esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Model Input Data  
Eddy covariance data from FLUXNET2015 ([[https://fluxnet.org/](https://fluxnet.org/)]([https://fluxnet.org/](https://fluxnet.org/)), accessed on 8 December 2024) and ICOS ([[https://www.icos-cp.eu](https://www.icos-cp.eu)]([https://www.icos-cp.eu](https://www.icos-cp.eu)), accessed on 8 December 2024) were used to parameterize the LUE models and validate the improvements of the models. The data we used included air temperature (TA_F), vapor pressure deficit (VPD_F), CO\({}_{2}\) mole fraction (CO\({}_{2}\)_F_MDS), shortwave radiation (SW_IN_F), and gross primary production (GPP_DT_NVT_MEAN).  
The LAI to drive the TL-LUE model is from Global Land Surface Satellite (GLASS), which has a temporal resolution of 8 days and a spatial resolution of 500 m. GLASS trained MOD09A1 based on an artificial neural network method to produce global land surface leaf area index products [40]. LAI is stored in HDF-EOS format and is spatially and temporally continuous (1980-2020) with no gaps and missing values. To match the daily scale of the site data, we resampled the 8-day resolution LAI to daily.  
The European Space Agency Climate Change Initiative (ESA-CCI) ([[https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)]([https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)), accessed on 8 December 2024) provides 300 m spatial resolution landcover data, which is used as model input in our study. This dataset is characterized by consistency over time, yearly updates, and a high level of thematic detail on a global scale, making it attractive for a wide range of applications.  
We used the MODIS Surface Reflectance products (MOD09A1) to calculate LSWI. MOD09A1 provides bands 1-7 at 500 m resolution in an 8-day gridded level-3 product in the Sinusoidal projection [41]. The data processing was conducted using Google Earth Engine (GEE). In addition, to match data time scales, we aggregated MOD09A1 data daily using the three-spline interpolation method.  
We extracted field capacity and wilting point at the site scale from a set of 1 km resolution global soil parameter data. This dataset was obtained by a new conversion function combined with machine learning, predicting the variance of pressure head, saturated hydraulic conductivity, field capacity, plant wilting coefficient, and other parameters of the surface soil at a global 1 km grid scale [42]. The dataset was proven to produce better results than other widely used models and provides necessary parameters for crop growth models, agricultural ecosystem models, land surface models, and regional and global climate models [43].
## 2 Methods
### Calculation of the 2-D Image Power Spectrum  
Historically, many methods of calculating an image power spectrum have been developed. For instance, spatial frequency response (SFR) based on ISO 12233 is widely used in the field of image quality assessment. It provides a complete profile of the power spectrum (spatial frequency) of images. In that document (ISO 12233), a test chart such as a Slanted-edge or Siemens star pattern, is employed to estimate the spatial resolving capability. However, this method no longer meets the demand in some cases. For example, to achieve passive focus function for natural scenes, or specific targets, such as fingerprints. In these cases, it is not possible to take designed targets or a constant scene; thus, it cannot obtain the fingerprint image quality by the traditional SFR method.  
Contributed to by modern computer performance, in this study, we determine the power spectrum of images calculating its 2D fast Fourier transformation (FFT). Given an image with \(M\times M\) pixels, \(h\left(x,y\right)\) is the gray level, with rectangular spatial coordinates \(x\) and \(y\) ranging from 0 to \(M-1\), and \(H\left(u,v\right)\) is the corresponding discrete Fourier transformation. The discrete Fourier transformation of the image is defined as Equation (2).  
\[H(u,v)=\sum_{x=0}^{M-1}\sum_{y=0}^{M-1}exp\left[-2\pi iy\frac{v}{M}\right]exp \left[-2\pi ix\frac{u}{M}\right]h(x,y),\ u,v=-\frac{M}{2}\ldots\frac{M}{2}. \tag{2}\]  
To relate the orthogonal \(u\) and \(v\) indices of \(H\left(u,v\right)\) to spatial frequency components of the input image, these indices must be normalized based on the number of pixels, \(M\), in the \(x\) and \(y\) directions. This process results in \(u/M\) and \(v/M\) in units of cycles per pixel width. We can acquire an estimate of the power spectrum by multiplying the Fourier terms \(H\left(u,v\right)\) by their complex conjugate, that is, the absolute square of \(H\left(u,v\right)\) is defined as Equation (3) [28].  
