esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 A New Approach for Optical Encoders
### Design Concept of the Demonstrator for the Miniaturized Optical Encoder  
Parallel to the development of the opto-ASIC, the demonstrator for a miniaturized optical encoder was designed. Figure 12 shows a cross-section of the demonstrator.  
The main emphasis was the miniaturized assembly and the development of the assembling concept based on chip-on-board technology. The dimensions of the demonstrator were about 10 mm in length and width, and 10 mm in height, measured without the connector and without the encoder shaft.  
The assembly concept for the optical module was a combination of chip-on-board and chip-on-chip technology. Figure 13 shows the assembling concept of the demonstrator. Firstly, the opto-ASIC was assembled onto a board. After this, the laser diode was assembled directly onto the opto-ASIC. It was important to assemble the laser diode concentric to the ring of photodiodes, as an eccentric arrangement would lead to an incorrectly measured angle during operation. After wire bonding of the opto-ASIC and laser diode, the lens was assembled on top of the opto-ASIC. Finally, the assembly of the housing, the encoder disc, the bearing, and the encoder shaft followed to complete the encoder.  
Figure 11: Read-out circuit of the segmented photodiode an optical application-specific integrated circuit (opto-ASIC).  
Figure 12: Cross-section of the demonstrator for the miniaturized optical encoder.  
Figure 13: Assembly concept of the demonstrator. (**a**) Assembly of the opto-ASIC onto the printed circuit board (PCB) and the laser diode onto the opto-ASIC; (**b**) assembly of the lens onto the opto-ASIC; (**c**) assembly of the housing; (**d**) assembly of the encoder disc, the bearing, and the shaft.
## 8 Applications of Calcium Carbonate Nanoparticles
### As a Biosensor  
CCPs can be used in developing a simple and economical biosensor due to their unique properties like high porosity, high surface area, and low mass transport barrier. Earlier investigators have reported the utilization of CCPs to detect phenols and glucose by immobilizing enzymes polyphenol oxidase (PPO) and glucose oxidase, respectively. The PPO/nano-CaCO\({}_{3}\) electrode possesses a wide detection range (i.e., \(6\times 10^{-9}\) to \(2\times 10^{-5}\) M), sub-nanomolar detection limit (0.44 nM at a signal to noise ratio of 3), good stability (70% remained after 56 days), the shorter response time (i.e., <12 s), large current density, and very high sensitivity (i.e., 474 mA/M). Similarly, the glucose biosensor also provided the same benefits and was very little influenced by other compounds (like ascorbic acid, glutathione, L-cysteine, and p-acetaminophen) at their normal levels. The development of a highly sensitive amperometric phenol biosensor by using CCNPs of size around 80 nm. The sensor was developed by casting (nano-CaCO\({}_{3}\))-PPO bio composite on the surface of a glassy carbon electrode by glutaraldehyde cross-linking. The special three-dimensional structure, porous morphology, hydrophilicity, and biocompatibility of the nano-CaCO\({}_{3}\) matrix provided high enzyme loading, and the enzyme entrapped in this matrix retained its activity to a large extent. In addition, the effects of pH value applied potential, temperature, and electrode construction were also studied [100]. In one of the experiments, investigators have used the polyelectrolyte capsules (with or without CaCO\({}_{3}\) core) as a pH indicator that works even in the presence of salt in the solution. High removal capacities of heavy metals, i.e., Cd\({}^{2+}\) 515, Pb\({}^{2+}\) 1028, Cr\({}^{3+}\) 259, Fe\({}^{3+}\) 321, and Ni\({}^{2+}\) 537 mg/g were achieved from aqueous solutions using precipitated amorphous calcium carbonate (ACC) NPs. The removal process was via precipitation, so the ACC became non-reusable. Few investigators have developed multi-structured CaCO\({}_{3}\)/magnetite composite crystals by microwave radiation method which was used for the photocatalytic splitting of water to liberate oxygen. One special advantage with such photocatalyst is that it can be recycled after extraction from the solution.
