)\right)\) Endfor If\(f(\mathbf{x}^{\prime}_{i})<f(\mathbf{x}_{i})\)  Replace \(\mathbf{x}_{i}\) by the new solution \(\mathbf{x}^{\prime}_{i}\) Endif If\(rand(0,1)<p_{a}\)  Init the worst nest \(\mathbf{x}_{worst}\) Endif Endwhile
```  
**Algorithm 2**Improved cuckoo search
## 4 Result Analysis
### Proposal and Showcase Studies
The studies examined highlighted the applicability of this approach to be integrated into different educational levels and contexts. The studies emphasized the importance of evaluating learners' interaction with the applications and systems, their actions within the virtual environments, and their performance. Additionally, the ability to support diverse learners by identifying and tracking issues and challenges that they face and by monitoring their actions, preferences, and behaviors was highlighted. The studies also commented upon the need to define suitable design guidelines and objectives, to emphasize realism and interactivity, and to compare the effectiveness of this approach with traditional teaching and learning approaches. Through the pilot applications, the ability to offer teachers meaningful visualized information and assist them to more effectively manage and coordinate their classrooms was revealed. However, the need to develop appropriate learning material and resources was also highlighted. The specific studies highlighted the need to monitor learners' engagement, interactions, experiences, and performance within virtual learning environments and highly regarded the use of artificial intelligence and virtual agents to enhance the provision of personalized learning.
## 2 Materials and Methods
### Point Set Mapping and Point Cloud Geometric Segmentation  
#### 2.2.1 Mapping the Mesh Model to a Point Set  
The spatial positions of the vertices of a triangular mesh representing a continuous 3D space will determine the geometric information of the triangular surface, including the location, area, normal vector, and texture information. Solving the problem of each triangular surface relies on the information from its three vertices when extracting spatial features. In geometric space, the processing of points is easier and more direct than that of surfaces in terms of computational complexity. To facilitate calculations, this study selected the vertices of triangular slices as computational units (Figure 2). The vertices of numerous triangular slices constitute a point aggregation model, thus transforming a triangular mesh's semantic segmentation into a point cloud's semantic segmentation. In contrast to a traditional LiDAR point cloud, the point cloud obtained through the transformation of mesh data possesses spatial geometry, texture, and color information while retaining a topological connection between points, which is of great value in describing local features.  
Considering the textural information of the vertices from the oblique photography 3D model, the data model following transformation and reconstruction is described as follows: In the continuous 3D space, the point cloud of the triangle model is expressed as \(\text{S}=\left\{\text{F},\text{P},\text{T}\right\}\), where F is the collection of facets that constitute the triangulation network, P is the collection of vertices that constitute the triangulation network, and T is the topological relationship between the triangles. This can be expressed as \(F=\left\{F_{i}\,|\,i=1,\,2,\,3,\dots,\,n\right\}\) and \(F_{i}=\left\{P_{i1},\,P_{i2},\,P_{i3}\right\}\), where \(P_{i1},\,P_{i2},\) and \(P_{i3}\) are the three vertices that constitute the triangular sheet \(F_{i}\). The vertex feature \(f(P_{i})\) includes coordinate and texture information: \(f(P_{i})=\left\{x_{p},\,\,y_{p},z_{p},r_{p},g_{p},b_{p},N_{p}\right\}\), where \(x_{p},\,\,y_{p},z_{p}\) are the coordinate values of the vertex, \(r_{p},g_{p},b_{p_{i}}\) are the texture color values of the vertex, and \(N_{p_{i}}\) is the set of points connected to the vertex P. The subsequent rough data segmentation utilizes the topological relationship (T) between triangular vertices.
## 1 Introduction
In this study, we used an approach that integrates hydrogeological flow simulation outputs with statistical analysis for finding critical monitoring locations in freshwater lenses in the LIB. The first aspect was hydrogeological characterization, so we characterized the aquifer properties using lithological logs, and calibrated a flow model for the study area. The calibrated model was simulated to generate monthly temporal heads. Then we applied a hexagonal pattern of sampling for continuous spatial functions on the model grid to select initial monitoring locations. [PERSON] (1984) [30] stated that the semi-variance and drift are inherent in continuous spatial functions and are therefore unmanageable factors. The manageable factors in sampling continuous spatial functions are sampling pattern and sampling density. It is not economically feasible to have dense sampling. The only remaining factor which can be managed is the pattern of sampling. A hexagonal pattern of sampling has the least average standard error in the case of continuous spatial functions [30]. Average standard error is a measure of the sampling efficiency of continuous spatial functions. Since groundwater is also a continuous spatial function, a hexagonal pattern of sampling was selected for the sampling of observation grid cells in the flow simulation model. The second aspect, accounting for temporal variations, was considered using PCA. PCA is a dimensionality reduction technique, which uses an orthogonal transformation to eliminate the correlation in the observations of variables by converting them into linearly uncorrelated variables called principal components [3,4]. The third aspect, the representation of potentiometric surface with minimum wells, was addressed through groundwater potentiometric surface classification. The potentiometric head surface used for the head classification was obtained through an inverse distance weighting (IDW) technique. IDW is a technique of spatial interpolation that estimates the unknown values based on the known values by giving more weight to nearer points and less weight to farther points [31]. This interpolation technique does not abruptly alter the interpolated surfaces, so, in our study, this technique was applied to obtain the potentiometric head surface. Finally, we rationalized the identified locations by comparing standard errors between identified location and base case locations, which were based on \(1\times 1\) km model grids. We simulated the future levels on the historical climatic cycles to determine whether the monitoring network could replicate the future hotspots for management.
