esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Design of Experiments
### Metrics for Evaluation  
The models were trained using datasets with varying degrees of training data to assess their accuracy, false positive rate, accuracy, and detection rate, as well as their F1-score. These measurements were constructed using true negative (TN), true positive (TP), false negative (FN), and false positive (FP) data. The improperly classified legitimate and attack vectors were FP and FN, respectively. TP and TN denote the numbers of successfully classified legitimate and attack vectors [33,34].  
Accuracy (ACC): The system's capability to categorize attack packets as normal or attack. For all samples, any percentage forecasts are acceptable. Accuracy is mathematically stated as follows:  
\[\text{Accuracy}=\frac{TP+\text{TN}}{TP+FN+TN+FP}\times 100 \tag{1}\]  
Precision (Pre): Defines the percentage of genuinely detected attacks versus all packets designated as attacks; arithmetically expressed as follows:  
\[\text{Precision}=\frac{TP+\text{TN}}{TP+FP}\times 100 \tag{2}\]  
F1-score (F1) is theoretically defined as the harmonic average of recall and precision.  
\[\text{F1}-\text{Score}=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{3}\]  
Recall (Rec): The system's ability to correctly detect attacks when a security breach occurs; often known as the true positive rate, and expressed mathematically as follows:  
\[\text{Recall}=\frac{TP}{TP+FN}\times 100 \tag{4}\]
## 2 Materials and Methods
### GPR Data Preprocess and Alignment
#### 2.1.2 Spatial Alignment  
The spatial alignment refers to performing an interpolation of the multi-frequency GPR data and making sure the different frequency data align in the same number of sampling channels. This mainly includes horizontal and vertical spatial alignment; the detailed calculation process is illustrated as follows:  
Assume that the GPR sampled data in two different frequencies can be denoted as \(X^{m_{1}}(i,j)\) and \(X^{m_{2}}(i^{*},j^{*})\), where \(i=1,2,\cdots,m_{1}\), \(j=1,2,\cdots,n_{1}\) and \(i^{*}=1,2,\cdots,m_{2}\), \(j^{*}=1,2,\cdots n_{2}\). Suppose \(m_{1}>m_{2}\) and \(t_{1}<t_{2}\); thus, the horizontal spatial alignment equation for two different frequencies of GPR data can be calculated as  
\[\hat{X}^{m_{2}}(i,j^{*})=\sum_{k=1}^{d}X^{m_{2}}\bigg{(}\varphi\bigg{(}\frac{i (m_{2}+d)}{m_{1}}\bigg{)},j^{*}\bigg{)}\text{sinc}(-d) \tag{1}\]  
where \(X^{m_{1}}\) and \(X^{m_{2}}\) denote GPR data with different sampled frequencies of \(m_{1}\) and \(m_{2}\); \(\varphi(\cdot)\) denotes the rounding function; and \(d\) is a positive integer value.  
Similarly, the vertical spatial alignment equation for different frequencies of the GPR data is expressed as  
\[\tilde{X}^{m_{2}}(i,j^{*})=\sum_{k=1}^{d}\hat{X}^{m_{2}}\bigg{(}i,\varphi \bigg{(}\frac{j^{*}(n_{1}\,+d)}{n_{1}^{*}}\bigg{)}+d\bigg{)}\text{sinc}(-d) \tag{2}\]  
where \(n_{1}^{*}=t_{1}n_{2}/t_{2}\).  
Figure 2: The fusion process of multi-frequency GPR data.
# A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals
## 2 Methods
### Vector Tracking Loops
#### 2.3.3 Vector-Scalar Hybrid Tracking  
[PERSON] [35] was the first to propose leveraging the strengths of both vector and scalar tracking, as shown in Figure 7. In this approach, both the scalar tracking loop (STL) and vector tracking loop (VTL) are implemented. The results from the VTL are used to assist the STL by correcting errors when detected. Building on this concept, [PERSON] [39; 104] enhanced this hybrid approach through weighted tracking, which resulted in smoother satellite signal processing, improving both efficiency and accuracy.  
