esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Chlorophyll Fluorescence at Different Scales: A Review
## 4 SIF Retrieval Methods in Space Scale  
The weak signal of chlorophyll fluorescence compared with the reflected signal makes it difficult to detect chlorophyll fluorescence from space, and it is about 2-5% of the reflected radiance in the near infrared spectral region. Numerous efforts have been made to decouple SIF from the reflected signal. The steady-state fluorescence at 685 nm was about 1.5-3.4 mW\(\cdot\)m\({}^{-2}\)\(\cdot\)sr\({}^{-1}\)\(\cdot\)nm\({}^{-1}\), and that at 740 nm was 2.4-5.4 mW\(\cdot\)m\({}^{-2}\)\(\cdot\)sr\({}^{-1}\)\(\cdot\)nm\({}^{-1}\)[91; 92; 93; 94]. How to retrieve the SIF from the reflected signal is an important issue. In the past few years, numerous approaches have been proposed to retrieve SIF from the radiance received by a sensor. In short, these methods have two characteristics, one mainly using a physical method, and the other exploiting a statistical method.  
The SIF retrieval algorithms based on the physical model are developed using the radiative transfer theory in the visible-near-infrared region. The assumptions are that both the surface reflectance and the fluorescence follow [PERSON]'s law and that the surface is uniform. Under these assumptions the radiation transmission equation can be simplified and fitted in the retrieval window to obtain the fluorescence radiance [33; 36; 38; 44; 45]. In addition, differential optical absorption spectroscopy (DOAS) is used to retrieve the fluorescence [46; 47; 48]. The fluorescence inversion algorithm based on a physical model has a clear physical meaning and a simple inversion process. However, the estimation of the atmospheric influence at the Fraunhofer line needs to be improved.  
A statistical retrieval algorithm uses statistical methods, such as principal component analysis (PCA) [95] or singular value decomposition (SVD) [43; 49; 50], to estimate atmospheric effects and fit the fluorescence radiance in the spectral region of interest. These algorithms select the oxygen absorption bands or Fraunhofer lines for the medium spectral resolution data as the retrieval window. The wide window can improve the signal-to-noise ratio and reduce the sensitivity of the algorithm to the sensor noise. In the oxygen absorption band, the main atmospheric effects including atmospheric scattering and oxygen absorption, are estimated using statistical models to avoid computing the relative atmospheric parameters. Currently, most algorithms use the O\({}_{2}\)-A band to estimate the near infrared fluorescence [34; 37; 38; 40; 51], and some algorithms use the O\({}_{2}\)-B band to estimate the red fluorescence [96; 97].
## 4 Discussion
### Environmental Controlling Factors of Different Climate Zones  
With the increasing impact exerted by global climate change, the response of terrestrial ecosystems is receiving more and more attention from the international community about the potential carbon sink  
Figure 7: (**a**) and (**b**) represent the spatial patterns of MODIS GPP in 2014 and 2015, respectively. (**c**) provides an overlay of (**a**) and (**b**), with the red box encompassing an area where numerous zero values for GPP were obtained in 2015.  
capacity [62]. The climatic variables are considered to be the main causes of land surface vegetation changes, whereas the dominant environmental factor controlling the interannual changes of GPP would be distinctly different across various climate zones [40; 63]. From the correlation analysis of annual mean BESS GPP products in terrestrial ecosystems across China along the 2001-2015 period (Figure 8, Table 2), GPP and temperature in most regions had a significant positive correlation over 59% of the study area, which indicated that the increase of temperature mainly stimulated the growth of vegetation, particularly in the southern area (VII) and the Tibet Plateau region (VI). However, the correlation analysis showed that GPP in Inner Mongolia and tropical humid region were negatively correlated with temperature (Figure 8a). Generally, high temperature occurs during a period of little precipitation, and so, the GPP is being limited by the lack of rainfall. Meanwhile, heat-induced damage to vegetation is often accompanied by water stress, and thus, VPD and water stress are drivers of GPP declines. Recently, many studies also found that the annual and seasonal maximum and minimum temperatures in China have amplified in the last few decades [62; 63; 64; 65; 66; 67], especially in North China with extreme heat wave events, which exerted a significant impact on economic activities, consumption of electricity and water, forest fires, and a reduction in grain yield. In addition, the correlation analysis between terrestrial GPP and precipitation in entire China indicated that the area with positive correlation (55.85%) was higher than that with negative correlation (44.15%). Consistent with the findings of [PERSON] et al. [20], for South China, with abundant precipitation, water is not a limiting factor for vegetation growth. However, arid and semiarid areas such as the Inner Mongolia region in Northern China are mainly controlled by yearly precipitation (Figure 8b), which agreed well with the finding that water availability is a dominant factor in drylands across China, while climate warming will greatly exacerbate the severity of drought [68]. Overall, contrasting mechanisms affect the variability of GPP over China. In the northern regions, terrestrial GPP is more sensitive to precipitation, while the thermal condition in the southern regions plays a critical role on vegetation GPP.  
