esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel and Adaptive Angle Diversity-Based Receiver for 6G Underground Mining VLC Systems
## 4 Results and Discussion  
In this section, we simulate and present graphical results based on numerical simulations using the Monte Carlo method in Matlab. The findings obtained are based on evaluation metrics of traditional wireless communication systems. These parameters together with the application of the UM-VLC system allow us to examine the capacity, behavior, and performance of the adaptive orientation receiver solution presented. Furthermore, to validate and compare our proposal with typical state-of-the-art solutions, we make a fair comparison with pyramidal and hemidodecahedral ADRs with fixed PDs and with the ToA method. Among the metrics used to evaluate the presented solution are the power received and the user data rate distributed in the scenario together with the BER. Both the parameters of the simulation model that were used in this manuscript as well as the description of the UM-VLC scenario are presented in Table 1.
# Auditory Localization in Low-Bitrate Compressed Ambisonic Scenes
###### Abstract  
The increasing popularity of Ambisonics as a spatial audio format for streaming services poses new challenges to existing audio coding techniques. Immersive audio delivered to mobile devices requires an efficient bitrate compression that does not affect the spatial quality of the content. Good localizability of virtual sound sources is one of the key elements that must be preserved. This study was conducted to investigate the localization precision of virtual sound source presentations within Ambisonic scenes encoded with Opus low-bitrate compression at different bitrates and Ambisonic orders (1 st, 3 rd, and 5 th). The test stimuli were reproduced over a 50-channel spherical loudspeaker configuration and binaurally using individually measured and generic Head-Related Transfer Functions (HRTFs). Participants were asked to adjust the position of a virtual acoustic pointer to match the position of virtual sound source within the bitrate-compressed Ambisonic scene. Results show that auditory localization in low-bitrate compressed Ambisonic scenes is not significantly affected by codec parameters. The key factors influencing localization are the rendering method and Ambisonic order truncation. This suggests that efficient perceptual coding might be successfully used for mobile spatial audio delivery.  
spatial audio; perceptual evaluation; listening tests; ambisonics; binaural; bitrate compression; auditory localization; audio codec; opus; streaming Article
## 2 Preamble
### On the Use of Windowed Fourier Transform  
Signal analysis is traditionally done using Fourier decompositions on a trigonometric function basis for a periodic signal, or more generally using the Fourier transform \(\hat{f}\) of any function \(f\in\mathbf{L}^{2}(\mathbb{R})\) as indicated in Equation (9). These methods allow for a signal of finite duration to express its frequency content. However, the Fourier transform is not adapted to non-stationary signals and to the localization of high frequencies.  
\[\hat{f}(\omega)=\int_{-\infty}^{\infty}f(x)e^{-i\omega x}dx \tag{9}\]  
In order to overcome this problem, a first idea is to perform windowed Fourier transforms on predefined signal portions in order to better identify the high frequency components. Considering the windowing function \(g\), we can define in Equation (10) a windowed Fourier transform \(Gf\), centered around an observation point \(x_{0}\). The \(g\) function allows us to spatially filter the \(f\) function around an interval of interest centered on \(x_{0}\) and therefore to detect the associated frequency components.  
\[Gf(\omega,x_{0})=\int_{-\infty}^{\infty}f(x)e^{-i\omega x}g(x-x_{0})dx \tag{10}\]  
By defining \(\sigma_{x}\) and \(\sigma_{\omega}\) as the standard deviations associated with the functions \(g\) and \(\hat{g}\), respectively, the uncertainty principle applied to the function \(g\) implies the following relation \(\sigma_{x}^{2}\sigma_{\omega}^{2}\geq\frac{\pi}{\pi}\). This inequality defines the compromise to be made between spatial and frequency resolutions. Figure 5a illustrates this inequality in the 2D dimension \(x/\omega\). \(Gf\) which only allows for the description of \(f\) around the point \((x_{0},\omega_{0})\) and for an interval \([x_{0}-\sigma_{x};x_{0}+\sigma_{x}]\times[\omega_{0}-\sigma_{\omega};\omega_{ 0}+\sigma_{\omega}]\). Thus cells of constant area and identical form are defined. This regular decomposition thus imposes that the discretization in frequencies of the studied problem is also regular and uniform. All the frequency intervals are therefore of the same size and they are distributed linearly, which therefore imposes a compromise between the spatial and temporal refinements of the windowing function \(g\). In our study, the signals to be analyzed are composed of non-uniformly distributed frequencies, which makes the windowed Fourier transform unsuitable.  
