esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Chitosan-Containing Materials and Composites as Adsorbents
### Magnetically Separable Chitosan Composites
However, after five consecutive adsorption-desorption cycles, the adsorption capacity of Fe\({}_{3}\)O\({}_{4}\)/CS/PEI decreased by 40%. [PERSON] et al. [130] studied the adsorption of long-chain perfluorinated chemicals (PFCs)--namely, perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS)--from water using reduced graphene oxide (rGO)-modified zinc ferrite (ZnFe\({}_{2}\)O\({}_{4}\))-immobilized chitosan beads (rGO-ZF@CB) as an adsorbent. Figure 20: Functionalization of CS with PPIMB [128]. rGO-ZF@CB was prepared in three steps:  
1. ZnFe\({}_{2}\)O\({}_{4}\) (ZF) NPs were synthesized usingthe chemical coprecipitation method;
2. rGO nanosheets were synthesized by using a modified Hummer's method with natural graphite;
3. Magnetic rGO-ZF@CB was synthesized by using a crosslinking reaction with GA (2.5%). Before magnetic bead preparation, rGO nanosheets and ZF NPs were added into the ethanol solution. A suspension of rGO and ZF NPs was mixed with CS solution in acetic acid. Then, this reaction mixture (rGO-ZF@CS) was dripped into 1 M NaOH solution using a syringe needle. The resulting wet magnetic beads, washed with water until reaching neutral pH, were treated with GA, which allowed magnetic rGO-ZF@CB to be obtained. The rGO-ZF@CB possessed a maximum adsorption capacity of 16.07 mg/g for PFOA and 21.64 mg/g for PFOS. Both the electrostatic attractions and hydrophobic interactions drove the removal of PFOA and PFOS using prepared rGO-ZF@CB [130]. Carbon-Fe\({}_{3}\)O\({}_{4}\) (C/Fe\({}_{3}\)O\({}_{4}\)) core-shell NPs modified with CS were synthesized [131] for the removal of MO from aqueous solutions. The C/Fe\({}_{3}\)O\({}_{4}\) NPs were prepared using the hydrothermal method, in which glucose served as a carbon source. The resulting C/Fe\({}_{3}\)O\({}_{4}\) NPs were covered with CS with immunoblot that to the one described above [129], using GA as crosslinking agent. Fast adsorption of MO in the wide pH range of 3-11 using CS-covered C/Fe\({}_{3}\)O\({}_{4}\)NPs was shown, and the maximum adsorption capacity was found to be 425 mg/g at 45 \({}^{\circ}\)C.  
A comparison of different adsorbents is presented in Table 1. As can be seen, there is a number of CS-based composite materials. In spite of this variety, the general trend, allowing for the achievementof high adsorption capacity, is the use of crosslinking agents (preferably covalent crosslinking) and grafting of functional groups, resulting in a noticeable increase in number of adsorption sites.
# An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates  
[PERSON]  
1 Collaborative Innovation Center of Steel Technology, University of Science and Technology, Beijing 100083, China; [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (J.X.)1 Collaborative Innovation Center of Steel Technology, University of Science and Technology, Beijing 100083, China; liuyang_ustb_1988@163.  
simulations. The defect in billets can be detected regardless of the defect position even in the vicinity of the surfaces of a billet. [PERSON] et al. [7] proposed a mathematical morphology detection method based on multi-scale element, which can not only filter the noise effectively, but can also delete the false characteristics of the cracks, scales, and slag. [PERSON] et al. [8] proposed a robust detection method based on the vision attention mechanism and deep learning of feature map in order to relieve the problem of the false and missed detection of casting defects in x-ray detection. The experimental results showed that the false rate and missed rate for the detection of casting defects were less than 4%, and the accuracy of the defect detection was more than 96%. [PERSON] et al. [9] proposed a new semi-supervised learning method based on convolutional auto encoder (CAE) and semi-supervised generative adversarial networks (SGAN) to classify the surface defects of steels. [PERSON] et al. [10] suggested a method for detecting the appearance defect of castings based on a deep residual network. This method divides the casting into multiple regions, preprocesses the image of each region, and then inputs the processed image into the convolutional neural network to extract the features, before finally determining whether the sample has defects.  