## 3 Results and Discussion
### Antioxidant Activity of Extracts from Unground and Ground Purslane
Values represent the means \(\pm\) standard deviations of three biological replicates and three technical replicates. Different letters indicate significant differences between the samples for each concentration separately (one-way ANOVA, Duncan test, \(p\leq 0.05\)). Control = freeze-dried, unground purslane; PPK = purslane powder after grinding with a knife mill; PPB = purslane powder after grinding with a ball mill; PPP = purslane powder after grinding with a planetary ball mill. Figure 7 shows the results of the ferric reducing capacity of unground and ground purslane extracts. FRAP is a good method to measure the reducing capacity of water-soluble compounds such as phenols, and the FRAP values correlate positively with the content of total phenols, which is also confirmed through the results obtained [56]. The PPB sample had a significantly higher percentage of FRAP inhibition than all other samples and dose-dependent FRAP values, while the PPP sample, at the same \(p\) value \(\leq\) 0.05, had the lowest FRAP values (Figure 7). [PERSON] et al. [50] reported on the influence of particle size on FRAP values and concluded that a reduction in particle size increases FRAP values, which is consistent with the results obtained in this study. The mechanical force used during grinding breaks open the cell wall of the plant material, which increases the release and accessibility of biologically active compounds, such as phenols [26]. In addition, by increasing the surface area and reducing the particle size, the release of bioactive compounds from the plant material is improved. For this reason, the PPB sample showed higher antioxidant activity in all methods compared to the other samples tested.
## 5 Conclusions  
Dynamics information in human motion can effectively capture movement trends. In this work, we estimate joint torques as dynamics information through a human dynamics model and integrate this with traditional kinematic information to extract rich motion representations using modules such as multiscale features fusion and mixed-graph convolution. Comparative and ablation experiments on the H3.6M dataset demonstrate that the proposed model outperforms existing approaches by reducing the average prediction error by 9.1%, 12.2%, 10.9%, and 7.1% at 160 ms, 320 ms, 400 ms, and 560 ms, respectively.  
Although we extract joint torques as dynamic information, as discussed in Section 4.4.1, the effect of joint torques is influenced by the prediction time length, and our approach has not yet addressed the effective time range for joint torques. On the other hand, independently learning features for the kinematic and dynamic branches increases the computational cost of the model, resulting in a higher computational load compared to existing models.  
To overlook the further investigation, we suggest focusing on a few key actions for prediction, where the predictive performance needs to be improved further. The related reason is that these actions lack prominent dynamics features; thus, the additional dynamics information may not facilitate prediction. This information instead introduces noise that interferes with the network's ability to learn these motion patterns. Furthermore, the joint torques we employ reflect the dynamics of historical motion. In the future, we suggest conducting a detailed discussion of their effective temporal range and variation trends in prediction. This issue warrants further investigation in future work.  
Conceptualization, methodology, software, validation, formal analysis, and investigation, resources, data curation, writing--original draft preparation, B.W., L.H., R.Z. and C.L.; writing--review and editing, R.Z., Y.M. and Y.C., and visualization, B.W., Y.M. and L.H.; supervision and project administration, R.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 72374154).  
Not applicable.  
The Human3.6 m dataset is available online. The code is available at [[https://github.com/Under-no-Flag/DJT-HMP](https://github.com/Under-no-Flag/DJT-HMP)]([https://github.com/Under-no-Flag/DJT-HMP](https://github.com/Under-no-Flag/DJT-HMP)) (accessed on 20 January 2025).  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{Models} & \multicolumn{6}{c}{Average MPJPE} \\ \cline{2-7}  & \multicolumn{3}{c}{Short-Term} & \multicolumn{3}{c}{Long-Term} \\ \cline{2-7}  & 80 ms & 160 ms & 320 ms & 400 ms & 560 ms & 1000 ms \\ \hline Direct-Addition Fusion & 11.1 & 20.9 & 41.2 & 52.1 & 74.7 & 130.8 \\ Weighted Fusion & **9.6** & **19.8** & **41.0** & **51.3** & **71.9** & **112.5** \\ \hline \hline \end{tabular} Note, the best results are highlighted in **bold**.  
\end{table}
Table 6: Experiments on H3.6M with different fusion methods.
