esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Organization of the Paper  
The rest of the paper is structured as follows. In Section 2, the circuit design and system architecture of the passive wake-up radios are demonstrated. State-of-the-art in the RF energy transfer and harvesting techniques used for the system is presented and a survey of the existing passive wake-up radio hardware architecture available in the literature is provided in the section. Section 3 discusses the communication principles and reviews the existing MAC protocols used for the passive wake-up radio. In Section 4, the key evolving application areas utilizing the passive wake-up radios are discussed. Section 5 describes the open research issues and practical challenges in the passive wake-up radio and describes the future research direction that can be taken to solve these challenges. Finally, a conclusion for the survey is presented in Section 6.
###### Abstract  
Leak detection in oil and gas pipeline networks is a climacter and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in engineering; and advanced technologies providing cost-effective, rapidly executed, and easy to implement solutions lead to building an efficient oil pipeline leak detection and real-time monitoring system. In this area, wireless sensor networks (WSNs) are increasingly required to enhance the reliability of checkups and improve the accuracy of real-time oil pipeline monitoring systems with limited hardware resources. The real-time transient model (RTTM) is a leak detection method integrated with LoRaWAN technology, which is proposed in this study to implement a wireless oil pipeline network for long distances. This study will focus on enhancing the LoRa network parameters, e.g., node power consumption, average packet loss, and delay, by applying several machine learning techniques in order to optimize the durability of individual nodes' lifetimes and enhance total system performance. The proposed system is implemented in an OMNeT++ network simulator with several frameworks, such as Flora and Inet, to cover the LoRa network, which is used as the system's network infrastructure. In order to implement artificial intelligence over the FLoRa network, the LoRa network was integrated with several programming tools and libraries, such as Python script and the TensorFlow libraries. Several machine learning algorithms have been applied, such as the random forest (RF) algorithm and the deep extreme learning machine (DELM) technique, to develop the proposed model and improve the LoRa network's performance. They improved the LoRa network's output performance, e.g., its power consumption, packet loss, and packet delay, with different enhancement ratios. Finally, a hybrid deep extreme learning machine model was built and selected as the proposed model due to its ability to improve the LoRa network's performance, with perfect prediction accuracy, a mean square error of 0.75, and an exceptional enhancement ratio of 39% for LoRa node power consumption.  
oil pipeline monitoring system; machine learning; random forest (RF); wireless sensor networks (WSNs); OMNeT++; LoRaWAN; deep extreme learning machine (DELM)
# Kirchhoff Migration for Identifying Unknown Targets Surrounded by Random Scatterers
## Appendix A Basic Idea of Kirchhoff Migration
The singular value decomposition (SVD) of \(\mathbb{K}\) can be represented as  
\[\mathbb{K}=\mathbb{U}\mathbb{D}\mathbb{V}^{*}=\sum_{n=1}^{N}\tau_{n}\mathbf{U} _{n}\mathbf{V}_{n}^{*}\approx\sum_{n=1}^{N_{0}}\tau_{n}\mathbf{U}_{n}\mathbf{ V}_{n}^{*}, \tag{12}\]  
where the superscript \(*\) represents the Hermitian operator, \(\mathbf{U}_{n}\) and \(\mathbf{V}_{n}\) are the left- and right-singular vectors of \(\mathbb{K}\), respectively, and \(\tau_{n}\) denotes singular value of \(\mathbb{K}\) such that  
\[\tau_{1}\geq\tau_{2}\geq\cdots\geq\tau_{N_{0}}>0\quad\text{and}\quad\tau_{n} \approx\rho\approx 0\quad\text{for}\quad n>N_{0}.\]  
Note that on the basis of (10), the value of \(\tau_{n}\) is significantly depending on the size, shape, permittivity, and permeability of \(\mathbf{A}_{m}\) and \(\mathbf{R}_{s}\). Then, by comparing (10)-(12), orthonormal property of the singular vectors, and relationship [38], we can observe that for some \(\mathbf{y}_{n}\in\Lambda\cup\mathrm{Y}\),  
\[ \begin{array}{l}\mathbf{U}_{n}\approx e^{i\tau_{n}^{(1)}}\mathbf{W}( \mathbf{y}_{n}),\quad\mathbf{V}_{n}\approx e^{-i\tau_{n}^{(2)}}\overline{ \mathbf{W}}(\mathbf{y}_{n}),\quad\text{and}\quad\gamma_{n}^{(1)}+\gamma_{n}^{( 2)}=\arg(\tau_{n}),\\ \langle\mathbf{W}(\mathbf{x}),\mathbf{U}_{n}\rangle\approx 1\quad\text{and} \quad\langle\mathbf{W}(\mathbf{x}),\overline{\mathbf{V}}_{n}\rangle\approx 1 \quad\text{if}\quad\mathbf{x}\in\Lambda\cup\mathrm{Y},\\ \langle\mathbf{W}(\mathbf{x}),\mathbf{U}_{n}\rangle\approx 0\quad\text{and} \quad\langle\mathbf{W}(\mathbf{x}),\overline{\mathbf{V}}_{n}\rangle\approx 0 \quad\text{if}\quad\mathbf{x}\in\mathbb{R}^{2}\rangle(\overline{\Lambda} \cup\overline{\mathrm{Y}}),\end{array}  \tag{13}\]  
