esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Characterization of High Damping Alloy Mounted Structures Calculations  
The vibration problem of the lubricating oil tank mounting structure is mainly investigated in this paper. As shown in Figure 11, the lubricating oil tank is connected to the aero-engine casing through the mounting structure. The mounting structure is mostly made of ordinary carbon steel with a low damping coefficient and high stiffness. The energy of vibration is often directly transferred from the aero-engine casing to the lubricating oil tank. The use of high damping alloys in the mounting structure can reduce the vibration of the tank system. Therefore, this section investigates the superiority of the vibration reduction performance of the mounting structure with Fe-12 Cr-3 Al high damping alloy. The real lubricating oil tank mounting structure was used to design the mounting structure fixture, as shown in Figure 12. Here, the surface details of the bolts of the mounting mass block are neglected, and the reason is that such a treatment has a limited impact on the vibration response results of the whole mounting structure. The results in Ref. [21] show that the local details of the threads have less influence on the overall structural vibration characteristics at the macroscale, especially when the relationship between the thread preload and the vibration response is more critical at the macroscale when considering the overall stiffness and damping characteristics of the structure.  
The damping coefficient of the high damping alloy is taken as the average value of first-order damping 0.01787 obtained from the tests in Section 2.3, and other specific material parameters are shown in Table 3.  
Figure 11: The mounting structure of the lubricating oil tank.  
Figure 12: The tooling systems of the mounting structure.  
The ANSYS software (The version number of the software is: ANSYS 2023 R1) is utilised to mesh the model and set the boundary condition, and the mesh result is shown in Figure 13. Where the mass blocks use the sweep method, and the remaining parts use the hex dominant method. The mesh of the mounting structure was encrypted as it was the main study component. The body sizing value of the mounting structure is 2.5 mm, and the body sizing value of other parts is 5 mm. The model includes a total of 262,244 nodes and 63,465 elements, with good mesh quality. All contact regions are set as bonded type, and the bottom surface of the base is set as a fixed support surface. In addition, using the Adaptive Convergence function in ANSYS, we performed adaptive mesh refinement based on the solution results to check the convergence of the results [22]. This was done by setting different mesh densities and observing the changes in the solution results until the results stabilised, thus ensuring the convergence and reliability of the analysis results.
# Empowering Clinical Engineering and Evidence-Based Maintenance with IoT and Indoor Navigation +
## 4 Results
### HiWay Mobile Application  
The previous version of HiWay provided navigation to fixed points of interest for patients and external users ([PERSON] et al., 2019). The upgraded version includes new features specifically designed for managing medical equipment maintenance in conjunction with the OHIO  
Figure 8: OHIO management web application interface. The devices associated with the last two work orders are currently not collected in the EEH (the green check mark is missing).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Code** & **Description** \\ \hline NPF & No problem found \\ BAT & Battery failure \\ ACC & Accessory failure (including supplies) \\ NET & Failure related to network \\ USE & Failure induced by use (e.g., abuse, accident) \\ UPF & Unpreventable failure caused by normal wear and tear \\ PPF & Predictable and preventable failure \\ SIF & Induced by service (i.e., caused by a technical intervention not properly completed or premature failures of a part just replaced) \\ ENV & Environmental causes (e.g., construction yards, cleaning services, flooding) \\ UTIL & Induced by utilities (e.g., electrical power, medical gas or vacuum, ventilation) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Failure Codes.  
management web application. HiWay now allows BMETs to access the WOs, take charge of them, navigate to the medical equipment, and classify the maintenance. Specifically, the new GUI (graphical user interface) of the application displays the assigned WOs once a technician has logged into the app (see Section 4.3). For each device, the basic information is displayed (inventory number and description) as well as the room code of the designed EEH, highlighted in green if the medical equipment is currently collected (Figure 9a). BMET can access any documentation related to the equipment and stored inside the SPOT document manager thanks to the data exchange features provided by HiWay and its communication with the other KERs through the ESB of the ODIN platform (Figure 10a). The IndoorAtlas RTLS integrated into the app, along with the deployed BLE beacons, allows technicians to navigate in real time to the selected device (Figure 9b). Once the maintenance is completed, BMETs can close the intervention by clicking the red stop icon. The user interface then asks for a fault class to be selected (Figure 10b) while the current date and time are saved inside the mobile device. When all the maintenance duties are completed, technicians are asked to upload the locally stored information to the OHIO server for storage and analysis.  
