esa-sceva/satcom-chunk-collection huggingface dataset:
# Probing Ferroic States in Oxide Thin Films Using Optical Second Harmonic Generation
## 3 Probing Ferroic States in Thin Films Using SHG
### Imaging of Ferroic Switching Events
(**a**,**b**) Coupled switching of ferroelectric, (**a**) and antiferromagnetic (**b**) orders in BFO thin films. The one-to-one domain correlation persists after local voltage application (within the area delimited by the yellow dashed line). Reprinted by permission from [108], Copyright Macmillan Publishers Ltd.: Nature Materials, 2006. (**c**–**e**) SHG images of an SPM tip-poled 100 \(\times\) 50 \(\upmu\)m\({}^{2}\) field in BFO on DSO (110), using surface SHG interference to selectively image domain walls (**c**), surface (**d**) or domain polarization direction (**e**), respectively. (**f**,**g**) Corresponding SHG polarization dependence and in-plane PFM images outside (**f**) and inside (**g**) the switched area (**c**–**g**). Reproduced with permission from [88], Copyright Wiley-VCH Verlag GmbH and Co. KGaA, 2015. It has to be emphasized here, that the polarization orientation in thin films can be probed with SHG even when the layer is capped with a top electrode [88]. Figure 14 shows the PFM and SHG response of a BFO thin film capped with 4 nm Pt. While no in-plane PFM domain contrast is observed, the polarizer SHG measurement can identify the direction of the macroscopic polar axis of the buried BFO layer (see Figure 9b). Contrary to SPM techniques, the SHG signal can therefore be used to probe a polar state in a buried layer and is therefore appropriate for in-operand monitoring of multiferroic magnetoelectric switching events and ferroic domain dynamics. Figure 14: Ferroelectric domain structure in a BFO thin film on La\({}_{0.7}\)Sr\({}_{0.3}\)MnO\({}_{3}\) buffered DSO capped with 4 nm Pt. **(a)** PFM image of the Pt-capped BFO shows no ferroelectric domain contrast. For comparison, the inset shows the domain structure of the uncapped BFO layer. **(b)** SHG polarizer measurement identifies a polar axis along the vertical orientation, corresponding to the macroscopic net polarization direction, indicated by the white arrows in the insets of **(a,b)**. Reproduced with permission from [88], Copyright Wiley-VCH Verlag GmbH and Co. KGaA, 2015. Figure 13: Multiferroic domain mapping in a BFO thin film. **(a)** SHG imaging set up. **(b)** The spatially resolved anisotropy corresponding to antiferromagnetic domains. **(c)** Polarizer measurements in different areas of the sample showing a spatially distributed anisotropy in the polarizer symmetry. Reprinted by permission from [113]. Copyright Macmillan Publishers Ltd.: Nature Materials, 2017.
# Underestimates of Grassland Gross Primary Production in MODIS Standard Products
## 1 Introduction
However, these studies were mostly based on the previous versions of the MODIS GPP product (Collection 4 or Collection 5), but the latest version (Collection 6) significantly improved the value of the maximum light use efficiency (for example, from 0.68 g C m\({}^{-2}\) d\({}^{-1}\) MJ\({}^{-1}\) to 0.86 g C m\({}^{-2}\) d\({}^{-1}\) MJ\({}^{-1}\) for grass) and the product footprint sizes (from 1 km to 500 m) [28]. Whether performance has improved for collection 6 or not in these ecosystems has not been studied on a global scale. Compared to the forest and savanna biomes, validation of the MODIS GPP in grassland has also been studied extensively. [PERSON] et al. [29] found that MODIS data underestimated the mean annual GPP by 30% to 50% in the alpine meadow sites in China. [PERSON] et al. [30] also found that grassland GPP (MOD17A2) was underestimated at the Haibei grassland site during the summer. In the US, [PERSON] et al. [31] also found that GPP\({}_{\text{MOD}}\) performed poorly at the improved pasture sites, the nativepasture sites, and the winter wheat sites when compared with GPP\({}_{\text{EC}}\). However, these analyses were based primarily on limited spatial and temporal coverage of in situ observations. Regional-scale and global-scale evaluations of GPP\({}_{\text{MOD}}\) for grassland ecosystems has not yet been conducted. With the increasing global distribution of eddy flux towers and the formation of networks (e.g., FLUXNET [32], AmeriFLux [33], EuropeanFLux [34], AsiaFLux [12]), a large scale validation of GPP products has become feasible [24; 35]. Recently, the latest global flux dataset of FLUXNET2015 [36] was released and it provided a great opportunity for the validation of GPP\({}_{\text{MOD}}\) for grassland ecosystems. In this study, we aimed to conduct a systematic validation of GPP\({}_{\text{MOD}}\) for grassland ecosystems by using the FLUXNET2015 dataset. The overarching goals of this study are to: (1) examine the performance of GPP\({}_{\text{MOD}}\) across a network of grassland flux sites, (2) assess the uncertainties of GPP\({}_{\text{MOD}}\) under different grassland biomes (temperate, tropical, and alpine), and (3) discuss the potential reasons that cause the underestimation of GPP in the MOD17A2H products.
