esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Recovery of Coverage and Abundance Precipitation on Nine Plant Species after Drought Stress  
In the recovery period, the coverage of _Stipa kijlovii_ under the drought treatment plots was 7.60% higher (absolute changes, \(p\) < 0.05, Table 1) than that of control. The abundance of _Stipa kijlovii_ under the drought treatment was marginally higher than that under the control treatment (_p_ < 0.10, Table 1). The abundance of _Heteropappus altaicus_ was significantly lower under the drought treatment than that under the control treatment (_p_ < 0.05, Table 1). No difference in coverage and abundance of other species was found between control and drought treatment (_p_ > 0.05, Table 1).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Coverage**} & \multicolumn{2}{c}{**Abundance**} \\ \hline  & **C** & **D** & **C** & **D** \\ \hline _Heteropappus altaicus_ & 0.96 \(\pm\) 0.30 a & 0.43 \(\pm\) 0.20 a & 12.83 \(\pm\) 2.40 a & 4.00 \(\pm\) 2.40 b \\ _Potentilla bifraca_ & 2.82 \(\pm\) 0.70 a & 2.24 \(\pm\) 0.50 a & 11.20 \(\pm\) 2.19 a & 13.00 \(\pm\) 4.23 a \\ _Artemisia scoparia_ & 0.56 \(\pm\) 0.40 a & 0.56 \(\pm\) 0.20 a & 9.75 \(\pm\) 5.50 a & 13.80 \(\pm\) 4.90 a \\ _Artemisia frigida_ & 7.50 \(\pm\) 1.00 a & 8.33 \(\pm\) 2.00 a & 10.00 \(\pm\) 1.70 a & 7.50 \(\pm\) 1.60 a \\ _Dontostemon dentatus_ & 0.01 \(\ast\) & 0.09 \(\ast\) & 1.00 \(\pm\) 0.00 b & 2.00 \(\pm\) 0.00 a \\ _Melissilus uthenticus_ & 1.18 \(\pm\) 0.40 a & 0.78 \(\pm\) 0.30 a & 2.33 \(\pm\) 0.90 a & 2.60 \(\pm\) 0.60 a \\ _Agoryro cristatum_ & 2.97 \(\pm\) 1.30 a & 1.00 \(\pm\) 0.50 a & 125.67 \(\pm\) 50.00 a & 39.00 \(\pm\) 15.60 a \\ _Stipa kijlovii_ & 4.58 \(\pm\) 0.20 a & 12.18 \(\pm\) 2.60 b & 9.20 \(\pm\) 2.60 a & 16.50 \(\pm\) 2.60 a \\ _Cleistocene squarrosa_ & 2.38 \(\pm\) 0.70 a & 1.92 \(\pm\) 0.40 a & 18.33 \(\pm\) 3.40 a & 16.83 \(\pm\) 2.80 a \\ \hline \hline \end{tabular} Note: Values are the means \(\pm\) standard error (\(n=6\)). Different lower letters indicate significant differences (_p_ < 0.05) between control and drought treatment \(\ast\). Standard error on the abundance and coverage of _Dontestemon dentatus_ Ledeb cannot be calculated because this species was detected in less than 3 plots in each treatment.  
\end{table}
Table 1: The coverage and abundance of dominant plant species in control and drought treatment in the recovery stage.  
Figure 2: Effects of drought on coverage of _Heteropappus altaicus_ (**a**), _Potentilla bifurca_ (**b**), _Artemisia scoparia_ (**c**), _Artemisia frigida_ (**d**), _Dontostemon dentatus_ (**e**), _Melissilus ruthenticus_ (**f**), _Agoryroro cristatum_ (**g**), _Stipa kijlovii_ (**h**), and _Cleistocene squarrosa_ (**i**). C: ambient precipitation; D: precipitation treatment; \(n\) = 6.
