esa-sceva/satcom-chunk-collection huggingface dataset:
# Encapsulation of Fennel Essential Oil in Calcium Alginate Microbeads via Electrostatic Extrusion
## 2 Materials and Methods
### Characterization of Fennel EO Microbeads
#### 2.5.4 Swelling Kinetics  
The method for determining beads' swelling is based on measuring the change in mass of capsules after absorbing water at specific time intervals (0-200 min in the present study). To determine swelling behavior, 1 g of dry fennel EO alginate microbeads were immersed in 100 mL of distilled water with continuous agitation at 100 rpm. At fixed time intervals (every 20 min), the beads were separated from the medium, and the mass of the beads was measured.  
In order to describe the swelling kinetics, firstly, the swelling ratio (S) was calculated according to Equation (6) [12]:  
\[\rm S\ =\ \frac{(m_{1}-m_{2})}{m_{2}} \tag{6}\]where \(m_{1}\) was the mass of beads after swelling, and \(m_{2}\) was the mass of beads prior to swelling.  
After calculating the swelling ratio, data were fitted to a Korsmeyer-Peppas model following a simple Fick's law in order to describe swelling kinetics and diffusion of the polymeric structures according to Equation (7) [18]:  
\[F\ =\ \frac{S_{t}}{S_{e}}\ =\ kt^{n} \tag{7}\]  
where \(F\) represents the swelling fraction; \(S_{t}\) is the swelling content at a given time; \(S_{e}\) is the equilibrium swelling content; \(t\) is time; \(n\) is the diffusion exponential of the solvent, and \(k\) is the constant that changes according to the gel network structure.
# Effects of Boundary Condition Models on the Seismic Responses of a Container Crane
## 2 Description of a Numerical Model of the Container Crane  
In this study, a 3D finite element model (FEM) of the container crane is generated by SAP2000. Figure 1 shows that the FEM uses the real dimensions of the Korean container crane. The container crane has crucial dimensions of: total height of 77.8 m from the ground to the top of the container crane; length from the end of the trolley girder to the end of the boom of around 136 m; the portal frame that controls the critical portal sway mode has a width of 30.5 m and its height is 17.5 m; it has an outreach of 63 m, and a back reach of 20.0 m. Most of the structural components are made of built-up stiffened box sections, except for diagonal braces that were made of tube sections; the forestay and backstay are made of wide-flange shapes. The properties of the material complied with the Japanese industrial standards (JIS) JIS-SM490Y and JIS-STK490. The FEM is composed of 9916 elements; 9912 frame elements, and 4 link elements that are used to simulate the connection of ground and structure. The moment in the end and beginning of elements, i.e., the forestay, backstays, and diagonal braces, are released; thus, these elements work as truss elements. All non-structural loads, i.e., machinery house, drive trucks, stairs, stowed pins, snag device, and boom hoist rope, are simulated as concentrated or distributed loads. The total weight of the container crane is 13,883 kN.
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Ref.** & \begin{tabular}{c} **Tracking** \\ **Type** \\ \end{tabular} & **STE** & **Character. Methodology** & \begin{tabular}{c} **Cost** \\ **(USD)** \\ \end{tabular} & \
\begin{tabular}{c} **Approx.** \\ **TRL** \\ \end{tabular} &
\begin{tabular}{c} **Annual Energy Increase** \\ **vs. a Fixed PV System** \\ \end{tabular} \\ \hline
1 & [26] & Dual & NM & NM & \$92 & 3–4 & 44.89\% \\
2 & [15] & Dual & NM & NM & NM & 3–4 & 48.20\% \\
3 & [16] & Dual & NM & Shadow bar with concentric circles & NM & 4–5 & 35.00\% \\
4 & [19] & Dual & 1\({}^{\circ}\) & STE measured with the sensor that give & NM & 4–5 & 39.50\% \\
5 & [14] & Dual & 0.11\({}^{\circ}\) & STE measured with the sensor that give & NM & 3–4 & 43.60\% \\  & & & & feedback to the control system & & \\
6 & [27] & Dual & 0.5\({}^{\circ}\) & STE measured with the sensor that give & NM & 4–5 & 26.90\% \\
7 & [28] & Single & NM & NM & NM & 4–5 & 29.90\% \\
8 & [18] & Dual & NM & NM & \$913 & 3–4 & 18.00\% \\
9 & [20] & Dual & 0.2\({}^{\circ}\) & NM & \$1484 & 7–8 & NM. STS for a parabolic concentrator system \\
10 & [18] & Dual & 2\({}^{\circ}\) & Shadow bar with concentric circles. & NM & 3–4 & NM \\
11 & [29] & Dual & 0.15\({}^{\circ}\) & Shadow bar with concentric circles & NM & 4–5 & 23.6\% and 31.8\% \\