## 4 ARIMA-BP-Based Selective Offloading Strategy  
### Research Motivation  
In MEC, the edge cloud has relatively large storage and computation resources. Compared with the computation capacity of mobile devices themselves, the computation capacity of the edge cloud is huge. Mobile edge computing brings computation resources to the mobile devices and reduces energy consumption in comparison with local execution, but leading to increased duration of transmission. Hence we need to consider both energy consumption and time delay comprehensively. For this issue, the computation capacity of the edge cloud is a key factor.  
In the experimental environment, we usually set the computation capacity of the edge cloud as a fixed value. However, the computation capacity of the edge cloud cannot be static in the process of task offloading. As the number of tasks processed at the edge cloud increases, its remaining computation resources will decrease, reducing its computation capacity. When the number of tasks processed at the edge cloud decrease, its computation capacity will increase. Therefore, It is very critical and effective to obtain the computation capacity of the edge cloud accurately for task offloading.  
In practice, the computation capacity of the edge cloud is hard to represent and is not available ahead of time due to the dynamic of tasks. We hence can only get its estimated value by prediction. The increasingly advanced big data analysis technology makes it possible to accurately predict computation resources. By predicting the resource usage of the edge cloud, we can get the estimated value of its the computation capacity.
# Mind, Machine, and Meaning: Cognitive Ergonomics and Adaptive Interfaces in the Age of Industry 5.0
## 4 Challenges and Future Directions in Cognitive Ergonomics for HMIs
### Major Challenges in Cognitive Ergonomics for HMIs  
* Increasing System Complexity: Modern interfaces integrate vast amounts of data and functionality, potentially overwhelming users. Cognitive overload can reduce task performance, increase error rates, and impact mental well-being [149]. For instance, smart manufacturing systems in Industry 4.0 often require users to interpret complex data visualizations and make real-time decisions [150].
* Multimodal and Adaptive Interfaces: These interfaces increasingly incorporate voice, gesture, haptic, and biometric inputs. Designing intuitive, seamless, and cognitively ergonomic multimodal systems remains a major challenge. [PERSON] et al. [151] reviewed EMG-, FMG-, and EIT-based biosensors, highlighting the difficulty in harmonizing input accuracy and user comfort.
* Situational Awareness in Shared Control Environments: Maintaining shared control between the human and machine is critical, especially in safety-sensitive domains like autonomous driving. [PERSON] et al. [152] emphasized the importance of external HMIs (eHMIs) for communicating AV intentions to vulnerable road users (VRUs) in shared spaces.
* Latency and Tactile Feedback in Real-Time Systems: Real-time applications such as teleoperation and VR/AR demand an ultra-low latency. [PERSON] et al. [150] describe how 5G and the tactile internet promise to enhance cognitive ergonomics by enabling haptic feedback and low-latency control, but such systems also raise new cognitive demands and safety concerns.
# A THz Passive Image Generation Method Based on Generative Adversarial Networks
## 3 Experiments
### Dataset  
As mentioned in Section 1, terahertz passive imaging is a cutting-edge security technology and has not been widely used yet, so there are no public dataset resources at present. To verify the method proposed in this paper, the self-developed 0.2 THz band passive imager which combined a simple quasi-optical scanning and a single-channel radiometric receiver was used for imaging experiments (the schematic is shown in Figure 5). A two-dimensional (2D) field of view (FOV) is obtained based on a raster scan of the beam spot along the X and Y directions. The sub-reflector is a rotating one with a small size, capable of steering the beam quickly in a horizontal X-direction through its rotation around the Y-axis. A concave main reflector is employed to focus the terahertz signal emitted from the target to the feed horn of the radiometric receiver. The imager operates at a center frequency of 0.2 THz with a bandwidth of 30 GHz.  
