esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Based on the complex topography and climate conditions over the Tianshan Mountains (TSM) in Xinjiang, China, the new precipitation product, the Global Precipitation Measurement (GPM) (IMERG), and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) 3B42 (TMPA), were evaluated and compared. The evaluation was based on daily-scale data from April 2014 to March 2015 and analyses at annual, seasonal and daily scales were performed. The results indicated that, overall, the annual precipitation in the Tianshan area tends to be greater in the north than in the south and greater in the west than in the east. Compared with the ground reference dataset, GPM and TRMM datasets represent the spatial variation of annual and seasonal precipitation over the TSM well; however, both measurements underestimate the annual precipitation. Seasonal analysis found that the spatial variability of seasonal precipitation has been underestimated. For the daily assessment, the coefficient of variation (CV), correlation coefficient (R) and relative bias (RB) were calculated. It was found that the GPM and TRMM data underestimated the larger CV. The TRMM data performed better on the daily variability of precipitation in the TSM. The R and RB data indicate that the performance of GPM is generally better than that of TRMM. The R value of GPM is generally greater than that of TRMM, and the RB value is closer to 0, indicating that it is closer to the measured value. As for the ability to detect precipitation events, the GPM products have significantly improved the probability of detection (POD) (POD values are all above 0.8, the highest is 0.979, increased by nearly 17%), and the critical success index (CSI) (increased by nearly 9% in the TSM) is also better than TRMM, although it is only slightly weaker than TRMM in terms of the false alarm ratio (FAR) and frequency bias index (FBI). Overall, GPM underestimates the low rainfall rate by 6.4% and high rainfall rate by 22.8% and overestimates middle rain rates by 29.1%. However, GPM is better than TRMM in capturing all types of rainfall events. Based on these results, GPM-IMERG presents significant improvement over its predecessor TRMM 3B42. Considering the performance of GPM in different subregions, a lot of work still needs to be done to improve the performance of the satellite before being used for research.  
GPM; TRMM; Tianshan Mountains; spatial distribution of precipitation; precipitation estimation 2020 1 3088 10.3390/w12113088 10.
## 3 Results and Discussion
### Bio-Medical Potential of CPL-Rx
#### 3.5.3 Antiproliferative Potential  
The antiproliferative effect of CPL-Rx was tested on SW480 (colon) and A549 (lung) cancer cell lines. CPL-Rx exhibited slight activity against both cell lines (Figure 3). The maximum concentration tested (200 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) caused a decrease in viability by 19.24% and 20.01% after 24 and 48 h of incubation, respectively, in the case of the A549 cells. In turn, the values for the SW480 cells were 18.13% and 14.1%, respectively. In addition, higher proliferation was observed at the extended incubation time (48 h), which may be a result of the reduced effect of the substance. In this case, this may be caused by the gradual adaptation of the cells to the content of CPL-Rx in the medium or the possible degradation of the compound in the cell culture conditions, and thus its lower stability. Nevertheless, the chemopre potential of this compound is worth exploring further, especially due to the fact that it is a compound of natural origin, which may require a prolonged use, an increase in the dose, or maintenance of its concentration at a constant level through regular use.
# An Interpretable XGBoost Framework for Predicting Oxide Glass Density
## 3 Results  
### Models' Predictions  
Predicted vs. experimental glass densities for the ElasticNet, XGBoost, and MLP models for considered feature sets are presented in Figures 2-4. The model's statistics like R\({}^{2}\), mean square error (MSE), and mean absolute error (MAE) are also included in the figures.  
