esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results  
### Study System  
This section numerically illustrates the performance of the proposed model on a CVPP test system, which has two RES units: one PV power station and one wind farm with installed capacities of 25 MW and 30 MW, respectively. The CVPP also comprises one ESS with a rated inverter power/energy capacity of 10 MW/40 MWh and a charge/discharge efficiency of 0.95. At the start of the 24-h scheduling horizon, the ESS energy level is assumed to be always 50% of its rated capacity. The customers within CVPP have an aggregated peak capacity of up to 35 MW. Overall, the CVPP can fully satisfy its customers based on the internal RES and ESS.  
In Figure 6, the red lines illustrate the typical daily profile curves of PV, WP, and demand, treated as the day-ahead forecast data. The light gray areas describe 99.7% confidence intervals of forecast data, assuming that the forecast errors of all resources and customers follow the normal distribution with means of 0 and standard deviations of 5%. With these forecast data, ten scenarios are generated as follows:  
* The Monte Carlo simulation is applied to generate 1000 samples from the distribution functions of uncertain parameters.
* Use the K-means method to divide these samples into ten clusters. The centroid of each cluster is treated as a scenario with a probability equal to the total probability of all samples in the corresponding cluster.  
The dark gray lines in Figure 6 describe the scenarios of each resource and demand; for easy observation, this figure shows only five scenarios out of the ten created.  
Figure 7 depicts the DA price forecast used for the CVPP test case. In the BE market, the CVPP can provide upward reserve energy with a price equal to 1.5 times the DA price, while the downward reserve energy price is 0.6 times the DA price.  
On the other hand, the proposed model considers that CVPP must satisfy the pre-existing BC contract. It is assumed that CVPP needs to ensure an upward reserve capacity of at least 3 MW, while the probability of reserve activation can vary from 1% to 10%. To evaluate the effectiveness of the proposed model, this study considers two plans for the BC contract, as shown in Table 2. In this table, the light orange cell with 1 is when CVPP has a pre-signed BC contract, while the white cell with 0 is the period without a BC contract. The simulations are conducted on a 64-bit core i5.1.9 GHz personal computer with 16 GB RAM using GAMS 40.3.0 software [51] and the CPLEX 12.10.0 solver [52].  
Figure 6: The forecast data of PV, WP, and demand for the CVPP test case.  
Figure 7: The DA price forecast for the CVPP test case.
# Development of Highly Sensitive Temperature Microsensors for Localized Measurements
## 3 Results and Discussion  
### Excitation Current  
According to the previous numerical simulations, the current for excitation the RTD sensor must be small to avoid self-heating. For the required resolution, a current of 100 \(\upmu\)A provides a negligible self-heating. Lower currents (10 and 40 \(\upmu\)A) were also experimentally studied to understand their influence on the 24 windings RTD sensitivity as lower currents give an even lower self-heating and lower power consumption allowing long-term monitoring. However, as expected, a considerable reduction on the sensitivity was achieved (Figure 9) and thus, the choice focused on 100 \(\upmu\)A for the following tests.
## 3 Building Energy Consumption Forecasting
### Objectives of Forecasting in Buildings  
Forecasting in buildings aims to achieve different objectives (see Figure 1) such as: (a) confirm and measure the impact of energy-saving activities [19] and once the guidelines have been drawn up and implemented to obtain reductions in energy consumption, this objective pursues to validate that these guidelines have the expected results; (b) assume the energy needs considering normal requirements, which seeks to anticipate events that may modify the usual energy consumption of the systems, such as a sudden change in temperature that may affect the occupant's comfort; and (c) indicate the variations in energy consumption of events at specific times; once an event that may alter energy consumption has occurred, this objective pursues to determine how much the variation in energy consumption would be. These forecasts can be used in the organization and distribution of goods to meet the need [20].  
Building energy consumption forecasting is critical for building performance enhancements, energy management and reserves, detecting system faults and optimization of intelligent building process [21,22]. It is commonly a noteworthy segment to permit the control framework to decide ideal control settings to accomplish energy or cost investment funds. The exactness of energy use estimation legitimately impacts the general control execution and energy/cost investment funds [23]. Consequently, the more precise the prediction, the more prominent the advantages the building proprietors procure. Despite what might be expected, a vulnerability in the estimation lessens the space for efficient energy management, which brings about pointless expenses. Thus, it is important to improve energy utilization forecasting [24].
