esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The purpose of this study was to optimize the effect of particle size, time and ultrasonic intensity on the extraction of phenolic compounds and energy efficiency. Sonication was performed with a VC750 Sonics processor at the following amplitudes: 30, 50 and 70%, which corresponds to the ultrasonic intensity, respectively: 1.6; 5.1 and 8.6 W\(\cdot\)cm\({}^{-2}\). The frequency of ultrasound was 20 kHz. Extraction was carried out in a 5 s on-10 s off pulse system. The content of polyphenols and their antioxidant activity were assayed by the spectrophotometric method. Response surface methodology (RMS) was used to optimize the investigated variables. On the basis of the developed model, the highest polyphenols yield was obtained under the following extraction conditions (particle size 0.65 mm, extraction time 13.14 min, ultrasound intensity 6.92 W\(\cdot\)cm\({}^{-2}\), which resulted in a maximum value of 15.24 mg GAE\(\cdot\)g\({}^{-1}\) dry matter of hemp. Taking into account the lowest unit energy consumption, the best conditions were obtained for particle size of approx. 0.5 mm, extraction time 7.7 min, and ultrasound intensity 1.8 W\(\cdot\)cm\({}^{-2}\), which resulted in a phenolic yield of 10.14 mg GAE\(\cdot\)g\({}^{-1}\) dry matter of hemp. The best agreement between values of optimization variables within investigated criteria was obtained for the variable particle size. The developed models of pulsed ultrasound-assisted extraction can be used for obtaining polyphenols from _Camubis sativa_ L. at low unit energy consumption.  
extraction yield; specific energy; pulsed ultrasonic treatment; polyphenols; response surface methodology; ultrasonic processor; FRAP; DPPH; plant materials Article
## 2 Materials and Methods  
### Study Area  
The study area and methods have been previously described [24]. In brief, the four study lakes lie within Voyageurs National Park, a park covering 883 km\({}^{2}\) in northeastern Minnesota (Figure 1; Table 2). The four lakes are drainage lakes, three of them fed by a small stream discharging from an upstream lake; the fourth (Ryan) fed only by a small headwaters stream. Although the upstream lakes may exert some influence on chemistry of the study lakes, given the small size of the inflowing streams, and relatively long water renewal times (0.6-1.2 year), the lake chemistry likely is governed more by catchment and deposition to the lake surface than by inflows from upstream lakes. Land cover within the park is largely boreal forest, with thin soils, and outcrops of Precambrian bedrock common throughout the park [26]. The lakes have been sampled for methylmercury and total mercury in lake water since 2000 (Shoepack Lake) or 2001 (Brown, Peary, and Ryan lakes) as part of earlier studies [21; 22; 23; 24].  
No monitoring of mercury deposition occurs within the park's borders; the two nearest NADP/MDN sites ([[http://nadp.slh.wisc.edu/mdn/](http://nadp.slh.wisc.edu/mdn/)]([http://nadp.slh.wisc.edu/mdn/](http://nadp.slh.wisc.edu/mdn/)), accessed on 1 February 2021) are used to characterize wet deposition of mercury: sites MN16 (Marcell, Minnesota), approximately 120 km south-southwest of Voyageurs National Park and MN18 (Fernberg, Minnesota), approximately 120 km southeast of the park. Due to the lack of large urban or industrial centers, relatively flat topography (hence lack of orographic effects), and variable wind direction in the region, data from these two monitoring sites are expectedto be representative of mercury and sulfate deposition in the region. We examined trends in wet Hg deposition from 1998-2018; trends in aqueous methylmercury (MeHg\({}_{\text{aq}}\)) and total mercury (THg\({}_{\text{aq}}\)) from 2000 (Shoepack Lake) or 2001 (Brown, Ryan, and Peary lakes) through 2018; and trends in biotic THg as described below.
## 4 Experiment and Analysis  
### C2Q-Kg  
The detailed metadata of the C2Q-KG used in this paper is shown in Table 2. The C2Q-KG contains 1626 continuous casting triples, involving 1144 entities and 5 types of relations.  
The \"describe\" relation denotes basic information about cast product defects, such as their location. The \"feature is\" relation denotes specific manifestations and related attributes of cast product defects, such as depth and temperature. The \"cause\" relation denotes that a certain root cause leads to a dynamic cast product defect phenomenon, while the \"lead to\" relation denotes a cause resulting in a static cast product defect. The \"solution\" relation denotes the optimization measures adopted for cast product defects.  
