esa-sceva/satcom-chunk-collection huggingface dataset:
# Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors
## 3 Results
### Initial Comparison: Scattered Field and B-Scan  
Before applying the inversion algorithm, the simulated scattered fields obtained with each method were compared, in both the frequency and time domains. This comparison is shown in Figures 5-7 for the multimonostatic scenario with a metallic target buried in the soil. The normalized scattered field in the frequency domain is shown for two observation domain positions: \(x=-0.8\) m (Figure 5) and \(x=0\) m (Figure 6). The inverse Fourier Transform is used to compute the scattered field in the time domain (B-scan), as shown in Figure 7. The agreement between the scattered field simulated with RT and TE FDFD modeling \(E_{z}\) (or TM\({}_{z}\), relative to z-axis) is good. The main difference is that in FDFD the amplitude at the air-soil interface is larger than the amplitude at the target, whereas in RT both amplitudes are similar. This might be due to the fact that RT only considers the specular reflection at the air-soil interface. This fact also explains that the scattered fields in the frequency domain are more similar at \(x=-0.8\) m (left side of the observation domain) than at \(x=0\) m (center of the observation domain, exactly over the target).  
Figure 5: Normalized scattered field at the first transmitter-receiver position (\(x=-0.8\) m): multimonostatic scenario with a metallic target. Real part (**a**) and imaginary part (**b**).  
Figure 6: Normalized scattered field in the middle of the observation domain (\(x=0\) m): multimonostatic scenario with a metallic target. Real part (**a**) and imaginary part (**b**).
## 2 Methods
### Data Items
#### 2.7.2 Measurement Parameters  
* Thermal comfort parameters:
* Air temperature (\({}^{\circ}\)C).
* Relative humidity (\(\%\)).
* Air velocity (\(\mathrm{m}\,\mathrm{s}^{-1}\)).
* PMV/PPD values (where available).
* Indoor air quality parameters:
* CO\({}_{2}\) concentration (ppm).
* Particulate matter (\(\mathrm{PM}_{2.5}\), \(\mathrm{PM}_{10}\)).
* Ventilation rates.
* Other pollutants (where measured).
* Acoustic parameters:
* Background noise level (dBA).
* Reverberation time (s).
* Speech transmission index (where measured).
* Lighting parameters:
* Illuminance (lux).
* Luminance (\(\mathrm{cd}\,\mathrm{m}^{-2}\)).
* Daylight factor (\(\%\)).
* Uniformity ratio.
## 3 Results
### Statistical Results
The lack of convergence could be due to a number of reasons, such as poor model specification or complex data structures, and this indicates that further investigation is required to improve the model fit and stability of the estimates. The mixed linear model results confirm the previously reported significant interaction effects and provide additional insight into the dynamics of these interactions. The statistical significance of the three-way interaction shows that the effect of a particular temperature class on the time share depends not only on the engine type but also on the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source of Variation** & **Sum of Squares** & **Degrees of Freedom (df)** & **F-Value** & _p-Value_ \\ \hline Engine type (C(Engine type)) & 5.293 \(\times\) 10\({}^{-22}\) & 1 & 4.575 \(\times\) 10\({}^{-24}\) & 1 \\ Temperature class (C(TC)) & 7.323 \(\times\) 10\({}^{7}\) & 5 & 1.266 \(\times\) 10\({}^{3}\) & \textless{}0.001 \\ Ambient temperature class (C(Ambient, Temp,Xlasses)) & 3.136 \(\times\) 10\({}^{-2}\) & 4 & 6.778 \(\times\) 10\({}^{-35}\) & 1 \\ Interaction: Engine type \({}^{*}\) Temperature class & 2.321 \(\times\) 10\({}^{6}\) & 5 & 4.017 \(\times\) 10\({}^{3}\) & \textless{}0.001 \\ Interaction: Engine type \({}^{*}\) Ambient temperature class & 1.936 \(\times\) 10\({}^{-22}\) & 4 & 4.182 \(\times\) 10\({}^{-25}\) & 1 \\ Interaction: Temperature class \({}^{*}\) Ambient temperature class & 3.269 \(\times\) 10\({}^{6}\) & 20 & 1.412 \(\times\) 10\({}^{3}\) & \textless{}0.001 \\ Three-way interaction: Engine type \({}^{*}\) Temperature class \({}^{*}\) Ambient temperature class & 2.881 \(\times\) 10\({}^{5}\) & 20 & 1.245 \(\times\) 10\({}^{3}\) & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: ANOVA results for the effects of engine type, temperature class, and ambient temperature class on the proportion of number of vehicles and their interaction (*). ambient temperature class, indicating a complex dependence on multiple environmental and operational conditions. Overall, the application of the mixed linear model emphasizes the need to consider the interactions between different factors when optimizing the time fraction and generalizing the results to real-world application scenarios.
