esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Flow Calculation Methods for Power Systems with Novel Structure UPFC
###### Abstract  
Latest unified power flow controller (UPFC) projects adopt novel device structures to meet the requirements of practical applications. Developing power flow calculation methods for these new UPFC structures is of great significance for the design and operation of related projects. To address this issue, this paper deduces an equivalent model of the general structure UPFC, and presents the modified power mismatch equations and Jacobian matrix of the system. Then, three power flow calculation methods suitable for the novel structure UPFC are proposed based on the Newton-Raphson algorithm. The main difference between the three methods is the processing method of UPFC equivalent power injections and the Jacobian matrix. The characteristics of these methods are compared by case studies. Results demonstrate the effectiveness of the three methods for different UPFC structures, and it is found that the improved method, which considers the impact of the Jacobian matrix modifications in UPFC equivalent power injections, can realize power flow calculation in existing calculation programs, and improve the convergence characteristics.  
flexible AC transmission systems (FACTS); unified power flow controller (UPFC); power flow calculation; Newton-Raphson algorithm; novel structure
## 3 Results and Discussion
### Analysis of Geosmin-Associated Genes: Origin and Evolution
#### 3.4.1 Inconsistent Topologies between Geosmin Gene and 16S rDNA Phylogenetic Trees
Figure 6: Clustering of cyanobacterial _geo_, single-domain sesquiterpene gene and MIB-synthesis-associated open reading frames. The gene array and constitution of two sesquiterpene synthase operons or open reading frames were significantly different, representative of the _geo_ with two _cnb_ genes and the single-domain one with cytochrome P450 and hybrid two-component protein genes (Figure 6). The P450 gene plays an important role in the modification of sesquiterpene products in fungi and cyanobacteria [12,30]. The large difference in the structure of the gene cluster and gene function of these two types of sesquiterpene genes suggests that they have independent origins and evolutionary lineages. Although they have the same substrate (FPP) and similar catalytic mechanism of the N-domain and single-domain synthase [10,12], the low homology of their sequences (Figure S3) implies an independent evolutionary lineage of geosmin from other sesquiterpene in cyanobacteria. Interestingly, the high homologies between the _cnb_ genes of geosmin and MIB operon in cyanobacteria (Table 2) elucidated that geosmin/germacradientol might be closely related to MIB in origin and evolution in cyanobacteria. However, this presumption needs further investigation.
###### Abstract  
In light of the burgeoning interest in mitigating anthropogenic CO\({}_{2}\) emissions, carbonate reservoirs have emerged as promising sequestration sites due to their substantial storage potentials. However, the complexity of CO\({}_{2}\) storage in carbonate reservoirs exceeds that in conventional sandstone reservoirs, predominantly due to the rapid interactions occurring between the injected CO\({}_{2}\), brine, and carbonate rock matrix. In this study, a numerical model integrated with the chemical CO\({}_{2}\)-brine-rock matrix interaction was developed to analyze the carbonate rock dissolution process and the physical property variations of different carbonate gas reservoirs during the CO\({}_{2}\) injection and sequestration process. More specifically, a total of twenty scenarios were simulated to examine the effects of lithology, pore size, pore-throat structures, and CO\({}_{2}\) injection rate on carbonate rock matrix dissolution and reservoir property variation. Calcite is significantly easier and quicker to react with CO\({}_{2}\)-solvated brine than dolomite; as a result, limestones exhibit an expedited rock dissolution and pore volume increase, along with a slower pressure buildup in comparison to dolomites. Also, the carbonate reservoir with a smaller pore size has a higher rock dissolution rate than one with a larger pore size. Furthermore, the simulation results show injected CO\({}_{2}\) can modify the pore-dominant carbonate reservoir to a more pronounced extent than the fracture-dominant carbonate reservoir. Lastly, the carbonate rock dissolution is more obvious at a lower CO\({}_{2}\) injection rate. The insights derived from this research aid evaluations related to CO\({}_{2}\) injectivity, storage capacity, and reservoir integrity, thereby paving the way for environmentally and structurally sound carbon sequestration strategies.  
