esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Moisture Content  
The moisture content (MC%) present in a powder product has an important impact on its stability and, consequently, on the durability of the material, commonly known as shelf-life. Additionally, powder products are considered the most practical type of material to employ in the industrial field. Therefore, low water contents are particularly desirable [14]. One of the major advantages of using spray drying for the microencapsulation of active materials is the fact that the particles obtained (in powder form) have a low moisture content and water activity, making them resistant to microbial and oxidative degradation and, therefore, attractive for the food and pharmaceutical industry [14].  
The results of MC%, presented in Table 3, showed average values of approximately 2.0% for all obtained particles. Similar values of MC% were observed for all tested conditions, which can be associated with the high inlet temperatures.  
In the food industry, low values of moisture content (0.2-5%) are desirable to maintain microbiological stability [14]. On the other hand, in the paint industry, specifically in the encapsulation of corrosion inhibitors, this parameter has not yet been discussed. Nonetheless, it is expected that a material with low water content is better for preventing corrosion,  
\begin{table}
\begin{tabular}{c c c} \hline
**Condition Number** & **Average Diameter (\(\upmu\)m)** & **Size Distribution (\(\upmu\)m)** \\ \hline
1 & \(1.7\pm 0.97\) & 0.52–9.88 \\
2 & \(2.0\pm 1.2\) & 0.12–11.4 \\
3 & \(2.2\pm 1.5\) & 0.73–14.8 \\
4 & \(1.8\pm 1.1\) & 0.38–11.5 \\
5 & \(2.0\pm 1.2\) & 0.52–9.08 \\
6 & \(1.8\pm 1.0\) & 0.67–7.67 \\ \hline \end{tabular}
\end{table}
Table 2: Effect of the set experimental conditions on the average diameter and size distribution range of the CMC.BTA-MS obtained by spray drying.  
as water molecules are involved in the electrochemical processes associated with corrosion at the metal interface [4; 37; 38; 39]. The proposed methodology for corrosion inhibitor microencapsulation in CMC microspheres resulted in low values of moisture content; this is of utmost importance, since the stimulus-responsive mechanism involved in the release of the encapsulated material is triggered by contact with water molecules [4; 37; 38; 39]. In summary, the MC % results were considered satisfactory, which validates the viability of the encapsulating technique used.
# A Novel Bitrate Adaptation Method for Heterogeneous Wireless Body Area Networks
###### Abstract  
In the article, a novel bitrate adaptation method for data streams allocation in heterogeneous Wireless Body Area Networks (WBANs) is presented. The efficiency of the proposed algorithm was compared with other known algorithms of data stream allocation using computer simulation. A dedicated simulator has been developed using results of measurements in the real environment. The usage of the proposed adaptive data streams allocation method by transmission rate adaptation based on radio channel parameters can increase the efficiency of resources' usage in a heterogeneous WBANs, in relation to fixed bitrates transmissions and the use of well-known algorithms. This increase of efficiency has been shown regardless of the mobile node placement on the human body.  
Wireless Body Area Networks; off-body communication; narrowband; ultra-wide band; data allocation; bitrate adaptation Article  
A Novel Bitrate Adaptation Method for Heterogeneous Wireless Body Area Networks [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]  
Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland; [EMAIL_ADDRESS] (S.J.A.); [EMAIL_ADDRESS] (P.R.); [EMAIL_ADDRESS] (J.S.); [EMAIL_ADDRESS] (J.S.); [EMAIL_ADDRESS]; Tel.: +48-58-347-1162
# Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models
## 6 Results  
The onset of the seizure was identified at the three channels RAST3, RAST4 and RMST4. Based on these three channels, the ARMA(2,2)-GARCH(1,1) models were estimated for all channels. Using Equations (1) and (2), these models take the following form where \(Y_{i,t}\) is the value of EEG signal \(i\) at time \(t\):  
\[Y_{i,t}=c_{1}Y_{i,t-1}+c_{2}Y_{i,t-2}+d_{1}\epsilon_{i,t-1}+d_{2}\epsilon_{i, t-2}+\epsilon_{i,t},\ (\text{ARMA}(2,2))\]  
where \(\epsilon_{i,t}=z_{i,t}\epsilon_{i,t}\) and  
\[\sigma_{i,t}^{2}=a_{0}+a_{1}\epsilon_{i,t-1}^{2}+b_{1}\sigma_{i,t-1}^{2}\ ( \text{GARCH}(1,1))\]  
Models were estimated for all segments of the seizure data. The models based on the seizure and post-seizure resulted in integrated models; thus, results only for the pre-seizure data are included. For the other three datasets, models were estimated for all segments.  
