esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Adsorption Studies
#### 2.5.2 Adsorption Modelling  
The percentage of metal ion removal (\(Re\), %) and the equilibrium adsorption capacity (\(q_{e}\), mg/g) were computed using the following equations:  
\[R_{e}\%=\left(\frac{C_{0}-C_{e}}{C_{0}}\right)\times 100 \tag{1}\]  
\[q_{e}=\frac{\left(C_{0}-C_{e}\right)\ V}{m} \tag{2}\]  
where \(C_{0}\) and \(C_{e}\) (mg/L) are respectively the initial and equilibrium metal concentrations, \(V\) (L) is the volume of the metal solution, and \(m\) (g) is the mass of adsorbent used.  
Adsorption isotherm was evaluated using [PERSON] and [PERSON] adsorption models [23]. The [PERSON] model generally assumes monolayer adsorption, whereas [PERSON] suggests a heterogeneous surface with an exponential distribution of active sites and their energies. The linear forms of the two isotherms can be expressed by Equations (3) and (4) [23].  
\[\frac{C_{e}}{q_{e}}=\ \frac{1}{K_{L}q_{m}}+\frac{C_{e}}{q_{m}} \tag{3}\]  
\[lnq_{e}=lnK_{F}+\left(lnC_{e}\right)/n \tag{4}\]  
where \(C_{e}\), \(q_{e}\), and \(q_{m}\) (mg/g) have the same meaning as above, \(K_{L}\) (L/mg) is the Langmuir constant, describing the adsorption affinity for the adsorbent, \(K_{F}\) (mg/g) is the Freundlich coefficient, reflecting the adsorption capacity, and \(1/n\) is the adsorption intensity, with the \(n\) value describing whether the adsorption is a physical (\(n<1\)) or chemical (\(n>1\)) process. If \(n=1\), then the adsorption is linear.  
To evaluate the adsorption kinetic, the pseudo-first-order (PFO) and pseudo-second-order (PSO) models were used as described, respectively, by [PERSON] and [PERSON] [23; 24]. The linear form of [PERSON] first-order rate equation is:  
\[log(q_{e}-q_{t})=log(q_{e})-\ \frac{k_{1}t}{2.303} \tag{5}\]  
where \(q_{e}\) and \(q_{t}\) (mg/g) are, respectively, the adsorption capacity of metal ion at equilibrium and after time \(t\) (min), and \(k_{1}\) (1/min) is the PFO rate constant.  
The linear form of the second-order rate equation is:  
\[\frac{t}{q_{t}}=\ \frac{1}{k_{2}\ q_{e}^{2}}+\ \frac{t}{q_{e}} \tag{6}\]  
where \(k_{2}\) is the PSO equilibrium constant (mg/(g\(\cdot\)min)).  
Adsorption thermodynamic parameters, including Gibbs free energy (\(\Delta G^{\circ}\)), enthalpy (\(\Delta H^{\circ}\)), and entropy (\(\Delta S^{\circ}\)) were calculated using the van't Hoff model, Equations (7)-(9).  
\[\Delta G^{\circ}=-RTlnK_{s} \tag{7}\]  
\[lnK_{s}=-\frac{\Delta H^{\circ}}{RT}+\frac{\Delta S^{\circ}}{R} \tag{8}\]  
where  
\[K_{s}=\frac{q_{e}}{C_{e}} \tag{9}\]  
where \(K_{s}\) (L/g) is the apparent equilibrium constant, R (8.314 J/(mol\(\cdot\)K)) is the universal gas constant, \(T\) (K) is the adsorption temperature; all the other parameters reflect the same meaning as described in the isotherms above.
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 6 Experimental Results  
In this section, some considerations on the experimental results are provided, starting with the precision of the distance estimation carried out thanks to the RSSI index and how variable this can be under certain conditions. Then, in the following step, the selected location algorithms are compared, and it is determined which of the three is more accurate in the estimation of the position. Tables 1 and 2 show RSSI measurements associated with multiple distances, both indoor and outdoor, calculated by placing two devices (one configured as the target and the other as the Anchor) at different distances.  
\begin{table}
\begin{tabular}{c c} \hline
**Environment** & **Path Loss Exponent (n)** \\ \hline Free space & 2 \\ \hline Urban area cellular radio & 2.7 to 3.5 \\ \hline Shadowed urban cellular radio & 3 to 5 \\ \hline Inside a building–line of sight & 1.6 to 1.8 \\ \hline Obstructed in building & 4 to 6 \\ \hline Obstructed in factory & 2 to 3 \\ \hline \end{tabular}
\end{table}
Table 2: Propagation exponent.  
