esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### XRD Analysis
Particle size (in nm) of the MCC and CNCs samples was determined concerning the (002) plane using Scherrer Equations (5) and (6) as shown in Table 2.  
\[D_{hkl}=\frac{K\lambda}{\beta_{hkl}Cos\theta} \tag{5}\]  
\[\beta_{hkl}=\frac{2\theta\Delta x\pi}{180} \tag{6}\]  
where \(D_{hkl}\) is particle size in nanometers, \(\lambda\) is radiation wavelength (1.54056 A for CuK\(\alpha\) radiation), \(k\) is a constant equal to 0.94, \(\beta_{hkl}\) is peak width at half maximum intensity and \(\theta\) is peak position. According to this research (see Table 2), the average crystallite size of the MCC samples was 7.06 nm, and the average crystallite size of the CNCs sample was 5.57 nm. Table 2 also included a summary of the full width at half maximum (FWHM) and the highest peak intensity of the samples. It was easily seen that the crystallite size decreased together with the broadening of the most significant peak [39]. In accordance with previously reported findings, it was also found that the crystallite size of the CNCs sample was noticeably smaller than that of the MCC sample. Specifically, it was found that the crystallite size of the nano-cellulose obtained via acid hydrolysis consistently has a smaller crystallite size than other methods, with a value of 5-10 nm [7]. The distribution of lattice strain due to faults and dislocations in the crystals was measured using Equation (7):  
\[\epsilon=\frac{\beta Cos\theta}{4} \tag{7}\]  
Table 3 displays the estimated strain values of the CNCs sample calculated using Equation (7): (110), (002), and (004) diffraction peaks correspond to strain values of \(2.5\times 10^{-3}\), \(6.4\times 10^{-3}\), and \(3.04\times 10^{-3}\), respectively. Hence, a function of \(2\theta\) was found to help distinguish between the impacts of size and strain on the peak broadening, i.e., the Bragg width contribution from the crystallite size is inversely proportional to the crystallite size, as suggested by Equation (7). According to the Bragg width contribution, it was also found that lattice strain reduces with increasing particle size. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{3}{c}{**Lattice Parameters**} & \multicolumn{2}{c}{**Particle Size**} & \multicolumn{2}{c}{**Grain Size**} & \multirow{2}{*}{**Intensity** (cps)**} \\  & & & & & **(nm)** & & \\ \hline \multirow{4}{*}{ \begin{tabular}{c} MCC \\ \end{tabular}  } & **Crystal** & **Peak** & \begin{tabular}{c} **Peak** \\ **Width at Half** \\ **(_hkl_)** \\ \end{tabular}  & **Peak Width at Half** & \ \begin{tabular}{c} **Peak Width at Half** \\ **(_o_)** \\ \end{tabular}  & & & & \\ \cline{1-1}  & **Crystal** & **Peak** & **Peak Width at Half** & \begin{tabular}{c} **Peak Width at Half** \\ **Maximum** \\ **Intensity** \\ \end{tabular}
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 2 Routing on Wireless Sensor Networks
### Hierarchical Based Routing  
Clustering is an efficient topology control approach for maximizing the lifetime and scalability of WSNs. The hierarchical based routing is a part of the group-based routing and consists of creating a virtual hierarchy among the nodes of the sensor network [21]. This class of routing techniques is generally designed for large scale networks and aims to efficiently maintain the energy consumption of sensor nodes and increase the network lifetime by cutting the whole network into clusters [14]. Each cluster is led by a node called Cluster Head (CH) which receives data from nodes within the cluster. CHs communicate each other in order to find a better route up to the sink node or the BS. This is done in order to reduce the energy consumption of sensor nodes by reducing the number of transmitted/received messages to the sink node. In addition to CH election, a second special node called Vice Cluster Head (VCH) can be elected in order to improve the lifetime of the CH as shown in [22]. Mechanisms like multihop communication, data aggregation, and data fusion are performed so that the energy is efficiently used within the cluster [12; 13]. The most popular clustering algorithm is the Low-Energy Adaptive Clustering Hierarchy (LEACH), it uses probability computing and the received signal strengths to locally select the CHs which have to serve as router of the data up to the BS. In LEACH, local data fusion and aggregation are performed by local CH [3]. For a large scale network, LEACH is able to increase the network lifetime [21]. However, due to its single hop configuration, the CH on LEACH is assumed to have a long communication range. Thus, the data sent by the CH has to reach the BS directly. Another approach subdivides the problem into two layers: an organization layer to manage communications and a distribution layer made up of cluster members [23]. Many hierarchical based routing algorithms are proposed in the literature, such as the PEGASIS, TEEN, EEHC, PEACH, or HEED. Authors of [4; 6; 16; 18; 19] present several classical hierarchical algorithms and show how the scalability, the energy efficiency, network lifetime, data delivery, and the fault tolerance are greatly improved on large scale sensor network.  
