esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This paper discusses the parameter estimation problem for multi-input OEAR systems. Based on the data filtering technique, an F-RGLS algorithm and an F-LSI algorithm are developed. The proposed methods are effective for estimating the parameters of multi-input OEAR systems. The simulation results indicate that the proposed F-LSI algorithm achieves higher estimation accuracies than the F-RGLS algorithm, and the convergence rate of the proposed methods can be improved by increasing the data length. The methods used in this paper can be extended to study the identification of other linear systems, nonlinear systems, state space systems and time delay systems. **Acknowledgments:** This work was supported by the Visiting Scholar Project for Young Backbone Teachers of Shandong Province, China. **Conflicts of Interest:** The author declares no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(k\) & \(a_{11}\) & \(a_{12}\) & \(b_{11}\) & \(b_{12}\) & \(a_{21}\) & \(a_{22}\) & \(b_{21}\) & \(b_{22}\) & \(c_{1}\) & \(\delta\) (\%) \\ \hline
1 & \(-0.00611\) & \(0.03601\) & \(0.00000\) & \(0.00000\) & \(0.00000\) & \(0.00000\) & \(0.00000\) & \(0.00000\) & \(0.00165\) & \(99.63926\) \\
2 & \(0.00000\) & \(0.00000\) & \(0.49960\) & \(0.67716\) & \(0.00000\) & \(0.00000\) & \(0.41144\) & \(0.84617\) & \(-0.26452\) & \(43.6442\) \\
3 & \(0.48512\) & \(0.14379\) & \(0.50775\) & \(0.90619\) & \(-0.22359\) & \(0.25926\) & \(0.39984\) & \(0.75210\) & \(-0.26452\) & \(15.35917\) \\
4 & \(0.35231\) & \(0.33645\) & \(0.50464\) & \(0.84218\) & \(-0.25181\) & \(0.31725\) & \(0.39804\) & \(0.73802\) & \(-0.10385\) & \(10.48969\) \\
5 & \(0.35968\) & \(0.29443\) & \(0.50270\) & \(0.84889\) & \(-0.24823\) & \(0.31243\) & \(0.39831\) & \(0.73841\) & \(-0.04162\) & \(14.44370\) \\
6 & \(0.36474\) & \(0.29771\) & \(0.50305\) & \(0.84987\) & \(-0.24911\) & \(0.31358\) & \(0.39819\) & \(0.73823\) & \(-0.24951\) & \(1.76580\) \\
7 & \(0.36336\) & \(0.29924\) & \(0.50326\) & \(0.84956\) & \(-0.24869\) & \(0.31289\) & \(0.39808\) & \(0.73850\) & \(-0.25696\) & \(1.73424\) \\
8 & \(0.36328\) & \(0.29861\) & \(0.50327\) & \(0.84966\) & \(-0.24868\) & \(0.31286\) & \(0.39809\) & \(0.73855\) & \(-0.25698\) & \(1.73382\) \\
9 & \(0.36336\) & \(0.29862\) & \(0.50327\) & \(0.84967\) & \(-0.24865\) & \(0.31286\) & \(0.39808\) & \(0.73856\) & \(-0.25703\) & \(1.73724\) \\
10 & \(0.36335\) & \(0.29864\) & \(0.50327\) & \(0.84966\) & \(-0.24865\) & \(0.31285\) & \(0.39808\) & \(0.73856\) & \(-0.25702\) & \(1.73638\) \\ \hline True values & \(0.35000\) & \(0.30000\) & \(0.50000\) & \(0.84000\) & \(-0.25000\) & \(0.30000\) & \(0.40000\) & \(0.75000\) & \(-0.25000\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The F-LSI parameter estimates and errors for Example 2.
## 3 Experiments and Results  
### Dataset  
All the experiments performed in this work were conducted on the OULAD benchmark dataset [22]. OULAD is a dataset extracted from the Open University database at Walton Hall, Milton Keynes, UK in 2015. It contains data about courses, students, and their interactions in a Virtual Learning Environment (VLE) in seven selected courses (called modules). The dataset consists of a dump of a relational database, made of data tables connected using unique identifiers, distributed as several comma separated values (.csv) files. A brief overview of the OULAD database schema is reported in Figure 2, where it is immediate to see that three main data categories are reported: the module presentation, in green, devoted to the courses and assessment data storage; the student activities, in purple; and the student demographical information, in yellow.  
