esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 3 Sensors and Actuators in the IoMT
### Influence of Actuators in the IoMT  
Actuators translate electrical impulses into physical actions or movements, facilitating various functions that contribute to an individual's wellbeing. In essence, actuators respond to instructions based on the data acquired by sensors, which are then processed and computed to execute the desired actions. For instance, sensors such as blood glucose monitors measure the body's blood glucose levels, while actuators respond by regulating insulin levels. These actuators can be categorized into body-centric actuators [51] and environment-centric actuators. Body-centric actuators can be further classified into therapy (therapeutic), diagnosis (diagnostic), and treatment actuators [52]. Several body-centric actuators are described in Table 4. The main challenge for these body-centric actuators is to ensure safety and compatibility with the human body. In addition to body-centric actuators, which directly interact with the human body, other types of actuators play crucial roles in various healthcare applications. Surgical robot actuators, for instance, enable precise and controlled movements during surgical procedures, enhancing surgical outcomes and patient safety. Drug delivery pump actuators regulate the flow rate of medication administration, ensuring accurate dosage delivery to patients [51]. Similarly, infusion pump actuators facilitate the controlled infusion of fluids into patients' circulatory systems, which is vital for administering medications and nutrients. Radiology table actuators enable precise positioning of patients during imaging procedures, ensuring optimal imaging quality while prioritizing patient comfort and safety. Rehabilitation robot actuators support physical therapy and rehabilitation exercises, providing varying levels of resistance and assistance to patients undergoing rehabilitation [51]. Despite their indispensable roles, these actuators pose challenges related to accuracy, compatibility, safety, and integration with existing healthcare systems. Addressing these challenges is paramount to harnessing the full potential of these actuators in improving healthcare delivery and patient outcomes. These environment-centric actuators operate outside the human body and interact with features of the external environment such as medical devices, equipment, and facilities. They can be described as follows:
# Optimisation of Cable Dome Structure Design for Progressive Collapse Resistance
## 2 Dynamic Response and Model Analysis of Progressive Collapse
### Dynamic Response and Collapse Model Analysis for the Entire Process of Structural Collapse
Table 2 presents the analysis results of the dynamic response and collapse model of the structure after each member was removed. The results indicated that the removal of various members resulted in different dynamic responses and collapse models. Particularly, the removal of the IHC and OHC resulted in relatively large collapsed areas and vertical displacement, followed by those observed after the removal of the IUS. The removal of the DC1, DC2, DC3, RC1, RC2, RC3, OS, MS, IS, and ILS yielded the smallest collapsed areas and vertical displacement. Hence, each factor contributed differently to the structure's progressive collapse. The correlation between each member's importance level and collapse model was further explored using the US UFC4-023-03 criteria [40] for the progressive collapse of cable dome structures. These criteria are as follows: First, a progressive collapse is said to have occurred in a cable dome if the maximum vertical displacement of a node is greater than 1/50 of the span and if the area of failure reaches 30% of the dome's total planar area. Second, a local progressive collapse is said to have occurred in a cable dome if the maximum vertical displacement of a node is greater than 1/50 of the span and if the area of failure is less than 30% of the dome's total planar area. Third, a progressive collapse is said to have not occurred if the maximum vertical displacement of a node is less than 1/50 of the span or if the maximum vertical displacement of the node is greater than 1/50 of the span, with an area of failure less than 15% of the dome's total planar area. In this study, these criteria were used to categorise cable dome collapse after the removal of members into three models: a progressive collapse model, a partial collapse model, and a nonprogressive collapse model. According to the model used, the corresponding removed component was then identified as a key component, an important component, or a common component (Table 2). Figure 4: Time–history of energy response. Figure 5: Time–history of internal force response.
