esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hardware Implementation
### ECG R-Peak Detection Engine
#### 3.2.3 Positive Zero-Crossing (PZC) Section Unit  
This unit performs the following steps to detect the R-peaks from the Hilbert filter output: First, it compares the sign bit of the current and the previous filter output. A sign bit change from 1 to 0 indicates a positive zero-crossing point, which is considered a potential R-peak. Second, it calculates the memory address of the potential R-peak by subtracting the computation delay incurred during the R-peak detection process. Third, it searches for the maximum value within a window of \(\pm\)25 samples around the potential R-peak address and updates it as the new R-peak. Fourth, it compares the candidate R-R interval, which is the time difference between two consecutive R-peaks, with the normal heart rate range. If the candidate R-R interval is outside the normal range, the R-peak detection is deemed erroneous. Finally, it provides the R-R interval as an input for the linear classifier.
# A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images
## 4 Conclusions  
The GEO remote sensing satellite has wide application prospects in sea surveillance over a wide area, and the GF-4 satellite has the highest spatial resolution in GEO satellites. In this paper, a ship-tracking algorithm applied to the GF-4 satellite is proposed, which is designed based on ship detection, position correction and ship-tracking three-step processing. A simple saliency method based on PSNR is designed for ship detection. Ship positioning accuracy is improved by RPCs model using AIS data as dynamic GCPs. Finally, ship tracking and motion parameter estimation in geographic coordinate are realized using the MHT tracker aided by amplitude information. The tracking performance is analyzed using GF-4 sequential images by calculation of recall, precision and so on. The experiment results show that the proposed algorithm has better performance with high detection rates, low false alarm rate, high association rate and high tracking precision. However, there are still potential developments to improve the accuracy of detection and tracking, especially for small ships. Using information (such as ships' spectral and wake information), tracking before detection (TBD) and machine learning methods will be pursued in our future work.  
The proposed algorithm also has potential applications in other research areas, such as target tracking in small satellite videos. As a single sensor cannot solve all problems in space-borne maritime surveillance, multi-sensor information fusion based on GEO medium resolution remote sensing satellites, LEO high-resolution remote sensing satellites, AIS satellites and signals intelligence (SIGINT) satellites, electronic intelligence (ELINT) satellites and other assets is the trend of future development. It is also challenging work worth studying.  
All the listed authors contributed to the research design. [PERSON] and [PERSON] conceived and designed the algorithm and experiment, collected the data, wrote the source code and the paper together. [PERSON] provided the instructive suggestions on the entire algorithm and experiment, revised the entire paper.  
This work is supported by the National Science Foundation of China (Nos. 91538201). The authors thank China Center for Resources Satellite Data and Application for providing GF-4 data sets.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Determination of Lignin Hydrophobicity by Rose Bengal  
Hydrophobic interactions are considered to be the primary driving force behind the non-productive adsorption process of both lignin and cellulase. The hydrophobic properties of the lignin surface of ground wood were assessed using the Rose Bengal dye method to examine the alterations in hydrophobic interactions between lignin and cellulase prior to and following ozone pretreatment. It was found that ozone pretreatment resulted in a significant reduction in the hydrophobicity of MWL. Additionally, the surface hydrophobicity of OL 60 and OL 120 decreased from 0.951 L/g to 0.889 L/g and 0.615 L/g, respectively, representing a decrease of 6.5% and 35.3% (Table 4). This reduction can be attributed primarily to the ozone pretreatment, which induced the formation of hydrophilic groups such as carboxyl, carbonyl, p-coumarate, and ferulate, in lignin. Additionally, ozone's demethylation of methoxyl groups on the lignin benzene ring contributed to this reduction. These findings align with the characterization conducted using \({}^{31}\)P-NMR and HSQC-NMR techniques. Consequently, the decrease in the hydrophobic properties of the lignin surface led to a reduction in its binding affinity to cellulase, as reported by [PERSON] et al. [39]. This subsequently minimized the ineffective adsorption of cellulase onto lignin [40].
