esa-sceva/satcom-chunk-collection huggingface dataset:
# ReliaMatch: Semi-Supervised Classification with Reliable Match
## 1 Introduction
When assigning pseudo-labels to unlabeled data, as plotted in Figure 2, assigning labels to samples with low predicted confidence may lead to confirmation bias [16], where the model overfits to incorrect labels and reduces its performance. Using a fixed threshold method cannot adapt to the dynamic changes of the dataset. On the contrary, a global dynamic threshold method dynamically adjusts the threshold based on the confidence distribution of unlabeled samples in the current iteration, thus avoiding this problem. Figure 1: Error information in feature space. The **(left)** figure shows how the semantic inconsistency between adjacent samples, and the **(right)** figure illustrates how to use anchors to set confidence thresholds to filter features. To address these issues, we proposed ReliaMatch, a semi-supervised classification method that filters unreliable information based on a confidence threshold. ReliaMatch adopts a confidence threshold filtering strategy, which matches the similarity of labeled data and unlabeled data in feature space by setting anchor points, thus filtering out outliers and demarcation points with ambiguous semantics. Dynamic threshold is used to select reliable pseudo-labels, so as to eliminate the confirmation deviation of the model to pseudo-labels and improve classification performance. Additionally, ReliaMatch adopts the training mode of Curriculum Learning [18], which combines the screened samples and their pseudo-labels with labeled data, gradually increasing the difficulty of training datasets and participating in model training in a supervised way, thus further improving the classification performance. In summary, we make the following three main contributions:  
1. We proposed a semi-supervised classification method (Reliable Match), which addresses the issue of confirmation bias that arises from unlabeled data having different semantics and low prediction confidence near the classification boundary. 2. ReliaMatch employs a confidence threshold filtering strategy that matches the similarity of labeled and unlabeled data in feature space by setting anchor points, which filter out outliers and demarcation points with ambiguous semantics. To eliminate confirmation deviation of the model to pseudo labels and improve classification performance, ReliaMatch uses a dynamic threshold to select reliable pseudo-labels. 3. ReliaMatch employs the Curriculum Learning training mode, which combines the screened samples and their pseudo-labels with labeled data and gradually increases the difficulty of the training dataset, thereby participating in model training in a supervised manner and further improving classification performance.
## 2 PEMFC-MH System Matching Design
### Design of the PEMFC-MH Coupling System  
The thermal management subsystem of the solid-state hydrogen storage device provides effective thermal management to maintain the temperature inside the device at a beneficial level to ensure that the MH material has the desired rate of hydrogen absorption/release. The thermal coupling methods for solid-state hydrogen storage devices and fuel cells during hydrogen discharge can be divided into two categories: active thermal coupling and passive thermal coupling. Although passive thermal coupling does not generate additional parasitic power, it has high requirements for the design of the system structure, and the heat exchange efficiency is generally low compared to that of active thermal coupling, and for most mobile applications, the active thermal coupling method has obvious advantages. System structures with active thermal coupling are shown in Figure 2, including a series structure (Figure 2a,b) and a parallel structure (Figure 2c,d), where the thermal coupling in the structure of Figure 2b is the air-mass heat transfer, and the others are the coolant heat transfer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Maximum mass hydrogen storage capacity & 1.3 & wt \% \\ Density of unabsorbed hydrogen & 7259 & kg/m\({}^{3}\) \\ Density of saturated absorbed hydrogen & 7164 & kg/m\({}^{3}\) \\ Molar mass & 0.43 & kg/mol \\ Specific heat capacity & 0.970 & kJ/(kg-K) \\ Heat conductivity & 3.18 & W/(m-K) \\ Permeability & \(10^{-8}\) & m\({}^{2}\) \\ Porosity & 0.63 & - \\ Hydrogen absorption rate constant & 59.187 & 1/s \\ Hydrogen release rate constant & 9.57 & 1/s \\ Hydrogen absorption activation energy & 21,170 & J/mol \\ Hydrogen release activation energy & 16,420 & J/mol \\ Reaction enthalpy & 30,100 & J/mol \\ Reaction entropy & 108 & J/(mol-K) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material property parameters of LaNi\({}_{\text{S}}\).  
