esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Localization Algorithm Performance
The utilization of all six types of pseudo-range observations offers the best signal geometry in both data sets. This effect is more pronounced in data set 1, where a PDOP less than four is attained in approximately 84% of epochs, proving that multi-constellation GNSS is most beneficial in situations with a high amount of signal blockage. For data set 2, a PDOP less than four is achieved in approximately 98% of epochs, offering no significant benefit in comparison to single-frequency GPS. Looking at the complete graphs emphasizes the benefits of being able to process all types of pseudo-ranges simultaneously: When compared to the results obtained with L2C-L1 C/A and E5a-E1 observations, the PDOP is lower in all epochs. The largest improvement is observed in the right third of each plot, corresponding to epochs with a relatively low number of available L2C-L1 C/A and E5a-E1 observations. The following subsections deal with estimation accuracy. Accuracy is quantified by the estimation error \(\hat{x}\) and its statistical distribution. \(\hat{x}\) is defined as the difference between the value \(\hat{x}\), estimated by the localization algorithm being evaluated, and the reference value \(x_{ref}\). \[\hat{x}(k)=\hat{x}(k)-x_{ref}(k) \tag{15}\]  
\(k\) is an arbitrary point in time and takes on values between 1 and \(N_{j}\in\mathbb{N}\) during one data set \(j\). The reference value is provided by the reference trajectory. Depending on the type of quantity being evaluated, \(\hat{x}\) can be a scalar or a vector. In both cases, the error is a random variable and fully characterized by its _cumulative distribution function_ (CDF) [19]. Section 5.1 focuses on the accuracy of attitude and velocity estimation. Because the main improvement in these quantities compared to the basic GPS L1 algorithm without odometry stems from odometry measurements, the multi-frequency/multi-constellation algorithm with odometry turned off is compared to the same algorithm, but with odometry turned on. Section 5.2 focuses on the accuracy of position estimation. Because the main improvement in the determination of absolute position compared to the basic GPS L1 algorithm stems from the inclusion of multi-frequency/multi-constellation pseudo-ranges, the three different stages of algorithm development (see Sections 2.1-2.3) are compared to each other. To illustrate the improvement in positioning accuracy provided by odometry measurements in GNSS-denied environments, the position error when driving through tunnels with and without odometry aiding is presented as well.
## Appendix A The MISR Instrument and MISR-HR Products
### The MISR-HR Processing System
The parametric bidirectional reflectance model of [PERSON] et al. [49] is subsequently inverted against the nine surface directional measurements available for each location, in the four spectral bands, to document the spectral anisotropy of the surface. Three model parameters are retrieved in each band, together with estimates of their associated uncertainties: a base reflectance \(\rho\), which determines the brightness level of the reflectance, the Minnaert parameter \(k\), which defines the bowl or bell shape of the reflectance as a function of the geometry of illumination and observation, and the asymmetry parameter \(\Theta\), which controls whether the reflectance is predominantly due to forward or backward scattering (also see [114]). 5. Those RPV results are spectrally and directionally integrated to generate the broadband albedos in the visible (VIS) and near-infrared (NIR) broadband regions required by the next step. These intermediary results are physically contained in the output files of the last processing step. 6. Finally, the Joint Research Centre (JRC) Two-stream Inversion Package (TIP) is inverted against the albedos just computed to offer a complete description of the radiation fluxes at the surface, specifically through estimates of the reflectance, transmittance and absorptance of the plant canopy, in addition to estimates of the Leaf Area Index (LAI), the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and optical properties of the leaves, as well as the implied albedo of the underlying surface. This inversion procedure also delivers estimates of the variance associated with each output variable, and the final value of the goodness of fit criterion, which can be used to cull data items that are obviously wrong or of doubtful reliability [76]. These products have been found useful in a variety of applications, including to describe the heterogeneity of plant canopies [115; 116], the foliage projective cover of plant canopies [117], the structural parameters of forests [26], or the response of grasslands to rainfall regimes [118].
