esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Effect of Static Corrosion on Weight Loss  
To determine the weight loss due to static corrosion after 4 h of immersing in salt solution at 31 \(\pm\) 2 \({}^{\circ}\)C, Linear Polarization Resistance (LPR) measurements were performed. However, LPR technique requires the corresponding Tafel constants to be obtained from the anodic and cathodic curve. Hence to determine these constants, a dynamic polarization test was performed for the mild steel sample by sweeping voltage between \(-1.2\) V to 0.8 V with respect to the open circuit potential. The curve as shown in Figure 8 was obtained after the dynamic polarization test. Analyzing the curve using Gamry Echem analyst software, the values for anodic constant \(\beta_{a}\) and cathodic constant \(\beta_{c}\) were found as 0.2016 V/decade and 0.3256 V/decade, respectively.  
After performing the LPR test and using Gamry Echem analyst software the \(R_{p}\) value obtained for samples after 4 h was 33.76 k\(\Omega\). This value, along with the Tafel constants when used in Equation (3), gives the \(i_{corr}\) value as 1.602 uAcm\({}^{-2}\). By converting this \(i_{corr}\) value using the Faraday's law, the weight loss is calculated as 0.03 mg which is relatively very low compared to \(TWL\) and \(E_{0}\).  
Figure 6: Surface of the specimen 2 mm away from the center after 4 h test at 20 m/s and 600 mg/L.  
Figure 7: Surface of the specimen 3 mm away from the center after 4 h of testing at 20 m/s and 600 mg/L.
# Generating Process Models by Interacting with Chatbots--A Literature Review
## 5 Reporting of the Systematic Literature Review
### Generating Process Models from Natural Language Text Input (RQ1)
Studies such as 'A Combined Model of NLP with process modeling for Sentiment Analysis' (S3) and 'Assisted Declarative Process Creation from Natural Language Descriptions' (S5) aim to automate the conversion of text to process models, or vice versa. Improving accuracy. Studies like 'Extracting Declarative Process Models from Natural Language' (S10), 'A Semi-automatic Approach to Identify Business Process Elements in Natural Language Texts' (S11), and 'Process Model Generation from Natural Language Text' (S15) focus on improving the accuracy and semantic consistency of the models or descriptions. Evaluation metrics.Several studies, including 'Supporting the Process of Learning and Teaching Process Models' (S12), and 'A Machine Translation Like Approach to Generate process model from Textual Description' (S17), use various metrics to evaluate the accuracy and effectiveness of their methods. Differences between the selected studies. Application focus. Studies like 'A Combined Model of NLP with process modeling for Sentiment Analysis' (S3) focus on specific applications, such as sentiment analysis. Methodologies. Different studies use varying methodologies. For example, 'A Proposed Technique for process modeling Diagram using NLP' (S2) uses probabilistic latent semantic analysis, while 'Automatic Process Model Discovery from Textual Methodologies'(S13) focuses on verb semantics. 'Live Process Modeling with the BPMN Sketch Miner' (S14) emphasizes interactive tools, whereas 'A Concept for Generating process models from Natural Language Description' (S16) uses intermediate representations. Type of process models. Some studies, such as 'Generating BPMN Diagram from Textual Requirements' (S8) and 'Live Process Modeling with the BPMN Sketch Miner' (S14), focus on BPMN diagrams, while others, like 'Extracting Declarative Process Models from Natural Language' (S10) and 'Assisted Declarative Process Creation from Natural Language Descriptions' (S5), work on declarative models. User interaction. Studies like 'A Visual Approach for Identification and Annotation of Business Process Elements in Process Descriptions' (S9) and 'Live Process Modeling with the BPMN Sketch Miner' (S14) emphasize interactive tools for process modeling, whereas others, such as 'Automatic Process Model Discovery from Textual Methodologies' (S13), focus on automated methods. Domain specificity.Some studies, like 'A Case Study on Designing Business Processes Based on Collaborative and Mining Approaches' (S6), apply their methods to specific domains (e.g., the Ghana cocoa supply chain), while others are more general.