The dataset contains data relating to 32,593 students each marked with one of the following labels: \"fail\", \"withdrawn\", \"pass\", and \"distinct\". In this study, to perform a binary classification process, the \"fail\" and \"withdrawn\" labels were aggregated considering them on the same level, and, therefore, both were labeled as \"fail\", same thing  
Figure 2: Database schema of OULAD dataset (online available at [[https://analyse.kmi.open.ac.uk/open_dataset](https://analyse.kmi.open.ac.uk/open_dataset)]([https://analyse.kmi.open.ac.uk/open_dataset](https://analyse.kmi.open.ac.uk/open_dataset))).  
was done with the labels \"pass\" and \"distinct\" aggregated under the label \"pass\". At the end of the aggregation process, there were 17,208 students labelled as \"fail\" and 15,385 students labelled as \"pass\". From the OULAD data, information about the course, the number of studied credits, the student, the course previous attempts for the student, the assessments (either evaluated by a tutor or a computer), and the examination, as well as the number of interactions with the learning platform are extracted. For each variable, grouped information such as minimum, maximum, sum, and average values over time are reported as well. This way, a total number of 97 features has been automatically grouped and extracted to define the classification problem. A script developed in PHP 7.0 is responsible for extracting such features.
## 4 Experiments and Results
### Baselines  
ML-DTR Model [35]. The ML-DTR Model encodes the articles' text sequence into a vector representation employing GRUs. To be consistent with the ML-DTR, the content of articles is used to implement the model. This method uses the title and content of the paper as the input data for the model.  
LightGCN [9]: Graph convolution has become a widespread technique in recommender systems for collaborative filtering methods. Feature transformation and nonlinear activation have little effect on collaborative filtering performance. As a representative model for collaborative filtering recommendation, lightGCN discards the two standard GCN operations of feature transformation and nonlinear activation: simple, linear, concise, easier to train, better generalization ability, and representative. Because GCN cannot apply relationship information between entities, this paper uses query terms and extensions as LightGCN input data.  
VOPRec [20]: VOPRec is a state-of-the-art hybrid paper recommendation model (GB+CB) in which the core idea is to represent papers with vectors in the citation network so that the similarity between papers can be calculated using the vectors. Specifically, it uses word2 vec and structural2 vec to reconstruct a citation network to derive representative vectors through graph learning and recommends similar papers to scholars according to their cosine similarity. In this paper, the entities of query terms and query extension terms have been used as the citation information of the paper as the input data of VOPRec.  
PRHN-without KG (paper recommendation model via heterogeneous network embedding without knowledge graph): The ablation model with the knowledge graph was removed from the proposed two-tower recommendation model. The query terms and the hotspot network extended by the query terms serve as the model's input data.  
PRHN-without hotspot (paper recommendation model via heterogeneous network embedding without hotspot): This paper proposes an ablation model for removing domain hotspot information in a two-tower recommendation model. The query terms and the domain knowledge graph extended by the query terms are used as the model's input data.  
PRHN (paper recommendation model via heterogeneous network embedding): This paper presents a proposed two-tower recommendation model combining a domain knowledge graph and hotspot information. The query terms, domain knowledge graph, and hotspot network, extended by the query terms, are used as the model's input data.
# Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
## 2 Proposed Methodology
### Optimization Algorithms
#### 2.4.2 Firefly Algorithm
To ensure fair comparison and reproducibility, both the FLA and the DOA were configured with consistent parameters throughout all experiments. FLA used a population size of 30, a maximum of 100 iterations, and control parameters \(\alpha=0.2\), \(\beta_{0}=1\), and \(\gamma=1\). For DOA, the population size was also set to 30 with 100 iterations. Probability-based parameters were P = 0.7 and Q = 0.3, with dynamic adjustment factors \(\beta_{1}\)\(\sim\)U(\(-2\), \(2\)) and \(\beta_{2}\)\(\sim\)U(\(-1,1\)). These values were selected based on empirical trials and consistency with prior studies. In order to facilitate reproducibility and transparency, we consider the sensitivity of the hyper parameters in the metaheuristic optimization. The chosen population size (30) and maximum iterations (100) were based on the previous benchmark works on the medical image analysis and feature selection problems. When a subset of the training data was used to perform some preliminary experiments, it was found that growth in population size beyond 30 and iterations beyond 100 did not deliver improvements in significance (<0.5% increase in accuracy) but increased too much in the amount of computation being performed. On the other hand, it yielded premature convergence when the values were low. Therefore, the selected optimal setup is an economical balance between the performance effectiveness, which appears in the biomedical optimization articles of similar applications. Further research can implement a complete sensitivity analysis: ablation or grid-based search.