12 & [14] & Dual & 0.43\({}^{\circ}\) & \$190 & 3–4 & 19.1\% and 30.2\% \\
13 & [30] & Dual & 0.15\({}^{\circ}\) & NM & 3–4 & 60.45\% \\
14 & [31] & Dual & 2\({}^{\circ}\) & STE measured with the sensor that give & NM & 3–4 & NM \\
15 & [32] & Single & 2\({}^{\circ}\) & Shadow bar with concentric circles & NM & 7–8 & NM \\
16 & [33] & Dual & NM & NM & 4–5 & 44.44\% \\
17 & [34,35] & Dual & \textless{}0.1\({}^{\circ}\) & STE measured with the sensor that give & NM & 7–8 & NM \\
18 & [36] & Single & NM & NM & 3–4 & 36\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: STS developments reported in the literature (NM: not mentioned). This paper presents the development, control and evaluation of a dual-axis active STS with a mean tracking accuracy of \(\pm\)0.4\({}^{\circ}\) and \(\pm\)0.55\({}^{\circ}\) for sunny and partly cloudy days, respectively, using a low-cost control system based on Arduino. The STS was tested in an operational environment at the Laboratory for Innovation and Characterization of Solar Thermal and Photovoltaic Systems (LICS-TF) of the Optical Research Center A.C., in Aguascalientes, Mexico. The present work is organized as follows: Section 2 describes the materials and methods used in the design and construction of the STS and its control and monitoring panel. In Section 3, the manual and automatic operating modes as well as the control strategy are detailed. Experimental solar tracking testing results under real environmental conditions are presented in Section 4. In addition, this section presents the optical-energetic analysis, considering a conventional PV system and a typical concentration PV module as the payloads, as well as the cost analysis of the developed STS. Finally, a brief discussion and conclusions are presented in Sections 5 and 6, respectively.
# A Compact Broadband Planar Inverted-F Antenna with Dual-Resonant Modes
## 4 Experiment Result  
To confirm the predicted antenna performance, an antenna prototype was fabricated and measured. Figure 7 shows the photograph of the fabricated antenna.  
The measured and simulated S-parametations and gains of the PIFA are shown in Figure 8. The measured impedance bandwidth (\(\mathrm{1S_{11}\,|<-10\;\mathrm{dB}}\)) is 14.5%, which is from 3.17 GHz to 3.67 GHz, showing good agreement with the simulated result. The measured gain varies from 5 to 6.23 dBi over the operating band, which is slightly lower than the simulated result varying from 5.2 to 6.36 dBi. The discrepancies between the measured and simulated results are mainly caused by the fabrication tolerance, the loss from the SMA connector and its imperfect manual assembly, as well as the possible effects of the measurement setup close to the antenna under test conditions. The measured total efficiency of the PIFA is higher than 83% within the operating frequency band.  
The measured and simulated radiation patterns in the E-plane and H-plane of the proposed antenna are illustrated in Figure 9. The measured results show that the antenna has stable radiation patterns with 3-dB beamwidths of 90 \(\pm\) 2\({}^{\circ}\) in the E-plane and 88 \(\pm\) 2\({}^{\circ}\) in the H-plane. Meanwhile, the H-plane radiation patterns have low cross-polarizations  
Figure 8: The simulated and measured \(\mathrm{1S_{11}\,|\,s}\), gains, and efficiency.  
Figure 7: Photograph of the fabricated antenna.  
of less than \(-17.3\) dB within the 3-dB beamwidths. There is good agreement between the measured and simulated results. The above results all prove that the antenna can have a good radiation performance.  
Table 3 shows the performance comparison between the proposed antenna and the previous reported PIFA/patch antennas. It can be seen that the proposed antenna operates with different resonant modes. In this context, the antenna combines the merits of wide bandwidth and compact size. It also demonstrates good radiation characteristics. It is worth noting that although the antennas in [25,27] have slightly wider impedance bandwidths than the proposed values, their overall volumes are much larger, and the radiation patterns have also been deteriorated to some extent. In all, the proposed antenna shows a better overall performance when a number of factors of bandwidth, size, and radiation patterns are comprehensively considered.  
