esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Based on the field survey and the collection of landslide data, a 1:150 scale physical model of the Meilishi H\({}_{3}\) landslide surge was devised to investigate the influence of landslide volume and channel characteristics on the temporal and spatial distribution of landslide surge. It can be found that river course characteristics have a greater impact on the propagation of landslide surge. Considerations of landslide surge in the study need to pay attention to the diversion area and curved area. Both the head wave crest and waveform were affected and changed, especially in the curved area; hence, attention should be paid to the sudden change in the wave crest. In the area where the river cross-sectional area changes a lot, risk prevention and control measures against landslide surge need to be taken. The influence of landslide volume on the spatial and temporal distribution characteristics of landslide surge is also very significant, and the disaster risk of landslide surge increases in the case of large volume. For the surge wave speed, the influence of the square volume is small, so for the project, it can implement the test with small square volume to carry out the related disaster risk warning.  
There are some advantages in the current research. This experiment, compared to the generalized model experiment method, is more desirable to the reality of landslides. The granular material, compared to other materials chosen as the slide body material, is closer to the process of landslide disintegration. In the analysis of the factors, the cross-sectional area of the river was considered as one of the factors of the river characteristics. The analysis also systematically synthesized the influence of landslide volume and river course characteristics.  
There are some shortcomings in the current research. The selection of materials of the landslide did not consider the existence of cohesion in the landslide body, so in the future, the factor of cohesion can be taken into account in the selection of materials. The curved area constructed in the experiment was relatively small, and a certain model size limited the accuracy of the study of the curved area. Improving the construction of the curved area model can be considered to improve the accuracy of the test results.  
In the future, for the Meilishi H\({}_{3}\) landslide, it will be meaningful to study the pressure change induced by landslide surge on the two banks of the pile in the curved area, which will aid determination of whether the two banks of the pile will experience landslides or not. A numerical calculation can be conducted to compare and verify the results of the physical experiment. Experiments testing other landslide variables such as the material of the landslide body and the landslide velocity can be conducted, and it will be meaningful to fit some of the relevant empirical formulas.
# Stable and Low-Spurs Optoelectronic Oscillators: A Review
## 2 Optoelectronic Oscillators  
The schematic diagram of a fundamental optoelectronic oscillator is shown in Figure 1a and Figure 1b shows the example spectrum of a fundamental OEO. An optoelectronic regenerative feedback loop consists of a laser, an electro-optical modulator (EOM), a pool of fiber, a photodetector (PD), a band-pass filter, an electronic amplifier, a phase shifter (PS) and a power coupler (PC). The optical carrier is provided by the laser and then introduced into the electro-optical modulator. The oscillation signal of an OEO plays the role of driving RF signal which modulates the optical carrier. Then, the modulated optical signal is produced and transmitted through the high Q optical energy storage element, such as low loss long fiber. The photodetector is the bridge between the optical and electrical domain, which achieves photo-electric conversion. The recovered RF oscillation signal is then amplified, filtered, and phase shifted, respectively. Finally, the RF signal is fed back to the electro-optical modulator for the loop close and output via the power coupler. Once the amplitude and phase conditions of oscillation are met, the OEO will export a high-quality microwave signal. Because of the long fiber with low transmission loss as the energy storage element, an OEO can obtain long delay, and then the generated microwave signal features high spectral purity and low phase noise. The recorded ultra-low phase noise of the most advance OEO is \(-163\) dBc\(/\)Hz @ 6 kHz offset for a 10-GHz carrier via 16-km optical fiber [20]. In addition, the energy storage performance of fiber has nothing to do with the microwave oscillation frequency, since the frequency band of microwave signal is much lower than optical frequency. Therefore, the phase noise performance of OEO is theoretically independent of the oscillation frequency [18].  
After nearly two decades of continuous exploration, the research on optoelectronic oscillators has made rapid progress [21]. However, it is still necessary to improve the performance and stability of OEOs for wider application. Nowadays, research on OEOs mainly focuses on the reduction of phase noise [22], the improvement of spurious suppression ratio [23], frequency stability [24], the expansion of oscillating frequency [25], frequency tuning [26], miniaturization [27], and multi-frequency oscillation [28]. This paper mainly analyzes and summarizes the work of spurious suppression and frequency stabilization for optoelectronic oscillators.
