esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In many angiosperms, floral volatiles are an important factor in attracting pollinating insects [1]. Floral scents have played important roles in the early evolution of flowers, and most are considered to be pleasant aromas by humans [2; 3; 4]. There is a never-ending need to characterize and synthesize new aroma compounds [5]. Therefore, over the past decade, there have been many studies on floral scents, their pattern differences among various flowers, and the biosynthesis, emission, regulation and ecological impact of emitted floral volatiles [1; 6].  
_Chrysanthemum_ (_Chrysanthemum morifolium_, tribe Anthemideae, family Asteraceae) is one of 10 well-known traditional Chinese flowers [7]. Together with the rose, it is one of the four most popular cut-flowers in the world. _Chrysanthemum_ species account for 30% of world cut flower production [8]. Chrysanthemums are also widely used as a health food and as a Traditional Chinese Medicine (TCM) [9; 10]. Early studies on the aroma compounds of _Chrysanthemum_ and its wild relatives were largely based on essential oils, and focused on their antioxidant activity and antifungal activity [11; 12; 13]. The odor compounds in _Chrysanthemum_ flowers include \(\beta\)-pinene, eucalypt, camphor, borneol and bornyl acetate [14; 15]. In recent years, there have been some studies on the floral volatile compounds in _Chrysanthemum_ and its wild relatives [16; 17; 18; 19]. However, _Chrysanthemum_ flower volatiles need to be investigated using more accessions.  
Solid-phase microextraction (SPME) is a new type of sample pretreatment technology that allows the rapid and simple extraction of small amounts of volatile compounds. This technique has high reproducibility under the same test conditions and is suitable for floral scent analysis [20]. In this investigation, we performed headspace solid-phase microextraction (HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) to analyze the floral scent volatiles from 29 chrysanthemum cultivars and five _Chrysanthemum_ relatives (a total of 10 accessions) using fresh flowers as plant material. We aimed to identify the cultivars with desirable floral scent by comparing the odor volatiles compositions between different cultivars. Cultivars with desirable scents are promising parents for chrysanthemum breeding.
# A Compact Thevenin Model for a Rectenna and Its Application to an RF Harvester with MPPT
## 5 Experimental Results and Discussion
One particular case is the upper limit, which would be achieved for \(P_{\rm{av}}{\rightarrow}\infty\) (or \(V_{\rm{\gamma}}{\rightarrow}0\)), in our case 74%. The resulting efficiencies (\(\eta_{\rm{rect,max}}\)) are among the highest published in the literature for similar designs [33]. On the other hand, \(V_{\rm{oc}}\) from Table 1 nearly matches \(V_{\rm{oc,exp}}\). Finally, \(V_{\rm{MPP,exp}}\) equates or nearly matches 0.5 \(V_{\rm{oc,exp}}\), the regulated voltage at the input of the MPPT. Thus, the proposed and implemented MPPT will be able to extract the maximum power (or nearly) from the rectenna. As for the whole RF harvester (rectenna plus the MPPT), Figure 8 shows the experimental values of \(\eta_{\rm{T}}\) versus \(P_{\rm{av}}\). At \(-20\) dBm, \(\eta_{\rm{rect,max}}=39.3\%\) (Table 2) but \(\eta_{\rm{T}}=6.5\%\), resulting, from (22), in \(\eta_{\rm{MPPT}}=16.5\%\). This low value of \(\eta_{\rm{MPPT}}\) is due to both a low input voltage value (140 mV = 0.5 \(V_{\rm{oc,exp}}\)) and a low value of \(P_{\rm{o}}\) (3.9 \(\mu\)W = \(\eta_{\rm{rect,max}}P_{\rm{av}}\)). Contrariwise, at \(-10\) dBm, \(\eta_{\rm{rect,max}}=60.3\%\) and \(\eta_{\rm{T}}=48.6\%\), resulting in \(\eta_{\rm{MPPT}}=80.6\%\), which agrees with the data from the BQ25504 chip's datasheet. At higher values of \(P_{\rm{av}}\) (\(-5\) dBm), \(\eta_{\rm{T}}\) reached a value of 55.6%. Compared to [6], where a similar chip for the MPPT was used, \(\eta_{\rm{T}}\) is quite higher. When powering the sensor node, the required value of \(P_{\rm{av}}\) was \(-17.6\) dBm. This value fits well with (23), considering the corresponding efficiency in Figure 8 (\(\approx\)24%). This performance was also tested with the antennas at a distance of 0.5 and 1 m. The power output of the remote RF generator was tuned at appropriate values so as to operate the node, resulting in 8.0 and 13.2 dBm, respectively. These values accounted for the respective link budgets. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(P_{\rm{av}}\) (dBm) & \(\eta_{\rm{rect,max}}\) (\%) & \(V_{\rm{MPP,exp}}\) (mV) & \(V_{\rm{oc,exp}}\) (mV) \\ \hline \(-10\) & 60.3 & 480 & 960 \\ \hline \(-20\) & 39.3 & 130 & 280 \\ \hline \(-30\) & 13.6 & 27 & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental values of \(\eta_{\rm{rect,max}}\), \(V_{\rm{MPP,exp}}\), and \(V_{\rm{oc,exp}}\) at different values of \(P_{\rm{av}}\). Figure 8: Overall efficiency (\(\eta_{\rm{T}}\)) of the RF harvester.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 4 Performance Metric and Simulation Setup
### Results
#### 4.4.3 Comparative Analysis 3  
This scenario is based on a comparison of propagation models with constant routing protocol and interfacing protocol with varying numbers of nodes for a fixed time of 60 s. The end-to-end delay is minimum for the Log-Distance model compared to other models as per Figure 6(a). However, Two-Ray Ground and FRIIS outperform other models in terms of packet delivery ratio, throughput, average routing goodput, with the smallest value of MAC/PHY overhead depicted in Figure 6(b)-e, respectively.
