esa-sceva/satcom-chunk-collection huggingface dataset:
# Acoustic Emission Spectroscopy: Applications in Geomaterials and Related Materials
## 7 Conclusions  
The anisotropy and heterogeneity of materials lead to the typical avalanche phenomenon in the failure process. This rapid discontinuous stepwise evolution of internal strain fields can be effectively captured by acoustic emission technology. The acoustic emission signal parameters, including acoustic emission energy, amplitude, and duration, follow power-law distributions. The power-law distribution is the only functional relationship with scale invariance, which means that avalanches satisfy the same statistical distribution and the same physical mechanism on different scales.  
A very important aspect of the study of material damage is the development of early warning systems for disaster processes. The AE energy distribution exponent reflects the correlation effects near failure points during material deformation. With the increase in damage, the damage mechanism evolves from random failure points (e.g., point defects) to defect aggregation. This effect is accompanied by a decrease in the AE energy exponent. This reduction of the power-law exponent is best observed by analyzing the superjerk events in the acoustic emission spectrum. This has already been used in early warning systems for catastrophic failure.  
In the typical scenario that more than one power-law mechanism exists with several interacting power-law mechanisms, the composite probability density function is no longer a straight line in double logarithmic representation but shows some curvature. The recommended method to analyze these distributions is the maximum likelihood (ML) method, to estimate the respective exponent values of the two power laws and the proportion of their mixing.  
We reviewed the acoustic emissions of common materials such as sandstone, coal, calcium carbonate sands, and sugar. While the avalanche characteristics are now well established there is still work to be done. One is to optimize the search process of aftershock sequences. Others involve applications such as pico-seismology in bore holes to assess the geological configuration and the stability of a well. How can we make the effective early warning and prevention of geological disasters possible? The challenge of separating the main action mechanism from the mixed mechanisms and carrying out grouting oranchoring for the shear or friction mechanism of the fault or crack remains unsolved. In the fields of materials sciences, physics and chemistry, AE spectroscopy is already used to optimize materials such as better alloys from space crafts to bicycles, and to assess the frequency range of ferroic modulators and switches in the gigacycle regime.  
This research was funded by EPSRC (No. EP/P024904/1), the EU's Horizon 2020 programme under the [PERSON] grant agreement No 861153, the financial support from the Natural Science Foundation of China (No. 51908088), and the Natural Science Foundation of Chongqing (cstc2019 cjyj-xfkxX0001).  
Not applicable.  
Data Availability Statement: Data will be made available under reasonable request.  
The authors declare no conflict of interest.
## 11 Hardware and Testbeds
### Testbeds
Currently, WiSNAP includes device libraries for Agilent's ADCM-1670 camera module, Agilent's ADNS-3060 optical mouse sensor, and Chipcon's CC2420 DB IEEE 802.15.4, but it can be extended to include and support any kind of sensor or wireless mote as it is an open source architecture. WiSNAP consists of two application program interfaces: 1) an image sensor API that enables frame capturing from image sensors after identifying the type of image sensor and number of frames, and 2) a wireless mote API that provides access to wireless motes through functions for initialization and MAC packet transmission and reception. Then these set  
of functions provided by the mentioned APIs are matched with the corresponding device libraries that lie below the API layer in WiSNAP program stack and provide a set of hardware-dependent functions (such as Agilent ADCM-1670 image sensor). Two application examples of using WiSNAP development platform are presented for event detection and node localization. Event detection is based on tracking of the number of changed pixels that exceed a certain threshold between successive image frames. For node localization, the distance is estimated using the received signal strength indicator RSSI from the node and the direction is calculated by extracting the relative angle of a continuously blinking LED on that node from the captured images using frame differencing of adjacent frames. Also in [102], a software testbed based on address event representation of image sensing is developed. The software testbed consists of an emulator of AER imagers - written in VisualC++ and runs under windows. the AER Emulator takes an 8-bit grayscale input stream from a COTS USB camera and outputs a queue of events to a text file. The AER classification is done in a way similar to the Hidden Markov Models (HMMs). At the receiver side, the image array can be obtained by converting the event frequency data into the original feature (e.g., light intensity) using two ways, Histogram reconstruction or Inter-event reconstruction.
