esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, the UWB and MEMS IMU integrated positioning method was proposed for a system for accurately identifying and monitoring the position of work tools which are used in the assembly process of automobile production. Our method measures the accurate position, velocity, and attitude through a MEMS IMU sensor that is derived from the inertial navigation formula in 3 coordinate frames of indoor positioning frame (E-frame), local navigation frame (N frame), EGI frame (I-frame). Then, we computed the distance between an anchor node and a tag node by the TWR method. Here, we used the confidence distance compensation trilateration localization method to prevent the distance error by obstacles or changes in the indoor environment. Our method used the improved EKR by the error dynamic equation derived from 8 \(\times\) 8 sparse state-space matrix, for the integrated positioning algorithms of MEMS IMU and UWB sensors. We compared our method to the previous UWB and MEMS IMU positioning method-based moving averaging filter and Kalman filter [7]. The results verified that our method has less errors (4.2 mm\(\sim\)13.3 mm) than the previous methods. Furthermore, we verified that the bandwidth is wider than 499.2 MHz and it is within the regularized frequency range, through DAA experiments. Also, we developed to monitor the accurate position of a work  
Figure 11: Tool position tracking experiment in real working environment.  
Figure 12: Monitoring display of the position of the tool (red circle).  
tool operating in an automobile model. Recently, the wireless positioning in the IoT has been researched much for indoor location identification [22; 23]. Thus, we intend to study the applicability of the wireless positioning in the IoT for the future works.  
Conceptualization, [PERSON]-G.K. and S.-H.L.; methodology, S.-G.K., [PERSON]-J.K., [PERSON]-R.K. and S.-H.L.; software, S.-G.K. and S.-H.L.; validation, O.-J.K. and K.-R.K.; formal analysis, S.-H.L.; investigation, S.-G.K. and S.-H.L.; resources, S.-G.K.; data curation, S.-G.K.; writing--original draft preparation, S.-G.K. and S.-H.L.; writing--review and editing, O.-J.K. and K.-R.K.; visualization, S.-H.L.; supervision, K.-R.K.; project administration, K.-R.K. and S.-H.L.; funding acquisition, K.-R.K. and S.-H.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1I1A306659411, 2020R1F1A1069124), and also supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2021-2020-0-01797) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).  
Not applicable.  
The authors declare no conflict of interest.
# An Improved Approach to DNS Covert Channel Detection Based on DBM-ENSec
## 2 Related Work
### Detection Methods Based on Traffic Grouping
This paper presents a detection model known as DBM-ENSec, which integrates an improved Deep Boltzmann Machine (DBM) with an ensemble learning strategy. The model utilizes a singular feature derived from DNS traffic and amalgamates the advantages of DBM with an ensemble classifier to improve the detection accuracy of DCC attacks. This integration facilitates the model's ability to more effectively identify complex attack patterns. Initially, features with 23 dimensions are extracted from DNS traffic and subsequently transformed into a 5 \(\times\) 5 grayscale image. These features, which encompass message length, Time to Live (TTL) value, and subdomain entropy, provide a comprehensive representation of the characteristics inherent in DNS traffic. The processed grayscale image is then input into the enhanced DBM, which optimizes and extracts high-dimensional, more discriminative features to augment the accuracy of subsequent classification tasks. The optimized features are subsequently fed into the ENSec module, which incorporates XGBoost, Random Forest, LightGBM, and CatBoost. This integration aims to leverage the strengths of each model to enhance detection performance. Specifically, XGBoost is proficient in managing large-scale data, Random Forest effectively mitigates overfitting, LightGBM accelerates training through a histogram-based algorithm suitable for large datasets, and CatBoost improves the model's robustness by uniquely addressing categorical features. The integrated approach has been shown to significantly enhance the generalization capability and accuracy of the detection model. Furthermore, it has been demonstrated to reduce the false positive rate and improve adaptability to novel covert channel attacks. The model effectively mitigates the risk of data exfiltration prior to the detection of abnormal traffic by employing deep feature extraction with the enhanced DBM and integrating the features through ensemble classifiers. The model achieves an accuracy rate of 99.92%, surpassing traditional machine learning and deep learning methodologies based on traffic grouping.
# SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach
## 4 Machine Learning Algorithms and Performance Measurements
### Performance Measurements  
Traditionally, the performance of ML algorithms is measured by metrics which are derived from the confusion matrix [25]. Table 3 shows the confusion matrix in the IDS context.  
In the IDS context, the following parameters are used to create the confusion matrix:  
* TN: Represents the number of normal flows correctly classified as normal (e.g., normal traffic);
* TP: Represents the number of abnormal flows (attacks) correctly classified as abnormal (e.g., attack traffic);
* FP: Represent the number of normal flows incorrectly classified as abnormal;
* FN: Represents the number of abnormal flows incorrectly classified as normal;  
Next, we present several evaluation metrics and their respective formulas which are derived from the confusion matrix parameters:  
* Accuracy: The percentage of correctly predicted flows considering the total number of predictions:  
\[\text{Accuracy }\%=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}\times 100 \tag{1}\]
* False Alarm Rate (FAR): This represents the percentage of the normal flows misclassified as abnormal flows (attack) by the model: \[\text{FAR }\%=\frac{\text{FP}}{\text{FP}+\text{TN}}\ \times 100\] (2)
* Un-Detection Rate (UND): The fraction of the abnormal flows (attack) which are misclassified as normal flows by the model: \[\text{UND }\%=\frac{\text{FN}}{\text{FN}+\text{TP}}\ \times 100\] (3)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Class** & **Classified as Normal** & **Classified as Abnormal** \\ \hline Normal & True Negative (TN) & False Negative (FN) \\ Abnormal & False Positive (FP) & True Positive (TP) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix in the intrusion detection system (IDS) context.  
Accuracy (Equation (1)) is the most frequently used metric for evaluating the performance of learning models in classification problems. However, this metric is not very reliable for evaluating the ML performance in scenarios with imbalanced classes [26]. In this case, one class is dominant in number, and it has more samples relatively compared to another class. For example, in IDS scenarios, the proportion of normal flows to attack flows is very high in any realistic dataset. That is, the number of samples in the dataset which represent the normal flows is enormous compared to the number of samples which represent the attack flows. This problem is prevalent in scenarios where anomaly detection is crucial, such as fraudulent transactions in banks, identification of rare diseases, and in the identification of cyber-attacks in critical infrastructure. New metrics have been developed to avoid a biased analysis [27]. So, in addition to the accuracy, we also used the FAR and UND metrics.
## 4 Training and Performance Evaluation of the CCBA Model
### Performance Evaluation and Results
#### 4.3.1 Classification Accuracy  
The classification accuracies of various models across different SNRs are presented in Table 1, where the proposed CCBA model has been highlighted. By examining the results of different training stages within each model combination, we observe that the second stage, targeted fine-tuning and knowledge consolidation, consistently improves the average classification accuracy by up to 2%, as seen with the CCBA. However, the Dual-stream ResNet18-CNN-LSTM using the CNN-Audio block showed only a 0.3% improvement from the first stage, suggesting that the two-stage training strategy is more effective on single-stream networks. The CCBA model, after two stages of training, achieved an average accuracy of 72.2%, outperforming all other benchmark student models utilizing the CNN-Audio block by approximately 3.8%. The advantage of the multi-head attention mechanism is evident when comparing CCBA to CNN-CNN-BiLSTM, as CCBA demonstrates a 4% higher average  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Feature (or Equivalent)**} & \multirow{2}{*}{**Classifier**} & \multicolumn{4}{c}{**Train**} & \multicolumn{4}{c}{**SNR**} & \multirow{2}{*}{**Clean**} & \multirow{2}{*}{**Avg**} \\  & & & **Stage** & **0 dB** & **4 dB** & **8 dB** & **12 dB** & **16 dB** & **20 dB** \\ \hline \multirow{3}{*}{(Teacher)} & \multirow{3}{*}{SPWVD-MFCC} & Dual-stream & \multirow{3}{*}{-} & \multirow{3}{*}{31.7\%} & \multirow{3}{*}{51.5\%} & \multirow{3}{*}{74.0\%} & \multirow{3}{*}{86.2\%} & \multirow{3}{*}{90.0\%} & \multirow{3}{*}{91.8\%} & \multirow{3}{*}{92.1\%} & \multirow{3}{*}{73.9\%} \\  & & ResNet50-CNN-LSTM & & & & & & & & \\ \hline \multirow{8}{*}{ \begin{tabular}{c} **This Study (Student)** \\ \end{tabular}
