esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results
### Analyzing the Effects of Cybersecurity and Cyberattack Costs on \(E_{6}\)
#### 5.2.2 The Impact of Rewards for Commitment to Cybersecurity  
We now numerically evaluate the impacts of rewards (\(R_{11}\) and \(R_{20}\)) and profits (\(P_{20}\)) for commitment to cybersecurity. Recall that \(R_{11}>0.1\) is required by (15) for making \(E_{6}\) ESS. Similarly, \(R_{20}>0.05\) and \(P_{20}>0.15\) are required by (16). Figure 6a presents the evolution of population fractions over \(R_{11}\). We can see that the system converges to \(E_{6}=(1,1,0,0)\) when \(R_{11}>0.1\). On the other hand, the population fractions \(x\) and \(z_{1}\) fluctuate when \(R_{11}<0.1\). Figure 6b,c shows that the population fractions \(x\), \(y\), \(z_{1}\), and \(z_{2}\) over \(R_{20}\) and \(P_{20}\) remain constant and equivalent to \(E_{6}\) when \(R_{20}>0.05\) and \(P_{20}>0.15\)  
Figure 4: Population evolution of \(x\), \(y\), \(z_{1}\), and \(z_{2}\) over time. (**a**) Evolution of population fractions over time with initial values \(x(0)=0.05\), \(y(0)=0.1\), \(z_{1}(0)=0.4\), and \(z_{2}(0)=0.5\). (**b**) Evolution of population fractions over time with initial values \(x(0)=0.7\), \(y(0)=0.1\), \(z_{1}(0)=0.3\), and \(z_{2}(0)=0.1\).  
Figure 5: The impact of cybersecurity costs on the ESS \(E_{6}\). (**a**) Evolution of population fractions \(x\), \(y\), \(z_{1}\), and \(z_{2}\) over \(C_{12}\). (**b**) Evolution of population fractions \(x\), \(y\), \(z_{1}\), and \(z_{2}\) over \(C_{20}\).  
respectively. When \(R_{20}<0.05\) and \(P_{20}<0.15\), \(y\) and \(z_{2}\) fluctuate. The evaluation of the profit and reward parameters shows that \(R_{11}>0.1\), \(R_{20}>0.05\), and \(P_{20}>0.15\) satisfy the ESS conditions for \(E_{6}\).
# An Efficient MAC Protocol for Blockchain-Enabled Patient Monitoring in a Vehicular Network
## 3 Proposed Superframe Structures
### Proposed Scheme  
The proposed MAC is specifically designed to meet the adaptive data requirements of patients in vehicular networks. Vehicles are attached with RSU during their journey to form a cluster, and the RSU adjacent to Mining Node (MN) acts as a cluster head. All vehicles attached to this mining node are its member nodes.  
_RSU_ remains a cluster head throughout, and vehicles in its communication areas become its member nodes only for the time during which they are connected with this _RSU_. The proposed communication round comprises multiple sessions, and each session comprises a handshaking period and data transmission phase as shown in Figure 3.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**MAC Protocol** & **Delay** & **Link Utilization** & **Nodes Capacity Improved** & **Duty Cycle** & **Purpose/Procedure** \\ \hline PASAGA & Low & High & No & Conventional & Duty cycle adjustment by prioritizing GTS requests \\ \hline OGTS & NA & High & Yes & Conventional & Optimal GTS allocation by considering their data requests \\ \hline BEST-MAC & Low & High & No & Adaptive & Applied knapsack algorithm to optimally adjust GTS in a session \\ \hline BS-MAC & Low & Low & No & Adaptive & Applied Shortest Job First algorithm \\ \hline ESAD & Low & High & Yes & Adaptive & An algorithm to optimally adjust a duty cycle of the next superframe by considering data requests \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative table between different MAC.  
Figure 3: A communication round of the proposed MAC for mining nodes in vehicular networks.
## 5 Conclusions  
One of the major problems inherent in pseudolite-based positioning systems is the receiver modification problem. Due to this problem, pseudolites cannot be used with general legacy GPS receivers. To resolve this problem, this study proposed a pseudolite-based positioning system that can be used by unmodified legacy GPS receivers. The proposed system uses GPS-simulating pseudolites whose signal can be processed by legacy GPS receivers. The position output of the GPS receiver is then reprocessed using the position information of the simulated GPS satellites and pseudolites.  
