esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Energy Management Strategies for HEVs
### Optimization-Based Control Strategy
#### 3.2.1 Global Optimization
(2022) [149] used PMP, combined with long-term stochastic MPC and scenario-based optimization methods, to provide a new and highly efficient solution for the energy management of HEVs. [PERSON] et al. (2022) [150] introduced a novel adaptive EMS grounded on PMP. This system incorporates both past driving patterns and variable vehicle quality into its operation. The findings indicated that, when compared to a traditional rule-driven EMS, the proposed approach yielded an average improvement of 18.36% in fuel efficiency. [PERSON] et al. (2024) [151] proposed an adaptive EMS based on Artificial Neural Network-PMP for plug-in hybrid electric buses by considering the problem of the dynamic change of vehicle mass under dynamic driving conditions, which improves the effect of the overall efficiency and mobility while adapting to the different loading conditions, and solves the problem of the traditional EMS, which is unable to adapt to the dynamic change of the vehicle mass, leading to the poor optimization of energy. Overall, these studies emphasize the significance of advanced EMSs, optimization frameworks, and predictive models in improving HEV efficiency and performance, especially concerning battery lifespan, fuel economy, and battery degradation. (d) Linear Programming. The primary goal of LP is to optimally allocate various energy sources to meet the diverse needs of vehicle operation while maximizing energy efficiency and minimizing emissions. The core lies in constructing a linear mathematical model and seeking the optimal values of decision variables by setting linear objective functions and linear constraints. LP plays an important role in the fuel efficiency optimization of series HEVs. Applying LP to formulate the fuel efficiency optimization problem allows us to find the global optimal solution, use energy efficiently, and reduce waste, thereby promoting the sustainable development of the automotive industry. [PERSON] et al. (2020) [152] applied a hybrid life cycle sustainability assessment and multi-objective decision-making approach to promote the use of electric vehicles, suggesting that HEVs should comprise over 90% of the vehicle fleet for sustainability. [PERSON] et al. (2020) [153] presented a mixed integer LP model designed specifically for the efficient management of multicarrier energy systems, aiming to attain optimal solutions in a streamlined manner. [PERSON] et al. (2020) [154] formulated a real-time energy management algorithm for parallel HEVs, leveraging a nonlinear optimal control framework under an MPC scheme. [PERSON] et al. (2020) [155] discussed the optimal energy management for parallel HEVs, focusing on battery preservation in the context of aging. [PERSON] et al. (2020) [156] presented a minimum-fuel strategy for HEVs using iterative LP. [PERSON] et al. (2021) [157] explored the application of sequential programming for real-time predictive energy management in heavy-duty HEVs.
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 5 Experiments and Results
### Dependence of the TARA Method Performance on the Delay Function
#### 5.1.1 Constant Delay Function  
The Constant Delay function serves as a proof of concept that should show us whether the TARA method has the potential to improve the performance of an ad hoc network with untrusted nodes. For the experiments with the Constant Delay function, we chose the most pessimistic scenario SC-50. Experiments were conducted with several combinations of \(MD\) and \(THR\) parameter values for both DFT and DFF. The performance results for DFT are plotted in Figure 6 and for DFF in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Flag** & \(PDR_{avg}\) & \(PL_{avg}\) & \(RDD_{avg}\) & \(RDD_{max}\) \\ \hline DFT & 0.35 & 6.13 & 6.13 & 16 \\ DFF & 0.34 & 7.28 & 1.42 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance metrics for the reference experiment.  
Figure 6: Constant Delay function + DFT.  
It follows from these figures that \(PDR_{avg}\) achieves maximum for \(THR\) around 0.7. The reason is that the Constant Delay function penalizes nodes with \(PDR<THR\). If \(THR=1\), then nodes with high \(PDR\) values are penalized by the function, making it impossible to find routes through them. On the other hand, \(THR=0.5\) is a bad choice, as nodes with relatively low \(PDR\) values are not penalized. Thus \(THR\) value of these boundary values gives worse results.  
