esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Re-Planned Path Using Potential Fields
#### 2.3.2 Selection of Re-Planned Path Candidates  
After rejecting unsafe route candidates, one applies the APF for selecting the best route among the remaining safe-path candidates. In other words, the APF is used for a decision-making purpose here. The APF artificially generates the attractive and repulsive potential fields based on the potential functions by considering the goal, which is the target position, and obstacles. While a UAV is attracted by a goal in the attractive potential field, the UAV is repelled by obstacles in the repulsive potential field. Then, the UAV in a given space travels in a direction where the total potential function value has the minimum value. The total potential function for the safe-path candidates is defined as follows [7]:  
\[U_{\text{tot}}(\mathbf{p}_{\text{int},l})\ =U_{\text{att}}(\mathbf{p}_{\text{int},l})\ +\ \sum_{i}U_{\text{rep}}(\mathbf{p}_{\text{int},l},\ \mathbf{p}_{\text{obs},i}), \tag{14}\]  
Figure 3: Perpendicular plane and normal vector on tunnel or corridor and a enlarged figure to illustrate how to generate path candidates.  
where \(U_{\text{att}}\) and \(U_{\text{rep}}\) are the attractive and repulsive potential functions that are defined as  
\[U_{\text{att}}(\mathbf{p}_{\text{int},l}) =\frac{1}{2}\,k_{\text{att}}\,D(\mathbf{p}_{\text{int},l},\ \mathbf{p}_{f})^{2}, \tag{15}\] \[U_{\text{rep}}(\mathbf{p}_{\text{int},l},\ \mathbf{p}_{\text{obs},i}) =\begin{cases}\frac{1}{2}\,k_{\text{rep}}\bigg{(}\frac{1}{D( \mathbf{p}_{\text{int},l},\ \mathbf{p}_{\text{obs},i})}-\frac{1}{d_{\text{nd}}}\bigg{)}^{2},& \text{if }\,D(\mathbf{p}_{\text{int},l},\ \mathbf{p}_{\text{obs},i})\leq d_{\text{ thd}},\\ 0,&\text{if }\,D(\mathbf{p}_{\text{int},l},\ \mathbf{p}_{\text{obs},i})>d_{ \text{thd}},\end{cases} \tag{16}\]  
where \(k_{\text{att}}\) and \(k_{\text{rep}}\) are scaling factors for the attractive and repulsive potential functions, \(D(\mathbf{a},\mathbf{b})\) is the relative distance between arbitrary two vectors \(\mathbf{a}\) and \(\mathbf{b}\), \(\mathbf{p}_{\text{obs},i}\) is the position vector of measured \(i\)-th obstacle, and \(d_{\text{thd}}\) is the threshold distance influenced by the repulsive potential function. With the the concept of the APF, the \(l^{*}\)-th path that has the smallest total potential function value among the computed values for each path candidate is finally selected as follows:  
\[l^{*}=\operatorname*{arg\,min}_{l}U_{\text{tot}}(\mathbf{p}_{\text{int},l}). \tag{17}\]  
At the next time step, the above processes are repeated until the UAV arrives at the final goal position.
###### Abstract  
To address the issues of signal waveform distortion and inter-symbol interference, both of which lead to performance degradation in airborne laser communication due to multipath transmission in airborne air-to-air channels, theoretical analysis and Monte Carlo ray tracing (MCRT) simulation methods were employed. Based on airborne application conditions, this research conducted numerical simulations of the Gaussian beam multipath transmission in an air-to-air channel, with a focus on analyzing the impact of meteorological conditions, communication distance, transmitter power, and initial pulse width on the pulse time spreading characteristics of received optical signals. In addition, an analysis of these parameters' impact on the inter-symbol interference (ISI) and bit error rate (BER) was conducted. The research findings can serve as a reference for the design of anti-interference techniques in airborne laser communication links.  
airborne air-to-air channel; multipath transmission; inter-symbol interference; pulse time spreading; MCRT numerical simulation +
Footnote †: journal: _Photonics_
## 2 Overview of the Benchmark Problem in RTHS
### Partitioning Schemes  
It has been reported that the partitioning of the reference structure plays a critical role in the successful implementation of the RTHS [34]. The predictive stability indicator (PSI), which is defined as PSI = log\({}_{10}\)(\(\tau_{\text{cr}}\)), where \(\tau_{\text{cr}}\) is the critical time delay, is used to quantify the sensitivity of different partitioning choices to de-synchronization at the interface between the substructures. A smaller value of PSI indicates that the RTHS is more sensitive to the de-synchronization. In the Benchmark problem, four different partitioning schemes were considered with PSI values ranging between 0.6 (moderately sensitive) and 1.05 (slightly sensitive), which is equivalent to \(\tau_{\text{cr}}\) values between 4 and 11 ms. The four partitioning cases are listed in Table 1.  
