esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Registration Algorithms Based on Hierarchical Optimization
### Graph Matching (GM)-Based
The algorithm allows the number of samples larger than that the minimum number of samples required for model estimation, as well as a method of calculating point correspondences that may quickly eliminate erroneous point correspondences, resulting in generating high-quality hypotheses. [PERSON] et al. [60] adopted hierarchical nonconvexity to handle the spinner problem. This strategy utilizes Douglas-Rachford splitting to efficiently prove global optimality. This method resolves the problem of high computational cost in SDP relaxation. [PERSON] [61] presented a novel solution for the correspondence-based rotation search and point cloud registration called random sampling with invariant compatibility (RANSIC), which applies invariant compatibility to locate outliers from random samples. Higher-order GM methods cast about for correspondences by comparing hyperedges or hypernodes, whose benefit is that these methods are invariant to scale and affine changes. Higher-order GM algorithms are given a probabilistic interpretation by [PERSON] and [PERSON] [62]. [PERSON] et al. [63] expressed the higher-order matching problem as a tensor optimization problem. [PERSON] et al. [64] developed a higher-order GM method by hopping with a reweighting scheme. [PERSON] et al. [65] proposed a framework of tensor block coordinate ascent methods for high-order matching. [PERSON] and [PERSON] [66] introduced a K-nearest-neighbor-pooling matching method that pools features into GMs for second-order GMs, and then constructed sub-pattern structures for higher-order GMs. Generally speaking, a higher-order graph is a generalization of a graph where a hyperedge can connect any number of vertices. Or rather, it can determine higher-order relationships between pairs of features under certain constraints. Vertices often represent features or object pieces in higher-order graphs, while hyperedges describe connections between vertex tuples. These relationships are multifarious in that they may hold numerous aspects at the same time, whereas graph relationships can only function in pairs. Table 1 summarizes the typical algorithms based on hierarchical optimization as well as their strengths and weaknesses.
# Communication Transport Protocol Strategies for Rail Applications
## 2 The Railway Context  
### Rail Environment  
Rail lines differ according to the speed at which a train can travel. They can be divided into the following types: i. Mainline, which connects cities and sometimes crosses nations, and usually has a dedicated high-speed line for passengers; ii. Regional, used to connect between cities but is a low-capacity passenger line; iii. Metro/Urban, which crosses part of city, a city or connects neighbor cities, and is usually dedicated to urban mass transit; iv. Freight, which connects cities and sometimes crosses nations, and is dedicated to transporting freight, thus no passengers are allowed.
## Materials and Methods
### Processing of Satellite Data  
All Level-2A product images collected by Sentinel-2 mission satellites between 2018 and 2020 in the study area were utilized in the research. The Sentinel-2 mission acquires frequent optical imagery at high spatial resolution (10-60 m) in visible, near infrared, and short-wave wavelengths [70]. For each scene, cloud masking was undertaken and ten spectral bands (blue, green, red, red edge 1-3, near infrared, mid infrared, and shortwave infrared 1-2) were extracted; and seven spectral indexes, as shown in Table 2, were calculated.  
The spectral indexes were selected based on the consideration of diversity of the spectral bands used, for instance, inclusion of the red edge bands; and ease of computation associated with the normalization due to the large scope of the work. The selected vegetation indexes using the red edge bands were effective in the differentiation of land cover and community-level vegetation types, whereas other spectral indexes were useful in the classification of land cover and agricultural types. The spectral-bands and spectral-indexes images were composited by computing half-month median values pixel by pixel. This procedure resulted in 408 features in total. The pixel values, corresponding to the ground truth data (geolocation points), were extracted from the stack of Sentinel-2 feature images consisting of both spectral bands and spectral indexes and utilized for deep convolutional learning. Supplementary Material S2 is provided to describe the capacities of the Sentinel-2 images to discriminate between plant ecological communities by virtue of monthly composite images using spectral band combinations and spectral indexes. The potential of specific spectral vegetation indexes for distinguishing particular vegetation types is also illustrated in Supplementary Material S2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Spectral Indexes** & **Reference** \\ \hline Normalized difference vegetation index (NDVI) & [PERSON] et al. [71] \\ Normalized difference water index (NDWI) & [PERSON] [72] \\ Normalized difference snow index (NDSI) & [PERSON] et al. [73] \\ Land surface water index (LSWI) & [PERSON] et al. [74] \\ Green red vegetation index (GRVI) & [PERSON] et al. [75] \\ Red edge normalized difference vegetation index (RENDVI) & [PERSON] and [PERSON] [76] \\ Normalized inner reflectance in the green and red edge (NDVIRE) & [PERSON] et al. [77] \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of spectral indexes used in the research.
