esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Quality Control Algorithms for GPS Observations
### Satellite Elevation and Dilution of Precision (DOP)
Several _DOP_ factors are calculated as follows [11]. _GDOP_ (Geometric Dilution of Precision): \(GDOP=\frac{\sqrt{\sigma_{n}^{2}+\sigma_{e}^{2}+\sigma_{h}^{2}+\sigma_{t}^{ 2}c^{2}}}{\sigma}\) (3) _PDOP_ (Positional Dilution of Precision): \(PDOP=\frac{\sqrt{\sigma_{n}^{2}+\sigma_{e}^{2}+\sigma_{h}^{2}}}{\sigma}\) (4) _HDOP_ (Horizontal Dilution of Precision): \(HDOP=\frac{\sqrt{\sigma_{n}^{2}+\sigma_{e}^{2}}}{\sigma}\) (5) _VDOP_ (Vertical Dilution of Precision): \(VDOP=\frac{\sigma_{h}}{\sigma}\) (6) _TDOP_ (Time Dilution of Precision): \(TDOP=\frac{\sigma_{t}}{\sigma}\) (7)where \(\sigma\) is usually taken to be equal to the total user equivalent range error, and \(\sigma_{n}^{2}\), \(\sigma_{e}^{2}\), \(\sigma_{h}^{2}\) and \(\sigma_{i}^{2}\) are the corresponding variances of position in northing \(N\), easting \(E\), height \(H\) and time \(T\) on the local geodetic coordinate system, respectively [11]. The case of GPS point positioning, which requires the estimation of four parameters (3-D position and receiver clock error), the most appropriate _DOP_ factor is the _GDOP_. _GDOP_ can be interpreted as the reciprocal of the volume of a tetrahedron that is formed from four satellites and the receiver positions. Hence, the best geometric situation for point positioning is when the volume is a maximum, which therefore requires _GDOP_ to be a minimum. Generally, if the _GDOP_ rises above 6, the satellite geometry is not desirable [12].
## 5 Evaluation of the Security Aspects of the Designed Protocol
### Informal Security Evaluation of the Suggested Protocol  
5.1.1. Reply Attack  
Among the frequently occurring threats in network platforms, the reply attack stands out. Within this context, the attacker in the network seeks to intercept and capture the packets being exchanged. These intercepted packets are then retransmitted through the network over different time periods. The process of retransmission and the transmission delays can be started by either the sender or the network nodes. Therefore, it is crucial to have robust security measures to prevent such attacks. In the proposed protocol, two key factors have been incorporated to thwart such attacks: message freshness verification and the utilization of the numbers that are chosen randomly. As outlined in the protocol process description, parties of communication initially verify that the received message is fresh. the evaluation is done based on network rules that define the permissible time period for network messages (\(\Delta\)T = \(\mid\)T\({}_{2}\) - T\({}_{1}\) \(\mid\)). The freshness of the message is the permission to continue the process of the protocol, otherwise the entities will terminate the established connection. Additionally, the parameters generated in each communication session are combined with random numbers, which makes the values generated in each session unique. This uniqueness of the parameters helps the protocol to be safe against attacks such as repeating messages and DOS and maintains the security of the parameters. Consequently, upon receipt of repeated messages or those outside the specified time frame, network entities will terminate the communication and prevent a potential adversary from launching an attack.
## 4 Conclusions  
In this study, we presented a novel image feature for the RUL prediction based on the CWTCNN-HI. In the proposed method, the vibration signal was converted into the 2-D image feature using CWT instead of using the raw vibration signal or extracting multiple statistical features. The image features of the wavelet power spectrum clearly visualize the degradation process of the bearing. 2-D CNN analyzed the image by including information in both the time and frequency domains. It extracted features from the image and constructed the HI without prior knowledge of feature engineering and signal processing. The CNN model was used as a regression model to estimate the HI between 0 and 1. The estimated CWTCNN-HI was used for the RUL prediction. Since the failure type of the bearings is not defined, the bearings with similar degradation patterns are clustered to set the threshold and predict the RUL. This study has experimentally proved that the image feature can estimate the HI reflecting the condition of the bearings and improve the prediction performance of the RUL. Experiments were conducted on the popular PRONOSTIA bearing dataset to validate the effectiveness of the proposed method. As a result, CWTCNN-HI obtained the highest score and the lowest error among the comparative studies. The CNN model automatically extracted, selected, and fused features that reduce the prediction error of the HI from the image features. Therefore, the result demonstrates that using the CWT-based image features and the CNN model is accurate and effective in the RUL prediction.  
