esa-sceva/satcom-chunk-collection huggingface dataset:
# Resilience in the Internet of Medical Things: A Review and Case Study
###### Abstract  
The Internet of Medical Things (IoMT), an extension of the Internet of Things (IoT), is still in its early stages of development. Challenges that are inherent to IoT, persist in IoMT as well. The major focus is on data transmission within the healthcare domain due to its profound impact on health and public well-being. Issues such as latency, bandwidth constraints, and concerns regarding security and privacy are critical in IoMT owing to the sensitive nature of patient data, including patient identity and health status. Numerous forms of cyber-attacks pose threats to IoMT networks, making the reliable and secure transmission of critical medical data a challenging task. Several other situations, such as natural disasters, war, construction works, etc., can cause IoMT networks to become unavailable and fail to transmit the data. The first step in these situations is to recover from failure as quickly as possible, resume the data transfer, and detect the cause of faults, failures, and errors. Several solutions exist in the literature to make the IoMT resilient to failure. However, no single approach proposed in the literature can simultaneously protect the IoMT networks from various attacks, failures, and faults. This paper begins with a detailed description of IoMT and its applications. It considers the underlying requirements of resilience for IoMT networks, such as monitoring, control, diagnosis, and recovery. This paper comprehensively analyzes existing research efforts to provide IoMT network resilience against diverse causes. After investigating several research proposals, we identify that the combination of software-defined networks (SDNs), machine learning (ML), and microservices architecture (MSA) has the capabilities to fulfill the requirements for achieving resilience in the IoMT networks. It mainly focuses on the analysis of technologies, such as SDN, ML, and MSA, separately, for meeting the resilience requirements in the IoMT networks. SDN can be used for monitoring and control, and ML can be used for anomaly detection and diagnosis, whereas MSA can be used for bringing distributed functionality and recovery into the IoMT networks. This paper provides a case study that describes the remote patient monitoring (RPM) of a heart patient in IoMT networks. It covers the different failure scenarios in IoMT infrastructure. Finally, we provide a proposed methodology that elaborates how distributed functionality can be achieved during these failures using machine learning, software-defined networks, and microservices technologies.  
internet of medical things (IoMT); remote patient monitoring (RPM); resilience; software-defined network (SDN); machine learning (ML); microservices architecture (MSA)
## 2 Materials and Methods
### Micrometeorology Measurements  
#### 2.5.1 Micrometeorology Instrumentation and Processing  
The eddy covariance (EC) method [55; 56; 57] was used to measure net ecosystem CO\({}_{2}\) exchange (NEE) (\(\mathrm{\SIUnitSymbolMicro m^{-2}s^{-1}}\)), (latent heat (LE) (W m\({}^{-2}\)), and sensible heat (H) (W m\({}^{-2}\)) fluxes during the 2016 growth cycle. This consisted of a fast response (20 Hz) closed-path infrared gas analyzer (IRGA) (model EC155, Campbell Scientific Inc., Logan, UT, USA) and a three-dimensional sonic anemometer (model CSAT-3A, Campbell Scientific Inc.). The system was installed on a scaffold tower at 25 m above the forest floor. The sonic anemometer and IRGA share integrated electronics and communications to reduce logging lag, as part of the CPEC200 EC system (Campbell Scientific). Both sensors have a sampling frequency of 20 Hz, logged with a CR3000 datalogger (Campbell Scientific, Logan). A complete weather station(model HOBO U-30-NRC Weather Station, Onset Computer Corp., Bourne, MA, USA) provide ancillary meteorological variables including temperature (T\({}_{\rm air}\)), relative humidity (RH), vapor pressure deficit (VPD), precipitation, soil temperature, soil volumetric water content (WWC), soil heat fluxes, and net radiation. Remote communication to all micrometeorological instrumentation was provided through an Iridium satellite connection (Upward Innovations Inc., East Falmouth, MA, USA).  
