esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Gross Primary Productivity  
GPP (Gross Primary Productivity) was used as an available time-series basis to estimate aboveground plant biomass across the Brazilian Pantanal. Aboveground woody biomass density data were obtained for the year 2012 from the \"WHRC/biomes/tropical\" collection at a spatial resolution of 500 m [35]. This dataset was assembled from co-located field measurements, LiDAR observations, and imagery recorded by MODIS sensor.  
The 500 m-resolution MOD and MYD17A2 product related to GPP is a cumulative composite of GPP values based on the efficiency of solar radiation use by vegetation (\(\varepsilon\)). In this logic, primary production is linearly related to absorbed photosynthetically active radiation (APAR), according to Equation (3). APAR can be calculated as the product of the incident photosynthetically active radiation (_PAR_) in the visible spectral range of 0.4-0.7 um, assumed to be 45% of the total incident solar radiation, and the fraction of photosynthetically active radiation absorbed by the vegetation cover (_FPAR_) [36].  
\[GPP=\varepsilon\times PAR\times FPAR \tag{3}\]  
One of the greatest challenges in using these models is obtaining the light use efficiency \"\(\varepsilon\)\" over a large area due to its dependence on environmental and vegetation factors. A solution is to relate \"\(\varepsilon\)\" according to its maximum value (\(\varepsilon_{max}\)) plus the environmental contributions synthesized by the minimum air temperature (\(T_{misscalar}\)) and the water status of the vegetation (\(VPD_{scular}\)--water vapor pressure deficit) [37], according to Equation (4).  
\[\varepsilon=\varepsilon_{max}\times T_{misscalar}\times VPD_{scalar} \tag{4}\]  
In this study, we used MODIS GPP version 5.0 from 2001 to 2019. Pixel values referring to the digital numbers of MODIS images were converted into biophysical values (Kg C m\({}^{-2}\)) by multiplying by a 0.0001 scale factor [36]. GPP values were also transformed to the cumulative values for each year of the time series.
## 2 Islanding Detection Methods
### Signal Processing Based Methods
#### 2.5.3 S- Transformer Method  
The time-domain function is transformed into a two-dimensional frequency-domain function using this technique, which is an extension of the wavelet transformer technique. The voltage or current signal measured at PCC is used to construct the S-matrix and the associated time-frequency contours. Calculating the spectral energy content of the time-frequency contours that contain both frequency and amplitude aberrations allows for the detection of the islanding [41]. The main drawback of this method is that it takes longer to process and uses more computer resources than other ways.
# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 3 Experiments and Results
### Accuracy Analysis of Matching between ZY-1-02C Images and ETM+ Images
#### 3.3.3 Error Elimination  
A total of 5458 Harris feature points were extracted, and 2324 pairs of points were successfully matched in the image matching. For error elimination, all the matched points were manually checked. Thirty-two pairs of points were excluded as false matches; however, the algorithm of error elimination through LSV determined that 54 pairs of points were error matches, of which 32 pairs were real errors and 22 pairs were not. Although the algorithm has some incorrect judging problems, as some correct matches were judged as errors, it was generally effective in automatic error elimination. Figure 7 shows three sample error matches of the experiment, and Table 5 summarizes the statistics of these error matches.  
Figure 6: Results of image matching between ZY-1-02C and ETM+. (**a**,**c**,**e**,**g**,**i**) show the Digital Orthophoto Maps (DOM) of enhanced thematic mapper (ETM+), while (**b**,**d**,**f**,**h**) show the Level 1 image of ZY-1-02C.