Figure 2: Active thermal coupling structures. (**a,b**) Series structure; (**c,d**) parallel structure.  
For the heat dissipation of the hydrogen absorption process, for some stationary applications, the solid-state hydrogen storage device cannot use an external coolant for heat dissipation. Therefore, the thermal management subsystem also needs to satisfy the heat dissipation requirements of the MH hydrogen absorption process. The solid-state hydrogen storage device and fuel cell in the cooling circuit in the series structure shown in Figure 2a may lead to a deviation in the fuel cell's temperature from the optimal operating temperature point and reduce the fuel cell's efficiency [19]. In order to not change the existing fuel cell thermal management subsystem too much, the thermally coupled structure of Figure 2c is adopted, and the whole PEMFC-MH system structure is shown in Figure 3.
# Determining Optimal Power Flow Solutions Using New Adaptive Gaussian TLBO Method
## 4 Numerical Results
### IEEE 57-Bus Test System  
A standard IEEE 57-bus test system (Figure 4) is used to evaluate the performance of the AGTLBO algorithm in greater-scale power systems. The system comprises seven generating units, 80 branches, 15 tap shifting transformers, three shunt reactive compensators, and a combined demand of 1250.8 MW. Reference [63] was used to collect the data for this evaluation device. A general range of voltages for generator buses and the decision variables for tap shifting transformers is set to 1.06-0.94 p.u. All load bus voltages are set to 1.06 and 0.94 p.u., respectively. We selected 0.0 and 0.3 in p.u. as the minimum and maximum ranges for the shunt reactive power compensation.
## 4 Conclusions  
In this paper, we proposed a SSADNet that could simultaneously perform segmentation and detection of drivable regions and moving or non-moving vehicles in an autonomous driving system by generating a top view image using a point cloud obtained from 3D LiDAR.  
The network was trained using the KITTI dataset, which is commonly used in research on autonomous driving systems. For network input, we generated a point cloud top view image having three channels of distance, reflection intensity, and height using the LiDAR point cloud. The segmentation performance of the proposed network had a pixelwise classification accuracy of 96.9%. The \(mAP\) was 96.9%, while the detection performance had a \(mIoU\)@0.5 of 70.5% and a \(mIoU\)@0.7 of 79.0%. The inference speed was 33 _FPS_. The experimental results can be applied for lane keeping, steering control, evading obstacles, collision prevention, speed control, and creating a driving path in autonomous driving or driving assistance.  
The contributions of this paper are as follows: (1) we proposed a 3D point cloud-based detection and segmentation network using deep learning and (2) an algorithm that can perform two functions in one network structure simultaneously was proposed instead of a separate network structure that performs two functions. Namely, unlike previous algorithms need more than one processor for each network for detection and segmentation simultaneously, SSADNet can obtain detection and segmentation results for autonomous driving using a single network, so it can be done using a single processor.  
However, this study has some limitations, in that the detection performance is poorer than the segmentation performance. SSADNet was not used for pre-trained models, so performance is limited when using the same number of training data, and after detection, reliable detection of obstacles is required through tracking algorithms such as the Kalman filter. Therefore, we will conduct further research (1) on improving the detection performance while maintaining the segmentation performance by enhancing the proposed network, (2) on increasing the speed to be more suitable for an autonomous driving system using pre-trained models, and (3) implementing the tracking algorithm in the time domain using Kalman filter, etc.  
Conceptualization, S.P.; methodology, Y.L. and S.P.; software, Y.L.; validation, Y.L.; formal analysis, Y.L. and S.P.; data curation, Y.L.; writing--original draft preparation, Y.L. and S.P.; writing--review and editing, Y.L. and S.P.; visualization, Y.L.; supervision, S.P.; project administration, S.P.; funding acquisition, S.P. All authors have read and agreed to the published version of the manuscript.  
