esa-sceva/satcom-chunk-collection huggingface dataset:
# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 2 Modern ECG Signal Processing Techniques for Detection and Prediction of Ventricular Fibrillation
### Machine Learning Algorithms
#### 2.1.2 Unsupervised Methods  
Do Not Require Training Labels and Are Used to Discover Hidden Structures in ECG Data
* K-means Clustering: K-means is a clustering algorithm that partitions the data into k clusters, with each cluster being represented by its center (centroid). The algorithm alternates between assigning each data point to its closest centroid and recomputing the centroids based on the average of the data points assigned to each cluster. The algorithm continues until convergence, where the data points stop changing clusters. An example of application in VF detection is reported in [19].
* Principal Component Analysis (PCA): PCA is a dimensionality reduction method that transforms the data into a new coordinate system, where the largest variations are captured by the first principal components. PCA calculates the eigenvalues and eigenvectors of the covariance matrix of the data, using these components to project the data into a low-dimensional space. The first principal components capture most of the variance in the data, allowing dimensionality reduction without significant loss of information. Examples of works in the field of VF are [20,21].  
Several evaluation measures are used to evaluate the performance of these algorithms, including accuracy, sensitivity, specificity, and F1 score. In general, public and private datasets of ECG signals are used, and cross-validation techniques are applied to ensure the robustness of the results.  
In [13], the performances of SVM, random forest, and KNN in the detection of ventricular fibrillation using an ECG dataset were compared. The results showed that SVM achieved an accuracy of 96%, sensitivity of 95%, and specificity of 97%. Random forest achieved similar values, and KNN had a slightly lower accuracy of around 94%.  
The study [22] aimed to develop and evaluate machine learning models to predict the occurrence of arrhythmia after an acute myocardial infarction (AMI). Three ML models were used to predict the occurrence of tachyarrhythmias after AMI. After variable selection, the artificial neural network (ANN) model achieved the highest accuracy rate, outperforming the model built using the Grace variable set.
## 5 Conclusions
### A Spatial Correlation Analysis of the Space-Born and UAV-Born Vegetation Spectral Indices  
The correlation analysis shows a significant correlation \(\alpha\) = 0.01 ** between all VIs derived from air- and space-born data, however, with different intensity. In particular, the GLI, EVI, SAVI and NDVI VIs stand out, in the cases of which relatively strong correlations were obtained. The best correlations were detected between the GLI index derived from UAV and the GLI index from Planet Scope where r = 0.69; from Landsat where r = 0.50; and from Sentinel where r = 0.66. However, the strength of these correlations indicates that there may be some differences in the information received due to sensor variations or other environmental factors.  
According to the results of the regression model, the best results were recorded for Sentinel, where a higher value of R\({}^{2}\) = 0.39 was recorded, RMSE (0.04) tended to the minimum, and a weak RPD = 1.28 was observed. The application of linear regression model showed that, in such studies, it is more appropriate to use a correlation method to compare data obtained from different sources.
# Recent Advances in Hydrothermal Oxidation Technology for Sludge Treatment
## 2 Results and Discussion
### Research on Influencing Factors of Sludge Treatment by HTO
(2) Longer reaction times are beneficial for increasing COD removal rates, but there exists a threshold beyond which removal rates plateau. (3) VSS removal rates are generally higher than COD removal rates and can reach their peak in a shorter timeframe. This is because COD removal primarily results from the oxidation of organic matter by soluble oxidants, such as gaseous oxygen, which is more affected by gas-liquid mass transfer, while VSS removal mainly occurs through rapid thermal hydrolysis of sludge. (4) Changes in reaction time also influence ammonia nitrogen concentrations, facilitating the conversion of carbon from liquid to gas. NH\({}_{3}\)-N and NO\({}_{3}\)-Nconcentrations initially increase with extended reaction time before gradually declining. These findings indicate that the impact of a changing reaction time is not as significant as that of a changing reaction temperature. Identifying an appropriate reaction time is essential for optimizing sludge treatment efficiency, highlighting the necessity of selecting suitable reaction times to enhance the effectiveness of hydrothermal oxidation technology. (3) Initial oxygen pressure  
The principles of hydrothermal oxidation technology indicate that oxygen supply significantly affects sludge treatment. As oxygen supply increases, the contact opportunities between oxygen and organic matter in the sludge are enhanced, accelerating the reaction
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### Optimal-Cluster-Based Source Anonymity Protocol in Delay-Sensitive Wireless Sensor Networks (OSAP)
In contrast, if the cluster head received a real packet, it re-encrypts and forwards the packet towards the sink after an appropriate time that follows the exponential distribution. However, if there is no real packet, the cluster head sends an encrypted fake packet instead. In case a cluster head received a packet from another cluster head, it forwards the packet after an appropriate time whether it is real or fake without filtration. The total delay of the real event must be less than the maximum required delay by the application. In addition, it is obvious that member nodes at the edge of the network have more cluster heads on the path to the sink, which increases the delay. Therefore, selecting the appropriate \(\upmu\) for each cluster is mandatory to balance the latency between clusters and to avoid any time analysis attacks from the adversary. Finally, balancing the power consumption between clusters is made by adjusting the radius of the unequal clusters and transmission rate. _OSAP_ still has some limitations; it assumes that the sink location is in the center of the network. This assumption is impractical for many applications that require the sink to be at different locations in the network. Another limitation is that the communications within the network are based on the same cluster heads that do not change during the lifetime of the network. Using the same cluster heads every time decreases the lifetime of the network because the cluster heads will consume more power than cluster members causing the network to be imbalanced regarding power consumption. Figure 6: Overview of the unequally clustering in OSAP.
