esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Influencing Factors of Cropland and Grassland during Drought Period Based on Geodetector  
According to the temporal and spatial variation of the PEI during drought, the photosynthetic efficiency of grassland and farmland ecosystems is significantly different in response to drought in the southwest. We used geographical detectors based on statistical methods to reveal the variability of different meteorological factors on the photosynthetic efficiency of farmland and grassland ecosystems.  
The results of the geodetector showed that the photosynthetic efficiency of grassland ecosystems was more susceptible to drought. The correlation between temperature and the VPD and PEI could reach 0.75 and 0.65, indicating that grassland would respond to a higher VPD caused by increasing temperature and decreasing precipitation in southwest China. On the contrary, the cropland ecosystem was insensitive to drought, and the correlation between photosynthetic efficiency and temperature/VPD was only about 0.2 (Figure 9). The lag effect of soil moisture on drought resulted in a low correlation with light and efficiency.  
Figure 8: Comparison of PEI regional mean values across different ecosystems during drought (including farmland, forestland, grassland, and shrubland).  
Figure 9: The \(q\) value of the individual factors on the cropland and grassland ecosystem during the extreme drought in Southwest China. (These meteorological factors include the following: T: temperature, VPD: vapor pressure deficit, PAR: photosynthetically active radiation, RH: relative humidity, P: precipitation, SM: soil moisture).  
The explanatory power of a single factor is usually limited because photosynthetic efficiency in an ecosystem is affected by multiple factors. We leveraged the interaction in the geodetector tool to detect the strength of the combined effect of two factors on photosynthetic efficiency in the southwest region. The detection results of the interaction of farmland and grassland ecosystems are shown in Figure 10. Generally speaking, if the interaction of two independent influencing factors is more potent than that of a single individual factor [q(X\({}_{1}\cap\)X\({}_{2}\)) > q(X1) + q(X\({}_{2}\)), where \(\cap\) denotes the interaction between X\({}_{1}\) and X\({}_{2}\)], it indicates that the interpretation of the interaction effect on the distribution of light and efficiency in southwest China will be further enhanced.  
Among these interactions, the strength between temperature and other factors in the grassland ecosystem showed a strong explanatory power, in which T\(\cap\)RH reached the highest level (\(q\) = 0.77), followed by T\(\cap\)PAR (\(q\) = 0.76), which indicated that temperature had a great impact on the distribution of grassland photosynthetic efficiency in southwest China. The radiation conditions can also affect the photosynthesis efficiency of vegetation. On the contrary, the explanatory strength of various interactions in the farmland ecosystem is weak, and PAR\(\cap\)RH only reaches the level of \(q\) = 0.24.
# Parallel Complex Quadrature Spatial Modulation
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ABEP & Average bit error probability \\ BER & Bit error rate \\ CQSM & Complex quadrature spatial modulation \\ IQSM & Improved quadrature spatial modulation \\ IM & Index modulation \\ MA-SM & Multiple active spatial modulation \\ MIMO & Multiple-input multiple-output \\ ML & Maximum likelihood \\ PCQSM & Parallel complex quadrature spatial modulation \\ PQSM & Parallel quadrature spatial modulation \\ PSM & Parallel spatial modulation \\ PEP & Pairwise-error probability \\ PSK & Phase shift keying \\ QAM & Quadrature amplitude modulation \\ QSM & Quadrature spatial modulation \\ RF & Radio frequency \\ SM & Spatial modulation \\ SNR & Signal to noise ratio \\ \end{tabular}
# Optimal Harmonic Mitigation in Distribution Systems with Inverter Based Distributed Generation
## 2 Problem Formulation
### Objective Functions
#### 2.1.2 Improving the Voltage Profile (f\({}_{2}\))  
This objective function can be described as [22]:  
\[f_{2}=\min\sum_{i=1}^{N}\left(\frac{V_{i}-V_{i}^{spec}}{V_{i}^{\max}-V_{i}^{ \min}}\right)^{2} \tag{2}\]  
where \(V_{i}\) is the voltage of bus \(i\), \(V_{i}^{spec}\) is the Specified voltage magnitude (1.0 p.u), \(V_{i}^{max}\) and \(V_{i}^{min}\) are the maximum and minimum fundamental voltage at bus \(i\).  