## 4 Experiments
### Result Analysis  
To obtain stable clustering results, we repeat 10 experiments for each method, and the average clustering results are shown in Table 2. Since autoencoder-based methods make use of node features and graph structures jointly, they perform better than classic spectral clustering methods that exploit \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Nodes** & **Edges** & **Features** & **Classes** \\ \hline Cora & 2708 & 5429 & 1433 & 7 \\ Citeseer & 3327 & 4732 & 3703 & 6 \\ Pubmed & 19,717 & 44,338 & 500 & 3 \\ Wiki & 2405 & 17,981 & 4973 & 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of datasets. node features or graph structures independently. Unfortunately, these autoencoder-based methods only utilize the 2-hop or 3-hop neighborhoods of the center node to update node representation, which is insufficient to capture global structures. AGC [7] achieves better clustering results since it exploits adaptive graph convolution to select k-hop neighbors to aggregate information and update the central node representation. However, for Wiki that is more densely connected than other datasets, AGC [7] cannot perform well because of its weaker ability to capture smoothness. As for our AGCHK model, it outperforms all the baseline methods, achieving state-of-the-art results on the four datasets, especially on Wiki. AGCHK implements such a smooth adaptive graph convolution by enhancing low-frequency basic filters and discounting high-frequency basic filters that it makes the representation of connected nodes smoother. Furthermore, the scaling parameter \(s\) of the heat kernel is flexible to adjust the diffusion range to suit different applications and different networks. Especially for Wiki, AGCHK achieves its superiority significantly because it sets the scaling parameter \(s\) smaller to leverage fine graph structure information. To verify the reliability of the proposed cluster evaluation index \(d\_DBI(k)>0\), we plot \(d\_DBI(k)\) and the clustering performance w.r.t. \(k\) on Cora and Wiki respectively in Figure 4. The intra-cluster distance decreases and the inter-cluster distance increases in the early iteration, which is corresponding to \(d\_DBI(k)<0\). However, too many iterations will make node features over-smoothing, which leads to the inter-cluster distance rises. The cluster evaluation \(d\_DBI(k)\) takes intra and inter-cluster distance comprehensively, and it can select the most appropriate \(k\) to stop iterating, as shown in Figure 5. One can see that the Acc, NMI, and F1 scores evaluating clustering performance are the best or close to the best when \(d\_DBI(k)\) is greater than zero the first time, which demonstrates the validity of the proposed selection evaluation index \(d\_DBI(k)>0\). The selected iteration number \(k\) for Cora, Citeseer, Pubmed, and Wiki is 9, 9, 13, and 8 respectively, which are all values that are lower than the respective \(k\) values on these datasets in AGC [7]--12, 55, 60, and 8.
## 5 Discussion
### Accuracy-Influencing Factors Analysis
#### 5.1.2 The Number of Components in the Image  
Another factor that affects the accuracy of the structural component detection is the number of components to be detected in the image of 2D drawings. With the fixed resolution of the input image, if more components are contained in an image, each component will occupy a smaller pixel size and have less features. Consequently, the probability of missing or false detection will increase when classifying the structural components based on YOLO. To verify the relationship expounded above, in this paper, 300 images of structural drawings used for testing are classified into CLPIs and FPIs, and the total number of components in each image is counted. After that, the average of OA and AA for each image are calculated based on FP, TP, FN, and total number of components. The relationships between the number of structural objects and the detection accuracy in CLPI and FPI are shown in Figure 7.  
In Figure 7a, the total numbers of structural components of each CLPI in the test set range from 10 to 25. Meanwhile, the average OA and AA of CLPI decrease with the number of components increasing. The maximum average OA of 95.42% and AA of 93.67% are obtained in CLPI with 10 components, while the minimum average OA of 80.07% and AA of 77.81% are acquired in the image with 25 components. The same trend also happens with FPI, as shown in Figure 7b. Moreover, it is distinctly noticed that the value of average OA (95.01%) is nearly equal to the value of average AA (94.06%) when the number of structural objects in FPI is small. As the number of components increases, the declining trend of AA is bigger than that of OA. The main reason for this inconsistency, based on the definitions of OA and AA explained in Section 4.2.3, is that more missing errors are produced during the detection process rather than classification errors. Therefore, based on the aforementioned analysis, it can be concluded that the number of structural components does have a significant impact on the accuracy of structural component detection.