However, there are still some limitations in the proposed method. In this study, the threshold for each cluster of bearings was defined empirically. In future studies, thresholds will be defined more systematically and mathematically based on the large amounts of training data. In addition, the RUL will be determined by interaction between bearings and the other components in the system. By doing so, we will improve the reliability of the HI by reflecting the correlation between bearings and other parts in the future. The HI based on multisensory signals will also be studied by considering temperature and vibration sensor data.  
[PERSON] proposed the idea and carried out the experiments. [PERSON]-[PERSON] validated the proposed method and guided the research. All authors read and approved the final manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2016R1A2B4013678). This work was also supported by BK21 Plus program (Big Data in Manufacturing and Logistics Systems, Korea University) and by Samsung Electronics Co., Ltd.  
The authors declare no conflict of interest.
## 4 Discussion and Conclusions
### Prototypical Networks  
Prototypical networks [37] are a subcategory of the embedding learning models. Prototypical networks are used mainly for classification tasks, in both few-shot learning and zero-shot learning scenarios. These neural networks learn a metric space from the data, and then the classification is performed by computing distances to the prototype representations of each class, which are M-dimensional representations of each class cluster, based on an embedding function. They are computed by averaging the embedding vectors of all the training samples of a class (i.e., the neural network features predicted for the input data):  
\[v_{k}=\frac{1}{N_{s}}\sum_{i=1}^{N_{s}}f_{\phi}(x_{i}), \tag{1}\]  
where \(v_{k}\) is the prototype of each class, \(f_{\phi}\) is the embedding function, and \(x_{i}\) are the support images. The next step consists of classifying the query images. This is performed by computing the distance between each image and the prototypes:  
\[p_{\phi}(y=k|x)=\frac{exp\big{(}-d\big{(}f_{\phi}(x),\ v_{k}\big{)}\big{)}}{ \sum_{k^{\prime}}exp(-d\big{(}f_{\phi}(x),v_{k^{\prime}}\big{)})} \tag{2}\]  
During training, the loss is computed using:  
\[J(\phi)=-log\ \big{(}p_{\phi}(y=k|x)\big{)} \tag{3}\]  
where \(k\) is the true class.
# Context-Based Dynamic Meshed Backhaul Construction for 5G Heterogeneous Networks
## 3 SDN-Based Implementation As A Proof-Of-Concept  
A testbed is constructed to verify the effectiveness of our proposed scheme. The testbed architecture is explained in Section 3.1. Using the testbed, several experiments are conducted. First, Section 3.2 shows that the testbed validly works in real environment with its resiliency functionality. In the next step, we apply the proposed dynamic mesh construction method to the testbed in Section 3.3 and preliminary results are achieved to demonstrate the effect of route multiplexing of the proposed algorithm against intensive traffic. It should be noted that due to the limitation in the scale of the testbed, measurements on the effect of reducing power consumption through dynamic ON/OFF remain as our future works.