# An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks
## 3 Preliminaries
### Cluster Head Selection
#### 4.2.1 Remaining Energy Ratio  
In FANETs, the remaining energy is one of the critical factors affecting the network life. The energy of the UAV nodes is related to the number of neighbors, flight speed, and moving distance. The CH selection is regarded as a significant task for clustering because CHs are responsible for both inter- and intra-cluster communications and the management of clusters. If a node serves as the CH for a long time, its energy depletes dramatically, which reduces the lifetime of the whole network. Thus, the remaining energy needs to be involved in the CH selection criteria. In this work, the remaining energy ratio of node \(i\) is defined as  
\[E_{i}=\frac{E_{0}-E_{T}}{E_{0}} \tag{10}\]  
where \(E_{i}\) is the remaining energy ratio of \(i\), \(E_{0}\) is the initial energy of \(i\) and \(E_{T}\) is the total energy consumption of \(i\), which can be expressed as  
\[E_{T}=E_{Tx}+E_{Rx}+E_{f} \tag{11}\]  
where \(E_{Tx}\) and \(E_{Rx}\) are the energy spent for transmitting and receiving communication, which occupies the largest proportion of total energy consumption. \(E_{f}\) represents the energy consumed by flying. The communication energy consumption for transmitting \(m\)-bit packet is calculated as  
\[E_{T_{x}}=\begin{cases}mE_{elec}+m\varepsilon_{fs}d^{2}&d<~{}d_{0}\\ mE_{elec}+m\varepsilon_{mp}d^{4}&d~{}\geq~{}d_{0}\end{cases} \tag{12}\]  
where \(E_{elec}\) is the energy consumption of the transceiver circuit for processing a unit bit data, \(d_{0}\) is a threshold of the transmission distance, and \(d\) represents the distance between the transmitter and receiver. \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) are the power amplifier coefficients. The communication energy consumption for receiving \(m\)-bit packet is calculated as  
\[E_{Rx}=mE_{elec} \tag{13}\]  
The radio energy dissipation model is shown in Figure 3. When the transmission distance is less than the threshold \(d_{0}\), the free space channel model is used and the power amplifier coefficient \(\varepsilon_{amp}\) is set to \(\varepsilon_{fs}\). When the transmission distance is greater than or equal to the threshold \(d_{0}\), the multi-diameter decay model is used and the \(\varepsilon_{amp}\) is set to \(\varepsilon_{mp}\).  
The flying energy consumption of node \(i\) is calculated as follows:  
\[E_{f}=\int_{0}^{t_{f}}P_{f}dt \tag{14}\]  
\[P_{f}=\sqrt{\frac{\left(m_{ULAVs}\right)^{3}}{2\pi n_{w}r_{w}\rho_{air}}} \tag{15}\]  
where \(P_{f}\) and \(t_{f}\) are the UAV flying power and time, \(m_{ULAVs}\) is the total mass of UAVs, \(\rho_{air}\) and \(g\) are the air density and earth gravity, respectively. \(n_{w}\) is the number of wings, and \(r_{w}\) is the radius of wings.
## 3 Simulation of Underwater Electric Field Communication
### Underwater Electric Field Communication Simulation Flow Setup
The simulation results validate that the NS-3.31 simulation model can realize network communication based on the CSMA/CA protocol for underwater electric field communication, laying the foundation for the subsequent experimental verification of AUV multi-node channel backoff, and the communication result is shown in Figure 4.  
Figure 4: The networking results of underwater electric field communication with multiple nodes. Figure 3: Simulation process of underwater multi-node electric field communication network based on CSMA/CA.
# Optimal Particle Filter Weight for Bayesian Direct Position Estimation in a GNSS Receiver
## 8 Simulations and Real-World Results
### Impact of Different Multipath Amplitudes  
It is expected that an increase of the multipath amplitude \(\alpha_{MP}\) further shifts the mean and increases the variance. This case is outlined in Figure 11. Naturally, in the case that the multipath signal is as strong as the LOS signal, the resulting probability function automatically covers the whole uncertainty  
Figure 10: Impact of different multipath offsets \(\Delta\tau_{MP}\) on the probability function. For a better visualization, a strong relative multipath to the LOS signal with \(\alpha_{MP}=0.9\) is chosen. It can be seen that higher offsets increase the weighted mean \(\mu\) but do not necessarily increase the weighted standard deviation \(\sigma_{w}\). The dotted black line refers to the LOS signal.  