The maximum value of \(PDR_{avg}\) and the minimum value of \(RDD_{avg}\) and combinations of delay function parameters to reach them are presented in Table 3.  
The Constant Delay function does not reflect differences in trust values at all. Smaller values of \(MD\) are not enough for trusted path selection; on the other hand, too great \(MD\) value does not make a significant improvement of \(PDR_{avg}\), but instead worsens route discovery delay \(RDD_{avg}\).  
The best \(PDR_{avg}\) value is achieved for \(MD=16\). Further increase in \(MD\) does not bring additional improvement, only worsening the \(RDD_{avg}\). Results of the experiments with the Constant Delay function validated the proof of concept. The TARA method improved \(PDR_{avg}\) by 51% (DFT) and 54% (DFF) compared to the reference experiment.
# Parameter Design and Optimization of Grass Aerial Seeding Tower Based Computational Fluid Dynamics
## 2 Structure and Research Methods
### Imposing Constraints on Velocity Outlet Direction  
To validate the accelerating effect of the obtained nozzle unit, flow field simulation was conducted on a single unit by Fluent, and the velocity contour is presented in Figure 8.  
As observed from Figure 8, the rotation and alternation of nozzles within the unit disrupt the accelerating mechanism of the basic Laval nozzle, where high-speed airflow enters through a large inlet and accelerates as it exits through a smaller outlet. In this case, the airflow disperses from one large inlet to the other five outlets, and the expected acceleration effect is not achieved. Simulating this scenario, with an inlet airflow velocity of 0.5 m\(\cdot\)s\({}^{-1}\), the average velocity at the five outlets is calculated to be 0.287 m\(\cdot\)s\({}^{-1}\).  
Figure 6: Evolution of the process.  
Figure 7: Composite configuration and holistic formation.  
Figure 9 illustrates the ideal direction of airflow inlet and outlet; 'a' represents the direction of air inflow, while 'b' represents the direction of air outflow. If it is possible to control the direction of airflow in and out, ensuring that airflow from direction a enters only through the large opening in direction a and exits only through the small opening in direction a', then the desired acceleration effect can be achieved.  
Utilizing simple mechanical principles, a polymer unidirectional filtration membrane, like heart valves controlling blood flow, can be designed, as shown in Figure 10.  
The filtration membrane consists of two layers of membranes, with the openings precisely staggered. Membrane A is fixed, while membrane B can move within a limited range. When the fluid flows in the correct direction, it can push membrane B, allowing the fluid to flow out from the gap between the openings of membranes A and B. When the fluid flows in the opposite direction, membrane B is pushed to adhere tightly to membrane A, preventing the fluid from flowing out. Therefore, this mechanism enables acceleration of the airflow in its own direction. The filter is installed at the inlet of the nozzle, as shown in Figure 11.  
Figure 8: Flow field distribution under unrestricted conditions.  
Figure 9: Ideal direction of airflow in and out.
