esa-sceva/satcom-chunk-collection huggingface dataset:
# Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method
## 4 Experimental Results and Analysis
### Baseline  
To prove the superiority of KGBGCN, a series of typical methods that also utilize graph convolutional neural networks or knowledge incorporation are selected for comparison experiments:  
1. KNN: one of the simplest methods in data mining classification techniques.
2. NB: a statistical probability classification method, one of the classical and simple classification algorithms.
3. Linear SVM: in the field of machine learning, it is a supervised learning model, usually used for pattern recognition, classification, and regression analysis.
4. Random forest: the number of features is chosen randomly, the training data are chosen randomly, and the prediction label with the most occurrences for the same prediction data is taken as the final prediction label.
5. LSTM: it is a kind of temporal recurrent neural network, which is specially designed to solve the long-term dependency problem of the general RNN.
6. BiLSTM: bidirectional long- and short-term memory network, which is based on an improvement in LSTM.
7. TextCNN: the first model that applies a convolutional neural network to text classification, keeping the original network structure and simplifying the convolutional layer, with a simple network structure and, therefore, a small number of parameters, low computation, and fast training.
8. BERT: This model is a large-scale pre-trained model proposed by Google in 2018, and the model framework is based on Transformer, which implements a multilayer bidirectional Transformer encoder. Once released, the model achieved SOTA results in several NLP tasks. Although the main task of this model is not text classification, due to its excellent text representation capability, the Chinese BERT pre-training model is used as an encoder, and the results are fed into the softmax function and fully connected layers to achieve the text classification task.
9. TextGCN: the first model that applies GCN to text classification, models the entire corpus as a heterogeneous graph, and learns word and document embeddingstogether through graph neural networks without using pre-trained word embeddings or external knowledge, and also learns predicted words and document embeddings automatically.
* BertGCN [20]: This model combines the pre-trained model, BERT, with the graph network GCN for text classification. The same heterogeneous graph is constructed as TextGCN, but the document nodes in this model graph are initialized with the pre-trained BERT; after that, the BERT and GCN are jointly trained to fully integrate the ability of both to process data and extract features, and the training process uses techniques such as prediction interpolation, memory storage, and small learning rate; finally, the GCN is used for text classification.
## 4 Results and Discussion
### Importance Analysis of Factors Related to Students' Knowledge Level  
To more accurately assess the impact of each input parameter on the prediction of students' knowledge level, an importance analysis of the input parameters was conducted. As depicted in Figure 6, PEG and LPR exhibit the highest importance scores, namely, 9.5958 and 4.2821, respectively, while STR, STG, and SCG only possess importance scores of 0.1344, 0.0856, and 0.0328, respectively. A higher importance score indicates that the prediction model will be more inclined towards parameters with greater significance when forecasting students' knowledge levels; consequently, any changes in these parameters will significantly influence the final prediction outcome. PEG represents a student's performance in learning objectives, whereas LPR signifies their performance in other goals related to those objectives. However, factors such as time allocation (STR), study time (STG), and self-control ability (SCG) have minimal impacts on the predictive results of this model. This substantial deviation from expected outcomes may potentially compromise  
Figure 5: Prediction result of confusion matrix UNS.  
both accuracy and reliability in predicting students' knowledge levels, thus warranting further investigation and discussion.