# Adapting the GACT-X Aligner to Accelerate Minimap2 in an FPGA Cloud Instance
## 1 Introduction
All of these culminate into the objective of this work, which is to infer whether it is possible to accelerate Minimap2's extending step on a cloud FPGA design, given the memory challenges bound to long reads and the limitations of implementing memory-based algorithms on FPGA clouds. This work proposes an adaptation of the GACT-X long-read alignment architecture to achieve its best performance as a substitute for Minimap2's SK alignment in software. As a first contribution, a thorough profiling of Minimap2 with simulated and real human long-read datasets was carried out; by focusing on the throughput, observations were made to validate the use of the datasets and to confirm the algorithm's bottlenecks. Then, a series of tests were performed with the same datasets to obtain an optimized version of GACT-X; in order to validate its use when adapted to long-read processing, the performance and accuracy were compared to those of Minimap2's SK algorithm running as software regarding. This work also contributes to a full integration of Minimap2 and the adapted GACT-X's host on an AWS f1.2 xlarge instance, with one to two FPGA kernels and one to eight software threads. The results are presented regarding processing speed and acceleration in comparison to those of Minimap2 in software, allowing inferences from the measurements to further improve the performance. Finally, the results are analyzed against the observations presented in [32] about implementing applications in virtualized FPGA environments.
# Routing Schemes in FANETs: A Survey
## 5 Routing Schemes in FANETs
### Proactive
#### 5.2.1 Enhancing Routing Performance Using Shortest Route  
[PERSON] et al. [112] propose a proactive routing scheme for nodes equipped with directional antennas to find the shortest route, which has the least possible number of nodes in a route while reducing routing overhead. The main objectives are to enhance network coverage (O.2) as well as to enhance routing performance and QoS (O.3), in FANETs. The routing scheme addresses two challenges, namely high dynamicity (X.1) of network topology and cost (X.2), particularly routing overhead. The routing scheme is designed for hop-by-hop routing (C.1.1) approach.  
During the neighbor and route discoveries stage, each node broadcasts hello messages to one-hop neighboring nodes to form a neighbor set comprised of two-hop neighboring nodes. The hello messages are not forwarded to reduce cost, particularly routing overhead and energy consumption. During the route selection stage, each node selects a next-hop node by using mobility metrics (M.1) (i.e., the relative speed of nodes), whereby neighbor nodes with similar mobility metrics are selected. The farthest node is selected in case of more than a single potential next-hop node in order to reduce the number of hops of a route, which helps to reduce the end-to-end delay of packet transmission. During the route maintenance stage, link disconnections are detected by the absence of the periodic hello messages. The intermediate node sends a link failure notification message to the source node in order to initiate a route discovery mechanism [110].  
The routing scheme is compared with a state-of-the-art scheme that aims to provide lower end-to-end delay. Random waypoint (A.2) and semi-random circular movement (A.5) mobility models are used in simulation. The routing scheme has been shown to increase QoS (P.1) (i.e., higher throughput, as well as lower end-to-end delay and routing overhead).
## 4 Results
### Current Model Validation  
In this section, model validation was performed by means of four tests: (1) proper limits, (2) structure verification, (3) dimensional consistency and (4) extreme condition.  
According to [36], the first test should consider if the current model meets the purpose for which it was created. In this case study, the main objectives determined were cost reduction and distribution time. The proposed model considered cost indicators (vehicle maintenance, tire wearing, tolls, salary and diesel), as well as time (route and hours worked to complete delivery). The second test verified the model structure. In this test, the current system structure must be compared with the existing model, as shown in Figure 5.  
The dimensional consistency test, according to [36], seeks to assess and analyze every mathematical equation and model parameter. Stella(r) Architect, provides a tool to conduct the vehicle unit tests. A revision of the model vehicle units was performed considering 251 equations and 410 variables, finding 100% consistency. Finally, the extreme condition test was carried out with the aim of determining whether the model operated logically when the parameter values drastically changed. The change made to this test was the lack of demand; by eliminating the demand, the model should not reflect any behavior, since it is the demand that \"pulls\" the entire system--that is, the values must be zero. As both wholesale and retail demand were modified to 0 (zero), the model behaved as expected; i.e., no movement indication was observed in the flows. Thus, the structural tests conducted validated the developed model.  
Figure 5: Comparing the process activities under study with the simulation model. (1) Delivery order receipt; (2) delivery road truck; (3) transport customer product; (4) customer product delivery.
# Transfer Learning Strategies for Deep Learning-based PHM Algorithms
## 2 Deep Learning PHM Algorithm Transfer Scenario  
When transferring a deep learning PHM algorithm, it is necessary to specify the transfer scenario. The similarity between the target domain and the source domain and the amount of data in the target domain are important indicators for the choice of transfer method. Therefore, according to the similarity of data and the size of data set in the target domain, the transfer scenarios of deep learning PHM algorithm are divided into four categories. Figure 1 shows the four types of scenarios for deep learning PHM algorithm transfer.  
In scenario I (S1), the target domain data set is large and has a high degree of similarity with the source domain data, which means that the weights of network obtained from the training of the source domain data can be retained as far as possible. On this basis, using the target domain data for very few batches of training can obtain a satisfactory deep learning PHM algorithm in the target domain.  
