esa-sceva/satcom-chunk-collection huggingface dataset:
# Skeleton Tracking Accuracy and Precision Evaluation of Kinect V1, Kinect V2, and the Azure Kinect
###### Abstract  
The Azure Kinect, the successor of Kinect v1 and Kinect v2, is a depth sensor. In this paper we evaluate the skeleton tracking abilities of the new sensor, namely accuracy and precision (repeatability). Firstly, we state the technical features of all three sensors, since we want to put the new Azure Kinect in the context of its previous versions. Then, we present the experimental results of general accuracy and precision obtained by measuring a plate mounted to a robotic manipulator end effector which was moved along the depth axis of each sensor and compare them. In the second experiment, we mounted a human-sized figuring to the end effector and placed it in the same positions as the test plate. Positions were located 400 mm from each other. In each position, we measured relative accuracy and precision (repeatability) of the detected figuring body joints. We compared the results and concluded that the Azure Kinect surpasses its discontinued predecessors, both in accuracy and precision. It is a suitable sensor for human-robot interaction, body-motion analysis, and other gesture-based applications. Our analysis serves as a pilot study for future HMI (human-machine interaction) designs and applications using the new Kinect Azure and puts it in the context of its successful predecessors.  
depth sensor; Kinect; Azure Kinect; skeleton tracking; skeletal tracking; pose tracking +
Footnote †: 2021: 11, 5756. [[https://doi.org/10.3390/app11125756](https://doi.org/10.3390/app11125756)]([https://doi.org/10.3390/app11125756](https://doi.org/10.3390/app11125756))
## 3 Results  
### Cell Attachment  
The results of the cell attachment assessment according to the WST assays are shown in Figure 1. The results revealed that there were no significant differences in the results between the control group and all of the test groups except that of the hMSCs exposed for 4 min to the air-flowing NTAPPI by the direct method (Figure 1a). The findings indicated that, in general, neither the air- nor nitrogen-flowing NTAPPIJs had an effect on the attachment of the hMSCs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Genes** & **Forward Sequences** & **Backward Sequences** \\ \hline PPARG & 5’-GCAThe morphology of the cells after 4 h of incubation was examined using immunofluorescence images, and the results are shown in Figure 2. A rounded cell morphology with relatively undeveloped actin filaments was evident for the control cells (Figure 2a), which appeared similar to the morphology of the cells in the test groups (Figure 2b-e).  
Figure 1: Human mesenchymal stem cells (hMSCs) attached after 24 h of cell culture following different durations of plasma exposure to (**a**) the air-flowing non-thermal atmospheric pressure plasma jet (NTAPPJ) by the direct method, (**b**) the air-flowing NTAPPJ by the media-treatment method, (**c**) the nitrogen-flowing NTAPPJ by the direct method, and (**d**) the nitrogen-flowing NTAPPJ by the media-treatment method (* significant at \(p<0.05\)).  
Figure 2: Human mesenchymal stem cell morphology after 4 h of cell culture following (**a**) no exposure to plasma (control), (**b**) direct exposure to the air-flowing NTAPPJ for 4 min, (**c**) media treatment exposure to the air-flowing NTAPPJ for 4 min, (**d**) direct exposure to the nitrogen-flowing NTAPPJ for 4 min. Scale bar is 100 \(\upmu\)m.
