esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison of Spring Phenology in Boreal Forests at the Site Scale
Among these, SOS\({}_{\text{EVIZ}}\) exhibited the strongest correlation with SOS\({}_{\text{PEP725}}\) (\(p<0.001\)), followed by SOS\({}_{\text{GOSIF}}\) (\(p<0.001\)) and SOS\({}_{\text{GOME-2 SFI}}\) (\(p<0.05\)). This could be explained by the selection of leaf emergence time for SOS\({}_{\text{PEP725}}\) in this study, while the spring phenology detected by SIF in the evergreen needleleaf forests has already commenced before the emergence of new leaves [9]. Regarding the absolute values of spring phenology onset date, SOSs from remote sensing datasets (SIF and EVI2) typically occurred later than SOS\({}_{\text{FLUX-GPP}}\) but earlier than SOS\({}_{\text{PEP725}}\). In boreal forests, the presence of evergreen coniferous vegetation results in photosynthesis commencing earlier than greening. Notably, remote sensing detection of photosynthesis often lags behind ground-based photosynthetic observations, whereas remote sensing of greenness tends to precede ground-based greenness observations due to interference from evergreen coniferous vegetation and snowmelt [7; 10]. In addition, the partial correlation coefficients (Pcor) from GOSIF and EVI2 exhibited a strong correlation with those from PEP725, but a weak correlation with Pcor from FLUX-GPP (Figure 6). This discrepancy may arise because SOS\({}_{\text{PEP725}}\) represented the first leaves separated (greening) in this study, and both GOSIF and EVI2 involve greenness information. Several factors may influence the reliability of the results in this study. For instance, the number of ground sites for validation was limited and unevenly distributed. More ground observation data in the future should yield further understanding of vegetation season dynamics. Also, the low spatial resolution of the remote sensing datasets used in this study may result in mixed pixels of vegetation types [46]. New high-resolution primary satellite-retrieved SIF datasets (e.g., TROPOMI, FLEX, etc.) and downscaled SIF (e.g., CSIF, GOSIF, etc.) with spatially continuous could provide more precise insights into spring phenology in boreal forests [47; 48]. From another point of view, the impact of low spatial resolution on the application of GOME-2 SIF in boreal forests might be minimal, as efforts were made in this study to address this issue. For example, the phenology values of a mixed pixel were all considered in the phenology statistics of the vegetation types present within the mixed pixel, without strict regulation on the type to which the pixel belonged. This approach ensured that the mixed pixel phenology statistical method was relatively equitable across all vegetation types, and the results were consistent with real-world conditions. In summary, the primary GOME-2 SIF, the modeled GOSIF, and the vegetation index EVI2 (MCD12Q2) are capable of identifying or characterizing the onset of vegetation phenology and conducting large-scale related studies in boreal forests. These are crucial for phenology research in this region.
## 2 Materials and Methods
### Model Validation and Mapping  
The retrieval of LAI in Schiermmonikoog island through inversion of PROSAIL using Sentinel-2 and RapidEye data was validated using the measured LAI from the field campaigns. To examine the LAI accuracy, the coefficient of determination (R\({}^{2}\)), the RMSE, and the normalised RMSE (NRMSE, RMSE/range) [27] between measured and retrieved LAI were assessed. The LAI map of the island was then produced using RapidEye and Sentinel-2 images and PROSAIL inversion. The existing vegetation map of the island [109], was utilised to mask out the peripheral land covers (waterbodies, bare dunes and buildup area) from Sentinel-2 and RapidEye data. The masked images were then used as input to the inversion process, and the maps of predicted LAI were retrieved using the minimum RMSE criterion (best fitting spectra).