## 4 Discussion
The observed dose reductions across Hp(0.07), Hp(3), and Hp(10) metrics demonstrate that material composition influences shielding performance primarily at lower thicknesses, whereas increasing thickness minimizes compositional differences, supporting previous studies on polymer-based radiation shielding [64]. Furthermore, the statistically significant reductions in dose equivalents (\(p\) < 0.05) with increasing thickness suggest that WO\({}_{3}\)-reinforced NR composites exhibit dose attenuation characteristics that are dependent on both composition and geometry, which concurs with findings from gamma shielding studies on tungsten-infused elastomers. Notably, the diminishing differences in shielding efficiency at 6 mm thickness indicate a saturation effect, where additional WO\({}_{3}\) content does not yield proportionally greater attenuation, consistent with nonlinear attenuation behaviors reported for high-Z polymer matrices [65; 66]. Moreover, the superior shielding efficacy of high WO\({}_{3}\) content (\(\geq\)75%) at higher radiation activity (740 MBq) suggests that the material benefits from enhanced photon absorption due to increased effective atomic number (Zeff), reinforcing conclusions from prior work on tungsten-based shielding composites. However, the absence of statistically significant differences among WO\({}_{3}\)-reinforced NR formulations at greater thicknesses implies that material optimization should focus not only on composition but also on overall density and structural integrity to maximize shielding efficiency [67].
# Low Delay Inter-Packet Coding in Vehicular Networks
## 1 Introduction
### Roadside Units and Quasi Real-Time Transfers  
By leveraging remote connectivity supplied by Road-Side Units (RSUs) deployed along the road in VANETs, vehicles can retrieve/update maps or perform advertisement media downloading [4] via vehicle-to-roadside (V2R) communications. Moreover, vehicles may exploit storage and processing capabilities locally offered by RSUs, according to the recently proposed mobile edge computing (MEC) paradigm [5].  
V2R communications are expected to be short-lived and intermittent, due to the high mobility of the vehicles and to the high costs to deploy a ubiquitous roadside infrastructure. Because of the huge data traffic demands of vehicles coupled with the limitations of V2R communications, a practical design must make the best of connectivity opportunities in _drive-thru scenario_, where moving vehicles spend at most a couple of minutes in the coverage area of a RSU [6; 7; 8]. When a non-reliable transport protocol is used, an application layer inter-packet coding turns out to be an attractive solution if we aim at transferring as many data as possible.  
Drive-through scenario applies to C-ITS vehicular active safety testing also. At the AstaZero testing facility, one can test active safety by enacting carefully choreographed scenarios. Each scenario may involve multiple test objects such as vehicles, pedestrians, etc. During testing, each test object is fitted with a modern. The purpose is twofold. Firstly, the modems send and receive to and from the infrastructure, strictly real-time data consisting of trajectory updates to the test server, scenario abort signals from the test server, etc. Secondly the modems upload quasi-real time data consisting of sensor measurements and vehicular log information to the RSUs. In this way, some of the data inside  
Figure 1: Asta-Zero test track.  
the vehicle are available nearly real-time to the RSUs/server for diagnostics in the case a vehicle itself ends up damaged. It is this quasi real-time data transfer for which we design inter-packet coding.