This works was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP; Ministry of Science, ICT & Future Planning) (NRF-2017 RIC1B5018101) and Soonchunhyang university 20160844. The authors are very appreciate to journal editors and reviews.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Experimental Design  
The SBME of BES, TES, and PRO is subject to numerous influencing factors, necessitating an optimized experimental design to determine the optimal combination of factors with minimal experimental effort. For this purpose, chemometric approaches using CCD-based RSM using Chemoface(r) v1.66 [35] were applied at three levels for four test factors listed in the Supplementary Table for Materials (Table S1). Midpoints were usually replicated to estimate experimental errors. Experiments were then run on these design points, and the EE% response variable was used to measure each combination of factor levels listed in Table 1. Then, the obtained experimental data listed in Table 1 were used to fit a mathematical (quadratic or polynomial) model equation describing the relationship between factors and responses [35]. The essential factors and their optimal values can be estimated by analyzing the model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multicolumn{4}{c}{**Influence Factors**} & \multicolumn{4}{c}{**Analyte Response (EE%)**} \\ \cline{2-9}  & **A: Number of** & **B: Extraction Time** & **C: Volume of** & **D: Elution** & & & \\  & **HDE-SBME Devices** & **(min)** & **Elution (\(\mu\)L)** & **Time (min)** & **BES** & **TES** & **PRO** \\ \hline
1 & 5 & 10 & 300 & 2 & 27.33 & 32.50 & 32.51 \\
2 & 1 & 10 & 300 & 2 & 17.
## 7 Outcomes
### RQ2: What Is the Performance of Anomaly Intrusion Detection in IoT Using Deep Learning Techniques?
#### 7.3.1 Threat Detection  
Because IoT supports a wide range of applications that need different resource requirements in terms of processing, storage, and communication, the network becomes more complex, due to the heterogeneity of the IoT devices that are being connected. This makes it hard to provide a secure environment in the IoT ecosystem and even harder to detect security threats. In securing an IoT environment, it is important to acknowledge the features and criteria necessary for applying security analytics in deep learning algorithms [54]. However, the existing mechanisms lack the effective and efficient methods that can perceive the hidden correlation between these features. Nevertheless, the rapid growth of deep learning algorithms is believed to have the capability of handling the hidden parameters not limited to the IoT application, but also for finding the correlation of data variation. In addition, a higher detection rate toward detecting zero-day attacks efficiently is obtainable with deep learning [58].
## 4 Use Case and Simulation of the Proposed System
### Comparison of Various Handover Delay's  
Based on previous Equations (1)-(4), the subsequent Table 9 denotes the handover delays of various mobile IPv6 protocols such as MIPv6, PMIPV6, F-PMIPV6, PMIPv6/SAND and PMIPv6/AAND.
## 4 Results
### Intrasubject Classification  
A binary subject-specific (intrasubject) classification was performed by training, validating, and testing the model for each subject using the BSL features. A 10-fold cross-validation was performed to train, validate, and test the model. Subject 26 had the highest classification accuracy in the beta band (98%) among all classifications. The theta band had 97% accuracy for subject 20, followed by the gamma band, which had 89% classification accuracy for subject 05. Finally, the alpha band had an 85% classification accuracy for subject 19. The good-performance subjects (Figure 10) had the highest classification accuracies compared to the bad-performance subjects (Figure 11). The sensitivity and specificity results provided in Figure 12 offer insight into the model performance across individual classes. The results suggest that the proposed model performed well in identifying the negative cases (high specificity) and positive cases (high sensitivity). The beta band had high sensitivity and specificity in all classifications, followed by theta, gamma, and alpha bands. The receiver operating characteristic (ROC) curves are plotted for 10-fold cross-validation in all four frequency bands for top classifications (Figure 13). The ROC curve further represents that the model performance is consistent in all four frequency bands.  
Figure 12: Sensitivity and specificity ranges of GCN classifier in theta, alpha, beta, and gamma frequency bands.  
Figure 13: ROC curves for 10-fold cross-validation of top-classifications in theta, alpha, beta, and gamma frequency bands.