A clear trend is observed across all models: predictive accuracy improves significantly as the feature set becomes more physically informative. The performance systematically increases when moving from the purely Compositional descriptor to the Meredig and Magpie sets. Even the inclusion of attributes distinguishing the roles of atoms (Atomic types features) provides a noticeable, albeit slight, improvement over the baseline for all models. This effect is particularly pronounced for the linear ElasticNet model (Figure 2). Its limited ability to capture the complex, non-linear relationship between composition and density is evident when using only compositional features. However, its accuracy increases  
Figure 2: Predicted vs. experimental glass densities for ElasticNet model for feature sets: (**a**) Compositional; (**b**) Atomic types; (**c**) Meredig, (**d**) Magpie.  
substantially with the more advanced feature sets. In contrast, the non-linear models, XGBoost (Figure 3) and MLP (Figure 4), demonstrate high predictive power even with the simplest feature set, achieving a coefficient of determination (R\({}^{2}\)) of approximately 0.9. Nevertheless, their performance is still markedly enhanced by the advanced descriptors, with the R\({}^{2}\) value reaching up to 0.97 for the XGBoost model using Magpie attributes. This trend is mirrored by the mean squared error (MSE) and mean absolute error (MAE) values, which consistently decrease as the features become more sophisticated.  
Comparing the overall results, the XGBoost model delivered the most accurate and robust predictions across all feature sets, establishing it as the best-performing model in this study. Therefore, all further analysis will focus on the results obtained with this model.  
Figure 3: Predicted vs. experimental glass densities for XGBoost model for feature sets: (**a**) Compositional; (**b**) Atomic types; (**c**) Meredig, (**d**) Magpie.
# Face Swapping Consistency Transfer with Neural Identity Carrier
## 2 Related Work
### Face-Swapping Approaches  
Face-swapping has a long history in vision and graphic research, going back nearly two decades. They are proposed due to privacy concerns first, while they are more used for entertainment [1]. The earliest swapping methods require manual adjustment [2]. [PERSON] et al. propose an automatic face-swapping method [13]. However, these methods cannot produce satisfactory results. Recently, learning-based methods have achieved better performance. Deepfakes used auto-encoder to swap faces between identity and target [14]. [PERSON] et al. upgraded the structure and launched an open-source project, DeepFaceLab (DFL), which is the most popular one on the Internet [15]. [PERSON] et al. used a fixed 3D face shape as the proxy to increase the controllability of face-swapping [5]. [PERSON] et al. proposed subject-agnostic methods which can be applied to any pair of faces without training on them [4]. And [PERSON] et al. propose a two-stage method that can achieve high fidelity and occlusion aware face-swapping [3].  
Previous methods suffer from their backbone heavily. For example, auto-encoder-based methods utilize an encoder to disentangle the target person's attribute and identity person's identity information and reconstruct them back by a decoder--a large amount of effective information lost in the encoder-decoder process [15]. GAN-based methods cannot deal with the problem of temporal consistency and produce abnormal results occasionally [4]. In this paper, we leverage a _U-Net_ as neural identity carrier to carry the primary information from face-swapping proxy, significantly avoiding the loss of information and producing coherent results.
## 3 Discussion
### Plant Nutrient Uptake Capacity Might Be Activated by H\({}_{2}\)-Based Irrigation  
Subsequent molecular evidence supported another possibility: the activation of plant nutrient uptake capacity. Strawberry _NRT1.1_, _NRT2.1_, _NIA_, and _NiR_ are crucial genes for nitrogen uptake and assimilation [41; 42]. Our previous results showed that H\({}_{2}\) supplied by exogenous or genetic manipulation could increase nitrate uptake and seed size in Arabidopsis by regulating the dephosphorylation of nitrate reductase under controlled laboratory conditions [22] and positively improve quantitative traits of rice by up-regulating the expression of _NRT2.3_ and _NiR_ in field trials [26]. These findings may have implications for minimizing fertilizer use achieved by H\({}_{2}\).  
Meanwhile, three P transporter genes, _PT4_, _PT5_, and _PT8_, in strawberry have been identified and found to be highly induced by mycorrhiza, contributing to efficient P transport and acquisition [43]. In addition, fifteen K\({}^{+}\) uptake transporter genes (_KUPS_) were previously identified in wild strawberry, with nine of them largely upregulated in response to K\({}^{+}\) deficiency [44]. A further functional complementation experiment in bacterial mutant indicated that _FveKIP8_ could utilize external K\({}^{+}\) under the neutral proton environment.  