Figure 4: Residual structures used in discriminator. (**a**) “ResBlock Downsample” block. (**b**) Traditional residual block.  
A pistol model and a mobile phone are used as the hidden objects. The imager and the imaging results are shown in Figure 6.  
As shown in Figure 7, there are three categories of images in the dataset: carrying a mobile phone, carrying a pistol model, and carrying above two objects. After a long-term imaging experiment, a total of 1800 images of three categories are collected. The experimenters include different body types, and the number of each category is almost equal. The original size of the terahertz passive image is a rectangle of 392 \(\times\) 192 and the physical size corresponding to one pixel is 5 cm. For the convenience of model design, the original images need to be preprocessed. Firstly, the original image is zero-padded to obtain a 392 \(\times\) 392 square image. There are no valid pixels in the zero-padded area, so it does not affect the network performance. Then, the filled images are scaled to 256 \(\times\) 256 as the final dataset.  
Figure 5: Simple architecture of the imager.  
Figure 6: Typical terahertz passive imaging system. (**a**) The passive imaging system. (**b**) Imaging targets. (**c**) The person to be tested. (**d**) Passive imaging results.
###### Abstract  
Cost efficient and robust transport systems are of critical importance to future economic prosperity as well as for the society's social and environmental well-being. However, current performance shortcomings in the transport infrastructure formations induced by extreme climatic events cause excessive maintenance requirements with increased costs and disruptions to commuters and loss of productivity in the freight services. This is particularly important in locations where soils are sensitive to moisture changes caused by extreme climatic events. In this paper the role of soil stabilisation in halting volumetric deformation and associated reduction in shear strength derived from the wetting processes (e.g., rainfall periods) is examined for an expansive soil. Two stabilizers commonly used in road construction are examined, i.e., hydrated lime and Portland cement. An additional non-traditional stabiliser composed of a blend of ground granulated blast furnace slag and hydrated lime is also considered. A series of one-dimensional swelling and direct shear box tests were conducted adopting vertical stresses relevant for pavements and simulate wetting process that can take place after a period of rainfall. Results indicate that while all stabilizers contribute to a reduction of swelling and smaller losses in shear strength upon wetting, the blend of blast furnace slag and hydrated lime is the most favourable in terms of carbon footprint.  
climate change; expansive soil; soil stabilisation; pavements +
Footnote †: journal: Applied Sciences
## Design and Implementation of Real-Time Software Radio for Anti-Interference GPS/WAAS Sensors  
**[PERSON] 1, [PERSON] 2, [PERSON] 3,*, [PERSON], [PERSON] 4, [PERSON] 1 and [PERSON] 1**  
1 Department of Aeronautics and Astronautics, Stanford University, 496 Lomita Mall, Stanford, CA 94305, USA; E-Mails: [EMAIL_ADDRESS] (Y.-H.C.); [EMAIL_ADDRESS] (S.L.); [EMAIL_ADDRESS] (D.S.D.); [EMAIL_ADDRESS] (P.E.)  
2 Department of Electrical Engineering, National Cheng Kung University, 1 University Road, Tainan 70101, Taiwan; E-Mail: [EMAIL_ADDRESS]  
3 School of Integrated Technology, Yonsei University, 162-1 Songdo-dong, Yeonsu-gu, Incheon 406-840, Korea; E-Mail: [EMAIL_ADDRESS]  
4 Department of Aerospace Engineering Sciences, University of Colorado, 1111 Engineering Drive, Boulder, CO 80309, USA; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-32-749-5833; Fax: +82-32-818-5801.  