High-frequency eddy covariance data was processed using EddyPro(r) software (LI-COR Inc., Lincoln, NE, USA) and IBM streams(r) (IBM, Armonk, NY, USA). Vertical CO\({}_{2}\) flux (NEE) (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)) was expressed as the product of mean air density and the covariance between instantaneous vertical wind velocity and concentration fluctuations.  
\[\mathrm{NEE}=-\rho_{\rm a}\overline{w^{\prime}s^{\prime}} \tag{10}\]  
where \(\rho_{\rm a}\) is the dry air density (mol m\({}^{-3}\)), \(w\) is the instantaneous vertical wind speed (m s\({}^{-1}\)), and \(s\) is the molar mixing ratio (mol mol\({}^{-1}\) dry air). The negative sign follows meteorological notation, where negative NEE values represented net CO\({}_{2}\) uptake into an ecosystem, and positive values represent net CO\({}_{2}\) release into the atmosphere.  
Eddy covariance data corrections included time lag correction [58], despiking data outliers [59], double coordinate rotation [60], high-pass and low-pass filtering [56; 61], and Webb-Pearman-Leuning (WPL) correction [62]. Fluxes were calculated at 30-minute block averages. Flux-footprint analysis' were performed following the [PERSON] et al. [63] parametrization. Precipitation events were not explicitly removed from the dataset. However, diagnostic/error flags associate with IRGA and SAT instrument failure, including during heavy rainfall, were used as a filter. A requirement of 80% data coverage was applied for each 30-minute averaging interval.
# Comparing Augmented Reality-Assisted Assembly Functions--A Case Study on Dougong Structure
## 4 Major Assisted Functions
### Assembly Demonstration  
* Stepwise color labeling: This information presentation method was proposed in a previous study [17] for state awareness during a complex process. The subjects participating in the experiments also confirmed the effectiveness of this design. The component models newly added at the current step are coded in a color different from that of the existing models. The user can visualize the correct orientation and position of the new parts with respect to the others.  
Figure 3: Improved paper-based assembly instructions: (**a**) original form and (**b**) improved version.  
* AR animation: [PERSON] et al. [18] suggested that displaying assembly animation containing both 3D models and real components helps users understand their size proportion and relative position/orientation in 3D space. This method follows a similar AR concept that combines virtual information with a real scene. The part models and real components usually have mutual occlusions when placing them in the same coordinate system. Occlusion processing is applied to hide rear portions, in order to produce a high visualization quality of the combined scene.
* Two assembly assisted systems that implement the above functional designs were proposed (see Figure 4). The first system mainly contains the interactive 3D model display and stepwise color labeling functions. A 3D animation demonstrates the component models and assembly process to the user. The second version includes a part identification function, which confirms the user's component selection by giving instant feedback. It also shows the assembly process by aligning the part models precisely with the actual components in an AR animation. The AR system provides additional assistance supported by object recognition and spatial reasoning intelligence. Table 1 lists the assisted functions provided by paper-based instructions and the two systems.
## 3 Results and Discussion
To correlate the EMI shielding properties with the composite structures, both the average SE\({}_{\text{T}}\), SE\({}_{\text{A}}\), and SE\({}_{\text{R}}\) values of SR/POE/CB-IL and SR/POE/CB-CNT-IL composites at 9.0 GHz were plotted with filler content as shown in Figure 3b. The SE\({}_{\text{A}}\) and SE\({}_{\text{R}}\) showed an increase as the filler content increased due to the synergistic effect between ILs and carbon fillers, which enhanced the electrical conductivity. Furthermore, the contribution of absorption to SE\({}_{\text{T}}\) is greater than that of the reflection because network conductivity supports abundant interfaces which form multiple reflections and attenuate the incident EM waves [19]. The filler content in the SR/POE/CB-CNT-IL composites should be well above their percolation thresholds. Herein, the excellent EMI SE of the SR/POE/CB-CNT-IL composites is attributed to the presence of highly conductive networks, which effectively reflect, multi-scatter and adsorb the incident radiations. Figure 4a presents the SE\({}_{\text{T}}\), SE\({}_{\text{A}}\), and SE\({}_{\text{R}}\) of SR/POE/CB, SR/POE/CB-CNT, SR/POE/CB-IL and SR/POE/CB-CNT-IL composites at 9.0 GHz. The EMI shielding performance of SR/POE/CB and SR/POE/CB-CNT composites obviously decreased compared with that of SR/POE/CB-IL and SR/POE/CB-CNT-IL composites. The results reveal that ILs promote the dispersion of carbon fillers due to the cation-\(\pi\) interactions between ILs and carbon fillers. Compared to SR/POE/CB-IL composites, the SR/POE/CB-CNT-IL composites with co-continuous structures have enhanced carrier mobility due to the presence of denser conductive networks and more interfacial structures formed by the uniformly-dispersed CB-CNT-IL. The schematic illustration of EMI shielding mechanisms for the SR/POE/CB-CNT-IL composites is shown in Figure 4b. The excellent EMI SE in this work can be attributed to the highly assembled carbon nano-fillers conductive networks that effectively reflect, multiple scatter and adsorb the incident radiation [20]. Figure 3: (**a**) Electromagnetic interference shielding effectiveness (EMI SE) of the SR/POE/CB-IL and SR/POE/CB-CNT-IL composites in the frequency range of 8.0 to 12.0 GHz, and (**b**) comparison of SE\({}_{\text{T}}\), SE\({}_{\text{A}}\), and SE\({}_{\text{R}}\) of the SR/POE/CB-IL and SR/POE/CB-CNT-IL composites at 9.0 GHz.