Figure 5: Diagram of observation cells and variable mesh in the 2D dimension \(x/\omega\)**(a)** Fourier representation (**b**) Wavelet representation.
# On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
## Appendix B Sampling Algorithm
### Resulting Gibbs Sampler  
The proposed auxiliary variable Gibbs sampler iterates between the sampling step of the unknown main variables \(\mathbf{x},\mathbf{\Theta},\tau\) and the auxiliary variables \(\mathbf{u}\). However, since we replace the sampling step of \(\tau\) by sampling from its marginalized distribution with respect to \(\mathbf{u}\) and this of \(\mathbf{z}\) with its marginalized distribution with respect to \(\mathbf{x}\), we actually consider the partially collapsed version of the Gibbs sampler so that the target stationnary distrubtion is preserved [67]. Each one of the conditional distributions belongs either to the Normal, Bernoulli or Inverse Gamma families which make all sampling steps exact. The sampling step of \(\mathbf{u}\) from (15) can be easily done since \(\mathbf{H}\mathbf{H}^{\top}=\mathbf{I}_{M}\) (non-regular sampling matrix) [58]. For more general sensing matrices, for example when \(\mathbf{H}\) is a Gaussian sampling matrix, one can still sample efficiently the auxiliary variable following [68]. Note the auxiliary variable trick improves the computational complexity of one iteration of the Gibbs sampler but may induce high correlation between successive samples. One way to improve the mixing properties of the considered Gibbs sampler is to use additional data augmentation strategies [69] (see [70] for an example).
# Biochar Improved Sugarcane Growth and Physiology under Salinity Stress
## 1 Introduction
Therefore, according to recently published papers, biochar is suggested as a conditioner for salt-affected soils for various crops [24,25]. For instance, [PERSON] et al. [26] used biochar to improve soil nutrient availability and growth of tomatoes under saline water irrigation, while [PERSON] et al. [27] concluded that biochar improved wheat's growth, physiological responses, yield, and nutrient uptake under salt stress. Sugarcane, with a total area of 129,000 ha planted over the country, is among the most important industrial crops in Vietnam [28]. Sugarcane is cultivated across Vietnam and its cropping area can be divided into six main regions, namely Midlands and North Mountain, North Central, South Central Coast, Highlands, Western South, and Mekong Delta [29]. Mekong Delta, with 20% of the area and 30% of the productivity of the nation, is one of the largest sugarcane production regions in Vietnam. Sugarcane cultivated in the Me-kong Delta region also has the highest crop yield (87 tons/ha) and sugar quality [29]. However, like many other coastal areas in the world such as Australia, America [15], and Japan [14], sugarcane production in the Mekong Delta region has been decreasing as a result of salinity stress. In the dry season, salinity in agricultural canals and ditches was reported to reach 4-6% in the region [30]. To make matters worse, the increasing impacts of sea level rises resulted in increasing soil salinity (both in frequency and intensity), thereby threatening sustainable sugarcane production in the region. Since then, several sugar processing factories have been closed due to a lack of raw materials, hence adversely affecting the livelihood of local farmers. Previous studies demonstrated the positive effects of biochar on growth, photosynthesis, soil parameters, and nutrient distribution in sugarcane [31; 32]. However, to date, there is no information on applying biochar to improve sugarcane growth under salinity stress. Our study was conducted to obtain a better understanding of the effectiveness of biochar in minimizing the negative impact of salinity stress on the growth and physiology of sugarcane. The information from this study will be useful for using biochar as a soil amendment to ameliorate sugarcane production in Mekong Delta and other regions with similar climatic problems.