## 3 System Hardware Architecture
### Three-Dimensional Scanning System  
In the production line, the transfer of lasted uppers between stations, 3D scanning at the scanning station, and coating at the treatment agent and water glue stations all require the lasted uppers to be clamped for operation. Therefore, a specialized clamping mechanism was designed based on the shape of the shoe last, as shown in Figure 5a. This custom fixture uses a cylinder to drive the clamping jaws and position pins for securing the shoe last in place. With this design, the custom clamping mechanism facilitates the transfer, scanning, and coating operations. After exiting the first oven, the lasted upper is gripped by a pneumatic gripper (Figure 5b) and transferred to the 3D scanning system. This system is composed of a transfer platform designed for the lasted upper and a distance measurement unit based on laser triangulation.  
To reduce costs, the 3D scanning system employs a self-developed laser triangulation technique to capture the geometry of the upper and reconstruct its 3D model. This system is constructed by setting up three laser triangulation systems, each composed of a camera and a line laser, on the two sides and the top of the lasted upper. Three laser beams from different directions form three lines, two of which are parallel, and the third one (top) is perpendicular to both, and three laser lines form a common plane, as shown in Figure 6a. The pneumatic gripper holds the lasted upper, and the transfer platform moves it while synchronized signals from the transfer system trigger the camera to capture the laser lines on the upper. Putting these components all together, the overall system implemented is shown in Figure 6b.  
Figure 4: Diagram for system architecture of hardware components.  
The system simultaneously scans the left and right sides, as well as the bottom of the upper. The data from the three sets of scans are integrated to acquire the complete 3D information of the upper. This enables the real-time generation of 3D models for various shoe designs, allowing the system to determine the precise adhesive spray positions and angles tailored to each shoe model. It ultimately generates a customized spray path for the robotic arm. Once the path is constructed, the rotary table mechanism transfers the lasted upper to the next station for treatment agent coating.
# The Assisted Positioning Technology for High Speed Train Based on Deep Learning
## 2 Related Works
### Object Detection  
The main purpose of this paper is to establish a detection model that can detect and locate the kilometer post quickly and accurately. The existing studies on the detection and location of kilometer posts are all based on traditional methods. For example, reference [12] used a combination method based on color and projection to detect and locate kilometer posts. The concept of visual balise is proposed in [13], and the interference effect of other white garbage is considered in the process of detecting kilometer posts. In addition, the RDS company in Europe has developed a train positioning method based on image sequence [14]. They corrected cumulative errors using an image recognition technology to identify kilometer posts, and considered the experimental results in different environments, such as rainy, snowy, nighttime and sunny conditions. However, the kilometer post pictures used in the experiment were processed, which did not detect the actual kilometer post. To sum up, these methods are complex, time-consuming and inefficient, and they do not fully consider the influence of interference such as different weather or trackside objects in the real environment. Therefore, in order to optimize the traditional detection methods, this paper proposes an object detection algorithm based on deep learning.  
The object detection algorithm based on deep learning has been successfully applied to many object detection scenarios. Such as, reference [20], which proposed a detection network based on an improved SSD algorithm to realize the real-time detection of traffic signs. Reference [29] proposed a multi-scale object detection network by improving the deep learning algorithm. Reference [30] studied real-time detection and motion recognition based on deep learning and multi-scale feature fusion. In short, there are many kinds of object detection networks based on deep learning, and their performance is better than those of traditional detection methods. Furthermore, among the most common detection algorithms, the SSD algorithm achieves a good balance between detection speed and accuracy, making it more suitable for practical application. Therefore, in this paper, we chose the optimized SSD algorithm for modeling and the model was simplified according to the specific application scenarios. The analysis of the experimental results shows that our design is effective.
## 3 Results and Analysis
### Spatial Auto-Correlation of EVI and GPP  
The Global Moran's I indices for EVI and GPP in China are 0.554 (z = 106.85, \(p<0.01\)) and 0.206 (z = 189.39, \(p<0.01\)), respectively, indicating significant spatial clustering of high values for both variables. Specifically, the distribution of EVI hotspots and coldspots exhibits pronounced polarization, with high-value areas predominantly located in the southern part of the study area, while low-value areas are mainly distributed in the central and western regions (Figure 6a). Similarly, GPP hotspots and coldspots display clear polarization, with high-value areas primarily concentrated in the southern part of the study area and low-value areas mainly found in the northeastern region. Notably, a clustering of low GPP values is observed in the downstream area of the Yangtze River (Figure 6b).  
Figure 5: Inter-annual variation in EVI and GPP for different land cover types (**notes:** (**a–e**): inter-annual variation in EVI in croplands, forests, shrubs, grasslands and wetlands; (**f–j**): inter-annual variation in GPP in croplands, forests, shrubs, grasslands, and wetlands).  