Figure 9: Influence of constructive and destructive multipath on the probability function with a relative multipath offset of \(\Delta\tau_{MP}=50\) m and an amplitude of \(\alpha_{MP}=0.5\) with respect to the line-of-sight (LOS) signal. The black dotted line refers to the LOS signal. It can be seen that the multipath variants shift the weighted mean \(\mu\) in the opposite direction and that the destructive multipath significantly increases \(\sigma_{w}\) due to the lower amplitude in \(|P|\).  
range with a mean exactly between the two signals. This case is shown with the dash-dotted blue line in Figure 11.
## 3 Drone Routing Algorithms
### Charging
#### 3.2.4 Cloud-Based Drone Navigation and Charging (CDNC)  
If a drone chooses the CSs individually without considering any central coordination and presence of other drones, they may encounter congestion to acquire access to the CSs for their battery replenishment. To minimize the congestion at CSs, in [51], the authors proposed the CDNC, which collects the drones' traffic information and delivers the optimal drone routes using central cloud management systems. The CDNC has three different components: traffic control center, CSs, and drones. Each drone transmits their source location, destination, and average speed to the cloud-assisted traffic control center. According to the collected information, the traffic control center computes the arrival, charging,and waiting time of each drone at a given CS, utilizes the estimated travel time to assign the most suitable path to each drone, and reserves the CS usage time for each drone to minimize the average travel time. The CDNC utilizes [PERSON]'s shortest path algorithm to generate the optimal route for drone, while considering the charging time and waiting time for charging at CSs, and drone flight time required for the end-to-end path. In addition, the CDNC can predict the congestion of drones at CSs utilizing the historical information of drone trajectories. Based on this prediction, the central controller changes the navigation path of the drones in advance to avoid congestion at specific CSs.  
Apart from the above charging strategy, recently, researchers are also utilizing the auction-based game theory [45], and deep reinforcement learning [52] to generate a cost-optimized charging scheduling. In these charging strategies, each drone participates in the auction to buy charging timeslots from the CSs. According to the bidding price, the CSs allocate timeslot to the drone to acquire energy replenishment. To improve the transaction security, the communication between CSs and drone is secured by using different energy-efficient consensus mechanism of blockchain technology such as the Internet of things application (IOTA) [46]. IOTA is a consensus protocol in blockchain specially designed for low power Internet of things (IoT) networks and more detailed study can be found in [53].
# Dynamic Correlation between Platelet Aggregation and Inflammatory-like State in Athlete Horses
## 2 Materials and Methods  
Ten Italian saddle horses (geldings; 7-10 years; mean body weight 465 \(\pm\) 60 kg) from the same horse training center were enrolled in this study with the owner's informed consent. All animals enrolled in the study were healthy as assessed by the clinical examination, routine hematology and biochemistry analyses performed at rest conditions. All horses were kept in individual boxes (3.50 \(\times\) 3.50 m\({}^{2}\)) and fed as described previously [12]. Horses were subjected to a standardized obstacle course preceded by a warm-up consisting of walking, trotting and galloping, including six jumps (5 verticals, 4 long jumps) of 0.80-1.20 m height and 1.15 m width. The exercise consisted of a 350 m long trail with eleven 1.25 high jumps (five vertical jumps, five long jumps and one double vertical and long jump). From each horse, blood samples were collected at rest (TREST), within 5 min from the end of exercise (TPES), and 30 min and 60 min after exercise (TPES30 and TPE60, respectively) by jugular vein puncture into vacutainer tubes with a clot activator (Terumo Corporation Japan, Tokyo, Japan) for the assessment of serum concentrations of C-reactive protein (CRP), total proteins and globulin fractions, and into 3.6 mL tubes containing 3.8% sodium citrate (1 part citrate/9 parts blood) for platelet aggregation measurements. Blood samples collected into vacuum tubes containing clot activator were allowed to clot for 20 min at room temperature prior to centrifugation at 3000 rpm for 10 min and the obtained sera were stored at \(-\)20 \({}^{\circ}\)C until analysis. Through an ELISA kit specific for equine species (ab190527--CRP Horse ELISA kit, abcam, Boston, MA, USA; sensitivity 1.198 ng/mL, intra-assay coefficient of variation <10%, inter-assay coefficient of variation <10%), the serum concentration of C-reactive protein (CRP) was determined by a micro-well plate reader (Sirio, SEAC, Florence, Italy). The serum values of total protein were investigatedby the Biuret method through an automated UV spectrophotometer ([PERSON], SEAC, Florence, Italy; commercially available kit, Biosystems S.A., Barcelona, Spain). The electrophoresis of serum protein fractions was performed through an automated system (Selvet 24, Seleo Engineering, Naples, Italy) following the procedures defined by the manufacturer as previously described [19]. Albumin and \(\alpha\)1-, \(\alpha\)-2, \(\beta\)1-, \(\beta\)2- and \(\gamma\)-globulins were the major protein fractions obtained. The determination of platelet aggregation was performed by obtaining platelet-rich plasma (PRP) and platelet-poor plasma (PPP) by centrifugation of blood samples collected into tubes containing 3.8% sodium citrate. The detailed description of the complete methodology was previously described [20; 21]. Platelet aggregation response was evaluated by an analysis of the maximum degree of aggregation and the initial velocity of aggregation (slope, expressed in percentage/min) [20].