# Effects of Monochromatic and Composite Light Withering on Black Tea Aroma
## 4 Discussion
However, it is reported that no significant cumulative effect was found in the hybrid light withering treatments on the flavor components of black tea [33]. This discrepancy may be due to variations in the intensity and duration of the monochromatic light used. Little difference in volatiles was observed among different monochromatic light-treated groups, which coincided with the findings of [PERSON] et al. [33] but disagreed with those of [PERSON] et al. [14]. In the latter, blue light withering promoted the accumulation of geraniol and citral [14]. Such disparities may stem from differences in experimental parameters like light intensities and treatment time [15]. Notably, the effects of monochromatic yellow light withering on volatiles and biochemical components of black tea appeared to occur prior to those of red and blue light in this study, as evidenced by the lowest content of TP and the highest level of soluble sugars, (E,7R,11R)-3,7,11,15-tetramethylhexadec-2-en-1-ol, (1S,2R,5S,7R,8R)-2,6,6,8-tetramethyltricyclo [5.3.1.01,5] undecan-8-olde, and [(Z)-hex-3-enyl] pentanoate, which exhibited distinct floral and fruity notes [31]. This is consistent with a previous study that showed that yellow light (5000 lx) surpassed red and blue light in improving the aroma of white tea [35]. In practical, natural light withering is more widely used than single-wavelength LED treatments, suggesting that potential advantages of composite light withering emerge. This is supported by [PERSON] et al. [36] who demonstrated that moderate-intensity composite LED light treatment produced a better aroma compared to natural light. This result is consistent with our findings showing that the RB group exhibited a higher aroma score. Red-yellow light withering increased the content of hydrocarbons, certain aldehydes, and alcohols compared to monochromatic lights, which resulted in an almond-like and roasted aroma of the black tea. This indicates that composite light withering is effective in improving tea aroma. However, this finding disagrees with a prior study where monochromatic yellow light withering outperformed red-blue light in enhancing white tea aroma [37]. The discrepancy may arise from subsequent processing steps (e.g., rolling, fermentation) in the present study, which were also crucial for the development of aroma [37]. Notably, our data showed that red-yellow light exerted stronger effects on black tea volatiles than the red-blue treatment, aligning partially with the result obtained above that yellow light is superior to monochromatic red and blue light.
# Block Recovery Rate-Based Unequal Error Protection for Three-Screen TV
## 2 Background  
Before explaining the proposed UEP algorithm and layer-switching method, this paper describes in detail the source coding technology with SVC and channel coding technologies including FEC.  
Figure 1: Conceptual diagram of three-screen TV using scalable video. VSP, video service provider; FEC, forward error correction; SVC, scalable video coding.
# Robust Burst Detection Algorithm for Distributed Unique Word TDMA Signal
## 2 System Model  
In TDMA communication, MPSK modulation is often used. The received signal model after down-conversion is as follows:  
\[r(k)=s(k)e^{\mathrm{i}(\omega_{0}kT+\theta)}+n(k) \tag{1}\]  
Herein, \(s(k)=e^{\mathrm{i}\varphi(k)}\) is complex base-band signal, the value of \(\varphi(k)\) is \(2\pi q/M\), \((q=0,1,2,\cdot\cdot\cdot,M-1)\), \(T\) is symbol period, \(\omega_{0}\) is frequency offset, \(\theta\) is phase offset, \(n(k)\) is additive white gaussian noise (AWGN).  
TDMA burst signals usually contain site identification code, UW, carrier and bit timing recovery code, instruction signal and information data. Since UW is not easily replicated by random data, it does not cause false detection and is used as a TDMA burst time base [19]. UW of traditional TDMA burst signal is generally continuous and concentrated in the start part of burst data, and its frame structure is shown in Figure 1.  
Figure 1: Traditional TDMA frame structure.  
At present, a class of single carrier TDMA signals with new type DUW structure appears in satellite communication. The frame structure is shown in Figure 2. There are \(P\) groups of UW, and each group of UW has \(N\) symbols. It is inserted into the burst signal at equal intervals with \(L_{0}\) symbols as a period. In one burst signal, the total length of UW is \(D=N\times P\) symbols.  
As shown in Figure 3, the known local UW sequence \(a(k)\) is composed of the above \(U_{1}\) to \(U_{P}\) groups arranged in series.  
The expression of \(a(k)\) is:  
\[\begin{array}{l}a(k)=a(iN+j)=e^{\mathrm{i}\phi(i,j)},\ \ \ i=0,1,\cdot\cdot\cdot,P-1,\\ j=0,1,\cdot\cdot\cdot,N-1,\ \ \ k=iN+j\end{array} \tag{2}\]  
Herein, \(e^{\mathrm{i}\phi(i,j)}\) represents the j-th UW symbol in the i-th group, and its position in sequence \(a(k)\) is shown by \(k=iN+j\).