# Remote Sensing of the Water Quality Parameters for a Shallow Dam Reservoir
## 2 Data, Methods, and Techniques
### Data for a Remote Sensing Research  
Images from Sentinel 2 (LLC product), registered from 13 April 2016 to 31 December 2021, were used for the teledetection quality modeling of water in Lake Dobczyce. The choice of specific images was determined by the dates of water quality tests in situ. A significant limitation in the number of images resulted from the rejection of images in which clouds or ice covered the lake.  
The content of chlorophyll a and suspended solids (as turbidity) in the water was measured, in the period from 2016 to 2021, in the laboratory of the Waterworks of the City of Krakow, located at the water intake. The study covered the period from 2016 to 2021. Chlorophyll a was analyzed in acetone extracts using the monochromatic spectrophotometric method, with correction for pheopigment a [2,37]; absorbance was measured on a Hach DR 4000 U spectrophotometer, while turbidity was measured on a TL 2360 spectrophotometer, according to the standard methods [38].
## 4 Discussion  
The results of experimental studies show the dependence of the AE data on the specimen's thickness. The reasons for this can be both a general increase in the deformable volume of the metal, and a more complex stress-strain state of the metal during the deformation of specimens with greater thickness. Figure 11 represents the distributions of strain fields near the stress concentrator for specimens of 1 mm and 6 mm thick.  
Figure 10: Influence of the specimens’ thickness \(t\) on the cumulative energy of AE signals \(E_{\Sigma}\).  
It can be seen from the data presented that the plastic deformation zone in a specimen of 6 mm thick is higher than for a specimen of 1 mm thick. The maximum strain near the crack is obviously higher (about 0.48 (Figure 11f)) for a 6-mm specimen compared to the corresponding value for a 1-mm specimen (about 0.34 (Figure 11e)). Presumably, during loading of a specimen of greater thickness, a more complex stress-strain state was formed, characterized by increased stress intensity due to larger transverse strains.  
Generally, the obtained results are consistent with the well-known theory of the scale factor impact on mechanical strength. According to it, specimens with smaller linear dimensions are characterized by lower values of the ultimate uniform strain and total strain to failure. The higher ductility of the thicker specimen was confirmed by the  
Figure 11: Photos of strain fields for a specimens of 1 mm thick (left) and 6 mm thick (right) at different stages of crack development: crack initiation (**a,b**); crack propagation (**c,d**); before failure (**e,f**).  
quantitative measurements made during the tests using the DIC. Figure 12 shows the dependence of the mean strain degree in the region of the stress concentrator for specimens of various thicknesses. It can be seen from the results that the mean strain values increase monotonically with thickness increasing (Figure 12b-e). For a specimen of 6 mm thick, the mean strain values are considerably higher on the whole: the mean elongation at the moment before failure is about 0.17 (Figure 12e) while for a specimen of 1 mm thick, this value is 0.11 (Figure 12b).  
Figure 12: The mean strain in the area of stress concentrator: the area under consideration (a); specimen of 1 mm thick (**b**); 3 mm thick (**c**); 5 mm thick (**d**) and 6 mm thick (**e**).
