esa-sceva/satcom-chunk-collection huggingface dataset:
# Fourier Neural Operator Network for Fast Photoacoustic Wave Simulations
## 2 Materials and Methods  
### Photoacoustic Signal Generation and Imaging  
The photoacoustic signal is generated by irradiating the medium with a nanosecond laser pulse (Figure 1). Chromophores within the image medium are excited by the laser and undergo thermoelastic expansion to generate acoustic pressure waves. Assuming negligible thermal diffusion and volume expansion during illumination, the initial photoacoustic pressure \(x\) can be defined as  
\[x(r)=\Gamma(r)A(r), \tag{1}\]  
where \(A(r)\) is the spatial absorption function, and \(\Gamma(r)\) is the Gruneisen coefficient describing the conversion efficiency from heat to pressure [32]. The photoacoustic pressure wave \(p(r,t)\) at position \(r\) and time \(t\) can be modeled as an initial value problem, where \(c\) is the speed of sound [33].  
\[\left(\partial_{tt}-c_{0}^{2}\Delta\right)p(r,t)=0,\;\;p(r,t=0)=x,\quad\partial _{t}p(r,t=0)=0 \tag{2}\]  
In photoacoustic imaging, sensors located along a measurement surface \(\mathrm{S_{o}}\), surrounding the medium, are used to measure a time series signal. The linear operator \(\mathcal{M}\) acts on \(p(r,t)\) restricted to the boundary of the computational domain \(\Omega\) over a finite time  
Figure 1: Diagram illustrating the process of photoacoustic signal generation and detection. Chromophores absorb the incident pulsed laser light and undergo thermoelastic expansion to generate acoustic waves. Acoustic detectors along the measurement boundary So are used to measure the acoustic waves.  
and provides a linear mapping to the measured time-dependent signal \(y\). The forward photoacoustic operator \(W\) maps the initial acoustic pressure to the measured signal.  
\[y=\mathcal{M}_{p|\partial\Omega\times(0,T)}=Wx \tag{3}\]  
The measured sensor data are then used to form an image representing the initial acoustic pressure distribution. Photoacoustic image reconstruction is a well-studied inverse problem that can be solved using analytical solutions, numerical methods and model-based iterative methods [11; 12; 13; 15; 34]. The adjoint photoacoustic operator \(W^{*}\) maps the measured signal to the initial acoustic pressure.  
\[x=W^{*}y \tag{4}\]  
Time reversal is a robust reconstruction method that works well for homogenous and heterogeneous mediums and also for any arbitrary detection geometry [15; 34]. The acoustic waves that are generated are measured along the measurement surface \(\mathsf{S}_{0}\). After a long period of time \(T\), the acoustic field within the medium becomes zero, which is guaranteed by [PERSON] principle in homogeneous mediums [35]. A PAT image is formed by running a numerical model of the forward problem and transmitting the measured sensor data in a time-reversed order into the medium, where the detectors along \(\mathsf{S}_{0}\) are time-varying pressure sources. Thus, time reversal is modeled as a time-varying boundary value problem, and the resulting acoustic field at \(t=0\) is the initial acoustic pressure distribution to be recovered.
## 5 Results
### Robustness Analysis
#### 5.2.3 Initial Model Sensitivity Analysis  
The initial models are shown in Figure 15. Figure 16 shows the inversion results of the FAFWI method with different initial constant-velocity models. In (a), with an initial model speed of 1800 m/s, the FAFWI method successfully reconstructed the velocity structures in both the shallow and middle layers, showing a high trend consistency in the deep regions. In (b), with a velocity of 2100 m/s, the overall velocity structure remains clear but shows slightly less consistency than (a). When the initial velocity increased to 2300 m/s, as shown in (c), there was a noticeable increase in the regions trapped in the local minima. Finally, when the initial velocity reached 2500 m/s, approaching the average velocity of the middle layer, large areas of the inversion results became trapped in local minima, leading to a decreased inversion accuracy. The experimental results indicate that the inversion is more accurate when the initial constant-velocity model is closer to the average velocity of the shallow layers near the well. Conversely, higher initial velocities are more likely to result in local minimum traps. Overall, although the FAFWI method demonstrated some insensitivity to the initial models, inversion results may fail because of local minima when the initial model velocities are excessively high.  
Figure 14: Comparison of inversion results for the Marmousi model under noisy conditions. (**a**) Inversion results without noise. (**b**) Inversion results at the 2\(\sigma_{0}\) noise level.  