## 4 Multi-Structural Optimization of BPMSM
### Multi-Structural Optimization of BPMSM by GA  
To solve the multi-input and multi-output optimization problem in (12), a GA is utilized to simultaneously determine \(k_{x}\), \(k_{z}\), \(P_{F}\), \(P_{C\mu}\), \(k\), and \(T\). According to the dimensionless multi-structural optimization function constructed, the fitness is calculated out using a GA based in MATLAB 9.0. Since the initial population selection of the GA is random, to ensure the reliability of the optimization results, 180 sets of experimental data are analyzed using the statistical method for each condition. The structural parameter optimization results are calculated under different weights, as shown in Table 5.  
From Table 5, group F of the mechanical structural parameters with the smallest fitness is selected as the optimal structural parameters. Finally, the mechanical structural parameters \(\delta_{0}\) = 3.09 mm, \(h\) = 8 mm, \(r\) = 15.33 mm, \(N_{1}\) = 146, and \(N_{2}\) = 640 are determined. The iteration stops at the 134 th step, reaching the minimum value. The optimization results of group F are selected as the optimal mechanical structural parameters. Since each torque winding has two coils per phase, \(N_{1}\) must be an even number. The optimal mechanical structural parameters are substituted into the finite element model, and BPMSM performance are determined again. The dominant BPMSM performances, before and after optimization, are compared in Table 6. The comparison results show that comprehensive performance of the optimized BPMSM has been significantly improved. Specifically, the passive axial stiffness, passive tilting stiffness, force-current coefficient, and motor efficiency increased by 56.4%, 71.3%, 19.6%, and 8.7%, respectively.
The enhanced signals were subsequently analyzed through the Fourier transform to extract their frequency domain characteristics, demonstrating the method's effectiveness. Finally, Section 6 summarizes the main conclusions and discusses potential applications and future research directions.
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 2 Background  
### Low Power Wide Area Network (LPWAN)  
Low-power wide area networks (LPWANs) are characterised by low-power, long-range, and low-throughput wireless transmission technologies, mainly operating at sub-gigahertz (sub-GHz) frequency bands. When compared with the short-range transmission techniques, i.e., Wi-Fi, Bluetooth, NFC, ZigBee, etc., the LPWANs are capable of offering an adequate level of connectivity to the low-power devices that are distributed over a large geographical area. LPWAN can be considered to be a fundamental enabler of the IoT paradigm, and it is gaining momentum in the context of smart city and massive machine type communication (mMTC) in 5G and beyond. More specifically, LPWAN technologies are designed for a wide area coverage, and they are effective for both indoor and outdoor IoT or machine-to-machine (M2M) type scenarios. Ultra-low power operation enables the long-term stability, and makes it easy to maintain IoT devices for longer period of time, and it decreases the cost, energy consumption, and devices maintenance. Related experiments and their analysis on the coverage, throughput, and power consumption across different LPWAN technologies can be found at references [6; 7; 8; 9; 10].
# Advances in Piezoelectric Jet and Atomization Devices
## 2 Advances in Piezoelectric Micro Jet Devices
### Push Mode  
In 2011, a push-type piezoelectric micro jet device was invented [30], in which laminated ceramics were used to provide the driving force, thus reducing the driving voltage of the inkjet printing head. Subsequently, the evolution of the inkjet process was simulated, and the relative experimental verification was also carried out, showing a good consistency with each other [31]. In 2013, [PERSON] et al. [32] proposed a piezoelectric-driven thin-film micro jet technology. Meanwhile, [PERSON] et al. [33] designed and developed a piezoelectric micro-spray device with high output performance. The mode of resonant braking of the piezoelectric vibrator unit was adopted, and the micro-displacement was dramatically amplified using additional mass block. It indicated that the high efficiency injection of the liquid can be realized by using the fluid inertia effect of liquid in cavity at high frequency.  