\[\left|H(u,\ v)\right|^{2}=H\overline{H}=(a+bi)(a-bi)=a^{2}+b^{2}. \tag{3}\]
Since LUI share similarities with conventional ultrasound, combining PAI and LUI is also of interest. During the last decade, constant improvements have been made in materials and structures design and control. However, in some problems (e.g., in the field of the reliability), conventional methods (like X-ray computed tomography, conventional ultrasound etc.) are approaching their limits, and the use of the new alternative technologies may be advantageous. LUI can be used for measurements of residual stresses, porosity, local elastic moduli, detection of microscopic internal cracks, and accurate measurements of the surface profile in various materials [47; 48; 49; 50]. LUI systems can employ raster scanning with 1D depth profiling using a single LU transducer, or it can gather scattering information using multiple receivers forming a tomogram. The latter can be called LU tomography (LUT), analogous to PAT. In addition, LUI can be used to reconstruct the distribution of speed of sound in the investigated object with transmission mode time-of-flight measurements [51]. LUT is an acoustic imaging modality that has much in common with conventional ultrasound imaging--the inhomogeneities of compressibility and density in the object reflect and scatter ultrasonic probe pulse. However, the probe pulse is generated in a dedicated light-absorbing material via a photoacoustic effect. While PAT and LUT image different physical properties, they share a common underlying principle of ultrasound generation and reception and, therefore, can share a common laser light source and a common ultrasound detection system, providing complementary information about the object under study. In the following sections, we will discuss the progress in PAT systems, and compare it with LUT with array-based ultrasound transducers. The separation of the ultrasound generation and detection processes in LUI allows for their separate optimization. The amplitude and the temporal shape of the probe pulse can be optimized for a given task by choosing appropriate laser and material for the PA generator. The piezoelectric transducers can be damped to widen their reception bandwidth (therefore increasing the image quality) at the cost of lower reception sensitivity. However, since the amplitude of the probe pulse can be optimized separately, the overall sensitivity of the system can be maintained. LUT was applied to the immersion ultrasound profilometry of solid samples [52; 53], measurements of internal geometry of solid samples with complex external surface [54], inspection of thick welded joints [55], and evaluation of internal structure of rock samples [56] and biological objects [45; 57; 58; 59; 60; 61] (often in combination with PAT). These applications will be reviewed, in more detail, below.
## 6 Experimental Results
### Case Study: Periodic Surface Flow Monitoring  
Once assessed the performance of the OTV algorithm, we address a case study motivated by practical considerations. Specifically, it is standard practice to perform surface flow measurement periodically, for instance, every 15 min. Consequently, in this case, it is possible to divide the whole 15-min cycle activity into the four phases outlined in Figure 10. Although the actual velocity estimation task takes place in the OTV phase, to obtain the energy drained in a 15-min cycle, three other phases (video acquisition, image extraction, and a low power idle state for the remaining time) must be taken into account. In our experiments, the video acquisition stage lasts 20 s and uses a USB camera, the next stage consists of extracting images from the video stored in RAM using the FFMPEG library. Besides OTV processing, the final phase (Idle) consists of a low-power state waiting for the following 15-min measurements. This latter phase (referred to as halt state according to the Raspberry terminology [16]), which lasts more than any other phase in our experiments, turns out to be critical since it impacts energy consumption significantly. Unfortunately, Raspberry PI 3B and 3B+ struggle compared to model 4 to save energy in the Idle phase; therefore, as detailed below, their efficacy is further decreased besides the considerations drawn in the previous section.  
Figure 9: OTV algorithm: energy drained during a single run at (**Top**) full resolution and (**Bottom**) half resolution.