###### Abstract  
We propose a weighted singular value decomposition (WSVD) to reduce the random noise in the Rayleigh backscattering signal of the phase-sensitive optical time domain reflectometry (\(\Phi\)-OTDR) to enhance the detection performance of the distributed vibration sensing. A 2D image is formed by assembling the raw Rayleigh backscattering traces into a matrix form, and slowly varying fluctuation and random noise can be removed using the WSVD. Consequently, the location information and the frequency of vibration induced by the external vibration event can be extracted. A vibration event with 9 m spatial resolution is detected along a 2.4 km single mode fiber. The signal-to-noise ratio (SNR) of location information for the 102 Hz physical vibration and the 525 Hz acoustic vibration was found to be 10.7 and 12.2 dB, respectively. The SNR of the vibration events demonstrate an increase of 6-7 dB compared to the conventional method, showing the excellent denoising capability of this new approach.  
phase-sensitive OTDR (optical time domain reflectometry); singular value decomposition; distributed vibration sensing; structural health monitoring 2021  
11
## 5 Application
### CCA-Secure Elgamal Kem
Choose the random exponent \(w\in\mathbb{Z}_{p}\). Set \(\text{stmt}=(g,h,g^{w},h^{w})\) and \(\text{wt}=w\). Compute \(\pi\gets P^{H_{1}}(\text{stmt},\text{wt})\). Choose the random exponent \(r\in\mathbb{Z}_{p}\) and set \(\text{com}=(g^{r},h^{r})\in\mathbb{G}^{2}\). Set \(c\,=\,H_{1}(\text{stmt},\text{com})\) and \(s=r+wc\in\mathbb{Z}_{p}\). Output the proof \(\pi=(s,c)\). Set \(\text{K}\gets H_{2}(\text{stmt},\text{com},\pi)\). Output \(\text{K}\) and \(\text{CT}=(g^{w},\pi)\). For a public key \(\text{PK}=(\mathbb{G},\,p,\,g,\,h,\,H_{1},\,H_{2})\), a ciphertext \(\text{CT}=(\text{ct},\pi=(s,c))\), and a private \(\text{SK}=x\), the Decap algorithm proceeds as follows. Set \(\text{k}=(\text{ct})^{x}\) and \(\text{stmt}=(g,h,\text{ct},\text{k})\in\mathbb{G}^{4}\). If \(1\gets P^{H_{1}}(\text{stmt},\pi)\), go on. Otherwise, abort. Set \(\text{com}=(g^{s}\cdot\text{ct}^{-c},h^{s}\cdot\text{k}^{-c})\in\mathbb{G}^{2}\). If \(c=H_{1}(\text{stmt},\text{com})\), output \(1\). Otherwise, output \(0\). Set \(\text{K}\gets H_{2}(\text{stmt},\text{com},\pi)\) and output \(\text{K}\). Suppose that the \((t_{ddh},\epsilon_{ddh})\) DDH assumption holds in \(\mathcal{G}\), and \(H_{1}\) and \(H_{2}\) are random oracles. Then, the ElGamal KEM is \((t,\epsilon,q_{d})\)-IND-CCA-secure, where  
\[\epsilon\leq\frac{\epsilon_{ddh}}{2}+\frac{q_{H_{1}}^{2}}{2^{k-1}}+\frac{q_{H _{2}}^{2}}{2^{\ell-1}}+\frac{2q_{H_{1}}^{2}+2q_{d}\cdot(q_{H_{1}}+q_{H_{1}}^{2 })+1}{p},t_{ddh}\approx t+\mathcal{O}(q_{d}t_{e}).\]  
Here, \(\{q_{H_{1}},q_{H_{2}}\}\) are the numbers of \(\{H_{1},H_{2}\}\) queries, \(t_{e}\) is the required time for exponentiation in \(\mathcal{G}\), and \(p\) is the group order. Proof. : We can obtain the proof by applying Theorem 3 to the facts that \(e^{\prime}\leq\epsilon_{ddh}\), \(t_{ddh}\approx t^{\prime}\), \(\epsilon_{\text{ZK}}\leq q_{H_{1}}^{2}/p\), and \(\epsilon_{\text{SS}}\leq(q_{H_{1}}+q_{H_{1}}^{2})/p\). Moreover, we use the fact that \(t_{v}\) becomes similar to \(t_{e}\) in Theorem 3.
