esa-sceva/satcom-chunk-collection huggingface dataset:
# Linear Parameter-Varying Model Predictive Control of AUV for Docking Scenarios
## 4 Simulation Result
### No Current Test  
The results of the first test, without any tidal current acting on the vehicle, are first presented. The objective of this test was to evaluate the performance of the AUV when moving to the docking position in zero current ideal conditions. The measured linear and angular positions of the controlled vehicle during the test are shown in Figure 3. This result shows the ability of the control system to drive the vehicle to the docking station meeting point in terms of linear and angular positions. The steady-state tracking errors in vertical position and in the pitch-angle are due to the mismatch between the actuators' nonlinear model and the LPV design model. The mismatch causes an error in mapping the MPC control action into the actuator control signals space. Despite this mismatch, the performance of the controlled system satisfies the design specification.  
The control actions driving the AUV actuators and computed by the TA policy are shown in Figure 4 that shows the main thruster control signal with respect to the saturation and backlash characteristics of the actuator. This reveals that the control signal managed  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**AUV Control System Tuning Parameters**} \\ \hline
**Parameter** & **Symbol** & **Value** \\ \hline Prediction horizon & \(N_{p}\) & 10 \\ \hline Control horizon & \(N_{u}\) & 1 \\ \hline Control rate weights & \(R\) & \([1,\ 1,\ 1,\ 0.1,\ 0.1,\ 0.1]\) \\ \hline Controlled output weights & \(Q\) & \([0.1,\ 0.1,\ 0.1,\ 0.01,\ 0.01,\ 0.01]\) \\ \hline Terminal weights & \(P\) & \([2\times 10^{3},\ 3\times 10^{7},\ 10^{8},\ 10^{7},\ 5\times 10^{7},10^{7}]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: AUV Controller Tuning Parameters.  
Figure 3: No current test: controlled variables (blue plot) with respect to the target position (dashed black plot).  
by the TA satisfies the saturation (\(\pm 2000\) rpm) limit values and is adjusted to values (\(\pm 400\) rpm) due to the backlash. The main thruster control signal jumps between the limits of the backlash zone (\(\pm 400\) rpm), as defined in the control policy. When the MPC controller computes a large control effort to be provided by this thruster, the TA policy maps it into a control signal satisfying the saturation max/min constraints (e.g., at time \(t=5\) s).  
Figure 5 shows the signals controlling the AUV fins, as they are driven almost symmetrically by the TA, such that for the rudders the control signal trajectory is different only
# A Novel Computational Imaging Algorithm for Electrical Capacitance Tomography
## 6 Experiment Results and Analysis  
A series of experiments are carried out to validate the performance of the proposed method in practical applications. As shown in Figure 13, the ECT imaging system is built by our research team and mainly consists of the following parts: (1) the sensor device. This part is a 12-electrode square sensor with a copper foil wrapped cylindrical shield on the outside, whose cross section is shown in Figure 7. The size parameters of the sensor are presented in Table 1. (2) Data acquisition system. The data acquisition system can switch up to 12 channels, and its resolution is 0.5 Ff. (3) Imaging system. In this experiment, quartz sand is used as the measured material with high dielectric constant, and air is used as the background material with low dielectric constant. The measured objects are plastic polycarbonate tubes filled with quartz sand of different diameters, as shown in (4) in Figure 13. We implement three sets of experiments, and their two-dimensional imaging targets are shown as (a)-(c) in Figure 14. These targets include a concentric rectangular ring (the side length of the large square = 40 mm, and the thickness = 15 mm), big circle (diameter = 16 mm), and small circle (diameter = 6 mm). The empty field calibration and the full field calibration are performed by filling the square tube with air and quartz sand, respectively. For the imaging targets (a)-(c) in Figure 14, the NHL in the ELM algorithm and the RELM algorithm is 1000, and the parameters of various iterative algorithms are listed in Tables 8-10. Tables 11 and 12 are the images reconstructed of various imaging algorithms, and we quantitatively compare the imaging quality using REs, as presented in Figure 15.  
Figure 12: Imaging errors under different numbers of hidden layer neuron (a–g correspond to the imaging errors for the imaging targets in Figure 7a–g).  
Figure 13: Experimental set-up. (1) Sensor device, (2) data acquisition system, (3) imaging system, (4) measured objects.  