# Data-Aided Frequency Offset Estimation for CE-OFDM Broadband Satellite Systems
## 1 Introduction
Since CE-OFDM has sacrificed spectral efficiency in order to achieve 0 dB PAPR, using a time domain training signal for frequency offset estimation will further lead to a decrease in spectral efficiency. Therefore, it is of considerable significance to design the CFO estimation strategy according to the CE-OFDM broadband satellite system requirements (e.g., the range of CFO) and the structure of the CE-OFDM signal itself. A CFO estimation scheme based on the null subcarrier is proposed for the CE-OFDM satellite system under the additive white Gaussian noise (AWGN) channel and multipath fading channel in [13 ]. However, the channel state information is assumed to be ideally known at the receiver in [14 ], which is difficult to achieve in the actual system. In this paper, we proposed a data-aided CFO estimation strategy for CE-OFDM broadband satellite communication systems. The purpose of this paper is to focus only on the estimation of CFO. Therefore, we assume that the timing offset is perfectly compensated at the receiver. The main contributions of this paper are summarized as follows:  
(1) We design a pilot symbol structure placed on the frequency domain subcarriers of the CE-OFDM signal, which can be used to estimate the integer and fractional CFO under AWGN and flat fading channel. The CRB of the proposed structure is analyzed by mathematical analysis. (2) We analyze the effect of the number of pilot symbols on the estimation performance and prove that based on the characteristics of the CE-OFDM signal, only a small overhead is required to obtain good estimation performance. (3) We propose a low computation complexity CFO estimator named the pilot symbol-based CFO estimator (PSCE), which is composed of a phase demodulator and a discrete Fourier transform (DFT) module. The estimation performances under different CFOs are evaluated under an LTE-like frame structure. The simulation results show that the PSCE is susceptible to the well-known threshold effect caused by the phase demodulator at a low carrier-to-noise ratio (CNR), and the PSCE is accurate in the moderate and high CNR region. The rest of this paper is organized as follows. Section 2 presents the broadband satellite system architecture and the propagation environment, including both the CFO and the channel fading. Section 3 shows the CE-OFDM transceiver structure and analyzes the impact of the CFO. Section 4 presents the proposed pilot symbol structure. Section?? describes the PSCE and the CRB. Section?? shows the simulation results. Section?? concludes the paper.
## 5 Discussion
### Soil Salinity and Geologic Unit  
The strongly and very strongly saline soil classes of HH and VV modes covered areas of two geologic units, the Maha Sarakham formation and Quaternary sediments, about 81% and 90%, respectively. This finding is consistent with the previous report about soil salinity in the Northeast region of Thailand [6; 7; 8; 9; 10; 11]. Based on annual surveys from 1973 to 1982 by the Economic Geology Division, Department of Mineral Resources, the Maha Sarakham formation comprises evaporites and redbeds, attaining a probable original thickness of about 300 m. The rock units (member status) begin with the Basal Anhydrite, Lower Salt, Lower Clastic, Middle Salt, Middle Clastic, Upper Salt, and Upper Clastic at the top. Sedimentary features preserved in cores also point toward a nonmarine origin. This interpretation leads to inferences about the formation of saline soils in northeast Thailand in many cases, such as the continuity of the potash horizons and the occurrence and migration of subsurface brine [79].  
Likewise, [PERSON] [78] studied salinization in northeast Thailand using various exposures and analytical data from the Maha Sarakham formation's rock salt member at three points in the Non Thai district: (1) dug pond at Ban Khok Krasang, Non Thai district, in which were found red siltone at a depth of 2.8 m with pH 9.5, ECe 24,000 mS/m, and Na total base of 6.5%; (2) slightly undulating rice field with termite mounds, 1 km west of Ban Khang Phlu, on the Khok Kruat-Non Thai road, in which were found white sandy clay, many calcium carbonate nodules at a depth of 1.2 m with pH 8.8, ECe 128,000 mS/m, and Na total base of 28.64%; (3) very slightly undulating rice field, north of Khok Sung village along with Cho Ho-Non Thai road, in which were found light olive gray, loamy clay at a depth of 2.2 m with pH 9.2, ECe 62,000 mS/m, and Na total base of 18.97%. All three had ECe values greater than 1600 mS/m and soil salinity severity classes of very strongly saline.
