esa-sceva/satcom-chunk-collection huggingface dataset:
# Anti-Hyperglycemic Activity of Major Compounds from _Calea terifolia_
## 2 Results and Discussion
### HS-SPME Analysis  
In order to complete the volatile profile of the plant HS-SPME-adsorbed compounds were analyzed by GC-MS analysis. The divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) coated fiber extracted the highest total amount of volatile compounds, followed by the polydimethylsiloxane (PDMS). The total amount of volatile compounds extracted  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Compounds** & **IR** & **DVB/CAR/PDMS** & **PDMS** & **CAR/PDMS** & **PDMS/DVB** \\ \hline
12 & \(\alpha\)-Pinene & 996 & 4.25 & 0.44 & 3.55 & 3.19 \\
13 & Camphene & 1018 & 4.35 & 0.45 & 2.56 & 1.60 \\
14 & \(\beta\)-Pinene & 1046 & 7.19 & 0.82 & 2.41 & 1.21 \\
42 & \(\alpha\)-Cymene & 1109 & 0.95 & - & - & 5.45 \\
43 & \(d\)-Limonene & 1111 & 6.80 & - & - & 5.45 \\
15 & Eucalyptus & 1113 & 1.91 & 0.36 & 2.00 & 5.45 \\
16 & Camphor & 1242 & 4.06 & 1.79 & 9.89 & 5.10 \\
17 & Pinoxenone & 1261 & 1.40 & - & 1.84 & 0.88 \\
18 & 1,2,3-Trimethylcyclopentane & 1277 & - & 0.31 & 2.75 & 1.22 \\
19 & \(\delta\)-Pinocamphone & 1278 & 1.16 & 0.29 & 2.75 & - \\
44 & 4-Carvorneth & 1288 & 0.32 & - & - & - \\
45 & Thym & 1429 & 0.25 & - & - & - \\
46 & \(\alpha\)-Cubehene & 1526 & - & 3.83 & - & - \\
20 & \(\beta\)-Caryophyllene & 1527 & 3.31 & 3.83 & 4.01 & 1.99 \\
21 & \(\beta\)-Cubehene & 1539 & 1.17 & 1.24 & - & - \\
22 & \(\alpha\)-CJimene & 1563 & 0.98 & & 4.07 & 1.79 \\
23 & Germarcene D & 1580 & 1.33 & 0.41 & 4.07 & 1.55 \\
24 & Curcuminene & 1591 & 5.02 & 10.90 & 1.04 & 8.90 \\
47 & \(\alpha\)-Zingiberence & 1604 & 1.11 & 1.80 & - & - \\
25 & \(\alpha\)-Selmeene & 1607 & 0.66 & 0.79 & 1.19 & - \\
26 & Germarcene A & 1627 & 0.67 & 1.25 & 0.42 & - \\
27 & \(\delta\)-Cadinene & 1632 & 1.79 & - & 2.71 & 2.61 \\
28 & Calamenene & 1639 & 0.33 & 0.81 & 1.42 & 0.88 \\
29 & \(cis\)-Farnesene & 1641 & 1.37 & 1.83 & - & 0.50 \\
30 & \(\alpha\)-Calacorene & 1666 & 0.11 & 0.01 & - & 0.08 \\
31 & Hedicarol & 1678 & 1.36 & 1.22 & 2.18 & - \\
32 & (E)-Nerolidol & 1682 & 2.68 & 1.97 & 1.61 & 1.94 \\
33 & Aromadendrene oxide 2 & 1698 & 0.22 & 0.13 & - & - \\
34 & Spathulenol & 1714 & 4.40 & 7.58 & 3.83 & 4.12 \\
35 & Caryophyllene oxide & 1716 & 3.77 & 7.69 & 3.54 & 5.02 \\
48 & _trans_-Chrysanthemal & 1723 & - & 7.64 & 14.35 & 1.89 \\
49 & Carotol & 1738 & 3.15 & 4.38 & 4.00 & 7.00 \\
50 & Humulene epoxide II & 1748 & 0.87 & 0.93 & - & - \\
51 & \(\alpha\)-Muurololol & 1780 & - & 2.73 & - & - \\
38 & \(\tau\)-Cadinol & 1783 & 2.90 & - & - & 3.31 \\
39 & \(\beta\)-Eudesmol & 1800 & 1.12 & 3.81 & 2.14 & 5.49 \\
40 & \(\alpha\)-Bisabolol & 1829 & 0.52 & - & - & - \\
41 & Ledene oxide II & 1843 & 0.56 & - & - & - \\
1 & Demethylisoeneccalin & 1883 & 2.68 & 10.68 & - & 8.48 \\ \hline \end{tabular}
\end{table}
Table 1: Volatile components identified in _C. ternifolia_ by HS-SPME-GC-MS.  