## 2 Numerical Models
### Delamination-Buckling Analysis  
The delamination-buckling of an initially partially delaminated clamped-clamped fiber-metal laminate subjected to an axial impact was simulated, with the geometry and dimensions of the original sample reported in Figure 4a. An equivalent simplified model, as shown in Figure 4b, consisting of three components was constructed. The model consisted of a 0.5-mm thick magnesium skin, a 0.5-mm thick adhesive layer, and a 2-mm thick fiberglass substrate. The symmetry in geometry and boundary conditions warranted modeling only one-half of the specimen, thus, reducing CPU computation. As shown in Figure 4b, the transverse displacement (\(u_{y}\)) of the nodes located at the far-end of the specimen was restrained, and the same nodes were displaced at a rate of 1 m/s in the negative x-direction (-\(u_{x}\)), to simulate the applied impact. In addition, the rotation (in xy-plane) of the nodes were also restrained. This combination of restrains mimics the actual clamped boundary condition. As also shown in the figure, the symmetric boundary condition at the left end of the half-symmetry model was ensured by restraining the longitudinal displacement (\(u_{x}\)) and rotation about the y-axis at that location, while displacement in the transverse direction was permitted. Lastly, the out-of-plane displacement of all nodes (i.e., (\(u_{z}\))) was restrained to guarantee a purely planar deformation.  
Similar to the DCB specimen's model, the adherends of the FML were modeled using elastic elements, while the adhesive layer was modeled using (i) the cohesive element only and (ii) a combination of both xFEM and cohesive elements. Moreover, similar to the previous case-study, the models will be referred to as COHESIVE and MIXED. The XFEM model was not considered here because of the inconsistent results obtained when the xFEM element was used in modeling the DCB, as will be discussed in Section 3.1. Moreover, the adhesive thickness was assumed to be 0.5 mm, so to facilitate more discrete simulation of the influence of the through-thickness location of a crack within the adhesive layer. Therefore, the mesh, established based on a convergence study, has nine layers of elements through the thickness of the adhesive.  
In addition, the upper and lower delaminated portions of the specimen were assumed to have a sinusoidal geometric imperfection with small amplitudes of 0.1 mm and \(-\)0.02 mm in the y-direction, respectively, to promote the instability and to ensure that the upper and lower adherends would deflect in two opposite directions.
