esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Accuracy Assessment of OBIA Results  
To assess the classification accuracy, we compared the OBIA results to results from visual image interpretation. Reference data were exemplarily created based on three selected images, whereby one image from each Landsat sensor was used (Landsat 8 from 4 December 2016; Landsat 7 from 24 August 2008; Landsat 5 from 27 September 2000). Another criterion for the selection of these images was the presence of a lake close to the landslide area so that the accuracy of both classes could be assessed. Manual digitizing from the satellite imagery was carried out in ArcGIS 10.7 at a scale of 1:25,000. We assessed the classification accuracy based on the overlapping area between the manual and the OBIA mappings. Producer's accuracies were computed by dividing the correctly classified area (overlap area) by the total area of the reference data (i.e., the manual mapping results), and user's accuracies were obtained by dividing the correctly classified area by the total area mapped by OBIA [37; 45; 69]. The producer's accuracy is the map accuracy from the point of view of the creator of the map (here: the producer of the OBIA classification). It is a measure of omission error (error of exclusion) [69]. The user's accuracy is the accuracy from the point of view of a map user, not the map creator. It is a measure of commission error (error of inclusion) [69].
# Antarctic Surface Ice Velocity Retrieval from MODIS-Based Mosaic of Antarctica (MOA)
## 4 Result and Analysis
### Residual in Rock Outcrop  
Mismatches or outliers are identified and removed using a threshold value of SNR, which is calculated by the correlation peak and background in every tracking's computation as the matching confidence indicator. The choice of the threshold is a compromise between removing most of the mismatches and retaining the interesting information at the same time [24]. Assuming that the coregistration of MOA 2004 and MOA 2009 is accurate enough and outcrops are presumed to be stationary in this scale during the observational period, the residual velocity field in the stable rock area can be utilized to determine the appropriate SNR threshold. Figure 5 shows the distributions of velocity measurement's SNR in each drainage and their mean residual speed in rock mask with respect to SNR. The actually thresholds we used in the processing step are shown as vertical dashlines.  
Figure 4: Velocity comparison between MOA-based velocity and manually selected tie points offsets in the (**a**) \(X\) dimension, (**b**) \(Y\) dimension and (**c**) magnitude, and (**d**) direction domains.  
The corresponding thresholds of the SNR filter were detected as the tipping point where residual is going to slash abruptly. Due to various flow characteristics, the climate regime, and the observation conditions such as the illuminating condition and the imaging quality in seven drainages, there are slightly different SNR thresholds in different regions. It is quite clear that the residual speed decreases to zero as the SNR threshold approaches one, both in our result and MEaSUREEs dataset. Furthermore, in most cases, the residual of MEaSUREEs (blue line) exceeds that of our result (red line). Since the random error originating from the stable rock area of MOA-based velocity by feature tracking is in no way better than that from the MEaSUREEs dataset by InSAR methods, this residual difference indicate that the mask area of the Landsat-8 Rock Outcrop over-extends to the non-stationary ice sheet. The only exception occurs in the Wilkes Land Drainage, where the Transantarctic Mountains occupy a large amount of area of Victoria Land, while only a few small-scale inactive ice areas exist. For the same reason, the largest residual also appears in the Wilkes Land drainage for our result. However, for the MEaSUREEs dataset, the largest residual occurs in Amundsen, where that of MOA-based velocity seems to remain invariable. By visual inspection, both overspread rock mask, resulting from snow coverage and from the shadow effect [54] in the ice sheet, and too much data void for the MOA-based velocity in this drainage contribute to this phenomenon. Generally, the residual error in the rock mask is well within 15 m-a\({}^{-1}\) when the SNR is set beyond 0.985. Thus the systemic errors can be removed if the residual was accessed properly in a small basin. However, this value cannot be assigned to a constant in a large division such as our drainage because it is spatially dependent as well.
