esa-sceva/satcom-chunk-collection huggingface dataset:
# Formal Modeling of IoT and Drone-Based Forest Fire Detection and Counteraction System
## 1 Introduction
The animal sensor is deployed on the animal's body to detect their body temperature and behavior, where other sensors are deployed on the trees and ground. Drones are known as flying robots, so they can efficiently control the fire at early stages. If sensors detect anything that indicates fire, then information will be passed to the control room. The controller sends the signals to the drone to visualize the forest area where the sensors detected the fire. In the case of fire, the monitoring drones will check the intensity of the fire and send back information to the control room for timely decisions to control the fire before it spreads. Retaining connection of the network is another essential problem, since whenever a device stops working, the network is isolated in different parts. To deal with the failure of nodes, there are three approaches: proactive, reactive, and hybrid. The proactive methods focus on building and preserving the biconnected topology, which results in a huge number of actors, increased costs, and inefficiency. Reactive methods are useful for observing faults at an early stage and for initiating repair procedures. Pre-failure preparation and post-failure recovery are assumed in hybrid methods, which are a combination of reactive and proactive methods. In the proposed work, the reactive method is used to identify the failure of network nodes, and recovery will be established by the control room. The control room is responsible for installing a new sensor and drone in case of a failure report, as shown in Figure 1.  
To minimize human interaction in network maintenance, graph theory is used to provide an effective data structure for information storage [10]. The semi-formal model is converted into a formal model by utilizing the different resources available in the VDM-SL toolbox for correct functioning of the system. Formal methods are useful to ensure correctness in order to resolve simulation and testing limitations. Mathematical notations represent the formal approaches that are beneficial for modeling, defining, and evaluating the characteristics of security, operational structures, and complicated systems. Today's researchers have adopted formal methods for formalizing systems in different areas [11; 12; 13; 14; 15].
## 3 Proposed Scheme
### Performance Requirements For Mission-Critical Data Delivery  
The U.S. Army Unified Capabilities (UC) Reference Architecture (RA) report provides resource assurances and a service differentiation between real-time and non-real-time mission-critical data over the network [20]. UC services provide support to all operational phases and facilitate the convergence of the operating and generating forces. UC services facilitate more timely delivery of emerging UC technologies and provide increased mission effectiveness.  
Army UC services are required to be delivered in accordance with different priority/precedence levels with connectivity [20]. Accordingly, voice, video, audio, and data for multimedia conferencing are delivered over the networks using multi-level precedence and preemption (MLPP). MLPP-based services are also known as precedence-based assured services (PBAS), with five priority levels from the lowest to the highest [21]: ROUTINE (R), PRIORITY (P), IMMEDIATE (I), FLASH (F), and FLASH OVERRIDE(FO), which can be mapped to future combat system (FCS) attributes. FCS is a joint networked system of systems and is connected using an advanced network architecture that facilitates situational awareness, joint connectivity, and synchronized operations [22]. The system operates as a system of systems that networks the existing systems and has the capacity to add yet to be developed systems also to meet the future requirements of the Army's FCS brigade combat teams. The FCS data traffic includes information regarding collaboration command control (C2), situational awareness, target data, fire requests, medical states, sensor tasking data, and terrain data. In addition, the system can classify the per-hop behavior (PHB) into expedited forwarding (EF), assured forwarding (AF), and best effort (BE) according to the transmission priority and can map it according to the required FCS traffic attributes. Table 1 summarizes the performance requirements of the various FCS traffic data elements, UC applications, and QoS mapping factors based on the priority/precedence levels referred to in [21]. In the present study, the elements were applied to an algorithm by considering the mapping relationships among the priority-based traffic attributes.
# ALPINE: Active Link Prediction Using Network Embedding
## 5 Experiments and Discussion
### Quantitative Evaluation of ALPINE
#### 5.3.2 Case 2: \(P\subset U\) and \(T=U\)  
In the second case, we applied ALPINE with a smaller pool, while we were still interested in predicting all the unknown link statuses. The experiment setting was similar to the previous case, but the pool size \(|P|\) was set to 10 times the step size--10s--and the budget \(k=5\)s, i.e., only five iterations were performed. The candidates in the pool were randomly sampled from the unobserved part for each PON in our experiments.  