Except for the network structure, protocols in WSN field can also be classified according to the path establishment (proactive, reactive, hybrid); the protocol operations (multipath-based, query-based, negotiation-based, delivery-based, QoS-based, coherent-based); the next hop selection (broadcast, location, comment, probabilistic). Figure 1 gives a global overview of this classification. More details about each category can be found in [4; 24; 25; 26; 27].  
Figure 1: Classification of routing protocol in a Wireless Sensor Network (WSN).
## 3 Proposed PLL Validation  
In order to validate the performance of the SF-EP-MAF-PLL, three case studies using Matlab/Simulink were investigated on a basic and sample microgrid [36]. The tuning coefficients are represented in Table 1 for both EP-MAF-PLL and SF-EP-MAF-PLL structures. There are different ST values to determine the system dynamic response, which are slow (4 times of a period where T = 20 ms), medium (3T) and fast (2T), while the simulations are carried out with medium ST conditions.
## Abstract  
Existing traffic information acquisition systems suffer from high cost and low scalability. To address these problems, the application of wireless sensor networks (WSNs) has been studied, as WSN-based systems are highly scalable and have a low cost of installing and replacing the systems. Magnetic, acoustic and accelerometer sensors have been considered for WSN-based traffic surveillance, but the use of ultrasonic sensors has not been studied. The limitations of WSN-based systems make it necessary to employ power saving methods and vehicle detection algorithms with low computational complexity. In this paper, we model and analyze optimal power saving methodologies for an ultrasonic sensor and present a computationally-efficient vehicle detection algorithm using ultrasonic data. The proposed methodologies are implemented and evaluated with a tiny microprocessor on real roads. The evaluation results show that the low computational complexity of our algorithm does not compromise the accuracy of vehicle detection.  
**Keywords:** power saving; low computational complexity; ultrasonic sensors; traffic information acquisition systems; wireless sensor networks
## 5 Conclusions  
Instead of comparing model performance among different biomes, this study evaluated the performance of eight widely used satellite-based GPP models in vegetation subtypes limited by different environmental factors in a biome. Taking the grasslands in northern China as an example, our results indicated that the performance of the satellite-based GPP models was obviously higher in the temperature-limited alpine meadow than in the water-limited temperate steppe. Additionally, performance varied considerably by model in the temperate steppe due to the different methods being used to address the water stresses. The MVPM and VI in general performed better than the other LUE and statistical models, respectively, owing to their better representation of moisture conditions. The relatively worse model performance in the temperate steppe can be significantly degraded by extreme drought events, regardless of model type. Comparatively, these remote sensing models showed small differences between the alpine shrub meadow and the alpine swamp meadow, except for the relatively worse performances of the MODIS-GPP in the alpine shrub meadow and the GR model in the alpine swamp meadow. Our results highlight the contrasting performances of satellite-based GPP models in grasslands limited by different environmental stresses and in different precipitation years in water-limited areas. Given the varying environmental stresses of different vegetation subtypes [127; 128; 129] and the increase in drought events worldwide [63], this study provides important insights for future model improvements and applications. In particular, more effort should be focused on the quantification of the effects of water stress on GPP in water-limited ecosystems and in drought years. Note that the conclusions in this study are based on only four EC flux sites, and further studies incorporating data at more flux sites are needed to see the generality of the findings from this study.  