## 5 Discussion  
System dynamics allows the simulation of real-world situations through complex models [9; 10; 11; 12; 16]. In this case study, the model represented the company's capacity needed to carry out the five distribution routes based on the proposal of three pessimistic, normal and optimistic scenarios. The scenarios could be evaluated by changing their parameters to predict situations which incurred risk or in which the company operation may be the most favorable [28]. The pessimistic and optimistic scenarios were constructed based on the model that reflected the normal situation of the company in the study.  
One of the factors that had the most impact on distribution costs was that of the distance at which each customer is located: the greater the distances, the higher the costs. This was influenced by fuel consumption, as it represented on average 56.46% of the total distribution cost. Based on this, the organization must seek a fuel provider that offers the best diesel price, which will positively impact the total costs.  
In addition, maintenance and tire wearing costs were found to have a minor impact on the total costs, representing 9.21% and 3.66% of the total, respectively. On the other hand, by considering more customers and the creation of more distribution routes in the optimistic scenario, an increase in route profitability could be observed despite the rise in costs, which leads to the conclusion that greater vehicle usage capacity will translate into greater profits.  
The validation process of the models using techniques such as the mean error test [37], unit consistency and extreme tests allowed us to state that the model was close to reality; however, the implementation of models based on abstractions of reality should take into consideration that events that were not considered could exist and affect the operation of the organization; for example, the Covid-19 pandemic is causing supply chains to contract, and their method of operation now has to be based on stricter health standards.  
According to [16], the development of a graphical user interface using Stella(r) Architect (Isee Systems) is an available option to organizations. However, two important facts should be considered: (1) a person with knowledge of the use of the software and the basic concepts of system dynamics methodology is required for future modifications of the model, based on new constraints, variables or parameters; and (2) the price of the software and of investing in licenses, which have a significant cost in the market, should be considered. However, the cost-benefit ratio of doing so is beneficial due to the information that the model for data-driven decision making will provide.
###### Abstract
Although many theoretical and empirical models have been developed, some problems are still unsolved. For example, the TSM originally proposed to estimate brightness temperature at higher frequencies, uses the sea surface wave spectrum by multiplying a factor of 2. However, whether this modification can be applied to L-band is still unclear, and the choice of cutoff wavenumber is arbitrary and needs to be clarified in the L-band. Moreover, the sea surface reflection of downwelling atmospheric emission is another contribution to satellite-observed brightness temperature [31], thus the determination of the cutoff wavenumber is required not only for sea surface emission but also for reflection. In addition, the widely used empirical models decouple the wind effect from \(SSS\) and \(SST\) effects, which means that the surface emission is due to a perfectly flat sea surface and the wind-roughened sea surface. The wind-roughened sea surface is associated with the increased brightness temperature (due to sea surface roughness effect), which induces regional biases when applied to different areas [32]. Furthermore, the satellite measurements of \(SSS\) are hampered by the effect of radio frequency interference (RFI) in offshore areas of China (i.e., Bohai sea, Yellow sea and East China sea), which causes a large amount of data to be discarded [33]. Thus, the compatibility of these models in coastal area of China needs to be assessed and tested in order to achieve higher accuracies of \(SSS\) from the space-borne observations. In this paper, two cruises have been carried out in the East China Sea (ECS) to test the performance of different sea surface emissivity models at the L-band, which cover a wide range of salinity, water temperature and wind speed (19 to 34 psu, 10 to 20 \({}^{\circ}\)C, and 0 to 11 m/s). Moreover, based on the in situ data, the model parameters are further optimized for \(SSS\) retrieval in the ECS. In what follows, Section 2 presents the cruise experiments, Section 3 describes the data and methods used in this study, including the radiometric calibration, correction of sea surface reflected sky radiation, auxiliary data preparation and sea surface emissivity models, Section 4 presents the results and discusses the findings and finally Section 5 gives the summary.