# Cardiac Radiofrequency Ablation Simulation Using a 3D-Printed Bi-Atrial Thermochromic Model
## 2 Materials and Methods
### X-ray Imaging, Mapping, and Ablation  
The simulator was taken to the cardiac catheterization laboratory, placed on the patient table and connected to the CARTO3 system. X-ray fluoroscopy imaging was performed at several standard view angles using the standard electrophysiology protocol on the system. A cone beam CT scan was performed using the standard protocol.  
An 8F ThermoCool SmartTouch SF (Biosense Webster, USA) ablation catheter was inserted via an 11 cm 8F introducer sheath (Cordis, Santa Clara, CA, USA) into the inferior vena cava. The ablation catheter was connected to the CARTO3 system, the irrigation system, and the RF generator. The right side of the model was mapped to generate the geometry of the right atrium and the venae cavae. The introducer sheath was replaced with a 60 cm 8.5F 55\({}^{\circ}\) Heartspan Transseptal Fixed Sheath (Biotronik, Berlin, Germany). This was manipulated under X-ray fluoroscopy guidance to pass the ablation catheter into the left atrium through the transseptal puncture in the heart model. Mapping was then performed of the left atrium.  
Ablations were performed only in the right side of the heart model. A line of five ablations was performed at the superior aspect of the posterior intercaval line with increasing ablative power. From inferior to superior, the power settings for each ablation point were as follows: 15.9 W; 16.0 W; 22.0 W; 23.0 W; and 30.6 W. The ablation duration was fixed to 45 s. Subsequently, using a fixed power setting of 21.5 W and the same duration, a line of five ablations was performed in the posterior wall of the right atrium. The heart model was then removed from the simulator and cut open to examine and measure the lesions.
# Advanced and Potential Methods for Extraction of Bioactive Compounds from Avocado Peel--A Review
## 4 Advanced Methods for Extraction of Bioactive Compounds from Avocado Peel
### Three-Phase Partitioning (TPP)  
Three-phase partitioning (TPP) is a simple and effective one-step technique for separating, purifying, and concentrating biomolecules such as enzymes, lipids, polysaccharides, and other biomolecules from complicated mixtures. This technique is also scalable and fast and considered an economical and efficient process for separations. In the TPP method, a solvent, such as t-butanol, is mixed with an aqueous anti-chaotropic salt, ammonium sulfate, at ambient temperature to generate two phases: the aqueous layer and the organic layer. It has been used to extract a wide range of biomolecules, most of which are proteins [49, 50]. The application of TPP has developed as a more ecologically friendly alternative to hexane extraction, allowing for the separation of important biological components. As a result, searching for new suitable salts and solvents that would allow for more appealing and cost-effective phase systems appears promising and potentially expands the scope of this technique's uses [51]. Indeed, the TPP technique is still a relatively new method for biomolecule recovery. It has yet to be widely used to extract compounds from avocado peel. [PERSON] and his co-workers [52] extracted the avocado peels using polyethylene glycol with 24.9-14.5% sodium nitrate and 12.2-15.5% magnesium sulfate. They found that the recovery was more than 82% of flavonoids, phenols, and condensable tannin from the avocado peel. The possibility of incorporating external approaches into TPP, such as ultrasound high pressure and even microwaves, to improve the separation process is very promising and may be interesting for future research. Optimization steps can also be established and recommended to assess the effect of various process parameters on the recovery yield and purity of the extraction of compounds. This would bolster TPP's advantages in biomolecule separation and purification in the future. A summary of TPP extraction, as well as other techniques that have been used to extract bioactive compounds from avocado peels, is shown in Table 1.
# Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications
## 2 Techniques in Alzheimer's Disease Detection
### Deep Learning Models
#### 2.2.1 Convolutional Neural Network (CNN)  
Studies by [14; 16; 21; 22; 25; 30] have shown that CNNs can outperform traditional ML methods in classifying AD stages by extracting high-level abstract features from MRI data. CNNs consist of multiple layers, such as convolutional layers, pooling layers, and fully connected layers. The convolution operation in CNNs can be represented as:  
\[f_{i,j}=\sum_{m}x_{i+m,j+n}\cdot w_{m,n}+b \tag{3}\]  
where \(X\) is the input feature map, \(W\) is the filter (or kernel), \(b\) is the bias term, and is the output feature map at position (\(i\),\(j\)). This operation allows CNNs to learn spatial hierarchies of features in the input data. CNN architectures such as VGGNet and ResNet have shown impressive results in distinguishing between healthy brains, MCI, and brains with AD as in [35]. The use of transfer learning, where pre-trained CNN models are fine-tuned on AD datasets, has improved diagnostic accuracy and reduced the need for large labeled datasets. Using brain MRI scans, [57] used two CNN models, MobileNetV3 and DenseNet121, to detect AD. MobileNetV3 achieved 93% accuracy, while DenseNet121 achieved 88% accuracy. The GAN-augmented dataset in article [58] achieved an accuracy of 81% using a conventional CNN model.
# Enhancing Resilience in Digital Twins: ASCON-Based Security Solutions for Industry 4.0
## 3 Proposed Solution
### Design Considerations and Requirements
#### 3.1.2 Out of Scope Requirements  
Even though proper key management is essential, it is an out-of-scope requirement for us. We assume that symmetric keys are preshared before communication. Additionally, we do not incorporate encryption technologies like SSL/TLS on the application level to avoid computation overhead on resource-constrained IoT devices.  
* The proposed solution does not cover a secure key exchange mechanism between the communicating parties. Instead, symmetric keys are assumed to be preshared before communication.
* The communication protocol (MQTT) at the application level is not encrypted using technologies like SSL/TLS to avoid computation overhead on the constrained device.
# A Delay-Aware and Reliable Data Aggregation for Cyber-Physical Sensing
## 4 Design of the TSR-DARDA Scheme
### Discussion on Parameters Selection  
In the section, how to select parameters of the proposed TSR-DARDA scheme is discussed. As described in Section 4.2 above, we know that the selection of key parameters including the outmost tier width \(d_{k}=\delta r\) and the number of the circular tiers plays an important role in the implementation the proposed algorithm.
# Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs
## 4 Bandwidth Allocation and AP Association Algorithms  
In this section, we first present the FBA-BP algorithm to calculate the bandwidth demanded by the clients and try to provide fair service to all clients. Then, we propose the CAA-BD algorithm to solve the performance anomaly problem and achieve the load balance among APs.
# Thermal Conductivity and Rheology of Graphene Oxide Nanofluids and a Modified Prediction Model
## 3 Results and Discussion
### Thermal Conductivity of Graphene Oxide Nanofluids
#### 3.3.3 Existing Models for the Effective Thermal Conductivity of Nanofluid  
Figure 7 shows the comparison between our experimental results and those calculated based on the formula proposed in the literature [18; 19]. The temperature range is 25-50 \({}^{\circ}\)C, and the mass concentration range is 0.002-0.010%. [PERSON] et al. proposed a formula for calculating the relative thermal conductivity of nanofluids [18]. As shown in Equation (1), they focused on the effect of particle concentration and believed that the effect of temperature is negligible:  
\[\frac{k_{nf}}{k_{bf}}=1+2.25\varphi+2.27\varphi^{2} \tag{1}\]  
where \(k_{nf}\) is the thermal conductivity of nanofluids, \(k_{bf}\) is the thermal conductivity of base nanofluids and \(\varphi\) is the mass concentration percentage of nanofluid. In this equation, temperature is ignored, however, as our experimental results demonstrate, temperature has a great effect on the thermal conductivity of nanofluids. As a result, a more accurate equation is proposed by [PERSON] (RS) [20]:  
\[\frac{k_{nf}}{k_{bf}}=0.0031*\left(T^{1.185}\right)*\left(\varphi^{0.863} \right)+1.006 \tag{2}\]  
where \(k_{nf}\) is the thermal conductivity of nanofluids, \(k_{bf}\) is the thermal conductivity of base nanofluids, \(\varphi\) is the mass concentration percentage of nanofluid and t is the temperature of nanofluid. The Equation (2) relates the relative thermal conductivity with temperature and particle concentration, which are two key factors affecting the relative thermal conductivity. As can be seen in Figure 7, this equation has better accuracy in the range of higher concentration, but it has a larger error for lower concentration nanofluids.