All of the above methods effectively solve the problem of recognition accuracy. However, in actual production, the steel production line runs very fast, so the surface defect detection algorithm needs to meet the real-time requirements while ensuring the accuracy.  
In this paper, we propose the multi-block local binary pattern (MB-LBP) algorithm to extract the features of the surface defects of steel plates. The experimental results show that the MB-LBP algorithm meets the requirements of the online detection of steel plate defects in terms of both speed and accuracy.  
The rest of this paper is organized as follows. Section 2 introduces the surface defects of steel plates. Section 3 introduces the principle of MB-LBP. Section 4 introduces the experiments and analysis of surface defect detection based on MB-LBP. Finally, the conclusions are discussed in Section 5.
# A Method for Translating Automotive Body-Related CAN Messages Based on Labeled Bits
## 4 Performance Evaluation
### Strengths and Efficiency  
#### 4.3.1 Strengths  
In this paper, we achieve bit-level inversion of CAN messages related to body behaviors based on the labeled bits of data fields, and the results showed an accuracy level of 99.35%. In the experiments, in addition to the test messages generated using the fuzzing scheme, the packet analysis method was employed to compensate for the reverse results of vehicle behaviors that could not be triggered by the test messages. In addition, the AND bit-by-bit method was used for the valid bits in the final location. On the one hand, this improved the accuracy compared with manual inversion. On the other hand, it does not require a high number of test messages and thus increases the speed of inversion.  
To translate those operations that fuzzing could not identify, we performed a packet analysis of the subject's operation. In the case of braking, for example, the fuzzing method does not work due to the checksum in the data field. Without any other operation, we captured the CAN bus data from the test vehicle, which had brakes that had never been applied to the stepped state. Through the packet analysis, we obtained two sets of valid information. One group had an ID of 0x165 with the corresponding bits 1, 2, 4, 8, and 9, and the other had an ID of 0x415 with the corresponding bits 11 and 12. Considering the CAN bus's arbitration mechanism, we know that ID 0x165 corresponds to the control signal of the brake, while ID 0x415 indicates the status of the brake.  
When undergoing the AND bit-by-bit process, the method described in this paper can translate vehicle body behaviors using just 10 candidate messages. In the case that the number of candidate messages is \(n\), if the probability of any bit in the data field is set to 1 is \(p\), the probability of the irrelevant bit AND bit-by-bit resulting in 0 is \(1-p^{n}\). Then the correct rate at this point is \((1-p^{n})^{m}\) (where \(m\) is the number of irrelevant bits). During our experiments, we calculated that the maximum number of potential body data bits in the data field of 12. However, a more extreme value of 20 bits was used to verify the accuracy, and it was assumed that only 15 bits were valid (which also means that 5 bits are actually not body-related). Since these are independent, we considered the value of P to be 0.5. Figure 9 showed an accuracy of 99.5% when \(n\) was 10 for the assumed extreme case. In actual vehicle experiments, the bit-tagging method in this paper would not result in \(m\) being greater than 5, so the accuracy would be higher in the actual case.  
Figure 9: Accuracy versus candidate messages.
# Fast Gaussian Filter Approximations Comparison on SIMD Computing Platforms
###### Abstract
* We estimate the accuracy using the same notation for all the methods and present their computational complexity calculated in terms of the amount of additive and multiplicative operations. * We compute and provide the parameters for lower-order Deriche and Vliet-Young-Verbeek filters in addition to those that had previously been considered in the original papers, following the authors' methodologies. * We consider quantized versions for the described methods except for the Deriche and Vliet-Young-Verbeek filters and describe the approach to vectorizing all the methods using SIMD extensions. * We conduct experiments on CPUs with x86_64 and ARMv8 architectures and measure the processing time for the methods under consideration. In Section 3, we describe the procedure of Gaussian filtering and its most basic optimizations. In Section 4, we provide a comparative overview of several methods proposed in the literature to approximate the Gaussian filter, and, in Section 5, we compare them in terms of speed and accuracy. Section 6 outlines our implementation of the considered methods. The results of the computational experiments are presented and discussed in Section 7. The paper is concluded in Section 8.