# Timed-Release Silica Microcapsules for Consistent Fragrance Release in Topical Formulations
###### Abstract  
Microcapsules are widely utilized in various applications to preserve active ingredients for prolonged durations while enabling controlled release. However, limited release of active ingredients often hampers their effectiveness in daily-use products. In this study, we demonstrated the synthesis of silica core-shell microcapsules designed for controlled fragrance release in topical formulations. The microcapsules were synthesized via the sol-gel polymerization of tetraethyl orthosilicate (TEOS) on the surface of an oil/water emulsion, leveraging the shrinkage and deformation characteristics of sol-gel-derived silica during drying. The concentrations of dipalmitoylethyl dimethylammonium chloride, a cationic emulsifier used in cosmetics, and TEOS were optimized to sustain fragrance release for up to 24 h after topical application. An additional silica coating on the microcapsules reduced the Brunauer-Emmett-Teller surface area by 76.54%, enhancing fragrance stability for long-term storage. The timed-release behavior was assessed using fragrance evaluation tests and gas chromatography-mass spectrometry. The fragrance intensity and release profiles confirmed the potential of these microcapsules in daily-use cosmetics. These findings suggest that silica microcapsules with extended-release properties have application potential in both cosmetic and pharmaceutical products.  
core-shell microcapsules; sol-gel method; cationic surfactant; controlled release; fragrance retention; topical application; daily use
# Selection Methodology of an Electric Actuator for
## 1 Introduction
A gearbox is used in order to transform low torque and high speed from the servo motor of actuator mechanism into low speed and high torque. Different types of gearbox, like Harmonic, cycloidal, or planetary gearboxesm are used for this purpose. However, planetary gearboxes are an effective selection, since their compact design uses less space and provide high effectiveness. A large part of the actuator mass can be dedicated to the servo motor when using planetary gearboxes because of its low gear ratio, as compared to the high reduction ratios of cycloidal and harmonic reducers. Next to the gear box, a screw mechanism is used in order to convert rotary motion into linear motion with a required force by transforming the torque and speed of servo motor. There are three primary kinds of linear actuation, which function by using a lead, roller, or ball screw. The Electronic Control Unit (ECU) controls the EMA module, which can run on 28 V-DC power source. The power  
Figure 1: Electro-mechanical Actuator (EMA) schematic. This figure identifies the components of an electro-mechanical actuator. control system of ECU defines the pulse width modulation for the motor control in the response to a torque command signal or position signal. The sensors in EMA directs the position, load and speed information towards the ECU for the feedback of landing gear position. For the flight state in which the external loads on landing gear actuator are high, an extra sensor for measuring the temperature is installed, so that the temperature of electronic components can be recorded and if the temperature is high, the ECU can let the components to cool down by limiting the current and voltage within range and give feedback about high temperature.
## 4 Discussion
### Reconstruction Accuracy Evaluation
It also systematically explored the effects of different dropout configurations on reconstruction performance, as shown in Figure 4. Statistical analysis revealed that the reconstruction errors between each method and the true HR DBM were ranked from largest to smallest as follows: A, B, C, D, and E. This result demonstrates that the improved network architectures significantly outperform the baseline methods in terms of detail recovery and structural fidelity, while simpler reconstruction methods, such as bicubic interpolation, tend to produce blurry artifacts and fail to recover high-frequency information. The differences between methods are visually highlighted in the black rectangular boxes within the figure, which further illustrate the excessive smoothing issues in bicubic interpo  
Figure 3: Comparison of reconstruction accuracy between five networks with different network structures: (**a**) MAE, (**b**) MSE, (**c**) PSNR, and (**d**) SSIM. lation and basic regularization strategies. The introduction of uncertainty quantification modules significantly improved artifact suppression, outperforming other networks and bilinear interpolation. Moreover, the HR DBM results reconstructed using various dropout techniques showed noticeable differences, suggesting that appropriate dropout strategies not only do not harm reconstruction but also help achieve more faithful results [54; 55]. The specific impact of dropout techniques and their application in multiple reconstructions will be discussed in more detail in subsequent sections. Figure 4: Comparison of the reconstruction results of the double cubic interpolation method with five networks.