Figure 19: **(a)** Estimation results for User #1 (good DOP) **(b)** Estimation results for User #2 (poor DOP).  
Figure 18: **(a)** Skyplot of live GPS satellites **(b)** Local positions of pseudolites and user.  
Several simulation tests were conducted using MATLAB to verify the proposed system. It was shown that the proposed system results in an accurate user position, unlike the biased GPS receiver outputs. Even under poor geometries of pseudolites or simulated GPS satellites, the proposed algorithm successfully obtained users' positions. Because GPS-simulating pseudolites were not available, live GPS signals were used to verify the system. Intentional delays were added to the pseudorange measurements in order to simulate the GPS signals transmitted by pseudolites and received by a local user's receiver. The results show that the algorithm works well for various pseudolite geometries and corresponding simulated GPS satellite geometries.  
This paper proposed a new concept of the pseudolite based navigation to overcome the receiver modification problem of the pseudolite-based positioning systems. The feasibility of the system is shown by test results using simulated data. In the future, proposed system should be tested by physically real data using real GPS-simulating pseudolites and general legacy GPS receivers.  
If this system concept matures, then pseudolite-based indoor or outdoor navigation will be available for many people with GPS receivers in their smartphones. The only other requirement would be an application that reprocesses the output of the GPS receiver.
# Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment
## 1 Introduction
This comprehensive evaluation framework validates the theoretical robustness of the proposed model, affirming its high degree of practicality within real-world SDN environments. The main contributions are:  
Figure 1: DDoS attacks in SDN [3]. * Introduction of OptMLP-CNN: This research introduces a novel DDoS-attack-detection method referred to as \"OptMLP-CNN\". This method combines two critical elements: SHAP-feature selection and a hybrid neural network architecture. The primary goal is to create an optimized and effective DDoS attack detector. * Optimization Using Bayesian and ADAM Optimizers: We optimized the proposed model by applying two optimization techniques: the Bayesian optimizer and the ADAM optimizer. These optimization methods fine tune the model to enhance its performance and effectiveness in detecting DDoS attacks. * In-Depth Analysis of DDoS-Attack-Detection Systems: The research extends beyond introducing a new model by conducting a detailed analysis of DDoS-attack-detection systems. This analysis encompasses systems that incorporate DL techniques and were selected based on specific criteria. The criteria include evaluating and comparing their DDoS-detection performance, the datasets used, optimization methods applied, and the types of systems they are designed to protect. * Evaluation of Effectiveness of Using Public Datasets: This study evaluates the proposed method by applying it to two publicly available datasets. One of these datasets is deliberately constructed to simulate an SDN environment. The results of this evaluation demonstrate that OptMLP-CNN, the proposed method, achieves a high accuracy rate and outperforms other existing methods in detecting DDoS attacks in the context of SDN infrastructure. * Promising Solution for SDN Security: This research's overarching contribution is the presentation of a promising solution for enhancing the security of SDN and addressing the growing threat of DDoS attacks. By introducing and optimizing the OptMLP-CNN method, this study aims to enhance the resilience of SDN environments against DDoS attacks, which have significant security implications in contemporary networked environments. This paper unfolds as follows: In Section 2, an extensive survey of related research is presented, focusing on advancing DDoS detection through utilizing ML techniques. Section 3 explores the foundational concepts and overarching background within this domain. Section 4 introduces a novel model for DDoS detection in SDN, incorporating a feature-selection method and an optimized hybrid DL model. Section 5 presents an in-depth analysis of the proposed model's performance, along with a detailed comparative assessment of the experimental outcomes. Section 6 encapsulates the paper with final insights, synthesizing findings and delineating potential future research directions, providing a holistic framework for advancing DDoS detection in SDN environments.