Conceptualization, L.Z. and D.Z.; methodology, L.Z. and D.Z.; writing--original draft preparation, L.Z.; writing--review and editing, D.Z., J.F., Q.G., S.C., R.W., and Y.L.; supervision, D.Z.  
This research was funded by the National Natural Science Foundation of China (Grant #: 41601196), the National Key R & D Program of China (Grant #: 2017 YFA0604804), the Chinese Academy of SciencesTechnology Service Plan (Grant #: KFJ_STS-ZDTP-013-02), the National Natural Science Foundation of China (Grant #: 31870413), and the Qinglan Project of Jiangsu Province of China.
# Experimental Study on the Surface Properties of Nanoalumina-Filled Epoxy Resin Nanocomposites
## 3 Results and Discussion
### Surface Roughness
For EPNCs with DDSA+MNA hardener, the lower value of the roughness corresponds to the higher value of CA. These results are in agreement with [PERSON] et al. [40], verifying that the surface hydrophobicity of epoxy resin is influenced by the  
Figure 5: Representative 2D and 3D atomic force microscopy (AFM) images for EPNCs with DDM: (**a**) control sample (CS); (**b**) 5 wt. (%) sample. Figure 6: Representative 2D and 3D AFM images for EPNCs with DDSA+MNA: (**a**) CS; (**b**) 3 wt. (%) sample. chemical surface of components. Also, [PERSON] et al. [42] observed that the roughness had a strong influence on the wettability of engineering surfaces. In fact, it was expected that as the roughness increases, the CA should also increase. This can be explained by the \"lotus effect\", whereby lotus leaves repel water because of the presence of small bumps at the micro- and nanometer scale on the surface [43]. In this case, a drop of water is supported between the peaks of the protuberances, which is the shortest contact, therefore acquiring a more spherical shape (that is, with a higher CA). The observed different behavior for the tested samples can be explained by the curing agents and manufacturing procedures used. In fact, in the case of DDSA+MNA, a postcuring cycle was performed to manufacture the EPNCS, while for the case of DDM the EPNCs, these were not subjected to postcuring. According to [PERSON] et al. [2], the degree of enhancement of a specific property is highly dependent on the type of curing agent used, and also on the state of the dispersion of the NPs throughout the EP resin matrix. In previous studies, a correlation between the mesoscopic structure and wettability has been reported for bionanocomposite films [44; 45]. In particular, it was observed that the inclusion of nanotubes into the studied composite films led to a significant decrease of the CA. The authors explained this observation as being due to the presence of hydrophilic nanotubes on the film surface. In this study, functionalized alumina NPs with APTES were used (Section 2.1). Hence, amine groups exist at the surface of the NPs, which make them hydrophilic. From this point of view, the results in this study should also show a general decrease of the CA due to the inclusion of the alumina NPs in the EP matrix. This is observed for EPNCs with DDM, but not in general for EPNCs with DDSA+MNA (Figure 7). As previously mentioned, these differences can be explained by the curing agents and manufacturing procedures used.
## 4 Simulation Results and Security Evaluation
### Speed Performance Test  
In our study, we conducted a speed test on encrypted images with a size of 256 \(\times\) 256, similar to the approach taken by [PERSON] [36]. We compared our results with those obtained in other relevant studies. Our proposed encryption method not only demonstrated its robustness but also proved to be lightweight and highly efficient in terms of performance. Based on our findings, as shown in Table 10, we believe that the proposed encryption method offers an excellent solution for secure and quick encryption.
