esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents a model for predicting the reference minimum-loss incidence and deviation angles of a blade arrangement with splitter vanes, which is probably a solution for future ultra-highly loaded axial compressor designs. The motivation of the modeling is to guide the blading design in splittered compressor design processes where the additional splitter vanes must be specially considered. The development of the model is based on a blade performance database from systematic numerical simulations. Basic correlations of the model are firstly proposed, which consider dominant blade geometry parameters related to blade loading, including camber angle and solidity. Secondly, geometric and aerodynamic corrections about orientation parameter, blade maximum thickness, inlet Mach number, and three-dimensional (3D) effects are empirically incorporated into the basic correlations. Eventually, a subsonic 3D splittered rotor is designed using the correlations coupled with the corrections obtained from the validation of the model. The results indicate that the model is able to achieve a good agreement within an error band of \(\pm 1.0^{\circ}\) for the predictions of both reference minimum-loss incidence and deviation angles, and the rotor designed using the model accomplishes the desired work input and flow deflection.  
incidence and deviation model; compressor aerodynamics; blade-to-blade design; blades with splitter vanes; axial compressors Article
###### Abstract  
Previous studies investigating cardiovascular indicators in people with deficit schizophrenia (D-SCZ) have provided mixed findings. None of these studies controlled for the effects of lifestyle characteristics. We aimed to compare cardiometabolic parameters between patients with deficit schizophrenia (D-SCZ), those with nondeficit schizophrenia (ND-SCZ) and healthy control subjects (HCs) while taking into consideration lifestyle characteristics. A total of 168 participants were assessed. The following parameters were determined from serum samples: total cholesterol, low- and high-density lipoproteins, triglycerides, insulin resistance index (HOMA-IR) and C-reactive protein (CRP). The levels of CRP, but not other parameters, were significantly higher in patients with D-SCZ compared with those with ND-SCZ and HCs after adjustment for body mass index, adherence to Mediterranean diet, level of physical activity, nicotine dependence and dosage of antipsychotics. Higher levels of CRP were associated with lower attention in patients with schizophrenia as well as lower immediate memory and global cognition in healthy controls. Moreover, a significant positive relationship between the concentration of CRP and severity of negative symptoms was observed. These findings suggest that individuals with D-SCZ show subclinical inflammation that cannot be simply explained by an unhealthy lifestyle. Elevated CRP levels might be associated with cognitive impairment in schizophrenia.  
Keywords:psychosis; comorbidity; obesity; insulin; diabetes +
Footnote †: journal: Medical Medical University, 50-367 Wroclaw, Poland 1
# Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability
## 4 Discussion
### The Significance for Ecosystem Management of Estimating the GPP Loss Probability  
We developed an optimal bivariate probabilistic model to derive the vegetation productivity loss probabilities under different drought scenarios using copula method. The vegetation productivity loss probability drought causes can also reflect the ability of terrestrial ecosystems to resist interference. Under the same drought conditions, the higher the probability of vegetation loss, the weaker the resistance to drought. Our results showed that arid and semiarid areas have higher conditional probabilities of vegetation productivity losses under different drought scenarios. The productivity loss probability of EBF, DNF, OS, SAV, and GRA showed an increasing trend during different seasons. Quantifying the probability of varying degrees of damage to vegetation productivity under predictable drought scenarios has great significance in mitigating and adapting to global changes. However, there is none of the comparisons either between vegetation are types or the prediction is self are not statistically significant. [PERSON] et al. [52] found that the persistence of a water deficit (11 months) with an intensity of \(-\)1.64 (SPEI) led to negligible growth of conifer species. [PERSON] et al. [53] showed that aridification can lead to systemic and abrupt changes in multiple ecosystem attributes. Therefore, in future studies, to determine how much drought can cause significant changes in ecosystem productivity and to estimate the corresponding GPP loss probability can better reflect the differences between arid to humid areas.
# An Internet of Things Access Control Scheme Based on Permissioned Blockchain and Edge Computing
## 3 Scheme Framework
### Blockchain  
Blockchain is a new distributed computing and storage paradigm integrating a variety of existing technologies [20], which has the characteristics of decentralization, non-tampering, and traceability. According to the degree of decentralization, it can be divided into the permissionless blockchain and the permissioned blockchain. In permissionless  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline OAR & Original Access Request \\ AAR & Attribute based Access Request \\ PDP & Policy Decision Point—the fundamental feature is to take a look at whether or not the foremost in AAR meets the coverage rules, and ship the outcomes lower back to PEP for execution \\ PAP & Policy Authority Point—the access control policy for PDP decision-making is provided, in which the decision rules, basis and related constraints of PDP are defined \\ PEP & Policy Enforcement Point—the essential characteristic is to set up AAR, ship AAR to PDP, and execute the choice \\  & end result of PDP \\ PIP & Policy Information Point—used to manipulate the key attribute facts of the system used for get right of entry to \\  & control judgment \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation and Description involved in ABAC model.  