Figure 8: Spatial correlations between annual mean BESS GPP and the corresponding temperature (**a**) and precipitation (**b**) across different climate zones in China from 2001 to 2015.
# Image Captioning with Word Gate and Adaptive Self-Critical Learning
## 4 Experiments
### Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **CIDEr** & **METEOR** & **ROUGE-L** & **Bleu-1** & **Bleu-2** & **Bleu-3** & **Bleu-4** \\ \hline MSM@MSRA [12] & 0.984 & 0.256 & 0.542 & 0.739 & 0.575 & 0.436 & 0.330 \\ Review Net [26] & 0.965 & 0.256 & 0.533 & 0.720 & 0.550 & 0.414 & 0.313 \\ ATT [27] & 0.943 & 0.250 & 0.535 & 0.731 & 0.565 & 0.424 & 0.316 \\ Google [9] & 0.943 & 0.254 & 0.530 & 0.713 & 0.542 & 0.407 & 0.309 \\ SCST [4] & 1.147 & 0.270 & 0.563 & 0.781 & 0.619 & 0.470 & 0.352 \\ \hline WG-ASCST & **1.179** & **0.275** & **0.572** & **0.786** & **0.630** & **0.485** & **0.368** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Automatic evaluation on the online official MSCOCO [8] test split. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **CIDEr-D** & **Bleu-1** & **Bleu-2** & **Bleu-3** & **Bleu-4** & **ROUGEL** & **METEOR** \\ \hline NICv2 [9] & 0.998 & - & - & - & 0.321 & - & 0.257 \\ Hard-Attention [10] & - & 0.718 & 0.504 & 0.357 & 0.25 & - & - \\ Soft-Attention [10] & - & 0.707 & 0.492 & 0.344 & 0.243 & - & - \\ e2e-gLSTM [24] & 0.951 & 0.712 & 0.54 & 0.401 & 0.292 & - & - \\ Sentence-condition [25] & 0.959 & 0.72 & 0.546 & 0.404 & 0.298 & - & - \\ Att2 in [4] & 1.013 & - & - & - & 0.313 & 0.543 & 0.260 \\ Att2 in + SCST [4] & 1.114 & - & - & - & 0.333 & 0.553 & 0.263 \\ \hline NIC (Resnext\_101\_64x4d) & 1.009 & 0.724 & 0.556 & 0.418 & 0.314 & 0.537 & 0.259 \\ Soft-Attention (Resnext\_101\_64x4d) & 1.046 & 0.737 & 0.571 & 0.433 & 0.326 & 0.550 & 0.263 \\ \hline Soft-Attention + SCST & 1.148 & 0.771 & 0.610 & 0.465 & 0.349 & 0.561 & 0.266 \\ Soft-Attention + ASCST & 1.170 & 0.777 & 0.616 & 0.471 & 0.354 & 0.564 & 0.269 \\ Soft-Attention + WG & 1.092 & 0.757 & 0.595 & 0.456 & 0.347 & 0.558 & 0.272 \\ Soft-Attention + WG + SCST & 1.187 & 0.779 & 0.620 & 0.470 & 0.349 & 0.569 & 0.274 \\ Soft-Attention + WG + ASCST & **1.212** & **0.785** & **0.630** & **0.486** & **0.368** & **0.571** & **0.277** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Single-model image captioning performance without RL on the MSCOCO Karpathy test split. BLEU-n is a geometric average of precision over 1-to n-grams.NIC: neural image caption. Figure 5: The CIDEr scores of the WG-ASCST model with different \(\beta\). The \(x\)-axis is the number of iterations, and the \(y\)-axis is the CIDEr score. All the models in this figure have the same alpha. alpha_5000_beta_01 means the \(\beta\) of the ASCST model is 0.1.
