esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
With the increasing emphasis on personalized learning, there is a growing demand for automated systems that analyze individual students' learning states and provide effective feedback. This study proposes a system that analyzes elementary school mathematics diagnostic assessment data to generate personalized feedback. The proposed system integrates Term Frequency-Inverse Document Frequency (TF-IDF) and eXtreme Gradient Boosting (XGBoost) to vectorize textual data and automatically classify learning error patterns. The study utilizes 15,000 diagnostic assessment records collected from 2020 to 2024. After preprocessing, TF-IDF was employed to extract relevant features, and XGBoost was used to train a classification model. To validate the model's performance, comparative experiments were conducted with Logistic Regression, Support Vector Machine (SVM), LightGBM, BERT, and DistilBERT. The TF-IDF + XGBoost model achieved an accuracy of 98.85% and an F1 Score of 0.9860, outperforming other models. Furthermore, the system demonstrated an average real-time processing speed of 1.3 s, ensuring instant feedback in educational settings. This study highlights the automation and scalability of educational data analysis, suggesting potential applications across various subjects and grade levels.  
TF-IDF; XGBoost; diagnostic assessment data; personalized learning Article
_Water_**2015**, \(7\), 2881-2899; doi:10.3390/w7062881  
_Water_  
**ISSN 2073-4441**  
www.mdpi.com/journal/water  
_Article_  
**Quantitative Impacts of Climate Change and Human Activities on Water-Surface Area Variations from the 1990s to 2013 in Honghu Lake, China**  
**[PERSON] 1,2, [PERSON] 1,*, [PERSON] 2,3,+ and [PERSON] 4,+**  
Footnote 1: Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China; E-Mail: [EMAIL_ADDRESS]  
Footnote 2: University of Chinese Academy of Sciences, Beijing 100049, China; E-Mail: [EMAIL_ADDRESS]  
Footnote 3: State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China  
Footnote 4: Regional Climate Center of Wuhan, Wuhan 430074, China; E-Mail: [EMAIL_ADDRESS]  
Footnote 5: These authors contributed equally to this work.  
Footnote 6: Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel./Fax: +86-27-6888-1312.  
Academic Editor: [PERSON]  
_Received: 15 May 2015 / Accepted: 9 June 2015 / Published: 15 June 2015_  
**Abstract:** The water-surface areas of the lakes in the mid-lower reaches of the Yangtze River, China, have undergone significant changes under the combined impacts of global climate change and local anthropogenic stress. As a typical lake in this region, the Honghu Lake features water-surface area variations that are documented in this study based on high-resolution remote sensing images from the 1990s to 2013. The impact of human activities is analyzed by a novel method based on land use data. The relative impacts of each driving force are further distinguished by the statistical analysis method. Results show that the water-surface area has significant inter-annual and seasonal variabilities, and the minimum of which generally occurs in spring. The degree to which climate factors and land use structure affect the water-surface area varies between different stages. In the April-May period, the sum of the water demands of paddies and aquaculture has a negative effect that is greater than the positive effect of the difference between the monthly precipitation and monthly evaporation. In the June-October period, the precipitation features a positive impact that is greater than the negative effect of the water demand of agriculture. Meanwhile, climate factors and human activities have no influence on the lake area in the November-March period. With the land use being altered when annual precipitations are close in value, paddy field areas decrease, ponds areas increase, and the water demand of agriculture rises in both flood and drought years. These findings provide scientific foundation for understanding the causes of water-surface area variations and for effectively maintaining the stability of the Honghu Lake area through adjustments in land use structure.  
water-surface area variations; climate change; human activities; land use change; water balance; Honghu Lake
# Crude Oil Price Forecasting Model Based on Neural Networks and Error Correction
## 2 Methodology  
### Structure of the Hybrid Model  
The modeling process for our hybrid model is given in Figure 1. The forecasting model includes three modules: data preprocessing and prediction, prediction results ensemble, and error correction. The detailed modeling process is as follows.  