Figure 9: The simulated and measured radiation patterns: (**a**) 3.2 GHz; (**b**) 3.6 GHz.
###### Abstract
This aims to resolve existing MA routing drawbacks, such as increased data  
Figure 1: NDN conceptual diagram. collection robustness, decreased path length and network resource constraints--mainly bandwidth. MINDS contributions are outlined as below:  
* Utilising a lightweight clustering algorithm (Hamming distance) with a minimised communication overhead to support early in-network data aggregation. * Taking the benefits of named data networking to form a context-aware tree infrastructure with minimised network traffic in a self-organising fashion. * Using a hop-count-aware Depth-First Search (DFS) algorithm [14] to forward MAs throughout the tree infrastructure to maximise the number of visiting nodes (results in increased data aggregation robustness) through minimised latency and loop-free routes. MINDS has the capacity to offer Internet of Things (IoT) applications a number of benefits, especially in the context of sensory data collection and aggregation. The key duty of IoT is to provide Machine-to-Machine (M2M) communications which interconnect (sensory) devices via Internet links to transmit data samples. Agent-based protocols are commonly used in IoT-enabled WSNs to enhance application automaticity and network interoperability [15]. Indeed, IoT establishes an Internet-based infrastructure to forward MAs throughout a network and autonomously complete a mission [16]. IoT sensory systems would take the benefits of MINDS, which forwards multiple MAs through Internet links in order that they visit connected sensory stations for data collection and aggregation. The rest of this paper is organised as follows. Section 2 reports a review on the applications of wireless sensor network data collection. Existing drawbacks and advances in this field of research are compared and contrasted. Section 3 introduces our proposed approach. Remarks on, features of and techniques within the designed MA itinerary planning are described. Section 4 explains the experimental plan and shows how the performance of MINDS was tested and evaluated. Section 5 provides the performance evaluation results of our proposed approach according to four key metrics: end-to-end delay, data robustness, path hop-count and traffic transmitted. The performance of MINDS is compared with two conventional protocols, ZMA [4] and TBID [17]. Section 6 explains MA routing benefits and outlines the key points of this research that have the potential to be addressed as further work.
## 3 Results and Discussion
### Implications  
The derivation of the model is based on part of the life cycle, which refers to the whole process of producing nitrogen, carbon dioxide, and water from the reaction of the reactants toluene and nitrate [49]. The effects of toluene degradation on the calcium carbonate system include many qualitatively complex reactions, and the mathematical model still has some limitations. Therefore, numerical simulation can be used to guide and apply the actual practice in the future, and the model can be better generalized [50]. Thus, it can serve to calculate the amount of degradation of petroleum pollutants by calculating the change in inorganic ion concentrations in the field, avoiding a series of problems associated with field organic tests, such as delayed test results, dangerous test drugs, and secondary pollution of the test solution. At the same time, with the help of the model, the degradation of toluene in a fixed period can be quickly determined, which is of great significance for improving the distribution efficiency of regional groundwater organic pollution.  
In the model, NO\({}_{3}\)\({}^{-}\) is converted to N\({}_{2}\) by the action of microorganisms. According to the research, fungi are capable of this reaction. They possess enzymes involved in denitrification, including nitrate reductase (Nar) and nitrite reductase (Nir). Similarly, bacteria such as nitrite-reducing bacteria and facultative denittifying bacteria also possess these enzymes and can carry out the reaction in the model [42; 51].  
In general, denittifying bacteria have diverse living conditions. The pH range for these microorganisms is generally between 6.5 and 9, and they can thrive in environments with temperatures ranging from 10 to 40 degrees Celsius. For bacteria, the reaction occurs under anaerobic conditions. For fungi, a small amount of oxygen can promote the reaction, but excessive oxygen inhibits the reaction [52]. The environment of most groundwater systems is suitable for the growth of microorganisms. At present, the living environment of microorganisms is not considered in the derivation of the model. In the future, the existing research will be expanded by a series of experiments with different initial conditions, including toluene concentration, intermediate organic acids, and initial pH.
# Promotion of Color Sorting in Industrial Systems Using a Deep Learning Algorithm
## 2 Materials and Methods
### Generalized Intersection over Union--GloU  
In neural networks, any loss function must be differentiable so as to execute learning by the backpropagation method. IoU has value 0 in cases when the predicted bounding box does not intersect with the real box; therefore, there is no derivative. On the other hand, GloU is always differentiable, and its range is from \(-1\) to 1. It covers the smallest possible area between the predicted and the real box. Therefore, it measures how close the predicted shape is to the real one, although they do not intersect, because some boxes are farther, and others are closer. An example is provided in Figure 7.  