Figure 13: Comparison of VSP shot gathers before and after the noise addition. (**a**) Far-offset shot gather without noise. (**b**) Far-offset shot gather with 2\(\sigma_{0}\) noise. (**c**) Near-offset shot gather without noise. (**d**) Near-offset shot gather with 2\(\sigma_{0}\) noise.
## 4 Results  
### Surface Deformation over The Entire Peninsula  
The LOS deformation rates of four different paths (Path 76, Path 69, Path 98 and Path 3) Sentinel-1 SAR data are firstly calculated independently. Then, all the deformation maps are transferred to the same coordinate system according to the external DEM. Finally, the annual land subsidence rate map over the entire peninsula from 2016 to 2019 is generated by mosaicking all four deformation rates shown in Figure 2. All the background images on which the InSAR-derived deformation maps are superimposed are from Google Earth. The accuracy of the InSAR measurements will be cross validated with independent InSAR observations from different tracks in Section 4.2.  
The surface deformation characteristics of six cities seated in the peninsula have been explored, that is Dongying, Weifang, Yantai, Weihai, Qingdao and Rizhao from north to south. We detected land subsidence in a total of twelve regions, eleven of which occur in or near the coastal areas defined by red rectangles from Region A to Region K (Figure 2). The remaining one is the subsidence over inland Guangrao, occupying an area as large as 110 by 35 km (green rectangle in Figure 2). On the whole, land subsidence is mainly concentrated in the northern Shandong peninsula (i.e., YRD), and the southern and southeastern areas are relatively stable. There is no large scale ground subsidence zone, except some scattered coastal zones, with the subsidence rate varying from 10 mm/a to 60 mm/a, which can be explained by the different coastal geological settings (i.e., YRD is mainly a silt mud coastline, and the south and southeast areas are mainly bedrock coastline). The most remarkable subsidence is located in Region C with a subsidence rate as large as 260 mm/a. The GPS results show that the vertical ground movement gradually changes from subsidence to slight uplift along the north to the south of Shandong peninsula during the period of 2010 to 2015 [38]. Although the observation periods between GPS and InSAR are different, the overall deformation trends are consistent. Detailed analysis of the main regional subsidence in Figure 2 will be given in the following Section 5.
## 1 Introduction
### Background and Related Works
#### 1.1.6. Smart Campus  
In Reference [79], a smart campus sensor system was developed at the University of Glasgow in the United Kingdom. The authored use Raspberry Pi to build sensor nodes called supersensors, which are directly connected to multiple sensors and responsible for retrieving and transmitting sensor data. The supersensors also behave autonomously to perform tasks such as dynamic reconfiguration and communicating with other local devices. A microservice architecture as also proposed in Reference [79], and a room temperature monitoring application based on the proposed architecture was also demonstrated in Referenece [79]. In Reference [80], a smart campus IoT framework was proposed to address issues related to energy consumption, classroom functionality, safety, and cybersecurity, and machine-learning algorithms were employed in the proposed framework. In reference [81], an Android-based school information system was made. The system provides functions such as notifications, location service, emergency contact list, temperature monitoring, and water tank monitoring. In Reference [82], a fast-paced development experience of a smart campus IoT platform was presented. Raspberry Pi, Arduino, sensor modules, and commercial all-in-one sensor devices were used in the platform. Raspberry Pi was used as a hub device, which is a bridge between sensor devices and cloud services. The authors of Reference [82] state that even students with no prior IoT knowledge could build a generic IoT platform within a short period. In Reference [83], the demand for a smart campus online teaching platform under the background of the 5G network was analyzed, and an online teaching platform based on the four initiatives operationmodel was illustrated. In Referene [84], a smart campus service discovery algorithm on the situational awareness system framework for smart campus was proposed. The authors of Reference [84] state that the smart campus platform based on situational awareness can efficiently fuse perceptual data and can make corresponding predictions of various behaviors.
