esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
amyloliquefaciens_ has been shown to suppress toxic substances produced by other microorganisms such as _Bacillus cereus_[12]. _B. amyloliquefaciens_ is known in the food processing sector for its ability to hydrolyze compounds and synthesize various enzymes (including thermoresistant enzymes), proteins, and carbohydrates, as well as for its prebiotic and probiotic abilities [13]. Although _B. amyloliquefaciens_ is known in the food industry, its use as a primary organism in fermentation is relatively unexplored. Moreover, the thermoresistant enzymes synthesized by _B. amyloliquefaciens_[14] offer potential benefits in fermented food production, where survival and functionality at higher temperatures are common challenges [15; 16]. Furthermore, while the primary fermentation microbe plays a major role in shaping the characteristics of a fermented food, other microorganisms present in the culture can also impact the final product [9; 10]. For example, although traditional amazake is primarily produced with _A. oryzae_, this microbe is part of a larger microbial community that has been reported on in koji [17] but remains unknown in amazake. Thus, it can be expected that fermented foods produced with other microorganisms such as _B. amyloliquefaciens_ would also support distinct microbial communities; if so, what are they and how might they contribute to the final product? Understanding the contributions of these secondary microorganisms could provide further insights into the fermentation process and the quality of the final product. In this study, we investigated the production of amazake using _A. oryzae_ (W-20) and _B. amyloliquefaciens_ (NCIMB 12077) strains under laboratory conditions to address several questions. (i) Can _B. amyloliquefaciens_ effectively ferment rice to produce amazake? (ii) If so, how does the fermentation processes compare between the two microorganisms? (iii) Are the sensory properties and compositional properties such as the sugar content, enzyme activity, and metabolomic profile of amazake produced by _B. amyloliquefaciens_ comparable to those of _A. oryzae_? (iv) Are the microbial communities in amazake produced by _B. amyloliquefaciens_ and _A. oryzae_ different, and if so, how?
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 5 Experiments with Real Data  
To further verify the effectiveness of the algorithm in this study, the following real-scene measured data were used for the algorithm verification. Here, a DJI M300 (It is manufactured in China by Shenzhen DJI Innovation Technology Co., Shenzhen, China)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Frame** & **20** & **70** & **100** & **123** \\ \hline \multirow{3}{*}{GNSS initial-aided positioning error} & MAE\_x(m) & 0.0223 & 0.0369 & 0.0844 & 0.1111 \\  & MAE\_y(m) & 0.0199 & 0.0681 & 0.0924 & 0.1230 \\  & MAE\_z(m) & 0.0114 & 0.0579 & 0.0833 & 0.1412 \\  & MAE\_x(m) & 0.0245 & 0.0338 & 0.0586 & 0.0560 \\  & MAE\_y(m) & 0.0189 & 0.0651 & 0.0687 & 0.0820 \\  & MAE\_z(m) & 0.0156 & 0.0191 & 0.0501 & 0.0626 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Error statistics for the different frame rates.  
Figure 10: Comparison of the error results.  
aircraft was used, and it was equipped with a Searle camera with five lenses (front, back, left, right, and down) and a high-precision PPK. The location of the collected image data is the north gate of the Zhengzhou University of Aeronautics, and the aircraft flew at a constant speed, collecting a total of 414 * 5 images and outputting the corresponding high-precision GNSS position of each image at the same time.  
In this experiment, the downward-looking lens image was the experimental data, and of the 414 images collected, for the images with high-precision position labeling, Figure 11a shows the trajectory below. The captured images could be 3D-reconstructed for tilt photogrammetry, and the airborne 3D measurement was calculated using Context Capture, as Figure 11b shows.  
In the following algorithm validation, the starting three frames of the image GNSS coordinates were used as the initial constraints, and three frames of the image GNSS coordinates were added as the continuous constraints at every subsequent interval of 35 frames.
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## 2 State of the Art
### Contributions  
While some of the work in Section 2.1 also includes communication from the sensor node back to the generator [5; 16; 18], none of them leveraged backscatter communication to enable even more energy-efficient sensor nodes. Our previous work showed the possibility of fast acoustic backscatter communication but did not employ wireless energy transfer to achieve completely batteryless sensor nodes. Furthermore, our prototype was not implemented energy-efficiently, so we did not investigate energy requirements for the backscatter communication. Moreover, the study was limited to a small, isolated specimen with a maximum distance of 3 m between the reader and tag.  