* Data preprocessing and prediction: The raw data are divided into three parts: training sets, validation sets, and test sets. Then, EWT is applied to decompose the three parts adaptively. The base predictors, the TCN, GRU, and ESN, predict the decomposed  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Refs.** & **Models** & **Year** & **Advantages** & & **Drawbacks** \\ \hline Traditional & [6] & ARIMA & 2003 & a. & Simple framework; & a. & Limited nonlinearity \\ methods & [7] & GM & 2019 & b. & Quick modeling. & b. & Requires stationarity. \\ \hline Deep & [12] & LSTM & 2019 & & & & \\ learning & [8] & DNN & 2024 & a. & Captures complex patterns; & & \\ methods & [15] & GMDH & 2021 & b. & Learns hierarchical features; & a. & Single-model accuracy \\  & [16] & TCN & 2024 & & & & constraints. \\ \hline Hybrid & [18] & EEMD-CNN-LSTM & 2023 & a. & Reduces data complexity & a. & Suboptimal usage of \\  & [19] & VMD-ANN & 2021 & & through decomposition; & & decomposed data; \\ Hybrid & [20] & CEEMD-ML-GRU & 2020 & b. & Adapts to diverse patterns; & b. & Heuristic methods risk \\ methods & [22] & PSO-based ensemble & 2023 & c. & Leverages multiple & & local optima. \\  & [24] & GWO-based ensemble & 2024 & & predictors’ strengths. & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The literature review for different models.  
sub-series. The details of EWT and predictors are given in Sections 2.2 and 2.3, respectively.
* Prediction results ensemble: In this paper, the ensemble method is completed by setting weights of the forecasting results of different predictors. The calculation method is shown in Equation (1). The SARSA algorithm, a reinforcement learning method, is applied to optimize the weights. The SARSA-based ensemble learning method is detailed in Section 2.4. \[\hat{Y}(t)=w_{1}\hat{y}_{1}(t)+w_{2}\hat{y}_{2}(t)+w_{3}\hat{y}_{3}(t)\] (1) where \(w_{i}\) represent the weights of different predictors, \(\hat{y_{i}}(t)\) represent the time-dependent forecasting results of the \(i\)-\(th\) predictor at time \(t\), derived from distinct predictive models, and \(\hat{Y}(t)\) represent the predicted results after the ensemble.  
Calculating error and error correction: The forecasting residuals still have predictable components after ensemble learning. Hence, there is still room for further improvement in forecasting accuracy. An extreme learning machine (ELM) is used for error correction. The final crude oil price prediction results are obtained by combining error correction results and model ensemble results. The ECM is detailed in Section 2.5.
## 5 Comparison of Design Equations  
The results of the parametric analysis were compared with the current design equations mentioned in Table 1. Each design equation is constructed as shown in Equation (4). By dividing the nominal yield stress (\(F_{y}\)) and the square of the chord thickness (\(t^{2}\)) and assuming that the CHS has no axial load and bending moment applied, the design equations are simply constructed as shown in Equation (5). \[R_{n}=Q_{u}Q_{f}F_{y}t^{2}, \tag{4}\]  
\[Q_{u}=A(1+B\eta), \tag{5}\]  
