esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A comprehensive SA was conducted for an ecosystem model, FBEM, based on a globally quantitative SA algorithm, EFAST. The sensitive parameters were distinguished from all parameters by quantifying the parameter's SIs and ranking their importance of the FBEM. The following findings were obtained by various numerical experiments. First, the effects of the range of parameter variation on the SIs were significant. It is found in the experiment that the SIs of parameters were changed with a different value range, therefore, the suggested range of parameter sensitivity analysis in FBEM was given. Second, Sensitive parameters of the FBEM model for GPP and NEE were identified, the most sensitive parameters were \(\alpha_{q}\), \(f_{Ci}\) and \(V_{m}^{25}\) for GPP, while \(ER_{0}\), \(Q_{10}\) and \(f_{Ci}\) were most sensitive for NEE. Second, an uncertainty analysis of the results of comparative experiments on parameter  
Figure 7: Correlation analysis of average air temperature (\(T_{a}\)) and variation in the sensitivity index of the main parameter from DOY10 to DOY340 when model output was NEE: (**a**) SI of \(ER_{0}\) vs. \(T_{a}\); (**b**) SI of \(\mathrm{E}_{Vm}\) vs. \(T_{a}\); (**c**) SI of \(f_{Ci}\) vs. \(T_{a}\); (**d**) SI of \(Q_{10}\) vs. \(T_{a}\).  
optimization was used to understand the performance of the model and the dynamics of the parameters. The results of this SA and uncertainty analysis are discussed in a more general framework. Furthermore, the temporal characteristics of the parameters' SI on GPP and NEE were subsequently described: the sensitivity index clearly changed in different growth stages of the plants. SI of \(E_{Vm}\) gradually decreased while SI of D0 and \(\alpha_{q}\) parameters were changing with a different value range, kept increasing at the beginning of the growth season with the model output GPP. SI of \(f_{ci}\), \(Q_{10}\) and \(E_{Vm}\) were variation in the growth season for NEE. The reasons for SI variation were discussed, temporal characteristic of \(\alpha_{q}\), \(Q_{10}\) and \(E_{Vm}\) were found to be mainly explained by the change of \(T_{a}\). Moreover, more importantly, the mild trade-off effect was observed in the variation of the parameter SIs during the growing season in the EBEM. This study provided an improved understanding of the uncertainty of the ecological model caused by the parameters' sensitivity and it also insights into potential approaches for the improvement of GPP simulation.  
[PERSON], [PERSON] and [PERSON] conceived of and designed this study. [PERSON] performed the analysis and calculations. [PERSON] and [PERSON] wrote and revised the paper. [PERSON], [PERSON] and [PERSON] contributed to revisions. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No. XDA20100104), the National Natural Science Foundation of China (Grant No.41701418), the National Natural Science Foundation of China (Grant No. 91425303), the CAS Light of West China Program.  
The authors declare no conflict of interest.
# Impact of Disruption on Ship Emissions in Port: Case of Pandemic in Long Beach
## 3 Model and Methodology
### Ship Emission Estimation Model
#### 3.3.4 Operating Time Statistics  
Ship operating time is crucial because it is directly related to fuel consumption. Since AIS data are updated every minute, we adopt the calculation in segments per minute for ships in motion; thus, the operating time value in the calculation formula is 1/60 h. The calculation ranges begin in the entering-statistical-region time, and end when the ship stops or leaves the statistical area. For a ship at berth or anchor, the time required to change its current state and the accompanying operation time is computed.  
In addition, it is vital to consider the type of engine the ship utilises, based on its status, while calculating the operating time. The result is obtained for a ship in motion by adding the ME, AE, and boiler, but, for a ship at anchor, the result is obtained by adding just the AE and boiler.
# Evaluating the Effectiveness of COVID-19 Bluetooth-Based Smartphone Contact Tracing Applications
## 5 Evaluation
### Impact of Accuracy  
In this subsection, we evaluate the impact of the detection accuracy on the number of infected and quarantined individuals. Particularly, we have evaluated how false positive (FP) and false negative (FN) ratios impact these aspects.  