## 5 Conclusions  
A plant disease recognition method that can support an extensible set of diseases is presented in this paper. This method can be implemented merely by the resources of a smart phone that does not have to be connected to a server. A few photographs of plant parts (e.g., less than 25% of the test set) that have been infected by a disease are used for the extraction of invariant features like the number and area of the spots, color histogram features, weather data, etc. The limits (wide and narrow) of these features are used to define disease signatures employed by a fuzzy clustering method that decides which is the most likely disease displayed in the analyzed photograph. The experimental results show that the disease recognition can be achieved with accuracy between 80% and 98% in most of the cases, competing with several popular classification methods. The achieved accuracy depends on the disease, the number and type of training samples used for the definition of the disease signatures and the employed spot-separation method. The most important advantages of the proposed approach are the extensibility of the supported disease signatures, the low complexity owed to the simple features used for the classification and the independence from the orientation, the resolution and the distance of the camera.  
Future work will focus on the support of different plants and diseases. Alternative classification methods like pure fuzzy clustering will also be tested as long as they provide extensibility of the supported set of diseases.
# A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree
###### Abstract  
Due to the development of photovoltaic (PV) technology and the support from governments across the world, the conversion efficiency of solar energy has been improved. However, the PV power output is influenced by environment factors, resulting in features of randomness and intermittency. These features may have a negative influence on power systems. As a result, accurate and timely power prediction data is necessary for power grids to absorb solar energy. In this paper, we propose a new PV power prediction model based on the Gradient Boost Decision Tree (GBDT), which ensembles several binary trees by the gradient boosting ensemble method. The Gradient Boost method builds a strong learner by combining weak learners through iterative methods and the Decision Tree is a basic classification and regression method. As an ensemble machine learning algorithm, the Gradient Boost Decision Tree algorithm can offer higher forecast accuracy than one single learning algorithm. So GBDT is of value in both theoretical research and actual practice in the field of photovoltaic power prediction. The prediction model based on GBDT uses historical weather data and PV power output data to iteratively train the model, which is used to predict the future PV power output based on weather forecast data. Simulation results show that the proposed model based on GBDT has advantages of strong model interpretation, high accuracy, and stable error performance, and thus is of great significance for supporting the secure, stable and economic operation of power systems.  
photovoltaic power prediction; machine learning; Gradient Boost Decision Tree +
Footnote †: journal: Applied Sciences
# Dual-Branch Luminance-Chrominance Attention Network for Hydraulic Concrete Image Enhancement
## 2 Related Work
### Traditional Methods  
The traditional methods can mainly be classified into those based on histogram equalization and those based on Retinex. The principle of histogram equalization entails the uniform dissemination of grayscale values in an initial image, originally confined within a relatively narrow range, across the entire grayscale spectrum [8]. [PERSON] [9] proposed Contrast Limited Adaptive Histogram Equalization (CLAHE). By imposing a clipping limit on the histogram prior to redistributing the intensities, CLAHE ensures that the enhancement process remains constrained within a reasonable range. [PERSON] et al. [10] utilized the Local Difference Representation (LDR) technique to augment the differentiation of neighboring pixel intensities within a two-dimensional histogram. [PERSON] et al. [11] proposed an underwater image enhancement method for defogging based on the underwater dark channel prior, which operates with minimal information loss and incorporates histogram prior distributions.  
Unlike the histogram equalization methods, Retinex theory, introduced by [PERSON] et al. [12], facilitates image enhancement by decomposing images into their illumination and reflectance components. Building on this foundation, [PERSON] et al. [13,14] developed Single-scale Retinex (SSR) and Multi-scale Retinex (MSR) techniques for improving low-light images. SSR is noted for its effective edge enhancement but may result in over-enhancement and subsequent information loss. Conversely, MSR offers advantages in color enhancement, color constancy, and dynamic range compression; however, it faceschallenges in preserving smooth edges and high-frequency details. To address these issues, [PERSON] et al. [15] proposed SSRBF, which integrates SSR with a Bilateral Filter (BF) to mitigate lighting problems and enhance video image quality. [PERSON] et al. [16] introduced a scaled Retinex method incorporating fast mean filtering in the hue-saturation-value (HSV) color space for rapid image enhancement. [PERSON] et al. [17] suggested a fractional-order variation-based Retinex model for enhancing severely underlit images, while [PERSON] et al. [18] presented a semi-decoupled decomposition approach for low-light image enhancement. [PERSON] et al. [19] transformed the Retinex approach into a statistical inference problem by utilizing a bidirectional perceptual similarity method to enhance underexposed images. [PERSON] et al. [20] proposed a composite algorithm for shadow removal from UAV remote sensing images by combining the Retinex algorithm with a two-dimensional gamma function. Nevertheless, when these algorithms are applied to images captured in the complex environment of hydraulic concrete structures, issues of distortion and restoration persist.
# Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency
## 4 Results
### Model Performance  
In this study, a comprehensive evaluation of the overall performance of the models was conducted using accuracy, precision, recall, F1-Score, and ROC curve (AUC value). The landslide dataset, composed of both landslide and non-landslide samples, was divided into 70% for model training and 30% for model testing. Using the time range as the dividing line, the performance of the RF and SVM models on the test set was compared, as detailed in Tables 2-5 and Figure 8.  
Essentially, this study is a binary classification task. The spatial probability analysis of landslide occurrence is a predictive analysis of the spatial scope of landslide occurrence, aiming to solve the problem of where landslides will occur. In this task, landslide areas are regarded as the positive class, while non-landslide areas are regarded as the negative class. Accuracy represents the proportion of correctly classified landslide and non-landslide areas by the model. Precision measures the proportion of areas predicted by the model as landslide areas that actually experience landslides. Recall focuses on the proportion of all areas that actually experience landslides that are successfully predicted by the model, reflecting the model's ability to identify landslides. F1-Score is the harmonic mean of precision and recall, used to comprehensively evaluate the model's performance. A higher F1-Score in this study indicates that the model performs well in both precision and recall.  
Specifically, the RF model outperforms the SVM model in terms of accuracy, precision, and F1-Score across all four time ranges. In terms of recall, the RF model leads the SVM model in the time ranges of 2000-2004 and 2005-2010. Additionally, ROC curves were plotted for the four study time ranges. Consistent with the results shown by the above-mentioned machine learning evaluation metrics, the ROC curves of the RF model are generally higher than those of the SVM model. The AUC values of the RF model lead the SVM model by between 0.4 and 0.8 across all time ranges. Overall, the model accuracy across the four time ranges is higher than 0.7, and the F1-Score and ROC curves both indicate that the models and parameters selected in this study are suitable for the actual situation and exhibit good performance.
## 1 Introduction
### Novelty and Contributions  
To the best of our knowledge, there currently exists no fully integrated, open-source framework that addresses all of these constraints simultaneously. This paper introduces JAVIS, a novel, healthcare-focused deployment framework for on-premises LLM and VLM solutions. Distinguishing itself from existing systems, JAVIS offers the following:  
* **Privacy Compliance:** Unlike OpenLLM or H2O LLM Studio, which rely on external infrastructures or rigid cloud-based workflows, JAVIS enables fully internal deployments, maintaining strict privacy compliance.
* **Customization and Modularity:** JAVIS's architecture supports real-time feedback loops inside private offline networks, customizable components, and dynamic workflows, overcoming the inflexibility seen in other solutions. This flexibility enables hospitals to tailor every aspect, from user interfaces to back-end processing pipelines, to institution-specific requirements.
* **Advanced Scalability and Orchestration:** Through seamless integration with Ray, JAVIS orchestrates multiple replicas of LLMs, VLMs, and supplemental modules (e.g., text-to-speech and multimodal fusion) at scale. This capability extends beyond the static scaling of other frameworks, ensuring that hospitals can efficiently handle increasing workloads, from a single workstation prototype to full hospital-wide deployment.
* **Future-Proof Integration and Maintenance:** While RayLLM introduced certain baseline capabilities, its archival status and lack of a user-friendly interface make it unsuitable for long-term healthcare integration. JAVIS builds upon these core ideas with ongoing support, updated containers, and straightforward maintenance procedures, ensuring sustainability and simplicity over time.  
