esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Forward Model Development
also showed with these phase functions that the Zaneveld approximation of the shape factor at nadir viewing, i.e.,\(f_{b}\approx 2\pi\beta(\pi-\theta_{s})/b_{b}\), had an average error of only \(5\%\) (their Figure 11) and this included overcast conditions that would not occur in remote sensing. To our knowledge, the only study attempting to test the Zaneveld model with directly measured data was [PERSON] et al. [20] with a subset of the NOMAD data set, which included no VSF measurements, where performance for the BRDF component of the model was comparable to the current state-of-the-art [7; 11]. Equation (2) demonstrates the direct link between \(L_{u}(\theta_{s},\theta_{v},\phi)\) and \(\beta(\psi)\). However, for the Zaneveld expression to be a practical tool for ocean color, the terms \(K_{Lu}\) and \(f_{L}\) must be expressed in terms of IOPs. Furthermore, the IOPs in the model should ideally be coefficients that are closely linked to reflectance and directly measurable with good accuracy using existing sensor technology. For example, the term in the denominator \([c-f_{L}(\theta_{s},\theta_{v},\phi)b_{f}]\) has two IOPs that are difficult to determine directly because of acceptance angle issues with standard transmissometer designs [38; 39]. However, since \(f_{L}\) is close to unity, this term is also closely related to \(a+b_{b}\), immediately recognizable from commonly used first order approximations. Goals of the next several sections are: 1) To represent Equation (2) entirely in terms of such IOPs, 2) to rework in terms of the commonly used remote sensing reflectance \(L_{u}/E_{d}\), since these are the measurements currently available in validation data sets, and 3) to include the inelastic effects associated with water Raman scattering.
## 3 Payload Design
### Suppayload 1: Data Acquisition System
#### 3.3.2 Electronics  
The Xilinx PYNQ-Z1 System on Chip (SoC) development board, acquired from Digilient, in Pullman, WA, USA [21] was chosen as the OBC for subpayload 1. The board is built around the Xilinx Zynq(r)-7000 SoC, which incorporates an FPGA and a dual-core ARM Cortex(tm)-A9 processor. The communication subsystem for telemetry via the Ethernet IP network protocol is accomplished by implementing the FPGA fabric using Python (version 3.11.3). The development of the image acquisition application and the management of its drivers were handled by the processor, which operates on a Linux-based system. The board is powered by the 12 V output line connected to its onboard power jack from the PDU. The OBC utilizes a USB-A to USB-C cable to connect to the optical system, powering the camera and facilitating the exchange of image data. Debugging was performed using the Secure Shell (SSH) and USB serial protocols.  
To host the operating system and store images, hardware drivers, and sensor data, we reused an Inmodisk industrial-grade 512 GB microSD card, which was sourced from Inmodisk Corporation in New Taipei City 221, Taiwan [22]. Since it stores crucial data, the microSD is a vital component of the system. However, the microSD card is categorized under the Ultra-High Speed (UHS) class 1, featuring a minimum speed of 10 MB/s [23]. The team did not find any other microSD card capable of matching or exceeding this level of performance under similar operating temperatures of stratospheric flight. Additionally, the card's ability to withstand thermal requirements and its performance during the previous year's flight validated the decision to reuse it for the current mission. It should be noted that the other, lower-priority systems of RSONAR II were tested with microSD cards with inferior ratings to prove the viability of using cheaper non-weather-rated microSD cards for future missions.
## 5 Experimental Results and Analyses
### Sensitivity of the Parameters  
In this section, an important parameter of the proposed algorithm, the group size K, is tested and compared to evaluate its overall effect on the algorithm. PSNR and SSIM are used as criteria to evaluate the algorithm objectively. Three images (\"Girl\", \"House\", and \"Lena\") with 30% noise level are selected, on which K is made to vary continuously from 1 to 10. The other parameters are adjusted to the optimum. PSNR and SSIM values are recorded and plotted into graphs, as shown in Figures 4 and 5. In Figure 4, PSNR and SSIM increase with the increase of K and reach their maximum at K = 5. Further increases in K leads to decreased PSNR values. Thus, the neighborhood information of an image has a positive impact on the performance of the algorithm. With K set to appropriate values, the edge information of the image is better preserved, and the noise resistance is improved. However, K should not be too large either, or nearby regions with drastic pixel changes could be taken, which will result in decreased PSNR and SSIM.  