These relations cover the four types of 1-1, 1-N, N-1 and N-N. The reasoning accuracy and stability of KGTBRN can be measured by using the standard knowledge graph reason  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Continuous** & **Continuous** & **Continuous** & **Relation Type** & **Reasoning Need** & **Reasoning Selection** \\
**Casting Head** & **Casting Relation** & **Casting Tail Entity** & & & \\ \hline Uneven water & & & & & \\ cooling in & & & & & \\ secondary cooling & & & & & \\ zone & & & & & \\ \hline Liquid steel & & & & & \\ enriched with & & & & & \\ solute elements is & & & & & \\ sucked into the & & & & & \\ void & & & & & \\ \hline Bridging & Lead to & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Relation classification and selection examples of two-branch reasoning network.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Number** \\ \hline continuous casting entities & 1144 \\ continuous casting relations & 5 \\ continuous casting triples & 1626 \\ describe & 144 \\ lead to & 349 \\ feature is & 261 \\ solution & 297 \\ cause & 575 \\ total & 2396 \\ \hline \end{tabular}
\end{table}
Table 2: The detailed metadata of the C2Q-KG.  
ing evaluation indicators in the domain. The main indicators include hit probability among the top k results (HITS@K) and mean reciprocal rank (MRR), as shown in Equations (11) and (12), where \(rank_{i}\) denotes the rank of the correct entity among all reasoning results and \(S\) denotes the number of data in the set.  
\[HITS@K=\frac{1}{|S|}\sum_{i=1}^{|s|}I(rank_{i}\leq k) \tag{11}\]  
\[MRR=\frac{1}{|S|}\sum_{i=1}^{|s|}\frac{1}{rank_{i}} \tag{12}\]
## 3 Algorithm Implementation and Case Study  
### Algorithm Implementation  
The path analysis algorithm proposed in this paper has been applied in the Shanghai Public Information Query System, which contains the data of the Shanghai road network  
Figure 6: Flowchart of the improved A* algorithm for path analysis of hybrid networks.  
of all grades, 8303 bus and rail stations, 1394 bus lines, 16 rail lines, and 20 P&R stations. Employing the improved A* algorithm proposed in this paper, the following tasks were accomplished in the implementation of the algorithm to realize the input of arbitrary starting point and ending point and quickly output the optimal transfer plan for the aforementioned public road traffic network data in Shanghai.
## 3 Simulation Results and Discussions
Therefore, notice that the best-and-worst cases in terms of detection performances from the perspective of correlated and uncorrelated received signals depend on each detector since the existence of correlation benefits some test statistics while it penalizes others. The robustness to any values of \(U\) and \(L\) is a significant advantage of the CFCPSC-DF over GLRT-DF and -EF, which can be noticed particularly for large values of \(U\) and small values of \(L\), independently of \(\rho\). Finally, see from Figure 3a,b that the GLRT-DF-MAJ and GLRT-DF-AND rules have identical detection performances with 2 SUs since that the number of decisions \(K=\lfloor U/2+1\rfloor\) equals the number of SUs \(U\). Now, in order to show a different scenario than \(U\times L=18\), as in the previous results, Table 3 shows some AUCs values for different combinations of \(U\) and \(L\), under DN (\(\xi=0.8\)) and correlation (\(\rho=0.8\)). The same conclusions, as the ones obtained in Figures 3 and 4, can be found from the results of Table 3.  
Figure 4: AUC versus DN factor, \(\xi\), for \(U=6\) SUs and \(L=3\) antennas (graphs (**a,b**)) and \(U=9\) SUs and \(L=2\) antennas (graphs (**c,d**)). This figure is better viewed in color.
## 5 Simulation Analysis
### Simulation Parameter Setting  
According to the data of an actual BSS in Shandong Province, the parameters of BSS are set as Table 1. The BSS provides battery-swapping services for 280 electric buses. The cumulative capacity of batteries actually switched by these 280 electric buses in each period of a certain day is selected as the forecast value of battery swapping demand, as shown in Figure 2.  