# Energy-Saving Network Ventilation Technology of Extra-Long Tunnel in Climate Separation Zone
## 3 Results and Discussion
### Energy-Saving Analysis
#### 3.3.2 Energy Saving with Different Control Strategies  
Division of control period is vital for ventilation energy saving in extra-long tunnels. The shorter the control period is, the closer the natural wind velocity will be to the actual condition. Based on the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Natural Wind** & **Resistance of** & **Other Resistance** & **Percentage of Natural Wind** & **Total Power (kW)** \\
**Velocity** & **Natural Wind** & **(kPa)** & **(kPa)** & **Resistance** & **(kW)** \\ \hline
0 & 0.00 & 51.86 & 0 & 8.78 \\
2 & 18.45 & 51.86 & 26.24\% & 41.26 \\
4 & 73.80 & 51.86 & 58.73\% & 138.69 \\
6 & 166.06 & 51.86 & 76.2\% & 301.08 \\
8 & 295.22 & 51.86 & 85.06\% & 820.72 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fan power at different velocities.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Design Wind** & **Line** & \multicolumn{2}{c}{**Section 1 (kW)**} & \multicolumn{2}{c}{**Section 2 (kW)**} & \multicolumn{2}{c}{**Section 3 (kW)**} & \multicolumn{2}{c}{**Total Power (kW)**} \\
**Velocity** & & **Jet Fans** & **Axial Flow** & **Supply** & **Jet Fans** & **Exhaust** & **Jet Fans** & **Axial flow** & **Fans** & \\ \hline
2.5 m/s & Left & 148 & 24 & 193 & 296 & 45 & 0 & 122 & \\  & Right & 148 & 22 & 157 & 296 & 32 & 0 & 117 & 1600 \\  & Total & 296 & 46 & 350 & 592 & 77 & 0 & 239 & \\ \hline
95\% guaranteed probability & Left & 148 & 24 & 193 & 370 & 45 & 148 & 122 & \\  & Right & 74 & 22 & 157 & 296 & 32 & 0 & 117 & 1748 \\  & Total & 222 & 46 & 350 & 666 & 77 & 148 & 239 & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Fan power in different design wind velocity.  
daily control strategy and the wind velocity with a guarantee rate of 95%, the power of fans and the corresponding energy saving effect are given in Table 8. The energy saving effect in July and August is the most obvious, totally 410.5 kW is saved in July and August, while the energy saving in January and December is quite little. It can be explained by the natural wind distribution of the Nibashan Tunnel in these months. The wind resource in summer is richer than in winter. Thus, more natural wind contributes to the tunnel ventilation, and the energy saving effect is better.  
The average power of jet fans in each period and condition, and the power of fans when wind is at 2.5 m/s are given in Table 9 for comparison. Table 9 shows that the effect of hourly control strategy is even better than the daily control strategy. If hourly control strategy were taken, it would lead to frequent change in the operational status of fans, eventually shortening the life span of jet fans.
## 5 System Performance  
With the system shown in Figure 1 set up, some preliminary measurements were carried out to assess the functioning and the performance of the developed system. Firstly, the multi-sensor was tested, and collected data were compared to those from other measurement instruments. An analysis of such measurement is reported in what follows. Then, performance results regarding the user interface are provided, and enhancements to decrease the related refresh time are discussed.
## 2 Results and Discussion
### Plant Growth Promotion Potential
#### 2.4.2 Synthesis of the Phytohormone Indole3-Acetic Acid (IAA)  
The ability of rhizospheric bacteria to promote plant growth is largely due to the production of auxin phytohormones, such as indole-3-acetic acid (IAA). IAA improves plant growth by stimulating root growth through cell proliferation and tissue expansion, as well as vascular bundle and nodule formation. Moreover, IAA plays a direct role in improving plant growth under stressful conditions; for example, it may prevent the deleterious effects of heavy metals [5]. It was found that bacterial IAA can slacken the cell walls of plants and, in this way, increase the amount of root exudates. These exudates control the higher metal concentration by chelating them in the rhizosphere and preventing them from entering the plant cellular pathways [56]. It has been reported that over 80% of bacteria isolated from the rhizosphere are capable of synthesizing IAA [57; 58]. This feature was also quantitatively estimated for the ZCR6 strain. The studied strain released approximately 3.6 mg IAA mL\({}^{-1}\) of medium (Table 1) and was classified as a low IAA producer. The main precursor for IAA biosynthesis in bacteria is L-tryptophan. Starting with tryptophan, different pathways have been reported for the synthesis of IAA; those that are more frequently described include (1) indole-3-pyruvate, (2) tryptamine, and (3) indole-3-acetamide. Genomic analysis of the ZCR6 strain revealed the presence of tryptophan (Trp) biosynthesis genes (_trpABCDE_) along with IAA biosynthesis genes involved in the indole-3-pyruvate pathway (_aspC_, _aldA_, _aldB_, and _ipdC_) (Table 4). In this pathway, indole-3-acetaldehyde is converted from indole-3-pyruvate by indolepyruvate decarboxylase (IpdC). In addition, the ZCR6 strain carried the _iaaM_ gene encoding tryptophan-2-monooxygenase (IaaM), which oxidizes tryptophan to indole-3-acetamide, but the _iaaH_ gene encoding indoleacetamide hydrolase (IaaH) was not found.