The PS is a portal frame that is built in the Intelligent Infrastructure Systems Lab (IISL), as described in Figure 1b. The mass, lateral stiffness, and damping coefficient of the PS are 29.12 kg, \(1.19\times 10^{6}\) N/m, and 114.6 Ns/m, respectively, which were identified using experimental data.
# Kiwifruit Detection Method in Orchard via an Improved Light-Weight YOLOv4
## 4 Results and Analysis
### Improvement Results of Different Backbone Feature Extraction Networks  
The original YOLOv4 network structure is prone to target fruit under-recognition during target fruit detection due to backlighting, high fruit density, fruit overlapping, and small target fruits. The improved MobileNetV3-YOLOv4 network improves the Precision by 4.78%, F1 value by 1.0%, and detection speed by 47 frames/s compared with the original YOLOv4. The improved GhostNet-YOLOv4 improves the mAP by 5.36%, Precision by 5.36%, F1 by 5%, Recall by 4.49%, and detection speed by 53 frames/s over the original YOLOv4, with a weight reduction of 201.5 MB (Table 3). By adding feature layers favorable to small target fruit detection in the path aggregation network and introducing depth-separable convolution and Ghost Module modules, the model size can be effectively compressed, the model recognition speed accelerated, and the model recognition accuracy improved. Compared with MobileNetV3 series as the backbone network, YOLOv4 with GhostNet as the backbone network has different degrees of improvement in Precision, Recall, detection speed, and F1 value.  
Figure 8: The training losses using different backbone networks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network Model** & **mAP** & **Recall** & **F1** & **FPS** & **Weights** & **Precision** \\ \hline YOLOv4 & 91.79 & 87.94 & 87.0 & 36 & 244 & 85.26 \\ MobileNetV3-YOLOv4 & 91.44 & 86.45 & 88.0 & 47 & 53.7 & 90.04 \\ GhostNet-YOLOv4 & 93.07 & 92.43 & 92.0 & 53 & 42.5 & 90.62 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performances of the models using different backbone networks.  
The two detection models were used to detect the dataset separately, and Figure 9. Shows the curves of the P and R relationships of the two networks. From the PR curves in Figure 9, the area enclosed under the PR curve of kiwifruit in the images of GhostNet-YOLOv4 model is significantly higher than that of MobileNetV3-YOLOv4. This indicated that GhostNet-YOLOv4 has higher detection accuracy and better performance.
# Encapsulation of Blackberry Phenolics and Volatiles Using Apple Fibers and Disaccharides
###### Abstract  
The objective of this study was to determine the effect of disaccharides on the encapsulation of the phenolics and volatiles of blackberry juice with the use of apple fiber. For this purpose, apple fiber/blackberry microparticles were prepared as the control, as well as microparticles additionally containing disaccharides, i.e., sucrose or trehalose. Fiber:disaccharide ratios were 1:0.5, 1:1, and 1:2. Formulated microparticles were characterized for total phenolics, proanthocyanidins, individual phenolics, antioxidant activity, flavor profiles, and color parameters. Both applied disaccharides affected the encapsulation of phenolics and volatiles by the apple fibers. Control microparticles had a higher content of phenolics than microparticles with disaccharides. Comparing disaccharides, the microparticles with trehalose had a higher content of phenolics than the ones containing sucrose. The amount of proanthocyanidins in the control microparticles was 47.81 mg PB2/100 g in trehalose, the microparticles ranged from 39.88 to 42.99 mg PB2/100 g, and in sucrose, the microparticles ranged from 12.98 to 26.42 mg PB2/100 g, depending on the fiber:disaccharide ratio. Cyanidin-3-glucoside was the dominant anthocyanin. Its amount in the control microparticles was 151.97 mg/100 g, while in the trehalose microparticles, this ranged from 111.97 to 142.56 mg /100 g and in sucrose microparticles, from 100.28 to 138.74 mg /100 g. On the other hand, microparticles with disaccharides had a higher content of volatiles than the control microparticles. Trehalose microparticles had a higher content of volatiles than sucrose ones. These results show that the formulation of microparticles, i.e., the selection of carriers, had an important role in the final quality of the encapsulates.  
apple fiber; trehalose; sucrose; blackberry phenolics; blackberry volatiles +
Footnote †: journal:  
Article
## 4 Adaptive OFDM-IM Method
### Scenario Model
#### 4.1.2 OFDM-IM Communication in Highway Scenarios  
In highway scenarios, as illustrated in Figure 5, the received signal is primarily composed of LOS components, while the NLOS components are relatively minimal, with a Rician factor of 20 dB. The reflection coefficients of the scatterers along both sides of the road are relatively low, denoted as \(\mu=0.2\). In this scenario, the good channel quality contrasts the inconvenience of charging electric vehicles, raising concerns about energy  
Figure 4: OFDM-IM communication in highway scenarios.  