# Variation of Fracture Toughness with Biaxial Load and T-Stress under Mode I Condition
## 2 Theoretical Model and Verification Method  
### Stress Field at Crack Tip Considering Higher-Order Term  
The fracture process of a cracked material is a separation process starting from the crack tip, while the stress at the crack tip is significantly higher than the rest of the material. Therefore, it is natural to analyze the crack tip stress field in the fracture process of the cracked material. Under the linear elasticity assumption, the mechanical solution to this problem is obtained by [PERSON] [20] and is known as the Williams series of solutions. Using (\(r\), \(\theta\)) centered at the crack tip as the coordinate system, the stress and strain can be written as:  
\[\sigma_{ij}=\sum_{1}^{\infty}A_{n}r^{\frac{n}{2}-1}f_{ij}{}^{(n)} \tag{1}\]  
\[\varepsilon_{ij}=\sum_{1}^{\infty}A_{n}r^{\frac{n}{2}-1}g_{ij}{}^{(n)} \tag{2}\]where \(\sigma_{ij}\) is the stress tensor, \(\varepsilon_{ij}\) is the strain tensor and \(f_{ij}{}^{(n)}\) and \(g_{ij}{}^{(n)}\) are the angular functions of the nth term for the stress and strain, respectively.  
Assuming that the stress field at the crack tip is the reference point for controlling the fracture behavior, then \(r\) approaches zero, according to Equations (1) and (2), the high-order terms except the first term in this asymptotic solution can be ignored. When only the first item is considered, one has:  
\[\sigma_{ij} = \frac{K_{\rm I}}{\sqrt{2\pi r}}f_{ij}{}^{(1)}(\theta) \tag{3}\] \[\varepsilon_{ij} = \frac{K_{\rm I}}{\sqrt{2\pi r}}g_{ij}{}^{(1)}(\theta) \tag{4}\]
## 4 Time Parameters
### Parameters Based on the Time-Size or Time-Distance Dispersions  
We have explored the Pixel Size-Time (ST space) and Pixel Distance-Time (DT space) parameter spaces (where pixel size is the charge content \(i_{p}\) of the single pixel and distance is intended as between each pixel and the image baricenter) searching for features that could be significant for the Gamma/Hadron discrimination. The latter space describes how the shower image evolves in time (i.e., at which distance from the image centroid the pixels light up first); the former tells if brighter pixels are faster to trigger. We can then apply to these plots a morphological analysis using the same algorithm as for the Hillas parameters (algorithms are described in [25]), to evaluate a width and a length for these dispersions. The quantities considererd for further analysis are width and length in the ST space (both in a simple form and normalized for the total SIZE of the image and the number of pixel \(n_{p}\): WID_ST, LEN_ST, WID_ST_NORM, and LEN_ST_NORM) and length in the DT space (LEN_DT).  
This concept can be expanded to 3-dimensional space. In particular, we have explored the two spaces defined by X and Y coordinates of the pixel plus pixel time (XYT space), and Pixel Size-Time-Pixel Distance (STD space) for each pixel, obtaining a width and a length. Here, we have examined the length and the width in the STD space (LEN_SDT, WID_SDT) and the length in the XYT space (LEN_XYT).
## 2 Retinal Vascular Diseases
### Cotton-Wool Spots
#### Diabetic Macular Edema (DME)  
Diabetic macular edema (DME) pathogenesis is linked to the blood barrier breakdown in retina with fluid leakage from microaneurysm. In diabetic eyes, VEGF is believed to be a main cause of hyperpermeability of retinal vasculature. Hence, anti-VEGF agents are usually considered the main and effective therapy for DME, which improves macular edema and visual prognosis in subjects with DME [55]. An OCTA study conducted by [PERSON] et al. [56] has found out that the high rate of the formation of macular microaneurysm (Mas) in diabetic patients is a predictor of the development of significant macular edema. Another OCTA study conducted by [PERSON] et al. [57] to analyze the relationship between DME and microaneuryss distribution in different retinal layers concluded that there is a correlation between microaneuryss existing in the DCP and DME pathogenesis.  
