esa-sceva/satcom-chunk-collection huggingface dataset:
# AoI Analysis of Satellite-UAV Synergy Real-Time Remote Sensing System
## 2 System Model  
### Scenario  
The satellite-UAV \"multiscale explanation\" synergy real-time sensing scenario is illustrated in Figure 1, which leverages the integrated space-air-ground communication network to provide environmental status information remote sensing and transmission services for military operations, disaster response, relief actions, etc. The satellite, which is recorded as SAT, is dedicated to continuously sensing the status information of a large-scale area, while the UAV focuses on a small-scale area. We assume that there are K GUs in the remote sensing scenario. For brevity, we denote the status information collected by SAT and UAV as \(m_{1}\) and \(m_{2}\), respectively. After each collection, the SAT and UAV immediately transmit the collected information to GUs via broadcast beams. Upon successful transmission of an \(m_{i}\) (\(i\in\{1,2\}\)) to GU \(k\), the SAT and UAV immediately initiate the next transmission. The GUs, within the beam coverage area, perform fusion processing on the latest received status information to generate the fused status information \(m_{0}\). Considering the spectrum constraints in SAGIN, the FFR strategy is employed by UAV and SAT. The spot beam coverage area of SAT and UAV is, respectively, denoted as \(A_{SAT}\) and \(A_{UAV}\) that \(A_{UAV}\subseteq A_{SAT}\). Additionally, note that in this section, the variables are measured in standard units.
# Lightweight Multiscale CNN Model for Wheat Disease Detection
## 2 Materials and Methods
### Model Performance Evaluation Metrics  
For image classification problems, the performance of a model is usually evaluated in five ways: accuracy, prediction rate, recall, F1 score, and confusion matrix. The accuracy is the proportion of the number of examples correctly classified by the model to the total number of examples, and it is a very intuitive evaluation metric. Precision refers to the proportion of samples predicted to be in the positive category that is predicted correctly, reflecting the model's ability to discriminate between negative samples. Recall indicates how many positive cases in the sample were predicted correctly. A higher recall indicates that the model is better at identifying positive cases and misses fewer positive cases. The F1-score reflects the balance of the model in terms of accuracy and recall. A higher F1-score indicates that the model performs better in both aspects. The confusion matrix is a table showing the prediction results of a classification model. It can count the number of samples that the model predicts correctly and incorrectly, as well as the actual and predicted categories of the samples. The confusion matrix can help us analyze the strengths and weaknesses of the model, as well as calculate other evaluation metrics.  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{4}\]  
\[F1-score=\frac{2 TP}{2 TP+FP+FN} \tag{5}\]  
where _TP_ is a positive example of a correct prediction, _FN_ is a positive example of a wrong prediction, _FP_ is a negative example of a wrong prediction, and _TN_ is a negative example of a correct prediction.
## 5 Discussion
### ML/DL Methods Challenges and Solutions  
In the ML method, the feature creation is the major challenge. For this, we used LBP, GLCM, and many other techniques to create the features that would be used for training and testing. Further, data quality is also essential and can be affected by factors like noisy and incorrect data, overfitting, and underfitting. For this, we created non-biased, balanced data that could be further cleaned via the exploratory data analysis (EDA) method. On the other hand, the primary use of CNNs is automatic feature selection. As a result of their immutability for transfer, rotation, and translation, the features learned by CNNs are robust against the variations seen in the same class of data. Therefore, a CNN was chosen as the best classifier model for the bamboo classification system. Since DL works well on large datasets, it is desirable to adopt it. For this, we created a large image dataset so that we obtained a good result for the classification model.  
Furthermore, choosing layers and their parameter values for the CNN architecture is very difficult. Instead of constructing a CNN from scratch and selecting relevant parameters like layers, filters, pooling techniques, and nonlinearity functions, opting for an existing pre-trained CNN network may save a significant amount of time and effort. According to several researchers, another benefit of pre-trained models is that the varied domains of datasets improve the CNN's capacity for generalization. Therefore, with the help of the transfer learning concept, we chose six pre-trained CNN models, AlexNet, VGGNet16, ResNet18, VGGNet19, and GoogleNet, for the implementation of our automated bamboo species classification system. Another problem in DL is overfitting, which happens mostly when the training dataset is limited. Thus, to overcome this problem, we used the most popular technique, data augmentation.  
