esa-sceva/satcom-chunk-collection huggingface dataset:
# Routing Attacks Detection in 6 LoWPAN-Based Internet of Things
## 5 Experiment Setup and Results
### Model Results and Evaluation
#### 5.4.1 Stage One: SIDS Results  
The proposed technique's performance is evaluated using the confusion matrix, with the results for the C4.5 classifier in the first stage shown in Table 7. Table 7 also details the accuracy of each of the classifiers, revealing that the C4.5 classifiers performed significantly better than the rest of the classifiers used in the study. The C4.5 classifier has the highest accuracy, with a score of 93.2609%, followed by Multilayer Perceptron, Naive Bayes, and SVM, in that order.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Classification Algorithm** & \multicolumn{2}{c}{**C4.5**} & \multicolumn{2}{c}{**Multilayer Perceptron**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**Naïve Bayes**} \\ \hline classified as & a & b & a & b & a & b \\ \hline a = normal & 1031 & 69 & 1067 & 33 & 1100 & 0 & 1100 & 0 \\ \hline b = anomaly & 86 & 1114 & 251 & 949 & 416 & 784 & 401 & 799 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix for different classification algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **C4.5** & **Multilayer Perceptron** & **SVM** & **Naïve Bayes** \\ \hline TP Rate & 0.933 & 0.877 & 0.819 & 0.826 \\ \hline FP Rate & 0.067 & 0.116 & 0.166 & 0.160 \\ \hline Accuracy & 93.26\% & 87.65\% & 81.91\% & 82.56\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy in detection using different algorithms.  
Table 8 provides a detailed analysis of the accuracy of the C4.5 decision tree classification. The table breaks down the accuracy of the C4.5 decision tree into more specific categories, which enables a more thorough evaluation of the model's performance.
## 3 Results
### \(foE\) and \(hmE\) Comparison
For Hermanus and Gakona the GNSS-RO \(hmE\) is found in a narrower altitude range than the ionograms, which is particularly noticeable for cases when the ionosonde \(hmE\) altitude is low. As a result, the average \(hmE\) is larger for the GNSS-RO profile, as can be seen in Table 6. There were several GNSS-RO profiles that had an \(hmE\) close to 100 km which drove the average value down, but nearly all of the profiles at this site had a higher \(hmE\) value than the ionogram \(hmE\). The profiles at Fortaleza did not follow this trend, and as a result, the average \(hmE\) difference is lower for this site. As with the \(foE\), a CDF was calculated for the difference between the GNSS-RO and ionosonde values and is shown in Figure 13.  
Figure 11: GNSS-RO—ionogram \(foE\) histograms and CDFs. The red line is the CDF, the blue boxes are relative frequency, the solid black line is the mean, and the dashed black lines are \(\pm 1\) standard deviation. Most of the sites show an average difference around zero MHz except for Gakona which shows a negative bias. \begin{table}
\begin{tabular}{l c c c c c} \hline
**Site** & **Hermanus** & **I-Cheon** & **Gakona** & **Fortaleza** & **Combined** \\ \hline R\({}^{2}\) & 0.64 & N/A & 0.43 & 0.33 & 0.58 \\ Avg. \(\Delta foE\) (MHz) & \(-0.07\) & 0.04 & \(-0.12\) & 0.02 & \(-0.05\) \\ \hline \end{tabular}
\end{table} 
Table 5: The \(foE\) R\({}^{2}\) and average difference for each site and for the combined results. A negative value indicates the average ionosonde \(foE\) is larger than the average GNSS-RO \(foE\), and a positive value indicates GNSS-RO is larger. All sites have an average \(hmE\) difference that was positive, and the standard deviation had a slight negative skew. Of note, the average \(hmE\) difference was roughly the same as the average shifted value at each site, meaning if the \(hmE\) value had also been shifted, the difference would be close to zero. Fortaleza had the widest spread, with two profiles having an absolute difference greater than 20 km. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Site** & **Hermanus** & **I-Cheon** & **Gakona** & **Fortaleza** & **Combined** \\ \hline R\({}^{2}\) & 0.30 & N/A & 0.13 & 0.24 & 0.30 \\ Avg. \(\Delta nmE\) (km) & 2.7 & 6.0 & 7.4 & 1.2 & 3.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The \(hmE\) R\({}^{2}\) and average difference for each site, and for the combined results. Positive values indicate the average GNSS-RO \(hmE\) is larger than the average ionosonde \(hmE\). Figure 12: A comparison of GNSS-RO and ionogram \(hmE\) measurements. The black line is the 1:1 line, and the red line is the linear fit. GNSS-RO \(hmE\) show less variation than the ionosonde \(hmE\) values. Most sites show a positive bias due to the limited variation in GNSS-RO \(hmE\) altitudes.