CO\({}_{2}\) injection and storage; carbonate reservoirs; pore-throat structures; CO\({}_{2}\)-brine-rock interactions; carbonate rock dissolution +
Footnote †: journal: Applied  
Article  
An Integrated Numerical Study of CO\({}_{2}\) Storage in Carbonate Gas Reservoirs with Chemical Interaction between CO\({}_{2}\), Brine, and Carbonate Rock Matrix
## 5 Online Classification Experiment
### Results and Discussions
#### 5.2.3 Classification of 9 Movements  
This scenario is intended to see how much the accuracy improves when the number of classes are reduced to nine. According to Figure 13, under the condition that training and testing are done on the same day, an overall correct rate of 72.9% across subject can be achieved for classification of 9 motions using the proposed method. Also, depending on the participant and the muscle condition, the system can accomplish a correct rate of 92.1% (participant L) or an average accuracy of up to 82.6% (participant F). These results demonstrate that reducing the number of motions helps improve the overall performance. Therefore, if the prosthetic hand is intended for a specific task, limiting the number of motions can significantly increase the performance and usability of the system. Furthermore, the above results also help strengthened the conclusion that a consumer-grade EMG sensor can serve as a modality in myoelectric prosthetic control.  
Compared with the scenario dealing with 17 motions (Figure 11), it is interesting to see that no significant statistical difference can be observed for participant B, E and H in this scenario. On the other hand, statistical difference can now be observed for participant A. This statistical difference was not present in the previous classification scenario with 17 movements. The above results suggest that for participant B, E and H, the accuracies of the selected 9 motions may already be relatively high to begin with and that the trainings done on day 1 may not be sufficient to significantly improve these accuracies. The opposite could be implied for participant A. Since the current skill level and the rate of improvement is different for each individual for each type of motion, a training method/menu specifically tailored for each individual may help improve the accuracy significantly.
## 3 Results
RSLDEOUT was identified via GC-MS as a mixture of glucose and mannitol (ratio 3.7:1), from the typical electron ionization (EI) patterns and from the retention times of authentic standards (Figure 5). The 1H NMR confirmed the occurrence of only one free reducing monosaccharide together with an alditol sugar, since only two anomeric signals at \(\delta\) 5.10 ppm (\(\alpha\)-glucose) and at \(\delta\) 4.65 ppm (\(\beta\)-glucose) were found (Figure 3D). Based on the concept that glucose per se does not significantly interfere with cell viability, except for concentrations beyond 20 mg mL-1 [19], and considering the ratio between glucose and mannitol in RSLDEOUT (ratio 3.7: 1), additional MTT tests on both cell lines treated with different concentrations (0, 0.6, 1.0, 1.2, 2.43, 4.5, 9.0 mg mL-1) of commercially available mannitol were performed at 24 and 48 h. The cell viability assays had the same trend on both cell samples after 24 and 48 h exposure. Figure 3: 1H NMR spectra of dialyzed extracts from hot water extraction (HWE) and rapid solid-liquid dynamic extraction (RSLDE): HWEIN (**A**), HWEOUT (**B**), RSLDEIN (**C**), and RSLDEOUT (**D**). In fact, significant cytotoxic activity was observed on Caco-2 cells up to 1.2 mg mL\({}^{-1}\) of mannitol whereas, between 1.0 and 1.2 mg mL\({}^{-1}\) of mannitol, there was a significant reduction in HCT-116 cell viability (around 40%) after 48 h exposure compared to the viability of Caco-2 treated with the same concentration of mannitol (Figure 6). This range 1.0-1.2 mg mL\({}^{-1}\) is of interest since it corresponds to the amount of mannitol contained (1.1 mg mL\({}^{-1}\)) in the RSLDE\({}_{\text{OUT}}\). Figure 4: Comparison of proliferative activities on Caco-2 (non-invasive) and HCT-116 (invasive) cell lines of the dialyzed extracts from hot water extraction (HWE\({}_{\text{IN}}\) and HWE\({}_{\text{OUT}}\)) and rapid solid-liquid dynamic extraction (RSLDE\({}_{\text{IN}}\) and RSLDE\({}_{\text{OUT}}\)) after 24 (**A**) and 48 h(**B**) exposure (* \(p<0.05\)). Figure 5: GC-MS chromatograms of acetylated methyl glycosides from RSLDE\({}_{\text{OUT}}\) (**A**); acetylated mannitol (**B**); acetylated methyl glycoside of glucose (**C**). Figure 6: Caco-2 and HCT-116 cytotoxicity under different mannitol concentrations (0, 1.0, 1.2, 2.43, 4.5, 9.0 mg mL\({}^{-1}\)). MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium) cell viability after 24 (**A**) and 48 h (**B**) exposure. From Figure 7 it can be seen that RSLDE\({}_{\text{OUT}}\) is more effective on cancer cells than pure mannitol at the same concentration.
# Warranty Cost Analysis for Multi-State Products Protected by Lemon Laws
## 3 Assumptions and Probability Analysis of \(\{\tau,N_{1},1\}\) Model
### Cost Analysis of \(\{\tau,N_{1},1\}\) Model  
Using the same method as that of Theorems 1 and 2, we can obtain the following two theorems.  