## 2 Materials and Methods
### Multi-Temporal SPOT5 Pan-Sharpened Surface Reflectance  
A complete coverage of SPOT5 imagery was acquired for NSW from 2008, 2009, 2010 and 2011, for a total of 1256 images, with all parts of the state covered by pixels from the four time periods. Multi-temporal data were used as many trees and areas of herbaceous ground cover were found to be indistinguishable in single images, but could be separated in multi-temporal imagery due to the persistence of the tree foliage compared to the fluctuations in ground cover caused by a closer relationship to rainfall. The pointable, push-broom sensor produced multispectral imagery in three bands with 10 m pixels, one shortwave infrared band with 20 m pixels, and one panchromatic band with 2.5 m pixels. The spectral ranges are green (0.49-0.61 \(\upmu\)m); red (0.61-0.68 \(\upmu\)m); near infrared (NIR) (0.78-0.89 \(\upmu\)m); shortwave infrared (SWIR) (1.58-1.75 \(\upmu\)m), and panchromatic (0.49-0.69 \(\upmu\)m). The images have a nominal swath width and path length of 60 km and were provided in units of calibrated, scaled, at-sensor radiance. Each image acquired needed to fulfil standard minimum specifications. They were nominally cloud free and the look angle was specified to be as close to nadir as possible and no more than 18 degrees off nadir. Images were preferentially obtained in summer months (November-March) when the grass cover was senescent, which provided good contrast to the green overstorey foliage [36].
## 3 Experimental Results: Baseline Data  
### HNSW Transducers and UPV Test  
Figure 6a shows one of the fifty waveforms recorded by the transducer P1 when it was placed above the samples 42A, 45A, and 50A. To ease the readability of the time waveforms, the amplitudes were offset. Figure 6b is a close-up view of Figure 6a and it shows that the TOF of the primary reflected wave increases with an increase in \(w/c\) ratio. Not presented here, but detailed in [38], the relative standard deviation (RSD), _i.e._, the ratio of the standard deviation to the mean, ranged from 0.2% to 0.9%. This proved the high repeatability of the setup. The figure reveals that the TOF increases with the increase of the \(w/c\) ratio.  
The static moduli estimated with Equation (4) are listed in Table 2. They are based on the values of the TOFs averaged from each of the fifty waveforms acquired for every transducer. The values from each transducer and from each cylinder with the same \(w/c\) ratio were then averaged and reported on the rightmost column together with the corresponding RSD. The data show that an increase in the \(w/c\) ratio decreases the static modulus.  
Table 3 presents instead the results associated with the UPV. When compared to the HNSW-based data, the ultrasonic test gives lower modulus and smaller RSD. The latter is due to the better repeatability of the commercial transducer, but it is also the effect of the fact that the ultrasonic test averages the effect of the through-thickness concrete. Finally, it must be noticed that the presence of an ultrasonic gel couplant between the transducer and the concrete mitigates any effect associated with the surface texture.  
Figure 6: HNSW (highly nonlinear solitary wave)-based measurement using the transducers. (**a**) time waveform relative to P1 for the cylinders with different \(w/c\) ratio; (**b**) close-up view of panel (**a**).
## 4 Simulation and Evaluation
### MAF Estimator Evaluation  
Due to the absence of a generic event model for WSNs, we represent the event-driven scenario by two types of detection distribution: Uniform and Gaussian distributions. In the first case, the variation on the detection ratio is represented by different number of sources generating data randomly during the simulation time. This distribution can represent the occurrence of uncorrelated event detections. In the second case, detections are distributed with a standard deviation around an average simulation time, and this distribution can represent the occurrence of correlated detections.  
To better understand how the _MAF_ estimator works and how the window size \(m\) affects its performance, we show in Figures 7 and 8 some simulation cases that consider the Uniform and Gaussian distributions, 50 source nodes and window sizes of 10 and 50. These graphics show instances of nodes detecting events along the simulation time. The number of detections is measured every 10 s [Figures 7(a) and 8(a)]. For each instance, the graph  
Figure 7: Multi-MAF’s adaptive model (uniform event detection).  
resulting operating mode of Multi-MAF (reactive or proactive mode) according with this MAF value.  