where \(\langle\mathbf{U},\mathbf{V}\rangle=\overline{\mathbf{U}}\cdot\mathbf{V}\). With this, we can introduce the following imaging function adopted by the Kirchhoff migration:  
\[\mathfrak{F}_{\text{KIR}}(\mathbf{x}):=\left|\mathbf{W}(\mathbf{x})^{*}\mathbb{ K}\overline{\mathbf{W}}(\mathbf{x})\right|=\left|\sum_{n=1}^{N}\tau_{n}\langle \mathbf{W}(\mathbf{x}),\mathbf{U}_{n}\rangle\langle\mathbf{W}(\mathbf{x}), \overline{\mathbf{V}}_{n}\rangle\right|.\]  
Following (13) we can easily observe that the map of \(\mathfrak{F}_{\text{KIR}}(\mathbf{x})\) will contain peak of magnitude \(\tau_{n}\) at the location \(\mathbf{x}\in\Lambda\cup\mathrm{Y}\).
## 3 Results
### Limitations of the Study  
Limitations of the study include that the satellite data may not accurately map forests, especially in areas with complex topography or dense canopy cover. In these cases, ground-based data may be needed to supplement the satellite data and improve the accuracy of the forest map. Second, satellite data may be subject to spatial resolution limitations, which can affect the accuracy of the forest map. For example, data with low spatial resolution may not provide sufficient detail to map small-scale features such as individual trees accurately.  
Third, the accuracy of the forest map may be affected by the quality and availability of ancillary data, such as digital elevation models and land cover maps, which are often used to improve the accuracy of the forest map. Finally, the analysis results may be limited by the sample size and the representativeness of the case study area. A larger sample size and a more diverse range of case study areas may provide more robust and generalizable results. The random forest method is a relatively complex algorithm that can be difficult to understand and interpret for some users. This can make it challenging to explain the results of a random forest model to non-technical stakeholders. The model's output may be unreliable if the input data are incomplete, noisy, or biased. Overfitting is another limitation of this study. The random forest can be prone to overfitting, which occurs when a model is too closely tailored to the training data and does not generalize well to new data. This can lead to poor performance on unseen data.
# Potential of Deep Learning Methods for Deep Level Particle Characterization in Crystallization
## 2 State of the Art
The mentioned segmentation and classification methods consist of multiple processing steps, many adjustable parameters and calculated features which must be chosen by the operator accordingly. Deep learning methods are emerging, which are capable of solving all these tasks in a bundled and fast way. Depending on the application goal, different tasks from detection to classification and segmentation can be solved separately or in combined fashion in an end-to-end approach. The performance can often keep up with that of humans and even outperformed them recently on image classification [41]. Convolutional neural networks (CNNs) are on the rise in a wide variety of areas where image analysis is crucial and are also beginning to gain importance in crystallization. In [PERSON] et al. [42] and [PERSON] et al. [43] images are classified into crystalline and precipitant products or undesired byproducts, respectively. Further, image analysis using CNNs is used to monitor crystal growth during crystallization [19; 44; 45]. Furthermore, the distinction of different particle shapes can be accomplished by CNNs, no matter if it is a multi component system [46] or different polymorphic forms with the expression of different habiti [45]. Efforts to segment overlapping particles are also being addressed [47]. A similar task of identifying primary crystals in agglomerates has been tackled for sintered and agglomerated silica nano-particles by applying a CNN [48]. It shows promising results and outperforms conventional methods. In this work, an CNN is applied to investigate agglomeration in cooling crystallization. Detailed information about crystals are provided including primary crystals and agglomerates so that a new level of particle characterization is reached.
## 4 Satellite Self-Organizing Network Simulation Platform Based on OMNeT++
### Function Domain  
The function domain mainly has four parts: initial network formation, cluster maintenance, time slots allocation, and data generation and forwarding.
