esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Antioxidant Activity
The radical scavenging activity of NEO was significantly stronger (\(p<0.001\)) than that of the alpha-tocopherol (IC50: 0.142 \(\pm\) 0.011 mg/mL) and BHA (0.086 \(\pm\) 0.006 mg/mL). Only BHT exhibits a significantly (\(p<0.001\)) better antioxidant activity than NEO (IC50: 0.013 \(\pm\) 0.009 mg/mL) (Table 2). Moreover, the relative antioxidant activity percentage (RAA %) of NEO (92.31 \(\pm\) 0.17%) in the \(\beta\)-carotene-linoleic acid assay was comparable to that of BHT (100%) (Table 2). The RAA% of NEO was determined with the following equation: RAA = A\({}_{\text{NEO}}\)/A\({}_{\text{BHT}}\), where A\({}_{\text{BHT}}\) is the absorption of BHT (positive control used) and A\({}_{\text{NEO}}\) is the absorption of NEO. To the best of our knowledge, this is the first study regarding NEO antioxidant activity. The present results are in disaccordance with previous studies that report weak antioxidant activity of EOs isolated from other _Nepeta_ species [32; 33; 34; 35]. Generally, _Nepeta_ species EOs are rich in repetalactone isomers known to be weak antioxidants [32; 36]. However, significant antioxidant activity was recorded in the case of _N. muda_ subsp. _muda_ oil, with 4a\(\beta\),7a\(\gamma\),7a\(\beta\)-repetalactone and 1,8-cineole as main components. In this case, the antioxidant activity was attributed to the relatively high presence of 1,8-cineole in the oil composition [37]. The presence of elemol (23,23%) in our oil composition, a sesquiterpene recognized for its antioxidant capacity [38], can partially explain the strong antioxidant capacity  
Figure 1: Antioxidant activity of samples treated with NEO, butylated hydroxyanisole (BHA), and butylated hydroxytoluene (BHT) in terms of peroxide values (PVs) (**a**) and thiobarbituric acid (TBA) values (**b**) of cold-pressed sunflowers oil during 24 days of the storage period. recorded. Moreover, the literature survey revealed that \(\alpha\)-pinene [39], beta-pinene [40], 1,8-cineole [37], cryophyllene [38], and cryophyllene oxide [40], minor compounds of our oil, possess a powerful antioxidant capacity. Based on these considerations, the NEO's shown antioxidant capacity might be associated with their terpenoid compounds.
## 4 Discussion and Conclusions  
Classical stochasticity via the instantaneous noise-based logic schemes may offer more than quantum computers because of the lack of the fundamental disadvantages of quantum systems [19] which are the decoherence, the statistical nature of quantum measurements, and the fact that measurements destroy the state. Examples of this superiority are shown in the present paper where, once the search has begun, the results come out deterministically without statistical errors, resulting in exponential speedup compared to [PERSON]'s quantum search algorithm.  
To begin the search, a single clock period with non-zero superposition amplitude is required. The whole search is done during this single period. Thus, a new research problem has emerged: Finding optimal reference signal systems where the superposition signals are zero with very low probability and, if they take the zero value, this value quickly changes to non-zero during the subsequent clock periods. Tested superposition amplitudes spend very short times at zero (see Figure 6) however a careful study of this issue is warranted. Zero superposition amplitude may introduce a random waiting time (typically with exponentially decaying probability to remain at zero, see [12]) before the search can begin; however, that does not change the fact that the search result is error-free and deterministic.  
The present paper is another demonstration that, despite the resemblance between instantaneous noise-based logic and quantum computing, the NBL schemes and their modes of operations are substantially different even if the achieved goals can be the same. While explorations of NBL are still taking place, it is already clear that the robustness and controllability of classical physical systems and information may sometimes offer competitive advantages over quantum informatics. The results in this paper show examples where NBL schemes not only outperform a known corresponding quantum algorithm, but can also achieve the goal with fewer resources. It is important to emphasize that the schemes shown in this paper _are not quantum-inspired_ and they utilize classical physical stochasticity.  