We repeated the experiments of Figure 4 with \(R_{0}=3\) and \(RC=0.6\), considering six different combinations of FP and FN ratios. Specifically, we evaluated the proportions of infected individuals and people quarantined depending on the utilization of the smartphone application for the following pairs of FP and FN ratios: \(\{(0.9,0.7),(0.7,0.7),(0.7,0.5),(0.5,0.5),(0.5,0.3),(0.3,0.3)\}\). From these pairs of values we can obtain the relative error \(\epsilon\) using expression (1): \(\{0.842,0.823,0.705,0.667,0.533,0.462\}\) (that is, the FP and FN pairs are in decreasing relative error). Figure 5 shows the results. Regarding the proportion of infected individuals (Figure 5b), we can see, as expected, that for lower ratios of FP and FN, the infected individuals are reduced, especially when the utilization is high. It is also important to point out that there is no significant difference between the red and blue curves--that is, when the FP ratio varies. For example, we can see that for the (FP, FN) pairs \(\{(0.5,0.3),(0.3,0.3)\}\), the curves are mostly the same. This means that the impact of the FP ratio on the infected individuals is very reduced. On the other hand, the FP ratio has a very significant impact on the people quarantined, as shown in Figure 5b. Actually, both ratios have a significant impact on the number of people in quarantine, since reducing the detection accuracy increases the people quarantined.  
Repeating the previous experiments with other values of \(R_{0}\) and \(RC\) led us to similar results and conclusions. Summing up, the results show that the false positive ratio has little effect on the final number of infected individuals, but it has a huge effect on the number of individuals quarantined. This is a logical outcome, since the consequence of a higher ratio of false positives is that more people are unnecessarily quarantined.  
Figure 5: Impact of accuracy. That is, the impacts of false positive and false negative ratios on the efficiency of contact tracing. All plots are for \(R_{0}=3\), \(1/\tau_{T}=1\) day and \(RC=0.6\): (**a**) percentage of population infected; (**b**) percentage of population quarantined.
# A Liquid-Solid Coupling Hemodynamic Model with Microcirculation Load
## 4 Conclusions  
Conclusively, the liquid-solid coupling hemodynamic model built in this paper considering microcirculation load effect is able to effectively simulate pulse wave propagation and reflection. It can also reflect the physiological features of human aortas and circulation system. The model is a hemodynamic model according with actual physiological process. Through data analysis, it was revealed that in the human blood circulation system, the coupling relationship of liquid, solid, and porous media seepage is exactly the deep reason why the various complex flowing phenomena of human blood occur in the circulation process. The effects of the propagation and reflection of the pulse wave should not be ignored in hemodynamic simulations. Meanwhile, through this model, we were able to simulate the motion and physiological features of flow in a blood vessel more rationally. This study thus provides an important theoretical foundation and technical methods for analyzing cause,  
Figure 12: Comparison of tube wall deformation under different cardiac cycle lengths: (**a**) 0 s; (**b**) 0.1 s; (**c**) 0.16 s; (**d**) 0.2 s.  
development, and clinical treatment of atherosclerosis, aneurysms, high blood pressure, and other cardiovascular and cerebrovascular diseases in the future.  
The constitutive equation of microcirculation load has a great influence on the bloodstream, and has decisive influence on various phenomena in the flow field. Therefore, in hemodynamic study and simulation, the influence of microcirculation load must be considered. The simulations will achieve a better correspondence with human physiology through deep study of microcirculation load properties and improving the constitutive equation of the model.  
The model is able to realize the transition of the circulation system under different flow field conditions, providing the possibility of simulating the dynamic condition of the human circulation system. Through studying dynamic properties, we cannot only analyze the properties of the blood circulation system under different physiological states, but also simulate the transition process of the human body between different physiological states. These have important meanings for research on aneurysms and other blood diseases.  
However, the present work is limited by physiological inaccuracies in the geometrical shape of the models. Further work will be conducted on the simulation in actual arterial geometries extracted from CT angiography or MRI. High-precision grids will be employed. In addition, the effect of non-Newtonian fluid on the small arteries will be considered.  