1.3 Minimizing the Cost of the Total Active Power Loss and That of the Filter Investment Cost (f\({}_{3}\))  
\[f_{3}=\min[k_{p}\times P_{loss}+C_{filter}] \tag{3}\]  
where \(k_{p}\) is the annual cost per unit of the real power loss (\(\$/\mathrm{kW}/\mathrm{year}\)); \(C_{filter}\) is the filter investment cost; \(P_{loss}\) is the active power loss which can be described as [31]:  
\[P_{loss}=\sum_{i=1}^{nb}P_{loss}^{1}\;\;+\sum_{i=1}^{nb}\sum_{h=3}^{h_{max}}P _{loss}^{h} \tag{4}\]  
where \(nb\) is the number of branches and \(h_{max}\) is the highest harmonic order of interest.  
\(C_{filter}\) can be calculated as follows [25]:  
\[C_{filter}=\sum_{i=1}^{nf}\left(k_{C}\times Q_{C_{i}}+k_{L}\times Q_{L_{i}}+k_{R} \times P_{R_{i}}\right) \tag{5}\]  
in which \(nf\) is the total number of filters, \(k_{c}\), \(k_{L}\), and \(k_{R}\) are the cost coefficients of the filter capacitor, inductor, and resistor, respectively, which are equals and equal to 3 \(\$/\)kVAR [32].  
This optimization problem is considered as a multi-objective optimization, which can be implemented using the weighting factors approach. So, the normalized fitness function (\(FF\)) can be formulated as:  
\[FF=\min(k_{1}\frac{f_{1}}{f_{1\max}}+k_{2}\frac{f_{2}}{f_{2\max}}+k_{3}\frac{f_ {3}}{f_{3\max}}) \tag{6}\]  
where, \(k_{1}\), \(k_{2}\), and \(k_{3}\) are the weighting factors.
## 4 The Proposed Selective Retransmission Mechanism
### Image Quality
After the performed analyses and mathematical verifications, we concluded that if the application cannot afford higher loss of quality of the reconstructed images, the semi-reliable transmission service should not be considered for the application, leading source nodes to apply only 1-level 2D DWT over original images. As a final comment, one may argue that if the application can afford some packet loss, it is better to discard packet at the source, avoiding transmission of less relevant information. However, such an approach would result in low quality images even when the packet error rates of the links are low. The proposed selective retransmission mechanism is more adequate to real-world applications where errors happen as burst, saving energy and keeping an acceptable average image quality over time.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 5 Methods for Improving Photocatalytic Efficiency
### Surface Dye Photosensitization
In the study of pollutant degradation, it was found that the photosensitization photocatalyst prepared by using Tween 20 surfactant had the best degradation performance, and the degradation rate of methyl orange (MB) dye (10 mg/L) by the photosensitization photocatalyst reached 60.3% under 150 min of visible light irradiation. [PERSON] et al. [330] employed a deposition method to cure the novel Cu(II)-phthalocyanine derivative on the surface of TiO\({}_{2}\) nanoparticles to achieve a photosensitization modification. The results showed that photosensitization has little effect on the morphology of TiO\({}_{2}\), but photosensitized TiO\({}_{2}\) enhances the response to visible light. Under visible light, the bactericidal capacity of photosensitized TiO\({}_{2}\) and the conversion rate of photosensitized TiO\({}_{2}\) to Cr\({}^{\beta+}\) ions are much higher than those of a pure TiO\({}_{2}\) photocatalyst. As a traditional modification method, surface dye photosensitization has been well developed in its development and application. One frequently used method of sensitization is to apply many dyes to the surface of the semiconductor. This method achieves good results in sensitization applications for nanoparticle solar cells, and there are articles about this method in famous journals such as _Nature_[331; 332]. While the photosensitization of surface dyes can improve the photo responsive range and photocatalytic efficiency of photosensitized semiconductor catalysts, the photosensitization of surface dyes also has some drawbacks. First, the presence of surface photosensizers limits the increase in photocatalytic efficiency of semiconductor photocatalysts. Second, the stability of photosensizers for long-term use is questioned. Photosensitizers can fall off from the surface of the catalyst and can result in the secondary contamination to water. Furthermore, some photosensitizing dyes, such as ruthenium-based dyes, are expensive and not suitable for a wide range of applications. In Table 3, we summarize both the advantages and disadvantages of the above modification methods.