## 3. Results and Discussion
### Experimental Results
#### 3.1.3 Optical Power  
Figure 3 shows the variations in the mean corneal optical power versus the changes in pressure obtained from the experimental results. We can see how the optical power decreased when the pressure rose and increased when the pressure dropped. This value was not reported in the consulted bibliography.  
Figure 2: Variations in pachymetry versus pressure changes.  
The observed behavior was directly related to the changes observed in the pachymetry and the curvature radii of both the anterior and posterior corneal surfaces, as discussed above. Obviously, the changes in the hydration state and other internal changes that could affect the corneal refraction index were not considered in this model and will be studied in a future work.
## 4 Analysis of the Influence of Water Depth on Air Layer Drag Reduction  
Numerical research was done on the target ship model to examine the effects of different water depths on the air layer morphology and efficiency of drag reduction. The World Association for Waterborne Transport Infrastructure (PIANC) guidelines were followed in the selection of water depth variations for this study. Following the recommendations, water depths classified as deep water have a depth-to-draft ratio greater than 3.0, moderately deep water has a ratio between 1.5 and 3.0, shallow water has a ratio between 1.2 and 1.5, and extremely shallow water has a ratio of less than 1.2. The navigational water depth conditions for this investigation, as shown in Table 6, were formed based on these categories, with the appropriate water depths being selected within each depth-to-draft ratio range.
# Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer
## 3 Results  
### Calculation of Atmospheric Drag Coefficient of Swarm-C Satellite  
Combining the atmospheric drag coefficient calculation method and the normal vector parameters of each satellite panel obtained by the abstraction of the surface shape of the Swarm-C satellite, the product of the drag coefficient and the effective area of the Swarm-C satellite was obtained. Figure 9a shows the variation of the drag coefficient's product and the Swarm-C satellite's effective area obtained using Equation (4).  
Figure 9: The Swarm-C satellite coefficient relationships. (**a**) Swarm-C satellite drag coefficient and effective area product; (**b**) atmospheric drag coefficient versus angle of incidence of gas molecules.  
The variation of the product of the atmospheric drag coefficient and cross-sectional area of the Swarm-C satellite with time during the day can be seen in Figure 9a. The main reason for this variation is the difference in atmospheric temperature between the daytime and night-time regions of the satellite. The atmospheric temperature has a significant effect on the drag coefficient. In addition, the different attitudes of the satellite during its orbital flight can also lead to different angles between the incident atmospheric molecules and the panel normal vectors of the satellite panels and, therefore, can impact the results [13]. Figure 9b shows the relationship between the angle of incidence of atmospheric molecules per unit area of the Swarm-C satellite and the drag coefficient.  
It can be seen from Figure 9b that the large-area damping coefficient gradually decreases as the incident angle of atmospheric molecules increases from 0\({}^{\circ}\) to 100\({}^{\circ}\). When the incident angle of atmospheric molecules is equal to 90\({}^{\circ}\), the atmospheric damping coefficient is not equal to 0, and its 0 value begins to appear from around 100\({}^{\circ}\) on the abscissa. The reason is that the direction of the thermal motion of atmospheric molecules is random, and the direction of motion of atmospheric molecules and the direction of relative motion of the atmosphere are superimposed on each other. Therefore, when the incident angle of the atmosphere is perpendicular to the satellite panel, the velocity is not completely perpendicular to the satellite panel due to the thermal motion of atmospheric molecules.
# Wind Lidar and Radiosonde Measurements of Low-Level Jets in Coastal Areas of the German Bight
## 3 Study Sites and Data Base
### Heligoland  
The WLS8-8 system was located at the island Heligoland at the site of the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research and almost at sea level (2 m) from 24 March 2017 to 23 March 2018. The data availability, defined as the percentage of wind profiles where at least half of the data points of the wind profiles were valid, was 98.5%. Since no wind farms are in close proximity to the island, only the orography of the island itself alters the wind field. The island is elevated up to 60 m with 50 m high cliffs in the north (Figure 2). The smaller island \"Dune\" is located to the northeast of the lidar. This limits the unobstructed wind sector to 90\({}^{\circ}\) to 270\({}^{\circ}\), neglecting the flat and narrow southern part of the island. The site will be referred to as HL.  