with the carboxen/polydimethylsiloxane (CAR/PDMS) and polydimethylsiloxane/divinylbenzene (PDMS/DVB) fibers was lower. Chromene **1** was detected in good yields with fibers PDMS (10.68%) and PDMS/DVB (8.48%), and to a less extend with the DVB/CAR/PDMS (2.68%) one.
## 3 Problem Description and Formulation
### Network Model Construction for Logistics Network with Shortcuts  
Consider a city logistics distribution network, which includes a set of warehouses denoted by \(K\), a set of DCs denoted by \(L\), a set of delivery stations denoted by \(J\), a set of existing transportation links (inter DC links) denoted by \(E^{E}\), and a set of transportation links to be built denoted by \(E^{B}\). Let a physical network \(G=(N,L)\) be a collection of possible physical network elements, where \(N=K\cup L\cup J\), and all available and existing links are denoted by the set \(E=E^{B}\cup E^{E}\). For the logistics distribution service, the physical network also consists of many origin and destination pairs. An origin is a warehouse with an index of \(k\in K\) where commodities originate from, and a destination is a station with an index of \(j\in J\) where commodities terminate.  
Figure 4 shows an illustrative network in our problem. To address the challenge of cross-layer connections within the same echelon, we apply a network decomposition strategy, i.e., splitting the original echelon \(L\) into two new echelons, namely \(L\) and \(L^{\prime}\), as shown in Figure 3(b). Each pair of nodes in the sets \((L,L^{\prime})\) with the same index represents a physical location node in the set \(L\). Specifically, the combination of \(\left(l_{1},l_{1}^{\prime}\right)\) indicates the actual node \(l_{1}\). Table 5 provides detailed information about the mapping between the original network and the extended network.  
\begin{table}
\begin{tabular}{l l} \hline \hline \(x_{kl}\) & =1 if assign warehouse \(k\) to distribution center \(l\), = 0 otherwise \\ \(y_{l^{\prime}j}\) & =1 if assign station \(j\) to the distribution center \(l^{\prime}\), = 0 otherwise \\ \hline \hline \end{tabular}
\end{table}
Table 4: Decision variables used in models.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Definition** \\ \hline Sets & \\ \(N\) & Set of nodes in the physical network \\ \(E\) & Set of transportation links in the physical network \\ \(E^{B}\) & Set of transportation links to be built in the physical network, \\ \(E^{E}\) & Set of transportation links existing in the physical network, \\ \(E^{E}\in E\) & \(E^{E}\in E\) \\ \(K\) & Set of warehouses, \(K\in N\) \\ \(L,L^{\prime}\) & Set of DCs, \(L\in N,L^{\prime}\in N\) \\ \(J\) & Set of stations, \(J\in N\) \\ Indices & \\ \(k\) & Index of set \(K,k\in K\) \\ \(l\) & Index of set \(L\), \(l\in L\) \\ \(l^{\prime}\) & Index of the set \(L^{\prime}\), \(l^{\prime}\in L^{\prime}\) \\ \(j\) & Index of set \(J\),\(j\in J\) \\ Parameters & \\ \(d_{kl}\) & Distance from warehouse \(k\) to DC \(l\) \\ \(c_{kl}\) & Estimated cost from warehouse \(k\) to DC \(l\) \\ \(d_{ll^{\prime}}\) & Distance from DC \(l\) to DC \(l^{\prime}\) \\ \(d_{l^{\prime}j}\) & Distance from DC \(l^{\prime}\) to station \(j\) \\ \(f_{kj}\) & Demand for station \(j\) at warehouse \(k\) \\ \(cap_{(l,l^{\prime})}\) & Capacity of the DC \(l\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sets, indices, and parameters used in models.
## 3 Proposed Framework
### Localization with Combined Dictionary
#### 3.4.7 DBN Structure During Testing  
The testing phase of DBN as shown in Figure 8 is designed to operate without the odometry sensor, relying solely on the LiDAR testing dataset for ego vehicle localization. The testing DBN builds on the insights learned during training, focusing on matching observed tracks to the pre-classified tracks and using this information to estimate the ego vehicle's position.  