# FPGA Implementation of a Real-Time Edge Detection System Based on an Improved Canny Algorithm
###### Abstract  
Canny edge detection is one of the most widely used edge detection algorithms due to its superior performance. However, it is a complex, time-consuming process and has a high hardware cost. To overcome these issues, an improved Canny algorithm is proposed in this paper. It uses the Sobel operator and approximation methods to calculate the gradient magnitude and direction for replacing complex operations with reduced hardware costs. [PERSON]'s algorithm is introduced to adaptively determine the image threshold. However, [PERSON]'s algorithm has division operations, and the division operation is complex and has low efficiency and slow speed. We introduce a logarithmic unit to turn the division into a subtraction operation that is easy to implement by hardware but does not affect the selection of the threshold. Experimental results show that the system can detect the edge of the image well without adjusting the threshold value when the external environment changes and requires only 1.231 ms to detect the edges of the 512 \(\times\) 512 image when clocked at 50 MHz. Compared with existing FPGA implementations, our implementation uses the least amount of logical resources. Thus, it is more suitable for platforms that have limited logical resources.  
image processing; Canny edge detection algorithm; FPGA; [PERSON]'s algorithm; logarithm approximation +
Footnote †: journal: Applied Sciences  
10.3390/app13020870
## 3 Federated Kalman Filter Design
### Inertial/Odometer Dead-Reckoning Error Model
#### 3.2.2 Dead-Reckoning Attitude Error Equation  
By analogy to the attitude error equation of strapdown inertial navigation, the attitude error equation of the navigation calculation is deduced below. When the error of the inertial measurement unit calibration term is not considered, the attitude error equation of dead reckoning is shown in Equation (9):  
\[\left\{\begin{array}{c}\dot{\text{\text{\text{\text{\text{\text{\text{\text{ \text{\text{\text{\text{\text  
and \(w(t)\) and \(v(t)\) are set as zero mean Gaussian white noise, that is, to satisfy the condition of Equation (12):  
\[\left\{\begin{array}{l}\mathrm{E}[w(t)]{=0,\;E}\big{[}w(t)w^{T}(t+\Delta t) \big{]}{=}\;\mathrm{Q}\delta(\Delta t)\\ \mathrm{E}[v(t)]{=0,\;E}\big{[}v(t)v^{T}(t+\Delta t)\big{]}{=}\;\mathrm{R} \delta(\Delta t)\\ \mathrm{E}\big{[}w(t)v^{T}(t+\Delta t)\big{]}{=}\;0\end{array}\right. \tag{12}\]  
where \(\mathrm{Q}\in\mathrm{R}^{1\times 1}\) is the variance matrix of system process noise, and \(\mathrm{Q}\in\mathrm{R}^{1\times 1}\) is assumed to be a nonnegative definite matrix; \(\mathrm{R}\in\mathrm{R}^{m\times m}\) is the measurement noise variance matrix of the system, and \(\mathrm{R}\in\mathrm{R}^{m\times m}\) is assumed to be positive definite matrix; and \(\Delta t\) is the sampling interval of system data.  
The basic equation of the standard discrete Kalman filter is as follows:  
\[\mathrm{\widehat{x}_{k/k-1}}{=}\;\mathrm{\Phi_{k/k-1}}\mathrm{\widehat{x}_{k -1}} \tag{13}\]  
\[\mathrm{P_{k/k-1}}{=}\;\mathrm{\Phi_{k/k-1}}\mathrm{P_{k-1}}\mathrm{\Phi_{k/k -1}^{T}}{+}\;\mathrm{G_{k-1}^{w}}\mathrm{Q_{k-1}}\big{(}\mathrm{G_{k-1}^{w}} \big{)}^{T} \tag{14}\]  
\[\mathrm{\widehat{x}_{k}}{=}\;\mathrm{\widehat{x}_{k/k-1}}{+}\;\mathrm{K_{k}} \mathrm{e_{k}} \tag{15}\]  
\[\mathrm{e_{k}}{=}\;\mathrm{y_{k}}-\mathrm{H_{k}}\mathrm{\widehat{x}_{k/k-1}} \tag{16}\]  
\[\mathrm{K_{k}}{=}\;\mathrm{P_{k/k-1}}\mathrm{H_{k}^{T}}\big{(}\mathrm{H_{k}} \mathrm{P_{k/k-1}}\mathrm{H_{k}^{T}}{+}\;\mathrm{R_{k}}\big{)}^{-1} \tag{17}\]  
\[\mathrm{P_{k}}{=}\;(\mathrm{I-K_{k}}\mathrm{H_{k}})\mathrm{P_{k/k-1}}\;( \mathrm{I-K_{k}}\mathrm{H_{k}})^{T}{+}\;\mathrm{K_{k}}\mathrm{R_{k}}\mathrm{K_ {k}^{T}} \tag{18}\]