The above-mentioned piezoelectric jet devices are all push-type piezoelectric micro jet devices as shown in Figure 1b. The working principle of the device can be summarized as follows: the telescopic deformation of cylindrical piezoelectric ceramics is used to produce the driving force, the volume of the ink cavity is changed via such driving force, and finally the ink inhalation and injection process is completed. The structure is characterized by the use of laminated ceramics to push the film, leading to change in the volume of the ink cavity. A push rod is needed to provide the driving force to transfer the film. The relative low energy utilization rate will seriously restrict its popularization and application. Therefore, there are few works focusing on this type of piezoelectric injection device.
# QoSComm: A Data Flow Allocation Strategy among SDN-Based Data Centers for IoT Big Data Analytics
## 4 Simulation Model and Experimental Setup  
### Simulation Model  
The simulation model implements a network topology considering host-to-host data flow transfers between two or more SDN-enabled distributed data centers. For the scope of our research,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**OFS** & **ID** & **Match** & **Action** & **Priority** \\ \hline S0 & 1 & source: 10.0.0.1, dest: 10.0.0.6, ip-protocol: 6 & output-node-connector: 1 & 201 \\ S2 & 2 & source: 10.0.0.2, dest: 10.0.0.5, ip-protocol: 6 & output-node-connector: 2 & 202 \\ S6 & 3 & source: 10.0.0.3, dest: 10.0.0.4, ip-protocol: 6 & output-node-connector: 2 & 202 \\ S0 & 10 & source: 10.0.0.1 dest: 10.0.0.6, ip-protocol: 17 & output-node-connector: 1 & 210 \\ S2 & 11 & source: 10.0.0.2, dest: 10.0.0.5, ip-protocol: 17 & output-node-connector: 2 & 211 \\ S6 & 12 & source: 10.0.0.3, dest: 10.0.0.4, ip-protocol: 17 & output-node-connector: 2 & 212 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Flow rules configuration example for each OFS.  
Figure 3: QoSComm communication process and its reference in the SDN architecture.  
the experimental setup considers a small-medium network topology based on three-layer for data centers [32]. This topology consists of the access layer to connect hosts (compute nodes), an aggregation or region layer in the middle to connect the access switches and a _core_ layer in the root of the tree.  
Figure 4 shows the topology used by the simulation model. It is based on three layers, where two data centers defined as Domain A and Domain B are connected. Each domain has its SDN network controller, and they do not communicate with each other. The domains A and B are connected by a link; this could be the case of the Internet Service Provider (ISP) and the bandwidth, delay, packet loss, and jitter link values will be simulated. QoSComm gets the feasible paths based on the application requirements and configures them through each domain SDN controller. Since QoSComm does not require SDN controllers to communicate with each other, there is no need to modify the SDN controller, which is one of the main QoSComm contributions.
## 4 Performance Evaluations
### Experimental Results  
#### 4.2.1 Accuracy Results  
Our experiments evaluated the performance of the Dynamic Method across various frame rates, comparing it with the Fixed Method and the Original Method. Figure 4 shows the results  
Figure 4 indicates that at lower frame rates of 5 fps and 10 fps, our Dynamic Method significantly outperforms the Fixed Method, achieving 77.37% accuracy at 5 fps compared to only 10.83% with the Fixed Method. As the frame rate increases, both methods generally show improved accuracy due to more frequent sampling. The Dynamic Method consistently maintains higher accuracy across all tested frame rates, reaching its highest accuracy at 25 fps with a top-1 accuracy of 92.29%, demonstrating its effectiveness in adapting to higher frame rates. Figure 5 shows the top-5 accuracy results according to the frame rate. As shown in Figure 5, the pattern of accuracy performance is similar to the top-1 results.  
Based on Figures 4 and 5, it is clear that the Dynamic Method consistently outperforms the Fixed Method and Original Method across all tested frame rates, with significant improvements in accuracy at lower frame rates of 5 fps and 10 fps. This highlights its effectiveness in scenarios with computational limitations, such as mobile environments. As the frame rate increases, the performance gap between the Dynamic and Fixed Methods is reduced, but the Dynamic Method maintains a competitive edge, indicating its robustness in varying operational conditions. The observed performance enhancement validates the efficacy of the Dynamic Method in enhancing the accuracy and reliability of sign language recognition systems deployed in mobile environments.