Conceptualization and methodology, L.B.K.; validation, L.B.K., W.C.D.; formal analysis, L.B.K.; investigation, L.B.K., W.C.D.; resources, L.B.K.; data curation, L.B.K.; writing-original draft preparation, L.B.K.; writing-review and editing, L.B.K., W.C.D.; visualization, L.B.K.; supervision, L.B.K., W.C.D.; project administration, L.B.K.; funding acquisition, L.B.K.  
This research received internal Texas A & M funding in the form of a T3 grant (2019-2020).  
We are grateful for [PERSON] for pointing out the potential problems of zero instantaneous amplitude of a superposition that needed to be addressed. Discussions with [PERSON], [PERSON], [PERSON] and [PERSON] are appreciated.  
The authors declare no conflict of interest.
## 6 Discussion
### Matrix-Dominated Behavior and Engineering Implications  
The statistical analysis provided a principal finding of this study: the dynamic tensile strength of UDRC, as measured by the pull-back method, is governed primarily by the properties of the concrete matrix rather than the reinforcement configuration. This matrix-dominated behavior is likely attributable to scale effects, where the spall fracture process occurs at a scale larger than the reinforcement diameter, and the fact that under these rapid, one-dimensional tensile conditions, the reinforcement is less effective at resisting crack initiation. This insight has significant engineering implications. For the design of protective structures against impact or blast, it suggests that plain concrete properties can be used for a conservative and simplified assessment of dynamic tensile capacity in the investigated strain rate regime. Furthermore, the choice between plain and deformed bars may have minimal influence on initial spall strength, allowing other factors like static performance or cost to guide the selection.
## 4 Statistical Optimization and Model Validation  
This section is dedicated to explaining the methodology of the parameter optimization and validation applied to the previously described thermo-chemical model. Optimization aims at finding the most probable parameter regions of the input parameters, presented in Table 4. This is done by comparing the model predictions with the data, acquired by two experimental setups, introduced in Section 2.3. In this study, we take a statistical inference approach to the optimization task. The benefit is twofold: contrary to the customary parameter-optimization techniques, we can include the prior belief of the modeler on the parameter values in the form of distribution or even ranges. Moreover, the different sources of errors, such as numerical and measurement error can also be considered in the optimization procedure.  
One of the most common approach to optimization in the statistical community is _Bayesian inference_. In a Bayesian setting, one tends to proceed the following three steps. First, a plausible hypothesis related to a simulation is formulated. Second, rational degrees of belief in these hypotheses are considered. Thirdly, the Bayesian principles are applied. Following these steps yields posterior probabilities revealing the degree of trust one shall rationally invest in the hypothesis. In the context of inference, a hypothesis could be identifying the values of input parameters of a computational model. This can be done by fitting a statistical model to the problem acknowledging the errors in both the computational models and the experiment.  
Here, we aim at calibrating and validating the computational model defined in the previous section in a stochastic manner. This requires many model evaluations, which renders the validation and parameter inference procedure infeasible for computationally demanding models. To remedy this problem, we accelerate the procedure by replacing the original computational model with an easy-to-evaluate surrogate model. Note that this will introduce an additional source of uncertainty into the computation due to prediction error. This error will be provided by the surrogate model and will be taken into account later in the task of inference. More details about all different sources of errors will be given in Section 4.4.
# Robust Visual Localization with Dynamic Uncertainty Management in Omnidirectional SLAM
## 2 Monocular Omnidirectional System
### Epipolarity Adaption Definitions
Angular and radius variables for the omnidirectional view are expressed by \(\alpha\in[0,2\pi]\) and \(r\in[r_{min},r_{min}+r_{step},\cdot\cdot\cdot,r_{min}+(n-1)r_{step},r_{max}]\), so that the dimensions of the panoramic view can be tuned in order to get the desired areas of the scene, with the final image of \(size(r\times\alpha)\) in pixels. Figure 3 presents a real example for this projection conversion, with feature points indicated. Figure 3a depicts the conversion seen on the rays' model side. Omnidirectional projection rays are backprojected from the mirror to lie on the Cartesian panoramic reference system, as denoted in Equation (3). Figure 3b,c represents the same image in its omnidirectional and panoramic projection, after conversion. It may be noticed that the angular variable in the omnidirectional reference, \(\alpha\), transfers into the \(u\) coordinate in the panoramic reference, highlighted as a blue line. Likewise, the radius \(r\) and \(v\) are represented in red. Finally, Figure 3d presents a sample image set for evaluating the benefits of the panoramic conversion when extracting feature points. Along 858 real images, the number of feature points detected on the panoramic projection is on average \(\sim\)14% higher. In addition, they are only concentrated on bottom areas of the image, thus considering only points between \(r_{min}-r_{max}\), with a final length of 445 in the \(v\) coordinate, leading thus to avoiding features at top pixel areas, where the mirror induces significant non-linearities. This fact implies a valuable aid in the robustness of the feature extraction. A proper calibration is crucial when dealing with backprojections. In this respect, the calibration toolbox presented in [52], was used. Figure 2: Epipolar constraint adaption to the omnidirectional geometry of the camera.