# Hom Antenna on Chip Operating at 180 GHz Using the SiGe CMOS Process
## 2 Antenna Design  
### Configuration  
All antenna designs and GSG feeds are within this range in Figure 1a. The overall CMOS size is 1200 \(\upmu\)m \(\times\) 1200 \(\upmu\)m \(\times\) 533.4 \(\upmu\)m, and the ground area is 850 \(\upmu\)m \(\times\) 850 \(\upmu\)m. The upper layer and the transmission line that feeds into the antenna body are made of M6 material. Its dielectric coefficient \(\varepsilon\)r = 1, loss tangent = 0, and the conductivity (bulk conductivity) is 21,939,477.13 siemens/m. After parameter analysis, the width of the feed transmission line is 10 \(\upmu\)m, which is optimal. The size of the upper cuboid is 650 \(\upmu\)m \(\times\) 450 \(\upmu\)m \(\times\) 3.5 \(\upmu\)m in Figure 1a, while the size of the cuboid on the lower layer is 530 \(\upmu\)m \(\times\) 325 \(\upmu\)m \(\times\) 2.91 \(\upmu\)m in Figure 1b, the material is M1 to M5, its dielectric coefficient \(\varepsilon\)r = 1, Loss tangent = 0, the conductivity (Bulk conductivity) is 24,189,646.83 siemens/m, and the middle aperture size is 390 \(\upmu\)m \(\times\) 190 \(\upmu\)m in Figure 1b. Height is 533.4 \(\upmu\)m in Figure 1c. All the parameters described above are presented in Table 1. All simulation results are obtained by electromagnetic simulation software, and the boundary conditions of the simulation process are all symmetrical. The chip horn antenna proposed comprises components M4-M6, as illustrated in Figure 1d.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Length** & **Value** & **Width/Height** & **Value** \\ \hline L\({}_{1}\) & 1200 & W\({}_{1}\) & 1200 \\ L\({}_{2}\) & 850 & W\({}_{2}\) & 850 \\ L\({}_{3}\) & 650 & W\({}_{3}\) & 450 \\ L\({}_{4}\) & 530 & W\({}_{4}\) & 325 \\ L\({}_{5}\) & 350 & W\({}_{5}\) & 190 \\ L\({}_{6}\) & 10 Figure 1: _Cont._
# Improving the Accuracy of Predicting Bank Depositor's Behavior Using a Decision Tree
## 5 Conclusions  
This research resulted in a classifier to predict whether a customer will subscribe to a long-term deposit or not. The combination of resampling and feature selection has been applied to 10% of the whole dataset (41188), resulting in 4119 instances from the UCI repository of a Portuguese bank. The proposed method addresses the imbalance issue and also removes unnecessary features through dimension reduction, based on the J48 model. To evaluate the performance of the classifier, three metrics, specificity, sensitivity and accuracy, were calculated. A comparison of the metrics could prove that the attribute evaluator has increased the important values of accuracy (94.39%), sensitivity (0.975) and specificity (0.709) by the J48 classifier. The evaluator selected 12 attributes out of the 20 attributes that were input according to the output. The aim of this paper was to predict the success of selling long-term deposits by telemarketing.  
Nevertheless, this study has important limitations that need to be addressed, which can lead to new future research directions, as follows:  
* This paper was limited to the telemarketing case--in the future, we aim to assess our approach on other marketing cases and datasets, such as direct marketing through email or instant messaging.
* While we have shown that resampling and feature selection improved the J48 algorithm beyond the results achieved by previous studies, we still do not have evidence if such an approach (resampling plus feature selection) would significantly improve other algorithms such as those based on neural networks, perhaps with results that are better than those from the J48 overall.  
Conceptualization, F.S.; methodology, F.S.; software, F.S.; validation, F.S. and S.M.; formal analysis, F.S.; investigation, F.S.; resources, F.S.; data curation, F.S.; writing--original draft preparation, F.S.; writing--review and editing, S.M., and F.S.; visualization, F.S.; supervision, S.M.; funding acquisition, S.M. Both authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Categories** & **References** & **Methods** & **Algorithm** & **Best Accuracy** \\ \hline \multirow{6}{*}{Bank dataset [11]} & \multirow{2}{*}{Current paper} & Resampling and feature selection & J48 & 94.39\% \\ \cline{2-5}  & [35] & Training and tested & C4.5 & 93.96\% \\ \cline{2-5}  & [36] & Compared 5 algorithms & LR & 91.21\% \\ \cline{2-5}  & [37] & Data preparation by preprocessing & NB & 88.86\%. \\ \cline{2-5}  & [38] & Data partitioning and three-dimensional & J48 & \textless{}89\% \\ \cline{2-5}  & [39] & Using fuzzy algorithm model & FMLP-SVM & 92.89\% \\ \hline \multirow{2}{*}{[40]} & \multirow{2}{*}{Using oversampling (SMOT)} & \multirow{2}{*}{J48} & \multirow{2}{*}{89.43\%} \\  & & & & \\ \end{tabular}
\end{table}
Table 4: Metrics comparison with previous studies.  