In many research articles, a CNN is introduced as a black box because it has complex layers of architecture and lacks theoretical support and internal comprehension. CNNs primarily present two problems: first, it is not apparent which weights or patterns can be learned using convolutional kernels; second, the architecture-related parameters are uncertain, including the layer depth, kernel size, and pooling approach. As a result, there has not been much work done to fully comprehend CNNs, although this is currently a popular area of investigation. Despite these restrictions, it is employed in many real-world applications, such as self-driving vehicles, medical image processing, etc.
###### Abstract
With the intention of developing a SPICE compatible model for UTC-PDs, a first compact modeling effort was presented in [11]. This compact model has been validated on type-I heterostructure UTC-PDs. Moreover, it is compatible with the Verilog-A design tools. In a previous study, a validation of the HiCuM/L2 compact model, developed mainly for SiGe HBT, on III-V type I (InGaAs/InP-based) and type II (GaAsSb/InP-based) double heterojunction bipolar transistors (DHBTs) [12; 13] has also been presented in a first attempt to combine the two technological paradigms. Since UTC-PDs and DHBTs could share the  
Figure 1: Schematic view showing future monolithic integration of the Uni-traveling-Carrier photodiodes (UTC-PD) with Double Hetero junction Bipolar Transistors (DHBT) to be used, for instance, in integrated THz transceivers. same InP substrate and possibly base-collector epitaxial heterostructure, this combined modeling approach can pave the way for the design of monolithic integrated OEICs. In continuation of previous works, this study presents improvements of the first UTC-PD compact model. We have introduced more physical equations describing, in particular, the dark current, the intrinsic series resistance and the junction capacitance as well as their dependence on the applied bias. As a consequence, the new model now takes into account more material parameters such as doping, mobility and permittivity. Scaling laws have also been implemented by analyzing the geometry of the UTC-PDs. In addition, new accurate de-embedding methods are proposed to overcome the limitations of conventional methods. A scalable parameter extraction methodology is presented for type I and type II heterostructure UTC-PDs, i.e., InGaAs/InGaAsP and GaInAsSb/InP, respectively, providing very good results over several geometries, bias points, and frequencies. It is important to note that the model validation does not focus primarily on the type of heterostructure but on the design of the UTC-PDs, test structures as well as the frequency used in the study. This paper is organized as follows: Section 2 provides the formulation of the improvements that have been implemented in the new model and presents the parameter extraction methodology. Section 3 reports the compact model validation and the results, including the extraction of the junction capacitances, intrinsic series resistances, current parameters, and the bandwidths. Finally, Section 4 concludes the study and discusses future perspectives.
# Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model
## 2 Materials and Methods  
### Datasets  
In the realm of YOLOv8 wildfire detection, the dataset's quality significantly shapes the outcomes of the training process. We utilized a web-scraping approach to gather images relevant to forest fires, encompassing a diverse range of both typical fire scenarios and non-fire environments, for model training. Additionally, we extracted images depicting ground fires, canopy fires, and other large-scale instances exceeding 322 pixels from downloaded wildfire videos. This meticulous approach ensured the dataset's comprehensiveness and diversity, which are essential for effective model training. Furthermore, some researchers have generously shared fire datasets publicly, such as the BoWFireDataset [23].  
In total, we amassed 2692 images, consisting of 816 photos depicting woodland-fire situations and 1876 photos depicting fire-free wooded-area environments. Subsequently, this collection was used to compile a diverse dataset focusing on forest fires. To ensure a robust evaluation, we allocated 80% of the dataset for training purposes, reserving 10% for validation and an additional 10% for the test set. It is worth noting that the partitioning process was carried out randomly to maintain the diversity and representativeness of the training and validation sets. Examples of traditional forest fire scenes and aerial imagery captured by drones are depicted in Figure 1.
## 3 Results  
### Area 1  
Two antennas (200 and 400 MHz) were tested in Area 1 (Braganca). The GPR prospection reached a depth of 8-9 m but with the occurrence of noise at depths of below approximately 1 m (Figure 5c,d). The AB profile (Figure 5a) revealed an abrupt depression at a depth of up to 4.5 m. A compact layer was also identified near the surface (0.25-0.5 m).  
The BC profile (Figure 5b) revealed the same depression as the AB profile, extending to a depth of 3 m, and the same compacted layer (0.25-0.50 m). In the CD profile (Figure 5c), an abrupt depression was identified at a depth of 2.7 m. This profile was surveyed near a stream (Rio Grande), which runs perpendicularly to study Area 1 and is earmarked as the future source of water for the 12 fish-farming ponds excavated at this site (Figure 3b). The EF profile (Figure 5d) also identified an abrupt depression at a depth of up to 2.16 m. The yellow shading in the profiles shown in Figure 5 indicates the zones of low reflection, in which the electromagnetic wave has a low amplitude, due to the attenuation of the GPR signal, while the other colors indicate the zones with high reflection and high amplitude.  