# On the Use of Focused Incident Near-Field Beams in Microwave Imaging
## NF Beams and Results
### Interrogation of Objects by the Bessel Beam Launcher
#### 3.2.1 Analysis of the Scattered NF Data  
The incident NF beams within the \(xy\) plane for Configurations I and II are shown in Figure 13a,b. In addition, the measurement domain (receivers' locations) is taken to be a line in the \(xy\) plane and parallel to the \(x\)-axis, which is \(2\lambda\) away from the Ol's center. Over this line, we have distributed 61 receivers that are evenly spaced. The incident NFs on the measurement domain have also been plotted in Figure 14a,b. (Note that the first-null beamwidth of the Bessel beam in Figure 14a is about \(\lambda\).) The scattered NF data (i.e., the scattered fields at the receivers' locations) for Cases I to IV for both configurations are shown in Figures 14c,f. As can be seen in these figures, the change in the scattered NF data due to the introduction of the undesired scatterer is less in Configuration I than that in Configuration II. Based on the discussion presented in Section 2.2, we speculate that this is due to the fact that the induced contrast sources have undergone less changes in Configuration I when the undesired scatterers were introduced. This has been demonstrated in Figure 15 where the contrast sources for both configurations regarding Cases I and II have been plotted: as can be seen, the contrast sources in Case I and II are more similar under Configuration I. (In addition, note that the contrast sources \(w\) outside the box in Case I for both configurations are exactly zero since the contrast \(\chi\) outside the box is zero.)  
Figure 12: The simulated Bessel beam launcher setup. This represents Case IV where the light green circular cylinder (with the radius of \(\lambda/4\) and the height of \(\lambda/3\)) represents the OI and the dark green circular cylinder, with the same size and dielectric properties as the OI, represents the undesired scatterer.  
Figure 14: **(a,b)** normalized magnitude of the incident NF at the receiver line; **(c–f)** scattered NF at the receiver line for Configurations I and II regarding Case I (single small box), Case II (elongated box consisting of an OI and two undesired scatterers), Case III, (single small cylinder) and Case IV (small cylinder with an offset extra undesired scatterer) respectively. In Configuration I, these fields correspond to the \(\hat{y}\) component of the electric field, and, in Configuration II, they correspond to the \(\hat{x}\) component of the electric field.  
Figure 13: Normalized magnitude of the incident NF distribution for **(a)** Configuration I (Bessel beam) and **(b)** Configuration II (dipole).
## 1 Introduction
In South Korea, the safety of LMOs has also been managed through the Transboundary Movement, Et. of the Living Modified Organisms Act (LMO Act), implemented in 2008 [45]. The Ministry of Environment (MOE) and the National Institute of Ecology (NIE) are responsible for monitoring, evaluating, and managing the environmental impact of LMOs in accordance with the LMO Act. The importation of LMO crops into South Korea has steadily increased from 8.57 million tons in 2008 to 11.15 million tons in 2021 [9]. With the rise in imports, concerns have been raised about whether LMOs may spread into natural ecosystems due to accidental spills during transportation and handling. For instance, LM canola has been found on roadsides near import ports in Japan and Canada [46; 47; 48]. Since 2009, the MOE has implemented an LMO monitoring project involving nationwide surveys, suspicious plant sampling, LMO detection analysis, and post-management in order to prevent the potential risks posed by LMOs in natural environments [49]. Despite these efforts, LMOs have been re-collected at the same survey sites. Thus, a priority management system, for areas where LMOs had been re-collected twice or more within the last five years, was established in 2014 to address concerns about gene flow to non-LMOs or related species and re-spreading by LMO volunteers. This study aimed to assess the effectiveness of this novel priority management system. This study suggests that intensive surveys using the system developed in 2014 effectively minimized the risks posed by LMOs to natural ecosystems and ensured their safety.
## 3 Results
### Chemical Analysis of Volatiles  
The volatiles from healthy, mock-inoculated, and MMWV-infested plants (10 days after inoculation) were analyzed by GC-MS (Figure 4). In total, 31 compounds were identified, of which, \(\beta\)-cubebene (12.0%), germacrene D (9.7%), \(\beta\)-himachalene (8.9%), \(\alpha\)-zingiberene (6.9%), and \(\beta\)-caryophyllene (6.3%) were the main VOCs released from the healthy plants (Figure 4). In contrast, \(\alpha\)-curcumene (12.6%), \(\beta\)-cubebene (12.3%), \(\alpha\)-zingiberene (8.9%), and \(\beta\)-caryophyllene (6.1%) were the main compounds from MMWV-infected plants (Figure 4). Compared to the healthy plants, MMWV-infected plants significantly increased their emission of \(\alpha\)-curcumene (94.1%), humulene (88.7%), and \(\alpha\)-ocimene (86.7%), but decreased the emission of \(\beta\)-himachalene (586.4%), \(\beta\)-sesquiphellandrenee (371.7%), and c-muurolene (321.7%), respectively (Figure 4). Interestingly, isocaryophyllene (13.5%), isoledene (11.6%), and \(\beta\)-caryophyllene (9.4%), trans-\(\alpha\)-bergamotene (7.0%), and \(\alpha\)-muurolene (6.0%) were found to be the major VOCs from mock-inoculated plants, distinct from those identified from healthy plants (Figure 4). On the other hand, no VOCs were detected from the negative control (pot wrapped in aluminum foil without plants). Perhaps plant handling, i.e., applying buffer to plants, could have introduced slight damage to the plants which was sufficient to cause changes in VOCs production. Such discrepancy has also been reported by other researchers [14; 25]. GC-MS analysis revealed a significant increase in quantities of volatiles released by MMWV-infected plants relative to the healthy and mock-inoculated plants, but no qualitative differences were detected (Figure 4). The PCA analysis separated healthy, mock-inoculated, and MMWV-infected plants from each other (Figure 5, PC1 = 46.84%, PC2 = 32.79%). Therefore, MMWV-infection and plant handling significantly changed the VOCs profile of _M. micranthu_.  