Figure 9: HiWay mobile application (1). (**a**) The collecting room code is highlighted in green, and the medical device the work order is referring to is placed inside that filter zone and can be accessed by the technician. (**b**) The navigation module shows the shortest route from the current position to the target room, enabling real-time navigation.
# Matrix Stiffness Influences Tubular Formation in Renal Tissue Engineering
###### Abstract  
Tubular structures contribute to essential organ functions. Therefore, controlling tubulogenesis is essential for bottom-up tissue engineering approaches. Tissue engineering strategies to form tubular structures utilize extracellular matrix (ECM) components and micropatterned molds. To improve the tubular formation rate, we studied the substrate stiffness's influence on the tubulogenesis of murine inner medullary collecting duct (mlMCD) cells. mIMCD cells were seeded in micropatterned molds with different compositions of polydimethylsiloxane (PDMS) (1:5, 1:10, 1:15, 1:20, 1:30) and agarose (1%, 2%, 3% 5%). We established the Young's modulus of the PDMS and agarose substrates and determined the ideal substrate stiffness for tube formation to be between 277 kPa and 2610 kPa. Within our parameters, optimal tube formation was observed at 439.9 kPa, a value similar to the Young's Modulus found in the basement membrane of the murine renal tubular compartment. We also found that different substrate concentrations of agarose or PDMS are associated with different expression levels of the apical polarization marker Zonula occludens 1 (ZO-1) in the generated tubular structures. In addition to the substrate stiffness, we observed that the tube formation differed based on the substrate material, with agarose showing a generally greater tube formation rate. While previous research demonstrated that ECM stiffness influences cellular behavior towards tube formation, our results suggest that the stiffness of the substrate influences tubular formation independently of the ECM.  
tissue engineering; kidney development; tubulogenesis; organogenesis; developmental engineering; stiffness; biomechanics; PDMS; agarose Article
## 3 Results and Discussion
### Factorial Design  
The results of the evaluated dependent parameters are presented in Table 3 and Figures 6 and 7, including R\({}^{2}\), Q2, validity, and reproducibility.  
Screening for influencing factors offered additional data (Figure 6).  
The experimental data were analyzed from a statistical point of view. R2 (Figure 6) presented values between 0.82 and 0.99 and Q2 (Figure 6) showed values between 0.44 and 0.76. None of the validity values were under 0.25. The values for reproducibility were over 0.75 in all cases.  
The needle-to-collector distance was the significantly influencing parameter for fiber diameter (Figure 7). The disintegration times were positively influenced by the needle-to-collector distance and highly influenced by the interactions between the needle-to-collector distance and the flow rate.  
The dissolved amounts of ITZ after 5 min were negatively influenced by the flow rate, positively by the distance, and negatively by the combination of the two factors. The dissolved amounts of ITZ were dependent on the distance and flow rate.  
Our study had some limitations, such as the lack of in vivo testing to confirm antifungal efficacy and the limited strain variability (there were only two Candida species tested). Future studies will be required to prove the antifungal effect of these fibrous sheets on mice infected with _Candida albicans_ and _Candida parapsylosis_ via susceptibility and safety testing  
Figure 6: Summary plot of the factorial design (**a**) and factors influencing disintegration, including (**b**) replicates, (**c**) summary of fit, (**d**) coefficients, and (**e**) residuals.  
of the drug delivery system. Scaling up this process may lead to the development of an oral or topical delivery system.
# Human Height Estimation by Color Deep Learning and Depth 3D Conversion
## 3 Proposed Method
### Human Body Region Extraction
#### 3.1.2 Human Body Region Extraction Using Depth Frames  
If the depth camera is fixed in a certain position, then the pixels of the human body region in the depth frame have different values from the depth pixels of a background. Therefore, the body region can be extracted by comparing depth pixels between the depth frame and the background depth image which has depth information about background. In order to extract the human body region accurately, the background depth image should be generated from several depth frames that capture the background because the depth video includes temporary noises. A depth value at the certain position of the background depth image is determined as a minimum value among pixels in the corresponding position of the depth frames capturing the background.  