blockchain, it has no control over who can join the network. All nodes in the permissionless blockchain take part in the wide consensus main block verification. Due to the nature of the permissionless blockchain network, the consensus protocol ought to be extra stringent to keep away from any collusion strife between nodes. Bitcoin is a common example of the permissionless blockchain community that operates using the workload proof consistency approach. The permissioned blockchain is a semi-open blockchain, which is controlled and supervised. Only the node that has been granted permission can access it, while the node without permission cannot. Due to the regulatory nature of the network, compared with the permissionless blockchain, the consensus algorithm is simpler and less intensive.  
According to distinct blockchain applications, the records amassed in the block can be of specific fact types. The hash function converts a block and its contents into a special constant-size output that is restrained to that block [21]. Each current block has to refer to the hash price of the preceding block. This will result in an orderly association of blocks to create a blockchain, as proven in Figure 1. Changing the hash value of any block will invalidate subsequent blocks due to the fact the hash values of preceding blocks have to be changed. This characteristic enhances the safety of the block and protects it from any variability and tampering. The blockchain permits the layout of a complete and strong point-to-point network, while eliminating any single-factor errors in the setup.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 9 Smart Port Use Case
### Design Overview  
Integration of ML techniques with SDN/NFV architecture will help to manage the resources more efficiently. Handover behavior can also be predicted by using ML algorithms. In our proposed model, we use ML techniques in order to slice the network with our above mentioned requirements. Identification of application in earlier stages is considered to be an important step to distinguish between mobile traffic, this approach can be useful to slice the network. In this regard, the SVM algorithm can be used to extract the data, which helps to classify the mobile traffic. Then K-means algorithm is used to group applications into different clusters according to similar traffic patterns. We categorize the above-mentioned requirements into three different services: application-as-a-service (AaaS), platform-as-a-service (PaaS), and infrastructure-as-a-service (IaaS). Every slice will be grouped into these three services, which will help to manage the traffic load evenly. For example, if a field operator wants to visit the port in a VR environment, they can achieve that by using the AaaS platform. In contrast, the IaaS platform will provide administration privileges to the port authority of this particular slice (eMBB). Figure 8 describes the architectural design of our model.  
We grouped the model into five layers: terminal layer, physical layer, service layer, network layer, and application layer. The terminal layer is used to collect the data from the sensors and then these data are passed onto the physical layer via 5G-CPE wireless terminal. IoT devices need to be 5G-enabled in order to connect these IoT devices to a 5G base station. Thus, due to the resource limitations of IoT devices we can use 5G-CPE [48].  
Data are analyzed and processed at the physical layer. The physical network is grouped into three slices (i.e., mMTC, eMBB, and URLLC). At the service layer, ML techniques are used and every sliced network is grouped into three more slices according to the services:AaaS, PaaS, and IaaS. The network layer, also called the core network,  
Figure 8: Proposed smart port network slicing architecture.  
offers various services, such as providing the control and data plane functions and storage services. the client-server environment is provided at the application layer.
# A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks
###### Abstract  
Wireless visual sensor networks have been adopted in different contexts to provide visual information in a more flexible and distributed way, supporting the development of different innovative applications. Although visual data may be central for a considerable set of applications in areas such as Smart Cities, Industry 4.0, and Vehicular Networks, the actual visual data quality may be not easily determined since it may be associated with many factors that depend on the characteristics of the considered application scenario. This entails several aspects from the quality of captured images (sharpness, definition, resolution) to the characteristics of the networks such as employed hardware, power consumption, and networking efficiency. In order to better support quality analysis and performance comparisons among different wireless visual sensor networks, which could be valuable in many monitoring scenarios, this article surveys this area with special concern on assessment mechanisms and quality metrics. In this context, a novel classification approach is proposed to better categorize the diverse applicable metrics for quality assessment of visual monitoring procedures. Hence, this article yields a practical guide for analyzing different visual sensor network implementations, allowing fairer evaluations and comparisons among a variety of research works. Critical analysis are also performed regarding the relevance and usage of the proposed categories and identified quality metrics. Finally, promising open issues and research directions are discussed in order to guide new developments in this research field.  
quality of monitoring; dependability; reliability; availability; wireless visual sensor networks; visual sensing Article
## 5 Challenges and Future Research Trends  
Deep learning techniques have demonstrated the capability to effectively process large amounts of data and enhance the channel estimation performance. However, it is essential to acknowledge that several challenges in this field remain unresolved. According to the current advancements in wireless communications, the prospective research trends for deep-learning-based channel estimation are outlined below.