[PERSON] et al. [58] studied the relation between the retinal capillary dropout areas in DME and the location of cystoid spaces. The authors found out that cystoid spaces theco-localized with areas of the capillary dropout, particularly in the DCP. There is no reperfusion that occurs after the DME resolution. OCTA has limitations in imaging of DME due to its inability to detect leakage. Hence, it cannot differentiate leaking from non-leaking microaneuryss. Another limitation is that there are defects of OCTA segmentation in the edematous macula. Decisions regarding treatment for DME are still primarily based on the presence of central cystoid spaces on structural OCT in combination with decreased visual acuity. OCTA does not typically play a role in this setting. However, severe capillary dropout in the macula that persists even with resolution of DME is something the treating clinician should consider. Macular ischemia can limit vision, and patients with center-involving DME whose vision fails to improve with resolution of edema often have significant macular ischemia that has damaged photoreceptors and reduced visual potential. In these cases, the clinician may consider withholding further treatment due to its futility. A further discussion of capillary dropout in the macula and abnormalities of the foveal avascular zone follows below.
## 3 Results
### Correlation Analysis  
#### 3.4.1 NDVI and Meteorological Factors  
Considering that the PWV is highly related to temperature and precipitation, its variation with NDVI was also analyzed. The Pearson correlation coefficients of NDVI with PWV, RHU, and SSD were calculated (Figure 9). Strong correlations existed between NDVI and PWV with a correlation value of 0.94. Many studies have shown that vegetation growth is highly influenced by precipitation [36; 37; 38; 39], and precipitation mainly originates from atmospheric water vapor [24]; thus, a strong correlation exists between NDVI and PWV. Most parts of Loess Plateau are semi-arid regions, and a large part of the moisture in the near-surface air comes from the evapotranspiration of the vegetated land surface. Thus, a moderate correlation of 0.52 between NDVI and RHU was observed. SSD is among the most influential factors contributing to the vegetation photosynthetic active radiation [40], and a good correlation of 0.464 was found between NDVI and SSD.
## 3 Methodology
### Proposed ML Models
#### 3.7.1 Decision Tree  
A decision tree is a non-parametric supervised learning algorithm that is adept at classification and regression tasks. It iteratively partitions the dataset based on the input feature values and makes decisions along the branches of the tree. In classification, it assigns data points to distinct classes, while in regression, it predicts the target variable's value. Renowned for simplicity and interpretability, decision trees offer insights into feature importance and decision processes. However, they may be prone to overfitting, necessitating techniques like pruning for improved generalization [31].
## 4 Application in Determination of Rock-Mass Properties  
The determination of rock mass mechanical properties is one of the essential parts of rock engineering design. To consider the scale effect for rock masses, field tests are desired, as laboratory testing on rock masses is not easy and is very unwieldly. However, field tests to obtain these properties are time-consuming, difficult to perform and expensive. Therefore, correlation between the \(RMR\) and \(Q\) with mechanical properties of rock masses have been made. The mechanical properties include not only the deformation modulus (\(E_{m}\)) and _UCS_ (\(\sigma_{cm}\)) of rock masses but also Poisson's ratio (\(v_{m}\)),frictional angle (\(\hat{\phi}_{m}\)), cohesion (\(c_{m}\)), Hoek-Brown constant and tensile strength (\(\sigma_{tm}\)), among the others, which are critical for tunnel design. Correlations between empirical rock-mass classification systems and mechanical rock-mass properties are a common practice observed in rock engineering, as they create an easy approach for assessing the rock-mass conditions.
# 5G NPN Performance Evaluation for I4.0 Environments
###### Abstract  
This paper aims to develop an open Asset Administration Shell (AAS) solution for 5G Non-Public Network (NPN) management, focusing on manufacturing digitization and complete Information and Operational Technology (IT/OT) convergence. The proposed 5G NPN framework is evaluated in a factory-like simulation scenario considering network slicing for I4.0, and demonstrates the outlook of 5G communication in the industrial domain, achieving an upload data rate of up to 86 Mbps, and a Round-Trip Time (RTT) for end-to-end communication as low as 11 ms. The proposed framework integrates OPC UA as an enabler and middleware across different protocols, equipment, and the manufacturing shop floor, with the target of aggregating different industrial data and creating insights on production optimization in a unified manner. The framework combines 5G NPNs with I4.0 environments, in the form of a complete FNS and its corresponding AAS. In parallel, a set of I4.0 enablers are investigated within the framework of the project, covering a Time-Sensitive Network (TSN) on the shop floor. The main objective of this paper is to propose a method for the unified integration of various enablers in the I4.0 domain and their combination with 5G technology, and to evaluate the feasibility of hosting industrial applications and services over 5G channels through the implementation of different slicing schemas. The paper presents detailed experimental data regarding 5G downlink/uplink data rates and RTT delays.  
