esa-sceva/satcom-chunk-collection huggingface dataset:
# Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery
## 2 Materials and Methods
### Planet Imagery Download and Processing  
For both the 2021 and 2022 cover crop cycles, weekly (5-7 days based on cloud coverage on experimental fields) cloud-free Planet imagery was downloaded. The acquisition time period for these Planet data ranged from the approximate planting date (1 November) until CC termination (30 April). Planet imagery was accessed through the NASA-CSDA program.  
Figure 2: Temperature during 2021 and 2022 cover crop cycles. Solid lines are minimum temperature while dotted lines are maximum temperature. Two dashed lines represent frost period initiation (blue dashed line, 0 °C) and second growing degree day base temperature (red dashed line, 4 °C) for winter cover crops.  
Analysis-ready surface reflectance images (PlanetScope AnalyticMS Level 3B product) were downloaded using Planet Explorer. This imagery had a spatial resolution of 3 m, with blue (464-517 nm), green (547-585 nm), red (650-682 nm) and NIR (846-888 nm) bands. Since all four study sites fell within one of the Planet satellite's swath size of 24 km \(\times\) 16 km, no further postprocessing for geometric correction was performed on the downloaded product. A total of 47 Planet images (representing the weekly image of the study sites for two years) were used for further processing and analysis.  
Each of the 47 Planet images with their surface reflectance bands (blue, green, red and NIR) were overlaid on the experimental plot boundaries (i.e., the 68 plots at the four study sites), and the pixel values were extracted using the R _raster:extract_ function. Imagery pixels that were 50% or more, by area, within the experimental plot boundary were included in the calculation of the average pixel value per plot. Vegetation indices (Table 2) were derived from the plot-level average pixel value of the blue, green, red and NIR bands. Most of the vegetation indices (NDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2), optimized soil-adjusted vegetation index (OSAVI), simple ratio (SR), and modified simple ratio (MSR) are closely related to biomass, leaf area index, and photosynthetically active radiation absorption, and have been reported widely for the purpose of vegetation estimation [22; 27; 28; 29; 30; 31; 32; 33; 34]. Excess green index (ExG) and excess red index (ExR) are, however, used to enhance vegetation features in RGB imagery and count plant density [35; 36; 37; 38] remotely.  
Based on the temperature and frost periods observed during the 2021 and 2022 cover crop cycle (Figure 2), the dataset (plot-level bands and VI values) from both years was divided into four time periods for statistical analysis: November, March, April_A (1-15 April), and April_B (15-30 April). For each time period, the average bands and VI values were calculated using the weekly value extracted in the earlier step. Hence, A total of 544 observations of reflectance values of bands and VIs (68 plots \(\times\) 4 time periods \(\times\) 2 years) returned by these steps were used for further statistical analysis and model building process.
## 2 Materials and Methods
### Field Data  
The field data were collected during the 2018 and 2019 time period as part of the F.OR.MA. (Fuel models and fORest roads Maps) project in collaboration with the Civil Protection Department of the Apulia region. To assess fine dead fuel load, 251 plots were randomly selected from the Apulian land-cover layer ([[http://www.sit.puglia.it](http://www.sit.puglia.it)]([http://www.sit.puglia.it](http://www.sit.puglia.it)), accessed on 23 February 2021) provided by the regional government. The field sampling protocol is illustrated in Figure 2.  
Figure 1: Location of the Apulia region, Italy (**left panel**). Location of 251 field plots and the pilot area across the region (**right panel**).  
Sampling of the fine dead fuel load was conducted according to [PERSON] et al. [18] and the method developed by [PERSON] et al. [42]. During the survey, circular plots with a radius of 5 m each were identified by geographical coordinates using a GPS receiver. Within each plot, a transect measuring 15 m was determined. If the piece of wood intersected the transect more than once, each intersection was measured. For the purposes of the F.OR.MA. project, dead woody materials were divided into three time-lag classes (1-h, 10-h, and 100-h) according to the diameter measured by the caliper at the point of intersection with the transect. In this study, the 1-h fuel load was chosen as the response variable since it represents the primary carrier of wildfire occurrence [43]. The 1-h time-lag fuels consist of needles, leaves, and small twigs with a diameter between 0 and 0.65 cm. The small size of this fuel makes its moisture content more responsive to changes in atmospheric conditions, affecting its ignitability [44,45]. Its rapid flammability makes it prone to fire ignition, especially in the hot and dry season.