# Numerical Simulation of Williamson Nanofluid Flow over an Inclined Surface: Keller Box Analysis
###### Abstract  
The study of nanofluids has become a key research area in mathematics, physics, engineering, and materials science. Nowadays, nanofluids are widely used in many industrial applications to improve thermophysical properties such as thermal conductivity, thermal diffusivity, convective heat transfer, and viscosity. This article discusses the effects of heat generation/absorption and chemical reaction on magnetohydrodynamics (MHD) flow of Williamson nanofluid over an inclined stretching surface. The impact of Williamson factor on velocity field is investigated numerically using Keller box analysis (KBA). Suitable similarity transformations are used to recover ordinary differential equations (ODEs) from the boundary flow equations. These ordinary differential equations are addressed numerically. The numerical computations revealed that energy and species exchange decrease with rising values of magnetic field. Moreover, it is found that increasing the chemical reaction parameter increases the Nusselt number and decreases skin friction. Further, the effect of Lewis parameter diminishes energy transport rate. In the same vein, it is also observed that increasing the inclination can enhance skin friction, while the opposite occurred for the energy and species transport rate. As given numerical computations demonstrate, our results are in reasonable agreement with the reported earlier studies.  
KBA; Williamson nanofluid; MHD; heat generation or absorption; inclined surface +
Footnote †: journal: Applied Sciences
# Detecting Multiple Mixed Bacteria Using Dual-Mode Hyperspectral Imaging and Deep Neural Networks
## 1 Introduction
Four kinds of pathogenic bacteria, _Staphylococcus aureus_ (_S. aureus_), _Escherichia coliform_ (_E. coli_), _Pseudomonas aeruginosa_ (_P. aeruginosa_), and _Serratia marcescens_ (_S. marcescens_), were selected and studied in the experiments. They are widespread in natural environment, such as soil, sewage, and excrement, and serve as the main culprits for many infectious diseases, inflammation, and various other abnormal symptoms. Our self-developed hyperspectral detection system collected both transmission and fluorescence spectral data of the mixed bacterial samples, which can be used as the characteristics of concentration estimation. The system can also generate transmission hyperspectral images of mixed samples for sample observation and calculation of the total concentration mixed bacteria using cell counting methods. In terms of algorithms, we developed a multiple mixed bacteria measurement network (MB-Net) with hybrid deep learning (DL) architectures based on spectral feature fusion. The DL architectures depending on different spectral types are jointly optimized, which are then stacked together using a fusion strategy, forming a MB-Net for processing and optimizing dual-mode hyperspectral data. The experimental results show that the predicted proportion of the four types of pathogenic bacteria indeed corresponds well to the actual proportion both in training sets and validation sets. Combining the predicted proportion with total concentration results measured through the transmission hyperspectral images, the detailed concentration of each bacterium in mixed samples can be calculated. Compared with commercial microscope and spectrometer, our self-developed system has the advantages of low-cost, favorable scalability, and convenient operation. Our study presents a highly effective quantitative analysis method for multiple bacteria found in clinical samples.
## Network Setting and Computational Model
### Computation-Offloading Model  
This section presents the model for computational task offloading. The computational task can be offloaded to the SBS for execution at the sMEC through a wireless link or to the MBS (from SBS to MBS) for execution at the mMEC via fiber-optic links. Therefore, we classified this activity into two schemes for computational task offloading: (1) through the sMEC or (2) through the mMEC. Here, the total  
Figure 1: Multi-tier computation-offloading strategy in HetNets.  
energy consumption for completing the computational task comprises the energy consumption for the uploading transmission, for task performance on the MEC servers, and for returning the output data. In this study, we regarded optimization from the user perspective; therefore, we ignored the energy consumption associated with execution on the MEC server and only considered that required for the upload transmission [16]. This assumption is reasonable because it has been used by existing studies [16; 35; 36] and allows that the MEC server is usually operated using electricity supplied from the grid. Moreover, the power required to transmit the result back to the MD is small compared with that required from transmission of the original computational task [18; 34; 38].