I4.0; 5G; NPN; OPC UA; network slicing +
Footnote †: journal: Applied Sciences  
_Accepted: 3 August 2022_  
Published: 6 August 2022  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Copyright**: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
###### Abstract  
To more precisely monitor drought, a new remote sensing-based drought index, the Vapor Pressure Deficit-Soil Moisture-Sun-Induced Chlorophyll fluorescence Dryness Index (VMFDI), with a spatial resolution of 1 km based on vapor pressure deficit (VPD), soil moisture (SM), and sun-induced chlorophyll fluorescence (SIF) data was constructed via a three-dimensional spatial distance model, and it was used to monitor dryness in the Yellow River Basin during 2003-2020. The spatiotemporal variations in and main factors of the VMFDI and agroecosystem responses were analyzed via the Theil-Sen median and Mann-Kendall tests and Liang-Kleeman information flow. The results revealed the following: (1) The VMFDI effectively monitors regional drought and is more sensitive than other indices like the standardized precipitation evapotranspiration index (SPEI) and GRACE drought severity index and single variables. (2) VMFDI values fluctuated seasonally in the Yellow River Basin, peaking in August and reaching their lowest in March. The basin becomes drier in winter but wetter in spring, summer, and autumn, with the middle and lower reaches, particularly Shaanxi and Gansu, being drought-prone. The VMFDI values in the agroecosystem were lower. (3) SM and VPD dominated drought at the watershed and agroecosystem scales, respectively. Key agroecosystem indicators, including greenness (NDVI), gross primary productivity (GPP), water use efficiency (WUE), and leaf area index (LAI), were negatively correlated with drought (_p_ < 0.05). When VPD exceeded a threshold range of 7.11-7.17 ha, the relationships between these indicators and VPD shifted from positive to negative. The specific VPD thresholds in maize and wheat systems were 8.03-8.57 ha and 7.15 ha, respectively. Suggestions for drought risk management were also provided. This study provides a new method and high-resolution data for accurately monitoring drought, which can aid in mitigating agricultural drought risks and promoting high-quality agricultural development.  
draft monitoring; three-dimensional spatial distance model; spatiotemporal variation; Liang-Kleeman information flow; agroecosystem responses; multisource data 2024164666[[https://doi.org/10.3390/rs16244666](https://doi.org/10.3390/rs16244666)]([https://doi.org/10.3390/rs16244666](https://doi.org/10.3390/rs16244666))
## 2 The Proposed Method
### The Conventional GSC Method
The GSC method with the minimum cross-correlation coefficient (GSC-MCC) method was proposed by combining the conventional GSC method and the GSC-CC method to utilize the advantages of the two methods. The conventional GSC method and the GSC-CC method ran synchronously. The output signal frame, which had the smaller cross-correlation coefficient (between the estimated noise signal and the corresponding output signal) of the two methods, was adopted to synthesize the final output signal. Considering the overlap of speech frames and the nonstationary nature of a speech signal, the principle of the GSC-MCC method to choose the frame to synthesize the final output signal was modified using Equation (5). \[y_{out-final}(k)=\left\{ \begin{array}{ll}y_{out-corr}(k),&\sum\limits_{i=k -1}^{k+1}sign(|\rho_{conv}(i)|-|\rho_{corr}(i)|)\geq 2\\ y_{out-conv}(k),&\text{otherwise}\end{array} \right., \tag{5}\]  
where \(y_{out-corr}(k)\) and \(y_{out-conv}(k)\) represent the output signal frame of the GSC-CC method and the conventional GSC-CC method, respectively.