_Received: 14 August 2012; in revised form: 14 September 2012 / Accepted: 27 September 2012 / Published: 1 October 2012_  
Adaptive antenna array processing is widely known to provide significant anti-interference capabilities within a Global Navigation Satellite Systems (GNSS) receiver. A main challenge in the quest for such receiver architecture has always been the computational/processing requirements. Even more demanding would be to try and incorporate the flexibility of the Software-Defined Radio (SDR) design philosophy in such an implementation. This paper documents a feasible approach to a real-time SDR implementation of a beam-steered GNSS receiver and validates its performance. This research implements a real-time software receiver on a widely-available x86-based multi-core microprocessor to process four-element antenna array data streams sampled with 16-bit resolution. The software receiver is capable of 12 channels all-in-view Controlled Reception Pattern Antenna (CRPA) array processing capable of rejecting multiple interferers. Single Instruction Multiple Data (SIMD) instructions assembly coding and multithreaded programming, the key to such an implementation to reduce computational complexity, are fully documented within the paper. In conventional antenna array systems, receivers use the geometry of antennas and cable lengths known in advance. The documented CRPA implementation is architected to operate without extensive set-up and pre-calibration and leverages Space-Time Adaptive Processing (STAP) to provide adaptation in both the frequency and space domains. The validation component of the paper demonstrates that the developed software receiver operates in real time with live Global Positioning System (GPS) and Wide Area Augmentation System (WAAS) L1 C/A code signal. Further, interference rejection capabilities of the implementation are also demonstrated using multiple synthetic interferers which are added to the live data stream.  
Keywords:Global Positioning System (GPS) and Wide Area Augmentation System (WAAS) sensor; software-defined radio; controlled reception pattern antenna (CRPA); Space-Time Adaptive Processing (STAP); radio frequency interference
## 3 LMD-PNN Microseismic-Signal Recognition
### Automatic Recognition Process of Microseismic Signal  
The process of microseismic-signal-feature extraction and automatic recognition based on the LMD energy entropy and probabilistic neural network (PNN) is shown in Figure 1. The cross-correlation coefficient is used to select the optimal PF component to construct the feature vector, and the PNN model is learned and trained to realize the automatic identification of the microseismic signal. The specific process is as follows (in Figure 2).  
1. The vibration signal of the mine site is collected by microseismic equipment, and the collected signal is preprocessed to filter out the noise signal and obtain the mine microseismic signal.
2. The LMD algorithm is compiled to calculate the mine microseismic signal, and all PF components and residual \(u_{k}(t)\) are obtained by signal decomposition.
3. Since the extraction of all PF components of the original microseismic signal is likely to cause vector redundancy, which is not conducive to effective classification, the correlation analysis coefficient (Equation (7)) is used to screen the effective PF components.
4. First, the energy value of the PF component decomposed by the microseismic signal is obtained by Equation (3). Then, the energy value is normalized and substituted into Equation (4). Finally, the LMD-energy-entropy value of the microseismic signal is obtained by Equation (5), and the eigenvector values of all types of mine microseismic signals are obtained.
5. The PF component entropy with the correlation coefficient greater than 0.3 is taken as the eigenvector value of the mine-microseismic-signal type, and the eigenvector of the PNN mine-microseismic-signal-classification model is constructed.
6. The random method is used to automatically select the training and prediction samples, the optimal model parameters are automatically obtained by the learning and training system of the samples, and the PNN classification model is established.
7. Finally, using the established PNN model, the unknown microseismic samples are judged and classified.
## 5 Discussion  
Rubber seals for expansion joints are recognized worldwide as consumables; therefore, they are replaced when damaged or when damage occurs. Therefore, there are almost no research cases on rubber seal materials for expansion joints. This study explored the utilization of superelastic shape memory alloys as alternative materials to enhance the maintenance efficiency of rubber expansion joints in bridge expansion joint devices. The NiTi-based shape memory alloy used in this study is more difficult to manufacture and process compared to the typical Cu-based and Fe-based shape memory alloys, but it has an excellent shape memory effect and a higher maximum strain capacity. In addition, it can be restored to its original form through separate heat treatments or stress relief at room temperature, allowing continuous use. These findings suggest that, as the displacement tolerance increased, the maximum load in superelastic shape memory alloy expansion joints decreased, primarily due to the widening gap distance in the expansion joint devices. Remarkably, these alloys could revert to their original shape after load removal, even when subjected to plastic displacement beyond their elastic limits, without experiencing any residual displacement. This behavior closely resembled that of rubber expansion joints under similar analytical conditions. The analysis of the maximum load indicated that superelastic shape memory alloy expansion joints can endure significantly higher loads compared to their rubber counterparts, likely attributable to their material properties  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Maximum Force (kN)** & **Minimum Force (kN)** \\ \hline Case-1 & 3052.62 & \(-\)172.35 \\ \hline Case-2 & 1999.60 & \(-\)182.22 \\ \hline Case-3 & 1348.90 & \(-\)106.01 \\ \hline Case-4 & 967.36 & \(-\)75.38 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Member force results of shape memory alloys.  