# Inter-Story Drift Ratio Detection of High-Rise Buildings Based on Ambient Noise Recordings
## 3 Data and Processing Methods
### Meteorological Data  
The hourly ground wind speed data of the Shanghai Hongqiao International Airport provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) were used in the research, with wind speed referring to the wind speed at approximately 10 m above the ground.  
The distance between the wind data station and the experimental site is approximately 18.6 km, and the calculated average wind speed is 6.99 m/s. The wind propagation time was calculated using the average wind speed to correct the wind speed time. The corrected wind speed chart is shown in Figure 5.  
Figure 4: Diagram of relationship between deflection curve and \(\theta_{1}\) angle.  
In Figure 5, the maximum wind speed was 23.5 m/s at 10:15 a.m. on 30 November 2021, reaching the speed level of a strong gate, which may cause slight damage to the top of the building [31]. Additionally, the median wind speed was 9.33 m/s, representing the wind speed of the building under normal conditions.  
The parameter \(\theta_{1}\) at 10:15 on 30 November 2021 was taken as the polarization parameter under the strongest wind conditions, and the median of \(\theta_{1}\) was taken as the polarization parameter under normal conditions.
## 2 Materials and Methods
### Statistic Evaluation of Classification Results
#### 2.3.1 Cohen's Kappa  
To determine the agreement between the machine learning model-based classification and the ground truth we used Cohen's Kappa. Therefore, we started by calculating a \(k\) by \(k\) confusion matrix with \(k\) being the respective number of classes in this classification scenario. Within the matrix an element \(f_{ij}\) defines the items that belong to class \(i\) and are classified by the model to class \(j\). So, \(f_{jj}\) is the number of agreements between ground truth and model prediction for class \(j\).  
Then, (from [40]):  
\[P_{o} =\frac{1}{N}\sum_{j=1}^{k}f_{jj}, \tag{1}\] \[r_{i} =\sum_{j=1}^{k}f_{ij},\forall i,\text{ and }c_{j}=\sum_{i=1}^{k}f_{ij}, \forall j,\] (2) \[P_{e} =\frac{1}{N^{2}}\sum_{i=1}^{k}r_{i}c_{i}, \tag{3}\]  
where \(P_{o}\) the observed proportional agreement, \(r_{i}\) and \(c_{j}\) the row and column totals for category \(i\) and \(j\) and \(P_{e}\) the expected proportion of agreement by chance. The final measure of agreement is given by Equation (4).  
\[\kappa=\frac{P_{o}-P_{e}}{1-P_{e}}. \tag{4}\]  
The approximate standard deviation of \(\kappa\) is [40]:  
\[std(\kappa)=\sqrt{\frac{P_{o}(1-P_{o})}{N(1-P_{e})^{2}}} \tag{5}\]  
As proposed by [PERSON] and [PERSON] [41], Kappa values 0.6-0.8 indicate a substantial agreement and \(>\)0.8 an almost perfect agreement between two different raters.
## 4 Conclusions  
In summary, eco-friendly air filters based on PVA NFs and nonwoven fabric were fabricated by electrospinning to overcome the limitations of the disposal of existing filters by incineration or in landfills, which cause environmental pollution. The quality factor of the optimized PVA NF-based filter was comparable to that of commercial air filters, such as dust masks, cabin filters, and HEPA filters. Additionally, by measuring the reflectance spectrum of the contaminated filter, it was found that the color of the PVA NF-based filter changed from white to dark brown due to the light absorption by the captured PM particles in the visible light region. Finally, the filter was soaked in water to selectively remove the contaminated PVA NF layer, and the remaining nonwoven fabric could be reused to make the filter. Based on the above experimental results, we conclude that the PVA NF-based filters fabricated in this study can be used as eco-friendly filters for effective PM capture. Furthermore, we expect that the results of this study can be used as a basic research data for the development of NF-based flexible printed electronic devices [21; 22; 23].  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)]([https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)) _3390/app11093831/s1, Figure S1: Scheme of PM filtration set, Figure S2: PM removal device used in chamber test, Figure S3: Surface FE-SEM images of samples, Figure S4: Demonstration of the use of a PVA NF-based filter to block the PM entering from the right glass bottle to the left glass bottle, Figure S5: Comparison of chamber test results of a PVA NF-based filter by reusing nonwoven fabric substrate.  