The DBN for testing operates as follows:  
1. Track Matching: Observed tracks in the LiDAR testing dataset are matched with the learned static tracks using the Bhattacharyya distance. This step ensures that the six static tracks identified during training are accurately re-associated with their counterparts in the testing environment, which shares a similar structure.
2. Prediction with MJPF: Once the tracks are matched, the MJPF predicts the ego vehicle's positions. The MJPF leverages the matched tracks as static references, using the learned motion dynamics and interaction models to estimate the ego state.
3. Dictionary Fusion for Robust Estimation: The matched track dictionaries are combined into a unified dictionary, as in the training phase. This fused dictionary incorporates all static tracks and provides probabilistic corrections to the MJPF predictions, ensuring that the ego vehicle's trajectory remains consistent with the observed environment.
4. Final Ego Position Estimation: The leftmost nodes of the DBN represent the final estimated positions of the ego vehicle. These nodes aggregate probabilistic influences from all matched tracks and the fused dictionary, enabling accurate localization even in the absence of direct odometry input.
# FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
## 5 FedOpt Evaluation
### Analysis of Communication Efficiency w.r.t Accuracy  
In this subsection, we compare the proposed compression algorithm SCA with respect to the number of epochs and the communicated bits that are required to achieve the targeted accuracy on a FL  
Figure 5: FedOpt communication efficiency on MNIST dataset.  
Figure 6: FedOpt communication efficiency on CIFAR-10 dataset.  
task. In the above subsections, FedOpt performed significantly better than FedAvg and PPDL. In order to have a meaningful comparison, we choose 100 users for 50 and 100 epochs, where every user holds 10 different classes and uses a batch-size of 20 during training. This setup of less number of users and epochs favours the FedAvg and PPDL. The rest of the parameters of the learning environment is the same as given in Table 1. We train the datasets until the targeted accuracy is achieved in the given number of epochs and measure the total communicated bits both for upload and download. The required amounts of upstream and downstream communication bits to achieve the targeted accuracy is given in megabytes (MB) in Table 2.  
In Table 2, FedOpt communicates 14.6 MB and 172.3 MB of data on MNIST and CIFAR-10 datasets, which is a reduction in communication by a factor of \(\times 152\) and \(\times 207\) as compared to baseline configurations. Meanwhile, FedAvg and PPDL (\(epochs=100\)) requires 84.73 and 63.74 MB of data on MNIST dataset and 1665.7 and 958.3 MB of data on CIFAR-10 dataset which proves that proposed FedOpt have a minimum delay period in order to achieve the targeted accuracy within a given number of training epochs.
# Reliability Assessment of Space Station Based on Multi-Layer and Multi-Type Risks
## 4 Risk Features Analysis of Space Station  
In general, risk features analysis only contains occurrence likelihood and consequence severity when using the risk matrix method [40], and propagation characteristic analysis is not required. However, the propagation characteristic becomes the third important feature of the configuration risks because of the serial implementations of the space station configuration and risks' close association. In this section, the occurrence likelihood and consequence severity of risks are analyzed based on the qualitative risk matrix method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Risk Layer** & **Risk Type** & **Risk Number** \\ \hline \multirow{4}{*}{Between-mission risks (57)} & Technology risk & 12 \\  & Management Risk & 5 \\  & Product risk & 23 \\  & Operation risk & 17 \\  & Technology risk & 34 \\ Between-system risks (166) & Management risk & 11 \\  & Product risk & 87 \\  & Operation risk & 34 \\  & Technology risk & 56 \\ Inner-system risks (234) & Management risk & 18 \\  & Product risk & 113 \\  & Operation risk & 47 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Numbers of Multi-layer and Multi-type risks identified.  
Figure 2: Configuration risks definition of the space station.  
From Table 4, there are 52 level I risks, and these risks can be regarded as the key risks. Engineering experience shows that the other risks cause limited damage to the configuration mission, and they can be ignored because of their lower risk levels. This can reasonably simplify the configuration reliability model and assessment.