# Biosensors Based on Bivalent and Multivalent Recognition by Nucleic Acid Scaffolds
## 3 Biosensors Based on Multivalent Binding Modes
### Biosensors Based on DNA Nanostructures
In 2020, an important approach to multivalent systems was reported by [PERSON] et al. [129]; namely, they designed various multivalent DNA architectures and applied them to pattern recognition for viral sensing and inhibition. To develop multivalent agents that selectively recognize a specific virus, the authors suggested a strategy based on a precisely designed DNA architecture mimicking the unique spatial patterns of antigens expressed on the surface of the virus. By adopting this strategy, a star-shaped DNA architecture was found to be the best motif for targeting the dengue virus (DENV) (Figure 17, top). A viral sensor was designed based on this star shape; it comprises aptamers at every vertex that recognize each DENV epitope as well as five molecular beacon-like motifs on each side of its inner pentagon structure. This structural design allowed for a \"steric strain-induced distance change\" strategy to be used for fluorescence signal gain upon virus binding (Figure 17, bottom). As a result, the viral sensor constructed from a star-shaped DNA architecture provided high DENV-binding affinity and fluorescence signal enhancement. Additionally,  
Figure 15: Scheme for trivalent binding of DNA nanoscalfolds to cell membranes and the construction of DNA nanoassemblies for fluorescent imaging. Adapted with permission from _J. Am. Chem. Soc._ 2021, 143, 4585-4592, doi:10.1021/jacs.0c11245. Copyright (2021) American Chemical Society [119]. DENV was highly inhibited by the star-shaped DNA architecture. This study clearly shows that precisely designed DNA nanoarchitectures are very powerful scaffolds for the construction of biosensors with enhanced selectivity and sensitivity; consequently, this strategy could be utilized for combating other disease-causing pathogens by generating the requisite ligand patterns on target DNA nanoarchitectures.
## 3 DSM Use Case
### Use Case Description
Actor A controls the energy channel and needs to interact with Actor B in order to obtain information with respect to the users that have a contract with both Actors. Actor A is the one interacting with the consumer in order to invite him/her to participate in a potential DR/DSM program. It is assumed that a consumer application is available for this purpose through which the end-user sends the feedback. For completing the picture, at the customer premises, there is a Customer Energy Management System (CEMS), which is controlled externally by Actor A and further controls the smart device(s). In the presence of regular appliances within the house, a home automation end device is needed, which interacts with the CEMS and the non-smart appliance. Such a device can be a controllable plug or a software capable of controlling when a regular electrical device will be switched on and off. It is assumed that: the home customer has a contract both with Actor A and with Actor B; the data is transmitted without errors; user profile is correct; customer and Actor A can interact correctly with the consumer application. The step by step analysis of the described use case is described in Table 2. In the described steps, alternative scenarios have not been taken into consideration. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Actor Name Selection List** & **Actor Type** & **Actor Description, [18]** & **Further Information Specific to This Use Case** \\ \hline Customer Energy Management System (CEMS) & & Energy management system for energy customers to optimize the utilisation of energy according to supply contracts or other economic targets. Is responsible for gathering flexibilities within the customer premises and providing them to an aggregator, and therefore does not directly participate in flexibility markets. & \\ \hline Smart Device & External Actor & Smart device may be an appliance, generator or storage device (Local storage devices include direct and functional electricity storages such as electrochemical batteries, heat pumps and micro CHP such as fuel cells with heat buffers, air conditioning and cooling devices with thermal inertia, etc.... ). The smart device can receive data directly from the grid, though an interface with the CEM and can react to commands and signals from the grid in an intelligent way. & Smart device may be an appliance, generator