In this work, to cover larger communication distances in more realistic structures, we employ a method to reduce the reader's self-interference, which arises because the reader does not solely receive the backscattered waveform from the tag but also many other reflections, e.g., from the edges of the structure. When the tag is far away from the reader, the backscattered signal is much weaker than the self-interference, and the reflected signal can not be isolated. To extend communication ranges, the backscattered signal is shifted in frequency to distinguish it from the self-interference. We analyze the potential of WPT over longer distances and design an efficient harvester. Detailed investigation of harvestable power and power consumption of the tag node is provided, showing that WPT can easily exceed the distances reported in prior studies.  
Furthermore, we tackle the challenges linked with real-world implementations on low-cost and low-power Microcontroller Unit (MCU)s. Low-power oscillators on such devices are inaccurate and vary in frequency over time. Moreover, reusing the same transducer for harvesting as for communication introduces another source of interference to the backscatter communication. However, employing equalization for ISI mitigation requires accurate channel estimation and is highly sensitive to such time-varying interferences.
## 2 Materials and Methods
### Experimental Design
#### 2.2.1 Synchronous Measurements of CO\({}_{2}\) and Temperature Curve  
Measurements were collected on attached and healthy leaves of maize and soybean. After a >1 h dark adaptation period, a saturation flash from the PAM fluorometer was used to determine minimal fluorescence (F\({}_{\text{o}}\)) and maximal fluorescence in the dark (F\({}_{\text{m}}\)) of dark-adapted leaves. One minute later, the dark respiration (R\({}_{\text{d}}\), umol m\({}^{-2}\) s\({}^{-1}\)) at the leaf temperature of 25 \({}^{\circ}\)C was recorded by the gas exchange system. Next, the leaves were equilibrated inside the synchronous chamber at a CO\({}_{2}\) concentration of 400 umol mol\({}^{-1}\) and under an illumination of 800 umol m\({}^{-2}\) s\({}^{-1}\) (soybean) and 1200 umol m\({}^{-2}\) s\({}^{-1}\) (maize) for at least 30 min to reach a stable CO\({}_{2}\) assimilation. These two high light intensities were kept constant because the impact of the dark reaction aspects of photosynthesis becomes more marked at high light. For both species, the temperature and the CO\({}_{2}\) response curves were measured for each leaf and repeated for three individual leaves. During the measurements, the gas exchange system was set to a constant flow rate of 500 umol s\({}^{-1}\) and a relative humidity of 50%. For the CO\({}_{2}\) curve, the leaf temperature was set at 25 \({}^{\circ}\)C, and air CO\({}_{2}\) concentrations were manually adjusted as follows (in ppm): 50, 100, 200, 300, 400, 500, 700, 900, 1200; for the temperature curve, the air CO\({}_{2}\) concentration was set at 400 ppm, and leaf temperatures were manually adjusted as follows (in \({}^{\circ}\)C): 15, 20, 25, 30, 35, 40. Through the procedure of determining CO\({}_{2}\) and temperature response curves, spectral measurements (reflected radiance, transmitted radiance, backward-forward fluorescence spectral radiant energy flux) were constantly recorded at 1 s intervals. In this study, we focused on the total fluorescence radiant energy flux emitted from both abaxial and adaxial surfaces of a leaf at one oxygen absorption line frequently used in SIF-related applications, namely, 760 nm (TChIF\({}_{760}\), mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\)). We also estimated the fluorescence quantum yield at 760 nm (\(\Phi_{\text{ChIF},760}\)) from leaf reflectance (\(R\)) and transmittance (\(T\)), which is described in Text S1. Note that TChIF\({}_{760}\) comes from both upper and deeper leaf layers,  
Figure 2: Enlarged views of the leaf chamber and abscisic acid (ABA) treatment with a microcentrifuge tube. and thus it is also affected by the inner leaf structure and leaf pigment content [31]. To better interpret passive ChIP radiance, steady-state fluorescence emission (F\({}_{\text{s}}\)) induced by the measuring beam of the PAM fluorometer was also included in this study. Compared with passive ChIP, F\({}_{\text{s}}\) mainly comes from the upper leaf surface such that it is less likely to be reabsorbed/scattered.