# Identification of Rotundone as an Important Contributor to the Flavor of Oak-Aged Spirits
###### Abstract  
Experiments were conducted to identify a compound responsible for a _spicy_, _vocody_, _inceselike_ odor note in oak-aged spirits. The target compound was extracted from oak wood and various oak-aged spirits and analyzed by multidimensional (heart-cut) gas chromatography-mass spectrometry-olfactometry (MD-GC-MS-O), and was unambiguously identified as the sesquiterpene ketone, 5-isopropenyl-3,8-dimethyl-3,4,5,6,7,8-hexadydro-1(\(2H\))-azuleno (rotundone). Quantitation of the trace-level target compound was done by stable isotope dilution analysis (SIDA) in a variety of oak-aged spirits, including bourbon, rye, Tennessee whiskey, scotch, rum, and requila. The content of rotundone was found to increase as a function of years of barrel aging for 4-, 8-, and 12-year-old bourbons obtained from the same manufacturer, thus confirming its origin to be from oak. In addition, odor-activity values (OAVs) were compared for selected potent odorants, including rotundone, in the same 4-, 8-, and 12-year-old bourbons, which indicated the relative importance of rotundone in the overall flavor of oak-aged spirits.  
flavor; rotundone; whiskey; gas chromatography-olfactometry; stable isotope dilution Article
## 3 Materials and Methods
### Assumptions and Limitations  
In this section, we discuss the constraints that affect connector mating. The board-to-board connector has certain limitations based on the manufacturing and position of the BtB on the device. Figure 5 illustrates that the X and Y movement is limited to 0.18 mm and 0.23 mm, respectively. This tolerance means that as long as the male BtB connector remains in the range of 0.23 mm in the Y and 0.18 mm in the X direction, the mating will always be successful. Therefore, this study was performed in a controlled environment, ensuring that the connector did not exceed the tolerance range area in any data collection process.  
There was no extra bending in the PCB board, as the experiment was performed at a low speed [14]. The other factor based on the speed is the effect owing to the rate of assembly, which may involve extra force and sudden jerks, causing the force value to increase and damage it. Another assumption is that the entire movement is in the vertically downward direction, and the \(Z\)-axis of the force sensor is on the vertical axis. In this research, we analyzed the data of the \(Z\)-axis of the force sensor as the insertion mating was performed vertically downward by pushing the male connector in the female connector for mating. Thus, our research focuses on processing the collected force data using the encoding approach and predicting it using the CNN layer.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Design Specification** \\ \hline
**Number of Pins** & **50** \\ \hline
**Number of Pins** & 50 \\
**Pitch** & 0.40 mm \\
**Mated height** & 0.80 mm \\
**Total Width** & 2.50 mm \\
**Type** & Surface mount (SMT.) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Board-to-board connector specification.  
Figure 4: The pitch and the width of the 0.4 mm BtB connector.  
Since we needed to collect the force data first as our reference for the successful insertion, this research applies only to insertion applications with reference data (golden sample) to map the data first for the following mating process.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 5 Internetworking Strategies of Marine Internet  
The above discussion shows that none of the reviewed systems alone can provide cost-effective high-speed Internet services in large marine environments. Consider their characteristics summarized in Table 8 and cost-effectiveness as well as reliable communication for safety and rescue requirement in oceans. A large scale cooperative heterogeneous wireless network is proposed for marine Internet [19; 20; 73]. As illustrated in Figure 5, this network mainly consists of integrated coastline networks, various types of WANETs and sky networks as well as underwater networks. These networks try to interconnect each other as much as possible when necessary, as discussed below.
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 3 Method
### Skin Pixel Selection  
The first step in the pipeline includes face finding [40] and fitting an active appearance model (AAM) [28]. This AAM is then used to determine facial landmarks (from the AAM shape vector) as well as the head orientation (by measuring angular deviation from the mean frontal pose). The landmarks are used to define a region of interest (RoI) which only contains pixels on the face belonging to skin. This allows us to robustly track the pixels in this RoI over the course of the whole video. Our RoI consists of the upper region of the face excluding the eyes (determined empirically). An illustration of this can be seen in Figure 1. The head orientation is used to measure and track the pitch, roll, and yaw angles of the head per frame. Across all pixels in the RoI, the averages for each colour channel (R,G,B) is computed and tracked (concatenated) to create three colour signals.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 6 Evaluation Metrics for Localization Techniques
### Accuracy  
The accuracy of the localization algorithm indicates that how closely the estimated location coincides with the actual ground truth location of the nodes. Efficient algorithmsoffer maximal coincidence. Nevertheless, location precision is not only the prevailing objective of every localization scheme, but it is also reliant on application. The structure of node deployment affects the coherence of desired location precision. Location precision is determined by using average location error as defined,  
\[RE=\frac{\sum_{l=1}^{N}\sqrt{\left(u_{l}-\overline{u}_{l}\right)^{2}+\left(v_{l} -\overline{v}_{l}\right)^{2}+\left(w_{l}-\overline{w}_{l}\right)^{2}}}{N} \tag{3}\]  
where \(\left(u_{l},v_{l},w_{l_{{}_{L}}}\right)\) and \(\left(\overline{u}_{l},\overline{v}_{l},\overline{w}_{l}\right)\) are true and estimated positions, respectively.  
