esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### ESA Swarm In-Situ Electron Temperature Data and Application of the Lomidze Correction
In Equations (1)-(3), and throughout the paper, \(T_{\rm e}\) values are in K and \(N_{\rm e}\) values are in cm\({}^{-3}\). Equation (1) is valid for the dataset of HG Swarm A LP \(T_{\rm e}\) dataset, Equations (2) and (3) for the corresponding Swarm B and C datasets, respectively. As outlined by Equations (1)-(3), the Lomidze correction is based on a regression analysis of Swarm-measured \(T_{\rm e,LP}\) with data retrieved by the three ISRs located at Jicamarca, Millstone Hill, and Arecibo from December 2013 to June 2016 (for more information on the dataset selection on which Equations (1)-(3) are based see [24]). The shortness of the dataset did not allow a characterization of possible diurnal, seasonal, geographic, and solar activity dependences of the corrections introduced in Equations (1)-(3). As a consequence, Equations (1)-(3) were applied on the entire Swarm datasets although someresidual diurnal, seasonal, geographic, and solar activity dependences were somewhat expected. The application of these ISRs-based corrections allowed greatly improving the accuracy of Swarm \(T_{\rm e}\) data when compared to ISRs ones (stepping from median differences between 300 and 400 K to near zero) without affecting the corresponding precision and correlation [24]. As outlined by [PERSON] et al. [24], the inclusion of the term \(N_{\rm e,LP}\) in Equations (1)-(3) produced a worsening in the precision of the correction (less than 60 K) but with the advantage of removing some certain non-physical patterns from Swarm \(T_{\rm e}\) data. In this work, both Swarm \(T_{\rm e,LP}\) original data and Lomidze-corrected \(T_{\rm e,Lom}\) data were employed. In Section 3, a statistical comparison between \(T_{\rm e}\) data measured by Swarm, calibrated after applying the Lomidze correction, and the corresponding ones modeled by IRI is presented. In the same section, a statistical comparison with \(T_{\rm e}\) data recorded by Jicamarca, Arecibo, and Millstone Hill ISRs is also performed.
# UAV Communication Recovery under Meteorological Conditions
## 2 System Model
### Excessive Path Loss
Furthermore, we use \(\eta_{LoS}\) and \(\eta_{NLoS}\) to denote the mean values of excessive path loss between the UAV and receivers in LoS or NLoS conditions within each region. Specifically, we employ \(\eta_{NLoS1}\), \(\eta_{NLoS2}\), and \(\eta_{NLoS3}\) to represent the mean values of excessive path loss between the UAV and receivers for varying reflection counts. \[\eta_{n}=\text{PL}_{n}-\text{FSPL}_{n}. \tag{3}\]  
For a carrier frequency of 28 GHz, Figure 3 illustrates the calculated excessive path loss for each receiver (discrete points) against the UAV's elevation angle in the four regions. Solid lines with distinct colors represent the average values of excessive path loss between the UAV and receivers in various communication states (LoS or NLoS with different reflection counts). Table 2 summarizes the mean values of different types of excessive path  
Figure 2: LoS points and NLoS points with different numbers of reflections in four regions (Freq = 28 GHz). loss (\(\eta_{LoS}\), \(\eta_{NLoS1}\), \(\eta_{NLoS2}\), and \(\eta_{NLoS3}\)) at carrier frequencies of 28 GHz and 71 GHz across the four regions. The path loss between the UAV and receivers in different states (LoS or NLoS) can be calculated using Equation (4) from [17], where \(d\) represents the distance in meters from the UAV to a ground receiver, and \(\lambda\) denotes the wavelength in meters. \[ \begin{split} PL_{LoS}&=20\log\left(\frac{4\pi d}{ \lambda}\right)+\eta_{LoS},\\ PL_{NLoS}&=20\log\left(\frac{4\pi d}{\lambda} \right)+\eta_{NLoS},\\ (\eta_{NLoS}&=\eta_{NLoS1},\eta_{NLoS2},\eta_{NLoS3} ).\end{split}  \tag{4}\]  
By substituting Equations (2) and (4) into Equation (1), we derive the propagation model of UAV-ground user communications as a function of the UAV elevation angle under various weather conditions, as shown in Equation (5). \[ \begin{split} PL_{\text{max}}&=[i\cdot\sin(j\theta+ k)+l\cdot\sin(m\theta+n)]\times(\eta_{LoS}-\eta_{NLoS})+20\log d+20\log\left(\frac{4\pi}{ \lambda}\right)\\ &+\eta_{NLoS}+\frac{(\beta+\gamma)d}{1000},(0\leq\theta\leq 70^{ \circ})(\eta_{NLoS}=\eta_{NLoS1},\eta_{NLoS2},\eta_{NLoS3}).\end{split}  \tag{5}\]  
Figure 3: Excessive path loss versus elevation angle of the UAV in four regions (Freq = 28 GHz).