The human body region is extracted by comparing the pixels between the depth frame and the background depth image. A binarization image \(B\) is generated for the human body region extraction as follows:  
\[B(x,y)=\left\{\begin{array}{ll}1,&d_{b}(x,y)-d(x,y)>T_{b}\\ 0,&\text{otherwise}\end{array}\right., \tag{1}\]  
where \(d_{b}(x,y)\) and \(d(x,y)\) are the depth pixel values of the background depth image and the depth frame at position of \((x,y)\), respectively and \(T_{b}\) is a threshold for the binarization.
## 4 Discussion
### Aggregate Production and Effluent Disinfection  
The formation of large aggregates through the production of extracellular polymeric substances (EPS) may be another response to reduced photoperiod and low light [37; 105; 106]. Considering that unsterilized WW was used as a culture medium for both experiments, the role of bacteria in flocculation can be taken into account [107]. [PERSON] and colleagues [108] demonstrated the formation of more compact aggregates when the self-flocculating microalgae _Etthia_ sp. was grown in co-culture with bacteria thanks to the production of bacterial filamentous EPS. An even greater difference in flocculating activity has been reported by [PERSON] and colleagues [109] when a strain of _Chorella vulgaris_ was grown in axenic rather than xenic culture, with a flocculation activity of 94% and 2%, respectively. The high flocculating activity and high sedimentation recorded in our experiment are worth noticing since they make it possible to separate and recover both algal biomass and treated wastewater easily and cost-effectively [110]. In large-scale applications, though, culture mixing cannot be performed manually so automated systems, e.g., aeration or paddle wheel, should be considered for culture mixing when biomass separation from the medium is not needed, increasing maintenance costs [45; 46; 111].  
It is also been reported that pathogenic bacteria like _E. coli_ can be trapped in the EPS matrix and thus removed from the treated WW, contributing to effluent disinfection [36]. This could explain the decrease in _E. coli_ concentration reported in both indoor and outdoor setups, but other interactions, e.g., competition and metabolite production, cannot be excluded [36; 112]. Moreover, cold stress can also affect bacteria growth and both EPS production and composition, explaining the smaller aggregates in the outdoor culture, compared to the indoor one [113; 114]. The decrease in the _E. coli_ concentration, along with the entrapment of other organisms in EPS, as evidenced by SEM images, are promising results that pave the way for the study of the disinfecting capabilities of this microcalga.
## Animal Studies
### Animal Models of Multiple Sclerosis  
#### 2.1.1 Virally-Induced Demyelination  
Theiler's Murine Encephalomyelitis Virus (TMEV) is used to induce MS, epileptic seizures and virus-induced myocarditis in susceptible mouse strains such as the SJL mice [44; 45] initially reported by [PERSON] in 1934 [46]. Intracerebral infection of TMEV causes flaccid paralysis of the hind limbs and acute polioencephalitis and chronic demyelination two weeks post-infection. TMEV can be found primarily in the oligodendrocyteytes, microglia and astrocytes with apoptotic oligodendrocytesbeing identified pre-chronic demyelination in the spinal cord white matter. The host immune system tends to clear the virus quickly from the brain, but not from the spinal cord [47].  
Several advantages of using TMEV as a model of MS have been reported in the literature, and they include: a chronic disease that lasts the lifetime of the animal; pathology is limited to the CNS with brain, brainstem and spinal lesions in transgenic mice [48], which makes it useful for the study of specific pharmacological compounds like cannabinoids, as they also have effects outside of the CNS; T1 (longitudinal relaxation time) hypointensities in the cerebrum and T2 (transverse relaxation time) hypointensities in the thalamus similar to the human disease found by magnetic resonance imaging [27; 28]; haemorrhagic demyelination corresponding to brain and spinal cord atrophy similar to those observed in the human disease [49]; and, several viral strains similar to TMEV have been identified to cause demyelinating diseases like MS in humans [47].