Figure 18: Force–displacement curve of shape memory alloys.  
closely aligning with those of conventional steel types. Despite bearing higher loads, the primary role of expansion joints in bridges is not to directly resist external forces, but rather to ensure material restoration to the original shape and uphold durability. In future studies, based on the research results, in order to confirm the usability of the shape memory alloy, we plan to conduct verification through experimental methods.
# Mission-Oriented Real-Time Health Assessments of Microsatellite Swarm Orbits
## 3 Real-Time Health Assessment Algorithm  
Since intersatellite links are usually bidirectional, based on graph theory [14], the topology of a swarm of microsatellites at a specific moment can be represented by an undirected graph as follows.  
\[G(t)=(V(t),E(t)),0\leq t\leq T \tag{2}\]  
where \(V(t)\) is the set of all satellites in the swarm at moment \(t\), \(E(t)\) is the set of interstellar communication links at moment \(t\), and \(T\) is the lifetime of the swarm. Without loss of generality, the following assumptions are made for the swarm.  
1. All satellites are of the same type and model and are equivalent to nodes in a swarm system;
2. The swarm contains \(N\) satellites and \(M\) communication links at \(t\);
3. The swarm has sensors that measure state quantities (three-axis positions, etc.), and they are capable of real-time error analysis against the desired values.
4. The topology of the swarm is stable until the next change occurs after the topology of the swarm has been altered;
5. The failure forms are transient failure and degradation failure.
## 2 Materials and Methods
### Cocoa Sensory Analysis  
Chocolates were produced under a standard protocol previously described by [PERSON] et al. [12] with some modifications at the Center de Cooperation Internationale en Recherche Agronomique pour le Developpement (CIRAD) chocolate factory laboratory (Montpellier, France). Cocoa beans were roasted at 125 \({}^{\circ}\)C for 25 min in an oven ([PERSON], UL 30, Schwabach, Germany). The roasted cocoa nibs were subsequently cracked in limprimita cocoa breaker (Capco Cocoabreaker240-1, Ipswich, UK) and deshelled using a cocoa winnower machine (Capco Cocoavinnow, Ipswich, UK). The cocoa nibs were grinded by using a mortar and pestle alumina grinder (Capco Mortarmill1 SS, Ipswich, UK) over 60 min. Sugar was added after 30 min of grinding. A three-roll refiner (Exakt 80S, City, Germany) for refining stage was used. The refining stage was performed twice. The refined chocolate mass was conched for 2 h at 70 \({}^{\circ}\)C with 5% cocoa butter and 1% soy lecithin in a longitudinal conching machine with a rotating roller (ZumWald LR01, Erlenbach, Switzerland). The tempering stage comprised heating the chocolate above 42 \({}^{\circ}\)C, cooling it to 32 \({}^{\circ}\)C, reaching 29 \({}^{\circ}\)C, increasing to 31 \({}^{\circ}\)C, and finally heating it to 32 \({}^{\circ}\)C before being molded.  
A descriptive sensory analysis was performed using the consensus profile method (International Organization for Standardization, 2016) in the sensory analysis laboratory at CIRAD. The chocolate samples were blindly tasted by nine trained judges with experience evaluating chocolate.  
There were two types of evaluation sessions. First, the judges agreed on a vocabulary with sensory attributes to evaluate the samples, and 11 descriptors were selected to describe the flavor of the chocolate samples. The descriptors were classified as basic flavors (sweetness, acidity, bitterness, and astringency; fine flavors: cocoa, fruitiness, nuttiness, floral, and spices) and off-flavors (burned and animal). Finally, global quality was also considered. This parameter represents the overall impression of the chocolate, encompassing all sensory elements, including the presence and significance of atypical aromas and special characteristics. In the second session, the judges were seated in individual booths, and the chocolates (5 g) were served at 20 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C on identical plates prelabeled with a random three-digit code. Samples were presented successively, one by one, in a monadic presentation order. A completely randomized design was used for the sample presentation. Judges scored chocolate samples using a 10-point scale from 0 (not perceivable) to 10 (very intense) regarding the sensory attributes defined initially. For neutralization between samples, unsalted crackers and noncarbonated water were available.