Figure 3: Block diagram of OFDM-IM method that is adaptive to user requirements and scenario.  
consumption. Therefore, the optimization strategy focuses on minimizing the energy consumption while considering the BER and spectral efficiency, selecting parameters that provide the best overall performance to meet the specific requirements of the OFDM-IM system in highway scenarios.  
In vehicular networks, optimizing the performance of the OFDM-IM communication system to meet the requirements of various users and scenarios necessitates consideration of three key factors: the BER, spectral efficiency, and energy consumption. For instance, in highway scenarios, roadside reflectors are sparse and the roads are open, but charging is more challenging than on regular roads. Considering users' difficulties with charging, the focus should be on optimizing the energy consumption and BER, accepting some loss in spectral efficiency to enhance the overall system performance.
# Effect of Temperature on Channel Compensation in Optical Camera Communication
## 7 Conclusions
Regarding the BER performance, first, it was observed that for high input SNR values, the BER was below \(3.8\cdot 10^{-3}\) (FEC limit) for the majority of temperature differences between the reference CSI employed for compensating and the current CSIs. As the BER increased with temperature difference, approaches that are more sensitive to SINR variations than OOK transmissions would be strongly affected by the thermal impact during the compensation procedure. Second, for a medium input SNR, the results reinforced the usefulness of a Bayer-based image sensor over a Foveon device. While the BER was below the FEC limit for almost all the temperature difference in the IMX249 sensor, most of the values were above the limit in the X3. Finally, for a low-input SNR, this poor performance was not unexpected. In fact, the ZF scheme inevitably penalizes the BER in these low SNR cases. As in the SINR analysis, the blue LED was the most thermal-affected device in the BER performance. These results suggest that it would be recommended to not using blue LEDs when the transmitter is subject to high-temperature gradients that can cause variations in the emitted spectra. Nonetheless, those spectral changes on GaN and InGaN LED depend on the specific compositions and should be further characterized. It must be remarked that these results are limited by the use of Bayer and Foveon filtering schemes (with severely overlapped sub-filters) and by the essayed devices. Nevertheless, the results obtained in this work demonstrate that temperature effects must be taken into account when designing multi-wavelength links. To conclude, future research should consider the temperature effect during the channel compensation process. Besides, further work based on this topic needs to be performed to provide techniques that mitigate this effect, such as more robust compensation techniques than ZF equalization (e.g., using a minimum mean square error (MMSE) estimator) or temperature stabilization stages in the emitting system. Finally, a more comprehensive study linking the temperature-induced spectral variations of the optical sources and the design of the filter arrays is needed to provide more insight about this topic. Conceptualization, J.R. ([PERSON]): Formal analysis, J.R. ([PERSON]) and V.G. ; Funding acquisition, R.P.-[PERSON]; Investigation, D.M. ; Methodology, D.M. ; Resources, [PERSON] ([PERSON]); Software, V.G. ; Supervision, [PERSON] ([PERSON]) and R.P.-J. ; Writing--original draft, D.M. and V.G. ; Writing--review and editing, [PERSON] ([PERSON]), [PERSON] ([PERSON]), and R.P.-J. All authors have read and agreed to the published version of the manuscript. This research was funded by the Spanish Research Administration (MICINN project: OSCAR, ref. : TEC 2017-84065-C3-1-R) and was made possible from the Trainee Predoctoral Research Staff Program (2019) of the Universidad de Las Palmas de Gran Canaria. The authors declare no conflict of interest.
## Abbreviations  
CLC = Corine Land Cover; DTM = Digital Terrain Model; EGMS = European Ground Motion Service; GIS = Geographic Information System; HRL = High-Resolution Level; InSAR = Interferometric SAR; LC/LU = Land Cover / Land Use; SAR = Synthetic Aperture Radar; SL = Sea Level; SLR = Sea Level Rise.
## 5 Case Studies
### Validation with Person Trip Survey Data
#### 5.1.1 Survey Data Collection  
This paper conducted an on-site survey to statistically validate the developed elapsed time threshold for each station and time period.  