Figure 7(b) shows that when \(m=10\), _MAF_ does not properly capture the seasonality of detection occurrence in a Uniform distribution, causing several changes between the reactive and proactive modes. This behavior is clear reactive mode is set around 1100 s, due to an occasional empty interval, which do not reflect the overall behavior of detections. In the sequence, a new detection happens, which represents an additional cost, and Multi-MAF turns to proactive mode again. When \(m=50\) [Figure 7(c)], these transitions are not so frequent, and _MAF_ has a better performance. However, in this case, _MAF_ is slower to detect that the initial condition (initial occurrences around 500 s) has changed. The impact of \(m\) is also observed with Gaussian distribution in Figure 8(b),8(c). In this case, a lower \(m\) is better for responding  
Figure 8: Multi-MAF’s adaptive model (Gaussian event detection).  
faster to increases in the detections.  
In all cases, the lower the filter window \(m\), the faster the variation detection. On the other hand, the greater the filter window \(m\), the smoother the estimation. Based on more simulation cases with the adopted parameters, we obtained \(m=20\) as the better value to capture seasonal characteristics of uniform detections, which does not respond so slowly to variations of the Gaussian distribution. This value represents an detection estimation based on two infrastructure building intervals (200 s) and it was used in the simulations of Multi-MAF presented below.
## 2 Materials and Methods
### Contrastive Self-Supervised Learning  
BERT's pre-training method has proven highly effective for a range of NLP tasks, yet it often struggles to generalize to other domains, such as computer vision, where pretext tasks rely on heuristics [18]. In contrast, contrastive learning offers a more adaptable framework by forming positive and negative sample pairs. Positive pairs are typically created through augmentations of the same data instance, while negative pairs derive from different data instances. By minimizing the distance between positive pairs and maximizing the distance between negative pairs in latent space, the model acquires robust and generalizable feature representations.  
CERT (Contrastive Self-Supervised Encoder Representations from Transformers) builds on this principle, employing back-translation as a global sentence-level augmentation method. Specifically, sentences are translated into an intermediate language (e.g., German or Chinese) and then translated back, generating augmented sentence pairs that approximate real variations in text. In contrast, Easy Data Augmentation (EDA) [19] relies on local word-level techniques--such as synonym replacement, random insertion, and swapping--to form augmented samples. Figure 2 demonstrates how CERT pulls positive pairs (semantically similar sentences) closer while pushing negative pairs farther apart in latent space. By combining back-translation with the Momentum Contrast (MoCo) loss function [20], CERT outperforms BERT in multiple GLUE tasks, illustrating the effectiveness of contrastive learning for sentence-level understanding.  
In domain-specific contexts--like clinical or biomedical NLP--such augmentation strategies must be carefully adapted to preserve precise terminology. Our approach avoids back-translation errors by leveraging negative pairs sampled from unrelated sections or different documents, ensuring accurate domain context. This design choice aligns with the contrastive principle while mitigating semantic drift issues that can arise from purely generative augmentations in specialized fields.
## 2 Results
### Optimization of the HS-SPME Technique
#### 2.1.1 Type of Fiber  
The extraction capacity of the volatile matrix of CB was evaluated using three types of fibers (DVB/PDMS/CAR, DVB/PDMS, and PDMS), as shown in Figure 2. One of the crucial aspects in optimizing the HS-SPME technique is the choice of extraction phase or fiber, as this stage plays a fundamental role in the essential equilibria that guide the extraction process. Within this framework, special attention has been paid to the choice of coating [3].  
Figure 1: Optimization of HS-SPME variables (Ext.time: extraction time, Cond.time: conditioning time, Sample Vol.: sample volume, and Fiber: fiber type) based on response factors (total area and number of peaks).  
Figure 2: Chromatograms obtained from experiments E3 (extraction time = 50 min; sample volume = 4 mL; conditioning time = 15 min; fiber type = DVB/PDMS/CAR); E14 (extraction time = 50 min; sample volume = 4 mL; equilibrium time = 15 min; fiber type = PDMS); E29 (extraction time = 50 min; sample volume = 4 mL; equilibrium time = 15 min; fiber type = DVB/PDMS) with the fiber type optimization graph.  