Figure 5 shows the results for this case. Compared to the first case, the _AUC_U_ was lower for each individual subplot as the accessible information in the pool was more limited. The results confirmed again that all active learning strategies were better than passive learning, Shown more clearly in the last row in Figure 5 on the Polblogs_cc network is that the three strategies **page-rank.**, **max-deg.**, and **d-opt.** were the winning group for the first two \(r_{0}\) values. However, in the third and fourth subplot in the same row, **v-opt.**, **max-ent.**, and **d-opt.** performed best. The **d-opt.** strategy stayed as one of the top strategies across different percentages of the observed information.
###### Abstract  
This work builds upon previous efforts at calibrating PM (particulate matter) monitors with ambient-like aerosols produced in the laboratory under well-controlled environmental conditions at the facility known as PALMA (Production of Ambient-like Model Aerosols). In this study, the sampling system of PALMA was equipped with commercial PM\({}_{2.5}\) and PM\({}_{10}\) impactors, designed according to the EN 12341:2014 standard, to select different aerosol size fractions for reference gravimetric measurements. Moreover, a metallic frame was mounted around the PM impactor to accommodate up to eight low-cost PM sensors. This sampling unit was placed at the bottom of the 2-meter-long aerosol homogenizer, right above the filter holder for the reference gravimetric measurements. As proof of principle, we used the upgraded PALMA facility to calibrate the new AirVisual Outdoor (IQAir, Goldach, Switzerland) and the SDS011 (InovaFitness, Jinan, China) low-cost PM sensors in a traceable manner against the reference gravimetric method according to the EN 12341 standard. This is the first time that PM\({}_{2.5}\) and PM\({}_{10}\) calibrations of low-cost sensors have been successfully carried out with complex ambient-like aerosols consisting of soot, inorganic species, secondary organic matter, and dust particles under controlled temperature and relative humidity.  
Keywords:air quality monitoring; calibration; low-cost; optical sensor; particulate matter (PM); ambient-like aerosols; EN 12341 standard +
Footnote †: journal: Applied
# Positioning Methods and the Use of Location and Activity Data in Forests
## 6 Emerging Research Needs
### Evaluation of Data Network Quality in Mission-Critical Operations with many Resources  
Much of the applied research we have referenced on data sharing in forestry, including [PERSON] and [PERSON] (2018), [PERSON] and [PERSON] (2018), and [PERSON] et al. (2018), has resulted from small, controlled experiments, or sampling with a small number of networked location-sharing devices (typically less than ten). However, to effectively deploy data networks in mission-critical wildland fire, disaster response, and other public safety applications in remote areas, testing technologies on a larger scale with more resources is imperative. The behavior of ad hoc networks when hundreds or even thousands of resources are being monitored is likely to generate a variety of bandwidth and connectivity issues. Further research is needed to evaluate the potential use of unique frequencies for different component systems sharing data simultaneously at low-level nodes in the hierarchical network as well as the slowing and failure of data transfer as the number of users increases. There are many, potentially useful applications for sharing activity recognition model predictions among phones and wearable sensors, particularly in the context of work productivity and worker health monitoring to improve safety. However, ad hoc networks may become clogged with this data when they are most needed, namely, on large incidents where conditions are changing quickly, and the rapid transfer of information among many agents is most critical. For this reason, it may be useful for mesh networks to establish enhanced emergency override controls that throttle non-critical communications, prioritizing top-level uses as the system becomes impaired.
###### Abstract  
In an optical measurement system using an interferometer, a phase extracting technique from interferogram is the key issue. When the object is varying in time, the Fourier-transform method is commonly used since this method can extract a phase image from a single interferogram. However, there is a limitation, that an interferogram including closed-fringes cannot be applied. The closed-fringes appear when intervals of the background fringes are long. In some experimental setups, which need to change the alignments of optical components such as a 3-D optical tomographic system, the interval of the fringes cannot be controlled. To extract the phase from the interferogram including the closed-fringes we propose the use of deep learning. A large amount of the pairs of the interferograms and phase-shift images are prepared, and the trained network, the input for which is an interferogram and the output a corresponding phase-shift image, is obtained using supervised learning. From comparisons of the extracted phase, we can demonstrate that the accuracy of the trained network is superior to that of the Fourier-transform method. Furthermore, the trained network can be applicable to the interferogram including the closed-fringes, which is impossible with the Fourier transform method.  