# Study of Data Transfer in a Heterogeneous LoRa-Satellite Network for the Internet of Remote Things
###### Abstract  
In the absence of traditional communication infrastructures, the choice of available technologies for building data collection and control systems in remote areas is very limited. This paper reviews and analyzes protocols and technologies for transferring Internet of Things (IoT) data and presents an architecture for a hybrid IoT-satellite network, which includes a long range (LoRa) low power wide area network (LPWAN) terrestrial network for data collection and an Iridium satellite system for backhaul connectivity. Simulation modelling, together with a specialized experimental stand, allowed us to study the applicability of different methods of information presentation for the case of transmitting IoT data over low-speed satellite communication channels. We proposed a data encoding and packaging scheme called GDEP (Gateway Data Encoding and Packaging). It is based on the combination of data format conversion at the connection points of a heterogeneous network and message packaging. GDEP enabled the reduction of the number of utilized Short Burst Data (SBD) containers and the overall transmitted data size by almost five times.  
internet of remote things; heterogeneous networks; terrestrial-satellite networks; SATCOM; Iridium SBD; LoRa; Protobuf +
Footnote †: journal: Accepted: 30 July 2019; Published: 1 August 2019
## 3 Results and Discussion  
### Structure and Morphology of N-CQDs  
The N-CQDs solution exhibited stability and homogeneity at room temperature for two months, with no notable precipitation detected. Figure 1a illustrates the morphology of the N-CQDs by TEM, revealing evenly spherical particles. A total of 181 particle size data (N = 181) were obtained from Figure 1a. Figure 1b indicates that the particle size varies from 1.86 nm to 8.62 nm, and the arithmetic mean value of particle size is D = 4.69 nm. Then, the grain size histogram was drawn by Sturges method. The bin-width (W) is obtained from the relation W =(D\({}_{\mathrm{max}}\) \(-\) D\({}_{\mathrm{min}}\))/k, where k = 1 + 3.322 log(N) [25]. From the Gaussian curve fitting formula y = A \(\times\) exp(\(-2\) ((x - x0)/2\(\sigma\))\({}^{2}\)) + C, we obtained a median particle size of D\({}_{0}\) = 4.53 \(\pm\) 0.1 nm and \(\sigma\) = 3.38 \(\pm\) 0.7 nm. Given that the values of D\({}_{0}\) and \(\sigma\) are of comparable magnitude and the differences are not statistically significant, it is inappropriate to approximate the CQDs as an ideal system with a single particle size, because variations in particle size may lead to distinct effects. The clearly defined lattice fringes in the high-resolution TEM (HRTEM) image (Figure 1c) correspond to the lattice spacing of the graphene (100) faces, measuring 0.21 nm [26]. The XRD result of the CQDs have a part of graphite oxide carbon structure, so there is a diffraction peak of (002), and the wide peak (20 = 15-30\({}^{\circ}\)) can be attributed to the amorphous carbon structure [22; 27; 28].
# Application of the Supercapacitor for Energy Storage in China: Role and Strategy
## 3 Development of China's Supercapacitor Field
### Application Status
#### 3.2.4 Communication and Transportation  
The supercapacitor is widely used in electric vehicles (EV). Every time the vehicle is started, the supercapacitor supplies a strong current to the engine to drive its operation, promoting the starting speed of the motor as well as improving the performance of the EV and the state of the battery. As the vehicle brakes, energy recovery and recycling can be achieved easily by supercapacitors. Similarly, as the auxiliary power supply of electric vehicles, supercapacitors can effectively prolong the battery's service life [43]. Urban rail transit systems combine high transport capacity and low station distance. Similarly, with electric vehicles, the frequent start-brake produces energy losses. The supercapacitor meets the energy management requirement of large capacity, long service life, and high power density, having the comprehensive value of application in different urban rail transit systems vehicles. A kind of trolley bus equipped with carbon/carbon organic supercapacitor was independently developed in Ningbo in 2017. The urban rail train itself carries a huge amount of kinetic energy when it runs. When the train slows down and stops at the station, the energy is converted into electrical energy and sent back to the grid through the electromagnetic braking of the driving motor. The traditional method releases the braking energy through the brake resistance, resulting in the waste of energy. Such trolley bus with 1500V supercapacitor developed in Ningbo absorbs energy while braking, and then transmits the absorbed braking energy to the power grid, realizing the recovery and utilization of train braking energy and effectively avoiding energy waste caused by rising ambient temperature during the braking process [44].
# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 2 Materials and Methods
### Method for Anomaly and Trend Detection Model Selection
#### 2.2.2 Synthetic Data Generation
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multirow{2}{*}{**Signal**} & \multirow{2}{*}{**Parameter Type**} & \multicolumn{2}{c}{**Parameter Values**} \\ \cline{3-5}  & & **Synthetic Data Set 1** & **Synthetic Data Set 2** \\ \hline \multirow{4}{*}{\(x_{trend}(t)\)} & \multirow{2}{*}{Trend type} & Linear & \\ \cline{3-5}  & & Quadratic & \\ \cline{3-5}  & & Linear: 0.012 & Linear: \(4.58\times 10^{-4}\) \\ \cline{3-5}  & & Quadratic: \(7.09\times 10^{-5}\) & Quadratic: \(1.05\times 10^{-7}\) \\ \hline \multirow{4}{*}{\(x_{seasonality}(t)\)} & \multirow{2}{*}{Seasonality type} & Sine & \\ \cline{3-5}  & & Production cycle (Formula 2) & \\ \cline{3-5}  & & \begin{tabular}{l} Simple: 0.15 \\ \end{tabular} & Sine: 3 \\ \cline{3-5}  & & Production cycle: 1.1 & \begin{tabular}{l} Production cycle: 2 \\ \end{tabular} \\ \hline \multirow{4}{*}{\(x_{noise}(t)\)} & \multirow{2}{*}{Noise type} & Uniform noise & \\ \cline{3-5}  & & White noise & \\ \cline{3-5}  & & Uniform noise & \begin{tabular}{l} Uniform noise \\ range: 0.15 \\ \end{tabular} & \begin{tabular}{l} Uniform noise \\ range: 1 \\ \end{tabular} \\ \cline{3-5}  & & Noise parameters & \begin{tabular}{l} White noise mean: 0 \\ White noise \\ standard deviation: \\ 0.03 \\ \end{tabular} & \
\begin{tabular}{l} White noise mean: 0 \\ White noise \\ standard deviation: \\ 0.8 \\ \end{tabular} \\ \hline \multirow{4}{*}{\(x_{anomaly}(t)\)} & \multirow{2}{*}{Anomaly types} & Point anomaly & \\ \cline{3-5}  & & Collective anomaly & \\ \cline{3-5}  & & Amplitude: 2 & Amplitude: 1.1 \\ \cline{3-5}  & & Collective anomaly lengths: &
\begin{tabular}{l} Collective anomaly \\ lengths: 5 measurements \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of used parameter ranges for the synthetic time series. Figure 4: Example of a synthetic time series. It consisted of time series with low noise, trends with a high slope, and large anomaly magnitude values and lengths. Furthermore, a second data set (synthetic data set 2) with more difficult conditions for the detection of trends and anomalies was generated. Here, time series with high noise, low trend slopes, and low anomaly magnitudes and lengths were calculated. In each time series 40 anomalies were present. Each created time series was analysed by each model to detect trends and anomalies. In total, 16 unique time series were analysed per data set.
# An All-Digital Optical Phase-Locked Loop Suitable for Satellite Downlinks +
## 2 Coherent Optical Receiver  
### LEO Slant Path  
A general overview of the implemented system is shown in Figure 1. It shows a satellite downlink and its power level diagram considering the propagation of an optical signal digitally modulated with quadrature phase-shift keying (QPSK) and a bit rate of 20 Gbps. In addition, an optical band-pass filter (OBPF) filters the unwanted side lobes of the modulated optical signal, thereby reducing the transmitted bandwidth. The filtered optical signal is then amplified by an erbium-doped fiber amplifier (EDFA) to reach an  
Figure 1: Power level diagram for the FSO downlink. \(G_{T}\) and \(G_{R}\) are the transmitter and receiver telescope gains, respectively, and EIRP stands for Effective Isotropic Radiated Power. The \(a_{\text{scint}}\) is the attenuation caused by atmospheric turbulence.  
optical power of 30 dBm. Finally, the amplified pass through a telescope with a diameter \(D_{T}=12.5\) cm having a gain of approximately \(G_{T}=108\) dB and optics efficiency \(\eta_{T}=0.7\). Subsequently, the optical signal propagating through a slanted path of an LEO satellite, with a minimum elevation of \(20^{\circ}\) and an altitude of 700 km, is considered.  