## 2 Materials and Methods
### Fatty Acid Composition Analysis  
The fatty acid composition of the lentil samples was determined using a GCMS-QP2010 (Shimadzu, Kyoto, Japan) gas chromatograph with a mass spectrometer (GC-MS). The fatty acid methyl ester (FAME) concentration was determined using a calibration curve, and the results were expressed as percentage of total FAME concentration in fat. Samples were prepared by extracting 1 g of the homogenized sample with 10 mL of extraction solution [chloroform-methanol, 2:1 (volume/volume, \(v/v\))] by shaking for 1 h. Afterwards, the mixture was centrifuged at 4000 rpm for 5 min at room temperature, and 5 mL of the organic phase was washed with 1 mL of 0.9% aqueous sodium chloride solution. The resulting mixture was centrifuged at 4000 rpm for 5 min at room temperature, and 1 mL of organic phase was evaporated using nitrogen gas. The resulting residue was dissolved in 1 mL of hexane and reacted with 70 \(\upmu\)L of methylation reagent (2 mol/L of KOH in methanol) by vortexing and shaking using a laboratory shaker for 30 min. The mixture was centrifuged at 13.2 k rpm for 5 min at room temperature and the upper layer was used for the analysis. We used a Capillary Stabilwax-MS column (30 m \(\times\) 0.25 mm-\(\upphi\) internal diameter (ID) \(\times\) 0.25 \(\upmu\)m-\(\upphi\) film thickness). The mass spectrometer operated at full scan mode. The analyte was injected in split mode at a 1:60 split ratio. The following parameters were used: MS ion source temperature: 240 \({}^{\circ}\)C; MS interface temperature: 240 \({}^{\circ}\)C; helium (carrier gas) flow-rate: 0.90 mL/min; injector: 240 \({}^{\circ}\)C; oven temperature program: 50 \({}^{\circ}\)C (4 min), 10 \({}^{\circ}\)C min\({}^{-1}\) to 110 \({}^{\circ}\)C (1 min), 15 \({}^{\circ}\)C min\({}^{-1}\) to 160 \({}^{\circ}\)C (2 min), 2.5 \({}^{\circ}\)C min\({}^{-1}\) to 195 \({}^{\circ}\)C (1 min), 2 \({}^{\circ}\)C min\({}^{-1}\) to 230 \({}^{\circ}\)C (1 min), and 2 \({}^{\circ}\)C min\({}^{-1}\) to 240 \({}^{\circ}\)C (12 min).
# Joint Beam-Forming Optimization for Active-RIS-Assisted Internet-of-Things Networks with SWIPT
## 5 Conclusions  
This paper investigates how to solve the problem of the shortage of energy harvested by users in communication systems based on the multi-user MISO system model using the A-RIS-assisted SWIPT technique, which can significantly improve the sum of energy harvested by users. In this paper, the problem of maximizing the sum of energy harvested by the user is established under the constraints of the minimum signal-to-dry noise ratio required by the user, the maximum power of active RIS, the maximum power of the base station, and the PS ratio. The algorithm for maximizing the sum of energy captured by the user is proposed through the joint optimization of the BS beam-former, power splitting factor, RIS phase matrix, and signal strength increase. For the proposed optimization problem, Algorithms 1-3 are used to transform the original problem into a convex problem which is first solved by local iterations, and finally, the whole is optimized alternatively. The numerical values show that the proposed algorithm has good convergence. At the maximum allowable radiant power of 40 dBm and 45 dBm at the base station, the total energy captured by the user using the A-RIS-assisted algorithm is improved by 48.7% and 40.3%, respectively, compared to the P-RIS-assisted algorithm. In addition, with A-RIS assistance, the use of the dynamic power splitting algorithm improves the system degrees of freedom by 21.7% and 23.5% of the total energy collected by the user compared to the use of the average power splitting algorithm (\(\rho_{k}=0.5\)), respectively.  
Conceptualization, L.L., S.L., M.W., J.X. and B.Y.; methodology, L.L., S.L. and M.W.; software, L.L. and S.L.; validation, L.L., S.L. and J.X.; formal analysis, L.L., S.L. and B.Y.; data curation, J.X. and B.Y.; writing--original draft preparation, L.L.; writing--review and editing, S.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
Figure 7: Relationship between the total energy collected at the user end and the minimum signal-to-noise ratio at the user end.  