The models used to estimate and examine volatility were ARMA(2,2)-GARCH(1,1) models. Since the EEG is believed to be non-stationary, one-minute (12,000 points) segments were selected and assumed to be (weakly) stationary. This particular model was selected for this analysis based on fitting an ARMA(2,2) model to the data, and checking the innovations (errors) for heteroscedasticity using an Ljung-Box Q test, and examining the autocorrelation and partial autocorrelation plots for GARCH characteristics, namely serial correlation in the squared innovations. These models were initially fit to the three seizure channels (RAST3, RAST4 and RMST4) and the standardized residuals were tested using the Ljung-Box Q test.  
In addition to testing the standardized residuals, the fit of the GARCH(1,1) model to innovations of the ARMA(2,2) model, the estimated unconditional volatility from the GARCH coefficients and the variance of the innovations are listed in Figure 1a-d (all coefficients for models are included in supplementary Table S1a-d). For the three seizure channels, unconditional volatility estimates were within two percent of the innovation variance, with the exception of segment 3 of RAST3, which showed an approximate seven percent difference; this particular channel also had the highest estimated GARCH coefficient (0.7485) and coefficient sum (0.8763). The data for the other signals showed similar results with RAST3 awake segment 7, sleep to awake segment 3, sleep segments 1 and 3, RAST4 awake segment 5 and RMST4 sleep segments 1, 3, and 5 showing greater than five percent difference. For the other channels, three from the seizure data, fifteen from the awake data, twenty-two from the sleep to awake data and ninety from the sleep data had a greater than five percent difference between unconditional volatility and innovation variance.  
Figure 1: _Cont._  
Figure 1: GARCH parameters and unconditional volatility for three seizure channels (RAST3, RAST4, RMST4) compared to summarized values for non-seizure channels.
# On the Use of Focused Incident Near-Field Beams in Microwave Imaging
## Appendix B Simulation Details of the Separation Resolution Study  
In this Appendix, we elaborate on how the separation resolution simulation study (see Section 3.1.3) was carried out by the use of ANSYS HFSS. To this end, we have placed two dielectric objects with the separation of about 0.13\(\lambda\) from each other in the NF plate setup under Configurations I and II. Due to the simulation setup, it was not possible to keep the two dielectric objects stationary, and then move the NF plate to scan the objects in the three steps as described in Section 3.1.3. To handle this, instead of moving the NF plate and keeping the objects stationary, wedo the reverse procedure: the two separated objects are moved together parallel to the NF plate in three steps. In Step I, the first dielectric object is located exactly at the center and in front of the NF plate. In Step II, the gap between the two objects is located at the center with respect to the NF plate, and finally in Step III the second dielectric object is placed at the center with respect to the NF plate. The two separated objects at each of these three steps are interrogated, and then the resulting scattered NF data are collected at the receivers' line (located on the yellow plane in Figure 9).  
Now, we will explain how the scattered NF data have been collected in each of the steps above. In this study, we have placed the total number of 106 receiving points along the line of intersection between the yellow and brown planes in Figure 9. This line, which we refer to as the receivers' line, is parallel to the \(y\)-axis and has been separated by about \(\lambda/7.5\) from the NF plate. At each of the three steps above, only those receivers that reside exactly behind the dielectric object being illuminated (Steps I and III) or the gap being illuminated (Step II) are used for data collection. (In other words, the receiving points used in each step are located mainly within the main beam of the NF plate.) We then plot these three sets of the scattered NF data together to create Figure 10a for Configuration I and Figure 10b for Configuration II. Finally, it should be mentioned that, due to the symmetry of the NF plate setup as well as the symmetric arrangement of the two dielectric objects, we have obtained the scattered NF data in Step III by flipping the scattered NF data from Step I.