deep learning; convolutional network; U-net; phase extraction; fringe analysis; closed-fringe; interferometer +
Footnote †: journal: Applied Physics Letters
# MCST Scheme for UAV Systems over LoRa Networks
###### Abstract  
In recent years, low-power wide-area network (LPWAN) has received widespread popularity with long-range and wide-area communication at low power for the Internet of Things (IoT) systems. Among many vendors of LPWAN, long-range low-power wireless communications, also called LoRa, is one of the competing standards and is well known in both academia and industrial communities as an emerging research area. Among the LoRa applications, unmanned aerial vehicles (UAV) systems are emerging with the benefits of extended battery life and a long communication range. In this paper, we investigate the network capacity with the mixture of concurrent and sequential transmission (MCST) scheme over LoRa networks. From the simulation results, it can be seen that MCST is suitable for implementation in the LoRa network. Specifically, MCST can achieve higher throughput with low transmission latency and energy consumption compared to the existing CSMA approach LoRa MAC. Besides, we also propose a modified MCST over the LoRa (mMCST/LoRa) scheme to mitigate the transmission latency further. The simulation results reveal a better performance in terms of throughput, latency and energy consumption, regardless of the frame payload size and the number of nodes in the network.  
basic relaying flow; MCST; MAC protocol; LoRaWAN; ad hoc networks Article
## 2 Coding Metamaterials for RCS Reduction
### Coding Metamaterial Parametric Study
#### 2.3.1 Four Lattices  
First, the four lattices were adopted, and outcomes were analyzed based on the monostatic RCS values and scattering patterns of three different coding sequences, as shown in Figure 6 and Table 6. The analysis revealed that the scattering patterns do not have major discrepancies when compared to the unit-cell metamaterial structure, as in the above subsection. However, the maximum bistatic RCS values reduced tremendously. The element '1' had a peak point of \(-51.7\) dBm\({}^{2}\), while the proposed coding metamaterials were reduced to \(-27.8\) dBm\({}^{2}\) with a similar spherical scattering pattern. From this analysis, one can clearly see that the distinct coding sequence can manipulate the RCS values and scattering pattern. Moreover, considering the three different sequences, Coding Sequence 1 exhibited a slightly better performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Coding Sequence** & **Row 1** & **Row 2** & **Row 3** & **Row 4** \\ \hline
1 & 1010 & 1010 & 1010 & 1010 \\
2 & 1010 & 1111 & 1100 & 1001 \\
3 & 1100 & 0011 & 1100 & 0011 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Coding sequences of \(n=4\) for each row.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Descriptions** & **Dimension (mm)** \\ \hline a & 5 \\ b & 5 \\ cr1 & 0.4 \\ cd1 & 1.0 \\ s1 & 1.6 \\ sr1 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Dimension details of all four elements for two-bit coding metamaterials.
## 4 Ultrastrong Coupling: Results and Discussion
### The Case of Comparable Rabi- and Bloch Frequencies  
In this subsection, we consider another case: when the Rabi- and Bloch frequencies are comparable (Figures 11 and 12). The scenario dramatically changes; the total spectrum is not separable into low-frequency and high-frequency components. As one can see in Figure 12, there is the appearance of the combined sub-harmonics of Bloch and Rabi-frequencies, according to Equation (27) (RBO regime).  
Figure 10: Frequency spectrum of inversion density with RO and BO at the separated frequency ranges at the atomic position \(j=90\). All parameters are identical to Figure 3.  
Figure 9: Frequency spectrum of the dipole moment with RO and BO in the separated frequency ranges at the atom with position number \(j=90\). All parameters are identical to Figure 3.  
In Figures 13 and 14, the spectra of tunneling current and dipole moment densities for \(\Omega_{B}~{}=~{}\Omega_{R}~{}=~{}0.5\omega_{0}\) are presented. In this special case, a lot of sub-harmonics become degenerate. As a result, the density of spectral lines decreases and the ability of their resolution in real experiments grows, avoiding a strong overlap via dissipative broadening. Appearing are the lines \(\omega=\omega_{0}\)\(\omega=\omega_{0}\pm\Omega_{R}\) (see Figure 14), at first, it appears they may be associated with a Mollow triplet, but no rather convincing arguments for such an interpretation exist. The reason is the degeneracy of the modes of different origins in our case, in contrast with Lorentz single-oscillator lines in Mollow triplets [1].  