**Funding:** This work was supported in part by the Chunhui plan international cooperation project of China Education Ministry under Grant 202201406, in part by the key research and development plan of Xuzhou under Grant KC20027 and Grant KC18079, and in part by college students' innovative entrepreneurial training plan program under Grant 20221032006Z.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Concept Design and Development of an Electric Go-Kart
## 5 Design of Electrical System
### Battery System Modeling
Its purpose is to control the polarity of the connected battery cells, limiting current, temperature, overload, and other possible anticipated faults. The main purpose of the controller is to check each battery to make the most of its capacity and extend its life. Two main types of controllers are common--centralized and divided. In the case of centralized, the main controller and battery management system are located in one housing. In a split system, each battery has its own control system, which sends information to the controller. The centralized system has more wiring; however, its cost is significantly lower compared to the split system because there is no need for a board for each battery. The system described in this paper was chosen because of its simplicity and cost. The BMS controller was selected from the manufacturer Deligreen [49]. Another purpose of the BMS is to maintain balance between batteries or the designed battery groups or packages regarding nominal voltages. Not all batteries are produced the same due to unavoidable manufacturing errors, i.e., margins and, therefore, balancing functions. The whole series of batteries depends on the weakest, which means that the weakest battery determines the capacity of the series. It also means that charging the weakest battery determines how much energy can be stored in a series. For this reason, BMS can maintain balance in a passive or active way. In a passive way, the difference in capacity between the batteries is converted into heat until they all reach the same capacity. In an active way, BMS uses energy from \"stronger\" batteries and charges weaker ones until they all reach the same capacity. It is envisaged that each of the designed battery tanks in the presented conceptual design has its own BMS, and in the case of one empty series, the vehicle still runs on a different series. The selected BMS Deligreen is marked \"14S 60A 48V\". The \"14S\" refers to the number of batteries in the series; \"60A\" is the maximum current at which the BMS switches off the system. Such a BMS is shown in Figure 16[47]. The scheme of connecting the \"14S 60A 48V\" BMS assumes the connection to a maximum of 10 battery groups (B1 to B10) to the input connectors shown in Figure 16.  
Figure 15: Battery system concept design: (**a**) cell placement in a holder box; (**b**) box with the cover attached to the chassis distribution as the basis for the sub-modeling view; (**c**) lower battery cell inter-connecting plate view; (**d**) upper battery cell inter-connecting plate view.
## 4 Analysis of Optimization Calculation and Results
### Further Discussions
#### 4.3.3 Comparative Analysis of Decision Variables  
The comparison of decision variables based on the optimal solution under the two optimization strategies is shown in Figure 9. It can be noted, considering the constraint condition of maximum renewable energy penetration and the objective requirement of minimizing the carbon transaction cost, that the overall equipment capacity of the system is reduced after optimization with the bi-level collaborative optimization model. Owing to the limitation of solar panel installation capacity, the photovoltaic solar panel capacity is reduced by 31.73 m\({}^{2}\), but the flat-panel solar collector capacity is increased by 31.89 m\({}^{2}\). Analysis shows that the area where the case is located belongs to the hot summer and cold winter area, and the annual sunshine time is long. Under the double-optimization mode, the increase in solar collector area is due to the nature of the example building. Extending to the whole life cycle of the system, the swimming pool needs to increase the amount of heat supply equipment in order to maintain the water temperature. At the same time, the boiler capacity also decreases by 51.99 kW, and the heat storage equipment capacity increases by 17.47 kW--that is, natural gas consumption is reduced, while giving full play to the energy storage characteristics of the MCS. Under the double-layer optimization mode, the heat exchanger capacity decreases further, falling to only 22.5% of the single-level optimization calculation results, and the energy consumption in the life cycle of the system is reduced.  