###### Abstract  
Satellite communications face difficulties such as intensified environmental attenuation, dynamic time-varying links, and diverse business scenarios, which usually require channel coding schemes with high coding gain and high throughput. Low-density parity-check (LDPC) codes are dominant in satellite communication coding schemes due to their excellent performance in approaching the Shannon limit and the characteristics of parallel computing. The traditional weighted-Algorithm B decoding algorithm ignores the channel received information and involves frequent multiplication operations and iteration, which introduces the channel received information for hard-decision and constellation mapping processing. Meanwhile, we design the correlated reliability between the extrinsic information and the mapping processing information to improve the correctness of decoding. The multiplication operation in the iterative process can be replaced by the simple sum of the Hamming distance coefficient, the correlated reliability between the extrinsic information and the mapping processing information, and the extrinsic information frequency, thereby reducing the complexity and storage load of the system. The simulation results show that the presented MRAI-LDPC algorithm can obtain about 0.4 dB performance gain, and the average number of iterations is reduced by 68% under a low SNR. The algorithm can achieve better error-correcting performance and higher throughput, providing strong support for reliable transmission of satellite communications.  
satellite communications; channel coding; LDPC code; multi-dimensional information iterative decoding; reliable transmission +
Footnote †: journal: Applied Sciences  
1
# Kalman Filter and Wavelet Cross-Correlation for VHF Broadband Interferometer Lightning Mapping
## 2 Materials and Methods
### Hardware  
Figure 3 shows the configuration diagram of the proposed ITF system which consists of three sets of antennae, with two perpendicular baselines of 15 m in length (3\(\lambda\)), band-pass filters, low noise amplifiers (LNA), coaxial cables, and an oscilloscope. Two types of parallel-plate antennae of size A4 and A3 are used to capture the VHF E-field and Fast E-field (10 Hz up to 3 MHz) of lightning flashes, respectively. For the VHF E-field antenna, the gap between the two plates is about \(h=1\) cm and 3 cm for Fast E-field to avoid the fringing effects for the VHF sensor [27,28]. The Fast E-field antenna is used to determine the type of lightning flash, and the VHF E-field antenna is used to capture the lightning radiation emissions. Figure 3 illustrates the implemented antennae B, C, and D were arranged in perpendicular baselines with two sides BC and BD to measure the phase difference. The broadband ITF system was installed in Klebang Beach, Malacca Strait, Melaka, Malaysia (2.216304\({}^{\circ}\) N, 102.192288\({}^{\circ}\) E).  
The received VHF signal at each antenna is sequentially passed through preamplifier and BPF before being digitized and stored in external memory. The LNA amplifies the low-power signals preserving the signal-to-noise-ratio of the received signal. The bandwidth of the BPF is limited to the range 40-80 MHz with a center frequency of 60 MHz [17]. The filtered analog signal is then passed to the oscilloscope (PicoScope 3000 series [29] with 100 MHz bandwidth) through three coaxial cables of 15 m length. The output of the antenna was digitized in the oscilloscope at a sampling frequency of 250 MS/s with a vertical resolution of 8 bits. Data records were event-triggered and stored in segments of 200 ms length. Then, the retrieved data from the oscilloscope are transferred to a personal computer with MATLAB software for lightning localization analysis. A summary of the ITF's attributes is given in Table 1.
# Network Attack Classification in IoT Using Support Vector Machines +
###### Abstract  
Machine learning (ML) techniques learn a system by observing it. Events and occurrences in the network define what is expected of the network's operation. It is for this reason that ML techniques are used in the computer network security field to detect unauthorized intervention. In the event of suspicious activity, the result of the ML analysis deviates from the definition of expected normal network activity and the suspicious activity becomes apparent. Support vector machines (SVM) are ML techniques that have been used to profile normal network activity and classify it as normal or abnormal. They are trained to configure an optimal hyperplane that classifies unknown input vectors' values based on their positioning on the plane. We propose to use SVM models to detect malicious behavior within low-power, low-rate and short range networks, such as those used in the Internet of Things (IoT). We evaluated two SVM approaches, the C-SVM and the OC-SVM, where the former requires two classes of vector values (one for the normal and one for the abnormal activity) and the latter observes only normal behavior activity. Both approaches were used as part of an intrusion detection system (IDS) that monitors and detects abnormal activity within the smart node device. Actual network traffic with specific network-layer attacks implemented by us was used to create and evaluate the SVM detection models. It is shown that the C-SVM achieves up to 100% classification accuracy when evaluated with unknown data taken from the same network topology it was trained with and 81% accuracy when operating in an unknown topology. The OC-SVM that is created using benign activity achieves at most 58% accuracy.  