# The Role of Correlation in the Performance of Massive MIMO Systems
## 6 Numerical Results
### Performance Evaluation Based on the MSE  
In this subsection, we compare the MSE performances under different antenna configurations. Figures 4 and 5 show plots of the MSE in the ULA and the URA, respectively, versus the SNR values in a scenario where the azimuth standard deviation is \(\alpha_{\text{A}}(AS)=5\). The lines depict the numerical analysis based on Equation (20), while the colored markers denote simulation based on Equation (16). Clearly, an excellent agreement between the analytical and simulated results is obtained with the realistic channel model.  
Figures 4 and 5 demonstrate that increasing the number of base station antennas results in reducing the MSE of the channel estimate. The plots also illustrate that the MSE of the channel estimate is minimized using an array with a URA configuration. For  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Symbol** & **Value** \\ \hline Number of base station antennas & \(M\) & 16, 64, 128, 256 \\ Number of UEs & \(U\) & 10 \\ Base station height & \(h\) & 35 m \\ Number of training slots & \(T_{\text{tr}}\) & 10 symbols \\ Azimuth standard deviation & \(\alpha_{A}\) & \(5^{\circ},10^{\circ},15^{\circ},20^{\circ}\) \\ Elevation standard deviation & \(\alpha_{E}\) & \(2^{\circ}\) \\ Antenna distance spacing & \(d\) & \(\lambda/2\) \\ UEs’ location range from BS & \(D_{u}\) & \(<\)200 m \\ Coherence time & \(T\) & 100 symbols \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Conclusions from the MSE figures can be drawn as follows: as the transmit power increases, the estimation error decreases since the error variance approaches zero in the asymptotic power regime. Another important insight is that the error variance is reduced with the stronger eigendirections, which correspond to the largest eigenvalues of the  
Figure 6: MSE vs. SNR in dB for the ULA topology, \(U=10\) UEs, \(M=128\), with different values of angular spread \(\alpha_{\rm A}(AS)\).  
Figure 7: MSE vs. SNR in dB using the URA topology, \(U=10\) UEs, \(M=128\), with different values of angular spread \(\alpha_{\rm A}(AS)\).  
covariance matrices of the UEs. This is explained by the fact that the transmit power is relatively high, which is concentrated in the stronger eigendirections of the channel, thus improving the performance of channel estimation significantly.
## 4 Discussion
### Dependence of DDER on the NP Material and Concentration  
In Table 1, it can be seen that Au NPs produced a higher DDER than Fe\({}_{2}\)O\({}_{3}\) NPs. However, this also resulted in a higher beam attenuation with depth in the phantom. When NPs are only uptaken by the tumour with NP concentration in the surrounding normal tissues equal to zero, Au NPs are better than Fe\({}_{2}\)O\({}_{3}\) NPs in radiotherapy [24; 25]. In addition, the use of FFF photon beam also contributes to the DDE when the treatment depth is shallow. Therefore, it is important to develop a NP carrier in order to maximize the NP uptake at the tumour [28; 42].  
In Figure 1, it can be seen that high concentration of Au NPs had a high DDER but at the same time a high beam attenuation. Similarly, low concentration of Au NPs had a relatively lower DDER but a lower beam attenuation. These characteristics made the DDER curves with different concentrations overlap one another with intersections at about 6 cm (Figure 1a) and 8 cm (Figure 1b). This means that at certain depths, the DDE of the Au NPs would be offset by the beam attenuation due to the variation of the NP concentration.  
Considering the Au NP concentration, it is found that the DDERs increased from 1 to 1.004 and 1.001 to 1.015 for concentration range of 3-40 mg/mL, using the FF and FFF photon beams at a depth of 5 cm (Table 1a). However, when the depth was deeper than 5 cm, the DDER was found decreased with an increase of concentration for both the FF and FFF beams. This shows that the beam attenuation was not significant in the shallow depth, which contained more low-energy photons in the beam for an increase of energy deposition. For Fe\({}_{2}\)O\({}_{3}\) NPs, the DDER was found decreased with an increase of NP concentration. In this event, the atomic number of iron was not high enough compared to gold to produce a similar DDE in the shallow depth (\(d=5\) cm).  