# An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing
## 1 Introduction
Likewise, [PERSON] et al. (2020) suggested a lightweight authentication mechanism for communication in drones, although it is not very secure against advanced attack vectors. [PERSON] et al. (2020) proposed a blockchain-based authentication system for UAVs, which provides strong security assurances but imposes prohibitive delays for real-time use. Notwithstanding these efforts, there is still an important research gap regardingauthentication protocols that, at the same time, meet the ostensibly conflicting demands of strong security, low computational overhead, and low delay for real-time IoD operations. To meet these challenges, this paper introduces a new machine learning-augmented cryptographic protocol for secure user authentication in IoD systems. Our design employs behavioral biometrics and lightweight cryptographic primitives to establish an effective authentication framework that strikes a good balance between computational overhead reduction and security enhancement. The protocol proposed has adaptive security measures that automatically adjust to varying threat levels and usage environments for optimal balance between the strength of security and efficiency of performance. The major contributions of this work are:  
* The design of a lightweight authentication framework based on a hybrid machine learning model that incorporates random forest and LSTM models with comparable security and significantly lower computational demands. * Complete security verification by way of formal verification using AVISPA (Automated Validation of Internet Security Protocols and Applications) and informal security analysis to show immunity to typical attack mechanisms such as replay attacks, man-in-the-middle attacks, and impersonation attacks. * Extensive performance assessment with NS2 simulation, achieving a 43% decrease in computational overhead, 37% reduction in communication latency, and 28% less energy expenditure than current IoD authentication mechanisms. The rest of this paper is structured as follows: Section 2 surveys relevant work on IoD security and authentication protocols. Section 3 explains the system model and an assumption made by our approach and introduces the proposed authentication protocol and its building blocks. Section 4 provides performance evaluation and simulation results. Section 5 gives a discussion of the overall results. Lastly, Section 6 concludes the paper and states future directions of work.
## 2 The Generation of 3D Microstructure Models
### The Generation of RCPS-VM  
In general, a 3D Voronoi diagram is generated by a set of \(n\) random distribution points in 3D space \(R^{3}\), \(Q=\{p_{1},p_{2},\ldots,p_{n}\}\). If the distance between \(p_{i}\) and any other point \(p_{j}\) is denoted as \(d(p_{i},p_{j})\), a Voronoi cell (V-Cell) corresponding to \(p_{i}\) is defined as  
\[v(p_{i})=\Big{\{}p|p\in R^{3},d(p,p_{i})<d(p,p_{j}),j\
eq i\Big{\}}. \tag{1}\]  
where the set of points in 3D space, \(R^{3}\), is composed of points that are closer to the nucleus points \(p_{i}\) than to any other nucleus points in \(Q\). The space is separated by the V-Cells, \(V\) (\(p_{1}\)), \(V\) (\(p_{2}\)),..., \(V\) (\(p_{n}\)), and their boundaries. All V-Cells form a Voronoi diagram, which divides 3D space into an array of convex, space-filling, and non-overlapping polyhedrons.  
In this paper, Voronoi modelling based on random close packing of spheres (RCPS-VM) is used to simulate the microstructure of DP steel. Firstly, RCPS with two groups (corresponding to the two phases in our model) are generated, shown in Figure 1a. Each sphere in RCPS is close-packing with its neighbor sphere, and the distance between each sphere and its every neighbor sphere is basically 2 times the radius of a sphere. Accordingly, RCPS can provide the discreet positional information of every sphere and the point set \(Q\) consisting of all the centers of the spheres for the construction of Voronoi diagram. Secondly, the point set \(Q\) in RCPS is utilised to compute the Voronoi diagram. The expansion frame of every sphere in RCPS serves as a template in the generation of Voronoi diagram, which is mostly filled by RCPS, shown in Figure 1b. A 3D Voronoi diagram is drawn through a 4D convex hull constructed via the Qhull software package [25; 26]. Each V-Cell in the constructed Voronoi diagram represents a grain in the DP steel microstructure. V-Cells originating from the same group of spheres belong to the same phase in the DP steel microstructure, shown in Figure 1c. The Voronoi diagram as a whole is a model of DP steel with equiaxis grains. To facilitate EM finite element simulation and eliminate possible boundary effects caused by the RCPS algorithm, V-cells within the outermost shell (four times the sphere radius in this algorithm) were trimmed off to obtain a regular boundary [15]. As a result, each model consists \(\sim\)6400 effective grains.