###### Abstract  
The present work investigates contributions of different heating mechanisms and power efficiency of atmospheric-pressure helium dielectric-barrier discharges (APHeDBDs) containing a small amount of N\({}_{2}\) for temperature measurements by developing the numerical methodology combining the one-dimensional (1D) plasma fluid model (PFM) and 3D gas flow model (GFM) with simulated results validated by measurements including the discharge power consumption and temperature distribution. The discharge dynamics are modeled by the 1D PFM for evaluating the average heating source considering elastic collision, ion Joule heating, and exothermic reactions as the source term of energy equation solved in the 3D GFM. The simulated current density reaches 29 A m\({}^{-2}\) which is close to that measured as 35 A m\({}^{-2}\). The simulated power consumption is 2.0 W which is in good agreement with the average measured power consumption as 2.1 W. The simulated average gas temperature in the reactive zone is around 346 K which is also close to the rotational temperature determined. The analysis shows that elastic collision and ion Joule heating are dominant heating mechanisms contributing 23.9% and 65.8% to the heating source, respectively. Among ion species, N\({}_{2}\)\({}^{+}\) and N\({}_{4}\)\({}^{+}\) are dominant species contributing 44.1% and 50.7% to the heating source of ion Joule heating, respectively. The simulated average total heating source is around \(5.6\times 10^{5}\) W m\({}^{-3}\) with the maximum reaching \(3.5\times 10^{6}\) W m\({}^{-3}\) in the sheath region due to the contribution of ion Joule heating.  
helium plasma; dielectric-barrier discharge; gas flow model; plasma fluid model; gas heating +
Footnote †: journal: Applied Physics Letters
## 8 Conclusions and Future Work
### Quantum Threat Scenarios  
Given the pace of quantum computing development, it is essential to assess the resilience of cryptographic schemes under various quantum threat models. Accordingly, we evaluate the potential impact on our proposed scheme across three-time horizons: short-term (0-5 years), mid-term (5-10 years), and long-term (10+ years), along with corresponding mitigation strategies.  
1. Short-Term (5 Years)  
At present, quantum hardware remains limited in terms of qubit count, stability, and error correction. Consequently, quantum attacks such as those enabled by [PERSON]'s algorithm remain computationally infeasible for breaking elliptic curve cryptography (ECC) or ABE [37,38]. Accordingly, the proposed is expected to remain secure throughout this period. Nonetheless, incremental progress in quantum computing merits close attention, as unforeseen breakthroughs could significantly alter this projection.  
2. Mid-Term (5-10 Years)  
As quantum processors become increasingly viable, the practical implementation of [PERSON]'s algorithm may begin to pose a genuine threat to classical public-key cryptographic schemes. This development could eventually enable the compromise of cryptographic protocols based on discrete logarithms and factoring assumptions. In parallel, [PERSON]'s algorithm raises a separate concern for symmetric cryptography, as it offers a quadratic speed-up over classical brute-force search [39]. This reduction in complexity effectively lowers the security of symmetric algorithms, necessitating the use of longer symmetric keys to maintain adequate levels of protection [40].  
During this transitional phase, the adoption of hybrid cryptographic frameworks is strongly recommended. Such frameworks may combine classical public-key algorithms (e.g., ECC) with post-quantum key encapsulation mechanisms. These hybrid designs facilitate a gradual migration towards quantum-resilient infrastructures while maintaining interoperability with existing systems and standards [41].  
3. Long-Term (10 Years+)  
In a scenario where large-scale, fault-tolerant quantum computers become a reality, public-key schemes based on the discrete logarithm and factoring problems--such asbilinear pairings--will become obsolete. Ensuring long-term security will therefore require the migration to fully post-quantum attribute-based encryption schemes, including those constructed on lattice-based or code-based cryptographic [42; 43; 44]. System designs must therefore remain adaptable to accommodate emerging standards and algorithms, thus ensuring resilience against increasingly advanced quantum adversaries.
## 4 Simulation Results  
To assess the performance and the advantages of integrating a MEMS inertial sensor with a GPS receiver, the TCAPLL architecture is tested under different scenarios. We start by analyzing the behavior under normal conditions, _i.e._, open sky and seven visible satellite vehicles (SVs). Then, we degrade navigation conditions by decreasing the number of visible satellites to four, then three, and then zero (_i.e._, GPS total outage). Under all these conditions, different PLL loop bandwidths and integration times are tested. The assessment takes into consideration navigation accuracy, tracking robustness, sensitivity and quality of the feed-forward component.