The high similarity between target domain data and source domain data in scenario II (S2) makes the transfer process similar to S1, but fewer target domain data require more batches of training in order to obtain a satisfactory deep learning PHM algorithm for the target domain. The fault diagnosis task of the same rotating bearing under different loads can be regarded as a concrete expression of S1 and S2. According to the amount of data collected under different loads, the S1 or S2 can be specifically distinguished.  
In scenario III (S3), the data set of target domain is small so that it is difficult to achieve the desired diagnostic goal if the network trained directly in the target domain. We transfer as many weights of the network trained in the source domain as possible and fine-tune with a small amount of data in the target domain to let the new network acquire more information of source domain easily so that the desired diagnostic goal can more probably be achieved.  
In scenario IV (S4), the target domain data set is large enough. In this scenario, continuing to use the network weights gained in source domain may make the whole network weights fall into a local optimum. For this scenario, we only transfer the network structure, and initialization weights retraining will be more reasonable. Fault diagnosis tasks of different types of rotary bearings under the same working condition can be considered as the concrete manifestation of S3 and S4. According to the amount of data collected in the target domain, S3 or S4 can be specifically distinguished.  
Figure 1: Deep learning PHM algorithm transfer scenario map.
## 3 Results and Discussion
### Antioxidant Properties of SEWS
These observations might be due to the compositional differences in the two products. The SEWS contained a higher amount of total phenols and approximately three-fold higher total flavonoids than the rapeseed meal ethanol-wash solute [12]. Both groups of plant secondary metabolites, phenols, and flavonoids, are potent antioxidants being able to prevent biomolecules including fats, proteins, and DNA from damaging [1]. Another difference that should be noted is in the relative content of phenolic acids and their major constituents, namely chlorogenic and sinapic acid being either present or not in the products. Differences in the phenolic profile of SEWS and rapeseed meal ethanol wash-solute were previously identified by HPLC analyzes as well [69]. Although phenols and flavonoids are the antioxidant compounds in the highest amounts in the SEWS, there might be other molecules in the mixture such as trace elements and proteins that might contribute to the overall antioxidant capacity. The cytosolic defensive ability of copper, zinc, and selenium against reactive oxygen and nitrogen species is well documented [69; 70; 71]. Proteins and especially peptides, a product of the hydrolytic cleavage of the proteins, are considered to have high potential as food antioxidants. These molecules inhibit lipid oxidation by neutralization of free radicals, reactive oxygen species, or chelation of pro-oxidative transition metals [72]. The antioxidant properties of the SEWS were analyzed by using three methods differing by their principles to better evaluate the potential applicability of the product. The experiments were conducted in water and ethanol with a clear understanding of the influence of the solvent on the antioxidant capacity of the sample [73; 74] and plausible underestimation of the evaluated features. It is highly possible to obtain higher antioxidant activities in other solvents. However, water and ethanol are largely used in the food and nutraceutical industries, and obtained knowledge could be easily transferred to new technologies. In addition, ethanol is generally recognized as safe (GRAS) [75] and is recently reconsidered as a useful extracting agent in \"green\" technologies [33; 46; 47]. Due to the complexity of the SEWS composition, in vivo model systems could be used to complement  
Figure 3: Hydroxyl radical scavenging activity of sunflower meal ethanol-wash solute dissolved in water. \({}^{\text{a-f}}\) Means of one sample with different concentrations without a common letter differ significantly (\(p<0.05\)). \({}^{\text{A,B}}\) Means of the two samples with the same concentration without a common letter differ significantly (\(p<0.05\)). Standard deviations (SD) of the means varied from 0.27 to 1.98.  
the acquired knowledge and more completely characterize the antioxidant properties of the product [76; 77].
## 2 Materials and Methods
### Imagery and Climate Data Collection  
#### 2.2.1 Acquisition of UAV Imagery  
A total of 12 soybean field scenes were taken under natural daylight conditions. All imagery was captured during the early stage of soybean growth (emergence stage) from 11 days after sowing (DAS) until 22 DAS (Table 1). The image data sets were captured using a commercial UAV (Phantom 4 Pro, DJI, Shenzhen, China) equipped with a default RGB camera capturing 5472 \(\times\) 3648 pixel images for each scene. The number of scenes was different for each field depending on the area coverage. Images were captured from 15 m above the surface, resulting in a ground sampling distance of approximately 3.5 mm per pixel. The trajectory of the UAV was set to ensure a 60-70% front and side image overlap. The coordinates of ground control points (GCPs) for registering the camera location were measured using KlauPPK (Klau Geomatics, New South Wales, Australia) with 0.03 m precision. Using this georeferencing process, we could minimize the geographical error regarding the location of each soybean stand measured in the following procedure (Section 2.3). The orthomosaic for each field was generated using Structure from Motion (SfM) software (Pix4D, Pix4 Dmapper version 4.6.4, Lausanne, Switzerland). The average pixel size per field of the orthomosaics was 61,036 \(\times\) 28,113 pixels.