The predicted power of the RE company are taken from the actual output of a wind farm and a photovoltaic power station in Shandong Province, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Parameters** & **Quantity** \\ \hline
1 & \(Q_{\text{max}}\) & 55 MWh \\
2 & \(Q_{\text{min}}\) & 5.5 MWh \\
3 & \(Q_{0}\) & 16.5 MW \\
4 & \(Q_{\text{end}}\) & 16.5 MW \\
5 & \(P_{\text{cmax}}\) & 11 MW \\
6 & \(P_{\text{dmax}}\) & 7 MW \\
7 & \(s\) & 1300 \(\forall\)/MWh \\
8 & \(\eta_{\text{c}}\) & 95\% \\
9 & \(\eta_{\text{d}}\) & 92\% \\
10 & \(\alpha\) & 10\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the BSS.  
Figure 2: Prediction of battery-swapping demand.  
The decisions are taken at the end of the day for each of the 24 h of the following day. Therefore, \(N_{t}\) is 24, and \(\Delta t\) is 1 h. The distribution company purchase price of RE is known, and the TOU price was adopted as shown in Figure 4. The division of peak and valley periods draws lessons from the division method of industrial electricity consumption price in Shandong Province. The price of wind power in Shandong Province is about 600 \(\forall\)/MWh. Therefore, we set the purchase price in the normal period as 600 \(\forall\)/MWh. The price in peak period is 1.5 times the one in normal period, and the price in valley period is half the one in normal period.  
The upper limit of charge/discharge price shall be 1.5 times the RE purchase price of the distribution company, and the lower limit shall be 50% of the electricity purchase price of the distribution company. If the BSS directly charging the battery from the distribution company, the contract price \(\rho_{\text{DC}}\) is 720 \(\forall\)/MWh. In order to ensure the cooperative motivation of BSS, the average charge/discharge price \(\overline{\rho}\) is the same as \(\rho_{\text{DC}}\).
# Ocean Wind Retrieval Models for RADARSAT Constellation Mission Compact Polarimetry SAR
## Abbreviations  
CP compact-polarimetry  
GMF geophysical model function  
HH horizontal-horizontal  
HV horizontal-vertical  
NDBC National Data Buoy Center  
NRCSs normalized radar cross-sections  
PR polarization ratio  
RCM RADARSAT Constellation Mission  
RH right circular transmit and horizontal receive  
RL right circular transmit and left circular receive  
RMSEs root mean square errors  
RR right circular transmit and right circular receive  
RV right circular transmit and vertical receive  
SAF sensitivity analysis factor  
SAR synthetic aperture radar  
TOGA COARE Tropical Ocean and Global Atmosphere Response Experiment  
VH vertical-horizontal  
VV vertical-vertical
###### Abstract  
In wireless networks, low-power Zigbee is an excellent network solution for wireless medical monitoring systems. Medical monitoring generally involves transmission of a large amount of data and easily causes bottleneck problems. Although Zigbee's AODV mesh routing provides extensible multi-hop data transmission to extend network coverage, it originally does not, and needs to support some form of load balancing mechanism to avoid bottlenecks. To guarantee a more reliable multi-hop data transmission for life-critical medical applications, we have developed a multipath solution, called Load-Balanced Multipath Routing (LBMR) to replace Zigbee's routing mechanism. LBMR consists of three main parts: Layer Routing Construction (LRC), a Load Estimation Algorithm (LEA), and a Route Maintenance (RM) mechanism. LRC assigns nodes into different layers based on the node's distance to the medical data gateway. Nodes can have multiple next-hops delivering medical data toward the gateway. All neighboring layer-nodes exchange flow information containing current load, which is the used by the LEA to estimate future load of next-hops to the gateway. With LBMR, nodes can choose the neighbors with the least load as the next-hops and thus can achieve load balancing and avoid bottlenecks. Furthermore, RM can detect route failures in real-time and perform route redirection to ensure routing robustness. Since LRC and LEA prevent bottlenecks while RM ensures routing fault tolerance, LBMR provides a highly reliable routing service for medical monitoring. To evaluate these accomplishments, we compare LBMR with Zigbee's AODV and another multipath protocol, AOMDV. The simulation results demonstrate LBMR achieves better load balancing, less unreachable nodes, and better packet delivery ratio than either AODV or AOMDV.  
medical monitoring; multipath routing; load balancing; load estimation; route maintenance +
Footnote †: journal: International Journal of
# Determination of Nonlinear Creep Parameters for Hereditary Materials
## 5 Application of Proposed Algorithm and Methods
### Glass-Reinforced Plastic TC 8/3-250
#### 5.2.1 \(\Theta\) = 90\({}^{\circ}\)  
Values of creep strain for glass-reinforced plastic (\(\Theta\) = 90\({}^{\circ}\)) at four stresses are represented in Table 2.  