# Dynamic Behavior and Libration Control of an Electrodynamic Tether System for Space Debris Capture
###### Abstract  
This study focuses on the dynamic responses and libration angle control of an electrodynamic tether (EDT) system with a flexible tether that was designed for space debris capture. Utilizing the absolute nodal coordinate formulation (ANCF), we develop a comprehensive three-dimensional model to accurately capture the complex behavior of the EDT system, particularly the effects of tether deformation and electrodynamic forces. The analysis reveals that the behavior of the EDT system is strongly influenced by the Earth's magnetic field in space, while the current generated in the system depends on the length and diameter of the tether--key parameters for optimizing performance. The analysis reveals that the current generated in the EDT system is influenced by tether length and diameter, which are critical parameters for optimizing system performance. Furthermore, the study examines the feasibility of using on-off switch control to manage the libration angles induced during debris capture. The results demonstrate that the EDT system can effectively stabilize libration angles across various debris masses and velocities, highlighting its potential for enhancing the efficiency and reusability of space debris removal missions. This research contributes to the understanding of flexible tether dynamics in EDT systems and provides insights into their application in sustainable space operations.  
e _Applied Sciences_  
electronic tether; flexible tether dynamics; space debris removal; libration angle control; absolute nodal coordinate formulation
# Extending Battery Lifetime by Avoiding High SOC
## 3 Model Development
### Vehicle Model and Vehicle Parameters  
In order to investigate the impact in a vehicle application, three vehicles are designed and an energy consumption model is set up, based on the force balance of the vehicle according to  
\[F_{acc}=m_{p}\frac{d}{dt}v(t)=F_{powertrain}-|F_{brake}|-|F_{a}|-|F_{r}|-F_{g}, \tag{7}\]  
where the different forces are described as  
Figure 6: Capacity degradation in 0–30% SOC with 2C charge and discharge for the ageing function together with experimental data in 35 \({}^{\circ}\)C.  
\[\text{Air drag:}\quad F_{a}(t)=\frac{1}{2}\rho_{a}A_{f}c_{d}(v_{car}-v_{wind})^{2}, \tag{8}\]  
\[\text{Rolling resistance:}\quad F_{r}(t)=c_{r}m_{v}gcos(\alpha),\quad v_{car}>0, \tag{9}\]  
\[\text{Grading:}\quad F_{g}(t)=m_{v}gsin(\alpha), \tag{10}\]  
where \(\rho_{A}\) is the air density, \(c_{d}\) the coefficient of aerodynamic resistance, \(A\) the frontal area of the vehicle, \(v_{car}\) vehicle speed, \(v_{wind}\) wind speed, \(c_{r}\) the rolling resistance coefficient, \(m\) the vehicle mass, \(g\) gravitation constant on earth, and \(\alpha\) road inclination. Both the wind speed and road inclination are set to 0, since this will only introduce small errors [10], and for this study these discrepancies will not affect the results.  
The air drag force and rolling resistance force are always working against the direction of the movement. Many EVs have a regenerative braking system, though, for this study, the brake force is only considered to be a friction based brake system.  
To account for the losses in the different components in the powertrain, an efficiency factor was used. The efficiency factor for an electric powertrain depends on all the efficiencies of the separate components in the powertrain, which, in turn, depend on their different working ranges, i.e., the vehicle speed and torque. The efficiency has been measured to be 0.6-0.9 [10], and for this work an efficiency factor of 0.8 has been used for the whole drivetrain.  
Three vehicles are used representing a small Battery Electric Vehicle (BEV), Vehicle 1, a medium-sized BEV, Vehicle 2, and a Plugin Hybrid Electric Vehicle (PHEV), Vehicle 3. The data of the vehicles are presented in Table 3.