Measurements were made in different environments to test whether, and how, the relationship between RSSI and distance changes depending on the environment in which the radio signal propagates.  
The values obtained are the result of the average carried out on 25 samples because it has been observed that using fewer values makes the measurement too variable. Moreover, each time the average of the RSSI index is made on a smaller number of samples, it results in something too different from the previous calculation. Therefore, this number of samples can be considered sufficient to determine the RSSI value of the communication between two nodes in a WSN.  
It is also preferred to use the standard deviation [2] as an index of variability because, in the presence of a fairly high number of measurements, it best represents the absolute error and is therefore of fundamental importance in order to correctly determine the magnitude of the fluctuations found in the measurements.
# THz Data Analysis and Self-Organizing Map (SOM) for the Quality Assessment of Hazelnuts
## 3 Samples  
In Italy, there are two varieties of IGP (Protected Geographical Indication) branded hazelnuts: the so-called \"Tonda delle Langhe\" (from Piedmont) and the \"Tonda di Giffoni\" (from Campania) and one, with a DOP (Protected Designation of Origin) label, the \"Tonda Gentile Romana\".  
Figure 1: **(A)** Image of the experimental setup. (1) YIG Source; (2) transmission line; (3) launching horn; (4) petri dish with an Eccosorb layer and hazelnut sample; (5) collecting horn; (6) Schottky diode; (7) digital multimeter. **(B)** Diagram of the experimental setup. The radiation transmitted through the hazelnut is collected by a collecting horn and detected via a Schottky diode.  
In this study, the samples under examination belong to the Tonda Gentile Romana and San Giovanni varieties, which are typical cultivars of the Lazio and Campania regions. The objective of this research is to select hazelnuts without breaking the outer shell, so millimeter wave measurements were carried out on apparently healthy hazelnuts and rotten hazelnuts, as shown in Figure 2. A parameter to be considered is the amount of moisture present in hazelnuts in order to ensure proper storage of the product. In fact, during roasting, there is a decrease in moisture from 4-6% to 1-3%.  
Transmission measurements were performed on 212 random hazelnuts containing either healthy, rotten, shriveled and empty hazelnuts. A subset of fifty hazelnuts was used to train the neural network and allow automatic sorting by a self-organizing neural network using the Kohonen algorithm
# Enhanced Ant Colony Optimization for Vehicular Ad Hoc Networks Using Fittest Node Clustering
## 4 Results and Discussion
### Packet Drop Ratio Analysis  
The packet drop ratio quantifies the rate of unsuccessful packet transfers within the network. In comparison to the previous ACO method, the proposed technique achieved a significantly lower packet drop ratio, as depicted in Table 5 and Figure 4c. Notably, a substantial contrast is observed in the packet drop analysis. The increased distance between vehicles' placements can impact the packet transmission process. By using PED estimation to position vehicles closer to reduce travel time, the proposed technique substantially  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No. of Vehicles** & **Distance (m)** & **DA-TRPED** & **Conventional ACO** \\ \hline
20 & 0.5 & 94.21 & 74.21 \\ \hline
40 & 1.0 & 95.27 & 74.12 \\ \hline
60 & 1.5 & 94.71 & 75.65 \\ \hline
80 & 2.0 & 92.21 & 78.12 \\ \hline
100 & 2.5 & 94.85 & 79.63 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative results of packet delivery ratio (PDR) analysis for network performance evaluation.  
Figure 4: Comparative analysis of the existing ACO and proposed DA-TRPED models. (**a**) Throughput results analysis; (**b**) packet delivery ratio analysis; (**c**) packet dropping ratio analysis; (**d**) end-to-end analysis.  
decreased the drop ratio. Specifically, the drop ratio of the proposed technique is seven times lower than that of AODV and two times lower than that of ACO.