# Impact Properties of Novel Natural Fibre Metal Laminated Composite Materials
## 1 Introduction
The crack propagation direction and the energy absorption rate of those FMLs were governed by fibre orientation [18]. [PERSON] and [PERSON] [19] studied the dynamic response of CARALL (carbon fibre-reinforced aluminium laminates). In those FMLs, the amount of polymer was one of the main mechanisms leading to the increase in the impact peak force, as well as providing lower deflection and delamination areas. [PERSON] et al. [20] compared the low-velocity impact behaviour of conventional FMLs (carbon- and glass-based) with those made of core polyester foam. The amount of energy absorbed by the foam fibre metal laminates was greater than that of the conventional FMLs. [PERSON] and [PERSON] [21] investigated the impact resistance of hot-pressed polypropylene-based FMLs. The high strain to failure of the polypropylene fibres generates large values of absorbed energy during the failure process, therefore enhancing the perforation resistance of these layered structures. The highest specific perforation energy was provided by a simple sandwich construction with a thick composite core and thin outer aluminium plies. These findings also motivated the authors to investigate bio-based FMLs composed of simple sandwich configurations [11]. In previous work, [PERSON] et al. [11] investigated the mechanical properties of sisal fibre-reinforced aluminium laminates (SiRAL) under static loads. The present work describes the dynamic response of SiRALs using drop tower impact testing and its effect on damage, propagation and fracture mode based on different composite core configurations. In addition, the influence of the surface treatment of aluminium skins is here considered. Analysis of variance (ANOVA) and design of experiment (DoE) are carried out to identify the effects of the fibre orientation (FO) and Al treatment (Al T) factors on the impact responses. Based on the authors' knowledge, the work presented in this paper represents the first attempt to study the behaviour of natural fibre-based metal laminates in low kinetic energy impacts using drop tower tests.
# Formation of a Colloidal CdSe and ZnSe Quantum Dots via a Gamma Radiolytic Technique
###### Abstract  
Colloidal cadmium selenide (CdSe) and zinc selenide (ZnSe) quantum dots with a hexagonal structure were synthesized by irradiating an aqueous solution containing metal precursors, poly (vinyl pyrrolidone), isopropyl alcohol, and organic solvents with 1.25-MeV gamma rays at a dose of 120 kGy. The radiolytic processes occurring in water result in the nucleation of particles, which leads to the growth of the quantum dots. The physical properties of the CdSe and ZnSe nanoparticles were measured by various characterization techniques. X-ray diffraction (XRD) was used to confirm the nanocrystalline structure, energy-dispersive X-ray spectroscopy (EDX) was used to estimate the material composition of the samples, transmission electron microscopy (TEM) was used to determine the morphologies and average particle size distribution, and UV-visible spectroscopy was used to measure the optical absorption spectra, from which the band gap of the CdSe and ZnSe nanoparticles could be deduced.  
gamma radiolytic technique; CdSe; ZnSe; quantum dots; particle size; band gap +
Footnote †: journal: Applied Physics Letters
# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 3 EO-Based Railway Infrastructure Monitoring: Literature Review
### Innovations: Proposals for Improvement in EO-Based Monitoring  
To address the aforementioned limitations, several innovations can enhance EO-based monitoring techniques for railway infrastructure. First, leveraging hybrid models that integrate multispectral, thermal, and SAR data can provide a more comprehensive understanding of railway infrastructure conditions. Multispectral data can improve vegetation and water monitoring, while thermal data can detect anomalies in track temperature [16; 19]. These hybrid approaches require advanced machine learning algorithms, such as ensemble models, to efficiently process and analyze multimodal datasets [19].  
Second, improvements in satellite technology, such as the use of higher temporal resolution constellations like ICEYE and Capella Space, can mitigate the limitations of revisit times. Coupling these data with ground-based IoT sensors can bridge the gap between spatial coverage and temporal accuracy, enabling near-real-time monitoring and predictive maintenance strategies [17; 26].  
