esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Crystal Structure  
The crystal structure of the Gd\({}_{7}\)(BO\({}_{3}\))(PO\({}_{4}\))\({}_{2}\)O\({}_{6}\) compound is shown in Figure 1a, where each Gd\({}^{3+}\) ion is surrounded by seven oxygen atoms, forming a polyhedral structure. Figure 1b presents the crystal structure from different perspectives, clearly revealing the layered stacking feature along the \(a\)-axis direction. It is worth noting that the nearest-neighbor distance between the stacked Gd\({}^{3+}\) ions along the \(a\)-axis direction is 3.79 A, indicating the presence of complex three-dimensional magnetic interactions in this compound. To better illustrate the uniqueness of the crystal structure, Figure 1c provides a magnified view of the local structure. The black dashed lines serve as a visual guide to observe the occupancy characteristics of Gd\({}^{3+}\) ions in the lattice. The neighboring Gd\({}^{3+}\) ions form a magnetically frustrated structure, with the distances between adjacent Gd\({}^{3+}\) ions ranging from 3.68 to 3.88 A. This unique structure leads to disorder and competition among the magnetic moments, which is expected to result in a lower magnetic ordering temperature. Figure 2 presents the refined powder X-ray diffraction (XRD) pattern of the Gd\({}_{7}\)(BO\({}_{3}\))(PO\({}_{4}\))\({}_{2}\)O\({}_{6}\) compound at room temperature. The compound crystallizes in a monoclinic structure with space group No. 14 (P2\({}_{1}\)/c). The lattice parameters are determined to be \(a\) = 6.6924 A, \(b\) = 17.2453 A, and \(
## 3 Proposed Protocol
### Key Revocation Mechanisms
#### 3.3.1 Certificate Revocation List (CRL)  
CRL is a widely used method for revoking certificates. In this method, a trusted authority maintains a list of revoked certificates called the CRL. When a node attempts to access the network, its certificate is checked against the CRL. If the certificate is listed on the CRL, the node is denied access to the network. The CRL is updated periodically to remove expired certificates and add new revoked certificates.
## 2 Materials and Methods
### Cell Biological Investigations
#### 2.2.2 Scanning Electron Microscopy (SEM)  
MG-63s (20,000 cells/cm\({}^{2}\)) were cultured for 1 h on the PPAAm-modified Ti arrays as previously described [34]. Briefly, after fixing with 2.5% glutaraldehydehehyde (Merck, Darmstadt, Germany) at 4 \({}^{\circ}\)C overnight and rinsing with 0.1% sodium phosphate buffer (according to [PERSON], Merck, Darmstadt, Germany), the samples were dehydrated through an ethanol series of 30, 50, 75, 90, and 100% (for 5, 5, 15, 10, and 10 min, respectively) and dried in a critical point dryer (K 850, EMITECH, Taunusstein, Germany). The samples were sputtered with gold for 50 s (15 nm, SCD 004, BAL-TEC, Wetzlar, Germany). Field emission scanning electron microscopy (FE-SEM, ZEISS Merlin VP compact, Carl Zeiss, Oberkochen, Germany) was conducted with a high efficiency secondary electron detector HE-SE2 at magnifications of 2000\(\times\) and 5000\(\times\) (5 kV, 5.1 mm working distance).  
Actin Staining and FilaQuant Software  
Actin staining: 20,000 MG-63 cells/cm\({}^{2}\) were cultured on the Ti arrays for 1 and 24 h. After fixing them with 4% PFA, cells were washed twice with phosphate-buffered saline (PBS, Sigma-Aldrich Chemie, Taufkirchen, Germany) and permeabilized with 0.1% Triton X-100 (5 min, RT) (Merck, Darmstadt, Germany). Afterwards, cells were incubated with Alexa 546-phalloidin (diluted 1:100, Invitrogen, Germany) for 30 min in the dark at RT, washed twice, and embedded with a cover slip in FluoroshieldTM with 4\({}^{\prime}\),6-diamidino-2-phenylindole (DAPI, Sigma-Aldrich Chemie, Taufkirchen, Germany). Actin was examined using the LSM780 with a Plan-Apochromat 63x oil immersion objective (Carl Zeiss; zoom 0.6 and 2) and the ZEN software (ZEISS efficient navigation, ZEN 2011 SP4, black edition, Carl Zeiss). Microscope images were converted to 8 bit and a size of 512 \(\times\) 512 pixels using the software ImageJ 1.51f ([PERSON], National Institutes of Health, USA).  