# A Deep Convolutional Neural Network for Detecting Volcanic Thermal Anomalies from Satellite Images
## 3 Method
### Theoretical Background  
To detect and quantify the thermal emissions from volcanic targets, it is necessary to consider the relation between radiance (i.e., rate of energy flow per unit area) and wavelength. The radiance emitted from a surface, or the reflectance (i.e., a dimensionless measurement that describes the ratio between solar reflected and incident radiation on a given surface), depends on its temperature and radiating efficiency (emissivity) [48]. According to the Stefan-Boltzmann law, the higher the temperature of a surface, the greater the radiance it emits [48]. This applies to all wavelengths of emission but, according to Wien's displacement law, the peak wavelength of thermal emission also shifts to shorter wavelengths with increasing temperature. Hence, a hotter volcanic surface will radiate more energy compared to a cooler surface, and variations in the radiant flux, which is quantified in terms of radiance per unit of wavelength, are directly related to changes in volcanic activity. This can include phenomena such as a fresh lava flow, an active lava lake or a subtly warm fumarole field [48].  
In terms of the temperature of hot volcanic features, fresh basaltic lava may be between 1073 and 1273 K near the vent of the flow [49]. Lava lakes can reach extremely high temperatures up to 1473 K [50]. Gasses emitted from volcanic fumaroles are found to be between ambient temperature and 673 K [51], although temperatures up to 1173 K have been measured [52; 53]. In terms of spectral radiance, therefore, the emissions from very hot surfaces (e.g., >900 K) are enhanced in near infrared (NIR, ~0.9-1.1 \(\upmu\)m) and shortwave infrared (SWIR, ~1.2-2.5 \(\upmu\)m) regions of the spectrum compared with those in the middle infrared (MIR, ~3-5 \(\upmu\)m) and longwave (thermal) infrared (TIR, ~7-14 \(\upmu\)m) bands.  
Various satellites are equipped with sensors that are able to record these infrared radiations and, as such, can be used for detecting and monitoring active volcanism. However, MIR and TIR detections from space will be mainly due to the combination of emissions from both ambient and hot surface components, for which to accurately quantify the emissions related to active volcanism, it is necessary to remove the contributions from the ambient background. Instead, NIR and SWIR signals recorded from space will be primarily due to the temperature of a very hot emitting surface (e.g., fresh lava).
## 4 Results
### Trends in VI SOS Estimates  
The results of VI SOS trends will be shown as two maps (TS trends and TS trends of significant changes) as relatively low amount of significant trends based on per-pixel trend analysis are observed caused by a relatively short period of analysis (2000-2014) and coarse resolution of the earth observation (EO) time-series [70].  
\begin{table}
\begin{tabular}{c c c c} \hline
**IGBP \({}^{1}\) Land Cover Class** & **PPI (Date)** & **NDVI (\(\Delta\)Days)** & **EVI (\(\Delta\)Days)** \\ \hline Evergreen Needleleaf forest & 24 May \(\pm\) 10 & \(-16\pm 13\) & \(-7\pm 8\) \\ Deciduous Needleleaf forest & 26 May \(\pm\) 5 & \(-15\pm 5\) & \(-13\pm 5\) \\ Deciduous Broadleaf forest & 1 May \(\pm\) 10 & \(-16\pm 10\) & \(5\pm 9\) \\ Mixed forest & 22 May \(\pm\) 7 & \(-20\pm 13\) & \(-8\pm 7\) \\ Open shrublands & 2 June \(\pm\) 8 & \(9\pm 9\) & \(11\pm 9\) \\ Woody savannas & 3 June \(\pm\) 8 & \(4\pm 10\) & \(5\pm 6\) \\ Savannas & 4 June \(\pm\) 8 & \(-15\pm 10\) & \(-2\pm 7\) \\ Grasslands & 26 May \(\pm\) 15 & \(10\pm 19\) & \(9\pm 16\) \\ Permanent wetlands & 29 May \(\pm\) 10 & \(-42\pm 11\) & \(-34\pm 13\) \\ Croplands & 27 May \(\pm\) 11 & \(-21\pm 8\) & \(-14\pm 6\) \\ Cropland/Natural vegetation & 20 May \(\pm\) 10 & \(-20\pm 10\) & \(-12\pm 7\) \\ All & 29 May \(\pm\) 9 & \(-13\pm 11\) & \(-6\pm 9\) \\ \hline \end{tabular} \({}^{1}\) International Geosphere-Biosphere Programme.  