Theorem 3: _For a multi-state product with the failure process modelled by the homogeneous discrete-state continuous-time Markov chain \(\{X(t)\}\) and warranty terms renewed according to the \(\{\tau,N_{1},1\}\) regulation, the expected length of the warranty service is:_\[E(W_{\{\tau,N_{1},1\}})=\frac{\sum\limits_{m=0}^{N_{1}}\sum\limits_{k=1}^{m+1}p_{ mk}L_{mk}+p^{0}L^{0}+\sum\limits_{k=1}^{N_{1}+1}p_{k}^{1}L_{k}^{1}+\sum\limits_{m=0}^{ N_{1}}p_{m}^{0}L_{m}^{0}+\sum\limits_{m=1}^{N_{1}-1}\sum\limits_{k=1}^{m+1}p_{ mk}^{1}L_{km}^{1}+p_{01}^{1}L_{01}^{1}}{1-p_{\tau N_{1}}}+W_{1}. \tag{51}\]  
For a multi-state product with the failure process modeled by the homogeneous discrete-state continuous-time Markov chain \(\{X(t)\}\) and warranty terms renewed according to the \(\{\tau,N_{1},1\}\) regulation, the expected warranty service costs over the renewable warranty period are:  
\[\begin{array}{l}E(C_{\{\tau,N_{1},1\}}^{rw})=\frac{1}{1-p_{\tau N_{1}}}\;( \sum\limits_{m=0}^{N_{1}}\sum\limits_{k=1}^{m+1}p_{mk}(mc_{d_{1}}^{s}+c_{d_{2} }^{s})+p^{0}N_{1}c_{d_{1}}^{s}+\sum\limits_{k=1}^{N_{1}+1}p_{k}^{1}(c_{d_{2}} ^{s}+N_{1}c_{d_{1}}^{s})+\\ \sum\limits_{m=0}^{N_{1}-1}p_{m}^{0}(mc_{d_{1}}^{s}+c_{d}^{l})+p_{N_{1}}^{0}(N_ {1}c_{d_{1}}^{s}+c_{d_{2}}^{l})+\sum\limits_{m=1}^{N_{1}-1}\sum\limits_{k=1}^{ m+1}p_{mk}^{1}(mc_{d_{1}}^{s}+c_{d_{2}}^{s}+c_{d_{1}}^{l})+\\ p_{01}^{1}c_{d_{2}}^{l}+p_{\tau N_{1}}(c_{mw}-c_{s})),\end{array} \tag{52}\]  
In Equation (52), \(c_{d_{1}}^{s}\) and \(c_{d_{2}}^{s}\) are the costs for short repairs of minor and catastrophic failures, respectively. \(c_{d_{1}}^{l}\) and \(c_{d_{2}}^{l}\) are the costs for long repairs of minor and catastrophic failures, respectively. \(c_{d}^{l}\) is the cost for a long repair of a failure.  
Similarly, the expected cost over the nonrenewable warranty period is:  
\[E(C_{\{\tau,N_{1},1\}}^{rw})=\sum\limits_{m=1}^{N_{1}}\widetilde{p}_{m}^{0}mc _{d_{1}}^{s}+\sum\limits_{m=1}^{N_{1}}\widetilde{p}_{mk}^{1}(mc_{d_{1}}^{s}+c_ {d_{2}}^{s})+\widetilde{p}_{01}^{1}c_{d_{2}}^{s}. \tag{53}\]  
From Equations (51)-(53), the expected cost per unit time during the warranty period can be given as follows:  
\[CR_{\{\tau,N_{1},1\}}=\frac{E(C_{\{\tau,N_{1},1\}}^{rw})+E(C_{\{\tau,N_{1},1\} }^{rw})}{E(W_{\{\tau,N_{1},1\}}^{rw})}. \tag{54}\]
# GIS-Based Flood Assessment Using Hydraulic Modeling and Open Source Data: An Example of Application
## 5 Conclusions  
This study presents an integrated approach to flood analysis, combining GIS technologies, hydraulic modeling in HEC-RAS, and LiDAR data to assess flood risk in the Cigher River basin (Romania). The main objectives of the research were to identify flood-prone areas under different flow scenarios, evaluate the impact on agricultural and non-agricultural land, and develop a reproducible methodology applicable to other river basins.  