## 6 Conclusions  
In conclusion, a TA-MCF was proposed by combining the advantages of MCFs and MSFs. Compared with MSF, the TA-MCF proposed herein shows superiorities of an all solid scheme and simplified structure. Meanwhile, numerical simulations showed that it also has the merits of a more effective dispersion management, a broader single-supermode operation region, and a flatter-top optical intensity distribution compared with MCFs. Though optimization of structure parameters, a flattened dispersion six-core TA-MCF with dispersion from \(-1.03\) to \(0.75\) ps/(nm\(\cdot\)km) in the wavelength range 1.52 to 2.01 \(\upmu\)m, cut-off wavelength of 1197 nm, intensity ratio of 0.92, effective mode aera of 48.41 \(\upmu\)m\({}^{2}\), and bending loss less than 0.1 dB/km with bend radius larger than 45 mm at 1550 nm was obtained. To further reduce the bending loss, an extra updoped inclusion was added in the fiber center and a seven-core TA-MCF was presented. The numerical results showed that dispersion varying from \(-0.46\) to \(1.35\) ps/(nm\(\cdot\)km) in the wavelength range 1.50 to 2.04 \(\upmu\)m and bending loss as low as 0.085 dB/km with 35-mm bending radius at 1550 nm was achieved with index differences as low as 0.015 between the matrix and inclusions. The TA-MCFs proposed herein have all the advantages of wideband nearly zero flattened dispersion, single-mode operation, flat-top field distribution, quasi-single material, all solid scheme, and simplified structure, and can be used in long-distance optical fiber communication systems.  
[PERSON] and [PERSON] conceived and wrote the manuscript, and all authors contributed to the completion of the manuscript. This research received no external funding.  
This work was supported by the Open Subject of Jiangsu Key Laboratory of Meteorological Observation and Information Processing (KDXS1107) and College Science Research Program of Hebei Province (BJ2017108), and the Natural Science Foundation of Hebei Province (F2018105036), National Natural Science Foundation of China (61735011), and Natural Science Foundation of Hebei Province (F2016203389, 2015003053).  
The authors declare no conflict of interest.
## 1 Introduction
The present work aims to contribute to filling these gaps by providing tools to estimate the performance ratio of BIPV systems through a methodology that can be universally applied to any location where irradiation, ambient temperature, and wind speed values are available from in situ experimental measurements or online databases and sources. This methodology analyses the dependence of the performance ratio on BIPV module temperature and the angle of incidence of solar irradiance as the two main factors affecting system losses and their sensitivity to all the possible tilt angles and orientations. In addition, this dependence is determined with state-of-the-art models to compute temperature and angular losses. This leads to tools and graphical information that provide _PR_ estimations as a function of the tilt and azimuth angles of the PV modules. The developed graphs can be used as guidelines during the design stage to assess a PV system's performance based on the modules' positioning in the building's envelope. Therefore, the main novelty of the work is to provide direct information on the impact of meteorological conditions on the BIPV performance from available data retrievals (satellite-derived information) and assess it with experimental data on a specific building, which can be extrapolated to different emplacements. The proposed methodology constitutes the first step in evaluating potential BIPV solutions in buildings. Additional analysis of shading in the specific emplacement would complement this information afterwards. The methodology has been applied to a case study in Madrid (Spain), since monitoring data for three in situ BIPV systems are available for a whole year and can be compared to modelled values to assess the results. This paper aims to aid the decision-making process of justifying BIPV envelopes by taking an important step towards better assessing the behaviour of BIPV systems during the design stages of a project, especially regarding the output energy that should be expected from the installation. Contrary to regular PV plants, where PV modules are placed in certain optimal positions, BIPV modules can have any positioning in the building envelope, leading to boundary conditions that are specific to each system. The proposed methodology provides heatmap graphs that can be developed for any desired site to help assess the expected performance ratio for any desired timeframe based on the tilt and azimuth angle of the BIPV system. In addition, the methodology uses free tools and information, meaning that it can easily be replicated by other users for any desired location where the required input data are available.