###### Abstract  
This paper presents a novel parameter identification and uncertainty quantification method for flutter derivatives estimation of bridge decks. The proposed approach is based on free-decay vibration records of a sectional model in wind tunnel tests, which consists of parameter identification by a heuristic optimization algorithm in the sense of weighted least squares and uncertainty quantification by a bootstrap technique. The novel contributions of the method are on three fronts. Firstly, weighting factors associated with vertical and torsional motion in the objective function are determined more reasonably using an iterative procedure rather than preassigned. Secondly, flutter derivatives are identified using a hybrid heuristic and classical optimization method, which integrates a modified artificial bee colony algorithm with the [PERSON]'s algorithm. Thirdly, a statistical bootstrap technique is used to quantify the uncertainties of flutter derivatives. The advantages of the proposed method with respect to other methods are faster and more accurate achievement of the global optimum, and refined uncertainty quantification in the identified flutter derivatives. The effectiveness and reliability of the proposed method are validated through noisy data of a numerically simulated thin plate and experimental data of a bridge deck sectional model.  
flutter derivatives; bridge deck; artificial bee colony; bootstrap; weighting factors Article
# Influence of Random Modulated Power Converter on G3 Power Line Communication
## 2 Random Pulse Position Modulation  
In RPWM, one of the switching parameters of the PWM signal, such as switching period, pulse position, and pulse width, are varied randomly in order to spread the noise exiting power converters. Therefore, RPWM can be classified as Random Frequency Modulation (RFM), Random Pulse Position Modulation (RPPM), and Random Duty-Cycle Modulation (RDCM), depending on the parameter, which is made random [17]. Figure 1 shows the RPWM signal, where T' is switching period, '\(\Delta\)' is the pulse position, and 'd' denotes pulse width.  
In order to investigate the properties of RPWM, it is important to consider the Power Spectral Density (PSD) (in particular for RPPM). For conventional PWM, the PSD can be directly determined from the Fourier Series expansion of the PWM signal (1).  
\[S(f)=\sum_{n=-\infty}^{\infty}|a_{n}|^{2}\delta(f-nf_{1}) \tag{1}\]  
or indirectly, by using the autocorrelation/PSD relationship [18; 19; 20]:  
\[S(f)=\int_{-\infty}^{\infty}R(\tau)e^{-i\omega\tau}d\tau \tag{2}\]  
where \(S(f)\) is the PSD of the PWM signal, \(|a_{n}|\) is the magnitude of the Fourier Series coefficients, \(f_{1}\) is the switching frequency, and \(R(\tau)\) is the autocorrelation of the switching signal, respectively.  
However, it is not straightforward to apply the above formulas to find the PSD of RPWM signals, because they are random and can be described only by a probabilistic level using the theory of stochastic processes such as Wide-Sense Stationary (WSS) random processes.  