## 2 FSO Channel Model
### Atmospheric Turbulence Channel  
The atmospheric turbulence for the vertical FSO link becomes weaker as the altitude of transmissions increases, so we assumed the log-normally distributed channels for the weak turbulence regime as  
\[I^{mm}=e^{Z_{mm}}, \tag{1}\]  
where \(Z_{mm}\) is a Gaussian random variable with the mean \(\mu_{Z_{mm}}\) and the variance \(\sigma_{Z_{mm}}^{2}\). Assume that \(\mu_{Z_{mm}}=\mu_{Z}\) and \(\sigma_{Z_{mm}}^{2}=\sigma_{Z}^{2}\) for all \(m\in\{1,\cdots,M\}\) and \(n\in\{1,\ \cdots,N\}\) for the independent and identically distributed channels. The Rytov variance \(\sigma_{Z}^{2}=-\mu_{Z}/2\) for each transceiver to ensure \(E\{I^{m}\}=1\) is  
\[\sigma_{Z}^{2}=2.25k^{\frac{7}{6}}(L-h_{0})^{\frac{5}{6}}\times\int_{h_{0}}^{ L}C_{n}^{2}(I)\bigg{(}1-\frac{l-h_{0}}{L-h_{0}}\bigg{)}^{\frac{5}{6}}\bigg{(} \frac{l-h_{0}}{L-h_{0}}\bigg{)}^{\frac{5}{6}}dl, \tag{2}\]  
where \(k=2\pi/\lambda\). \(\lambda\), \(L\), and \(h_{0}\) are the wavelength, the altitude of the UAV, and the ground [19]. The perpendicular environment is assumed to make the transmission distance equal to the altitude of the UAV for the vertical link. The refractive index structure at altitude \(l\) follows the Hufnagel-Valley model, given by  
\[C_{n}^{2}(l)=0.00594(V_{rms}/27)^{2}\Big{(}10^{-5}l\Big{)}^{10}e^{-l/1000}+2.7 \times 10^{-16}e^{-l/1500}+C_{n}^{2}(0)e^{-l/100}, \tag{3}\]  
where \(V_{rms}\) is the rms wind speed and \(C_{n}^{2}(0)\) is the refractive-index structure at the ground [19].
# Joint Object Detection and Multi-Object Tracking Based on Hypergraph Matching
## 5 Experiments  
### Datasets and Evaluation Metrics  
We evaluate our method on MOTChallenges [53; 54; 55], including MOT16, MOT17 and MOT20 benchmarks. The MOT16 and MOT17 datasets both include 14 video sequences, and the MOT20 dataset includes 8 video sequences, with high density and extremely complex scenes. Following the CLEAR MOT metrics [56], HOTA [57], and IDF1 score [58], we apply some basic items for quantitative evaluation, e.g., Multiple Object Tracking Accuracy (MOTA \(\uparrow\)), Higher Order Tracking Accuracy (HOTA \(\uparrow\)), IDF1 (\(\uparrow\)), False Positives (FP \(\downarrow\)), False Negatives (FN \(\downarrow\)), and Identity Switches (IDS \(\downarrow\)). MOTA focuses on the performance of the detection branch, HOTA can comprehensively assess the performance of both the detection branch and data association, while IDF1 evaluates the ability to maintain identity, paying more attention to the association performance.