Figure 7: The cases when the predicted box (**a**) does not coincide at any point with real box: (**c**) has intersection with real box: (**b**,**d**) area C that serves to define how close the predicted box is to the real one.  
Input: two arbitrary shapes: \(\text{A},\text{B}\subseteq\text{S}\in\text{R}^{\text{n}}\). Output: GIoU  
A, B represent the predicted and real bounding box. C is the smallest convex envelope that includes A and B.  
\[\text{IoU}=\left|\frac{\text{A}\cap\text{B}}{\text{A}\cup\text{B}}\right| \tag{4}\]  
\[\text{GIoU}=\text{IoU}-\frac{|\text{C}\backslash(\text{A}\cup\text{B})|}{| \text{C}|} \tag{5}\]
# A Comprehensive Review of GNSS/INS Integration Techniques for Land and Air Vehicle Applications
## 3 GNSS Solution. Strapdown INS Approach. Strapdown INS Mechanization. Inertial Error Propagation
In most works, the dynamic errors of the accelerometer \(\sigma\mathbf{b}_{a}\) and gyro sensor \(\sigma\mathbf{b}_{g}\) are modeled as Gauss-Markov scalar processes [70; 71]:  
\[\sigma\dot{\mathbf{b}}_{a}=-\frac{1}{\tau_{a}}\sigma\mathbf{b}_{a}+G_{a}\mathbf{w},\; \sigma\dot{\mathbf{b}}_{g}=-\frac{1}{\tau_{g}}\sigma\mathbf{b}_{g}+G_{g}\mathbf{w} \tag{16}\]  
where \(\tau_{a}\), \(\tau_{g}\) is the correlation time and \(w\) is white noise.
## 6 Conclusions  
This paper uses HK CORS observation data and the multi-GNSS real-time orbit and clock products released by the CNES to realize multi-GNSS real-time PPP. Based on the ZTD products of IGS and the PWV data of the RS station and the ERA5 dataset, the positioning performance of real-time PPP during rainfall and nonrainfall periods and the accuracy of water vapor inversion are analyzed. The key results obtained from the current data suggest the following: (1) Compared with a single GPS system, real-time multi-GNSS PPP can reduce the convergence time of a single GPS system during both rainfall and nonrainfall periods. During nonrainfall periods, the convergence time of real-time multi-GNSS PPP is 12.06% less than that of single GPS systems. During rainfall periods, the convergence time of real-time multi-GNSS PPP is 29.20% less than that of single GPS systems. In terms of positioning accuracy, the multi-GNSS system has no obvious improvement effect on a single GPS system, and the average positioning accuracy in the E, N, and U directions under the two schemes is better than 3 cm. (2) Real-time PPP-ZTD experiments show that compared with IGS-ZTD, multi-GNSS real-time PPP-ZTD has higher accuracy. The accuracy of real-time PPP-ZTD during nonrainfall periods is higher than that during rainfall periods, whether using IGS-ZTD or NWP inversion ZTD as a reference. (3) The real-time PPP PWV experiment shows that the accuracy of real-time PPP PWV during nonrainfall periods is higher than that during rainfall periods, whether using RS PWV or NWP inversion PWV as a reference. The PWV value during rainfall periods is higher than that during nonrainfall periods.  
Conceptualization, Y.X., L.M. and F.Z.; methodology, L.M. and X.C.; software, L.M.; validation, Y.X., L.M. and X.C.; formal analysis, L.M.; investigation, L.M.; resources, Y.X.; data curation, L.M.; writing--original draft preparation, L.M., Y.X. and F.Z.; writing--review and editing, L.M.; visualization, L.M.; supervision, Y.X.; project administration, Y.X.; funding acquisition, Y.X. and Z.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (42174035) and the Talent Introduction Plan for the Youth Innovation Team in the Universities of Shandong Province (satellite position and navigation innovation team).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We acknowledge the University of Wyoming, the International GNSS Service (IGS), the Hong Kong Geodetic Survey Services (HKOSS), the National Climate Data Center (NCDC), and the European Centre for Medium-Range Weather Forecasts (ECMWF) for providing relevant data and products.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Voice Transformation Using Two-Level Dynamic Warping and Neural Networks
## 6 NN Architecture Experiments
### Phase Two  
After validating from Phase One that the NN was able to produce good transformed speech for a single phrase, the NN was trained to deal with multiple phrases. In this phase, training was conducted using 600 phrases from a single speaker for training, and a separate set of 120 phrases from a single speaker was used to test. The selected NN architecture was trained for 10,000 iterations.  