FilaQuant analysis: Contrast and brightness settings were adjusted identically for all LSM images. Subsequent actin filament quantification via mathematical image processing was conducted by the software FilaQuant 2011 (University of Rostock, Institute of Mathematics, Mathematical Optimization) [46]. The main algorithm is based on a relaxation of the analytical criterion for a ridge, which is expressed in terms of a ridgeness measurement (line detection and line tracking). The parameters filament number, total filament length, maximum filament length, and mean filament length were evaluated for single cells (\(n=10\) cells per surface). Software settings were as follows: top-hat radius 10-12, noise penalizer 0.2-0.3, min. edge-length 4.4-5, max. edge-length 30-34.7.
# Pido: Predictive Delay Optimization for Intertidal Wireless Sensor Networks
## 4 Design of Pido
### Performance Evaluation of Pido
#### 4.4.2 Simulation Settings  
We simulate IT-WSNs in MATLAB (2017a, MathWorks, Natick, MA, USA) with variant network sizes from 49 to 225 (number of nodes) and tidal periods, and we summarize the simulation parameters in Table 3. Nodes are uniformly distributed and the average distance between nodes is 50 m. The base station is located at the center of the network (on the ground, which will not be submerged by water). In the simulation, the maximum communication range of nodes is 150 m, and nodes are allocated with location coordinates \((x_{i},y_{i})\). The distance between node \(i\) and node \(j\) is calculated by \(\sqrt{(x_{i}-x_{j})^{2}+(y_{i}-y_{j})^{2}}\). Besides that, we set a countdown timer for each node to record at which time the node will be submerged. To simulate unpredictable waves, we add a strong Gaussian noise at each node.  
We implement Pido, CTP and ORW as routing protocols for the simulation. At the beginning of the simulation, each node is buffered with variant number of packets (from 20 to 80). Nodes generate one packet per cycle, and we set the system cycle as 1 min in the simulation. The ETX for CTP, the EDC for ORW and link delay for Pido are calculated by the signal attenuation model with a strong Gaussian noise we mentioned above. To evaluate the energy consumption equally, we set the retransmission times of Pido, CTP and ORW as 10.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Layout Related Problems in WSNs  
Layout of wireless sensor network deals with determining optimal location of sensor node in order to maximize the coverage, minimize energy consumption and to prolong the life time of the network. For example in [125], the authors have used a multi-objective optimization technique to solve the sensor layout problem with the objective of minimizing energy consumption and the number of nodes while considering the constraint of full coverage. Sensor layout problem has been addressed in [126] to minimize the number of sensors used while maximizing the quality of information contained in the measurement data for the identification of structural damage. A multi-objective optimization formulation has been suggested in [127] for the optimal layout of wireless sensor network. The proposed approach obtained a trade-off between maximization of the coverage and the life time of the network. Furthermore, the authors also investigated the impact of sensing range and communication range on the optimal layout. An energy efficient layout strategy has been proposed in [128] to maximize the coverage and life time of the network by using a multi-objective particle swarm optimization algorithm.  
\[\begin{split} f_{1}(x)&=Length(x)\\ f_{2}(x)&=Max\left(\{E(x_{i})\}_{i=1}^{f_{1}(x)} \right)\end{split} \tag{31}\]\[\begin{split} Coverage&=[U_{i=1}^{n}R_{Sensor}^{2}(x_{i},y_{i})/ Area]\\ Lifetime&=\min_{i=1,..,n}(T_{failure,i})/T_{ max}\end{split} \tag{32}\]  
where \(E()\) is the function related to consumed energy.
# Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks
## 4 Mathematical Models for Energy-Aware Routing Calculation
### Optimization Problems Complexity Estimation  
To assess the computational complexity of the optimization problems defined in the above section, we must calculate the number of variables and constraints. For the standard fat-tree topology with \(k\) port switches, the number of network nodes \(N\) and the maximal number of servers \(S\) are equal to \(N=k^{2}+(k/2)^{2}\) and \(S=k^{3}/4\), respectively. The number of links \(L\) connecting switches can be calculated as \(L=2k(k/2)^{2}\).  
Table 1 shows task dimensionality for a fat-tree network constructed of switches with port numbers varying in the range of 4 to 48 and a rather simplistic scenario of one flow per every of servers (\(M=S\)), where \(M\) denotes the total number of flows. The first row of the table corresponds to the fat-tree network with the number of servers reduced to 1 per lowest level switch.  
It can be seen that the number of variables and constraints grows extremely quickly and explodes for networks with switches larger than eight ports. Therefore, the exact solution with the MIP solver may be found only for the small dimension networks. To calculate energy-efficient routing for larger networks, we need fast heuristics; however, the MIP task solution may be used as a benchmark.