## 3 Results  
### Area Monitoring Results  
Based on the GEE cloud platform, this thesis performs feature construction with Sentinel-2 images as the data source, extracts and calculates the walnut planting area in Ganquan Town from 2017 to 2021 with the random forest classification algorithm, and calculates the relative error via a comparison with statistical data (as shown in Table 4). Additionally, when performing feature construction, this study adds vegetation index EVI; compared with the non-adding vegetation index EVI, the relative error of the walnut planting area extraction is reduced, and the average relative error is within 10%. The relative error of the walnut planting area was reduced from 8% to 6% in 2018-2019, from 2% to 1% in 2019-2020, and from 13% to 11% in 2020-2021, with good extraction effects. In addition to the above verification, this thesis calculates the overall classification accuracy, kappa coefficient, and walnut classification accuracy (Table 5) after classification, based on the confusion matrix for the verification samples. It was observed that, after adding EVI, the overall accuracy, kappa coefficient, and walnut classification accuracy improved. It was observed from the analysis above that the method adopted in this research has higher accuracy and better effects when extracting information regarding walnut planting areas in Ganquan Town.  
The extraction result of the walnut planting area shows that, based on the GEE cloud platform, the random forest classification algorithm can preprocess remote sensing image data quickly and quickly map the spatial distribution information of walnut planting areas in Ganquan Town. Spatial information on the walnut planting area in Ganquan Town from 2017 to 2021 is shown in Figure 4, and it can be observed that walnuts are mainly distributed around the center of Ganquan Town. On account of the special climate in Southern Xinjiang, Ganquan Town has been dry for a long time, and is rich in water resources near the center of the town, which is conducive to walnut growth. This shows that, in Figure 4, the walnut planting area measured by remote sensing in Ganquan Town from 2017 to 2021 is generally stable; the walnut planting area measured by remote sensing in 2020 increased compared with that of 2019, which is consistent with the area's change measured by the Statistical Bureau of Alar City.
# Automated Modal Analysis Using Stochastic Subspace Identification and Field Monitoring Data
###### Abstract  
The accurate identification of modal parameters is essential for structural health monitoring (SHM), as it provides critical insights into the presence of damage or degradation within the structure. A promising technique, stochastic subspace identification (SSI) has numerous advantages in operational modal analysis (OMA), particularly in implementing automated OMA. Hence, an improved procedure is proposed in this study, addressing the size of the SSI matrix, the estimation of system order, and the removal of spurious modes for automated modal analysis. A general instruction for user-defined parameters is first reviewed and summarized. Subsequently, a proposed procedure is then introduced and framed into three steps. Key advances include the preliminary identification of fundamental frequency, which helps the overall automated work, adequately assigning the size of the SSI matrix, which can improve decomposition, and a decay function, which provides a good estimation of system order. To demonstrate and verify the procedure, a numerical simulation of a ten-story shear-type building structure and two field datasets, collected from reinforced concrete (RC) frames in Taiwan, are utilized. Consequently, the results suggest that the proposed three-step procedure based on SSI can facilitate automated OMA for continuous and long-term SHM, in terms of autonomously adjusting user-defined parameters.  
stochastic subspace identification; reinforced concrete frame; operational modal analysis; automated modal analysis; structural health monitoring; resilient buildings Article  
1
# Design and Validation of a Cascading Vector Tracking Loop in High Dynamic Environments
## 1 Introduction
Given the limit of noise bandwidth and wavelength, the carrier loop, whether the phase-locked loop (PLL) or the frequency-locked loop (FLL), is more prone to lose locking than the delay-locked loop (DLL). To solve the receiver problem of signal loss in highly dynamic environments based on vector tracking, we designed and verified a cascade tracking structure to replace the original carrier loop, and as the first level of the cascade structure, this pre-filter based on the UKF can work independently when it is close-looped and be used for the vector structure when it is open-looped. Therefore, the switching between the scalar and vector structures can be unified well.The rest of the paper is organized as follows. Section 2 introduces our vector tracking method. Section 3 presents a series of experiments to prove the performance of vector loop from tracking error and robustness. Section 4 discusses the future work. Section 5 summarizes the main work of this paper.