# Improving PM2.5 Concentration Forecast with the Identification of Temperature Inversion
## 4 Experimental Results and Comparative Performances
### Pm\({}_{2.5}\) Concentration Estimation
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & **Expert** & **CART** & **CNN** & **AIINormal** & **AIITemp** \\ \hline
**2017** & **RMSE** & 15.01 & 14.97 & 14.01 & 15.32 & 16.63 \\
**MAE** & 11.42 & 11.56 & 10.63 & 11.43 & 13.08 \\
**RMSE** & 7.68 & 8.18 & 8.34 & 8.92 & 11.24 \\
**MAE** & 6.33 & 6.66 & 7.09 & 7.16 & 8.64 \\
**RMSE** & 7.65 & 7.74 & 7.56 & 8.32 & 8.45 \\
**MAE** & 5.8 & 6.27 & 6.89 & 6.78 & 6.85 \\ \hline \end{tabular}
\end{table} 
Table 4: PM\({}_{2.5}\) forecast performance of various model strategies with short-term training in seven days. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & & **Expert** & **CART** & **CNN** & **AIINormal** & **AIITemp** \\ \hline
**2017** & **RMSE** & 16.42 & 16.13 & 16.32 & 16.37 & 16.23 \\
**MAE** & 12.36 & 12.29 & 12.34 & 12.35 & 12.4 \\
**RMSE** & 9.01 & 9.21 & 9.74 & 10.37 & 11.97 \\
**MAE** & 6.56 & 6.88 & 6.87 & 7.34 & 8.58 \\
**RMSE** & 7.8 & 8.59 & 8.59 & 8.95 & 9.7 \\
**MAE** & 6.05 & 6.48 & 6.74 & 6.77 & 7.21 \\ \hline \end{tabular}
\end{table} 
Table 2: PM\({}_{2.5}\) forecast performance of various model strategies with short-term training in five days. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & & **Expert** & **CART** & **CNN** & **AIINormal** & **AIITemp** \\ \hline
**2017** & **RMSE** & 15.68 & 15.63 & 15.18 & 16.02 & 16.08 \\
**MAE** & 11.78 & 11.77 & 11.39 & 11.89 & 12.14 \\
**RMSE** & 8.74 & 8.99 & 9.22 & 10.52 & 11.59 \\
**MAE** & 6.65 & 6.87 & 6.61 & 7.77 & 8.44 \\
**RMSE** & 7.77 & 8.64 & 8.61 & 9.32 & 8.92 \\
**MAE** & 5.93 & 6.55 & 6.73 & 7.05 & 7.21 \\ \hline \end{tabular}
\end{table} 
Table 3: PM\({}_{2.5}\) forecast performance of various model strategies with short-term training in six days. The pollution event incurred by external sources definitely cannot be correctly modeled by local climate ecology such as temperature inversion. Our future research will be focused on finding correlated monitoring stations as forecast regulators. For example, the monitoring station at Lunbei may serve as a regulator for the Puli PM\({}_{2.5}\) forecast. If the test day for Puli is classified as normal, then the PM\({}_{2.5}\) measured at Lunbei with some hours ahead (which can be calculated from wind directions and speeds) can be used as an extra variable in the regression to adjust the forecast.
## 2 Materials and Methods
### Experimental Details  
#### 2.5.1 Study Area and Datasets  
This research was conducted at the Ayazaga Campus of Istanbul Technical University in Turkiye (Figure 3). This campus features a mix of buildings, roads, and dense trees. The UAV-based photogrammetry flights covered an area of roughly 95 hectares.  
Figure 2: The fundamental concept of RTK GNSS positioning through the use of a UAV.  
The research was carried out with the DJI Mavic 3M unmanned aerial vehicle (Table 1). The DJI Mavic 3M's lens features FOV: 84\({}^{\circ}\), equivalent focal length: 24 mm, aperture: f/2.8 to f/11, focus: 1 m to \(\infty\). The UAV collected images at an altitude of 60 m above the take-off point. Optimum flight parameters were determined as 80% longitudinal overlap ratio and 70% transverse overlap ratio. Therefore, around a 1.6 cm/pixel ground sampling distance (GSD) was obtained. The images were acquired from nadir angles. During the UAV flight, 2829 images in non-RTK mode and 3231 images in RTK mode were collected at 1 s intervals in the study area. The UAV's GNSS recorded data at 0.2-s (5 Hz) intervals.