Test results have shown that the sample of glass-reinforced plastic, cut at an angle of \(\Theta\) = 90\({}^{\circ}\) to textile warp, is deformed linearly at a specific temperature and up to stress 349 MPa. Instantaneous strain has been satisfactorily approximated by equation:  
\[\varepsilon_{0}^{m}\ =\ 3.3342\cdot 10^{-3}\cdot\sigma. \tag{27}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time \(t\), h**} & \multicolumn{3}{c}{**Strain \(\varepsilon_{E}\) (t), \%, at Stress \(\sigma\), MPa**} \\ \cline{2-5}  & **104.7** & **209.4** & **279.2** & **349.0** \\ \hline
0 & 0.3478 & 0.6957 & 0.9276 & 1.1595 \\
1 & 0.3616 & 0.7232 & 0.9643 & 1.2054 \\
10 & 0.3668 & 0.7337 & 0.9782 & 1.2228 \\
50 & 0.3709 & 0.7419 & 0.9892 & 1.2365 \\
100 & 0.3728 & 0.7457 & 0.9942 & 1.2428 \\
200 & 0.3746 & 0.7493 & 0.9991 & 1.2489 \\
300 & 0.3757 & 0.7515 & 1.0020 & 1.2525 \\
400 & 0.3765 & 0.7530 & 1.0040 & 1.2550 \\
500 & 0.3771 & 0.7542 & 1.0056 & 1.2570 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of creep strain for glass-reinforced plastic (\(\Theta\) = 90\({}^{\circ}\)).  
Figure 4: Creep curves of material Nylon 6 at various stresses: 1–5 MPa; 2–10 MPa; 3–15 MPa.  
Creep strains at minimum stress, equal to 104.7 MPa, have been approximated by Rabotnov's fractional exponential kernel. Creep parameters have the following values: \(\alpha\) = 0.8, \(\varepsilon_{0}\) = 0.3501, \(\beta\) = 0.3, and \(\lambda\) = 0.0462.  
The equation of creep curve for glass-reinforced plastic (\(\Theta\) = 90\({}^{\circ}\)) at minimum stress (\(\sigma\) = 104.7 MPa) has the form of:  
\[\varepsilon_{m}(t)\ =\ 0.3501\left[1+0.0462\sum_{n\ =\ 0}^{\infty}\ \frac{(-0.32)^{n}t^{0.2(1+n)}}{ \Gamma[0.2(1+n)+1]}\right]. \tag{28}\]  
From Equation (27) at \(\sigma\) = 104.7 MPa we obtain: \(\varepsilon_{0}^{m}\) = 0.3501%. Considering this, we determine the model rheological parameter:  
\[k_{m}(t)\ =\ \frac{\varepsilon_{m}(t)}{\varepsilon_{0}^{m}}\ =\ 1+0.0462\sum_{n\ =\ 0}^{\infty}\ \frac{(-0.32)^{n}t^{0.2(1+n)}}{\Gamma[0.2(1+n)+1]}. \tag{29}\]  
We calculate the values of instantaneous strain under Equation (27) at stresses 209.4 MPa, 279.2 MPa, and 349.0 MPa: \(\varepsilon_{0}^{m}\) = 0.6982%, 0.9309%, and 1.1636%, respectively.  
Now using the obtained values of instantaneous strain \(\varepsilon_{0}^{m}\), under formula (21) we can calculate the values of creep strain at stresses 209.4 MPa, 279.2 MPa, and 349.0 MPa.  
The experimental and calculated values of creep strain for glass-reinforced plastic (\(\Theta\) = 90\({}^{\circ}\)) at all four stresses are shown in Figure 5 for comparison. As it is seen, convergence of calculated creep strains to experimental ones is good.
# Vulnerable Road User Detection for Roadside-Assisted Safety Protection: A Comprehensive Survey
## 5 Discussion  
### Detection Models  
#### 5.1.1 Challenges  
The following are challenges found in the detection models:  
* Real-time Processing As GPU computing performance continues to advance, researchers are developing more complex models and exploring the fusion of diverse data across various modalities and scenarios. The goal is to train robust models with strong generalization capabilities that are not constrained by a single modality or scenario. However, in practical roadside assistance systems, deploying large-scale multi-modal models can be challenging due to limitations in computing performance and communication latency between sensors and the edge computer. Therefore, developing lightweight models that offer robust performance and high tolerance to variations presents a significant research direction in road-assisted detection systems.