Then, we tested how to select a good regularization parameter \(\mu\) for different images. We started with the value of \(\mu\) from a low value then increased the parameters empirically as the level of noise  
Figure 3: Original images. Top row: from left to right, (**a**) Girl.bmp, (**b**) Milk drop.tiff, (**c**) House.png, (**d**) Reagan.bmp, (**e**) Lena.png, (**f**) Shoulder.jpg, (**g**) Woman.tif, and (**h**) Pepper.tif.  
improved to get the best visual effect. For example, for the \"Girl\" image corrupted by impulse noise from 20% to 50%, \(\mu\) = 0.14, 0.15, 0.15, 0.18, respectively.  
In the selection of the parameter \(p\), the \(p\) value is set between 0 and 1. On the premise of fixing other parameters, we increase \(p\) by 0.1 step. After several rounds of experiments, we select the optimal when the image gets the best visual effect.  
The optimal parameters for different images with the noise level from 20% to 50% are given in Table 1.
# Experimental Study of Low Temperature Performance of Porous Asphalt Mixture
## 3 Experimental Method and Results
### Influence of Porosity on Maximum Bending Strain  
The porosity is amongst the most concerned indicator in the design of porous asphalt mixture. To further analyze the influence of porosity on the low temperature performance of porous asphalt mixture, the single factor influence test of porosity was conducted. The test selected the maximum bending strain as the evaluation indicator.  
The experiment adopted the same mineral aggregate gradation and raw materials as those in the orthogonal test, the modifier content was 12%, and the bitumen/aggregate ratio was 4.8%. The mixing and moulding process of porous asphalt mixture was consistent with that in the orthogonal test, and the porous asphalt mixture was not subjected to aging. According to the results analysis of orthogonal test, as the porosity increased from 16.2% to 23.8%, the maximum bending strain decreased by 3.3% only. To attain higher statistical confidence, in the single factor influence test of porosity, on the basis of the orthogonal test results, another two groups of specimens with porosity of 18.4% and 26.3% were added. The five groups of specimens were subjected to low temperature bending test at \(-10\)\({}^{\circ}\)C. With reference to the test results, the relationship between the maximum bending strain \(\varepsilon_{B}\) (in \(\mu\epsilon\)) and the porosity \(n_{0}\) (in %) was obtained, as depicted in Figure 10.  
It is evident from Figure 10 that there exists an approximately linear correlation between the maximum bending strain and the porosity of porous asphalt mixture, and the maximum bending strain decreases as the porosity increases. The relationship between the maximum bending strain and the porosity is expressed in Equation (2) with coefficient of determination \(R^{2}\) = 0.986.  
\[\varepsilon_{B}=7527.7-100.9n_{0}\quad(16.2\leq n_{0}\leq 26.3) \tag{2}\]  
when designing the mix proportion of porous asphalt mixture, in order to ensure good working performance of porous asphalt mixture in low temperature environment, the porosity should also conform to the required limits of the maximum bending strain in relevant technical standards. In the cold regions of northern China, when applying porous asphalt pavement, the maximum bending strain of porous asphalt mixture should not be less than 5000 \(\mu\)e in accordance with JTG F40-2004 [50]. From Equation (2), it is noted that when the porosity is controlled within 25%, thus the limit requirement on maximum bending strain is satisfied.  
Finally, the possible use of recycled asphalt pavement to enhance the sustainability of highway infrastructure has attracted vast research interest in recent years [59; 60; 61]. Recycled asphalt mixtures, which are prepared by blending reclaimed asphalt pavement, virgin bitumen, and mineral additives, offer various advantages including resources recycling, cost saving, and reduced environmental impact [62]. Further research on the utilization of reclaimed asphalt in porous asphalt pavements and their low temperature performance is recommended.
## 1 Introduction
Regarding the flexibility of the proposed calibrator, the following capabilities can be underlined: it is possible to specify the calibration mode, manual or automatic; to configure the calibration to be performed with any of the most common oscillometric algorithms (systolic/diastolic rates or maximum/minimum slope); to define the different parameters of the pulse envelope pressure signal; to generate artificial pulse waveforms and pulse envelopes that mimic physiological BP signals with specific characteristics, such as the typical oscillometric pulse envelope BP of people that suffer from heart arrhythmia, arteriosclerosis, very high or low BP values, or other heart diseases; and to perform ABPMD sensitivity tests using variable maximum envelope amplitudes that can vary between 1 and 50 mmHg, with a 0.1 mmHg resolution. The paper is organized as follows: part one, already presented, is the introduction; part two presents the common oscillometric blood pressure estimation algorithms and also the proposed method to implement an algorithm-independent calibrator of ABPMDs; part three presentsthe hardware and software descriptions of the developed prototype; part four includes the simulation and experimental results; and the last part, part five, is dedicated to conclusions.