\end{table}
Table 1: Average \(\pm\) one standard deviation Plant Phenology Index (PPI) start of season (SOS) dates for different land cover types (Figure 1) and the relative difference (in Julian days) in SOS to the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).  
Figure 3: (**A**) per pixel average PPI SOS (2000–2014). (**B**) relative difference in PPI and NDVI SOS and (**C**) relative difference in PPI and EVI SOS (2000–2014). Water bodies and pixels of forest loss are masked.
# A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
## 1 Introduction
Its primary purpose is to unify and enhance detection capabilities across diverse and evolving threat modalities--including zero-day attacks, botnets, and malware concealed within images--through a single, modular pipeline architecture. The framework specifically addresses this goal by starting with the synthetic data generation using GANs and continuing with the threat detection by combining transfer learning for image-based malware detection and ensemble learning for tabular network intrusion data analysis. This research directly builds upon the authors' prior work in GAN-based malware synthesis and classification [10], zero-day attack data generation [11], and multi-model ensemble evaluation for intrusion detection [12], aiming to create a comprehensive and scalable solution for heterogeneous cyber threats. The main added value of the approach presented in the current study lies in its highly flexible, scalable, and adaptable modular architecture, which uniquely supports different input data types (image-based threats and tabular network data) without requiring standardization. It features two independently operating branches: an image-processing branch optimized for malware classification using CNNs and transfer learning, and a tabular-processing branch employing a soft-voting ensemble classifier for network anomaly detection. Each branch functions autonomously, providing binary (malicious/benign) outputs/predictions. Crucially, these outputs can be fused at a decision layer when both data modalities pertain to the same input instance. This optional fusion combines the strengths of each specialized detection approach, significantly boosting the system's resilience against sophisticated, evasive, and zero-day threats--delivering enhanced, unified protection where traditional single-modality systems fall short. This design enables the system to operate effectively in complex threat scenarios, where multimodal indicators (e.g., obfuscated malware executables, traces of botnet or DDoS attacks) are present. Real-world platforms such as malware sandboxing systems, Security Information and Event Management (SIEM), and Extended Detection and Response (XDR) architectures often capture both static payloads and their dynamic behaviors across various channels. By unifying these modalities in a modular and flexible pipeline, the framework supports either independent or joint analysis, enhancing adaptability, improving detection accuracy, and reducing false positives. The remainder of the paper is organized as follows: Section 2 presents related works, focusing on the domain of cybersecurity and mainly revolving around GANs, zero-day attacks, transfer learning, and ensemble methods. Section 3 describes in detail the proposed methodology designed and developed, whilst Section 4 elaborates on the produced results. Finally, Section 5 concludes the paper.
## 1 Introduction
The motivation for most of the existing work [23; 24] is to achieve conversion using RF and LO signals in the system frequency band, typically the L-Ku band for the transceiver. However, this frequency conversion method will always bring about a lot of in-band spurs, mainly from the beating between RF/LO sidebands and the optical carrier. Thus, there is an urgent demand to decrease the in-band interference signals after frequency conversion. In this paper, the broadband characteristic of a microwave photonic converter and fine processing of a microwave converter are combined to propose a microwave photonic converter based on microwave pre-upconversion. The transmitted and received signals are upconverted to high frequency (HF) by microwave pre-treatment before entering the MZM. The interference components consisting of harmonics and intermodulation as well as original signals are all out of the system frequency band (0.8-18 GHz), and these spurs can be suppressed by taking advantage of the bandwidth limitation of the detector. Hence, the spurious suppression of both the in-band and out-band is achieved, which further realizes high-quality conversion and filtering for the upconversion and downconversion. Additionally, only a single MZM is employed in the converter system for the purpose of simplicity and low cost. The HF and LO signals are coupled to drive the MZM by means of the electrical combiner. CS-DSB modulation is introduced to the MZM by simply controlling bias voltages of the modulator, which leads to effective spectrum utilization.