When the Au NP concentration was increased from 3 to 40 mg/mL, the DDERs were decreased at rates from \(0.2\times 10^{-3}\) to \(2.2\times 10^{-3}\) cm\({}^{-1}\) and \(0.4\times 10^{-3}\) to \(2.8\times 10^{-3}\) cm\({}^{-1}\) for the FF and FFF photon beams, respectively. For Fe\({}_{2}\)O\({}_{3}\) NPs, relatively lower rates of \(0.1\)-\(1.7\times 10^{-3}\) cm\({}^{-1}\) and \(0.3\)-\(1.8\times 10^{-3}\) cm\({}^{-1}\) were found for the FF and FFF beams. Therefore, it is seen that increasing the NP concentration increased the rate of decreased dose enhancement with depth (Figures 1 and 2), and such effect was more significant in Au NPs.
# Efficient I/O Performance-Focused Scheduling in High-Performance Computing
## 2 Background
### Impact of IOPS on HPC Systems  
The previous section establishes that jobs performed on HPC systems typically require high IOPS. The I/O performance of these jobs significantly impacts the overall system performance, making it a crucial factor in determining the efficiency of HPC systems. Ensuring adequate I/O performance is even more critical in these environments for two key reasons. First, as Figure 1 shows, the architecture of an HPC system uses a distributed file system, unlike computational resources such as CPUs and GPUs. As shown in the figure, App 1 (represented by the green box) uses three CPU nodes, each dedicated to separate functions, while App 2 (represented by the red box) utilizes four GPU nodes independently for its computations. However, both applications store their computation results in the shared OSS. This means that while the applications use different compute nodes (CPU and GPU) for computation-intensive workloads, the results and intermediate data are stored in shared storage (OSS), requiring all jobs to be allocated and scheduled for access to the DFS. This shared nature of DFS makes I/O performance susceptible to interference from multiple concurrently running applications, potentially causing contention and reduced performance. As noted by [PERSON] et al. [5], concurrent access to DFS by multiple applications increases data access variability, leading to contention among applications. Similarly, [PERSON] et al. [6] highlight that this contention arises due to limited I/O bandwidth, which becomes overburdened by high-frequency read and write operations from various applications, resulting in resource bottlenecks and performance variability. Second, efficient I/O performance is essential for keeping all other resources fully utilized. Computation can only proceed if the input data are read from distributed storage and delivered to the node performing the computation in a timely manner. Delays in data transfer can result in data stalls, leaving nodes and their hardware idle and significantly degrading overall system efficiency [22; 23]. Therefore, in order to schedule jobs without experiencing performance degradation it is crucial to focus on meeting the IOPS requirements of each job, thereby ensuring that sufficient I/O resources are allocated.  
Figure 1: Architecture of HPC system with a distributed file system (OSS: Object Storage Server).
# Poly(lactic acid)-Based Electrospun Fibrous Structures for Biomedical Applications
## 5 Engineering of PLA-Based Electrospun Structures for Biomedical Applications
### Core-Shell Structure  
Electrospinning of PLA nanofibers with core-shell structure is an attractive strategy for biomedical applications. The coaxial electrospinning makes it possible to produce multicomponent fibers with a core-shell structure. Core-shell nanofibers compose of diverse materials for the core and the sheath (or shell). This method is also a versatile approach for producing hollow nanofibers by optionally eliminating the core part from the core-shell fibers. Coaxial electrospinning has boosted several challenges in the medical area, such as sustained drug release [9, 35, 42]. The ordinary use of this strategy is to encapsulate biological agents in the core site. Therefore, the shell part preserves the biomolecules and the delivery function occurs in a controlled manner by minimizing the burst release. Enhanced mechanical properties and the feasibility to functionalize the surface without affecting the core component are the other advantages of this technology; moreover, this approach is suggested to alleviate some limitations of PLA, such as its brittleness and hydrophobic nature [9, 42]. In a research work performed by [PERSON] et al. [9], coaxial electrospinning was applied to fabricate core-shell (PVA)-PLA nanofibers (Figure 6f). This allowed the hydrophilic feature of PVA and the biocompatibility of PLA to be combined in one structure.
