esa-sceva/satcom-chunk-collection huggingface dataset:
# PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism
## 2 Methods
### Multi-Scale Feature Merging Module  
In order to retain more detailed information about the vessels to improve the final vessel segmentation results, the Multi-scale Feature Merging Module (MFMM) is introduced. The MFMM merges the feature maps obtained at each stage of the decoder so that the final feature maps contain more information about the blood vessels, which, in turn, improves the final blood vessel segmentation accuracy and effectiveness. The MFMM realization step is shown in Equations (5) and (6). In this module, the feature maps of each scale are denoted as \(F_{x}\), and \(\mathbf{x}\) denotes each stage of the decoder. The feature maps at each stage of the decoder have different resolutions and channel numbers. Firstly, it is ensured that the feature maps obtained from each stage have the same number of channels by using a \(1\times 1\) convolutional layer. Then, using bilinear interpolation, they are upsampled to obtain feature maps with the same resolution, denoted as \(F_{x}^{\prime}\). Next, the obtained feature maps are added together to form the final feature map, \(F_{final}\). Finally, a \(1\times 1\) convolutional layer is used to obtain the final segmentation result, as illustrated in Figure 1.  
\[F_{x}^{\prime}=UP(1\times 1 Conv(F_{x}))\quad x=1,2,3 \tag{5}\]  
\[F_{final}=F_{1}^{\prime}+F_{2}^{\prime}+F_{3}^{\prime} \tag{6}\]  
Here, \(1\times 1 Conv()\) denotes the \(1\times 1\) convolution operation, \(UP\) denotes bilinear interpolation upsampling, and \(+\) denotes summation by channel.  
Figure 4: DropBlock_Diagonal schematic diagram. (**a**) represents a color fundus image (local) inputted into the network. (**b**) shows the effect of Dropout, where the activation units are randomly discarded. Figure (**c**) illustrates the schematic when _diag_1_ = 5, _diag_type_ = “primary”, _num_diag_ = 1 for DropBlock_Diagonal and Figure (**d**) illustrates the schematic when _diag_1_ = 5, _diag_type_ = “secondary”, _num_diag_ = 1.  
Figure 5: DropBlock_Diagonal placement. The convolutional block is illustrated in (**a**), DropBlock_Diagonal is specifically added at the location, after the convolutional layer and before BN [40] and Relu [41], as shown in (**b**) (here, Drop_Diag stands for DropBlock_Diagonal).
# Towards Automatic License Plate Recognition in Challenging Conditions
## 2 Related Work
Different versions of YOLO [45; 46; 47], which give state-of-the-art accuracy for object detection, have been published in the last few years. The attempts outlined above are just a few examples of the numerous object detection and recognition algorithms that aim to overcome various LP recognition challenges. The following are a few of the primary reasons that prompted us to create a state-of-the-art license plate recognition algorithm. * Most of the above-described methods and works have been carried out on standard databases that are gathered by researchers at different times under different conditions. Therefore, it prompted us to develop an algorithm that can reliably handle real-life images in real time while maintaining high recognition accuracy. * Our study indicates that the methods, which use RNNs as the OCR, are costly in terms of execution time. Similarly, segmentation-based methods are mostly dependent on segmentation performance and highly susceptible to environmental conditions, such as varying illumination conditions, wild weather, or blurring. Therefore, these methods result in low recognition accuracy in such conditions. Even a strong recognizer, if applied, would produce much lower recognition rates. Therefore, inspired by the aforementioned fact, we aimed to develop a license plate recognition method that could perform well under the scenarios described above. * The PKU dataset, which is also investigated in this study, contains five prominent classes of vehicles on main highways. These categories cover different day times, varying weather conditions, multiple vehicles and license plates per image, occlusions, and crosswalks on the main highways. The scenarios mentioned are from real life, in which the detection and recognition accuracy of any algorithm might be significantly challenged. * Many times, the cameras installed on the main highways of various countries in the world capture vehicle images in which license plates appear at an angle, tilted, or partially obscured. This motivates us to develop a system that could facilitate the traffic control and monitoring staff's ability to reliably recognize any suspicious license plate.
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 5 System Performance Evaluation  
Finally, the performance of the MAC design is evaluated both theoretically and experimentally. The system is analyzed in terms of update frequency, current consumption, accuracy and infrastructure requirements (communication range).