Root mean square localization error is determined by using  
\[RMSLE=\sqrt{\sum_{l=1}^{N}\frac{\left(u_{l}-\overline{u}_{l}\right)^{2}+\left( v_{l}-\overline{v}_{l}\right)^{2}+\left(w_{l}-\overline{w}_{l}\right)^{2}}{N_{L}}} \tag{4}\]  
where \(N_{L}\) is a number of nodes localized. Along with location error, corresponding geometry projected by the localization algorithms is of equal importance. [PERSON] et al. (1988) have proven that the few localization algorithms with acceptable average localization error show massive variation in relative geometry precision for the assessed and actual network. Consequently, defined new metric called Global Distance Error (GDE) as given by  
\[GDE=\frac{1}{D}\sqrt{\frac{\sum_{l=1}^{N}\sum_{m-l+1}^{N}\left(\frac{x_{lm}- \overline{x}_{lm}}{x_{lm}}\right)^{2}}{\frac{N(N-1)}{2}}} \tag{5}\]  
where \(x_{lm},\overline{x}_{lm}\) represents the distance between node \(l\) and \(m\) for actual and positions, respectively. \(D\) is the mean transmission range of a sensor node.
# In-Vehicle Data for Predicting Road Conditions and Driving Style Using Machine Learning
## 7 Results  
### Road Surface Conditions Classification Results  
Table 2 shows the accuracy, the precision, the recall and the F-score of the road surface conditions classification and Table 3 shows the confusion matrix of the predictions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Decision Trees** & **Random Forests** & **SVM** \\ \hline Accuracy & 0.954 & 0.983 & 0.94 \\ Precision & 0.974 & 0.99 & 0.942 \\ Recall & 0.975 & 0.984 & 0.941 \\ F-score & 0.975 & 0.989 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Road surface conditions classification results.  
Table 4 shows the top seven important features for road surface detection for the three algorithms. As shown in the results, engine coolant temperature is the most important feature for the three algorithms (decision trees, random forests and SVM). SVM was the only approach to have the longitudinal acceleration as one of the top seven important features for classification.
## 3 Fundamentals
### The Influence of the AAC Basicity
#### 3.1.3 Factor #3: Molecular Weight of a Surfactant  
The subject of research was an AAC with Mb = 1.1 (Mix 3, see Table 1) building mortar (reference composition). The criteria for evaluating the effectiveness of a surfactant were the consistency (flow) and consistency retention time of fresh mortar, as well as the compressive strength of mortar.  
Low-molecular-weight anionic aliphatic compounds (polyols) were used in building mortars, and their high effectiveness in the case of AAC with low basicity was discovered.  
Ethylene glycol (2-atomic polyol) increased the consistency of the AAC mortar mix by 57% (Figure 5a), glycerol (3-atomic polyol) by 26% (Figure 5b), and sorbitol (6-atomic polyol) by 40% (Figure 5c) compared to that of the reference composition.  
Thus, no linear interrelation between the quantity of the OH\({}^{-}\)-groups in a molecule of polyol (molecular weight) and its (polyol) influence on the consistency of the AAC mortar mix could be discovered. Apart from a plasticizing action, the consistency of the plasticized mortar remained constant over time.  
With the dosages of polyols, exceeding the optimal ones (1% by mass of AAC), the strength gain decelerated, though a high plasticizing effect remained (Figure 6).  