## 2 Results and Discussion
### Volatile Compounds  
A total of 51 volatile compounds, mainly monoterenes and sesquiterpenes, were identified in the CRP extract processed by SPME. The detailed information is shown in Table S1. Results were consistent with reported data obtained through hydro-distillation and other methods [11]. Meanwhile, HS-SPME provided an easier sampling procedure in a shorter time (30 min). As shown in Table 2, 21 peaks, which were simultaneously detected in all 54 samples, were finally selected, and the peak-area ratios relative to that of n-ridecane were imported to form a dataset. Collectively, they made up an average of 85.2% of the total peak area, thus covering the majority of the information contained in the volatile profiles. Based on a previous report [18], volatile components which appeared in minute quantities were excluded for better repeatability of quantification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Retention Time (RT) (min)**} & \multirow{2}{*}{**Compound**} & \multicolumn{4}{c}{**Peak Area (\%)**} \\ \cline{3-5}  & & & **Solid-Phase** & **SPME at 50 \({}^{\circ}\)C** & **SPME at 50 \({}^{\circ}\)C** \\  & & & **at 30 \({}^{\circ}\)C for 10 min** & **for 10 min** & **for 30 min** \\ \hline
13.84 & \(\Omega\)-Limonene & 81.52 & 61.46 & 52.35 & 28.09 \\
14.77 & \(\gamma\)-Terpinene & 7.44 & 8.37 & 4.51 & 3.05 \\
26.60 & \(\Omega\)-Germacrene & 0.33 & 1.94 & 1.18 & 4.22 \\
27.03 & \(\alpha\)-Farmesene & 1.11 & 7.18 & 8.44 & 14.26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main terpenes identified in the analyzed “Chenpi” (CP) sample (S5) using various extraction methods and parameters.  
Figure 1: GC-MS typical total ion chromatograms (TICs) of volatile compounds in the “Chenpi” (CP) sample (S5). (**a**) Extracted through hydro-distillation. (**b**) Extracted through headspace solid-phase microextraction (HS-SPME) at 30 \({}^{\circ}\)C for 10 min. (**c**) Extracted through HS-SPME at 50 \({}^{\circ}\)C for 10 min. (**d**) Extracted through HS-SPME at 50 \({}^{\circ}\)C for 30 min.
# Model for Cryogenic Flashing LNG Leak
## 3 Results  
### Leak Scenario  
The simulated scenario was chosen from SPARCLING, a 9 mm-diameter leak with an 8 bar initial stagnation pressure. The LNG composition is based on Australian LNG, which has a methane concentration of 99.8%. Given the extremely high methane concentration,LNG was considered as pure methane in the simulation. The assumption of pure methane composition provides a conservative gas dispersion result as heavier alkanes diffuse slower. The leak inventory volume is based on a cryogenic hose of 10 m, 8 inch in length [21]. A 5 s delay to cut off the upstream supply from the emergency shutdown valve was considered, and this increased the total leak volume [22]. The proposed model used the initial pressurized leak parameter at the breach orifice and mapped out the de-pressurized and flashed leak parameter at the entrainment zone. Table 1 summarizes the initial leak parameters at the breach orifice and entrainment zone after leak model application.  
The breach occurs at the hose joint connection point with the manifold. From a risk perspective, knowing how far a flammable leak source can disperse is critical for safety. According to Singapore risk analysis technical guidance, leak direction, wind direction, and wind speed should be considered when assessing the risk [10].  
Three probable leak directions, vertical upward, vertical downward, and horizontal, were considered. The vertical leaks only consider one wind direction, since the two directions are perpendicular to each other. For the horizontal leak, three different wind directions, east (tailwind), west (headwind), and northeast (crosswind), were considered to cover probable wind interaction with the leak. Figure 2 shows the three leak directions and the three wind directions.  
According to [PERSON] and co-workers, a lower wind speed has a slower dilution rate and leads to greater gas dispersion [23]. The wind speed was set at 1 m/s (reference height of 10 m), which belongs to roughly 1 percentile of Singapore's average wind speed range [24].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Jet Region** & **Parameter** & **Value** \\ \hline \multirow{4}{*}{Breach orifice} & A\({}_{1}\) & 0.0000636 m\({}^{2}\) \\  & V\({}_{1}\) & 35.03 m/s \\  & P\({}_{1}\) & 8.0 bar \\  & \(\rho_{1}\) & 418.3 kg/m\({}^{3}\) \\ \hline \multirow{4}{*}{Entrainment zone} & A\({}_{3}\) & 0.0239 m\({}^{2}\) \\  & V\({}_{3}\) & 21.70 m/s \\ \cline{1-1}  & P\({}_{2}\) & 1.0 bar \\ \cline{1-1}  & \(\rho_{3}\) & 1.797 kg/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Leak parameter before and after leak model application.  