# Multi-Label Weighted Contrastive Cross-Modal Hashing
## 4 Experiment
### Ablation Study
#### 4.5.1 Effectiveness of Compact Consistent Similarity Representation  
We evaluate the effectiveness of the compact consistent similarity representation module. Before experiment, we vary MLWCH method by the following two ways. Firstly, we replace the multi-label similarity measurement with a naive manner that is commonly used by prevailing solutions: If the cross-modal samples share at least one label, the similarity is 1; otherwise, it is 0. For the sake of discussion, this variation is named SLWCH. Secondly, we replace the multi-label similarity matrix in MLWCH with the multi-label similarity matrix in Bi_NCMH [29] and keep other parts unchanged. This variation is termed MLBRH. Afterward, we compare the hashing performance of MLWCH with SLWCH and MLBRCH  
Figure 5: Precision-recall curves on MIRFLICKR-25K, NUS-WIDE, and MS COCO datasets. The code length is 32 bits.  
Figure 6: Precision-recall curves on MIRFLICKR-25K, NUS-WIDE, and MS COCO datasets. The code length is 64 bits.  
on MIRFLICKR-25K, NUS-WIDE, and MS COCO. The corresponding MAP@50 values for hash code lengths of 16, 32, and 64 on the three datasets are presented in Tables 5, 6, and 7, respectively.  
As can be seen from Tables 5-7, on one hand, the MAP@50 of MLBRH and MLWCH are higher than that of SLWCH in all cases, which indicates that compared with single-label semantic similarity, the complex semantic relationships between instances are more accurately captured by multi-label semantic similarity. On the other hand, the performance of MLWCH is overall superior to MLBRH on three datasets, which confirms that the proposed compact consistent similarity representation module efficaciously improves the accuracy of cross-modal hashing retrieval. This is mainly because the proposed technique is instrumental in modeling sparse multi-labels so as to capture the semantic similarities between instances more accurately, which is a characteristic not possessed by Bi_NCMH [29], in contrast.
## 4 Raman Spectroscopy and Hepatitis Diagnosis
### Strategies Based on Methodology
#### 4.2.3 Immunoassays and Biosensors
The hairpins were stoichiometrically optimized to form a stable three-way junction (3 WJ) for enzyme-free isotherm amplification of the targeted DNA. Malachite green isothiamate (MGITC) was used as the Raman reporter for the SERS signal generation. The developed system targeted the 23-nucleotide sequence within the HCV's 5'-UTR, essential for RNA replication, facilitating pan-genotypic HCV detection that complemented direct-acting antiviral strategies. An HCV RNA concentration as low as 1.706 fM was measured by obtaining a 10-fold increase in sensitivity. This system is capable of the early-stage detection of HCV infections with an extremely low limit of detection for HCV RNA. A schematic illustration of this amplification process based on a hairpin mechanism is shown in Figure 3.
## 3 Results and Discussion
### Streamflow Simulation Scenarios and Results  
The widely used approach for addressing the basin-scale discrepancies between ground- and reanalysis/satellite-based precipitation estimates is to use them to drive hydrological models, subsequently evaluating the model performance against observational flood records [7]. Nevertheless, the error of precipitation inputs may result in substantial uncertainty in future flood forecasting. In this section, we compare the effects of various precipitation estimates for hydrological modeling at the outlets of the Beijing and Dongjiang River Basins. Three scenarios were assessed with regard to their day-to-day streamflow simulations and comparisons.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 4 Optimizing Ground Station Systems Through Numerical Design
### The Parameters for Calculating the Capabilities of the Ground Station
#### 4.2.2 Carrier-to-Noise Ratio (C/N)  
The carrier-to-noise ratio (C/N) can be determined the quality of the transmitted information and whether it can be retrieved properly or not. Equation (4) determines the carrier-to-noise ratio of the ground receiver system [19], where \(EIRP\) is the effective  
isotropic radiated power of a satellite in dBW; \(L_{p}\) is the path loss; \(k\) is Boltzmann's constant, equal to \(-228.6\,\mathrm{dBW/K/Hz}\); and \(B\) is the logarithmic value of the bandwidth in dBHz.  