## 4 Results and Discussions
### Improvements Interpolation  
To further explore the features of CFST-LSTM, this study interpolates the R\({}^{2}\) improvements geospatially. The inverse distance weighted (IDW) technique of GIS [48] is used to visualize and interpolate the value of \(R_{\Delta}^{2}=R_{C}^{2}-R_{O}^{2}\) in the map of California. The spatial change of \(R_{\Delta}^{2}\) is presented in Figure 10. Red means the value of \(R_{\Delta}^{2}\) is high while yellow means the value of \(R_{\Delta}^{2}\) is low. It can be seen that for areas with denser stations, the improvement is higher than those with sparser stations. This is because in areas with higher density of stations, more stations remained during the modeling process of CFST-LSTM, and therefore more related information are utilized to learn the temporal patterns. However, in areas with lower density of stations, only one or two stations might be retained by CFST-LSTM as the inputs. The prediction result, therefore, could be similar to the ordinary model, which uses the historical data of the target station only.
## 4 Results and Discussion
### Parametric Study
By comparing with Figure 24, it is found that this is the exact location of the maximum dislocation displacement. Thus, it can be perceived that the dislocation of the deformation joints absorbs energy and causes the corresponding stresses to decrease. Therefore, the existence of deformation joints makes the shear deformation of tunnel linings occur at two sides of the deformation joints, and thus partially releases the stress of the tunnel linings near work shafts. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multicolumn{3}{c}{**Largest Tensile Stress**} & \multicolumn{3}{c}{**Largest Compressive Stress**} \\ \cline{2-7}  & **Without** & **With** & **Change (\%)** & **Without** & **With** & **Change (\%)** \\ \hline S1 RI & 1.698 & 1.548 & \(-\)8.83 & 1.716 & 1.610 & \(-\)6.18 \\ S2 LE & 1.208 & 1.125 & \(-\)6.87 & 1.222 & 1.157 & \(-\)5.32 \\ S2 RI & 1.266 & 1.176 & \(-\)7.11 & 1.526 & 1.429 & \(-\)6.36 \\ S3 LE & 1.894 & 1.718 & \(-\)9.29 & 2.861 & 2.538 & \(-\)11.29 \\ S3 RI & 2.634 & 2.194 & \(-\)16.7 & 2.793 & 2.349 & \(-\)15.9 \\ S4 LE & 3.027 & 2.661 & \(-\)12.09 & 2.841 & 2.383 & \(-\)16.12 \\ S4 RI & 2.548 & 2.278 & \(-\)10.6 & 2.838 & 2.568 & \(-\)9.51 \\ S5 LE & 2.589 & 2.348 & \(-\)9.31 & 2.730 & 2.487 & \(-\)8.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Effect of the deformation joints on maximum stresses of the tunnel. Figure 24: Modulus of maximum dislocation displacement of each deformation joint.
## 3 Challenges and the State of the Art Progresses
### Performance Metrics
We also tested the scalability of Ethereum in terms of the time and energy consumption with the increase of stored data. We ran the evaluation in the following environment. An Ethereum client Geth (1.8.3-stable) ran on a computer (MacBook Pro 2017 version) as well as an Ethereum Wallet 0.10.0. The hardware configuration was a macOS 10.14.6 operating system, a CPU with 2.3 GHz, an i5 version Intel Core and a memory with 8 GB of 2133 MHz LPDDR3. We explain the performance by testing gas cost and time consumption for documents stored on the blockchain. As shown in Figure 3, we found that, with the increase of documents, gas cost increases steadily. When the amount of documents is 10, the gas cost is 151,408. For additional 10 documents stored, 100,000 more gas could be cost. As shown in Figure 4, we found that, with the increase of documents, it shows an increasing trend. When the amount of documents is 10, the time cost is 952.001 s. When the amount of documents is 100, the time cost is 5636 s. The growth range of time cost is almost linear, which proves that the storage performance of Ethereum is high performance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Ethereum** & **Parity** \\ \hline Throughput & hundreds of tps & tens of tps (no more than 80 tps) \\ Latency & around 100 seconds & several seconds \\ Scalability & degrade linearly & keep constant \\ Fault tolerance & unaffected & unaffected \\ Security & vulnerable & vulnerable \\ \hline \hline  & **Hyperledger** & **IOTA** \\ \hline Throughput & thousands of tps & 127 tps on a 250-node network \\ Latency & tens of seconds & approach zero when the network reaches a certain size \\ Scalability & can not scale up to 16 nodes & scalability increases with more users \\ Fault tolerance & stop working after \(\frac{1}{3}\) nodes fail & asynchronous BET and works with more than \(\frac{3}{3}\) honest nodes \\ Security & guaranteed safety & vulnerable \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of popular blockchain platforms. Figure 3: The gas cost with the increased documents in Ethereum.