# Visible Light Communication: An Investigation of LED Non-Linearity Effects on VLC Utilising C-OFDM
## 2 Test Setup
### Discrete Cosine Transform  
The discrete cosine transform is a function that expresses a finite sequence of data points as a sum of cosine functions oscillating at various frequencies. DCTs are a type of Fourier-related transform that only utilise a real part. Furthermore, since DCT is a real transform when data mapping is real, using it with the OFDM technique would avoid the in-phase/quadrature-phase (IQ) imbalance. Likewise, the DCT is less affected by the carrier frequency offset than the FFT [24; 25]. The DCT matrix could be given as:  
\[\text{DCT}_{k,n}=\sqrt{\frac{2}{N}}\sum_{n=0}^{N-1}\psi_{n}~{}~{}\cos\left[ \frac{\pi(2k+1)n}{2N}\right]. \tag{3}\]where \(k\) and \(n\) are the rows and columns position in the DCT matrix and \(\psi_{n}\) is the normalisation factor, which is determined by:  
\[\psi_{n}\ =\ \left\{\begin{array}{ll}\frac{1}{\sqrt{2}},&n=0\\ 1,&0<n<N-1\end{array}\right.. \tag{4}\]  
Therefore, a DCT matrix of size eight could be as follows:  
\[\text{DCT}_{8\times 8}=\left[\begin{array}{cccccccc}0.35&0.35&0.35&0.35&0.35&0.3 5&0.35&0.35\\ 0.5&0.41&0.28&0.1&-0.1&-0.28&-0.42&-0.5\\ 0.46&0.19&-0.19&-0.46&-0.46&-0.19&0.19&0.46\\ 0.41&-0.1&-0.5&-0.28&0.28&0.5&0.1&-0.42\\ 0.35&-0.35&-0.35&0.35&0.35&-0.35&-0.35&0.35\\ 0.28&-0.5&0.1&0.42&-0.41&-0.1&0.5&-0.28\\ 0.19&-0.46&0.46&-0.19&-0.19&0.46&-0.46&0.19\\ 0.1&-0.28&0.42&-0.5&0.5&-0.41&0.28&-0.1\end{array}\right]\]
# Phase Center Corrections for BDS IGSO and MEO Satellites in IGb14 and IGSR3 Frame
## 4 Results and Validation
### Estimated Phase Center Offsets
Besides the Z-PCO, the horizontal PCO estimates partly show significant deviations from their metadata values. For the X-PCO of C06, C07, and C08, we found biases of \(-\)16.65, \(-\)12.02 and \(-\)16.40 cm, respectively. Similar results have been obtained by [PERSON] et al. [21] and [PERSON] et al. [29]; the reasons for the discrepancy need further investigation.
# Modeling and Key Parameter Interaction Analysis for Ship Central Cooling Systems
## 4 Development of the Response Surface Model
### Establishment of the Response Prediction Model  
A multiple quadratic regression prediction model for the central cooling system's energy consumption was obtained by fitting using the least squares method. The expression is given by Equation (9):  
\[F(X)=87.91+18.68X_{1}+4.52X_{2}+5.98X_{3}+3.19X_{4}+1.31X_{5}-3.80X_{1}X_{2}+6.15 X_{1}X_{3}-1.14X_{1}X_{4}-0.4X_{1}X_{5}\\ -X_{2}X_{3}-1.14X_{2}X_{4}+0.275X_{3}X_{4}+1.06X_{3}X_{5}+1.31X_{4}X_{5}+7.46X _{1}{}^{2}-0.9619X_{2}{}^{2}-2.45X_{3}{}^{2}+0.944X_{4}{}^{2}-0.7944\\ X_{5}{}^{2} \tag{9}\]  
where \(F(X)\) is the energy consumption of the ship's central cooling system, in N; \(X_{1}\) is the main engine power, in kW; \(X_{2}\) is the seawater temperature, in \({}^{\circ}\)C; \(X_{3}\) is the seawater pump speed, in rpm; \(X_{4}\) is the three-way valve opening; and \(X_{5}\) is the low-temperature fresh water flow rate, in m\({}^{3}\)/s.
# Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests
## 2 Experimental Methodologies
### Experimental Setup  
#### 2.2.1 Wireless Standard and Sensor Node  
We adopt Crossbow's TeloSB sensor nodes in our experiments. The TelosB sensor node, formerly called the TmoteSky node [21], is an integrated platform including a TI MSP430 microcontroller, a ChipCon CC2420 radio transceiver [22] of 2.4 GHz and an onboard antenna. Compatible with the IEEE 802.15.4 standard, the TelosB node is very popular in practice, because it provides end users with  
Figure 1: Three typical forest environments. (**a**) Campus forest; (**b**) Bajia Rural Park forest; (**c**) Jiufeng Mountain forest.  
a set of external AD/DAand GPIOports that can easily interface with other sensing and actuating devices. The TelosB node transmits data with a power of at most 1 mW; so the link formed by two TelosB nodes is a typical low-power link. The TelosB node can be powered either by two AA batteries or by its built-in USB interface connecting to a host computer. One disadvantage of TelosB node is its low-gain antenna printed on the board, which diminishes the radio communication range. In our experiment, therefore, we expand the TelosB node with an undirected external antenna of 3 dBi, as shown in Figure 1c, besides encapsulating the TelosB node with the aluminum package.
## 4 Numerical Simulation of Slope Stability under Different Vegetation
### Numerical Model Establishment  
#### 4.2.1 Slope Geometry Model and Boundaries  
In order to further study the mechanical effect of vegetation, FEM was utilized to assess slope stability under different vegetation conditions. Slope geometry and boundaries were constructed, as shown in Figure 4. The height was 12 m and the slope angle was 45\({}^{\circ}\). In Figure 4, the boundary conditions of the model are the bottom constraints and the normal constraints on both sides are only subject to the body force; there are no other surface constraints. The ground water level is below the foot of the slope. The model implemented the Mohr-Coulomb failure criterion. Both the soil and root-soil composites are considered uniform materials in the simulation analysis.  
By changing the thickness of the root-soil composites, and the corresponding c and \(\varphi\) (Table 2), the FS in the different growth periods was assessed. Specifically, the thickness of the root-soil composite under M.m was set at 0.4 m, 0.6 m, 0.8 m and 1.0 m during the four growth periods, while under C.d it was 0.06 m, 0.18 m, 0.24 m, and 0.30 m.
## 4 Analysis of Numerical Simulation Results
### Results and Analysis  
(1) Surface subsidence  
A total of 52 monitoring points were set up in this simulation, including villages, rivers, highways, and mine industrial sites. Table 3 shows the settlement heights of the monitoringpoints under the two schemes. Comparing the two schemes, it was found that increasing the backfill ratio in the YZS mining area could reduce subsidence at most monitoring points. Under Scheme 1 in Table 3, 7 out of the 52 monitoring points experienced surface subsidence exceeding 30.00 mm, with the maximum subsidence of 53.98 mm occurring at YH6. Among the key monitoring points, BJ12, GC9, and YH17 exceeded the standard value of 30.00 mm, with subsidence values of 46.50 mm, 41.43 mm, and 33.72 mm, respectively. Under Scheme 2 in Table 3, 3 out of the 52 monitoring points experienced surface subsidence exceeding 30.00 mm, with the maximum subsidence of 51.93 mm occurring at YH6. Among the key monitoring points, BJ12 exceeded the standard value of 30.00 mm, with a maximum subsidence of 45.54 mm. Comparing the two filling schemes and improving the filling rate of YZS, the settlement of BJ12 is reduced by 0.96 mm, the settlement of GC9 is reduced by 39.58 mm, the settlement of YH5 is reduced by 2.5 mm, the settlement of YH6 is reduced by 2.05 mm, the settlement of YH8 is reduced by 1.68 mm, the settlement of YH17 is reduced by 32.94 mm, and the settlement of YH18 is reduced by 22.86 mm. Figure 7 shows the right diagonal cross-section of the model under the two scenarios. It can be observed that when the backfill ratio in the YZS mining area is 90%, a significant subsidence zone appears in the upper part of the mining area. However, after increasing the backfill ratio in the YZS mining area, the subsidence values in this zone significantly decreased. Nevertheless, small-scale roof collapses may occur within the BJ mining area, so additional measures are needed to mitigate potential roof collapses in this area. Figure 8 shows the surface subsidence contour maps under the two scenarios. By comparing Figure 8, it is evident that after increasing the backfill ratio in the YZS mining area, the subsidence center shifted, and the subsidence range was significantly reduced. (2) Surface tilt rate  
Figure 9 shows the surface tilt contour maps under the two scenarios. Under Figure 9a, Scheme 1, the surface tilt values are very small, with the maximum tilt value of 0.223 mm/m occurring at monitoring point YH8. Under Figure 9b, Scheme 2, the surface tilt values are also very small, with the maximum tilt value of 0.174 mm/m occurring at monitoring point BJ6, which is far below the standard requirement. An analysis of Figure 9 reveals that the surface tilt rates at all points under both scenarios are very small. Increasing the backfill ratio in the YZS mining area slightly affects the surface tilt rate, but the impact is not significant. (3) Terrain curvature  
Figure 10 shows the surface curvature contour maps under the two scenarios.