Figure 15, we can clearly see that the image errors of the RELM algorithm are the smallest for the four kinds of experiments, which are 1.43%, 2.54%, and 2.39%, respectively. From experimental results, we conclude that the RELM algorithm effectively solves the image reconstruction problem of the ECT and obtains satisfactory imaging results.
# Validation Analysis of Drought Monitoring Based on FY-4 Satellite
## 2 Materials and Methods  
### Typical Drought Events  
Focused on the effectiveness of TVDI based on FY-4A for drought monitoring in China, three typical drought events in 2021-2022 were selected for validation. The first drought event was from May to June 2022, located in the Huanghuai region, including Shandong, Henan, Anhui, and Jiangsu Provinces (Figure 1a). The second and third drought events were from January to March 2021, located in Yunnan Province (Figure 1b) and Guangdong Province (Figure 1c). These drought events were across China, in different climatic, topographical, and geographical regions. Yunnan Province is situated in the southwest plateau region, dominated by woodlands and grasslands. The climate includes subtropical and tropical monsoon types and the highland mountainous type. Guangdong Province is located on the southeastern coast, including the mid-subtropical, south-subtropical, and tropical climate types, mainly dominated by woodland. The Huanghuai region belongs to the temperate monsoon region, and the main land use type is arable land.  
Figure 1: Land use type of three regions and the location of each region in China: (**a**) Huanghuai region, (**b**) Yunnan Province, (**c**) Guangdong Province.
## 5 Numerical Example Results
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter \(a_{ij}\)** & \multicolumn{2}{c}{**Parameter \(b_{ij}\)**} \\ \hline \(a_{14}\) & 2,021,152,507 & \(b_{14}\) & \(-0.00379\) \\ \(a_{110}\) & 3,825,126,631 & \(b_{110}\) & \(-0.0042\) \\ \(a_{910}\) & 603,344,912 & \(b_{910}\) & \(-0.0023\) \\ \(a_{69}\) & 180,950,610 & \(b_{69}\) & \(0.001046\) \\ \(a_{67}\) & 241,253,384 & \(b_{67}\) & \(0.000921\) \\ \(a_{78}\) & 236,922,319 & \(b_{78}\) & \(4.19\times 10^{-5}\) \\ \(a_{89}\) & 140,398,411 & \(b_{89}\) & \(8.37\times 10^{-5}\) \\ \(a_{35}\) & 272,631,999 & \(b_{35}\) & \(-0.00126\) \\ \(a_{56}\) & 766,010,664 & \(b_{56}\) & \(-0.00126\) \\ \(a_{511}\) & 710,214,368 & \(b_{511}\) & \(-0.00021\) \\ \(a_{45}\) & 1,636,367,083 & \(b_{45}\) & \(-0.00042\) \\ \(a_{24}\) & 450,972,262 & \(b_{24}\) & \(-0.00084\) \\ \(a_{1112}\) & 196,766,854 & \(b_{1112}\) & \(0.00021\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters \(a_{ij}\) and \(b_{ij}\), which come from Equations (13) and (14). Figure 2: A simplified gas network of Ireland with 13 nodes and 14 branches.
## 4 Results and Discussion
### Three-Phase Evaluation
#### 4.2.1 Phase 1: Individual Dataset Performance Evaluation
However, their results on EduTalk suggest that the EduDCM framework mitigates some of the limitations associated with simpler modeling approaches, providing structured annotations that enhance model learning. To evaluate annotation quality, three domain experts reviewed a random sample of 300 instances from each dataset. Table 3 summarizes the results. Both datasets received high scores for annotation accuracy, category coverage, and consistency, with EduTalk achieving mean scores of 4.4, 4.3, and 4.4, respectively, and TalkMoves scoring slightly higher with 4.5, 4.4, and 4.5. Annotation accuracy was measured by how well the generated labels matched the intended pedagogical categories as defined in the taxonomy. Category coverage was evaluated by determining whether all relevant categories were adequately represented in the dataset. Consistency was assessed by verifying the uniformity of annotations across similar dialogue instances. The inter-rater reliability, measured by Cohen's Kappa, was 0.85 or higher for both datasets, reflecting strong agreement among the experts. Experts noted that EduTalk's structured and context-aware annotations, generated by the EduDCM framework, aligned well with pedagogical categories, enabling consistent interpretation. However, the manually annotated TalkMoves dataset exhibited slightly higher annotation accuracy due to the direct involvement of human annotators with domain expertise. In summary, the evaluation results demonstrate the reliability of the EduTalk dataset generated by EduDCM, as evidenced by the comparable performance of models trained on EduTalk to those trained on manually annotated datasets like TalkMoves. These findings validate the framework's potential to bridge the gap between distant supervision and manual annotation in educational NLP tasks.
# Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information
## 3 Methods
### CNN Model
#### 3.1.2 Activation Layer  
In the CNN model, the primary role of the activation function is to process nonlinear data and extract data features through nonlinear mapping, thereby improving the expressiveness of the network. The commonly used activation functions are sigmoid, tanh, and rectified linear unit (ReLU), which are respectively expressed as:  
\[\text{Sigmoid}(x)=\frac{1}{1+e^{-x}} \tag{2}\]  
\[\text{tanh}(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{3}\]\[\text{Re}LU=\left\{\begin{array}{ll}\max(0,x)&x>0\\ 0&x<0\end{array}\right. \tag{4}\]  
In this study, the ReLU function was added between the BN algorithm layer and the pooling layer to develop the nonlinear processing capability of the model.
## 2 Materials and Methods
### Mathematical Models
#### 2.2.1 One-Dimensional Riccati Model
In the case of the MPP-SBH, whose internal structure of ribs and slits is rather sparse, as it is explained below, the wall acoustic admittance is set as \(\tilde{Y}_{\mathrm{w}}\ =\ 0\) at the position of the ribs (which are assumed to be rigid), and \(\tilde{Y}_{\mathrm{w}}(x)\ =\ \bigl{[}\tilde{Z}_{\mathrm{MPP}}+1/\tilde{Y}_{ \mathrm{s}}(x)\bigr{]}^{-1}\) between the ribs, where \(\tilde{Z}_{\mathrm{MPP}}\) is the transfer impedance of the MPP (see [30; 31]), which reads  
\[\tilde{Z}_{\mathrm{MPP}}\ =\ \frac{\mathrm{i}\omega\rho_{0}t}{\phi}\biggl{[}1- \frac{2\mathrm{J}_{1}(k^{\prime}\sqrt{-\mathrm{i}})}{k^{\prime}\sqrt{- \mathrm{i}}\mathrm{J}_{0}(k^{\prime}\sqrt{-\mathrm{i}})}\biggr{]}^{-1}+\frac{8 \mathrm{i}\omega\rho_{0}d}{3\pi\phi}+\frac{\sqrt{32\omega\rho_{0}\mu}}{\phi}, \tag{3}\]  
where \(\mathrm{J}_{0}(),\mathrm{J}_{1}()\) are the first-kind Bessel functions of order \(0\) and \(1\), respectively, \(\phi\) is the MPP perforation ratio, \(t\) is its thickness, \(d\) is the diameter of the holes in the MPP, \(\rho_{0}\) is the air density, \(\mu\) is the air dynamic viscosity, and \(k^{\prime}\ =\ d\sqrt{\rho_{0}\omega/4\mu}\). The first term in Equation (3) accounts for the wave propagation in the orifics forming the structure of the MPP, the second one is the radiation reactance accounting for the air oscillation in the vicinity of the orifics, and the last term is the radiation resistance of the orifics. The Riccati equation (Equation (1)) is numerically integrated with an initial condition \(\tilde{Y}\ =\ 0\) at \(x=0\) (a rigid wall is assumed at the SAS's apex) along the spatial interval \(x\in\langle 0,L\rangle\), the result of which is the SAS's input specific acoustic admittance \(\tilde{Y}_{\mathrm{SAS}}\ =\ \tilde{Y}(L)\). The amplitude reflection coefficient \(r\) from the SAS can be then calculated as  
\[r=\frac{A+\rho_{0}c_{0}\mathrm{\theta}_{\mathrm{SAS}}\tilde{Y}_{\mathrm{SAS}}} {A-\rho_{0}c_{0}\mathrm{\theta}_{\mathrm{SAS}}\tilde{Y}_{\mathrm{SAS}}} \mathrm{e}^{2\mathrm{i}\omega L/c_{0}}, \tag{4}\]  
where \(a_{\mathrm{SAS}}\ =\ a(L)\leq A\) is the input half-height of the SAS. As a rigid wall is assumed to terminate the structure at \(x=0\), there is no transmission to the domain \(x<0\), and the absorption coefficient can be calculated as  
\[\mathcal{A}\ =\ 1-|r|^{2}. \tag{5}\]
# An Automated Behaviour-Based Clustering of IoT Botnets
## 4 The Proposed Approach
### Clustering  
The behaviour-based clustering of IoT botnet samples implies identifying groups of botnet samples with similar behaviour profiles. Thus, to cluster the samples it is necessary to measure the similarity between the behaviour profiles. Since the behaviour profiles are text documents, their similarity can be measured by transforming them into feature vectors and measuring the similarity between their feature vector representations.  