## 2 Theories  
This section offers a concise overview of pertinent SLAM (Simultaneous Localization and Mapping) algorithms, specifically Gmapping, ORB-SLAM3, SC-LeGO-LOAM, and the Cartographer algorithm. Gmapping stands out as one of the most widely-used SLAM algorithms; however, it relies on odometry data as the input, making it suitable solely as a reference for positional effects in Section 4. The primary focus of evaluation lies on ORB-SLAM3 and SC-LeGO-LOAM. Additionally, the Cartographer algorithm comes into play for calculating ground-truth trajectories. Ultimately, 3D reconstruction is employed to assess the mapping outcomes.
# Bayesian Filtering Multi-Baseline Phase Unwrapping Method Based on a Two-Stage Programming Approach
## 4 Results and Discussion
### Experiment 4  
To verify the noise robustness of the proposed TSPA-based Bayesian filtering MBPU methods, this study uses Figure 4b,c as a reference unwrapped phase, to add phase noise with different mean correlation coefficients to the data, and we obtained the wrapped phases. As shown in Figure 6, we added phase noise to the simulated data with phase correlation coefficients of 1, 0.99, 0.98, 0.96, 0.95, 0.93, 0.90, 0.88, 0.85, 0.88, 0.75, and 0.7. It can be seen from the results that when the wrapped phases are without noise, different MBPU methods can accurately obtain the PU results. However, with the decrease of the phase correlation coefficient, the results of traditional TSPA become significantly worse, especially when the correlation coefficient is less than 0.90, as the unwrap phase produces a serious unwrap error phenomenon. However, the proposed TSPA-based Bayesian filtering MBPU methods can still obtain results when the phase correlation coefficients are 0.8, which further validates that the methods are better than the traditional TSPA. However, there are also differences among the TSPA-based Bayesian filtering MBPU methods. For example, when the phase correlation coefficient is 0.93, the long baseline result of TSPACKF produces a phenomenon of unwrap error in the same areas, but this does not occur in the other two methods. This may be caused by different methods of variance estimation. This provides a research point for the more accurate TSPA-based Bayesian filtering MBPU methods proposed in the future.
###### Abstract  
Fault diagnosis of rolling bearings is important for ensuring the safe operation of industrial machinery. How to effectively extract the fault features and select a classifier with high precision is the key to realizing the fault recognition of bearings. Accordingly, a new fault diagnosis method of rolling bearings based on improved fast spectral correlation and optimized random forest (i.e., particle swarm optimization-random forest (PSO-RF)) is proposed in this paper. The main contributions of this study are made from two aspects. One is that an improved fast spectral correlation approach was developed to extract the fault features of bearings and form the feature vector more effectively. The other is that an optimized random forest classifier was developed to achieve highly accurate identification by exploiting particle swarm optimization to select the best parameters of random forest (RF). In the presented method, improved fast spectral correlation was first utilized to analyze the raw vibration signal caused by a faulty bearing to obtain the enhanced envelope spectrum. Then, the amplitudes of the four characteristic cyclic frequencies (i.e., the rotating frequency, the characteristic frequency of outer-race fault, the characteristic frequency of inner-race fault, and the characteristic frequency of rolling element fault) exhibited in the enhanced envelope spectrum were selected to form the feature vector. Finally, the PSO-RF method was introduced for identifying and classifying bearing faults. The experimental investigations demonstrate the proposed method can accurately identify bearing faults and outperform other state-of-art techniques considered.  