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 3 UAV Localization and Tracking
### UAV Tracking
#### 3.2.2 Deep Siamese Networks
The development of more efficient algorithms and the availability of more diverse and comprehensive datasets for training and testing are essential for further advancing the field of UAV tracking.
# Joint Embedding of Semantic and Statistical Features for Effective Code Search
## 3 Approach  
We present the technical framework of JessCS in Figure 1. JessCS consists of three steps: feature extraction, joint embedding of semantic and statistical features, and construction of a code search engine. First, we extract information from source code and query descriptions to build the code embedding network and natural language embedding network. The query description, which includes word stemming, keyword synonyms and word frequency, is obtained by tokenizing the identifier according to the camel case naming rule. Second, we implement the joint embedding method based on semantic and statistical features to overcome the incongruence of data dimension. Finally, we construct a code search engine based on the matching strategy of the query and source code.
###### Abstract  
We aimed to investigate the changes caused by focused extracorporeal shock wave pain therapy (f-ESWT) in patients with patellar tendinopathy by means of ultrasound imaging. We included 18 knees from 11 college athletes with patellar tendinopathy. We assessed the tendon thickness and blood flow of the patellar tendon using ultrasound imaging, rest pain using NRS and tenderness using a pressure pain gauge. We recorded four measurements: immediately before f-ESWT (PRE()) and after f-ESWT (POST()) and two weeks after the first irradiation before f-ESWT (PRE()) and after f-ESWT (POST()). Only the resting pain in both the first and second irradiations showed a significant difference immediately before and after the treatment. In terms of pain changes after two weeks later, we observed significant differences in the resting pain between PRE() and PRE() and also in the resting pain, tenderness and blood flow area between PRE() and POST(). No significant difference was seen in the tendon thickness. We concluded that pain in the patellar tendon at rest decreased before and after irradiation, suggesting that f-ESWT may have influenced the nociceptive structures and had an analgesic effect.  
p 2021 11 8748 10.0000-000  
Conservative therapy, including eccentric exercise, is the main treatment for patellar tendinopathy. However, over a long period, competitive athletes often develop intractable patellar tendinopathy with prolonged pain [16], and in some cases, surgery is required because conservative therapy is not successful.  
In addition, the change in tendon stiffness due to scarring reduces its ability to transmit tension, making patellar tendinopathy directly related to decreased athletic performance [17]. Intractable patellar tendinopathy is a serious condition that can cause the end of a career for athletes because surgical treatment requires prolonged withdrawal from sporting competitions.  
The efficacy of f-ESWT for patellar tendinopathy is 60-90%, but the mechanism of healing is unclear [18; 19; 20]. A few reports deny the efficacy of f-ESWT and suggest that the results of f-ESWT are poor in refractory patellar tendinopathy and tendinopathy with advanced lesions. Objectively evaluating the actual patellar tendinopathy site, rather than carrying out only subjective pain assessments, may lead to further clarification of the cause of tendinopathy [21; 22; 23; 24]. Ultrasound echo can be used not only to gain a more detailed picture of tendon morphology but also to map changes in tendon blood flow components in detail. In addition, recent developments in ultrasound echo have made it possible to capture the internal structure in detail before, making it possible to clearly see Doppler images. By using these, it can be used as an objective index to evaluate the effect of f-ESWT in more detail. Therefore, in this study, we examined the effects of f-ESWT on immediate and short-term pain changes by performing ultrasound imaging of the patellar tendon in patients with patellar tendinopathy.