# Reinforcement Learning-Based Intent Classification of Chinese Questions About Respiratory Diseases
## 3 Methodology
### CQRD Representation Model
#### 3.2.2 Keyword-Driven GCN  
The main idea of keyword-driven GCN (KD-GCN) is to build a CQRD representation by screening the most important words and utilizing these words to assign different weight values to the adjacency matrix. Likewise, it is expected to learn more task-relevant representations for classification.  
We construct a large-scale heterogeneous text graph comprising word nodes and CQRD nodes to explicitly model global word co-occurrence and facilitate the application of graph convolution. The total number of nodes in the text graph, denoted as \(|V|\), is the sum of the number of CQRDs (corpus size) and the number of unique words (vocabulary size) in the corpus. For simplicity, we initialize the feature matrix \(X=I\) as an identity matrix, meaning each word or document is represented as a one-hot vector, which serves as the input to the text Graph Convolutional Network (GCN). Edges between nodes are established based on two criteria: (1) word occurrence within CQRDs (CQRD-word edges) and (2) word co-occurrence across the entire corpus (word-word edges). To quantify the strength of associations between word nodes, we utilize point-wise mutual information (PMI), a widely recognized metric for measuring word relationships. Our preliminary experiments indicate that employing PMI yields superior results compared to using raw word co-occurrence counts. Additionally, the edge weight between a CQRD node and a word node is determined by whether the word is a keyword, with keywords assigned higher weights and non-keywords assigned relatively lower weights. This weighting strategy ensures that semantically significant words contribute more prominently to the graph structure.  
The specific values are established in accordance with Equation (10). Formally, the weight of the edge between node \(i\) and node \(j\) is defined as follows:  
\[A_{ij}=\left\{\begin{array}{ll}PMI(i,j),&\text{$i$ and $j$ are words and PMI ($i$,$j$) > 0}\\ log(\lambda-W_{xl}),&\text{word is keyword}\\ 1,&\text{$i$ = $j$ or word is not keyword}\\ 0,&\text{otherwise}\end{array}\right. \tag{10}\]  
where \(\lambda\) is the maximum value obtained by \(W_{xl}\) in all words within the current \(l\) category.
# An Image Hashing-Based Authentication and Secure Group Communication Scheme for IoT-Enabled MANETs
## 3 Problem Formalization and System Model  
Our proposed model is an IoT-based reliable authentication approach to provide the authentication and permission of a soldier by using a mobile device. The authentication is conducted at the fog node/NA and the GH. Each soldier acts as a smart node denoted by \(\mathrm{(N)}=\{\mathrm{N1,~{}N2,~{}N3~{}\dots~{}Nn}\}\), where \(\mathrm{n}\) is the total number of soldiers in the network. In our system model, the soldier sends an encrypted message to the GH to join the group. The GH authenticates the received soldier information by comparing the received values with the actual stored information received from the NA. Moreover, when a soldier is transferred from the present group to another group, the request message is forwarded to the GH. On receiving the request, the GH cooperates with a member node and transfers security credentials to the destination group GHs. Then, the soldier sends the group joining request to the new group. The GH of that group matches the security credentials of the soldier with the pre-received information of the soldier. The GH sends approval to the soldiers and permits soldiers to join the group when the security credentials are matched. Otherwise, the GH cannot join this group. Moreover, NA locally stored and processed the security credentials of the soldiers. The information of soldiers is not available, as the fog node request message is forwarded to the private cloud server for the information of a specific soldier. The information of all soldiers is in the permanent storage of a private cloud server. Security is a necessary element of our proposed scheme to protect the network from several security threats. The authentication process of most existing schemes that use image hashing techniques enhances the communication and computational cost while processing the image. As a solution to this, we provide a secure and lightweight image hashing model. In the case an attacker kills a soldier and takes his mobile device to join the group, the attacker cannot join the group until their security credentials are matched with the security credentials of the soldier that are stored in the private cloud server. In our proposed communication model, Ns, GHs, and NA are the main devices. We utilized asymmetric key-based encryption for secure communication. Moreover, each soldier can only communicate with his GH in the network. The GH can communicate with other GHs in the network and with the NA.