## 5 Fiber Transmission Performance Results and Discussion
### High-Baud-Rate QAM Transmission Matrix  
After transmitting 16-QAM and 64-QAM signals with various baud rates and different Tx side powers, we summarized the single-carrier signal transmission matrix, using the net data rate and achievable unamplified link distance. As shown in Figure 8, the link distance data points are interpolated at \(4.2\times 10^{-2}\) SD-FEC threshold. The net data rate calculates only the information bits, by excluding the 25% FEC OH. For each combination of baud rate and QAM order, we chose two points of optical power at the Tx side, \(P_{Tx\;Ref}\) and 6-dB more optical power than \(P_{Tx\;Ref}\). For the 64 GBd 16-QAM signals, these conditions provide 12 dB and 18 dB link margin, respectively. From Figure 8 one can see that, for the same modulation format, lowering the baud rate gives an extended reach at the sacrifice of the net data rate. Similarly, for the same baud rate, increasing the QAM order gives a higher net data rate at the expense of the system's reach. For \(P_{Tx\;Ref}\) Tx side optical power, the 45 GBd 16-QAM signal can still achieve 80 km reach in the unamplified link, which provides 288 Gb/s net data rate. For the Tx side with 6-dB more optical power than \(P_{Tx\;Ref}\), the 64 GBd 16-QAM signal can achieve both 80 km transmission and larger than 400 Gb/s net data rate. In such a condition, the 64 GBd 64-QAM signal can produce greater than 600 Gb/s net data rate for an unamplified link as long as 40 km. By comparing the two conditions of optical power at the Tx side, one can see that 6-dB more power can extend the fiber transmission reach to approximately 30 km. Consequently, it is critically important to increase the output power at the Tx side or improve the sensitivity at the Rx side. Increasing the laser's power, reducing the modulation loss, using two separate lasers for Tx and LO are a few potential options.  
Figure 8: Net data rate as a function of transmission distance for 16-QAM and 64-QAM signals at various baud rates.  
Figure 7: BER as a function of transmission distance in unamplified point-to-point link for (**a**) 45 GBd 64-QAM signals; (**b**) 64 GBd 64-QAM signals.
# Smart Contract Vulnerability Detection Based on Hybrid Attention Mechanism Model
## 3 Related Work
### Machine Learning  
In recent years, machine learning has achieved great success in the field of security. Machine learning has high automation and can effectively improve detection efficiency. [PERSON] et al. introduced BiLSTM model [27] and the attention mechanism to detect reentrancy vulnerabilities by capturing control flow information and semantic information in contracts. ContractWard chooses contract opcodes as the model input, and adopts a variety of machine learning algorithms to detect contract vulnerabilities. [PERSON] [49] proposed the concept of vulnerability candidate slices to capture key characteristics of vulnerabilities, focusing on reentrancy and time dependency. In the literature [50], the contract bytecode was first associated with RGB images, the contract bytecode was converted into fixed-size RGB encoded images, and the convolutional neural network was used for automatic feature extraction and learning to detect smart contract vulnerabilities. However, the interpretability of the contract bytecode represented by the RGB image is limited, and the vulnerability detection effect greatly depends on the performance of the neural network. [PERSON] et al. explored the use of the graph neural network (GNN) to detect smart contract vulnerabilities [28]. They constructed a contract graph to represent the syntactic and semantic structures for a smart contract function and designed a degree-free graph convolutional neural network (DB-GCN) and a novel Temporal Messaging Network (TMP). This methodimplements the contract graph generation separately for each vulnerability, but it is difficult to migrate and expand and the detection accuracy is limited. [PERSON] et al. (2019) proposed a deep learning-based scheme to detect vulnerabilities at an opcode level. And they rate smart contract as safe/vulnerable based on probability value for rewarding or penalizing the users who deploy them. But they uses a simple binary vector with length equal to the size of machine instruction list to represent opcodes, there will be large spare vectors when the number of instructions increases.
## 3 Active Guidance Controller Design for IRWs Based on MARL
### MARL-Based Controller Design
#### 3.4.4 Policy and Value Networks  
Each agent in our MADDPG algorithm is equipped with two different neural networks: a policy network and a value network. The policy network is responsible for mapping the dynamically observed state to deterministic actions, while the value network assesses the quality of these actions given the current policy. As Equation (14) defines, the actor (policy) network's input layer corresponds to each agent's local observation vector, comprising seven neurons to match the dimensionality of the observation. Conversely, the input layer of the critic (value) network integrates the global state with all collective actions, resulting in an input dimension of 16.  