Figure 2: (**a**) Section view of the manifold showing two vertical leaks and wind directions; (**b**) plane view of the manifold showing the horizontal leak and wind directions.
# Numerical Simulation of Seakeeping Performance on the Preliminary Design of a Semi-Planing Craft
## 4 Results and Discussion
### Sea State Comparisons
#### 4.2.3 Rolling Motion  
Figure 13a-e displays the speed polar diagrams of rolling motion in combination with sailing speeds and wave heading angles under different sea states (1-5). The results suggest that when the sea state worsened, rolling motion increased noticeably for wave heading angles between 45\({}^{\circ}\) and 135\({}^{\circ}\), and that the strongest rolling motion was observed for wave heading angles between 90\({}^{\circ}\) and 135\({}^{\circ}\). This indicated that for all sailing speeds, when wave heading angles ranged from beam to bow seas, rolling motion increased as the sea state worsened.  
For all Froude numbers, a discernible resonance of rolling motion was generated at the wave angles of 90\({}^{\circ}\) and 135\({}^{\circ}\). Additionally, at a wave heading angle of 135\({}^{\circ}\), increasing the Froude number caused the significant resonance of rolling motion. This signified that for all sailing speeds, rolling motion was strongly affected by beam and bow seas.  
Figure 12: Speed polar diagrams of heaving motion in combination with sailing speeds and wave heading angles under sea states of (**a**) 1, (**b**) 2, (**c**) 3, (**d**) 4, and (**e**) 5, respectively.
## 2 Ge Growth Techniques
### Two Step LT/HT Ge Growth  
The origin of the two-step LT/HT (low temperature/high temperature) growth technique can be traced back to 1986 for GaAs growth on Si by [PERSON] _et al_. [11]. Its application in the epitaxially grown Ge on Si was first proposed and utilized by [PERSON] _et al_. [12] in a ultra high vacuum chemical vapor deposition (UHVCVD) growth reactor in 1998, since when it has attracted wide interest for Ge epitaxial growth. In the two-step Ge growth procedure, first, after thorough cleaning, the substrate is maintained at low temperature (\(\sim\)300-400 \({}^{\circ}\)C), and a thin layer of Ge buffer layer (\(\sim\)50-100 nm) is grown to prevent strain release through undesirable island growth. Second, the substrate temperature is elevated to \(\sim\)550-700 \({}^{\circ}\)C and a thick Ge layer with reduced threading dislocation density is grown on top of the low-temperature thin Ge buffer. It should be noted that the two-step Ge method can be adopted not only in UHVCVD systems, but also in growth tools such as reduced-pressure CVD (RPCVD) [13] and molecule beam epitaxy (MBE) [14].  
The Ge layers growth by two-step Ge epitaxy typically suffers from a high threading dislocation density (TDD) in the order of 10\({}^{8}\)-10\({}^{9}\) cm\({}^{-2}\). Therefore, high temperature annealling is employed by many groups to reduce the TDD to an acceptable level. For example, the research of [PERSON] _et al_. indicates that the TDD in two-step Ge layer can be significantly reduced by cyclic thermal annealing. The optimized annealing condition (900 \({}^{\circ}\)C/10 min, 780 \({}^{\circ}\)C/10 min, cycle number: 10) can reduce the threading dislocation density to \(\sim\)2 \(\times\) 10\({}^{7}\) cm\({}^{-2}\)[15, 16]. Ge photodetectors based on this process were successfully demonstrated to have improved performance [15, 17]. However, the annealing process increases the thermal budget undesirable for photodetectors' integration with Si MOSFET. Therefore, a number of experiments have been reported to demonstrate high Ge detector performance which are based on low-temperature anneal or even no additional thermal anneal [18,19]. In Table 1, some of the currently active groups' Ge growth methods are summarized.
# New Resorbable Membrane Materials for Guided Bone Regeneration
## 1 Introduction  
Guided bone regeneration (GBR) is a widely used technique for bone regeneration on alveolar bone defects [1; 2]. GBR uses a membrane as a physical barrier to prevent epithelial and connective tissue in the bone defect [3; 4; 5; 6]. The selection criteria for a membrane used for GBR are important [7].  
Ideal membrane characteristics include tissue integration, cell occlusivity, clinical manageability, space-making, and biocompatibility [8; 9]. However, no membrane satisfies all of these conditions [9].  