## Appendix B FLUXNET2015 Sites Used in This Study
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Code** & **Name** & **Latitude** & **Longitude** & **Veg. Type** & **Year Start** & **Year End** \\ \hline JP-MBF & Moshiri Birch Forest Site & 44.3869 & 142.319 & DBF & 2003 & 2005 \\ JP-SMF & Seto Mixed Forest Site & 35.2617 & 137.079 & MF & 2002 & 2006 \\ NL-Hor & Horstermeer & 52.2404 & 5.07130 & GRA & 2004 & 2011 \\ NL-Loo & Loobos & 52.1666 & 5.74360 & ENF & 1996 & 2013 \\ NO-Adv & Adventdalen & 78.1860 & 15.9230 & WET & 2011 & 2014 \\ RU-Fyo & Fydorovskoye & 56.4615 & 32.9221 & ENF & 1998 & 2014 \\ SD-Dem & Demokeya & 13.2829 & 30.4783 & SAV & 2005 & 2009 \\ SN-Dhr & Dahra & 15.4028 & \(-\)15.4322 & SAV & 2010 & 2013 \\ US-AR1 & ARMUSDA UNLOUSwitchgrass 1 & 36.4267 & \(-\)99.4200 & GRA & 2009 & 2012 \\ US-AR2 & ARM USDA UNL OSU Switchgrass 2 & 36.6358 & \(-\)99.5975 & GRA & 2009 & 2012 \\ US-ARb & ARM Southern Great Plains burn - Lamont & 35.497 & \(-\)98.0402 & GRA & 2005 & 2006 \\ US-ARc & ARM Southern Great Plains control - Lamont & 35.5465 & \(-\)98.0400 & GRA & 2005 & 2006 \\ US-Blo & Blodgett Forest & 38.8953 & \(-\)120.633 & ENF & 1997 & 2007 \\ US-Cop & Corral Pocket & 38.0900 & \(-\)109.390 & GRA & 2001 & 2007 \\ US-H1 & Harvard Forest EMSTower (HFR1) & 42.5378 & \(-\)72.1715 & DBF & 1991 & 2012 \\ US-KS2 & Kennedy Space Center (scrub oak) & 28.6086 & \(-\)80.6715 & CSH & 2003 & 2006 \\ US-MMS & Morgan Monroe State Forest & 39.3232 & \(-\)86.4131 & DBF & 1999 & 2014 \\ US-Me1 & Metolius-Eyerly burn & 44.5794 & \(-\)121.500 & ENF & 2004 & 2005 \\ US-Me2 & Metolius-intermediate aged Ponderosa pine & 44.4523 & \(-\)121.557 & ENF & 2002 & 2014 \\ US-Me6 & Metolius Young Pine Burn & 44.3233 & \(-\)121.608 & ENF & 2010 & 2014 \\ US-NR1 & Niwot Ridge Forest (LITER NWT1) & 40.0329 & \(-\)105.546 & ENF & 1998 & 2014 \\ US-Ne1 & Mead-irrigated continuous maize & 41.1651 & \(-\)96.4766 & CRO & 2001 & 2013 \\ US-Ne2 & Mead-irrigated maize-soyeban rotation & 41.1649 & \(-\)96.4701 & CRO & 2001 & 2013 \\ US-Ne3 & Mead-rainfed maize-soyeban rotation & 41.1797 & \(-\)96.4397 & CRO & 2001 & 2013 \\ US-ORv & Olentangy River wetland Research Park & 40.0201 & \(-\)83.0183 & WET & 2011 & 2011 \\ US-Prr & Poker Flat Research Range & 65.1237 & \(-\)147.488 & ENF & 2010 & 2013 \\ US-SRG & Santa Rita Grassland & 31.7894 & \(-\)110.828 & GRA & 2008 & 2014 \\ US-SRM & Santa Rita Mesquite & 31.8214 & \(-\)110.866 & WSA & 2004 & 2014 \\ US-Syv & Sylvania Wilderness Area & 46.2420 & \(-\)89.3477 & MF & 2001 & 2014 \\ US-Ton & Tonzi Ranch & 38.4316 & \(-\)120.966 & WSA & 2001 & 2014 \\ US-Tw1 & Twitchell West Pond & 38.1074 & \(-\)121.647 & WET & 2012 & 2014 \\ US-Tw3 & Twitchell Alfafla & 38.1159 & \(-\)121.647 & CRO & 2013 & 2014 \\ US-Tw4 & Twitchell East End Wetland & 38.1030 & \(-\)121.641 & WET & 2013 & 2014 \\ US-Twt & Twitchell Island & 38.1087 & \(-\)121.653 & CRO & 2009 & 2014 \\ US-UMB & Univ.of Mich.Biological Station & 45.5598 & \(-\)84.7138 & DBF & 2000 & 2014 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Listing of flux tower sites used in this study (cont. ).