Figure 12 shows the warping paths for some of the segments, where the DW path (truth) is shown in blue and the learned path is in orange. The NN learned some of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Phase No.** & **Input Model** & **Architecture Type** & **NN Architecture** & **MCD [dB]** \\ \hline \multirow{8}{*}{
\begin{tabular}{c} Phase \\ Three \\ \end{tabular} } & & 5 & 256L 600R 1200R 1000R 964L & 20.5 \\  & & 6 & 256L 600R 1000R 1500R 1000R 964L & 18.01 \\  & & 7 & 256L 600R 1000R 1500R 2000R 1500R 964L & 18.002 \\  & & & 500L 1500R 2500R 3500R 4500R 5040R 6459R 7459R & 3.3 \\  & & 19 & 8459R 9459R 10459R 8459R 7939R 6939R 5500R 4500R & 3.3 \\  & & & 3500R 2500R 964L & \\  & & & 500L 1500R 2500R 3500R 4500R 5040R 6459R 7459R & 5.5 \\  & & & 8459R 9459R 10459R 9459R 8459R 7939R 6939R 5500R & \\  & & & 4500R 3500R 2500R 964L &  
warping paths well. Figure 12b, which was produced using the selected architecture after 10,000 training iterations, showed more matching between the \(\mathbf{a}_{F}\) warping path and the learned path than Figure 12a, which was produced at 2500 training iterations. Figure 12d, which was produced at 10,000 training iterations, showed more matching between the \(\mathbf{b}_{F}\) warping path and the learned path than Figure 12c, which produced at 2500 training iterations. By 10,000 training iterations, many of the warping functions were learned well, but there was still a significant number that were not learned well. Figure 13 shows the MSE between the true values of the warping paths and the estimated one.  
Figure 12: Learned warping path **a**, Phase Two. (**a**) Warping path **a**, 2500 iterations. (**b**) Warping path **a**, 10,000 iterations. (**c**) Warping path **b**, 2500 iterations (**d**) Warping path **b**, 10,000 iterations.
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
###### Abstract  
Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving 5G and 6G networks. A 5G/6G network can comprise various network slices from unique or multiple tenants. Network providers need to perform intelligent and efficient resource management to offer slices that meet the quality of service and quality of experience requirements of 5G/6G use cases. Resource management is far from being a straightforward task. This task demands complex and dynamic mechanisms to control admission and allocate, schedule, and orchestrate resources. Intelligent and effective resource management needs to predict the services' demand coming from tenants (each tenant with multiple network slice requests) and achieve autonomous behavior of slices. This paper identifies the relevant phases for resource management in network slicing and analyzes approaches using reinforcement learning (RL) and DRL algorithms for realizing each phase autonomously. We analyze the approaches according to the optimization objective, the network focus (core, radio access, edge, and end-to-end network), the space of states, the space of actions, the algorithms, the structure of deep neural networks, the exploration-exploitation method, and the use cases (or vertical applications). We also provide research directions related to RL/DRL-based network slice resource management.  
admission control; resource allocation; resource scheduling; resource orchestration; network slicing; deep reinforcement learning 2022
# Internet of Things (IoT) Cybersecurity: Literature Review and IoT Cyber Risk Management
## 2 Literature Review
### Cybersecurity in IoT Architecture
#### 2.1.3 Cybersecurity at the Processing Layer  
Cloud computing and fog computing have become a standard technology at the processing layer for storing and processing large-size data streams generated from large numbers of IoT devices concurrently. Fog computing uses network devices for latency-aware processing of collected data [31]. In fog computing, IDS can be used on a fog node to detect intrusion [32]. A hybrid method using IDS, Virtual Honeypot Devices (VHD), and Markov models in fog computing shows promising results in identifying malicious devices as well as decreasing the false alarm rate [31].  
The processing layer can utilize blockchain by publishing and storing data as a public ledger of every user or node in the system [33]. Some of the promising application areas of blockchain for the IoT include supply chains, smarter energy, and healthcare [34]. Blockchain can also be used to generate IoT security certificates for IoT devices to be deployed securely and automatically [35].