Figure 5: Changes in the consistency (flow) of fresh mortar based on the AAC with Mb = 1.1 (Mix 3) vs. the type of admixture (ethylene glycol—blue (**a**), glycerol—red (**b**), and sorbitol—yellow (**c**) and the time after mixing.
# Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
## 5 Experiments and Results
### Tests Results  
#### 5.3.1 UWB Ranging Estimates  
When evaluating the performance of the proposed positioning solution, the first step is to analyse the UWB ranging estimates over the followed trajectory. In order to avoid using NLOS UWB measurements, the UWB ranging estimates are post-processed, as explained in Section 4.2.  
Figure 11 shows the distance error of the UWB ranging estimates before (UWB error (raw)) and after (UWB error (post-processed)) being post-processed over the whole trajectory. As can be seen, applying our post-processing approach, the maximum distance error is reduced from 140 cm to 55 cm.  
Figure 12 shows the value of the post-processed UWB ranging estimates over the whole trajectory. There are some specific areas of the trajectory where we have NLOS measurements as in the areas near  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Channel** & **Fc** & **Bandwidth** & **Preamble** & **Preamble** & **PRF** & **Data** & **Max Mean** \\  & & & **Length** & **Code** & & **Rate** & **Spectral Density** \\ \hline
5 & 6489.6 MHz & 500 MHz & 128 symbols & 3 & 16 MHz & 6.8 Mbps & \(-\)41.3 dBm/MHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: UWB system configuration.  
Figure 11: Distance error of the UWB ranging estimates.  
the RSUs or some UWB signal loss due to the spatial orientation explained in Section 5.2. Nevertheless, the areas without UWB information do not affect the final results since our approach relies on the data fusion of different technologies as it will be shown in Section 5.3.2. Furthermore, it can be said that the post-processed UWB ranging estimates cover almost the whole trajectory with high accuracy and allow us to achieve a good final solution in terms of reliability and availability.
# An Improved CNN Model for Within-Project Software Defect Prediction
## 4 Experimental Setup
### Baseline Methods  
We compared our improved CNN method on the SPSC dataset with the following baseline methods:  
* CNN [24]: This is a state-of-the-art method of deep software defect prediction that extracts AST node information as model input, and it proposes deep models (CNN) combined with word embeddings for defect prediction.
* DBN [23]: This was the first method of deep software defect prediction conceived. It extracts AST node information as model input, and it proposes deep models (DBN) for defect predictions. We used the results from the CNN paper [24] for easy comparison.
* Traditional (LR) [29]: This is an empirical result of traditional software defect prediction, which utilizes 20 code metrics extracted manually in the PROMISE repository as model input, and it proposes logistic regression models for defect prediction. We also used the results from the CNN paper for ease of comparison. It should be noted that the traditional model [29] is not the most suitable baseline to represent traditional machine learning models.  
We then compared our improved CNN method on the PSC dataset with the following baseline methods:  
* Five machine learning models [31]: Five models were trained and validated in the within-version WPDP pattern. These five models were the decision tree (DT), logistic regression (LR), naive Bayes (NB), random forest (RF), and RBF network (NET). These models are the most prevalent machine learning models, which in general represent the top results for WPDP. We excluded the SVM model because it tends to work poorly in empirical scenarios [31].
* RANDOM [31]: This model predicts a source file as buggy at the probability of 0.5. If the RANDOM model beats any model on averaged metrics, it means that the model is worse than random guessing.
* FIX [31]: This model predicts all source files as buggy, which works as a null hypothesis model. If the FIX model beats any model, it means that the model should not be used in practical situations.  
In the original empirical paper [31], no detailed experimental data regarding each version were found. Therefore, we obtained access to the corresponding Github pages where detailed raw experiment data were available. We then filtered the experiment data to fit our PSC dataset and calculated the average results from multiple experiments. The Github website is as follows: [[https://github.com/sherbold/replication-kit-tse-2017-benchmark](https://github.com/sherbold/replication-kit-tse-2017-benchmark)]([https://github.com/sherbold/replication-kit-tse-2017-benchmark](https://github.com/sherbold/replication-kit-tse-2017-benchmark)).