Consistent with changes in soil nutrients with or without H\({}_{2}\) supply (Figure 2A), the qPCR results clearly showed that transcripts of _NRT1.1_, _NRT2.1_, _NIA_, _PT4_, _PT8_, _KUP4_, and _KUP8_ in strawberry roots at the fruiting stage were all significantly induced by H\({}_{2}\), regardless of whether fertilizers were added (Figure 6A). These changes were strongly and positively correlated with the enhanced soil nutrient reduction and strawberry yield (Figure 6B).  
Previous studies have documented ample evidence of PGPR stimulating the expression of plant nutrient uptake-related genes, thus facilitating nutrient absorption. For example, _Pseudomonas nitroreducens_ strain IHB B 13561 could induce a high expression of _NRT2.1_ in Arabidopsis and lettuce roots, thus enhancing their nitrate uptake and biomass [45]. _Pseudomonas_ sp. strain P34-L, isolated from the wheat rhizosphere, increased phosphate and biomass accumulation by regulating _PT4_ expression [46]. Accordingly, our study clearly suggested that the obvious enriched PGPR (i.e., _Burkholderia_, _Novosplingobium_, _Splingomonas_, _Splingobium_, and _Cupriavidus_) driven by H\({}_{2}\) was positively correlated with expressions of N- and P-absorption-related genes in strawberries and soil nutrient reduction (Figure 6C). Certainly, the detailed mechanisms involved are not well known, and these need to be further addressed in the near future.
# Smart Grid Testing Management Platform (SGTMP) +
## 3 Implementing SGTMP
### Platform Architecture  
The deployment diagram shows the SGTMP design from a high-level point of view (Figure 3). The primary component is the SGTMP core module, running within the Java virtual machine. The functionality is exposed via Java and REST APIs, together with an HTTP GUI server implementing a subset of the functionality, allowing technicians to upload test definitions containing the SG topology and the test pass criteria, to schedule tests for execution and inspect the results in a visual form. On the same server, the Mosaik interface must be running to support a connection to the Mosaik framework via inter-process communication (IPC). The Mosaik interface, along with Mosaik, is responsible for communication with other simulators. Each simulator must implement the Mosaik API to be able to communicate with Mosaik. The implementation of the Mosaik API takes care of communication and data exchange with both software and hardware simulators. Software simulators can be running on the same machine, or they can be accessed remotely: the Mosaik API implementation serves only as a bridge between them. Hardware simulators can be connected to the simulator via a hardware interface (e.g., RS-232, Ethernet, USB), and the API implementation is responsible for managing the connection and other administrative tasks. The database server is a data store for storing all of the related test data, which are generated before, during, and after each test run. The connection between SGTMP and the database is handled by Java Database Connectivity (JDBC).  
In the next sections, we discuss the main implementation details of SGTMP, first covering the testing management part and how tests are modeled, run, and executed. In the second part, we include the integration of simulations within the platform through the Mosaik framework.
## 5 Phase Field Methods
### Derivation of the Phase Field Evolution Equation in from the General Form  
The phase field evolution equation employed in Borden et al. (2019) can be obtained from the general expression of the strong form (Equations (71) and (72)), considering the expressions for \(F_{\Gamma}\) and \(g(c)\) from Equation (60) and (68), i.e.,  
\[\begin{split} F_{\Gamma}=\left[\frac{\left(c-1\right)^{2}}{4l_{0 }}+l_{0}|\
abla c|^{2}\right]&;\quad\delta_{c}(F_{\Gamma})= \left[\frac{\left(c-1\right)}{2l_{0}}-2l_{0}\Delta c\right]\\ g(c)=(1-k)c^{2}+k&;\quad g^{\prime}(c)=2(1-k)c\end{split} \tag{76}\]  
In the original formulations of Miehe et al. (2016), which is later also adopted in (Miehe et al., 2019), the crack driving force \(\tilde{D}\) was the positive part of the elastic strain energy density, i.e.,  
\[\tilde{D}=\psi_{e}^{+} \tag{77}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(g(\mathbf{c})\) & \(F_{\Gamma}\) & **Reference** \\ \hline \(\left[\left(1-k\right)c^{2}+k\right]\) Equation (68) & \(\left[\frac{\left(c-1\right)^{2}}{4l_{0}}+l_{0}|\
abla c|^{2}\right]\) & Equation (60) & Borden et al. (2019) \\ \hline \(c^{2}\) & Equation (69) & \(\frac{3}{8l_{0}}\left[1-c+l_{0}^{2}|\
abla c|^{2}\right]\) & Equation (65) & Geelen et al. (2019) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition variants for the degradation function \(g\left(c\right)\) and the functional \(F_{\Gamma}\).  