## 2 Related Work
### Low Power Communications  
The current state-of-the-art in the field of SMs is such that an increasing number of solutions are utilizing NB-IoT (e.g., Shenzhen gas company in China). An example of an early NB-IoT water metering system is given in [16]. Several commercial case studies in China are reviewed in [17]. Such solutions in Europe were in the mature pilot testing phase a few years ago (e.g., Kamstrup from Denmark [18]).  
Other solutions are based on LoRaWAN technologies [19]. Although the design seems to be low power and low cost (STM8 microcontroller unit), there are no data to support such a claim in the paper. Additionally, it can be used only on pulse output water meters. A hybrid water meter solution based on infrared and LoRaWAN is given in [20].
# Service Interface Translation. An Interoperability Approach
## 3 Service Interface Translation
### Interface Generation Requirements  
This paper proposes a novel approach based on autonomous runtime consumer interface generation. The presented approach translates information exchanges between systems with heterogeneous interfaces. The approach is defined based on the following requirements: (1) autonomous, (2) runtime, and (3) service contract-based.  
* Autonomous. To reduce the engineering effort, the generation needs to be autonomous. The generation must be performed without human intervention.
* Runtime. The interoperability problems that occur during operation time require more effort and cost to be resolved. The solution to interoperability mismatches at runtime allows the connection of heterogeneous systems to open new possibilities, including the integration of vertical domains without increasing the engineering cost.
* Service contract-based. The service contract is a key piece of the generation since the service metadata that compound the service contract is necessary for a complete generation and translation. The final objective is to provide a reliable lossless service interface translation.
## 1 Introduction  
Room-temperature ionic liquids (ILs) represent a very interesting class of materials because their properties can be tuned by choosing an appropriate combination of cation and anion. In this manner, tailor-made media exhibiting optimal physicochemical properties for specific applications can be synthesized, if the structure-property relationships are understood [1; 2]. Adding a solvent to an IL adds another dimension to this tunability as both the chemical nature of the solvent and its concentration can be varied. However, in such a mixture, not only the interactions between the ions must be decoded, but also the impact of the solvent. The molecular interactions between conventional solvents and ILs are complicated and typically comprise a variety of mechanisms including hydrogen bonding, coulombic forces, and dispersion interactions. Their interplay determines the molecular and macroscopic behavior of the mixture.  
The most common solvent considered in ILs is water. Due to the hygroscopic nature of many ILs, even careful handling under a dry atmosphere can result in contamination with water, and hence, its effects need to be studied. When water is added to, e.g., 1-alkyl-3-methylimidazolium-based ILs, the dominant interaction mechanism is the formation of hydrogen bonds (HBs). It was shown that water-ion HBs form with the anion and also with the cation, predominantly at the C(2) position, where the cation-anion interactions usually take place. As a consequence, the ion pairs dissociate and the individual ions are hydrated [3; 4; 5; 6; 7]. Mixtures with alkyl alcohols indicated the same behavior [8; 9]. However, spectroscopic and computational studies aiming at insights into the molecular interactions have focused on short-chain alcohols, mainly methanol and ethanol. Hence, conclusions about the general behavior of imidazolium ILs in alcohols can hardly be drawn at this point. For completeness, the macroscopic properties and thermodynamics of alcohol-IL solutions have already been studied for longer chain lengths, and interesting correlations could be identified there [10; 11; 12; 13]. Deducing information about the molecular scale solely from the measurement of macroscopic parameters, however, bears the danger of over-interpreting the data.  
In the present Communication, we show a case in which the ion pairs of an imidazolium-based IL do not dissociate, even at high dilution by an alcohol. For this purpose, binary mixtures of 1-butyl-3-methylimidazolium trifluoromentanesulfonate (C\({}_{4}\)mimTfO) and propan-1-ol (PrOH) are studied using Raman and FTIR spectroscopy. We have recently shown that TfO ILs represent interesting model systems for spectroscopic studies as the TfO\({}^{-}\) anion exhibits a single configuration, hence reducing the complexity of the spectra [14], e.g., in contrast to the common NTf\({}_{2}\) - ILs [15]. Moreover, we have chosen this combination of an IL and an alcohol as the thermodynamic behavior of their binary mixtures is well understood [11].