# The Design and FPGA-Based Implementation of a Stream Cipher Based on a Secure Chaotic Generator
## 3 Hardware Implementation of the Proposed SCbSC and Evaluation of Its Performance  
The implementation of the secure chaos-based stream cipher was realized on the PYNQ Z-2 FPGA prototyping board from Xilinx. For implementation, the SCbSC's code was written in VHDL with 32-bit fixed-point data formats, then synthesized, and implemented using the Xilinx Vivado design suite (V.2017.2). Vivado design tools essentially made it possible to carry out the various steps from design to implementation on the target FPGA board. It allows, among other things, description, synthesis, simulation, and implementation of a design, then programming it on a chip from one of the different families of Xilinx FPGAs. In Figure 5, we summarize the different steps of the design flow that were performed under Vivado for the performance evaluation of the proposed SPCNG.  
First, we describe the proposed SPCNG using a hierarchical description containing several modules described in VHDL. Second, the synthesis step checks the VHDL description of the SPCNG, converts it into a gate-level representation, and creates a netlist. Third, we perform a behavioral simulation of the SPCNG to check its validity and make sure that the results obtained X(n) are consistent with those gotten by MATLAB. The simulation was invoked directly by the Xsim simulator integrated into the Vivado tools and the results obtained are displayed in a chronogram (see Figure 6 (Behavioral simulation)). At this step we can assess the statistical performance of the SPCNG. Fourth, the design implementation performs: First, Translate merges the netlists resulting from the design synthesis and the specified constraints file (Xilinx Design Constraint XDC file); then Map fits the design with the available resources of the target FPGA. After that, the Place and Route process places the components and routes them, respecting the constraints specified during the translation, to obtain a configuration file. At this step, we get the maximum frequency and hardware resources summarized in the implementation reports. After the design implementation, we performed the post-implementation timing simulation to get the true timing delay  
Figure 5: FPGA conception flow (under Vivado) of the proposed SPCNG.  
information of the SPCNG as shown in the chronogram of Figure 6 (Post-implementation timing simulation).  
Finally, we generated a programming file (BIT) to program the Xilinx device PYNQ-Z2 FPGA.
## 4 Performance Analysis
### Complexity Analysis  
It is assumed that there are \(M\) nodes in the network, and from Figure 3, clustering in each time slot needs a maximum \(M\) comparisons for one node. That is to say, in each time slot there are maximum \(M\times M\) comparisons, so the complexity of clustering operation is O(\(M^{2}\)). On message forwarding, there are two scenarios to be considered. In the intra-clustering routing, since the Spray and Wait algorithm is adopted, there are limited numbers of moves for each message to be delivered and it needs only one comparison of contact probability for each move. Therefore the complexity of the intra-clustering routing is O(\(N\)) for \(N\) messages. On the other hand, in the inter-clustering routing, according to the algorithm in Figure 5, there are maximum of three comparisons for each move of a message. Like the intra-cluster routing, only limited number of moves are needed to deliver a message, so the complexity of the inter-clustering routing is O(\(N\)) for \(N\) messages. Finally, according to Definition 5, the algorithm needs O(\(N^{2}\)) to perform network coding and decoding for \(N\) messages. Based on the above analysis, the complexity of the protocol at the worst case is O(\(M^{2}+N^{2}\)) for \(M\) nodes and \(N\) messages.
## 4 MultiDS Model  
The main idea of this study is to empower DG-FSTC by introducing multi-level distributional signatures, which indicate rich and hierarchical information. To take full advantage of the power of distributional signatures, in our method, we mainly propose the following two modules.  
* Hierarchical attention generator via multi-level distributional signatures (Section 4.2): We utilize three levels of distributional signatures, which are domain-agnostic, domain-specific, and class-specific, to approximate hierarchical word importance. To denoise distributional signatures and get more accurate attentions, this generator is devised to translate different levels of distributional signatures into word-level attention weights;
* Domain calibrator via domain-specific information (Section 4.3): In addition to accessing word importance, a domain calibrator is applied to calibrate sentence-level feature representations. By generating calibration vectors using domain-related information, this calibrator guides the model to adapt to specific domain distributions.  