Figure 11: The space–time distribution of the tunnel current density with RBO in the ultrastrong coupling regime. Here, the quantum transition frequency is taken as the frequency unit, zero detuning (\(\omega=\omega_{0}\)), \(\Omega_{B}=0.7\), \(\Omega_{R}=0.8\), \(t_{a}=t_{b}=7.0\), and interatomic distance \(a=20\,\mathrm{nm}\). The initial state of the chain is an excited single Gaussian wave packet \(a_{j}(0)=g\exp\Bigl{[}-(j-j^{\prime})^{2}a^{2}/\sigma^{2}\Bigr{]}\), \(b_{j}(0)=0\). Gaussian initial position and width are \(j^{\prime}=80\), \(g=20\), respectively. Number of atoms \(N=128\).  
Figure 12: The frequency spectrum of tunnel current density with RBO in ultrastrong coupling regime at the atomic position \(j=60\). All parameters are identical to Figure 11.
# Advances in Piezoelectric Jet and Atomization Devices
## 2 Advances in Piezoelectric Micro Jet Devices
### Bend Mode  
In 2008, [PERSON] et al. [25] proposed a type of piezoelectric inkjet nozzle and fabricated the piezoelectric ceramic into the shape of a buzzer. The surrounding of the piezoelectric ceramic was fixed, and the middle part was deformed to drive the formation of droplets. In this research, a high-speed camera (CCD) was employed to record the formation and separation process of the droplets. In a previous study, [PERSON] et al. [26] carried out numerical simulation on the droplet injection process of a representative piezoelectric film nozzle, and the related findings revealed that the injection characteristics were strongly affected by many factors including the nozzle outlet diameter, injection time interval, and surface tension as well as fluid viscosity. Since then, with the development of MEMS technology, some scholars have begun to use MEMS technology to design piezoelectric micro jet devices. In 2010, [PERSON] et al. [27] designed a piezoelectric nozzle using MEMS technology, and found that the nozzle structure, driving voltage, ink viscosity, and meniscus vibration mode all affected the spray performance of the nozzle. In 2014, [PERSON] et al. [28] fabricated a piezoelectricity driven inkjet printhead on silicon wafer by MEMS technology, and carried out a numerical simulation on this course. It was found that the Helmholtz mode was the main resonance mode to influence the flow oscillation at the nozzle. As one of the core components of the piezoelectric nozzle, the piezoelectric vibration plate directly affects the jet performance of the print head. In 2018, the amplitude of the piezoelectric vibration plate was enhanced by releasing the long side of lead zirconate titanate film, and thereby a so-called spring structure was conducted, demonstrating that such a structure can significantly increase the amplitude and average deformation rate of a piezoelectric vibration plate in air throughout the experimental progress [29].  
All of the above-mentioned piezoelectric jet devices are assigned to curved piezoelectric micro jet devices as shown in Figure 1a. For the apparatus structure, the piezoelectric ceramic is stuck onto the substrate. The substrate can vibrate up and down with the piezoelectric ceramic under the excitation of the AC signal, which causes the volume change of the ink storage chamber, and then the fluid suction and injection are completed. Accordingly, both the jet velocity and droplet size are influenced by the parameters of piezoelectric ceramic, driving voltage, driving frequency, and nozzle size.
# A New Passive Islanding Detection Solution Based on Accumulated Phase Angle Drift
## 3 Composite Islanding Detection Solution
### Start Criterion  
The \"Normal Status\" and \"Abnormal Status\" of the power system are defined. The initial state of the power system without fault is in \"Normal Status\". The voltage phase angle changes under the \"Normal Status\" are expressed as follows:  
\[\Delta Ang_{X}(n)=Ang_{X}(n)-Ang_{X}(n-2),X=A,B,C \tag{6}\]  
In Equation (6), where \(Ang_{X}(n)\) is the phase angle calculated at the n cycle; \(Ang_{X}(n-2)\) is the phase angle calculated at the \(n-2\) cycle.  