This study was supported by \"Specialized Research Fund for the Doctoral Program of Higher Education\" (20101103110001). The authors are very grateful to the reviewers for reviewing this manuscript and giving very constructive suggestions.  
[PERSON] and [PERSON] conceived and designed the study. [PERSON] performed the simulation and wrote the paper. All authors read and approved the manuscript.  
The authors declare no conflict of interest.
# _Applied_
## 3 Methodology  
In this section, the background of audio classification is first set, before being linked to the methodology that is used in this work. After that, the selected datasets are presented, followed by the chosen audio features, the selected ML techniques and finally the metrics used to evaluate the classifiers.  
The goal of the experiments is to compare classical ML techniques to CNN classification and see if combining classical ML techniques can outperform CNNs. Like many other pattern classification tasks, audio classification is made up of three fundamental components:  
* Sensing: measuring the sound event or signal;
* Audio signal processing: extracting the characteristic features of the measured sound signal; and finally,
* Classification: recognizing the context of the sound event.  
The audio signal processing part mainly deals with the extraction of features from the recorded audio signal. The various methods of time-frequency analysis developed for processing audio signals, in many cases originally developed for speech processing, are used. That is, feature extraction quantizes the audio signal and transforms it into various characteristic features. This results in an \(N\) dimensional feature vector often representing each audio frame. A classifier then takes this feature vector and determines what it represents--that is, it determines the context of the audio event. The classification technique has two phases: the training phase and the classification phase. During the training phase the system receives prerecorded audio data from labeled training sets to train a representative model. In the classification phase, the system receives its audio inputs from different prerecorded audio data or directly from a microphone. The classification phase uses the models generated during the training phase to match and determine the type of audio received by the microphone. Figure 1 is a representation of this process.
# Cyanobacterial Potential for Restoration of Loess Surfaces through Artificially Induced Biocrusts
## 5 Conclusions  
The ongoing process of desertification represents a significant environmental threat in many parts of the world. The health and wellbeing of humans and animals are at risk in the affected areas, and the loss of arable land causes further socio-economic problems. The method frequently used in temperate zones to restore degraded land surfaces, namely revegetation, has its shortcomings in semi-arid and arid environments such as loess landscapes.  
This study demonstrated the potential of loess-derived cyanobacterial strains for restoration purposes as the strains lacked toxicity but were characterized by a high production of biomass and polysaccharides. It is proposed that special attention should be paid to _Chroococcilopsis_ sp. L1 in the restoration context as this strain showed a high potential for EPS production.  
The interaction between the cyanobacterial biofilm and airborne dust particles was found to lead to the formation of biocrusts. The mechanism of trapping, accumulation and stabilization of particles by cyanobacteria differed depending on the morphology of the colonies. The trapped dust particles were exposed to intensive weathering and subsequent mineral precipitation events, which led to the formation of organomineral structures in the contact zone between EPS and dust particles.  
The interaction between the cyanobacteria and airborne dust, which eventually led to the accumulation of particles in layers of biocrusts, can be understood through the recently introduced concept of synergism and modelled by the BLOCDUST hypothesis, which deals with the dual origin of loess. Applied studies concerning the development of the biocrusts in field conditions are still necessary but this study presents a proof-of-concept and progress in developing a strategy to combat desertification in loess landscapes.  
Conceptualization, Z.S., J.M.; Methodological design, Z.S., I.O., T.P.M. and T.D.; Data curation, T.P.M., Formal analyses, Z.T.; Experiments, T.P.M., T.D., Z.T., B.K., T.V. and R.M.; Writing--original draft preparation, T.P.M.; Writingreview and editing, Z.S., T.D., T.P.M., J.M., I.O., Z.T., T.V., R.M. and B.K.; Visualization, T.P.M.; Funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge financial support of the Ministry of Education, Science and Technological Development of the Republic of Serbia (Grant No. 451-03-68/2020-14/200125), ERASMUS+ mobility program signed between the University of Novi Sad Faculty of Sciences and Abo Akademi University, Project Code 2015-2-FI01-KA107-022151 and Project Code 2017-1-FI01-KA107-034440, as well as the support of the Abo Akademi University Doctoral Scholarship Programme.  