where \(\psi_{e}^{+}\) is the tensile part of strain energy density taken from [228].  
Substituting Equation (77) in Equation (72) and considering also Equation (76) the following evolution equation is derived, i.e.,  
\[\left(\frac{4l_{0}\left(1-k\right)\psi_{e}^{+}}{\mathcal{G}_{c}}+1\right)c-4l_{0 }^{2}\Delta c=1,\,\text{on}\,\,\Omega \tag{78}\]  
which is a linear differential equation with respect to \(c\). It is of interest to note that the Laplacian of the phase field in Equation (78) is scaled by the squared value of the length scale parameter hence it rapidly vanishes for small values of \(l_{0}\) compared to the \(c\).
## 4 Light Attenuation  
There are several mechanisms that contribute to reducing the radiation that reaches the target. For most studies of heliostat fields at work, involving geometric parameters or comparisons between different situations, these attenuation effects can usually be ignored (implicitly assuming that they affect the various situations equally). However, to estimate the flux of energy collected in the target, these effects should be taken into account. Shading and blocking simulations are important to optimize the heliostat field layout, maximizing land use without causing obstructions between heliostats.
###### Abstract  
Conventional drought indices based on stationary assumptions are no longer appropriate for drought assessments conducted under conditions with climate change or anthropogenic influences. In this study, a time-varying Nonstationary Standardized Precipitation Index (NSPEI) was developed by fitting a time covariate with the Generalized Additive Models for Location, Scale, and Shape (GAMLSS), with a time-scale of six months. Daily precipitation, maximum temperature, and minimum temperature datasets from 1979-2020 that were based on the Climate Prediction Center (CPC) global unified gauge-based analysis with a resolution of \(0.5^{\circ}\times 0.5^{\circ}\) were used. The results of the study indicated that both precipitation and evapotranspiration in China had increased significantly over the past 42 years in China and that Northwest China would become drier. By extracting the return objects of the GAMLSS, this study identified Northwest China (Continental River Basin) as the main region wherein the distribution parameters of the non-stationary models changed; this region was identified as the one concentrated with nonstationary hotspot response areas. A comparison of drought duration and severity showed that the stationary SPEI under-estimated the severity of the drought. The severity was under-estimated in the spring-summer and fall-winter seasons for Northwest and Southwest of China, respectively; more attention should be paid to these regions. This study provides results that can support nonstationary drought research; droughts can be identified more precisely, and early warnings can be provided for them.  
n ontationarity; GAMLSS model; SPEI index; drought characteristics; hotspot 2022128479.
# Experimental Study of Vegetative Properties in
## 3 Results and Analysis
### Analysis of the Effect of Substrate Ratio on Plant Growth  
#### 3.2.1 Plant Growth Rules of Different Substrates  
When analyzing the effects of ZL and BC on the plant height of C.d. seedlings, Figure 10 illustrates the varying average plant height under different ZL and BC contents, as well as different substrate ratios for substrates No. 1#-26#. Common growth patterns were observed across the different substrates. The C.d. growth cycle can be divided into three stages.  
In the germination stage (0-30 days), the growth height of the C.d. seedlings was very small and could be ignored. Between the 5 th and 10 th day after seeding, the C.d. on each substrate began germinating, resulting in small plant heights. Within 30 days after seeding, the maximum growth height did not exceed 3 cm, with an average height of 1.86 cm. The 11# substrate had the lowest seedling growth height at 1.2 cm, while the 1# substrate had the highest at 2.9 cm. No significant effects of ZL and BC on the root height of C.d. seedlings were observed during this stage.  