# Material Removal Model for Lapping Process Based on Spiral Groove Density
## 3 Experimental Results
### Oil Film Thickness and Frictional Force
Based on this, the decrease in groove density increases the oil film thickness in the lapping process, which changes the lubrication condition at the contact interface between the wafer and platen from boundary lubrication to mixed lubrication. Therefore, it is believed that the boundary lubrication condition exists at groove densities of 0.3 or higher with the wafer and abrasives in direct contact, while the mixed lubrication condition exists at groove densities lower than 0.3 with the development of an oil film. Therefore, the groove density at which the lubrication state changes at the contact interface was defined as the critical groove density (CGD), and the CGD in this experimental condition was 0.3. Thus, the material removal model based on indentation theory is not consistent with the actual experimental result in the low groove density range because the changes in the oil film thickness were not considered in the model. Hence, a modified model that incorporates the oil film thickness at the contact interface is presented in the next section. Figure 10: Film thickness as a function of groove density.
## 4 Experiment and Result Analysis  
### Experimental Setup  
The experiment was initially conducted on the KITTI 3D object dataset: 7481 training samples were divided into a training set containing 3712 samples and a validation set containing 3769 samples. A deep learning server was set up for the experiment, the configuration of which is shown in Table 2.  
The training epoch was set to 160 with a batch size of 6. The deep learning optimizer utilizes Adam (Adaptive Moment Estimation) with an initial learning rate of 2 \(\times\) 10\({}^{-4}\). The learning rate decays by 0.8 times every 15 epochs. Pass-through filtering is used to intercept regions of interest, with a specific range as shown in Equation (12).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Model** \\ \hline CPU & Inter i9 11,900 k \\ Memory & 32 G \\ Hard disk & 512 G + 1 T \\ GPU & NVIDIA GeForce RTX 3080 Ti 12 GB \\ OS & Ubuntu 18.04 LTS 64 bit \\ Programming language & Python \\ Dependency library & CUDA 11.1, CUDNNs0.5, PyTorch 1.8.1, Open3d 0.13.0, etc. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Deep learning server.  
Figure 7: Comparison of visualization results of GT-Aug and RS-Aug: (**a**) point cloud collection scenarios; (**b**) ground truth; (**c**) visualization result of GT-Aug; (**d**) visualization result of RS-Aug. Cars are represented by red bounding boxes, cyclists by blue bounding boxes, and pedestrians by green bounding boxes.  
\[0\leq x\leq 69.12\] \[-39.68\leq y\leq 39.68 \tag{16}\] \[-3\leq z\leq 1\]  
In this approach, the maximum number of pillars (denoted as P) in each sample is set to 12,000, with each pillar containing a maximum of 64 points. If the number of pillars in each sample and the number of points in each pillar exceed the preset threshold, random sampling will be adopted. Conversely, if the number is too small to form a tensor, zero padding will be utilized instead. When calculating the 2D Intersection over Union (IoU) metric, positive matches usually choose the highest values or those marked values that surpass the positive match threshold. Conversely, negative matches consider marked values below the negative threshold. Redundant anchor points are excluded during loss calculation. Following the methodology of VoxelNet [8], our study defines overlap thresholds for the car category at 0.7 IoU across easy, moderate, and hard recognition scenarios. For cyclists and pedestrians, the overlap thresholds are uniformly set at 0.5 IoU across easy, moderate, and hard recognition scenarios.
# Ultra-Low Interfacial Tension Foam System for Enhanced Oil Recovery
## 3 Results and Discussion
### Optimization of Foam System Performance  
The surfactants (SDS at 0.18 g/L and DHPB at 0.12 g/L) were compounded with XC polymer, and the impact of the polymer's concentration on the oil-liquid IFT and foaming properties was investigated. Figure 4 shows the influence of the XC concentration on the oil-liquid IFT.  