The neural network architectures consist of three fully connected internal layers for both the policy and value networks, achieving a balance between capturing the dynamic characteristics of vehicles and computational demands, as detailed in Figure 5. The policy network features 128, 256, and 128 neurons across its three hidden layers and is designed to extract complex dynamic features for generating torque commands. The value network, with hidden layers of 256, 512, and 256 neurons, evaluates the state-action pairs. For both networks, the rectified linear unit (ReLU) activation function is utilized in the hidden layer to introduce nonlinearity and alleviate vanishing gradients. Moreover, the output layers employ the tanh function for normalization. Training is conducted using the adaptive moment estimation (Adam) optimizer, with learning rates of \(10^{-3}\) for the value network and \(10^{-4}\) for the policy network.  
Figure 5: Definitions of the policy and value networks of the MADDPG. (**a**) Policy network, (**b**) Value network.
###### Abstract
It has been reported that ABA can have a positive effect on grape phenolic compounds by increasing the total anthocyanin and total phenolic content in grape berry skin at harvest [20]. ABA may increase low-molecular-weight polyphenols and anthocyanins in final wines [21]. It has been demonstrated that exogenous ABA can modulate transcription factors involved in the phenylpropanol pathway differently to increase the amount of petunidin- and malvidin- type anthocyanins, which improve the skin color of grapes [22]. Phenolic compounds are important for the grape and wine fruit color, astringency, and bittermess [23]. Phenolic compounds in grapes are generally synthesized in the berry and accumulated in the berry skin and seeds. The major phenolics in grapes are tannins, anthocyanins, hydroxycinnamates, flavan-3-ols, flavonoids, and stilbenes/stilbene derivatives [24]. Phenolic compounds are synthesized from the amino acid phenylalanine through the phenylpropanol pathway [25]. Stilbenes, like resveratrol, are produced in grapevine tissues as phytoalexins to mitigate biotic and abiotic stresses and serve to protect the plant from pathogens [26; 27]. Wine volatile compounds are important for the wine aroma and quality. Volatile compounds can be sourced from grapes, fermentation, and the aging process [28]. Grape-derived compounds include pyrazines, terpen, and norisoprenoids. Methoxypyrazines are nitrogenated heterocyclic products of the amino acid metabolism, which originate in the grape berry and are associated with green pepper and herbaceous aromas [29]. Terpenes contribute fruity and floral aromas to wines and are distinguishable between grape varieties [29]. C\({}_{13}\)-norisoprenoids are formed through carotenoid degradation and are present in grape berries as both free-form and nonvolatile glycosides, which can be transformed to the free-form through enzyme or acid hydrolysis [30; 31]. Many norisoprenoids have an extremely low sensory threshold; thus, small changes in concentration can have a large impact on wine aroma. Esters and higher alcohols are predominant fermentation metabolites and are commonly present in high concentrations in wine [28]. In this study, we investigated the impact of exogenous ABA application to vines in situ compared to untreated GRBV-positive vines over the course of a two-year study. Anthocyanins, phenolics, and volatile aroma were evaluated in wines created from GRBV-infectedgrapes with or without ABA applied to the cluster zones. The goal was to determine whether exogenous ABA application could improve berry maturation in GRBV-positive host plants by interfering with normal hormone and metabolite transport, and improve the sugar, flavonoid, and anthocyanin content in the berry.
###### Abstract
The GAN is an unsupervised deep learning model [20], which has good applications in the fields of image generation, image completion, and image quality improvement [21; 22; 23]. The GAN consists of a generator and a discriminator. Some researchers add a local discriminator to improve the brightness of the images [24], but this will make the images lose some colors. [PERSON] et al. extracted and fused multi-scale features under the framework of the GAN [25]. This method has good results on image enhancement, but it lacks image details. There are also some researchers who integrate attention mechanisms and global features into the GAN structure to enhance image quality [26]. In view of the facts that the accuracy of the satellite images 3D reconstruction results depends heavily on the quality of the input images, we use the GAN model to enhance the input images to obtain high-quality satellite images for 3D reconstruction. In this paper, we propose an improved 3D reconstruction method of satellite images based on GAN image enhancement. The contributions of this paper are as follows:  
1. A new improvement method for automatic 3D reconstruction of satellite images is proposed, which can generate high-quality reconstruction results without any ground control points. 2. The perceptual loss function is applied to GAN image enhancement to improve the clarity of satellite images, further improving the quality of 3D reconstruction results of satellite images. This article is organized as follows: The key imaging model and the network used in the proposed method are introduced in Section 2. The framework and details of the proposed method are described in Section 3. We compare our method with other satellite images 3D reconstruction methods and analyze the reconstruction results in Section 4. There are some useful conclusions in Section 5.