## 4 Conclusions  
In conclusion, a simple electrochemical aptasensor was developed for the sensitive and rapid detection of GA. The overall aptasensor fabrication process is relatively simple, excluding the necessity of nanomaterials other than GO, carboxylation treatment, chemical pre-treatment of SPCEs, prolonged incubation of aptamer and GO and the labelling of redox probes to aptamer, thereby making it possible to take advantage of other fabrication methods. The developed aptasensor, with a broad linearity over 1 to 10,000 \(\mathrm{\SIUnitSymbolMicro g}\) mL\({}^{-1}\) and a relatively low LOD (0.031 \(\mathrm{\SIUnitSymbolMicro g}\) mL\({}^{-1}\)), offers several benefits, such as selectivity, reproducibility, stability, and affordability of the aptasensor as compared with the previously reported methods for GA detection. The analytical performance (recovery, within-run and between-run CV) of this method support the great quality of the aptasensor. In addition, the excellent application of the developed aptasensor in clinical samples has intensified its practicality as a potential device for GA measurements in the screening and monitoring of diabetes mellitus with or without thalassemia. Moreover, the validation of the method developed with a commercial ELISA method verified the reliability of the aptasensor with high accuracy. The method proposed in this study may also be applied for the development of an electrochemical aptasensor for the detection of other biomolecules.  
The following are available online at [[https://www.mdpi.com/article/10.3](https://www.mdpi.com/article/10.3)]([https://www.mdpi.com/article/10.3](https://www.mdpi.com/article/10.3)) 390/app112110315/s1, Supplementary data associated with this article involved Figure S1: (A) Cyclic voltammograms of GO/SPCEs at different scan rates from 10 to 300 \(\mathrm{mVs^{-1}}\) using 5 mM [Fe (\(\mathrm{CN_{6}}\))]\({}^{\mathrm{a}+\mathrm{7/3}}\) in PBS as redox probe, (B) plot between anodic and cathodic peak current vs. square root of the scan rate (v), Figure S2: Different electrochemical measurements (DPV, SWV, CV) were applied for the detection of different concentrations of GA (10-10,000 \(\mathrm{\SIUnitSymbolMicro g}\) mL\({}^{-1}\)). Figure S3: Secondary structure of GA binding aptamer with \(\mathrm{dG-0.586}\) kcal/mol predicted at 37 \(\mathrm{\SIUnitSymbolCels
## 3 Decision Mechanism for Collaborative Search
### Path-Planning Algorithm
Since the arrival times at waypoints vary across UAVs, at most one UAV acquires situation information and makes decisions at any given time. Under this mechanism, coordination among UAVs occurs asynchronously, thereby reducing communication and decision-making consumption for inter-UAV negotiation. Based on the concept of a rolling optimization strategy, at each time step, the UAV has two waypoints, \(WP_{j}=\left[wp_{j,1}\quad wp_{j,2}\right]\), which it will sequentially pass through. Upon entering the approach threshold range \(R_{ap}\) of the next waypoint, the UAV calculates a new set of waypoints \(WP_{j+1}=\left[wp_{j+1,1}\quad wp_{j+1,2}\right]\) based on the environmental information it has acquired. Here, \(wp_{j,2}\) may not necessarily coincide with \(wp_{j+1,1}\) in terms of location, but the influence of \(wp_{j,2}\) is still considered when calculating the benefits of the waypoints.
## 6 Conclusions  
Reliable prediction of failure strain for sandstone is critical for engineering security in the construction industry given the wide use of sandstone materials in infrastructure systems. For the first time, through uniaxial compression experiments on sandstone specimens, combining the advanced  
Figure 18: Simulation results.  
noncontact digital image correlation (DIC) method, this paper studied the deformation process of sandstone to estimate its full-field failure strain. On classical digital photography principles, digital image correlation contrasts pre- and post-deformation images to obtain numerical strain information by image processing and statistical correlation techniques. A sandstone cylinder 50 mm in diameter and 100 mm in height was adopted as a specimen by considering two loading speed scenarios (loading speeds at 0.1 and 0.3 MPa/s, respectively). Four typical points were selected to obtain detailed investigations, with the strain values along the x-direction, y-direction, and shear direction being analyzed.  
It was observed that in both scenarios with the loading speeds of 0.1 and 0.3 MPa/s, the strain-time relationships at all the four selected points displayed linearity before failure occurred. Nevertheless, when strain approaches some critical value of failure, a sharp rise occurs. We defined the identified critical value to be the failure threshold. After comprehensive analysis on the four selected typical points at two different speeds, we concluded that failure may occur if the strain reaches or exceeds 0.004. Sandstone failure mode detected in the physical experiment process was additionally cross-checked by finite element analysis and the Hill-Tsai criterion. In this study, some resulting errors may exist, attributed to the fact that the Cartesian coordinate system was employed to define strain parameters, which may embed uncertainties for cylindrical surfaces subjected to investigation. This study provides a potential basis for future research on mechanical properties of prefabricated rocks associated with the process of failure, and their applications for life cycle deterioration modeling of various structure systems [30].  