The PSD of a RPPM is derived in [20] by assuming the constant switching period and pulse width of the RPWM shown in Figure 1.  
\[S(f)=\frac{1}{T}|U(f;d)|^{2}\Bigg{[}1-\Big{|}E\{e^{-i\omega\Delta}\}\Big{|}^{2 }+\frac{1}{T}\Big{|}E\{e^{-i\omega\Delta}\}\Big{|}^{2}\sum_{n=-\infty}^{ \infty}\delta\Big{(}f-\frac{n}{T}\Big{)}\Bigg{]} \tag{3}\]  
Figure 1: Random switching signal.  
where \(U\left(f;d\right)\) is the Fourier transform of the sampling pulse \(u(t)\):  
\[u(t)=\begin{cases}1,&\text{for }0\leq t\leq d\\ 0,&\text{otherwise}\end{cases} \tag{4}\]  
\(E[e^{-j\omega\Delta}]\) is the expectation of the probability density function of the random pulse position, which depends on the distribution of the random number, and \(\omega=2\pi\)_f_.  
As an example, the PSD of RPPM with 5 kHz switching frequency and 0.5 duty ratio is shown in Figure 2. In this case, a uniformly distributed random number is used to vary the random pulse position. Of course, the shape of the spectrum may change if the distribution of the random number changes, e.g., if a normal distribution is used. However, the spectrum always exhibits both the density and the harmonic part, unlike other modulation schemes. For instance, the RFM spectrum only exhibits the density part [20]. This makes RPPM less effective compared to RFM in minimizing the CE of power converters. Nevertheless, this property will play a significant role in making RPPM more compatible than RFM with communications systems.
## 3 Simulation Results
By deploying many pico-cells (200 pico-cells), the percentage of outage users reduces to 7% and 6.1% of user devices in the macro-cell for the path-loss models. Using Equation (6), the energy consumption for data collection can be modeled using our simulator, based on how many samples have been collected by the acquisition hardware and the reporting requirements for the control center. As an example, in Figure 7 we analysed the NB-IoT battery lifetime for transmitting short data packets of 50 bytes and 200 bytes versus the number of reporting intervals per day for different values of the maximum coupling loss (MCL) based on [5]. From the figure it can be seen that battery lifetime for NB-IoT user devices can be increased by the shortening the data packets and by reducing the number of reporting times per day. For example, the lifetime of the battery for a single data transmission of 50 bytes per day with an MCL of 164 dB is 20 years, while the lifetime of the battery will reduce to 15 years for one transmission per day of 200 bytes with the same MCL. One way to shorten the data packets is using lossless compression techniques described in [17]. Results for this approach making use of a realistic testbed system based on Raspberry Pi computers is described in the next section. Evaluation of the required energy consumption for data compression in real hardware is necessary to count the energy consumption of user devices which can significantly impact the battery lifetime of NB-IoT devices. Compression of NB-IoT data packets, in addition to increasing lifetime of the battery, can reduce the latency and increase reliability through using smaller data packets at a particular time. The energy consumption and compression of data is very important especially if the UE acts as a D2D node to extend the coverage of cellular IoT BS to outage users. By compressing data packets and the reducing the reporting interval to once or twice per day we can successfully increase the battery lifetime of MTC devices. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No. of Small Cells** & **20** & **60** & **100** & **140** & **180** & **200** \\ \hline \% Average Outage Users & 28 & 28 & 28 & 28 & 28 & 28 \\ \hline Equation (3) Femto Cell & 24.6 & 18.7 & 14.41 & 11.2 & 8.4 & 7.3 \\ \hline Equation (3) Pico-Cell & 23.7 & 18.4 & 14.1 & 10.7 & 8.4 & 7 \\ \hline Equation (4) Femto Cell & 23.6 & 18.3 & 13.9 & 10.5 & 8.2 & 6.8 \\ \hline Equation (4) Pico-Cell & 23.4 & 17.7 & 13.2 & 9.8 & 7.5 & 6.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results from Equations (3) and (4) for Femto and Pico-cells deployment and remaining users in Outage in each scenario.
# A Dual-Gas Sensor Using Photoacoustic Spectroscopy Based on a Single Acoustic Resonator
## 3 Results
### Simultaneous Measurement of CH\({}_{4}\) and NH\({}_{3}\)  
The performance of the PA instrument was evaluated for simultaneous measurement of CH\({}_{4}\) and NH\({}_{3}\), especially the potential interference from NH\({}_{3}\) and CH\({}_{4}\) on each other. The interference between NH\({}_{3}\) and CH\({}_{4}\) was observed through altering the concentrations of CH\({}_{4}\) and NH\({}_{3}\) that were controlled by MFCs. The input channel of the PA cell was composed of channels A and B, which always had identical flow rates, leading to a constant CH\({}_{4}\) concentration in the PA cell. Meanwhile, channel B for NH\({}_{3}\) was connected to two MFCs, which controlled the flow rates of channels 1 and 2 for NH\({}_{3}\) and N\({}_{2}\), respectively. The NH\({}_{3}\) concentration in the PA cell could be altered by changing the flow rate in channels 1 and 2.  