Figure 1: Location of measurement sites of the lidars (St. Peter-Ording, Heligoland, FINO1, Alpha-Ventus, Norderney) [56].
# Digital Twin and Internet of Things--Current Standards Landscape
## 3 Standards for Digital Twins and the Internet of Things
### Open Data Protocol  
The Open Data Protocol (OData) [49] allows the definition of standardized but data model-agnostic RESTful APIs. It was initially developed by Microsoft in 2007 and Version 1.0, 2.0, and 3.0 were released under the Microsoft Open Specification Promise. In 2014, Version 4.0 was standardized by the Organization for the Advancement of Structured Information Standards (OASIS). This is the version we are referring to in this paper. Although not developed with IoT or DT explicitly in mind, we consider OData relevant for them as it is all about describing and accessing resources (even though originally resources were not considered physical).  
The primary concept of OData is called'service'. Amongst others, a service comprises entity and entity set definitions as well as functions (must not have side effects, must have a result) and actions (may have side effects, may have a result). OData services are described by an Entity Data Model (EDM) expressed via the Common Schema Definition Language (CSDL), which in turn can be serialized via XML and JSON. The CSDL, therefore, corresponds to what we refer to as resource description in this article. The identification of resources happens primarily via URLs. Additionally, resources can also be identified via their entity type and ID, which, together with the URL conventions, implicitly defines the URL [50]. Resources can easily be interlinked and contain annotations, which can  
Figure 5: Class diagram depicting the metamodel hierarchy of the Next Generation Service Interfaces-Linked Data (NGSI-LD) API as defined by European Telecommunications Standards Institute (ETSI) (based on Figure 4.2.3-1 from [43]).  
be used to add (external) semantic. OData do not support defining events on resources, nor subscribing to property changes.  
Similar to NGSI-LD, there is no direct communication between producer and consumer in OData and all communication happens via a central server running the service. The standard defines a HTTP-based API for communication with the service that includes a powerful custom URL-based query language supporting geo-spatial filtering.  
As all communication, resource access in OData happens via HTTP(S). URLs in OData are defined by the URL conventions in combination with the data model defined by the EDM. An example URL is shown in Figure 6. The service root part is given by the OData service and can be followed by any entity set name in the resource path. After that, any path defined by the EDM is allowed, e.g., accessing a single entity in an entity set by appending its ID in parenthesis as well as following relations or addressing properties by adding a followed by the name. The query options part of a URL can be used to specify the desired result more fine-grained.
## 3 Sources of Uncertainty
### Uncertainty in Damping Ratio  
According to the on-site experiment conducted by [PERSON] et al. [20], the container crane was excited by the actuator at a height of about 40 m at the tie beam level. A cyclic loading at the same period with the natural periods of the crane was applied, the damping factor of the crane was then assessed from the decrease of amplitude during free vibration after cutting off the cyclic loading. It was shown that the damping factor ranged from approximately 0.2% to 0.4% and was always less than 2.5%, as for general steel structures. The damping ratios determined from the forced vibration test for two steel-frame buildings were proposed by [PERSON] [21]. The average of the observing damping ratios was reported as 1.1% with a COV of 0.3. [PERSON] et al. also used a COV of 0.4 for their study on sensitivity analysis of building loss estimates. The modal parameters of the container crane in Koper port, Slovenia were numerically obtained using FEM with experimental verification by performing a full-scale forced vibration experiment, introduced by [PERSON] et al. [12]. In particular, the structure was forced into the specific vibration mode with limited operational movements. Then, spectral analysis with Fourier transformation was conducted to obtain the frequency response. The portal sway in trolley travel direction behaved at a dominant frequency of 0.75 Hz (or period of 1.33 s). Thus, the average damping measured for this mode is 1.0% of critical damping. In addition, based on industry experience, a damping ratio of 1.5% is evaluated in the critical portal sway mode [5; 22]. A recent study on a 1:20 scale container crane experiment that included verification through a numerical approach found that the sway in the portal frame had a damping ratio of 0.84%. In this study, the Rayleigh damping is taken as 1.5% with a COV of 0.3 assumed as Gaussian distribution, as recommended by Kosbab B.D. [5].