[PERSON] [14] introduced a method using FLL-assisted VPLL, where the carrier loop generates the carrier frequency via the FLL-assisted PLL method based on the KF, maintaining a locked carrier signal. [PERSON] and [PERSON] [108; 109] later completed the theoretical framework for FLL-assisted VPLL tracking, further demonstrating the advantages of this approach.  
[PERSON] [110] proposed the DU-HTL (Dual Update-Rate Hybrid Tracking Loop), integrating the Dual Update-Rate Kalman Filter (DUKF) and the VFLL. [PERSON] [111] combined the PDR (pseudo-range) algorithm with VT to enhance positioning accuracy, broadening the application range of VT technology. [PERSON] [112] brought innovation to navigation filters by applying Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs) to assist KF-based VT, leading to more robust tracking performance.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Technique** & **Advantages** & **Challenges** & **Representative References** \\ \hline Extended Kalman Filter & Provides baseline for navigation filter; handles non-linearities in measurements. & Sensitive to model errors; requires linearization at each step. & [PERSON] (2011) [105], [PERSON] (2009) [33], [PERSON] (2017) [40] \\ \hline Adaptive EKF & Dynamically adjusts noise covariance; improves adaptability to signal variation. & Increased computational complexity; tuning difficulties. & [PERSON] (2012) [35], [PERSON] (2021) [36], [PERSON] (2021) [37], [PERSON] (2014) [34] \\ \hline Unscented Kalman Filter & No need for Jacobian; better performance in highly non-linear systems. & Higher complexity; increased memory and computation load. & [PERSON] (2022) [38] \\ \hline Graph Optimization & Flexible modeling of dynamic state changes and NLOS effects; scalable to large networks. & Model design is complex; sensitive to constraint inconsistencies. & [PERSON] (2020) [106], [PERSON] (2022) [107] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of navigation filter optimization techniques in vector tracking.  
Figure 7: Hybrid vector-scalar tracking loop methods.  
While several effective hybrid algorithms have been proposed, these methods primarily combine the advantages of scalar and VT (see Table 6). However, challenges remain when tracking in complex environments. Future research should focus on integrating these hybrid methods with emerging technologies, such as deep learning, to further enhance tracking performance.
# GaN-on-Si: Monolithically Integrated All-GaN Drivers for High-Voltage DC-DC Power Conversion
## 5 Discussions  
This article discusses a high-side driver circuit architecture for integrated half-bridge designs, primarily for buck converters, including device characteristics, circuit operation, and implementation issues. One of the advantages of the proposed driver is that it does not require the traditional level shifters typically employed in half-bridge designs. It is  
Figure 8: Overall system efficiency of buck converter and EED driver power consumption versus output power at switching frequency of 1 MHz.  
Figure 9: Overall power loss percentage comparison of an EED\(-\)driven GaN-based buck converter at 1 MHz switching frequency to achieve a maximum efficiency of 90.7%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & [21] & [22] & [23] & **This Work** \\ \hline Architecture & Offline & Asynchronous Buck & Synchronous Buck & Synchronous Buck \\  & Buck & Asynchronous Buck & Synchronous Buck & E-mode \\ Technology & GaN on Si & GaN on SOI & D-mode RF GaN on SiC & GaN on Si \\ Switching frequency (MHz) & 0.262 & 1\(\sim\)2 & 10 & \(>\)=1 \\ Max. output power (W) & 15 & 24 & 10 & 20.5 \\ Max. Efficiency (\%) & 95.6 & 89.8 & 95 & 90.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Buck converter performance comparisons using integrated driver solutions.  
easier for engineers to adjust the delay of input drive signals to avoid possible shoot-through current that can occur in the output power switches, particularly operating at high switching frequencies. The architecture has been validated for a buck converter with maximum output power around 20 W, and efficiency achieves about 90% over a wide load range. From the above results, there remains some areas for improvement in this architecture. First, the EED/ED driver still has leakage issues when the high-side power switch is turned off, making the overall power consumption about 0.6 W. This can be improved from a power consumption perspective, resizing the driver and reducing losses in the EED/ED driver. Secondly, the package selection can be further studied for higher power applications to avoid thermal problems and thereby increase the output power of the overall system.