* Non-rigid Small 3D Object Detection Given the small size of VRUs, accurately detecting them in 3D is inherently more challenging due to the limited information collected by sensors. Relying on a single sensor may not provide sufficient data, making sensor fusion a more effective solution to address the missing features and enhance detection accuracy. Considering the non-rigid characteristic of the VRUs, such as walking, running, sitting and standing, it will be more challenging to detect them compared with rigid objects like vehicles. Compared with 2D detection, there is much less research focusing on this issue in 3D detection with LiDAR, which may be the result of sparse points on VRU itself and most LiDAR's application in rigid object detection such as for vehicles. However, with the advancement of LiDAR resolution and the ability to capture more detailed VRU gestures, this area presents promising opportunities for future research.
* Domain Generalization from Onboard to Roadside In real-world applications, many pre-trained models are directly used for practical purposes, such as roadside surveillance. However, most pre-trained detection models that use point clouds as input are primarily designed and trained for onboard applications. A key question arises: how adaptable are these pre-trained onboard detection models to roadside detection tasks? Typically, when a LiDAR-based detection model trained on onboard datasets is directly applied to roadside data, performance degradation often occurs. This issue falls under domain generalization, where the challenge is ensuring the model performs well despite domain differences between development and deployment data, with minimal additional investment.
# Internet-of-Things and Information Fusion: Trust Perspective Survey
## 6 Internet-of-Things Architectures for Modeling Trust
### Summary  
Table 3 summarizes the existing architectures for modeling trust in IoT environments. Trust information records are stored either in the things, the fog, or the cloud. In the case of using an ordinary thing to store this information, critical constraints are applied when the candidate thing is selected. These constraints relate to power and computation capabilities, thing availability, and link quality metrics. As shown in Table 3, two architectures are suggested when an ordinary thing is used as a directory; centralized and distributed. With regards to the use of fog node as a directory, three architectures are suggested, namely centralized, distributed, or hybrid. Fully centralized architecture is suggested when the cloud is utilized for storing the trust information.  
From Table 3, it can be noticed that the centralized and distributed trust architectures have been considered thoroughly, while the hybrid architecture received insufficient consideration [13]. To the best of our knowledge, no work has been done for taking the advantages of centralized and distributed architectures except the study proposed by [44] which utilizes a cloudlet as an intermediate layer for storing trust information.
# QuietPlace: An Ultrasound-Based Proof of Location Protocol with Strong Identities
## 5 Technical Challenges
### Location Granularity Levels  
To support different location granularity levels, protocols in literature use mainly three techniques, with some variations. The first technique expresses the users' location using a grid. The granularity levels are the subdivisions of this grid [24; 25]. In the second one, multiple encryption, the prover encrypts all different location levels with a symmetric key and includes all of them in its proof [6]. Together with the proof, the prover sends the symmetric key that unlocks the desirable granularity level to the verifier. This technique produces proofs that are big in size, containing unnecessary information.  
In QuietPlace we used the third technique, hash chains, to include all possible location granularity levels in the proof without redundant information. There are many different variations of hash chains used in proof of location protocols [1; 2; 4; 16] all of which are compatible with our protocol. We used a simple hash chain as described in [24]. This chain can be constructed as shown in Figure 3.  
We consider n different location levels, where L\({}_{0}\) is the finest (highest accuracy) and L\({}_{n-1}\) the coarsest (lowest accuracy). The prover begins by calculating a random number, K\({}_{0}\) (seed). If h() is a hash function, then the prover produces the chain K = K\({}_{n}\)\(|\)K\({}_{n-1}\)\(|\)... \(|\)K\({}_{1}\)\(|\)K\({}_{0}\) where K\({}_{i+1}\) = h(L\({}_{i}\)\(|\)K\({}_{i}\)). We name K\({}_{n}\) as the head of the location chain.  
Witnesses receive the head K\({}_{n}\) and the finest information L\({}_{0}\), K\({}_{0}\) from the prover. By consecutive hashing, they can recalculate the head of the chain and make sure that it is correct and include it in their LPSs. In the final proof to the verifier, the prover has to include only the head of the chain, K\({}_{n}\) and the desirable granularity level \(L_{i}\) along with the corresponding part of the chain \(K_{i}\). Given this information, the verifier can reach the head of the chain, which has been validated as correct by the witnesses.  
The CA is unable to obtain any location-relevant information included in any message, as it is described in Section 6.