## 4 Numerical Simulation Results
### Mesoscopic Analysis of the Similar Material
#### 4.4.2 Mesoscopic Contact Number of the Similar Material  
In order to provide a more quantitative description of the contact between particles, information on the number of contacts in the particle system was extracted using the FISH language, and the average number of contacts (coordination number) was calculated. It is an important parameter to evaluate whether a model is dense or not and can reflect the state of the specimen to some extent. The average number of contacts in the specimen is calculated by Equation (9).  
\[z=\frac{2N_{c}}{N_{p}} \tag{9}\]  
where \(N_{c}\) represents the number of actual contacts (normal contact force greater than zero) in the specimen, and \(N_{p}\) is the total number of contacts in the specimen.  
The number of mesoscopic contacts in the numerical specimen with varying RFCAs is depicted in Figure 13. As shown, the total number of contacts in the specimen is 18,279, with an average number of contacts of 4.05 when \(H\) = 0.1. When \(H\) = 0.7, the total number of contacts in the numerical specimen increases to 157,113, representing an increase of 759.5%, and the average number of contacts increases to 4.99, a rise of 23.4%. The comparison shows that the increase in \(H\) increases the total number of contacts and the average number of mesoscopic contacts in the numerical specimen.  
Figure 12: Contact force distribution of the numerical specimen with different RFCAs under the uniaxial compressive stress of 0.5 MPa.  
On a mesoscopic level, an increase in the total number of contacts implies that a greater number of particles in the sample are involved in sharing the applied load. Consequently, the overall contact area between particles increases, and the sample's cohesion is likely to rise with the increasing \(H\). Furthermore, an increase in the average number of contacts signifies that the sample becomes denser, with each individual particle experiencing a rise in the number of surrounding particles it contacts. This is consistent with the above analysis. The internal friction angle \(\varphi\) is mainly controlled by the skeleton between coarse particles when \(H\leq 0.2\).  
As \(H\) increases, the skeleton between coarse particles weakens, and the internal friction angle \(\varphi\) is gradually controlled by the sliding friction between the particles. When \(H>0.2\), the average number of contacts between particles increases, which increases the resistance to relative sliding between particles and renders it more difficult for particles to slide against each other. Consequently, the internal friction angle \(\varphi\) increases.
## 2 Materials and Methods
### Data Collection  
Each measurement consisted of three MVC attempts, each lasting five seconds. The program identified the maximum weight within these intervals to pinpoint the three MVC attempts per test. From each peak, the program searched back to find the start of each attempt, marked when the weight dropped below 0.20 kg. Similarly, it searched forward from the peak to identify the end of the attempt using the same threshold criterion.  
In each attempt, there are two key areas of interest: the lifting period and the maximal contraction period. The lifting period, called the SubMax zone, is defined as the time from when the weight first exceeds the threshold value to when it reaches 80% of its maximum value. The maximal contraction period, called the Max zone, is when the weight consistently exceeds 80% of its maximum value.  
Raw data from the ADL-TTD was exported to Excel for further analysis using the programming language Python, Version 3.8, Available online: [[https://www.python.org/](https://www.python.org/)]([https://www.python.org/](https://www.python.org/)). The Python program imported raw data and converted the quaternion orientation measures into Euler angles for a more intuitive analysis of spatial orientation. To remove extremely large outliers from sudden bursts of effort (e.g., abrupt lifting movements), a 0.99 quantile filter was applied to the weight variable. The outliers refer to data points that lie beyond a specified quantile threshold. This threshold was selected because it effectively removes extreme spikes in the data while preserving the overall strength profile, ensuring that the maximal strength value remains largely unaffected.  
The Python program determined the MVC and the average tilt angle along the x-, y-, and z axes for the SubMax and Max zones. Each measurement includes data from three different attempts, from which the average is calculated. If one attempt is deemed incorrect, the average is recalculated from the remaining two attempts. Based on consensus, an attempt is incorrect if the deviation between MVCs is greater than 20% [22]. To ensure the accuracy of the automated scoring system, results were compared with manuallysscored data from five participants, and the outcomes were found to be similar, confirming the reliability of the automated approach.  
Figure 5: Water spilling angles with different tilt directions. Note: The calculation was not made for the AH/NAH forward rotation because movement in the horizontal plane is not very relevant as parameter causing spilling.