**Author Contributions:** Conceptualization, H.-J.K. and Y.K.; Methodology, H.-J.K., D.-IC., and Y.K.; Formal Analysis, H.-J.K., S.-K.S., S.-H.L., S.-J.K., J.K., and B.-S.H.; Data Curation, H.-J.K. and Y.K.; Investigation, H.-J.K. and D.-I.K.; Supervision, D.-I.K. and Y.K.; Validation, H.-J.K. and Y.K.; Writing-original draft, H.-J.K. and Y.K.; Writing-review and editing, H.J.K. and Y.K.; Funding Acquisition, Y.K.; Project Administration, Y.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the \"The Innovation Growth Engine for Planning and Demonstration\" of the Commercialization Promotion Agency for R&D Outcomes (COMPA) granted financial resources from the Ministry and ICT, Korea (2020K000049), and the Korea Innovation Foundation (INNOPOLIS) grant funded by the Korean government (MSIT) (2020-DD-UP-0278).  
Figure 4: (**a**) Selective removal of PM\(-\)captured poly(vinyl alcohol) nanofiber (PVA NF) layer by immersion in water; (**b**) surface field-emission scanning electron microscopy (FE-SEM) images of the PVA NF\(-\)based filters during recycling.
## 2 Related Work
Frequency domain analysis is a method that examines the characteristics of a signal in the frequency domain. [PERSON] et al. [30] revealed the differences in the contribution of frequency components to the overall signal at different speeds through the analysis of noise emissions from two distinct motorized sources. [PERSON] et al. [31] proposed a Global Navigation Satellite System (GNSS) spoofing detection method based on frequency domain processing. Combining time domain and frequency domain characteristics provides a more comprehensive perspective, improving the efficiency and accuracy of feature extraction and facilitating a better understanding and analysis of signal characteristics. Predictive anomaly detection methods typically require the construction of an accurate forecasting model. By comparing the difference between predicted and observed values against a predefined threshold, anomalies can be identified. Based on the literature review, the following key challenges exist in the field of traffic flow anomaly detection:  
1. Lack of specialized anomaly detection methods for traffic flow data  
While traffic flow data share typical characteristics of MTS data, they also exhibit strong spatial dependencies. However, existing methods often fail to account for these spatial properties. 2. Limited consideration of frequency-domain features  
Current predictive models primarily focus on time-domain methods, leveraging only temporal and spatial characteristics while overlooking frequency-domain properties. As a result, the feature learning process for MTS data remains incomplete. 3. Uncertainty in error threshold determination  
In predictive anomaly detection, setting an appropriate error threshold is challenging. Since error analysis is a crucial step in ensuring detection accuracy, the lack of a reliable threshold selection method can significantly impact the effectiveness of anomaly detection. Based on the above issues and situation analysis, this paper proposes a prediction-based anomaly detection method for traffic flow data that incorporates multi-domain feature extraction. This method leverages both time-domain and frequency-domain features to comprehensively capture the temporal and spectral characteristics of the data, enabling the construction of an effective time series prediction model. By analyzing the predicted and observed values, the error threshold is determined using Chebyshev's inequality, and anomalies are detected by checking whether the error falls within the threshold range. This approach enables effective detection of anomalous data points.
###### Abstract  
Among various carbon capture and storage technologies to mitigate global warming and ocean acidification due to greenhouse gases, ocean geological storage is considered the most feasible for Korea due to insufficient inland space to store CO2. However, the risk of CO2 leakage and the behavior and environmental effects of the leaked CO2 need to be assessed for its successful implementation. Therefore, the behavior of CO2 bubbles/droplets dissolving into the surrounding seawater and the diffusion of dissolved CO2 by ocean flows should be accurately predicted. However, finding corresponding research has been difficult in Korea. Herein, the behavior and convection-diffusion of CO2 that was assumed to have leaked from the seafloor near the southeastern coast of Korea were numerically predicted using a multi-scale ocean model for the first time. In the simulation region, one of the pilot projects of CO2 ocean geological storage had started but has been temporarily halted. In the ocean model, hydrostatic approximation and the Eulerian-Lagrangian two-phase model were applied for meso- and small-scale regions, respectively. Parameters for the simulations were the leakage rate and the initial diameter of CO2. Results revealed that all leaked and rising CO2 bubbles were dissolved into the seawater before reaching the free surface; further, the change in the partial pressure of CO2 did not exceed 500 ppm during 30 days of leakage for all cases.  
carbon capture and storage; CO2 ocean geological storage; multi-scale ocean model; hydrostatic approximation; Eulerian-Lagrangian two-phase model; environmental impact +
Footnote †: journal: Applied Sciences