where \(Q_{u}\) is a partial design strength function that predicts the joint capacity without chord axial stress, and \(Q_{f}\) is a chord stress function that reduces the joint resistance based on the chord normal stress influence. \(Q_{u}\) is a value only affected by the longitudinal plate width-to-chord diameter ratio (\(\eta\)). The values of \(A\) and \(B\) are constants, which are 5.5 and 0.25 for AASC [2], 5.0 and 0.25 for Eurocode 3 [3], and 5.0 and 0.4 for ISO 14346 [4]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Specimen** & \multirow{2}{*}{\(R_{1\times D}\) (kN)} & \multirow{2}{*}{\(R_{3\times D}\) (kN)} & \multirow{2}{*}{\(R_{3\times D}\)/\(R_{1\times D}\)} & \multirow{2}{*}{\(R_{max}\) (kN)} & \multirow{2}{*}{\(R_{max}\)/\(R_{3\times D}\)} & **Det.** \\  & **(\(F_{y}\)-2)-\(\eta\)-\(U\)** & & & & & & \\ \hline
1 & 900-22.4-1-00 & 634.5 & 1508.9 & 2.38 & 1950.4 & (7.9) & 1.29 & F.P. \\
2 & 900-22.4-2-00 & 882.1 & 2092.1 & 2.37 & 2602.6 & (8.0) & 1.24 & F.P. \\
3 & 900-22.4-3-00 & 1141.5 & 2704.1 & 2.37 & 3271.5 & (7.7) & 1.21 & F.P. \\
4 & 900-22.4-4-00 & 1403.2 & 3327.5 & 2.37 & 3964.5 & (7.3) & 1.19 & F.P. \\
5 & 900-28.0-1-00 & 354.5 & 890.1 & 2.51 & 1213.4 & (8.8) & 1.36 & F.P. \\
6 & 900-28.0-2-00 & 474.1 & 1204.7 & 2.54 & 1618.4 & (9.3) & 1.34 & F.P. \\
7 & 900-28.0-3-0 & 602.5 & 1532.2 & 2.54 & 2018.8 & (9.6) & 1.32 & F.P. \\
8 & 900-28.0-4-00 & 733.8 & 1874.1 & 2.55 & 2436.6 & (8.8) & 1.30 & F.P. \\
9 & 900-37.3-1-00 & 169.7 & 446.1 & 2.63 & 643.4 & (9.1) & 1.44 & F.P. \\
10 & 900-37.3-2-00 & 216.1 & 575.3 & 2.66 & 871.4 & (10.3) & 1.51 & P.W. \\
11 & 900-37.3-3-00 & 267.8 & 717.4 & 2.68 & 1083.3 & (10.5) & 1.51 & P.W. \\
12 & 900-37.3-4-00 & 321.4 & 867.0 & 2.70 & 1301.4 & (10.6) & 1.50 & P.W. \\
13 & 900-56.0-1-00 & 65.0 & 174.9 & 2.69 & 263.6 & (10.3) & 1.51 & P.W. \\
14 & 900-56.0-2-00 & 76.8 & 210.3 & 2.74 & 371.1 & (12.6) & 1.76 & P.W. \\
15 & 900-56.0-3-00 & 91.0 & 249.9 & 2.75 & 464.2 & (13.1) & 1.86 & P.W. \\
16 & 900-56.0-4-00 & 106.5 & 293.4 & 2.76 & 562.5 & (14.0) & 1.92 & P.W. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Parametric analysis results of S900 (\(F_{y}=900\) MPa).
# Mixed-Integer-Based Path and Morphing Planning for a Tensegrity Drone
## 2 State of the Art  
### Protective Cage Design for Drones  
One of the important problems in drone mechanical design has been ensuring human safety in human-robot interactions and robot safety in collisions with the environment. In both cases, an emphasis has been put on preventing propeller blades from making contact interactions with objects in the environment; additionally, prevention of mechanical damage to the robot frame in the event of crash landing has been considered [3; 13; 14]. The most popular approach in both cases is the use of protective cages. In [15], a protective cage based on Euler springs was studied. In [3], a foldable origami-inspired cage was proposed, with the goal of providing sufficient space for cargo, while the drone itself is easy to transport in the folded form. In [16], an HVAC ducts inspection drone with a rigid cage was proposed; the cage design facilitated both hovering and rolling motion.  
In general, advancements in drone protective cage design aimed for some or all of the following goals: (1) lower weight, (2) comprehensive drone and cargo protection, (3) foldability and ease of transportation, and (4) additional maneuverability. The use of tensegrity structures in place of rigid robot frames can potentially lead to lowering the weight of the robot (tensegrity structures are known for good mass-to-stiffness ratios [7]), making the robots foldable in no pre-stress state [3], and facilitating mid-flight morphing [12]. Note that there have been works on using tensegrity structures in drone protective cage design [17]; however, the drones in question still used traditional rigid frames.