Figure 8: Comparison of iteration time and energy efficiency.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 6 Monitoring Phenomena Other than Rain  
The attenuation of microwave links can not only be used to measure rainfall, but also theoretically monitor all substances that affect microwave signals propagating in the atmospheric environment, such as water vapor, oxygen, CO\({}_{2}\), snow, dust, and so on [94; 95]. However, different substances have different absorption characteristics for specific frequency bands. For example, water vapor strongly absorbs the microwave in the frequency around 22.2 GHz (i.e., 1.35 cm in wavelength), and oxygen has a strong absorption of microwave in the frequency around 60 GHz (i.e., 0.5 cm in wavelength). Therefore, when measuring different atmospheric substances, the corresponding absorption bands should be avoided. Monitoring other meteorological phenomena by microwave link attenuation is summarized as follows:
## 3 Results and Discussion
### Effect of Neutralization on Biomethane Potential Test  
The purpose of the BMP test was to investigate the effects of different treatments on the biomethane potential. As shown in Figure 2a, the curves have a similar trend. And Figure 2b displays that the total methane yield for raw material (RM) was 218.9 mL\({}_{\text{N}}\cdot\text{gVS}_{\text{feed}}\)\({}^{-1}\) whereas it was 267.3, 253.7, and 221.6 mL\({}_{\text{N}}\cdot\text{gVS}_{\text{feed}}\)\({}^{-1}\) for neutralization with WW, CN, and LDN, respectively. The total methane yield of WW was the highest, seemingly due to a large amount of water washing that removed the non-fermentable inert components (e.g., lignin) from VS. This yielded the highest percentage of fermentability, while causing the large amounts of wastewater at the same time. There was no significant difference (\(p>0.05\)) between CN and LDN, but LDN was slightly lower than CN, seemingly because the acid consumers from the LD triggered a part of the loss of methane potential during the pH reduction process.  
Overall, these results suggest that different neutralization methods have different degrees of impact on the biomethane potential of CS. Compared to RM, 22.11% and 15.90% improvements were identified for WW and CN, respectively. The increase in methane production was associated with the lime pretreatment [15; 35; 36], but the difference in the degree of improvement emerged because the washing process had inevitably concentrated the fermentable fraction. Alas, LDN, with the drag of the acid consumers, completely diminished the increase caused by lime pretreatment.  
Figure 2: Effect of different neutralization treatments on BMP tests of lime-treated corn stover: (**a**) cuvers and (**b**) total yields. Data are shown as mean \(\pm\) SD, \(n=3\). Significant differences are indicated by different letters (\(p<0.05\)).
## 2 DAB Topology and Modeling Analysis  
### DAB Basic Topology  
Figure 1 presents the topology of the DAB converter, which comprises two full-bridge circuits, denoted as FB\({}_{1}\) and FB\({}_{2}\). Each full bridge is constructed from a pair of half-bridge modules, with FB\({}_{1}\) consisting of HB\({}_{1}\) and HB\({}_{2}\), and FB\({}_{2}\) comprising HB\({}_{3}\) and HB\({}_{4}\). FB\({}_{1}\) and FB\({}_{2}\) are interconnected via a high-frequency transformer with a defined turns ratio of \(n\):1. The inductor \(L\) represents the total inductance, encompassing both the leakage inductance  
of the transformer and any additional external series inductance. The direction of positive power flow is defined as flowing from \(V_{1}\) to \(V_{2}\). The voltage conversion ratio is given by \(k=V_{1}/nV_{2}\). A value of \(k>1\) corresponds to buck-mode operation, while \(k<1\) indicates operation in the boost mode.  
Figure 2 illustrates the operating waveforms of the DAB converter under TPS modulation. Here, \(D_{1}\) and \(D_{2}\) represent the duty cycles of \(v_{1}\) and \(v_{2}\), respectively, while \(\Phi\) denotes the phase shift between the centers of \(v_{1}\) and \(v_{2}\). The ranges of \(D_{1}\), \(D_{2}\), and \(\Phi\) are within [0,1]. \(T_{\rm s}\) is the switching period, and \(f_{\rm s}\) is the switching frequency, with \(T_{\rm hs}=T_{\rm s}/2\). The parameters \(D_{1}\), \(D_{2}\), and \(\Phi\) serve as the three key control variables in the TPS modulation strategy. These variables collectively shape the leakage current waveform, define the peak inductor current, and govern the average power transferred from the primary side to the secondary side.