## 2 Research Methods
### Data Analysis  
The carbon budget is controlled by several major processes that describe the exchange of carbon dioxide between terrestrial ecosystems and the atmosphere. Satellite remote sensing provides consistent and systematic observations of vegetation and has played an increasingly important role in characterizing vegetation structure and in estimating the GPP of vegetation [16]. In this work, GPP was estimated using the following equation:  
\[\mathrm{GPP}=\epsilon\times\mathrm{fAPAR}\times\mathrm{PAR}=\epsilon\times \mathrm{APAR} \tag{2}\]  
PAR is restricted to the visible portion of the solar spectrum, _i.e._, from 400 to 700 nanometers [29]. PAR is assumed to be approximately half of the incoming solar radiation [30], with solar radiation data from the Indonesian Meteorology, Climatology and Geophysics Agency (BMKG). The fAPAR is related to the NDVI. NDVI has been widely used to estimate fAPAR because of the positive linear relationship between these variables [12]. In Southeast Asian countries, this relationship can be parameterized as [31]:  
\[\mathrm{fAPAR}=-0.08+1.075\times\mathrm{NDVI} \tag{3}\]  
NDVI is computed from image data using the following formula:  
\[\mathrm{NDVI}=\frac{\mathrm{Near\ Infra\ Red\ Band\ -\ Red\ Band}}{\mathrm{Near\ Infra\ Red\ Band\ +\ Red\ Band}} \tag{4}\]  
Light use efficiency (\(\epsilon\)) is a biome-specific value representing the optimal potential of the vegetation to convert PAR to GPP. Light use efficiency values are similar for all plant types and biomes [29]. Estimation of LUE has proven problematic since it varies with vegetation type and environmental conditions. Light use efficiency can be estimated using mechanistic models based on leaf biochemistry and micrometeorological parameters, but these models are complex and generally require many parameters that cannot be directly estimated by remote sensing [8]. Light use efficiency may be assumed to be constant under non-stress conditions, but it is affected by stresses, phenological stages, and the physical environment [10]. In some Asian countries, the value of \(\epsilon\) has been estimated as 1.5 gC \(\mathrm{MJ}^{-1}\)[31]. Here, we compare the output of mechanistic models of light use efficiency to the MODIS GPP product (MOD17) in the Denpasar area.  
The main analysis in this research is to calculate GPP on different types of land use, but similar measures have been applied in analyzing the entire area of research. GPP maximum and minimum values are the maximum and minimum value of GPP in all research areas or a land use. Meanwhile the average value of GPP is obtained from division between the total value of GPP with the number of image pixels in all research areas or in each land use type. Analyses were carried out using ENVI 4.4 and ArcView GIS (version 3.2) software with the Spatial Analyst Extensions package.
# Comparative Study for Sentiment Analysis of Financial Tweets with Deep Learning Methods
## 4 Experimental Setup and Results
### Supporting Findings and Conclusions
#### 4.6.3 Experiment Design and Outcome  
This study furnishes an elaborate description of the experimental setup, incorporating the application of five-fold cross-validation during both training and testing phases. Outcomes for both binary and multi-class classifications are presented, accompanied by a comparative analysis of model performance based on different embedding techniques.
## 5 Rate-Compatible IR-HARQ Transmission Scheme Based on PCPP Codes
### Optimization Method for the 1 st PCPP Coding Block of the RC IR-HARQ Transmission Scheme
In addition, puncturing does not need extra encoders and decoders, and the transmission in this subcase only needs one pair of encoder and decoder. Last, if \(I^{(n)}(W)\leq I^{(n+1)}(W)<R_{U}^{(n)}\), then a 3-level PCPP codes for the \((n+1)\)-th data block needs to be constructed with code-rate \(R_{1}^{(n+1)}=I_{max}\), \(R_{2}^{(n+1)}=R_{U}^{(n)}\) and \(R_{3}^{(n+1)}=R_{L}^{(n)}\), respectively. Furthermore, the data delivery is finished in three time transmissions with two pairs of encoders and decoders. Else, if the channel capacity decreases, similarly, two subcases can be distinguished: if the channel capacity satisfies \(R_{U}^{(n)}\leq I^{(n+1)}(W)<I^{(n)}(W)\), the \((n+1)\)-th data block with a initial code-rate \(R_{1}^{(n+1)}=I_{max}\) 2-level PCPP codes can be transmitted successfully in two time transmissions as well as the above subcase in Case 3. Else, if \(I^{(n+1)}(W)<I^{(n)}(W)<R_{U}^{(n)}\), then the sender needs to construct 3-level PCPP codes for the \((n+1)\)-th data block transmission, with code-rate \(R_{1}^{(n+1)}=I_{max}\), \(R_{2}^{(n+1)}=R_{U}^{(n)}\) and \(R_{3}^{(n+1)}=R_{L}^{(n)}\), respectively. Furthermore, the final state feedback to the sender is \(I^{(n+1)}(W)\in[R_{L}^{(n)},R_{U}^{(n)})\). In summary, in our RC IR-HARQ transmission scheme, the main idea to determine an optimal initial code-rate for the PCPP coding block is according to the capacity interval feedback from the last successful data block transmission. If the initial code-rate lower than \(I_{min}\), the PCPP coding block can be transmitted successfully in one time transmission, but it will be suboptimal if the channel capacity increases in the next several data blocks transmission. If we choose an initial code-rate larger than \(I_{max}\), then the number of transmission may be larger than 2. As analyzed above, in most situations, a 1-/2-level PCPP coding construction can reliably transmit the data blocks by using our optimal initial code-rate method over a time-varying channel.
# Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis
## 4 Results
### Markovian Analysis  
The main feature used in order to capture the task dynamics is the IBI. By separating the IBIs distribution in two categories it is an open question whether the generation of blinks is subject to two attractor states, one in which blinks occur in a short time, resulting in short IBIs, and another in which blinks occur in a long time, resulting in long IBIs. These different states of blink generation during watching neutral, relax and stressful video was analyzed by applying first-order Markovian Analysis. The state transition probability matrices (STPMs) \(P\) were calculated and compared with the \(P^{(0)}\) matrices giving the probability of being at state \(i\), in order to test whether the generation of blinks during relaxing/stress procedures is a stochastic process. The statistical significance between these two matrices was calculated and the results are presented in Table 6.  
In Table 6 the matrices \(S,\ P,\ P^{(0)},\ X^{2}\) are presented for each experiment, phase and task. Based on chi-square statistics with 2 degrees of freedom at \(a=0.05\) significance level, \(P\) is significantly different from \(P^{(0)}\) when the values of \(X^{2}\) is larger than the value 5.991, based on chi-square distribution tables. This was the case for all tasks. Thus the null hypothesis that matrix \(P\) is equal to matrix \(P^{(0)}\) was rejected and a first-order Markov process was assumed for all tasks. Therefore, it was conjectured that the process generating blinks possessed, at least, first-order \"memory\" in all tasks. For each original element value of the matrix \(P\), a distribution of 1000 Bootstrap values was derived, and the 2.5% and 97.5% percentiles of this distribution were calculated and used as the 95% confidence interval for the particular original element value of the P matrix. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experimental Phase** & **Accuracy\%** & **Precision\%** & **Recall\%** \\ \hline  & & **CNN** & \\ Social exposure & 50.4 & 49.3 & 74.0 \\ Emotional recall & 74.5 & 63.5 & 80.0 \\ Mental tasks & 76.4 & 74.5 & 80.0 \\ Stressful videos & 67.6 & 68.4 & 75.0 \\ \hline  & & **LSTM** & \\ Social exposure & 74.1 & 80.6 & 72.2 \\ Emotional recall & 65.7 & 84.6 & 66.2 \\ Mental tasks & 79.5 & 99.0 & 76.7 \\ Stressful videos & 64.3 & 86.1 & 68.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The average classification accuracy, precision and recall between cognitive processes on the testing set after 10 runs. Values are averages over 10 trials.
# Prototypical Graph Contrastive Learning for Recommendation
## 4 Experiments
### Robustness Study (RQ5)  
To evaluate the robustness of ProtoRec under different types of data noise, we added noise to both the graph structure and embedding distributions at noise ratios of {0.1, 0.2} and conducted extensive experiments on the Yelp2018 dataset. The \"noise ratio\" is used to control the degree of different types of noise added. In edge noise, we randomly discard edges in the interaction graph with a probability determined by the noise ratio, simulating data sparsity. In feature noise, the noise ratio controls the intensity of uniform noise added to each dimension of the embeddings, allowing us to evaluate the model's generalization ability. Experiments were also carried out with the baseline methods SimGCL and NCL for comparison, with the results shown in Figure 8. The performance degradation due to feature noise is lower than that caused by edge noise across all methods, indicating that adding noise to the graph structure affects the model's ability to learn neighborhood information. As the noise ratio increases, the performance degradation becomes more apparent. ProtoRec outperforms SimGCL and NCL in all experiments, demonstrating its strong adaptability to various types of noise. The robustness of ProtoRec can be attributed to the contrastive learning framework and prototype-enhanced information, which enable it to extract users' and items' latent preferences from substantial noise and effectively optimize the embedding representations by leveraging the differences between positive and negative samples.  
Figure 8: Evaluating the robustness of different baseline methods on the Yelp2018 dataset under the recall@20 metric with respect to various types of noise. Edge noise is introduced by removing edges in the interaction graph. Feature noise is added to the embeddings by injecting uniform noise.  
Figure 7: Hyperparameter analysis on Amazon-book.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 3 Environments for Communication in Oceans  
This section describes communication environments in oceans, which refer to deployment locations, objects and resources that might be used to set up communication infrastructure and act as networking nodes, main communication media as well as power sources applicable and available in oceans.