# Influence of Yeast Strain on Odor-Active Compounds in Fiano Wine
## 2 Materials and Methods  
### Wines and Yeast Strains  
Fiano grapes were harvested at 22\({}^{\circ}\) Brix in vineyards located in the town of Taurasi (AV-coordinates 41.0125\({}^{\circ}\) N, 14.9681\({}^{\circ}\) E), a DOCG area in the Campania region, Italy.  
A total of 1200 kg of grapes were crushed (0.9 atm), using a pneumatic press of 80 q. SO\({}_{2}\) (50 mg/L), and pectic enzyme (1 g/hL) were added to the must. Then, it was immediately cooled to 10 \({}^{\circ}\)C and submitted to static decantation to 80-100 NTU (Number Torbidity Unity) in stainless-steel tanks (15 hL).  
Four tanks were inoculated each with a selected yeast strain, while the fifth tank was not inoculated (control). Inoculations were carried out at 30 g/hL, after the yeasts were rehydrated in warm water for 30 min, as described by the manufacturers. Then, a homogenization was carried out for 10 min, and the must from each stainless-steel tank was transferred to a new barrique (Troncay, MTL+). Fermentation took place in five barriques (Troncay) at 12 \({}^{\circ}\)C. Upon completion of alcoholic fermentation (30 days), wines were cold stabilized for a 3-month period at 10 \({}^{\circ}\)C, filtered on 5 \(\upmu\)m membranes and bottled, previously added with 30 mg/L of SO\({}_{2}\).  
Three strains of _S. cerevisiae_ (Lalvin D47; Lalvin CY3079; Zymaflore VL1) and one strain of _S. bayanus_ (Lalvin QA23) were used. The D47, CY3079 and QA23 strains were supplied by LALLEMAND S.A. company (Montreal, QC, Canada) as dry active yeasts, while Zymaflore VL1 by Laffort Oenologie (Bordeaux, France). The _S. cerevisiae_ strains were previously selected for white winemaking.  
The QA23 strain is defined as \"aromatic\", due to its higher production of esters and alcohols. It has an alcohol resistance of up to 15% and works an optimal fermentation temperature of 15-35 \({}^{\circ}\)C. The CY3079 strain is defined as \"varietal\", due to its ability to release the primary aroma of the grapes. It ferments over a wide temperature range above 13 \({}^{\circ}\)C and has an alcohol resistance of up to 16%. The D47 strain has a good aptitude for fermenting in wooden barrels. It has an alcohol resistance of up to 15% and an optimal fermentation temperature of 12-30 \({}^{\circ}\)C. The VL1 strain is defined as highly varietal. It has an alcohol resistance of up to 14.5% and an optimal fermentation temperature of 16-20 \({}^{\circ}\)C.  
The chemical standards were supplied by Sigma-Aldrich (St. Louis, MO, USA).
## 4 Experimental Studies
When the comparison table is examined, it is seen that PSNR values are obtained with similar capacities. In the proposed study, it reached 2.5 bpp using a new method and is resistant to RS analysis attacks. In addition, with the proposed new method, secret data cannot be obtained without the original cover image. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**Embedding Capacity (EC) Ratio**} \\ \cline{2-11}  & & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** & **100\%** \\ \hline \multirow{3}{*}{\(n=1\)} & EC (bits) & 156,148 & 312,306 & 468,466 & 62462 & 780,780 & 936,950 & 1,093,106 & 1,249,262 & 1,405,424 & 1,561,416.5 \\ \cline{2-11}  & PSNR & 61.17 & 58.17 & 56.40 & 55.16 & 54.18 & 53.39 & 52.72 & 52.15 & 51.63 & 51.18 \\ \cline{2-11}  & EC (bits) & 233,880 & 467,809.5 & 701,741 & 935678 & 1,169,605 & 1,403,535 & 1,637,465 & 1,871,393 & 2,105,321 & 2,339,292 \\ \cline{2-11}  & PSNR & 55.93 & 52.84 & 51.09 & 49.83 & 48.85 & 48.06 & 47.39 & 46.81 & 46.30 & 45.84 \\ \cline{2-11}  & EC (bits) & 310,680 & 621,446 & 932,218 & 1242986 & 1,553,752 & 1,864,518 & 2,175,286 & 2,486,050 & 2,796,818 & 3,107,663 \\ \cline{2-11}  & PSNR & 50.21 & 47.11 & 45.37 & 44.10 & 43.14 & 42.34 & 41.67 & 41.09 & 40.58 & 40.13 \\ \cline{2-11}  & EC (bits) & 384,641 & 769,402 & 1,154,175 & 1538936 & 1,923,702 & 2,308,465 & 2,693,227 & 3,077,991 & 3,462,753 & 3,847,631 \\ \cline{2-11}  & PSNR & 44.29 & 41.22 & 39.47 & 38.21 & 37.23 & 36.45 & 35.77 & 35.19 & 34.68 & 34.23 \\ \cline{2-11}  & EC (bits) & 446,355 & 892,873.5 & 1,339,386 & 1,785,895 & 2,232,403 & 2,678,914 & 3,125,422 & 3,571,933 & 4,018,444 & 4,465,063 \\ \cline{2-11}  & PSNR & 38.49 & 35.42 & 33.67 & 32.41 & 31.44 & 30.65 & 29.97 & 29.39 & 28.87 & 28.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of average P, EC and PSNR values for \(n=1\) to 5 in all colored images used in experimental studies. Figure 7: Comparisons of embedding capacity and PSNR values for \(n=1\) to 5 in grayscale images.