Overview: As illustrated in Figure 2, our method is divided into following steps: (1) we construct word embeddings by pretrained language model, BERT, in Section 4.1; (2) we calculate multi-level distributional signatures, and convert them to word-level attentions by hierarchical attention generator in Section 4.2; (3) we calibrate the feature representations by generating calibration vectors from domain-specific distributional signatures in Section 4.3; (4) we apply a ridge regressor as the classifier to categorize query samples in Section 4.4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Explanation** \\ \hline \(N\) & Number of class in an N-way-K-shot task \\ \(K\) & Number of instance for each class in support set \\ \(Q\) & Number of instance for each class in query set \\ \(t\) & An N-way-K-shot task \\ \(t^{s}\) & Support set \\ \(t^{\mu}\) & Query set \\ \(x\) & Instance \\ \(y\) & Label \\ \(\theta\) & Model parameter \\ \(D_{train}\) & Dataset for meta-training \\ \(D_{test}\) & Dataset for meta-testing \\ \(w\) & Word sequence \\ \(\epsilon\) & BERT embedding \\ \(s(\cdot)\) & General word importance matrix \\ \(d(\cdot)\) & Preliminary word weight matrix \\ \(g(\cdot)\) & Hidden state \\ \(att(\cdot)\) & Attention matrix \\ W & Weight \\ \(z\) & Bias \\ \(I\) & Identity matrix \\ \(\tilde{y}\) & One-hot label \\ \(\eta\) & Hyperparameter \\ \(\alpha\), \(\beta\), \(\mu\), \(\phi\), \(\epsilon\) & Learnable parameter \\ \(r\) & Sentence representation \\ \(df\) & Domain feature \\ \(cal\) & Calibration vector \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations and explanations.
# Star Identification Algorithm Based on Dynamic Distance Ratio Matching
## 5 Experiment and Analysis
### Replenish Adjacent Stars  
Using actual star images for testing, the identification results are shown in Figure 13. Here, the red circles represent successfully recognized stars, while the blue circles indicate stars obtained through the neighbor supplementation algorithm after identification was completed. On the left side of the Figure 14, the star distribution is relatively sparse, providing fewer features, which led to unsuccessful identification when directly applying the identification algorithm. However, with the assistance of the recognized stars, multiple star numbers were further supplemented, thereby increasing the overall number of recognized stars.  
To further verify the reliability of these star identification results, the current attitude of the star sensor is calculated based on these star points, and the simulated star image is presented in Figure 14.  
From the simulated star image in Figure 14, it can be observed that the use of the neighbor star supplementation method results in accurate star point identifications, effectively increasing the number of recognized stars. By comparing the relationship between the ideal star point positions and the actual star point positions, this approach provides a possibility of calibrating various parameters of the star sensor. Specifically, the estimated correction for the star image can be made based on the displacement of star point positions, achieving distortion correction.  
Figure 13: Supplemental adjacent stars results.
###### Abstract
Despite growing interest in the effects of thermal processing on the bioactive compounds in carrots, research on the phenolic compound profile remains scarce and inconclusive, indicating variability in the presence and/or degree of degradation of phenolic acids during processing [4,28,29]. Additionally, studies on the levels of specific bioactive compounds often yield ambiguous conclusions, suggesting either their degradation or release as a result of thermal treatments. For example, [PERSON] et al. [17] demonstrated that phenolic compounds were better preserved in vegetables cooked sous-vide than in conventional methods such as boiling or steaming. In contrast, [PERSON] et al. [26] reported a slightly higher total phenolic content in steamed carrots compared to those cooked sous-vide, emphasizing the role of both the method and duration of thermal treatment. On the other hand, [PERSON] and [PERSON] [21] observed a significant increase in the total phenolic content of carrots processed by various methods, including boiling, steaming, pressure cooking, microwaving, and sauteing. However, in all cases this increase was accompanied by a decrease in total flavonoid content. Variations in carotenoid levels were also observed. [PERSON] et al. [4] reported that both steam cooking and sous-vide processing induced significant changes in carotenoid profiles compared to raw samples, generally resulting in an increase in all carotenoids, except lutein, which exhibited similar levels in sous-vide and raw carrots. In contrast, [PERSON] et al. [30] documented significant losses of \(\alpha\)-carotene and \(\beta\)-carotene in carrots subjected to various cooking methods, including boiling, sauteing, pressure cooking, and microwaving. Interestingly, lutein content in their study increased slightly after boiling in water containing 1% NaCl but decreased when other home-cooking methods were applied. Importantly, no studies to date have directly compared the impact of traditional steaming in a steel pot with steaming in a combi oven on the chemical composition of carrot roots. Furthermore, most research has focused on carrot roots that were peeled and cut into cylindrical specimens [4; 26; 28; 29], cubes [21; 22], or slices [20; 24] prior to processing. This approach may result in different quality attributes compared to those observed in whole carrot roots, which remain underexplored. The objective of the present study was to evaluate the effects of different heat treatment methods on whole, unpeeled carrot roots, focusing on their color, total phenolic content, total flavonoid content, phenolic compound and carotenoid profiles, and antioxidant capacity.