## 4 Experimental Results and Discussions
### Permeability Test  
The permeability test, conducted in accordance with the specifications outlined in KS F 2394, aimed to determine the permeability characteristics of the soil brick [24]. The results of the test revealed a permeability coefficient for the soil brick ranging from 1.5 to 1.9 \(\times\) 10\({}^{-2}\) cm/s. This coefficient is higher than that observed in conventional cementitious bricks. The permeability coefficient was found to increase by up to 1.13 times as the wood chip content increased from 0.5% to 1.5%, as shown in Figure 10. In contrast, an increase in the stabilizer content resulted in a decrease in the permeability coefficient by as much as 7%. Considering the quality standards stated in KS F 4419 [28], which indicate that permeable bricks should have a permeability coefficient of 0.01 cm/s and soil-concrete bricks should have a permeability coefficient of 1.0 \(\times\) 10\({}^{-3}\) cm/s, the soil brick employed in this study satisfies the requirement for a permeable brick.
# Design and SAR Analysis of a Dual Band Wearable Antenna for WLAN Applications
###### Abstract  
This paper presents the design of three types of dual band (2.5 & 5.2 GHz) wearable microstrip patch antennas. The first one is based on a conventional ground plane, whereas the other two antennas are based on two different types of two-dimensional electromagnetic band gap (EBG) structures. The design of these two different dual-band EBG structures using wearable substrates incorporates several factors in order to improve the performance of the proposed conventional ground plane (dual band) wearable antenna. The second EBG with plus-shaped slots is about 22.7% more compact in size relative to the designed mushroom-like EBG. Subsequently, we have demonstrated that the mushroom-like EBG and the EBG with plus-shaped slots improve the bandwidth by 5.2 MHz and 7.9 MHz at lower resonance frequencies and by 33.6 MHz and 16.7 MHz at higher resonance frequencies, respectively. Furthermore, improvements in gain of 4.33% and 16.5% at a frequency of 2.5 GHz and improvements in gain of 30.43% and 4.57% at 5.2 GHz have been achieved by using the mushroom-like EBG and EBG with plus-shaped slots, respectively. The operation of the conventional ground plane antenna is investigated under different bending conditions, such as wrapped around different rounded body parts. The proposed conventional ground plane antenna is placed over a three-layered (flat body phantom (chest)) and four-layered (rounded body parts) tissue models, and a thorough SAR analysis has been performed. It is concluded that the proposed antenna reduces SAR effects (<2 W/kg) on the human body, thereby making it useful for numerous critical wearable applications.  
w +
Footnote †: journal: Medical Imaging  
1
Footnote 1: email: _Applied_  
28 July 2022  
Accepted: 12 September 2022  
Published: 14 September 2022
###### Abstract  
In numerical simulations, achieving high accuracy without significantly increasing computational cost is often a challenge. To address this issue, this paper proposes an improved finite volume Weighted Essentially Non-Oscillatory (WENO) scheme for structured grids. By employing a single-point quadrature rule to perform flux integration on the control volume faces, this scheme is designed for use in NUAA-Turbo three-dimensional fluid solvers based on structured grids, utilizing RANS and RANS/LES coupling to simulate turbomachinery flows. Firstly, the new WENO scheme is validated against classical numerical test cases to evaluate its stability and reliability in handling discontinuities, double Mach reflection problems, and Rayleigh-Taylor (RT) instability. Compared to the original scheme, this improved finite-volume WENO scheme demonstrates better stability near discontinuities and more effectively resolves flow features at the same grid resolution. Next, for engineering applications related to turbomachinery, such as compressor and turbine characteristics, calculations using RANS are performed and the results obtained with WENO-ZQ3 and WENO-JS3 are compared. Finally, the new fifth-order WENO scheme is applied to RANS/LES coupling simulations of turbine wake and film cooling. The results indicate that the improved finite-volume WENO scheme provides better stability and accuracy in engineering applications. For instance, the average error in calculating compressor efficiency characteristics is reduced from 0.76% to 0.05%, the error in turbine vane pressure distribution compared to the experimental values is within 1%, and the error in film cooling efficiency centerline distribution compared to the experimental values is within 3%. Additionally, the qualitative results of turbine wake and film cooling show that even with a small number of grid points, more detailed flow physics can be captured, thereby reducing computational costs in aerodynamic applications.  
WENO scheme; turbomachinery flow; NUAA-Turbo; RANS; RANS/LES coupled simulations 2024145085 202414
## 4 Experimental Results
### Experiment Analysis  
#### 4.2.1 Synthetic Data  
In the experiments, we varied the support from 0.07% to 0.03% for FDCNB-MINING and FLR-mining in six different simulation cases of network bandwidth to study its effect on proportions of execution time. The number of transactions, average frequent itemset length, number of items, and average transaction length were 100 K, 5, 100 K, and 20, respectively. Network bandwidth had six simulation cases, which are shown in Table 1.  