## 3 Simulation Results and Discussion
### Simulation Results
#### 3.3.3 Effect of Scanning Speed on the Ablation Depth of Different Micro-Structure  
The total ablation depth in this model is determined by both the energy and the rate of spot overlap. As previously stated, it is difficult to significantly increase the material removal rate by increasing the energy, so it can be investigated by increasing the spot overlap rate. One of the simplest methods, as shown in Equation (1), changes the spot overlap rate by adjusting the scanning speed to change the effective number of pulses at a single spot. Figure 7a depicts the total ablation depth at various scanning speeds. It has been discovered that slowing down the scanning speed can effectively increase the ablation depth at each point, and the ablation of the material can reach more than 8 \(\upmu\)m at 100 mm/s, with the ablation depth increasing as one moves away from the upper surface of the specimen. It is demonstrated that slowing down the scanning speed increases the ablation rate and thus the material removal rate. Meanwhile, the rate of change in the total ablation depth, as shown in Figure 7b, indicates that the rate of change is faster at low scanning speed. When a specific roughness is required at a faster rate, we can use a low scanning speed and then adjust the single pulse energy or defocus amount for precise control later.
# GPU Parallel Implementation for Real-Time Feature Extraction of Hyperspectral Images
## 3 GPU Parallel Implementation Based on CUDA
### Reduction  
The reduction is a typical parallel algorithm that uses a binary compound combination law operator to generate \(O\)\((1)\) results for the incoming \(O\)\((N)\) input data. In addition, one of CUDA's efficient strategies is the use of shared memory. When one block starts executing, GPU will allocate a certain amount of shared memory, and the address space of shared memory will be shared by all threads in the block, which can significantly reduce the global memory bandwidth required by the kernel.  
According to a large amount of hyperspectral data, we set up a two-level reduction for the calculation of the sum of each column of the matrix. When inputting a matrix of \(M\) rows and \(N\) columns (\(M>>N\)), each thread loads one element from global to shared memory. For first level reduction block, which is specified by \(dim3\)\(dimBlock(block1,\ 1)\), there are threads whose number is block1, so that the grid dimension can be calculated as,  
\[dim3\;dimGrid((N/(block1))\ +\ (!(N\;\%\ (block1))\?\ 0\ :\ 1),\ M) \tag{2}\]  
Here, we allocate memory for first level reduction results, \(r1\) is a static floating-point pointer,  
\[static\;int\;r1 size\ =\ dimGrid.x*dimGrid.y \tag{3}\]  
\[cudaMalloc((void*)\&r1,\ sizeof(float)*r1 size) \tag{4}\]  
Then, we perform the second level reduction on the results of the first level with the pointer r1 as input. Threads organization of the second level reduction can be set as,  
\[dim3\;dimBlock2(block2,\ 1) \tag{5}\]  
\[dim3\;dimGrid2((dimGrid.x/(block2))\ +\ (!(dimGrid.x\ \%\ (block2))\?\ 0\ :\ 1)\,\ M \tag{6}\]  
The design using two-level reduction can effectively improve the calculation efficiency when considering large hyperspectral data and limited shared memory resources.
# Simulation Study on Nanofluid Heat Transfer in Immersion Liquid-Cooled Server
## 4 Conclusions  
In this paper, a liquid-cooled server is used as the research object, and the computational fluid dynamics method is used to simulate the flow heat transfer of five types of nanofluid, respectively, using coolant FC-40 heat transfer as the reference benchmark. The following conclusions are drawn:  
1. The nanofluid has greater thermal conductivity compared to the base fluid, which can carry away more heat from electronic core components under the same working conditions.
2. Among the five types of nanofluid flow heat transfer, Al-FC40 nanofluid has the best comprehensive heat transfer effect. The lowest temperature of the GPU surface is 326.5 K; the lowest temperature of the chip heat sink surface is 347.8 K; the highest temperature of the server as a whole is 392.4 K; and the temperature of the outlet is 385.6 K. The frictional resistance coefficient/ of the Al-FC40 nanofluid is the smallest under the same working condition.