## 5 Experimental Results and Analysis
### GCNN-Based Model Experiment and Result Analysis  
(1) Experimental results  
The data are divided randomly and the ratio of data in the training set and test set is 8:1:1. The accuracy of the test set is close to 90% and the Loss value is 0.43. This is shown in Figure 27.  
The results of the graphical convolutional neural network for traffic black spot identification at road intersections are visualized in 2D space, as shown in Figure 28.  
According to the experimental results of the graph convolutional neural network, it can be found that the GCNN model can be better used in the identification of traffic black spots. Through the extraction of semantic information between the built environment features of adjacent road intersections, the accuracy of the identification of traffic black spots has been improved to a certain extent, and the model value Loss has decreased significantly, which also confirms that, in daily life, the built environment is more similar between the neighboring roads and that, when a traffic accident occurs in a certain place, it brings about the negative effects of vehicular congestion, crowd gathering, and other  
Figure 27: Graph of the result of GCNN model to recognize traffic black spots.  
negative effects which will be transmitted to the neighboring roads, which will make the probability of the occurrence of traffic accidents greatly increased.
## 3 Panoptic Segmentation
### Metrics for Panoptic Segmentation  
PS is a joint segmentation task between SS and IS (\"stuff\" and \"things\") and earlier works focused on evaluating their performance metrics independently [6; 28; 29; 30] (seen in Figure 1). The pursuit of a cohesive metric for unifying these distinct tasks introduces several algorithmic complexities. In their work [10], the authors introduced a trio of metrics: panoptic quality (PQ), segmentation quality (SQ) and recognition quality (RQ).  
PQ measures the predicted PS quality relative to some ground truth. This involves tasks such as segment matching between the predicted segments and the ground truth. SQ is computed as the intersection over union (IoU) score (shown in equation below). RQ represents quality estimation in identification settings [32] and is the familiar F1 score [33]. It is assumed that both predicted and ground truth segments match only if the IoU exceeds a value of 0.5.  
\[IoU=\frac{|Target\ \cap\ Predicted|}{|Target\cup Predicted|}\]  
where, for a set A, \(|\)A\(|\) denotes its size.  
Panoptic quality (PQ): PQ is calculated independently for each class and an average over all the classes is obtained. The unique matching of each class divides the predicted and the ground truth segments into three categories namely: true-positives (TPs), false-positives (FPs), and false-negatives (FNs). These categories represent matched and unmatched predicted segments, and unmatched ground truth segments, respectively. PQ is calculated as  
\[\mathbf{PQ}=\frac{\sum_{(p,g)\in TP}\mathbf{IoU}(\mathbf{p},\mathbf{g})}{|\ TP\ |+\frac{1}{2}\ |\ FP\ |+\frac{1}{2}\ |\ FN\ |}\]  
where \(\frac{1}{|TP|}\sum_{(p,g)\in TP}\mathbf{IoU}(\mathbf{p},\mathbf{g})\), is the average \(IoU\) of matched segments, and to penalize the segments that have no matches, \(\left(\frac{1}{2}|FP|+\frac{1}{2}|FN|\right)\) is included in the denominator. It is important to know that regardless of their area, all the segments receive equal consideration. Also, on multiplying and dividing PQ proportional to the TP set size, PQ can be expressed as a product of SQ and RQ (SQ \(\times\) RQ) as follows:  
\[\mathbf{PQ}=\frac{\sum_{(p,g)\in TP}\mathbf{IoU}(\mathbf{p},\mathbf{g})}{|\ TP\ |}\times\frac{|\ TP\ |}{|\ TP\ |+\frac{1}{2}\ |\ FP\ |+\frac{1}{2}\ |\ FN\ |}\]  
Figure 1: PS demonstrating the combined instance and semantic segmentation results. (Input image to the left is from Google search and the output to the right is extracted using Detectron2 [31]).  
The first factor of the product is referred to as the segmented quality (SQ) whereas the second is referred to as the recognition quality (RQ), i.e., PQ = SQ \(\times\) RQ. However, SQ and RQ are not independent as SQ is measured only over the segments matched. It is also interesting to consider the patches with void regions and group of instances [9].
## 3 Analysis and Results
### Accuracy
#### 3.1.2 Electric Resistivity Tomography  
Following the same criterium used for the accuracy evaluation of ultrasonic tomography (Section 3.1.1), we now consider a reference stone back--XY section, 600 mm width, and 250 mm height--with a limited number of veins (Figure 18). Applying both procedures (automatic and manual) on this reference section, the accuracy of the results can be comparatively evaluated.  