## 3 Wind Tunnel Test of Section Model
### VIV Responses of Aerodynamic Measures of Deflector Plate
#### 3.3.5 Lower Chord and Web Deflector Plates  
The web deflector plate and the lower chord deflector plate were added to the original section at the same time. The web deflector plate was arranged symmetrically on both outsides of the webs along the truss girder, and the lower chord deflector plate was arranged symmetrically on the outside along the lower chord of the truss. The position and shape of the lower chord and web deflector plates are shown in Figure 19.  
After the lower chord and web deflector plates were added, the relationship between the vertical and torsional VIV amplitudes and the wind speed under attack angles of \(+3^{\circ}\) and \(+5^{\circ}\) was determined (Figure 20).  
Figure 18: Wind speed–amplitude curve of WD working condition.  
Figure 19: Working condition of lower chord and web deflector plates.  
Figure 20: Wind speed–amplitude curve of LD + WD working condition.  
It can be seen from Figure 20 that when the wind attack angle was +3\({}^{\circ}\), the vertical and the torsional peak amplitudes of the UD working condition decreased compared with those of the OS working condition. After setting the lower chord and web deflector plates on the original section, the peak amplitude of vertical VIV decreased from 75.54 mm to 33.83 mm, and the vertical VIV locking range also changed from 5.24 m/s-6.72 m/s to 5.49 m/s-6.43 m/s. The peak amplitude of torsional VIV decreased from 0.181\({}^{\circ}\) to 0.062\({}^{\circ}\), and the torsional VIV major locking range also changed from 12.27 m/s-18.06 m/s to 13.85 m/s-15.92 m/s. At a +5\({}^{\circ}\) attack angle, the peak amplitude of vertical VIV decreased from 98.23 mm to 43.44 mm, and the vertical VIV locking range also changed from 5.01 m/s-6.69 m/s to 4.94 m/s-6.34 m/s after setting the lower chord and web deflector plates on the original section. The peak amplitude of torsional VIV decreased from 0.211\({}^{\circ}\) to 0.077\({}^{\circ}\), and the torsional VIV major locking range also changed from 13.75 m/s-19.49 m/s to 14.68 m/s-18.04 m/s. The right-angled lower chord and web deflector plates can well suppress vertical and torsional VIV at +3\({}^{\circ}\) and +5\({}^{\circ}\) wind attack angles, greatly reduce the peak amplitude of VIV, and shorten the length of the VIV locking range. At wind attack angles of +3\({}^{\circ}\) and +5\({}^{\circ}\), the suppression effect on the maximum amplitude of vertical VIV reaches 55.22% and 55.78%, respectively, and the suppression effect on the maximum amplitude of torsional VIV reaches 65.75% and 62.92%, respectively.
Molecules2010159184-9196; doi:10.3390/molecules15129184  
_molecules_ ISSN 1420-3049  
www.mdpi.com/journal/molecules  
_Article_  
Volatile Compounds of Young Wines from Cabernet Sauvignon, Cabernet Gernischet and Chardonnay Varieties Grown in the Loess Plateau Region of China  
**[PERSON] 1** and [PERSON] 1,2,*  
* 1 College of Enology, Northwest A&F University, Yangling 712100, China; E-Mail: [EMAIL_ADDRESS] (B.J.)
* 2 Shaanxi Engineering Research Center for Viti-Viniculture, Northwest A&F University, Yangling 712100, China
* 3 Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86 29 87091847; Fax: +86 29 87091099.  
_Received: 6 October 2010; in revised form: 6 December 2010 / Accepted: 8 December 2010 / Published: 10 December 2010_  
**Abstract**: In order to elucidate the aroma components of wine produced in the Loess Plateau region of China, volatile compounds of young wines from Cabernet Sauvignon, Cabernet Gernischet and Chardonnay varieties grown in the new ecological region were investigated for the first time in this research. Among the volatile compounds analyzed by HS-SPME with GC-MS, a total of 45, 44 and 42 volatile compounds were identified and quantified in Cabernet Sauvignon, Cabernet Gernischet and Chardonnay wines, respectively. In the volatiles detected, alcohols formed the most abundant group in the aroma compounds of the three wines, followed by esters and fatty acids. According to their odor active values (OAVs), 18 volatile compounds were always present in the three wines at concentrations higher than their threshold values, but ethyl octanoate, ethyl hexanoate, and isoamyl acetate were found to jointly contribute to 92.9%, 93.3%, and 98.7%, of the global aroma of Cabernet Sauvignon, Cabernet Gernischet and Chardonnay wines, respectively. These odorants are associated with \"fruity\" and \"ripe fruit\" odor descriptors.  