# A Context-Aware Neural Embedding for Function-Level Vulnerability Detection
## 1 Introduction
It can also be fine-tuned on domain-specific data [15], which means that domain transfer can be achieved. We evaluate five mainstream word embedding techniques, demonstrating that the code representations produced by the ELMo module outperformed the selected embedding models on our software source code data sets for vulnerability discovery. The structure of our proposed framework consists of the ELMo module followed by a bidirectional long short-term memory (Bi-LSTM) network for converting source code functions to vector representations indicative of vulnerable patterns. The proposed framework inherits the capabilities of the ELMo model, being able to learn meaningful representations of source code tokens based on code semantics. It is also capable of capturing long-term contextual dependencies associated with the source-sink patterns, which is the key for vulnerable patterns recognition. Experiments showed that our framework could be easily integrated with many conventional ML algorithms and is capable of capturing vulnerable semantics for detecting vulnerabilities. In summary, our contributions are three-fold:  
* We compare the performance of five mainstream word embedding models, including Word2 Vec, GloVe, FastText, ELMo, and bidirectional encoder representations from transformers (BERT) [32]) in the scenario of vulnerable function detection. We demonstrate that among the selected NLP embedding solutions, ELMo is the most suitable one for generating code embedding for vulnerability detection when there are not many code samples available for pre-training. * Based on the evaluation of embedding solutions, we design a deep neural network built on top of the ELMo module to capture code semantic that is context aware and is capable of learning the long-term dependencies, reflecting potentially vulnerable source-sink patterns in the source code. * On top of the ELMo-based neural model, we develop a framework for transforming source code functions to meaningful embeddings optimized for vulnerable function detection, without the need for other code analysis tools. A performance evaluation is carried out to examine the effectiveness of the proposed framework with four baseline systems. The results confirm that the proposed framework achieved state-of-the-art performance. The rest of this paper is organized as follows: Section 2 describes how the code-embedding framework for vulnerability detection is designed for capturing the vulnerable patterns in details. The experiments for the evaluation of the proposed framework are presented in Section 3. Section 4 lists the related studies, and Section 5 concludes the paper.
# The Current State and Future Potential of Microbial Control of Scarab Pests
## 8 Viruses
### Orytes Nudivirus  
The _Oryctes_ virus (_Rhudovirus oryctes_; OrNV) is a rod-shaped non-occluded virus in the family of Nudiviruses, which are closely related to baculoviruses [229]. In one of the most successful examples of viral biocontrol, OrNV was used against the rhinoceros beetle (_O. monocerus_), a scarab species that infests coconuts and other palms throughout the South Pacific [35]. In the Palau Islands, rhinoceros beetle infestations were reported to have destroyed as many as 50% of coconut palms in 10 years after introduction [230; 231]. The discovery and use of the OrNV has provided the only effective control option for growers in these areas. The virus was initially found in infected grubs from oil palms in Malaysia [232] and adult inoculation programs were carried out in 10 India-Pacific Island nations throughout the 1970s. Early inoculation trials with grubs quickly determined that adults were highly susceptible, averaging a 40% infection rate, and able to spread the virus quickly and effectively to new areas [35; 231].  
OrNV has been found to infect several other scarab species, including _H. arator_, _C. zealandica_, _Adorophorus coulina_, and _Pericoptus_ species, but no non-scarab insects [233]. After ingestion, the virus initially infects midgut epithelial cells but largely proliferates in the fat body. Days after infection, grubs stop eating, their abdomen becomes turbid and distended, and they display severe diarrhea. The virus is spread effectively through contact with infected feces. Mortality occurs between 6 to 30 days post-infection [231; 232].  
The OrNV is effective in producing epizootics and reducing local populations. In fact, the success of the biocontrol programs in the South Pacific is largely due to OrNVs ability persist and recycle locally, which has reduced the need for additional financial inputs into the program. In 2012, an OrNV strain was implicated in an epizootic in Japanese rhinoceros beetle (_Allomyrina dichotomous_) populations throughout Korea. It was presumably introducedto the area, though its source has not been identified [234]. Although local inoculations are still performed in the South Pacific, OrNV is not commercially available for insect control.
## 4 SIP Protocol Analysis
### SIP Request  
SIP Requests have a request-line that shows request type. As shown in Figure 5, the SIP request messages are used in three different call flows: registration, call and message. The SIP request messages are mentioned below, as follows [43];  
* **REGISTER**: Message used for subscriber registration.
* **INVITE**: Message used for initiating call session.
* **OPTIONS**: Request used to query Server capabilities
* **MESSAGE**: Transport instant messaging message.
* **SUBSCRIBE**: Subscribes to event notification
* **REFER**: Asks a recipient to issue a SIP request
* **UPDATE**: Modifies state of a session without changing the state of the dialog and notify the subscriber of a new event.
* **CANCEL**: Cancels any pending requests.
* **PUBLISH**: Publishes an event to the server.
* **PRACK**: Provisional acknowledgment.