### Objectives  
To address the limitations of conventional PI and fixed-parameter sliding mode controllers under dynamic system conditions, this study aims to achieve the following:  
1. Develop a VSMC strategy to enhance the robustness of PMSM servo systems used in water-fertilization equipment.
2. Design a recursive least squares (RLS)-based observer to estimate real-time system parameters, including rotational inertia and load torque.
3. Adaptively adjust the sliding surface parameters in the VSMC using the observer's output to maintain control accuracy under varying operating conditions.
4. Compare two parameter derivation strategies--analytical modeling and data-driven fitting--for determining sliding mode parameters.
5. Validate the proposed method through simulations and field experiments by assessing its performance in maintaining target conductivity and improving fertilizer distribution uniformity.
# Energy Saving Potential of PCMs in Buildings under Future Climate Conditions
## 2 Background  
### Energy Consumption in Buildings  
Population growth, comfort level, building services, building age, and the hours spent inside a building have all caused the energy consumption levels of buildings to increase until they rival the levels of the industry and transport sectors [16]. In the EU, 40% of all annual energy is consumed by the building sector, and 50% of that is consumed by HVAC systems [4]. Forty-one percent of all the annual energy used in the US is consumed by commercial and residential buildings, and, of that, 37% and 43% are consumed for heating and cooling, respectively [11]. Several studies have reported that energy consumption in developing countries relates to population growth, urbanization levels, and growing living standards, which are related to urbanization rates [10; 17]. In India, 30% of the population lives in cities. In China, it is 45%, and those percentages are expected to exceed 50% and 70% in the next 25 years in India and China, respectively. In 2030, the Chinese population will reach the saturation level, whereas the Indian population is expected to grow until 2050. Meanwhile, Brazil and Russia do not expect a significant change in their populations despite urbanization that leads to high rates of new construction [10].  
According to the International Energy Agency, 50% of the population living in cities consumes two-thirds of the world's energy, and that rate will rise to 73% by 2030, at which point it will be causing 70% of global CO\({}_{2}\) emissions Therefore, reducing the energy consumed in and by buildings is important [18; 19; 20]. Several researchers have predicted that global energy demand in buildings will double by 2050 because 0.8 billion people lack adequate housing, 1.3 billion people currently lack electricity, and 3 billion people around the world still depend on traditional fuels such firewood, straw, and animal waste for cooking and heating [10; 21]. Meanwhile, the migration of populations to cities involves lifestyle changes that increase the living space per person and contribute to the increasing energy demand expected in the future [22; 23]. Building energy consumption data in developing countries have shown high increases in recent years, an average annual growth of about 2.2% in the past decade. In China, the energy consumed in and by buildings exceeds that consumed in US buildings, and it is expected to increase significantly in the coming years, driven by demand for new buildings [24]. In Sweden, the residential and services sector consumed 40% of the country's total energy in 2011, and 60% of that was for heating and hot water generation [1]. [PERSON] et al. [25] reported that, in Asia, household energy consumption will rise in the future, which will affect developing countries in Asia as well. As shown by this review of global energy demand, existing policies are insufficient to reduce energy consumption in general.
## Materials and Methods
### The Method of PI and BRS Phase Synchronization Measurement  
To estimate the mutual synchronization behaviour of the two signals quantitatively we used the method based on the comparison of their phases [36]. Instantaneous phase values were determined by the Hilbert transform which is widely used in mathematics, physics and signal analysis. For the overall estimation algorithm design, see Figure 1.  