improved fast spectral correlation; particle swarm optimization-random forest; rolling element bearings; fault diagnosis Article
# Simultaneous Quantitation of Isoprenoid Pyrophosphates in Plasma and Cancer Cells Using LC-MS/MS
## Materials and Methods
### Selectivity and Specificity  
Selectivity and specificity were processed following our extraction procedure. To evaluate selectivity, we analyzed the blank samples spiked with analytes and the IS. Six different lots for blank plasma were evaluated for any interference at the retention times of analytes and the IS to assess the specificity of this method.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 7 Fixed Routing and Dynamic Sink Mobility  
In our proposed scheme 3D-SM, we consider sink mobility to enhance the performance of the network. We consider a network field with dimensions 500 m \(\times\) 500 m \(\times\) 1000 m. The network is logically divided into four RCs. In each RC, either the MS or CN is deployed for data gathering from nodes at the least distance. The CN gathers data from its RC and further sends it to the MS. In order to find the difference between fixed routing and sink mobility, we perform simulations, where the network dimensions and number of CNs and MS are kept the same for both fixed routing in 3D-SM. In Figure 19, the architecture of fixed routing is shown. The sink and CN are static, and they are receiving data through direct communication (in order to keep the comparison fair). Far away CNs send their data through multi-hop communication, shown in Figure 19.  
We compare the results of both schemes in the same environment; one is fixed routing, and the other is the proposed scheme, 3D-SM. As we defined NLT as the time duration from the start of the network till the death of the first node, in Figure 20, the NLT of 3D fixed routing is less compared to 3D-SM, because of the static sink and CNs. The load between the nodes is managed in 3D-SM, which prolongs the network lifetime. In fixed routing, the distance between nodes and the sink (or CNs) is fixed, so the nodes that are far drain their energy quickly.  
Figure 18: Packet drop ratio.  
The MS and CNs, when moving in the network, actively gather data from the nodes in 3D-SM. However, nodes are sending the data through fixed routes to CNs (or the sink), and the throughput of fixed routing is less compared to 3D-SM, as shown in Figure 21. In Figure 22, the packet drop of both schemes are shown. In fixed routing, the ratio of the packet drops is high compared to the throughput (the number of packets successfully delivered). Because of the distance and underwater harsh environment, nodes that are far from the sink (or CNs) lose the transmitted packet. The packet dropped ratio also increases when CNs forward the received data through multi-hop communication.  
In Figure 23, 3D-SM has higher transmission losses compared to fixed routing, because when the network operation starts, nodes quickly die in fixed routing. With the decrease in the number of alive nodes, the transmission loss also decreases. In Figure 24, the path loss of both schemes is represented. Due to the far distance transmission, path losses increase in fixed routing. Nodes send their data directly to the MS (or sink) in both schemes, 3D-SM and fixed routing. In fixed routing, nodes and CNs (sink) are fixed, and far away nodes consume more energy during data transmission; also, the delay increases, as shown in Figure 25.  
Figure 19: Architecture for fixed routing.  
Figure 20: NLT of fixed and dynamic routing.  
Figure 23: Transmission loss of fixed and dynamic routing.  
Figure 21: Throughput of fixed and dynamic routing.  
Figure 22: Packet drops of fixed and dynamic routing.
# Research on Human-Robot Collaboration Method for Parallel Robots Oriented to Segment Docking
## 2 Application Scenario Description  
This paper proposes a framework wherein the Stewart parallel platform collaborates with the operator to accomplish the cabin segment docking task. Within this framework, the operator exerts his expertise to guide the robot's actions by applying force. The setup, depicted in Figure 1, consists of a fixed cabin segment fastened on a bracket, a six-axis force sensor on the end-effector, and a movable cabin segment meant for docking positioned above. Under the operator's guidance, the robot is tasked with moving the movable cabin segment for assembly between different working points. The assembly task can be divided into two subtasks. The first is the initial mobile phase, where the robot requires swift movement. When nearing the fixed cabin segment, the operator applies minimal force, expecting precise movements from the robot, and slight orientation adjustments are required to ensure assembly accuracy. During the entire docking process, if the operator tries to counteract additional movements caused by inertial forces by applying opposing force, the robot should promptly respond with the corresponding reverse motion.  