The authors recruited of eight local volunteers and collected field transfer data at four metro stations in Shenzhen. The survey time included two periods: (1) 7:00-9:00 on 21-22 March (Thursday and Friday), 2019; and (2) 18:00-20:00 on 23-24 March (Saturday and Sunday), 2019. Every two volunteers were assigned into one group, where one person was in charge of recording major passengers' swiping card time, and the other was responsible for counting transfer volume.  
In the real-world survey, all volunteers firstly recorded the alighting time of the majority of passengers at one exit of each metro station, and followed them to the ground-level bus station. Then, our volunteers recorded the passengers' boarding time on the specific bus line. Next, they returned to the next metro station exit and repeated the process. To eliminate random errors, the volunteers were required to replicate the survey several times for each metro exit while covering all exiting gates of four stations (five exits at Guomao Station, six exits at Shenzhen University Station, five exits at Daxin Station, and four exits at Airport east Station).
## 3 Fundamentals of GLI-Split
### General Idea and Nomenclature  
Edge networks like those of companies that implement GLI-Split are called GLI-domains while others are called _classic IPv6_ domains. Nodes of a GLI-domain are GLI-nodes and its border routers are GLI-gateways. GLI-nodes with a special GLI-(networking-)stack are called _upgraded_ while others are called _classic IPv6_ nodes. GLI-nodes and GLI-gateways are identified by a globally unique identifier (ID). They have a local locator (LL) that describes their position within their GLI-domain and serves for local routing. Furthermore, each GLI-gateway has a globally unique global locator (GL) that describes its position in the IPv6 backbone. A global mapping system (MS) maps IDs to global locators and a local mapping system maps IDs to local locators. This setting is illustrated in Figure 1. IDs are denoted by integral numbers, local locators by lowercase letters, and global locators by uppercase letters. In the examples of this paper, we refer to parts of the setting in this figure. We designate GLI-nodes by their IDs, _i.e_., node 1 is the node with ID 1.
# Joint Channel Estimation Algorithm Based on DFT and DWT
## 6 Simulation and Analysis
### Channel Estimation Algorithm Performance Based on ST-DFT  
Figures 2 and 3 show the performance of different channel estimation algorithms under different channel models. The performance of the DFT-based algorithm in [11] appears floor quickly. Although the MSE of the T-DFT algorithm in [13] is significantly better than that of [11], it is still inferior to that of the LS algorithm in high SNR range. The performance of the ST-DFT algorithm proposed in this paper is better than that of the T-DFT algorithm, especially under low SNR. The reason can be explained as follows. The threshold of the T-DFT algorithm is fixed at twice the average power of the samples in the noise observation area. When the SNR is low, the large noise in the CIR cannot be effectively removed. When the SNR is high, some of the samples that should be reserved will be misjudged as noise samples and removed. The ST-DFT algorithm avoids the above problems and thus has a better channel estimation performance. However, since all samples in the time-domain contain channel components, the threshold decision will always misjudge a portion of the channel components as noise and set them to zero. T-DFT and ST-DFT algorithms will inherently suffer from performance loss under high SNR, so they still have a trend of performance floor. The wavelet-denoising algorithm transforms the signal into the wavelet domain for denoising. Based on the appropriate wavelet base and decomposition layers, the useful signals and noise can be separated correctly, so the estimation performance loss is relatively small under high SNR.  
In addition, the maximum delay spread and the number of taps are different for the two models. Although the outdoor channel has more severe frequency selectivity than that of the indoor channel, the proposed algorithms are still valid. The MSE of the threshold-based DFT algorithm for the UMi model is worse than that estimated for the D model because the UMi model has a larger delay spread and more taps. When conducting \(N\)-point IFFT, more channel components diffuse into the noise observation area and thus are more likely to be misjudged during threshold judgments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Channel bandwidth & 20 MHz & OFDM Symbols & 12.8 μs \\ \hline Poilt modulation scheme & BPSK & Circular prefix & 3.2 μs \\ \hline Number of subcarriers & 256 &
\begin{tabular}{c} Number of active \\ subcarriers \\ \end{tabular} & 242 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameter settings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Indoor** & **Outdoor** \\ \hline Channel Model & Model D & Model UMi \\ \hline Number of taps & 18 & 19 \\ \hline Maximum delay spread & 390 ns & 730 ns \\ \hline Movement speed & 0.089 km/h & 3 km/h \\ \hline \hline \end{tabular}
\end{table}
Table 3: IEEE 802.11 ax channel model key parameters.