# Analysis on Target Detection and Classification in LTE Based Passive Forward Scattering Radar
## 3 Target Classification in Passive Forward Scattering Radar
### Evaluation of Target's Baseline Crossing
Figure 11c shows a finer resolution for a moving target with a higher speed, 20 km/h. The 5 m range (blue stars) is concentrated on the right side of the PCA space, while the 10 m range (red triangles) is localized in the middle area and the 20 m range (black dots) is confined to the lower-left area of the principal component space. In the case of 30 km/h, which is illustrated in Figure 11d, it seems that each type of target's baseline has their own area where the black dots (20 m) are localized on the left area, the red triangles (10 m) in the middle area and the blue stars (5 m) are localized on the upper-right area of the PCA space. The results prove that the higher speed of the moving target offers finer clustering on the target's baseline recognition using PCA analysis. This result also reflects the variance information as mentioned in the previous section, which shows that 82% of variance is achieved at the speed of 30 km/h, thus higher speed indicates better target baseline crossing recognition. Figure 11: Location of training data for ground moving target with speed of (**a**) 5 km/h; (**b**) 10 km/h; (**c**) 20 km/h and (**d**) 30 km/h in the PCA space.
## 1 Introduction
### Neural Network  
The neural network model receives digitized data of ECG and TMT-ECG signals and generates proper recommendations as output. The basic features (age, QRS complex, ST segment drop, etc.) in addition to input data lead to the improved performance of the neural network [40; 41; 42]. The deep neural network is an automatic learning process consisting of multiple layers and activation functions from the lower level to higher-level representation data. The automated convolutional neural network is a version of the deep neural network finding application in predicting medical disease. CNN models have been developed to analyze ECG signals to detect arrhythmia conditions [17; 20], intervals of the ECG segment [43], atrial fibrillation (AF) [23], and classes of ECG signals [44] using 4-layer architecture [45; 46], 11-layer architecture [43], 16-layer architecture [44], and 34-layer [5] architecture, respectively.  
The performance of any convolution neural network is assessed by the data behavior. CNNs find wide applications in the area of image processing [47], object recognition [48], and handwriting data classification [49]. In an ECG-related analysis for medication, CNNs are mostly designed for arrhythmia, AF, and MI diseases under a MIT-BIH [17; 35; 40] dataset. Thus far, no major work has been reported (as per the literature surveyed) regarding the application of the CNN model for the prediction of CAD.  
The highlight of the present study for CAD detection involved the incorporation of activation functions for different layers based on detailed analyses of various combinations (for optimum performance). This study proposes the best CNN combinatorial model with end-to-end architecture (from data extraction to effective disease prediction) (Figure 2), focused on ethically approved fresh ECG and TMT-ECG datasets (IRB approved), which were collected from a reputed hospital in a printed image file format. Data extraction was conducted with a hybrid image processing technique and an optimized CNN model was developed thereafter, which was more accurate.
# Performance Analysis of UAV-IRS Relay Multi-Hop FSO/THz Link
## 2 System and Channel Models  
Before analyzing this system, the following two assumptions need to be made:  
* Assume that the UAVs are equipped with advanced stabilization technologies and GPS systems that provide high positional accuracy. This allows us to focus on the effects of environmental factors such as wind and turbulence under controlled conditions.
* Assume that the IRS can maintain a consistent performance level despite changes in the environment. This involves considering that the IRS components are robust enough to handle variations in temperature, humidity, and physical disruptions without significant degradation in performance.  
The IRS-assisted multi-hop FSO/THz link system for UAVs is schematically diagrammed in Figure 1. It consists of an IRS-UAV relay node, a receiving user with a directional antenna, and a transmitting base station. To utilize their complementary benefits, this system combines FSO and THz links, with THz as the secondary link and FSO as the primary link. It improves communication efficiency and reliability by using both hard- and soft-switching strategies to ease the transition between FSO and THz networks.  