## 4 Results
### REE Concentrations in the Weathering Crust
#### 4.1.2 REE Concentrations in the Weathering Profile  
Two weathering profiles were distributed in hornblende-gneiss. The \(\Sigma\)REE values of bedrock were 233 \(\mu\)g\(\cdot\)g\({}^{-1}\) and 264 \(\mu\)g\(\cdot\)g\({}^{-1}\), respectively, in profiles 1 and 2, indicating their similar REEs concentrations. Both weathering profiles in NGHs showed similar chondrite-normalized REE patterns from the bottom of bedrock up to regolith, and onto the soil, with enrichment of LREEs and moderately negative Eu anomalies (Table 2, Figure 4), which is in accordance with the weathering characteristics in mountain terrain that bedrock was in-situ weathered to the soil, and the latter has inherited REE concentrations from the former. However, different enrichments and fractionations within REEs have occurred during weathering. As shown in profile 1 (Figure 4a), the \(\Sigma\)REE was overall higher in the regoliths in which HREEs were deficient, but LREEs were enriched as compared to bedrock, while topsoil was deficient in both LREEs and HREEs. Regoliths contained higher REE concentrations with \(\Sigma\)REE values ranging 242-289 \(\mu\)g-g\({}^{-1}\), and that of soil was 169 \(\mu\)g-g\({}^{-1}\). LREE/HREE varied in the range of 8.28-11.2 and La\({}_{N}\)/Yb\({}_{N}\) varied in the range of 9.24-13.8, which were slightly different with bedrock (LREE/HREE = 8.56, La\({}_{N}\)/Yb\({}_{N}\) = 9.48). However, profile 2 exhibited that the \(\Sigma\)REE was overall lower in the regoliths and soils, and that LREEs were more deficient than HREEs in both regolith and soil as compared to bedrock, while topsoil was more enriched in REEs than regolith (Figure 4b). Regoliths contained lower REE concentrations with \(\Sigma\)REE values ranging 142-238 \(\mu\)g-g\({}^{-1}\), and that of topsoil was 238 \(\mu\)g-g\({}^{-1}\). LREE/HREE varied in the range of 6.08-10.7 and La\({}_{N}\)/Yb\({}_{N}\) varied in the range of 5.83-14.3, which were different with bedrock (LREE/HREE = 20.6, La\({}_{N}\)/Yb\({}_{N}\) = 35.8). Figure 3: Chondrite-normalized REE patterns of rhizosphere soils and roots of _Scutellaria bacialensis_. (**a**) Rhizosphere soils in NGH; (**b**) Roots in NGH; (**c**) Rhizosphere soils in APHs; (**d**) Roots in APHs; (**e**) Rhizosphere soils in BCHs; (**f**) Roots in BCHs.
## 2 Geological Setting
### Geology of Fe-Ti-V Oxide Deposits
#### 2.2.1 The Krzemianka Fe-Ti-V Oxide Deposit  
The Krzemianka deposit has an arcuate shape (2.5 km wide and 5 km long), with an area of approximately 13.2 km\({}^{2}\), and is located in the western part of SAM, close to its contact with granite and diotriotide rock cover (Figure 1) [28]. The deposit lies under the Cambrian, Permian, Triassic, Jurassic, Cretaceous, and Cenozoic sediments; the sedimentary cover may reach a total thickness of about 900 m. The Fe-Ti-V oxide ores in the deposit lie at depths of 850 to 2300 m; however, the majority of ore lies at a depth of 1100-1700 m [28]. The largest orebody is hosted by anorthosite. The orebodies are in the form of lenses, schlieren, pseudo-seams, and veins dipping generally at an angle of about 45\({}^{\circ}\) towards the SE (Figure 2), [28,39]. Their orebodies are variable in size. Their thicknesses vary from a few centimetres to 150 m. The contacts of orebodies with anorthosites and diorites are sharp [28]. The construction of the south-western ore zone in the upper part is made of norites and anorthosites which gradually turn downwards from rich ores to poor mineralization. The lower part consists of plagioclase-type ore, the ore bodies having sharp boundaries with the anorthosites. Plagioclase-type ore has been distinguished for macroscopic classification, e.g., a massive titanomagnetic ore mixture with anorthosite of labradore-andesine composition [23,28].  
The deposit is made up of titanium- and vanadium-bearing magnetite, containing ilmenite and hematite-ilmenite, occurring in various proportions from 1:1 to 5:1 and even 10:1 [28,39]. Magnetic contains many decomposition products, including ulvospinels, ilmenite, and Al-spinel solid solutions [23,39]. The Fe-Ti-V mineralization is accompanied by Fe and Cu sulphides: pyrrhotite, chalcopyrite, pyrite, marcasite, cubanite, chalcolcite, and Ni and Co sulphides such as pentlandite, bravoite, millerite, linnaeite, and violarite [23; 26; 27; 28]. Sulphides may form up to 1-3% of the volume of the Fe-Ti-V ores. In the Krzemianka deposit, about 1.07 billion tons of ilmenite-magnetite ore containing vanadium have been documented. This Fe-Ti-V mineralization has an average grade of 27% Fe\({}_{2}\)O\({}_{3}\), 7% TiO\({}_{2}\), and 0.3% V\({}_{2}\)O\({}_{5}\)[30].