# Federated Learning for Cybersecurity: A Privacy-Preserving Approach
## 4 Experimental Setup and Evaluation
### Evaluation Metrics
Although the FL approach introduces moderate communication overhead, this is offset by improved data confidentiality and system resilience. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Dataset** & **Model** & **Accuracy (\%)** & **Learning Rate** & **Batch Size** & **Local Epochs** \\ \hline FedAvg & CICIDS2017 & CNN & 92.3 & 0.01 & 32 & 5 \\ FedProx & TON\_IoT & LSTM & 89.7 & 0.005 & 64 & 10 \\ MoFL & CICIDS2017 & Transformer & 93.5 & 0.001 & 32 & 5 \\ Ours & CICIDS2017/ & Hybrid & 95.2 & 0.001 & 64 & 5 \\  & TON\_IoT & CNN-LSTM & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental setup and baseline comparison. Figure 5: Comparative performance metrics: federated vs. centralized learning. These findings confirm the effectiveness of our FL approach in balancing performance and privacy while maintaining communication efficiency. The consistent improvements across key evaluation metrics highlight the framework's suitability for deployment in privacy-sensitive, resource-constrained environments, which are common in IoT networks.
# Opportunities and Challenges for Error Correction Scheme for Wireless Body Area Network--A Survey
## 5 Review on ECS Methods on WBAN
### Harq  
As we discussed in Introduction, WBAN standard permits perform error correction using FEC or HARQ, depending on type of PHY layers. In UWB, error correction can used both techniques, depending upon the mode of communications (refer to Table 2). From the literature review, it is observed that the researchers have applied both HARQ-I and HARQ-II error correction techniques in WBAN communications. In both of these approaches, bottom two layers of OSI are involved.
## Materials and Methods
### Sequence Assembly and Annotation  
The data obtained by sequencing is known as raw reads or raw data. Quality reads are performed on the raw reads to determine whether the sequencing data is suitable for subsequent analysis. Fragments were assembled using Trinity software after QC was qualified [49]. The assembled Unigene was compared with the seven databases, nr ([[http://www.ncbi.nlm.nih.gov/](http://www.ncbi.nlm.nih.gov/)]([http://www.ncbi.nlm.nih.gov/](http://www.ncbi.nlm.nih.gov/)), NCBI non-redundant protein sequences) (access on 9 November 2017), nt (NCBI nucleotide sequences), Pfam (Protein family, [[http://pfam.sanger.ac.uk/](http://pfam.sanger.ac.uk/)]([http://pfam.sanger.ac.uk/](http://pfam.sanger.ac.uk/))) (access on 9 November 2017), KOG (euKaryotic Ortholog Groups, [[http://www.ncbi.nlm.nih.gov/cog/](http://www.ncbi.nlm.nih.gov/cog/)]([http://www.ncbi.nlm.nih.gov/cog/](http://www.ncbi.nlm.nih.gov/cog/))) (access on 9 November 2017), Swiss-Prot (a manually annotated and reviewed protein sequence database, [[http://www.expasy.ch/sprot/](http://www.expasy.ch/sprot/)]([http://www.expasy.ch/sprot/](http://www.expasy.ch/sprot/))) (access on 9 November 2017), KEGG (Kyoto Encyclopedia of Genes and Genomes, [[http://www.genome.jp/kegg/](http://www.genome.jp/kegg/)]([http://www.genome.jp/kegg/](http://www.genome.jp/kegg/))) (access on 9 November 2017), and GO (Gene Ontology, [[http://www.geneontology.org/](http://www.geneontology.org/)]([http://www.geneontology.org/](http://www.geneontology.org/))) (access on 9 November 2017), by the BLAST program for sequence similarity analysis and functional annotation of unigenes, and the DETs were screened out according to the expression level of genes.