## 2 Mathematical Model
### Numerical Modeling  
The equation for colloidal particle transport presented in Equation (1) was solved first at the inlet concentration of 5 g L\({}^{-1}\) for 30 pore volumes (PVs, 1 PV represents the volume of colloidal particle suspension equal to the pore volume of sand medium) of injection. The obtained retention profile of colloidal particles (\(S_{c}\) in Equation (2)) was applied to the initial retained colloidal particles for solving the contaminant transport equation (Equation (7)). An inlet contaminant concentration of 5 mg L\({}^{-1}\) was applied to the boundary condition for 30 PVs of injection. All parameters used in simulations were identical, except the mean and the standard deviation of the particle size distribution of colloidal particles. The backward Euler method was used for discretizing the governing equations, and the Picard iteration [33; 34] was implemented in the model for the convergence in each time step. The dimension of the sand medium domain in all the numerical simulations was set to 30.48 cm (=1 foot) long, except the model validation (the dimension of domain = 4.61 cm). All parameters adopted in the numerical simulation are summarized in Table 1.
# A Systematic Review of Radio Frequency Threats in IoMT
### Recommended Solutions  
There are still organizational, technological, and governance barriers that prevent the adoption of cybersecurity in healthcare IoT, but the coronavirus disease (COVID-19) pandemic has brought to light the need for a secure IoT to coordinate the transfer of confidential information, for temperature control of medical supplies and vaccines, radio frequency-based machine implantable communication systems, wearable technologies for remote patient monitoring, and patient-controlled drug delivery systems. The need to drive greater adoption of IoT security policies in healthcare cybersecurity makes it imperative to remove some of these barriers in concerted efforts to drive greater adoption. Although external factors such as COVID-19 alone may push the adoption of these technologies, such factors cannot achieve lasting and sustained effects [9]. We aimed to systematically review the IoMT to understand the research gap and identify RF hackers' locations. Our goal was to provide the healthcare community with better understanding, literacy, and appropriate advancements, as well as bring together IoMT and physical layer scientists. Additionally, we hope that this work will foster a greater interest in integrating IoMT systems into future healthcare applications and beyond. The following are the main contributions. 1. A description of the current state of research on internet of things (IoT) side-channel attacks. Figure 1: Physical attack and attack system by SDR devices. 2. The aim of this study was to understand the research gap about sniffing and replaying IoMT attacks in the healthcare ecosystem. 3. The research papers were reviewed top-down to facilitate our future research, including those on cybersecurity systems, cybersecurity frameworks, cyber-attacks on layers and protocols, radio frequency machine learning, and deep learning in a cybersecurity field experimentation. 4. The conclusions we have reached, and our plans for future work are presented. In Table 2, cyber spectrum attacks are analyzed across journals. There is a lack of research on sniffing and tampering attacks, according to the results. Our future research will be facilitated by reviewing research papers from the top down. Topics included systems for cybersecurity, cybersecurity frameworks, attacks on layers and protocols, radio frequency machine learning, and deep learning in the cybersecurity field. Table 3 shows the papers we reviewed. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attack Search** &
\begin{tabular}{c} **Web of** \\ **Science** \\ \end{tabular} & **Scopus** & **PubMed** & **Total** \\ \hline Jamming Attack & 17 & 330 & 0 & 347 \\ Replay Attack & 50 & 372 & 23 & 445 \\ Side-Channel Attack & 30 & 447 & 0 & 477 \\ Sniffing Attack & 0 & 18 & 1 & 19 \\ Spoofing Attack & 23 & 316 & 5 & 344 \\ Tampering Attack & 25 & 46 & 14 & 85 \\ Total & 145 & 1529 & 43 & 1717 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Displays the search analysis of cyber spectrum attacks across journals.
# On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies
## 5 Performance Evaluation
### Peak Signal-to-Noise Ratio
However, this can be misleading, since the drop in quality is not as high as it may seem. This is due to the use of the CRF encoding method. CRF compresses different frames by different amounts, taking the motion into account. Therefore, frames with higher motion will have a greater compression, discarding more information from them. The reason for doing so is that the human eye perceives more detail in still objects than in moving objects. Consequently, in spite of having a worse PSNR because of greater compression, subjectively, the quality of the video sequences with high motion will still be acceptable. To this extent, the video sequences with higher motion, Foreman and Mobile QCIF, obtained after the simulation using a 5-3 (WO-AO) configuration for the 6 \(\times\) 6 scenario can be seen [38], where, although its quality is rated as \"very annoying\" according to the PSNR to MOS conversion, the video can be considered as acceptable from a subjective point of view.