Third, incorporating advanced AI techniques, including deep learning and explainable AI, can enhance the accuracy and interpretability of EO data analyses. For instance, convolutional neural networks (CNNs) trained on high-resolution SAR and optical images can improve anomaly detection in deformation monitoring. Additionally, integrating data fusion techniques that downscale coarser resolution thermal data to finer spatial resolutions can address gaps in temperature monitoring for railway tracks [27]. Large models recently released, such as the Segment Anything Model (SAM) by Meta AI [81], are used for image segmentation to assist in the task of feature extraction without additional training. [PERSON] et al. [PERSON] et al. (2017) proposed the SAMUnet model, a dual-branch semantic segmentation network with the fusion of large models, to address safety hazards from floating objects during train operations in high-speed rail networks. Using two publicly available datasets, the model's effectiveness was validated. It effectively extracts hazards (roof buildings, dust nets, farm mulch) in high-speed railway environments, improving semantic segmentation accuracy.  
Finally, fostering collaborations between satellite data providers and railway infrastructure managers can lower the cost barriers associated with high-resolution commercial data, promoting widespread adoption of EO-based monitoring solutions.
## 1 Introduction  
Antiferroelectric materials have a high energy storage density and low loss characteristics. Therefore, there is a growing interest in the application of multi-layer ceramic capacitors (MLCCs) in electronic and power systems used in high electric fields [1, 2, 3]. A representative ceramic composition exhibiting antiferroelectricity is (Pb, La)(Zr, Ti)O\({}_{3}\) (PLZT) [4]. In PLZT, La\({}^{3+}\) is substituted with the Pb\({}^{2+}\) site of the A-site in the PZT structure. At this time, the paraelectric, ferroelectric, and antiferroelectric properties are determined according to changes in the Pb\({}^{2+}\) and La\({}^{3+}\) of the A-site and the Zr\({}^{4+}\) and Ti\({}^{4+}\) content of the B-site. In the past, many studies were conducted on the change in electrical characteristics according to the composition change of the A- and B-sites [5, 6, 7].  
The ferroelectric materials are known to have low dielectric properties at high electric fields, while in anti-ferroelectric materials, dielectric properties increase linearly in high electric fields. Therefore, many studies were conducted to apply anti-ferroelectric properties as capacitors used in high electric fields [2, 8]. However, in order to be used in a high electric field, the electrical and physical destruction of the ceramic should not have occurred when the breakdown electric field of the material is high. Recently, many studies have been conducted to increase the breakdown of the electric field and energy storage density. Representatively, many research results have been reported on the composition of (Pb, La)(Zr, Sn, Ti)O\({}_{3}\) (PLZT) [9, 10, 11]. PLZT has a structure in which Sn\({}^{4+}\) is substituted for the B-site, which is an element site with a similar ionic radius in the PLZT composition. PLZT is reported to have a high breakdown electric field. However, most of the reports mainly investigated the characteristic change according to a small amount of Sn\({}^{4+}\) substitution. In addition, in order to simultaneously sinter the Cu inner electrode to manufacture MLCC by applying the composition to the thick film process, the sintering temperature should be lowered to 950 \({}^{\circ}\)C [12; 13]. It is necessary to systematically study additives for low-temperature sintering and the improvement in the breakdown electric field.  
In the present study, we selected (Pb\({}_{0.88}\), La\({}_{0.12}\))(Zr\({}_{0.86}\), Ti\({}_{0.14}\))O\({}_{3}\) as the basic composition and PbO, ZnO, and NiO as low-temperature sintering additives and sintered them at 950 \({}^{\circ}\)C. Additives were added after the calcination process to avoid their effect on the crystal structure of PLZT. To obtain a high breakdown electric field and energy storage density, Sn\({}^{4+}\) was substituted up to a content of 0.1 to 0.6 to investigate changes in electrical properties. The electrical properties of the composition were investigated by calibrating the content of Sn (0.1 to 0.6 mol ratio) to find the highest breakdown electric field and energy storage density.