\[\frac{C}{N}=EIRP-L_{p}+\frac{G}{T}-k-B \tag{4}\]
# Shear Wave Velocity Prediction Based on the Long Short-Term Memory Network with Attention Mechanism
## 4 Prediction and Discuss  
### Evaluation Indicators  
In this study, the average absolute error (\(MAE\)) between the actual value and the predicted value is selected as the loss optimization function of the network structure. In addition, the adaptive momentum optimization algorithm (Adam) is used as an optimization algorithm for the backpropagation descending gradient of the network. The prediction performance of the proposed network in this paper is evaluated by the mean absolute error (\(MAE\)) and coefficient of determination (\(R^{2}\)). The formula for \(MAE\) and \(R^{2}\) can be expressed as follows:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|m_{i}-\hat{m}_{i}| \tag{12}\]  
Figure 9: Plot of various relevant variables versus shear wave velocity (Well A).  
\[R^{2}=\frac{\sum\limits_{i=1}^{n}{(\hat{m}_{i}-\overline{m}_{i})^{2}}}{\sum \limits_{i=1}^{n}{(m_{i}-\overline{m}_{i})^{2}}} \tag{13}\]  
where \(m_{i}\) is the measured value, \(\hat{m}_{i}\) is the estimated result, \(n\) is the sample size, and \(\overline{m}_{i}\) is the average of the measured values.
# Recent Advances and Insights into Bromelain Processing, Pharmacokinetics and Therapeutic Uses
## 3 Extraction and Purification Methods
### Unconventional Approaches  
Although conventional methods are frequently and widely used both in laboratories and on an industrial scale, they often present limitations, principally in terms of yield, purification and processing costs. Modern techniques permit the above-mentioned conventional approaches to be used in association with unconventional processes, including gel filtration, aqueous two-phase extraction, extraction via reversed micelles, ion exchange and affinity chromatography. The use of both types of approaches allows an initial pre-purification step, which concentrates the enzymatic proteases to be performed with a subsequent step to remove the remaining impurities [5]. The most appropriate technique for the isolation of bromelain also depends on the intended application of the purified enzyme. Nevertheless, a combination of low-resolution (filtration and liquid-liquid extraction) and high-resolution techniques (chromatography) is probably the best approach to limit costs, provide high yields and low-impurity extracts and to retain enzymatic activity.
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## Appendix A Model Description  
### Dynamic of Surface Water  
When the rain falls to the ground, one part is left on the ground and the other part will infiltrate into the soil. The infiltration rate is based on the surface vegetation biomass because the vegetation reduces the surface crust and the root system increases the fraction sites of macro-pore sites near the soil surface. The dynamic of the surface water can be characterized as:  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Dryland Areas of China** & **Dryland Areas of America** \\ \hline
1 & Most areas show a green trend over the past decades. However, there exists desertification in Xinjiang, Qinghai and Inner Mongolia of China. & 1 & Most areas show a green trend over the past decades. Desertification mainly exists in Colorado, Kansas and California. \\ \hline
2 & The most dominant driver for vegetation changes in Chinese dryland areas is CC. China is undergoing fast development. Urbanization has changed most areas from a natural state to cities. & 2 & The most dominant driver for America is CO\({}_{2}\) fertilization effects. \\ \hline
3 & The main driver for positive trends is CC. However, the primary driver for desertification is LU. & 3 & The primary factor for positive trends is the CO\({}_{2}\) fertilization effect. For desertification, LU is the primary factor. Overgrazing is also an unignorable factor for desertification. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptions of detection and attribution on dryland areas in China and America.  
\[\frac{dS}{dt}=R-\alpha S\frac{P+k_{2}W_{o}}{P+k_{2}}. \tag{1}\]  
In which, R (mm/d) is the precipitation. \(\alpha\) (d\({}^{-1}\)) is the maximum infiltration rate. \(k_{2}\) (gm\({}^{-2}\)) is the water infiltration constant. \(W_{o}\) is dimensionless, characterizing a measure of the infiltration contrast between vegetated and bare soil.