## 3 Results
### Mycobione Abundance Analysis
\begin{table}
\begin{tabular}{c|c c c|c c} \hline
**Groups of Fungi** & **Control** & **BCS** & **BCR** & **WW** & **S** \\ \hline _Sagenomella_ & 6.21 & 2.20 & 0.84 & 0.73 & 2.01 \\ _Wilcoxina_ & 6.06 & 0.13 & 2.05 & 0.04 & 0.13 \\ _Oidiodendron_ & 6.03 & 5.04 & 2.14 & 7.12 & 5.06 \\ _Melinotypes_ & 6.02 & 0.41 & 2.02 & 1.04 & 8.10 \\ _Penicillium_ & 5.10 & 10.02 & 4.03 & 6.14 & 7.03 \\ _Tricholoma_ & 2.13 & 1.06 & 4.12 & 1.07 & 1.04 \\ _Cortinarius_ & 2.04 & 1.14 & 0.05 & 0.01 & 1.06 \\ _Mortierella_ & 2.03 & 2.11 & 0.52 & 0.81 & 1.02 \\ _Geomyces_ & 1.14 & 0.31 & 2.07 & 0.81 & 0.09 \\ _Solicoccozyme_ & 1.04 & 0.41 & 0.41 & 0.31 & 0.82 \\ _Hyaloscopylpha_ & 1.02 & 4.05 & 0.21 & 0.23 & 0.72 \\ _Inocybe_ & 0.91 & 5.03 & 7.02 & 2.04 & 0.02 \\ _Exophilia_ & 0.70 & 0.32 & 0.21 & 1.08 & 1.03 \\ _Hydrum_ & 0.31 & 0.24 & 0.04 & 2.04 & 0.00 \\ _Trichoderma_ & 0.21 & 0.92 & 0.32 & 0.90 & 0.41 \\ _Russula_ & 0.12 & 0.13 & 1.03 & 9.13 & 5.07 \\ _Cenangium_ & 0.12 & 0.09 & 2.03 & 0.40 & 0.14 \\ _Collarina_ & 0.06 & 0.00 & 0.00 & 1.08 & 0.00 \\ _Apiotrichum_ & 0.06 & 0.11 & 1.03 & 0.08 & 0.07 \\ _Hygrohorus_ & 0.02 & 0.13 & 1.14 & 1.06 & 0.00 \\ _Amphinema_ & 0.02 & 5.01 & 0.00 & 0.00 & 0.00 \\ _Cenococcum_ & 0.01 & 0.01 & 0.32 & 0.00 & 2.12 \\ \hline \end{tabular}
\end{table} 
Table 1: Dominance of fungi groups for various soil conditions studied: C—control, BCS—bark compost on the surface, BCR—bark compost under the roots, WW—wood waste and S—sawdust. Yellow—dominants (>5%), blue—subdominants (1–5%), grey—accessory (<1%).
## 3 Preliminaries
### Four State Model  
A four-state transition model based on sensor internal components was developed to ensure that no events are missed while the states change dynamically. Combining these modules generates a total of sixteen possible states. However, in this case, only four states are employed to increase energy efficiency and avoid missing any important events. In other words, when the four-state model is used, the sensor never misses an event and may change states without compromising network performance. After calculating a reward function \(F(S_{n},Ac_{n})\), the state can change to another based on the agent's values. The sensing data slices are marked as \(\mu\); using the Markov process of changing states, this can be represented as follows: A state will remain the same if the detected data slice \(\mu\) remains the same. The values of each state will change if the change is based on the number of times it is shown as \(1-\mu\). The four states with symbols and transitions are shown in Figure 1. This figure also shows the distributions of the states that change from one state to another based on the detected data packets \(\mu\).  
The Markov Decision Process is used to calculate the state-changing policy due to the stochastic nature of dynamic state change using a four-state model. Using the above, we have the four-state model with different probabilities because ASC-RL moves from one state to another, and these transitions may occur simultaneously or asynchronously among multiple nodes. With the above state transition values, the probabilities of each row in ASC-RL, the states changing, can be modeled as  
\[Pr(S_{t})=\begin{bmatrix}\mu&\frac{1-\mu}{3}&\frac{1-\mu}{3}&\frac{1-\mu}{3} \\ \frac{1-\mu}{3}&\mu&\frac{1-\mu}{3}&\frac{1-\mu}{3}\\ \frac{1-\mu}{3}&\frac{1-\mu}{3}&\mu&\frac{1-\mu}{3}\\ \frac{1-\mu}{3}&\frac{1-\mu}{3}&\frac{1-\mu}{3}&\mu\end{bmatrix} \tag{1}\]  
Figure 1: Four state model.  