The experimental results shown in Figure 2 reveal that the proportion of the execution time of FDCNB-MINING/FLR-mining was close to 1 in Case 1. This finding represents the addition of a monitor node in the stable network; our proposed method exerted a minimal effect on the overall execution efficiency. In the unstable bandwidth in Case 2, FDCNB-MINING required 59.60% of the execution time of FLR-mining when the support was 0.07%. FDCNB-MINING required 51.79% of the execution time of FLR-mining when the support was 0.03%. Thus, FLR-mining had poor execution efficiency when an unequal or unstable network bandwidth was encountered. FLR-mining asks the connection node to obtain the available computing node by node order, and it does not consider the unequal or unstable bandwidth in a real network.  
\begin{table}
\begin{tabular}{c|c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Network Bandwidth (Mbps)**} \\ \hline
**Node** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Case 1 & \multicolumn{8}{c}{Bandwidths are all 100} \\ \hline Case 2 & 3 & 80 & 6 & 77 & 35 & 69 & 87 & 63 & 14 & 22 \\ \hline Case 3 & 38 & 60 & 88 & 95 & 100 & 36 & 80 & 69 & 68 & 20 \\ \hline Case 4 & 87 & 19 & 36 & 100 & 38 & 92 & 25 & 61 & 76 & 68 \\ \hline Case 5 & 34 & 15 & 42 & 41 & 50 & 82 & 90 & 57 & 56 Therefore, we propose the FDCNB-MINING method to improve the problem of varying network bandwidth environments through the monitor node helping the kernel node to select the best node. For other simulation cases, the results show that the proposed method FDCNB-MINING delivered excellent performance in terms of execution efficiency and scalability compared with the FLR-mining algorithm. The detailed execution time ratio is shown in Table 2. In the experiments, we also observed that the average proportion of execution time of FDCNB-MINING to that of FLR-mining was about 85%, meaning that the proposed method can save 15% of the execution time of FLR-mining for the synthetic datasets.
# Influence of Partial and Total Replacement of Used Foundry Sand in Self-Compacting Concrete
## 2 Materials and Methods
### Mix Proportions  
For this study, three mixes were designed based on the EFNARC guidelines [22] with the amount of coarse aggregate set at 60% vol. All mixes use the same amount of CEM 52.5 R, effective water and Master Ease 5025 superplasticizer. The same amount of LC is also used but a different amount of three types of sand. The three mixes are described below based on the use of one sand or another:  
* A control mix (SCC-C) using only LS and SS adjusted by [PERSON]'s method.
* A mix that, without using SS, combines UFS with LS (SCC-UFS-100) to obtain an overall particle size distribution as similar as possible to the SCC-C mix. For this purpose, adjustment equations with the granulometric modulus of the sands were proposed.
* A mix that uses half the UFS of the previous mix (SCC-UFS-50) and allows for the use of SS. The proportion of SS and LS is calculated using the adjustment equations with their granulometric modulus.
* The mix proportions are shown in Table 3.  
The proposed procedure for mixing concrete is necessary because of the use of UFS. Its high absorption compared to LS and SS makes the correct homogenization in the mixture  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Material** & **SiO2** & **CaO** & **Al2O3** & **Cr2O3** & **Fe2O3** & **SO3** & **K2O** & **MgO** & **TiO2** & **P2O5** & **Others** \\ \hline Cement & 17.81 & 65.6 & 4.65 & - & 3.25 & 4.50 & 0.60 & 1.2 & 0.2 & - & - \\ \hline UFS & 83.9 & 7.83 & 2.87 & 1.67 & 1.40 & 0.72 & 0.70 & 0.53 & 0.13 & 0.06 & \textless{}0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: XRF chemical composition of cement and UFS.  
Figure 1: Grading curves of the aggregates.  
of the materials difficult, so to guarantee this, longer mixing and resting times are proposed. The fabrication procedure of the three SCC mixes is described below (Figure 2):  
* Determine the moisture content of aggregates.
* Weigh the aggregates, and moisten the concrete mixer beforehand.
* Add the aggregates from the largest to smallest and add the cement and powder material.
* Mix all the materials for 3 min.
* Add half of the mixing water without superplasticizer (this addition will not take longer than 20 s) and mix for 3 min.
* Add the second part of the water with the superplasticizer and mix for 3 min.
* Stop the concrete mixer and allow the concrete to settle for 3 min.
* Finally, start the concrete mixer and mix the concrete for a further 5 min.