## 2 System Design
### Clock Model  
Owing to the reference clock being considered as a perfect frequency source, we only need to model the VC-OCXO on-board the slave satellite. The instantaneous voltage of the VC-OCXO can be modeled as:  
\[V_{0}(t)=(A_{0}+\varepsilon(t))\sin(2\pi\!\!f_{0}t+\phi(t)) \tag{1}\]  
where \(t\) is the reference time generated by the master clock, \(A_{0}\) and \(f_{0}\) are the nominal amplitude and frequency values of the signal respectively, and \(\varepsilon(t)\) and \(\phi(t)\) correspond to the amplitude and phase random fluctuations [18; 19].  
The instantaneous frequency is:  
\[f\left(t\right)=\frac{1}{2\pi}\frac{d}{dt}\left(2\pi\!\!f_{0}t+\phi\left(t \right)\right)=f_{0}+\frac{1}{2\pi}\frac{d}{dt}\phi\left(t\right) \tag{2}\]  
The phase and frequency deviations are expressed as:  
\[x\left(t\right)=\frac{\phi\left(t\right)}{2\pi\!\!f_{0}^{\prime}} \tag{3}\]  
\[y\left(t\right)=\frac{f\left(t\right)-f_{0}}{f_{0}}=\frac{1}{2\pi\!\!f_{0}^{ \prime}}\frac{d}{dt}\phi\left(t\right) \tag{4}\]  
The quantity \(x\left(t\right)\) is the clock error between two clocks. It is usually modeled by Equation (5) [20]:  
\[x\left(t\right)=a_{0}+a_{1}t+\frac{1}{2}a_{2}t^{2}+\psi\left(t\right) \tag{5}\]  
where \(a_{0}\), \(a_{1}\) and \(a_{2}\) represent the clock bias, clock drift and clock drift rate respectively, while \(\psi\left(t\right)\) denotes the random noise, which can be expressed by the sum of five independent noise terms [21]. Its power-law spectral density is represented as:  
\[S_{y}\left(f\right)=h_{-2}f^{-2}+h_{-1}f^{-1}+h_{0}f^{0}+h_{1}f^{1}+h_{2}f^{ 2}=\sum\limits_{a=-2}^{2}h_{a}f^{a} \tag{6}\]  
These five random noise terms are known in the metrological literature as:  
\(\alpha=-2:\) random walk frequency modulation (RWFM).  
\(\alpha=-1:\) flicker frequency modulation (FFM).  
\(\alpha=0:\) white frequency modulation (WFM).  
\(\alpha=1:\) flicker phase modulation (FPM).  
\(\alpha=2:\) white phase modulation (WPM).  
Oscillator performance also can be described in time domain by using the Allan variance:  
\[\sigma_{y}^{2}\left(m_{0}\right)=\left\langle\left(\overset{-}{\underset{y_{ i+1}}{\overset{-}{\underset{y_{i}}{\overset{-}{\underset{y_{i}}{\overset{-}{ \underset{y_{i}}{\overset{-}{\underset{y_{i}}{\overset{-}{\underset{y_{i}}{ \overset{-}{\underset{y_{i}}{\overset{-}{\underset{y_{i
# Resonant Airborne Acoustic Emission for Nondestructive Testing and Defect Imaging in Composites
## 1 Introduction  
The forecast for the global market of lightweight, fibre-reinforced composite materials projects about 40% growth by 2024 due to increasing demands in aerospace and transportation industries [1]. In these safety-relevant applications, an assessment of the probability of failure is necessary for individual components and entire systems. Because of the often-manual manufacturing of composite components, the structural parts have to go through 100% nondestructive testing (NDT). A commonly used method is an ultrasonic inspection by using an array of multiple elements to scan over the whole specimen area. However, with the increasing use and field applications of fibre-reinforced composites, new NDT methods have to be promoted and must shape up to these new materials.  
Acoustic emission (AE) is an established NDT method based on monitoring transient, elastic wave packages in a material, which accompany irreversible changes (cracking, microfracture) in its internal structure [2]. The AE events are then detected by acoustic wave transducers attached to the specimen. To locate the damage, the transducers are set in arrays and the beamforming computation tool is applied [3]. Acoustic emission has several advantages, including inspection of large components and the capability to determine the location of damage. The downside is that the component must be further damaged to generate the AE signal.  