Due to the absence of a direct LoS path between the transmitting base station and the receiving user, by equipping UAVs with a hypersurface-based IRS platform and using them as relay nodes, a virtual line-of-sight path can be created. We consider an IRS composed of programmable reflective elements, engineered to tailor communication system performance for specific scenarios. The transmitting base station is equipped with laser transmitters and an equal number of THz antennas, facilitating concurrent FSO and THz signal transmission. These signals are reflected by the IRS's elements at each IRS-UAV relay node, traversing such nodes before reception by the user. The system employs intensity modulation with direct detection and opts for the BPSK modulation scheme. The data, modulated using BPSK, are conveyed over the FSO and THz links and, after relay through the IRS-UAV nodes, are directly detected by the receiving user. Table 1 summarizes all the parameters used in the paper.  
Figure 1: Multi-hop FSO/THz communication system model with UAV-based IRS relays.
# Preconception Health Attitudes and Behaviours of Women: A Qualitative Investigation
## 3 Results
### Attitudes to Preconception Health  
The majority of women felt that preconception was an important time to ensure good health, with regular engagement in physical activity being regarded as one of the most important priorities.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Demographic Details and Characteristics** & **Median (IQR *) or Count (\%)** \\ \hline Median (IQR *) age in years & 33 (36–31) \\ \hline Reproductive status & \\ Preconception * & 7 (46.7) \\ Pregnancy & 7 (46.7) \\ Postpartum * & 1 (6.7) \\ \hline Country of birth & \\ Australia & 13 (86.7) \\ Other & 2 (13.3) \\ \hline Language other than English & \\ Yes & 1 (6.7) \\ No & 14 (93.3) \\ \hline Location of residence & \\ Major city & 12 (80.0) \\ Inner regional & 3 (20.0) \\ \hline Previous pregnancy & \\ Yes & 5 (33.3) \\ No & 10 (66.7) \\ \hline Previous births & \\ Yes & 4 (26.7) \\ No & 11 (73.3) \\ \hline Number of children & \\
0 & 11 (73.3) \\
1 & 4 (26.7) \\ \hline Employment status & \\ Full time paid & 12 (80.0) \\ Part time/casual paid & 3 (20.0) \\ \hline Post-school education & \\ Certificate & 1 (6.7) \\ Bachelor degree & 8 (53.3) \\ Graduate diploma/graduate certificate & 1 (6.7) \\ Postgraduate degree (Masters/PhD+) & 5 (33.3) \\ \hline Marital status & \\ Married & 11 (73.3) \\ De-facto & 4 (26.7) \\ \hline Partner’s median (IQR*) age in years & 36 (37.5–31.5) \\ \hline Partner’s employment status & \\ Full time paid & 14 (93.3) \\ Unemployed & 1 (6.7) \\ \hline \hline \end{tabular} Abbreviations: * IQR: Interquartile range. Notes. * Preconception period included women who, at the time of recruitment, were planning to conceive within the next 12 months. * Postpartum period included women who, at the time of recruitment, had given birth within the past 12 months.  
\end{table}
Table 1: Demographic details and characteristics of \(n\) = 15 participating women.  
Maintaining a healthy diet and taking pre-pregnancy supplements, including folic acid, iodine, iron and vitamin D, were also considered important preconception behaviours by the majority of women.  
\"Basically, healthy lifestyle things like exercise and having a base level of fitness and um, and healthy food. Those things were pretty important to me.\"--(P11, pregnant, 37 years old, one child).  
\"I think for me it was really important, I liked going on the multivitamins at you know four months beforehand, because then I'd know that my levels were all worked out and things like that... Because I would hate for something to go wrong in pregnancy and think, oh, what if I could have done something to change that?\"--(P3, pregnant, 30 years old, no children).  
However, some women were predominantly concerned with their fertility and ability to conceive and overall pre-pregnancy health was not a priority.  
\"...healthy pregnancy has not even really occurred to me at this point. I am just like trying to get pregnant.\"--(P7, preconception, 33 years old, no children).
## 5 Discussions  
### Extraction of Ground Surface  
Four parameters are needed in the proposed IMF algorithm, i.e., the pixel size used in the rasterization of point cloud, the window size used in median filtering, the threshold of vertical distance to the template, and the number of iterations. The requirement for pixel size is not rigorous. The ranges from 1.0 m to 10.0 m are acceptable for stereo imagery with a resolution smaller than 0.1 m, as used in this study. There will be no sufficient number of points for the interpolation if the pixel size is too small, while the small terrain features will be missed if the pixel size is too large.  