* **BYE**: used in call termination.  
Our focus in this paper is settled on call drops and failures. Therefore, we only chose SIP request types that are present in Mobile Originating (MO)/Mobile Terminating (MT) call flows (highlighted red in Figure 5). We find that **Invite**, **PRACK**, **CANCEL** and **BYE  
Figure 4: IMS architecture.  
SIP requests are present in any successful or failed call. Meanwhile, **Refer**, **Notify** and **Subscribe** SIP requests are present in any successful or failed conference call, specifically [45]. Therefore, we will only monitor these SIP requests.
# A Cross-Medium Uplink Communication Model Merging Acoustic and Millimeter Waves
###### Abstract
Further research and technological optimization are needed to promote the application of TARF technology in these critical areas and overcome these limitations and challenges. Model building is an effective approach for understanding physical processes and communication systems [23; 24]. In the context of surface wave modeling in water, significant contributions were made by [PERSON], who derived a linear control equation for small amplitude wave theory. This was further explored by [PERSON] and [PERSON], who investigated the initial value problem associated with the general linear water wave equation. In 2020, the Ocean College of Zhejiang University developed an end-to-end channel theoretical model based on linear microamplitude wave theory, which was validated through low-frequency experiments [25]. However, despite some progress being made in the investigation of this communication system, the physical processes of the system remain relatively understudied, and there is a notable absence of a corresponding channel model for assessing its communication performance metrics. In this paper, a signal-to-noise ratio (SNR) channel model for evaluating the performance metrics of communication systems was successfully constructed, which was rigorously validated through experimental verification. The main contributions of this paper can be summarized as follows:  
1. The SNR channel model of TARF was proposed for the first time, and the corrections for multipath effects, box vibrations, and a second-order nonlinear coefficient for amplitudes of the water surface wave were completed;
2. The experimental validation of the SNR ratio channel model was conducted;
3. A water-to-air cross-medium communication system was constructed, and a series of experiments were conducted. The remainder of this paper is structured as follows: Section 2 provides a comprehensive exposition of the physical process of bridging acoustic waves and millimeter waves. It elaborates on the construction of the SNR channel model and presents the experimental validation. In Section 3, a series of communication experiments are described, along with the proposal of a stable and effective filtering algorithm. Furthermore, the utilization of a sliding Fourier transform approach to mitigate intercode errors is elucidated. Section 4 concludes the paper by summarizing the key findings and discusses the scope for future work.
# Further Advances in Atrial Fibrillation Research: A Metabolomic Perspective
## 2 Application of Metabolomics in Atrial Fibrillation
### Blood-Derived and Heart Tissue Biomarkers Analysis in Atrial Fibrillation
It was observed that amino acid metabolism was altered in patients with AF, indicating that amino acid levels could be a potential diagnostic marker of AF. In addition, the association between the amino acids measured and the clinical characteristics shown by the patients was evaluated. The results showed a significant correlation between amino acids and cholesterol levels or coagulation in some patients. Furthermore, the circulating level of 4-hydroxypyrrolidin-2-carboxylic acid decreased in the presence of persistent AF. All of this indicates the importance of the metabolic profile in AF, suggesting a therapeutic strategy of amino acid supplementation as a potential treatment for the disease. [PERSON] et al. [16] performed a combination of metabolomics and proteomic analyses to identify potential biomarkers and related metabolic networks in AF. They found that 24 metabolites, 16 lipids, and 16 proteins were significantly dysregulated in AF patients. Specifically, purine metabolic pathway and fatty acid metabolism were perturbed by AF onset. [PERSON] et al. [17] studied the shifts in gut microbiome and metabolome associated with risk of recurrent AF after percutaneous radiofrequency catheter ablation, which represents an important treatment strategy for AF patients. Compared with non-AF controls, gut microbiota composition and metabolomic profile from serum and feces samples were significantly altered between patients with recurrent AF and non-recurrent AF group. Finally, a multiomics study was performed by [PERSON] et al. [18] to identify potential biomarkers for chronic AF in mitral valve disease. They found 57 proteins and 55 metabolites differentially expressed between AF and sinus rhythm patients. The authors conclude that some identified proteins and metabolites may be futher developed as biomarkers for mitral valve disease-associated AF.