# Challenges and Implications of Virtual Reality in History Education: A Systematic Review
## 3 Results and Discussion
### Technical Issues  
From a technical standpoint, the primary limitations are related to the lack of interoperability between platforms and the absence of open standards that would facilitate the efficient use of VR technologies. These issues are further exacerbated by the substantial hardware and software requirements needed to ensure proper functionality, which in turn impacts both accessibility and sustainability in educational settings [44; 45; 51]. Additionally, problems with visualization and navigation within virtual environments have been identified, which can result in disorientation and frustration for users [38]. Based on the findings, the primary technical issues identified were related to internet access, including downtime, delays, connectivity problems, and disruptions. Additionally, the energy consumption of devices was identified as another significant challenge.
## 1 Introduction  
The biological effects of extremely low-frequency electric fields (ELF-EFs) have been extensively studied for their safe use in various conditions, and lowering the electric field intensity has been found to be effective in avoiding adverse influences, including brain and cardiac symptoms [1, 2, 3]. At the same time, beneficial changes in humans have also been reported. Low-intensity ELF-EF ameliorates pain due to rheumatoid arthritis [4] and chronic pain of undetermined origin [5]. Low-intensity ELF-EF generates physiological changes related to arousal reduction, leading to relaxation [6], which may underly its beneficial effects. In rodents, it is known that an ELF-EF reduces stress-related hormonal changes [7]. These observations suggest that, when used at a low intensity, an ELF-EF can have therapeutic effects on psychosomatic disturbances, but the mechanisms underlying the effects are not well understood. The beneficial and adverse effects can be related to different biological mechanisms depending on the intensity of the ELF-EF. In contrast to a high-intensity ELF-EF, a low-intensity ELF-EF may be able to modify psychosomatic activities within their functional ranges. And the autonomic nervous system is a candidate to be assessed because it controls various psychosomatic functions in humans.  
Previous research reported that the heart rate (HR) and heart rate variability (HRV) indices are altered during the application of a low-intensity ELF-EF and suggested that autonomic activity may be involved in the generation of the beneficial changes [6]. However, among the HRV indices, the high-frequency (HF) component, which reflects parasympathetic nerve activity, is not affected during or after the treatment. HRV is a fluctuation in the inter-beat interval, resulting in several rhythms, including a breathing-dependent HF component, which is related to parasympathetic nerve activity, and a blood-pressure-dependent low-frequency (LF) component, which is related to both sympathetic and parasympathetic nerve activities [8; 9]. HRV analysis itself is not fully capable of assessing sympathetic nerve activity [10; 11].  
Among various autonomic measures, skin conductance is accepted as a convenient tool for the evaluation of sympathetic nerve activity [12]. Skin conductance reflects sweating, which is solely controlled by the sympathetic nervous system [13; 14]. Two types of skin conductance are measured: the skin conductance level (SCL) and skin conductance fluctuations (SCFs). The former represents the total sweating on the skin, and the latter is related to psychological status, including anxiety and tension [15]. SCFs are elicited by external stimuli (skin conductance response) or are generated spontaneously in the resting condition (spontaneous fluctuations in skin conductance; SC-SFs). The present study aimed to evaluate the effects of low-intensity ELF-EF on the sympathetic activity in the resting condition using two measures of skin conductance, SCL and SC-SF, in addition to the parasympathetic HRV index.
## 5 Proposed Predictive Maintenance (PdM) Structure Using Internet of Things (IoT)
### Proposed PdM Structure Using IoT  
Referring to the late studies and proposed architecture [28; 29; 70; 77], the proposed PdM structure using IoT is presented by different interconnected components, as shown by the block diagram in Figure 9.  
Figure 8: Program running into Arduino Uno.  
Figure 9: Proposed PdM Structure bloc diagram.  
Basing on each component role, the first two components must be settled together, as they serve for data gathering and processing. The communication module transmits data to a remote database from where the user can remotely obtain information through a computer or mobile application. At the same time, a Short Messaging System (sms) may be sent to the user in case of critical condition. The detailed proposed PdM structure illustrated in Figure 10 is presented into three main interdependent parts, which are:  
* Data acquisition, processing, and analytics.
* Results transmission.
* Application.