# A Deep Learning-Based Model for Tree Species Identification Using Pollen Grain Images
## 4 Discussion
Our results obtained without data augmentation cannot be used to interpret misclassification trends clearly due to the small number of overall data; however, based on our results with data augmentation (Figure 4), P.a. and Q.ser. were candidates for misclassification in many cases. Because we maintained a constant magnification level in this study, pollen grain size may have influenced these results. For example, C.cr. may have shown higher classification accuracy because its pollen grains are smaller than those of other tree species. However, our tree species classification accuracy results have not been verified in detail, and the issue of pollen size remains to be addressed in a future study. Misclassification frequently occurred between species with the same pollen type (Figure 3 and Table 4). However, pollen grain misclassification rates were particularly high for certain tree species. For example, tricolporate was misidentified to Carj. of triporate and C.t. of Stephanoporate, triporate was misidentified to Q.c., Q.ser., and Q.ses. of tricolporate and U.d. of Stephanoporate, and Stephanoporate was misidentified to P.a., Q.ser. Q.ser., and Q.ses. of tricolporate and B.p. of triporate. In our previous studies on tree species identification using deep learning based on leaf images [39; 40], these three groups were classified according to leaf morphological characteristics, and misclassification tended to occur most frequently (but not always) within the same group. However, as 100% identification is difficult, it is expected that even manual pollen identification can misclassify pollen grains among tree species with the same pollen type. Therefore, although it may be desirable to identify 100% of pollen grains to species, it is necessary to consider an evaluation method that assumes that misclassification will occur. For example, mean reciprocal ranking, which evaluates classified results in terms of probability and outputs them as ranks [41], may be effective for analyzing final classification results. Therefore, when constructing a practical identification system, it is necessary to consider several candidates in terms of the probability that the output matches the actual pollen grain, rather than presenting a single pollen grain type as the classified result. Comparing our results with those of previous pollen identification studies using deep learning, we found F values of 0.967 [15], 0.963 [17], and 0.978 [18], which were higher than those reported in this study. However, these studies differed in various ways, including the implementation of a database of preprocessed pollen images; the application of fine-tuning [43], which is a method for re-training and adjusting parameters using weights from a model already trained on another image set; transfer learning [27], in which only the output layer is trained using hyperparameters from a trained model; or image preprocessing. Any of these methods could improve classification accuracy, such that our results cannot be compared directly with those of the cited studies.
# Rapid Construction of Aerocapture Attainability Sets Using Sequential Convex Programming
## 4 Solving with Sequential Convex Programming  
Solving _Problem 1_ is an essential step in the algorithm, which is marked in Figure 5 with shaded blocks. Key procedures in solving _Problem 1_ such as convexification, discretization, and successive iteration are introduced in this section.  
As the dynamic equations are nonlinear, _Problem 1_ is a highly nonlinear optimal control problem. To solve it using convex optimization, all the nonconvex items in _Problem 1_ need to be first converted into convex functions, resulting in a sequence of convex subproblems to approximate the solution of the original _Problem 1_. Such a method is called sequential convex programming. Note that the subproblems are not equivalent to the original nonlinear problem. As a result, successive approximation is then used until the solution of the convex subproblems is sufficiently close to the solution of the original problem.
## 4 Implementation Results
The ACLR improvement of 3 dB showcases the advancement in linearity through IMD3 cancellation. Moreover, the fractional bandwidth expanded, thanks to the compact output combiner design and lumped-element impedance transformation. Despite the increased circuit complexity, the chip area remains under 1.5 mm\({}^{2}\)[4; 10; 25], highlighting the high level of integration achieved in recent DPA designs. The trade-off between size and performance has been carefully managed, demonstrating the maturity of the DPA technology for 5G handset front-end modules.