## 2 Theoretical Formulation
### The Formulation in ISO 14163  
ISO 14163 defines the pressure loss, \(\Delta p_{t}\), as the difference between the mean total (i.e., static and dynamic) pressures upstream and downstream of the silencer. The standard also mathematically defines the pressure loss as follows:  
\[\Delta p_{t}=(\xi_{s}+\xi_{f})\frac{\rho}{2}\
u^{2}=\xi\frac{\rho}{2}\
u^{2} \tag{1}\]  
where \(\xi_{s}\) is the pressure loss coefficient in relation to the total duct cross section, \(\xi_{f}\) is the pressure loss coefficient due to friction, \(\rho\) is the density of the medium, and \(\
u\) is the mean flow velocity in the inlet cross section.  
The pressure loss coefficient in relation to the total duct cross section, \(\xi_{s}\), is defined as follows:  
\[\xi_{s}=\bigg{(}\frac{d}{2}\bigg{)}^{2}\bigg{[}0.5\xi_{1}\bigg{(}\frac{s}{d}+ 1\bigg{)}+\xi_{2}\bigg{]} \tag{2}\]  
where \(\xi_{1}\) is the form factor for the upstream side (for rectangular splitters, \(\xi_{1}=1\); for splitters with a semihexadecagonal upstream side profile, \(\xi_{1}=0.1\)), \(\xi_{2}\) is the form factor for the downstream side (for rectangular splitters, \(\xi_{2}=1\); for splitters with a semihexadecagonal upstream side profile, \(\xi_{1}=0.7\)), \(s\) is the width of the airway, and \(d\) is the thickness of the splitters.  
The pressure loss coefficient due to friction, \(\xi_{f}\), can be estimated from the following:  
\[\xi_{f}=0.025\frac{l}{s}\bigg{(}1+\frac{d}{s}\bigg{)}^{2} \tag{3}\]  
where the value 0.025 is typical for half the friction coefficient of dissipative silencers and \(l\) is the length of the silencer.  
It is important to note that the pressure loss coefficients vary depending on the adopted profiles. Nevertheless, since the main goal of this work is to consider the limitations of the standards, the original coefficients shown in the ISO standard are considered.
## 3 Theoretical Background of the SRM  
### Torque Generation  
The principle of the torque generation in an SRM is based on the reluctance minimization of the magnetic flux linkage paths. Each phase, the torque strongly varies with the phase current and rotor position variation. Generally, the torque phase \(T_{k}\) can be calculated from the co-energy as follows:  
\[T_{k}(i_{k},\theta)=\frac{\partial\int_{0}^{i_{k}}\psi_{k}(i_{k},\theta)di_{k}} {\partial\theta}\,k=1,2,\cdots,m \tag{1}\]  
where \(k\) is the identifier of the phases, \(\theta\) is the rotor position, \(i_{k}\) is the phase current, and \(\Psi_{k}\) is the phase flux linkage. All of the position angles refer to mechanical angles (except where noted). The two basic equations that describe each phase flux linkage and the machine model of the SRM are as follows:  
\[\psi_{k}(i_{k},\theta)=L_{k}(i_{k},\theta)\times i_{k} \tag{2}\]  
\[u_{k}=Ri_{k}+\frac{d\psi_{k}}{\mathrm{d}t} \tag{3}\]  
where \(L_{k}\) is the \(k\)th phase inductance, \(u_{k}\) is the \(k\)th phase instantaneous voltage, and \(R\) is the phase winding resistance. Because of the highly nonlinear and saturation effects of the electromagnetic characterization in the SRM, the inductance \(L_{k}\) in Equation (2) varies not only with phase current value but also with rotor position variation.  
Figure 3: Linear interpolation results: (**a**) 3-D relationships of \(\Psi\)-\(i\)-\(\theta\); (**b**) 3-D relationships of \(T\)-\(i\)-\(\theta\).  
Figure 2: The phase inductance, the flux linkage profiles and the torque characteristics of the studied SRM: (**a**) inductance profiles; (**b**) flux linkage profiles; (**c**) torque profiles.  
By neglecting the saturated nature, we simplify the formula and assume that the inductance \(L_{k}\) varies only with the rotor position \(\theta\). According to Equations (1) and (2), the torque \(T_{k}\) can be expressed as:  
\[T_{k}(i_{k},\theta)=0.5i_{k}^{2}\times\frac{dL_{k}(\theta)}{d\theta}=0.5L_{kp}( \theta)i_{k}^{2} \tag{4}\]  
where \(L_{kp}\) is denoted as the derivative of \(L_{k}\). In this case, assuming that \(L_{kp}\) is known, Equation (4) enables a fast and simple torque calculation.