# Smart Facility Management System Based on Open BIM and Augmented Reality Technology
## 1 Introduction
### Study Scope and Method  
The main purpose of this study is to improve the application of BIM to the existing FM work and to increase work productivity through the application of AR. To this end, the research proceeded by focusing on operations and maintenance work carried out by the on-site FM crew. In order to apply efficient AR to tasks carried out by the on-site FM crew, the existing maintenance procedures and work processes were analyzed, and a new work process was proposed by separating the task categories to which AR technology could be applied and implemented. Within the proposed process, we defined the property information (attribute data) category required for FM and established COBie datasheet parameters for each category to enable interfacing with BIM data. Through this,it was possible to convert BIM data to AR visualization data without further modification, while manual input for some additionally required information was made available. Such converted AR data was used to build AR-based smart FMS, which supports the on-site FM crew and automates history track record management. Finally, the developed system was evaluated and validated through survey instruments.  
This research adopted a methodology, as outlined in Figure 1, to develop a smart FMS by applying BIM and AR for use in the management of facilities in the public sector. The research procedure followed five major steps: (1) analysis of preceding studies on existing BIM-based FMS and AR technology. At this stage, we w derived ICT technology applicable to existing maintenance processes through a literature review and investigated the current problems and issues of FM; (2) categorization of smart FM work processes to which a COBie system based on BIM and AR would be applied, followed by the definition of COBie data required at each work stage. Information that required AR visualization was mapped from the BIM attribute information to connect BIM and AR; (3) development of a scenario-based Business Process Modeling Notification (BPMN), which is a segmented data exchange process system necessary for the smart FMS prototype. The period proposal process was subdivided, and the information exchange system was schematized; (4) designing the UI for the smart FMS functional implementation method. At this stage, a web-based maintenance AR information DB was constructed, and the UI functions were defined; and (5) development of the AR-based smart FMS prototype, prototype implementation and testing and verification. An experimental control group study was designed and evaluated to implement and verify the prototype system.  
Figure 1: Research content and procedure.
## 5 Discussion  
The factors affecting PRI variation at different spatiotemporal scales were reviewed in this study based on the most recent publications in which PRI has received increasing attention and on the previous publications reviewed by [PERSON] et al. [20]. Our more than 20 years' analysis shows that diurnal changes in PRI were strongly correlated with EPS at both the foliar and canopy levels (Figures 1 and 5) across more vegetation species than those reported in the first study reviewed by [PERSON] et al. [20]. Interestingly, the relationships of seasonal PRI with Chl/Car ratios were stronger than with EPS at both foliar and canopy levels for different species (Figures 1 and 3). Such results indicate the significant impacts of xanthophyll-cycle pigments on short-term changes of PRI, and chlorophyll and carotenoid pool sizes on long-term changes of PRI, in accordance with the reported studies by [PERSON] and [PERSON] [54; 55]. Our analysis thus further supports that taking into account the changes in chlorophyll and carotenoid pool sizes is critical for PRI interpretation on long timescales. Our summarized correlations between PRI and ecophysiological variables linked to RUE (Figures 1-8) show that PRI is a good proxy of photosynthetic efficiency at foliar, canopy and ecosystem levels. The mean \(R^{2}\) values between PRI and RUE at diurnal and seasonal scales from foliar to ecosystem levels in this study (Figure 9) were even higher than in the first study reviewed by [PERSON] et al. [20]. Further, the significant relationships of PRI with water status (relative water content (RWC) and water potential) suggest that PRI also could be appropriate for water stress assessment.  
Nevertheless, the factors complicating PRI interpretation still hinder the use of generalized PRI-RUE relationships. Our summarized review of improvements in PRI implementation found, however, that key factors driving PRI variation, such as the sizes of pigment pools and canopy structure could be minimized or avoided in part to make PRI an efficient indicator of RUE. Such improvement could be possible through subtracting the dark-adapted PRI and introducing the optical signal of pigment pools or the structural-related signal. Moreover, our analysis also showed that combining RUE-related PRI with APAR-related greenness biomass indices (e.g., NDVI, MTCI and EVI; Table 2) efficiently explores GPP (Figure 10), as expected in the previous review by [PERSON] et al. [20]. On the other hand, most reported publications focused on broadleaf and herbaceous and crops during last two decades (Table 1), so expanding the study to a wide range of plant functional types is necessary.  
The strong correlations of PRI with actual photochemical efficiency, especially with RUE and net CO\({}_{2}\) uptake across different plant functional types and spatiotemporal scales, indicate the possibility of assessing photosynthetic activity at larger scales. Besides, MODIS PRI has been increasingly and successfully used to assess ecosystem RUE, allowing us to expect a PRI product in platforms such as the commonly used NDVI.