Figure 4: VOCs released from healthy, Mikania micranthu wilt virus (MMWV)-infected, or mock-inoculated plants. Quantitative analyses of each volatiles organic compound (expressed as area percentage) were carried out by a peak area normalization measurement. Values are mean \(\pm\) SE (three replicates). 1, Hexanal; 2, 2-Hexanal; 3, \(\alpha\)-Ocimene; 4, \(\alpha\)-Cubebene; 5, \(\alpha\)-Copaene; 6, \(\alpha\)-Curcumene; 7, \(\beta\)-Cubebene; 8, Isocaryophyllene; 9, \(\beta\)-Caryophyllene; 10, trans-\(\alpha\)-Bergamotene; 11, Cedrene; 12, cis-\(\beta\)-Farmesene; 13, Humulene; 14, \(\beta\)-Himachalene; 15, Isoledene; 16, (+)-Epi-bicyclosesquiphellandrenee; 17, \(\alpha\)-Zingiberene; 18, \(\alpha\)-Muurolene; 19, \(\beta\)-Bisabolene; 20, \(\beta\)-Sesquiphellandrenee; 21, \(\delta\)-Cadinene; 22, \(\beta\)-Vatirenene; 23, \(\alpha\)-Guaiene; 24, Cyclohexene; 25, Isolongifolene; 26, (E)-Nerolidol; 27, \(\xi\)-Himachalene; 28, \(\xi\)-Muurolene; 29, Germacrene D; 30, trans-Sesquisibinene hydrate; 31, 8-oxo-9H-Cycloisolongifolen.
## 3 MIMO Radar Simulation Platform  
This section introduces the structure of the traditional MIMO radar simulation platform, and then compares the performance of the MIMO radar simulation platform with the MIMO radar simulation platform optimized by the single RF link.  
The composition of a MIMO radar system is complex and can be divided into several modules, including the signal generation module, the target module, the channel module, the signal receivingmodule, and the data analysis module. The structure of the traditional MIMO radar simulation platform is shown in Figure 3.  
In the signal generation module and receiving module, an antenna array is used for transmitting and receiving. The signal-processing module includes filtering, frequency conversion, and demodulation processes. For different kinds of applications, it may employ the FFT principle, pulse compression, space-time adaptive processing, and intelligent algorithm. The data analysis process uses various algorithms to obtain the target distance, velocity, detection probability, and other physical quantities. The final processing results are obtained to complete the whole radar detection process.
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 3 Out-of-Equilibrium Dynamics
### Modified Dynamics with Entropy Coupling
#### 3.9.1 Physical Regimes  
3.9.1 Case 1: Purely Dissipative (\(\zeta_{p}=0\))  
* Standard damped oscillator: \[m\dot{q}+\zeta_{q}\dot{q}+kq=0\] (25)
* Phase-space contraction (Figure 2(Left))
## 4 Experiments
### Datasets  
Table 1 presents the data statistics for the Kazakh and Turkish datasets in terms of dataset size, OOV rates, and AT. The size of the Turkish dataset is much greater than the size of the Kazakh datasets. The OOV rates for Kazakh are higher at 27.58% and 43.9% for kD1 and kD2, while the Turkish dataset has an OOV rate of 10.24%. This complexity is further manifest in the average number of analyses per token where the Kazakh datasets have an AT of 2.95 and 2.85 for kD1 and kD2, respectively, compared to 1.76 for the Turkish dataset. These statistics suggest that while both languages exhibit rich morphological structures, Kazakh presents more challenges due to its smaller datasets and higher OOV rates. These factors are important in model development because fine-tuning PLMs could enhance performance for low-resource languages such as Kazakh.