3. With an increase in \(\alpha\) and the acceleration of \(u\), the surface temperature of the server components can be effectively reduced. However, it is unrealistic to expect an improvement in heat transfer performance by increasing the volume fraction of nanoparticles and increasing the inlet flow rate of the server without restriction, which will not only increase the energy consumption of nanofluid transport, but will also make it difficult to maintain uniform stability of nanoparticles in the nanofluid.  
The results of the above study can provide a technical-level reference for the application of nanofluid in liquid-cooled data centers. Nanofluid cooling as an innovative cooling technology provides an attractive and promising option for liquid-cooled data centers. In this paper, simulation software is used to conduct an experimental study of nanofluid cooling applied to submerged liquid cooling; however, the actual effect of its application needs to be further verified in practice by building an experimental platform, and at the same time, factors such as its cost, maintenance requirements, and environmental sustainability need to be considered.  
Conceptualization, S.W. and G.C.; methodology, S.W. and G.C.; software, S.W.; validation, S.W., Q.W. and Y.L.; formal analysis, S.W.; investigation, Q.W.; resources, Y.L.; data curation, Q.W. and Y.L.; writing-original draft preparation, S.W. and G.C.; writing-review and editing, S.W.; visualization, Q.W. and Y.L.; supervision, S.W. and G.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 12: Changes in \(Nu\) and \(f\) with \(u\).
# A Critical Review of Flood Risk Management and the Selection of Suitable Measures
## 3 Risk-Based Flood Management
### Software Tools  
Several tools/software programs have been developed to carry out flood simulations in order to estimate flood damage/risk. The methodology/approaches of these tools vary at a large scale, extending from hydrodynamic modeling to statistical, artificial intelligence-based, and even stage-damage or discharge damage empirical models. The majority of tools are hydrodynamic models that simulate the flood wave in 1D, 2D, and 3D [74; 75]. These include models that have compatibility with geographic information systems (GIS) [76]. A few tools have additional extensions/modules to perform statistical analysis and damage calculations as well. The inclusion of GIS capabilities has become more popular in recent years in hydrodynamic models. However, damage estimation tools recognize GIS capabilities on a larger scale. Most common software takes basic hydrologic, hydraulic, and topographical inputs to simulate flooding, while others estimate the flood damage considering the depth of flood and land-uses parameters in an empirical way. Table 3 lists a few software tools, along with their capability for GIS and country of origin.
## 4 Results
### High-Sensitivity Acquisition Latency  
The authors assessed the latency of the two-stage acquisition scheme by conducting evaluations with both a TCXO and an OCXO, using the test setup described in Section 4.1. The variable attenuator was used to decrease the \(C/N_{0}\) of the received signals to as low as 20 dB-Hz. The GESTAT testbed rooftop GNSS antenna facility [46] was used for the tests. The acquisition latency was assessed using the procedure shown in Table 9. The software was temporarily modified to measure the worst-case acquisition latency as follows: the acquisition process consistently performed seven GPDIT iterations, with the receiver recording the acquisition latency during each execution. The latency was measured as the time it takes for the receiver to execute each acquisition stage.