# 3 GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach
## 1 Introduction and Literature Review
The next step of this study is to utilize the resulting knowledge and to draw conclusions, which will be used to create a coding/decoding scheme based on machine learning (ML), where the ML coding scheme will learn using data from the optimal coding/decoding. This scheme will consider both the FER performance and QoS parameters for every possible SNR value in order to achieve optimal overall performance in dynamic 5G V2X communication environments.
# Malware Variants Detection Model Based on MFF-HDBA
## 6 Experimental Results and Discussion  
As the number of malware variants continues to multiply, they pose a significant threat to network security. This paper proposed an improved vision-based intelligent malware detection method. The proposed method is divided into two parts: data preprocessing, and feature extraction and classification. The generated malware images are normalized via bilinear interpolation algorithm, and data augmentation is utilized to resolve the problem of imbalanced malware data sets. With this lightweight design idea, a novel CNN structure was proposed that combined with multi-scale feature fusion and channel attention mechanism to enhance feature extraction capabilities. In addition, we proposed a hyperparameter optimization algorithm, named HDBA, to improve the performance of the model. Experimental results indicated that our model achieved an accuracy of 99.36%, which is higher than state-of-the-art malware detection methods. Moreover, the MTTD of our model was only 9.63 ms with low parameters of 1.11 M. Based on validation on the DataCon malware data set, our model can effectively identify malware variants from real and daily networks.  
Funding acquisition, Y.S.; Investigation, J.W.; Methodology, S.W.; Resources, J.W.; Validation, S.L. and W.H.; Visualization, S.W.; Writing--original draft, S.W.; Writing--review & editing, Y.S. All authors have read and agreed to the published version of the manuscript. This research is supported by the National Science Foundation of China (61806219, 61703426 and 61876189), by the National Science Foundation of Shaanxi Provence (2021 JM-226) by Young Talent fund of University and Association for Science and Technology in Shaanxi, China (20190108, 20220106), and by the Innovation Capability Support Plan of Shaanxi, China (2020 KJXX-065). Not applicable. Not applicable. Not applicable. Not applicable. Data Availability StatementAll data used in this paper can be obtained by contacting the authors of this study. The authors declare no conflict of interest.
# Prediction of Intensive Care Unit Length of Stay in the MIMIC-IV Dataset
###### Abstract  
Accurately estimating the length of stay (LOS) of patients admitted to the intensive care unit (ICU) in relation to their health status helps healthcare management allocate appropriate resources and better plan for the future. This paper presents predictive models for the LOS of ICU patients from the MIMIC-IV database based on typical demographic and administrative data, as well as early vital signs and laboratory measurements collected on the first day of ICU stay. The goal of this study was to demonstrate a practical, stepwise approach to predicting patient's LOS in the ICU using machine learning and early available typical clinical data. The results show that this approach significantly improves the performance of models for predicting actual LOS in a pragmatic framework that includes only data with short stays predetermined by a prior classification.  
intensive care unit; length of stay; predictive modeling; machine learning +
Footnote †: journal: Medical Data Science, Leipzig University Medical Center, 04107 Leipzig, Germany 12 Institute for Medical Informatics, Statistics and Epidemiology, Leipzig University, 04107 Leipzig, Germany 2
## 2 System Model Network  
An HD EH relay network over the different fading environment (Rayleigh and Rician Fading Channel), with the effect of HI, is illustrated in Figure 1. This system model is working in AF mode and PS protocol, in which the source (S) and destination (D) can exchange their signal via the helping Relay (R), as shown in Figure 1. The PS protocol of the system model is plotted in Figure 2. Here, \(T\) denotes the block time for EH and IT processes in the PS protocol. In the first half interval time \(T\)/2, the S simultaneously transfers information and energy to the R with the PS factor \(\rho\in\left(0,\,1\right)\), \(\rho\)\(P\) is used for energy harvesting at the R and \(\left(1-\rho\right)P\) is used for transmitting information to the R node. So, the remaining \(T\)/2 interval time is used for transferring information from the R to the D [17; 18; 19; 20].  