For investigating the interference of NH\({}_{3}\) in CH\({}_{4}\) measurement, a constant CH\({}_{4}\) concentration of 50 ppm was generated with the same flow rate of 0.15 L/min in channels A and B. Different NH\({}_{3}\) concentrations of 50, 100, 180 and 220 ppm were obtained by changing the flow rate in channels 2 and 3. Figure 8a shows the measured CH\({}_{4}\) concentrations with different NH\({}_{3}\) concentrations. The dashed line presents the fluctuation of the measured CH\({}_{4}\) concentration without taking into account the interference of NH\({}_{3}\), while the retrieved CH\({}_{4}\) concentration remains constant after eliminating the NH\({}_{3}\) interference as mentioned in Section 2.3.  
Figure 8a shows the CH\({}_{4}\) interference in the measurement of NH\({}_{3}\) by recording the fluctuation of NH\({}_{3}\) concentration through altering the CH\({}_{4}\) concentration. As shown in Figure 8b, the measured NH\({}_{3}\) concentration is constant across different CH\({}_{4}\) concentrations, which shows that different CH\({}_{4}\) concentrations cannot affect the measurement of NH\({}_{3}\).  
Figure 7: (**a**) Combination of NH\({}_{3}\) and CH\({}_{4}\) PAS under one scan cycle of DFB laser diodes. The purple line is 100 ppm CH\({}_{4}\) without NH\({}_{3}\) detected by DFB 2, while the green line and yellow line mark the PA spectrum of NH\({}_{3}\) without CH\({}_{4}\) detected by DFB 1. (**b**) Response linearity of the crosstalk signal induced by NH\({}_{3}\) at different concentrations.
## 2 Materials and Methods
### Particle Swarm Optimization-Based Human Detection and Initialization for Tracking
#### 2.2.2 PSO-Based Human Detection and Initialization  
In a typical tracking study, the state vector is mostly initialized to a random value. However, the closer the initial value is to the actual target, the faster the particle converges accurately to the target. If there is a human in the depth image extracted from the Section 2.1, the depth values of the human are almost the same. Therefore, in this study, we use the PSO method to discover the optimal window size and position at which the variance of depth values of the window region is the minimum. Since the window position and size can sufficiently enclose the human by the PSO method, the inside and the outside of the window serve as the seed of foreground and background, respectively, to the graph-cuts method [36]. This gives the initial value of the ellipse parameter for tracking the human.  
\[\begin{array}{l}{argminf(P_{i})}\\ {w,h,x,y}\\ 0<x<I_{w}-L_{w}\\ 0<y<I_{h}-L_{h}\\ \end{array} \tag{4}\]  
where \(f(P_{i})=\sum_{i=0}^{w}\sum_{j=0}^{h}\left(I(x+i,y+j)-\overline{I}\right)\) and \(P_{i}=\{w,h,x,y\}\). The function \(f(P_{i})\) is the objective function to be minimized to find the optimum size \((w,h)\) and location of the local window \((x,y)\) enclosing a human body shape. The symbols, \(w,h,x,y\), indicate the window width, window height, and the \(x\) and \(y\) coordinates of the center of the window, respectively. \(I_{w},I_{h}\) and \(L_{w},L_{h}\) denote the image width and height and the window width and height, respectively. \(I(x,y)\) is the intensity value at \((x,y)\) and \(\overline{I}\) is the mean of all pixels in the local window. Figure 4 shows the human detection process using the PSO method. In the depth image, the region having the smallest variance is found and the depth information in the region is used to expand the region. In this way, the final (red) rectangular box is detected and the inside (foreground) and the outside (background) information are separated from each other and used for the graph-cuts method.