Figure 8: pH Improved substrate pH with different ratios.  
During the growth stage, from 30 to 75 days after seeding, C.d. seedlings as a whole experienced a period of rapid growth lasting approximately 40 days. The timing of this rapid growth varied among seedlings with different substrate numbers. On average, the height of the C.d. seedlings was 16.87 cm, with the tallest and shortest substrates being 4# and 24#, measuring 21.5 cm and 13.5 cm, respectively. This height discrepancy reached 8 cm, demonstrating significant growth changes in all groups. Relative to day 30, the C.d. growth height for substrates 2# to 6#, 7# to 11#, 12# to 16#, 17# to 21#, and 22# to 26# increased by 888.12%, 807.92%, 645.54%, 699%, and 682.18%, respectively. At this stage, the growth height of each substrate group was progressively graded, showing advantages in plant height ratios when BC content was 0% and 1%. Additionally, root height was notably higher when ZL content was at 6%, followed by 3% ZL content.  
Figure 9: pH and seed germination index fit map: (**a**) germination percentage; (**b**) germination energy; (**c**) germination index.  
During the stable stage (75-90 days after seeding), the growth curve of the 1#-26# substrate C.d. seedlings began to smooth out, and the growth fluctuations decreased. While a few scattered groups of substrate seedlings still experienced some rapid growth, this was only temporary. Overall, all groups of C.d. seedlings entered a stable growth stage, with an average growth height of 19.98 cm, representing an 18.43% increase compared to the previous stage. The substrate showed the highest average plant height when the BC content was 1% and the ZL content was 6%.
# An All-Digital Optical Phase-Locked Loop Suitable for Satellite Downlinks +
###### Abstract  
The optical signal propagation used in satellite uplinks and downlinks is influenced by absorption, scattering, and changes in the atmospheric refractive index or turbulence, causing optical signal attenuation. A free space optics (FSO) communications system using coherent communication can improve the link sensitivity and reach higher distances. This article proposes a new architecture for the phase detector in an all-digital optical phase-locked loop (OPLL) for coherent optical detection. Firstly, the performance of the proposed phase detector is evaluated under Gaussian noise, where the best operation point is found for the OPLL working with two sample rates: 625 MSa/s and 10 GSa/s. The system analyses also take a non-negligible delay into account. Then, it will be evaluated and compared with an OPLL using an analog phase detector in the presence of atmospheric turbulence. Finally, in three different atmospheric turbulence conditions, the effect of wind speed on communication quality is investigated through the obtained bit error rate (BER) from the recovered data for a bit rate of 20 Gbps. The results show that the proposed digital phase detector can track a signal under longer feedback loop delays and fading signals.  
atmospheric turbulence; coherent detection; optical digital phase-locked loop +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Performance Comparison of H.264 and H.265 Encoders in a 4K FPV Drone Piloting System
## 4 Experimental Results
### Energy Consumption  
Energy consumption for the encoding of each video sequence is proportional to the amount of time consumed for the encoding of the sequence. Encoding can be performed in parallel on several cores or by using just one core. The total amount of time spent in processing is almost the same whether only one core is used or several cores are used. For this kind of high-resolution video, parallelization of both the H.264 and H.265 encoder is highly efficient and they scale almost perfectly, especially in the range of up to 16 cores [31; 32; 33].  
Figure 10: Classes A–D VMAF scores.  
Depending on the codec used, different parallelization strategies can be used such as slice-level, frame-level, intra-frame macroblock or 3D-Wave [34]. In addition to them, hardware implementations (or additional hardware specialized for multimedia processing) are often seen in SoCs used for drones. Those implementations using hardware-accelerated encoders achieve better performance than the software implementation. Different vendors have their video encoder and decoder semiconductor IP cores, such as the Hantro series from Veri Silicon. In SoCs, they are used for encoding and decoding together with processors such as ARM Cortex-A53 with several processing cores. For a precise calculation of the hardware needed for the real-time encoding of the 4K video, additional research should be performed with particular hardware, which is intended to be deployed on the drone.