**Keywords:** volatile compounds; young wines; Loess Plateau region; GC-MS
## 3 Principals of Routing Protocols and Their Breakdowns
### Geographical Information-Based Routing Protocols
#### 3.2.10 VBF [54]  
Vector-based forwarding (VBF), a new routing protocol, is suggested to offer robust and energy-efficient routing for UWSNs. Each data packet in VBF contains location information for the source, destination, and forwarding nodes, and the forwarding path is defined by a vector placed inside a routing pipe from the source to the destination node. The VBF protocol is a location-based routing method: data is sent from the source to the destination by nodes adjacent to the vector. Furthermore, VBF has a self-adapting algorithm that allows nodes to assess the advantages of forwarding data packets to conserve energy. However, using the same node over time drains them faster than other nodes, which creates an unbalanced system.  
* Comparison Here, the network cost ranges from medium to low. All of the protocols use a Hello packet in the initialization phase. The differences between the protocols are illustrated in Table 12.
* Pressure-based Routing Protocols We can detect the sensors at different sea levels using pressure sensors as the pressure at different levels is different and distinguishable. The greedy routing approach is preferred for routing path selection as most sensors are inexpensive and equipped with low-budget pressure sensors. Nodes calculate the position using the pressure sensor, and most protocols use a greedy approach to find the shortest part from the node to the sink. One advantage of pressure-based sensors is that they use only the location or the depth information to forward the data; thus, there is no extra overhead; pressure-based routing protocols give more accurate outcomes than others.
# Dynamic Mechanical Characteristics of Horseshoe Tunnel Subjected to Blasting and Confining Pressure
## 3 Dynamic Response with Different Blast Loading Direction  
### Dynamic Stress Field Propagation  
Numerical modeling was conducted under different impact conditions, and the stress field was discussed in the following sections. Figure 5 shows the Von Mises stress field in five specimens in which the sidewall, spandrel, vault, corner and bottom floor were selected respectively as front blasting faces. The reflected wave is generated near the tunnel-free face. The stress wave interaction has a significant influence on radial crack growth. The central shock wave origin from the left borehole at 5 \(\upmu\)s after blast loading, and the pressure is 150 MPa around the wall, which exceeds the rock dynamic strength. Smaller cracks appear around the borehole, and many radial cracks extend to the free boundary. The propagation of stress waves and cracks are similar around the borehole in five specimens before 40 \(\upmu\)s.  
The initial stress wave arrives at the tunnel-free face, and the reflected wave is formed at 40 \(\upmu\)s, as seen in Figure 5a. The superposition of stress waves is significant and promotes horizontal crack growth. When the cracks reach the tunnel at 80 \(\upmu\)s, the central through crack connects the borehole and tunnel in spacemen 1, 3 and 4, and the oblique cracks are formed in specimens 2 and 5. The stress wave reduces with the spreading distance increase, and the average values are lower than rock strength. The initial stress wave decreases when arriving at the tunnel's back face, and the superimposed stress zone is formed at 100 \(\upmu\)s. The cracks appear mostly around the corner position in specimens 2 and 3 (Figure 5b,c). Two cracks grow along the vertical and horizontal directions in the tunnel's lower right corner, as seen in Figure 5c.  
Figure 4: Finite element model with confining pressure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Confining Pressure** & \(P_{\text{h}}\)**/MPa** & \(P_{\text{v}}\)**/MPa** & \(k=P_{\text{h}}\)**/\(P_{\text{v}}\)** \\ \hline \multirow{3}{*}{Uniform confining pressure} & 5 & 5 & 1 \\  & 10 & 10 & 1 \\  & 15 & 15 & 1 \\ \hline \multirow{3}{*}{Non-uniform rock pressure} & 15 & 2.5 & 6 \\  & 15 & 5 & 3 \\ \cline{1-1}  & 15 & 7.5 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Different initial in-situ stress loading conditions (\(P_{\text{h}}\) is the horizontal confining pressure; \(P_{\text{v}}\) is the vertical confining pressure, \(k=P_{\text{h}}\)/\(P_{\text{v}}\)).  
The stress wave superposition process is complicated after 100 us because the reflected stress wave shape is controlled by the tunnel structure and free boundary. The orthogonal damage area appears around the borehole, and the partial failure around the tunnel is controlled by concentrated stress.