We considered two techniques for vectorising text documents, namely, TF-IDF and Doc2 Vec. The TF-IDF is a statistic which reflects how important a word is to a document in a corpus of documents. It is computed by multiplying the TF and IDF metrics. The TF indicates how often a word appears in a document, while IDF reflects how common or rare a word is across the corpus. Doc2 Vec, also known as paragraph2 vec, computes a feature vector for every document in a corpus, irrespective to its length. It expands the Word2 Vec technique [32], used for vectorising words, with another vector referred to as paragraph ID [33]. To select the more feasible of the two techniques for vectorising the behaviour profiles, we investigated the characteristics of the behaviour profiles and identified that they are relatively short, with an average length of 221 characters. A study investigating the effectiveness of text similarity measuring techniques on short-length text documents indicates that Doc2 Vec may have poor performance for short-length documents [34]. Thus, we used TF-IDF to vectorise the behaviour profiles.  
To cluster the TF-IDF vectors, it is necessary to select a metric for measuring the similarity between a pair of TF-IDF vectors. The botnet samples may execute different number of actions, resulting in behaviour profiles with different lengths. The length of the behaviour profiles in the corpus varies in the range from 221 characters to 21,418 characters. Thus, the similarity between behaviour profiles should be computed irrespective of their length. Considering this requirement, we selected the cosine similarity metric for measuring the similarity between the TF-IDF vectors, since it measures the cosine of the angle between the vectors, and not their magnitude.  
Because there is no prior knowledge about the botnet samples comprising the dataset, the number and the shape of the clusters cannot be identified beforehand. Therefore, we clustered the TF-IDF vectors using the DBSCAN algorithm which does not require the number of clusters to be specified beforehand. The DBSCAN algorithm performs well with arbitrary shaped clusters and is robust to outliers. The vectorisation of the behaviour profiles, the computation of the cosine similarity matrix, and the DBSCAN clustering were performed using the Python implementation of TF-IDF, cosine similarity, and DBSCAN, provided by the scikit-learn machine learning framework [35].
## 5 Conclusions  
This paper applies two standard types of FF ANN models with a novel, simple, and specially designed input training dataset for day-ahead short-term electricity load forecasting in the Greek electricity market. The basic meteorological input comprised the daily heating and cooling degree days from a representative station of Athens. Additional input was the hour of the day, day of the week, a holiday/weekend indicator, and the 24- and 168-hour lagged load of the system. The forecasting capabilities of the two models were compared with the respective predictions of the Greek system's operator's model against the actual data reported in the European platform. The mean value of MAPE of the FF ANN predictions for the 12 months of 2022 was 3.66% (\(\sigma\) = 0.30%) vs. 2.61% (\(\sigma\) = 0.33%) of the official predictions, and the nRMSE was 0.049 (\(\sigma\) = 0.005) vs. 0.036 (\(\sigma\) = 0.005) of the official predictions, respectively. However, our forecasts are true 24-hour-ahead predictions without the noon correction, which is routinely applied by the system's operator. The results suggest that the proposed approach based on a simple and robust FF ANN model, aided by the new design and specific structure of input data, achieves the major goal in 24-hour-ahead prediction. It performs equally well with commercial state-of-the-art tools in day-ahead load forecasting. Specific instances of failure were analyzed, being common to all prediction methodologies and tools examined. They comprise periods with rapid temperature drops with weather changes during summer, which lead to unexpected sudden drops in electricity load for air conditioning. In the future, the effect of incorporating further weather parameters on short-term load forecasting will be examined in the form of air humidity and wind average speed and prevailing direction. Moreover, economic and societal events that may impact grid operation in the form of tourist resident numbers during summer may also be highlighted. Finally, the behavior of Saturday could be distinctively studied and taken care of as a third day typology, instead of just discriminating between workdays and weekends/holidays.  
Conceptualization, G.S. and T.S.; methodology, T.S.; software, G.S.; validation, G.S.; investigation, G.S.; resources, T.S.; data curation, G.S.; writing--original draft preparation, G.S.; writing--review and editing, T.S.; supervision, T.S.; project administration, T.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 20: Incident of Thursday 7–Saturday 9 July: load prediction failure of both models during a sudden change to drier weather conditions and ambient temperature drop.