## 4 Results and Discussion
### Simulation Result  
#### 4.2.1 Simulation Setup and Observations  
The generated results were input into CARLA and SUMO for simulation. The NRV followed the generated trajectory during the simulation. However, the actual driving trajectory was slightly different from the generated trajectory, and this difference made the vehicle's driving state more in line with reality. The MSV only specified its travel task of merging in the roundabout, without limiting the specific travel trajectory.  
During the simulation, it was observed that some generated scenarios extended beyond the roadway boundaries, causing vehicles to appear outside the driving area. But such occurrences were relatively rare and predominantly concentrated at specific entry points. Analysis revealed that the sample size of the entrance in the total sample accounted for a very small proportion. This scarcity of samples may have hindered the model's ability to learn the trajectories of vehicles entering through these specific points. The lack of samples may cause the model not to learn the vehicle trajectory of this entrance well, so that the occurrence of these particular scenarios is understandable. In future research, targeted scenario generation can be performed for these entry points. Out of the 1046 scenarios generated, the scenarios beyond the boundary only accounted for less than 5 percent, which further indicates that it is feasible to use the generative model to generate more vehicle travel scenarios.
## 2 The Application Framework  
It is well known that the key to JAD strategy is to avoid traffic jams by controlling the headway and velocity of a single vehicle. The mechanism diagram of the JAD strategy is depicted in Figure 1.  
The description of the vehicle's trajectory over some time in Figure 1 is as follows. When the vehicles are in a congested state, absorbing vehicle m decelerates from point A to point B at speed \(v_{a}\) to prevent the traffic wave from propagating upstream, and then the absorbing vehicle m accelerates from point B to point C and travels at free flow speed to eliminate the compression wave caused by a series of actions. Subsequent vehicles follow vehicle m in a car-following behavior. The absorbing vehicle induces a compression wave with speed \(v_{e}\) and an expansion wave with speed \(v_{f}\) arising from A and B, respectively. Eventually, these two waves cancel each other out at point C. Hence, subsequent vehicles at point C run at the initial speed \(v_{M}\).  
Figure 1: The schematic diagram of the jam-absorption driving (JAD) strategy.  
To dissipate stop-and-go waves, an MPC scheme is developed based on a car-following model for the JAD strategy. Due to the linearity and intuitiveness of the Helly model, it is chosen for modeling manual vehicles. The MPC is employed to predict the evolution of traffic dynamics through the traffic flow model and compute the optimal control action according to the current state of the system. The schematic view of the overall framework system is depicted in Figure 2. In mixed traffic, connected vehicles use onboard sensors to obtain the position and speed of the vehicle in real time and then transmit the vehicle data back to the traffic management center through the roadside communication system. The processing system analyzes the traffic information to determine if traffic oscillations are occurring on the road. The optimal controller is activated once the phenomenon is detected. In the optimization process, the control input (acceleration) of the system is obtained according to the traffic state of the vehicle and the objective function and constraint conditions. Consequently, vehicles adhere to the advised acceleration for navigation.