# Deep-Learning-Based Approach for IoT Attack and Malware Detection
## 2 Datasets
### CIC-MalMem-2022 Dataset  
The CIC-MalMem-2022 dataset is designed to test memory-based detection methods for concealed malware [33]. Created using malware commonly found in the real world, this dataset aims to closely represent real-world scenarios. CIC-MalMem-2022 includes spyware, ransomware, and Trojan horse malware, providing a balanced dataset for testing concealed malware detection systems. During the memory dump process, the debug mode is utilized to ensure the dump process remains invisible in memory dumps, offering a more accurate representation of programs an average user might run during a malware attack. The CIC-MalMem-2022 dataset is balanced, with 50% malicious memory dumps and 50% benign memory dumps. Out of a total of 58,596 records, 29,298 are benign and 29,298 are malicious memory dumps. This dataset, created using real-world malware, incorporates concealment techniques that make malware detection challenging. Its balanced nature allows for an objective evaluation of detection algorithms' accuracy. The dataset includes spyware, ransomware, and Trojan horse malware, with examples from various malware families within each category. Additionally, the use of debug mode during the memory dump process ensures the invisibility of the dump process in memory dumps, enabling more accurate testing of malware detection systems. The CIC-MalMem-2022 dataset is a crucial resource for evaluating the effectiveness of malware detection methods and advancing research in this field. Representing real-world malware and concealment techniques, this dataset aids security analysts and researchers in developing more effective detection methods.  
In this dataset, the BENIGN class represents benign memory dumps, while the Malware classes include various types of malware such as spyware, ransomware, and Trojan horses. The dataset contains concealment techniques that complicate malware detection and maintain a balanced structure, allowing for an objective evaluation of detection algorithms' accuracy.
# Impact of Subsurface Drainage System Design on Nitrate Loss and Crop Production
## 3 Results and Discussion
### Crop Yield Response  
Crop yield in each plot was mapped during harvesting using a yield mapping monitor mounted on the grain combine harvester. The harvester recorded information such as grain flow, moisture, area covered, and location. Crop yield data were recorded in 2018, 2020, 2021, and 2022 for each plot. However, due to technical issues with the combine harvester's yield mapping system, the crop yield data for 2019 were lost. Table 5 shows the average annual yield for each plot in 2018, 2020, 2021, and 2022. We observed a consistent crop yield pattern across the plots during the recorded years. The soybean yield followed a similar trend to the corn yield in 2021. On average, plots with the same design combination, CS-2 and CS-4, showed a 5.1% to 2.6% higher corn yield (3-year average) compared to CS-1 and CS-3, and CS-5 and CS-6, respectively. Among the individual plots, the highest average corn yield recorded was 11,332 kg ha\({}^{-1}\) in CS-2, followed by CS-3, CS-6, CS-4, and CS-5. The lowest average corn yield recorded was 9976 kg ha\({}^{-1}\) in CS-1. We observed a substantial difference in corn yield between the plots with different drainage spacing and depth.  
Our particular interest lay in the observations regarding drainage design, nitrate losses, and productivity within the CS-2 plot. The results observed in the CS-2 plot showed relatively lower tile flow and nitrate loss while also demonstrating the highest corn productivity, despite having a wider drainage design. These findings differ from those reported in previous research studies [25,34]. Generally, subsurface drainage systems with a narrower design are more effective at controlling excess water than wider systems. Previous research on yield results suggests that narrower subsurface drainage positively affects productivity. From the overall results of our observations, we can infer that corn and soybean plants in CS-2 utilized more water and nitrate from the soil. The higher water  
Figure 7: Nitrate concentration of soil below 0–15 cm and 15–30 cm. The nitrate concentration in the soil exhibits variation between depths and years across the plots.  
retention in the soil also influences longer microbial processes facilitated by saturated water, which may have contributed to the higher crop yield observed in the CS-2 plot compared to other plots. Therefore, we suggest optimizing the drainage design with a correctly wider and deeper system to enhance productivity while pursuing sustainability by reducing nutrient loss for the environment.