# Additive Manufactured Waveguide for E-Band Using Ceramic Materials
## 2 Manufacturing Process
### Printing Process  
To produce the desired components, a so-called DLP printer was used. DLP is a resin-based printing process in which an ultraviolet (UV) sensitive resin (photopolymer) is cured layer by layer by irradiating it with light [11; 12]. The process was developd to 3D print polymers, but if ceramic powder is added to the resin, ceramics can also be produced using this process. After printing, the samples were heat-treated, the resin was burnt out (debinded) and the ceramic sintered to the desired component. The choice of the ceramic material is critical for the printing as well as the sintering process. In this work, a low temperature cofired ceramic powder (Ferro(r) L5) was used, which has a suitable d\({}_{50}\)-particle size of 2.65 um [36] and can be mixed well with the resin (Tethon3D(r) Gensis). The resulting glass-ceramic provides sufficient temperature resistance for the desired application scenario with simultaneous low sintering temperature, which simplifies the fabrication. The samples were sintered in a laboratory furnace (Naberthem(r) LE 14/11/P300) at 900 \({}^{\circ}\)C.  
A powder-resin mixture was prepared. The rheological properties of the powder-resin mixture are a crucial parameter for the 3D-printing process [37; 38; 39; 31]. Higher mixing ratios enable a better loading content. However, a lower mixing ratio leads to a lower viscosity and allows for a better printing performance. Therefore, different mixing ratios (30 wt% (weight percent powder content), 40 wt%, 50 wt%) were examined in a previous parameter study. A mixing ratio of 40 wt% ceramic powder enabled the best printing quality. Before printing, the viscosity of the resin and the ceramic filled resin was measured, and the results are shown in Section 3.  
Before printing, this mixture was degassed in order to remove air bubbles, which would otherwise lead to defects in the component. The printing process was then performed using a conventional desktop-grade DLP printer (Elegoo(r) Mars 2 Pro). The layer thickness was set to 0.01 mm. Each layer was photo cured for six seconds under a light intensity of 8.5 mW/cm\({}^{2}\) at 405 nm wavelength.
# Application of Internet of Things in a Kitchen Fire Prevention System
## 4 Results
After the connection and testing of sensors, control board, and controlled devices were completed, we triggered the gas sensor, temperature sensor, and flame detector by using gas from the lighter, hot air from the hair dryer, and strong light from the mobile phone flashlight, respectively. The detected changes of gas concentration, temperature, and light intensity shown on the LCD are presented in Figure 14. When the gas concentration, temperature, and light intensity reached the set threshold, the alarm device emitted a warning light and sound (Figure 15), the gas shutoff device was activated to turn 90 degrees to turn off the gas supply (Figure 16), Line notification messages were sent to the user's mobile phone (Figure 17), and the main entrance door was automatically unlocked (Figure 18). When the system reads that any detected value exceeds the safe range, then the warning system, Line notification, opening the door lock and closing the gas will be triggered immediately. The automatic gas shut-off device takes about 5 s to completely cut off the gas supply. Figure 14: Changes of gas concentration, light intensity and temperature. Figure 13: Architecture of the smart kitchen fire prevention system. This study has mainly used three kinds of sensors to detect the possible fire in the kitchen. When any of these sensors exceeds our set range, the relevant alarms, notifications, gas shuts and door locks are immediately activated. Their startup values, response time and completion/ended time are shown in Table 3. When the detected values reach the startup values, the system accurately starts the relevant facilities all the time. Figure 16: Gas shutoff device turned 90 degrees to turn off the gas supply. Figure 17: Line notification messages on the mobile device. Figure 18: Door being automatically unlocked. Figure 15: Activation of the alarm device. Regarding the remote monitoring of the gas stove, the IP camera was turned on to check whether the stove had been turned off. If not, the user clicked on the \"Turn off the main switch\" button (Figure 19) to activate the gas shutoff device and turn off the gas supply (Figure 16). Figure 20 shows images of the gas stove before and after the gas was shut off.
# Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping
## 2 Material and Methodology
### Data Preprocessing  
In this section, the pre-processing for the acquired dataset is presented.  
1. To investigate only the operating periods of the ship, this study extracted the \"Underway using engine\" data from the Navigational Status features, which meant the mooring and anchoring periods were rejected.
2. Shipping speed can decrease due to different sea state resistances; however, there is also a probability that it may be reduced by the operator, especially around the port at the start and end of the voyage. To reduce this kind of measurement error, this study discarded the data with less than 5 knots of SOG, which is considered as maneuvering.