Owing to elastic coupling with ambient air, acoustic waves that hit the surface of the solid produce airborne sound. As a result, conventional (internal) AE is accompanied by airborne (external) AE events, which can be received remotely, e.g., by air-coupled transducers [4]. A similar beamforming approach that uses an array of acoustic microphones (acoustic camera) is further applied to locate and detect the airborne sound sources [5].  
However, commercial acoustic cameras are barely applicable to the detection of ultrasound, since they mainly operate in audible frequency ranges [6; 7].  
The efficiency of the airborne sound radiation is expected to rise, provided that a standing wave resonance develops in the subsurface source. Such an approach to the enhancement of acoustic responses of defects in composites was proposed by using frequency-selective acoustic activation based on the concepts of Local Defect Resonance (LDR) [8] and Resonant Air-Coupled Emission (RACE) [9]. In this paper, the effect of RACE is assessed as a background of a new mode of AE in view of applications for NDT and defect imaging in composites. The AE imaging is achieved by using different mechanical scanning units: a three-axis-scanning table and a six-axis industrial robot. A new approach to instant, full-field ultrasound defect imaging is proposed by using an acoustic camera. Adapting acoustic cameras to sound-field imaging enables researchers to save testing time and to make progress in the development of new, fully acoustic methods for NDT of composite materials.
## 2 Materials and Methods  
The system consists of a gas turbine (Brayton) cycle as the topping cycle, a steam turbine (Rankine) cycle next below it, and then an ammonia heat pump that can be used for space heating, as displayed in Figure 1. Air at ambient temperature and pressure (state 1) is fed into the compressor of the gas turbine cycle. The air at high pressure (state 2) is then transferred into the mixing chamber where it is mixed with the fuel (state 3) needed for the gas power plant. Methane gas (CH\({}_{4}\)) is used from the Thermolib setup block, and its data are loaded into the MATLAB workspace. In the presence of air and at constant pressure, the fuel is burnt in the combustion chamber. This gives rise to a high-temperature gas (state 4) which is transferred into the turbine. Inside the turbine, the high-temperature gas expands and produces power or electricity. The exhaust gas (state 5) which leaves the turbine possesses considerably high thermal energy, i.e., it leaves at a very high temperature (about 650 \({}^{\circ}\)C); therefore, it is used as the heat source for the steam cycle.  
The exhaust gas from the gas cycle is transferred into the steam cycle by entering the heat recovery steam generator (HRSG). The water from the steam cycle (state 8) which enters the HRSG comes out as steam with high pressure and temperature (state 9). This steam enters the steam turbine and expands, thereby generating additional electricity. The steam leaving the turbine after expansion (state 10) is then passed through a condenser where it is cooled down into a saturated liquid, using a stream of water/air (states 16 and 17). The saturated liquid (state 11) leaving the condenser is then pumped into the HRSG to recycle the process. The exhaust gas after leaving the heat recovery steam generator (state 6) still possesses enough energy which is fed into the ammonia heat pump. Here, the ammonia flowing into the evaporator (state 8) receives heat from the exhaust gas and leaves at a higher temperature (state 13). The ammonia is then compressed into the high-pressure level of the heat pump, thereby causing its temperature to be further increased. The hot fluid is then passed through the condenser where it exchanges heat with the process air at ambient temperature (state 18), which leaves it at a much higher temperature (state 19). The ammonia leaving the condenser (state 14) is then passed through an expansion valve to reduce its pressure (state 12) to that of the HRSG.