## 2 Procedures in Deriving Rainfall Maps from Attenuation
### Calibration of the \(\gamma\)-R Relationship and Rainfall Estimation  
For the coefficients \(a\) and \(b\) in the relationship between specific rain-induced attenuation and rain rate, the International Telecommunication Union Recommendations (ITU-R) [77] provide a reliable reference in the absence of microphysical information for rainfall. However, the ITU-R model, as a statistical model fitted from global data, has been found to potentially perform sub-optimally in localized areas in several reports [78; 79; 80; 81; 82]. As shown in Figure 4, for the Nanjing area, the ITU-R model would significantly overestimate rainfall under high rain rate conditions compared to the local \(\gamma\)-\(R\) relationship. Therefore, based on T-matrix or the Mie scattering theory, some scholars have also utilized DSD data to fit power-law parameters appropriate to the local climate. Using data from the dishormeter deployed in Nanjing, China, [PERSON] et al. [83] estimated rainfall using the improved \(\gamma\)-\(R\) relationship. The study concluded that the results are more accurate than the ITU-R model and highlighted the significance of establishing the local \(\gamma\)-\(R\) relationship. [PERSON] et al. [84] then fit the power-law relationship for stratiform and convective rainfall separately, demonstrating superior performance over the ITU-R relationship.  
After calibration of the power-law relationship, the path-averaged rain rate can be estimated based on Equations (4) and (6):  
\[\hat{R}\overset{b\approx 1}{=}\left(\frac{A}{aL}\right)^{1/b} \tag{8}\]
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Advanced Antenna Designs for High-Performance Satellite Communication Systems
#### 3.6.5 Example Designs
[127]) capabilities, and wide-beam applications with the simple use of a parasitic element on top of the patch element, broadening the beam by \(20^{\circ}\). Moreover, phased array designs utilize the advantages of patch microstrip antennas by placing them in an array configuration that provides various benefits that improve performance and functionality, such as compactness, enhanced gain, directivity and efficiency, and beamforming and steering. Tensorial metasurfaces, in combination with phased arrays, enhance their capabilities further. A tensor metasurface overlaying on a phased array achieves advanced polarization control and improved aperture efficiency (ref. [131]), which are critical for satellite applications. The combination of tensor metasurfaces and the phased array enables dual-frequency operation and adaptive radiation patterns. Designs such as (ref. [122]) that utilize a metasurface in front of an array are examples of their effectiveness and later usage in LEO satellite applications Furthermore, one important technological trend is to make metantennas intelligent for configurability in both spectral and temporal domains, making them suited for 5G-Beyond and 6G applications. Finally, URAs (ref. [125]) take into consideration the path loss variation due to Earth's curvature, quality of service constraints, and constant modulus constraints for maximum power amplification efficiency. The proposed method (ref. [125]) formulates the URA design in two linear and uniform arrays, giving constant SNR for specific beamwidths, low sidelobe interference, and channel capacity improvement.
## 3 Methodology
### Dataset and Labeling Strategy  
A dataset consisting of breast DCE-MRI data from 44 patients was used for the training and testing of the proposed segmentation model. Data were acquired on a 1.5 T scanner (MAGNETOM Avanto, Siemens Healthcare GmbH, Erlangen, Germany) with the patient positioned lying face down. The TR/TE/flip angle was 4.33 s/1.32 s/10\({}^{\circ}\) for each scan, with a slice thickness of 1.1 mm, with no gaps between slices. The resolution of each slice was 448 \(\times\) 448 pixels. Each breast DCE-MRI protocol consisted of one pre-contrast T1-weighted sequence and seven post-contrast T1-weighted sequences collected at intervals of 1:01 min between sequences.  
A subset of the slices of each sequence was manually segmented, and the segmentation was checked for accuracy by a breast radiologist. Before manual segmentation, the slices were cropped to size to remove the posterior half of the thorax. In this developmental phase of the segmentation, the bottom row of 131 pixels was removed as this gave the best cropping result for our dataset. For manual segmentation, eleven evenly spaced slices throughout the central 70 slices of the scan volume of the pre-contrast sequence were selected to address anatomical variations along the upper body. To address changes in tissue brightness because of CA injection, the six central slices of the pre-contrast sequence were also manually segmented. The manual segmentation of these slices was repeated for the corresponding slices in all seven post-contrast sequences. A total of 2552 slices were manually segmented. Segmented slices from 37 patients (n = 2146 slices) were used for the training of the model. The model was tested with segmented slices from the remaining seven patients (n = 406 slices).