Through cluster analysis of EVI and GPP, we found that EVI across China exhibits strong spatial correlation, whereas the spatial correlation of GPP is primarily concentrated in the southern and northeastern regions, consistent with the results of hotspot analysis. Specifically, the spatial distribution of EVI is characterized by two types: low-low (L-L) and high-high (H-H). Low-value aggregations are predominantly located in the northern regions, while high-value aggregations are concentrated in the southern regions. Additionally, low-high (L-H) clusters are mainly distributed in the Huaihe River Basin, the southwestern river basins, and the Pearl River Basin, whereas high-low (H-L) clusters are more dispersed, primarily appearing in the eastern coastal areas (Figure 7a). For GPP, its spatial distribution is characterized by three types: L-L, L-H, and H-H. Low-value aggregations are mainly concentrated in the northeastern regions, the transition from low to high values is primarily found in the Yangtze River Basin, and high-value aggregations are concentrated in the southern regions. H-L clusters are more dispersed, mainly distributed in the downstream areas of the Yangtze River Basin (Figure 7b).  
Figure 6: Spatial distribution of cold- and hotspots of EVI and GPP in China from 2001 to 2020 (notes: (**a**): spatial distribution of cold- and hotspots of EVI; (**b**): spatial distribution of cold- and hotspots of GPP).  
Figure 7: Spatial distribution of Local Morans’s I of EVI and GPP in China from 2001 to 2020 (notes: (**a**): spatial distribution of Local Morans’s I of EVI; (**b**): spatial distribution of Local Morans’s I of GPP).
# LiDAR-Based Dense Pedestrian Detection and Tracking
## 2 Methodology
### Dense Pedestrian Tracking
#### 2.4.2 Data Association  
Data association is the key part of the traditional multi-target tracking problem [40; 41; 42]. Assuming that the targets' trajectories at time \(k-1\) are \(T^{k-1}=\{t_{1}^{k-1},t_{2}^{k-1},\ldots,t_{N}^{k-1}\}\), the predicted target trajectories at time \(k\) are \(\hat{T}^{k}=\{t_{1}^{k},t_{2}^{k},\ldots,t_{N}^{k}\}\) according to Kalman filtering. If \(M\) pedestrians \(Z^{k}=\{z_{1}^{k},z_{2}^{k},\ldots,z_{M}^{k}\}\) are detected at time \(k\), the purpose of the association is to match the measurements \(Z^{k}\) with the predicted trajectories \(\hat{T}^{k}\).  
Assignment matrix \(A=[a_{ij}]_{N\times M},a_{ij}\in\{0,1\}\) can be used to describe the relationship between the trajectories and the measurements. \(a_{ij}=1\) indicates that trajectory \(i\) is matched to the measurement \(j\) and \(a_{ij}=0\) indicates that they are not matched. In tracking, location is the basis of target association. At the same time, the LiDAR echo intensity is related to the color and material of clothes, so the echo intensity and position can be combined to construct the cost matrix \(C=[c_{ij}]_{N\times M}\). The association cost between trajectory \(i\) and measurement \(j\) is as follows:  
\[c_{ij}=\left\{\begin{array}{cc}\omega_{ij}(v_{j}-u_{i})^{T}(v_{j}-u_{i}),&(v_ {j}-u_{i})^{T}(v_{j}-u_{i})<\tau_{c}\\ \infty,&\text{Otherwise}\end{array}\right. \tag{26}\]  
\[\omega_{ij}=1-\frac{R}{2\sigma_{m}} \tag{27}\]  
where \(u_{i}\) and \(v_{j}\) are the positions of trajectory \(i\) and the measurement \(j\) respectively. The \(\omega_{ij}\) represents the different degree of echo intensity. To the trajectory \(i\), we can obtain the range (\(I_{m}-\sigma_{m}\), \(I_{m}+\sigma_{m}\)) of its echo intensity, where \(I_{m}\) and \(\sigma_{m}\) are the mean and standard deviation of the echo intensity of all the measurements that comprise the trajectory \(i\), respectively. To the measurement \(j\), we can obtain the range (\(I_{j}-\sigma_{j}\), \(I_{j}+\sigma_{j}\)) of its echo intensity, where \(I_{j}\) and \(\sigma_{j}\) are the mean and standard deviation of the measurement \(j\) respectively. The \(R\) represents the length of overlap between (\(I_{j}-\sigma_{j}\), \(I_{j}+\sigma_{j}\)) and (\(I_{m}-\sigma_{m}\), \(I_{m}+\sigma_{m}\)).  