or storage device (Local storage devices include direct and functional electricity storages such as electrochemical batteries, heat pumps and micro CHP such as fuel cells with heat buffers, air conditioning and cooling devices with thermal inertia, etc.... ). The smart device can receive data directly from the grid, though an interface with the CEM and can react to commands and signals from the grid in an intelligent way. & Smart device providing additional functionalities enabling consumers to interact with their own environment. \\ \hline Appliance(s) & System & Object device(s) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# EDC-Net: Edge Detection Capsule Network for 3D Point Clouds
## 4 Experimental Results
### Implementation Details  
We trained the EDC-Net model by ADAM optimizer [52] with a learning rate of \(10^{-3}\), momentum of 0.9, and reduced the learning rate by cosine annealing until \(10^{-4}\). Cosine annealing is a learning rate scheduler that has the effect of starting with a large learning rate that is relatively rapidly decreased to a minimum value before being increased rapidly again [53]. Furthermore, we applied weight regularization by a weight-decay equal to \(10^{-5}\) to reduce the overfitting problem in our network. In all experiments, the number of neighbors (k) to build the features graph was either equal to 20 or to 40 when we respectively sampled either 1024 or 2048 points. We have performed three iterations in dynamic routing procedure between capsules. We kept the batch-size as 32 or 16 during training and testing respectively, and did the training for 300 epochs on ABC and 200 epochs on ShapeNet for the weakly-supervised transfer learning experiments.  
All the experiments of this paper were conducted on a system with a single GeForce RTX 2080 Ti GPU and EDC-Net implementation is based on Pytorch library.
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 2 Related Work
### Character Recognition in Different Characters Using YOLO
#### 2.1.2 YOLO for Handwritten Bangla Character Recognition  
This line of research focuses on the recognition of Bangla characters using the BanglaLekha dataset, which comprises 84 distinct classes, including 50 basic characters, 10 digits, and 23 compound characters. A YOLOv5-based convolutional neural network (CNN) model was trained to perform character detection and recognition with high accuracy. The methodology encompasses data preprocessing, model training, and classification. Prior studies have shown substantial progress in Bangla handwriting recognition: ref. [4] achieved 98.8% accuracy in numeral recognition, ref. [5] examined Bangla handwritten characters and showed recognition accuracies of 99.50% for Bangla digits, 93.18% for vowels, 90.00% for consonants, and 92.25% for combined classes, and ref. [6] attained 98% for digits. CNN architectures consistently outperform traditional approaches, and recent developments such as 3D CNNs and residual networks have further enhanced recognition capabilities. Additionally, knowledge distillation techniques have been explored to improve accuracy, particularly for ambiguous or poorly written characters. The overarching goal is to develop robust systems capable of converting handwritten Banglatext into digital form, thereby advancing automation in handwriting recognition and document digitization.
# Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture
## 2 Materials and Methods
### Description of the Base Station Coordinator Node
#### 2.2.3 Communication Module XBee 868 LP of the Base Station  
The module XBee 868 LP that is an important part of the base station node. It is configured in coordination mode through the appropriate programming by sequence of AT commands generated from the microprocessor, and is responsible for implementing the wireless transmission of image data. More specifically, the sequence of steps executed in the request and reception of the images is depicted in Figure 8.  
Figure 7: Sample view of the control and verification console, with check commands received.  
The configuration in AT transparent mode allows obtaining the data stream of the captured image in JPEG format, avoiding the need for a later compression. In this way, the image file is available in a readable format immediately after capture and transmission. Additionally, the XBee configuration in AT mode improves the transmission speed due to the direct transfer of the image data stream.