## 3 Results and Discussion
### Chemical Composition of the Waterborne UV/Al Low Infrared Emissivity Coating  
According to Figure 4 and Table 7, the wavenumber of 3343 cm\({}^{-1}\) was an -OH stretching vibration [24]. A wavenumber of 1724 cm\({}^{-1}\) was the characteristic absorption peak of C=O [25], while wavenumber 1530 cm\({}^{-1}\) was the N-H bending vibration of the amide II band [26], indicating that there were a large number of carbamate bonds in the product. At wavenumber 1670 cm\({}^{-1}\), the characteristic absorption peak of the C=C double bond could appear [27]. Moreover, the characteristic absorption peaks of -CH were 2930 cm\({}^{-1}\) and 1462 cm\({}^{-1}\)[28], and the C-O located at 1049 cm\({}^{-1}\) and 1120 cm\({}^{-1}\)[29]. In addition, the N-C-O stretching vibration at 1256 cm\({}^{-1}\) was found too [30]. According to the intensity of the wave peak, at the 40.0% concentration, the wave peak of the infrared spectrum was basically the same, which had a high reflectivity to the infrared, so it was more instrumental in reducing the coating surface emissivity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Al Powder Concentration (\%)** & **Hardness** & **Adhesion (level)** & **Impact Resistance (kg cm)** \\ \hline
0 & H \(\pm\) 0 & 3 \(\pm\) 0 & 25.00 \(\pm\) 0.02 \\
10.0 & 6H \(\pm\) 0 & 0 \(\pm\) 0 & 50.00 \(\pm\) 0.20 \\
15.0 & 6H \(\pm\) 0 & 0 \(\pm\) 0 & 50.00 \(\pm\) 0.20 \\
20.0 & 6H \(\pm\) 0 & 0 \(\pm\) 0 & 50.00 \(\pm\) 0.10 \\
25.0 & 6H \(\pm\) 0 & 0 \(\pm\  
### Chemical Composition of the Waterborne UV/Al Low Infrared Emissivity Coating  
After the infrared emissivity, lightness, mechanical properties and infrared spectrum tests, it was found that the comprehensive properties of samples with the 20.0-30.0% Al powder were better. Therefore, the electrochemical polarization curve in Figure 5 was obtained after the corrosion resistance test of coating with a concentration of 20.0-30.0%. The main electrochemical parameters of Figure 5 were acquired from the Tafel polarization curves in Table 8. R\({}_{\rm P}\) is the polarization resistance of the coated substrate; E\({}_{\rm corr}\) is the corrosion potential of the coating, I\({}_{\rm corr}\) is the corrosion current density; and \(\beta_{\rm a}\) and \(\beta_{\rm c}\) are the anode constant and cathode constant, respectively. The corrosion resistance was positively correlated with the potential E\({}_{\rm corr}\) (V). The corrosion resistance of the waterborne UV coating at 25.0% Al powder was the best.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Band (cm\({}^{-1}\))** & **Assignment** \\ \hline
3343 & -OH stretching vibration \\
1724 & C=O absorption \\
1530 & N–H bending vibration of the amide II band \\
1670 & C=C absorption \\
2930, 1642 & -CH absorption \\
1049, 1120 & C–O absorption \\
1256 & N–C–O stretching vibration \\ \hline \hline \end{tabular}
\end{table}
Table 7: Assignment of the bands.  
Figure 4: Influence of the Al powder concentration on the infrared spectrum.
## 3 Results and Discussion
### Characterization of the Rolled Powder (RP)
The pore analysis showed D\({}_{10}\) of 90.1 \(\upmu\)m, D\({}_{50}\) of 131.2 \(\upmu\)m, and D\({}_{90}\) of 204.8 \(\upmu\)m, and the mean diameter was 264.0 \(\upmu\)m. These values are slightly lower than those found by [PERSON] et al. [58]. However, this difference can be explained by the granulometry of the aggregate. The higher the granulometry of the aggregates, the higher the diameter of the bubbles, whereas the lower the granulometry, the smaller the diameter of the bubbles; moreover, nanoscale particles can form an interfacial layer between the water and the foaming agent, thereby increasing the resistance of the air-voids [38]. The distribution of the macropores is shown in Figure 6.  