GBR membranes can be divided into two types--resorbable and non-resorbable--depending on their degradation characteristics [10]. Non-resorbable membranes have the disadvantage of requiring secondary surgery for removal that can cause additional pain, discomfort, infection, and economic burden [3]. Resorbable membranes have been developed to overcome this problem [1].  
The purpose of this paper is to selectively review new resorbable membrane materials used for GBR. In this paper, the changes in characteristics of membranes by adding additives to commercial membranes are described, unlike previous papers.
## 4 Simulations and Analysis
### Comparison of Beampatterns  
Beampatterns were used to clarify the meaning of the spectrogram results in Section 4.2. Figure 3 shows the beampatterns and the values of the color bar represent the beampattern amplitude in dB. Figure 3 shows that the main lobes of the CMB, HKB, LNR, SMF, BPR, GLC and PFLI could not be directed at the real DOA of the target source of 70\({}^{\circ}\), and the main lobe of the CMB and BPR were evidently cancelled by themselves. This explained why the target speech was seriously distorted in these two methods. Moreover, since the main lobes of the HKB, LNR, SMF, GLC and NRP-TMMSE were not directed at the real DOA of the target source, the target speech recovered by them was low-pass filtered. This explained why the target speech was slightly distorted in these five methods. Conversely, in the IMB, IDL-SIM and proposed method, the direction of the main lobe was close to the real DOA of the target source, so the target speech was well recovered.  
However, the nulls of the SMF, GLC, NRP-TMMSE, PFLI and IDL-SMI were not effectively formed in the direction of the interference source, which explained why the residual components of the interference sources were maintained. Moreover, the proposed method not only made the main lobe closer to the real DOA of the target source, but also formed a null at the direction of the interference source. Hence, the proposed method has better performance for recovering the target source and suppressing the interference source.  
Figure 2: A comparison of spectrograms. The number of snapshots was 100 and the SNR was 10 dB.
# Experimental Performance Analysis of Wi-SUN Channel Modelling Applied to Smart Grid Applications
## 3 Channel Modeling Methodology
### Statistical or Empirical Modeling  
Statistical or empirical modeling is created from the statistical analysis of the large amounts of measurement data [24]. The Rayleigh fading model is typically used in an urban environment, where there is no dominant signal, i.e., a non-line-of-sight (nLOS). The uncorrelated Rayleigh fading could be modeled by a complex Gaussian distribution with equal variance and zero mean. In the Rician fading model, a dominant transmitted signal component is included, i.e., LOS path, in its model. In this article, the Rician fading model for the RSSI estimation in the Wi-SUN channel is studied by using the empirical model due to its low complexity property, given a tradeoff in compromised accuracy in the model.
###### Abstract  
In addition to combat missions, military ships often participate in search-and-rescue missions or interception of ships with refugees or migrants. The communication systems and modes of work that may be used during these missions will be in accordance with the International Convention for the Safety of Life at Sea (SOLAS). This paper aims to demonstrate by theoretical methods (analytical and numerical) the noise stability of communication systems using analog modulation in high-noise conditions, characteristic of the marine environment. The stability of analog systems employing amplitude (AM), frequency (FM), and phase (PM) modulations is investigated. The analyzed systems are currently under use in distress maritime communications.  
modulation; electromagnetic disturbances; electromagnetic compatibility; military ship; communication systems Article
# A Dynamic Service Reconfiguration Method for Satellite-Terrestrial Integrated Networks
## 3 Problem Descriptions
### Optimal Objective
At the same time, the nodes passing through the migration path can be replaced by the number of routing hops, and the migration cost can be measured by the extra propagation delay caused by the number of migration path hops and the extra transmission delay related to the bandwidth of the migration path. Since the cost of instantiating a new VNF is independent of the migration location, only the extra delay is considered, so the migration cost is dwindled. The migration cost \(M_{c}\) can be expressed as Equation (5):  
\[M_{c}(t)=\sum_{hop(f_{i}f_{v})}\left[d_{p}^{f_{i}f_{v}}(t)+d_{tr}^{f_{i}f_{v} }(t)\right] \tag{5}\]  
where \(hop(f_{i},f_{v})\) represents the number of routing hops between VNF nodes \(f_{k}\) and \(f_{v}\), and \(d_{p}^{f_{i}f_{v}}(t)\) and \(d_{tr}^{f_{i}f_{v}}(t)\) represent the extra propagation delay and transmission delay generated by each hop. Since the forwarding node will not process packets during migration, it will not increase extra forwarding processing delay.