# Application of Spinel and Hexagonal Ferrites in Heterogeneous Photocatalysis
## 4 TiO\({}_{2}\)-Based Magnetic Photocatalysts  
Titanium(IV) oxide (TiO\({}_{2}\), titania, titanium dioxide) is probably the most broadly investigated semiconductor photocatalyst due to various advantages, including high photocatalytic activity, stability, abundance, low price, and low toxicity [85; 86; 87; 88; 89]. However, the wide bandgap of titania, resulting in good redox properties (both oxidation and water reduction capabilities) is not recommended for light harvesting efficiency. Moreover, titania, as all semiconductors, suffers from charge carriers' recombination. In addition, the most active titania, i.e., that used in a particulate form, cannot be easily recycled, as already discussed in the introduction. Accordingly, various methods of titania modification, doping, coupling, and immobilizing have been proposed to improve its photocatalytic performance [90; 91; 92; 93; 94; 14; 90; 94]. Ferrites have also been proposed as titania modifiers for three  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Photocatalyst**} & **Pollutants** & **Experimental Conditions** & **Findings** & **Ref.** \\ \hline BaFe\({}_{12}\)O\({}_{19}\) & & & & 37\% deg. & \\ \hline BaFe\({}_{12}\)O\({}_{19}\) doped with Nb and Cu & MG & Pakistan sunlight; 882–890 W\(\cdot\)m\({}^{-2}\); & & [78] \\ \hline BaFe\({}_{12}\)O\({}_{19}\) & RXD & 250 W Hg lamp (\(\lambda_{\text{max}}\) 254 nm); 300 W Hal. lamp (350–1100 nm); pH 3–10; & 100\% deg. (Hg lamp); 90\% deg. (Hal. lamp) & [81] \\  & & 0.2–1.4 g\(\cdot\)L\({}^{-1}\); 40 ppm; 240 min & & \\ \hline SrFe\({}_{12}\)O\({}_{19}\) & MB & 20 W UV light; 50 ppm; 120 min & 46\% deg. & [82] \\ \hline CdFe\({}_{12}\)O\({}_{19}\) (with Fe\({}_{2}\)O\({}_{3}\) impurity) & & & 19\% deg. MO; 0\% deg. & \\ \cline{3-4}  & MO; MB; MV & 400 W UV light; 1 g\(\cdot\)L\({}^{-1}\); 5 ppm; 70 min & & MB; 0 deg. MV & [83] \\ \hline CdFe\({}_{12}\)O\({}_{19}\)/CdTiO\({}_{3}\) & & & 70\% deg. MO; 61\% deg. & \\ \cline{3-4}  & & & MB; 55\% deg. MV & \\ \hline Ba\({}_{1\text{-x}}\)Co\({}_{8}\)Fe\({}_{12\text{-y}}\)Cr\({}_{9}\)O\({}_{19}\) & CV &
\begin{tabular}{c} Bahawalpur (Pakistan) sunlight \\ (881–892 W\(\cdot\)m\({}^{-2}\)); 1 g\(\cdot\)L\({}^{-1}\); 10 ppm; 60 min \\ \end{tabular} & \(\sim\)65.5\% deg. & [84] \\ \hline \hline \multicolumn{4}{c}{CV—Crystal Violet dye; deg.—degradation; Hal.—halogen; MB—Methylene Blue; MG—Methyl Green; MO—Methyl Orange; MV—Methyl Violet RXD—hexogen} \\ \end{tabular}
\end{table}
Table 2: Summary of M-type hexaferrite photocatalysts.  
main purposes, i.e., (i) improved activity by hindering the charge carriers' recombination, (ii) extension of activity towards vis range of solar spectrum, and (iii) easy and cheap recycling, as presented in this Section.
## 5 Experiments and Results
### Pre-Processing  
Figure 4 shows the normalized median intensity (NMI) information for each hospital and method, which is plotted as a violin plot. The NMI is used to assess the effectiveness of each CN technique in normalizing the data set. It involves calculating the median intensity value of pixel values within an image and then normalizing this value. NMI values were computed for individual images within the data set, and metrics such as the standard deviation (NMI SD) and coefficient of variation (NMI CV) were employed. Lower NMI SD and NMI CV values suggest a more consistent normalization across the data set. Ref. [36] presented numerical results for BKSVD, Mac, and Vah across individual clinical centers as well as the entire CAM 17 data set. In this study, Figure 4 visually displays the distribution, variability, and potential outliers of values across different groups, facilitating comparisons between these groups.  