## 5 Performance Analysis
### Performance Analysis on Healthcare Application  
The results of the cross-sectional descriptive design [35], bibliometric analysis [36], sustainable healthcare systems [37], energy-saving technique [38], and NDTDT [39] are discussed with healthcare dataset collected from kaggle. The dataset is published at [67]. In total, 500 sensor nodes are deployed to conduct the simulation. The DSR routing protocol is used to perform delay-efficient data transmission in WSN. The Random Waypoint model is considered a mobility model.  
Figure 5 shows the Packet Delivery Ratio \(PDR\) presented in Equation (1) for existing methods. The number of healthcare data points is taken from the dataset. A number of data packets is taken from the range of 100 to 1000 for experimental purposes. The number of healthcare data points, 100, is considered in the sixth iteration. The \(PDR\) obtained for different approaches is 92%, 87%, 84%, 80%, and 97%, respectively. The \(PDR\) of NDTDT is higher than other techniques. This is owing to the application of intelligent decision-making processes in healthcare centers. The \(PDR\) of NDTDT is improved by 6%, 11%, 17%, and 23%, respectively, compared to other approaches.  
The overall performance results of the \(PDR\) are described in Figure 6. NDTDT outperforms other methods in terms of achieving a higher \(PDR\). When the experiment was conducted, the \(PDR\) was observed as 89%, 85%, 80%, 76%, and 94% for existing methods, respectively.
# JSON Schemas with Semantic Annotations Supporting Data Translation
## 5 Translating JSON Schema to XML Schema
### Mapping Data Types  
There may be incompatibilities between JSON Schemas and XML Schemas regarding the data types. XSD's and JSON Schemas do not share all data types, with the latter being a more general, less detailed implementation. A datatype conversion mapping, as shown in Table 1, was established for making the translation possible. This table has the opposite direction of the table presented in [51], which proposes the translation from XSD to JSON Schema data types.  
JSON objects have a keyword that specifies the **required items** to be in the JSON instance. There is not a direct translation for this property to XSD since, in XML Schema, this is specified for each element as the minimum and the maximum number of occurrences. The translator must verify for each created element if it is present in the required array of the JSON Schema.
## 2 CEMs--State of the Art
### Performance Parameters of Coordination Energetic Materials  
The performance parameters of CEMs reported in the literature are extremely varied, and the materials are only able to undergo decomposition via deflagration to substances, whose parameters are comparable to those of the strongest classical EMs [10]. A similar variation can be observed in terms of the safety parameters of the reported CEMs as some of the reported substances show sensitivity comparable to that of lead(II) azide, whereas other compounds are virtually insensitive to mechanical stimuli. The majority of works dedicated to CEMs, however, report substances whose performance parameters are comparable to those of 1,3,5-trinitro-1,3,5-triazinane (\"hexogen\", RDX) and that are slightly more sensitive to stimuli than RDX. The performance and safety parameters of selected CEMs and classical EMs containing lead are collated in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Chemical Structure** & **Nitrogen Content [wt. \%]** & **Name Abbreviation** & **Ref.** \\ \hline
5 & 66.64 & 1A123 TrAz & [11] \\
6 & 61.74 & bisTrAz & [12] \\
7 & 59.58 & DNAT & [13] \\
8 & 57.11 & DMfetAz & [12] \\
9 & 46.61 & EDA & [8.9] \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multicolumn{2}{c}{**Sensitivity Parameters**} & \multicolumn{3}{c}{**Performance Parameters**} \\ \cline{2-5}  & **Friction [N]** & **Impact [J]** & **Detonation** & **Detonation Pressure** & **Ref.** \\ \hline Lead(II) azide & 0.1–1.0 & 2.5–4.0 & 5877 & 33.4 & [19; 20] \\ Lead(II) styphnate & 0.196 \({}^{\text{a}}\) & 0.41 \({}^{\text{b}}\) & 4630 & 16.1 & [8] \\ Lead(II) styphnate & 0.5–1.5 & 2.5–5.0 & - & - & [21] \\
[Ni(Hz)\({}_{2}\)](No) & 15.69 \({}^{\text{a}}\) & 0.82 \({}^{\text{b}}\) & 7000 & 20.8 & [8] \\
[Ag(A124 TrAz)(H\({}_{2}\)O)]ClO\({}_{4}\) & 30 & 2 & - & - & [22] \\
[Ag(CNO)\({}_{3}\)(bisTrAz)] & 10 & 5 & - & - & [12] \\
[Ag(CNO)\({}_{3}\)(bisTetrAzM)] & 64 & 7 & - & - & [12] \\
[Cu(OxTA)[O]] & 3–360 & \textgreater{}98 & 8969 & 13.5 & [10] \\
[Fe(DNAT)(H\({}_{2}\)O)] & 3 & 16 & 8147 & 29.8 & [13] \\
[CuK\({}_{2}\)(DNAT)\({}_{2}\)(H\({}_{2}\)O)] & 3 & 16 & 8348 & 31.5 & [13] \\
[NiK\({}_{2}\)(DNAT)\({}_{2}\)(H\({}_{2}\)O)] & 6 & 26 & 8169 & 29.7 & [13] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensitivity and performance parameters of selected CEMs compared with classical EMs.