This study selected the DVB/PDMS/CAR extraction phase based on the chromatographic signal performance. Compared with the other evaluated phases, this combination exhibited more detected signals and a more prominent signal intensity. The corresponding chromatograms are shown in Figure 2. Experiments E3, E14, and E29 (Table S1) were analyzed, and all variables were kept constant except for the type of fiber. This result demonstrates that the fiber choice significantly influences the number of peaks and the total area of the extracted compounds.  
In experiment E14, where the PDMS (non-polar) fiber was used, a lower extraction of analytes and their respective concentrations was observed, indicating a lower affinity for specific compounds. In contrast, for DVB/PDMS/CAR and DVB/PDMS fibers with different polarities, broader and more varied extractions of the compounds were achieved. Experiment E3, which used the triple fiber (DVB/PDMS/CAR), yielded the highest response parameters compared with the other fibers. This position is the most suitable option for the extraction methodology because it can extract compounds with various polarities, as its coating is composed of three types of phases with different characteristics [1; 3; 14; 15; 16; 17; 18]. This quality is consistent with findings in the literature describing the volatile composition of coffee preparations, indicating the presence of compounds with a wide range of polarities. Additionally, several extraction methods proposed in similar studies agree on the efficacy of triple fibers [1; 19; 20].  
This detailed analysis of the type of fiber used provides a solid basis for selecting DVB/PDMS/CAR FIBER as the preferred option for extracting volatile compounds from CB.
# Post Quantum Cryptographic Keys Generated with Physical Unclonable Functions
## 3 Public Key Infrastructure
### PKI with Network of PUFs  
The use of networks of PUFs can mitigate the vulnerabilities of PKIs. PUF technology exploits the variations created during fabrication to differentiate each device from all other devices, acting as a hardware \"fingerprint\" [27; 28; 29]. Solutions based on PUFs embedded in the hardware of each node can mitigate the risk of an opponent reading the keys stored in non-volatile memories. The keys for the PKI can be generated on demand with a one-time use; stealing a key becomes useless as new keys are needed at each transaction. During enrollment cycles, the images of the PUFs are stored in look-up tables in the CA (see Figure 5); enrollment has to be done only once in a secure environment. Handshake protocols [30] can select a portion of the PUFs--and their image is stored in the CA--to extract a data stream that generates the key pairs. The PUFs can be erratic, therefore the generation of cryptographic keys, the focus of this work, is challenging. A single-bit  
Figure 4: Communication protocol between two client devices with shared public keys. Each client device is set with its key pair. For KEM, Client 1 uses the public key of Client 2 to encrypt a shared key; Client 2 retrieves the key with their private key. For DSA, Client 1 signs a message with their private key, Client 2 verifies the message with the public key of Client 1.  
mismatch in a cryptographic key is not acceptable for most encryption protocols. Therefore, the use of error correcting code (also use the acronym ECC, not to be confused with \"elliptic curve cryptography) methods, helper data, and fuzzy extractors can minimize the levels of errors [31; 32; 33]. The alternate method is one where the CA has search engines, such as response-based-cryptography (RBC), that can handle the validation of erratic keys [34; 35; 36; 37].  
The RBC engine that validates public keys, shown in Figure 5, generates public/private key pairs until the public key matches the client's provided key. The server searches over a seed (e.g., a 256 bit seed), and uses that seed for key generation. If the generated public key matches the client's public key, then the client is authenticated. If the public keys do not match, then the server flips one bit of the seed at a time (increasing the Hamming distance) until the public keys match. Thus, the search is carried out by generating the public/private key pairs by iterating over the seed and increasing the Hamming distance until the seed is found that matches the client's public key. The search space for a 256 bit key is 256 and it would be nearly impossible to authenticate a user in a fixed time without the use of parallel computing. High-performance computing HPC and graphics processing unit (GPU) technologies are valuable to enhance the ability of the CA to validate the public key generated by the client devices. For instance, graphics processing units (GPUs) can be employed to parallelize and accelerate the authentication process. By using a GPU, the server can search over multiple keys in parallel.
## 1 Introduction  
The viscoplastic self-consistent model (VPSC) [1] allows simulating the plastic behavior of polycrystalline materials, taking into account their crystal structure, grain shape and crystallographic texture. Additionally, a hardening law that determines the hardening in each slip system resulting from accumulated deformation is needed. The model uses an iterative method to predict the mechanical response of the material to the specified boundary conditions and its microstructural evolution with plastic deformation.  