## 3 Results and Discussion
### Changes in Soil Cu
#### 3.3.2 Changes of Cu Bioavailability  
Cu bioavailability, estimated by CaCl\({}_{2}\) extraction [69], was highest in the acid conventional soil (0.55 mg kg\({}^{-1}\)), followed by the acid organic soil (0.41 mg kg\({}^{-1}\)) and calcareous organic soil (0.32 mg kg\({}^{-1}\)), and was lowest in the calcareous conventional soil (0.12 mg kg\({}^{-1}\)) (Figure 3, Table S3). These values are certainly a consequence of soil pH,but the quantity of DOC seems to also play a crucial role. In fact, in soils before incubation, whatever the soil management, concentrations of bioavailable Cu are correlated to the concentration of DOC (Table 1). This behavior is not confirmed after 6 months' incubation, because no clear trend was found between bioavailable Cu and DOC after induced climate stresses; in fact, correlation was not significant (Figure S3). Bioavailable Cu is influenced by several factors; two of them are DOC and pH, which behaved in opposite ways after climate stress.  
Cu bioavailability was in our samples below 0.66% of the total Cu in the soil. Similar to Cu leaching, Cu bioavailability also decreased after incubation (climate change simulations), with the exception of the acidic conventional soil. Here, the bioavailable Cu concentration almost doubled (from 0.55 mg kg\({}^{-1}\) to 0.95 mg kg\({}^{-1}\)) after 6 months of simulating high temperature. We found that the Cu bioavailability from soils in a CaCl\({}_{2}\) extraction solution was affected by pH, as the Pearson coefficient was 0.86 for all soil samples (Figure S3). Another soil parameter negatively correlated to Cu bioavailability is TN, whereas the correlation of TOC was not significant. Bioavailable Cu was strongly correlated to Cu extracted in Step 1 and in Step 3.
# Towards the Detection of GPS Spoofing Attacks against Drones by Analyzing Camera's Video Stream
## 1 Introduction
(2) We develop a simulator that allows us to simulate a drone hovering over a Google Earth map, allowing us to simulate flights in different conditions (various altitudes, terrains, speeds, and level of ambient light) and locations. The code of the simulator has been uploaded to GitHub [6]. The remainder of this article is organized as follows. We provide an overview of related work in Section 2. The proposed method is described in Section 3. In Section 4, we discuss the analysis performed in our simulation environment. The results of our real-world evaluation are presented in Section 5. In Section 6, we present the method's limitations. In Section 7, we discuss our plans for future work. Our article concludes in Section 8, where we summarize our findings.
###### Abstract  
This study aims to address the need for design guidelines in developing a cultural-heritage-based metaverse educational system. Using the UTAUT, the TTF model, and Flow Theory, a theoretical framework is constructed. Through qualitative research based on the GT, three user perception factors--presence, interactivity, and narrativity--are introduced as external variables to explore the relationship between these factors and users' willingness to adopt the cultural heritage metaverse system. The study examines this relationship from the dual perspectives of user perception and technology acceptance. A scale was designed to test the theoretical model empirically, and 298 valid responses were collected through a structured process involving GT coding, pre-testing, and formal surveys. The findings indicate that interactivity, narrativity, and presence significantly enhance the flow experience, while factors such as performance expectancy, effort expectancy, social influence, facilitating conditions, technology-task fit, and flow positively influence users' intention to adopt the system. Among these, technology-task fit emerged as the most influential factor. This integrated approach reduces subjectivity and bias in criteria determination, enhancing the objectivity and precision of cultural heritage metaverse system assessments and making the system more responsive to user needs.  
cultural education; metaverse; Unified Theory of Acceptance and Use of Technology (UTAUT); technology-task fit (TTF); Flow Theory; intention to use +
Footnote †: journal: Applied sciences
# NOx Formation Mechanism and Emission Prediction in Turbulent Combustion: A Review
## 3 NOx Formation-Kinetics Approach  
The mechanism utilization for NOx formation could be divided into two categories: the simplified mechanisms and the detailed mechanisms.