## 2 Antenna Modelling
### The E and H Planes  
A study of the antenna's radiation patterns in the E- (Elevation) and H- (Azimuthal) planes shows how its design affects its ability to send out signals in all directions. In the E-Plane (Elevation), the vertical radiator and ground plane make a mirror picture, which promotes even radiation in the vertical plane. In the H-Plane (Azimuthal), symmetry and the ground plane equally send radiation in all directions, creating an omni-directional pattern. The way these parts are put together in the antenna design makes for efficient, even radiation, which is perfect for applications that need coverage in all directions, like wireless communication systems and broadcasting.
## 4 Results
### Discussion on the Effectiveness of Different Planting Design Approaches  
Throughout this research process, eight distinct planting schemes were proposed for simulation in Envi-met, including the following:  
1. A single row of trees planted on the north side of the sidewalk without shrubs.
2. A single row of trees planted on the north side of the sidewalk with one-meter-high shrubs.
3. A single row of trees planted on the south side of the sidewalk without shrubs.
4. A single row of trees planted on the south side of the sidewalk with one-meter-high shrubs.
5. Aligned planting of double rows of trees along the sidewalk without shrubs.
6. Aligned planting of double rows of trees along the sidewalk with one-meter-high shrubs.
7. Staggered planting of double rows of trees along the sidewalk without shrubs.
8. Staggered planting of double rows of trees along the sidewalk with one-meter-high shrubs.  
As a result, the average Physiological Equivalent Temperature (PET) for each scheme is as follows and as shown in Figure 9:  
* Scheme a (single row of trees planted on the north side of the sidewalk without shrubs): 46.27 \({}^{\circ}\)C
* Scheme b (single row of trees planted on the north side of the sidewalk with one-meter-high shrubs): 46.39 \({}^{\circ}\)C  
Figure 8: Current PET (*: abbreviation).  
* Scheme c (single row of trees planted on the south side of the sidewalk without shrubs): 45.03 \({}^{\circ}\)C
* Scheme d (single row of trees planted on the south side of the sidewalk with one-meter-high shrubs): 45.15 \({}^{\circ}\)C
* Scheme e (aligned planting of double rows of trees along the sidewalk without shrubs): 45.23 \({}^{\circ}\)C
* Scheme f (aligned planting of double rows of trees along the sidewalk with one-meter-high shrubs): 45.31 \({}^{\circ}\)C
* Scheme g (staggered planting of double rows of trees along the sidewalk without shrubs): 44.84 \({}^{\circ}\)C
* Scheme h (staggered planting of double rows of trees along the sidewalk with one-meter-high shrubs): 44.87 \({}^{\circ}\)C.  
Figure 9: PET of proposed schemes.  
Thermal comfort is influenced by factors such as air temperature, humidity, radiant temperature, and wind speed. Based on the data from various design schemes, it is evident that different tree arrangements and planting configurations exert varying impacts on microclimates, thereby indirectly affecting sidewalk thermal comfort. Among these factors, tree arrangement plays a crucial role in microclimate modulation due to the shading effect of vegetation. Consequently, the average PET effect of double rows of trees surpasses that of single rows, indicating better thermal comfort outcomes.