# The Sliding-Aperture Transform and Its Applicability to Deep-Level Transient Spectroscopy
## 2 Methods
Because traditional capacitance-based DLTS requires a temperature scan to determine the temperature which maximizes the DLTS signal, each data point requires the system to first come into temperature equilibrium with its surroundings, followed by any signal averaging required to improve the signal-to-noise ratio. This effort produces a single point of the traditional DLTS, the temperature domain spectrum. The SLAP methodology also requires the temperature to equilibrate followed by signal averaging to improve the signal-to-noise ratio. However, the SLAP transform algorithm effectively searches for that rate window which will maximize the SLAP signal at the current temperature, with minimal computational overhead. Thus, the SLAP methodology can produce a full rate domain spectrum in the time it takes to produce one point of a DLTS temperature domain spectrum. Although relatively simple for a single defect, the challenge is to resolve the signatures of multiple defects, whose concentration, type, and ionization parameters are unknown [2; 3; 4; 5; 6; 7; 23; 24; 25; 26]. In traditional boxcar-based, temperature-swept DLTS, two neighboring peaks can be distinguished if their emission rates differ by at least \(\sim\)8 times [6]. Other, more recent DLTS analysis techniques utilize the Laplace transform (L-DLTS) or Fourier transform (deep-level Fourier spectroscopy) to improve signal acquisition and rate extraction to improve emitter distinguishability [6; 7; 23]. Both techniques are constant-temperature approaches, such as the SLAP technique, allowing for unlimited signal averaging for improved signal-to-noise ratio. Both techniques offer improvements over the original double-boxcar technique in that the judgment of the user is not required to determine peak location in temperature or rate, instead relying on curve-fitting algorithms to obtain peak location and height, sharing a similar strength with the proposed SLAP technique. Both L-DLTS and deep-level Fourier spectroscopy identify defects as a spectral feature, as does SLAP, with the SLAP technique offering minimal computational overhead and a simple curve-fitting function for peak characterization. Significantly, both L-DLTS and Fourier spectroscopy are based on integrals summed from zero to infinity. This is in stark contrast to the SLAP technique, which obtains results as an integer sum with each integer representing a unique defect. As will be shown, the SLAP technique also provides both minority and majority carrier defect information from a single transient. In all cases, a constant-temperature approach is used for more precise determination of defect parameters through signal averaging, with a signal-to-noise ratio of at least 1000 required for Laplace DLTS to provide better rate resolution than offered by the traditional double-boxcar DLTS technique [23].
## 3 Adaptive TrimTree Mechanism
### Power Management Module  
Generally selective connectivity implementation in the Internet involves two approaches for putting switches into \"sleep\" mode: uncoordinated and coordinated. In \"uncoordinated sleeping\", devices (routers/LAN switches) make their own decisions about when to go to \"sleep\" based on traffic monitoring on interface(s). In \"coordinated sleeping\", a network-wide approach is taken where sleep decisions are coordinated and made at the level of an area in an Autonomous System (AS) and selected routers/switches are informed to \"sleep\" [32]. In many of today's end systems, a \"Wake-on-LAN\" functionality is built into the Network Interface Card (NIC), such that the NIC wakes the host when a special packet arrives.  
The Power Management module receives active and passive network subsets information from the TrimTree module and implements \"coordinated sleeping\" to toggle the power state of ports, line-cards, and switches which have to be turned \"off\" while keeping the active network subset \"on\". It also togles the passive network subset components to \"sleep\" mode. The precise way of implementing this in a real data center network is not elaborated which can use any of the suitable mechanism such as command line interface or SNMP or some other control mechanism.  
Subsequent toggling of passive subset switches into \"on\" and \"sleep\" mode is managed by the switches, which is uncoordinated approach. Different methods of implementing uncoordinated sleeping are proposed in literature along with solutions of issues involved in each method. We propose an approach where an active switch upon deciding to invoke an adjacent passive switch sends a wake-up packet to turn it to \"on\" state. We suggest that passive switches once invoked must remain \"on\" in order to avoid unnecessary complexity because since TrimTree module re-computes network subsets periodically, there would be new subsets soon and new states will be implemented nevertheless.
## 4 Discussion
### General Discussion
#### 4.2.2 Monitoring Strategy--Ways of Improvement  
The surface monitoring of the Lacq-Rousse pilot site does not pretend to be exhaustive. It is part of a larger-scale project that was entirely funded by the operator, which is not so common [17]. It should be mentioned again that the French Administration approved all operations at the time the actions were planned, when these operations were considered to provide sufficient information to ensure storage safety and conformance. However, since those days progress has been made in several fields of knowledge that merit further discussion.  
The first field concerns the use of monitoring mostly restricted to characterization of the CO\({}_{2}\) phase in the near-surface environment, using a spot-sampling approach. Gas-source attribution techniques [85; 86] rely on complementary gas phases to account for the specificities of the CO\({}_{2}\)-gas phase (non-uniqueness of the source, ability to interact with water, etc.). Similarly, the use of methods with a wider geographical coverage and/or allowing continuous monitoring is commonly included in compliance monitoring [18].  