The total association cost can be represented as \(\Psi\):  
\[\Psi=\sum_{i=1}^{N}\sum_{j=1}^{M}c_{ij}a_{ij} \tag{28}\]  
The data association is to find the optimal assignment matrix \(A^{*}\) that minimizes the \(\Psi\).  
\[A^{*}=\operatorname*{arg\,min}_{A}\Psi \tag{29}\]  
Equation (29) can be solved using the Hungarian algorithm [43].
## 2 Materials and Methods
### Expression and Purification of-Glt20 Variants  
The best results for production of high amounts of soluble Glt-proteins were obtained by expression them as fusion with the maltose binding protein (MBP). In order to facilitate purification of the recombinant Glt-proteins the sequence of a cleavage site (smt3) for the Ulp-protease was introduced between the MBP- and Glt sequence and further a His6- tag was added at the C-terminal end of the protein. After expression of the fusion gene constructs in _E. coli_ the proteins were purified via IMAC. _E. coli_ JM109 harboring pHWG1106 and the two newly constructed variant plasmids, designated pLB101 and pLB102, containing the _glt20:E120Q_ and the _glt20:E209Q_ mutant genes, respectively, were cultivated in LB medium (200 mL) containing 100 \(\upmu\)g/mL ampicillin. For gene expression, the cultures were grown at 37 \({}^{\circ}\)C until cell density reached an OD\({}_{600}\) of 0.3, and were then induced by adding of 0.1% rhamnose and further grown for 4 h at 30 \({}^{\circ}\)C. The cells were harvested by centrifugation at 4500\(\times\)_g_ for 20 min at 4 \({}^{\circ}\)C, washed, resuspended in 10 mM potassium phosphate buffer pH 6.5 and disrupted by passing them twice through a French press. After centrifugation (13,000\(\times\)_g_ for 15 min at 4 \({}^{\circ}\)C), the supernatants of the crude cell extracts were subjected to purification of the MalE-Glt20-His6 protein variants by immobilized metal ion affinity chromatography (IMAC). For this, 25 mg _E. coli_ protein was applied onto a 2 mL Talon(r) metal affinity resin (Clontech, Mountain View, CA, USA) in a column using gravity flow. The resin was washed with 10 mL of washing buffer (50 mM potassium phosphate, 300 mM NaCl, 5 mM imidazole pH 7.0). Bound protein was eluted with 3 mL of elution buffer (50 mM potassium phosphate, 300 mM NaCl, 150 mM imidazole pH 7.0. Fractions containing the respective His\({}_{6}\)-tagged MBP-Glt-fusion protein were combined and stored in elution buffer at 4 \({}^{\circ}\)C and was used for activity analysis. Subsequently, the MalE domain was separated from the Glt20 domain and its mutant derivatives by proteolytic cleavage using specific Ulp1 protease as previously described [29]. After proteolytic cleavage the products were purified via IMAC to remove protease and tags and the eluted Glt-His6 was used to confirm the identity of the Glt and was verified by SDS-PAGE [30], using 12% gels and Coomassieie Brilliant Blue R250 staining (data not shown).
## 3 Principles of Signal Transmission and Processing  
### Carrier Frequency Analysis  
In the present study, a 4 FSK modulation scheme was employed. During the process of carrier frequency selection, comprehensive theoretical analysis and derivation were conducted by the researchers. The influence of carrier frequencies on system transmission performance and the relationship between frequency selection and symbol transmission rate were rigorously considered.  
On the one hand, given the uneven modulation frequency characteristics of the mercury lamp, minimizing the interval between carrier frequencies for symbol modulation is desirable. This optimization ensures uniform emission flatness across different carrier frequencies, thereby maximizing spectral resource utilization.  
On the other hand, the selected frequencies must satisfy orthogonality within symbol intervals. Assuming a single symbol transmission period of \(T_{s}\), orthogonality among sub-carriers within Ts is essential, with orthogonal carrier frequencies adhering to the following relationship:  
\[\int_{0}^{T_{s}}\cos(2\pi f_{i}t)\cos(2\pi f_{j}t)dt=0 \tag{9}\]  
Within the interval from 0 to \(T_{s}\), the frequencies \(f_{i}\) and \(f_{j}\) exhibit orthogonality. The derivation of the minimum sub-carrier spacing yields  
\[\min(f_{i}-f_{j})=\min\frac{n}{T_{s}}=\Delta f_{\min}=\frac{1}{T_{s}} \tag{10}\]  
Hence, in scenarios where the symbol period is \(T_{s}\), ensuring carrier frequency differences are integer multiples of \(1/T_{s}\) guarantees orthogonality within the \(T_{s}\) interval.