Figure 1: System model.
## 5 Empirical Studies
### System Environments  
The experiment was conducted on a system equipped with an Intel(r) CoreTM i7-6700 HQ CPU and NVIDIA GeForce GTX 960M GPU, with 16.0 GB installed RAM. The operating system installed was Ubuntu 22.04. This setup provides sufficient computational power needed by the FCP2 Vec model. For software dependencies, Python version (V) 3.9.12 was used as the primary programing language. A variety of Python libraries were also utilized, each of a specific version: Pandas V. 1.4.4, Gensim V. 4.2.0, Numpy V. 1.18.5, Matplotlib V. 3.5.2, scikit-learn V. 1.1.1, tqdm V. 4.64.1, using the Pythonic version of Word2 vec from the Gensim [74] open-source library.
# A Risk Assessment Analysis to Enhance the Security of OT WAN with SD-WAN
## 2 Related Works
### SD-WAN-Related Research
#### 2.2.2 SD-WAN Security Related  
The authors in [36] compared two SD-WAN solutions--Flexiwan and Fortinet--to evaluate their cybersecurity mechanisms in response to common attacks such as man-in-the-middle, DoS, and brute force. The paper concludes that commercial solutions provide superior cybersecurity mechanisms and mitigations. On the other hand, the authors in [37] compared various open-source SD-WAN solutions--Flexiwan, OPNSense, and pfSense--and found that while all three provide comprehensive security features, they also have vulnerabilities that can be addressed. The study in [52] (pp. 1981-1986) explored the applications of SD-WAN within an OT network, emphasizing the use of physical isolation, symmetric key encryption, automated data transfer, and VPNs. The authors underscore the importance of network segregation, point out that certain servers and computers contain sensitive information, and highlight the roles of symmetric key encryption and dynamic data routing in automated data transfer.
# Novel Aromatic Estolide Esters from Biobased Resources by a Green Synthetic Approach
## 3 Results and Discussion  
### Synthesis of Ricinoleic Acid by Saponification of Castor Oil  
The ricinoleic acid (RCA) used as bio-based monomer was obtained by a well-known chemical method, saponification of castor oil followed by acidification with concentrated HCl. The fatty acid composition (as methyl esters) of the purified product was assayed bygas chromatography (Figure S1) and the calculated RA content was 88%, similar to that of most commercial products.  
The physico-chemical characteristics of the obtained RCA were determined by standard methods [41], as presented below:  
* Color: intense brown-yellow;
* Melting point: 5.5-6 \({}^{\circ}\)C;
* Boiling point: 246 \({}^{\circ}\)C;
* Density at 20 \({}^{\circ}\)C: 940 kg/m\({}^{3}\);
* Acid index: 186.34 mgKOH/g;
* Iodine index: 85.23 gl\({}_{2}\)/100 g;
* Refractive index at 20 \({}^{\circ}\)C: 1.4694.  
These values of the physico-chemical properties are close to those reported in the literature for RCA obtained by a similar procedure or calculated for pure ricinoleic acid [42]. A more advanced purification of RCA by vacuum distillation (4-5 mm Hg) could not be achieved because of the small differences in volatility compared with the accompanying fatty acids and the ease to undergo, when heated, secondary reactions to form oligoesters (estolides) due to the carboxyl and hydroxyl functional groups and the C = C double bond present in its structure.
# Method for Constructing a Facade Dataset through Deep Learning-Based Automatic Image Labeling
## 7 Conclusions  
This study proposed a method for efficiently constructing facade datasets through the process of collecting facade data from road-view images and automatically labeling them using deep learning. The goal of this study was to automatically label facade images to quickly generate large-scale facade datasets with much less effort than the existing research methods. We tested the possibility of calculating the data required by the facade database to comprehend the design characteristics of the street.  