The apparent conductivity recorded in Area 1 was low, with values ranging from 3.5 to 17 mS.m\({}^{-1}\) (Figure 6). This indicates the possible presence of a clayey subsoil [38]. The saturation of the substrate tends to increase its electrical conductivity, causing attenuation of the electromagnetic signal. The soils of the study area, in the municipality of Braganca (Para), are yellow latosols which are derived from the clayey and sandy-clayey sedimentary substrates of the Barreiras formation [65].  
Figure 5: The GPR profiles (200 MHz antenna with a 200 ns time window) recorded in Area 1 (Braganca): (**a**) AB; (**b**) BC; (**c**) CD; (**d**) EF.  
In Area 1 (Braganca) organic matter was identified, which was derived from the decomposition of vegetation at the site. The sedimentological analysis of the soil detected the presence of three layers, one of clay (top), then silt, and finally sand (bottom). The soil is clayey, with a 40-60% clay content.  
Three Standard Penetration Test (SPT) probes--SP 01 (01\({}^{\circ}\)02.685' S, 46\({}^{\circ}\)42.231' W), SP 02 (01\({}^{\circ}\)02.679' S, 46\({}^{\circ}\)42.228' W), and SP 03 (01\({}^{\circ}\)02.669' S, 46\({}^{\circ}\)42.240' W)--were implemented in study Area 1 (Figure 3b). The depth limits of these probes were between 10.45 m (SP 01, SP 02) and 10.50 m (SP 03). The lithological profiles for each SPT probe are shown in Tables 1 and 2.  
lithology of the different layers of the subsoil in Area 1 (Table 3). The values obtained for these parameters revealed a lithological profile that aligned closely with the findings of the SPT probes in the same area.
## 2 Basic Theory
### Multi-Head Attention  
The Transformer is a novel network architecture centered around an attention mechanism [29]. Initially introduced for natural language processing (NLP) tasks, it has since been extended to other domains, such as computer vision and time series analysis, owing to its superior performance in handling long sequences of data. Unlike the CNNs, the Transformer relies on multi-head attention (MHA) and multi-layer perceptron (MLP) components, which offer distinct advantages in capturing global dependencies.  
Figure 1: The structure of ACGAN.  
The core structure of the Transformer consists of key components, including MHA, MLP, and layer normalization (LN), as illustrated in Figure 2.  
The MHA decomposes the input feature sequence into query (\(Q\)), key (\(K\)), and value (\(V\)) components. It dynamically adjusts feature associations using attention weights, enabling the modeling of global dependencies in sequence data. The calculation formula is expressed as follows:  
\[\text{Attention}(Q,K,V)=\text{softmax}\left(\frac{QK^{\top}}{\sqrt{d_{k}}} \right)V \tag{8}\]  
where \(d_{k}\) is the feature dimension. The multi-head attention mechanism captures multi-level feature associations, thereby enhancing the model's expressive power. Additionally, the MLP performs feature mapping through a combination of linear transformations and nonlinear activation functions, such as GELU. To accelerate training and improve model stability, the Transformer incorporates layer normalization at each layer, ensuring consistency in feature distributions. The residual connection further mitigates the issue of gradient vanishing in deep networks.
## 2 Materials and Methods
### Proposed Solution
#### 2.3.1 Connector Module  
This module offers user-configurable parameters for Primavera P6 data input. This functionality enables users to define the specific Primavera version used for the project schedule. Additionally, users can specify XML tags to be excluded or included during the parsing process. This granular control ensures that the extraction of relevant data points is tailored to the specific project requirements. Following the user configuration and project data upload, a dedicated data preprocessing step is initiated. This step extracts information from the uploaded project data encompassing comprehensive information about the scheduled activities, including the activity name, unique identification code, duration, start and finish dates, predecessor and successor relationships, resource assignments, and user-defined parameters specified during the configuration phase. The extracted data are then transformed into instances of the Optimizio-RCPSP classes. This transformation process essentially converts the raw project data into a structured format that is specifically designed for subsequent analysis within the Optimizio application framework.  