Figure 4: Violin plots of NMI values for each center. The histogram of NMI values for each plot is represented by the blue shadow. Bars mark the maximum, median, and minimum NMI values for each plot. The x-axis corresponds to the hospital numbers.  
In refs. [32; 33], the color of images for each hospital could be transferred to a similar distribution, but with a larger inter/intra-center than the original images' distribution. Among these three CN methods, BKSVD exhibited the best performance in approximately normalizing the images of all five centers to the same NMI interval. This means that BKSVD transforms the images from each hospital with the lowest intra-center variance and most similar median values, which makes its outputs more interpretable and reliable. To provide a more detailed statistical analysis to strengthen the evidence for the effectiveness of BKSVD compared with Vah and Mac, a quantitative comparison was conducted, Table 1[36], based on the peak signal to noise ratio (PSNR). The table shows that BKSVD outperforms the rest of the methods, obtaining a higher mean PSNR while requiring a similar time to that required by the Vah method.  
Figure 5 provides visual information about the impact of each CN technique on the color distribution of five random images. Randomly chosen images from all five centers exhibit noticeable color variations, as depicted in the initial line showing the original images. Notably, BKSVD effectively mitigated this color variation, achieving a harmonized and consistent color version that surpassed the performance of Vah and Mac as the classical methods.
## 4 Discussion
### Effects of Spatial and Temporal Resolutions on SIF-Based Phenology  
Numerous previous studies have shown phenology derived from optical satellite data could be subjected to coarse temporal resolution and landscape heterogeneity [21; 23; 66]. By comparison with \(\text{SIF}_{\text{Ground}}\), we also found that phenological metrics from the GOME-2 SIF likely contain large uncertainties due to its low spatial and temporal resolutions (see Section 3.4). Vegetation understory may start to green more than three weeks earlier than forest canopy [67]. Due to its coarse spatial resolution, a large contribution of understory phenology may significantly shift these phenological  
Figure 4: Comparison of surface air temperature (\(T_{\text{air}}\)) and Photosynthetically Active Radiation (PAR) versus: 90 th percentile of the green chromatic coordinates (GCC90) (**a**); ground-based NDVI (\(\text{NDVI}_{\text{Ground}}\)) (**b**); ground-based SIF (\(\text{SIF}_{\text{Ground}}\)) and tower-based GPP (**d**). All the time series are normalized and have a daily time step. PAR time series is the 5-day average.  
events. Particularly, we found that the start and end of spring/autumn (S1, S3, A1 and A3) derived from GOME-2 SIF may contain large biases, while dates of the middle of spring/autumn (S2 and A2) can be still estimated from them reasonably well. Therefore, GOME-2 SIF is still useful in predicting the GPP-based phenology, especially in the autumn season. Although our analysis showed that SIF measurements retrieved from current satellites were too coarse for phenology studies, future space-borne sensors such as Fluorescence Explorer (FLEX, [68]) will provide SIF observations with higher spatial and temporal resolutions, such that variations in phenology could be better monitored through the upcoming sensors.  
The temporal resolution of satellite data is another important factor affecting the performance of GOME-2 SIF in capturing phenological events [69]. We show that the low temporal resolution of GOME-2 SIF may also lead to large uncertainties in estimating phenological metrics. In addition to improving temporal resolution of future SIF satellites, a previous study [22] showed that exact observation dates of values used in temporal composition can help to reduce errors associated with its coarse temporal resolution. Considering temporal composite is usually necessary in processing remote sensing data, retaining exact day of acquisition is highly needed in future satellite SIF products to improve accuracy of remote sensing phenological indicators.  
Among all the time series, SIF\({}_{\text{Ground}}\) demonstrates the best capacity to predict the GPP-based phenology in both the spring and fall seasons. Although the effects of spatial and temporal resolutions on the performance of SIF in estimating the phenological metrics need to be analyzed in much more detail, our results clearly show that there is a great need to combine both site-level and satellite-based SIF observations in future phenology studies.