## 11 Further Research Directions and Future Considerations
### Software-Defined Optical Transmission
#### 11.5.1 Software-Defined Optical Transceivers  
There has been considerable migration from static networks that offer limited flexibility to software-defined optical networks (SDONs) by the telecommunications industry. The rationale behind the adaptation is due to the fact that the perpetual increase in traffic has to be supported by dynamic network throughput. The usable fiber bandwidth is limited, and an increase in the SE is at the expense of the achievable reach. Another challenge being faced by the telecommunications industry is the growing increase in the dynamic environment and the heterogeneous nature in the optical transport network (OTN) [1; 161]. The variation can be attributed to an increase in the uncertainty of the traffic sources and the heterogeneity of the demands, as well as the ever-changing models being employed by the content providers to address them [175].  
In addition, the optical network operators usually intend to accomplish the fairest possible optimal tradeoff between the SE and the reach. Consequently, for effective support of the network requirements in the SDON environment, the transceivers need to have good controls and adaptations for a number of parameters and functionalities that are related to the symbol rate, modulation format, FEC overhead, channel spacing, optical spectrum shaping, and coding gain (CG). In light of this, software-defined transceivers have been attracting significant attention due to the offered benefits such as improved flexibility, increased robustness to channel impairments, and an upgrade path for the content-aware future transmission systems. In this regard, they have been envisaged to play a significant function in dynamic access network development [178; 179].  
Based on the transceivers' programmability and parameter adaptation, optimal trade-offs among the reach, power dissipation, and data rate can be realized in accordance with the bandwidth requirements and prevailing channel conditions [161; 162; 178; 180]. This will facilitate the ability of the transceivers to support \"any-haul\". Therefore, the same transceiver can be employed for different applications through appropriate parameter re configuration, making it a promising candidate for SDON applications [161]. Furthermore, it is noteworthy that reconfigurable transceivers' development will demand different innovations in both software and hardware parts that might be more demanding than the fixed counterparts. Similarly, network control and management could be more challenging [175].
## 3 Results
### Trend of Carbon Fluxes  
The analysis of GPP trends in Yunnan forest ecosystems revealed that the SSP1-2.6 scenario showed an increasing trend, accounting for 97.50% of the area, with a decreasing trend in GPP in the northwestern sporadic area (Figure 8). Under the SSP2-4.5 and SSP5-8.5 scenarios, the Xishuangbanna forests showed a decreasing trend, while in the northwestern and eastern regions, the forests showed an increasing trend (\(>\)25 gcm\({}^{-2}\cdot\)yr\({}^{-1}\)). Additionally, 28.24% and 29.05% of the area with an increasing trend of \(>\)20 gcm\({}^{-2}\cdot\)yr\({}^{-1}\) under the SSP1-2.6 and SSP5-8.5 scenarios were higher than the value of 22.14% under the SSP2-4.5 scenario.  
Figure 7: Spatial distribution of the carbon flux coefficient of variation in Yunnan Province from 2020 to 2060 under different emission scenarios: (**a**) SSP1-2.6; (**b**) SSP2-4.5; and (**c**) SSP5-8.5.  
The analysis of forest ecosystem ER trends in Yunnan revealed that the ER of forest ecosystems in the three scenarios showed an increasing trend in an area of \(>\)95%, but there were differences in the spatial distribution. The SSP1-2.6 scenario showed an increasing trend in Baoshan and Puer in the central and southern parts of the country and a slight decrease in the northwestern part. The SSP2-4.5 and SSP5-8.5 scenarios showed a decreasing trend in the ER of the Xishuangbanna forest in the southern part of the country and an increasing trend in the ER of the Yuxi forest in the eastern part of Diqing in the northwestern part (\(>\)20 gCm\({}^{-2}\cdot\)yr\({}^{-1}\)).  
Figure 8: Spatial distribution of carbon flux trends in Yunnan Province from 2020 to 2060 under different emission scenarios: (**a**) SSP1-2.6; (**b**) SSP2-4.5; and (**c**) SSP5-8.5.  