Based on the probability distribution over these four states, the system must move somewhere; the total probability of all transitions from any given state must equal one. In Equation (1), the valid value of the stochastic matrix is 1 in each row; otherwise, the probability is marked as erroneous. For this matrix, the sums are 1.  
\[\mu+3\cdot\frac{1-\mu}{3}=\mu+(1-\mu)=1 \tag{2}\]  
For each cognitive sensor, it measures the surroundings and processes the data using its \(RM_{n}\), and based on these values, it will adjust the state of the sensor. These values of \(\mu\) in the \(n\)-th slot can be written as  
\[\mu(n)_{St}=\mu(1)_{St1},\mu(2)_{St2},...,\mu(n-1)_{Stn-1},...\mu(n+1)_{Stn+1} \in\{0,1\})^{i\times j} \tag{3}\]  
where \(\mu(n)_{St}=\mu(1)_{St1},\mu(2)_{St2},...,\mu(n-1)_{Stn-1},...\mu(n+1)_{Stn+1}\) are the values sensed by different sensors at different times.
# Intelligent End-Edge Computation Offloading Based on Lyapunov-Guided Deep Reinforcement Learning
## 5 Experimental Results And Analysis  
### Experimental Setup  
In this section, we evaluate the performance of the proposed algorithm. All experiments are evaluated on a platform equipped with Intel Core i7-11700 2.5 GHz CPU and Pytorch. During the experiments, we choose two benchmark DRL algorithms for comparison, including A2C-based and TD3-based algorithms.  
During experiments, it is assumed that the channel gain \(h_{n,m}\) follows the path loss model as \(\overline{h_{n,m}}=A_{d}\Big{(}\frac{3\times 10^{8}}{4\pi^{2}c_{d}h_{n,m} }\Big{)}^{d_{o}}\), where \(d_{n,m}\) denotes the distance between \(n\)-th industrial terminal and \(m\)-th industrial base station and is measured in meters. The noise power is denoted by \(N_{0}=Wv_{0}\), indicates that the task arrivals of all industrial terminals obey an exponential distribution with equal average rates \(\mathbb{E}[(A_{n})^{2}]=\eta_{n}{<}\infty,n=1,\ldots,N\). The number of industrial base stations is set to 3 and the number of industrial terminals is set to  
Figure 3: Flowchart of the LyA3C algorithm.  
10, and all industrial terminals are identical industrial terminals with the specific parameter values shown in Table 1.
# A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data
## 5 Discussion  
### The Extracted Ground Surface Phases by the Three-Stage Method and TSVD  
From Equation (10), it can be seen that the ground surface phase can also be estimated by the TSVD-based method. Since the ground surface phase plays an important part in the estimation of the underlying topography [30; 32; 33], the extracted ground surface phases obtained by the three-stage method and TSVD are shown in Figure 8a,b, respectively.  
It is difficult to see any difference between Figure 8a,b. This indicates that the three-stage method and TSVD perform similarly in extracting the ground surface phase. For a more in-depth analysis, the ground surface phase obtained by TSVD subtracted from that obtained by the three-stage method is shown in Figure 8c. It can be seen that most of the values in Figure 8c are close to zero. This further validates that TSVD is unable to improve the accuracy of the estimation of the ground surface phase and the underlying digital elevation model (DEM) [30].  
The line-fit approach is used in the three-stage method to determine the ground surface phase. In order to reconstruct the straight line accurately from noisy coherence sets, 10 polarizations are used by least-squares based line-fit approach. Similarly, the same 10 polarizations are used in TSVD for the estimation of model parameters. Therefore, the observations used by line-fit approach and TSVD have  
Figure 8: (**a**) ground surface phase estimated by the three-stage method; (**b**) ground surface phase estimated by TSVD; (**c**) the difference between the ground surface phases obtained by the three-stage method and TSVD.  
Figure 7: (**a**) forest heights estimated by the three-stage method; (**b**) forest heights estimated by the TSVD-based method.  
the same noise and information, and the basic criterion in the line-fit approach and TSVD both are least-squares. This is a possible reason for this result.