## 6 Evaluation  
Our experimentation involves two metrics: the total energy consumption and the processing time.  
The total energy consumption (Etotal) consists of three different types of energy: the transmission of the packets (Etr); the classification of the packets (Ec) into high, medium, and low priority; and the resource allocation of the categorized packets (Ea) as follows:  
\[\text{Etotal}=\text{Etr}+\text{Ec}+\text{Ea} \tag{4}\]  
Etr is calculated based on the size of the packets as in Equation (5):  
\[\text{Etr}=\sum_{i=1}^{n}\text{Sn}\times\text{Eob} \tag{5}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Node Name** & **Computational Capacity** \\ \hline F1 & 360 \\ F2 & 60 \\ F3 & 120 \\ F4 & 280 \\ F5 & 40 \\ F6 & 160 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fog nodes’ computational capacity.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Configuration** \\ \hline Processor & 11 th Gen Intel(R) Core(TM) i7-1165G7 @ 2.80 GHz 1.69 GHz \\ Language & Java \\ Integrated Development Environment (IDE) & Eclipse \\ Development Kit & Java Development Kit (JDK) 17 \\ Fuzzy rules integration & MATLAB \\ \hline \hline \end{tabular}
\end{table}
Table 4: System configuration.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Nodes** & **Description** \\ \hline
1, 2, 3, 4, 5, 6 & BT, HR, GL nodes at the Edge layer \\ MB & Mist broker \\ FB & Fog broker \\ F1, F2, F3, F4, F5, F6 & Nodes at the Fog layer \\ CL1, CL2, CL3 & Nodes at the Cloud layer \\ C1 and C2 & The two Fog clusters \\ M1 and M2 & Mist nodes \\ CC & Computational capacity \\ \hline \hline \end{tabular}
\end{table}
Table 5: Nodes’ description.  
where Eob is the energy cost of transmitting one byte for a single hop, n is the total of transmitted packets, and Sn is the size of the n packet. In this experiment, the energy cost of transmitting one byte of packets for a single hop is assumed to be 0.5 mJ.  
In a similar manner, the total processing time (Ttotal) is determined by considering the same factors as the total energy cost. It can be calculated using the following equation:  
\[\mathrm{Ttotal}=\mathrm{T}\mathrm{r}\mathrm{r}+\mathrm{T}\mathrm{c}+\mathrm{T}\mathrm {a} \tag{6}\]  
where Trr represents the packet transmission time, Tc denotes the packet classification time, and Ta represents the packet allocation time.
## 4 Numerical Simulation  
### Construction and Parameter Selection of Numerical Model  
As a 3D continuum mechanical analysis software developed by Itasca company, FLAC3D 5.0 was applied in the present research to conduct numerical simulation. It adopts the explicit Lagrange algorithm and mixed-discrete partition technology to solve the dynamic equation, which can achieve simulation. Adjusting the three-dimensional mesh can simulate and study the real shape of the object, and the post-processing function is powerful. It combines Mohr-Coulomb failure criterion and tensile failure criterion to form its Mohr-Coulomb constitutive model from the incremental equation of plastic flow theory [30; 31; 32].  
As per the geological conditions of the Wudong Coal Mine, a FLAC3D numerical model with a size of long \(\times\) wide \(\times\) height = 47 m \(\times\) 20 m \(\times\) 27 m was established; after cell division, the computational grid is formed associated with 80,000 cells and 89,892 nodes. The vertical load is applied to the rock mass from the roof of the roadway to the surface. The roller boundary is used to limit the horizontal movement on the model sides, and the fixed boundary is used to limit the bottom of the model. The boundary in-situ stress is applied to the roof and two sides of the model. Figure 5 shows the model parameters, and Mohr-Coulomb criteria are applied. The mechanical parameters of the surrounding rock of the roadway are shown in Table 3.  
For the sake of grasping the deformation and instability features of the surrounding rock, four measuring lines were set up. As depicted in Figure 5, the first line is set in the center of the roadway roof and floor and the second one is placed at the center of the left and right sides of the roadway. The third measuring line is at the bottom corner of the right side of the roadway, while the fourth line is arranged at the bottom corner of the left rib of the roadway. Because the constant measuring point with an interval of 50 cm was given, there are 12 measuring points arranged on each measuring line.  
Figure 4: Relationship between stress concentration coefficient and N value around the roadway.