The semantic segmentation method was used for automatic image labeling and a SegNet model with high performance in terms of accuracy was used. The labeling subjects in this study were walls, windows, entrances, and background. The facade information calculated was the dominant color, secondary color, number of floors, and number of buildings. Six hundred images of CMP database-based training data were used to train the SegNet model-based automatic image labeling algorithm and 30 road-view images were used to test the algorithms. The automatic labeling of the test images showed a result of one per 2.31 s. Automatic labeling and facade information calculation showed results of 0.54, 0.75, and 0.87 for mIoU, pixel accuracy, and accuracy of facade information, respectively.  
This study confirmed that the computer could recognize the visual information of the facade image and calculate meaningful facade information of the road-view images at a high speed. In addition, the results of the automatic labeling and facade information analysis were analyzed using the method of calculating the facade information proposed in this study. Detailed criteria for the required mIoU and pixel accuracy values based on the characteristics of the data will be prepared and applied in further studies to increase the facade information accuracy calculation.  
**Author Contributions:** [PERSON] conceived experiments, analyzed data, and wrote papers; [PERSON] investigated prior research and edited thesis; [PERSON] supervised the research. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 22 AATD-C163269-02). This research is a basic research project in the field of Ph.D. student research incentive project that was conducted with the support of the Korea Research Foundation with funding from the government (Future Creation Science) in 2021. Assignment number: NRF-2021R1A6A3A13045849.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interest or personal relationships that could have influenced the work reported in this study.
## 3 Key Technologies in Trust Management for WSNs
### Approaches to Extracting Trust Evidence
#### 3.3.2 Extracting Packet Receiving Rate  
Among the models we reviewed, only two models used the receiving rate of data packets or control packets [15; 35]. Both models used the packet receiving rate to calculate the packet loss rate. The extraction of the packet receiving rate was performed by tracking the packets received by the node during direct interactions.  
We propose that the packet receiving rate can be used to detect attacks that attract traffic, such as blackhole and sinkhole attacks. The approach can be summarized as follows. The sensor node works in promiscuous receiving mode. By analyzing the destination address and type of each received packet, the node to which the data or control packet was sent can be identified, and the corresponding count can be increased accordingly.
## 4 Experimental Datasets and Evaluation  
### Datasets  
#### 4.1.1 Building Image Dataset of Typical Rural Areas in China  
To solve the problems of insufficient sample data and large regional differences in rural buildings, this study constructed an open-source dataset of typical rural buildings in China for training rural building automatic extraction models based on deep learning. Considering the vast territory and complex diverse landforms in China, rural buildings in different regions have significant differences. This dataset selected sample images from nine geographical regions, including the Northeast Plain, North China Plain, and Yungui Plateau, representing the main geomorphological types, climate characteristics and regional architectural styles in China, ensuring the comprehensiveness and representativeness of the samples [31]. All samples of this dataset come from 2020 Google remote sensing images with a resolution of 0.29 m, clearly reflecting the details of rural buildings. Based on the manual drawing of fine annotations containing building outlines and categories, the diversity of samples is enriched through rotation, flipping and other augmentation means, which can effectively enhance the robustness of models and prevent overfitting problems [34], making up for the overall shortage of rural building samples. This dataset contains 13,428 images and annotations, of which 10,742 were randomly selected for model training and 2686 for testing. Figure 6 shows an original image and its corresponding label, where black represents the background and red represents the building.
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 6 Numerical Results and Discussions
### Application Case  
To illustrate the application of our energy model, the considered use case is the monitoring of high voltage electrical network pylons. The purpose of this application is to measure the pylon movement. The system composition is depicted in Figure 7. The whole hardware/software system is powered by a battery. The sensor unit is composed of an accelerometer which measures the pylon deviation and sends data measurements to the processing unit. The processing unit will retrieve the acceleration measurements and it will do the necessary treatment. Then, the LoRa transceiver ensures the data transmission to the corresponding gateway.  
Table 4 depicts the main parameters of this application.  
In summary, the communicating sensor realizes periodical measurements of the acceleration. After the sensing phase, data frames are sent to the access point using LoRa technology. Then, energy consumption of LoRa Class A will be discussed taking into account the acknowledgment transmission.