## 4 Results and Discussion
### Research Trends and Key Findings  
To enhance the discussion on recent research trends and key findings in patent retrieval for prior art and novelty searches, it is crucial to align these insights with the initially set research questions. This approach ensures a structured analysis and understanding of the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Study** & **QC1:** & **QC2:** & **QC3:** & **QC4:** & **QCs:** & **QC6:** & **QC7:** & **Total** & **Normalized** \\ \hline  & **Publication** & **Puneuw** & **Fremework** & **Proof of** & **Dataset** & **Study** & **Citations** & **Future** & & \\  & **Venue** & **Concept** & & **Limitations** & **Clations** & **Directions** & & & \\ \hline
[41] & 1 & 1 & 0.5 & 1 & 0 & 0 & 0 & 3.5 & 46 \\ \hline
[40] & 0 & 1 & 1 & 1 & 0 & 0 & 0.5 & 3.5 & 46 \\ \hline
[64] & 0 & 0.5 & 1 & 1 & 0 & 0 & 1 & 3.5 & 46 \\ \hline
[94] & 0 & 1 & 1 & 1 & 0 & 0 & 0.5 & 3.5 & 46 \\ \hline
[59 evolving landscape in patent information retrieval. By examining the advancements and methodologies developed in recent studies, one can identify the direction in which the field is moving and the potential areas for future investigation. This includes recognizing the adoption of sophisticated machine learning and natural language processing techniques, addressing the challenges posed by complex patent language and diverse document formats, and the increasing need for systems capable of navigating the intricacies of global patent databases.
# Research on Secure Community Opportunity Network Based on Trust Model
###### Abstract
This selection of secure and efficient relay nodes alleviates network congestion, prolongs node lifespan, and enhances data transmission efficiency within the opportunistic network, thereby improving network service quality and user experience. This study contributes as follows:  
1. This study introduces the notion of delineating secure communities grounded in node trust, coupled with a delineated methodology for computing node trust derived from historical interactions. It further advances the segmentation of dynamic secure communities predicated on trust thresholds and trust decay, aiming to fortify the efficacy of community segmentation and facilitate secure data transmissions. 2. We propose a novel routing algorithm that determines next-hop relay nodes by leveraging secure community divisions, node centrality, residual cache capacity, and remaining energy levels. This algorithm enhances data transmission success rates, alleviates network congestion, and prolongs network longevity. 3. Simulation experiments substantiate that the secure community opportunistic routing algorithm, underpinned by the trust model, surpasses conventional algorithms in network data transmission, showcasing a superior transmission success rate and diminished transmission delays.
## 3 Results and Discussion
### Optimizing CLG Efficiency
For the given boundary conditions, FR temperatures are projected to directly correlate with AR temperatures, which means that for the given reduction in AR temperatures from 1050 to 1000 \({}^{\circ}\)C, a corresponding drop in FR temperatures from 928 to 880 \({}^{\circ}\)C entails. This means that when attempting to prevent the ensuing drop in FR temperatures, related to negative effects on volatile and carbon conversion, OC circulation rates have to be increased accordingly as a counter-measure. Although these insights allow for a first glimpse on process optimization approaches, it becomes clear that a detailed consideration of reaction kinetics and reactor hydrodynamics is quintessential, when aiming for a holistic optimization of the CLG process, as both phenomena have a pronounced effect on the process parameters. As it is well known that the conversion of char and other hydrocarbons is kinetically governed [25; 55; 56; 59], the impact of reactor temperature, residence time, and gas concentrations on reaction kinetics need to be established in detail, allowing for accurate predictions of the governing reactions in a realistic environment. Moreover, reactor hydrodynamics are a crucial factor in chemical looping systems [74; 75], making it a pre-requisite to consider them in advanced CLG process models. Through considering these phenomena, it thus becomes feasible to assess to which extent the preceding approaches can be utilized to obtain a CLG process exhibiting not only a high cold gas efficiency, but also excellent carbon capture efficiencies. Nonetheless, the preceding explanations offer valuable insights on the fundamental challenges associated with the autothermal CLG process, which require catering to, when implementing the technology in large scale.