The oil-liquid IFT increased with the XC concentration over time, as shown in Figure 4. Additionally, the adsorption rate of surfactant molecules from the liquid phase to the oil-liquid interface decreased as the XC concentration increased. However, the rate of the surfactant molecules' desorption from the oil-liquid interface to the oil phase did not change significantly, which eventually led to a decrease in the surfactant molecule concentration at the oil-liquid interface when the adsorption equilibrium was reached.  
Figure 4: Influence of the xanthan gum (XC) concentration on oil-liquid interfacial tension.  
Figure 3: (a) Viscosity versus shear rate and (b) viscosity versus temperature.  
With an increasing XC concentration, the foaming volume and the foam half-life of the system decreased slightly, but the drainage half-life of the foam increased significantly (Table 9). The foam viscosity increased with the XC concentration; thus, more energy was needed for the foaming process. However, the external energy obtained by the system stayed the same due to the same foaming method and stirring time, leading to a reduction in the foam volume. Meanwhile, the diffusion resistance of surfactant molecules increased with the liquid's viscosity. This resulted in localized thinning of the liquid film, a delay in the liquid film's self-repair, and a decline in the foam half-life. As the viscosity of the liquid increased, the liquid film's drainage rate and the gas diffusion rate decreased, which led to an increase in the drainage half-life.  
Temperature could affect the stability of foam system by affecting viscosity, molecular motion, intermolecular force and molecular structure. According to the results in Table 9, the system with higher XC's concentration performed better foaming properties and foam stability at high temperature, and the system with lower XC's concentration had better performance at low temperature. This was mainly caused by the viscosity change [26], the viscosity of the system with high XC's concentration was too high at low temperature, but it could maintain a certain viscosity at high temperature. Therefore, it was concluded that the most suitable concentration of XC was 0.075 g/L based on the experimental results.
# FLaMAS: Federated Learning Based on a SPADE [PERSON]
## 3 Multi-Agent Learning Based on Federated Learning
### Agent Interaction Model
#### 3.3.2 New Agent Entering the System  
When a new agent enters the system, it will be playing the Client role, and will be linked to an IoT device, acquiring all the information related to the IoT device sensors.  
In this way, the new agent will communicate with the agent playing the Server role, to communicate that it is going to play a new Client. Figure 3 shows the protocol or sequence of messages between the two agents in this stage:  
1. The new agent sends a message to the agent playing the Server role indicating that it wants to _join_ the system as another Client.
2. The agent playing the Server role will subscribe to the Presence of the new Client agent.  
Figure 1: Finite state machine for the agents in the _FLaMAS_ system.  
Figure 2: Agent interaction protocol for an iteration in the normal execution cycle.  
When the Server agent receive the confirmation of the new agent Presence subscription, it will add it to its clients list for the normal execution cycle.
4. The Server agent will send the _init_ message with the parameters needed to initialize the model in the Client agent. As the Server agent receives this message, it will begin the other protocol commented above (normal execution cycle) in both agents.  
In this section, we have presented \(FLaMAS\) as a federated learning algorithm developed using SPADE agents as a way to have all the advantages of both developments: an enhanced learning process that uses traditional isolated learning, but which allows for preservation of the privacy of the local data used for each agent to train their local model (federated learning main advantages), along with a scalable distributed solution that adapts quickly to failures in agents (the Server only waits for currently available Client agents through their Presence feature), or even to new agents entering the process when it is still running.  
Figure 4 shows a brief schema summarizing how \(FLaMAS\) works: After all agents (Clients and Server) are created and registered in a XMPP Server, the system enters in a cycle where the following steps are executed:  
1. All Client agents that are active in the system (so their Presence would indicate it), will get input data from the environment.
2. All active Client agents will train their local models.
3. If the Server agent is active (indicated by its Presence), all active Client agents will send their trained local model parameters to the Server agent.
4. The Server agent calculates a de-aggregated global model.
5. The new global model is sent by the Server agent to the active Client agents, which put it in their systems as the new local model.  
To conclude this summary, we would like to underline the use of the Presence in the SPADE agents, which not only ensures that the system will adapt to agents failing in the system, and recovers from fails after a while, but also allows new Client agents to enter in the system during its execution.  
Figure 3: Agent interaction protocol for a new agent entering the system.