The transmitted optical signal is recovered in the optical ground station (OGS), where the telescope has a diameter of \(D_{R}=100\) cm, having a gain of approximately \(G_{R}=126\) dB, and an optics efficiency of \(\eta_{R}=0.65\). The received optical power after the propagation through the slant path, the telescope gains, and the transmission loss can be found in the link budget calculation [4; 10; 11]. Considering additional losses of 9 dB, such as coupling loss, the recovered optical signal has an optical power of approximately \(-16\) dBm. The optical signal will be more attenuated according to the effects of the atmospheric turbulence \(\mathrm{a_{scint}}\), indicated in the power level diagram.  
The OGS employs a coherent optical detector to retrieve the transmitted data. The coherent detector has a photodetector with a responsivity of \(R=0.04\) A/W, which gives a margin of 4 dB over the required input power. Consequently, the signal-to-noise ratio SNR can be determined by the following expression [12]:  
\[\mathrm{SNR}=\frac{RPs}{qR_{s}} \tag{1}\]  
where \(R\) is the photodetector responsivity, \(Ps\) is the received optical power, \(q\) is the electron charge (\(1.602\times 10^{-19}\) C), and \(R_{s}\) is the symbol rate. For QPSK modulation, the symbol-error rate (SER) is approximately denoted as [13]:  
\[\mathrm{SER}=\frac{1}{2}\mathrm{erfc}\left(\sqrt{\frac{SNR}{2}}\right) \tag{2}\]  
The above equation is approximately equal to the BER when the transmitted symbols are encoded with Gray code. Therefore, the modulation schemes used in this study will be encoded with the Gray code, where the above relations will be used to determine the theoretical BER curve.
## 4 Problem Analysis and Algorithm Description
### Algorithm Description
#### 4.2.2 Formation Process of 2D Convex Hull Spanning Tree  
The sink node is the tree root node \(r\) found at the center of the water surface. It broadcasts information to other active nodes, according to the received signal strength from node \(S_{i}\), to calculate distance \(ds_{i}\) (\(1\leqslant i\leqslant N_{A}\)) between \(S_{i}\) and the sink node, and \(N_{A}\) denotes the number of active nodes. Moreover, the sink node assigns a unique activation identifier \(ID_{A}\) to all active nodes, where \(ID_{A}\) is recorded from \(1\) to \(N_{A}\) according to the size of \(ds_{i}\). Furthermore, all active nodes in the convex hull are recorded as \(CH\_nodes\).  
1. The tree root node \(r\) and set of active neighbor nodes \(Nei\_A\) form an initial spanning tree \(Tree_{init}\) as shown in Figure 7a. The iterative strategy is adopted to produce a next spanning tree generation according to the size of \(ID_{A}\), until all active nodes of the 2D convex hull form a complete initial spanning forest \(Forest_{init}\). Whether a parent node and its child nodes on spanning forest \(Forest_{init}\) are able to communicate, if not, by Section 3.1.3, the communication radius is incremented until the parent node is connected to its child nodes so that the initial spanning forest \(Forest_{init}\) can maintain full network connectivity.
2. The time marker \(T_{mark}\) (\(0\leqslant T_{mark}\leqslant T\))of root node \(r\) is initialized to \(0\), and the next generation of child nodes' time marker increments by \(1\), until all active nodes of the initial spanning forest \(Forest_{init}\) are marked to the \(T\) level (i.e., all of \(CH\_node\) are marked by the \(r\) node). The \(T_{mark}\) of the same subtree parent node set \(\{f_{i}^{j}\}\) (\(j\) denotes \(ID_{A}\); \(i\) denotes \(T_{mark}\)) is recorded.  
Figure 6: Flowchart of the optimal activation strategy.  
3. The active node inside the 2D convex hull \(Sa\_in\) (\(ID_{A}\)) acts as the parent of its neighboring sleeping nodes. Building a small spanning tree of individual nodes on \(Forest_{init}\), we define a small spanning tree consisting of hybrid construction where the parent is the active node and the child is the sleeping node, as shown in Figure 7b.
4. To achieve the 3D global network coverage, the algorithm needs to deploy neighboring sleeping nodes \(Nei(f_{j}^{i})\) distributed at different depths and calculate the depth adjustment of the set of subtree parent nodes \(\{f_{j}^{i}\}\) for their child sleeping nodes.