# Interactive 3D Vase Design Based on Gradient Boosting Decision Trees
## 2 Algorithm and Principle
### Decision Tree  
Decision trees [27] are some of the commonly used machine learning algorithms for building regression prediction models. The principle is as follows:  
1. Nodes and splits: The decision tree consists of nodes and branches. Each non-leaf node in the tree represents a test on a feature attribute; each branch represents the test result; and each leaf node stores a category label or regression value. The process of constructing a decision tree is to select appropriate feature attributes and test them, dividing the dataset into different subsets until the predetermined stopping conditions are met.
2. Feature selection: At each node, the decision tree algorithm needs to select an optimal feature for splitting to maximize the purity of the node. Generally, the decision tree uses information gain to select features. For the feature set \(a=\{a_{1},a_{2},\cdots,a_{i}\}\) in the dataset, the information gain of the feature \(a_{i}\) represents the degree to which the information of the feature \(a_{i}\) reduces the uncertainty of the target data \(Z\). The expression for calculating the information gain \(g(Z,a_{i})\) of the feature \(a_{i}\) on target data \(Z\) is shown in Equation (1): \[g(Z,a_{i})=H(Z)-H(Z|a_{i}).\] (1)
3. Recursive splitting: The process of constructing a decision tree is recursive. Starting from the root node, select the optimal feature for splitting, and then recursively process each child node until the stopping condition is met, such as if the number of samples in the node is less than a certain threshold, the depth of the tree reaches a predetermined value, or there are no more features to choose from.
4. Pruning: Decision trees are prone to overfitting training data, and to improve generalization ability, pruning of decision trees is necessary. The purpose of pruning is to simplify the model and reduce overfitting. Common pruning methods include pre-pruning and post-pruning. Pre-pruning is the process of stopping the growth of a tree in advance based on certain conditions during the construction of the tree; post-pruning involves first constructing a complete tree and then reducing the complexity of the tree by deleting nodes or subtrees.
5. Classification and regression: Decision trees can be used for classification and regression problems. In classification problems, each leaf node represents a category; in regression problems, leaf nodes store a numerical value.
# An Effective Wide-Bandwidth Channel Access in Next-Generation WLAN-Based V2X Communications
## 3 Proposed Scheme and Its Variants  
### Proposed Channel Access Scheme for 20-MHz Transmission  
A channel access scheme for 20-MHz frame transmission should ensure fairness with stations using an extended 10-MHz secondary channel, with the definitions of the primary channel and secondary channel as in [14]. In a BSS environment, the primary channel is a main channel used by all stations, and the secondary channels can be other BSSs' primary channels, which may be geographically separated where adjacent channel interference and channel extension fairness problems are not very serious.  
However, in the OCB environment, each channel is equally important, with its own purpose. Stations using other channels cannot be geographically separated owing to the moving nature of vehicles. Therefore, adjacent channel interference and channel extension fairness problems are taken into account more seriously and carefully in an OCB environment (e.g., IEEE 802.11p and IEEE 802.11 bd) than a BSS environment (e.g., IEEE 802.11n, IEEE 802.11 ac, and IEEE 802.11 ax). In the design  
Figure 3: Application of conventional channel-extension scheme to 20-MHz channel access.  
of the 20-MHz channel access scheme, fairness and adjacent interference avoidance are considered, and a minimized channel-access delay must be provided. In the proposed scheme, while fairness with stations operating in an extended 10-MHz channel is ensured, minimized channel-access delay is provided by preventing overprotection caused by dynamic sensing methods.