Regarding the manual procedure, two cases were considered: (1) 14 \(\times\) 10 grid (Figure 19), which is a regular measuring grid achievable by a skilled operator and (2) 14 \(\times\) 15 grid (Figure 20), the manual best case scenario, which is considered the highest accuracy that can be performed by manual measurements.  
Regarding the new developed automatic procedure, we considered a 20 \(\times\) 41 grid (Figure 21), whose accuracy is allowed by the current prototype setup.  
Figure 19: Grid 14 \(\times\) 10 (manual—skilled operator).  
Figure 20: Grid 14 \(\times\) 15 (manual best case).  
Analyzing the numerical results shown in Table 6, it is possible to detect a major difference between the automatic and manual procedures. The manual procedures show a very low capacity of veins detection when compared with the automatic procedure. In fact, both manual grids (14 \(\times\) 10 and 14 \(\times\) 15) detect at the highest detection capacity, only 59% of the target area, while the automatic procedure (20 \(\times\) 41 grid) arrives to a detection rate of 84.3%. The proposed automatic measurement procedure can easily achieve much finer grids that will result in much more accurate results. Visually, we can see that the automatic procedure provided accurate results, maintaining most of the vein's original shapes and dimensions.  
Finally, it is worth mentioning that these results assume that the reading's precision is 100%, which can be a good approximation for the automatic system but not for the manual one, resulting in even poorer results for the manual procedures.
# Knowledge Distillation-Based GPS Spoofing Detection for Small UAV
## 4 Problem Statement  
With GPS, a UAV can localize its positions and adjust the output of its motors so that it can follow the trajectory designed by the GCS and finish missions. IMUs provide the motion information that enables the UAV to adjust its posture in flight to maintain safety. With the RSSI, the UAV can measure the distance between its localization and the GCS. In our scenarios, the hackers utilize SDR to generate forged GPS signals to send to the GPS module of the UAV. The signal generated from the SDR cannot impact the functionality of IMUs or the telecommunication radio. The telecommunication radio works at frequencies of 433 MHz or 915 MHz.  
The attacker model is as follows. The attackers use compact SDR devices (like BladeRF 2.0 [32] and HackRF One [33]) to acquire satellite information. And then, the attacker uses a GPS simulator [34] to generate the forged signal. When the attacker broadcasts the forged signal, the UAV will calculate its localization with the forged signal. The strength of the forged signal surpasses the signal generated from the satellites, which enables the forged signal to be the only source of the GPS for the UAV. With forged signals, the UAV calculates the wrong localization. The wrong localization causes an inconsistency in the velocity and acceleration of the UAV. With this inconsistency, we can detect GPS spoofing attacks Figure 1. However, in practical scenarios, the noise derived from the motion and environment also has effects on the stability of GPS localization.  
Conventional approaches have sensitive detection with good environmental noise acquisition, but it is hard to deploy this detection method on compact vehicles like UAVs. UAVs have limited capacities for power supply and loading. In this paper, we will deploy a compact neural network model on a compact device that can be installed on UAVs with less weight and a lower power supply. The compact device can acquire information from GPS, IMUs, and telecommunication radio and detect the status of the UAV to identify if the UAV is under a GPS spoofing attack. In this paper, we focus on GPS spoofing attack detection and lightweight deployment on devices that can be installed in UAVs.
###### Abstract
Although the equivalent factor method is relatively popular, this approach relies on LULC to calculate EVs, and different (even the same) LULC products have different spatial scales. Nevertheless, while the different spatial scales of different LULC products may affect ESVs calculations, the impacts of variations in the spatial scale have not been clearly investigated. To address the problem of different scales among different LULC products, we used data from two LULC products and obtained two sets of data at different spatial scales by resampling. We then calculated the EVs by implementing the equivalent factor method to obtain the differences caused by different LULC products. In this study, data from two LULC products in 2010 were used. Each product consisted of 13 spatial scales ranging from 300 m to 3900 m, and the LULC data had a scale difference of 300 m between the two products. Taking the QTP as the study area, the EVs were calculated by the equivalent factor method. The main research objectives were as follows: (1) to analyze the differences in different LULC products with changes in the spatial scale; (2) to calculate the EVs from the LULC data of different products and from the same product at different spatial scales; and (3) to study the change trend of the EVs of LULC at different scales.