The IMF algorithm is also not sensitive to the filtering window size. The ranges from \(3\times 3\) to \(15\times 15\) are all acceptable. The difference between different window sizes is the work efficiency. More iterations are needed for smaller window sizes because most features of forests' vertical structures are retained. A filtering widow size that is too large is not suggested because detailed terrain features will be missed, as in the case of the large pixel size used in the rasterization.  
The threshold of vertical distance to the template is a critical parameter of IMF because points having a vertical distance larger than the threshold are directly discarded. The larger threshold will slow down the filtering process while a smaller threshold will remove the point of the ground surface on the slope. The minimum of the threshold should be the integrated consideration of terrain slopes, pixel size, and filtering window size to guarantee that the threshold is larger than the elevation fluctuation of ground surfaces. In this study, the fixed threshold was used because the elevation dynamics over the coverage area are narrow. In future studies, it would be worthwhile to adjust the threshold adaptively for application over larger areas with broader dynamics of ground surface elevations.  
The last parameter of IMF is the number of iterations. The results in this study showed that the filtering should be stopped when the number of points removed is lower than 1% of the original point number. This parameter, in fact, is not particularly important because the intermediate results can be saved in the filtering process. Users can determine which one is the best result by comparing these intermediate results.
## 3 Proposed Sensor Fusion Framework Model Using PDR and W-Fi Localization Systems
### Wi-Fi Positioning
#### 3.2.3 Proposed Wi-Fi Fusion Algorithm  
The idea of combining Wi-Fi fingerprint algorithm with trilateration algorithm is explained in [43; 44]. The position results from the Wi-Fi fusion algorithm show better position accuracy than individual Wi-Fi position estimation algorithm results. A state dynamic model and a measurement model are used for the Wi-Fi fusion process. The Wi-Fi fusion algorithm uses a simple linear model and it combines the position results for better user position estimation. The Wi-Fi fusion algorithm utilizes the features of LKF, and its implementation is shown in Figure 5.  
Figure 4: Location estimation procedure.  
Figure 5: LKF implementation.  
The unique characteristics of LKF give Wi-Fi fusion results which are free from localization errors and it improves the user position accuracy. The variables used for LKF implementation are shown in Table 1.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 3 Instruments for TD-NIRS
### Other New TD-NIRS Systems
#### 3.3.7 Compact TD-NIRS System Incorporating Devices Used in Telecommunications  
Very recently, [PERSON] et al. [3] developed a single-channel TD-NIRS system using a spread spectrum technique for TOF measurements. As a light source, the system incorporates a low-cost commercially available optical transceiver module, widely used in telecommunications applications. The system can generate an IRF under 600 ps, exhibits good accuracy and low-noise properties, requires very short warm-up times in contrast to conventional pulsed laser diodes, and is very compact compared to traditional TD-NIRS systems.
## 4 Low Complexity Non-Linear Detection
### Two Approaches for Non-Linear Data Detection  
Once the preprocessing of the received signal, and the QR decomposition of the estimated channel matrix, have been completed, the next step is to perform data detection. For accomplishing this task, this work considered the following two non-linear detectors:
## 3 Advantages, Characteristics and Types of Digital Twin
### Advantages of Digital Twin
Remote monitoring and controlling of equipment and systems becomes a necessity in a situation where local access is limited, like during the COVID-19 pandemic when lockdowns have been enforced by governments and working remotely or non-contact is the only viable option [71]. * _Safer than the Physical Counterpart:_ In industries such as oil and gas or mining where the working conditions are extreme and hazardous, the capability of DT to remotely access its physical twin, as well as its predictive nature, can reduce the risk of accidents and hazardous failures. However, DT's advantage of accessing remotely is not limited to preventing accidents. During the global COVID-19 pandemic, not having human contact and in-person monitoring is also a way to guarantee safety. According to a recent Gartner survey, almost one-third of companies are using DT amidst the global COVID-19 pandemic to increase the safety of employees and customers through remote monitoring [72]. * _Waste Reduction:_ Using DT to simulate and test product or system prototypes in a virtual environment significantly reduces wastage. Prototype designs can be probed and scrutinized virtually, under a variety of different test scenarios, to finalize the final product design prior to manufacture. This not only saves on material wastage but also reduces development costs and time to market. * _Documentation and Communication:_ To create a DT, it is important to synchronize data scattered across different software applications, databases, hard copies, etc., which simplifies the process of accessing and maintaining the data in one place [33]. DT enable a better understanding of system reactions and thus it can be used to document and communicate the behaviour and mechanisms of the physical twin [32]. * _Training:_ DT can be used to develop more efficient and illustrative safety training programmes than the traditional one [73]. Before working on a high-risk site or hazardous machinery, operators can be trained using a DT to reduce the dangers, as exposing and educating them about different processes or scenarios will make them confident in dealing with the same situations in person. For example, mining is a high-risk environment where new employees can be trained using DT on machinery operations, as well as how to deal with emergency scenarios [74]. DT can also be a great tool in closing the knowledge gap from experienced workers to newcomers.