The phase angle calculation is based on full cycle DFT. When the vector shifts in cycle \(n-1\) as shown in Figure 5, the DFT calculation with the samples in cycle \(n-1\) is not accurate. So the calculation of the phase angle changes shall apply \(Ang_{X}(n)\) and \(Ang_{X}(n-2)\) for better accuracy.  
Figure 5: Phase angle change calculation based on Discrete Fourier Transformer (DFT).  
If Equation (7) is satisfied, the state of the power system is switched into \"Abnormal Status\" from \"Normal Status\".  
\[\left\{\begin{array}{c}\Delta Ang_{Max}(n)>\Delta Ang_{set}\\ \Delta Ang_{Max}(n)=Max\{|\Delta Ang_{A}(n)|,|\Delta Ang_{B}(n)|,|\Delta Ang_{C }(n)|\}\end{array}\right. \tag{7}\]  
Here \(\Delta Ang_{set}\) is the angle change threshold.  
The voltage phase angle changes under the \"Abnormal Status\" are expressed as follows:  
\[\Delta Ang_{X}(n)=Ang_{X}(n)-Ang_{X}(n_{0}),X=A,B,C \tag{8}\]  
In Equation (8), \(Ang_{X}(n)\) is the phase angle calculated at the n cycle; \(Ang_{X}(n_{0})\) is the last phase angle calculated in the \"Normal Status\".
# An Analytical Solution to Two-Region Flow Induced by Constant-Head Pumping in an Unconfined Aquifer
###### Abstract
Practically, the use of flow modeling by considering the Dupuit assumption is preferred to meet the engineering requirements when the drawdown is significantly smaller than the initial hydraulic head in the unconfined aquifer [24; 25; 26; 27]. Otherwise, previous studies have generally assumed that the non-Darcian flow happens in the entire pumped aquifer [17]. Such an assumption is doubtful by field observations [28]. Theoretically speaking, at the beginning of the pumping, the specific discharge nearby the pumping well is sufficiently large, which cannot be ignored owing to an intensive decline of the hydraulic head. A sufficiently large specific discharge can make the Reynolds number greater than the critical Reynolds number, suggesting the presence of a non-Darcian flow. As the radial distance increases, the Reynolds number gradually decreased with the specific discharge. Thus, it can further result in the conversion from non-Darcian flow to Darcian one, indicating the development of a two-region flow in the pumped aquifer [28; 29; 30]. So far, the mechanics of such two-region flow by constant-head tests in an unconfined aquifer have remained unclearly. In order to address the issues mentioned above, this paper proposes an analytical two-region, non-Darcian and Darcian, model for the flow induced by the constant-head pumping test in an unconfined aquifer when the drawdown inside the pumping well is small. [PERSON]'s equation is used to depict the relationship between the specific discharge and hydraulic gradient for mathematical modeling purposes. The solution is derived using the Boltzmann transform, and the Dupuit assumption can make the equation more concise. The acceptability of the two-region model is verified by comparing it with the numerical solution of COMSOL Multiphysics and the measured data from a constant-head pumping in Wisconsin. Such investigation on the mechanics of the two-region flow by constant-headtests in an unconfined aquifer has significant engineering values, which can provide a concise and reliable method for solving environmental damage in landfills.
# Could We Realize the Fully Flexible System by Real-Time Computing with Thin-Film Transistors? +
## 5 Simulations and Analysis
### Performance Analysis  
#### 5.2.1 Settling Time  
The following simulations are based on the HSPICE level 62 model for oxide TFT. As we have stated above, the speed of calculation basically depends on the settling time of the Gaussian convolution unit. We simulate the settling time for several units with different scales and different step-input current sources. The normalized settling time curves are shown in Figure 9a. The settling time of Gaussian convolution circuit is about 300 ns. It is equivalent to perform the convolution operation at a frequency of 3.3 MOPS/s, which is much faster than the conventional digital circuit driven by kHz-level clock on TFT [9]. As to the feature processing, the settling time of flexible MLP is about 100 us, which means that the classification speed is up to 10 k frames/s, and is totally enough for real-time image processing with the speed of 30-100 fps. This result is really significant because it proves that, by exploiting the physical characteristic, the slow devices could exceed its limitation to reach a settling-time-equivalent calculation speed. These, in fact, demonstrate our thoughts of analog-to-information processing.