Since the presentation of the original VPSC model in 1993, several improvements have been proposed. Some works have extended the model to also take into account elastic deformation, in an elasto-viscoplastic scheme [2, 3]. Other authors have focused on the usage of different hardening laws [4, 5] or have incorporated into the model additional phenomena, such as strain rate hardening [6, 7] and thermal softening [8]. It has also been proposed to modify the main VPSC algorithm, either to include the effect of intragranular fluctuations [9] or to use the model in conjunction with finite element analysis software [2, 10].  
In this work, the VPSC90 version of the VPSC model, presented in [10], is modified for its application to the simulation of multiphase steels. A novel approach is used to model strain rate sensitivity, while work hardening is described combining several physical and phenomenological laws. The model is applied to the study of bainitic steels. Dueto their good combination of properties, which include competitive production costs with enhanced mechanical properties that can range from very high levels of strength and toughness to improved ductility, hardenability or weldability, depending on specific composition and treatments [11; 12], bainitic steels are some of the most promising materials as third-generation advanced high-strength steels (AHSS), with increasing importance in the automotive industry [13; 14].  
In the rest of this Introduction, it is discussed how the work hardening, strain rate sensitivity and formability of steels have been previously studied. In Section 2, the materials and experimental methods used in this work are presented in detail. The modeling framework is described in Section 3. Sections 4 and 5 present the obtained results and discuss them in detail. Finally, the conclusions of the study are presented in Section 6.
###### Abstract  
Accurate peak detection in the frequency domain is fundamental to reliable range estimation in Frequency-Modulated Continuous-Wave (FMCW) radar systems, particularly in challenging conditions characterized by a low signal-to-noise ratio (SNR) and phase noise impairments. This paper presents a comprehensive comparative analysis of five peak detection algorithms: FFT thresholding, Cell-Averaging Constant False Alarm Rate (CA-CFAR), a simplified Matrix Pencil Method (MPM), SVD-based detection, and a novel Learned Thresholded Subspace Projection (LTSP) approach. The proposed LTSP method leverages singular value decomposition (SVD) to extract the dominant signal subspace, followed by signal reconstruction and spectral peak analysis, enabling robust detection in noisy and spectrally distorted environments. Each technique was analytically modeled and extensively evaluated through Monte Carlo simulations across a wide range of SNRs and oscillator phase noise levels, from \(-100\) dBc/Hz to \(-70\) dBc/Hz. Additionally, real-world validation was performed using a custom-built harmonic FMCW radar prototype operating in the 2.4-2.5 GHz transmission band and 4.8-5.0 GHz harmonic reception band. Results show that CA-CFAR offers the highest resilience to phase noise, while the proposed LTSP method delivers competitive detection performance with improved robustness over conventional FFT and MPM techniques. Furthermore, the hardware feasibility of each algorithm is assessed for implementation on a Xilinx FPGA platform, highlighting practical trade-offs between detection performance, computational complexity, and resource utilization. These findings provide valuable guidance for the design of real-time, embedded FMCW radar systems operating under adverse conditions.  
harmonic FMCW radar; passive harmonic transponder; range estimation; power consumption; simulation; measurement +
Footnote †: journal: _Article_  
_Academic Editor:_ [PERSON]  
Received: 9 June 2005  
Revised: 17 July 2005  
Accepted: 24 July 2025  
Published: 30 July 2025  
**Citation**: [PERSON]; [PERSON]; [PERSON]; [PERSON] Robust Peak Detection Techniques for Harmonic FMCW Radar Systems: Algorithmic Comparison and FPGA Feasibility Under Phase Noise. _Signal_**2025**, 6, 36.  
[[https://doi.org/10.3390/390/](https://doi.org/10.3390/390/)]([https://doi.org/10.3390/390/](https://doi.org/10.3390/390/))  
**Copyright**: 62 2025 by the authors.  
License MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
[[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).