## 2 Experiments
### Technical Principle of DIC  
Digital image correlation (DIC) technique is a non-contact optical technique known for its practicability and effectiveness [28]. This technology can be used to measure the deformation of an object's surface. Currently, DIC has been widely applied to experimental studies on the deformation of rocks and rock-like materials [33; 34; 35; 36; 37; 38; 39]. The core algorithm used in the DIC tracks the relative displacement of the speckles by comparing the current image against the reference one. The reference image is subdivided into smaller subsets, whose changes are tracked on the deformation images. The \(x\) and \(y\) coordinates of the point \(P_{cur}\) (\(x_{cur}\), \(y_{cur}\)) in the current subset are related to the coordinates of the point \(P_{rpf}\) (\(x_{rpf}\), \(y_{rpf}\)) in the reference subset as follows:  
\[x_{cur}=x_{ref}+u_{rc}+\frac{\partial u}{\partial x_{rc}}\Big{(}x_{ref}-x_{ refc}\Big{)}+\frac{\partial u}{\partial y_{rc}}\Big{(}y_{ref}-y_{refc}\Big{)} \tag{1}\]  
\[y_{cur}=y_{ref}-v_{rc}+\frac{\partial v}{\partial x_{rc}}\Big{(}x_{ref}-x_{ refc}\Big{)}+\frac{\partial v}{\partial y_{rc}}\Big{(}y_{ref}-y_{refc}\Big{)} \tag{2}\]  
where \(x_{ref}\) and \(y_{ref}\) are the \(x\) and \(y\) coordinates of the point \(P_{ref}\) in the reference subset, respectively; \(x_{refc}\) and \(y_{refc}\) are the \(x\) and \(y\) coordinates of the central point \(Q_{ref}\) in the reference subset, respectively; \(u_{rc}\) and \(v_{rc}\) are the horizontal and vertical displacements of the subset, respectively; \(\frac{\partial u}{\partial x_{rc}}\frac{\partial u}{\partial y_{rc}}\), \(\frac{\partial v}{\partial x_{rc}}\), and \(\frac{\partial v}{\partial y_{rc}}\) are the gradients of the subset's displacement components, respectively.  
The main DIC algorithm used in the software is based on the RG-DIC (reliability guided digital image correlation) framework. The image takes pixels as coordinate units and the sub-area size is 16 \(\times\) 16 pixels, and the default step size is 8 pixels. In theory, the sub-area with a small size should be selected as much as possible to prevent disordered displacement data. Following the recommendations of the International DIC Society ([[https://idics.org/guide/](https://idics.org/guide/)]([https://idics.org/guide/](https://idics.org/guide/))) (accessed on 7 December 2021), HD industrial camera (resolution 2560 \(\times\) 1922) conduct data was acquired with a sampling frequency of 0.5 Hz and a pattern frame number of 14.2 fps.
## 2 Related Work
Moreover, since the MN does not configure a LCOA every time it moves within a domain; the handoff latency is minimized as there is no need to perform Duplicate Address Detection (DAD) of the IP address. On the other hand, the interface between the MN and MAG is applied for one- hop communication at the network layer. The interface between the MAG and a MN is not defined for multi-hop communication between GW and MN. Also the mobility-related signaling packets between LMA and MAG are carried by the IP traffic. Moreover, at the network layer, PMIPv6 demands an extra tunneling overhead between the MAG and LMA. The authors in [14] proposed a network mobility management scheme for wireless mesh networks. The wireless mesh network is composed of several Routing Access Points (RAPs) that not only serve as an access point for the MNs, but also route MN's data packets. However, the RAPs are much more powerful devices, in terms of processing power and energy consumption, as compared to the 6 LoWPAN SNs. In [15, 16, 17], we have introduced mobility management architecture for 6 LoWPAN. The architecture considered the mobility of a single 6 LoWPAN node within a PAN with multiple GWs and with the help of the existing static nodes. However, the proposed architecture required a SN to send a LU to GW every time a new MN associates with it. This reduced the lifetime of the SNs and eventually the whole PAN. Moreover, the proposed architecture did not consider route optimization between multiple MNs located in the same PAN. This paper aims to reduce the number of LUs sent from SNs to the GW by proposing a distributed version of the architecture presented in [15, 16, 17]. The proposed scheme further considers route optimization between multiple corresponding MNs, located in the same PAN.