## 3 Results and Discussion  
### Analysis of Microcapsule Morphology and Composition  
The SEM morphology of microcapsules with and without rice husk powder was analyzed as shown in Figure 1. Compared with Figure 1A, the microcapsules with rice husk powder content of 5.5%, which was showed in Figure 1B, had little agglomeration, good morphology, and uniform particle size. In Figure 2, the absorption peak at 1547 cm\({}^{-1}\) belongs to -NH- stretching vibration peak, which is the characteristic peak of melamine resin. Compared with the microcapsules without rice husk powder, the infrared spectrum of the microcapsules with rice husk powder in the wall material split at 1157 cm\({}^{-1}\), and the peak type changes. It can be inferred that this peak is affected by the C-H vibration of aromatic core and the C-O-C antisymmetric \"bridge\" stretching vibration peak in rice husk powder, and it can be judged that the wall material of microcapsule contains cellulose. The absorption peaks at other positions of microcapsules with 5.5% rice husk powder in the infrared spectra are consistent with the microcapsules without rice husk powder. The chemical composition of the microcapsules has not changed, so it can be concluded that the microcapsules are successfully prepared.
## 2 Dataset and Methodology
### Dataset and Criteria
#### 2.3.3 Slope  
The slope of the land directly influences the efficiency and installation costs of PV farms, as flat or gently sloping terrains are more cost-effective and easier to install solar panels on. In areas with steep slopes, solar panel installations are typically more complex and costly due to the need for specialized mounting systems, and the panels may also receive less sunlight due to shading from surrounding terrain. The topography of the land significantly impacts the selection of suitable areas for PV farms in Gaziantep [33]. The region is largely characterized by flat or gently sloping areas, particularly in its rural zones, making it favorable for solar farm development. Digital Elevation Model (DEM) data obtained from ASTER satellite images were used to produce the slope data in percentage terms for Gaziantep [48]. A review of the literature indicates that the most suitable land for solar energy projects has slopes ranging from 0% to 2% or 4%. In the study, the very suitable criteria and other sub-criteria were classified as follows: 0-2%, 2-5%, 5-8%, 8-10%, and greater than 10% [17; 23; 27].
## 3 Related Work
The advantage of using an AE over using other neural networks such as Multilayer Perceptron (MLP) [41] is that the sensor's uncertainty due to process or measurement errors can be fine-tuned. Qualified simulation and dynamic testing constitute the basis for characterizing the operation conditions. Such measurement data are fundamentally time series data, i.e., sequential data over time (e.g., the daily values of a currency exchange rate). There are specific classification algorithms for solving the corresponding classification problem, such as AutoRegression Moving-Average (ARMA) [42]. The performance of such classical time series analysis methods depends however often on the availability of a big amount of data with strong seasonality. The deep learning approaches, such as RNN [43], LSTM [44], and GRU [45], may have a good performance in predicting the data, while working only as black boxes. The state-based methods [46], such as Hidden Markov Model (HMM), and Dynamic Bayesian Network (DBN), allow the incorporation of some existing domain-knowledge about system observability, dynamics, and causality, and thereby could be better optimized for situations with delimited data as well as uncertain measurements. In other words, these methods could have better resilience against the sparsity of data, compared to neural networks with the performance depending on the amount of data available for training. For the design of our perception system, we consider therefore HMMs as a core method for coping with the targeted dynamic process, which is inherently stochastic and only partially observable. Similar work can be found in the area of robotics with promising results [47; 48]. As a special type of Bayesian Inference (BI) [46; 49], HMM is widely used in speech recognition [50; 51], natural language modeling [52], on-line handwriting recognition [53], and for the analysis of biological sequences such as proteins and DNA [54; 55]. Anomaly detection refers to the identification and classification of system conditions that differ from some preferred conditions. It could be supported directly by a reasoning of patterns in data, such as with SVM and K-NN [52; 56; 57; 58]. The classification could also be supported with probabilistic classification [59]. On the basis of HMM, probabilistic classification allows a quantification of the discrepancies through statistical inference.