## 1 Introduction
This allows for a more precise definition of the power profiles required from the network for vehicle charging. Hence, the objectives of this study are as follows:  
1. Characterize the growth and distribution of EV charging infrastructure in the region. 2. Analyze energy consumption patterns and charging behaviours across different types of charging stations. 3. Identify temporal and spatial trends in charging station usage. 4. Compare the monitored data with results from grid impact simulation models to validate and refine predictive approaches. The remainder of this article is structured as follows: Section 2 describes the study area, provides an overview of the recharging infrastructure, and details the data collection and analysis methods. Section 3 presents the results, including the distribution and expansion of charging points, energy consumption and charging session statistics, and detailed profiles of charging behaviours. Section 4 discusses the implications of these findings for grid impact modelling and infrastructure planning. Section 5 concludes the study by summarizing key insights and suggesting directions for future research.
# Enhancing Robotic-Assisted Lower Limb Rehabilitation Using Augmented Reality and Serious Gaming
## 6 Proprioception Stimulation
#### 3.3.2 Colors Mode  
In this mode, the patient executes combined movements to touch different colored circles. This mode specifically targets the activation of the hip, knee, and ankle joints, providing benefits such as the following:  
1. Cognitive Challenge  
The patient must make quick decisions about which color to touch and how to move their foot, thus stimulating cognitive functions.  
2. Improvement of Coordination and Timing  
The patient is required to visually track the position of the colors and coordinate their movements to accurately hit the target. This helps restore visual-motor coordination.  
3. Quick Reactions  
The changing position of the colors prompts the patient to react quickly, improving their reaction times and reflexes.  
4. Increase in Flexibility and Range of Motion  
The varying positions of the colors require the patient to perform a wide range of movements, promoting joint flexibility and enhancing joint mobility.  
5. Strengthening Muscles and Joints  
The exercises engage different muscle groups as the patient moves their foot toward the colored targets. These repetitive movements strengthen the muscles and joints.
# SemKoRe: Improving Machine Maintenance in Industrial IoT with Semantic Knowledge Graphs
## 2 Motivating Scenario and Requirements
### Motivating Scenario  
The following scenario is based on our interactions with real customers who want to improve their existing maintenance process. Let us consider three actors: [PERSON] the machine operator, [PERSON] the maintenance technician and [PERSON] the OEM machine builder. On a given day, [PERSON] is working on factory floor operating several machines when suddenly one machine stops working. [PERSON] spends some time to fix the issue himself but is unable to do so, since [PERSON]'s main job is to operate the machine. He might be able to fix small issues due to his experience but he is supposed to call a qualified technician for anything major. He then calls [PERSON] to come to factory floor to check on the machine. When [PERSON] checks the machine she finds that she is also not able to solve the issue so she calls the OEM or Schneider Electric service bureau, where a machine expert guides her through the repair process. Finally, [PERSON] is able to fix the issue and the machine starts working.  
The whole process took a long time and while [PERSON] is now able to operate his machine, if the same issue occurs in a similar type of machine located in a different city the same process would likely be repeated because only [PERSON] knows how to quickly solve this particular issue. However, if [PERSON] can describe what she learned from the service bureau and share her experience with the technicians in other sites by using some appropriate mechanism, they could all benefit from this common knowledge.  
Another beneficiary of this common knowledge is [PERSON]. Today, when [PERSON] gets reports about the issues with his machines from different customers, he has no easy way to get the finer details that can only come from the technicians like [PERSON]. These details could be useful and help him to understand why some of his machines are facing particular issues. This can help him to improve the design and engineering process of his machines, especially in the case of hundreds or thousands of machines being used worldwide, the scale of problem and timely action in resolving the issue becomes hugely difficult. Another benefit is that using the insights from customer A, [PERSON] can help customer B to quickly respond to machine issues while respecting of privacy and sensitive nature of the information, if both customers have the same type of machines. The importance of the quick resilience after failure aspect is discussed in details by [PERSON] et al. in [6].