* **Enhanced Compatibility and Containerization:** JAVIS leverages Docker to ensure uniform execution across diverse environments, including legacy systems. This containerization mitigates compatibility issues and streamlines deployment across varying infrastructures.
* **Seamless Deployment and Scaling:** JAVIS provides effortless transitions from small-scale development environments to full hospital-wide production setups. Through modular container configurations, scaling up or down, adding new LLMs, or switching between development and production modes requires minimal commands and no intrusive reconfigurations.
* **Comprehensive Monitoring and Analytics:** JAVIS integrates detailed analytics dashboards and Ray performance-monitoring tools, offering real-time insights into user interactions, resource utilization, and model performance. This enables administrators to make data-driven decisions for capacity planning, resource allocation, and system optimization.
* **Robust Privacy and Regulatory Compliance:** Operating within a secure, closed internal network, JAVIS adheres to stringent privacy and data protection laws (e.g., Korea's Data 3 Laws), ensuring safe, lawful handling of sensitive medical data. Its privacy-by-design approach aligns with healthcare regulations, providing a trusted foundation for secure LLM/VLM deployments.
# A Software-Defined Radio for Future Wireless Communication Systems at 60 GHz
## 3 Results Discussion
### Back-to-Back Performance
This subjective quality evaluation indicates that the OFDM SDR system is thus very accurate. Figure 18: OFDM transmitted signal power versus digital gain and IF frequency. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**IF Config.**} & \multicolumn{2}{c}{**EVM 1**} & \multicolumn{2}{c}{**EVM Q**} & \multicolumn{2}{c}{**EVM 1Q**} \\ \cline{2-7}  & **[\%]** & **[dB]** & **[\%]** & **[dB]** & **[\%]** & **[dB]** \\ \hline
0 & - & - & - & - & 320.76 & 11.17 \\
2 & 0.85 & \(-41.29\) & 0.77 & \(-42.21\) & \(4.36^{*}\) & \(-27.16^{*}\) \\
4 & 0.84 & \(-41.51\) & 0.78 & \(-42.10\) & 0.87 & \(-41.23\) \\
6 & 0.97 & \(-41.22\) & 0.90 & \(-40.86\) & 0.72 & \(-42.75\) \\ \hline \hline \end{tabular} (\({}^{*}\)) Received level power exceeds the ADC saturation point.  
\end{table} 
Table 5: Summary of the OFDM system performance, in B2B configuration, for various IF values, DAC/ADC channel configurations, and max. digital gain.
## 3 The Proposed Fuzzy Relevance-Based Cluster Head Selection Algorithm
### Cluster Head Selection
#### 3.3.3 Signal Strength  
We denote \(RS(x_{i})\) as the received signal strength of node \(x_{i}\) Typically, the signal strength between the sender and receiver depends on the physical distance between the nodes, and it is shown as \(d_{x_{i},x_{j}}\)[11], where \(d_{x_{i},x_{j}}\) is the distance between the cluster i and the member node j. However, in the real \(ad\)\(hoc\) network, the measured signal strength is ambiguous and inaccurate due to the dynamic mobility. This ambiguity and inaccuracy have a negative effect on the selection of cluster head.  
Here, the signal strength based on FRD is introduced to solve the problems issued from the ambiguity and inaccuracy in the signal strength of member node j with respect to the cluster i. \(FRD_{i}^{\mu}(x_{i})\) represents the relevance degree of the signal strength from the cluster i to member nodes and obtains the relevance information according to the signal strength between the cluster i and member node. The received signal strength function for node \(x_{i}\), \(RS(x_{i})\), is to measure the signal strength ratio of the cluster head and member nodes, which is defined by Equation (4):  
\[RS(x_{i})=10\log_{10}\frac{\sum\limits_{x_{j}\in\{neighborhooded\,of\,x_{j}\}} \prod(x_{j})}{FRD(clusteral\,of\,\,x_{j})} \tag{4}\]
## 5 Formulations for Isolated Piles Undergoing Horizontal Load
### Horizontal Reaction Modulus Model  
The reaction modulus may be related with the soil deformation modulus by the expression (Equation (4)):  
\[K_{h}=A\frac{E}{D} \tag{4}\]  
where: \(E\): soil deformation modulus for the level of deformation considered; \(D\): diameter of the pile; \(A\): constant with value between from 0.8 to 1.5.  