The results of hydraulic simulations revealed that as discharge increases, flood extent expands significantly, with the most severe scenario (S4, 60 m\({}^{3}\)/s) leading to the inundation of 871 hectares of land. The analysis demonstrated that agricultural land (arable land and grasslands) is the most affected, followed by forested areas, highlighting the need for sustainable flood risk management strategies. By integrating high-resolution geospatial data and computational modeling, the study provided valuable data and cartographic materials that can support decision-makers in reducing risks and efficiently planning land use.  
The proposed methodology demonstrates the effectiveness of open-source tools in hydraulic modeling and geospatial analysis, making it accessible for further research and practical applications in flood risk management. Future studies should focus on dynamic flood simulations, integrating the impact of climate change and exploring adaptive mitigation strategies for sustainable water resource management.  
Conceptualization, L.C., E.T.M., S.H., L.L.C., C.A.P., C.-B.V., R.B., M.V.H., O.S.C., and A.C.; methodology, L.C., E.T.M., S.H., and L.L.C.; software, L.C., C.-B.V., R.B., M.V.H., and A.C.; validation, E.T.M., S.H., L.L.C., and C.A.P.; formal analysis, C.-B.V., O.S.C., and A.C.; investigation, L.C., L.L.C., and R.B.; resources, L.C., S.H., and C.A.P.; data curation, L.L.C., M.V.H., and O.S.C.; writing--original draft preparation, L.C. and L.L.C.; writing--review and editing, C.-B.V., R.B., M.V.H., and A.C.; visualization, E.T.M., S.H., L.L.C., and C.A.P.; supervision, L.C., L.L.C., and E.T.M.; project administration, L.C., L.L.C., and E.T.M.; funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The publication of the present paper was supported by the University of Life Sciences \"[PERSON] Mihai I\" from Timisoara, Romania.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** The authors thank the GEOMATICS Research Laboratory, \"[PERSON]\" University of Life Sciences in Timisoara, for the facility of software used for this study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Acute Toxicity Assays
#### 3.3.3 Ionic Liquid vs. Heavy Metal Toxicity  
Concentrations of dissolved IL after extraction significantly exceeded the calculated EC\({}_{50}\) values in all samples. Even in the most favorable cases, leaching still equaled 18 mg IL L\({}^{-1}\) (Table 3). To better understand these results in light of possible applications, the retention in treatment plants and biodegradability of these ILs needs to be assessed. Amongst others, several readily biodegradable (\(>\) 60% in the 28-d bottle test) naphthenic acid-based ILs were synthesized [53]. Moreover, guidelines on designing biodegradable ILs recommend long, hydrophobic alkyl chains on the cation and anions derived from organic salts [24]. This suggests that the ILs studied in this work have potential in this regard. This should be the subject of further investigation. The potential biodegradability described for ILs stands in contrast to the environmental properties of heavy metals, which are persistent and non-degradable [54]. Algal toxicity for heavy metals already has been reported for different algal strains and under various testing conditions. For the two target algae of interest, diverging results have been published: For instance, EC\({}_{50}\) values regarding the growth inhibition of _R. subcapitata_ range from 13 to 110 \(\mu\)g L\({}^{-1}\) for Cd, 10-280 \(\mu\)g L\({}^{-1}\) for Cu or 27-330 \(\mu\)g L\({}^{-1}\) for Hg [55; 56; 57; 58]. Concerning _T. obliquus_, the reported values lie in the same order of magnitude, with, e.g., 58 \(\mu\)g L\({}^{-1}\) for Cd [59]. Likewise, a significantly higher impact on overall photosynthetic performance was reported for the target metals compared to the tested ILs. Thus, [PERSON] et al. determined EC\({}_{50}\) (96 h) values of 50 \(\mu\)g L\({}^{-1}\) for Cu [60] and 37 \(\mu\)g L\({}^{-1}\) for Hg [33] towards _R. subcapitata_. Although the different data are not fully comparable due to varying setups, available data suggest that heavy metal toxicity greatly exceeds that of the tested ILs.  
Our results clearly indicate that environmentally favorable conditions in the sample solution after extraction cannot be easily achieved even when using highly hydrophobic ILs. Certainly, the leaching behavior of ILs can be improved in several ways: we demonstrated that immobilizing ILs on solidsupports can substantially decrease leaching by up to 88% [34; 61], leaving residual IL concentrations in the feed solution at concentrations approximate to the EC\({}_{50}\) values.