The second field concerns the restricted number of monitoring locations. Site selection was based on several technical factors, but also on the cost of the monitoring actions, using the ratio 'number of locations' to 'information collected' as an economic parameter. The will to deploy several monitoring points per monitoring site, which enhances scientific relevance, did not match this economic constraint. Today, conformance monitoring would certainly refer more to the use of continuous monitoring and the use of systems surveying the atmosphere [37].  
Other fields of potential improvement concern the lack of monitoring deep aquifers, if present close to the injection site, or the absence of monitoring of mineral-water sites at a larger geographical scale. Some local mineral waters that are of sulphide type, such as Eaux Chaudes or Eaux Bonnes [87]. CO\({}_{2}\) intrusion in water has a direct effect on pH and delayed effects on water mineralization, especially in the case of mineral waters that naturally have low CO\({}_{2}\) content.  
A final field in which progress has been made concerns the planning of monitoring sessions, particularly the focus on \"low season\" (winter) monitoring, recommended by [PERSON] [55] and others, leakage certainly being independent from climatic conditions. The way baseline data were used for computing initial threshold levels and the way data from the injection period were used for defining new threshold levels are also questionable. This last point is discussed in the next section.
## 3 Case Study--Goldeneye CCS Site
### Making Decisions Based on BCNN Output with Varying Cost  
In Section 2.7 we presented Algorithm 1 that can assist in taking difficult decisions during environmental monitoring. The reminder of this Section exemplifies the use of Algorithm 1, given a specific cost function. We have created \(T=200\) realizations as an approximation of the two posterior distributions \(P_{t}(c_{1}|\mathbf{x}^{*})\) and \(P_{t}(c_{2}|\mathbf{x}^{*})\). Since we use binary classification, we have that \(P_{t}(c_{2}|\mathbf{x}^{*})=1-P_{t}(c_{1}|\mathbf{x}^{*})\). The 200 realization can be viewed as distinct expert opinions of what class the time series belongs. By applying the two summary function Equations (5) and (6) to the posterior predictive distribution we get the following decision rule in the binary classification setting, that is, we decide action \(\alpha_{1}\) if  
\[(\lambda_{21}-\lambda_{11})P(c_{1}|\mathbf{x}^{*})>(\lambda_{12}-\lambda_{22}) P(c_{2}|\mathbf{x}^{*}).\]  
Since it is difficult to estimate the real cost associated with confirming a leakage, we use a simple cost function and vary some parameters to determine the impact of the cost function on the optimal decision. The cost associated with confirming a no-leak situation is the baseline cost, i.e., we assign a value of 1 for this purpose. This cost can be interpreted as the operational cost with sending a cruise or initiate a unmanned operation to check for a leakage. We assume that the cost with confirming a leakage is dependent on the operational cost. Furthermore, the cost of not confirming a leakage is \(\kappa\) times more expensive than the operational cost. The parameter \(\gamma\) is the factor that describes the cost of not confirming a leakage compared to the cost of confirming a leakage. The cost associated with not confirming if there is no leakage is assumed to be 0. Based on these assumptions we can set up a decision rule dependent on the summary function over the posterior distribution; confirm a leak if  
\[(\gamma-1)\kappa>\frac{P(c_{1}|\mathbf{x}^{*})}{P(c_{2}|\mathbf{x}^{*})}. \tag{7}\]  
Table 3 shows the cost function in terms of true and false positives and true and false negatives.  
Figure 11: **(Left panel) ROC curve with Gaussian noise is simulated with a standard deviation of 0.01 is added to the test data set. The drop of in accuracy is quite large, even with relatively low level of noise added to the test data. (Right Panel) ROC curve for the case where we have excluded the 300T scenario.**In Figure 12 we show the percentage of the total area to be monitored for \(\kappa\in\{10,100,1000\}\) and \(1\leq\gamma\leq 100\).  
This cost function shows the trade-off between confirming a leak and not confirming a leak. By using the MAP, the predictive distribution becomes more distinct on which class it belongs. We see this in Figure 12, where the percentage area needed to be monitored under the same cost function is significantly lower.