# PSDFH: A Phase-Space-Based Depth from Hologram Extraction Method
## 2 Related Works
### Depth-From-Focus Methods  
A simple depth estimation method is the use of the DFF method [3; 4; 5]. First, a reconstruction volume is built from the input hologram, with a predefined number of holographic reconstructions within a fixed depth interval. Each numerical reconstruction can be computed using the angular spectrum method [6] defined as  
\[\mathcal{P}_{z_{i}}\{H\}=\mathcal{F}^{-1}\bigg{\{}\mathcal{F}(H)e^{j2\pi z_{i} \sqrt{\lambda^{-2}-f_{i}^{2}-f_{j}^{2}}}\bigg{\}}, \tag{1}\]  
where \(H\) is the hologram, \(\mathcal{F}\) is the Fourier transform, \(\lambda\) is the acquisition wavelength, \(f_{x}\) and \(f_{y}\) are the spatial frequencies along the \(X\)- and \(Y\)-axis, \(\lambda\) is the acquisition wavelength, and \(z_{i}\) is the reconstruction depth, given by  
\[z_{i}=\frac{z_{\text{max}}-z_{\text{min}}}{N}i+z_{\text{min}}, \tag{2}\]  
where \(N\) is the total number of holographic reconstructions, and \(z_{\text{min}},z_{\text{max}}\) are the boundaries of the reconstruction interval. Then, the focus level of each pixel \((m,n)\) in the reconstruction volume is evaluated locally using a focus measure \(FM\) applied on a centered patch \(R_{m,n}\) around the pixel. Finally, the pixel depth is set to the depth \(d_{m,n}\) at which the maximum focus level \(FM(R)\) is reached. More formally,  
\[d_{m,n}=\operatorname*{arg\,max}_{i\in\{1,N\}}\{FM(R_{m,n,i})\}, \tag{3}\]where  
\[R_{m,n,i}(u,v)=|\mathcal{P}_{z_{i}}\{H\}|(m+u-s/2,n+v-s/2), \tag{4}\]  
and \(s\) is the used patch size. Despite its time-consuming nature, the DFF method can produce relevant results under optimal conditions in which the holographic reconstruction parameters, focus measure, and patch sizes are carefully chosen.  
In practice, determining the proper reconstruction parameters is difficult. It is simpler to use a very large reconstruction interval \([0,z]\) that spans the entire space and a high sampling rate \(N\) to cover all possible depths. Even if this methodology appears to be advantageous, the obtained results will depend on the focus measure \(FM\), which must be sensitive enough to distinguish the correct optimal focus plane.  
A detailed benchmark of focus measurement operators can be found in [7]. More recently, [8] studied various types of focus measurements and how different choices of hyperparameters can affect their performance when used in CGH. The results obtained demonstrate that the focusing curves are subject to a change in polarity depending on the region treated. Therefore, the authors propose an automatic switching method to automatically choose the global minimum or maximum depending on the offset from the mean value.
## 4 Conclusions  
In this paper, research on HALE flight trajectory with minimal battery mass for SPA based on gravity energy reserving and mission altitude is conduced to achieve the following.  
1. A flight path phase model based on gravity energy reserving and mission altitude has been carried out for analysis and research background for engineering applications is taken into consideration for the day and night cycle flight and tasks.
2. GPM is feasible for the research on flight trajectory optimization for high-altitude SPA due to its advantages in calculation efficiency and accuracy as well as the integration between GPOPS and function files in MATLAB.
3. This flight trajectory optimization results shows that Zephyr 7 can reduce the battery mass from 16 kg to 12.61 kg for the day and night cycle flight and missions, which equals to reducing its total mass by 6.4%.
4. According the optimization results, the yaw angel may have more influence on high-altitude SPA 3D flight strategy in energy harvesting than the roll angle and the battery charge rate has good following to the solar irradiation intensity, which is instructive for SPA to use solar radiation reasonably at noon phase in mission execution.  