## 3 Results
### Probiotics and Vitamin B6 Affect the Fecal Metabolome of LI Patients  
We investigated the differences within fecal volatile organic compounds (VOCs) in the ZR group compared with the PL group. VOCs identified from fecal samples (62 compounds) were grouped according to chemical classes, that is, alcohols (12), aldehydes (5), aromatic heterocyclic (2), hydrocarbons (8), ketones (8), short and medium chain fatty acids (SCFA) (10), sulfur compounds (2), esters and methyl esters (1), and terpen (14). The content of the metabolites within the same group largely varied; nevertheless, some significant differences were figured out between the two conditions (Table 2). Compared with PL, some compounds (acetic acid, 2-methyl-propanoic acid, nonenal, and indolizine 3-methyl) were found at significantly (\(p<0.05\)) higher levels in the feces of ZR. Within significantly different alcohols (\(p<0.05\)), alcohols 1-butanol, phenol, and 1-hexadecanol showed the highest level in the fecal samples of PL. Between the aldehydes compounds, hexanal was found at the highest levels in the feces samples of PL. Terpens were variously distributed within groups, except for limone, which was significantly higher in the PL group.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compounds** & **ZR** & **PL** & \(p\)**-value** \\ \hline
1-Butanol & 0 (0, 0) & 11.22 (0, 12.58) & 0.037 \\ \hline
5-Hepten-2-ol, 6-methyl- & 51.74 (5.98, 72.3) & 30.81 (0.79, 39.28) & 0.029 \\ \hline Phenol & 4.12 (0, 8.56) & 19.78 (3.48, 28.56) & 0.048 \\ \hline
1-Hexadecanol & 4.03 (0, 6.29) & 8.17 (0, 13.94) & 0.043 \\ \hline Acetic Acid & 44.53 (26.62, 63.82) & 23.34 (7.72, 37.47) & 0.042 \\ \hline Propanoic Acid, 2-methyl- & 29.78 (13.4, 45.04) & 11.26 (0.71, 17.35) & 0.009 \\ \hline Hexanal & 5.81 (0, 13.22) & 11.3 (2, 19.38) & 0.009 \\ \hline
2-Nonenal & 5.38 (2.24, 8.9) & 3.35 (0, 4.34) & 0.045 \\ \hline Indolizine, 3-methyl- & 248.96 (87.94, 372.62) & 99.53 (31, 93.91) & 0.045 \\ \hline
5-Hepten-2-One, 6-methyl- & 310.76 (55.31, 396.75) & 124.03 (19.91, 210.84) & 0.048 \\ \hline D-Limonene & 87.43 (11.19, 187.9) & 483.68 (55.46, 804.44) & 0.042 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Volatile organic compounds (VOCs). Concentration (min, max) of VOCs (ppm) headspace fecal samples of treated (ZR) and placebo (PL) patients.  
Figure 5: Relative proportions (percent) of genera showing significant (\(p<0.05\)) differences between the fecal samples of treated patients (ZR) and those of placebo (PL) patients.
## 1 Introduction  
Endodontic success is strictly related to the removal of pulp tissue and to the disinfection of root canal system that may be colonized by microorganisms [1]. Irrigation is reported to have an important antimicrobial/antibiofilm effect through the whole root canal, as well as the ability to remove the smear layer and/or debris produced by mechanical instrumentation [2, 3]. Moreover, the use of irrigants may enhance the cutting ability of endodontic files decreasing their friction with dental tissue as well as the risk of fractures [3, 4].  
The irrigation procedure in endodontics is clinically based on sodium hypochlorite (NaOCI), due to its antibacterial effects and ability to dissolve the organic tissue, coupled with ethylenediaminetetraacetic acid (EDTA), used to remove the smear layer. Finally, the rinsing agents may be used with different effective techniques to promote deep penetration into the entire anatomical aspect of the endodontic canal system, even those not reached by mechanical instrumentation [5]. The correct combination of endodontic irrigants and mechanical preparation techniques would contribute to provide an ideal cleaning [6].  
Temperature has a positive effect on the antibacterial and lytic action of NaOCl [7]; indeed, an enhancement in the temperature of NaOCl solution would increase the cleaning potential as well as the decontamination effect of the root canal system [7]. In addition, hot NaCl solution removes organic debris from dentin crisps better than unheated solution [8]. NaOCl at 25 \({}^{\circ}\)C used for five minutes was demonstrated to be effective in the elimination of _Enterococcus faecalis_, showing a 100-fold increase in its efficacy [9]. In addition, preheating to 37 \({}^{\circ}\)C enhances NaOCl effectiveness on _Candida albicans_ at five minutes of contact time showing a significant antifungal property [9]. Moreover, heating of NaOCl may allow the use of a less concentrated solution; indeed, the ability of NaOCl to dissolve human dental pulp at 1% and at a temperature of 45 \({}^{\circ}\)C is reported to be equivalent to that at 5.25% and 20 \({}^{\circ}\)C [10]. The enhancement of the temperature of NaOCl may be obtained through extraoral (in special containers or by preheating of a syringe), intracanal using heat carriers (pluggers), or by means of ultrasonic inserts or laser devices.  
Therefore, the aim of the present in vitro investigation was to compare the efficacy of different methods of NaOCl heating by evaluating the temperature profiles developed at different levels of the root canal system.