Soil deformation modulus can be obtained from the soil profile based on SPT test. The correlation used in this case is the following Equation (5):  
\[E=1.5\;N_{SPT}+10 \tag{5}\]  
Therefore, the soil profile used is (Table 3):  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Depth**} & \multirow{2}{*}{**Material Type**} & \multirow{2}{*}{**Apparent Density (KN/m3)**} & \multirow{2}{*}{**SPT**} & \multirow{2}{*}{**E MPa**} & \multicolumn{2}{c}{**Horizontal Reaction Modulus MN/m3**} \\ \cline{5-8}  & & & & & **A = 0.8** & **A = 1.5** \\ \hline From 0.0 to & \multirow{2}{*}{Material compacted by layers} & \multirow{2}{*}{20.0} & \multirow{2}{*}{15} & \multirow{2}{*}{32.5} & \multirow{2}{*}{43} & \multirow{2}{*}{80} \\
3.0 m & & & & & & \\ \hline From 3.0 to & \multirow{2}{*}{Material compacted by \(7.0\) m} & \multirow{2}{*}{20.0} & \multirow{2}{*}{20} & \multirow{2}{*}{40} & \multirow{2}{*}{52} & \multirow{2}{*}{98} \\
7.0 m & & & & & & \\ \hline From 7.0 to & \multirow{2}{*}{Material compacted by \(16\) m} & \multirow{2}{*}{20.0} & \multirow{2}{*}{30} & \multirow{2}{*}{55} & \multirow{2}{*}{72} & \multirow{2}{*}{135} \\
16 m & & & & & & \\ \hline From 16 to & \multirow{2}{*}{Deep material compacted by \(20\) m} & \multirow{2}{*}{45} & \multirow{2}{*}{77.5} & \multirow{2}{*}{100} & \multirow{2}{*}{190} \\
20 m & & & & & & \\ \hline Below 20 m & Dense sand. Natural terrain. & 21.0 & 50 & 85 & 110 & 209 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Soil Profile used to determinate the horizontal reaction modulus.  
Figure 9: Graphic of the pile deformation.  
The Figure 10, compares the results between the four tests of the piles and the analytical model. It is observed, again, how the piles test on TP-1 and TP-2 fall within the limits defined by upper and lower boundaries.  
The tests TP-3 and TP-4 are somewhat lower, although, as previously mentioned, it may be considered an acceptable fitting.
# Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach
## 2 Existing Methods for Detection and Identification of Objects in an Image
### Cascade Classifier (Haar-Like Features)  
This classifier is primarily intended for the classification of objects that are stable [10]. As an example, the figure of the human body or human face can be mentioned. The human body and face in most cases have the same proportions, i.e., there are always hands, legs, head, and so on. The drone shape analysis showed that the drone does not fall into such a category of objects. As stated in [16], there are two main reasons why a Haar classifier is used to recognize objects. One is that Haar-like features can effectively describe a region of interest, which is a challenging task with limited training data. If Haar features are compared to raw pixels, they can increase or reduce the variability of data belonging or not into a common class by their properties. Haar features are capable of recognizing and effectively describing the value of the ratio between light and dark parts of the scene. As mentioned in [16], they are also successful in classical computer vision problems, namely scene variability and varying illumination of the scene. The second reason to use this method of classification is its speed, as working with Haar features is considered highly effective.