## 4 Proposed Protocol
### Fuzzy Logic
It is well known that using the fuzzy logic to choose the forwarding node becomes very easy compared to SPRINT [4], which uses the weights method using normalized values to select the forwarding node and RECRP [6], which uses RSSI and Doppler scale shift measurement to estimate distance using optimal min-max method, and next hop selection is based on the information in a routing table that is updated from the beginning to the ending node. In [6], due to regular updates of the routing table, the energy consumption will be increased. Similarly, among the neighbors, the forwarding node is chosen based on the largest value. The process is continued until the sink is reached. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **No.** & **No.** & **No.** & \\
**No.** & **Hops** & **Neighbors** & **Distance** & **Chance** \\ \hline
1 & 7 & 6 & 8 & 0.375 \\
2 & 6 & 6 & 6 & 0.474 \\
3 & 5 & 8 & 6 & 0.312 \\
4 & 3 & 4 & 5 & 0.625 \\
5 & 7 & 3 & 2 & 0.564 \\
6 & 2 & 3 & 4 & 0.875 \\
7 & 1 & 2 & 7 & 0.637 \\
8 & 5 & 8 & 7 & 0.196 \\
9 & 1 & 1 & 1 & 0.929 \\
10 & 1 & 1 & 2 & 0.917 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Result of fuzzy operation. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**No.** & **No.** & **No.** & **No.** & **No.** & **No.** & **No.** & **Distance** & **Chance** \\
**Rule** & **Hops** & **Neighbors** & **Distance** & **Chance** & **Rule** & **Hops** & **Neighbors** & **Distance** & **Chance** \\ \hline
**1** & Min. & Min. & Near & Very Best & 15 & Max. & Avg. & Near & Good \\
**2** & Min. & Min. & Close & Best & 16 & Min. & Avg. & Near & Good \\
**3** & Max. & Min. & Close & Best & 17 & Avg. & Min. & Near & Good \\
**4** & Min. & Avg. & Close & Best & 18 & Min. & Avg. & Far & Good \\
**5** & Max. & Min. & Close & Best & 19 & Min. & Min. & Far & Good \\
**6** & Max. & Max. & Close & Far Better & 20 & Max. & Min. & Near & Good \\
**7** & Max. & Avg. & Close & Far Better & 21 & Avg. & Avg. & Far & Fair \\
**8** & Min. & Min. & Far & Far Better & 22 & Avg. & Max. & Near & Fair \\
**9** & Min. & Max. & Close & Better & 23 & Avg. & Max. & Far & Bad \\
**10** & Avg. & Max. & Close & Better & 24 & Max. & Max. & Far & Bad \\
**11** & Avg. & Avg. & Close & Better & 25 & Max. & Max. & Near & Bad \\
**12** & Avg. & Min. & Near & Better & 26 & Avg. & Max. & Far & Worse \\
**13** & Avg. & Avg. & Near & Better & 27 & Max. & Max. & Far & Worst \\
**14** & Avg. & Min. & Far & Better & & & & & \\ \hline \hline \end{tabular} Note: Min. = minimum, Med. = medium, Max. = maximum, Avg. = average.  
\end{table} 
Table 3: Fuzzy rules established for the proposed scheme.
# Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China
## 2 Materials and Methods
### Remote Sensing Images  
MODIS (Moderate Resolution Imaging Spectroradiometer) is a vital instrument on Terra (initially called EOS AM-1) and Aqua (initally called EOS PM-1) satellites. Terra's orbit around the earth is fixed so that it crosses the equator from north to south in the morning, and Aqua crosses the equator from south to north in the afternoon. Terra MODIS and Aqua MODIS acquire data in 36 spectral bands by observing the entire Earth's surface every 1 to 2 days, covering the visible to infrared bands. These data help us study and understand the global dynamics and processes in the ocean, the lower atmosphere, and land. MODIS plays an essential role in the development of earth system models that can accurately predict global changes, and can help decision-makers take relevant measures to protect our environment.  
Figure 2 is a false-color image of northern Henan Province on 26 July 2021 acquired from the Moderate Resolution Imaging Spectrometer (MODIS) on NASA's Aqua satellite ([[https://modis.gsfc.nasa.gov](https://modis.gsfc.nasa.gov)]([https://modis.gsfc.nasa.gov](https://modis.gsfc.nasa.gov)), accessed on 30 July 2021).  
Figure 1: The red areas are the main 11 cities affected by the Henan flood, of which Xinxiang (blue dot) and Zhengzhou (red dot) are the most severely affected areas. From top to bottom, the blue jagged lines represent Wei River, Jialu River, Ying River, and Sha River.  
Figure 2: MODIS images in northern Henan after the flood (on 26 July 2021). In this type of image, the vegetation is bright green and the water is dark blue.  