Not applicable.  
The data presented in this study are available on request from the corresponding author T.P.M. The data are not publicly available due to later utilization in PhD theses.  
The authors thank [PERSON] for his support during this research.  
The authors declare no conflict of interest.
## 3 Experiment
### Comparison with State-of-the-Art Methods
\begin{table}
\begin{tabular}{c c c c} \hline
**Method** & **MIoU (\%)** & **MAcc (\%)** & **PAcc (\%)** \\ \hline DeepLabv3+ [8] & 72.25 & 79.22 & 81.07 \\ CAE(ViT-L, UperNet) [31] & 71.90 & 78.27 & 80.99 \\ ABCNet [32] & 76.57 & 82.39 & 82.82 \\ BANet [33] & 77.38 & 82.59 & 82.43 \\ SegFormer [34] & 78.43 & 83.24 & 83.37 \\ UNetFormer [19] & 79.35 & 84.57 & 85.97 \\ GLFFNet & **80.42** & **85.08** & **86.23** \\ \hline \end{tabular}
\end{table} 
Table 6: Quantitative comparison results on the GID dataset with some state-of-the-art methods. \begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & **MIoU (\%)** & **MAcc (\%)** & **PAcc (\%)** & **Inference Time** (ms)** \\ \hline DeepLabv3+ [8] & 80.64 & 88.38 & 89.26 & 25.87 \\ CAE(ViT-L, UperNet) [31] & 79.67 & 87.51 & 88.34 & 85.14 \\ ABCNet [32] & 84.02 & 89.64 & 91.15 & 78.34 \\ BANet [33] & 85.67 & 89.91 & 91.34 & 73.51 \\ SegFormer [34] & 85.43 & 91.95 & 92.33 & 82.65 \\ UNetFormer [19] & 86.80 & 93.23 & 93.89 & 53.41 \\ GLFFNet & **87.96** & **93.84** & **94.75** & **56.37** \\ \hline \end{tabular}
\end{table} 
Table 5: Comparative experimental results on the Potsdam dataset with other methods.
## 2 Data and Methods  
### Study Area  
The study area is located in the central Tibetan Plateau around the city of Naqu, where the CTP-SMTMN (91.5\({}^{\circ}\)E-92.5\({}^{\circ}\)E, 31\({}^{\circ}\)N-32\({}^{\circ}\)N) is situated (Figure 1). This region falls into the cold semi-arid climate zone and is characterized by a relatively flat terrain with rolling hills. The ecosystem is dominated by alpine grasslands and alpine meadows, which exerts less impact on the L-band passive microwave remote sensing of soil moisture (e.g., SMAP). The main soil texture classes are sands and sandy loam [42] with a high infiltration rate, making it hard for precipitation to form surface runoff. Precipitation mainly occurs during the rainy season (i.e., June-September, JJAS), accounting for about 70% of the total precipitation throughout the year. There is a typical freeze-thaw cycle here [35], and therefore, the analysis was conducted during the rainy season to avoid the impacts from freezing soil and/or snow cover.  
Figure 1: (**a**) The Tibetan Plateau collocated with the Central Tibetan Plateau Soil Moisture Monitoring Network (CTP-SMTMN). (**b**) Spatial distribution of the soil moisture stations within the CTP-SMTMN (black dots) as well as the China Meteorological Administration (CMA) rain gauge station (white triangle).
## 3 Methodology and Model
### IPSA-DV-Hop Algorithm  
The proposed algorithm in this paper performs the first hop distance refinement during the hop count acquisition phase. In the node distance calculation phase, an improved weighted hyperbolic algorithm is adopted to replace the least squares solution. The combination of the improved DV-Hop algorithm and the optimized PSA further reduced localization error.
# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
###### Abstract  
Hyperspectral sensors are passive instruments that record reflected electromagnetic radiation in tens or hundreds of narrow and consecutive spectral bands. In the last two decades, the availability of hyperspectral data has sharply increased, propelling the development of a plethora of hyperspectral classification and target detection algorithms. Anomaly detection methods in hyperspectral images refer to a class of target detection methods that do not require any a-priori knowledge about a hyperspectral scene or target spectrum. They are unsupervised learning techniques that automatically discover rare features on hyperspectral images. This review paper is organized into two parts: part A provides a bibliographic analysis of hyperspectral image processing for anomaly detection in remote sensing applications. Development of the subject field is discussed, and key authors and journals are highlighted. In part B an overview of the topic is presented, starting from the mathematical framework for anomaly detection. The anomaly detection methods were generally categorized as techniques that implement structured or unstructured background models and then organized into appropriate sub-categories. Specific anomaly detection methods are presented with corresponding detection statistics, and their properties are discussed. This paper represents the first review regarding hyperspectral image processing for anomaly detection in remote sensing applications.  
target detection; Reed-Xiaoli algorithm; background models; kernel-based methods; representation models
## 7 Treatment Methods
### Blocking and Inactivation of AQP4, IL-6, and Complement Factors  
The single target antibody AQP4 could be used to treat NMOSD. Blocking the cell surface polyclonal AQP4 antibodies with engineered, high-affinity antibodies prevents  
Figure 5: The illustration describes the pharmacological treatment methods for neuromyclitis optical spectrum disorder (NMOSD). (Figure created using BioRender.com; accessed on 11 April 2023).  
downstream cytotoxicity and reduces NMO lesions [212]. The binding of pathogenic AQP4-IgG with the AQP4 channel on the astrocyte initiates pathogenesis in AQP4 seropositive NMOSD cases. Inactivation, deletion, or blocking of the AQP4-IgG binding with its respective antigen using a small molecule can be used as a therapeutic method. Engineered monoclonal antibodies block AQP4-IgG from binding to AQP4 with high efficacy in NMOSD seropositive animal models [212; 213].  
Bacterial enzymes are used to inactivate AQP4-IgG. Endoglycans S (Endo S) and IgG degrading enzyme (IdeS) from _Streptococcus pyogenes_ help digest asparagine-linked glycans on heavy chains of IgG [214] and cleave IgG antibodies into non-pathogenic Fc and F(ab) fragments, which inhibits AQP4-IgG from binding with AQP4 [215]. The administration of Endo S and IdeS through the apheresis method would prevent the disease. A high-affinity antibody (aquorumab) blocked AQP4-IgG and prevented CDC in seropositive NMOSD patients. Thus, intravenous antibody-blocking therapy could reduce NMO pathology and the frequency of exacerbations [212]. Targeting the neonatal Fc receptor (FcRn) of AQP4 by its inhibitor (rozanolixizumab) reduced IgG concentration in human subjects [216]. AQP4 immunoassay adsorption-based removal of circulatory AQP4-IgG by AQP4-affinity resin, tryptophan, or phenylalanine-linked polyvinyl alcohol needs to be further studied before use [217; 218].  
Targeting IL-6 and blocking its functions through the use of tocilizumab, an antibody used to block the IL-6 receptor, reduces the frequency of relapse in MOGAD cases that did not respond to other immunotherapies [219]. Satalizumab is another drug engineered to dissociate IL-6 from its receptor in a pH-dependent manner, which promotes lysosomal degradation of the IL-6 receptor [220]. However, satalizumab was ineffective in AQP4-IgG seronegative NMOSD cases [209; 210]. Upper respiratory tract infection, nasopharyngealitis, and headaches are the adverse side effects of satalizumab [221]. Therefore, certain pre-treatment procedures are needed before satalizumab treatment, including screening for the hepatitis B virus, tuberculosis, liver transaminases, and serum bilirubin [221].  
Blocking the IL-6 receptor through the use of tocilizumab has been approved for treating rheumatoid arthritis and NMOSD [222]. Tocilizumab could bind to IL-6R in the astrocyte or T cell membrane and initiate downstream signalling cascades such as JAK/STAT, mitogen-activated protein kinase, and the phosphatidylinositol 3 kinase (PI3k) pathway [223].