Funding: This work is funded by national funds through FCT--Fundacao para a Ciencia e Tecnologia, I.P., under the project FCT UIDB/04466/2020.
# Optimization of Blended Biochar Pellet by the Use of Nutrient Releasing Model
## 1 Introduction
Biochar pellets mixed with various ratios of pig manure compost was induced, and its sorption capacity and kinetic models have already been investigated [26]. For sorption test of NH\({}_{4}\)-N with various loading rates, it has been shown that the maximum sorption of NH\({}_{4}\)-N in the biochar pellet is 2.94 mg g\({}^{-1}\) where lettuce yield increased at approximately 13% relative to the control. However, there is little information on major plant releasing nutrient from the biochar pellet during leaching periods. For the releasing model, [PERSON] and [PERSON] presented the leaching behavior of heavy metals from solidified and stabilized forms of biofilms using Michaelis-Menten kinetics [27]. The used model predictions confirmed that Michaelis-Menten-type kinetics is probably the most dominant mechanism for the leaching of heavy metals from cement based waste forms. Furthermore, Michaelis-Menten kinetics has been used to explore the nitrogen deposition and climate change with laboratory manipulations [28]. It is hypothesized that (1) blended biochar pellets could prolong the nutrient releasing period, and could be affected by their nutrient releasing characteristics. In addition, (2) tests should be conducted to see whether biochar pellets fit a modified Hyperbola model. Therefore, this experiment was conducted to investigate the plant nutrient releasing characteristics, and to determine an optimum blended rate of biochar for the production of biochar pellets using a modified Hyperbola model.
## 3 Background
### Mpi  
Message Passing Interface (MPI) is a library for message passing and refers to the basic functions, Application Programming Interface (API), required for data exchange in distributed and parallel processing [9; 10]. This standard supports both point-to-point and collective communication. The MPI application consists of several processes that exchange data with each other. Each process starts in parallel simultaneously when the MPI application is running and exchanges data with each other during execution. The advantage of MPI is that it can exchange data and information easily between multiple machines. Parallel MPI is used not only in parallel computers that exchange information at high speed, such as shared memory or InfiniBand, but also in PC clusters. MPI has various open libraries on different HW environments, such as MPICH, OpenMPI, and Intel MPI [32; 33].  
MPI allocates tasks based on process. Each process corresponds to a set called the communicator. A communicator defines a group of processes that have the ability to communicate. In the same communicator, messages can be exchanged with different processes. In the communicator, processes are identified by rank. There are two types of communication methods in MPI: point-to-point communication and collective communication. MPI_Send and MPI_Recv are common point-to-point communication methods of MPI. Group communication is communication in which all processes, which are communicators, participate together. MPI_Bcast, MPI_Gather, MPI_Allgather, MPI_Reduce, and MPI_AllReduce are all representative collective communications. Figure 1 shows the procedure for collective communication. The experiment presented in this study performs MPI_Bcast, MPI_Gather, and MPI_AllReduce communication. MPI_Bcast is a Broadcast communication that sends data of the root process to all other processes in the same communicator. MPI_Gather gathers data from multiple processes in the Communicator to the root process. MPI_AllReduce combines the values of all processes in the communicator and redistributes the results to all processes.
## 2 Materials and Methods  
### Soil Properties  
The soil sample of the scaled-down model test in this study was collected from a cut slope at a construction site located at Ulju-gun, in the Ulsan Metropolitan City area in Korea.  
Table 1 shows the geotechnical index properties of the specimen used in this study. Specific gravity test, grain size test, standard proctor test and relative density test and physical properties of the soil were analyzed. The specific gravity of the soil was 2.69, the maximum dry unit weight was 18.27 kN/m\({}^{3}\), and the minimum dry weight was 12.43 kN/m\({}^{3}\). The optimum moisture content was 12.5% and the Arterberg limit test showed Non-Plastic (NP) for Plastic Index (PI). The maximum and minimum void ratios were 1.123 and 0.443, respectively. The fines content of the soil was 10.8% and the soil was classified as SW-SM according to the Unified Soil Classification System. For the 1 \(g\) shaking table test, the specimen was selected for the sample passing through the No. 4 sieve after the physical property tests, and the sample remaining in the No. 4 sieve was about 1%. The elastic modulus was 2 \(\times\) 10\({}^{8}\) Pa. According to the existing experimental data, the friction angle of 30\({}^{\circ}\) and the dilatancy angle of 24.4\({}^{\circ}\) were used in the study [9].  