It can be seen from Figure 2 that the flooding caused by heavy rain in mid-July 2021 is still visible.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 7 Communication Standards/Protocols in Telemedicine
UDP is the preferred transport protocol for streaming video over fading channels, and can be used in combination with other protocols to further benefit from their advantages. The UDP/IP protocol was utilized in [129]; refs. [130; 131] recommend that UDP should be employed for delay-sensitive tasks with tolerance to minor loss of frame traffic, such as video streaming, in preference to TCP. In the work of [92], the UDP protocol was chosen instead of other protocols because of its swiftness and efficiency in real life applications, although [29] mention that video traffic or signals take the lowest priority in the UDP protocol. The UDP protocol uses a simple datagram without congestion control. This issue can be addressed by adopting a rate adjustment measure, as pointed out in [131; 132]. While UDP may not be reliable for transmission of packet-based data, it is very good for networked games and streaming media [96]. 6. Real-Time Protocol (RTP) and Real-Time Transport Control Protocol (RTCP): the RTP and RTCP protocols are internet protocols belonging to the family of transport protocols for media streaming. For real-time applications, the RTP protocol provides an end-to-end transport function. RTCP is often used as a companion control protocol to the RTP protocol, where it helps to improve Quality of Service (QoS) feedback. The authors in [133] used RTP and RTCP protocols in their measurement of the QoS of video streams. 7. TCP/IP: another wireless communication protocol utilised in telemedicine is the transmission control protocol (TCP)/internet protocol (IP). Due its low-cost and multiple functions, there has been significant rise in the use of IP-based networks in telemedicine applications [2]. A study by [84] presented an IP-based medical data transmission system that used the TCP/IP protocol to efficiently transmit data over the network, achieving a high bandwidth and low error rate. In their proposed design, data are transmitted between the server and the client in the form of packets using TCP.
# Recovery/Reuse of Heterogeneous Supported Spent Catalysts
## 3 Methods for Recycling Heterogenous Catalysts
### Magnetic Separation
#### 3.2.4 Surface Modification of Magnetic Nanoparticles  
Unfortunately, it has been found that MNPs commonly tend to agglomerate into a large cluster due to their small interparticle distances, high surface energy, and the existence of van der Waals forces. As a result, all of this involves a non-uniform dispersion of the magnetic catalyst in the reaction mixture and then a rapid loss of catalytic activity and magnetism [71]. This problem is solved by coating their surface with suitable protecting agents to prevent direct contact between the nanoparticles and accordingly improve their chemical stability. Generally, magnetic nanoparticles (NPs) are coated with carbon (graphene) or silica so that they form magnetic core-shell structures. Carbon or silica coating provides the possibility to prevent metal oxidation and/or to facilitate the introduction of functional groups via sp\({}^{2}\) carbon of graphene or silanol groups of silica [72], which allow, in turn, the attachment of various catalytic species on the magnetic NPs surface including ionic liquids through covalent bonds or electrostatic interactions. Recently, much attention has been paid to other metal oxides, such as ceria and titania, for coating of the magnetic core in order to obtain magnetically recoverable catalyst supports. [PERSON] et al. have found that the pre-coating of magnetic supports containing ceria or titania with a uniform layer of silica provides thermal resistant magnetic materials without modifying the magnetic properties of the iron oxide core [27].  
The process of synthesis of functionalized magnetic nanoparticles usually consists of three steps: the formation of the magnetic core, the coating of the nanoparticles with an outer shell and the modification of the resultant core-shell structure.
## 2 Materials and Methods
### Characterization of Extracts  
The phenolic compounds, flavonoid content and antioxidant activity of the Graviola extracts obtained from the five different extraction methods was evaluated. Phenolic compounds in the PB-WE, CB-WE, S-WE, MW-WE and SMW-WE of the Graviola leaves were analyzed based on the spectrophotometric method described by [PERSON] et al., 2007 [18]. Briefly, 50 \(\mu\)L of the respective extract was mixed with 1150 \(\mu\)L of distilled water and 200 \(\mu\)L of dilute Folin-Ciocalteu reagent was added. The mixture was thoroughly shaken and allowed to stand for 7 min at room temperature. 600 \(\mu\)L of 20% sodium carbonate aqueous solution was added to this mixture and incubated for 60 min and the absorbance measured at 765 nm against the blank using a Shimadzu UV-1700 spectrophotometer (Shimadzu Corporation, Kyoto, Japan). Different concentrations of gallic acid (0.0325-0.5 mg/mL) were used to construct the calibration curve. The results were expressed as mg of gallic acid equivalents per gram dry weight (mg GAE/g DW).  
The total flavonoid content in the different Graviola extractions was estimated by a colorimetric assay according to [PERSON] et al., 2002 [19] with slight modifications. Briefly, 0.5 mL of the respective extract was mixed with 0.1 mL of 10% aluminum chloride and 4.3 mL of distilled water. The mixture was incubated for 30 min at room temperature and the absorbance measured at 415 nm using a Shimadzu UV-1700 spectrophotometer. Rutin was used as the standard (15.15-500 \(\mu\)g/mL) and the results were expressed as \(\mu\)g rutin equivalent per gram dry weight (\(\mu\)g RE/g DW).  