This module extends its functionality by facilitating the import of a clash report generated by Synchro4D in an Excel format. This report contains pre-identified pairs of locations that are deemed incompatible due to potential spatial conflicts arising during the 4D simulation process. The imported clash report data undergo a transformation process to integrate them with the Optimizio model. Each resource identified in the clash report is converted into a corresponding resource class instance containing an additional attribute that stores information regarding all incompatible resources associated with that particular resource. This process creates a mapping between resources and their associated incompatibility constraints, effectively capturing the spatial conflict information within the model. By incorporating this information, the model accounts for potential spatial conflicts that may arise during the construction-planning phase. The verification of these resource incompatibilities will be addressed in the subsequent section, the resource incompatibility verification module.  
In addition to facilitating the retrieval of input data, this module is responsible for exporting the output of the Optimizio solver to Primavera P6, enabling further analysis and integration with downstream processes. The module generates an XML representation of the feasible schedule produced by the Optimizio solver. This output adheres to the Primavera P6 EPPM data format, ensuring seamless interoperability and enhancing usability for project managers and their teams.
## 2 Materials and Methods
### Structure of the Fuzzy Controller
These amplifiers (zero drift) have offset voltage and temperature drift minimization, due to the small measurement signal output from shunt at the minimum current of 1 A (see Figure 4--\(I_{L}\)). The measurement circuit must have an isolated galvanic power supply (e.g., using a push-pull converter consisting of a transformer driver connected to a transformer with a reference voltage). The gain in the measurement circuit should be such that it matches the value of the measured signal to the range of the ADC converter (12-bit SAR type) (see Figure 4). In the case of large changes in the value of the measured current, instead of an operational amplifier a PGA system  
Figure 4: Structure \(AGC\) of the fuzzy controller in the current measurement. with programmable gain PGA or a cascade of PGA circuits with a VGA should be used (see Figure 4). The programmable amplifier has the possibility of voltage gain from \(1\,v/v\) to 2048 V/V. In the case of a measuring shunt with a resistance of 125 \(\mu\Omega\), a current of 1 A is amplified by the input amplifier to a voltage level 0.1 V. Next, this signal is amplified by a programmable amplifier so that it reaches a value equal to 80% FS (4 V at \(V_{ref}\) = 5 V, and full scale (FS) = \(V_{ref}\)) of the ADC converter. For this purpose, the PGA sets the gain at 32 V/V. In this case, the voltage at its output is equal to 3.2 V, which means that the 80% of the \(FS\) range is not used (unused 0.8 V). There are 655 unused transitions (3276 \(-\) 2621 = 655). This implies a loss in the converted signal accuracy. The maximum rate of voltage change is described by the slew rate. The measurement structure is estimated on the basis of the relationship min[\(SR_{OA}\), \(SR_{PGA}\)] (where \(SR_{OA}\) is the slew rate of the input amplifier and \(SR_{PGA}\) is the slew rate of the PGA amplifier). These parameters should not differ from each other, otherwise the system will be determined by the inferior parameter. Using a AGC fuzzy system allows for continuity of amplification and adjustment to the ADC dynamic range with an error resulting from the properties of the elements used for its implementation. The proposed solution is simple to implement and enables the measurement of small values of the current.
## 5 Conclusions  
This research study proposed two categories of feature extraction suite, a very high-resolution suite and an ultra-resolution suite at 2 m and 0.5 m resolutions, respectively, and applied them for the differentiation and mapping of land cover and community-level vegetation types. In spite of a limited number of multi-spectral and panchromatic images available to the research, the ultra-resolution suite showed superior performance over the very high spatial resolution suite in all three sites studied. Achieving a 5.3% increase in the classification accuracy in terms of the F1-score using single-date images and a 4.9% increase in the classification accuracy using bi-seasonal images by the ultra-resolution suite are remarkable contributions. The ultra-resolution mapping suite based on pan-sharpened images and image textural properties has the capacity to capture plant communities cl to the individual canopy level. The image textural information incorporated by the ultra-resolution suite is not available for the very high-resolution suite. The ultra-resolution suite can be suitable for discovering small patches of rare and endangered species which would otherwise be difficult to detect solely from coarse-resolution images. The features extraction suites presented in this research can also be applied with more temporal images. Addition of more temporal images can be useful for retrieving detailed phenological information, and thus can increase the classification accuracy further. The ultra-resolution suite presented in this research is expected to meet the growing need for differentiating a wide variety of land cover and vegetation types, particularly at large regions of interest, irrespective of limited temporal images.  
The WorldView images utilized in this research were provided by JAXA-commissioned research in 2019-2021. Copyright of the WorldView data is inherited to Maxar (formally DigitalGlobe). [PERSON] is appreciated for logistic support to the research. The author is thankful to the anonymous reviewers and editors of the Journal.  
The author declares no conflict of interest.