Compressive strength was the parameter chosen to define which composition presents the best result, thereby seeking to comply with the Brazilian standard NBR 13438 [32] for ACCB. Figure 7 shows the results of the compressive strength tests. NBR 13438 requires that the compressive strength should have values of at least 1.2 to 1.5 MPa at densities between 400 and 600 kg/m\({}^{3}\)[32]. In addition to the curing process, the result of the compressive strength is also a function of the density and the physicochemical composition of the mixture. For these tests, the variation of the added foam mass defines what the density and compressive strength will be, since besides  
Figure 6: Typical binary images for the sixth mix (Table 2). Figure 7: Compressive strength as a function of foam amount for different w/c rates. the direct introduction of the air-voids, this leads to an increased water content of the mixture because the preformed foam consists of about 98.6% water. These findings agree with previous works [31; 52]. The best value obtained for compressive strength was 1.07 MPa for AFCB with 1.28 w/c and 18% foam (test six--Table 2) by mass added. It was observed that the tests with a w/c of 1.08 did not develop a homogeneous mass prior to adding the preformed foam, due to a lack of hydration. On the other hand, the tests with respect to a w/c of 1.48 showed a high fluidity due to the excess of hydration water. As observed, the compression strength results are below the limit of the Brazilian standard (NBR 13438), in which the minimum value of compressive strength is 1.2 MPa, but this rule is just for ACCB. These lower results (AFCB) can be explained by the absence of a controlled cure process (autoclave), where the literature states that curing at pressure and temperature produce hydration products from the strongest cements [36; 37]. Also, the high amount of cadmium oxide in the cement, generated by the addition of furnace slag in cement manufacture, may have influenced the decrease in compressive strength.
# A New Online Service for the Validation of Multi-GNSS Orbits Using SLR
## 1 Introduction
### The Objective and Structure of This Paper  
This paper describes the functionality and methodology of computations offered by IGG ILRS AAC. The developed service called GOVUS is focused on generating scientific products based on the SLR observations to the GNSS satellites on the operational basis in the near real-time mode. Section 2 provides the core information about the components of the system with a description of the computational algorithms. Furthermore, it describes the possible range of use with regard to the needs and expectations for SLR tracking of GNSS built up by the scientific community, especially in the context of improving the MGEX products. Section 3 provides sample analyses performed using the publicly available dataset and tools delivered by GOVUS. The analyses concern mainly the assessment of overall multi-GNSS orbit quality. The conclusions and the overview of prospects for the multi-GNSS community resulting from GOVUS are drawn in the last section.
## 3 DC Bus Voltage Control Strategy
### Active Disturbance Rejection Controller Combined with Adaptive Filter Structure  
In order to achieve precise control of the DC-side voltage, it can be seen from the first-order LADRC control structure that the discrepancy among the reference and estimated variables must be addressed prior to the proportional section of the control framework. Therefore, an adaptive filter part is introduced before the proportional section to filter the deviation signal, reducing signal fluctuations during the input filtering stage, and thus minimizing the impact on DC-side voltage control [28]. Figure 5 shows the adaptive filter combined with linear active disturbance rejection control (A-LADRC).  
The specific internal structure of the adaptive filter is shown in Figure 6.  
Figure 4: LADRC control structure block diagram.  
Figure 5: A-LADRC structure block diagram.  
Figure 6: Adaptive filter structure.  
In the above model, \(x(n)\) is the input signal, \(y(n)\) is the output signal, and \(h(n)\) represents the filter weights. As shown in figure, the filter handles the input by assigning weights, with the weights in the predictor being updated iteratively through an adaptive algorithm.  