# A Design of a Parcel Delivery System for Point to Point Delivery with IoT Technology
## 2 Background: Underlying IoT Technology
### LoRa Technology  
LoRa is a technology that is actively being developed and distributed around the world, mainly around the LoRa Alliance. Unlike conventional smartphone networks such as 3G and LTE, the technology is capable of communicating at low power and over 10 km at a minimum power consumption. They do not need separate base stations or relaying equipment, can send and receive data directly, and have low building costs and high scalability. Although it does not show a high data rate, it is specialized for certain periodic data transmission rather than real-time data, enabling long-distance communication with low power. Existing low-power wireless communication protocols such as Bluetooth and Zigbee exist, but the scope of communication is short and the cost of building infrastructure to extend the scope of communication is additional. However, LoRa technology can solve problems very simply. In the security field, which has recently become a hot topic in IoT, LoRa adapts AES128 to security. That is, by using LoRa, telecommunications services can be provided with mobility, localization and security.
###### Abstract
A limited number of works have considered EMF exposure in urban areas using outdoor drive test measurements [31; 32]. Moreover, there are no studies of EMFs in a car cabin during travel. According to the data presented in [33], in almost all European countries, the car is the dominant mode of transport for commuting to work and school, and people spend around 55 min every day in their vehicles [34]. Data presented in [35] show that in 2018, travel by car accounted for 82.9% of passenger kilometers across the European Union. In addition, more than 40% of people in Europe travel to a different country for recreation or a vacation [36]. Consequently, more studies are needed to estimate EMF exposure in a car during travel. In our study, we measured the electric field in a vehicle cabin during short (between cities) or long (between countries) trips on several European roads (E60, E61, E65, E70, E75, and E80), with the expectation that these data may be helpful for adequate and accurate exposure assessment of both car driver and passengers. Our objective was to evaluate the electric field in the car cabin during driving on urban routes and motorways to investigate the relationship between different roads and electric field strength. We performed measurements in seven European countries: Austria, Bulgaria, Croatia, Hungary, Italy, Slovenia, and the Republic of Serbia in the period April 2022-April 2023. The total distance over which the measurements were carried out was more than 1900 km. Finally, a comparison of the measured values of the electric field in each of the seven countries is presented. We also present information for the field strength of _Ex_, _Ey_, and _Ez_ components as an indicator of the orientation of the electric field, which is valuable for the design of both new absorbers of EMF for the car interior and new flexible rectennas for EMF energy harvesting in vehicles.
# Gaussian Process Model-Based Performance Uncertainty Quantification of a Typical Turboshaft Engine
## 4 Results and Discussion
### Gaussian Process Modeling for the Turboshaft Engine Performance
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Component** & **Uncertain Parameter** & **Notation** & **High Level** & **Low Level** & **Probabilistic Distribution** \\ \hline \multirow{2}{*}{Compressor} & Flow capacity & \(w_{\text{COM}}\) & \(0.02\) & \(-0.02\) & Truncated Gaussian distribution \\  & Adiabatic efficiency & \(\eta_{\text{COM}}\) & \(0.02\) & \(-0.02\) & Truncated Gaussian distribution \\ \hline \multirow{2}{*}{HPT} & Flow capacity & \(w_{HPT}\) & \(0.02\) & \(-0.02\) & Truncated Gaussian distribution \\  & Adiabatic efficiency & \(\eta_{HPT}\) & \(0.02\) & \(-0.02\) & Truncated Gaussian distribution \\ \hline \multirow{2}{*}{LPT} & Flow capacity & \(w_{LPT}\) & \(0.02\) & \(-0.02\) & Truncated Gaussian distribution \\  & Adiabatic efficiency & \(\eta_{LPT}\) & \(0.02\) & \(-0.02\) & Truncated Gaussian distribution \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The description of the uncertain parameters. gradually. Table 4 gives the mean values of NRMSE and \(Q^{2}\) with respect to the LHD size. It can be found that the maximum mean value of NRMSE is only 0.001237, while the minimum mean value of \(Q^{2}\) is very close to 1, confirming that the three GPMs are of high accuracy. After making a tradeoff between the computational cost and the prediction accuracy, the final choice of the LHD is of size 180. Three GPMs with better prediction performance are established. The validation shows that the criteria of the final three GPMs are all better than the mean values shown in Table 4.  
Figure 3: (**a**) The boxplots of NRMSE for \(T_{45}\) against Latin hypercube design (LHD) size; (**b**) the boxplots of \(Q^{2}\) for \(T_{45}\) against LHD size; (**c**) the boxplots of NRMSE for \(P_{e}\) against LHD size; (**d**) the boxplots of \(Q^{2}\) for \(P_{e}\) against LHD size; (**e**) the boxplots of NRMSE for specific fuel consumption (SFC) against LHD size; (**f**) the boxplots of \(Q^{2}\) for SFC against LHD size.