This study focuses on flight strategy optimization of the HALE SPA in three-dimensional, and gives the optimal flight trajectory with minimal battery mass based on gravity energy reserving and mission altitude. It will be very interesting to have the result of another case in different simulation conditions based on this flight strategy. As a result for the future works, there are two aspects of the related work. Firstly, we will further design mission path planning frame-work for the mission altitude in the  
Figure 10: Optimal attack angle and attitude angle profile with minimal battery mass.  
Figure 9: Optimal yaw angle profile with minimal battery mass.  
integrated model, which is more complicated than the proposed framework. Secondly, the energy management for this flight strategy and the influences of other environmental factors for SPA such as the wind will also be taken into our considerations in future research.  
**Author Contributions:** \"Conceptualization, M.S. and K.S.; methodology, M.S.; software, M.S.; validation, M.S., X.J. and K.S.; formal analysis, K.S.; investigation, M.Z.; resources, K.S.; data curation, M.S.; writing--original draft preparation, M.S.; writing--review and editing, M.S.; visualization, K.S.; supervision, K.S.; project administration, M.Z.; funding acquisition, K.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant No. 51775021, Key Laboratory of Aircraft Advanced Design Technology (Beihang University) Ministry of industry.  
The authors declare no conflict of interest.
## 4 Materials and Methods
### Floral Scent Collection and Analysis  
Petals were collected at the full-opened stage (the first day the flower bloom from half open to full open). To avoid the closed state of flowers, the samples were collected from 10:00-11:00 in the sunny morning. Floral scent collections were conducted using the headspace SPEM (HS-SPME) method and performed with three replicates. This method has been proven to be linear in the range of 5-200 mg/l, and all R2 values were above 0.99. The minimum limit of detection was less than 4 \(\times\) 10\({}^{-3}\) ng. In five repeated experiments, it was found that the standard sample recovery rate was over 80%, and the relative standard deviation of the main volatile components was under 10%, indicating good reproducibility [73,74]. This method can accurately and effectively analyze volatile aromatic substances.  
The SPME fiber (50/30 \(\upmu\)m, DVB/CAR/PDMS fiber, Supelco, Bellefonte, PA, USA) was aged for 2 h in the GC injection (250 \({}^{\circ}\)C). After cutting the entire fresh herbaceous peony petals, the inner and outer petals were evenly mixed, and then accurately weighed at 0.5 g. Petals were put into a 40 mL collection bottle with 20 \(\upmu\)L 3-octanal (82 ng/\(\upmu\)L) as the internal standard. The bottle was placed on the magnetic stirrer. The aged SPME fiber was inserted into the headspace of the bottle and was extracted at 40 \({}^{\circ}\)C for 40 min. Then, the fiber was inserted into the GC/MS (ISQ & TRACE GC Ultra, Thermo Fisher Scientific, Waltham, MA, USA) system and desorbed at 250 \({}^{\circ}\)C for 3 min. Three biological replicates of the experiments were carried out.  
GC conditions are as follows. Carrier gas: helium; chromatographic column: DB-5 MS (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent, Palo Alto, CA, USA); carrier gas flow rate: 1 mL/min; injection temperature: 250 \({}^{\circ}\)C; injection mode: no split flow; column temperature: 40 \({}^{\circ}\)C for 2.5 min, increased from 40 \({}^{\circ}\)C to 230 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min, and then held for 5 min. MS conditions are as follows. Eletron ionization: EI source, 70 eV; mass range: 35-450 m/z.  
The volatile compounds in each sample were identified by the combination of the National Institute of Standards and Technology (NIST) library. The identification results were selected with a Match Factor (SI) or Reverse Match Factor (RSI) thresholds greater than 800 (900 and above, mass spectrometry matching is excellent; 800-900, good; 700-800, fair; <600, Poor), and they were ultimately determined based on the retention index. The content of each component is determined by the internal standard method. The calculation formula is as follows: each component content (ng/g) = [(peak area of each component \(\times\) internal standard content (ng/uL) \(\times\)internal standard volume (\(\upmu\)L)/peak area of internal standard)/sample weight (g).
# A Software-Defined Radio Platform for Teaching Beamforming Principles
## 4 Beamforming Platform Validation and Testing  
The beamforming platform validation and testing were conducted using a customized array of patch antennas, with experimental measurements performed in both indoor and outdoor environments. The system was tested for different beam orientations by applying phase shifts between the patch elements and measuring the resulting radiation patterns.