## 3 Graph Neural Networks for Next-Generation Networks
### Link Scheduling
#### 3.2.2 Traffic Prediction
Evaluations performed with real-world 4G LTE traffic data show that STGCN-HO outperforms the baseline models, including Historical average (HA) [74], Auto-Regressive Integrated Moving Average (ARIMA) [75], and LSTM models [76], at both the cellular and BS levels. However, all those models primarily focus on predicting future traffic load for a city, an urban area, or a BS, which is vague for fine-granular user-level traffic prediction. In this context, a fine-grained prediction is proposed by [PERSON] et al. [77], where the authors present a spatio-temporal fine-granular user traffic prediction mechanism for cellular networks called _STEP_. Specifically, STEP is based on the integration of GCN and GRU models to capture the spatiotemporal features of the individual user traffic. In addition, [PERSON] et al. [78] introduce an attention mechanism to assign appropriate weights to each node. They propose a time-series similarity-based graph attention network for cellular traffic prediction known as _TSGAN_. This model uses dynamic time warping [79] to compute the time-series similarity between the network traffic of every pair of cells and GATs to extract spatial features. Comparative experiments conducted on the Telecom Italia and Abilene datasets against GNNs and GRU models demonstrated the superior performance of TSGAN. To conclude, we summarize the reviewed methods along with their experiment set-ups and quantitative results in Table 3.
# Geometric Processing and Accuracy Verification of Zhuhai-1 Hyperspectral Satellites
## 3 Results and Discussion
### Band-to-Band Registration Accuracy  
The registration accuracy of adjacent spectral segments was verified by using 2018-9-12-OHS-Beijing, 2018-8-5-OHS-Tianjing, and 2018-10-10-OHS-Shandong images. A high-precision matching algorithm [16] is used to extract the same-named points from the adjacent spectral segments of the above basic products, and the spectral segment registration accuracy is evaluated by calculating and counting the coordinate deviations of the same-named image points of the adjacent spectral segments [4]. The results are shown in Figure 11.  
As can be seen from Figure 11, the spectral registration accuracy of the evaluation of the three-field experimental data is 0.3 to 0.5 pixels. As mentioned above, this study uses the method of positioning consistency constraints for spectral segment registration. The registration accuracy mainly depends on the elevation error, attitude and orbit error, and internal orientation element error. Among them, the influence of the global 90 m SRTM elevation data on spectral segment registration is less than 0.1 pixels. In addition, the imaging interval of adjacent spectral bands is only within 0.1 s, the attitude error is mainly systematic error, and the influence on spectral segment registration can be neglected. The  
Figure 11: Evaluation results of spectral segment registration accuracy.  
Figure 10: Calibration accuracies of other spectral calibrations.  
accuracy of spectral segment registration depends mainly on the accuracy of spectral band calibration. A comparison of the results shown in Figures 10 and 11 shows that the accuracy of the spectral alignment in the evaluation is comparable to the calibration accuracy between the spectral bands. Finally, the overlay display effect of the spectrum segment of OHS basic products is shown in Figure 12.
# Modulation Characteristics of High-Speed Transistor Lasers
## 3 Modulation Characteristics of TL
### Optimizations of QW
#### 3.1.1 Number of QWs and Cavity Length  
For a high-speed TL, the modulation bandwidth is dependent on the cavity length and number of QWs. However, the photon lifetime increases as the cavity length increases, which is not conducive to the rapid modulation of optical signals. The higher the number of QWs, the more carriers are captured, which indicates more recombination, more photons, and greater bandwidth.  
According to the carrier transport model shown in Figure 4b, the continuity equations that analyze the cavity length and optical bandwidth of TL has been reported [26]. Comparing the modulation bandwidth under different cavity lengths, the bandwidth of the TL with a long cavity length decreases sharply when the base current exceeds a certain threshold (see Figure 5a). The presence of the threshold is caused by the carrier transport effect. Therefore, the cavity length should be minimized under a large injection. This finding is congruent with previous reports on DML [29].  
The relationship between cavity length and QW numbers with the bandwidth size can be explained using Figure 5b. First, although the increase in the number of QWs will increase the photon concentration and the carrier trapping efficiency of QWs (thus increasing the bandwidth), the accumulation of carriers and photons will lead to relaxation oscillations. Therefore, to balance photon density and relaxation oscillations, the optimal number of QWs is five [30]. Second, increasing the cavity length reduces the bandwidth because the longer cavity length increases the photon lifetime, which produces stronger relaxation oscillations. However, as shown in Figure 5b, there is clearly a cavity length that results in the maximum bandwidth. This is because the shorter the cavity length, the smaller the carrier gain, which leads to a decrease in photon density. However, shorter cavity lengths can result in lower photon lifetimes. Therefore, optimal values for the number of QW and the cavity length that result in a maximum bandwidth exist [31].  
Considering the influence of the cavity length and the number of QWs on the bandwidth and cut-off current, various factors should be weighed when designing TLs.