As illustrated in Figure 2, the cabin segment human-robot collaboration docking system encompasses the Stewart parallel platform, fixed bracket, movable cabin segment, and fixed cabin segment, among other components. The pivotal Stewart parallel platform serves as the mechanism to control and adjust the posture of the movable cabin segment. This comprises an active platform, a fixed platform, support chains, servo motors, drivers, force sensors, and industrial control computers. To aid the operator in accomplishing the docking task, target point positions and coordinate systems are defined across different components of the docking system, as depicted in Figures 2 and 3. The definitions of coordinate systems and key points are outlined as follows:  
1. Geodetic coordinate system (base), denoted as \(O_{0}-X_{0}Y_{0}Z_{0}\);
2. The coordinate system firmly linked to the static platform of the parallel mechanism (hereafter called the static coordinate system), noted as \(O_{1}-X_{1}Y_{1}Z_{1}\);
3. The coordinate system firmly connected to the moving platform of the parallel mechanism (hereinafter termed the moving coordinate system), denoted as \(O_{2}-X_{2}Y_{2}Z_{2}\);
4. The docking points for the movable segments include the following: \(H_{1}\), \(H_{2}\), \(H_{3}\), \(H_{4}\), \(H_{5}\), \(H_{6}\);
5. The docking points for the fixed compartment segments consist of the following: \(G_{1}\), \(G_{2}\), \(G_{3}\), \(G_{4}\), \(G_{5}\), \(G_{6}\).  
Figure 1: Stewart human-robot collaborative parallelism platform.  
Figure 2: Schematic diagram of horizontal docking of compartments.
# Temporal Changes in Land Use, Vegetation, and Productivity in Southwest China
## 3 Results  
### Land Types Changes  
During the study period, the composition of LULC types in the study area changed significantly, with a high spatial heterogeneity between the LULC types (Figure 1). The conversion of farmland into forests was the major driving force of LULC change. Overall, the forest area showed increasing trends after 2000, but it showed a decreasing trend first and then increased (1.04 year\({}^{-1}\), \(p\)\(<\) 0.01). Forest area increased by 16,180.0 km\({}^{2}\), accounting for 58.7% of the regional area from 2000 to 2019 (Figure 2, Table 1). The growing regions of forest were mainly distributed in northeastern Sichuan and southwestern Yunnan, and the decreasing regions were mainly distributed in southeastern Guizhou, northwestern Guangxi, and northwestern Yunnan. The cropland area first increased and then decreased, showing a significant downward trend overall (\(-\)0.707 year\({}^{-1}\), \(p\)\(<\) 0.01). The area of conversion of farmland into forests was 61,318.48 km\({}^{2}\), which was the LULC change type with the greatest land area transfer, mainly concentrated in the Sichuan Basin, northern Guizhou, and central and southern Guangxi (Figure 1). The grassland area showed a continuously decreasing trend (\(-0.64\) year\({}^{-1}\), \(p<0.01\)), of which 10,948.30 km\({}^{2}\) of the grassland area was converted into forest. The change regions were concentrated in the Hengduan Mountains in the northwest of Yunnan, Kunming-Zhaotong regions in the northwest, and the western Sichuan plateau region. A grassland area of 4231.28 km\({}^{2}\) was converted into cropland, the change regions were mainly concentrated in the northeast part of Yunnan, the western part of Guizhou, and there were also sporadic changes in southern Sichuan.
# Multi-Scale Factor Image Super-Resolution Algorithm with Information Distillation Network
## 2 Materials and Methods  
### Network Structure  
IDMF-SR mainly includes two parts: a deep feature learning block and a multi-scale factor up-sampling block, as shown in Figure 2. First, a Conv-3 is used to extract coarse image features. The key component of IDMF-SR utilizes multiple-stacked information distillation blocks (IDBs). After each information distillation block, the feature maps flow into the next IDB and flows on to the last IDB. When several convolution operations are completed, the retained multi-scale feature maps are fused through concatenation. The upsampling module mainly includes position projection, weight prediction, and feature mapping, as shown in Figure 2. Details are introduced in Section 2.3.