## 2 Materials and Methods
### Computation Methods  
The real energy input (E\({}_{\text{wpT}}\)) was sourced from measurements made with the Orno OR-WAT-435-watt meter (Elmak Ltd., Poland). The energy output (E\({}_{\text{CH4 out}}\)) generated from methane production was calculated using the following equation:  
\[\text{E}_{\text{CH4 out}}=\text{Y}_{\text{CH4 out}}\times\text{E}_{\text{CH4}} \times\text{M}_{\text{CODin}}\text{ [Wh]} \tag{1}\]  
where:  
YCH4 out--methane yield [dm\({}^{3}\)/g COD];  
E\({}_{\text{CH4}}\)--methane energetic value (Wh/cm\({}^{3}\)); and  
M\({}_{\text{CODin}}\)--COD mass fed to the respirometer (gCOD).  
The unit-specific energy input (E\({}_{\text{uin}}\)) per COD\({}_{\text{in}}\). mass load was calculated using Equation (2):  
\[\text{E}_{\text{uin}}=\text{E}_{\text{sin}}/\text{M}_{\text{CODin}}\text{ [Wh/gCOD]} \tag{2}\]  
where:  
E\({}_{\text{uin}}\)--unit specific energy input (Wh/g COD);  
E\({}_{\text{s}}\)--the specific energy input [Wh]; and  
M\({}_{\text{CODin}}\)--COD mass (g COD).  
The total energy input to the respirometer (E\({}_{\text{totin}}\)) was calculated using Equation (3):  
\[\text{E}_{\text{totinR}}=\text{E}_{\text{uin}}/\text{M}_{\text{CODinR}}\text{ [Wh]} \tag{3}\]  
where:  
E\({}_{\text{totinR}}\)--unit specific energy input to respirometer (Wh);  
E\({}_{\text{uin}}\)--unit specific energy input (Wh/g COD); and  
M\({}_{\text{CODinR}}\)--COD mass fed to the respirometer (g COD).  
The net energy gain (E\({}_{\rm net}\)) was calculated according to Equation (4):  
\[\rm E_{net}=E_{CH4 out}-E_{\rm totink}\ [Wh] \tag{4}\]  
The respirometric measurements allowed for the determination of the rate of biogas production (r) and the reaction rate constant (k). An iterative method was deployed, in which the convergence coefficient (\(\varphi^{2}\)) between the model and the experimental data was assumed at 0.2. It represents the ratio of the sum of the squares of the deviations of the values calculated based on the determined function from the experimental values to the sum of the squares of the deviations of the experimental values from the mean value.
# Laser Spot Centering Algorithm of Double-Area Shrinking Iteration Based on Baseline Method
## 2 Traditional Central Positioning Method  
### Circle Fitting Algorithm  
The laser spot can be regarded as a circle under ideal conditions, and the center of laser spot is the circle's center. However, it will cause the asymmetry of the laser spot distribution, and the obtained spot is often not a standard circle due to the presence of scatter, uneven reflection from the measured object surface, and the influence of the optical system in the actual optical measurement. The laser spot center detection algorithm based on circle fitting uses a circle to approximate the contour of the laser spot according to the principle of least squares, such that the center of the spot, as well as the radius, can be calculated.  
The least squares circle fitting curve is  
\[R^{2}=(x-\overline{x})^{2}+(y-\overline{y})^{2}. \tag{1}\]It can be expanded to obtain  
\[R^{2}=x^{2}-2\overline{x}x+\overline{x}^{2}+y^{2}-2\overline{y}y+\overline{y}^{2}, \tag{2}\]  
where \(R\) is the radius of the circle; \(\overline{x}\) and \(\overline{y}\) are constants. Another expression for the circle is  
\[x^{2}+y^{2}+ax+bx+c=0. \tag{3}\]  
Let \(a=-2\overline{x},\ b=-2\overline{y},\ c=\overline{x}^{2}+\overline{y}^{2}-R^{2}\); then, only the parameters \(a\), \(b\), and \(c\) are needed to find the center and radius of the circle.