intrusion detection systems; anomaly detection; Internet of Things; support vector machines +
Footnote †: This paper is an extended version of a paper published in International Conference on Distributed Computing in Sensor Systems (DCOSS), Intelligent Systems for the Internet of Things (ISIoT) 2019 workshop, entitled \"Classifying Security Attacks in IoT Networks Using Supervised Learning\", ISBN 978-1-7281-0570-3, doi:10.1109/DCOSS.2019.00118, published by IEEE.
## 3 Proposed Method  
### Data Acquisition  
The primary crops cultivated in the Jeonbuk region of South Korea include paprika, tomatoes, and strawberries. The Fruit and Vegetable Research Institute at Jeonbuk Agricultural Research & Extension Services generously offered us access to their paprika greenhouse, consequently leading to the selection of paprika as the focal crop for our study. The paprika testbed was established to collect environmental data for smart farm. It was equipped with a main board, sensor board, router, converter, and sensors. The structure of the established paprika testbed is depicted in Figure 5.  
The main board plays a pivotal role in collecting data from three sensor nodes and transmitting it to the database. The router is responsible for facilitating data transmission, acting as a communication hub. The converter ensures that measured data is transformed into the appropriate format, while the sensors are dedicated to gathering environmental data. Lastly, the sensor node serves the function of transmitting information collected by the sensors to the Main Board.  
Figure 5: Testbed structure.  
To acquire environmental data, a total of nine sensors (air temperature, humidity, CO\({}_{2}\), soil temperature, soil moisture, insolation, soil electrical conductivity (EC), drainage electrical conductivity (EC), drainage pH) were installed. Figure 6 shows the appearance of the established smart farm testbed.
# Routing Schemes in FANETs: A Survey
## 2 Background and Motivating the Need for FANETs
### What Are the Roles of FANETs?  
FANETs provide connectivity among flying nodes (e.g., UAVs) to support various short-term applications and tasks [61]. Multiple UAVs can be deployed to fly over a targeted region and to perform surveillance, detection, and monitoring. The UAVs use sensors and cameras to capture real-time images, audios, and videos at remote and difficult-to-access areas and send the data to a BS immediately [62]. Subsequently, the BS processes the data and generates messages, such as alert messages upon the detection of a disturbance or an event [63]. The multi-UAV scenario has a significant impact to human life and activities [64]. For instance, the use of the multi-UAV scenario to detect and monitor wildfire is foreseen to reduce the current 340,000 cases of casualties and the $10 million USD cost annually [62; 65].  
The multi-UAV scenario has been deployed to detect targets (i.e., to identify the position of a target) in search missions [66], to provide bird's-eye view for surveillance [67; 68; 69], to monitor crops (e.g., identifying ripe and unripe crops) in agriculture [70; 71], to monitor disaster [72; 73; 74], and to monitor environment (e.g., wind [75], temperature, humidity, light intensity, and the pollution level [76; 77; 78]). Multi-UAVs also support other kinds of ad hoc networks, particularly MANETs and VANETs. For instance, the multi-UAV scenario has been deployed to monitor and manage traffic in VANETs [79; 80]. The multi-UAV swarm scenario has been deployed to monitor and manage disaster and to perform surveillance in smart cities [81].
# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 2 Literature Review and Applications in Disaster Management
### Applications
#### 2.2.5 Medical Help  
UAVs can be used for emergency medical response in the disaster-hit area to provide medical help and supplies. Since time is critical in the case of medical emergency and chances of survival rapidly decrease with time, UAVs can be used to quickly reach people in need before the other medical help arrives. In [48], UAVs are used to provide medical aid with the help of onboard sensors and the robotic arm. Particularly, the authors studied the case of cardiac arrest and present a framework for handling such a medical emergency. The health of soldiers in the battlefield is monitored using UAVs and wearable sensors in [49]. The objective is to efficiently direct medical help in order to save soldiers lives. In [5], UAVs are considered for delivering essential medical supplies in disaster-hit areas with damaged or destroyed transport infrastructure.
# Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis
## 2 ITFRSNP System
### Actual Case Analysis  
This study verifies the effectiveness of the proposed fault diagnosis model and diagnosis process with actual fault cases from a city power system in Shandong Province, 2020. The wiring diagram of the power system associated with the fault case is shown in Figure 6. It contains seven substations, nine buses, twenty-one lines, and two transformers.
# Analyzing and Forecasting Vessel Traffic Through the Panama Canal: A Comparative Study
## 2 Materials and Methods
### Methodology
#### 2.2.3 Neural Network Autoregressive (NNAR)  
Artificial neural networks, based on a mathematical model that mimics brain function, are widely used in time series modeling and forecasting [15]. Their fundamental architecture consists of an input layer, one or more hidden layers, and an output layer. The input layer receives the initial data, while the hidden layers process this information through weighted connections and activation functions to detect complex patterns. Finally, the output layer generates the network's prediction or output. This structure enables the network to learn and model nonlinear relationships within the data [42]. Once a neural network has been trained, it can be used to predict outputs for new inputs that have not been seen before. The following figure (Figure 4) presents the architecture of the neural network, showing the arrangement of its layers and the flow of data between them.  
In the Neural Network Autoregressive (NNAR) model, the initial layer, known as the input layer, receives lagged values of the time series and distributes the data to the next layer; the hidden layer performs the necessary intermediate computations, enabling the capture of nonlinear relationships within the data; and the output layer produces the model's predictions or estimates. The NNAR model is capable of handling both non-seasonal and seasonal data [43].  
In general, for seasonal time series, NNAR \((p;P,\ k)_{12}\) describes a neural network model that uses as inputs the last \(p\) observations of the monthly time series and \(P\) seasonal lagged observations corresponding to multiples of 12 periods, with \(k\) nodes in the hidden layer. Thus, the inputs to the hidden layer are the following:  
\[y_{t-1},\ y_{t-2},\...,\ y_{t-p},\ y_{t-12},\ y_{t-24},\...,\ y_{t-12P}\]  
where the first \(p\) terms correspond to the non-seasonal lags, and the remaining \(P\) terms represent the seasonal lags. An NNAR \((p;P,\ k)_{m}\) model is equivalent to an ARIMA (P,0,0) (p,0,0)\({}_{\text{m}}\) model but without parameter constraints.  
This process is summarized and visually presented in the following flowchart, which illustrates the architecture and data flow within the NNAR model (Figure 5).  
Figure 3: Holt–Winters Modeling Process Flowchart.
# MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems
## 7 Preprocessing Methodology
### Feature Ranking Methods
#### 7.1.5 Monotonicity  
The monotonicity ranking method [48] evaluates the degree to which a feature changes consistently with respect to time or degradation progression, making it particularly useful for fault diagnosis and prognostics. Monotonicity can be computed using two approaches, depending on the specified method: the Signum method and [PERSON]'s rank correlation coefficient method. The Signum method measures monotonicity based on the consistency of directional changes (increases or decreases) in a feature over time. Mathematically, it is computed as  
\[Monotonicity=\frac{1}{M}\sum_{j=1}^{M}\left|\frac{1}{N_{j}-1}\sum_{k=1}^{N_{j}- 1}\text{sgn}(x_{j}(k+1)-x_{j}(k))\right| \tag{8}\]  
where \(x_{j}\) represents the vector of measurements for a feature on the \(j\)-th system, \(M\) is the total number of systems monitored, \(N_{j}\) is the number of measurements on the \(j\)-th system, and \(\text{sgn}(\cdot)\) is the signum function. The method evaluates the fraction of consistent changes in the feature in all monitored systems, with higher values indicating stronger monotonicity. The Spearman rank method computes monotonicity based on the correlation between the ranks of the feature measurements and their corresponding time points. The formula for monotonicity using this method is  
\[Monotonicity=\frac{1}{M}\sum_{j=1}^{M}\lvert\text{corr}(\text{rank}(x_{j}),\text{ rank}(t_{j}))\rvert \tag{9}\]  
where \(x_{j}\) represents the feature measurement vector on the \(j\)-th system, \(t_{j}\) is the vector of time points corresponding to the measurements \(x_{j}\), \(M\) is the total number of systems monitored, and \(\text{rank}(\cdot)\) denotes the ranking of elements in a vector.