The elastic modulus is obtained indirectly using experimental data. The calculation of the modulus of elasticity is explained here. The average shear wave velocity was determined to be 72 m/s by combining the hammer test and the calculation formula of [PERSON] (1966). In a homogeneous and isotropic soil, the speed of the shear wave is controlled by the elastic modulus, as shown in Equation (1):  
\[\mathrm{G}=\rho\mathrm{V_{s}}^{2} \tag{1}\]  
where G is the shear modulus, \(\rho\) is the unit weight of soil, \(\mathrm{V_{s}}\) is the shear wave velocity.  
The relationship between elastic modulus and shear modulus is:  
\[\mathrm{G}=\frac{\mathrm{E}}{2(1+\mathrm{v})} \tag{2}\]  
where, G is the shear modulus, E is the elastic modulus, \(\mathrm{v}\) is the Poisson's ratio.  
According to Equations (1) and (2), knowing the unit weight of soil, \(\rho\) is 1.47 \(\times\) 10\({}^{5}\) N/m\({}^{3}\) and shear wave velocity, \(\mathrm{V_{s}}\) is 72 m/s. The shear modulus can be obtained as 7.6 \(\times\) 10\({}^{7}\) Pa. Then knowing the shear modulus, G is 7.6 \(\times\) 10\({}^{7}\) Pa, and the Poisson's ratio, \(\mathrm{v}\) is equal to 0.3, the elastic modulus can be obtained as 2 \(\times\) 10\({}^{8}\) Pa. This modulus was used in the study. In order to make the parameters fit better, the concept of adjusted elastic modulus is also used later.
# Design of MIMO Antenna with Double L-Shaped Structure for 5G NR
## 3 Results and Discussion
### Diversity and Multiplexing Performance  
In general, the performance of MIMO antennas is primarily determined by the antenna elements' diversity and multiplexing capabilities. As a result, it is critical to examine the multiplexing and diversity performance of MIMO antenna arrays in addition to the S-parameters and radiation characteristics. In this regard, the Envelope Correlation Coefficient (ECC) is the most important metric for estimating and validating antenna diversity characteristics, which can be calculated using Equation (1) [27]. It can be seen from Figure 12 that the calculated ECC value is less than 0.08 in the entire operating frequency range for the proposed design, thereby indicating a good diversity performance.  
\[ECC=\frac{\left|S_{12}^{*}S_{12}+S_{21}^{*}S_{22}\right|^{2}}{\left(1-\left|S_{ 11}\right|^{2}-\left|S_{21}\right|^{2}\right)\left(1-\left|S_{22}\right|^{2}- \left|S_{12}\right|^{2}\right)} \tag{1}\]
## 7 Limitations
### Limitations in Computational Resources and Real-Time Performance  
The SE-TransUNet model has a parameter count of 223 million, which is substantially higher than most of the comparative models. Classical segmentation models such as UNet (23M) and SegNet (14M) have parameter counts approximately one-tenth that of SE-TransUNet. Even compared with similar Transformer-based models like TransUNet (176M), SE-TransUNet has approximately 20% more parameters. This results in significantly longer training times and increased demands on GPU memory. In real-time detection scenarios, the model's inference speed is approximately 6.53 frames per second (FPS), considerably lower than that of the comparison models. Lightweight models such as UNet and SegNet achieve 16-17 FPS, which is more than 2.5 times faster than SE-TransUNet. Even among Transformer-based models, TransUNet exhibits an FPS approximately 2.2 times higher than SE-TransUNet.  
Thus, while SE-TransUNet meets the accuracy requirements for tunnel leakage detection, its high parameter count and low inference speed result in high training costs and challenges in real-time deployment. Future studies should explore model lightweighting strategies to better balance accuracy and computational efficiency, thereby promoting practical engineering applications.