The proportion of area with a decreasing NPP trend under the three scenarios was 15-20%, with the largest proportion of area decreasing under SSP5-8.5 (up to 19.13%). The largest proportions of area with an increasing NPP trend between 0 and 5 gCm\({}^{-2}\cdot\)yr\({}^{-1}\) were 53.51, 60.30, and 52.96% for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. Under SSP1-2.6, NPP showed a decreasing trend mainly in Diqing and Nujiang, and SSP2-4.5 and SSP5-8.5 mainly had decreasing NPP trends in Chuxiong and Kunming.  
Over the next 40 years, the NEP of more than 85% of forest ecosystems will decrease, with that in SSP2-4.5 and SSP5-8.5 decreasing mainly in Lijiang and Dali in the north with a downward trend of \(-\)5 gCm\({}^{-2}\cdot\)yr\({}^{-1}\) and that in SSP1-2.6 decreasing mainly in Wenshan in the south. Because the rising rate of ER in this region was higher than the rising rate of GPP, NEP showed a downward trend. The forests with an increasing NEP trend under the three scenarios were mainly located in the northwestern and eastern regions.
###### Abstract  
This study tried to propose an innovated idea of solidifying the resolution of gamma camera in routine quality control and recommended a quantified index as minimum detectable difference (MDD) of gamma camera SPECT images using the Taguchi analysis and an indigenous V-shaped slit gauge. The gauge was customized to fulfill the quantitative requirement of the Taguchi analysis. The MDD among slit gauge of derived SPECT image was calculated from two overlapped peak profiles collected from a tangent slice of the V-shaped slit gauge with two nearby peaks. In particular, MDD was evaluated as minimum distance between two peak centers through the Student's \(t\)-test with a constant, 1.96, which indicates that two peak centers separated distant enough to create a 95% confidence level of separation. Eighteen combinations of six gamma camera scanned factors were organized according to Taguchi analysis. Accordingly, (A) collimator, (B) detector to large distance, (C) total counts, (D) acquired energy width, (E) Matrix size, and (F) zoom of collected ROI with each of two or three levels were organized into 18 groups to collect the slit gauge images according to Taguchi L\({}_{18}\) orthogonal array. Then, three well-trained radiologists were ranked the scanned gauge images to derive the fish-bone-plot of signal-to-noise ratio (S/N, dB) and correlated ANOVA. Furthermore, the quantified MDD was proposed to verify the optimal suggestion of gamma camera scanned protocol, and obtained the MDD as 8.4, 7.9, and 7.1 mm for the second group of original L\({}_{18}\) preset, conventional, and the optimal preset, respectively. Thus, the optimal preset of gamma camera was achieved in this study. The MDD proved to be a successful index in quantifying the imaging resolution of a gamma camera.  
SPECT; gamma camera; Tc-99 mTc MDP; spatial resolution; minimum detectable difference iacn
## 3 Results and Discussion
### Model-Based Optimization and Validation  
Through this experiment, a regression model that can predict \(Y_{leg}\) and \(Y_{pen}\) was derived, and R\({}^{2}\) was 94% for \(Y_{leg}\) and 99% for \(Y_{pen}\). Figure 11 shows contour plots of the response variables with respect to the independent variables. \(Y_{leg}\) and \(Y_{pen}\) decreased as \(X_{1}\) increased within the range of \(X_{1}\) (123-157 cm/min). As \(X_{2}\) and \(X_{3}\) increased, the response variables exhibited the same trend. Points 1-3 in Figure 11 are the test points for the estimation models. Table 9 lists the predicted and actual values at the test points. The test was repeated thrice. Consequently, in the case of point 1, the average deviations of \(Y_{leg}\) and \(Y_{pen}\) were 0.16 and 0.13 mm. For point 2, the average deviations of \(Y_{leg}\) and \(Y_{pen}\) were both 0.37 mm. Finally, at point 3, the average deviations of \(Y_{leg}\) and \(Y_{pen}\) were 0.37 and 0.23 mm.  
If the optimal welding conditions are determined based on the three points shown in Table 9, in terms of productivity, the condition of point 3 is considered an optimal condition because the welding speed is the fastest and the size of the weld also satisfies the required standard for the manufacturer.  
Figure 11: Contour plots for predicted \(Y_{leg}\) and \(Y_{pen}\): (**a**) predicted \(Y_{leg}\) when \(X_{1}\): 140 cm/min; (**b**) predicted \(Y_{pen}\) when \(X_{1}\): 140 cm/min; (**c**) predicted \(Y_{leg}\) when \(X_{1}\): 145 cm/min; (**d**) predicted \(Y_{pen}\) when \(X_{1}\): 145 cm/min.