The antioxidant activity of the five different Graviola extracts was determined following [PERSON] et al., 2000 [20]. 0.25 mL of each extract was mixed with 2.5 mL of 0.1 mM 2,2-diphenyl-1-picrylhydrazyl (DPPH) and made up to 3 mL with distilled water. The mixture was vortexed and incubated for 80 min at room temperature in a dark place. The absorbance was measured at 517 nm using a Shimadzu UV-1700 spectrophotometer. The extract replaced with distilled water served as the control. The scavenging activity was calculated using the following equation:  
\[\text{Free radical scavenging effect}\%=(1-\text{Abs}_{\text{sample}}/\text{Abs}_{\text{ control}})\times 100\]  
The absorbance of the 5 different Graviola extracts was estimated using a NanodropND-1000 v 3.3.1 spectrophotometer, (Nanodrop Technologies, Inc., Wilmington, DE, USA), by scanning from 220 to 700 nm.
## 6 Evaluation
### User Study 3: Overall Realism
#### 6.4.2 Result and Discussion  
The mean scores for the questionnaire are plotted in Figure 13. The Type B surface using simultaneous feedback achieved the maximum score of 81.5, followed by the Type-C and Type-A surfaces. Again, the actuator with single vibrotactile feedback showed less fidelity for all three types of surfaces. The two-way ANOVA test showed a significant difference for the two methods (\(p=5.9\times 10^{-6}\)).  
With the result from User Study 2, this experiment's results clearly showed that the additional pressure feedback improved the overall quality of the feedback. In particular, the dual-layered design in our new actuator enabled simultaneous feedback, making this improvement possible.
###### Abstract
In addition to B-mode images, shear-wave elastography, which represents tissue characteristics instead of the anatomical structure, has also been utilized for breast cancer diagnosis [11]. Recently, ensemble network approaches have been utilized with multiple CNN models and/or inputs representing different aspects of an image in either time or frequency domains. [PERSON] et al. proposed an ensemble network by combining two CNN models (VGG-19 and ResNet-152) with a heat map and concluded that many breast masses were not detected by conventional CNNs as the important regions for correct classification [12]. [PERSON] et al. proposed an ensemble learning architecture using B-mode image, tumor shape image (TSI), which is manually extracted along mass boundary by an expert, and the segmented tumor image represents the tumor region only [13]. The fusion image made by RGB-like concatenation of three images was also used as one of the inputs for an ensemble network and achieved good classification performances due to manually extracted image features. Ensemble transfer learning architecture using elastography as well as B-mode images was proposed and showed benefits of different features rather than morphological information in breast tumor classification [14]. In this paper, we propose a classification framework for breast US images using a CNN with multiple feature images produced in the time domain. Although the virtue of CNNs is a direct abstraction of image features from the raw data by the learning process, it is difficult to learn some features that are not represented (partially or entirely) in the raw image. For example, because B-mode images are generated only by the magnitude of the echoed radiofrequency signals, the reflections from large organ boundaries or tumors represent the anatomical structure relatively well. However, the magnitude of reflected signals from interior tumors or tissue microstructures, which are generally related to the pathological status of soft tissue, is relatively small due to the nature of diffuse scattering and absorption.
###### Abstract  
Over the past decades, hardware and software technologies for wireless sensor networks (WSNs) have significantly progressed, and WSNs are widely used in various areas including Internet of Things (IoT). In general, existing WSNs are mainly used for applications that require delay-tolerance and low-computation due to the poor resources of traditional sensor nodes in WSNs. However, compared to the traditional sensor nodes, today's devices for WSNs have more powerful resource. Thus, sensor nodes these days not only conduct sensing and transmitting data to servers but also are able to process many operations, so more diverse applications can be applied to WSNs. Especially, many applications using audio data have been proposed because audio is one of the most widely used data types, and many mobile devices already have a built-in microphone. However, many of the applications have a requirement that heavy-operations should be done by a tight deadline, so it is difficult for a single node in WSNs to run relatively heavy applications by itself. In this paper, to overcome this limitation of WSNs, we propose a new emerging system, _HeaLow_, a cooperative computing system for heavy-computation and low-latency processing in WSNs. We designed _HeaLow_ and carried out the practical implementation on real devices. We confirmed the effectiveness of _HeaLow_ through various experiments using the real devices and simulations. Using _HeaLow_, nodes in WSNs are able to perform heavy-computation processes while satisfying a completion time requirement.  
wireless sensor networks; Internet of Things; cooperative computing; heavy computation; low-latency processing; audio data processing 2018 1866 doi:10.3390/s18061686