The Hilbert transform produces complex function \(\dot{s}(t)\) from the original real signal \(s(t)\) (that stands for either SBP or PI) by adding the imaginary component \(s_{\perp}(t)\), which is defined as:  
\[s_{\perp}(t)=\frac{1}{\pi}\int\limits_{-\infty}^{\infty}\frac{s(\tau)}{t-\tau} \,d\tau. \tag{1}\]The resulting complex function is known as the complex analytical signal \(\dot{s}(t)=s(t)+\dot{s}_{\perp}(t)\).  
The real and imaginary parts of the analytical signal allowed us to determine the envelope \(S(t)\) as the absolute value of the analytic signal that characterized the laws governing its amplitude modulation, and the phase \(\Phi(t)\) as the argument of the analytical signal that characterized the laws governing its angular modulation. Accordingly;  
\[S(t)=\sqrt{s^{2}(t)+s_{\perp}^{2}(t)},\ \Phi(t)=\text{arctg}\frac{s_{\perp}(t)}{s( t)}. \tag{2}\]  
Of note, the signal phase has a clear physical interpretation only in the case of harmonic or narrowband oscillations, while the above formalism was not restricted to these assumptions and thus allowed us to calculate phase values for arbitrary data sequences. Next, we determined the differences between the phases \(\Phi_{\text{PI}}\)-\(\Phi_{\text{SBP}}\). Following [27, 28] we next applied moving average filtering in a gliding window of size \(\tau\) and calculated the standard deviation of the phase differences. Consecutive phase points where the standard deviation remained below a given threshold, equal to \(2\pi\)/\(\delta\), were treated as belonging to synchronization episodes, once their duration exceeded \(T\) seconds. This procedure was applied to the entire record in a gliding window while counting episodes of synchronous behavior. As a result, a quantitative measure of the phase synchronization was the synchronization coefficient Sync defined as the percentage of the synchronous behaviour episodes duration within the total duration of the analysis window.
# Spatiotemporal Evolution and Impact Mechanisms of Gross Primary Productivity in Tropics
## Discussion
### Summaries and Limitations  
In this paper, the improved P model was used to calculate the gross primary productivity (GPP) of vegetation in the tropics using remote sensing data and meteorological reanalysis information. The spatial and temporal distribution trend of GPP in the tropics and the influence mechanisms of factors such as temperature and land cover change were explored through Pearson correlation analysis and contribution analysis. The results indicated that the annual average value of GPP in the tropics exhibited a fluctuating pattern, ranging from 2603.9 to 2757.1 cm\({}^{-2}\) a\({}^{-1}\). GPP showed a significant increasing trend over the past 20 years. With regard to the factors influencing GPP, it can be observed that climate exerts its influence primarily in inland areas, where precipitation is the dominant factor, whereas in coastal areas, temperature is the primary determinant. The average annual GPP exhibited a significant variation among different land cover types. The conversion of bare land into cropland was associated with the largest increase in GPP, amounting to 1.38 \(\times\) 10\({}^{7}\) GgC, while the conversion of broadleaf evergreen forest into cropland was associated with the largest decrease in GPP, amounting to 4.96 \(\times\) 10\({}^{6}\) GgC. By calculating the contribution of the influencing factors, with the exception of mixed forests, deciduous coniferous forests, and urban areas, where the main influencing factor for GPP was land cover change, the other regions were climate change factors. These findings indicate that climate has a significant effect on tropical vegetation productivity, and in the context of global climate change, it is crucial to identify strategies to reduce the negative impact of climate change on productivity.  