The weight vector is denoted as:  
\[H(n)=\left[h_{1}(n),h_{2}(n),\ldots,h_{D-1}(n)\right]^{T} \tag{12}\]  
The input vector is denoted as:  
\[X(n)=\left[x_{1}(n),x_{2}(n),\ldots,x_{n}(n)\right]^{T} \tag{13}\]  
By setting the initial values of the weight vector \(h(n)\) and the sampling signal \(x(n)\), the desired signal \(u_{dc}^{*}\) is determined, and the output \(y(n)\) is calculated as follows:  
\[y(n)=\sum_{n=0}^{D-1}h(n)x(n-m+D) \tag{14}\]  
We define the target output of the filter as \(d(n)\), the difference between the target output and the predicted output is the error, as follows:  
\[e(n)=u_{dc}(n)-z_{1}(n) \tag{15}\]  
From the relationship between the output and input of the filter, it can be observed that as the error signal of the filter continuously approaches zero, the filtering effect of the deviation signal can be effectively guaranteed, minimizing influence of disturbances on the system. The weight update process of the filter is associated with the adaptive algorithm module in the model. Among these algorithms, the LMS algorithm is the most widely used due to its simple structure, strong robustness, and ease of implementation [29; 30].  
\[\
abla e^{2}(n)=-2e(n)x(n) \tag{16}\]  
Using the gradient descent method, the weight vector can be updated as:  
\[h(n+1)=h(n)+\mu e(n)x(n) \tag{17}\]  
The control strategy for the DC side voltage using the LADRC method is as follows:  
\[\left\{\begin{array}{l}\dot{z}_{1}(n)=z_{2}(n)-\beta_{1}[z_{1}(n)-u_{dc}(n) ]+b_{u0}i_{d}^{*}(n)\\ \dot{z}_{2}(n)=-\beta_{2}[z_{1}(n)-u_{dc}(n)]\\ x(n)=u_{ref}(n)-z_{1}(n)\\ y(n)=h^{T}(n)X(n)\\ e(n)=\left[u_{ref}(n)-z_{1}(n)\right]-y(n)\\ h(n+1)=h(n)+\mu e(n)X(n)\\ u_{0}(n)=k_{p}\cdot y(n)\\ i_{d}^{*}(n)=\left[u_{0}(n)-z_{2}(n)\right]/b_{0}\end{array}\right. \tag{18}\]
# A Survey of the Applications of Text Mining for the Food Domain
## 6 Text Mining in Recipe Recommendation and Personalized Nutrition
### Food and Nutritional Sciences  
In the contemporary era, global health concerns are increasingly linked to diet-related chronic diseases resulting from deviations from nutritional norms. Scholarly efforts are underway to establish correlations between individuals' nutritional profiles and overall wellness [89]. The field of food science and nutrition has generated a vast amount of textual data, both structured and unstructured, ready for analysis through advanced text mining methodologies to extract key insights on dietary and nutritional knowledge. Recent research emphasizes the adoption of a quantitative index reflecting food processing levels and physicochemical attributes, moving away from traditional nutrient-centric approaches [103].  
Text mining is instrumental in examining documentary sources such as dietary regulations and nutrition facts labels, automating the extraction of essential knowledge on the interrelation among food, nutrients, and diseases. Pioneering work by [PERSON] et al. [104] introduced the Nutritional Epidemiology Ontology (ONE), a framework based on normative guidelines, enriching comprehensive knowledge graphs for intelligent querying and decision support. Text mining also serves as a valuable tool for synthesizing and distilling nutrition research literature, providing scholars with expedited access to scientific discoveries, enhancing research productivity. Additionally, text mining aids in scrutinizing nutrient composition studies, as exemplified by [PERSON] et al. [105], who employed text mining to analyze gluten-free versus gluten-laden provisions. This investigation highlights the compositional nuances and constraints of gluten-free products, emphasizing the need for individuals with celiac disease to be vigilant about nutritional content. [PERSON] et al. [106] used digital datasets to elucidate Londoners' dietary patterns and their link to metabolic syndrome-associated pathologies, revealing counterintuitive findings.  
There is much promise in text mining technology for analyzing scientific data related to nutrition and food. Its capabilities hold the potential to spur new discoveries, improve the effectiveness of research, and create more intelligent knowledge-based services, all of which will advance empirical illumination in the fields of food science and nutritional practices.