# Analysis of Dual-Band Direction of Arrival Estimation in Multipath Scenarios
## 5 Validation of the Simulation Environment
Anyway, the data combination enhances both mean and 90-th percentile error, as expected from measurements. A further step has been carried out to fully validate the proposed method. The latter refers to a direct comparison between simulated and measured results. With reference to the simulated scenarios described at the beginning of the present section, three real scenarios were implemented for \(D\) ranging from 2 m to 3 m, in according to those described in [16]. An attenuation range from \(\chi=0\) dB to \(\chi=10\) dB has been used. Figures 17-19 illustrate the comparison between the experimental results and the simulated ones for both Mean DoA estimation and 90-th percentile DoA estimation error, considering the combined use of the two analysis frequencies. The results depicted in Figure 17, and corresponding to \(D=2\) m, show an optimum agreement between simulation and measurement in both Mean DoA estimation error and 90-th percentile estimation error. Maximum discrepancies of about 1.5\({}^{\circ}\) and 2\({}^{\circ}\) exist in Mean DoA and in 90-th percentile estimation error, respectively. Conversely, Figure 18 exhibits a difference of 4\({}^{\circ}\) and 7\({}^{\circ}\) respectively in Mean DoA error and 90-th percentile error, between simulation and measure at \(\chi=0\) with \(D=2.5\) m. Comparison results for \(D=3\) m are highlighted in Figure 19, where at \(\chi=0\) values of about 6.5\({}^{\circ}\) and 4\({}^{\circ}\) are achieved. Figure 16: 90-th percentile DoA estimation error, synthetic graph for all \(D\) values. Figure 15: Mean DoA estimation error, synthetic graph for all \(D\) values. Figure 19: Comparison for \(D=3\) m between measurement and simulation environment of 90-th percentile combined DoA estimation error (**a**) and Mean combined DoA estimation error (**b**). Figure 17: Comparison for \(D=2\) m between measurement and simulation environment of 90-th percentile combined DoA estimation error (**a**) and Mean combined DoA estimation error (**b**). Figure 18: Comparison for \(D=2.5\) m between measurement and simulation environment of 90-th percentile combined DoA estimation error (**a**) and Mean combined DoA estimation error (**b**). The above results show how both errors decrease in the case of shorter distance, and even though the analysis method slightly underestimates the DoA, confirm the ability to predict correctly performance of the dual-band approach applied to real scenarios. This important result represents a key objective in assessing the ability of this method to overcome the limitation involved in a real-world scenario to fully exploit the proposed new dual-band methodology for DoA-based localization philosophy.
## 3 Short-Reach Systems Classification and Enabling Technologies
### Data Center Network Requirements and Challenges
#### 3.2.3 Traffic  
It has been observed that the majority of network traffic processing is within the DCNs (intra-DCNs). This is mainly due to a large amount of hosted applications that are based on parallel programming frameworks. This setup demands significant interaction among the distributed storage and processing nodes for huge data management. This implies considerable communication requirements among the servers. The intra-DCNs' traffic increase is also owed to the introduction of innovative high-performance server processors. The processors host interfaces that can support 10 Gb/s data rates. In addition, with the integration of different high-performance processing cores into a single processor, higher-bandwidth interfaces are needed for effective communication between those cores and other cores hosted on separate racks. DCN operators have been adopting 100 Gb/s Ethernet and beyond to meet the network demands [74].
## 3 Methodology
### System Optimization  
System optimization is crucial to ensure that the proposed framework operates efficiently and effectively, minimizing unnecessary computations while maintaining high accuracy in hallucination detection. The optimization process focuses on balancing various factors, including reducing dialogue rounds, minimizing response variance, and ensuring timely task completion.  
One of the key objectives in system optimization is to find the optimal trade-off between accuracy and efficiency. This can be formulated as a multi-objective optimization problem, where we aim to minimize both the total response time \(T_{response}\) and the number of tokens used \(T_{total}\), while also minimizing hallucination occurrence. The optimization problem can be expressed as follows:\[\min_{W}\bigl{(}\lambda_{1}T_{response}(W)+\lambda_{2}T_{total}(W)+\lambda_{3}H_{ rate}(W)\bigr{)}, \tag{1}\]  
where \(W\) represents the model parameters, \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) are weighting factors that balance the importance of each term, and \(H_{rate}\) is the hallucination rate, defined as the percentage of responses that are flagged as hallucinations.