3. From the AIS data report [19], if the data value is not-available (missed data), there is a default outlier value for each feature such as 102.2 for SOG, 511 for heading, 91 for latitude and 181 for longitude [20]. Those values were observed in our data and used to discard the missed data.
4. The scatter plot of the features can be used to show that the data may have noise/outliers because of the inconsistencies in the measurement of the sensors or human errors which must be rejected before training the models. Z-score is a parametric outlier detection method for different numbers of dimensional feature space [21]. However, this method assumed that the data had a Gaussian distribution; hence, the outliers were considered to be distributed at the tails of the distribution, which meant that the data point was far from the mean value. Before deciding a threshold that we set as \(Z_{thr}\), the given data point \(x_{i}\) was normalized as \(Z_{i}\) using the following equation. \[Z_{i}=\frac{x_{i}-\mu}{\sigma},\] (1) where \(\mu\) and \(\sigma\) are the mean and standard deviation of all \(x_{i}^{\prime}\)'s, respectively. An outlier is then a data point that has an absolute value greater than or equal to \(Z_{thr}|Z_{i}|\geq Z_{thr}\).  
Usually, the threshold value is set to \(\pm 3\)[22]; however, our data is extremely non-linear and this study only aims to remove extreme cases. Therefore, the study used a threshold value of \(\pm 5\) for all features to reject values which were extremely far from the mean value on both tails. Figures 2 and 3 show examples of the data distribution of SOG, including normal and outlier data, which are detected using the Z-score.
## 3 Procedure to Synthesize the IKM Using Groebner Basis
### First Step: Information Input  
The procedure begins requesting all the relevant information about the analyzed robot. The user has to supply only two data: the Denavit-Hartenberg (D-H) parameters of the robot and the movement range of its actuators.  
The first data, the D-H parameters, are necessary to solve the Forward Kinematics problem, which is a required step to calculate the Groebner Basis that will constitute the IKM. These parameters represent the fundamental information of any robotic system, so they are also indispensable for all traditional methods that solve the Kinematic Problem.  
The robot actuators' movement range is employed to eliminate all the solutions that are not reachable by the robotic system. This way, the IKM is able to discard those solutions that are out of the robot's workspace.
# Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
## 4 Computer Simulation Experiments
### Underwater Acoustic Channels
#### 4.1.1 Fuxian Lake  
Fuxian Lake is a freshwater lake, and its underwater acoustic environment is relatively pure due to the lack of human-made noise such as boat activity. From Figure 7a, at least three transmission paths can be observed that are clear and constant. There are also several transmission paths that are not clear enough and can be ignored. The maximum Delay of these clear paths is 3 ms.
# State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks
## 5 Experimental Setup of the Proposed Modified LSTM-Based State Estimator
### State Estimator Performance Validation  
The performance of all state estimators is validated by calculating the error value between the real simulated voltage value and the value estimated by the estimator. The types of error considered in this paper are the mean absolute error (MAE) in Equation (23), mean absolute percentage error (MAPE) in Equation (24), mean squared error (MSE) in Equation (25), and root mean squared error (RMSE) in Equation (26). In Equations (23)-(26), \(n\) is the number of samples in the dataset, \(x_{i}\) denotes the actual output, and \(y_{i}\) denotes the predicted output.  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|x_{i}-y_{i}\right| \tag{23}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Modified LSTM** & **MLP** & **LSTM** \\ \hline Epoch & & 100 & 100 & 100 \\ \hline Batch & & 9 & 9 & 9 \\ \hline \multirow{5}{*}{Number of layers} & Hidden 1 & 100 & 100 & 100 \\ \cline{2-5}  & Hidden 2 & 50 & 50 & 50 \\ \cline{2-5}  & Hidden 3 & 50 & 50 & 50 \\ \cline{2-5}  & Output & 9 & 9 & 9 \\ \hline Activation function & ReLU & ReLU & ReLU \\ \hline Loss function & Modified WLS & MSE & MSE \\ \hline Optimizer & Adam & Adam & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the deep-learning-based state estimator.  
\[MAPE=\frac{100\%}{n}\sum_{i=1}^{n}\left|\frac{x_{i}-y_{i}}{x_{i}}\right| \tag{24}\]  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\left(x_{i}-y_{i}\right)^{2} \tag{25}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(x_{i}-y_{i}\right)^{2}} \tag{26}\]