Clearly, this study has its limitations. Primarily, the analysis of the spatiotemporal evolution pattern of GPP in the tropics did not include a comparison with existing GPP products. Furthermore, combining multi-source datasets would be beneficial to derive more reliable conclusions. Secondly, when analyzing the impact of climate change on vegetation GPP in the tropics, only two climate factors, temperature and precipitation, were considered, while other climate factors, such as surface scattering and solar radiation, also play a role in influencing GPP growth. Thirdly, the remote sensing data employed to calculate and analyze GPP have low temporal and spatial resolution, limiting the analysis of its long-term, large-scale evolution pattern. Additionally, in order to facilitate the calculation and analysis, reclassifying the initial dataset's 38 land types into 14 may have reduced the accuracy of the study. In future studies, expanding the data sources, utilizing satellite or drone data with higher resolution and timeliness, and constructing more sophisticated models should be considered to enhance the objectivity and credibility of the analysis results.
## 5 Discussion  
### Selection of the Optical Flow Calculation Method  
In this section, we discuss the choice of optical flow calculation method. The traditional optical flow calculation method has a high calculation accuracy, but the high computation cost make itusuitable for real-time detection. The optical flow calculation method based on deep learning has the advantage of fast calculation speed and acceptable precision, which has been an important research subject in respect of deep learning in recent years.  
As shown in Figure 8, we compared 4 optical flow calculation methods which are based on deep learning. Figure 8a,b show the two adjacent frames, (c) shows the TV ground truth of frame (a), and frames (d) to (g) are the visualized images of optical flow calculated using the 4 methods. It can be seen from Figure 8d,e that flownet2.0 [19] and LiteFlowNet2 [13] can effectively capture the motion of the shadow regions where the defocused TV is located. Finally, we chose the LiteFlowNet2 [13] method, which has the faster calculation speed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Optical Flow** & **Slicing** & **Rough** & **Fine** & **Total** \\  & **Calculation** & & **Detection** & **Detection** & **Total** \\ \hline Running times & 0.545 s & 0.050 s & 1.556 s & 0.875 s & 3.026 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: The average running times for one calculation process of our algorithm.  
Figure 8: Comparison between 4 optical flow calculation methods based on deep learning. (**a**,**b**) show the two adjacent frames; (**c**) shows the TV ground truth of frame (**a**); (**d**–**g**) are the visualized images of optical flow calculated using the 4 methods. It can be seen from Figure 8d,e that flownet2.0 [19] and LiteFlowNet2 [13] can effectively capture the motion of the shadow regions where the defocused TV is located. Finally, we chose the LiteFlowNet2 [13] method, which has the faster calculation speed.
## 4 Discussion  
Table 2 gives a comparison between the proposed shared-aperture phased array antenna and other related works. For dual-band, dual-CP antenna arrays, some of them [18,19] can achieve large-angle scanning, but the phased array with a multi-layer structure is too complex and has the potential risk of delamination in a satellite environment (alternatively operated from \(-90\)\({}^{\circ}\)C to \(90\)\({}^{\circ}\)C in LEO). A dual-band, single-layer, shared-aperture antenna array [15,20], is mainly composed of traditional PCB CP patch elements, and they are limited by the larger element spacing and cannot provide the capability of large-angle scanning. Our proposal, with a single-layer shared-aperture structure, can provide stable wide-angle scanning of larger than \(\pm 50^{\circ}\) for S-/C-bands without a serious grating lobe. It is thus a potential good candidate for direct-to-cellular satellite payload.  
\begin{table}
\begin{tabular}{c c c} \hline
**Operating Frequency** & **S @ 1.975 GHz** & **C @ 3.55 GHz** \\ \hline Bandwidth (GHz) & 1.93–2.03 (5.3\%) & 3.36–3.75 (11\%) \\ Polarization & RHCP & LHCP \\ Element Spacing & 0.58 \(\lambda\) & 0.52 \(\lambda\) \\ Boresight Gain of 64-Elements Array (dB) & 23.57 & 23.34 \\ Scanning Range & \(\pm 50^{\circ}\) & \(\pm 50^{\circ}\) \\ \(\theta\) = 50\({}^{\circ}\) Side Lobe Level (dB) & \(-12.5\) & \(-13.1\) \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of measured parameters between two bands of the proposed antenna array.  
Figure 15: Simulated and measured AR at the different scanning directions (**a**) at 1.97 GHz; (**b**) 64-element array at 3.55 GHz.