## 4 Discussion
The practice of the other three spectral subsets from Sentinel-2 data did not improve the retrieval accuracy signifying the properly located spectral bands from the sensor. For the generation of the LAI maps, the smallest RMSE criterion was used in the inversion of RapidEye and Sentinel-2 LUTs. Overall, the LAI maps obtained from both sensors presented the variation of LAI in the island well. Although the spatial variation of LAI from the two maps were somehow comparable, higher LAI values were observed in the derived Sentinel-2 map of 2016 which was in agreement with the larger range of measured LAI in 2016. As was expected, the map produced from RapidEye data, due to its higher spatial resolution of 5 m, presented a detailed overview of the variation of LAI. Nevertheless, a concise study of the maps revealed that due to absence of SWIR spectral bands in RapidEye sensor, part of the island in 2015 (in the northeast) which was dominated by sand rather than vegetation cover (low reflectance in red and very high reflectance in NIR) was identified as region with very high LAI values (LAI > 7). In the presented maps, this area is identified in the 2015 map as the sand class. Our findings emphasise the importance of multi-spectral satellite sensors such as Sentinel-2 with spectral bands at NIR and SWIR regions for regional mapping of vegetation LAI in wetland ecosystems using radiative transfer models. Monitoring vegetation status and tracking ESVs such as LAI in these systems is vital for their sustainability and proper use and promote the Ramsar Convention's common agenda to halt loss of biodiversity.
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 2 Materials and Methods
### Image Processing
#### 2.4.1 World View-3 Satellite Image Pre-Processing  
The World-View 3 satellite images are provided as 8-band digital number (DN) products. These images were pre-processed in GEE to obtain surface reflectance. First, they were converted to at-sensor radiance images using the data in [21]. Then, to obtain surface reflectance, the dark object subtraction (DOS) atmospheric correction method was applied [22].
# Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities
## 2 Enabling Technologies
### Wi-Fi 6  
Wi-Fi 6 or IEEE 802.11 ax is the latest wave of Wi-Fi technology. ABI Research predicts widespread adoption and domination of WiFi 6 chipsets in the automotive industry. These predictions imply that almost half of the Wi-Fi chipsets will be Wi-Fi 6 in 2023 and will subsequently reach a market domination of 70% in 2024. Other predictions claim that Wi-Fi 6 will be the in-vehicle wireless interface technology for CVs as it holds many benefits over previous Wi-Fi versions. Due to higher spectral efficiency/channel capacity, broader outdoor coverage, and flexibility, more customers can operate on the same access point and use cases demanding higher bandwidth, such as high-quality streaming videos, which can be supported by Wi-Fi 6. Overhead reduction due to small-sized traffic data is also endorsed. Wi-Fi 6 also degrades signal congestion by incorporating orthogonal frequency division multiple access (OFDMA) (for multiple users with differing bandwidth requirements) and a basic service set, which is referred to as BSS (for signal identification from heterogeneous radio devices). The aforementioned benefits of Wi-Fi 6 technology will meet the demand of the exponential growth of automotive safety/convenience applications and use cases by allowing a seamless wireless connectivity inside/outside the vehicle and an enhanced quality of experience (QoX) for users [26; 27]. Even with the aforementioned benefits, Wi-Fi 6 will have a smaller coverage range in comparison to 5G-cellular technology.
## 2 Methodology
### Determining Design Parameters Weights
#### 2.2.2 DEMATEL Results  
The results of the DEMATEL analysis are summarized in Table 1. The analysis shows that the design factor with the highest importance is the curve radius (\(q_{i}=2\)), followed by horizontal alignment change (angle of deflection) and vertical alignment change (\(q_{i}=1.83\)), horizontal curve length (\(q_{i}=1.33\)), and longitudinal grades (\(q_{i}=1.167\)).  
Environmental hydrology and geological factors exhibit moderate relationships with other factors (\(q_{i}=0.67\)), while tangent length shows loose associations (\(q_{i}=0\)).  
Moreover, causal factors were identified based on the net effect values (\(n_{i}\)). Curve radius, environmental hydrology and geological factors, horizontal curve length, longitudinal grade, and vertical curve parameters have values of \(n_{i}\) greater than 0, establishing them as causal factors. Consequently, they should be carefully considered in highway design as they significantly influence route selection.