## 3 Experimental Results and Discussion
### Evaluation Indicators  
In this study, we used precision, recall, accuracy, and the \(F1\) score as evaluation metrics. We used the values of these evaluation metrics to evaluate the model in a comprehensive manner. Precision, recall, accuracy and the \(F1\) score were calculated as follows.  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{3}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{4}\]  
\[\text{Accuracy}=\frac{TP+TN}{TP+FN+FP+TN} \tag{5}\]  
\[F1=\frac{2 TP}{2 TP+FP+FN} \tag{6}\]  
where \(TP\) is the number of true positive samples, \(TN\) is the number of true negative samples, \(FP\) is the number of false positive samples, and \(FN\) is the number of false negative samples. Accuracy is the ratio of the number of correctly predicted samples to the total number of samples used for model experiments. Precision is the ratio of the number of correctly predicted positive samples to the total number of positive samples. The \(F1\) score is a comprehensive evaluation index that considers precision and recall, and the average of precision and recall.  
We chose the cross-entropy loss function as the loss function for our experiments. The cross-entropy loss function is commonly used for classification problems. It measures the difference between the model output and the true label, making it a preferred loss function for training classification models. The cross-entropy loss function is widely used in deep learning because of its good performance, ability to directly optimize classification probability, and ability to handle category imbalance. The cross-entropy loss function is shown in Equation (7), and we can calculate it by using the equation where \(M\), \(P_{i,c}\) and \(y_{i,c}\) represent the class, predicted probabilities, and ground truth, respectively, for specific images.  
\[Loss=-\sum_{c=1}^{M}y_{i,c}log(P_{i,c}) \tag{7}\]
# LSH Models in Federated Recommendation
## 3 NCF Federated Recommendation Based on DP Results
### Local Parameter NCF Recommendation  
In order to prevent getting the user's rating or other data from the historical interaction by back propagation attacks, a local parameter training method is proposed for NCF federated recommendation. This is as shown in Equation (6):  
\[\underset{x\in R^{d}}{\text{min}}F(x)=E_{i\sim p}[f_{i}(x)] \tag{6}\]where \(f_{l}(x)\) is the local optimization target for user \(i\); \(x\) is the vector of model parameters with dimension d; and p is the probability distribution for the clients. Some parameters in this model are stored locally, not transmitted, and do not participate in the aggregation process of federated learning. These parameters are referred to as the local parameters of the federated learning. In the NCF model, the embedding layer maps the user ID and the item ID to the hidden layer using one-hot encoding. Therefore, its parameters can be considered as local parameters that do not participate in the aggregation and the issue process of the federated learning. Each user's or item's ID is independent during both the training and prediction processes. Only the correct user hidden feature vector is required as input. As a result, the user embedding layer can serve as a local model parameter, saved and updated locally on each client. The training process is shown in Figure 2 below:  
As depicted in Figure 3, the process unfolds as follows: firstly, the recommendation system server dispatches the initialized NeuMF model M, excluding the embedding layer, to each online client. Secondly, each client initializes its own embedding layer and combines model M into a complete recommendation model M\({}^{\prime}\). Thirdly, the newly trained model M\({}^{\prime}\) is obtained by training local data on the client side based on M\({}^{\prime}\), and the embedding layer parameters of M\({}^{\prime}\) will be replaced by those of the model M\({}^{\prime}\). Fourthly, the model M\({}^{\prime\prime}\) excluding the user embedding layer is uploaded to the recommendation system server. Finally, the recommendation system server aggregates the uploaded parameters according to each client's weight.  
In this local parameter NCF federated recommendation, the local model M\({}^{\prime}\) needs to be updated, and the model M issued by the recommendation system server does not contain the user embedding layer. After updating and merging with the embedding layer, the model M  
Figure 3: Different weight setting in federated recommendation.  
Figure 2: Local parameter NCF recommendation model.  
has been evolved into the model M\({}^{\
u}\). The local parameter NCF federated recommendation reduces the transmission or communication cost, but improves the data privacy.