Conceptualization, F.C., E.W., B.Z., L.Z., and F.M.; methodology, F.C.; software, F.C.; validation, E.W., B.Z., L.Z., and F.M.; formal analysis, F.C. and E.W.; investigation, F.C.; resources, F.C.; data curation, F.C.; writing--original draft preparation, F.C. and E.W.; writing--review and editing, F.C. and E.W.; visualization, F.C.; supervision, F.C.; project administration, F.C.; funding acquisition, F.C. All authors have read and agreed to the published version of the manuscript. This research was funded by NATIONAL NATURAL SCIENCE FOUNDATION OF CHINA, grant number #11942203 and grant number #11472145. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Measurement Results and Discussion
### Experiments under Winter Conditions
#### 3.2.3 Analysis Results and Discussion  
In order to perform the same analysis as in the summer experiment, we classified the objects in the images into four categories. Figure 12 shows the heat load on the human body for each of the structures.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Location** & **Date** & **Temperature** & **Humidity** & **Barometric Pressure** & **Starting Time of Measurement** \\ \hline The Imperial Palace & 4 February 2020 & 12.2 & 26 & 1006.9 & 12:00 noon \\ \hline The Imperial Palace & 5 February 2020 & 9.9 & 38 & 1016.0 & 12:00 noon \\ \hline \end{tabular}
\end{table}
Table 2: Environmental conditions of measurement location in winter experiment.  
Figure 11: Measured image in winter experiment at the Imperial Palace with (I) visible, (II) near-infrared, and (III) far-infrared light camera.  
The major changes when compared to the summer experiment were seen in the results of the far-infrared experiment. In summer, the temperature of all structures except the sky was higher than that of the human body, resulting in a positive heat load on the human body. In winter, however, the temperature of all structures was lower than that of the human body, resulting in a negative heat load. Furthermore, in the visible and near-infrared radiation, the radiation energy from the sky and ground were decreased. This is due to the fact that the angle of incidence of sunlight is larger in winter than in summer, and the flux of sunlight reaching the ground surface is smaller. A comparison focusing on the difference in wavelengths showed that the negative heat load of far-infrared radiation was dominant in winter, while visible and near-infrared radiation energy had little effect on the heat load to the human body.
# Signal-to-Noise Ratio Analysis of Bandpass Sampling Time-Modulated Fourier Transform Spectroscopy
## 4 Simulation Analysis and Verification
### Simulation Analysis of Signal-to-Noise Ratio for Different \(r_{B}\cdot m\) Values  
When \(r_{B}\cdot m\) is a fixed value, according to the analysis, the average SNR of the interferogram is a fixed value, and the average SNR of the reconstructed spectrum decreases with the increase in \(m\). Figures 13 and 14, respectively, show the variation trend of the average SNR of the interferogram and spectrum with \(m\) when \(r_{B}\cdot m\) is a fixed value of 0.5. From the figure, we can clearly see that with the increase in \(m\), \(r_{B}\) decreases at this time, and the corresponding average SNR of the interferogram, although slightly fluctuates up and down due to the different positions of OPD when \(m\) is taken, basically maintains at around 176.7. The average SNR of the reconstructed spectrum decreases with the increase in \(m\), that is, it increases with the increase in \(r_{B}\). Of course, it is not difficult to prove that when \(r_{B}\cdot m\) takes other values, although the average SNR of the interferogram and the average SNR of the reconstructed spectrum are not the same at each \(r_{B}\cdot m\), the trend of the curve is consistent with the trend of the curve in Figure 14, which also verifies our previous conclusion.  
Figure 11: Average SNR of interferogram when \(r_{B}=0.5\).  
Figure 10: Locally enlarged image. It is part of Figure 9. It can be seen from Figure 10 that under different \(m\), the average SNR of the spectrum is only approximately equal. As the \(m\) changes, since the interferogram is a signal that oscillates with the change in OPD, there will be a slight oscillation phenomenon. This also verifies our previous conclusion.