# Energy Demand and Supply Simultaneous Optimization to Design a Nearly Zero-Energy House
## 3 Results
### Financial Objective--Sequential Optimization and Energy Supply
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Sequential Optimization**} & \multicolumn{4}{c}{**Integrated Optimization**} \\ \hline  & & & **Relative** & & & & **Relative** & & **Relative** \\  & **Parameter** & **Unit** & **Cost-Optimal Value** & **Frequency** & **Frequency** & **Cost-Optimal Value** & **Frequency** & **Frequency** \\  & & & & **(All)** & **(Neigh.)** & & **(All)** & **(Neigh.)** & **(All)** & **(Neigh.)** \\ \hline  & ResO & [m\({}^{2}\)K/W] & 2.7 & 27\% & 43\% & 1.0 & 51\% & 72\% \\  & TO & [m] & 0.1 & 52\% & 83\% & 0.1 & 74\% & 90\% \\  & ResR & [m\({}^{2}\)K/W] & 1.8 & 16\% & 28\% & 2.0 & 33\% & 46\% \\  & ResS & [m\({}^{2}\)K/W] & 2.7 & 47\% & 64\% & 1.0 & 38\% & 60\% \\  & WT-South & [-] & Double gll, air & 74\% & 81\% & Double gll, air & 37\% & 41\% \\  & WT-EastWest/North & [-] & Triple gll, low-e, air & 49\% & 61\% & Triple gll, low-e, air & 83\% & 88\% \\  & RWT & [-] & Triple gll, low-e, Argon & 100\% & 100\% & Triple gll, low-e, Argon & 100\% & 100\% \\  & GWW & [m] & 2.2 & 63\% & 87\% & 2.2 & 73\% & 89\% \\  & FWW & [m] & 0.2 & 58\% & 73\% & 0.2 & 38\% & 44\% \\  & RWH & [m] & 0 & 74\% & 98\% & 0 & 74\% & 98\% \\ \hline  & N\_coll & [-] & 4 & 33\% & 47\% & 2 & 60\% & 73\% \\  & Slope\_coll & [-] & 45 & 59\% & 66\% & 30 & 41\% & 49\% \\  & Type\_coll & [-] & Excuated tube & 65\% & 55\% & Excuated tube & 92\% & 97\% \\  & Ratio\_st & [l/m\({}^{2}\)] & 50 & 40\% & 41\% & 75 & 60\% & 70\% \\  & N\_PV & [-] & 8 & 32\% & 50\% & 8 & 32\% & 45\% \\  & Slope\_PV & [-] & 30 & 37\% & 45\% & 30 & 25\% & 23\% \\  & Type\_PV & [-] & Polycr. & 59\% & 80\% & Polycr. & 57\% & 67\% \\  & Type\_heat & [-] & Geo HP & 59\% & 66\% & GeoHP & 94\% & 99\% \\ \hline  & Global cost & [kWh/m\({}^{2}\)] & & 915.59 & & & 906.47 & \\  & Solar coverage–heating & [kWh/m\({}^{2}\)] & & 50.04\% & & & 36.40\% & \\ Results & Self-consumption–electricity & [kWh/m\({}^{2}\)] & & 2714.94 & & & 2714.94 & \\  & EP\({}_{\rm norm}\) & [kWh/m\({}^{2}\)] & & 64.87 & & & 76.10 & \\  & EP\({}_{\rm tot}\) & m\({}^{3}\) & & 90.83 & & & 95.47 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of sequential and integrated optimizations with financial objectives.